Repository: lightgbm-org/LightGBM Branch: master Commit: 8e7f3dcf4207 Files: 509 Total size: 16.7 MB Directory structure: gitextract_uh184o35/ ├── .appveyor.yml ├── .ci/ │ ├── README.md │ ├── append-comment.sh │ ├── build-docs.sh │ ├── check-dynamic-dependencies.sh │ ├── check-omp-pragmas.sh │ ├── check-python-dists.sh │ ├── check-workflow-status.sh │ ├── conda-envs/ │ │ ├── README.md │ │ ├── ci-core-py39.txt │ │ └── ci-core.txt │ ├── create-nuget.py │ ├── download-artifacts.sh │ ├── install-opencl.ps1 │ ├── install-r-deps.R │ ├── lint-all.sh │ ├── lint-powershell.ps1 │ ├── lint-r-code.R │ ├── parameter-generator.py │ ├── pip-envs/ │ │ ├── requirements-latest.txt │ │ └── requirements-oldest.txt │ ├── rerun-workflow.sh │ ├── run-r-cmd-check.sh │ ├── set-commit-status.sh │ ├── setup.sh │ ├── test-python-latest.sh │ ├── test-python-oldest.sh │ ├── test-r-package-valgrind.sh │ ├── test-r-package-windows.ps1 │ ├── test-r-package.sh │ ├── test-windows.ps1 │ └── test.sh ├── .editorconfig ├── .git-blame-ignore-revs ├── .github/ │ ├── CODEOWNERS │ ├── ISSUE_TEMPLATE/ │ │ ├── BUG_REPORT.md │ │ └── FEATURE_REQUEST.md │ ├── dependabot.yml │ ├── release-drafter.yml │ └── workflows/ │ ├── build.yml │ ├── cpp.yml │ ├── cuda.yml │ ├── lock.yml │ ├── lychee.yml │ ├── no_response.yml │ ├── optional_checks.yml │ ├── python_package.yml │ ├── r_configure.yml │ ├── r_package.yml │ ├── r_valgrind.yml │ ├── release_drafter.yml │ ├── static_analysis.yml │ └── swig.yml ├── .gitignore ├── .gitmodules ├── .pre-commit-config.yaml ├── .readthedocs.yaml ├── .typos.toml ├── .yamllint.yml ├── CMakeLists.txt ├── CODE_OF_CONDUCT.md ├── CONTRIBUTING.md ├── LICENSE ├── MAINTAINING.md ├── R-package/ │ ├── .Rbuildignore │ ├── AUTOCONF_UBUNTU_VERSION │ ├── DESCRIPTION │ ├── LICENSE │ ├── NAMESPACE │ ├── R/ │ │ ├── aliases.R │ │ ├── callback.R │ │ ├── lgb.Booster.R │ │ ├── lgb.DataProcessor.R │ │ ├── lgb.Dataset.R │ │ ├── lgb.Predictor.R │ │ ├── lgb.convert_with_rules.R │ │ ├── lgb.cv.R │ │ ├── lgb.drop_serialized.R │ │ ├── lgb.importance.R │ │ ├── lgb.interprete.R │ │ ├── lgb.make_serializable.R │ │ ├── lgb.model.dt.tree.R │ │ ├── lgb.plot.importance.R │ │ ├── lgb.plot.interpretation.R │ │ ├── lgb.restore_handle.R │ │ ├── lgb.train.R │ │ ├── lightgbm.R │ │ ├── metrics.R │ │ ├── multithreading.R │ │ └── utils.R │ ├── README.md │ ├── cleanup │ ├── configure │ ├── configure.ac │ ├── configure.win │ ├── cran-comments.md │ ├── demo/ │ │ ├── 00Index │ │ ├── basic_walkthrough.R │ │ ├── boost_from_prediction.R │ │ ├── categorical_features_rules.R │ │ ├── cross_validation.R │ │ ├── early_stopping.R │ │ ├── efficient_many_training.R │ │ ├── leaf_stability.R │ │ ├── multiclass.R │ │ ├── multiclass_custom_objective.R │ │ └── weight_param.R │ ├── inst/ │ │ ├── Makevars │ │ └── make-r-def.R │ ├── man/ │ │ ├── agaricus.test.Rd │ │ ├── agaricus.train.Rd │ │ ├── bank.Rd │ │ ├── dim.Rd │ │ ├── dimnames.lgb.Dataset.Rd │ │ ├── getLGBMThreads.Rd │ │ ├── get_field.Rd │ │ ├── lgb.Dataset.Rd │ │ ├── lgb.Dataset.construct.Rd │ │ ├── lgb.Dataset.create.valid.Rd │ │ ├── lgb.Dataset.save.Rd │ │ ├── lgb.Dataset.set.categorical.Rd │ │ ├── lgb.Dataset.set.reference.Rd │ │ ├── lgb.configure_fast_predict.Rd │ │ ├── lgb.convert_with_rules.Rd │ │ ├── lgb.cv.Rd │ │ ├── lgb.drop_serialized.Rd │ │ ├── lgb.dump.Rd │ │ ├── lgb.get.eval.result.Rd │ │ ├── lgb.importance.Rd │ │ ├── lgb.interprete.Rd │ │ ├── lgb.load.Rd │ │ ├── lgb.make_serializable.Rd │ │ ├── lgb.model.dt.tree.Rd │ │ ├── lgb.plot.importance.Rd │ │ ├── lgb.plot.interpretation.Rd │ │ ├── lgb.restore_handle.Rd │ │ ├── lgb.save.Rd │ │ ├── lgb.slice.Dataset.Rd │ │ ├── lgb.train.Rd │ │ ├── lgb_predict_shared_params.Rd │ │ ├── lgb_shared_dataset_params.Rd │ │ ├── lgb_shared_params.Rd │ │ ├── lightgbm.Rd │ │ ├── predict.lgb.Booster.Rd │ │ ├── print.lgb.Booster.Rd │ │ ├── setLGBMThreads.Rd │ │ ├── set_field.Rd │ │ └── summary.lgb.Booster.Rd │ ├── pkgdown/ │ │ └── _pkgdown.yml │ ├── recreate-configure.sh │ ├── src/ │ │ ├── Makevars.in │ │ ├── Makevars.win.in │ │ ├── install.libs.R │ │ ├── lightgbm_R.cpp │ │ └── lightgbm_R.h │ ├── tests/ │ │ ├── testthat/ │ │ │ ├── helper.R │ │ │ ├── test_Predictor.R │ │ │ ├── test_basic.R │ │ │ ├── test_custom_objective.R │ │ │ ├── test_dataset.R │ │ │ ├── test_learning_to_rank.R │ │ │ ├── test_lgb.Booster.R │ │ │ ├── test_lgb.convert_with_rules.R │ │ │ ├── test_lgb.importance.R │ │ │ ├── test_lgb.interprete.R │ │ │ ├── test_lgb.model.dt.tree.R │ │ │ ├── test_lgb.plot.importance.R │ │ │ ├── test_lgb.plot.interpretation.R │ │ │ ├── test_metrics.R │ │ │ ├── test_multithreading.R │ │ │ ├── test_parameters.R │ │ │ ├── test_utils.R │ │ │ └── test_weighted_loss.R │ │ └── testthat.R │ └── vignettes/ │ └── basic_walkthrough.Rmd ├── README.md ├── SECURITY.md ├── VERSION.txt ├── biome.json ├── build-cran-package.sh ├── build-python.sh ├── build_r.R ├── cmake/ │ ├── IntegratedOpenCL.cmake │ ├── Sanitizer.cmake │ └── modules/ │ ├── FindLibR.cmake │ └── FindNCCL.cmake ├── docker/ │ ├── README.md │ ├── dockerfile-cli │ ├── dockerfile-python │ ├── dockerfile-r │ └── gpu/ │ ├── README.md │ ├── dockerfile-cli-only-distroless.gpu │ ├── dockerfile-cli-only.gpu │ └── dockerfile.gpu ├── docs/ │ ├── .lychee.toml │ ├── Advanced-Topics.rst │ ├── C-API.rst │ ├── Development-Guide.rst │ ├── Experiments.rst │ ├── FAQ.rst │ ├── Features.rst │ ├── GPU-Performance.rst │ ├── GPU-Targets.rst │ ├── GPU-Tutorial.rst │ ├── GPU-Windows.rst │ ├── Installation-Guide.rst │ ├── Key-Events.md │ ├── Makefile │ ├── Parallel-Learning-Guide.rst │ ├── Parameters-Tuning.rst │ ├── Parameters.rst │ ├── Python-API.rst │ ├── Python-Intro.rst │ ├── Quick-Start.rst │ ├── README.rst │ ├── _static/ │ │ └── js/ │ │ └── script.js │ ├── build-docs.sh │ ├── conf.py │ ├── env.yml │ ├── gcc-Tips.rst │ ├── index.rst │ ├── logo/ │ │ ├── LightGBM-logo-hex.cdr │ │ └── LightGBM_logo.cdr │ └── make.bat ├── examples/ │ ├── README.md │ ├── binary_classification/ │ │ ├── README.md │ │ ├── binary.test │ │ ├── binary.test.weight │ │ ├── binary.train │ │ ├── binary.train.weight │ │ ├── forced_splits.json │ │ ├── predict.conf │ │ ├── train.conf │ │ └── train_linear.conf │ ├── lambdarank/ │ │ ├── README.md │ │ ├── predict.conf │ │ ├── rank.test │ │ ├── rank.test.query │ │ ├── rank.train │ │ ├── rank.train.query │ │ └── train.conf │ ├── multiclass_classification/ │ │ ├── README.md │ │ ├── multiclass.test │ │ ├── multiclass.train │ │ ├── predict.conf │ │ └── train.conf │ ├── parallel_learning/ │ │ ├── README.md │ │ ├── binary.test │ │ ├── binary.train │ │ ├── predict.conf │ │ └── train.conf │ ├── python-guide/ │ │ ├── README.md │ │ ├── advanced_example.py │ │ ├── dask/ │ │ │ ├── README.md │ │ │ ├── binary-classification.py │ │ │ ├── multiclass-classification.py │ │ │ ├── prediction.py │ │ │ ├── ranking.py │ │ │ └── regression.py │ │ ├── dataset_from_multi_hdf5.py │ │ ├── logistic_regression.py │ │ ├── notebooks/ │ │ │ └── interactive_plot_example.ipynb │ │ ├── plot_example.py │ │ ├── simple_example.py │ │ └── sklearn_example.py │ ├── regression/ │ │ ├── README.md │ │ ├── forced_bins.json │ │ ├── forced_bins2.json │ │ ├── predict.conf │ │ ├── regression.test │ │ ├── regression.test.init │ │ ├── regression.train │ │ ├── regression.train.init │ │ └── train.conf │ └── xendcg/ │ ├── README.md │ ├── predict.conf │ ├── rank.test │ ├── rank.test.query │ ├── rank.train │ ├── rank.train.query │ └── train.conf ├── include/ │ └── LightGBM/ │ ├── application.h │ ├── arrow.h │ ├── arrow.tpp │ ├── bin.h │ ├── boosting.h │ ├── c_api.h │ ├── config.h │ ├── cuda/ │ │ ├── cuda_algorithms.hpp │ │ ├── cuda_column_data.hpp │ │ ├── cuda_metadata.hpp │ │ ├── cuda_metric.hpp │ │ ├── cuda_nccl_topology.hpp │ │ ├── cuda_objective_function.hpp │ │ ├── cuda_random.hpp │ │ ├── cuda_rocm_interop.h │ │ ├── cuda_row_data.hpp │ │ ├── cuda_split_info.hpp │ │ ├── cuda_tree.hpp │ │ ├── cuda_utils.hu │ │ └── vector_cudahost.h │ ├── dataset.h │ ├── dataset_loader.h │ ├── export.h │ ├── feature_group.h │ ├── meta.h │ ├── metric.h │ ├── network.h │ ├── objective_function.h │ ├── prediction_early_stop.h │ ├── sample_strategy.h │ ├── train_share_states.h │ ├── tree.h │ ├── tree_learner.h │ └── utils/ │ ├── array_args.h │ ├── binary_writer.h │ ├── byte_buffer.h │ ├── chunked_array.hpp │ ├── common.h │ ├── file_io.h │ ├── json11.h │ ├── log.h │ ├── openmp_wrapper.h │ ├── pipeline_reader.h │ ├── random.h │ ├── text_reader.h │ ├── threading.h │ └── yamc/ │ ├── alternate_shared_mutex.hpp │ ├── yamc_rwlock_sched.hpp │ └── yamc_shared_lock.hpp ├── python-package/ │ ├── README.rst │ ├── lightgbm/ │ │ ├── __init__.py │ │ ├── basic.py │ │ ├── callback.py │ │ ├── compat.py │ │ ├── dask.py │ │ ├── engine.py │ │ ├── libpath.py │ │ ├── plotting.py │ │ ├── py.typed │ │ └── sklearn.py │ └── pyproject.toml ├── src/ │ ├── application/ │ │ ├── application.cpp │ │ └── predictor.hpp │ ├── boosting/ │ │ ├── bagging.hpp │ │ ├── boosting.cpp │ │ ├── cuda/ │ │ │ ├── cuda_score_updater.cpp │ │ │ ├── cuda_score_updater.cu │ │ │ ├── cuda_score_updater.hpp │ │ │ ├── nccl_gbdt.cpp │ │ │ ├── nccl_gbdt.hpp │ │ │ └── nccl_gbdt_component.hpp │ │ ├── dart.hpp │ │ ├── gbdt.cpp │ │ ├── gbdt.h │ │ ├── gbdt_model_text.cpp │ │ ├── gbdt_prediction.cpp │ │ ├── goss.hpp │ │ ├── prediction_early_stop.cpp │ │ ├── rf.hpp │ │ ├── sample_strategy.cpp │ │ └── score_updater.hpp │ ├── c_api.cpp │ ├── cuda/ │ │ ├── cuda_algorithms.cu │ │ └── cuda_utils.cpp │ ├── io/ │ │ ├── bin.cpp │ │ ├── config.cpp │ │ ├── config_auto.cpp │ │ ├── cuda/ │ │ │ ├── cuda_column_data.cpp │ │ │ ├── cuda_column_data.cu │ │ │ ├── cuda_metadata.cpp │ │ │ ├── cuda_row_data.cpp │ │ │ ├── cuda_tree.cpp │ │ │ └── cuda_tree.cu │ │ ├── dataset.cpp │ │ ├── dataset_loader.cpp │ │ ├── dense_bin.hpp │ │ ├── file_io.cpp │ │ ├── json11.cpp │ │ ├── metadata.cpp │ │ ├── multi_val_dense_bin.hpp │ │ ├── multi_val_sparse_bin.hpp │ │ ├── parser.cpp │ │ ├── parser.hpp │ │ ├── sparse_bin.hpp │ │ ├── train_share_states.cpp │ │ └── tree.cpp │ ├── main.cpp │ ├── metric/ │ │ ├── binary_metric.hpp │ │ ├── cuda/ │ │ │ ├── cuda_binary_metric.cpp │ │ │ ├── cuda_binary_metric.hpp │ │ │ ├── cuda_pointwise_metric.cpp │ │ │ ├── cuda_pointwise_metric.cu │ │ │ ├── cuda_pointwise_metric.hpp │ │ │ ├── cuda_regression_metric.cpp │ │ │ └── cuda_regression_metric.hpp │ │ ├── dcg_calculator.cpp │ │ ├── map_metric.hpp │ │ ├── metric.cpp │ │ ├── multiclass_metric.hpp │ │ ├── rank_metric.hpp │ │ ├── regression_metric.hpp │ │ └── xentropy_metric.hpp │ ├── network/ │ │ ├── linker_topo.cpp │ │ ├── linkers.h │ │ ├── linkers_mpi.cpp │ │ ├── linkers_socket.cpp │ │ ├── network.cpp │ │ └── socket_wrapper.hpp │ ├── objective/ │ │ ├── binary_objective.hpp │ │ ├── cuda/ │ │ │ ├── cuda_binary_objective.cpp │ │ │ ├── cuda_binary_objective.cu │ │ │ ├── cuda_binary_objective.hpp │ │ │ ├── cuda_multiclass_objective.cpp │ │ │ ├── cuda_multiclass_objective.cu │ │ │ ├── cuda_multiclass_objective.hpp │ │ │ ├── cuda_rank_objective.cpp │ │ │ ├── cuda_rank_objective.cu │ │ │ ├── cuda_rank_objective.hpp │ │ │ ├── cuda_regression_objective.cpp │ │ │ ├── cuda_regression_objective.cu │ │ │ └── cuda_regression_objective.hpp │ │ ├── multiclass_objective.hpp │ │ ├── objective_function.cpp │ │ ├── rank_objective.hpp │ │ ├── regression_objective.hpp │ │ └── xentropy_objective.hpp │ ├── treelearner/ │ │ ├── col_sampler.hpp │ │ ├── cost_effective_gradient_boosting.hpp │ │ ├── cuda/ │ │ │ ├── cuda_best_split_finder.cpp │ │ │ ├── cuda_best_split_finder.cu │ │ │ ├── cuda_best_split_finder.hpp │ │ │ ├── cuda_data_partition.cpp │ │ │ ├── cuda_data_partition.cu │ │ │ ├── cuda_data_partition.hpp │ │ │ ├── cuda_gradient_discretizer.cu │ │ │ ├── cuda_gradient_discretizer.hpp │ │ │ ├── cuda_histogram_constructor.cpp │ │ │ ├── cuda_histogram_constructor.cu │ │ │ ├── cuda_histogram_constructor.hpp │ │ │ ├── cuda_leaf_splits.cpp │ │ │ ├── cuda_leaf_splits.cu │ │ │ ├── cuda_leaf_splits.hpp │ │ │ ├── cuda_single_gpu_tree_learner.cpp │ │ │ ├── cuda_single_gpu_tree_learner.cu │ │ │ └── cuda_single_gpu_tree_learner.hpp │ │ ├── data_parallel_tree_learner.cpp │ │ ├── data_partition.hpp │ │ ├── feature_histogram.cpp │ │ ├── feature_histogram.hpp │ │ ├── feature_parallel_tree_learner.cpp │ │ ├── gpu_tree_learner.cpp │ │ ├── gpu_tree_learner.h │ │ ├── gradient_discretizer.cpp │ │ ├── gradient_discretizer.hpp │ │ ├── leaf_splits.hpp │ │ ├── linear_tree_learner.cpp │ │ ├── linear_tree_learner.h │ │ ├── monotone_constraints.hpp │ │ ├── ocl/ │ │ │ ├── histogram16.cl │ │ │ ├── histogram256.cl │ │ │ └── histogram64.cl │ │ ├── parallel_tree_learner.h │ │ ├── serial_tree_learner.cpp │ │ ├── serial_tree_learner.h │ │ ├── split_info.hpp │ │ ├── tree_learner.cpp │ │ └── voting_parallel_tree_learner.cpp │ └── utils/ │ └── openmp_wrapper.cpp ├── swig/ │ ├── ChunkedArray_API_extensions.i │ ├── StringArray.hpp │ ├── StringArray.i │ ├── StringArray_API_extensions.i │ ├── lightgbmlib.i │ └── pointer_manipulation.i ├── tests/ │ ├── c_api_test/ │ │ └── test_.py │ ├── cpp_tests/ │ │ ├── predict.conf │ │ ├── test.py │ │ ├── test_array_args.cpp │ │ ├── test_arrow.cpp │ │ ├── test_byte_buffer.cpp │ │ ├── test_chunked_array.cpp │ │ ├── test_common.cpp │ │ ├── test_main.cpp │ │ ├── test_serialize.cpp │ │ ├── test_single_row.cpp │ │ ├── test_stream.cpp │ │ ├── testutils.cpp │ │ ├── testutils.h │ │ └── train.conf │ ├── data/ │ │ └── categorical.data │ ├── distributed/ │ │ ├── _test_distributed.py │ │ └── conftest.py │ └── python_package_test/ │ ├── __init__.py │ ├── conftest.py │ ├── test_arrow.py │ ├── test_basic.py │ ├── test_callback.py │ ├── test_consistency.py │ ├── test_dask.py │ ├── test_dual.py │ ├── test_engine.py │ ├── test_plotting.py │ ├── test_sklearn.py │ ├── test_utilities.py │ └── utils.py └── windows/ ├── LightGBM.sln ├── LightGBM.vcxproj └── LightGBM.vcxproj.filters ================================================ FILE CONTENTS ================================================ ================================================ FILE: .appveyor.yml ================================================ version: 4.6.0.99.{build} image: Visual Studio 2017 platform: x64 configuration: - '3.9' # only build on 'master' and pull requests targeting it branches: only: - master environment: matrix: - COMPILER: MSVC TASK: python - COMPILER: MINGW TASK: python clone_depth: 5 install: - git submodule update --init --recursive # get `external_libs` folder - set PATH=C:\mingw-w64\x86_64-8.1.0-posix-seh-rt_v6-rev0\mingw64\bin;%PATH% - set PYTHON_VERSION=%CONFIGURATION% - ps: | $env:APPVEYOR = "true" $env:CMAKE_BUILD_PARALLEL_LEVEL = 4 $env:MINICONDA = "C:\Miniconda3-x64" $env:PATH = "$env:MINICONDA;$env:MINICONDA\Scripts;$env:PATH" $env:BUILD_SOURCESDIRECTORY = "$env:APPVEYOR_BUILD_FOLDER" # tell scripts where to put artifacts # (this variable name is left over from when jobs ran on Azure DevOps) $env:BUILD_ARTIFACTSTAGINGDIRECTORY = "$env:APPVEYOR_BUILD_FOLDER/artifacts" build: false test_script: - conda config --remove channels defaults - conda config --add channels nodefaults - conda config --add channels conda-forge - conda config --set channel_priority strict - conda init powershell - powershell.exe -ExecutionPolicy Bypass -File %APPVEYOR_BUILD_FOLDER%\.ci\test-windows.ps1 ================================================ FILE: .ci/README.md ================================================ Helper Scripts for CI ===================== This folder contains scripts which are run on CI services. Dockerfile 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). ================================================ FILE: .ci/append-comment.sh ================================================ #!/bin/bash # # [description] # Post a comment to a pull request. # # [usage] # append-comment.sh # # PULL_REQUEST_ID: ID of PR to post the comment on. # # BODY: Text of the comment to be posted. set -e -E -u -o pipefail if [ -z "$GITHUB_ACTIONS" ]; then echo "Must be run inside GitHub Actions CI" exit 1 fi if [ $# -ne 2 ]; then echo "Usage: $0 " exit 1 fi pr_id=$1 body=$2 body=${body/failure/failure ❌} body=${body/error/failure ❌} body=${body/cancelled/failure ❌} body=${body/timed_out/failure ❌} body=${body/success/success ✔️} data=$( jq -n \ --argjson body "\"$body\"" \ '{"body": $body}' ) curl -sL \ --fail \ -X POST \ -H "Accept: application/vnd.github.v3+json" \ -H "Authorization: token ${GITHUB_TOKEN}" \ -d "$data" \ "${GITHUB_API_URL}/repos/lightgbm-org/LightGBM/issues/${pr_id}/comments" ================================================ FILE: .ci/build-docs.sh ================================================ #!/bin/bash set -e -E -u -o pipefail conda env create \ --name test-env \ --file ./docs/env.yml \ || exit 1 # shellcheck disable=SC1091 source activate test-env make -C docs html || exit 1 ================================================ FILE: .ci/check-dynamic-dependencies.sh ================================================ #!/bin/bash # # [description] # Helper script for checking versions in the dynamic symbol table. # This script checks that LightGBM library is linked to the appropriate symbol versions. # Linking to newer symbol versions at compile time is problematic because it could result # in built artifacts being unusable on older platforms. # # Version history for these symbols can be found at the following: # * GLIBC: https://sourceware.org/glibc/wiki/Glibc%20Timeline # * GLIBCXX: https://gcc.gnu.org/onlinedocs/libstdc++/manual/abi.html # * OMP/GOMP: https://github.com/gcc-mirror/gcc/blob/master/libgomp/libgomp.map # # [usage] # check-dynamic-dependencies.sh # # PATH: Path to the file. # Path to the file with the dynamic symbol table entries of the file # (result of `objdump -T` command). set -e -E -u -o pipefail if [ "$#" -ne 1 ]; then echo "Usage: $0 " exit 1 fi INPUT_FILE="$1" if [ ! -f "$INPUT_FILE" ]; then echo "Error: File '$INPUT_FILE' not found." exit 1 fi awk ' BEGIN { glibc_count = 0 glibcxx_count = 0 gomp_count = 0 has_error = 0 } # --- Check GLIBC --- /0{16}[ \t\(]+GLIBC_[0-9]+\.[0-9]+/ { match($0, /GLIBC_([0-9]+)\.([0-9]+)/, parts) if (RSTART > 0) { match($0, /GLIBC_[0-9]+\.[0-9]+/) ver_str = substr($0, RSTART+6, RLENGTH-6) # skip "GLIBC_" split(ver_str, v, ".") major = v[1] + 0 minor = v[2] + 0 glibc_count++ if (major > 2 || (major == 2 && minor > 28)) { print "Error: found unexpected GLIBC version: \x27" major "." minor "\x27" has_error = 1 } } } # --- Check GLIBCXX --- /0{16}[ \t\(]+GLIBCXX_[0-9]+\.[0-9]+/ { match($0, /GLIBCXX_[0-9]+\.[0-9]+(\.[0-9]+)?/) if (RSTART > 0) { ver_str = substr($0, RSTART+8, RLENGTH-8) # skip "GLIBCXX_" n = split(ver_str, v, ".") major = v[1] + 0 minor = v[2] + 0 patch = (n >= 3) ? v[3] + 0 : 0 patch_str = (n >= 3) ? v[3] : "" glibcxx_count++ msg_ver = major "." minor if (n >= 3) msg_ver = msg_ver "." patch if (major != 3 || minor != 4) { print "Error: found unexpected GLIBCXX version: \x27" msg_ver "\x27" has_error = 1 } if (n >= 3 && patch > 22) { print "Error: found unexpected GLIBCXX version: \x27" msg_ver "\x27" has_error = 1 } } } # --- Check OMP/GOMP --- /0{16}[ \t\(]+G?OMP_[0-9]+\.[0-9]+/ { match($0, /G?OMP_[0-9]+\.[0-9]+/) if (RSTART > 0) { full_match = substr($0, RSTART, RLENGTH) us_idx = index(full_match, "_") ver_str = substr(full_match, us_idx + 1) split(ver_str, v, ".") major = v[1] + 0 minor = v[2] + 0 gomp_count++ if (major > 4 || (major == 4 && minor > 5)) { print "Error: found unexpected OMP/GOMP version: \x27" major "." minor "\x27" has_error = 1 } } } END { if (glibc_count <= 1) { print "Error: Not enough GLIBC symbols found (found " glibc_count ", expected > 1)" has_error = 1 } if (glibcxx_count <= 1) { print "Error: Not enough GLIBCXX symbols found (found " glibcxx_count ", expected > 1)" has_error = 1 } if (gomp_count <= 1) { print "Error: Not enough OMP/GOMP symbols found (found " gomp_count ", expected > 1)" has_error = 1 } if (has_error == 1) { exit 1 } } ' "$INPUT_FILE" ================================================ FILE: .ci/check-omp-pragmas.sh ================================================ #!/bin/bash set -e -u echo "checking that all OpenMP pragmas specify num_threads()" get_omp_pragmas_without_num_threads() { grep \ -n \ -R \ --include='*.c' \ --include='*.cc' \ --include='*.cpp' \ --include='*.h' \ --include='*.hpp' \ 'pragma omp parallel' \ | grep -v ' num_threads' } # 'grep' returns a non-0 exit code if 0 lines were found. # Turning off '-e -o pipefail' options here so that bash doesn't # consider this a failure and stop execution of the script. # # ref: https://www.gnu.org/software/grep/manual/html_node/Exit-Status.html set +e PROBLEMATIC_LINES=$( get_omp_pragmas_without_num_threads ) set -e if test "${PROBLEMATIC_LINES}" != ""; then get_omp_pragmas_without_num_threads echo "Found '#pragma omp parallel' not using explicit num_threads() configuration. Fix those." echo "For details, see https://www.openmp.org/spec-html/5.0/openmpse14.html#x54-800002.6" exit 1 fi echo "done checking OpenMP pragmas" ================================================ FILE: .ci/check-python-dists.sh ================================================ #!/bin/sh set -e -u DIST_DIR=${1} # defaults METHOD=${METHOD:-""} TASK=${TASK:-""} echo "checking Python-package distributions in '${DIST_DIR}'" pip install \ -qq \ check-wheel-contents \ twine || exit 1 echo "twine check..." twine check --strict "$(echo "${DIST_DIR}"/*)" || exit 1 if { test "${TASK}" = "bdist" || test "${METHOD}" = "wheel"; }; then echo "check-wheel-contents..." check-wheel-contents "$(echo "${DIST_DIR}"/*.whl)" || exit 1 fi PY_MINOR_VER=$(python -c "import sys; print(sys.version_info.minor)") if [ "$PY_MINOR_VER" -gt 7 ]; then echo "pydistcheck..." pip install 'pydistcheck>=0.9.1' if { test "${TASK}" = "cuda" || test "${METHOD}" = "wheel"; }; then pydistcheck \ --inspect \ --ignore 'compiled-objects-have-debug-symbols'\ --ignore 'distro-too-large-compressed' \ --max-allowed-size-uncompressed '500M' \ --max-allowed-files 800 \ "$(echo "${DIST_DIR}"/*)" || exit 1 elif { test "$(uname -m)" = "aarch64"; }; then pydistcheck \ --inspect \ --ignore 'compiled-objects-have-debug-symbols' \ --max-allowed-size-compressed '5M' \ --max-allowed-size-uncompressed '15M' \ --max-allowed-files 800 \ "$(echo "${DIST_DIR}"/*)" || exit 1 else pydistcheck \ --inspect \ --max-allowed-size-compressed '5M' \ --max-allowed-size-uncompressed '15M' \ --max-allowed-files 800 \ "$(echo "${DIST_DIR}"/*)" || exit 1 fi else echo "skipping pydistcheck (does not support Python 3.${PY_MINOR_VER})" fi echo "done checking Python-package distributions" ================================================ FILE: .ci/check-workflow-status.sh ================================================ #!/bin/bash # [description] # # Look for the last run of a given GitHub Actions workflow on a given branch. # If there's never been one (as might be the case with optional workflows like valgrind), # exit with 0. # # Otherwise, check the status of that latest run. # If it wasn't successful, exit with a non-0 exit code. # # [usage] # # check-workflow-status.sh # # BRANCH: name of a branch involved in a pull request. # # WORKFLOW_FILE: filename (e.g. 'r_valgrind.yml') defining the GitHub Actions workflow. # set -e -u -o pipefail BRANCH="${1}" WORKFLOW_FILE="${2}" PR_NUMBER="${3}" # Limit how much data is pulled from the API and needs to be parsed locally. OLDEST_ALLOWED_RUN_DATE=$(date --date='7 days ago' '+%F') echo "Searching for latest run of '${WORKFLOW_FILE}' on branch '${BRANCH}' " LATEST_RUN_ID=$( gh run list \ --repo 'lightgbm-org/LightGBM' \ --event 'workflow_dispatch' \ --created ">= ${OLDEST_ALLOWED_RUN_DATE}" \ --workflow "${WORKFLOW_FILE}" \ --json 'createdAt,databaseId,name' \ --jq "sort_by(.createdAt) | reverse | map(select(.name | contains (\"pr=${PR_NUMBER}\"))) | .[0] | .databaseId" ) if [[ "${LATEST_RUN_ID}" == "" ]]; then echo "No runs of '${WORKFLOW_FILE}' found on branch from pull request ${PR_NUMBER} (on or after ${OLDEST_ALLOWED_RUN_DATE})." exit 0 fi echo "Checking status of workflow run '${LATEST_RUN_ID}'" gh run view \ --repo "lightgbm-org/LightGBM" \ --exit-status \ "${LATEST_RUN_ID}" ================================================ FILE: .ci/conda-envs/README.md ================================================ # conda-envs This directory contains files used to create `conda` environments for development and testing of LightGBM. The `.txt` files here are intended to be used with `conda create --file`. For details on that, see the `conda` docs: * `conda create` docs ([link](https://conda.io/projects/conda/en/latest/commands/create.html)) * "Managing Environments" ([link](https://conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html)) ================================================ FILE: .ci/conda-envs/ci-core-py39.txt ================================================ # [description] # # Similar to ci-core.txt, but specific to Python 3.9. # # Unlike ci-core.txt, this includes a Python version and uses # `=` and `<=` pins to make solves faster and prevent against # issues like https://github.com/lightgbm-org/LightGBM/pull/6370. # # [usage] # # conda create \ # --name test-env \ # --file ./.ci/conda-envs/ci-core-py39.txt # # python python=3.9.* # direct imports cffi=1.15.* # dask and distributed versions below are the first versions that support tornado >=6.2 # which is required for testing dask=2022.12.* distributed=2022.12.* joblib=1.3.* matplotlib-base=3.5.* numpy=1.22.* pandas=1.3.* pyarrow=9.0.* python-graphviz=0.20.* scikit-learn=1.0.* scipy=1.7.* # testing-only dependencies cloudpickle=2.2.* pluggy=1.0.* psutil=5.9.3 pytest=7.4.* # https://github.com/lightgbm-org/LightGBM/issues/6990 graphite2=1.3.14=*_0 # other recursive dependencies, just # pinned here to help speed up solves bokeh=2.4.* fsspec=2023.1.* msgpack-python=1.0.* pytz=2024.1 setuptools=59.8.* snappy=1.1.* tomli=2.0.* tornado=6.2.* wheel=0.42.* zict=2.2.* zipp=3.15.* ================================================ FILE: .ci/conda-envs/ci-core.txt ================================================ # [description] # # Core dependencies used across most LightGBM continuous integration (CI) jobs. # # 'python' constraint is intentionally omitted, so this file can be reused across # Python versions. # # These floors are not the oldest versions LightGBM supports... they're here just to make conda # solves faster, and should generally be the latest versions that work for all CI jobs using this. # # [usage] # # conda create \ # --name test-env \ # --file ./.ci/conda-envs/ci-core.txt \ # python=3.10 # # direct imports cffi>=1.16 dask>=2023.5.0,<2024.12 joblib>=1.3.2 matplotlib-base>=3.7.3 numpy>=1.24.4 pandas>2.0 pyarrow-core>=6.0 python-graphviz>=0.20.3 scikit-learn>=1.3.2 scipy>=1.1 # testing-only dependencies cloudpickle>=3.0.0 psutil>=5.9.8 pytest>=8.1.1 # other recursive dependencies, just # pinned here to help speed up solves pluggy>=1.4.0 setuptools>=69.2 wheel>=0.43 ================================================ FILE: .ci/create-nuget.py ================================================ # coding: utf-8 """Script for generating files with NuGet package metadata.""" import datetime import sys from pathlib import Path from shutil import copyfile if __name__ == "__main__": source = Path(sys.argv[1]) nuget_dir = Path(__file__).absolute().parent / "nuget" print(f"Creating nuget directory '{nuget_dir}'") linux_folder_path = nuget_dir / "runtimes" / "linux-x64" / "native" linux_folder_path.mkdir(parents=True, exist_ok=True) osx_folder_path = nuget_dir / "runtimes" / "osx-x64" / "native" osx_folder_path.mkdir(parents=True, exist_ok=True) windows_folder_path = nuget_dir / "runtimes" / "win-x64" / "native" windows_folder_path.mkdir(parents=True, exist_ok=True) build_folder_path = nuget_dir / "build" build_folder_path.mkdir(parents=True, exist_ok=True) print(f"Looking for libraries in '{source}'") copyfile(source / "lib_lightgbm.so", linux_folder_path / "lib_lightgbm.so") copyfile(source / "lib_lightgbm.dylib", osx_folder_path / "lib_lightgbm.dylib") copyfile(source / "lib_lightgbm.dll", windows_folder_path / "lib_lightgbm.dll") copyfile(source / "lightgbm.exe", windows_folder_path / "lightgbm.exe") version = (nuget_dir.parents[1] / "VERSION.txt").read_text(encoding="utf-8").strip().replace("rc", "-rc") print(f"Setting version to '{version}'") nuget_str = rf""" LightGBM {version} Guolin Ke Guolin Ke MIT https://github.com/lightgbm-org/LightGBM false A fast, distributed, high performance gradient boosting framework Copyright {datetime.datetime.now().year} @ Microsoft machine-learning data-mining distributed native boosting gbdt """ prop_str = r""" PreserveNewest false PreserveNewest false """ target_str = r""" true """ (nuget_dir / "LightGBM.nuspec").write_text(nuget_str, encoding="utf-8") (nuget_dir / "build" / "LightGBM.props").write_text(prop_str, encoding="utf-8") (nuget_dir / "build" / "LightGBM.targets").write_text(target_str, encoding="utf-8") print("Done creating NuGet package") ================================================ FILE: .ci/download-artifacts.sh ================================================ #!/bin/bash # [description] # Collect and download artifacts from all workflow runs for a commit. # # [usage] # ./download-artifacts.sh # set -e -u -E -o pipefail COMMIT_ID="${1}" OUTPUT_DIR="./release-artifacts" get-latest-run-id() { gh run list \ --repo "lightgbm-org/LightGBM" \ --commit "${1}" \ --workflow "${2}" \ --json 'createdAt,databaseId' \ --jq 'sort_by(.createdAt) | reverse | .[0] | .databaseId' } # ensure directory for storing artifacts exists echo "preparing to download artifacts for commit '${COMMIT_ID}' to '${OUTPUT_DIR}'" mkdir -p "${OUTPUT_DIR}" # get core artifacts echo "downloading core artifacts" gh run download \ --repo "lightgbm-org/LightGBM" \ --dir "${OUTPUT_DIR}" \ "$(get-latest-run-id "${COMMIT_ID}" 'build.yml')" echo "done downloading core artifacts" # get python-package artifacts echo "downloading python-package artifacts and NuGet package" gh run download \ --repo "lightgbm-org/LightGBM" \ --dir "${OUTPUT_DIR}" \ "$(get-latest-run-id "${COMMIT_ID}" 'python_package.yml')" echo "done downloading python-package artifacts and NuGet package" # get R-package artifacts echo "downloading R-package artifacts" gh run download \ --repo "lightgbm-org/LightGBM" \ --dir "${OUTPUT_DIR}" \ "$(get-latest-run-id "${COMMIT_ID}" 'r_package.yml')" echo "done downloading R-package artifacts" # get SWIG artifacts echo "downloading SWIG artifacts" gh run download \ --repo "lightgbm-org/LightGBM" \ --dir "${OUTPUT_DIR}" \ "$(get-latest-run-id "${COMMIT_ID}" 'swig.yml')" echo "done downloading SWIG artifacts" # 'gh run download' unpackages into nested directories like {artifact-name}/{file}. # # This moves all files to the top level and then deletes those {artifact-name}/ directories, # to make it easier to bulk upload all files to a release. echo "flattening directory structure" find "${OUTPUT_DIR}" -type f -mindepth 2 -exec mv -i '{}' "${OUTPUT_DIR}" \; find "${OUTPUT_DIR}" -type d -mindepth 1 -exec rm -r '{}' \+ echo "downloaded artifacts:" find "${OUTPUT_DIR}" -type f ================================================ FILE: .ci/install-opencl.ps1 ================================================ Write-Output "Installing OpenCL CPU platform" $installer = "AMD-APP-SDKInstaller-v3.0.130.135-GA-windows-F-x64.exe" Write-Output "Downloading OpenCL platform installer" $ProgressPreference = "SilentlyContinue" # progress bar bug extremely slows down download speed $params = @{ OutFile = "$installer" Uri = "https://github.com/lightgbm-org/LightGBM/releases/download/v2.0.12/$installer" } Invoke-WebRequest @params if (Test-Path "$installer") { Write-Output "Successfully downloaded OpenCL platform installer" } else { Write-Output "Unable to download OpenCL platform installer" Write-Output "Setting EXIT" $host.SetShouldExit(-1) exit 1 } # Install OpenCL platform from installer executable Write-Output "Running OpenCL installer" Invoke-Command -ScriptBlock { Start-Process "$installer" -ArgumentList '/S /V"/quiet /norestart /passive /log opencl.log"' -Wait } $property = Get-ItemProperty -Path Registry::HKEY_LOCAL_MACHINE\SOFTWARE\Khronos\OpenCL\Vendors if ($null -eq $property) { Write-Output "Unable to install OpenCL CPU platform" Write-Output "OpenCL installation log:" Get-Content "opencl.log" Write-Output "Setting EXIT" $host.SetShouldExit(-1) exit 1 } else { Write-Output "Successfully installed OpenCL CPU platform" Write-Output "Current OpenCL drivers:" Write-Output $property } ================================================ FILE: .ci/install-r-deps.R ================================================ # Install R dependencies, using only base R. # # Supported arguments: # # --all Install all the 'Depends', 'Imports', 'LinkingTo', and 'Suggests' dependencies # (automatically implies --build --test). # # --build Install the packages needed to build. # # --exclude= Comma-delimited list of packages to NOT install. # # --include= Comma-delimited list of additional packages to install. # These will always be installed, unless also used in "--exclude". # # --test Install packages needed to run tests. # # [description] Parse command line arguments into an R list. # Returns a list where keys are arguments and values # are either TRUE (for flags) or a vector of values passed via a # comma-delimited list. .parse_args <- function(args) { out <- list( "--all" = FALSE , "--build" = FALSE , "--exclude" = character(0L) , "--include" = character(0L) , "--test" = FALSE ) for (arg in args) { parsed_arg <- unlist(strsplit(arg, "=", fixed = TRUE)) arg_name <- parsed_arg[[1L]] if (!(arg_name %in% names(out))) { stop(sprintf("Unrecognized argument: '%s'", arg_name)) } if (length(parsed_arg) == 2L) { # lists, like "--include=roxygen2,testthat" values <- unlist(strsplit(parsed_arg[[2L]], ",", fixed = TRUE)) out[[arg_name]] <- values } else { # flags, like "--build" out[[arg]] <- TRUE } } return(out) } args <- .parse_args( commandArgs(trailingOnly = TRUE) ) # which dependencies to install ALL_DEPS <- isTRUE(args[["--all"]]) BUILD_DEPS <- ALL_DEPS || isTRUE(args[["--build"]]) TEST_DEPS <- ALL_DEPS || isTRUE(args[["--test"]]) # force downloading of binary packages on macOS COMPILE_FROM_SOURCE <- "both" PACKAGE_TYPE <- getOption("pkgType") # CRAN has precompiled binaries for macOS and Windows... prefer those, # for faster installation. if (Sys.info()[["sysname"]] == "Darwin" || .Platform$OS.type == "windows") { COMPILE_FROM_SOURCE <- "never" PACKAGE_TYPE <- "binary" } options( install.packages.check.source = "no" , install.packages.compile.from.source = COMPILE_FROM_SOURCE ) # always use the same CRAN mirror CRAN_MIRROR <- Sys.getenv("CRAN_MIRROR", unset = "https://cran.r-project.org") # we always want these deps_to_install <- c( "data.table" , "jsonlite" , "Matrix" , "R6" ) if (isTRUE(BUILD_DEPS)) { deps_to_install <- c( deps_to_install , "knitr" , "markdown" ) } if (isTRUE(TEST_DEPS)) { deps_to_install <- c( deps_to_install , "RhpcBLASctl" , "testthat" ) } # add packages passed through '--include' deps_to_install <- unique(c( deps_to_install , args[["--include"]] )) # remove packages passed through '--exclude' deps_to_install <- setdiff( x = deps_to_install , args[["--exclude"]] ) msg <- sprintf( "[install-r-deps] installing R packages: %s\n" , toString(sort(deps_to_install)) ) cat(msg) install.packages( # nolint: undesirable_function. pkgs = deps_to_install , dependencies = c("Depends", "Imports", "LinkingTo") , lib = Sys.getenv("R_LIB_PATH", unset = .libPaths()[[1L]]) , repos = CRAN_MIRROR , type = PACKAGE_TYPE , Ncpus = parallel::detectCores() ) ================================================ FILE: .ci/lint-all.sh ================================================ #!/bin/bash set -e -E -u -o pipefail pwsh -command "Install-Module -Name PSScriptAnalyzer -Scope CurrentUser -SkipPublisherCheck" echo "Linting PowerShell code" pwsh -file ./.ci/lint-powershell.ps1 || exit 1 conda create -q -y -n test-env \ "python=3.13[build=*_cp*]" \ 'pre-commit>=3.8.0' \ 'r-lintr>=3.3.0' # shellcheck disable=SC1091 source activate test-env echo "Running pre-commit checks" pre-commit run --all-files || exit 1 echo "Linting R code" Rscript ./.ci/lint-r-code.R "$(pwd)" || exit 1 ================================================ FILE: .ci/lint-powershell.ps1 ================================================ $ErrorActionPreference = 'Stop' $settings = @{ Severity = @( 'Information', 'Warning', 'Error' ) IncludeDefaultRules = $true # Additional rules that are disabled by default Rules = @{ PSAvoidExclaimOperator = @{ Enable = $true } PSAvoidLongLines = @{ Enable = $true MaximumLineLength = 120 } PSAvoidSemicolonsAsLineTerminators = @{ Enable = $true } PSPlaceCloseBrace = @{ Enable = $true NoEmptyLineBefore = $true IgnoreOneLineBlock = $true NewLineAfter = $false } PSPlaceOpenBrace = @{ Enable = $true OnSameLine = $true NewLineAfter = $true IgnoreOneLineBlock = $true } PSUseConsistentIndentation = @{ Enable = $true IndentationSize = 4 PipelineIndentation = 'IncreaseIndentationAfterEveryPipeline' Kind = 'space' } PSUseConsistentWhitespace = @{ Enable = $true CheckInnerBrace = $true CheckOpenBrace = $true CheckOpenParen = $true CheckOperator = $true CheckSeparator = $true CheckPipe = $true CheckPipeForRedundantWhitespace = $true CheckParameter = $true IgnoreAssignmentOperatorInsideHashTable = $false } PSUseCorrectCasing = @{ Enable = $true } } } Invoke-ScriptAnalyzer -Path ./ -Recurse -EnableExit -Settings $settings ================================================ FILE: .ci/lint-r-code.R ================================================ loadNamespace("lintr") args <- commandArgs( trailingOnly = TRUE ) SOURCE_DIR <- args[[1L]] FILES_TO_LINT <- list.files( path = SOURCE_DIR , pattern = "\\.r$|\\.rmd$" , all.files = TRUE , ignore.case = TRUE , full.names = TRUE , recursive = TRUE , include.dirs = FALSE ) # text to use for pipe operators from packages like 'magrittr' pipe_text <- paste0( "For consistency and the sake of being explicit, this project's code " , "does not use the pipe operator." ) # text to use for functions that should only be called interactively interactive_text <- paste0( "Functions like '?', 'help', and 'install.packages()' should only be used " , "interactively, not in package code." ) LINTERS_TO_USE <- list( "absolute_path" = lintr::absolute_path_linter() , "any_duplicated" = lintr::any_duplicated_linter() , "any_is_na" = lintr::any_is_na_linter() , "assignment" = lintr::assignment_linter() , "backport" = lintr::backport_linter() , "boolean_arithmetic" = lintr::boolean_arithmetic_linter() , "braces" = lintr::brace_linter() , "class_equals" = lintr::class_equals_linter() , "commas" = lintr::commas_linter() , "conjunct_test" = lintr::conjunct_test_linter() , "duplicate_argument" = lintr::duplicate_argument_linter() , "empty_assignment" = lintr::empty_assignment_linter() , "equals_na" = lintr::equals_na_linter() , "fixed_regex" = lintr::fixed_regex_linter() , "for_loop_index" = lintr::for_loop_index_linter() , "function_left" = lintr::function_left_parentheses_linter() , "function_return" = lintr::function_return_linter() , "implicit_assignment" = lintr::implicit_assignment_linter() , "implicit_integers" = lintr::implicit_integer_linter() , "infix_spaces" = lintr::infix_spaces_linter() , "inner_combine" = lintr::inner_combine_linter() , "is_numeric" = lintr::is_numeric_linter() , "lengths" = lintr::lengths_linter() , "length_levels" = lintr::length_levels_linter() , "length_test" = lintr::length_test_linter() , "line_length" = lintr::line_length_linter(length = 120L) , "literal_coercion" = lintr::literal_coercion_linter() , "matrix" = lintr::matrix_apply_linter() , "missing_argument" = lintr::missing_argument_linter() , "non_portable_path" = lintr::nonportable_path_linter() , "numeric_leading_zero" = lintr::numeric_leading_zero_linter() , "outer_negation" = lintr::outer_negation_linter() , "package_hooks" = lintr::package_hooks_linter() , "paren_body" = lintr::paren_body_linter() , "paste" = lintr::paste_linter() , "quotes" = lintr::quotes_linter() , "redundant_equals" = lintr::redundant_equals_linter() , "regex_subset" = lintr::regex_subset_linter() , "routine_registration" = lintr::routine_registration_linter() , "scalar_in" = lintr::scalar_in_linter() , "semicolon" = lintr::semicolon_linter() , "seq" = lintr::seq_linter() , "spaces_inside" = lintr::spaces_inside_linter() , "spaces_left_parens" = lintr::spaces_left_parentheses_linter() , "sprintf" = lintr::sprintf_linter() , "string_boundary" = lintr::string_boundary_linter() , "todo_comments" = lintr::todo_comment_linter(c("todo", "fixme", "to-do")) , "trailing_blank" = lintr::trailing_blank_lines_linter() , "trailing_white" = lintr::trailing_whitespace_linter() , "true_false" = lintr::T_and_F_symbol_linter() , "undesirable_function" = lintr::undesirable_function_linter( fun = c( "cbind" = paste0( "cbind is an unsafe way to build up a data frame. merge() or direct " , "column assignment is preferred." ) , "dyn.load" = "Directly loading or unloading .dll or .so files in package code should not be necessary." , "dyn.unload" = "Directly loading or unloading .dll or .so files in package code should not be necessary." , "help" = interactive_text , "ifelse" = "The use of ifelse() is dangerous because it will silently allow mixing types." , "install.packages" = interactive_text , "is.list" = paste0( "This project uses data.table, and is.list(x) is TRUE for a data.table. " , "identical(class(x), 'list') is a safer way to check that something is an R list object." ) , "rbind" = "data.table::rbindlist() is faster and safer than rbind(), and is preferred in this project." , "require" = paste0( "library() is preferred to require() because it will raise an error immediately " , "if a package is missing." ) ) ) , "undesirable_operator" = lintr::undesirable_operator_linter( op = c( "%>%" = pipe_text , "%.%" = pipe_text , "%..%" = pipe_text , "|>" = pipe_text , "?" = interactive_text , "??" = interactive_text ) ) , "unnecessary_concatenation" = lintr::unnecessary_concatenation_linter() , "unnecessary_lambda" = lintr::unnecessary_lambda_linter() , "unreachable_code" = lintr::unreachable_code_linter() , "unused_import" = lintr::unused_import_linter() , "vector_logic" = lintr::vector_logic_linter() , "whitespace" = lintr::whitespace_linter() ) noquote(paste0(length(FILES_TO_LINT), " R files need linting")) results <- NULL for (r_file in FILES_TO_LINT) { this_result <- lintr::lint( filename = r_file , linters = LINTERS_TO_USE , cache = FALSE ) print( sprintf( "Found %i linting errors in %s" , length(this_result) , r_file ) , quote = FALSE ) results <- c(results, this_result) } issues_found <- length(results) noquote(paste0("Total linting issues found: ", issues_found)) if (issues_found > 0L) { print(results) } quit(save = "no", status = issues_found) ================================================ FILE: .ci/parameter-generator.py ================================================ # coding: utf-8 """Helper script for generating config file and parameters list. This script generates LightGBM/src/io/config_auto.cpp file with list of all parameters, aliases table and other routines along with parameters description in LightGBM/docs/Parameters.rst file from the information in LightGBM/include/LightGBM/config.h file. """ import re from collections import defaultdict from pathlib import Path from typing import Dict, List, Tuple def get_parameter_infos(config_hpp: Path) -> Tuple[List[Tuple[str, int]], List[List[Dict[str, List]]]]: """Parse config header file. Parameters ---------- config_hpp : pathlib.Path Path to the config header file. Returns ------- infos : tuple Tuple with names and content of sections. """ is_inparameter = False cur_key = None key_lvl = 0 cur_info: Dict[str, List] = {} keys = [] member_infos: List[List[Dict[str, List]]] = [] with open(config_hpp) as config_hpp_file: for line in config_hpp_file: if line.strip() in {"#ifndef __NVCC__", "#endif // __NVCC__"}: continue if "#pragma region Parameters" in line: is_inparameter = True elif "#pragma region" in line and "Parameters" in line: key_lvl += 1 cur_key = line.split("region")[1].strip() keys.append((cur_key, key_lvl)) member_infos.append([]) elif "#pragma endregion" in line: key_lvl -= 1 if cur_key is not None: cur_key = None elif is_inparameter: is_inparameter = False elif cur_key is not None: line = line.strip() if line.startswith("//"): key, _, val = line[2:].partition("=") key = key.strip() val = val.strip() if key not in cur_info: if key == "descl2" and "desc" not in cur_info: cur_info["desc"] = [] elif key != "descl2": cur_info[key] = [] if key == "desc": cur_info["desc"].append(("l1", val)) elif key == "descl2": cur_info["desc"].append(("l2", val)) else: cur_info[key].append(val) elif line: has_eqsgn = False tokens = line.split("=") if len(tokens) == 2: if "default" not in cur_info: cur_info["default"] = [tokens[1][:-1].strip()] has_eqsgn = True tokens = line.split() cur_info["inner_type"] = [tokens[0].strip()] if "name" not in cur_info: if has_eqsgn: cur_info["name"] = [tokens[1].strip()] else: cur_info["name"] = [tokens[1][:-1].strip()] member_infos[-1].append(cur_info) cur_info = {} return keys, member_infos def get_names(infos: List[List[Dict[str, List]]]) -> List[str]: """Get names of all parameters. Parameters ---------- infos : list Content of the config header file. Returns ------- names : list Names of all parameters. """ names = [] for x in infos: for y in x: names.append(y["name"][0]) return names def get_alias(infos: List[List[Dict[str, List]]]) -> List[Tuple[str, str]]: """Get aliases of all parameters. Parameters ---------- infos : list Content of the config header file. Returns ------- pairs : list List of tuples (param alias, param name). """ pairs = [] for x in infos: for y in x: if "alias" in y: name = y["name"][0] alias = y["alias"][0].split(",") for name2 in alias: pairs.append((name2.strip(), name)) return pairs def parse_check(check: str, reverse: bool = False) -> Tuple[str, str]: """Parse the constraint. Parameters ---------- check : str String representation of the constraint. reverse : bool, optional (default=False) Whether to reverse the sign of the constraint. Returns ------- pair : tuple Parsed constraint in the form of tuple (value, sign). """ try: idx = 1 float(check[idx:]) except ValueError: idx = 2 float(check[idx:]) if reverse: reversed_sign = {"<": ">", ">": "<", "<=": ">=", ">=": "<="} return check[idx:], reversed_sign[check[:idx]] else: return check[idx:], check[:idx] def set_one_var_from_string(name: str, param_type: str, checks: List[str]) -> str: """Construct code for auto config file for one param value. Parameters ---------- name : str Name of the parameter. param_type : str Type of the parameter. checks : list Constraints of the parameter. Returns ------- ret : str Lines of auto config file with getting and checks of one parameter value. """ ret = "" univar_mapper = {"int": "GetInt", "double": "GetDouble", "bool": "GetBool", "std::string": "GetString"} if "vector" not in param_type: ret += f' {univar_mapper[param_type]}(params, "{name}", &{name});\n' if len(checks) > 0: check_mapper = {"<": "LT", ">": "GT", "<=": "LE", ">=": "GE"} for check in checks: value, sign = parse_check(check) ret += f" CHECK_{check_mapper[sign]}({name}, {value});\n" ret += "\n" else: ret += f' if (GetString(params, "{name}", &tmp_str)) {{\n' type2 = param_type.split("<")[1][:-1] if type2 == "std::string": ret += f" {name} = Common::Split(tmp_str.c_str(), ',');\n" else: ret += f" {name} = Common::StringToArray<{type2}>(tmp_str, ',');\n" ret += " }\n\n" return ret def gen_parameter_description( sections: List[Tuple[str, int]], descriptions: List[List[Dict[str, List]]], params_rst: Path ) -> None: """Write descriptions of parameters to the documentation file. Parameters ---------- sections : list Names of parameters sections. descriptions : list Structured descriptions of parameters. params_rst : pathlib.Path Path to the file with parameters documentation. """ params_to_write = [] lvl_mapper = {1: "-", 2: "~"} for (section_name, section_lvl), section_params in zip(sections, descriptions): heading_sign = lvl_mapper[section_lvl] params_to_write.append(f"{section_name}\n{heading_sign * len(section_name)}") for param_desc in section_params: name = param_desc["name"][0] default_raw = param_desc["default"][0] default = default_raw.strip('"') if len(default_raw.strip('"')) > 0 else default_raw param_type = param_desc.get("type", param_desc["inner_type"])[0].split(":")[-1].split("<")[-1].strip(">") options = param_desc.get("options", []) if len(options) > 0: opts = "``, ``".join([x.strip() for x in options[0].split(",")]) options_str = f", options: ``{opts}``" else: options_str = "" aliases = param_desc.get("alias", []) if len(aliases) > 0: aliases_joined = "``, ``".join([x.strip() for x in aliases[0].split(",")]) aliases_str = f", aliases: ``{aliases_joined}``" else: aliases_str = "" checks = sorted(param_desc.get("check", [])) checks_len = len(checks) if checks_len > 1: number1, sign1 = parse_check(checks[0]) number2, sign2 = parse_check(checks[1], reverse=True) checks_str = f", constraints: ``{number2} {sign2} {name} {sign1} {number1}``" elif checks_len == 1: number, sign = parse_check(checks[0]) checks_str = f", constraints: ``{name} {sign} {number}``" else: checks_str = "" main_desc = f'- ``{name}`` :raw-html:`🔗︎`, default = ``{default}``, type = {param_type}{options_str}{aliases_str}{checks_str}' params_to_write.append(main_desc) params_to_write.extend([f"{' ' * 3 * int(desc[0][-1])}- {desc[1]}" for desc in param_desc["desc"]]) with open(params_rst) as original_params_file: all_lines = original_params_file.read() before, start_sep, _ = all_lines.partition(".. start params list\n\n") _, end_sep, after = all_lines.partition("\n\n.. end params list") with open(params_rst, "w") as new_params_file: new_params_file.write(before) new_params_file.write(start_sep) new_params_file.write("\n\n".join(params_to_write)) new_params_file.write(end_sep) new_params_file.write(after) def gen_parameter_code( config_hpp: Path, config_out_cpp: Path ) -> Tuple[List[Tuple[str, int]], List[List[Dict[str, List]]]]: """Generate auto config file. Parameters ---------- config_hpp : pathlib.Path Path to the config header file. config_out_cpp : pathlib.Path Path to the auto config file. Returns ------- infos : tuple Tuple with names and content of sections. """ keys, infos = get_parameter_infos(config_hpp) names = get_names(infos) alias = get_alias(infos) names_with_aliases = defaultdict(list) str_to_write = r"""/*! * Copyright (c) 2018-2026 Microsoft Corporation. All rights reserved. * Copyright (c) 2018-2026 The LightGBM developers. All rights reserved. * Licensed under the MIT License. See LICENSE file in the project root for license information. * * \note * This file is auto generated by LightGBM\.ci\parameter-generator.py from LightGBM\include\LightGBM\config.h file. */ """ str_to_write += "#include \n\n" str_to_write += "#include \n" str_to_write += "#include \n" str_to_write += "#include \n" str_to_write += "#include \n\n" str_to_write += "namespace LightGBM {\n" # alias table str_to_write += "const std::unordered_map& Config::alias_table() {\n" str_to_write += " static std::unordered_map aliases({\n" for pair in alias: str_to_write += f' {{"{pair[0]}", "{pair[1]}"}},\n' names_with_aliases[pair[1]].append(pair[0]) str_to_write += " });\n" str_to_write += " return aliases;\n" str_to_write += "}\n\n" # names str_to_write += "const std::unordered_set& Config::parameter_set() {\n" str_to_write += " static std::unordered_set params({\n" for name in names: str_to_write += f' "{name}",\n' str_to_write += " });\n" str_to_write += " return params;\n" str_to_write += "}\n\n" # from strings str_to_write += "void Config::GetMembersFromString(const std::unordered_map& params) {\n" str_to_write += ' std::string tmp_str = "";\n' for x in infos: for y in x: if "[no-automatically-extract]" in y: continue param_type = y["inner_type"][0] name = y["name"][0] checks = [] if "check" in y: checks = y["check"] tmp = set_one_var_from_string(name, param_type, checks) str_to_write += tmp # tails str_to_write = f"{str_to_write.strip()}\n}}\n\n" str_to_write += "std::string Config::SaveMembersToString() const {\n" str_to_write += " std::stringstream str_buf;\n" for x in infos: for y in x: if "[no-save]" in y: continue param_type = y["inner_type"][0] name = y["name"][0] if "vector" in param_type: if "int8" in param_type: str_to_write += f' str_buf << "[{name}: " << Common::Join(Common::ArrayCast({name}), ",") << "]\\n";\n' else: str_to_write += f' str_buf << "[{name}: " << Common::Join({name}, ",") << "]\\n";\n' else: str_to_write += f' str_buf << "[{name}: " << {name} << "]\\n";\n' # tails str_to_write += " return str_buf.str();\n" str_to_write += "}\n\n" str_to_write += """const std::unordered_map>& Config::parameter2aliases() { static std::unordered_map> map({""" for name in names: str_to_write += '\n {"' + name + '", ' if names_with_aliases[name]: str_to_write += '{"' + '", "'.join(names_with_aliases[name]) + '"}},' else: str_to_write += "{}}," str_to_write += """ }); return map; } """ str_to_write += """const std::unordered_map& Config::ParameterTypes() { static std::unordered_map map({""" int_t_pat = re.compile(r"int\d+_t") # the following are stored as comma separated strings but are arrays in the wrappers overrides = { "categorical_feature": "vector", "ignore_column": "vector", "interaction_constraints": "vector>", } for x in infos: for y in x: name = y["name"][0] if name == "task": continue if name in overrides: param_type = overrides[name] else: param_type = int_t_pat.sub("int", y["inner_type"][0]).replace("std::", "") str_to_write += '\n {"' + name + '", "' + param_type + '"},' str_to_write += """ }); return map; } """ str_to_write += "} // namespace LightGBM\n" with open(config_out_cpp, "w") as config_out_cpp_file: config_out_cpp_file.write(str_to_write) return keys, infos if __name__ == "__main__": current_dir = Path(__file__).absolute().parent config_hpp = current_dir.parent / "include" / "LightGBM" / "config.h" config_out_cpp = current_dir.parent / "src" / "io" / "config_auto.cpp" params_rst = current_dir.parent / "docs" / "Parameters.rst" sections, descriptions = gen_parameter_code(config_hpp, config_out_cpp) gen_parameter_description(sections, descriptions, params_rst) ================================================ FILE: .ci/pip-envs/requirements-latest.txt ================================================ # nightlies for: matplotlib, numpy, pandas, pyarrow, scipy, scikit-learn --extra-index-url https://pypi.anaconda.org/scientific-python-nightly-wheels/simple # runtime dependencies (using `.dev0` suffix to allow nightlies) # # pinning rules: # # * latest versions of lightgbm's dependencies, # * including pre-releases and nightlies # cffi>=1.17.1 matplotlib>=3.11.0.dev0 numpy>=2.4.0.dev0 pandas>=3.0.0.dev0 pyarrow>=21.0.0.dev0 scikit-learn>=1.8.dev0 scipy>=1.17.0.dev0 # testing-only dependencies cloudpickle>=3.1.1 psutil>=7.0 pytest>=8.4.1 ================================================ FILE: .ci/pip-envs/requirements-oldest.txt ================================================ # oldest versions of dependencies published after # minimum supported Python version's first release, # for which there are wheels compatible with the # python:{version} image # # see https://devguide.python.org/versions/ # cffi==1.15.1 numpy==1.19.3 pandas==1.1.3 pyarrow==6.0.1 scikit-learn==0.24.2 scipy==1.6.0 ================================================ FILE: .ci/rerun-workflow.sh ================================================ #!/bin/bash # # [description] # Rerun specified workflow for given pull request. # # [usage] # rerun-workflow.sh # # WORKFLOW_ID: Identifier (config name of ID) of a workflow to be rerun. # # PR_BRANCH: Name of pull request's branch. set -e -E -u -o pipefail if [ -z "$GITHUB_ACTIONS" ]; then echo "Must be run inside GitHub Actions CI" exit 1 fi if [ $# -ne 2 ]; then echo "Usage: $0 " exit 1 fi workflow_id=$1 pr_branch=$2 # --branch for some GitHub GLI commands does not respect the difference between forks and branches # on the main repo. While some parts of the GitHub API refer to the branch of a workflow # as '{org}:{branch}' for branches from forks, others expect only '{branch}'. # # This expansion trims a leading '{org}:' from 'pr_branch' if one is present. pr_branch_no_fork_prefix="${pr_branch/*:/}" RUN_ID=$( gh run list \ --repo 'lightgbm-org/LightGBM' \ --workflow "${workflow_id}" \ --event "pull_request" \ --branch "${pr_branch_no_fork_prefix}" \ --json 'createdAt,databaseId' \ --jq 'sort_by(.createdAt) | reverse | .[0] | .databaseId' ) if [[ -z "${RUN_ID}" ]]; then echo "ERROR: failed to find a run of workflow '${workflow_id}' for branch '${pr_branch}'" exit 1 fi echo "Re-running workflow '${workflow_id}' (run ID ${RUN_ID})" gh run rerun \ --repo 'lightgbm-org/LightGBM' \ "${RUN_ID}" ================================================ FILE: .ci/run-r-cmd-check.sh ================================================ #!/bin/bash set -e -u -o pipefail PKG_TARBALL="${1}" declare -i ALLOWED_CHECK_NOTES=${2} # 'R CMD check' redirects installation logs to a file, and returns # a non-0 exit code if ERRORs are raised. # # The '||' here gives us an opportunity to echo out the installation # logs prior to exiting the script. check_succeeded="yes" R CMD check "${PKG_TARBALL}" \ --as-cran \ --run-donttest \ || check_succeeded="no" CHECK_LOG_FILE=lightgbm.Rcheck/00check.log BUILD_LOG_FILE=lightgbm.Rcheck/00install.out echo "R CMD check build logs:" cat "${BUILD_LOG_FILE}" if [[ $check_succeeded == "no" ]]; then echo "R CMD check failed" exit 1 fi # WARNINGs or ERRORs should be treated as a failure if grep -q -E "WARNING|ERROR" "${CHECK_LOG_FILE}"; then echo "WARNINGs or ERRORs have been found by R CMD check" exit 1 fi # Allow a configurable number of NOTEs. # Sometimes NOTEs are raised in CI that wouldn't show up on an actual CRAN submission. set +e NUM_CHECK_NOTES=$( grep -o -E '[0-9]+ NOTE' "${CHECK_LOG_FILE}" \ | sed 's/[^0-9]*//g' ) if [[ ${NUM_CHECK_NOTES} -gt ${ALLOWED_CHECK_NOTES} ]]; then echo "Found ${NUM_CHECK_NOTES} NOTEs from R CMD check. Only ${ALLOWED_CHECK_NOTES} are allowed" exit 1 fi ================================================ FILE: .ci/set-commit-status.sh ================================================ #!/bin/bash # # [description] # Set a status with a given name to the specified commit. # # [usage] # set-commit-status.sh # # NAME: Name of status. # Status with existing name overwrites a previous one. # # STATUS: Status to be set. # Can be "error", "failure", "pending" or "success". # # SHA: SHA of a commit to set a status on. set -e -E -u -o pipefail if [ -z "$GITHUB_ACTIONS" ]; then echo "Must be run inside GitHub Actions CI" exit 1 fi if [ $# -ne 3 ]; then echo "Usage: $0 " exit 1 fi name=$1 status=$2 status=${status/error/failure} status=${status/cancelled/failure} status=${status/timed_out/failure} status=${status/in_progress/pending} status=${status/queued/pending} sha=$3 data=$( jq -n \ --arg state "${status}" \ --arg url "${GITHUB_SERVER_URL}/lightgbm-org/LightGBM/actions/runs/${GITHUB_RUN_ID}" \ --arg name "${name}" \ '{"state":$state,"target_url":$url,"context":$name}' ) curl -sL \ --fail \ -X POST \ -H "Accept: application/vnd.github.v3+json" \ -H "Authorization: token ${GITHUB_TOKEN}" \ -d "$data" \ "${GITHUB_API_URL}/repos/lightgbm-org/LightGBM/statuses/$sha" ================================================ FILE: .ci/setup.sh ================================================ #!/bin/bash set -e -E -u -o pipefail # defaults IN_UBUNTU_BASE_CONTAINER=${IN_UBUNTU_BASE_CONTAINER:-"false"} SETUP_CONDA=${SETUP_CONDA:-"true"} ARCH=$(uname -m) if [[ $OS_NAME == "macos" ]]; then # Check https://github.com/actions/runner-images/tree/main/images/macos for available # versions of Xcode macos_ver=$(sw_vers --productVersion) if [[ "${macos_ver}" =~ 15. ]]; then xcode_path="/Applications/Xcode_16.0.app/Contents/Developer" else xcode_path="/Applications/Xcode_15.0.app/Contents/Developer" fi sudo xcode-select -s "${xcode_path}" || exit 1 if [[ $COMPILER == "clang" ]]; then brew install libomp else # gcc brew install 'gcc@14' fi if [[ $TASK == "mpi" ]]; then brew install open-mpi fi if [[ $TASK == "swig" ]]; then brew install swig fi else # Linux if type -f apt > /dev/null 2>&1; then sudo apt-get update sudo apt-get install --no-install-recommends -y \ ca-certificates \ curl else sudo yum update -y sudo yum install -y \ ca-certificates \ curl fi CMAKE_VERSION="3.30.0" curl -O -L \ "https://github.com/Kitware/CMake/releases/download/v${CMAKE_VERSION}/cmake-${CMAKE_VERSION}-linux-${ARCH}.sh" \ || exit 1 sudo mkdir /opt/cmake || exit 1 sudo sh "cmake-${CMAKE_VERSION}-linux-${ARCH}.sh" --skip-license --prefix=/opt/cmake || exit 1 sudo ln -sf /opt/cmake/bin/cmake /usr/local/bin/cmake || exit 1 if [[ $IN_UBUNTU_BASE_CONTAINER == "true" ]]; then # fixes error "unable to initialize frontend: Dialog" # https://github.com/moby/moby/issues/27988#issuecomment-462809153 echo 'debconf debconf/frontend select Noninteractive' | sudo debconf-set-selections sudo apt-get update sudo apt-get install --no-install-recommends -y \ software-properties-common sudo apt-get install --no-install-recommends -y \ build-essential \ git \ libcurl4 \ libicu-dev \ libssl-dev \ locales \ locales-all || exit 1 if [[ $COMPILER == "clang" ]]; then sudo apt-get install --no-install-recommends -y \ clang \ libomp-dev elif [[ $COMPILER == "clang-17" ]]; then sudo apt-get install --no-install-recommends -y \ wget wget -qO- https://apt.llvm.org/llvm-snapshot.gpg.key | sudo tee /etc/apt/trusted.gpg.d/apt.llvm.org.asc sudo apt-add-repository deb http://apt.llvm.org/jammy/ llvm-toolchain-jammy-17 main sudo apt-add-repository deb-src http://apt.llvm.org/jammy/ llvm-toolchain-jammy-17 main sudo apt-get update sudo apt-get install -y \ clang-17 \ libomp-17-dev fi export LANG="en_US.UTF-8" sudo update-locale LANG=${LANG} export LC_ALL="${LANG}" fi if [[ $TASK == "r-package" ]] && [[ $COMPILER == "clang" ]]; then sudo apt-get install --no-install-recommends -y \ libomp-dev fi if [[ $TASK == "mpi" ]]; then if [[ $IN_UBUNTU_BASE_CONTAINER == "true" ]]; then sudo apt-get update sudo apt-get install --no-install-recommends -y \ libopenmpi-dev \ openmpi-bin else # in manylinux image sudo yum update -y sudo yum install -y \ openmpi-devel \ || exit 1 fi fi if [[ $TASK == "gpu" ]]; then if [[ $IN_UBUNTU_BASE_CONTAINER == "true" ]]; then sudo apt-get update sudo apt-get install --no-install-recommends -y \ libboost1.74-dev \ libboost-filesystem1.74-dev \ ocl-icd-opencl-dev else # in manylinux image sudo yum update -y sudo yum install -y \ boost-devel \ ocl-icd-devel \ opencl-headers \ || exit 1 fi fi if [[ $TASK == "gpu" || $TASK == "bdist" ]]; then if [[ $IN_UBUNTU_BASE_CONTAINER == "true" ]]; then sudo apt-get update sudo apt-get install --no-install-recommends -y \ patchelf \ pocl-opencl-icd elif [[ $(uname -m) == "x86_64" ]]; then sudo yum update -y sudo yum install -y \ ocl-icd-devel \ opencl-headers \ || exit 1 fi fi if [[ $TASK == "cuda" ]]; then echo 'debconf debconf/frontend select Noninteractive' | debconf-set-selections if [[ $COMPILER == "clang" ]]; then apt-get update apt-get install --no-install-recommends -y \ clang \ libomp-dev fi fi fi if [[ "${TASK}" != "cpp-tests" ]] && [[ "${TASK}" != "r-package" ]] && [[ "${TASK}" != "swig" ]]; then if [[ $SETUP_CONDA != "false" ]]; then curl \ -sL \ -o miniforge.sh \ "https://github.com/conda-forge/miniforge/releases/latest/download/Miniforge3-$(uname)-${ARCH}.sh" sh miniforge.sh -b -p "${CONDA}" fi conda config --set always_yes yes --set changeps1 no conda update -q -y conda # print output of 'conda info', to help in submitting bug reports echo "conda info:" conda info fi ================================================ FILE: .ci/test-python-latest.sh ================================================ #!/bin/bash set -e -E -u -o pipefail echo "installing lightgbm and its dependencies" pip install \ --prefer-binary \ --upgrade \ -r ./.ci/pip-envs/requirements-latest.txt \ dist/*.whl echo "installed package versions:" pip freeze echo "" echo "running tests" pytest tests/c_api_test/ pytest tests/python_package_test/ ================================================ FILE: .ci/test-python-oldest.sh ================================================ #!/bin/bash set -e -E -u -o pipefail echo "installing lightgbm and its dependencies" pip install \ --prefer-binary \ --upgrade \ -r ./.ci/pip-envs/requirements-oldest.txt \ dist/*.whl echo "installed package versions:" pip freeze echo "" echo "checking that examples run without error" # run a few examples to test that Python-package minimally works echo "" echo "--- advanced_example.py ---" echo "" python ./examples/python-guide/advanced_example.py || exit 1 echo "" echo "--- logistic_regression.py ---" echo "" python ./examples/python-guide/logistic_regression.py || exit 1 echo "" echo "--- simple_example.py ---" echo "" python ./examples/python-guide/simple_example.py || exit 1 echo "" echo "--- sklearn_example.py ---" echo "" python ./examples/python-guide/sklearn_example.py || exit 1 echo "" echo "done testing on oldest supported Python version" ================================================ FILE: .ci/test-r-package-valgrind.sh ================================================ #!/bin/bash set -e -E -u -o pipefail RDscriptvalgrind ./.ci/install-r-deps.R --test || exit 1 sh build-cran-package.sh \ --r-executable=RDvalgrind \ --no-build-vignettes \ || exit 1 RDvalgrind CMD INSTALL --preclean --install-tests lightgbm_*.tar.gz || exit 1 cd R-package/tests ALL_LOGS_FILE="out.log" VALGRIND_LOGS_FILE="valgrind-logs.log" RDvalgrind \ --no-readline \ --vanilla \ -d "valgrind --tool=memcheck --leak-check=full --track-origins=yes --gen-suppressions=all" \ -f testthat.R \ > ${ALL_LOGS_FILE} 2>&1 || exit 1 cat ${ALL_LOGS_FILE} echo "writing valgrind output to ${VALGRIND_LOGS_FILE}" cat ${ALL_LOGS_FILE} | grep -E "^\=" > ${VALGRIND_LOGS_FILE} bytes_definitely_lost=$( cat ${VALGRIND_LOGS_FILE} \ | grep -E "definitely lost\: .*" \ | sed 's/^.*definitely lost\: \(.*\) bytes.*$/\1/' \ | tr -d "," ) echo "valgrind found ${bytes_definitely_lost} bytes definitely lost" if [[ ${bytes_definitely_lost} -gt 0 ]]; then exit 1 fi bytes_indirectly_lost=$( cat ${VALGRIND_LOGS_FILE} \ | grep -E "indirectly lost\: .*" \ | sed 's/^.*indirectly lost\: \(.*\) bytes.*$/\1/' \ | tr -d "," ) echo "valgrind found ${bytes_indirectly_lost} bytes indirectly lost" if [[ ${bytes_indirectly_lost} -gt 0 ]]; then exit 1 fi # one error caused by a false positive between valgrind and openmp is allowed # ==2063== 352 bytes in 1 blocks are possibly lost in loss record 153 of 2,709 # ==2063== at 0x483DD99: calloc (in /usr/lib/x86_64-linux-gnu/valgrind/vgpreload_memcheck-amd64-linux.so) # ==2063== by 0x40149CA: allocate_dtv (dl-tls.c:286) # ==2063== by 0x40149CA: _dl_allocate_tls (dl-tls.c:532) # ==2063== by 0x5702322: allocate_stack (allocatestack.c:622) # ==2063== by 0x5702322: pthread_create@@GLIBC_2.2.5 (pthread_create.c:660) # ==2063== by 0x56D0DDA: ??? (in /usr/lib/x86_64-linux-gnu/libgomp.so.1.0.0) # ==2063== by 0x56C88E0: GOMP_parallel (in /usr/lib/x86_64-linux-gnu/libgomp.so.1.0.0) # ==2063== by 0x1544D29C: LGBM_DatasetCreateFromCSC (c_api.cpp:1286) # ==2063== by 0x1546F980: LGBM_DatasetCreateFromCSC_R (lightgbm_R.cpp:91) # ==2063== by 0x4941E2F: R_doDotCall (dotcode.c:634) # ==2063== by 0x494CCC6: do_dotcall (dotcode.c:1281) # ==2063== by 0x499FB01: bcEval (eval.c:7078) # ==2063== by 0x498B67F: Rf_eval (eval.c:727) # ==2063== by 0x498E414: R_execClosure (eval.c:1895) bytes_possibly_lost=$( cat ${VALGRIND_LOGS_FILE} \ | grep -E "possibly lost\: .*" \ | sed 's/^.*possibly lost\: \(.*\) bytes.*$/\1/' \ | tr -d "," ) echo "valgrind found ${bytes_possibly_lost} bytes possibly lost" if [[ ${bytes_possibly_lost} -gt 1104 ]]; then exit 1 fi # ensure 'grep --count' doesn't cause failures set +e echo "checking for invalid reads" invalid_reads=$( cat ${VALGRIND_LOGS_FILE} \ | grep --count -i "Invalid read" ) if [[ ${invalid_reads} -gt 0 ]]; then echo "valgrind found invalid reads: ${invalid_reads}" exit 1 fi echo "checking for invalid writes" invalid_writes=$( cat ${VALGRIND_LOGS_FILE} \ | grep --count -i "Invalid write" ) if [[ ${invalid_writes} -gt 0 ]]; then echo "valgrind found invalid writes: ${invalid_writes}" exit 1 fi ================================================ FILE: .ci/test-r-package-windows.ps1 ================================================ # Download a file and retry upon failure. This looks like # an infinite loop but CI-level timeouts will kill it function Get-File-With-Tenacity { param( [Parameter(Mandatory = $true)][string]$url, [Parameter(Mandatory = $true)][string]$destfile ) $ProgressPreference = "SilentlyContinue" # progress bar bug extremely slows down download speed do { Write-Output "Downloading ${url}" sleep 5 Invoke-WebRequest -Uri $url -OutFile $destfile } while (-not $?) } # External utilities like R.exe / Rscript.exe writing to stderr (even for harmless # status information) can cause failures in GitHub Actions PowerShell jobs. # See https://github.community/t/powershell-steps-fail-nondeterministically/115496 # # Using standard PowerShell redirection does not work to avoid these errors. # This function uses R's built-in redirection mechanism, sink(). Any place where # this function is used is a command that writes harmless messages to stderr function Invoke-R-Code-Redirect-Stderr { param( [Parameter(Mandatory = $true)][string]$rcode ) $decorated_code = "out_file <- file(tempfile(), open = 'wt'); sink(out_file, type = 'message'); $rcode; sink()" Rscript --vanilla -e $decorated_code } # Remove all items matching some pattern from PATH environment variable function Remove-From-Path { [CmdletBinding(SupportsShouldProcess)] param( [Parameter(Mandatory = $true)][string]$pattern_to_remove ) if ($PSCmdlet.ShouldProcess($env:PATH, "Removing ${pattern_to_remove}")) { $env:PATH = ($env:PATH.Split(';') | Where-Object { $_ -notmatch "$pattern_to_remove" }) -join ';' } } # remove some details that exist in the GitHub Actions images which might # cause conflicts with R and other components installed by this script $env:RTOOLS40_HOME = "" Remove-From-Path ".*Amazon.*" Remove-From-Path ".*Anaconda.*" Remove-From-Path ".*android.*" Remove-From-Path ".*Android.*" Remove-From-Path ".*chocolatey.*" Remove-From-Path ".*Chocolatey.*" Remove-From-Path ".*cmake.*" Remove-From-Path ".*CMake.*" Remove-From-Path ".*\\Git\\.*" Remove-From-Path "(?!.*pandoc.*).*hostedtoolcache.*" Remove-From-Path ".*Microsoft SDKs.*" Remove-From-Path ".*mingw.*" Remove-From-Path ".*msys64.*" Remove-From-Path ".*PostgreSQL.*" Remove-From-Path ".*\\R\\.*" Remove-From-Path ".*R Client.*" Remove-From-Path ".*rtools40.*" Remove-From-Path ".*rtools42.*" Remove-From-Path ".*rtools43.*" Remove-From-Path ".*rtools44.*" Remove-From-Path ".*rtools45.*" Remove-From-Path ".*shells.*" Remove-From-Path ".*Strawberry.*" Remove-From-Path ".*tools.*" Remove-Item C:\rtools40 -Force -Recurse -ErrorAction Ignore Remove-Item C:\rtools42 -Force -Recurse -ErrorAction Ignore Remove-Item C:\rtools43 -Force -Recurse -ErrorAction Ignore Remove-Item C:\rtools44 -Force -Recurse -ErrorAction Ignore Remove-Item C:\rtools45 -Force -Recurse -ErrorAction Ignore # Get details needed for installing R components # # NOTES: # * some paths and file names are different on R4.0 $env:R_MAJOR_VERSION = $env:R_VERSION.split('.')[0] if ($env:R_MAJOR_VERSION -eq "4") { $RTOOLS_INSTALL_PATH = "C:\rtools43" $env:RTOOLS_BIN = "$RTOOLS_INSTALL_PATH\usr\bin" $env:RTOOLS_MINGW_BIN = "$RTOOLS_INSTALL_PATH\x86_64-w64-mingw32.static.posix\bin" $env:RTOOLS_EXE_FILE = "rtools43-5550-5548.exe" $env:R_WINDOWS_VERSION = "4.3.1" } else { Write-Output "[ERROR] Unrecognized R version: $env:R_VERSION" Assert-Output $false } $env:CMAKE_VERSION = "3.30.0" $env:R_LIB_PATH = "$env:BUILD_SOURCESDIRECTORY/RLibrary" -replace '[\\]', '/' $env:R_LIBS = "$env:R_LIB_PATH" $env:CMAKE_PATH = "$env:BUILD_SOURCESDIRECTORY/CMake_installation" $env:PATH = @( "$env:RTOOLS_BIN", "$env:RTOOLS_MINGW_BIN", "$env:R_LIB_PATH/R/bin/x64", "$env:CMAKE_PATH/cmake-$env:CMAKE_VERSION-windows-x86_64/bin", "$env:PATH" ) -join ";" $env:CRAN_MIRROR = "https://cran.rstudio.com" $env:MIKTEX_EXCEPTION_PATH = "$env:TEMP\miktex" # don't fail builds for long-running examples unless they're very long. # See https://github.com/lightgbm-org/LightGBM/issues/4049#issuecomment-793412254. if ($env:R_BUILD_TYPE -ne "cran") { $env:_R_CHECK_EXAMPLE_TIMING_THRESHOLD_ = 30 } if (($env:COMPILER -eq "MINGW") -and ($env:R_BUILD_TYPE -eq "cmake")) { $env:CXX = "$env:RTOOLS_MINGW_BIN/g++.exe" $env:CC = "$env:RTOOLS_MINGW_BIN/gcc.exe" } Set-Location "$env:BUILD_SOURCESDIRECTORY" tzutil /s "GMT Standard Time" [Void][System.IO.Directory]::CreateDirectory("$env:R_LIB_PATH") [Void][System.IO.Directory]::CreateDirectory("$env:CMAKE_PATH") # download R, RTools and CMake Write-Output "Downloading R, Rtools and CMake" $params = @{ url = "$env:CRAN_MIRROR/bin/windows/base/old/$env:R_WINDOWS_VERSION/R-$env:R_WINDOWS_VERSION-win.exe" destfile = "R-win.exe" } Get-File-With-Tenacity @params $params = @{ url = "https://github.com/lightgbm-org/LightGBM/releases/download/v2.0.12/$env:RTOOLS_EXE_FILE" destfile = "Rtools.exe" } Get-File-With-Tenacity @params $params = @{ url = "https://github.com/Kitware/CMake/releases/download/v{0}/cmake-{0}-windows-x86_64.zip" -f $env:CMAKE_VERSION destfile = "$env:CMAKE_PATH/cmake.zip" } Get-File-With-Tenacity @params # Install R Write-Output "Installing R" $params = @{ FilePath = "R-win.exe" NoNewWindow = $true Wait = $true ArgumentList = "/VERYSILENT /DIR=$env:R_LIB_PATH/R /COMPONENTS=main,x64,i386" } Start-Process @params ; Assert-Output $? Write-Output "Done installing R" Write-Output "Installing Rtools" $params = @{ FilePath = "Rtools.exe" NoNewWindow = $true Wait = $true ArgumentList = "/VERYSILENT /SUPPRESSMSGBOXES /DIR=$RTOOLS_INSTALL_PATH" } Start-Process @params; Assert-Output $? Write-Output "Done installing Rtools" Write-Output "Installing CMake" Add-Type -AssemblyName System.IO.Compression.FileSystem [System.IO.Compression.ZipFile]::ExtractToDirectory("$env:CMAKE_PATH/cmake.zip", "$env:CMAKE_PATH") ; Assert-Output $? # Remove old CMake shipped with RTools Remove-Item "$env:RTOOLS_MINGW_BIN/cmake.exe" -Force -ErrorAction Ignore Write-Output "Done installing CMake" Write-Output "Installing dependencies" Rscript.exe --vanilla ".ci/install-r-deps.R" --build --include=processx --test ; Assert-Output $? Write-Output "Building R-package" # R CMD check is not used for MSVC builds if ($env:COMPILER -ne "MSVC") { $PKG_FILE_NAME = "lightgbm_$env:LGB_VER.tar.gz" $LOG_FILE_NAME = "lightgbm.Rcheck/00check.log" if ($env:R_BUILD_TYPE -eq "cmake") { if ($env:TOOLCHAIN -eq "MINGW") { Write-Output "Telling R to use MinGW" $env:BUILD_R_FLAGS = "c('--skip-install', '--use-mingw', '-j4')" } elseif ($env:TOOLCHAIN -eq "MSYS") { Write-Output "Telling R to use MSYS" $env:BUILD_R_FLAGS = "c('--skip-install', '--use-msys2', '-j4')" } elseif ($env:TOOLCHAIN -eq "MSVC") { $env:BUILD_R_FLAGS = "'--skip-install'" } else { Write-Output "[ERROR] Unrecognized toolchain: $env:TOOLCHAIN" Assert-Output $false } Invoke-R-Code-Redirect-Stderr "commandArgs <- function(...){$env:BUILD_R_FLAGS}; source('build_r.R')" Assert-Output $? } elseif ($env:R_BUILD_TYPE -eq "cran") { $params = -join @( "result <- processx::run(command = 'sh', args = 'build-cran-package.sh', ", "echo = TRUE, windows_verbatim_args = FALSE, error_on_status = TRUE)" ) Invoke-R-Code-Redirect-Stderr $params ; Assert-Output $? Remove-From-Path ".*msys64.*" # Test CRAN source .tar.gz in a directory that is not this repo or below it. # When people install.packages('lightgbm'), they won't have the LightGBM # git repo around. This is to protect against the use of relative paths # like ../../CMakeLists.txt that would only work if you are in the repoo $R_CMD_CHECK_DIR = "tmp-r-cmd-check" New-Item -Path "C:\" -Name $R_CMD_CHECK_DIR -ItemType "directory" > $null Move-Item -Path "$PKG_FILE_NAME" -Destination "C:\$R_CMD_CHECK_DIR\" > $null Set-Location "C:\$R_CMD_CHECK_DIR\" } Write-Output "Running R CMD check" if ($env:R_BUILD_TYPE -eq "cran") { # CRAN packages must pass without --no-multiarch (build on 64-bit and 32-bit) $check_args = "c('CMD', 'check', '--as-cran', '--run-donttest', '$PKG_FILE_NAME')" } else { $check_args = "c('CMD', 'check', '--no-multiarch', '--as-cran', '--run-donttest', '$PKG_FILE_NAME')" } $params = -join ( "result <- processx::run(command = 'R.exe', args = $check_args, ", "echo = TRUE, windows_verbatim_args = FALSE, error_on_status = TRUE)" ) Invoke-R-Code-Redirect-Stderr $params ; $check_succeeded = $? Write-Output "R CMD check build logs:" $INSTALL_LOG_FILE_NAME = "lightgbm.Rcheck\00install.out" Get-Content -Path "$INSTALL_LOG_FILE_NAME" Assert-Output $check_succeeded Write-Output "Looking for issues with R CMD check results" if (Get-Content "$LOG_FILE_NAME" | Select-String -Pattern "NOTE|WARNING|ERROR" -CaseSensitive -Quiet) { Write-Output "NOTEs, WARNINGs, or ERRORs have been found by R CMD check" Assert-Output $False } } else { $INSTALL_LOG_FILE_NAME = "$env:BUILD_SOURCESDIRECTORY\00install_out.txt" Invoke-R-Code-Redirect-Stderr "source('build_r.R')" 1> $INSTALL_LOG_FILE_NAME ; $install_succeeded = $? Write-Output "----- build and install logs -----" Get-Content -Path "$INSTALL_LOG_FILE_NAME" Write-Output "----- end of build and install logs -----" Assert-Output $install_succeeded # some errors are not raised above, but can be found in the logs if (Get-Content "$INSTALL_LOG_FILE_NAME" | Select-String -Pattern "ERROR" -CaseSensitive -Quiet) { Write-Output "ERRORs have been found installing lightgbm" Assert-Output $False } } # Checking that the correct R version was used if ($env:TOOLCHAIN -ne "MSVC") { $checks = Select-String -Path "${LOG_FILE_NAME}" -Pattern "using R version $env:R_WINDOWS_VERSION" $checks_cnt = $checks.Matches.length } else { $checksParams = @{ Path = "${INSTALL_LOG_FILE_NAME}" Pattern = "R version passed into FindLibR.* $env:R_WINDOWS_VERSION" } $checks = Select-String @checksParams $checks_cnt = $checks.Matches.length } if ($checks_cnt -eq 0) { Write-Output "Wrong R version was found (expected '$env:R_WINDOWS_VERSION'). Check the build logs." Assert-Output $False } # Checking that we actually got the expected compiler. The R-package has some logic # to fail back to MinGW if MSVC fails, but for CI builds we need to check that the correct # compiler was used. if ($env:R_BUILD_TYPE -eq "cmake") { $checks = Select-String -Path "${INSTALL_LOG_FILE_NAME}" -Pattern "Check for working CXX compiler.*$env:COMPILER" if ($checks.Matches.length -eq 0) { Write-Output "The wrong compiler was used. Check the build logs." Assert-Output $False } } # Checking that we got the right toolchain for MinGW. If using MinGW, both # MinGW and MSYS toolchains are supported if (($env:COMPILER -eq "MINGW") -and ($env:R_BUILD_TYPE -eq "cmake")) { $checks = Select-String -Path "${INSTALL_LOG_FILE_NAME}" -Pattern "Trying to build with.*$env:TOOLCHAIN" if ($checks.Matches.length -eq 0) { Write-Output "The wrong toolchain was used. Check the build logs." Assert-Output $False } } # Checking that MM_PREFETCH preprocessor definition is actually used in CI builds. if ($env:R_BUILD_TYPE -eq "cran") { $checks = Select-String -Path "${INSTALL_LOG_FILE_NAME}" -Pattern "checking whether MM_PREFETCH work.*yes" $checks_cnt = $checks.Matches.length } elseif ($env:TOOLCHAIN -ne "MSVC") { $checks = Select-String -Path "${INSTALL_LOG_FILE_NAME}" -Pattern ".*Performing Test MM_PREFETCH - Success" $checks_cnt = $checks.Matches.length } else { $checks_cnt = 1 } if ($checks_cnt -eq 0) { Write-Output "MM_PREFETCH preprocessor definition wasn't used. Check the build logs." Assert-Output $False } # Checking that MM_MALLOC preprocessor definition is actually used in CI builds. if ($env:R_BUILD_TYPE -eq "cran") { $checks = Select-String -Path "${INSTALL_LOG_FILE_NAME}" -Pattern "checking whether MM_MALLOC work.*yes" $checks_cnt = $checks.Matches.length } elseif ($env:TOOLCHAIN -ne "MSVC") { $checks = Select-String -Path "${INSTALL_LOG_FILE_NAME}" -Pattern ".*Performing Test MM_MALLOC - Success" $checks_cnt = $checks.Matches.length } else { $checks_cnt = 1 } if ($checks_cnt -eq 0) { Write-Output "MM_MALLOC preprocessor definition wasn't used. Check the build logs." Assert-Output $False } # Checking that OpenMP is actually used in CMake builds. if ($env:R_BUILD_TYPE -eq "cmake") { $checks = Select-String -Path "${INSTALL_LOG_FILE_NAME}" -Pattern ".*Found OpenMP: TRUE.*" if ($checks.Matches.length -eq 0) { Write-Output "OpenMP wasn't found. Check the build logs." Assert-Output $False } } if ($env:COMPILER -eq "MSVC") { Write-Output "Running tests with testthat.R" Set-Location R-package/tests # NOTE: using Rscript.exe intentionally here, instead of Invoke-R-Code-Redirect-Stderr, # because something about the interaction between Invoke-R-Code-Redirect-Stderr # and testthat results in failing tests not exiting with a non-0 exit code. Rscript.exe --vanilla "testthat.R" ; Assert-Output $? } Write-Output "No issues were found checking the R-package" ================================================ FILE: .ci/test-r-package.sh ================================================ #!/bin/bash set -e -E -u -o pipefail # defaults ARCH=$(uname -m) # set up R environment export CRAN_MIRROR="https://cran.rstudio.com" export R_LIB_PATH=~/Rlib mkdir -p $R_LIB_PATH export R_LIBS=$R_LIB_PATH export PATH="$R_LIB_PATH/R/bin:$PATH" # don't fail builds for long-running examples unless they're very long. # See https://github.com/lightgbm-org/LightGBM/issues/4049#issuecomment-793412254. if [[ $R_BUILD_TYPE != "cran" ]]; then export _R_CHECK_EXAMPLE_TIMING_THRESHOLD_=30 fi # Get details needed for installing R components R_MAJOR_VERSION="${R_VERSION%.*}" if [[ "${R_MAJOR_VERSION}" == "4" ]]; then export R_MAC_VERSION=4.3.1 export R_MAC_PKG_URL=${CRAN_MIRROR}/bin/macosx/big-sur-${ARCH}/base/R-${R_MAC_VERSION}-${ARCH}.pkg export R_LINUX_VERSION="4.3.1-1.2204.0" export R_APT_REPO="jammy-cran40/" else echo "Unrecognized R version: ${R_VERSION}" exit 1 fi # installing precompiled R for Ubuntu # https://cran.r-project.org/bin/linux/ubuntu/#installation # adding steps from https://stackoverflow.com/a/56378217/3986677 to get latest version # # `devscripts` is required for 'checkbashisms' (https://github.com/r-lib/actions/issues/111) if [[ $OS_NAME == "linux" ]]; then mkdir -p ~/.gnupg echo "disable-ipv6" >> ~/.gnupg/dirmngr.conf sudo apt-key adv \ --homedir ~/.gnupg \ --keyserver keyserver.ubuntu.com \ --recv-keys E298A3A825C0D65DFD57CBB651716619E084DAB9 || exit 1 sudo add-apt-repository \ "deb ${CRAN_MIRROR}/bin/linux/ubuntu ${R_APT_REPO}" || exit 1 sudo apt-get update sudo apt-get install \ --no-install-recommends \ -y \ devscripts \ r-base-core=${R_LINUX_VERSION} \ r-base-dev=${R_LINUX_VERSION} \ texinfo \ texlive-latex-extra \ texlive-latex-recommended \ texlive-fonts-recommended \ texlive-fonts-extra \ tidy \ qpdf \ || exit 1 if [[ $R_BUILD_TYPE == "cran" ]]; then sudo apt-get install \ --no-install-recommends \ -y \ "autoconf=$(cat R-package/AUTOCONF_UBUNTU_VERSION)" \ automake \ || exit 1 fi fi # Installing R precompiled for Mac OS 10.11 or higher if [[ $OS_NAME == "macos" ]]; then brew update-reset --auto-update brew update --auto-update if [[ $R_BUILD_TYPE == "cran" ]]; then brew install automake || exit 1 fi brew install \ checkbashisms \ qpdf || exit 1 brew install basictex || exit 1 export PATH="/Library/TeX/texbin:$PATH" sudo tlmgr --verify-repo=none update --self || exit 1 sudo tlmgr --verify-repo=none install inconsolata helvetic rsfs || exit 1 curl -sL "${R_MAC_PKG_URL}" -o R.pkg || exit 1 sudo installer \ -pkg "$(pwd)/R.pkg" \ -target / || exit 1 # install tidy v5.8.0 # ref: https://groups.google.com/g/r-sig-mac/c/7u_ivEj4zhM TIDY_URL=https://github.com/htacg/tidy-html5/releases/download/5.8.0/tidy-5.8.0-macos-x86_64+arm64.pkg curl -sL ${TIDY_URL} -o tidy.pkg sudo installer \ -pkg "$(pwd)/tidy.pkg" \ -target / # ensure that this newer version of 'tidy' is used by 'R CMD check' # ref: https://cran.r-project.org/doc/manuals/R-exts.html#Checking-packages export R_TIDYCMD=/usr/local/bin/tidy fi # {Matrix} needs {lattice}, so this needs to run before manually installing {Matrix}. # This should be unnecessary on R >=4.4.0 # ref: https://github.com/lightgbm-org/LightGBM/issues/6433 Rscript --vanilla -e "install.packages('lattice', repos = '${CRAN_MIRROR}', lib = '${R_LIB_PATH}')" # manually install {Matrix}, as {Matrix}=1.7-0 raised its R floor all the way to R 4.4.0 # ref: https://github.com/lightgbm-org/LightGBM/issues/6433 Rscript --vanilla -e "install.packages('https://cran.r-project.org/src/contrib/Archive/Matrix/Matrix_1.6-5.tar.gz', repos = NULL, lib = '${R_LIB_PATH}')" # Manually install dependencies to avoid a CI-time dependency on devtools (for devtools::install_deps()) Rscript --vanilla ./.ci/install-r-deps.R --build --test --exclude=Matrix || exit 1 cd "${BUILD_DIRECTORY}" PKG_TARBALL="lightgbm_$(head -1 VERSION.txt).tar.gz" BUILD_LOG_FILE="lightgbm.Rcheck/00install.out" LOG_FILE_NAME="lightgbm.Rcheck/00check.log" if [[ $R_BUILD_TYPE == "cmake" ]]; then Rscript build_r.R -j4 --skip-install || exit 1 elif [[ $R_BUILD_TYPE == "cran" ]]; then # on Linux, we recreate configure in CI to test if # a change in a PR has changed configure.ac if [[ $OS_NAME == "linux" ]]; then ./R-package/recreate-configure.sh num_files_changed=$( git diff --name-only | wc -l ) if [[ ${num_files_changed} -gt 0 ]]; then echo "'configure' in the R-package has changed. Please recreate it and commit the changes." echo "Changed files:" git diff --compact-summary echo "See R-package/README.md for details on how to recreate this script." echo "" exit 1 fi fi ./build-cran-package.sh || exit 1 # Test CRAN source .tar.gz in a directory that is not this repo or below it. # When people install.packages('lightgbm'), they won't have the LightGBM # git repo around. This is to protect against the use of relative paths # like ../../CMakeLists.txt that would only work if you are in the repo R_CMD_CHECK_DIR="${HOME}/tmp-r-cmd-check/" mkdir -p "${R_CMD_CHECK_DIR}" mv "${PKG_TARBALL}" "${R_CMD_CHECK_DIR}" cd "${R_CMD_CHECK_DIR}" fi if [[ $PRODUCES_ARTIFACTS == "true" ]]; then cp "${PKG_TARBALL}" "${BUILD_ARTIFACTSTAGINGDIRECTORY}/lightgbm-${LGB_VER}-r-cran.tar.gz" fi declare -i allowed_notes=0 bash "${BUILD_DIRECTORY}/.ci/run-r-cmd-check.sh" \ "${PKG_TARBALL}" \ "${allowed_notes}" # ensure 'grep --count' doesn't cause failures set +e used_correct_r_version=$( cat $LOG_FILE_NAME \ | grep --count "using R version ${R_VERSION}" ) if [[ $used_correct_r_version -ne 1 ]]; then echo "Unexpected R version was used. Expected '${R_VERSION}'." exit 1 fi if [[ $R_BUILD_TYPE == "cmake" ]]; then passed_correct_r_version_to_cmake=$( cat $BUILD_LOG_FILE \ | grep --count "R version passed into FindLibR.cmake: ${R_VERSION}" ) if [[ $passed_correct_r_version_to_cmake -ne 1 ]]; then echo "Unexpected R version was passed into cmake. Expected '${R_VERSION}'." exit 1 fi fi # this check makes sure that CI builds of the package actually use OpenMP if [[ $OS_NAME == "macos" ]] && [[ $R_BUILD_TYPE == "cran" ]]; then omp_working=$( cat $BUILD_LOG_FILE \ | grep --count -E "checking whether OpenMP will work .*yes" ) elif [[ $R_BUILD_TYPE == "cmake" ]]; then omp_working=$( cat $BUILD_LOG_FILE \ | grep --count -E ".*Found OpenMP: TRUE.*" ) else omp_working=1 fi if [[ $omp_working -ne 1 ]]; then echo "OpenMP was not found" exit 1 fi # this check makes sure that CI builds of the package # actually use MM_PREFETCH preprocessor definition # # _mm_prefetch will not work on arm64 architecture # ref: https://github.com/lightgbm-org/LightGBM/issues/4124 if [[ $ARCH != "arm64" ]]; then if [[ $R_BUILD_TYPE == "cran" ]]; then mm_prefetch_working=$( cat $BUILD_LOG_FILE \ | grep --count -E "checking whether MM_PREFETCH work.*yes" ) else mm_prefetch_working=$( cat $BUILD_LOG_FILE \ | grep --count -E ".*Performing Test MM_PREFETCH - Success" ) fi if [[ $mm_prefetch_working -ne 1 ]]; then echo "MM_PREFETCH test was not passed" exit 1 fi fi # this check makes sure that CI builds of the package # actually use MM_MALLOC preprocessor definition if [[ $R_BUILD_TYPE == "cran" ]]; then mm_malloc_working=$( cat $BUILD_LOG_FILE \ | grep --count -E "checking whether MM_MALLOC work.*yes" ) else mm_malloc_working=$( cat $BUILD_LOG_FILE \ | grep --count -E ".*Performing Test MM_MALLOC - Success" ) fi if [[ $mm_malloc_working -ne 1 ]]; then echo "MM_MALLOC test was not passed" exit 1 fi # this check makes sure that no "warning: unknown pragma ignored" logs # reach the user leading them to believe that something went wrong if [[ $R_BUILD_TYPE == "cran" ]]; then pragma_warning_present=$( cat $BUILD_LOG_FILE \ | grep --count -E "warning: unknown pragma ignored" ) if [[ $pragma_warning_present -ne 0 ]]; then echo "Unknown pragma warning is present, pragmas should have been removed before build" exit 1 fi fi ================================================ FILE: .ci/test-windows.ps1 ================================================ function Assert-Output { param( [Parameter(Mandatory = $true)][bool]$success ) if (-not $success) { $host.SetShouldExit(-1) exit 1 } } $env:CONDA_ENV = "test-env" $env:LGB_VER = (Get-Content $env:BUILD_SOURCESDIRECTORY\VERSION.txt).trim() # Use custom temp directory to avoid # > warning MSB8029: The Intermediate directory or Output directory cannot reside under the Temporary directory # > as it could lead to issues with incremental build. # And make sure this directory is always clean $env:TMPDIR = "$env:USERPROFILE\tmp" Remove-Item $env:TMPDIR -Force -Recurse -ErrorAction Ignore [Void][System.IO.Directory]::CreateDirectory($env:TMPDIR) # create the artifact upload directory if it doesn't exist yet [Void][System.IO.Directory]::CreateDirectory($env:BUILD_ARTIFACTSTAGINGDIRECTORY) if ($env:TASK -eq "r-package") { & .\.ci\test-r-package-windows.ps1 ; Assert-Output $? exit 0 } if ($env:TASK -eq "cpp-tests") { cmake -B build -S . -DBUILD_CPP_TEST=ON -DUSE_DEBUG=ON -A x64 cmake --build build --target testlightgbm --config Debug ; Assert-Output $? .\Debug\testlightgbm.exe ; Assert-Output $? exit 0 } if ($env:TASK -eq "swig") { $env:JAVA_HOME = $env:JAVA_HOME_8_X64 # there is pre-installed Eclipse Temurin 8 somewhere $ProgressPreference = "SilentlyContinue" # progress bar bug extremely slows down download speed $params = @{ Uri = "https://sourceforge.net/projects/swig/files/latest/download" OutFile = "$env:BUILD_SOURCESDIRECTORY/swig/swigwin.zip" UserAgent = "curl" } Invoke-WebRequest @params Add-Type -AssemblyName System.IO.Compression.FileSystem [System.IO.Compression.ZipFile]::ExtractToDirectory( "$env:BUILD_SOURCESDIRECTORY/swig/swigwin.zip", "$env:BUILD_SOURCESDIRECTORY/swig" ) ; Assert-Output $? $SwigFolder = Get-ChildItem -Name -Path "$env:BUILD_SOURCESDIRECTORY/swig" -Attributes Directory $env:PATH = @("$env:BUILD_SOURCESDIRECTORY/swig/$SwigFolder", "$env:PATH") -join ";" $BuildLogFileName = "$env:BUILD_SOURCESDIRECTORY\cmake_build.log" cmake -B build -S . -A x64 -DUSE_SWIG=ON *> "$BuildLogFileName" ; $build_succeeded = $? Write-Output "CMake build logs:" Get-Content -Path "$BuildLogFileName" Assert-Output $build_succeeded $checks = Select-String -Path "${BuildLogFileName}" -Pattern "-- Found SWIG.*${SwigFolder}/swig.exe" $checks_cnt = $checks.Matches.length if ($checks_cnt -eq 0) { Write-Output "Wrong SWIG version was found (expected '${SwigFolder}'). Check the build logs." Assert-Output $False } cmake --build build --target ALL_BUILD --config Release ; Assert-Output $? if ($env:PRODUCES_ARTIFACTS -eq "true") { cp ./build/lightgbmlib.jar $env:BUILD_ARTIFACTSTAGINGDIRECTORY/lightgbmlib_win.jar ; Assert-Output $? } exit 0 } # setup for Python conda activate ; Assert-Output $? conda config --set always_yes yes --set changeps1 no ; Assert-Output $? conda config --remove channels defaults ; Assert-Output $? conda config --add channels nodefaults ; Assert-Output $? conda config --add channels conda-forge ; Assert-Output $? conda config --set channel_priority strict ; Assert-Output $? conda install -q -y conda "python=$env:PYTHON_VERSION[build=*_cp*]" ; Assert-Output $? # print output of 'conda info', to help in submitting bug reports Write-Output "conda info:" conda info if ($env:PYTHON_VERSION -eq "3.9") { $env:CONDA_REQUIREMENT_FILE = "$env:BUILD_SOURCESDIRECTORY/.ci/conda-envs/ci-core-py39.txt" } else { $env:CONDA_REQUIREMENT_FILE = "$env:BUILD_SOURCESDIRECTORY/.ci/conda-envs/ci-core.txt" } $condaParams = @( "-y", "-n", "$env:CONDA_ENV", "--file", "$env:CONDA_REQUIREMENT_FILE", "python=$env:PYTHON_VERSION[build=*_cp*]" ) conda create @condaParams ; Assert-Output $? # print output of 'conda list', to help in submitting bug reports Write-Output "conda list:" conda list -n $env:CONDA_ENV if ($env:TASK -ne "bdist") { conda activate $env:CONDA_ENV } Set-Location "$env:BUILD_SOURCESDIRECTORY" if ($env:TASK -eq "regular") { cmake -B build -S . -A x64 ; Assert-Output $? cmake --build build --target ALL_BUILD --config Release ; Assert-Output $? sh ./build-python.sh install --precompile ; Assert-Output $? cp ./Release/lib_lightgbm.dll "$env:BUILD_ARTIFACTSTAGINGDIRECTORY" cp ./Release/lightgbm.exe "$env:BUILD_ARTIFACTSTAGINGDIRECTORY" } elseif ($env:TASK -eq "sdist") { sh ./build-python.sh sdist ; Assert-Output $? sh ./.ci/check-python-dists.sh ./dist ; Assert-Output $? Set-Location dist; pip install @(Get-ChildItem *.gz) -v ; Assert-Output $? } elseif ($env:TASK -eq "bdist") { # Import the Chocolatey profile module so that the RefreshEnv command # invoked below properly updates the current PowerShell session environment. $module = "$env:ChocolateyInstall\helpers\chocolateyProfile.psm1" Import-Module "$module" ; Assert-Output $? RefreshEnv Write-Output "Current OpenCL drivers:" Get-ItemProperty -Path Registry::HKEY_LOCAL_MACHINE\SOFTWARE\Khronos\OpenCL\Vendors conda activate $env:CONDA_ENV # TODO: restore --integrated-opencl as part of https://github.com/lightgbm-org/LightGBM/issues/6968 sh "build-python.sh" bdist_wheel ; Assert-Output $? sh ./.ci/check-python-dists.sh ./dist ; Assert-Output $? Set-Location dist; pip install @(Get-ChildItem *py3-none-win_amd64.whl) ; Assert-Output $? cp @(Get-ChildItem *py3-none-win_amd64.whl) "$env:BUILD_ARTIFACTSTAGINGDIRECTORY" } elseif (($env:APPVEYOR -eq "true") -and ($env:TASK -eq "python")) { if ($env:COMPILER -eq "MINGW") { sh ./build-python.sh install --mingw ; Assert-Output $? } else { sh ./build-python.sh install; Assert-Output $? } } if (($env:TASK -eq "sdist") -or (($env:APPVEYOR -eq "true") -and ($env:TASK -eq "python"))) { # cannot test C API with "sdist" task $tests = "$env:BUILD_SOURCESDIRECTORY/tests/python_package_test" } else { $tests = "$env:BUILD_SOURCESDIRECTORY/tests" } if ($env:TASK -eq "bdist") { # Make sure we can do both CPU and GPU; see tests/python_package_test/test_dual.py # TODO: set LIGHTGBM_TEST_DUAL_CPU_GPU back to "1" as part of https://github.com/lightgbm-org/LightGBM/issues/6968 $env:LIGHTGBM_TEST_DUAL_CPU_GPU = "0" } pytest $tests ; Assert-Output $? if (($env:TASK -eq "regular") -or (($env:APPVEYOR -eq "true") -and ($env:TASK -eq "python"))) { Set-Location "$env:BUILD_SOURCESDIRECTORY/examples/python-guide" @("import matplotlib", "matplotlib.use('Agg')") + (Get-Content "plot_example.py") | Set-Content "plot_example.py" # Prevent interactive window mode (Get-Content "plot_example.py").replace( 'graph.render(view=True)', 'graph.render(view=False)' ) | Set-Content "plot_example.py" conda install -y -n $env:CONDA_ENV "h5py>=3.10" "ipywidgets>=8.1.2" "notebook>=7.1.2" # Run all examples foreach ($file in @(Get-ChildItem *.py)) { @( "import sys, warnings", -join @( "warnings.showwarning = lambda message, category, filename, lineno, file=None, line=None: ", "sys.stdout.write(warnings.formatwarning(message, category, filename, lineno, line))" ) ) + (Get-Content $file) | Set-Content $file python $file ; Assert-Output $? } # Run all notebooks Set-Location "$env:BUILD_SOURCESDIRECTORY/examples/python-guide/notebooks" (Get-Content "interactive_plot_example.ipynb").replace( 'INTERACTIVE = False', 'assert False, \"Interactive mode disabled\"' ) | Set-Content "interactive_plot_example.ipynb" jupyter nbconvert --ExecutePreprocessor.timeout=180 --to notebook --execute --inplace *.ipynb ; Assert-Output $? } ================================================ FILE: .ci/test.sh ================================================ #!/bin/bash set -e -E -o -u pipefail # defaults CONDA_ENV="test-env" IN_UBUNTU_BASE_CONTAINER=${IN_UBUNTU_BASE_CONTAINER:-"false"} METHOD=${METHOD:-""} PRODUCES_ARTIFACTS=${PRODUCES_ARTIFACTS:-"false"} SANITIZERS=${SANITIZERS:-""} ARCH=$(uname -m) LGB_VER=$(head -n 1 "${BUILD_DIRECTORY}/VERSION.txt") # create the artifact upload directory if it doesn't exist yet mkdir -p "${BUILD_ARTIFACTSTAGINGDIRECTORY}" if [[ $OS_NAME == "macos" ]] && [[ $COMPILER == "gcc" ]]; then export CXX=g++-14 export CC=gcc-14 elif [[ $OS_NAME == "linux" ]] && [[ $COMPILER == "clang" ]]; then export CXX=clang++ export CC=clang elif [[ $OS_NAME == "linux" ]] && [[ $COMPILER == "clang-17" ]]; then export CXX=clang++-17 export CC=clang-17 fi if [[ $IN_UBUNTU_BASE_CONTAINER == "true" ]]; then export LANG="en_US.UTF-8" export LC_ALL="en_US.UTF-8" fi # Setting MACOSX_DEPLOYMENT_TARGET prevents CMake from building against too-new # macOS features, and helps tools like Python build tools determine the appropriate # wheel compatibility tags. # # ref: # * https://cmake.org/cmake/help/latest/envvar/MACOSX_DEPLOYMENT_TARGET.html # * https://github.com/scikit-build/scikit-build-core/blob/acb7d0346e4a05bcb47a4ea3939c705ab71e3145/src/scikit_build_core/builder/macos.py#L36 if [[ $ARCH == "x86_64" ]]; then export MACOSX_DEPLOYMENT_TARGET=10.15 else export MACOSX_DEPLOYMENT_TARGET=12.0 fi if [[ "${TASK}" == "r-package" ]]; then bash "${BUILD_DIRECTORY}/.ci/test-r-package.sh" || exit 1 exit 0 fi cd "${BUILD_DIRECTORY}" if [[ $TASK == "swig" ]]; then cmake -B build -S . -DUSE_SWIG=ON cmake --build build -j4 || exit 1 if [[ $OS_NAME == "linux" ]] && [[ $COMPILER == "gcc" ]]; then objdump -T ./lib_lightgbm.so > ./objdump.log || exit 1 objdump -T ./lib_lightgbm_swig.so >> ./objdump.log || exit 1 ./.ci/check-dynamic-dependencies.sh ./objdump.log || exit 1 fi if [[ $PRODUCES_ARTIFACTS == "true" ]]; then cp ./build/lightgbmlib.jar "${BUILD_ARTIFACTSTAGINGDIRECTORY}/lightgbmlib_${OS_NAME}.jar" fi exit 0 fi if [[ "$TASK" == "cpp-tests" ]]; then cmake_args=( -DBUILD_CPP_TEST=ON -DUSE_DEBUG=ON ) if [[ $METHOD == "with-sanitizers" ]]; then cmake_args+=("-DUSE_SANITIZER=ON") if [[ -n $SANITIZERS ]]; then cmake_args+=("-DENABLED_SANITIZERS=$SANITIZERS") fi fi cmake -B build -S . "${cmake_args[@]}" cmake --build build --target testlightgbm -j4 || exit 1 ./testlightgbm || exit 1 exit 0 fi # including python=version=[build=*_cp*] to ensure that conda prefers CPython and doesn't fall back to # other implementations like pypy CONDA_PYTHON_REQUIREMENT="python=${PYTHON_VERSION}[build=*_cp*]" if [[ $TASK == "if-else" ]]; then conda create -q -y -n "${CONDA_ENV}" "${CONDA_PYTHON_REQUIREMENT}" numpy # shellcheck disable=SC1091 source activate "${CONDA_ENV}" cmake -B build -S . || exit 1 cmake --build build --target lightgbm -j4 || exit 1 cd "$BUILD_DIRECTORY/tests/cpp_tests" ../../lightgbm config=train.conf convert_model_language=cpp convert_model=../../src/boosting/gbdt_prediction.cpp ../../lightgbm config=predict.conf output_result=origin.pred ../../lightgbm config=predict.conf output_result=ifelse.pred python test.py exit 0 fi if [[ $PYTHON_VERSION == "3.9" ]]; then CONDA_REQUIREMENT_FILE="${BUILD_DIRECTORY}/.ci/conda-envs/ci-core-py39.txt" else CONDA_REQUIREMENT_FILE="${BUILD_DIRECTORY}/.ci/conda-envs/ci-core.txt" fi conda create \ -y \ -n "${CONDA_ENV}" \ --file "${CONDA_REQUIREMENT_FILE}" \ "${CONDA_PYTHON_REQUIREMENT}" \ || exit 1 # print output of 'conda list', to help in submitting bug reports echo "conda list:" conda list -n ${CONDA_ENV} # shellcheck disable=SC1091 source activate $CONDA_ENV cd "${BUILD_DIRECTORY}" if [[ $TASK == "sdist" ]]; then sh ./build-python.sh sdist || exit 1 sh .ci/check-python-dists.sh ./dist || exit 1 pip install "./dist/lightgbm-${LGB_VER}.tar.gz" -v || exit 1 if [[ $PRODUCES_ARTIFACTS == "true" ]]; then cp "./dist/lightgbm-${LGB_VER}.tar.gz" "${BUILD_ARTIFACTSTAGINGDIRECTORY}" || exit 1 fi pytest ./tests/python_package_test || exit 1 exit 0 elif [[ $TASK == "bdist" ]]; then if [[ $OS_NAME == "macos" ]]; then sh ./build-python.sh bdist_wheel || exit 1 sh .ci/check-python-dists.sh ./dist || exit 1 if [[ $PRODUCES_ARTIFACTS == "true" ]]; then cp "$(echo "dist/lightgbm-${LGB_VER}-py3-none-macosx"*.whl)" "${BUILD_ARTIFACTSTAGINGDIRECTORY}" || exit 1 fi else sh ./build-python.sh bdist_wheel --integrated-opencl || exit 1 # print some debugging logs about the wheel's GLIBC version and dependencies on shared libraries pip install 'auditwheel>=6.5.1' auditwheel show ./dist/lightgbm*.whl # pass through 'auditwheel repair' to set the appropriate wheel tags. # # intentionally avoid vendoring libgomp, to reduce the risk of multiple OpenMP libraries # being loaded in the same process. auditwheel repair \ --exclude 'libgomp.so*' \ --lib-sdir '' \ --wheel-dir dist-fixed/ \ ./dist/lightgbm*.whl # overwrite the original wheel with the new one rm ./dist/lightgbm*.whl mv ./dist-fixed/lightgbm*.whl ./dist # check wheel properties sh .ci/check-python-dists.sh ./dist || exit 1 if [[ $PRODUCES_ARTIFACTS == "true" ]]; then # hard-code expected tag so CI will fail if 'auditwheel repair' has a surprising result (e.g. newer # manylinux tag than we intended) if [[ $ARCH == "x86_64" ]]; then PLATFORM="manylinux_2_27_x86_64.manylinux_2_28_x86_64" else PLATFORM="manylinux2014_aarch64.manylinux_2_17_aarch64" fi cp "dist/lightgbm-${LGB_VER}-py3-none-${PLATFORM}.whl" "${BUILD_ARTIFACTSTAGINGDIRECTORY}" || exit 1 fi # Make sure we can do both CPU and GPU; see tests/python_package_test/test_dual.py export LIGHTGBM_TEST_DUAL_CPU_GPU=1 fi pip install -v ./dist/*.whl || exit 1 pytest ./tests || exit 1 exit 0 fi if [[ $TASK == "gpu" ]]; then sed -i'.bak' 's/std::string device_type = "cpu";/std::string device_type = "gpu";/' ./include/LightGBM/config.h grep -q 'std::string device_type = "gpu"' ./include/LightGBM/config.h || exit 1 # make sure that changes were really done if [[ $METHOD == "pip" ]]; then sh ./build-python.sh sdist || exit 1 sh .ci/check-python-dists.sh ./dist || exit 1 pip install \ -v \ --config-settings=cmake.define.USE_GPU=ON \ "./dist/lightgbm-${LGB_VER}.tar.gz" \ || exit 1 pytest ./tests/python_package_test || exit 1 exit 0 elif [[ $METHOD == "wheel" ]]; then sh ./build-python.sh bdist_wheel --gpu || exit 1 sh ./.ci/check-python-dists.sh ./dist || exit 1 pip install "$(echo "./dist/lightgbm-${LGB_VER}"*.whl)" -v || exit 1 pytest ./tests || exit 1 exit 0 elif [[ $METHOD == "source" ]]; then cmake -B build -S . -DUSE_GPU=ON fi elif [[ $TASK == "cuda" ]]; then sed -i'.bak' 's/std::string device_type = "cpu";/std::string device_type = "cuda";/' ./include/LightGBM/config.h grep -q 'std::string device_type = "cuda"' ./include/LightGBM/config.h || exit 1 # make sure that changes were really done # by default ``gpu_use_dp=false`` for efficiency. change to ``true`` here for exact results in ci tests sed -i'.bak' 's/gpu_use_dp = false;/gpu_use_dp = true;/' ./include/LightGBM/config.h grep -q 'gpu_use_dp = true' ./include/LightGBM/config.h || exit 1 # make sure that changes were really done if [[ $METHOD == "pip" ]]; then sh ./build-python.sh sdist || exit 1 sh ./.ci/check-python-dists.sh ./dist || exit 1 pip install \ -v \ --config-settings=cmake.define.USE_CUDA=ON \ "./dist/lightgbm-${LGB_VER}.tar.gz" \ || exit 1 pytest ./tests/python_package_test || exit 1 exit 0 elif [[ $METHOD == "wheel" ]]; then sh ./build-python.sh bdist_wheel --cuda || exit 1 sh ./.ci/check-python-dists.sh ./dist || exit 1 pip install "$(echo "./dist/lightgbm-${LGB_VER}"*.whl)" -v || exit 1 pytest ./tests || exit 1 exit 0 elif [[ $METHOD == "source" ]]; then cmake -B build -S . -DUSE_CUDA=ON fi elif [[ $TASK == "mpi" ]]; then if [[ $METHOD == "pip" ]]; then sh ./build-python.sh sdist || exit 1 sh ./.ci/check-python-dists.sh ./dist || exit 1 pip install \ -v \ --config-settings=cmake.define.USE_MPI=ON \ "./dist/lightgbm-${LGB_VER}.tar.gz" \ || exit 1 pytest ./tests/python_package_test || exit 1 exit 0 elif [[ $METHOD == "wheel" ]]; then sh ./build-python.sh bdist_wheel --mpi || exit 1 sh ./.ci/check-python-dists.sh ./dist || exit 1 pip install "$(echo "./dist/lightgbm-${LGB_VER}"*.whl)" -v || exit 1 pytest ./tests || exit 1 exit 0 elif [[ $METHOD == "source" ]]; then cmake -B build -S . -DUSE_MPI=ON -DUSE_DEBUG=ON fi else cmake -B build -S . fi cmake --build build --target _lightgbm -j4 || exit 1 sh ./build-python.sh install --precompile || exit 1 pytest ./tests || exit 1 if [[ $TASK == "regular" ]]; then if [[ $PRODUCES_ARTIFACTS == "true" ]]; then if [[ $OS_NAME == "macos" ]]; then cp ./lib_lightgbm.dylib "${BUILD_ARTIFACTSTAGINGDIRECTORY}/lib_lightgbm.dylib" else if [[ $COMPILER == "gcc" ]]; then objdump -T ./lib_lightgbm.so > ./objdump.log || exit 1 ./.ci/check-dynamic-dependencies.sh ./objdump.log || exit 1 fi cp ./lib_lightgbm.so "${BUILD_ARTIFACTSTAGINGDIRECTORY}/lib_lightgbm.so" fi fi cd "$BUILD_DIRECTORY/examples/python-guide" sed -i'.bak' '/import lightgbm as lgb/a\ import matplotlib\ matplotlib.use\(\"Agg\"\)\ ' plot_example.py # prevent interactive window mode sed -i'.bak' 's/graph.render(view=True)/graph.render(view=False)/' plot_example.py # requirements for examples conda install -y -n $CONDA_ENV \ 'h5py>=3.10' \ 'ipywidgets>=8.1.2' \ 'notebook>=7.1.2' for f in *.py **/*.py; do python "${f}" || exit 1; done # run all examples cd "$BUILD_DIRECTORY/examples/python-guide/notebooks" sed -i'.bak' 's/INTERACTIVE = False/assert False, \\"Interactive mode disabled\\"/' interactive_plot_example.ipynb jupyter nbconvert --ExecutePreprocessor.timeout=180 --to notebook --execute --inplace ./*.ipynb || exit 1 # run all notebooks # importing the library should succeed even if all optional dependencies are not present conda uninstall -n $CONDA_ENV --force --yes \ cffi \ dask \ distributed \ joblib \ matplotlib-base \ pandas \ psutil \ pyarrow \ python-graphviz \ scikit-learn || exit 1 python -c "import lightgbm" || exit 1 fi ================================================ FILE: .editorconfig ================================================ root = true [*] charset = utf-8 trim_trailing_whitespace = true insert_final_newline = true end_of_line = lf indent_style = space indent_size = 2 [*.{py,sh,ps1,js,json}] indent_size = 4 max_line_length = 120 skip = external_libs known_first_party = lightgbm # Tabs matter for Makefile and .gitmodules [{makefile*,Makefile*,*.mk,*.mak,*.makefile,*.Makefile,GNUmakefile,BSDmakefile,make.bat,Makevars*,*.gitmodules}] indent_style = tab ================================================ FILE: .git-blame-ignore-revs ================================================ # introduce ruff-format (#6308) 6330d6269c81dfd4c96e664b99239b8ff39ccf91 # enable ruff format on tests and examples (#6317) 1b792e716682254c33ddb5eb845357e84018636d # enable ruff-format on main library Python code (#6336) dd31208ab7a7aea86762830697b00666f843ded9 # enable whitespace/indent_namespace rule from cpplint (#7056) 50f11a9f3c066eadee475ea567f9e9d49e6bb827 ================================================ FILE: .github/CODEOWNERS ================================================ # This file controls default reviewers for LightGBM code. # See https://help.github.com/en/articles/about-code-owners # for details # # Maintainers are encouraged to use their best discretion in # setting reviewers on PRs manually, but this file should # offer a reasonable automatic best-guess # catch-all rule (this only gets matched if no rules below match) * @guolinke @jameslamb @shiyu1994 @jmoralez @borchero @StrikerRUS ================================================ FILE: .github/ISSUE_TEMPLATE/BUG_REPORT.md ================================================ --- name: Bug Report 🐞 about: Something isn't working as expected? Here is the right place to report. --- ## Description ## Reproducible example ## Environment info LightGBM version or commit hash: Command(s) you used to install LightGBM ```shell ``` ## Additional Comments ================================================ FILE: .github/ISSUE_TEMPLATE/FEATURE_REQUEST.md ================================================ --- name: Feature Request 💡 about: Suggest a new idea for the project. labels: enhancement, feature-request --- ## Summary ## Motivation ## Description ## References ================================================ FILE: .github/dependabot.yml ================================================ version: 2 updates: - package-ecosystem: github-actions directory: / schedule: interval: monthly groups: ci-dependencies: patterns: - "*" cooldown: # only accept releases that have been out for at least 15 days default-days: 15 commit-message: prefix: "[ci]" labels: - maintenance ================================================ FILE: .github/release-drafter.yml ================================================ name-template: 'v$NEXT_PATCH_VERSION' tag-template: 'v$NEXT_PATCH_VERSION' categories: - title: '💡 New Features' label: 'feature' - title: '🔨 Breaking' label: 'breaking' - title: '🚀 Efficiency Improvement' label: 'efficiency' - title: '🐛 Bug Fixes' label: 'fix' - title: '📖 Documentation' label: 'doc' - title: '🧰 Maintenance' label: 'maintenance' change-template: '- $TITLE @$AUTHOR (#$NUMBER)' template: | ## Changes $CHANGES ================================================ FILE: .github/workflows/build.yml ================================================ # builds core artifacts, intended to be attached to releases # or used by other workflows name: Build on: push: branches: - master pull_request: branches: - master # automatically cancel in-progress builds if another commit is pushed concurrency: group: ${{ github.workflow }}-${{ github.ref }} cancel-in-progress: true env: # tell scripts where to put artifacts BUILD_ARTIFACTSTAGINGDIRECTORY: '${{ github.workspace }}/artifacts' jobs: archive: runs-on: ubuntu-latest timeout-minutes: 15 steps: - name: Checkout repository uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6.0.2 with: fetch-depth: 5 persist-credentials: false submodules: true - name: Create source archive run: | mkdir -p "${BUILD_ARTIFACTSTAGINGDIRECTORY}" tar \ -czvf \ /tmp/LightGBM-complete_source_code_tar_gz.tar.gz \ . mv \ /tmp/LightGBM-complete_source_code_tar_gz.tar.gz \ ${BUILD_ARTIFACTSTAGINGDIRECTORY}/ - name: Create commit.txt shell: bash run: | # for pull requests, github.sha refers to the merge commit from merging the PR and # target branch... we want the actual commit that was pushed if [[ "${{ github.event_name }}" == "pull_request" ]]; then COMMIT_SHA="${{ github.event.pull_request.head.sha }}" else COMMIT_SHA="${{ github.sha }}" fi echo "${COMMIT_SHA}" > "${BUILD_ARTIFACTSTAGINGDIRECTORY}/commit.txt" - name: Upload artifacts uses: actions/upload-artifact@bbbca2ddaa5d8feaa63e36b76fdaad77386f024f # v7.0.0 with: name: source-archive path: | ${{ env.BUILD_ARTIFACTSTAGINGDIRECTORY }}/commit.txt ${{ env.BUILD_ARTIFACTSTAGINGDIRECTORY }}/LightGBM-complete_source_code_tar_gz.tar.gz if-no-files-found: error all-build-jobs-successful: if: always() runs-on: ubuntu-latest needs: - archive steps: - name: Note that all tests succeeded uses: re-actors/alls-green@05ac9388f0aebcb5727afa17fcccfecd6f8ec5fe # v1.2.2 with: jobs: ${{ toJSON(needs) }} ================================================ FILE: .github/workflows/cpp.yml ================================================ name: C++ on: push: branches: - master pull_request: branches: - master # automatically cancel in-progress builds if another commit is pushed concurrency: group: ${{ github.workflow }}-${{ github.ref }} cancel-in-progress: true env: # tell scripts where to put artifacts # (this variable name is left over from when jobs ran on Azure DevOps) BUILD_ARTIFACTSTAGINGDIRECTORY: '${{ github.workspace }}/artifacts' # where repo sources are cloned to BUILD_DIRECTORY: '${{ github.workspace }}' # in CMake-driven builds, parallelize compilation CMAKE_BUILD_PARALLEL_LEVEL: 4 jobs: test: name: ${{ matrix.task }} (${{ matrix.os-display-name || matrix.os }}, ${{ matrix.compiler }}) runs-on: ${{ matrix.os }} container: ${{ matrix.container }} timeout-minutes: 60 strategy: fail-fast: false matrix: include: ############# # C++ tests # ############# - os: ubuntu-latest task: cpp-tests compiler: clang-17 method: with-sanitizers container: 'ubuntu:22.04' os-display-name: 'ubuntu22.04' - os: macos-15-intel task: cpp-tests compiler: clang method: with-sanitizers sanitizers: "address;undefined" container: null - os: windows-2022 task: cpp-tests compiler: msvc container: null ########### # if-else # ########### # These test CLI-generated C++ inference code. # # They need Python because they're written with pytest, but they # do not need the LightGBM Python package - os: ubuntu-latest task: if-else compiler: clang-17 container: 'ubuntu:22.04' os-display-name: 'ubuntu22.04' python_version: '3.13' setup-conda: 'true' - os: macos-15-intel task: if-else compiler: clang python_version: '3.10' container: null - os: ubuntu-latest task: if-else compiler: gcc python_version: '3.11' container: 'lightgbm.azurecr.io/vsts-agent:manylinux_2_28_x86_64' os-display-name: 'manylinux_2_28' steps: - name: Install packages used by third-party actions if: matrix.container == 'ubuntu:22.04' shell: bash run: | apt-get update -y apt-get install --no-install-recommends -y \ dirmngr \ gpg \ gpg-agent \ software-properties-common \ sudo # install newest version of git # ref: # - https://unix.stackexchange.com/a/170831/550004 # - https://git-scm.com/download/linux add-apt-repository ppa:git-core/ppa -y apt-get update -y apt-get install --no-install-recommends -y \ git - name: Trust git cloning LightGBM if: startsWith(matrix.os, 'ubuntu') run: | git config --global --add safe.directory "${GITHUB_WORKSPACE}" - name: Checkout repository uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6.0.2 with: fetch-depth: 5 persist-credentials: false submodules: true - name: Setup and run tests on Linux and macOS if: startsWith(matrix.os, 'macos') || startsWith(matrix.os, 'ubuntu') shell: bash run: | export COMPILER="${{ matrix.compiler }}" export CONDA="${HOME}/miniforge" export METHOD="${{ matrix.method }}" export PATH=${CONDA}/bin:${PATH} export PYTHON_VERSION="${{ matrix.python_version }}" export SANITIZERS="${{ matrix.sanitizers }}" export SETUP_CONDA="${{ matrix.setup-conda }}" export TASK="${{ matrix.task }}" if [[ "${{ matrix.os }}" =~ ^macos ]]; then export OS_NAME="macos" elif [[ "${{ matrix.os }}" == "ubuntu-latest" ]]; then export OS_NAME="linux" fi if [[ "${{ matrix.container }}" == "ubuntu:22.04" ]]; then export DEBIAN_FRONTEND=noninteractive export IN_UBUNTU_BASE_CONTAINER="true" fi $GITHUB_WORKSPACE/.ci/setup.sh $GITHUB_WORKSPACE/.ci/test.sh - name: Setup and run tests on Windows if: startsWith(matrix.os, 'windows') shell: pwsh -command ". {0}" run: | $env:BUILD_SOURCESDIRECTORY = $env:BUILD_DIRECTORY $env:TASK = "${{ matrix.task }}" & "$env:GITHUB_WORKSPACE/.ci/test-windows.ps1" all-cpp-jobs-successful: if: always() runs-on: ubuntu-latest needs: - test steps: - name: Note that all tests succeeded uses: re-actors/alls-green@05ac9388f0aebcb5727afa17fcccfecd6f8ec5fe # v1.2.2 with: jobs: ${{ toJSON(needs) }} ================================================ FILE: .github/workflows/cuda.yml ================================================ name: CUDA Version on: # Run manually by clicking a button in the UI workflow_dispatch: # automatically cancel in-progress builds if another commit is pushed concurrency: group: ${{ github.workflow }}-${{ github.ref }} cancel-in-progress: true jobs: test: # yamllint disable-line rule:line-length name: ${{ matrix.task }} ${{ matrix.cuda_version }} ${{ matrix.method }} (${{ matrix.linux_version }}, ${{ matrix.compiler }}, Python ${{ matrix.python_version }}) # yamllint disable-line rule:line-length # ref: https://docs.github.com/en/actions/concepts/runners/about-larger-runners#specifications-for-gpu-larger-runners runs-on: github-hosted-linux-gpu-amd64 container: image: nvcr.io/nvidia/cuda:${{ matrix.cuda_version }}-devel-${{ matrix.linux_version }} env: CMAKE_BUILD_PARALLEL_LEVEL: 4 COMPILER: ${{ matrix.compiler }} CONDA: /tmp/miniforge DEBIAN_FRONTEND: noninteractive METHOD: ${{ matrix.method }} OS_NAME: linux PYTHON_VERSION: ${{ matrix.python_version }} TASK: ${{ matrix.task }} SKBUILD_STRICT_CONFIG: true options: --gpus all timeout-minutes: 30 strategy: fail-fast: false matrix: include: - method: wheel compiler: gcc python_version: "3.11" cuda_version: "12.8.0" linux_version: "ubuntu22.04" task: cuda - method: source compiler: gcc python_version: "3.13" cuda_version: "12.2.2" linux_version: "ubuntu22.04" task: cuda - method: pip compiler: clang python_version: "3.12" cuda_version: "11.8.0" linux_version: "ubuntu20.04" task: cuda steps: - name: Install latest git and sudo run: | apt-get update apt-get install --no-install-recommends -y \ ca-certificates \ software-properties-common add-apt-repository ppa:git-core/ppa -y apt-get update apt-get install --no-install-recommends -y \ git \ sudo - name: Checkout repository uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6.0.2 with: fetch-depth: 5 persist-credentials: false submodules: true - name: Setup and run tests run: | export BUILD_ARTIFACTSTAGINGDIRECTORY="${{ github.workspace }}/artifacts" export BUILD_DIRECTORY="$GITHUB_WORKSPACE" export PATH=$CONDA/bin:$PATH # check GPU usage nvidia-smi # build and test $GITHUB_WORKSPACE/.ci/setup.sh $GITHUB_WORKSPACE/.ci/test.sh all-cuda-jobs-successful: if: always() runs-on: ubuntu-latest needs: [test] steps: - name: Note that all tests succeeded uses: re-actors/alls-green@05ac9388f0aebcb5727afa17fcccfecd6f8ec5fe # v1.2.2 with: jobs: ${{ toJSON(needs) }} ================================================ FILE: .github/workflows/lock.yml ================================================ name: 'Lock Inactive Threads' on: schedule: # midnight UTC, every Wednesday, for Issues - cron: '0 0 * * 3' # midnight UTC, every Thursday, for PRs - cron: '0 0 * * 4' # allow manual triggering from GitHub UI workflow_dispatch: permissions: issues: write pull-requests: write concurrency: group: lock jobs: action: runs-on: ubuntu-latest steps: - uses: dessant/lock-threads@7266a7ce5c1df01b1c6db85bf8cd86c737dadbe7 # v6.0.0 with: github-token: ${{ github.token }} # after how many days of inactivity should a closed issue/PR be locked? issue-inactive-days: '365' pr-inactive-days: '365' # do not close feature request issues... # we close those but track them in https://github.com/lightgbm-org/LightGBM/issues/2302 exclude-any-issue-labels: 'feature request' # what labels should be removed prior to locking? remove-issue-labels: 'awaiting response,awaiting review,blocking,in progress' remove-pr-labels: 'awaiting response,awaiting review,blocking,in progress' # what message should be posted prior to locking? issue-comment: > This issue has been automatically locked since there has not been any recent activity since it was closed. To start a new related discussion, open a new issue at https://github.com/lightgbm-org/LightGBM/issues including a reference to this. pr-comment: > This pull request has been automatically locked since there has not been any recent activity since it was closed. To start a new related discussion, open a new issue at https://github.com/lightgbm-org/LightGBM/issues including a reference to this. # what should the locking status be? issue-lock-reason: 'resolved' pr-lock-reason: 'resolved' process-only: ${{ github.event.schedule == '0 0 * * 3' && 'issues' || 'prs' }} ================================================ FILE: .github/workflows/lychee.yml ================================================ name: Link checks on: # Run manually by clicking a button in the UI workflow_dispatch: # Run once a day at 8:00am UTC schedule: - cron: '0 8 * * *' env: COMPILER: gcc OS_NAME: 'linux' TASK: 'check-links' jobs: check-links: timeout-minutes: 60 runs-on: ubuntu-latest steps: - name: Checkout repository uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6.0.2 with: fetch-depth: 5 persist-credentials: false submodules: false - name: Build docs run: | export CONDA=${HOME}/miniforge export PATH=${CONDA}/bin:${HOME}/.local/bin:${PATH} $GITHUB_WORKSPACE/.ci/setup.sh || exit 1 $GITHUB_WORKSPACE/.ci/build-docs.sh || exit 1 - name: Check links uses: lycheeverse/lychee-action@8646ba30535128ac92d33dfc9133794bfdd9b411 # v2.8.0 with: args: >- --config=./docs/.lychee.toml -- "**/*.rst" "**/*.md" "./R-package/**/*.Rd" "./docs/_build/html/*.html" fail: true failIfEmpty: true ================================================ FILE: .github/workflows/no_response.yml ================================================ name: No Response Bot permissions: issues: write pull-requests: write on: issue_comment: types: [created] schedule: # "every day at 04:00 UTC" - cron: '0 4 * * *' jobs: noResponse: runs-on: ubuntu-latest steps: - uses: lee-dohm/no-response@9bb0a4b5e6a45046f00353d5de7d90fb8bd773bb # v0.5.0 with: closeComment: > This issue has been automatically closed because it has been awaiting a response for too long. When you have time to to work with the maintainers to resolve this issue, please post a new comment and it will be re-opened. If the issue has been locked for editing by the time you return to it, please open a new issue and reference this one. Thank you for taking the time to improve LightGBM! daysUntilClose: 30 responseRequiredLabel: awaiting response token: ${{ github.token }} ================================================ FILE: .github/workflows/optional_checks.yml ================================================ name: Optional checks on: pull_request: branches: - master jobs: all-optional-checks-successful: timeout-minutes: 30 runs-on: ubuntu-latest env: GITHUB_TOKEN: ${{ github.token }} steps: - name: Checkout repository uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6.0.2 with: fetch-depth: 5 persist-credentials: false submodules: false - name: Check valgrind workflow shell: bash run: | # ref: https://docs.github.com/en/actions/reference/workflows-and-actions/contexts#github-context PR_BRANCH="${GITHUB_HEAD_REF:-${GITHUB_REF#refs/heads/}}" echo "checking status for branch '${PR_BRANCH}'" ./.ci/check-workflow-status.sh \ "${PR_BRANCH}" \ 'r_valgrind.yml' \ ${{ github.event.number }} ================================================ FILE: .github/workflows/python_package.yml ================================================ name: Python-package on: push: branches: - master pull_request: branches: - master # automatically cancel in-progress builds if another commit is pushed concurrency: group: ${{ github.workflow }}-${{ github.ref }} cancel-in-progress: true env: # tell scripts where to put artifacts # (this variable name is left over from when jobs ran on Azure DevOps) BUILD_ARTIFACTSTAGINGDIRECTORY: '${{ github.workspace }}/artifacts' # where repo sources are cloned to BUILD_SOURCESDIRECTORY: '${{ github.workspace }}' CMAKE_BUILD_PARALLEL_LEVEL: 4 # avoid interactive prompts in Debian-based distributions DEBIAN_FRONTEND: noninteractive # On runners using nvidia-container-runtime with Docker, ensure GPUs are available to running processes. # # ref: https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/docker-specialized.html NVIDIA_VISIBLE_DEVICES: 'all' SKBUILD_STRICT_CONFIG: true jobs: test-linux-aarch64: name: bdist wheel (manylinux2014-arm, gcc, Python 3.13) runs-on: ubuntu-24.04-arm timeout-minutes: 60 steps: - name: Checkout repository uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6.0.2 with: fetch-depth: 5 persist-credentials: false submodules: true - name: Setup and run tests shell: bash # this uses 'docker run' instead of just setting 'container:' # because actions/checkout requires GLIBC 2.28 and that is too # new for manylinux2014 env: BUILD_DIRECTORY: /LightGBM run: | cat > ./docker-script.sh <> tests.log 2>&1 || exit_code=-1 cat ./tests.log exit ${exit_code} test-r-extra-checks: name: r-package (${{ matrix.image }}, R-devel) timeout-minutes: 60 strategy: fail-fast: false matrix: # references: # * CRAN "additional checks": https://cran.r-project.org/web/checks/check_issue_kinds.html # * images: https://r-hub.github.io/containers/containers.html image: - clang16 - clang17 - clang18 - clang19 - clang20 - gcc14 - intel - rchk runs-on: ubuntu-latest container: ghcr.io/r-hub/containers/${{ matrix.image }}:latest steps: - name: Checkout repository uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6.0.2 with: fetch-depth: 5 persist-credentials: false submodules: true - name: Install pandoc uses: r-lib/actions/setup-pandoc@6f6e5bc62fba3a704f74e7ad7ef7676c5c6a2590 # v2.11.4 - name: Install LaTeX shell: bash run: | if type -f apt 2>&1 > /dev/null; then apt-get update apt-get install --no-install-recommends -y \ devscripts \ texinfo \ texlive-latex-extra \ texlive-latex-recommended \ texlive-fonts-recommended \ texlive-fonts-extra \ tidy \ qpdf else yum update -y yum install -y \ devscripts \ qpdf \ texinfo \ texinfo-tex \ texlive-latex \ tidy fi - name: Install packages and run tests shell: bash run: | Rscript ./.ci/install-r-deps.R --build --test --exclude=testthat sh build-cran-package.sh # 'rchk' isn't run through 'R CMD check', use the approach documented at # https://r-hub.github.io/containers/local.html if [[ "${{ matrix.image }}" =~ "rchk" ]]; then r-check "$(pwd)" \ | tee ./rchk-logs.txt 2>&1 # the '-v' exceptions below are from R/rchk itself and not LightGBM: # https://github.com/kalibera/rchk/issues/22#issuecomment-656036156 if grep -E '\[PB\]|ERROR' ./rchk-logs.txt \ | grep -v 'too many states' \ > /dev/null; \ then echo "rchk found issues" exit 1 else echo "rchk did not find any issues" exit 0 fi fi # 'testthat' is not needed by 'rchk', so avoid installing it until here Rscript -e "install.packages('testthat', repos = 'https://cran.rstudio.com', Ncpus = parallel::detectCores())" if [[ "${{ matrix.image }}" =~ "clang" ]]; then # allowing the following NOTEs (produced by default in the clang images): # # * checking compilation flags used ... NOTE # Compilation used the following non-portable flag(s): # ‘-Wp,-D_FORTIFY_SOURCE=3’ # # even though CRAN itself sets that: # https://www.stats.ox.ac.uk/pub/bdr/Rconfig/r-devel-linux-x86_64-fedora-clang # declare -i allowed_notes=1 else declare -i allowed_notes=0 fi bash .ci/run-r-cmd-check.sh \ "$(echo lightgbm_$(head -1 VERSION.txt).tar.gz)" \ "${allowed_notes}" all-r-package-jobs-successful: if: always() runs-on: ubuntu-latest needs: [test, test-r-sanitizers, test-r-extra-checks] steps: - name: Note that all tests succeeded uses: re-actors/alls-green@05ac9388f0aebcb5727afa17fcccfecd6f8ec5fe # v1.2.2 with: jobs: ${{ toJSON(needs) }} ================================================ FILE: .github/workflows/r_valgrind.yml ================================================ name: R valgrind tests # 'run-name' is used here to distinguish in the API between different runs from forks. # # When this was added, it was the only way to feed workflow inputs into something that # would show up in the output of 'gh run list'. # # See https://github.com/orgs/community/discussions/73223#discussioncomment-11862624 run-name: R valgrind tests (pr=${{ inputs.pr-number }}) on: workflow_dispatch: inputs: pr-branch: type: string description: | Branch the PR was submitted from. Branches from forks should be prefixed with the user/org they originate from, like '{user}:{branch}'. pr-number: type: string description: Pull request ID, found in the PR URL. permissions: actions: write checks: write contents: read deployments: none discussions: none id-token: write issues: none packages: none pages: none pull-requests: write repository-projects: none security-events: none statuses: write jobs: test-r-valgrind: name: r-package (ubuntu-latest, R-devel, valgrind) timeout-minutes: 360 runs-on: ubuntu-latest container: wch1/r-debug env: GITHUB_TOKEN: ${{ github.token }} steps: - name: Install essential software before checkout shell: bash run: | apt-get update apt-get install --no-install-recommends -y \ curl \ jq - name: Install GitHub CLI run: | GH_CLI_VERSION="2.83.0" curl \ --fail \ -O \ -L \ https://github.com/cli/cli/releases/download/v${GH_CLI_VERSION}/gh_${GH_CLI_VERSION}_linux_amd64.tar.gz tar -xvf ./gh_${GH_CLI_VERSION}_linux_amd64.tar.gz mv ./gh_${GH_CLI_VERSION}_linux_amd64/bin/gh /usr/local/bin/ - name: Trust git cloning LightGBM run: | git config --global --add safe.directory "${GITHUB_WORKSPACE}" - name: Checkout repository uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6.0.2 with: fetch-depth: 5 submodules: true persist-credentials: false repository: lightgbm-org/LightGBM ref: "refs/pull/${{ inputs.pr-number }}/merge" - name: Run tests with valgrind shell: bash run: ./.ci/test-r-package-valgrind.sh - name: Send final status if: ${{ always() }} run: | $GITHUB_WORKSPACE/.ci/set-commit-status.sh \ "${{ github.workflow }}" \ "${{ job.status }}" \ "${{ github.sha }}" comment="Workflow **${{ github.workflow }}** has been triggered! 🚀\r\n" comment="${comment}\r\n${GITHUB_SERVER_URL}/lightgbm-org/LightGBM/actions/runs/${GITHUB_RUN_ID} \r\n" comment="${comment}\r\nStatus: ${{ job.status }}" $GITHUB_WORKSPACE/.ci/append-comment.sh \ "${{ inputs.pr-number }}" \ "${comment}" - name: Rerun workflow-indicator if: ${{ always() }} run: | bash $GITHUB_WORKSPACE/.ci/rerun-workflow.sh \ "optional_checks.yml" \ "${{ inputs.pr-branch }}" \ || true ================================================ FILE: .github/workflows/release_drafter.yml ================================================ name: Release Drafter permissions: contents: read on: push: branches: - master jobs: updateReleaseDraft: permissions: contents: write pull-requests: read runs-on: ubuntu-latest steps: - uses: release-drafter/release-drafter@6a93d829887aa2e0748befe2e808c66c0ec6e4c7 # v6.4.0 with: config-name: release-drafter.yml disable-autolabeler: true env: GITHUB_TOKEN: ${{ github.token }} ================================================ FILE: .github/workflows/static_analysis.yml ================================================ # contains non-functional tests, like checks on docs # and code style name: Static Analysis on: push: branches: - master pull_request: branches: - master # automatically cancel in-progress builds if another commit is pushed concurrency: group: ${{ github.workflow }}-${{ github.ref }} cancel-in-progress: true env: COMPILER: 'gcc' MAKEFLAGS: '-j4' OS_NAME: 'linux' jobs: lint: name: lint runs-on: ubuntu-latest timeout-minutes: 60 steps: - name: Checkout repository uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6.0.2 with: fetch-depth: 5 persist-credentials: false submodules: false - name: Setup and run tests shell: bash run: | export TASK=lint export CONDA=${HOME}/miniforge export PATH=${CONDA}/bin:$HOME/.local/bin:${PATH} $GITHUB_WORKSPACE/.ci/setup.sh || exit 1 $GITHUB_WORKSPACE/.ci/lint-all.sh || exit 1 check-docs: name: check-docs runs-on: ubuntu-latest timeout-minutes: 60 steps: - name: Checkout repository uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6.0.2 with: fetch-depth: 5 persist-credentials: false submodules: false - name: Setup and run tests shell: bash run: | export TASK=check-docs export CONDA=${HOME}/miniforge export PATH=${CONDA}/bin:$HOME/.local/bin:${PATH} $GITHUB_WORKSPACE/.ci/setup.sh || exit 1 $GITHUB_WORKSPACE/.ci/build-docs.sh || exit 1 r-check-docs: name: r-package-check-docs timeout-minutes: 60 runs-on: ubuntu-latest container: rocker/verse steps: - name: Trust git cloning LightGBM run: | git config --global --add safe.directory "${GITHUB_WORKSPACE}" - name: Checkout repository uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6.0.2 with: fetch-depth: 5 persist-credentials: false submodules: true - name: Install packages shell: bash run: | Rscript ./.ci/install-r-deps.R --build --include=roxygen2 sh build-cran-package.sh || exit 1 R CMD INSTALL --with-keep.source lightgbm_*.tar.gz || exit 1 - name: Test documentation shell: bash --noprofile --norc {0} run: | Rscript --vanilla -e "roxygen2::roxygenize('R-package/', load = 'installed')" || exit 1 num_doc_files_changed=$( git diff --name-only | grep --count -E "\.Rd|NAMESPACE" ) if [[ ${num_doc_files_changed} -gt 0 ]]; then echo "Some R documentation files have changed. Please re-generate them and commit those changes." echo "" echo " sh build-cran-package.sh" echo " R CMD INSTALL --with-keep.source lightgbm_*.tar.gz" echo " Rscript -e \"roxygen2::roxygenize('R-package/', load = 'installed')\"" echo "" exit 1 fi all-static-analysis-jobs-successful: if: always() runs-on: ubuntu-latest needs: [lint, check-docs, r-check-docs] steps: - name: Note that all tests succeeded uses: re-actors/alls-green@05ac9388f0aebcb5727afa17fcccfecd6f8ec5fe # v1.2.2 with: jobs: ${{ toJSON(needs) }} ================================================ FILE: .github/workflows/swig.yml ================================================ name: SWIG on: push: branches: - master pull_request: branches: - master # automatically cancel in-progress builds if another commit is pushed concurrency: group: ${{ github.workflow }}-${{ github.ref }} cancel-in-progress: true env: # tell scripts where to put artifacts # (this variable name is left over from when jobs ran on Azure DevOps) BUILD_ARTIFACTSTAGINGDIRECTORY: '${{ github.workspace }}/artifacts' # in CMake-driven builds, parallelize compilation CMAKE_BUILD_PARALLEL_LEVEL: 4 # all SWIG jobs produce artifacts PRODUCES_ARTIFACTS: 'true' # all jobs here have the same 'TASK' TASK: swig jobs: test-swig: name: swig (${{ matrix.os-display-name || matrix.os }}, ${{ matrix.compiler }}) runs-on: ${{ matrix.os }} container: ${{ matrix.container }} timeout-minutes: 60 strategy: fail-fast: false matrix: include: - os: ubuntu-latest compiler: gcc container: 'lightgbm.azurecr.io/vsts-agent:manylinux_2_28_x86_64' artifact-name: swig-linux-x86_64-jar os-display-name: 'manylinux_2_28' - os: macos-15-intel compiler: clang container: null artifact-name: swig-macos-x86_64-jar # Visual Studio 2022 - os: windows-2022 compiler: msvc container: null artifact-name: swig-windows-x86_64-jar steps: - name: Checkout repository uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6.0.2 with: fetch-depth: 5 persist-credentials: false submodules: true - name: Setup and run tests on Linux and macOS if: startsWith(matrix.os, 'macos') || startsWith(matrix.os, 'ubuntu') shell: bash run: | export BUILD_DIRECTORY="${GITHUB_WORKSPACE}" export COMPILER="${{ matrix.compiler }}" export CONDA="${HOME}/miniforge" export PATH=${CONDA}/bin:${PATH} if [[ "${{ matrix.os }}" =~ ^macos ]]; then export OS_NAME="macos" elif [[ "${{ matrix.os }}" == "ubuntu-latest" ]]; then export OS_NAME="linux" fi $GITHUB_WORKSPACE/.ci/setup.sh $GITHUB_WORKSPACE/.ci/test.sh - name: Setup and run tests on Windows if: startsWith(matrix.os, 'windows') shell: pwsh -command ". {0}" run: | $env:BUILD_DIRECTORY = $env:GITHUB_WORKSPACE $env:BUILD_SOURCESDIRECTORY = $env:BUILD_DIRECTORY & "$env:GITHUB_WORKSPACE/.ci/test-windows.ps1" - name: Upload artifacts uses: actions/upload-artifact@bbbca2ddaa5d8feaa63e36b76fdaad77386f024f # v7.0.0 with: name: ${{ matrix.artifact-name }} path: ${{ env.BUILD_ARTIFACTSTAGINGDIRECTORY }}/*.jar if-no-files-found: error all-swig-jobs-successful: if: always() runs-on: ubuntu-latest needs: - test-swig steps: - name: Note that all tests succeeded uses: re-actors/alls-green@05ac9388f0aebcb5727afa17fcccfecd6f8ec5fe # v1.2.2 with: jobs: ${{ toJSON(needs) }} ================================================ FILE: .gitignore ================================================ ## Ignore Visual Studio temporary files, build results, and ## files generated by popular Visual Studio add-ons. # User-specific files *.suo *.user *.userosscache *.sln.docstates # User-specific files (MonoDevelop/Xamarin Studio) *.userprefs # Build results [Dd]ebug/ [Dd]ebugPublic/ [Rr]elease/ [Rr]eleases/ x64/ x86/ bld/ [Bb]in/ [Oo]bj/ [Ll]og/ [Bb]uild/ # Visual Studio 2015 cache/options directory .vs/ # Uncomment if you have tasks that create the project's static files in wwwroot #wwwroot/ # MSTest test Results [Tt]est[Rr]esult*/ [Bb]uild[Ll]og.* # NUNIT *.VisualState.xml TestResult.xml # Build Results of an ATL Project [Dd]ebugPS/ [Rr]eleasePS/ dlldata.c # DNX project.lock.json artifacts/ *_i.c *_p.c *_i.h *.ilk *.meta *.obj *.pch *.pdb *.pgc *.pgd *.rsp *.sbr *.tlb *.tli *.tlh *.tmp *.tmp_proj *.log *.vspscc *.vssscc .builds *.pidb *.svclog *.scc # Chutzpah Test files _Chutzpah* # Visual C++ cache files ipch/ *.aps *.ncb *.opendb *.opensdf *.sdf *.cachefile # Visual Studio profiler *.psess *.vsp *.vspx *.sap # TFS 2012 Local Workspace $tf/ # Guidance Automation Toolkit *.gpState # ReSharper is a .NET coding add-in _ReSharper*/ *.[Rr]e[Ss]harper *.DotSettings.user # JustCode is a .NET coding add-in .JustCode # TeamCity is a build add-in _TeamCity* # DotCover is a Code Coverage Tool *.dotCover # NCrunch _NCrunch_* .*crunch*.local.xml nCrunchTemp_* # MightyMoose *.mm.* AutoTest.Net/ # Web workbench (sass) .sass-cache/ # Installshield output folder [Ee]xpress/ # DocProject is a documentation generator add-in DocProject/buildhelp/ DocProject/Help/*.HxT DocProject/Help/*.HxC DocProject/Help/*.hhc DocProject/Help/*.hhk DocProject/Help/*.hhp DocProject/Help/Html2 DocProject/Help/html # Click-Once directory publish/ # Publish Web Output *.[Pp]ublish.xml *.azurePubxml *.pubxml *.publishproj # NuGet Packages *.nupkg nuget/ # The packages folder can be ignored because of Package Restore **/packages/* # except build/, which is used as an MSBuild target. !**/packages/build/ # Uncomment if necessary however generally it will be regenerated when needed #!**/packages/repositories.config # NuGet v3's project.json files produces more ignoreable files *.nuget.props *.nuget.targets # Microsoft Azure Build Output csx/ *.build.csdef # Microsoft Azure Emulator ecf/ rcf/ # Microsoft Azure ApplicationInsights config file ApplicationInsights.config # Windows Store app package directory AppPackages/ BundleArtifacts/ # Visual Studio cache files # files ending in .cache can be ignored *.[Cc]ache # but keep track of directories ending in .cache !*.[Cc]ache/ # Others ClientBin/ ~$* *~ .*.swp *.dbmdl *.dbproj.schemaview *.pfx *.publishsettings node_modules/ orleans.codegen.cs # RIA/Silverlight projects Generated_Code/ # Backup & report files from converting an old project file # to a newer Visual Studio version. Backup files are not needed, # because we have git ;-) _UpgradeReport_Files/ Backup*/ UpgradeLog*.XML UpgradeLog*.htm # SQL Server files *.mdf *.ldf # Business Intelligence projects *.rdl.data *.bim.layout *.bim_*.settings # Microsoft Fakes FakesAssemblies/ # GhostDoc plugin setting file *.GhostDoc.xml # Node.js Tools for Visual Studio .ntvs_analysis.dat # Visual Studio 6 build log *.plg # Visual Studio 6 workspace options file *.opt # Visual Studio LightSwitch build output **/*.HTMLClient/GeneratedArtifacts **/*.DesktopClient/GeneratedArtifacts **/*.DesktopClient/ModelManifest.xml **/*.Server/GeneratedArtifacts **/*.Server/ModelManifest.xml _Pvt_Extensions # Paket dependency manager .paket/paket.exe # FAKE - F# Make .fake/ # Compiled Object files *.slo *.lo *.o *.obj # Precompiled Headers *.gch *.pch # Compiled Dynamic libraries *.so *.dylib *.dll # Fortran module files *.mod # Compiled Static libraries *.lai *.la *.a *.lib # Executables *.exe *.out *.app /windows/LightGBM.VC.db /lightgbm /testlightgbm # Created by https://www.gitignore.io/api/python ### Python ### !/python-package/lightgbm/ # Byte-compiled / optimized / DLL files __pycache__/ *.py[cod] *$py.class # C extensions *.so # Distribution / packaging .Python env/ build/ develop-eggs/ dist/ downloads/ eggs/ .eggs/ lib/ lib64/ parts/ sdist/ var/ *.egg-info/ .installed.cfg *.egg # PyInstaller # Usually these files are written by a python script from a template # before PyInstaller builds the exe, so as to inject date/other infos into it. *.manifest *.spec # Installer logs pip-log.txt pip-delete-this-directory.txt # Unit test / coverage reports htmlcov/ .tox/ .nox/ .coverage .coverage.* .cache nosetests.xml prof/ *.prof coverage.xml *.cover *.py.cover .hypothesis/ .pytest_cache/ cover/ **/coverage.html **/coverage.html.zip **/Rplots.pdf # Translations *.mo *.pot # Django stuff: *.log local_settings.py # Flask stuff: instance/ .webassets-cache # Scrapy stuff: .scrapy # Sphinx documentation docs/_build/ docs/pythonapi/ *.flag # Doxygen documentation docs/doxyoutput/ # PyBuilder target/ # Jupyter Notebook .ipynb_checkpoints Untitled*.ipynb # pyenv .python-version # celery beat schedule file celerybeat-schedule # dotenv .env # virtualenv .venv/ venv/ ENV/ # Spyder project settings .spyderproject # Rope project settings .ropeproject # R testing artefact lightgbm.model # saved or dumped model/data *.model *.pkl *.bin *.h5 # macOS **/.DS_Store # VSCode .vscode # IntelliJ/CLion .idea *.iml /cmake-build-debug/ # Files from local Python install lightgbm-python/ python-package/LICENSE python-package/build_cpp/ python-package/compile/ python-package/lightgbm/VERSION.txt # R build artefacts **/autom4te.cache/ R-package/conftest* R-package/config.status !R-package/data/agaricus.test.rda !R-package/data/agaricus.train.rda !R-package/data/bank.rda R-package/docs R-package/src/CMakeLists.txt R-package/src/Makevars R-package/src/lib_lightgbm.so.dSYM/ R-package/src/src/ R-package/src-x64 R-package/src-i386 R-package/**/VERSION.txt **/Makevars.win lightgbm_r/* lightgbm*.tar.gz lightgbm*.tgz lightgbm.Rcheck/ miktex*.zip *.def # Files created by examples and tests *.buffer **/lgb-Dataset.data **/lgb.Dataset.data **/model.txt **/lgb-model.txt examples/**/*.txt tests/distributed/mlist.txt tests/distributed/train* tests/distributed/model* tests/distributed/predict* # Files from interactive R sessions .Rproj.user **/.Rapp.history **/.Rhistory *.rda *.RData *.rds # Files generated by aspell **/*.bak # GraphViz artifacts *.gv *.gv.* # Files from local Dask work dask-worker-space/ # credentials and key material *.env *.pem *.pub *.rdp *_rsa # hipify-perl -inplace leaves behind *.prehip files *.prehip # pixi environments .pixi # mypy .mypy_cache/ .dmypy.json dmypy.json # files created by release tasks /release-artifacts ================================================ FILE: .gitmodules ================================================ [submodule "include/boost/compute"] path = external_libs/compute url = https://github.com/boostorg/compute [submodule "eigen"] path = external_libs/eigen url = https://gitlab.com/libeigen/eigen.git [submodule "external_libs/fmt"] path = external_libs/fmt url = https://github.com/fmtlib/fmt.git [submodule "external_libs/fast_double_parser"] path = external_libs/fast_double_parser url = https://github.com/lemire/fast_double_parser.git ================================================ FILE: .pre-commit-config.yaml ================================================ # exclude files which are auto-generated by build tools exclude: | (?x)^( build| external_libs| lightgbm-python| lightgbm_r| )$ |R-package/configure$ |R-package/inst/Makevars$ |R-package/inst/Makevars.win$ |R-package/man/.*Rd$ repos: - repo: https://github.com/pre-commit/pre-commit-hooks rev: v6.0.0 hooks: - id: check-toml - id: check-xml - id: end-of-file-fixer - id: trailing-whitespace - repo: https://github.com/cmake-lint/cmake-lint rev: '1.4.3' hooks: - id: cmakelint args: ["--linelength=120"] - repo: https://github.com/cpplint/cpplint rev: '2.0.2' hooks: - id: cpplint args: - --root=.. # workaround to get correct header guard pattern - --recursive - --filter=-build/include_subdir,-whitespace/line_length - repo: local hooks: - id: check-omp-pragmas name: check-omp-pragmas entry: sh args: - .ci/check-omp-pragmas.sh language: system pass_filenames: false - repo: https://github.com/adrienverge/yamllint rev: v1.37.1 hooks: - id: yamllint args: ["--strict"] - repo: local hooks: - id: regenerate-parameters name: regenerate-parameters entry: python args: - ./.ci/parameter-generator.py language: python pass_filenames: false - repo: https://github.com/rstcheck/rstcheck rev: v6.2.5 hooks: - id: rstcheck args: ["--config", "./python-package/pyproject.toml"] additional_dependencies: - breathe>=4.36.0 - sphinx>=8.1.3 - sphinx_rtd_theme>=3.0.1 - repo: https://github.com/astral-sh/ruff-pre-commit rev: v0.14.10 hooks: - id: ruff-check args: ["--config", "python-package/pyproject.toml"] types_or: [python, jupyter] - id: ruff-format args: ["--config", "python-package/pyproject.toml"] types_or: [python, jupyter] - repo: https://github.com/biomejs/pre-commit rev: v2.3.10 hooks: - id: biome-ci args: - --config-path=./biome.json - --diagnostic-level=info - --error-on-warnings - repo: https://github.com/shellcheck-py/shellcheck-py rev: v0.11.0.1 hooks: - id: shellcheck - repo: https://github.com/crate-ci/typos rev: v1.40.0 hooks: - id: typos args: ["--force-exclude"] exclude: (\.gitignore$)|(^\.editorconfig$) - repo: https://github.com/henryiii/validate-pyproject-schema-store rev: 2025.11.21 hooks: - id: validate-pyproject files: python-package/pyproject.toml$ - repo: https://github.com/pre-commit/mirrors-mypy rev: v1.19.1 hooks: - id: mypy args: ["--config-file", "python-package/pyproject.toml", "python-package/"] pass_filenames: false verbose: true additional_dependencies: - matplotlib>=3.9.1 - pandas>=2.0 - pyarrow>=17.0 - scikit-learn>=1.5.2 ================================================ FILE: .readthedocs.yaml ================================================ version: 2 build: os: "ubuntu-24.04" tools: python: "mambaforge-23.11" conda: environment: docs/env.yml formats: - pdf sphinx: builder: html configuration: docs/conf.py fail_on_warning: true submodules: include: all recursive: true ================================================ FILE: .typos.toml ================================================ default.extend-ignore-re = [ "/Ot", "mis-alignment", "mis-spelled", "posix-seh-rt", ] [default.extend-words] MAPE = "MAPE" datas = "datas" indx = "indx" interprete = "interprete" mape = "mape" splitted = "splitted" [default.extend-identifiers] ERRORs = "ERRORs" GAM = "GAM" ND24s = "ND24s" WARNINGs = "WARNINGs" fullset = "fullset" thess = "thess" ================================================ FILE: .yamllint.yml ================================================ # default config: https://yamllint.readthedocs.io/en/stable/configuration.html#default-configuration extends: default rules: document-start: disable line-length: max: 120 truthy: # prevent treating GitHub Workflow "on" key as boolean value check-keys: false ================================================ FILE: CMakeLists.txt ================================================ option(USE_MPI "Enable MPI-based distributed learning" OFF) option(USE_OPENMP "Enable OpenMP" ON) option(USE_GPU "Enable GPU-accelerated training" OFF) option(USE_SWIG "Enable SWIG to generate Java API" OFF) option(USE_TIMETAG "Set to ON to output time costs" OFF) option(USE_CUDA "Enable CUDA-accelerated training " OFF) option(USE_ROCM "Enable ROCm-accelerated training " OFF) option(USE_DEBUG "Set to ON for Debug mode" OFF) option(USE_SANITIZER "Use sanitizer flags" OFF) set( ENABLED_SANITIZERS "address" "leak" "undefined" CACHE STRING "Semicolon separated list of sanitizer names, e.g., 'address;leak'. \ Supported sanitizers are address, leak, undefined and thread." ) option(USE_HOMEBREW_FALLBACK "(macOS-only) also look in 'brew --prefix' for libraries (e.g. OpenMP)" ON) option(BUILD_CLI "Build the 'lightgbm' command-line interface in addition to lib_lightgbm" ON) option(BUILD_CPP_TEST "Build C++ tests with Google Test" OFF) option(BUILD_STATIC_LIB "Build static library" OFF) option(INSTALL_HEADERS "Install headers to CMAKE_INSTALL_PREFIX (e.g. '/usr/local/include')" ON) option(__BUILD_FOR_PYTHON "Set to ON if building lib_lightgbm for use with the Python-package" OFF) option(__BUILD_FOR_R "Set to ON if building lib_lightgbm for use with the R-package" OFF) option(__INTEGRATE_OPENCL "Set to ON if building LightGBM with the OpenCL ICD Loader and its dependencies included" OFF) cmake_minimum_required(VERSION 3.28) # If using Visual Studio generators, always target v10.x of the Windows SDK. # Doing this avoids lookups that could fall back to very old versions, e.g. by finding # outdated registry entries. # ref: https://cmake.org/cmake/help/latest/variable/CMAKE_VS_WINDOWS_TARGET_PLATFORM_VERSION.html if(CMAKE_GENERATOR MATCHES "Visual Studio") set(CMAKE_SYSTEM_VERSION 10.0 CACHE INTERNAL "target Windows SDK version" FORCE) endif() project(lightgbm LANGUAGES C CXX) set(CMAKE_CXX_STANDARD 17) set(CMAKE_CXX_STANDARD_REQUIRED ON) list(APPEND CMAKE_MODULE_PATH "${PROJECT_SOURCE_DIR}/cmake/modules") #-- Sanitizer if(USE_SANITIZER) if(MSVC) message(FATAL_ERROR "Sanitizers are not supported with MSVC.") endif() include(cmake/Sanitizer.cmake) enable_sanitizers("${ENABLED_SANITIZERS}") endif() if(__INTEGRATE_OPENCL) set(__INTEGRATE_OPENCL ON CACHE BOOL "" FORCE) set(USE_GPU OFF CACHE BOOL "" FORCE) message(STATUS "Building library with integrated OpenCL components") endif() if(__BUILD_FOR_PYTHON OR __BUILD_FOR_R OR USE_SWIG) # the SWIG wrapper, the Python and R packages don't require the CLI set(BUILD_CLI OFF) # installing the SWIG wrapper, the R and Python packages shouldn't place LightGBM's headers # outside of where the package is installed set(INSTALL_HEADERS OFF) endif() if(CMAKE_CXX_COMPILER_ID STREQUAL "GNU") if(CMAKE_CXX_COMPILER_VERSION VERSION_LESS "4.8.2") message(FATAL_ERROR "Insufficient gcc version (${CMAKE_CXX_COMPILER_VERSION})") endif() elseif(CMAKE_CXX_COMPILER_ID STREQUAL "Clang") if(CMAKE_CXX_COMPILER_VERSION VERSION_LESS "3.8") message(FATAL_ERROR "Insufficient Clang version (${CMAKE_CXX_COMPILER_VERSION})") endif() elseif(CMAKE_CXX_COMPILER_ID STREQUAL "AppleClang") if(CMAKE_CXX_COMPILER_VERSION VERSION_LESS "8.1.0") message(FATAL_ERROR "Insufficient AppleClang version (${CMAKE_CXX_COMPILER_VERSION})") endif() elseif(MSVC) if(MSVC_VERSION LESS 1900) message(FATAL_ERROR "Insufficient MSVC version (${MSVC_VERSION})") endif() endif() if(USE_SWIG) find_package(SWIG REQUIRED) find_package(Java REQUIRED) find_package(JNI REQUIRED) include(UseJava) include(UseSWIG) set(SWIG_CXX_EXTENSION "cxx") set(SWIG_EXTRA_LIBRARIES "") set(SWIG_JAVA_EXTRA_FILE_EXTENSIONS ".java" "JNI.java") set(SWIG_MODULE_JAVA_LANGUAGE "JAVA") set(SWIG_MODULE_JAVA_SWIG_LANGUAGE_FLAG "java") set(CMAKE_SWIG_OUTDIR "${CMAKE_CURRENT_BINARY_DIR}/java") include_directories(Java_INCLUDE_DIRS) include_directories(JNI_INCLUDE_DIRS) include_directories($ENV{JAVA_HOME}/include) if(WIN32) set(LGBM_SWIG_DESTINATION_DIR "${CMAKE_CURRENT_BINARY_DIR}/com/microsoft/ml/lightgbm/windows/x86_64") include_directories($ENV{JAVA_HOME}/include/win32) elseif(APPLE) set(LGBM_SWIG_DESTINATION_DIR "${CMAKE_CURRENT_BINARY_DIR}/com/microsoft/ml/lightgbm/osx/x86_64") include_directories($ENV{JAVA_HOME}/include/darwin) else() set(LGBM_SWIG_DESTINATION_DIR "${CMAKE_CURRENT_BINARY_DIR}/com/microsoft/ml/lightgbm/linux/x86_64") include_directories($ENV{JAVA_HOME}/include/linux) endif() file(MAKE_DIRECTORY "${LGBM_SWIG_DESTINATION_DIR}") endif() set(EIGEN_DIR "${PROJECT_SOURCE_DIR}/external_libs/eigen") include_directories(${EIGEN_DIR}) # See https://gitlab.com/libeigen/eigen/-/blob/master/COPYING.README add_definitions(-DEIGEN_MPL2_ONLY) add_definitions(-DEIGEN_DONT_PARALLELIZE) set(FAST_DOUBLE_PARSER_INCLUDE_DIR "${PROJECT_SOURCE_DIR}/external_libs/fast_double_parser/include") include_directories(${FAST_DOUBLE_PARSER_INCLUDE_DIR}) set(FMT_INCLUDE_DIR "${PROJECT_SOURCE_DIR}/external_libs/fmt/include") include_directories(${FMT_INCLUDE_DIR}) if(__BUILD_FOR_R) find_package(LibR REQUIRED) message(STATUS "LIBR_EXECUTABLE: ${LIBR_EXECUTABLE}") message(STATUS "LIBR_INCLUDE_DIRS: ${LIBR_INCLUDE_DIRS}") message(STATUS "LIBR_LIBS_DIR: ${LIBR_LIBS_DIR}") message(STATUS "LIBR_CORE_LIBRARY: ${LIBR_CORE_LIBRARY}") include_directories(${LIBR_INCLUDE_DIRS}) add_definitions(-DLGB_R_BUILD) endif() if(USE_TIMETAG) add_definitions(-DTIMETAG) endif() if(USE_DEBUG) add_definitions(-DDEBUG) endif() if(USE_MPI) find_package(MPI REQUIRED) add_definitions(-DUSE_MPI) else() add_definitions(-DUSE_SOCKET) endif() if(USE_CUDA) set(CMAKE_CUDA_HOST_COMPILER "${CMAKE_CXX_COMPILER}") enable_language(CUDA) set(USE_OPENMP ON CACHE BOOL "CUDA requires OpenMP" FORCE) endif() if(USE_ROCM) enable_language(HIP) set(USE_OPENMP ON CACHE BOOL "ROCm requires OpenMP" FORCE) endif() if(USE_OPENMP) if(APPLE) find_package(OpenMP) if(NOT OpenMP_FOUND) if(USE_HOMEBREW_FALLBACK) # libomp 15.0+ from brew is keg-only, so have to search in other locations. # See https://github.com/Homebrew/homebrew-core/issues/112107#issuecomment-1278042927. execute_process(COMMAND brew --prefix libomp OUTPUT_VARIABLE HOMEBREW_LIBOMP_PREFIX OUTPUT_STRIP_TRAILING_WHITESPACE) set(OpenMP_C_FLAGS "-Xpreprocessor -fopenmp -I${HOMEBREW_LIBOMP_PREFIX}/include") set(OpenMP_CXX_FLAGS "-Xpreprocessor -fopenmp -I${HOMEBREW_LIBOMP_PREFIX}/include") set(OpenMP_C_LIB_NAMES omp) set(OpenMP_CXX_LIB_NAMES omp) set(OpenMP_omp_LIBRARY ${HOMEBREW_LIBOMP_PREFIX}/lib/libomp.dylib) endif() find_package(OpenMP REQUIRED) endif() else() find_package(OpenMP REQUIRED) endif() set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} ${OpenMP_CXX_FLAGS}") endif() if(USE_GPU) set(BOOST_COMPUTE_HEADER_DIR ${PROJECT_SOURCE_DIR}/external_libs/compute/include) include_directories(${BOOST_COMPUTE_HEADER_DIR}) find_package(OpenCL REQUIRED) include_directories(${OpenCL_INCLUDE_DIRS}) message(STATUS "OpenCL include directory: " ${OpenCL_INCLUDE_DIRS}) if(WIN32) set(Boost_USE_STATIC_LIBS ON) endif() find_package(Boost 1.56.0 COMPONENTS filesystem system REQUIRED) if(WIN32) # disable autolinking in boost add_definitions(-DBOOST_ALL_NO_LIB) endif() include_directories(${Boost_INCLUDE_DIRS}) add_definitions(-DUSE_GPU) endif() if(__INTEGRATE_OPENCL) if(APPLE) message(FATAL_ERROR "Integrated OpenCL build is not available on macOS") else() include(cmake/IntegratedOpenCL.cmake) add_definitions(-DUSE_GPU) endif() endif() if(BUILD_CPP_TEST AND MSVC) # Use /MT flag to statically link the C runtime set(CMAKE_MSVC_RUNTIME_LIBRARY "MultiThreaded$<$:Debug>") endif() if(USE_CUDA) find_package(CUDAToolkit 11.0 REQUIRED) find_package(NCCL REQUIRED) include_directories(${CUDAToolkit_INCLUDE_DIRS}) set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -Xcompiler=${OpenMP_CXX_FLAGS} -Xcompiler=-fPIC -Xcompiler=-Wall") # reference for mapping of CUDA toolkit component versions to supported architectures ("compute capabilities"): # https://en.wikipedia.org/wiki/CUDA#GPUs_supported set(CUDA_ARCHS "60" "61" "62" "70" "75") if(CUDAToolkit_VERSION VERSION_GREATER_EQUAL "11.0") list(APPEND CUDA_ARCHS "80") endif() if(CUDAToolkit_VERSION VERSION_GREATER_EQUAL "11.1") list(APPEND CUDA_ARCHS "86") endif() if(CUDAToolkit_VERSION VERSION_GREATER_EQUAL "11.5") list(APPEND CUDA_ARCHS "87") endif() if(CUDAToolkit_VERSION VERSION_GREATER_EQUAL "11.8") list(APPEND CUDA_ARCHS "89") list(APPEND CUDA_ARCHS "90") endif() if(CUDAToolkit_VERSION VERSION_GREATER_EQUAL "12.8") list(APPEND CUDA_ARCHS "100") list(APPEND CUDA_ARCHS "120") endif() # Generate PTX for the most recent architecture for forwards compatibility list(POP_BACK CUDA_ARCHS CUDA_LAST_SUPPORTED_ARCH) list(TRANSFORM CUDA_ARCHS APPEND "-real") list(APPEND CUDA_ARCHS "${CUDA_LAST_SUPPORTED_ARCH}-real" "${CUDA_LAST_SUPPORTED_ARCH}-virtual") message(STATUS "CUDA_ARCHITECTURES: ${CUDA_ARCHS}") if(USE_DEBUG) set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -g") else() set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -O3 -lineinfo") endif() message(STATUS "CMAKE_CUDA_FLAGS: ${CMAKE_CUDA_FLAGS}") add_definitions(-DUSE_CUDA) if(NOT DEFINED CMAKE_CUDA_STANDARD) set(CMAKE_CUDA_STANDARD 17) set(CMAKE_CUDA_STANDARD_REQUIRED ON) endif() endif() if(USE_ROCM) find_package(HIP) include_directories(${HIP_INCLUDE_DIRS}) set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -D__HIP_PLATFORM_AMD__") set(CMAKE_HIP_FLAGS "${CMAKE_HIP_FLAGS} ${OpenMP_CXX_FLAGS} -fPIC -Wall") # avoid warning: unused variable 'mask' due to __shfl_down_sync work-around set(DISABLED_WARNINGS "${DISABLED_WARNINGS} -Wno-unused-variable") # avoid warning: 'hipHostAlloc' is deprecated: use hipHostMalloc instead set(DISABLED_WARNINGS "${DISABLED_WARNINGS} -Wno-deprecated-declarations") # avoid many warnings about missing overrides set(DISABLED_WARNINGS "${DISABLED_WARNINGS} -Wno-inconsistent-missing-override") # avoid warning: shift count >= width of type in feature_histogram.hpp set(DISABLED_WARNINGS "${DISABLED_WARNINGS} -Wno-shift-count-overflow") set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} ${DISABLED_WARNINGS}") set(CMAKE_HIP_FLAGS "${CMAKE_HIP_FLAGS} ${DISABLED_WARNINGS}") if(USE_DEBUG) set(CMAKE_HIP_FLAGS "${CMAKE_HIP_FLAGS} -g -O0") else() set(CMAKE_HIP_FLAGS "${CMAKE_HIP_FLAGS} -O3") endif() message(STATUS "CMAKE_HIP_FLAGS: ${CMAKE_HIP_FLAGS}") # Building for ROCm almost always means USE_CUDA. # Exceptions to this will be guarded by USE_ROCM. add_definitions(-DUSE_CUDA) add_definitions(-DUSE_ROCM) endif() include(CheckCXXSourceCompiles) check_cxx_source_compiles(" #include int main() { int a = 0; _mm_prefetch(&a, _MM_HINT_NTA); return 0; } " MM_PREFETCH) if(${MM_PREFETCH}) message(STATUS "Using _mm_prefetch") add_definitions(-DMM_PREFETCH) endif() include(CheckCXXSourceCompiles) check_cxx_source_compiles(" #include int main() { char *a = (char*)_mm_malloc(8, 16); _mm_free(a); return 0; } " MM_MALLOC) if(${MM_MALLOC}) message(STATUS "Using _mm_malloc") add_definitions(-DMM_MALLOC) endif() if(UNIX OR MINGW OR CYGWIN) set( CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -pthread -Wextra -Wall -Wno-ignored-attributes -Wno-unknown-pragmas -Wno-return-type" ) if(MINGW) # ignore this warning: https://gcc.gnu.org/bugzilla/show_bug.cgi?id=95353 set( CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Wno-stringop-overflow" ) endif() if(USE_DEBUG) set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -g -O0") else() set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -O3") endif() if(USE_SWIG) set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -fno-strict-aliasing") endif() if(NOT USE_OPENMP) set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Wno-unknown-pragmas -Wno-unused-private-field") endif() if(__BUILD_FOR_R AND CMAKE_CXX_COMPILER_ID STREQUAL "GNU") set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Wno-cast-function-type") endif() endif() if(WIN32 AND MINGW) set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -static-libstdc++") endif() # Check if inet_pton is already available, to avoid conflicts with the implementation in LightGBM. # As of 2022, MinGW started including a definition of inet_pton. if(WIN32) include(CheckSymbolExists) list(APPEND CMAKE_REQUIRED_LIBRARIES "ws2_32") check_symbol_exists(inet_pton "ws2tcpip.h" WIN_INET_PTON_FOUND) if(WIN_INET_PTON_FOUND) add_definitions(-DWIN_HAS_INET_PTON) endif() list(REMOVE_ITEM CMAKE_REQUIRED_LIBRARIES "ws2_32") endif() if(MSVC) # compiling 'fmt' on MSVC: "Unicode support requires compiling with /utf-8" set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} /W4 /MP /utf-8") if(__BUILD_FOR_R) # MSVC does not like this commit: # https://github.com/wch/r-source/commit/fb52ac1a610571fcb8ac92d886b9fefcffaa7d48 # # and raises "error C3646: 'private_data_c': unknown override specifier" add_definitions(-DR_LEGACY_RCOMPLEX) endif() if(USE_DEBUG) set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} /Od") else() set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} /O2 /Ob2 /Oi /Ot /Oy") endif() else() if(NOT BUILD_STATIC_LIB) set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -fPIC") endif() if(NOT USE_DEBUG) set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -funroll-loops") endif() endif() set(LightGBM_HEADER_DIR ${PROJECT_SOURCE_DIR}/include) set(EXECUTABLE_OUTPUT_PATH ${PROJECT_SOURCE_DIR}) set(LIBRARY_OUTPUT_PATH ${PROJECT_SOURCE_DIR}) include_directories(${LightGBM_HEADER_DIR}) if(USE_MPI) include_directories(${MPI_CXX_INCLUDE_PATH}) endif() set( LGBM_SOURCES src/boosting/boosting.cpp src/boosting/gbdt_model_text.cpp src/boosting/gbdt_prediction.cpp src/boosting/gbdt.cpp src/boosting/prediction_early_stop.cpp src/boosting/sample_strategy.cpp src/io/bin.cpp src/io/config_auto.cpp src/io/config.cpp src/io/dataset_loader.cpp src/io/dataset.cpp src/io/file_io.cpp src/io/json11.cpp src/io/metadata.cpp src/io/parser.cpp src/io/train_share_states.cpp src/io/tree.cpp src/metric/dcg_calculator.cpp src/metric/metric.cpp src/network/linker_topo.cpp src/network/linkers_mpi.cpp src/network/linkers_socket.cpp src/network/network.cpp src/objective/objective_function.cpp src/treelearner/data_parallel_tree_learner.cpp src/treelearner/feature_histogram.cpp src/treelearner/feature_parallel_tree_learner.cpp src/treelearner/gpu_tree_learner.cpp src/treelearner/gradient_discretizer.cpp src/treelearner/linear_tree_learner.cpp src/treelearner/serial_tree_learner.cpp src/treelearner/tree_learner.cpp src/treelearner/voting_parallel_tree_learner.cpp src/utils/openmp_wrapper.cpp ) set( LGBM_CUDA_SOURCES src/boosting/cuda/cuda_score_updater.cpp src/boosting/cuda/cuda_score_updater.cu src/boosting/cuda/nccl_gbdt.cpp src/metric/cuda/cuda_binary_metric.cpp src/metric/cuda/cuda_pointwise_metric.cpp src/metric/cuda/cuda_regression_metric.cpp src/metric/cuda/cuda_pointwise_metric.cu src/objective/cuda/cuda_binary_objective.cpp src/objective/cuda/cuda_multiclass_objective.cpp src/objective/cuda/cuda_rank_objective.cpp src/objective/cuda/cuda_regression_objective.cpp src/objective/cuda/cuda_binary_objective.cu src/objective/cuda/cuda_multiclass_objective.cu src/objective/cuda/cuda_rank_objective.cu src/objective/cuda/cuda_regression_objective.cu src/treelearner/cuda/cuda_best_split_finder.cpp src/treelearner/cuda/cuda_data_partition.cpp src/treelearner/cuda/cuda_histogram_constructor.cpp src/treelearner/cuda/cuda_leaf_splits.cpp src/treelearner/cuda/cuda_single_gpu_tree_learner.cpp src/treelearner/cuda/cuda_best_split_finder.cu src/treelearner/cuda/cuda_data_partition.cu src/treelearner/cuda/cuda_gradient_discretizer.cu src/treelearner/cuda/cuda_histogram_constructor.cu src/treelearner/cuda/cuda_leaf_splits.cu src/treelearner/cuda/cuda_single_gpu_tree_learner.cu src/io/cuda/cuda_column_data.cu src/io/cuda/cuda_tree.cu src/io/cuda/cuda_column_data.cpp src/io/cuda/cuda_metadata.cpp src/io/cuda/cuda_row_data.cpp src/io/cuda/cuda_tree.cpp src/cuda/cuda_utils.cpp src/cuda/cuda_algorithms.cu ) if(USE_CUDA OR USE_ROCM) list(APPEND LGBM_SOURCES ${LGBM_CUDA_SOURCES}) endif() if(USE_ROCM) set(CU_FILES "") foreach(file IN LISTS LGBM_CUDA_SOURCES) string(REGEX MATCH "\\.cu$" is_cu_file "${file}") if(is_cu_file) list(APPEND CU_FILES "${file}") endif() endforeach() set_source_files_properties(${CU_FILES} PROPERTIES LANGUAGE HIP) endif() add_library(lightgbm_objs OBJECT ${LGBM_SOURCES}) if(BUILD_CLI) add_executable(lightgbm src/main.cpp src/application/application.cpp) target_link_libraries(lightgbm PRIVATE lightgbm_objs) endif() set(API_SOURCES "src/c_api.cpp") # Only build the R part of the library if building for # use with the R-package if(__BUILD_FOR_R) list(APPEND API_SOURCES "src/lightgbm_R.cpp") endif() add_library(lightgbm_capi_objs OBJECT ${API_SOURCES}) if(BUILD_STATIC_LIB) add_library(_lightgbm STATIC) else() add_library(_lightgbm SHARED) endif() # R expects libraries of the form .{dll,dylib,so}, not lib_.{dll,dylib,so} if(__BUILD_FOR_R) set_target_properties( _lightgbm PROPERTIES PREFIX "" OUTPUT_NAME "lightgbm" ) endif() # LightGBM headers include openmp, cuda, R etc. headers, # thus PUBLIC is required for building _lightgbm_swig target. target_link_libraries(_lightgbm PUBLIC lightgbm_capi_objs lightgbm_objs) if(MSVC AND NOT __BUILD_FOR_R) set_target_properties(_lightgbm PROPERTIES OUTPUT_NAME "lib_lightgbm") endif() if(USE_SWIG) set_property(SOURCE swig/lightgbmlib.i PROPERTY CPLUSPLUS ON) list(APPEND swig_options -package com.microsoft.ml.lightgbm) set_property(SOURCE swig/lightgbmlib.i PROPERTY SWIG_FLAGS "${swig_options}") swig_add_library(_lightgbm_swig LANGUAGE java SOURCES swig/lightgbmlib.i) swig_link_libraries(_lightgbm_swig _lightgbm) set_target_properties( _lightgbm_swig PROPERTIES # needed to ensure Linux build does not have lib prefix specified twice, e.g. liblib_lightgbm_swig PREFIX "" # needed in some versions of CMake for VS and MinGW builds to ensure output dll has lib prefix OUTPUT_NAME "lib_lightgbm_swig" ) if(WIN32) set(LGBM_SWIG_LIB_DESTINATION_PATH "${LGBM_SWIG_DESTINATION_DIR}/lib_lightgbm_swig.dll") if(MINGW OR CYGWIN) set(LGBM_LIB_SOURCE_PATH "${PROJECT_SOURCE_DIR}/lib_lightgbm.dll") set(LGBM_SWIG_LIB_SOURCE_PATH "${PROJECT_SOURCE_DIR}/lib_lightgbm_swig.dll") else() set(LGBM_LIB_SOURCE_PATH "${PROJECT_SOURCE_DIR}/Release/lib_lightgbm.dll") set(LGBM_SWIG_LIB_SOURCE_PATH "${PROJECT_SOURCE_DIR}/Release/lib_lightgbm_swig.dll") endif() elseif(APPLE) set(LGBM_LIB_SOURCE_PATH "${PROJECT_SOURCE_DIR}/lib_lightgbm.dylib") set(LGBM_SWIG_LIB_SOURCE_PATH "${PROJECT_SOURCE_DIR}/lib_lightgbm_swig.jnilib") set(LGBM_SWIG_LIB_DESTINATION_PATH "${LGBM_SWIG_DESTINATION_DIR}/lib_lightgbm_swig.dylib") else() set(LGBM_LIB_SOURCE_PATH "${PROJECT_SOURCE_DIR}/lib_lightgbm.so") set(LGBM_SWIG_LIB_SOURCE_PATH "${PROJECT_SOURCE_DIR}/lib_lightgbm_swig.so") set(LGBM_SWIG_LIB_DESTINATION_PATH "${LGBM_SWIG_DESTINATION_DIR}/lib_lightgbm_swig.so") endif() add_custom_command( TARGET _lightgbm_swig POST_BUILD COMMAND "${Java_JAVAC_EXECUTABLE}" -d . java/*.java COMMAND "${CMAKE_COMMAND}" -E copy_if_different "${LGBM_LIB_SOURCE_PATH}" "${LGBM_SWIG_DESTINATION_DIR}" COMMAND "${CMAKE_COMMAND}" -E copy_if_different "${LGBM_SWIG_LIB_SOURCE_PATH}" "${LGBM_SWIG_LIB_DESTINATION_PATH}" COMMAND "${Java_JAR_EXECUTABLE}" -cf lightgbmlib.jar com ) endif() if(USE_MPI) target_link_libraries(lightgbm_objs PUBLIC ${MPI_CXX_LIBRARIES}) endif() if(USE_OPENMP) if(CMAKE_CXX_COMPILER_ID MATCHES "Clang") target_link_libraries(lightgbm_objs PUBLIC OpenMP::OpenMP_CXX) # c_api headers also includes OpenMP headers, thus compiling # lightgbm_capi_objs needs include directory for OpenMP. # Specifying OpenMP in target_link_libraries will get include directory # requirements for compilation. # This uses CMake's Transitive Usage Requirements. Refer to CMake doc: # https://cmake.org/cmake/help/v3.16/manual/cmake-buildsystem.7.html#transitive-usage-requirements target_link_libraries(lightgbm_capi_objs PUBLIC OpenMP::OpenMP_CXX) endif() endif() if(USE_GPU) target_link_libraries(lightgbm_objs PUBLIC ${OpenCL_LIBRARY} ${Boost_LIBRARIES}) endif() if(USE_CUDA) target_link_libraries(lightgbm_objs PUBLIC ${NCCL_LIBRARY}) endif() if(USE_ROCM) find_package(rccl) target_link_libraries(lightgbm_objs PUBLIC ${RCCL_LIBRARY}) endif() if(__INTEGRATE_OPENCL) # targets OpenCL and Boost are added in IntegratedOpenCL.cmake add_dependencies(lightgbm_objs OpenCL Boost) # variables INTEGRATED_OPENCL_* are set in IntegratedOpenCL.cmake target_include_directories(lightgbm_objs PRIVATE ${INTEGRATED_OPENCL_INCLUDES}) target_compile_definitions(lightgbm_objs PRIVATE ${INTEGRATED_OPENCL_DEFINITIONS}) target_link_libraries(lightgbm_objs PUBLIC ${INTEGRATED_OPENCL_LIBRARIES} ${CMAKE_DL_LIBS}) endif() if(USE_CUDA) set_target_properties( lightgbm_objs PROPERTIES CUDA_ARCHITECTURES "${CUDA_ARCHS}" CUDA_SEPARABLE_COMPILATION ON ) set_target_properties( _lightgbm PROPERTIES CUDA_ARCHITECTURES "${CUDA_ARCHS}" CUDA_RESOLVE_DEVICE_SYMBOLS ON ) if(BUILD_CLI) set_target_properties( lightgbm PROPERTIES CUDA_ARCHITECTURES "${CUDA_ARCHS}" CUDA_RESOLVE_DEVICE_SYMBOLS ON ) endif() endif() if(USE_ROCM) target_link_libraries(lightgbm_objs PUBLIC hip::host) endif() if(WIN32) if(MINGW OR CYGWIN) target_link_libraries(lightgbm_objs PUBLIC ws2_32 iphlpapi) endif() endif() if(__BUILD_FOR_R) # utils/log.h and capi uses R headers, thus both object libraries need to link # with R lib. if(MSVC) set(R_LIB ${LIBR_MSVC_CORE_LIBRARY}) else() set(R_LIB ${LIBR_CORE_LIBRARY}) endif() target_link_libraries(lightgbm_objs PUBLIC ${R_LIB}) target_link_libraries(lightgbm_capi_objs PUBLIC ${R_LIB}) endif() #-- Google C++ tests if(BUILD_CPP_TEST) find_package(GTest CONFIG) if(NOT GTEST_FOUND) message(STATUS "Did not find Google Test in the system root. Fetching Google Test now...") include(FetchContent) # lint_cmake: -readability/wonkycase FetchContent_Declare( # lint_cmake: +readability/wonkycase googletest GIT_REPOSITORY https://github.com/google/googletest.git GIT_TAG v1.14.0 ) # lint_cmake: -readability/wonkycase FetchContent_MakeAvailable(googletest) # lint_cmake: +readability/wonkycase add_library(GTest::GTest ALIAS gtest) endif() set(LightGBM_TEST_HEADER_DIR ${PROJECT_SOURCE_DIR}/tests/cpp_tests) include_directories(${LightGBM_TEST_HEADER_DIR}) set( CPP_TEST_SOURCES tests/cpp_tests/test_array_args.cpp tests/cpp_tests/test_arrow.cpp tests/cpp_tests/test_byte_buffer.cpp tests/cpp_tests/test_chunked_array.cpp tests/cpp_tests/test_common.cpp tests/cpp_tests/test_main.cpp tests/cpp_tests/test_serialize.cpp tests/cpp_tests/test_single_row.cpp tests/cpp_tests/test_stream.cpp tests/cpp_tests/testutils.cpp ) if(MSVC) set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} /permissive-") endif() add_executable(testlightgbm ${CPP_TEST_SOURCES}) target_link_libraries(testlightgbm PRIVATE lightgbm_objs lightgbm_capi_objs GTest::GTest) endif() if(BUILD_CLI) install( TARGETS lightgbm RUNTIME DESTINATION ${CMAKE_INSTALL_PREFIX}/bin ) endif() if(__BUILD_FOR_PYTHON) set(CMAKE_INSTALL_PREFIX "lightgbm") endif() # The macOS linker puts an absolute path to linked libraries in lib_lightgbm.dylib. # This block overrides that information for LightGBM's OpenMP dependency, to allow # finding that library in more places. # # This reduces the risk of runtime issues resulting from multiple {libgomp,libiomp,libomp}.dylib being loaded. # if(APPLE AND USE_OPENMP AND NOT BUILD_STATIC_LIB) # store path to {libgomp,libiomp,libomp}.dylib found at build time in a variable get_target_property( OpenMP_LIBRARY_LOCATION OpenMP::OpenMP_CXX INTERFACE_LINK_LIBRARIES ) # get just the filename of that path # (to deal with the possibility that it might be 'libomp.dylib' or 'libgomp.dylib' or 'libiomp.dylib') get_filename_component( OpenMP_LIBRARY_NAME ${OpenMP_LIBRARY_LOCATION} NAME ) # get directory of that path get_filename_component( OpenMP_LIBRARY_DIR ${OpenMP_LIBRARY_LOCATION} DIRECTORY ) # get exact name of the library in a variable get_target_property( __LIB_LIGHTGBM_OUTPUT_NAME _lightgbm OUTPUT_NAME ) if(NOT __LIB_LIGHTGBM_OUTPUT_NAME) set(__LIB_LIGHTGBM_OUTPUT_NAME "lib_lightgbm") endif() if(CMAKE_SHARED_LIBRARY_SUFFIX_CXX) set( __LIB_LIGHTGBM_FILENAME "${__LIB_LIGHTGBM_OUTPUT_NAME}${CMAKE_SHARED_LIBRARY_SUFFIX_CXX}" CACHE INTERNAL "lightgbm shared library filename" ) else() set( __LIB_LIGHTGBM_FILENAME "${__LIB_LIGHTGBM_OUTPUT_NAME}.dylib" CACHE INTERNAL "lightgbm shared library filename" ) endif() # Override the absolute path to OpenMP with a relative one using @rpath. # # This also ensures that if a {libgomp,libiomp,libomp}.dylib has already been loaded, it'll just use that. add_custom_command( TARGET _lightgbm POST_BUILD COMMAND install_name_tool -change ${OpenMP_LIBRARY_LOCATION} "@rpath/${OpenMP_LIBRARY_NAME}" "${__LIB_LIGHTGBM_FILENAME}" WORKING_DIRECTORY ${CMAKE_CURRENT_SOURCE_DIR} COMMENT "Replacing hard-coded OpenMP install_name with '@rpath/${OpenMP_LIBRARY_NAME}'..." ) # add RPATH entries to ensure the loader looks in the following, in the following order: # # - (R-only) ${LIBR_LIBS_DIR} (wherever R for macOS stores vendored third-party libraries) # - ${OpenMP_LIBRARY_DIR} (wherever find_package(OpenMP) found OpenMP at build time) # - /opt/homebrew/opt/libomp/lib (where 'brew install' / 'brew link' puts libomp.dylib) # - /opt/local/lib/libomp (where 'port install' puts libomp.dylib) # # with some compilers, OpenMP ships with the compiler (e.g. libgomp with gcc) list(APPEND __omp_install_rpaths "${OpenMP_LIBRARY_DIR}") # with clang, libomp doesn't ship with the compiler and might be supplied separately if(CMAKE_CXX_COMPILER_ID MATCHES "Clang") list( APPEND __omp_install_rpaths "/opt/homebrew/opt/libomp/lib" "/opt/local/lib/libomp" ) # It appears that CRAN's macOS binaries compiled with -fopenmp have install names # of the form: # # /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/lib/libomp.dylib # # That corresponds to the libomp.dylib that ships with the R framework for macOS, available # from https://cran.r-project.org/bin/macosx/. # # That absolute-path install name leads to that library being loaded unconditionally. # # That can result in e.g. 'library(data.table)' loading R's libomp.dylib and 'library(lightgbm)' loading # Homebrew's. Having 2 loaded in the same process can lead to segfaults and unpredictable behavior. # # This can't be easily avoided by forcing R-package builds in LightGBM to use R's libomp.dylib # at build time... LightGBM's CMake uses find_package(OpenMP), and R for macOS only provides the # library, not CMake config files for it. # # Best we can do, to allow CMake-based builds of the R-package here to continue to work # alongside CRAN-prepared binaries of other packages with OpenMP dependencies, is to # ensure that R's library directory is the first place the loader searches for # libomp.dylib when clang is used. # # ref: https://github.com/lightgbm-org/LightGBM/issues/6628 # if(__BUILD_FOR_R) list(PREPEND __omp_install_rpaths "${LIBR_LIBS_DIR}") endif() endif() set_target_properties( _lightgbm PROPERTIES BUILD_WITH_INSTALL_RPATH TRUE INSTALL_RPATH "${__omp_install_rpaths}" INSTALL_RPATH_USE_LINK_PATH FALSE ) endif() install( TARGETS _lightgbm RUNTIME DESTINATION ${CMAKE_INSTALL_PREFIX}/bin LIBRARY DESTINATION ${CMAKE_INSTALL_PREFIX}/lib ARCHIVE DESTINATION ${CMAKE_INSTALL_PREFIX}/lib ) if(INSTALL_HEADERS) install( DIRECTORY ${LightGBM_HEADER_DIR}/LightGBM DESTINATION ${CMAKE_INSTALL_PREFIX}/include ) install( FILES ${FAST_DOUBLE_PARSER_INCLUDE_DIR}/fast_double_parser.h DESTINATION ${CMAKE_INSTALL_PREFIX}/include/LightGBM/utils ) install( DIRECTORY ${FMT_INCLUDE_DIR}/ DESTINATION ${CMAKE_INSTALL_PREFIX}/include/LightGBM/utils FILES_MATCHING PATTERN "*.h" ) endif() ================================================ FILE: CODE_OF_CONDUCT.md ================================================ # Microsoft Open Source Code of Conduct This 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. ## Our open source communities strive to: * 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. * 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. * 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: 1. Violent threats or language. 2. Discriminatory or derogatory jokes and language. 3. Posting sexually explicit or violent material. 4. Posting, or threatening to post, people's personally identifying information ("doxing"). 5. Insults, especially those using discriminatory terms or slurs. Behavior that could be perceived as sexual attention. Advocating for or encouraging any of the above behaviors. * Understand disagreements: Disagreements, both social and technical, are useful learning opportunities. Seek to understand the other viewpoints and resolve differences constructively. * 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. ## Scope This 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. Note: 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. ## Reporting Code of Conduct Issues We 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. In your report please include: * Your contact information. * Names (real, usernames or pseudonyms) of any individuals involved. If there are additional witnesses, please include them as well. * 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. * Any additional information that may be helpful. All 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. Anyone 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. This 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. ================================================ FILE: CONTRIBUTING.md ================================================ # contributing LightGBM has been developed and used by many active community members. Your help is very valuable to make it better for everyone. ## How to Contribute - 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. - Contribute to the [tests](https://github.com/lightgbm-org/LightGBM/tree/master/tests) to make it more reliable. - Contribute to the [documentation](https://github.com/lightgbm-org/LightGBM/tree/master/docs) to make it clearer for everyone. - Contribute to the [examples](https://github.com/lightgbm-org/LightGBM/tree/master/examples) to share your experience with other users. - 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! - [Open an issue](https://github.com/lightgbm-org/LightGBM/issues) to report problems or recommend new features. ## Development Guide ### Linting Every commit in the repository is tested with multiple static analyzers. When developing locally, run some of them using `pre-commit` ([pre-commit docs](https://pre-commit.com/)). ```shell pre-commit run --all-files ``` That command will check for some issues and automatically reformat the code. ================================================ FILE: LICENSE ================================================ The MIT License (MIT) Copyright (c) Microsoft Corporation Copyright (c) The LightGBM developers Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. ================================================ FILE: MAINTAINING.md ================================================ # maintaining This document is for LightGBM maintainers. ## Releasing ### Step 1: Put up a Release PR Create a pull request into `master` which prepares the source code for release. Copy the description and checklist from the previous release PR (for example: https://github.com/lightgbm-org/LightGBM/pull/6796). This should usually also include a checklist of other issues and PRs that should be completed for the release, and the PR should be used to discuss what makes it into the release. ### Step 2: Merge the Release PR Once the PR is approved, merge it. Do not merge any other PRs into `master` until the rest of the release is complete. ### Step 3: Wait for a New CI Run on `master` Wait for all CI runs triggered by the merge to `master` to complete successfully. These runs build and test the official artifacts that will be attached to the GitHub release and published to package managers. ### Step 4: Create a Release Navigate to https://github.com/lightgbm-org/LightGBM/releases. Click "edit" on the draft release that `release-drafter` has created there. * update the tag and release title to match the version of LightGBM, in the format `v{major}.{minor}.{patch}` * ensure that tag points at the commit on ``master`` created by merging the release PR When you're satisfied with the state of the release, click "Publish release". ### Step 5: Upload Artifacts After creating a release, run the following from the root of the repo to populate it with artifacts. ```shell # download all artifacts to a local directory ./.ci/downloads-artifacts.sh ${COMMIT_ID} # attach them to the GitHub release gh release upload \ --repo lightgbm-org/LightGBM \ "${TAG}" \ ./release-artifacts/* ``` Where: * `COMMIT_ID` = full commit hash of the commit on `master` corresponding to the release * `TAG` = the tag for the release (e.g. `v4.6.0`) ### Step 6: Complete All Other Post-merge Release Steps These include things like publishing to package managers, updating build configs for repackagers like ``conda-forge``, and many other steps. See the release checklist on the PR for details. ================================================ FILE: R-package/.Rbuildignore ================================================ \.appveyor\.yml AUTOCONF_UBUNTU_VERSION ^autom4te.cache/.*$ ^.*\.bin ^build_r.R$ \.clang-format ^.*\.clusterfuzzlite$ ^cran-comments\.md$ ^docs$ ^.*\.dll \.drone\.yml ^.*\.dylib \.git \.gitkeep$ ^.*\.history ^Makefile$ ^.*\.o ^.*\.out ^pkgdown$ ^recreate-configure\.sh$ ^.*\.so ^src/build/.*$ ^src/CMakeLists.txt$ ^src/external_libs/compute/.appveyor.yml$ ^src/external_libs/compute/.coveralls.yml$ ^src/external_libs/compute/.travis.yml$ ^src/external_libs/compute/test/.*$ ^src/external_libs/compute/index.html$ ^src/external_libs/compute/.git$ ^src/external_libs/compute/.gitignore$ ^src/external_libs/compute/CONTRIBUTING.md$ ^src/external_libs/compute/README.md$ src/external_libs/fast_double_parser/benchmarks src/external_libs/fast_double_parser/Makefile src/external_libs/fast_double_parser/.*\.md src/external_libs/fast_double_parser/tests src/external_libs/fast_double_parser/.*\.yaml src/external_libs/fast_double_parser/.*\.yml src/external_libs/fmt/.*\.md src/external_libs/fmt/.travis.yml src/external_libs/fmt/doc src/external_libs/fmt/support/Android\.mk src/external_libs/fmt/support/bazel/.bazel.* src/external_libs/fmt/support/.*\.gradle src/external_libs/fmt/support/.*\.pro src/external_libs/fmt/support/.*\.py src/external_libs/fmt/support/rtd src/external_libs/fmt/support/.*sublime-syntax src/external_libs/fmt/support/Vagrantfile src/external_libs/fmt/support/.*\.xml src/external_libs/fmt/support/.*\.yml src/external_libs/fmt/test ================================================ FILE: R-package/AUTOCONF_UBUNTU_VERSION ================================================ 2.71-2 ================================================ FILE: R-package/DESCRIPTION ================================================ Package: lightgbm Type: Package Title: Light Gradient Boosting Machine Version: ~~VERSION~~ Date: ~~DATE~~ Authors@R: c( person("Yu", "Shi", email = "yushi2@microsoft.com", role = c("aut")), person("Guolin", "Ke", email = "guolin.ke@outlook.com", role = c("aut")), person("Damien", "Soukhavong", email = "damien.soukhavong@skema.edu", role = c("aut")), person("James", "Lamb", email="jaylamb20@gmail.com", role = c("aut", "cre")), person("Qi", "Meng", role = c("aut")), person("Thomas", "Finley", role = c("aut")), person("Taifeng", "Wang", role = c("aut")), person("Wei", "Chen", role = c("aut")), person("Weidong", "Ma", role = c("aut")), person("Qiwei", "Ye", role = c("aut")), person("Tie-Yan", "Liu", role = c("aut")), person("Nikita", "Titov", role = c("aut")), person("Yachen", "Yan", role = c("ctb")), person("Microsoft Corporation", role = c("cph")), person("Dropbox, Inc.", role = c("cph")), person("Alberto", "Ferreira", role = c("ctb")), person("Daniel", "Lemire", role = c("ctb")), person("Victor", "Zverovich", role = c("cph")), person("IBM Corporation", role = c("ctb")), person("David", "Cortes", role = c("aut")), person("Michael", "Mayer", role = c("ctb")) ) Description: Tree based algorithms can be improved by introducing boosting frameworks. 'LightGBM' is one such framework, based on Ke, Guolin et al. (2017) . This package offers an R interface to work with it. It is designed to be distributed and efficient with the following advantages: 1. Faster training speed and higher efficiency. 2. Lower memory usage. 3. Better accuracy. 4. Parallel learning supported. 5. Capable of handling large-scale data. In recognition of these advantages, 'LightGBM' has been widely-used in many winning solutions of machine learning competitions. 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. Encoding: UTF-8 License: MIT + file LICENSE URL: https://github.com/lightgbm-org/LightGBM BugReports: https://github.com/lightgbm-org/LightGBM/issues NeedsCompilation: yes Biarch: true VignetteBuilder: knitr Suggests: knitr, markdown, processx, RhpcBLASctl, testthat Depends: R (>= 4.0) Imports: R6 (>= 2.4.0), data.table (>= 1.9.6), graphics, jsonlite (>= 1.0), Matrix (>= 1.1-0), methods, parallel, utils SystemRequirements: C++17 RoxygenNote: 7.3.3 ================================================ FILE: R-package/LICENSE ================================================ YEAR: 2016 COPYRIGHT HOLDER: Microsoft Corporation ================================================ FILE: R-package/NAMESPACE ================================================ # Generated by roxygen2: do not edit by hand S3method("dimnames<-",lgb.Dataset) S3method(dim,lgb.Dataset) S3method(dimnames,lgb.Dataset) S3method(get_field,lgb.Dataset) S3method(predict,lgb.Booster) S3method(print,lgb.Booster) S3method(set_field,lgb.Dataset) S3method(summary,lgb.Booster) export(getLGBMthreads) export(get_field) export(lgb.Dataset) export(lgb.Dataset.construct) export(lgb.Dataset.create.valid) export(lgb.Dataset.save) export(lgb.Dataset.set.categorical) export(lgb.Dataset.set.reference) export(lgb.configure_fast_predict) export(lgb.convert_with_rules) export(lgb.cv) export(lgb.drop_serialized) export(lgb.dump) export(lgb.get.eval.result) export(lgb.importance) export(lgb.interprete) export(lgb.load) export(lgb.make_serializable) export(lgb.model.dt.tree) export(lgb.plot.importance) export(lgb.plot.interpretation) export(lgb.restore_handle) export(lgb.save) export(lgb.slice.Dataset) export(lgb.train) export(lightgbm) export(setLGBMthreads) export(set_field) import(methods) importClassesFrom(Matrix,CsparseMatrix) importClassesFrom(Matrix,RsparseMatrix) importClassesFrom(Matrix,dgCMatrix) importClassesFrom(Matrix,dgRMatrix) importClassesFrom(Matrix,dsparseMatrix) importClassesFrom(Matrix,dsparseVector) importFrom(Matrix,Matrix) importFrom(R6,R6Class) importFrom(data.table,":=") importFrom(data.table,as.data.table) importFrom(data.table,data.table) importFrom(data.table,rbindlist) importFrom(data.table,set) importFrom(data.table,setnames) importFrom(data.table,setorder) importFrom(data.table,setorderv) importFrom(graphics,barplot) importFrom(graphics,par) importFrom(jsonlite,fromJSON) importFrom(methods,is) importFrom(methods,new) importFrom(parallel,detectCores) importFrom(stats,quantile) importFrom(utils,modifyList) importFrom(utils,read.delim) useDynLib(lightgbm , .registration = TRUE) ================================================ FILE: R-package/R/aliases.R ================================================ # Central location for parameter aliases. # See https://lightgbm.readthedocs.io/en/latest/Parameters.html#core-parameters # [description] List of respected parameter aliases specific to lgb.Dataset. Wrapped in a function to # take advantage of lazy evaluation (so it doesn't matter what order # R sources files during installation). # [return] A named list, where each key is a parameter relevant to lgb.Dataset and each value is a character # vector of corresponding aliases. .DATASET_PARAMETERS <- function() { all_aliases <- .PARAMETER_ALIASES() return(all_aliases[c( "bin_construct_sample_cnt" , "categorical_feature" , "data_random_seed" , "enable_bundle" , "feature_pre_filter" , "forcedbins_filename" , "group_column" , "header" , "ignore_column" , "is_enable_sparse" , "label_column" , "linear_tree" , "max_bin" , "max_bin_by_feature" , "min_data_in_bin" , "pre_partition" , "precise_float_parser" , "two_round" , "use_missing" , "weight_column" , "zero_as_missing" )]) } # [description] Non-exported environment, used for caching details that only need to be # computed once per R session. .lgb_session_cache_env <- new.env() # [description] List of respected parameter aliases. Wrapped in a function to take advantage of # lazy evaluation (so it doesn't matter what order R sources files during installation). # [return] A named list, where each key is a main LightGBM parameter and each value is a character # vector of corresponding aliases. .PARAMETER_ALIASES <- function() { if (exists("PARAMETER_ALIASES", where = .lgb_session_cache_env)) { return(get("PARAMETER_ALIASES", envir = .lgb_session_cache_env)) } params_to_aliases <- jsonlite::fromJSON( .Call( LGBM_DumpParamAliases_R ) ) for (main_name in names(params_to_aliases)) { aliases_with_main_name <- c(main_name, unlist(params_to_aliases[[main_name]])) params_to_aliases[[main_name]] <- aliases_with_main_name } # store in cache so the next call to `.PARAMETER_ALIASES()` doesn't need to recompute this assign( x = "PARAMETER_ALIASES" , value = params_to_aliases , envir = .lgb_session_cache_env ) return(params_to_aliases) } # [description] # Per https://github.com/lightgbm-org/LightGBM/blob/master/docs/Parameters.rst#metric, # a few different strings can be used to indicate "no metrics". # [returns] # A character vector .NO_METRIC_STRINGS <- function() { return( c( "na" , "None" , "null" , "custom" ) ) } .MULTICLASS_OBJECTIVES <- function() { return( c( "multi_logloss" , "multiclass" , "softmax" , "multiclassova" , "multiclass_ova" , "ova" , "ovr" ) ) } .BINARY_OBJECTIVES <- function() { return( c( "binary_logloss" , "binary" , "binary_error" ) ) } ================================================ FILE: R-package/R/callback.R ================================================ # constants that control naming in lists .EVAL_KEY <- function() { return("eval") } .EVAL_ERR_KEY <- function() { return("eval_err") } #' @importFrom R6 R6Class CB_ENV <- R6::R6Class( "lgb.cb_env", cloneable = FALSE, public = list( model = NULL, iteration = NULL, begin_iteration = NULL, end_iteration = NULL, eval_list = list(), eval_err_list = list(), best_iter = -1L, best_score = NA, met_early_stop = FALSE ) ) # Format the evaluation metric string .format_eval_string <- function(eval_res, eval_err) { # Check for empty evaluation string if (is.null(eval_res) || length(eval_res) == 0L) { stop("no evaluation results") } # Check for empty evaluation error if (!is.null(eval_err)) { return(sprintf("%s\'s %s:%g+%g", eval_res$data_name, eval_res$name, eval_res$value, eval_err)) } else { return(sprintf("%s\'s %s:%g", eval_res$data_name, eval_res$name, eval_res$value)) } } .merge_eval_string <- function(env) { # Check length of evaluation list if (length(env$eval_list) <= 0L) { return("") } # Get evaluation msg <- list(sprintf("[%d]:", env$iteration)) # Set if evaluation error is_eval_err <- length(env$eval_err_list) > 0L # Loop through evaluation list for (j in seq_along(env$eval_list)) { # Store evaluation error eval_err <- NULL if (isTRUE(is_eval_err)) { eval_err <- env$eval_err_list[[j]] } # Set error message msg <- c(msg, .format_eval_string(eval_res = env$eval_list[[j]], eval_err = eval_err)) } return(paste(msg, collapse = " ")) } cb_print_evaluation <- function(period) { # Create callback callback <- function(env) { # Check if period is at least 1 or more if (period > 0L) { # Store iteration i <- env$iteration # Check if iteration matches moduo if ((i - 1L) %% period == 0L || is.element(i, c(env$begin_iteration, env$end_iteration))) { # Merge evaluation string msg <- .merge_eval_string(env = env) # Check if message is existing if (nchar(msg) > 0L) { cat(.merge_eval_string(env = env), "\n") } } } return(invisible(NULL)) } # Store attributes attr(callback, "call") <- match.call() attr(callback, "name") <- "cb_print_evaluation" return(callback) } cb_record_evaluation <- function() { # Create callback callback <- function(env) { if (length(env$eval_list) <= 0L) { return() } # Set if evaluation error is_eval_err <- length(env$eval_err_list) > 0L # Check length of recorded evaluation if (length(env$model$record_evals) == 0L) { # Loop through each evaluation list element for (j in seq_along(env$eval_list)) { # Store names data_name <- env$eval_list[[j]]$data_name name <- env$eval_list[[j]]$name env$model$record_evals$start_iter <- env$begin_iteration # Check if evaluation record exists if (is.null(env$model$record_evals[[data_name]])) { env$model$record_evals[[data_name]] <- list() } # Create dummy lists env$model$record_evals[[data_name]][[name]] <- list() env$model$record_evals[[data_name]][[name]][[.EVAL_KEY()]] <- list() env$model$record_evals[[data_name]][[name]][[.EVAL_ERR_KEY()]] <- list() } } # Loop through each evaluation list element for (j in seq_along(env$eval_list)) { # Get evaluation data eval_res <- env$eval_list[[j]] eval_err <- NULL if (isTRUE(is_eval_err)) { eval_err <- env$eval_err_list[[j]] } # Store names data_name <- eval_res$data_name name <- eval_res$name # Store evaluation data env$model$record_evals[[data_name]][[name]][[.EVAL_KEY()]] <- c( env$model$record_evals[[data_name]][[name]][[.EVAL_KEY()]] , eval_res$value ) env$model$record_evals[[data_name]][[name]][[.EVAL_ERR_KEY()]] <- c( env$model$record_evals[[data_name]][[name]][[.EVAL_ERR_KEY()]] , eval_err ) } return(invisible(NULL)) } # Store attributes attr(callback, "call") <- match.call() attr(callback, "name") <- "cb_record_evaluation" return(callback) } cb_early_stop <- function(stopping_rounds, first_metric_only, verbose) { factor_to_bigger_better <- NULL best_iter <- NULL best_score <- NULL best_msg <- NULL eval_len <- NULL # Initialization function init <- function(env) { # Early stopping cannot work without metrics if (length(env$eval_list) == 0L) { stop("For early stopping, valids must have at least one element") } # Store evaluation length eval_len <<- length(env$eval_list) # Check if verbose or not if (isTRUE(verbose)) { msg <- paste0( "Will train until there is no improvement in " , stopping_rounds , " rounds.\n" ) cat(msg) } # Internally treat everything as a maximization task factor_to_bigger_better <<- rep.int(1.0, eval_len) best_iter <<- rep.int(-1L, eval_len) best_score <<- rep.int(-Inf, eval_len) best_msg <<- list() # Loop through evaluation elements for (i in seq_len(eval_len)) { # Prepend message best_msg <<- c(best_msg, "") # Internally treat everything as a maximization task if (!isTRUE(env$eval_list[[i]]$higher_better)) { factor_to_bigger_better[i] <<- -1.0 } } return(invisible(NULL)) } # Create callback callback <- function(env) { # Check for empty evaluation if (is.null(eval_len)) { init(env = env) } # Store iteration cur_iter <- env$iteration # By default, any metric can trigger early stopping. This can be disabled # with 'first_metric_only = TRUE' if (isTRUE(first_metric_only)) { evals_to_check <- 1L } else { evals_to_check <- seq_len(eval_len) } # Loop through evaluation for (i in evals_to_check) { # Store score score <- env$eval_list[[i]]$value * factor_to_bigger_better[i] # Check if score is better if (score > best_score[i]) { # Store new scores best_score[i] <<- score best_iter[i] <<- cur_iter # Prepare to print if verbose if (verbose) { best_msg[[i]] <<- as.character(.merge_eval_string(env = env)) } } else { # Check if early stopping is required if (cur_iter - best_iter[i] >= stopping_rounds) { if (!is.null(env$model)) { env$model$best_score <- best_score[i] env$model$best_iter <- best_iter[i] } if (isTRUE(verbose)) { cat(paste0("Early stopping, best iteration is: ", best_msg[[i]], "\n")) } # Store best iteration and stop env$best_iter <- best_iter[i] env$met_early_stop <- TRUE } } if (!isTRUE(env$met_early_stop) && cur_iter == env$end_iteration) { if (!is.null(env$model)) { env$model$best_score <- best_score[i] env$model$best_iter <- best_iter[i] } if (isTRUE(verbose)) { cat(paste0("Did not meet early stopping, best iteration is: ", best_msg[[i]], "\n")) } # Store best iteration and stop env$best_iter <- best_iter[i] env$met_early_stop <- TRUE } } return(invisible(NULL)) } attr(callback, "call") <- match.call() attr(callback, "name") <- "cb_early_stop" return(callback) } # Extract callback names from the list of callbacks .callback_names <- function(cb_list) { return(unlist(lapply(cb_list, attr, "name"))) } .add_cb <- function(cb_list, cb) { # Combine two elements cb_list <- c(cb_list, cb) # Set names of elements names(cb_list) <- .callback_names(cb_list = cb_list) if ("cb_early_stop" %in% names(cb_list)) { # Concatenate existing elements cb_list <- c(cb_list, cb_list["cb_early_stop"]) # Remove only the first one cb_list["cb_early_stop"] <- NULL } return(cb_list) } .categorize_callbacks <- function(cb_list) { # Check for pre-iteration or post-iteration return( list( pre_iter = Filter(function(x) { pre <- attr(x, "is_pre_iteration") !is.null(pre) && pre }, cb_list), post_iter = Filter(function(x) { pre <- attr(x, "is_pre_iteration") is.null(pre) || !pre }, cb_list) ) ) } ================================================ FILE: R-package/R/lgb.Booster.R ================================================ #' @importFrom R6 R6Class #' @importFrom utils modifyList Booster <- R6::R6Class( classname = "lgb.Booster", cloneable = FALSE, public = list( best_iter = -1L, best_score = NA_real_, params = list(), record_evals = list(), data_processor = NULL, # Initialize will create a starter booster initialize = function(params = list(), train_set = NULL, modelfile = NULL, model_str = NULL) { handle <- NULL if (!is.null(train_set)) { if (!.is_Dataset(train_set)) { stop("lgb.Booster: Can only use lgb.Dataset as training data") } train_set_handle <- train_set$.__enclos_env__$private$get_handle() params <- utils::modifyList(params, train_set$get_params()) params_str <- .params2str(params = params) # Store booster handle handle <- .Call( LGBM_BoosterCreate_R , train_set_handle , params_str ) # Create private booster information private$train_set <- train_set private$train_set_version <- train_set$.__enclos_env__$private$version private$num_dataset <- 1L private$init_predictor <- train_set$.__enclos_env__$private$predictor if (!is.null(private$init_predictor)) { # Merge booster .Call( LGBM_BoosterMerge_R , handle , private$init_predictor$.__enclos_env__$private$handle ) } # Check current iteration private$is_predicted_cur_iter <- c(private$is_predicted_cur_iter, FALSE) } else if (!is.null(modelfile)) { # Do we have a model file as character? if (!is.character(modelfile)) { stop("lgb.Booster: Can only use a string as model file path") } modelfile <- path.expand(modelfile) # Create booster from model handle <- .Call( LGBM_BoosterCreateFromModelfile_R , modelfile ) params <- private$get_loaded_param(handle) } else if (!is.null(model_str)) { # Do we have a model_str as character/raw? if (!is.raw(model_str) && !is.character(model_str)) { stop("lgb.Booster: Can only use a character/raw vector as model_str") } # Create booster from model handle <- .Call( LGBM_BoosterLoadModelFromString_R , model_str ) } else { # Booster non existent stop( "lgb.Booster: Need at least either training dataset, " , "model file, or model_str to create booster instance" ) } class(handle) <- "lgb.Booster.handle" private$handle <- handle private$num_class <- 1L .Call( LGBM_BoosterGetNumClasses_R , private$handle , private$num_class ) self$params <- params return(invisible(NULL)) }, # Set training data name set_train_data_name = function(name) { # Set name private$name_train_set <- name return(invisible(self)) }, # Add validation data add_valid = function(data, name) { if (!.is_Dataset(data)) { stop("lgb.Booster.add_valid: Can only use lgb.Dataset as validation data") } if (!identical(data$.__enclos_env__$private$predictor, private$init_predictor)) { stop( "lgb.Booster.add_valid: Failed to add validation data; " , "you should use the same predictor for these data" ) } if (!is.character(name)) { stop("lgb.Booster.add_valid: Can only use characters as data name") } # Add validation data to booster .Call( LGBM_BoosterAddValidData_R , private$handle , data$.__enclos_env__$private$get_handle() ) private$valid_sets <- c(private$valid_sets, data) private$name_valid_sets <- c(private$name_valid_sets, name) private$num_dataset <- private$num_dataset + 1L private$is_predicted_cur_iter <- c(private$is_predicted_cur_iter, FALSE) return(invisible(self)) }, reset_parameter = function(params) { if (methods::is(self$params, "list")) { params <- utils::modifyList(self$params, params) } params_str <- .params2str(params = params) self$restore_handle() .Call( LGBM_BoosterResetParameter_R , private$handle , params_str ) self$params <- params return(invisible(self)) }, # Perform boosting update iteration update = function(train_set = NULL, fobj = NULL) { if (is.null(train_set)) { if (private$train_set$.__enclos_env__$private$version != private$train_set_version) { train_set <- private$train_set } } if (!is.null(train_set)) { if (!.is_Dataset(train_set)) { stop("lgb.Booster.update: Only can use lgb.Dataset as training data") } if (!identical(train_set$predictor, private$init_predictor)) { stop("lgb.Booster.update: Change train_set failed, you should use the same predictor for these data") } .Call( LGBM_BoosterResetTrainingData_R , private$handle , train_set$.__enclos_env__$private$get_handle() ) private$train_set <- train_set private$train_set_version <- train_set$.__enclos_env__$private$version } # Check if objective is empty if (is.null(fobj)) { if (private$set_objective_to_none) { stop("lgb.Booster.update: cannot update due to null objective function") } # Boost iteration from known objective .Call( LGBM_BoosterUpdateOneIter_R , private$handle ) } else { if (!is.function(fobj)) { stop("lgb.Booster.update: fobj should be a function") } if (!private$set_objective_to_none) { self$reset_parameter(params = list(objective = "none")) private$set_objective_to_none <- TRUE } # Perform objective calculation preds <- private$inner_predict(1L) gpair <- fobj(preds, private$train_set) # Check for gradient and hessian as list if (is.null(gpair$grad) || is.null(gpair$hess)) { stop("lgb.Booster.update: custom objective should return a list with attributes (hess, grad)") } # Check grad and hess have the right shape n_grad <- length(gpair$grad) n_hess <- length(gpair$hess) n_preds <- length(preds) if (n_grad != n_preds) { stop(sprintf("Expected custom objective function to return grad with length %d, got %d.", n_preds, n_grad)) } if (n_hess != n_preds) { stop(sprintf("Expected custom objective function to return hess with length %d, got %d.", n_preds, n_hess)) } # Return custom boosting gradient/hessian .Call( LGBM_BoosterUpdateOneIterCustom_R , private$handle , gpair$grad , gpair$hess , n_preds ) } # Loop through each iteration for (i in seq_along(private$is_predicted_cur_iter)) { private$is_predicted_cur_iter[[i]] <- FALSE } return(invisible(self)) }, # Return one iteration behind rollback_one_iter = function() { self$restore_handle() .Call( LGBM_BoosterRollbackOneIter_R , private$handle ) # Loop through each iteration for (i in seq_along(private$is_predicted_cur_iter)) { private$is_predicted_cur_iter[[i]] <- FALSE } return(invisible(self)) }, # Get current iteration current_iter = function() { self$restore_handle() cur_iter <- 0L .Call( LGBM_BoosterGetCurrentIteration_R , private$handle , cur_iter ) return(cur_iter) }, # Number of trees per iteration num_trees_per_iter = function() { self$restore_handle() trees_per_iter <- 1L .Call( LGBM_BoosterNumModelPerIteration_R , private$handle , trees_per_iter ) return(trees_per_iter) }, # Total number of trees num_trees = function() { self$restore_handle() ntrees <- 0L .Call( LGBM_BoosterNumberOfTotalModel_R , private$handle , ntrees ) return(ntrees) }, # Number of iterations (= rounds) num_iter = function() { ntrees <- self$num_trees() trees_per_iter <- self$num_trees_per_iter() return(ntrees / trees_per_iter) }, # Get upper bound upper_bound = function() { self$restore_handle() upper_bound <- 0.0 .Call( LGBM_BoosterGetUpperBoundValue_R , private$handle , upper_bound ) return(upper_bound) }, # Get lower bound lower_bound = function() { self$restore_handle() lower_bound <- 0.0 .Call( LGBM_BoosterGetLowerBoundValue_R , private$handle , lower_bound ) return(lower_bound) }, # Evaluate data on metrics eval = function(data, name, feval = NULL) { if (!.is_Dataset(data)) { stop("lgb.Booster.eval: Can only use lgb.Dataset to eval") } # Check for identical data data_idx <- 0L if (identical(data, private$train_set)) { data_idx <- 1L } else { # Check for validation data if (length(private$valid_sets) > 0L) { for (i in seq_along(private$valid_sets)) { # Check for identical validation data with training data if (identical(data, private$valid_sets[[i]])) { # Found identical data, skip data_idx <- i + 1L break } } } } # Check if evaluation was not done if (data_idx == 0L) { # Add validation data by name self$add_valid(data, name) data_idx <- private$num_dataset } # Evaluate data return( private$inner_eval( data_name = name , data_idx = data_idx , feval = feval ) ) }, # Evaluation training data eval_train = function(feval = NULL) { return(private$inner_eval(private$name_train_set, 1L, feval)) }, # Evaluation validation data eval_valid = function(feval = NULL) { ret <- list() if (length(private$valid_sets) <= 0L) { return(ret) } for (i in seq_along(private$valid_sets)) { ret <- append( x = ret , values = private$inner_eval(private$name_valid_sets[[i]], i + 1L, feval) ) } return(ret) }, # Save model save_model = function( filename , num_iteration = NULL , feature_importance_type = 0L , start_iteration = 1L ) { self$restore_handle() if (is.null(num_iteration)) { num_iteration <- self$best_iter } filename <- path.expand(filename) .Call( LGBM_BoosterSaveModel_R , private$handle , as.integer(num_iteration) , as.integer(feature_importance_type) , filename , as.integer(start_iteration) - 1L # Turn to 0-based ) return(invisible(self)) }, save_model_to_string = function( num_iteration = NULL , feature_importance_type = 0L , as_char = TRUE , start_iteration = 1L ) { self$restore_handle() if (is.null(num_iteration)) { num_iteration <- self$best_iter } model_str <- .Call( LGBM_BoosterSaveModelToString_R , private$handle , as.integer(num_iteration) , as.integer(feature_importance_type) , as.integer(start_iteration) - 1L # Turn to 0-based ) if (as_char) { model_str <- rawToChar(model_str) } return(model_str) }, # Dump model in memory dump_model = function( num_iteration = NULL, feature_importance_type = 0L, start_iteration = 1L ) { self$restore_handle() if (is.null(num_iteration)) { num_iteration <- self$best_iter } model_str <- .Call( LGBM_BoosterDumpModel_R , private$handle , as.integer(num_iteration) , as.integer(feature_importance_type) , as.integer(start_iteration) - 1L # Turn to 0-based ) return(model_str) }, # Predict on new data predict = function(data, start_iteration = NULL, num_iteration = NULL, rawscore = FALSE, predleaf = FALSE, predcontrib = FALSE, header = FALSE, params = list()) { self$restore_handle() if (is.null(num_iteration)) { num_iteration <- self$best_iter } if (is.null(start_iteration)) { start_iteration <- 0L } # possibly override keyword arguments with parameters # # NOTE: this length() check minimizes the latency introduced by these checks, # for the common case where params is empty # # NOTE: doing this here instead of in Predictor$predict() to keep # Predictor$predict() as fast as possible if (length(params) > 0L) { params <- .check_wrapper_param( main_param_name = "predict_raw_score" , params = params , alternative_kwarg_value = rawscore ) params <- .check_wrapper_param( main_param_name = "predict_leaf_index" , params = params , alternative_kwarg_value = predleaf ) params <- .check_wrapper_param( main_param_name = "predict_contrib" , params = params , alternative_kwarg_value = predcontrib ) rawscore <- params[["predict_raw_score"]] predleaf <- params[["predict_leaf_index"]] predcontrib <- params[["predict_contrib"]] } # Predict on new data predictor <- Predictor$new( modelfile = private$handle , params = params , fast_predict_config = private$fast_predict_config ) return( predictor$predict( data = data , start_iteration = start_iteration , num_iteration = num_iteration , rawscore = rawscore , predleaf = predleaf , predcontrib = predcontrib , header = header ) ) }, # Transform into predictor to_predictor = function() { return(Predictor$new(modelfile = private$handle)) }, configure_fast_predict = function(csr = FALSE, start_iteration = NULL, num_iteration = NULL, rawscore = FALSE, predleaf = FALSE, predcontrib = FALSE, params = list()) { self$restore_handle() ncols <- .Call(LGBM_BoosterGetNumFeature_R, private$handle) if (is.null(num_iteration)) { num_iteration <- -1L } if (is.null(start_iteration)) { start_iteration <- 0L } if (!csr) { fun <- LGBM_BoosterPredictForMatSingleRowFastInit_R } else { fun <- LGBM_BoosterPredictForCSRSingleRowFastInit_R } fast_handle <- .Call( fun , private$handle , ncols , rawscore , predleaf , predcontrib , start_iteration , num_iteration , .params2str(params = params) ) private$fast_predict_config <- list( handle = fast_handle , csr = as.logical(csr) , ncols = ncols , start_iteration = start_iteration , num_iteration = num_iteration , rawscore = as.logical(rawscore) , predleaf = as.logical(predleaf) , predcontrib = as.logical(predcontrib) , params = params ) return(invisible(NULL)) }, # Used for serialization raw = NULL, # Store serialized raw bytes in model object save_raw = function() { if (is.null(self$raw)) { self$raw <- self$save_model_to_string(NULL, as_char = FALSE) } return(invisible(NULL)) }, drop_raw = function() { self$raw <- NULL return(invisible(NULL)) }, check_null_handle = function() { return(.is_null_handle(private$handle)) }, restore_handle = function() { if (self$check_null_handle()) { if (is.null(self$raw)) { .Call(LGBM_NullBoosterHandleError_R) } private$handle <- .Call(LGBM_BoosterLoadModelFromString_R, self$raw) } return(invisible(NULL)) }, get_handle = function() { return(private$handle) } ), private = list( handle = NULL, train_set = NULL, name_train_set = "training", valid_sets = list(), name_valid_sets = list(), predict_buffer = list(), is_predicted_cur_iter = list(), num_class = 1L, num_dataset = 0L, init_predictor = NULL, eval_names = NULL, higher_better_inner_eval = NULL, set_objective_to_none = FALSE, train_set_version = 0L, fast_predict_config = list(), # finalize() will free up the handles finalize = function() { .Call( LGBM_BoosterFree_R , private$handle ) private$handle <- NULL return(invisible(NULL)) }, # Predict data inner_predict = function(idx) { # Store data name data_name <- private$name_train_set if (idx > 1L) { data_name <- private$name_valid_sets[[idx - 1L]] } # Check for unknown dataset (over the maximum provided range) if (idx > private$num_dataset) { stop("data_idx should not be greater than num_dataset") } # Check for prediction buffer if (is.null(private$predict_buffer[[data_name]])) { # Store predictions npred <- 0L .Call( LGBM_BoosterGetNumPredict_R , private$handle , as.integer(idx - 1L) , npred ) private$predict_buffer[[data_name]] <- numeric(npred) } # Check if current iteration was already predicted if (!private$is_predicted_cur_iter[[idx]]) { # Use buffer .Call( LGBM_BoosterGetPredict_R , private$handle , as.integer(idx - 1L) , private$predict_buffer[[data_name]] ) private$is_predicted_cur_iter[[idx]] <- TRUE } return(private$predict_buffer[[data_name]]) }, # Get evaluation information get_eval_info = function() { if (is.null(private$eval_names)) { eval_names <- .Call( LGBM_BoosterGetEvalNames_R , private$handle ) if (length(eval_names) > 0L) { # Parse and store privately names private$eval_names <- eval_names # some metrics don't map cleanly to metric names, for example "ndcg@1" is just the # ndcg metric evaluated at the first "query result" in learning-to-rank metric_names <- gsub("@.*", "", eval_names) private$higher_better_inner_eval <- .METRICS_HIGHER_BETTER()[metric_names] } } return(private$eval_names) }, get_loaded_param = function(handle) { params_str <- .Call( LGBM_BoosterGetLoadedParam_R , handle ) params <- jsonlite::fromJSON(params_str) if ("interaction_constraints" %in% names(params)) { params[["interaction_constraints"]] <- lapply(params[["interaction_constraints"]], function(x) x + 1L) } return(params) }, inner_eval = function(data_name, data_idx, feval = NULL) { # Check for unknown dataset (over the maximum provided range) if (data_idx > private$num_dataset) { stop("data_idx should not be greater than num_dataset") } self$restore_handle() private$get_eval_info() ret <- list() if (length(private$eval_names) > 0L) { # Create evaluation values tmp_vals <- numeric(length(private$eval_names)) .Call( LGBM_BoosterGetEval_R , private$handle , as.integer(data_idx - 1L) , tmp_vals ) for (i in seq_along(private$eval_names)) { # Store evaluation and append to return res <- list() res$data_name <- data_name res$name <- private$eval_names[i] res$value <- tmp_vals[i] res$higher_better <- private$higher_better_inner_eval[i] ret <- append(ret, list(res)) } } # Check if there are evaluation metrics if (!is.null(feval)) { # Check if evaluation metric is a function if (!is.function(feval)) { stop("lgb.Booster.eval: feval should be a function") } data <- private$train_set # Check if data to assess is existing differently if (data_idx > 1L) { data <- private$valid_sets[[data_idx - 1L]] } # Perform function evaluation res <- feval(private$inner_predict(data_idx), data) if (is.null(res$name) || is.null(res$value) || is.null(res$higher_better)) { stop( "lgb.Booster.eval: custom eval function should return a list with attribute (name, value, higher_better)" ) } # Append names and evaluation res$data_name <- data_name ret <- append(ret, list(res)) } return(ret) } ) ) #' @name lgb_predict_shared_params #' @title Shared prediction parameter docs #' @param type Type of prediction to output. Allowed types are:\itemize{ #' \item \code{"response"}: will output the predicted score according to the objective function being #' optimized (depending on the link function that the objective uses), after applying any necessary #' transformations - for example, for \code{objective="binary"}, it will output class probabilities. #' \item \code{"class"}: for classification objectives, will output the class with the highest predicted #' probability. For other objectives, will output the same as "response". Note that \code{"class"} is #' not a supported type for \link{lgb.configure_fast_predict} (see the documentation of that function #' for more details). #' \item \code{"raw"}: will output the non-transformed numbers (sum of predictions from boosting iterations' #' results) from which the "response" number is produced for a given objective function - for example, #' for \code{objective="binary"}, this corresponds to log-odds. For many objectives such as #' "regression", since no transformation is applied, the output will be the same as for "response". #' \item \code{"leaf"}: will output the index of the terminal node / leaf at which each observations falls #' in each tree in the model, outputted as integers, with one column per tree. #' \item \code{"contrib"}: will return the per-feature contributions for each prediction, including an #' intercept (each feature will produce one column). #' } #' #' Note that, if using custom objectives, types "class" and "response" will not be available and will #' default towards using "raw" instead. #' #' If the model was fit through function \link{lightgbm} and it was passed a factor as labels, #' passing the prediction type through \code{params} instead of through this argument might #' result in factor levels for classification objectives not being applied correctly to the #' resulting output. #' #' \emph{New in version 4.0.0} #' #' @param start_iteration int or None, optional (default=None) #' Start index of the iteration to predict. #' If None or <= 0, starts from the first iteration. #' @param num_iteration int or None, optional (default=None) #' Limit number of iterations in the prediction. #' If None, if the best iteration exists and start_iteration is None or <= 0, the #' best iteration is used; otherwise, all iterations from start_iteration are used. #' If <= 0, all iterations from start_iteration are used (no limits). #' @param params a list of additional named parameters. See #' \href{https://lightgbm.readthedocs.io/en/latest/Parameters.html#predict-parameters}{ #' the "Predict Parameters" section of the documentation} for a list of parameters and #' valid values. Where these conflict with the values of keyword arguments to this function, #' the values in \code{params} take precedence. #' @details This page contains shared documentation for prediction-related parameters used throughout the package. #' @keywords internal NULL #' @name predict.lgb.Booster #' @title Predict method for LightGBM model #' @description Predicted values based on class \code{lgb.Booster} #' #' \emph{New in version 4.0.0} #' #' @details If the model object has been configured for fast single-row predictions through #' \link{lgb.configure_fast_predict}, this function will use the prediction parameters #' that were configured for it - as such, extra prediction parameters should not be passed #' here, otherwise the configuration will be ignored and the slow route will be taken. #' @inheritParams lgb_predict_shared_params #' @param object Object of class \code{lgb.Booster} #' @param newdata a \code{matrix} object, a \code{dgCMatrix}, a \code{dgRMatrix} object, a \code{dsparseVector} object, #' or a character representing a path to a text file (CSV, TSV, or LibSVM). #' #' For sparse inputs, if predictions are only going to be made for a single row, it will be faster to #' use CSR format, in which case the data may be passed as either a single-row CSR matrix (class #' \code{dgRMatrix} from package \code{Matrix}) or as a sparse numeric vector (class #' \code{dsparseVector} from package \code{Matrix}). #' #' If single-row predictions are going to be performed frequently, it is recommended to #' pre-configure the model object for fast single-row sparse predictions through function #' \link{lgb.configure_fast_predict}. #' #' \emph{Changed from 'data', in version 4.0.0} #' #' @param header only used for prediction for text file. True if text file has header #' @param ... ignored #' @return For prediction types that are meant to always return one output per observation (e.g. when predicting #' \code{type="response"} or \code{type="raw"} on a binary classification or regression objective), will #' return a vector with one element per row in \code{newdata}. #' #' For prediction types that are meant to return more than one output per observation (e.g. when predicting #' \code{type="response"} or \code{type="raw"} on a multi-class objective, or when predicting #' \code{type="leaf"}, regardless of objective), will return a matrix with one row per observation in #' \code{newdata} and one column per output. #' #' For \code{type="leaf"} predictions, will return a matrix with one row per observation in \code{newdata} #' and one column per tree. Note that for multiclass objectives, LightGBM trains one tree per class at each #' boosting iteration. That means that, for example, for a multiclass model with 3 classes, the leaf #' predictions for the first class can be found in columns 1, 4, 7, 10, etc. #' #' For \code{type="contrib"}, will return a matrix of SHAP values with one row per observation in #' \code{newdata} and columns corresponding to features. For regression, ranking, cross-entropy, and binary #' classification objectives, this matrix contains one column per feature plus a final column containing the #' Shapley base value. For multiclass objectives, this matrix will represent \code{num_classes} such matrices, #' in the order "feature contributions for first class, feature contributions for second class, feature #' contributions for third class, etc.". #' #' If the model was fit through function \link{lightgbm} and it was passed a factor as labels, predictions #' returned from this function will retain the factor levels (either as values for \code{type="class"}, or #' as column names for \code{type="response"} and \code{type="raw"} for multi-class objectives). Note that #' passing the requested prediction type under \code{params} instead of through \code{type} might result in #' the factor levels not being present in the output. #' @examples #' \donttest{ #' \dontshow{setLGBMthreads(2L)} #' \dontshow{data.table::setDTthreads(1L)} #' data(agaricus.train, package = "lightgbm") #' train <- agaricus.train #' dtrain <- lgb.Dataset(train$data, label = train$label) #' data(agaricus.test, package = "lightgbm") #' test <- agaricus.test #' dtest <- lgb.Dataset.create.valid(dtrain, test$data, label = test$label) #' params <- list( #' objective = "regression" #' , metric = "l2" #' , min_data = 1L #' , learning_rate = 1.0 #' , num_threads = 2L #' ) #' valids <- list(test = dtest) #' model <- lgb.train( #' params = params #' , data = dtrain #' , nrounds = 5L #' , valids = valids #' ) #' preds <- predict(model, test$data) #' #' # pass other prediction parameters #' preds <- predict( #' model, #' test$data, #' params = list( #' predict_disable_shape_check = TRUE #' ) #' ) #' } #' @importFrom utils modifyList #' @export predict.lgb.Booster <- function(object, newdata, type = "response", start_iteration = NULL, num_iteration = NULL, header = FALSE, params = list(), ...) { if (!.is_Booster(x = object)) { stop("predict.lgb.Booster: object should be an ", sQuote("lgb.Booster", q = FALSE)) } additional_params <- list(...) if (length(additional_params) > 0L) { additional_params_names <- names(additional_params) if ("reshape" %in% additional_params_names) { stop("'reshape' argument is no longer supported.") } old_args_for_type <- list( "rawscore" = "raw" , "predleaf" = "leaf" , "predcontrib" = "contrib" ) for (arg in names(old_args_for_type)) { if (arg %in% additional_params_names) { stop(sprintf("Argument '%s' is no longer supported. Use type='%s' instead." , arg , old_args_for_type[[arg]])) } } warning(paste0( "predict.lgb.Booster: Found the following passed through '...': " , toString(names(additional_params)) , ". These are ignored. Use argument 'params' instead." )) } if (!is.null(object$params$objective) && object$params$objective == "none" && type %in% c("class", "response")) { warning("Prediction types 'class' and 'response' are not supported for custom objectives.") type <- "raw" } rawscore <- FALSE predleaf <- FALSE predcontrib <- FALSE if (type == "raw") { rawscore <- TRUE } else if (type == "leaf") { predleaf <- TRUE } else if (type == "contrib") { predcontrib <- TRUE } pred <- object$predict( data = newdata , start_iteration = start_iteration , num_iteration = num_iteration , rawscore = rawscore , predleaf = predleaf , predcontrib = predcontrib , header = header , params = params ) if (type == "class") { if (object$params$objective %in% .BINARY_OBJECTIVES()) { pred <- as.integer(pred >= 0.5) } else if (object$params$objective %in% .MULTICLASS_OBJECTIVES()) { pred <- max.col(pred) - 1L } } if (!is.null(object$data_processor)) { pred <- object$data_processor$process_predictions( pred = pred , type = type ) } return(pred) } #' @title Configure Fast Single-Row Predictions #' @description Pre-configures a LightGBM model object to produce fast single-row predictions #' for a given input data type, prediction type, and parameters. #' @details Calling this function multiple times with different parameters might not override #' the previous configuration and might trigger undefined behavior. #' #' Any saved configuration for fast predictions might be lost after making a single-row #' prediction of a different type than what was configured (except for types "response" and #' "class", which can be switched between each other at any time without losing the configuration). #' #' In some situations, setting a fast prediction configuration for one type of prediction #' might cause the prediction function to keep using that configuration for single-row #' predictions even if the requested type of prediction is different from what was configured. #' #' Note that this function will not accept argument \code{type="class"} - for such cases, one #' can pass \code{type="response"} to this function and then \code{type="class"} to the #' \code{predict} function - the fast configuration will not be lost or altered if the switch #' is between "response" and "class". #' #' The configuration does not survive de-serializations, so it has to be generated #' anew in every R process that is going to use it (e.g. if loading a model object #' through \code{readRDS}, whatever configuration was there previously will be lost). #' #' Requesting a different prediction type or passing parameters to \link{predict.lgb.Booster} #' will cause it to ignore the fast-predict configuration and take the slow route instead #' (but be aware that an existing configuration might not always be overridden by supplying #' different parameters or prediction type, so make sure to check that the output is what #' was expected when a prediction is to be made on a single row for something different than #' what is configured). #' #' Note that, if configuring a non-default prediction type (such as leaf indices), #' then that type must also be passed in the call to \link{predict.lgb.Booster} in #' order for it to use the configuration. This also applies for \code{start_iteration} #' and \code{num_iteration}, but \bold{the \code{params} list must be empty} in the call to \code{predict}. #' #' Predictions about feature contributions do not allow a fast route for CSR inputs, #' and as such, this function will produce an error if passing \code{csr=TRUE} and #' \code{type = "contrib"} together. #' @inheritParams lgb_predict_shared_params #' @param model LightGBM model object (class \code{lgb.Booster}). #' #' \bold{The object will be modified in-place}. #' @param csr Whether the prediction function is going to be called on sparse CSR inputs. #' If \code{FALSE}, will be assumed that predictions are going to be called on single-row #' regular R matrices. #' @return The same \code{model} that was passed as input, invisibly, with the desired #' configuration stored inside it and available to be used in future calls to #' \link{predict.lgb.Booster}. #' @examples #' \donttest{ #' \dontshow{setLGBMthreads(2L)} #' \dontshow{data.table::setDTthreads(1L)} #' library(lightgbm) #' data(mtcars) #' X <- as.matrix(mtcars[, -1L]) #' y <- mtcars[, 1L] #' dtrain <- lgb.Dataset(X, label = y, params = list(max_bin = 5L)) #' params <- list( #' min_data_in_leaf = 2L #' , num_threads = 2L #' ) #' model <- lgb.train( #' params = params #' , data = dtrain #' , obj = "regression" #' , nrounds = 5L #' , verbose = -1L #' ) #' lgb.configure_fast_predict(model) #' #' x_single <- X[11L, , drop = FALSE] #' predict(model, x_single) #' #' # Will not use it if the prediction to be made #' # is different from what was configured #' predict(model, x_single, type = "leaf") #' } #' @export lgb.configure_fast_predict <- function(model, csr = FALSE, start_iteration = NULL, num_iteration = NULL, type = "response", params = list()) { if (!.is_Booster(x = model)) { stop("lgb.configure_fast_predict: model should be an ", sQuote("lgb.Booster", q = FALSE)) } if (type == "class") { stop("type='class' is not supported for 'lgb.configure_fast_predict'. Use 'response' instead.") } rawscore <- FALSE predleaf <- FALSE predcontrib <- FALSE if (type == "raw") { rawscore <- TRUE } else if (type == "leaf") { predleaf <- TRUE } else if (type == "contrib") { predcontrib <- TRUE } if (csr && predcontrib) { stop("'lgb.configure_fast_predict' does not support feature contributions for CSR data.") } model$configure_fast_predict( csr = csr , start_iteration = start_iteration , num_iteration = num_iteration , rawscore = rawscore , predleaf = predleaf , predcontrib = predcontrib , params = params ) return(invisible(model)) } #' @name print.lgb.Booster #' @title Print method for LightGBM model #' @description Show summary information about a LightGBM model object (same as \code{summary}). #' #' \emph{New in version 4.0.0} #' #' @param x Object of class \code{lgb.Booster} #' @param ... Not used #' @return The same input \code{x}, returned as invisible. #' @export print.lgb.Booster <- function(x, ...) { # nolint start handle <- x$.__enclos_env__$private$handle handle_is_null <- .is_null_handle(handle) if (!handle_is_null) { ntrees <- x$current_iter() if (ntrees == 1L) { cat("LightGBM Model (1 tree)\n") } else { cat(sprintf("LightGBM Model (%d trees)\n", ntrees)) } } else { cat("LightGBM Model\n") } if (!handle_is_null) { obj <- x$params$objective if (is.null(obj)) { obj <- "(default)" } if (obj == "none") { obj <- "custom" } num_class <- x$.__enclos_env__$private$num_class if (num_class == 1L) { cat(sprintf("Objective: %s\n", obj)) } else { cat(sprintf("Objective: %s (%d classes)\n" , obj , num_class)) } } else { cat("(Booster handle is invalid)\n") } if (!handle_is_null) { ncols <- .Call(LGBM_BoosterGetNumFeature_R, handle) cat(sprintf("Fitted to dataset with %d columns\n", ncols)) } # nolint end return(invisible(x)) } #' @name summary.lgb.Booster #' @title Summary method for LightGBM model #' @description Show summary information about a LightGBM model object (same as \code{print}). #' #' \emph{New in version 4.0.0} #' #' @param object Object of class \code{lgb.Booster} #' @param ... Not used #' @return The same input \code{object}, returned as invisible. #' @export summary.lgb.Booster <- function(object, ...) { print(object) } #' @name lgb.load #' @title Load LightGBM model #' @description Load LightGBM takes in either a file path or model string. #' If both are provided, Load will default to loading from file #' @param filename path of model file #' @param model_str a str containing the model (as a \code{character} or \code{raw} vector) #' #' @return lgb.Booster #' #' @examples #' \donttest{ #' \dontshow{setLGBMthreads(2L)} #' \dontshow{data.table::setDTthreads(1L)} #' data(agaricus.train, package = "lightgbm") #' train <- agaricus.train #' dtrain <- lgb.Dataset(train$data, label = train$label) #' data(agaricus.test, package = "lightgbm") #' test <- agaricus.test #' dtest <- lgb.Dataset.create.valid(dtrain, test$data, label = test$label) #' params <- list( #' objective = "regression" #' , metric = "l2" #' , min_data = 1L #' , learning_rate = 1.0 #' , num_threads = 2L #' ) #' valids <- list(test = dtest) #' model <- lgb.train( #' params = params #' , data = dtrain #' , nrounds = 5L #' , valids = valids #' , early_stopping_rounds = 3L #' ) #' model_file <- tempfile(fileext = ".txt") #' lgb.save(model, model_file) #' load_booster <- lgb.load(filename = model_file) #' model_string <- model$save_model_to_string(NULL) # saves best iteration #' load_booster_from_str <- lgb.load(model_str = model_string) #' } #' @export lgb.load <- function(filename = NULL, model_str = NULL) { filename_provided <- !is.null(filename) model_str_provided <- !is.null(model_str) if (filename_provided) { if (!is.character(filename)) { stop("lgb.load: filename should be character") } filename <- path.expand(filename) if (!file.exists(filename)) { stop(sprintf("lgb.load: file '%s' passed to filename does not exist", filename)) } return(invisible(Booster$new(modelfile = filename))) } if (model_str_provided) { if (!is.raw(model_str) && !is.character(model_str)) { stop("lgb.load: model_str should be a character/raw vector") } return(invisible(Booster$new(model_str = model_str))) } stop("lgb.load: either filename or model_str must be given") } #' @name lgb.save #' @title Save LightGBM model #' @description Save LightGBM model #' @param booster Object of class \code{lgb.Booster} #' @param filename Saved filename #' @param num_iteration Number of iterations to save, NULL or <= 0 means use best iteration #' @param start_iteration Index (1-based) of the first boosting round to save. #' For example, passing \code{start_iteration=5, num_iteration=3} for a regression model #' means "save the fifth, sixth, and seventh tree" #' #' \emph{New in version 4.4.0} #' #' @return lgb.Booster #' #' @examples #' \donttest{ #' \dontshow{setLGBMthreads(2L)} #' \dontshow{data.table::setDTthreads(1L)} #' library(lightgbm) #' data(agaricus.train, package = "lightgbm") #' train <- agaricus.train #' dtrain <- lgb.Dataset(train$data, label = train$label) #' data(agaricus.test, package = "lightgbm") #' test <- agaricus.test #' dtest <- lgb.Dataset.create.valid(dtrain, test$data, label = test$label) #' params <- list( #' objective = "regression" #' , metric = "l2" #' , min_data = 1L #' , learning_rate = 1.0 #' , num_threads = 2L #' ) #' valids <- list(test = dtest) #' model <- lgb.train( #' params = params #' , data = dtrain #' , nrounds = 10L #' , valids = valids #' , early_stopping_rounds = 5L #' ) #' lgb.save(model, tempfile(fileext = ".txt")) #' } #' @export lgb.save <- function( booster, filename, num_iteration = NULL, start_iteration = 1L ) { if (!.is_Booster(x = booster)) { stop("lgb.save: booster should be an ", sQuote("lgb.Booster", q = FALSE)) } if (!(is.character(filename) && length(filename) == 1L)) { stop("lgb.save: filename should be a string") } filename <- path.expand(filename) # Store booster return( invisible(booster$save_model( filename = filename , num_iteration = num_iteration , start_iteration = start_iteration )) ) } #' @name lgb.dump #' @title Dump LightGBM model to json #' @description Dump LightGBM model to json #' @param booster Object of class \code{lgb.Booster} #' @param num_iteration Number of iterations to be dumped. NULL or <= 0 means use best iteration #' @param start_iteration Index (1-based) of the first boosting round to dump. #' For example, passing \code{start_iteration=5, num_iteration=3} for a regression model #' means "dump the fifth, sixth, and seventh tree" #' #' \emph{New in version 4.4.0} #' #' @return json format of model #' #' @examples #' \donttest{ #' library(lightgbm) #' \dontshow{setLGBMthreads(2L)} #' \dontshow{data.table::setDTthreads(1L)} #' data(agaricus.train, package = "lightgbm") #' train <- agaricus.train #' dtrain <- lgb.Dataset(train$data, label = train$label) #' data(agaricus.test, package = "lightgbm") #' test <- agaricus.test #' dtest <- lgb.Dataset.create.valid(dtrain, test$data, label = test$label) #' params <- list( #' objective = "regression" #' , metric = "l2" #' , min_data = 1L #' , learning_rate = 1.0 #' , num_threads = 2L #' ) #' valids <- list(test = dtest) #' model <- lgb.train( #' params = params #' , data = dtrain #' , nrounds = 10L #' , valids = valids #' , early_stopping_rounds = 5L #' ) #' json_model <- lgb.dump(model) #' } #' @export lgb.dump <- function(booster, num_iteration = NULL, start_iteration = 1L) { if (!.is_Booster(x = booster)) { stop("lgb.dump: booster should be an ", sQuote("lgb.Booster", q = FALSE)) } # Return booster at requested iteration return( booster$dump_model( num_iteration = num_iteration, start_iteration = start_iteration ) ) } #' @name lgb.get.eval.result #' @title Get record evaluation result from booster #' @description Given a \code{lgb.Booster}, return evaluation results for a #' particular metric on a particular dataset. #' @param booster Object of class \code{lgb.Booster} #' @param data_name Name of the dataset to return evaluation results for. #' @param eval_name Name of the evaluation metric to return results for. #' @param iters An integer vector of iterations you want to get evaluation results for. If NULL #' (the default), evaluation results for all iterations will be returned. #' @param is_err TRUE will return evaluation error instead #' #' @return numeric vector of evaluation result #' #' @examples #' \donttest{ #' \dontshow{setLGBMthreads(2L)} #' \dontshow{data.table::setDTthreads(1L)} #' # train a regression model #' data(agaricus.train, package = "lightgbm") #' train <- agaricus.train #' dtrain <- lgb.Dataset(train$data, label = train$label) #' data(agaricus.test, package = "lightgbm") #' test <- agaricus.test #' dtest <- lgb.Dataset.create.valid(dtrain, test$data, label = test$label) #' params <- list( #' objective = "regression" #' , metric = "l2" #' , min_data = 1L #' , learning_rate = 1.0 #' , num_threads = 2L #' ) #' valids <- list(test = dtest) #' model <- lgb.train( #' params = params #' , data = dtrain #' , nrounds = 5L #' , valids = valids #' ) #' #' # Examine valid data_name values #' print(setdiff(names(model$record_evals), "start_iter")) #' #' # Examine valid eval_name values for dataset "test" #' print(names(model$record_evals[["test"]])) #' #' # Get L2 values for "test" dataset #' lgb.get.eval.result(model, "test", "l2") #' } #' @export lgb.get.eval.result <- function(booster, data_name, eval_name, iters = NULL, is_err = FALSE) { if (!.is_Booster(x = booster)) { stop("lgb.get.eval.result: Can only use ", sQuote("lgb.Booster", q = FALSE), " to get eval result") } if (!is.character(data_name) || !is.character(eval_name)) { stop("lgb.get.eval.result: data_name and eval_name should be characters") } # NOTE: "start_iter" exists in booster$record_evals but is not a valid data_name data_names <- setdiff(names(booster$record_evals), "start_iter") if (!(data_name %in% data_names)) { stop(paste0( "lgb.get.eval.result: data_name " , shQuote(data_name) , " not found. Only the following datasets exist in record evals: [" , toString(data_names) , "]" )) } # Check if evaluation result is existing eval_names <- names(booster$record_evals[[data_name]]) if (!(eval_name %in% eval_names)) { stop(paste0( "lgb.get.eval.result: eval_name " , shQuote(eval_name) , " not found. Only the following eval_names exist for dataset " , shQuote(data_name) , ": [" , toString(eval_names) , "]" )) } result <- booster$record_evals[[data_name]][[eval_name]][[.EVAL_KEY()]] # Check if error is requested if (is_err) { result <- booster$record_evals[[data_name]][[eval_name]][[.EVAL_ERR_KEY()]] } if (is.null(iters)) { return(as.numeric(result)) } # Parse iteration and booster delta iters <- as.integer(iters) delta <- booster$record_evals$start_iter - 1.0 iters <- iters - delta return(as.numeric(result[iters])) } ================================================ FILE: R-package/R/lgb.DataProcessor.R ================================================ DataProcessor <- R6::R6Class( classname = "lgb.DataProcessor", public = list( factor_levels = NULL, process_label = function(label, objective, params) { if (is.character(label)) { label <- factor(label) } if (is.factor(label)) { self$factor_levels <- levels(label) if (length(self$factor_levels) <= 1L) { stop("Labels to predict is a factor with <2 possible values.") } label <- as.numeric(label) - 1.0 out <- list(label = label) if (length(self$factor_levels) == 2L) { if (objective == "auto") { objective <- "binary" } if (!(objective %in% .BINARY_OBJECTIVES())) { stop("Two-level factors as labels only allowed for objective='binary' or objective='auto'.") } } else { if (objective == "auto") { objective <- "multiclass" } if (!(objective %in% .MULTICLASS_OBJECTIVES())) { stop( sprintf( "Factors with >2 levels as labels only allowed for multi-class objectives. Got: %s (allowed: %s)" , objective , toString(.MULTICLASS_OBJECTIVES()) ) ) } data_num_class <- length(self$factor_levels) params <- .check_wrapper_param( main_param_name = "num_class" , params = params , alternative_kwarg_value = data_num_class ) if (params[["num_class"]] != data_num_class) { warning( sprintf( "Found num_class=%d in params, but 'label' is a factor with %d levels. 'num_class' will be ignored." , params[["num_class"]] , data_num_class ) ) params$num_class <- data_num_class } } out$objective <- objective out$params <- params return(out) } else { label <- as.numeric(label) if (objective == "auto") { objective <- "regression" } out <- list( label = label , objective = objective , params = params ) return(out) } }, process_predictions = function(pred, type) { if (NROW(self$factor_levels)) { if (type == "class") { pred <- as.integer(pred) + 1L attributes(pred)$levels <- self$factor_levels attributes(pred)$class <- "factor" } else if (type %in% c("response", "raw")) { if (is.matrix(pred) && ncol(pred) == length(self$factor_levels)) { colnames(pred) <- self$factor_levels } } } return(pred) } ) ) ================================================ FILE: R-package/R/lgb.Dataset.R ================================================ #' @name lgb_shared_dataset_params #' @title Shared Dataset parameter docs #' @description Parameter docs for fields used in \code{lgb.Dataset} construction #' @param label vector of labels to use as the target variable #' @param weight numeric vector of sample weights #' @param init_score initial score is the base prediction lightgbm will boost from #' @param group used for learning-to-rank tasks. An integer vector describing how to #' group rows together as ordered results from the same set of candidate results #' to be ranked. For example, if you have a 100-document dataset with #' \code{group = c(10, 20, 40, 10, 10, 10)}, that means that you have 6 groups, #' where the first 10 records are in the first group, records 11-30 are in the #' second group, etc. #' @details This page contains shared documentation for dataset-related parameters used throughout the package. #' @keywords internal NULL # [description] List of valid keys for "info" arguments in lgb.Dataset. # Wrapped in a function to take advantage of lazy evaluation # (so it doesn't matter what order R sources files during installation). # [return] A character vector of names. .INFO_KEYS <- function() { return(c("label", "weight", "init_score", "group")) } #' @importFrom methods is #' @importFrom R6 R6Class #' @importFrom utils modifyList Dataset <- R6::R6Class( classname = "lgb.Dataset", cloneable = FALSE, public = list( # Initialize will create a starter dataset initialize = function(data, params = list(), reference = NULL, colnames = NULL, categorical_feature = NULL, predictor = NULL, free_raw_data = TRUE, used_indices = NULL, label = NULL, weight = NULL, group = NULL, init_score = NULL) { # validate inputs early to avoid unnecessary computation if (!(is.null(reference) || .is_Dataset(reference))) { stop("lgb.Dataset: If provided, reference must be a ", sQuote("lgb.Dataset", q = FALSE)) } if (!(is.null(predictor) || .is_Predictor(predictor))) { stop("lgb.Dataset: If provided, predictor must be a ", sQuote("lgb.Predictor", q = FALSE)) } info <- list() if (!is.null(label)) { info[["label"]] <- label } if (!is.null(weight)) { info[["weight"]] <- weight } if (!is.null(group)) { info[["group"]] <- group } if (!is.null(init_score)) { info[["init_score"]] <- init_score } # Check for matrix format if (is.matrix(data)) { # Check whether matrix is the correct type first ("double") if (storage.mode(data) != "double") { storage.mode(data) <- "double" } } # Setup private attributes private$raw_data <- data private$params <- params private$reference <- reference private$colnames <- colnames private$categorical_feature <- categorical_feature private$predictor <- predictor private$free_raw_data <- free_raw_data private$used_indices <- sort(used_indices, decreasing = FALSE) private$info <- info private$version <- 0L return(invisible(NULL)) }, create_valid = function(data, label = NULL, weight = NULL, group = NULL, init_score = NULL, params = list()) { # the Dataset's existing parameters should be overwritten by any passed in to this call params <- modifyList(private$params, params) # Create new dataset ret <- Dataset$new( data = data , params = params , reference = self , colnames = private$colnames , categorical_feature = private$categorical_feature , predictor = private$predictor , free_raw_data = private$free_raw_data , used_indices = NULL , label = label , weight = weight , group = group , init_score = init_score ) return(invisible(ret)) }, # Dataset constructor construct = function() { # Check for handle null if (!.is_null_handle(x = private$handle)) { return(invisible(self)) } # Get feature names cnames <- NULL if (is.matrix(private$raw_data) || methods::is(private$raw_data, "dgCMatrix")) { cnames <- colnames(private$raw_data) } # set feature names if they do not exist if (is.null(private$colnames) && !is.null(cnames)) { private$colnames <- as.character(cnames) } # Get categorical feature index if (!is.null(private$categorical_feature)) { # Check for character name if (is.character(private$categorical_feature)) { cate_indices <- as.list(match(private$categorical_feature, private$colnames) - 1L) # Provided indices, but some indices are missing? if (anyNA(cate_indices)) { stop( "lgb.Dataset.construct: supplied an unknown feature in categorical_feature: " , sQuote(private$categorical_feature[is.na(cate_indices)], q = FALSE) ) } } else { # Check if more categorical features were output over the feature space data_is_not_filename <- !is.character(private$raw_data) if ( data_is_not_filename && !is.null(private$raw_data) && is.null(private$used_indices) && max(private$categorical_feature) > ncol(private$raw_data) ) { stop( "lgb.Dataset.construct: supplied a too large value in categorical_feature: " , max(private$categorical_feature) , " but only " , ncol(private$raw_data) , " features" ) } # Store indices as [0, n-1] indexed instead of [1, n] indexed cate_indices <- as.list(private$categorical_feature - 1L) } # Store indices for categorical features private$params$categorical_feature <- cate_indices } # Generate parameter str params_str <- .params2str(params = private$params) # Get handle of reference dataset ref_handle <- NULL if (!is.null(private$reference)) { ref_handle <- private$reference$.__enclos_env__$private$get_handle() } # not subsetting, constructing from raw data if (is.null(private$used_indices)) { if (is.null(private$raw_data)) { stop(paste0( "Attempting to create a Dataset without any raw data. " , "This can happen if the Dataset's finalizer was called or if this Dataset was saved with saveRDS(). " , "To avoid this error in the future, use lgb.Dataset.save() or " , "Dataset$save_binary() to save lightgbm Datasets." )) } # Are we using a data file? if (is.character(private$raw_data)) { handle <- .Call( LGBM_DatasetCreateFromFile_R , path.expand(private$raw_data) , params_str , ref_handle ) } else if (is.matrix(private$raw_data)) { # Are we using a matrix? handle <- .Call( LGBM_DatasetCreateFromMat_R , private$raw_data , nrow(private$raw_data) , ncol(private$raw_data) , params_str , ref_handle ) } else if (methods::is(private$raw_data, "dgCMatrix")) { if (length(private$raw_data@p) > 2147483647L) { stop("Cannot support large CSC matrix") } # Are we using a dgCMatrix (sparse matrix column compressed) handle <- .Call( LGBM_DatasetCreateFromCSC_R , private$raw_data@p , private$raw_data@i , private$raw_data@x , length(private$raw_data@p) , length(private$raw_data@x) , nrow(private$raw_data) , params_str , ref_handle ) } else { # Unknown data type stop( "lgb.Dataset.construct: does not support constructing from " , sQuote(class(private$raw_data), q = FALSE) ) } } else { # Reference is empty if (is.null(private$reference)) { stop("lgb.Dataset.construct: reference cannot be NULL for constructing data subset") } # Construct subset handle <- .Call( LGBM_DatasetGetSubset_R , ref_handle , c(private$used_indices) , length(private$used_indices) , params_str ) } if (.is_null_handle(x = handle)) { stop("lgb.Dataset.construct: cannot create Dataset handle") } # Setup class and private type class(handle) <- "lgb.Dataset.handle" private$handle <- handle # Set feature names if (!is.null(private$colnames)) { self$set_colnames(colnames = private$colnames) } # Ensure that private$colnames matches the feature names on the C++ side. This line is necessary # in cases like constructing from a file or from a matrix with no column names. private$colnames <- .Call( LGBM_DatasetGetFeatureNames_R , private$handle ) # Load init score if requested if (!is.null(private$predictor) && is.null(private$used_indices)) { # Setup initial scores init_score <- private$predictor$predict( data = private$raw_data , rawscore = TRUE ) # Not needed to transpose, for is col_marjor init_score <- as.vector(init_score) private$info$init_score <- init_score } # Should we free raw data? if (isTRUE(private$free_raw_data)) { private$raw_data <- NULL } # Get private information if (length(private$info) > 0L) { # Set infos for (i in seq_along(private$info)) { p <- private$info[i] self$set_field( field_name = names(p) , data = p[[1L]] ) } } # Get label information existence if (is.null(self$get_field(field_name = "label"))) { stop("lgb.Dataset.construct: label should be set") } return(invisible(self)) }, # Dimension function dim = function() { # Check for handle if (!.is_null_handle(x = private$handle)) { num_row <- 0L num_col <- 0L # Get numeric data and numeric features .Call( LGBM_DatasetGetNumData_R , private$handle , num_row ) .Call( LGBM_DatasetGetNumFeature_R , private$handle , num_col ) return( c(num_row, num_col) ) } else if (is.matrix(private$raw_data) || methods::is(private$raw_data, "dgCMatrix")) { # Check if dgCMatrix (sparse matrix column compressed) # NOTE: requires Matrix package return(dim(private$raw_data)) } else { # Trying to work with unknown dimensions is not possible stop( "dim: cannot get dimensions before dataset has been constructed, " , "please call lgb.Dataset.construct explicitly" ) } }, # Get number of bins for feature get_feature_num_bin = function(feature) { if (.is_null_handle(x = private$handle)) { stop("Cannot get number of bins in feature before constructing Dataset.") } if (is.character(feature)) { feature_name <- feature feature <- which(private$colnames == feature_name) if (length(feature) == 0L) { stop(sprintf("feature '%s' not found", feature_name)) } } num_bin <- integer(1L) .Call( LGBM_DatasetGetFeatureNumBin_R , private$handle , feature - 1L , num_bin ) return(num_bin) }, # Get column names get_colnames = function() { # Check for handle if (!.is_null_handle(x = private$handle)) { private$colnames <- .Call( LGBM_DatasetGetFeatureNames_R , private$handle ) return(private$colnames) } else if (is.matrix(private$raw_data) || methods::is(private$raw_data, "dgCMatrix")) { # Check if dgCMatrix (sparse matrix column compressed) return(colnames(private$raw_data)) } else { # Trying to work with unknown formats is not possible stop( "Dataset$get_colnames(): cannot get column names before dataset has been constructed, please call " , "lgb.Dataset.construct() explicitly" ) } }, # Set column names set_colnames = function(colnames) { # Check column names non-existence if (is.null(colnames)) { return(invisible(self)) } # Check empty column names colnames <- as.character(colnames) if (length(colnames) == 0L) { return(invisible(self)) } # Write column names private$colnames <- colnames if (!.is_null_handle(x = private$handle)) { # Merge names with tab separation merged_name <- paste(as.list(private$colnames), collapse = "\t") .Call( LGBM_DatasetSetFeatureNames_R , private$handle , merged_name ) } return(invisible(self)) }, get_field = function(field_name) { # Check if attribute key is in the known attribute list if (!is.character(field_name) || length(field_name) != 1L || !field_name %in% .INFO_KEYS()) { stop( "Dataset$get_field(): field_name must be one of the following: " , toString(sQuote(.INFO_KEYS(), q = FALSE)) ) } # Check for info name and handle if (is.null(private$info[[field_name]])) { if (.is_null_handle(x = private$handle)) { stop("Cannot perform Dataset$get_field() before constructing Dataset.") } # Get field size of info info_len <- 0L .Call( LGBM_DatasetGetFieldSize_R , private$handle , field_name , info_len ) if (info_len > 0L) { # Get back fields if (field_name == "group") { ret <- integer(info_len) } else { ret <- numeric(info_len) } .Call( LGBM_DatasetGetField_R , private$handle , field_name , ret ) private$info[[field_name]] <- ret } } return(private$info[[field_name]]) }, set_field = function(field_name, data) { # Check if attribute key is in the known attribute list if (!is.character(field_name) || length(field_name) != 1L || !field_name %in% .INFO_KEYS()) { stop( "Dataset$set_field(): field_name must be one of the following: " , toString(sQuote(.INFO_KEYS(), q = FALSE)) ) } # Check for type of information data <- if (field_name == "group") { as.integer(data) } else { as.numeric(data) } # Store information privately private$info[[field_name]] <- data if (!.is_null_handle(x = private$handle) && !is.null(data)) { if (length(data) > 0L) { .Call( LGBM_DatasetSetField_R , private$handle , field_name , data , length(data) ) private$version <- private$version + 1L } } return(invisible(self)) }, slice = function(idxset) { return( Dataset$new( data = NULL , params = private$params , reference = self , colnames = private$colnames , categorical_feature = private$categorical_feature , predictor = private$predictor , free_raw_data = private$free_raw_data , used_indices = sort(idxset, decreasing = FALSE) ) ) }, # [description] Update Dataset parameters. If it has not been constructed yet, # this operation just happens on the R side (updating private$params). # If it has been constructed, parameters will be updated on the C++ side. update_params = function(params) { if (length(params) == 0L) { return(invisible(self)) } new_params <- utils::modifyList(private$params, params) if (.is_null_handle(x = private$handle)) { private$params <- new_params } else { tryCatch({ .Call( LGBM_DatasetUpdateParamChecking_R , .params2str(params = private$params) , .params2str(params = new_params) ) private$params <- new_params }, error = function(e) { # If updating failed but raw data is not available, raise an error because # achieving what the user asked for is not possible if (is.null(private$raw_data)) { stop(e) } # If updating failed but raw data is available, modify the params # on the R side and re-set ("deconstruct") the Dataset private$params <- new_params private$finalize() }) } return(invisible(self)) }, # [description] Get only Dataset-specific parameters. This is primarily used by # Booster to update its parameters based on the characteristics of # a Dataset. It should not be used by other methods in this class, # since "verbose" is not a Dataset parameter and needs to be passed # through to avoid globally re-setting verbosity. get_params = function() { dataset_params <- unname(unlist(.DATASET_PARAMETERS())) ret <- list() for (param_key in names(private$params)) { if (param_key %in% dataset_params) { ret[[param_key]] <- private$params[[param_key]] } } return(ret) }, # Set categorical feature parameter set_categorical_feature = function(categorical_feature) { # Check for identical input if (identical(private$categorical_feature, categorical_feature)) { return(invisible(self)) } # Check for empty data if (is.null(private$raw_data)) { stop("set_categorical_feature: cannot set categorical feature after freeing raw data, please set ", sQuote("free_raw_data = FALSE"), " when you construct lgb.Dataset") } # Overwrite categorical features private$categorical_feature <- categorical_feature # Finalize and return self private$finalize() return(invisible(self)) }, set_reference = function(reference) { # setting reference to this same Dataset object doesn't require any changes if (identical(private$reference, reference)) { return(invisible(self)) } # changing the reference removes the Dataset object on the C++ side, so it should only # be done if you still have the raw_data available, so that the new Dataset can be reconstructed if (is.null(private$raw_data)) { stop("set_reference: cannot set reference after freeing raw data, please set ", sQuote("free_raw_data = FALSE"), " when you construct lgb.Dataset") } if (!.is_Dataset(reference)) { stop("set_reference: Can only use lgb.Dataset as a reference") } # Set known references self$set_categorical_feature(categorical_feature = reference$.__enclos_env__$private$categorical_feature) self$set_colnames(colnames = reference$get_colnames()) private$set_predictor(predictor = reference$.__enclos_env__$private$predictor) # Store reference private$reference <- reference # Finalize and return self private$finalize() return(invisible(self)) }, # Save binary model save_binary = function(fname) { # Store binary data self$construct() .Call( LGBM_DatasetSaveBinary_R , private$handle , path.expand(fname) ) return(invisible(self)) } ), private = list( handle = NULL, raw_data = NULL, params = list(), reference = NULL, colnames = NULL, categorical_feature = NULL, predictor = NULL, free_raw_data = TRUE, used_indices = NULL, info = NULL, version = 0L, # finalize() will free up the handles finalize = function() { .Call( LGBM_DatasetFree_R , private$handle ) private$handle <- NULL return(invisible(NULL)) }, get_handle = function() { # Get handle and construct if needed if (.is_null_handle(x = private$handle)) { self$construct() } return(private$handle) }, set_predictor = function(predictor) { if (identical(private$predictor, predictor)) { return(invisible(self)) } # Check for empty data if (is.null(private$raw_data)) { stop("set_predictor: cannot set predictor after free raw data, please set ", sQuote("free_raw_data = FALSE"), " when you construct lgb.Dataset") } # Check for empty predictor if (!is.null(predictor)) { # Predictor is unknown if (!.is_Predictor(predictor)) { stop("set_predictor: Can only use lgb.Predictor as predictor") } } # Store predictor private$predictor <- predictor # Finalize and return self private$finalize() return(invisible(self)) } ) ) #' @title Construct \code{lgb.Dataset} object #' @description LightGBM does not train on raw data. #' It discretizes continuous features into histogram bins, tries to #' combine categorical features, and automatically handles missing and # infinite values. #' #' The \code{Dataset} class handles that preprocessing, and holds that #' alternative representation of the input data. #' @inheritParams lgb_shared_dataset_params #' @param data a \code{matrix} object, a \code{dgCMatrix} object, #' a character representing a path to a text file (CSV, TSV, or LibSVM), #' or a character representing a path to a binary \code{lgb.Dataset} file #' @param params a list of parameters. See #' \href{https://lightgbm.readthedocs.io/en/latest/Parameters.html#dataset-parameters}{ #' The "Dataset Parameters" section of the documentation} for a list of parameters #' and valid values. #' @param reference reference dataset. When LightGBM creates a Dataset, it does some preprocessing like binning #' continuous features into histograms. If you want to apply the same bin boundaries from an existing #' dataset to new \code{data}, pass that existing Dataset to this argument. #' @param colnames names of columns #' @param categorical_feature categorical features. This can either be a character vector of feature #' names or an integer vector with the indices of the features (e.g. #' \code{c(1L, 10L)} to say "the first and tenth columns"). #' @param free_raw_data LightGBM constructs its data format, called a "Dataset", from tabular data. #' By default, that Dataset object on the R side does not keep a copy of the raw data. #' This reduces LightGBM's memory consumption, but it means that the Dataset object #' cannot be changed after it has been constructed. If you'd prefer to be able to #' change the Dataset object after construction, set \code{free_raw_data = FALSE}. #' #' @return constructed dataset #' #' @examples #' \donttest{ #' \dontshow{setLGBMthreads(2L)} #' \dontshow{data.table::setDTthreads(1L)} #' data(agaricus.train, package = "lightgbm") #' train <- agaricus.train #' dtrain <- lgb.Dataset(train$data, label = train$label) #' data_file <- tempfile(fileext = ".data") #' lgb.Dataset.save(dtrain, data_file) #' dtrain <- lgb.Dataset(data_file) #' lgb.Dataset.construct(dtrain) #' } #' @export lgb.Dataset <- function(data, params = list(), reference = NULL, colnames = NULL, categorical_feature = NULL, free_raw_data = TRUE, label = NULL, weight = NULL, group = NULL, init_score = NULL) { return( invisible(Dataset$new( data = data , params = params , reference = reference , colnames = colnames , categorical_feature = categorical_feature , predictor = NULL , free_raw_data = free_raw_data , used_indices = NULL , label = label , weight = weight , group = group , init_score = init_score )) ) } #' @name lgb.Dataset.create.valid #' @title Construct validation data #' @description Construct validation data according to training data #' @inheritParams lgb_shared_dataset_params #' @param dataset \code{lgb.Dataset} object, training data #' @param data a \code{matrix} object, a \code{dgCMatrix} object, #' a character representing a path to a text file (CSV, TSV, or LibSVM), #' or a character representing a path to a binary \code{Dataset} file #' @param params a list of parameters. See #' \href{https://lightgbm.readthedocs.io/en/latest/Parameters.html#dataset-parameters}{ #' The "Dataset Parameters" section of the documentation} for a list of parameters #' and valid values. If this is an empty list (the default), the validation Dataset #' will have the same parameters as the Dataset passed to argument \code{dataset}. #' #' @return constructed dataset #' #' @examples #' \donttest{ #' \dontshow{setLGBMthreads(2L)} #' \dontshow{data.table::setDTthreads(1L)} #' data(agaricus.train, package = "lightgbm") #' train <- agaricus.train #' dtrain <- lgb.Dataset(train$data, label = train$label) #' data(agaricus.test, package = "lightgbm") #' test <- agaricus.test #' dtest <- lgb.Dataset.create.valid(dtrain, test$data, label = test$label) #' #' # parameters can be changed between the training data and validation set, #' # for example to account for training data in a text file with a header row #' # and validation data in a text file without it #' train_file <- tempfile(pattern = "train_", fileext = ".csv") #' write.table( #' data.frame(y = rnorm(100L), x1 = rnorm(100L), x2 = rnorm(100L)) #' , file = train_file #' , sep = "," #' , col.names = TRUE #' , row.names = FALSE #' , quote = FALSE #' ) #' #' valid_file <- tempfile(pattern = "valid_", fileext = ".csv") #' write.table( #' data.frame(y = rnorm(100L), x1 = rnorm(100L), x2 = rnorm(100L)) #' , file = valid_file #' , sep = "," #' , col.names = FALSE #' , row.names = FALSE #' , quote = FALSE #' ) #' #' dtrain <- lgb.Dataset( #' data = train_file #' , params = list(has_header = TRUE) #' ) #' dtrain$construct() #' #' dvalid <- lgb.Dataset( #' data = valid_file #' , params = list(has_header = FALSE) #' ) #' dvalid$construct() #' } #' @export lgb.Dataset.create.valid <- function(dataset, data, label = NULL, weight = NULL, group = NULL, init_score = NULL, params = list()) { if (!.is_Dataset(x = dataset)) { stop("lgb.Dataset.create.valid: input data should be an lgb.Dataset object") } # Create validation dataset return(invisible( dataset$create_valid( data = data , label = label , weight = weight , group = group , init_score = init_score , params = params ) )) } #' @name lgb.Dataset.construct #' @title Construct Dataset explicitly #' @description Construct Dataset explicitly #' @param dataset Object of class \code{lgb.Dataset} #' #' @examples #' \donttest{ #' \dontshow{setLGBMthreads(2L)} #' \dontshow{data.table::setDTthreads(1L)} #' data(agaricus.train, package = "lightgbm") #' train <- agaricus.train #' dtrain <- lgb.Dataset(train$data, label = train$label) #' lgb.Dataset.construct(dtrain) #' } #' @return constructed dataset #' @export lgb.Dataset.construct <- function(dataset) { if (!.is_Dataset(x = dataset)) { stop("lgb.Dataset.construct: input data should be an lgb.Dataset object") } return(invisible(dataset$construct())) } #' @title Dimensions of an \code{lgb.Dataset} #' @description Returns a vector of numbers of rows and of columns in an \code{lgb.Dataset}. #' @param x Object of class \code{lgb.Dataset} #' #' @return a vector of numbers of rows and of columns #' #' @details #' Note: since \code{nrow} and \code{ncol} internally use \code{dim}, they can also #' be directly used with an \code{lgb.Dataset} object. #' #' @examples #' \donttest{ #' \dontshow{setLGBMthreads(2L)} #' \dontshow{data.table::setDTthreads(1L)} #' data(agaricus.train, package = "lightgbm") #' train <- agaricus.train #' dtrain <- lgb.Dataset(train$data, label = train$label) #' #' stopifnot(nrow(dtrain) == nrow(train$data)) #' stopifnot(ncol(dtrain) == ncol(train$data)) #' stopifnot(all(dim(dtrain) == dim(train$data))) #' } #' @rdname dim #' @export dim.lgb.Dataset <- function(x) { if (!.is_Dataset(x = x)) { stop("dim.lgb.Dataset: input data should be an lgb.Dataset object") } return(x$dim()) } #' @title Handling of column names of \code{lgb.Dataset} #' @description Only column names are supported for \code{lgb.Dataset}, thus setting of #' row names would have no effect and returned row names would be NULL. #' @param x object of class \code{lgb.Dataset} #' @param value a list of two elements: the first one is ignored #' and the second one is column names #' #' @details #' Generic \code{dimnames} methods are used by \code{colnames}. #' Since row names are irrelevant, it is recommended to use \code{colnames} directly. #' #' @examples #' \donttest{ #' \dontshow{setLGBMthreads(2L)} #' \dontshow{data.table::setDTthreads(1L)} #' data(agaricus.train, package = "lightgbm") #' train <- agaricus.train #' dtrain <- lgb.Dataset(train$data, label = train$label) #' lgb.Dataset.construct(dtrain) #' dimnames(dtrain) #' colnames(dtrain) #' colnames(dtrain) <- make.names(seq_len(ncol(train$data))) #' print(dtrain, verbose = TRUE) #' } #' @rdname dimnames.lgb.Dataset #' @return A list with the dimension names of the dataset #' @export dimnames.lgb.Dataset <- function(x) { if (!.is_Dataset(x = x)) { stop("dimnames.lgb.Dataset: input data should be an lgb.Dataset object") } # Return dimension names return(list(NULL, x$get_colnames())) } #' @rdname dimnames.lgb.Dataset #' @export `dimnames<-.lgb.Dataset` <- function(x, value) { # Check if invalid element list if (!identical(class(value), "list") || length(value) != 2L) { stop("invalid ", sQuote("value", q = FALSE), " given: must be a list of two elements") } # Check for unknown row names if (!is.null(value[[1L]])) { stop("lgb.Dataset does not have rownames") } if (is.null(value[[2L]])) { x$set_colnames(colnames = NULL) return(x) } # Check for unmatching column size if (ncol(x) != length(value[[2L]])) { stop( "can't assign " , sQuote(length(value[[2L]]), q = FALSE) , " colnames to an lgb.Dataset with " , sQuote(ncol(x), q = FALSE) , " columns" ) } # Set column names properly, and return x$set_colnames(colnames = value[[2L]]) return(x) } #' @title Slice a dataset #' @description Get a new \code{lgb.Dataset} containing the specified rows of #' original \code{lgb.Dataset} object #' #' \emph{Renamed from} \code{slice()} \emph{in 4.4.0} #' #' @param dataset Object of class \code{lgb.Dataset} #' @param idxset an integer vector of indices of rows needed #' @return constructed sub dataset #' #' @examples #' \donttest{ #' \dontshow{setLGBMthreads(2L)} #' \dontshow{data.table::setDTthreads(1L)} #' data(agaricus.train, package = "lightgbm") #' train <- agaricus.train #' dtrain <- lgb.Dataset(train$data, label = train$label) #' #' dsub <- lgb.slice.Dataset(dtrain, seq_len(42L)) #' lgb.Dataset.construct(dsub) #' labels <- lightgbm::get_field(dsub, "label") #' } #' @export lgb.slice.Dataset <- function(dataset, idxset) { if (!.is_Dataset(x = dataset)) { stop("lgb.slice.Dataset: input dataset should be an lgb.Dataset object") } return(invisible(dataset$slice(idxset = idxset))) } #' @name get_field #' @title Get one attribute of a \code{lgb.Dataset} #' @description Get one attribute of a \code{lgb.Dataset} #' @param dataset Object of class \code{lgb.Dataset} #' @param field_name String with the name of the attribute to get. One of the following. #' \itemize{ #' \item \code{label}: label lightgbm learns from ; #' \item \code{weight}: to do a weight rescale ; #' \item{\code{group}: used for learning-to-rank tasks. An integer vector describing how to #' group rows together as ordered results from the same set of candidate results to be ranked. #' For example, if you have a 100-document dataset with \code{group = c(10, 20, 40, 10, 10, 10)}, #' that means that you have 6 groups, where the first 10 records are in the first group, #' records 11-30 are in the second group, etc.} #' \item \code{init_score}: initial score is the base prediction lightgbm will boost from. #' } #' @return requested attribute #' #' @examples #' \donttest{ #' \dontshow{setLGBMthreads(2L)} #' \dontshow{data.table::setDTthreads(1L)} #' data(agaricus.train, package = "lightgbm") #' train <- agaricus.train #' dtrain <- lgb.Dataset(train$data, label = train$label) #' lgb.Dataset.construct(dtrain) #' #' labels <- lightgbm::get_field(dtrain, "label") #' lightgbm::set_field(dtrain, "label", 1 - labels) #' #' labels2 <- lightgbm::get_field(dtrain, "label") #' stopifnot(all(labels2 == 1 - labels)) #' } #' @export get_field <- function(dataset, field_name) { UseMethod("get_field") } #' @rdname get_field #' @export get_field.lgb.Dataset <- function(dataset, field_name) { # Check if dataset is not a dataset if (!.is_Dataset(x = dataset)) { stop("get_field.lgb.Dataset(): input dataset should be an lgb.Dataset object") } return(dataset$get_field(field_name = field_name)) } #' @name set_field #' @title Set one attribute of a \code{lgb.Dataset} object #' @description Set one attribute of a \code{lgb.Dataset} #' @param dataset Object of class \code{lgb.Dataset} #' @param field_name String with the name of the attribute to set. One of the following. #' \itemize{ #' \item \code{label}: label lightgbm learns from ; #' \item \code{weight}: to do a weight rescale ; #' \item{\code{group}: used for learning-to-rank tasks. An integer vector describing how to #' group rows together as ordered results from the same set of candidate results to be ranked. #' For example, if you have a 100-document dataset with \code{group = c(10, 20, 40, 10, 10, 10)}, #' that means that you have 6 groups, where the first 10 records are in the first group, #' records 11-30 are in the second group, etc.} #' \item \code{init_score}: initial score is the base prediction lightgbm will boost from. #' } #' @param data The data for the field. See examples. #' @return The \code{lgb.Dataset} you passed in. #' #' @examples #' \donttest{ #' \dontshow{setLGBMthreads(2L)} #' \dontshow{data.table::setDTthreads(1L)} #' data(agaricus.train, package = "lightgbm") #' train <- agaricus.train #' dtrain <- lgb.Dataset(train$data, label = train$label) #' lgb.Dataset.construct(dtrain) #' #' labels <- lightgbm::get_field(dtrain, "label") #' lightgbm::set_field(dtrain, "label", 1 - labels) #' #' labels2 <- lightgbm::get_field(dtrain, "label") #' stopifnot(all.equal(labels2, 1 - labels)) #' } #' @export set_field <- function(dataset, field_name, data) { UseMethod("set_field") } #' @rdname set_field #' @export set_field.lgb.Dataset <- function(dataset, field_name, data) { if (!.is_Dataset(x = dataset)) { stop("set_field.lgb.Dataset: input dataset should be an lgb.Dataset object") } return(invisible(dataset$set_field(field_name = field_name, data = data))) } #' @name lgb.Dataset.set.categorical #' @title Set categorical feature of \code{lgb.Dataset} #' @description Set the categorical features of an \code{lgb.Dataset} object. Use this function #' to tell LightGBM which features should be treated as categorical. #' @param dataset object of class \code{lgb.Dataset} #' @param categorical_feature categorical features. This can either be a character vector of feature #' names or an integer vector with the indices of the features (e.g. #' \code{c(1L, 10L)} to say "the first and tenth columns"). #' @return the dataset you passed in #' #' @examples #' \donttest{ #' \dontshow{setLGBMthreads(2L)} #' \dontshow{data.table::setDTthreads(1L)} #' data(agaricus.train, package = "lightgbm") #' train <- agaricus.train #' dtrain <- lgb.Dataset(train$data, label = train$label) #' data_file <- tempfile(fileext = ".data") #' lgb.Dataset.save(dtrain, data_file) #' dtrain <- lgb.Dataset(data_file) #' lgb.Dataset.set.categorical(dtrain, 1L:2L) #' } #' @rdname lgb.Dataset.set.categorical #' @export lgb.Dataset.set.categorical <- function(dataset, categorical_feature) { if (!.is_Dataset(x = dataset)) { stop("lgb.Dataset.set.categorical: input dataset should be an lgb.Dataset object") } return(invisible(dataset$set_categorical_feature(categorical_feature = categorical_feature))) } #' @name lgb.Dataset.set.reference #' @title Set reference of \code{lgb.Dataset} #' @description If you want to use validation data, you should set reference to training data #' @param dataset object of class \code{lgb.Dataset} #' @param reference object of class \code{lgb.Dataset} #' #' @return the dataset you passed in #' #' @examples #' \donttest{ #' \dontshow{setLGBMthreads(2L)} #' \dontshow{data.table::setDTthreads(1L)} #' # create training Dataset #' data(agaricus.train, package ="lightgbm") #' train <- agaricus.train #' dtrain <- lgb.Dataset(train$data, label = train$label) #' #' # create a validation Dataset, using dtrain as a reference #' data(agaricus.test, package = "lightgbm") #' test <- agaricus.test #' dtest <- lgb.Dataset(test$data, label = test$label) #' lgb.Dataset.set.reference(dtest, dtrain) #' } #' @rdname lgb.Dataset.set.reference #' @export lgb.Dataset.set.reference <- function(dataset, reference) { if (!.is_Dataset(x = dataset)) { stop("lgb.Dataset.set.reference: input dataset should be an lgb.Dataset object") } return(invisible(dataset$set_reference(reference = reference))) } #' @name lgb.Dataset.save #' @title Save \code{lgb.Dataset} to a binary file #' @description Please note that \code{init_score} is not saved in binary file. #' If you need it, please set it again after loading Dataset. #' @param dataset object of class \code{lgb.Dataset} #' @param fname object filename of output file #' #' @return the dataset you passed in #' #' @examples #' \donttest{ #' \dontshow{setLGBMthreads(2L)} #' \dontshow{data.table::setDTthreads(1L)} #' data(agaricus.train, package = "lightgbm") #' train <- agaricus.train #' dtrain <- lgb.Dataset(train$data, label = train$label) #' lgb.Dataset.save(dtrain, tempfile(fileext = ".bin")) #' } #' @export lgb.Dataset.save <- function(dataset, fname) { if (!.is_Dataset(x = dataset)) { stop("lgb.Dataset.save: input dataset should be an lgb.Dataset object") } if (!is.character(fname)) { stop("lgb.Dataset.save: fname should be a character or a file connection") } return(invisible(dataset$save_binary(fname = fname))) } ================================================ FILE: R-package/R/lgb.Predictor.R ================================================ #' @importFrom methods is new #' @importFrom R6 R6Class #' @importFrom utils read.delim #' @importClassesFrom Matrix dsparseMatrix dsparseVector dgCMatrix dgRMatrix CsparseMatrix RsparseMatrix Predictor <- R6::R6Class( classname = "lgb.Predictor", cloneable = FALSE, public = list( # Initialize will create a starter model initialize = function(modelfile, params = list(), fast_predict_config = list()) { private$params <- .params2str(params = params) handle <- NULL if (is.character(modelfile)) { # Create handle on it handle <- .Call( LGBM_BoosterCreateFromModelfile_R , path.expand(modelfile) ) private$need_free_handle <- TRUE } else if (methods::is(modelfile, "lgb.Booster.handle") || inherits(modelfile, "externalptr")) { # Check if model file is a booster handle already handle <- modelfile private$need_free_handle <- FALSE } else if (.is_Booster(modelfile)) { handle <- modelfile$get_handle() private$need_free_handle <- FALSE } else { stop("lgb.Predictor: modelfile must be either a character filename or an lgb.Booster.handle") } private$fast_predict_config <- fast_predict_config # Override class and store it class(handle) <- "lgb.Booster.handle" private$handle <- handle return(invisible(NULL)) }, # Get current iteration current_iter = function() { cur_iter <- 0L .Call( LGBM_BoosterGetCurrentIteration_R , private$handle , cur_iter ) return(cur_iter) }, # Predict from data predict = function(data, start_iteration = NULL, num_iteration = NULL, rawscore = FALSE, predleaf = FALSE, predcontrib = FALSE, header = FALSE) { # Check if number of iterations is existing - if not, then set it to -1 (use all) if (is.null(num_iteration)) { num_iteration <- -1L } # Check if start iterations is existing - if not, then set it to 0 (start from the first iteration) if (is.null(start_iteration)) { start_iteration <- 0L } # Check if data is a file name and not a matrix if (identical(class(data), "character") && length(data) == 1L) { data <- path.expand(data) # Data is a filename, create a temporary file with a "lightgbm_" pattern in it tmp_filename <- tempfile(pattern = "lightgbm_") on.exit(unlink(tmp_filename), add = TRUE) # Predict from temporary file .Call( LGBM_BoosterPredictForFile_R , private$handle , data , as.integer(header) , as.integer(rawscore) , as.integer(predleaf) , as.integer(predcontrib) , as.integer(start_iteration) , as.integer(num_iteration) , private$params , tmp_filename ) # Get predictions from file preds <- utils::read.delim(tmp_filename, header = FALSE, sep = "\t") num_row <- nrow(preds) preds <- as.vector(t(preds)) } else if (predcontrib && inherits(data, c("dsparseMatrix", "dsparseVector"))) { ncols <- .Call(LGBM_BoosterGetNumFeature_R, private$handle) ncols_out <- integer(1L) .Call(LGBM_BoosterGetNumClasses_R, private$handle, ncols_out) ncols_out <- (ncols + 1L) * max(ncols_out, 1L) if (is.na(ncols_out)) { ncols_out <- as.numeric(ncols + 1L) * as.numeric(max(ncols_out, 1L)) } if (!inherits(data, "dsparseVector") && ncols_out > .Machine$integer.max) { stop("Resulting matrix of feature contributions is too large for R to handle.") } if (inherits(data, "dsparseVector")) { if (length(data) > ncols) { stop(sprintf("Model was fitted to data with %d columns, input data has %.0f columns." , ncols , length(data))) } res <- .Call( LGBM_BoosterPredictSparseOutput_R , private$handle , c(0L, as.integer(length(data@x))) , data@i - 1L , data@x , TRUE , 1L , ncols , start_iteration , num_iteration , private$params ) out <- methods::new("dsparseVector") out@i <- res$indices + 1L out@x <- res$data out@length <- ncols_out return(out) } else if (inherits(data, "dgRMatrix")) { if (ncol(data) > ncols) { stop(sprintf("Model was fitted to data with %d columns, input data has %.0f columns." , ncols , ncol(data))) } res <- .Call( LGBM_BoosterPredictSparseOutput_R , private$handle , data@p , data@j , data@x , TRUE , nrow(data) , ncols , start_iteration , num_iteration , private$params ) out <- methods::new("dgRMatrix") out@p <- res$indptr out@j <- res$indices out@x <- res$data out@Dim <- as.integer(c(nrow(data), ncols_out)) } else if (inherits(data, "dgCMatrix")) { if (ncol(data) != ncols) { stop(sprintf("Model was fitted to data with %d columns, input data has %.0f columns." , ncols , ncol(data))) } res <- .Call( LGBM_BoosterPredictSparseOutput_R , private$handle , data@p , data@i , data@x , FALSE , nrow(data) , ncols , start_iteration , num_iteration , private$params ) out <- methods::new("dgCMatrix") out@p <- res$indptr out@i <- res$indices out@x <- res$data out@Dim <- as.integer(c(nrow(data), length(res$indptr) - 1L)) } else { stop(sprintf("Predictions on sparse inputs are only allowed for '%s', '%s', '%s' - got: %s" , "dsparseVector" , "dgRMatrix" , "dgCMatrix" , toString(class(data)))) } if (NROW(row.names(data))) { out@Dimnames[[1L]] <- row.names(data) } return(out) } else { # Not a file, we need to predict from R object num_row <- nrow(data) if (is.null(num_row)) { num_row <- 1L } npred <- 0L # Check number of predictions to do .Call( LGBM_BoosterCalcNumPredict_R , private$handle , as.integer(num_row) , as.integer(rawscore) , as.integer(predleaf) , as.integer(predcontrib) , as.integer(start_iteration) , as.integer(num_iteration) , npred ) # Pre-allocate empty vector preds <- numeric(npred) # Check if data is a matrix if (is.matrix(data)) { # this if() prevents the memory and computational costs # of converting something that is already "double" to "double" if (storage.mode(data) != "double") { storage.mode(data) <- "double" } if (nrow(data) == 1L) { use_fast_config <- private$check_can_use_fast_predict_config( csr = FALSE , rawscore = rawscore , predleaf = predleaf , predcontrib = predcontrib , start_iteration = start_iteration , num_iteration = num_iteration ) if (use_fast_config) { .Call( LGBM_BoosterPredictForMatSingleRowFast_R , private$fast_predict_config$handle , data , preds ) } else { .Call( LGBM_BoosterPredictForMatSingleRow_R , private$handle , data , rawscore , predleaf , predcontrib , start_iteration , num_iteration , private$params , preds ) } } else { .Call( LGBM_BoosterPredictForMat_R , private$handle , data , as.integer(nrow(data)) , as.integer(ncol(data)) , as.integer(rawscore) , as.integer(predleaf) , as.integer(predcontrib) , as.integer(start_iteration) , as.integer(num_iteration) , private$params , preds ) } } else if (inherits(data, "dsparseVector")) { if (length(self$fast_predict_config)) { ncols <- self$fast_predict_config$ncols use_fast_config <- private$check_can_use_fast_predict_config( csr = TRUE , rawscore = rawscore , predleaf = predleaf , predcontrib = predcontrib , start_iteration = start_iteration , num_iteration = num_iteration ) } else { ncols <- .Call(LGBM_BoosterGetNumFeature_R, private$handle) use_fast_config <- FALSE } if (length(data) > ncols) { stop(sprintf("Model was fitted to data with %d columns, input data has %.0f columns." , ncols , length(data))) } if (use_fast_config) { .Call( LGBM_BoosterPredictForCSRSingleRowFast_R , self$fast_predict_config$handle , data@i - 1L , data@x , preds ) } else { .Call( LGBM_BoosterPredictForCSRSingleRow_R , private$handle , data@i - 1L , data@x , ncols , as.integer(rawscore) , as.integer(predleaf) , as.integer(predcontrib) , start_iteration , num_iteration , private$params , preds ) } } else if (inherits(data, "dgRMatrix")) { ncols <- .Call(LGBM_BoosterGetNumFeature_R, private$handle) if (ncol(data) > ncols) { stop(sprintf("Model was fitted to data with %d columns, input data has %.0f columns." , ncols , ncol(data))) } if (nrow(data) == 1L) { if (length(self$fast_predict_config)) { ncols <- self$fast_predict_config$ncols use_fast_config <- private$check_can_use_fast_predict_config( csr = TRUE , rawscore = rawscore , predleaf = predleaf , predcontrib = predcontrib , start_iteration = start_iteration , num_iteration = num_iteration ) } else { ncols <- .Call(LGBM_BoosterGetNumFeature_R, private$handle) use_fast_config <- FALSE } if (use_fast_config) { .Call( LGBM_BoosterPredictForCSRSingleRowFast_R , self$fast_predict_config$handle , data@j , data@x , preds ) } else { .Call( LGBM_BoosterPredictForCSRSingleRow_R , private$handle , data@j , data@x , ncols , as.integer(rawscore) , as.integer(predleaf) , as.integer(predcontrib) , start_iteration , num_iteration , private$params , preds ) } } else { .Call( LGBM_BoosterPredictForCSR_R , private$handle , data@p , data@j , data@x , ncols , as.integer(rawscore) , as.integer(predleaf) , as.integer(predcontrib) , start_iteration , num_iteration , private$params , preds ) } } else if (methods::is(data, "dgCMatrix")) { if (length(data@p) > 2147483647L) { stop("Cannot support large CSC matrix") } # Check if data is a dgCMatrix (sparse matrix, column compressed format) .Call( LGBM_BoosterPredictForCSC_R , private$handle , data@p , data@i , data@x , length(data@p) , length(data@x) , nrow(data) , as.integer(rawscore) , as.integer(predleaf) , as.integer(predcontrib) , as.integer(start_iteration) , as.integer(num_iteration) , private$params , preds ) } else { stop("predict: cannot predict on data of class ", sQuote(class(data), q = FALSE)) } } # Check if number of rows is strange (not a multiple of the dataset rows) if (length(preds) %% num_row != 0L) { stop( "predict: prediction length " , sQuote(length(preds), q = FALSE) , " is not a multiple of nrows(data): " , sQuote(num_row, q = FALSE) ) } # Get number of cases per row npred_per_case <- length(preds) / num_row # Data reshaping if (npred_per_case > 1L || predleaf || predcontrib) { preds <- matrix(preds, ncol = npred_per_case, byrow = TRUE) } # Keep row names if possible if (NROW(row.names(data)) && NROW(data) == NROW(preds)) { if (is.null(dim(preds))) { names(preds) <- row.names(data) } else { row.names(preds) <- row.names(data) } } return(preds) } ), private = list( handle = NULL , need_free_handle = FALSE , params = "" , fast_predict_config = list() , check_can_use_fast_predict_config = function(csr, rawscore, predleaf, predcontrib, start_iteration, num_iteration) { if (!NROW(private$fast_predict_config)) { return(FALSE) } if (.is_null_handle(private$fast_predict_config$handle)) { warning(paste0("Model had fast CSR predict configuration, but it is inactive." , " Try re-generating it through 'lgb.configure_fast_predict'.")) return(FALSE) } if (isTRUE(csr) != private$fast_predict_config$csr) { return(FALSE) } return( private$params == "" && private$fast_predict_config$rawscore == rawscore && private$fast_predict_config$predleaf == predleaf && private$fast_predict_config$predcontrib == predcontrib && .equal_or_both_null(private$fast_predict_config$start_iteration, start_iteration) && .equal_or_both_null(private$fast_predict_config$num_iteration, num_iteration) ) } # finalize() will free up the handles , finalize = function() { if (private$need_free_handle) { .Call( LGBM_BoosterFree_R , private$handle ) private$handle <- NULL } return(invisible(NULL)) } ) ) ================================================ FILE: R-package/R/lgb.convert_with_rules.R ================================================ # [description] get all column classes of a data.table or data.frame. # This function collapses the result of class() into a single string .get_column_classes <- function(df) { return( vapply( X = df , FUN = function(x) { paste(class(x), collapse = ",") } , FUN.VALUE = character(1L) ) ) } # [description] check a data frame or data table for columns that are any # type other than numeric and integer. This is used by lgb.convert_with_rules() # to warn if more action is needed by users # before a dataset can be converted to a lgb.Dataset. .warn_for_unconverted_columns <- function(df, function_name) { column_classes <- .get_column_classes(df = df) unconverted_columns <- column_classes[!(column_classes %in% c("numeric", "integer"))] if (length(unconverted_columns) > 0L) { col_detail_string <- toString( paste0( names(unconverted_columns) , " (" , unconverted_columns , ")" ) ) msg <- paste0( function_name , ": " , length(unconverted_columns) , " columns are not numeric or integer. These need to be dropped or converted to " , "be used in an lgb.Dataset object. " , col_detail_string ) warning(msg) } return(invisible(NULL)) } .LGB_CONVERT_DEFAULT_FOR_LOGICAL_NA <- function() { return(-1L) } .LGB_CONVERT_DEFAULT_FOR_NON_LOGICAL_NA <- function() { return(0L) } #' @name lgb.convert_with_rules #' @title Data preparator for LightGBM datasets with rules (integer) #' @description Attempts to prepare a clean dataset to prepare to put in a \code{lgb.Dataset}. #' Factor, character, and logical columns are converted to integer. Missing values #' in factors and characters will be filled with 0L. Missing values in logicals #' will be filled with -1L. #' #' This function returns and optionally takes in "rules" the describe exactly #' how to convert values in columns. #' #' Columns that contain only NA values will be converted by this function but will #' not show up in the returned \code{rules}. #' #' NOTE: In previous releases of LightGBM, this function was called \code{lgb.prepare_rules2}. #' @param data A data.frame or data.table to prepare. #' @param rules A set of rules from the data preparator, if already used. This should be an R list, #' where names are column names in \code{data} and values are named character #' vectors whose names are column values and whose values are new values to #' replace them with. #' @return A list with the cleaned dataset (\code{data}) and the rules (\code{rules}). #' Note that the data must be converted to a matrix format (\code{as.matrix}) for input in #' \code{lgb.Dataset}. #' #' @examples #' \donttest{ #' data(iris) #' #' str(iris) #' #' new_iris <- lgb.convert_with_rules(data = iris) #' str(new_iris$data) #' #' data(iris) # Erase iris dataset #' iris$Species[1L] <- "NEW FACTOR" # Introduce junk factor (NA) #' #' # Use conversion using known rules #' # Unknown factors become 0, excellent for sparse datasets #' newer_iris <- lgb.convert_with_rules(data = iris, rules = new_iris$rules) #' #' # Unknown factor is now zero, perfect for sparse datasets #' newer_iris$data[1L, ] # Species became 0 as it is an unknown factor #' #' newer_iris$data[1L, 5L] <- 1.0 # Put back real initial value #' #' # Is the newly created dataset equal? YES! #' all.equal(new_iris$data, newer_iris$data) #' #' # Can we test our own rules? #' data(iris) # Erase iris dataset #' #' # We remapped values differently #' personal_rules <- list( #' Species = c( #' "setosa" = 3L #' , "versicolor" = 2L #' , "virginica" = 1L #' ) #' ) #' newest_iris <- lgb.convert_with_rules(data = iris, rules = personal_rules) #' str(newest_iris$data) # SUCCESS! #' } #' @importFrom data.table set #' @export lgb.convert_with_rules <- function(data, rules = NULL) { column_classes <- .get_column_classes(df = data) is_data_table <- data.table::is.data.table(x = data) is_data_frame <- is.data.frame(data) if (!(is_data_table || is_data_frame)) { stop( "lgb.convert_with_rules: you provided " , paste(class(data), collapse = " & ") , " but data should have class data.frame or data.table" ) } # if user didn't provide rules, create them if (is.null(rules)) { rules <- list() columns_to_fix <- which(column_classes %in% c("character", "factor", "logical")) for (i in columns_to_fix) { col_values <- data[[i]] # Get unique values if (is.factor(col_values)) { unique_vals <- levels(col_values) unique_vals <- unique_vals[!is.na(unique_vals)] mini_numeric <- seq_along(unique_vals) # respect ordinal } else if (is.character(col_values)) { unique_vals <- as.factor(unique(col_values)) unique_vals <- unique_vals[!is.na(unique_vals)] mini_numeric <- as.integer(unique_vals) # no respect for ordinal } else if (is.logical(col_values)) { unique_vals <- c(FALSE, TRUE) mini_numeric <- c(0L, 1L) } # don't add rules for all-NA columns if (length(unique_vals) > 0L) { col_name <- names(data)[i] rules[[col_name]] <- mini_numeric names(rules[[col_name]]) <- unique_vals } } } for (col_name in names(rules)) { if (column_classes[[col_name]] == "logical") { default_value_for_na <- .LGB_CONVERT_DEFAULT_FOR_LOGICAL_NA() } else { default_value_for_na <- .LGB_CONVERT_DEFAULT_FOR_NON_LOGICAL_NA() } if (is_data_table) { data.table::set( x = data , j = col_name , value = unname(rules[[col_name]][data[[col_name]]]) ) data[is.na(get(col_name)), (col_name) := default_value_for_na] } else { data[[col_name]] <- unname(rules[[col_name]][data[[col_name]]]) data[is.na(data[col_name]), col_name] <- default_value_for_na } } # if any all-NA columns exist, they won't be in rules. Convert them all_na_cols <- which( sapply( X = data , FUN = function(x) { (is.factor(x) || is.character(x) || is.logical(x)) && all(is.na(unique(x))) } ) ) for (col_name in all_na_cols) { if (column_classes[[col_name]] == "logical") { default_value_for_na <- .LGB_CONVERT_DEFAULT_FOR_LOGICAL_NA() } else { default_value_for_na <- .LGB_CONVERT_DEFAULT_FOR_NON_LOGICAL_NA() } if (is_data_table) { data[, (col_name) := rep(default_value_for_na, .N)] } else { data[[col_name]] <- default_value_for_na } } .warn_for_unconverted_columns(df = data, function_name = "lgb.convert_with_rules") return(list(data = data, rules = rules)) } ================================================ FILE: R-package/R/lgb.cv.R ================================================ #' @importFrom R6 R6Class CVBooster <- R6::R6Class( classname = "lgb.CVBooster", cloneable = FALSE, public = list( best_iter = -1L, best_score = NA, record_evals = list(), boosters = list(), initialize = function(x) { self$boosters <- x return(invisible(NULL)) }, reset_parameter = function(new_params) { for (x in self$boosters) { x[["booster"]]$reset_parameter(params = new_params) } return(invisible(self)) } ) ) #' @name lgb.cv #' @title Main CV logic for LightGBM #' @description Cross validation logic used by LightGBM #' @inheritParams lgb_shared_params #' @param nfold the original dataset is randomly partitioned into \code{nfold} equal size subsamples. #' @param record Boolean, TRUE will record iteration message to \code{booster$record_evals} #' @param showsd \code{boolean}, whether to show standard deviation of cross validation. #' This parameter defaults to \code{TRUE}. Setting it to \code{FALSE} can lead to a #' slight speedup by avoiding unnecessary computation. #' @param stratified a \code{boolean} indicating whether sampling of folds should be stratified #' by the values of outcome labels. #' @param folds \code{list} provides a possibility to use a list of pre-defined CV folds #' (each element must be a vector of test fold's indices). When folds are supplied, #' the \code{nfold} and \code{stratified} parameters are ignored. #' @param callbacks List of callback functions that are applied at each iteration. #' @param reset_data Boolean, setting it to TRUE (not the default value) will transform the booster model #' into a predictor model which frees up memory and the original datasets #' @param eval_train_metric \code{boolean}, whether to add the cross validation results on the #' training data. This parameter defaults to \code{FALSE}. Setting it to \code{TRUE} #' will increase run time. #' @inheritSection lgb_shared_params Early Stopping #' @return a trained model \code{lgb.CVBooster}. #' #' @examples #' \donttest{ #' \dontshow{setLGBMthreads(2L)} #' \dontshow{data.table::setDTthreads(1L)} #' data(agaricus.train, package = "lightgbm") #' train <- agaricus.train #' dtrain <- lgb.Dataset(train$data, label = train$label) #' params <- list( #' objective = "regression" #' , metric = "l2" #' , min_data = 1L #' , learning_rate = 1.0 #' , num_threads = 2L #' ) #' model <- lgb.cv( #' params = params #' , data = dtrain #' , nrounds = 5L #' , nfold = 3L #' ) #' } #' #' @importFrom data.table data.table setorderv #' @export lgb.cv <- function(params = list() , data , nrounds = 100L , nfold = 3L , obj = NULL , eval = NULL , verbose = 1L , record = TRUE , eval_freq = 1L , showsd = TRUE , stratified = TRUE , folds = NULL , init_model = NULL , early_stopping_rounds = NULL , callbacks = list() , reset_data = FALSE , serializable = TRUE , eval_train_metric = FALSE ) { if (nrounds <= 0L) { stop("nrounds should be greater than zero") } if (!.is_Dataset(x = data)) { stop("lgb.cv: data must be an lgb.Dataset instance") } # set some parameters, resolving the way they were passed in with other parameters # in `params`. # this ensures that the model stored with Booster$save() correctly represents # what was passed in params <- .check_wrapper_param( main_param_name = "verbosity" , params = params , alternative_kwarg_value = verbose ) params <- .check_wrapper_param( main_param_name = "num_iterations" , params = params , alternative_kwarg_value = nrounds ) params <- .check_wrapper_param( main_param_name = "metric" , params = params , alternative_kwarg_value = NULL ) params <- .check_wrapper_param( main_param_name = "objective" , params = params , alternative_kwarg_value = obj ) params <- .check_wrapper_param( main_param_name = "early_stopping_round" , params = params , alternative_kwarg_value = early_stopping_rounds ) early_stopping_rounds <- params[["early_stopping_round"]] # extract any function objects passed for objective or metric fobj <- NULL if (is.function(params$objective)) { fobj <- params$objective params$objective <- "none" } # If eval is a single function, store it as a 1-element list # (for backwards compatibility). If it is a list of functions, store # all of them. This makes it possible to pass any mix of strings like "auc" # and custom functions to eval params <- .check_eval(params = params, eval = eval) eval_functions <- list(NULL) if (is.function(eval)) { eval_functions <- list(eval) } if (methods::is(eval, "list")) { eval_functions <- Filter( f = is.function , x = eval ) } # Init predictor to empty predictor <- NULL # Check for boosting from a trained model if (is.character(init_model)) { predictor <- Predictor$new(modelfile = init_model) } else if (.is_Booster(x = init_model)) { predictor <- init_model$to_predictor() } # Set the iteration to start from / end to (and check for boosting from a trained model, again) begin_iteration <- 1L if (!is.null(predictor)) { begin_iteration <- predictor$current_iter() + 1L } end_iteration <- begin_iteration + params[["num_iterations"]] - 1L # pop interaction_constraints off of params. It needs some preprocessing on the # R side before being passed into the Dataset object interaction_constraints <- params[["interaction_constraints"]] params["interaction_constraints"] <- NULL # Construct datasets, if needed data$update_params(params = params) data$construct() # Check interaction constraints params[["interaction_constraints"]] <- .check_interaction_constraints( interaction_constraints = interaction_constraints , column_names = data$get_colnames() ) # Update parameters with parsed parameters data$update_params(params = params) # Create the predictor set data$.__enclos_env__$private$set_predictor(predictor = predictor) if (!is.null(folds)) { # Check for list of folds or for single value if (!identical(class(folds), "list") || length(folds) < 2L) { stop(sQuote("folds"), " must be a list with 2 or more elements that are vectors of indices for each CV-fold") } } else { if (nfold <= 1L) { stop(sQuote("nfold"), " must be > 1") } # Create folds folds <- .generate_cv_folds( nfold = nfold , nrows = nrow(data) , stratified = stratified , label = get_field(dataset = data, field_name = "label") , group = get_field(dataset = data, field_name = "group") , params = params ) } # Add printing log callback if (params[["verbosity"]] > 0L && eval_freq > 0L) { callbacks <- .add_cb(cb_list = callbacks, cb = cb_print_evaluation(period = eval_freq)) } # Add evaluation log callback if (record) { callbacks <- .add_cb(cb_list = callbacks, cb = cb_record_evaluation()) } # Did user pass parameters that indicate they want to use early stopping? using_early_stopping <- !is.null(early_stopping_rounds) && early_stopping_rounds > 0L boosting_param_names <- .PARAMETER_ALIASES()[["boosting"]] using_dart <- any( sapply( X = boosting_param_names , FUN = function(param) { identical(params[[param]], "dart") } ) ) # Cannot use early stopping with 'dart' boosting if (using_dart) { if (using_early_stopping) { warning("Early stopping is not available in 'dart' mode.") } using_early_stopping <- FALSE # Remove the cb_early_stop() function if it was passed in to callbacks callbacks <- Filter( f = function(cb_func) { !identical(attr(cb_func, "name"), "cb_early_stop") } , x = callbacks ) } # If user supplied early_stopping_rounds, add the early stopping callback if (using_early_stopping) { callbacks <- .add_cb( cb_list = callbacks , cb = cb_early_stop( stopping_rounds = early_stopping_rounds , first_metric_only = isTRUE(params[["first_metric_only"]]) , verbose = params[["verbosity"]] > 0L ) ) } cb <- .categorize_callbacks(cb_list = callbacks) # Construct booster for each fold. The data.table() code below is used to # guarantee that indices are sorted while keeping init_score and weight together # with the correct indices. Note that it takes advantage of the fact that # someDT$some_column returns NULL is 'some_column' does not exist in the data.table bst_folds <- lapply( X = seq_along(folds) , FUN = function(k) { # For learning-to-rank, each fold is a named list with two elements: # * `fold` = an integer vector of row indices # * `group` = an integer vector describing which groups are in the fold # For classification or regression tasks, it will just be an integer # vector of row indices folds_have_group <- "group" %in% names(folds[[k]]) if (folds_have_group) { test_indices <- folds[[k]]$fold test_group_indices <- folds[[k]]$group test_groups <- get_field(dataset = data, field_name = "group")[test_group_indices] train_groups <- get_field(dataset = data, field_name = "group")[-test_group_indices] } else { test_indices <- folds[[k]] } train_indices <- seq_len(nrow(data))[-test_indices] # set up test set indexDT <- data.table::data.table( indices = test_indices , weight = get_field(dataset = data, field_name = "weight")[test_indices] , init_score = get_field(dataset = data, field_name = "init_score")[test_indices] ) data.table::setorderv(x = indexDT, cols = "indices", order = 1L) dtest <- lgb.slice.Dataset(data, indexDT$indices) set_field(dataset = dtest, field_name = "weight", data = indexDT$weight) set_field(dataset = dtest, field_name = "init_score", data = indexDT$init_score) # set up training set indexDT <- data.table::data.table( indices = train_indices , weight = get_field(dataset = data, field_name = "weight")[train_indices] , init_score = get_field(dataset = data, field_name = "init_score")[train_indices] ) data.table::setorderv(x = indexDT, cols = "indices", order = 1L) dtrain <- lgb.slice.Dataset(data, indexDT$indices) set_field(dataset = dtrain, field_name = "weight", data = indexDT$weight) set_field(dataset = dtrain, field_name = "init_score", data = indexDT$init_score) if (folds_have_group) { set_field(dataset = dtest, field_name = "group", data = test_groups) set_field(dataset = dtrain, field_name = "group", data = train_groups) } booster <- Booster$new(params = params, train_set = dtrain) if (isTRUE(eval_train_metric)) { booster$add_valid(data = dtrain, name = "train") } booster$add_valid(data = dtest, name = "valid") return( list(booster = booster) ) } ) # Create new booster cv_booster <- CVBooster$new(x = bst_folds) # Callback env env <- CB_ENV$new() env$model <- cv_booster env$begin_iteration <- begin_iteration env$end_iteration <- end_iteration # Start training model using number of iterations to start and end with for (i in seq.int(from = begin_iteration, to = end_iteration)) { # Overwrite iteration in environment env$iteration <- i env$eval_list <- list() for (f in cb$pre_iter) { f(env) } # Update one boosting iteration msg <- lapply(cv_booster$boosters, function(fd) { fd$booster$update(fobj = fobj) out <- list() for (eval_function in eval_functions) { out <- append(out, fd$booster$eval_valid(feval = eval_function)) } return(out) }) # Prepare collection of evaluation results merged_msg <- .merge_cv_result( msg = msg , showsd = showsd ) # Write evaluation result in environment env$eval_list <- merged_msg$eval_list # Check for standard deviation requirement if (showsd) { env$eval_err_list <- merged_msg$eval_err_list } # Loop through env for (f in cb$post_iter) { f(env) } # Check for early stopping and break if needed if (env$met_early_stop) break } # When early stopping is not activated, we compute the best iteration / score ourselves # based on the first first metric if (record && is.na(env$best_score)) { # when using a custom eval function, the metric name is returned from the # function, so figure it out from record_evals if (!is.null(eval_functions[1L])) { first_metric <- names(cv_booster$record_evals[["valid"]])[1L] } else { first_metric <- cv_booster$.__enclos_env__$private$eval_names[1L] } .find_best <- which.min if (isTRUE(env$eval_list[[1L]]$higher_better[1L])) { .find_best <- which.max } cv_booster$best_iter <- unname( .find_best( unlist( cv_booster$record_evals[["valid"]][[first_metric]][[.EVAL_KEY()]] ) ) ) cv_booster$best_score <- cv_booster$record_evals[["valid"]][[first_metric]][[.EVAL_KEY()]][[cv_booster$best_iter]] } # Propagate the best_iter attribute from the cv_booster to the individual boosters for (bst in cv_booster$boosters) { bst$booster$best_iter <- cv_booster$best_iter } if (reset_data) { lapply(cv_booster$boosters, function(fd) { # Store temporarily model data elsewhere booster_old <- list( best_iter = fd$booster$best_iter , best_score = fd$booster$best_score , record_evals = fd$booster$record_evals ) # Reload model fd$booster <- lgb.load(model_str = fd$booster$save_model_to_string()) fd$booster$best_iter <- booster_old$best_iter fd$booster$best_score <- booster_old$best_score fd$booster$record_evals <- booster_old$record_evals }) } if (serializable) { lapply(cv_booster$boosters, function(model) model$booster$save_raw()) } return(cv_booster) } # Generates random (stratified if needed) CV folds .generate_cv_folds <- function(nfold, nrows, stratified, label, group, params) { # Check for group existence if (is.null(group)) { # Shuffle rnd_idx <- sample.int(nrows) # Request stratified folds if (isTRUE(stratified) && params$objective %in% c("binary", "multiclass") && length(label) == length(rnd_idx)) { y <- label[rnd_idx] y <- as.factor(y) folds <- .stratified_folds(y = y, k = nfold) } else { # Make simple non-stratified folds folds <- list() # Loop through each fold for (i in seq_len(nfold)) { kstep <- length(rnd_idx) %/% (nfold - i + 1L) folds[[i]] <- rnd_idx[seq_len(kstep)] rnd_idx <- rnd_idx[-seq_len(kstep)] } } } else { # When doing group, stratified is not possible (only random selection) if (nfold > length(group)) { stop("\nYou requested too many folds for the number of available groups.\n") } # Degroup the groups ungrouped <- inverse.rle(list(lengths = group, values = seq_along(group))) # Can't stratify, shuffle rnd_idx <- sample.int(length(group)) # Make simple non-stratified folds folds <- list() # Loop through each fold for (i in seq_len(nfold)) { kstep <- length(rnd_idx) %/% (nfold - i + 1L) folds[[i]] <- list( fold = which(ungrouped %in% rnd_idx[seq_len(kstep)]) , group = rnd_idx[seq_len(kstep)] ) rnd_idx <- rnd_idx[-seq_len(kstep)] } } return(folds) } # Creates CV folds stratified by the values of y. # It was borrowed from caret::createFolds and simplified # by always returning an unnamed list of fold indices. #' @importFrom stats quantile .stratified_folds <- function(y, k) { # Group the numeric data based on their magnitudes # and sample within those groups. # When the number of samples is low, we may have # issues further slicing the numeric data into # groups. The number of groups will depend on the # ratio of the number of folds to the sample size. # At most, we will use quantiles. If the sample # is too small, we just do regular unstratified CV if (is.numeric(y)) { cuts <- length(y) %/% k if (cuts < 2L) { cuts <- 2L } if (cuts > 5L) { cuts <- 5L } y <- cut( y , unique(stats::quantile(y, probs = seq.int(0.0, 1.0, length.out = cuts))) , include.lowest = TRUE ) } if (k < length(y)) { # Reset levels so that the possible levels and # the levels in the vector are the same y <- as.factor(as.character(y)) numInClass <- table(y) foldVector <- vector(mode = "integer", length(y)) # For each class, balance the fold allocation as far # as possible, then resample the remainder. # The final assignment of folds is also randomized. for (i in seq_along(numInClass)) { # Create a vector of integers from 1:k as many times as possible without # going over the number of samples in the class. Note that if the number # of samples in a class is less than k, nothing is produced here. seqVector <- rep(seq_len(k), numInClass[i] %/% k) # Add enough random integers to get length(seqVector) == numInClass[i] if (numInClass[i] %% k > 0L) { seqVector <- c(seqVector, sample.int(k, numInClass[i] %% k)) } # Shuffle the integers for fold assignment and assign to this classes's data foldVector[y == dimnames(numInClass)$y[i]] <- sample(seqVector) } } else { foldVector <- seq(along = y) } out <- split(seq(along = y), foldVector) names(out) <- NULL return(out) } .merge_cv_result <- function(msg, showsd) { if (length(msg) == 0L) { stop("lgb.cv: size of cv result error") } eval_len <- length(msg[[1L]]) if (eval_len == 0L) { stop("lgb.cv: should provide at least one metric for CV") } # Get evaluation results using a list apply eval_result <- lapply(seq_len(eval_len), function(j) { as.numeric(lapply(seq_along(msg), function(i) { msg[[i]][[j]]$value })) }) # Get evaluation. Just taking the first element here to # get structure (name, higher_better, data_name) ret_eval <- msg[[1L]] for (j in seq_len(eval_len)) { ret_eval[[j]]$value <- mean(eval_result[[j]]) } ret_eval_err <- NULL # Check for standard deviation if (showsd) { # Parse standard deviation for (j in seq_len(eval_len)) { ret_eval_err <- c( ret_eval_err , sqrt(mean(eval_result[[j]] ^ 2L) - mean(eval_result[[j]]) ^ 2L) ) } ret_eval_err <- as.list(ret_eval_err) } return( list( eval_list = ret_eval , eval_err_list = ret_eval_err ) ) } ================================================ FILE: R-package/R/lgb.drop_serialized.R ================================================ #' @name lgb.drop_serialized #' @title Drop serialized raw bytes in a LightGBM model object #' @description If a LightGBM model object was produced with argument `serializable=TRUE`, the R object will keep #' a copy of the underlying C++ object as raw bytes, which can be used to reconstruct such object after getting #' serialized and de-serialized, but at the cost of extra memory usage. If these raw bytes are not needed anymore, #' they can be dropped through this function in order to save memory. Note that the object will be modified in-place. #' #' \emph{New in version 4.0.0} #' #' @param model \code{lgb.Booster} object which was produced with `serializable=TRUE`. #' #' @return \code{lgb.Booster} (the same `model` object that was passed as input, as invisible). #' @seealso \link{lgb.restore_handle}, \link{lgb.make_serializable}. #' @export lgb.drop_serialized <- function(model) { if (!.is_Booster(x = model)) { stop("lgb.drop_serialized: model should be an ", sQuote("lgb.Booster", q = FALSE)) } model$drop_raw() return(invisible(model)) } ================================================ FILE: R-package/R/lgb.importance.R ================================================ #' @name lgb.importance #' @title Compute feature importance in a model #' @description Creates a \code{data.table} of feature importances in a model. #' @param model object of class \code{lgb.Booster}. #' @param percentage whether to show importance in relative percentage. #' #' @return For a tree model, a \code{data.table} with the following columns: #' \itemize{ #' \item{\code{Feature}: Feature names in the model.} #' \item{\code{Gain}: The total gain of this feature's splits.} #' \item{\code{Cover}: The number of observation related to this feature.} #' \item{\code{Frequency}: The number of times a feature split in trees.} #' } #' #' @examples #' \donttest{ #' \dontshow{setLGBMthreads(2L)} #' \dontshow{data.table::setDTthreads(1L)} #' data(agaricus.train, package = "lightgbm") #' train <- agaricus.train #' dtrain <- lgb.Dataset(train$data, label = train$label) #' #' params <- list( #' objective = "binary" #' , learning_rate = 0.1 #' , max_depth = -1L #' , min_data_in_leaf = 1L #' , min_sum_hessian_in_leaf = 1.0 #' , num_threads = 2L #' ) #' model <- lgb.train( #' params = params #' , data = dtrain #' , nrounds = 5L #' ) #' #' tree_imp1 <- lgb.importance(model, percentage = TRUE) #' tree_imp2 <- lgb.importance(model, percentage = FALSE) #' } #' @importFrom data.table := setnames setorderv #' @export lgb.importance <- function(model, percentage = TRUE) { if (!.is_Booster(x = model)) { stop("'model' has to be an object of class lgb.Booster") } # Setup importance tree_dt <- lgb.model.dt.tree(model = model) # Extract elements tree_imp_dt <- tree_dt[ !is.na(split_index) , .(Gain = sum(split_gain), Cover = sum(internal_count), Frequency = .N) , by = "split_feature" ] data.table::setnames( x = tree_imp_dt , old = "split_feature" , new = "Feature" ) # Sort features by Gain data.table::setorderv( x = tree_imp_dt , cols = "Gain" , order = -1L ) # Check if relative values are requested if (percentage) { tree_imp_dt[, `:=`( Gain = Gain / sum(Gain) , Cover = Cover / sum(Cover) , Frequency = Frequency / sum(Frequency) )] } # adding an empty [] to ensure the table is printed the first time print.data.table() is called return(tree_imp_dt[]) } ================================================ FILE: R-package/R/lgb.interprete.R ================================================ #' @name lgb.interprete #' @title Compute feature contribution of prediction #' @description Computes feature contribution components of rawscore prediction. #' @param model object of class \code{lgb.Booster}. #' @param data a matrix object or a dgCMatrix object. #' @param idxset an integer vector of indices of rows needed. #' @param num_iteration number of iteration want to predict with, NULL or <= 0 means use best iteration. #' #' @return For regression, binary classification and lambdarank model, a \code{list} of \code{data.table} #' with the following columns: #' \itemize{ #' \item{\code{Feature}: Feature names in the model.} #' \item{\code{Contribution}: The total contribution of this feature's splits.} #' } #' For multiclass classification, a \code{list} of \code{data.table} with the Feature column and #' Contribution columns to each class. #' #' @examples #' \donttest{ #' \dontshow{setLGBMthreads(2L)} #' \dontshow{data.table::setDTthreads(1L)} #' Logit <- function(x) log(x / (1.0 - x)) #' data(agaricus.train, package = "lightgbm") #' train <- agaricus.train #' dtrain <- lgb.Dataset(train$data, label = train$label) #' set_field( #' dataset = dtrain #' , field_name = "init_score" #' , data = rep(Logit(mean(train$label)), length(train$label)) #' ) #' data(agaricus.test, package = "lightgbm") #' test <- agaricus.test #' #' params <- list( #' objective = "binary" #' , learning_rate = 0.1 #' , max_depth = -1L #' , min_data_in_leaf = 1L #' , min_sum_hessian_in_leaf = 1.0 #' , num_threads = 2L #' ) #' model <- lgb.train( #' params = params #' , data = dtrain #' , nrounds = 3L #' ) #' #' tree_interpretation <- lgb.interprete(model, test$data, 1L:5L) #' } #' @importFrom data.table as.data.table #' @export lgb.interprete <- function(model, data, idxset, num_iteration = NULL) { # Get tree model tree_dt <- lgb.model.dt.tree(model = model, num_iteration = num_iteration) # Check number of classes num_class <- model$.__enclos_env__$private$num_class # Get vector list tree_interpretation_dt_list <- vector(mode = "list", length = length(idxset)) # Get parsed predictions of data pred_mat <- t( model$predict( data = data[idxset, , drop = FALSE] , num_iteration = num_iteration , predleaf = TRUE ) ) leaf_index_dt <- data.table::as.data.table(x = pred_mat) leaf_index_mat_list <- lapply( X = leaf_index_dt , FUN = matrix , ncol = num_class , byrow = TRUE ) # Get list of trees tree_index_mat_list <- lapply( X = leaf_index_mat_list , FUN = function(x) { matrix(seq_along(x) - 1L, ncol = num_class, byrow = TRUE) } ) for (i in seq_along(idxset)) { tree_interpretation_dt_list[[i]] <- .single_row_interprete( tree_dt = tree_dt , num_class = num_class , tree_index_mat = tree_index_mat_list[[i]] , leaf_index_mat = leaf_index_mat_list[[i]] ) } return(tree_interpretation_dt_list) } #' @importFrom data.table data.table single.tree.interprete <- function(tree_dt, tree_id, leaf_id) { # Match tree id single_tree_dt <- tree_dt[tree_index == tree_id, ] # Get leaves leaf_dt <- single_tree_dt[leaf_index == leaf_id, .(leaf_index, leaf_parent, leaf_value)] # Get nodes node_dt <- single_tree_dt[!is.na(split_index), .(split_index, split_feature, node_parent, internal_value)] # Prepare sequences feature_seq <- character(0L) value_seq <- numeric(0L) # Get to root from leaf leaf_to_root <- function(parent_id, current_value) { value_seq <<- c(current_value, value_seq) if (!is.na(parent_id)) { # Not null means existing node this_node <- node_dt[split_index == parent_id, ] feature_seq <<- c(this_node[["split_feature"]], feature_seq) leaf_to_root( parent_id = this_node[["node_parent"]] , current_value = this_node[["internal_value"]] ) } } # Perform leaf to root conversion leaf_to_root( parent_id = leaf_dt[["leaf_parent"]] , current_value = leaf_dt[["leaf_value"]] ) return( data.table::data.table( Feature = feature_seq , Contribution = diff.default(value_seq) ) ) } #' @importFrom data.table := rbindlist setorder .multiple_tree_interprete <- function(tree_dt, tree_index, leaf_index) { interp_dt <- data.table::rbindlist( l = mapply( FUN = single.tree.interprete , tree_id = tree_index , leaf_id = leaf_index , MoreArgs = list( tree_dt = tree_dt ) , SIMPLIFY = FALSE , USE.NAMES = TRUE ) , use.names = TRUE ) interp_dt <- interp_dt[, .(Contribution = sum(Contribution)), by = "Feature"] # Sort features in descending order by contribution interp_dt[, abs_contribution := abs(Contribution)] data.table::setorder( x = interp_dt , -abs_contribution ) # Drop absolute value of contribution (only needed for sorting) interp_dt[, abs_contribution := NULL] return(interp_dt) } #' @importFrom data.table set setnames .single_row_interprete <- function(tree_dt, num_class, tree_index_mat, leaf_index_mat) { # Prepare vector list tree_interpretation <- vector(mode = "list", length = num_class) # Loop throughout each class for (i in seq_len(num_class)) { next_interp_dt <- .multiple_tree_interprete( tree_dt = tree_dt , tree_index = tree_index_mat[, i] , leaf_index = leaf_index_mat[, i] ) if (num_class > 1L) { data.table::setnames( x = next_interp_dt , old = "Contribution" , new = paste("Class", i - 1L) ) } tree_interpretation[[i]] <- next_interp_dt } if (num_class == 1L) { tree_interpretation_dt <- tree_interpretation[[1L]] } else { # Full interpretation elements tree_interpretation_dt <- Reduce( f = function(x, y) { merge(x, y, by = "Feature", all = TRUE) } , x = tree_interpretation ) # Loop throughout each tree for (j in 2L:ncol(tree_interpretation_dt)) { data.table::set( x = tree_interpretation_dt , i = which(is.na(tree_interpretation_dt[[j]])) , j = j , value = 0.0 ) } } return(tree_interpretation_dt) } ================================================ FILE: R-package/R/lgb.make_serializable.R ================================================ #' @name lgb.make_serializable #' @title Make a LightGBM object serializable by keeping raw bytes #' @description If a LightGBM model object was produced with argument `serializable=FALSE`, the R object will not #' be serializable (e.g. cannot save and load with \code{saveRDS} and \code{readRDS}) as it will lack the raw bytes #' needed to reconstruct its underlying C++ object. This function can be used to forcibly produce those serialized #' raw bytes and make the object serializable. Note that the object will be modified in-place. #' #' \emph{New in version 4.0.0} #' #' @param model \code{lgb.Booster} object which was produced with `serializable=FALSE`. #' #' @return \code{lgb.Booster} (the same `model` object that was passed as input, as invisible). #' @seealso \link{lgb.restore_handle}, \link{lgb.drop_serialized}. #' @export lgb.make_serializable <- function(model) { if (!.is_Booster(x = model)) { stop("lgb.make_serializable: model should be an ", sQuote("lgb.Booster", q = FALSE)) } model$save_raw() return(invisible(model)) } ================================================ FILE: R-package/R/lgb.model.dt.tree.R ================================================ #' @name lgb.model.dt.tree #' @title Parse a LightGBM model json dump #' @description Parse a LightGBM model json dump into a \code{data.table} structure. #' @param model object of class \code{lgb.Booster}. #' @param num_iteration Number of iterations to include. NULL or <= 0 means use best iteration. #' @param start_iteration Index (1-based) of the first boosting round to include in the output. #' For example, passing \code{start_iteration=5, num_iteration=3} for a regression model #' means "return information about the fifth, sixth, and seventh trees". #' #' \emph{New in version 4.4.0} #' #' @return #' A \code{data.table} with detailed information about model trees' nodes and leaves. #' #' The columns of the \code{data.table} are: #' #' \itemize{ #' \item{\code{tree_index}: ID of a tree in a model (integer)} #' \item{\code{split_index}: ID of a node in a tree (integer)} #' \item{\code{split_feature}: for a node, it's a feature name (character); #' for a leaf, it simply labels it as \code{"NA"}} #' \item{\code{node_parent}: ID of the parent node for current node (integer)} #' \item{\code{leaf_index}: ID of a leaf in a tree (integer)} #' \item{\code{leaf_parent}: ID of the parent node for current leaf (integer)} #' \item{\code{split_gain}: Split gain of a node} #' \item{\code{threshold}: Splitting threshold value of a node} #' \item{\code{decision_type}: Decision type of a node} #' \item{\code{default_left}: Determine how to handle NA value, TRUE -> Left, FALSE -> Right} #' \item{\code{internal_value}: Node value} #' \item{\code{internal_count}: The number of observation collected by a node} #' \item{\code{leaf_value}: Leaf value} #' \item{\code{leaf_count}: The number of observation collected by a leaf} #' } #' #' @examples #' \donttest{ #' \dontshow{setLGBMthreads(2L)} #' \dontshow{data.table::setDTthreads(1L)} #' data(agaricus.train, package = "lightgbm") #' train <- agaricus.train #' dtrain <- lgb.Dataset(train$data, label = train$label) #' #' params <- list( #' objective = "binary" #' , learning_rate = 0.01 #' , num_leaves = 63L #' , max_depth = -1L #' , min_data_in_leaf = 1L #' , min_sum_hessian_in_leaf = 1.0 #' , num_threads = 2L #' ) #' model <- lgb.train(params, dtrain, 10L) #' #' tree_dt <- lgb.model.dt.tree(model) #' } #' @importFrom data.table := rbindlist #' @importFrom jsonlite fromJSON #' @export lgb.model.dt.tree <- function( model, num_iteration = NULL, start_iteration = 1L ) { json_model <- lgb.dump( booster = model , num_iteration = num_iteration , start_iteration = start_iteration ) parsed_json_model <- jsonlite::fromJSON( txt = json_model , simplifyVector = TRUE , simplifyDataFrame = FALSE , simplifyMatrix = FALSE , flatten = FALSE ) # Parse tree model tree_list <- lapply( X = parsed_json_model$tree_info , FUN = .single_tree_parse ) # Combine into single data.table tree_dt <- data.table::rbindlist(l = tree_list, use.names = TRUE) # Substitute feature index with the actual feature name # Since the index comes from C++ (which is 0-indexed), be sure # to add 1 (e.g. index 28 means the 29th feature in feature_names) split_feature_indx <- tree_dt[, split_feature] + 1L # Get corresponding feature names. Positions in split_feature_indx # which are NA will result in an NA feature name feature_names <- parsed_json_model$feature_names[split_feature_indx] tree_dt[, split_feature := feature_names] return(tree_dt) } #' @importFrom data.table := data.table rbindlist .single_tree_parse <- function(lgb_tree) { tree_info_cols <- c( "split_index" , "split_feature" , "split_gain" , "threshold" , "decision_type" , "default_left" , "internal_value" , "internal_count" ) # Traverse tree function pre_order_traversal <- function(env = NULL, tree_node_leaf, current_depth = 0L, parent_index = NA_integer_) { if (is.null(env)) { # Setup initial default data.table with default types env <- new.env(parent = emptyenv()) env$single_tree_dt <- list() env$single_tree_dt[[1L]] <- data.table::data.table( tree_index = integer(0L) , depth = integer(0L) , split_index = integer(0L) , split_feature = integer(0L) , node_parent = integer(0L) , leaf_index = integer(0L) , leaf_parent = integer(0L) , split_gain = numeric(0L) , threshold = numeric(0L) , decision_type = character(0L) , default_left = character(0L) , internal_value = integer(0L) , internal_count = integer(0L) , leaf_value = integer(0L) , leaf_count = integer(0L) ) # start tree traversal pre_order_traversal( env = env , tree_node_leaf = tree_node_leaf , current_depth = current_depth , parent_index = parent_index ) } else { # Check if split index is not null in leaf if (!is.null(tree_node_leaf$split_index)) { # update data.table env$single_tree_dt[[length(env$single_tree_dt) + 1L]] <- c( tree_node_leaf[tree_info_cols] , list("depth" = current_depth, "node_parent" = parent_index) ) # Traverse tree again both left and right pre_order_traversal( env = env , tree_node_leaf = tree_node_leaf$left_child , current_depth = current_depth + 1L , parent_index = tree_node_leaf$split_index ) pre_order_traversal( env = env , tree_node_leaf = tree_node_leaf$right_child , current_depth = current_depth + 1L , parent_index = tree_node_leaf$split_index ) } else if (!is.null(tree_node_leaf$leaf_index)) { # update list env$single_tree_dt[[length(env$single_tree_dt) + 1L]] <- c( tree_node_leaf[c("leaf_index", "leaf_value", "leaf_count")] , list("depth" = current_depth, "leaf_parent" = parent_index) ) } } return(env$single_tree_dt) } # Traverse structure and rowbind everything single_tree_dt <- data.table::rbindlist( pre_order_traversal(tree_node_leaf = lgb_tree$tree_structure) , use.names = TRUE , fill = TRUE ) # Store index single_tree_dt[, tree_index := lgb_tree$tree_index] return(single_tree_dt) } ================================================ FILE: R-package/R/lgb.plot.importance.R ================================================ #' @name lgb.plot.importance #' @title Plot feature importance as a bar graph #' @description Plot previously calculated feature importance: Gain, Cover and Frequency, as a bar graph. #' @param tree_imp a \code{data.table} returned by \code{\link{lgb.importance}}. #' @param top_n maximal number of top features to include into the plot. #' @param measure the name of importance measure to plot, can be "Gain", "Cover" or "Frequency". #' @param left_margin (base R barplot) allows to adjust the left margin size to fit feature names. #' @param cex (base R barplot) passed as \code{cex.names} parameter to \code{\link[graphics]{barplot}}. #' Set a number smaller than 1.0 to make the bar labels smaller than R's default and values #' greater than 1.0 to make them larger. #' #' @details #' The graph represents each feature as a horizontal bar of length proportional to the defined importance of a feature. #' Features are shown ranked in a decreasing importance order. #' #' @return #' The \code{lgb.plot.importance} function creates a \code{barplot} #' and silently returns a processed data.table with \code{top_n} features sorted by defined importance. #' #' @examples #' \donttest{ #' \dontshow{setLGBMthreads(2L)} #' \dontshow{data.table::setDTthreads(1L)} #' data(agaricus.train, package = "lightgbm") #' train <- agaricus.train #' dtrain <- lgb.Dataset(train$data, label = train$label) #' #' params <- list( #' objective = "binary" #' , learning_rate = 0.1 #' , min_data_in_leaf = 1L #' , min_sum_hessian_in_leaf = 1.0 #' , num_threads = 2L #' ) #' #' model <- lgb.train( #' params = params #' , data = dtrain #' , nrounds = 5L #' ) #' #' tree_imp <- lgb.importance(model, percentage = TRUE) #' lgb.plot.importance(tree_imp, top_n = 5L, measure = "Gain") #' } #' @importFrom graphics barplot par #' @export lgb.plot.importance <- function(tree_imp, top_n = 10L, measure = "Gain", left_margin = 10L, cex = NULL ) { # Check for measurement (column names) correctness measure <- match.arg( measure , choices = c("Gain", "Cover", "Frequency") , several.ok = FALSE ) # Get top N importance (defaults to 10) top_n <- min(top_n, nrow(tree_imp)) # Parse importance tree_imp <- tree_imp[order(abs(get(measure)), decreasing = TRUE), ][seq_len(top_n), ] # Attempt to setup a correct cex if (is.null(cex)) { cex <- 2.5 / log2(1.0 + top_n) } # Refresh plot op <- graphics::par(no.readonly = TRUE) on.exit(graphics::par(op)) graphics::par( mar = c( op$mar[1L] , left_margin , op$mar[3L] , op$mar[4L] ) ) tree_imp[rev(seq_len(.N)), graphics::barplot( height = get(measure) , names.arg = Feature , horiz = TRUE , border = NA , main = "Feature Importance" , xlab = measure , cex.names = cex , las = 1L )] return(invisible(tree_imp)) } ================================================ FILE: R-package/R/lgb.plot.interpretation.R ================================================ #' @name lgb.plot.interpretation #' @title Plot feature contribution as a bar graph #' @description Plot previously calculated feature contribution as a bar graph. #' @param tree_interpretation_dt a \code{data.table} returned by \code{\link{lgb.interprete}}. #' @param top_n maximal number of top features to include into the plot. #' @param cols the column numbers of layout, will be used only for multiclass classification feature contribution. #' @param left_margin (base R barplot) allows to adjust the left margin size to fit feature names. #' @param cex (base R barplot) passed as \code{cex.names} parameter to \code{barplot}. #' #' @details #' The graph represents each feature as a horizontal bar of length proportional to the defined #' contribution of a feature. Features are shown ranked in a decreasing contribution order. #' #' @return #' The \code{lgb.plot.interpretation} function creates a \code{barplot}. #' #' @examples #' \donttest{ #' \dontshow{setLGBMthreads(2L)} #' \dontshow{data.table::setDTthreads(1L)} #' Logit <- function(x) { #' log(x / (1.0 - x)) #' } #' data(agaricus.train, package = "lightgbm") #' labels <- agaricus.train$label #' dtrain <- lgb.Dataset( #' agaricus.train$data #' , label = labels #' ) #' set_field( #' dataset = dtrain #' , field_name = "init_score" #' , data = rep(Logit(mean(labels)), length(labels)) #' ) #' #' data(agaricus.test, package = "lightgbm") #' #' params <- list( #' objective = "binary" #' , learning_rate = 0.1 #' , max_depth = -1L #' , min_data_in_leaf = 1L #' , min_sum_hessian_in_leaf = 1.0 #' , num_threads = 2L #' ) #' model <- lgb.train( #' params = params #' , data = dtrain #' , nrounds = 5L #' ) #' #' tree_interpretation <- lgb.interprete( #' model = model #' , data = agaricus.test$data #' , idxset = 1L:5L #' ) #' lgb.plot.interpretation( #' tree_interpretation_dt = tree_interpretation[[1L]] #' , top_n = 3L #' ) #' } #' @importFrom data.table setnames #' @importFrom graphics barplot par #' @export lgb.plot.interpretation <- function(tree_interpretation_dt, top_n = 10L, cols = 1L, left_margin = 10L, cex = NULL) { num_class <- ncol(tree_interpretation_dt) - 1L # Refresh plot op <- graphics::par(no.readonly = TRUE) on.exit(graphics::par(op)) # Do some magic plotting bottom_margin <- 3.0 top_margin <- 2.0 right_margin <- op$mar[4L] graphics::par( mar = c( bottom_margin , left_margin , top_margin , right_margin ) ) if (num_class == 1L) { # Only one class, plot straight away .multiple_tree_plot_interpretation( tree_interpretation = tree_interpretation_dt , top_n = top_n , title = NULL , cex = cex ) } else { # More than one class, shape data first layout_mat <- matrix( seq.int(to = cols * ceiling(num_class / cols)) , ncol = cols , nrow = ceiling(num_class / cols) ) # Shape output graphics::par(mfcol = c(nrow(layout_mat), ncol(layout_mat))) # Loop throughout all classes for (i in seq_len(num_class)) { # Prepare interpretation, perform T, get the names, and plot straight away plot_dt <- tree_interpretation_dt[, c(1L, i + 1L), with = FALSE] data.table::setnames( x = plot_dt , old = names(plot_dt) , new = c("Feature", "Contribution") ) .multiple_tree_plot_interpretation( tree_interpretation = plot_dt , top_n = top_n , title = paste("Class", i - 1L) , cex = cex ) } } return(invisible(NULL)) } #' @importFrom graphics barplot .multiple_tree_plot_interpretation <- function(tree_interpretation, top_n, title, cex) { # Parse tree tree_interpretation <- tree_interpretation[order(abs(Contribution), decreasing = TRUE), ][seq_len(min(top_n, .N)), ] # Attempt to setup a correct cex if (is.null(cex)) { cex <- 2.5 / log2(1.0 + top_n) } # create plot tree_interpretation[abs(Contribution) > 0.0, bar_color := "firebrick"] tree_interpretation[Contribution == 0.0, bar_color := "steelblue"] tree_interpretation[rev(seq_len(.N)), graphics::barplot( height = Contribution , names.arg = Feature , horiz = TRUE , col = bar_color , border = NA , main = title , cex.names = cex , las = 1L )] return(invisible(NULL)) } ================================================ FILE: R-package/R/lgb.restore_handle.R ================================================ #' @name lgb.restore_handle #' @title Restore the C++ component of a de-serialized LightGBM model #' @description After a LightGBM model object is de-serialized through functions such as \code{save} or #' \code{saveRDS}, its underlying C++ object will be blank and needs to be restored to able to use it. Such #' object is restored automatically when calling functions such as \code{predict}, but this function can be #' used to forcibly restore it beforehand. Note that the object will be modified in-place. #' #' \emph{New in version 4.0.0} #' #' @details Be aware that fast single-row prediction configurations are not restored through this #' function. If you wish to make fast single-row predictions using a \code{lgb.Booster} loaded this way, #' call \link{lgb.configure_fast_predict} on the loaded \code{lgb.Booster} object. #' @param model \code{lgb.Booster} object which was de-serialized and whose underlying C++ object and R handle #' need to be restored. #' #' @return \code{lgb.Booster} (the same `model` object that was passed as input, invisibly). #' @seealso \link{lgb.make_serializable}, \link{lgb.drop_serialized}. #' @examples #' \donttest{ #' library(lightgbm) #' \dontshow{setLGBMthreads(2L)} #' \dontshow{data.table::setDTthreads(1L)} #' data("agaricus.train") #' model <- lightgbm( #' agaricus.train$data #' , agaricus.train$label #' , params = list(objective = "binary") #' , nrounds = 5L #' , verbose = 0 #' , num_threads = 2L #' ) #' fname <- tempfile(fileext="rds") #' saveRDS(model, fname) #' #' model_new <- readRDS(fname) #' model_new$check_null_handle() #' lgb.restore_handle(model_new) #' model_new$check_null_handle() #' } #' @export lgb.restore_handle <- function(model) { if (!.is_Booster(x = model)) { stop("lgb.restore_handle: model should be an ", sQuote("lgb.Booster", q = FALSE)) } model$restore_handle() return(invisible(model)) } ================================================ FILE: R-package/R/lgb.train.R ================================================ #' @name lgb.train #' @title Main training logic for LightGBM #' @description Low-level R interface to train a LightGBM model. Unlike \code{\link{lightgbm}}, #' this function is focused on performance (e.g. speed, memory efficiency). It is also #' less likely to have breaking API changes in new releases than \code{\link{lightgbm}}. #' @inheritParams lgb_shared_params #' @param valids a list of \code{lgb.Dataset} objects, used for validation #' @param record Boolean, TRUE will record iteration message to \code{booster$record_evals} #' @param callbacks List of callback functions that are applied at each iteration. #' @param reset_data Boolean, setting it to TRUE (not the default value) will transform the #' booster model into a predictor model which frees up memory and the #' original datasets #' @inheritSection lgb_shared_params Early Stopping #' @return a trained booster model \code{lgb.Booster}. #' #' @examples #' \donttest{ #' \dontshow{setLGBMthreads(2L)} #' \dontshow{data.table::setDTthreads(1L)} #' data(agaricus.train, package = "lightgbm") #' train <- agaricus.train #' dtrain <- lgb.Dataset(train$data, label = train$label) #' data(agaricus.test, package = "lightgbm") #' test <- agaricus.test #' dtest <- lgb.Dataset.create.valid(dtrain, test$data, label = test$label) #' params <- list( #' objective = "regression" #' , metric = "l2" #' , min_data = 1L #' , learning_rate = 1.0 #' , num_threads = 2L #' ) #' valids <- list(test = dtest) #' model <- lgb.train( #' params = params #' , data = dtrain #' , nrounds = 5L #' , valids = valids #' , early_stopping_rounds = 3L #' ) #' } #' #' @export lgb.train <- function(params = list(), data, nrounds = 100L, valids = list(), obj = NULL, eval = NULL, verbose = 1L, record = TRUE, eval_freq = 1L, init_model = NULL, early_stopping_rounds = NULL, callbacks = list(), reset_data = FALSE, serializable = TRUE) { # validate inputs early to avoid unnecessary computation if (nrounds <= 0L) { stop("nrounds should be greater than zero") } if (!.is_Dataset(x = data)) { stop("lgb.train: data must be an lgb.Dataset instance") } if (length(valids) > 0L) { if (!identical(class(valids), "list") || !all(vapply(valids, .is_Dataset, logical(1L)))) { stop("lgb.train: valids must be a list of lgb.Dataset elements") } evnames <- names(valids) if (is.null(evnames) || !all(nzchar(evnames))) { stop("lgb.train: each element of valids must have a name") } } # set some parameters, resolving the way they were passed in with other parameters # in `params`. # this ensures that the model stored with Booster$save() correctly represents # what was passed in params <- .check_wrapper_param( main_param_name = "verbosity" , params = params , alternative_kwarg_value = verbose ) params <- .check_wrapper_param( main_param_name = "num_iterations" , params = params , alternative_kwarg_value = nrounds ) params <- .check_wrapper_param( main_param_name = "metric" , params = params , alternative_kwarg_value = NULL ) params <- .check_wrapper_param( main_param_name = "objective" , params = params , alternative_kwarg_value = obj ) params <- .check_wrapper_param( main_param_name = "early_stopping_round" , params = params , alternative_kwarg_value = early_stopping_rounds ) early_stopping_rounds <- params[["early_stopping_round"]] # extract any function objects passed for objective or metric fobj <- NULL if (is.function(params$objective)) { fobj <- params$objective params$objective <- "none" } # If eval is a single function, store it as a 1-element list # (for backwards compatibility). If it is a list of functions, store # all of them. This makes it possible to pass any mix of strings like "auc" # and custom functions to eval params <- .check_eval(params = params, eval = eval) eval_functions <- list(NULL) if (is.function(eval)) { eval_functions <- list(eval) } if (methods::is(eval, "list")) { eval_functions <- Filter( f = is.function , x = eval ) } # Init predictor to empty predictor <- NULL # Check for boosting from a trained model if (is.character(init_model)) { predictor <- Predictor$new(modelfile = init_model) } else if (.is_Booster(x = init_model)) { predictor <- init_model$to_predictor() } # Set the iteration to start from / end to (and check for boosting from a trained model, again) begin_iteration <- 1L if (!is.null(predictor)) { begin_iteration <- predictor$current_iter() + 1L } end_iteration <- begin_iteration + params[["num_iterations"]] - 1L # pop interaction_constraints off of params. It needs some preprocessing on the # R side before being passed into the Dataset object interaction_constraints <- params[["interaction_constraints"]] params["interaction_constraints"] <- NULL # Construct datasets, if needed data$update_params(params = params) data$construct() # Check interaction constraints params[["interaction_constraints"]] <- .check_interaction_constraints( interaction_constraints = interaction_constraints , column_names = data$get_colnames() ) # Update parameters with parsed parameters data$update_params(params) # Create the predictor set data$.__enclos_env__$private$set_predictor(predictor) valid_contain_train <- FALSE train_data_name <- "train" reduced_valid_sets <- list() # Parse validation datasets if (length(valids) > 0L) { for (key in names(valids)) { # Use names to get validation datasets valid_data <- valids[[key]] # Check for duplicate train/validation dataset if (identical(data, valid_data)) { valid_contain_train <- TRUE train_data_name <- key next } # Update parameters, data valid_data$update_params(params) valid_data$set_reference(data) reduced_valid_sets[[key]] <- valid_data } } # Add printing log callback if (params[["verbosity"]] > 0L && eval_freq > 0L) { callbacks <- .add_cb( cb_list = callbacks , cb = cb_print_evaluation(period = eval_freq) ) } # Add evaluation log callback if (record && length(valids) > 0L) { callbacks <- .add_cb( cb_list = callbacks , cb = cb_record_evaluation() ) } # Did user pass parameters that indicate they want to use early stopping? using_early_stopping <- !is.null(early_stopping_rounds) && early_stopping_rounds > 0L boosting_param_names <- .PARAMETER_ALIASES()[["boosting"]] using_dart <- any( sapply( X = boosting_param_names , FUN = function(param) { identical(params[[param]], "dart") } ) ) # Cannot use early stopping with 'dart' boosting if (using_dart) { if (using_early_stopping) { warning("Early stopping is not available in 'dart' mode.") } using_early_stopping <- FALSE # Remove the cb_early_stop() function if it was passed in to callbacks callbacks <- Filter( f = function(cb_func) { !identical(attr(cb_func, "name"), "cb_early_stop") } , x = callbacks ) } # If user supplied early_stopping_rounds, add the early stopping callback if (using_early_stopping) { callbacks <- .add_cb( cb_list = callbacks , cb = cb_early_stop( stopping_rounds = early_stopping_rounds , first_metric_only = isTRUE(params[["first_metric_only"]]) , verbose = params[["verbosity"]] > 0L ) ) } cb <- .categorize_callbacks(cb_list = callbacks) # Construct booster with datasets booster <- Booster$new(params = params, train_set = data) if (valid_contain_train) { booster$set_train_data_name(name = train_data_name) } for (key in names(reduced_valid_sets)) { booster$add_valid(data = reduced_valid_sets[[key]], name = key) } # Callback env env <- CB_ENV$new() env$model <- booster env$begin_iteration <- begin_iteration env$end_iteration <- end_iteration # Start training model using number of iterations to start and end with for (i in seq.int(from = begin_iteration, to = end_iteration)) { # Overwrite iteration in environment env$iteration <- i env$eval_list <- list() # Loop through "pre_iter" element for (f in cb$pre_iter) { f(env) } # Update one boosting iteration booster$update(fobj = fobj) # Prepare collection of evaluation results eval_list <- list() # Collection: Has validation dataset? if (length(valids) > 0L) { # Get evaluation results with passed-in functions for (eval_function in eval_functions) { # Validation has training dataset? if (valid_contain_train) { eval_list <- append(eval_list, booster$eval_train(feval = eval_function)) } eval_list <- append(eval_list, booster$eval_valid(feval = eval_function)) } # Calling booster$eval_valid() will get # evaluation results with the metrics in params$metric by calling LGBM_BoosterGetEval_R", # so need to be sure that gets called, which it wouldn't be above if no functions # were passed in if (length(eval_functions) == 0L) { if (valid_contain_train) { eval_list <- append(eval_list, booster$eval_train(feval = eval_function)) } eval_list <- append(eval_list, booster$eval_valid(feval = eval_function)) } } # Write evaluation result in environment env$eval_list <- eval_list # Loop through env for (f in cb$post_iter) { f(env) } # Check for early stopping and break if needed if (env$met_early_stop) break } # check if any valids were given other than the training data non_train_valid_names <- names(valids)[!(names(valids) == train_data_name)] first_valid_name <- non_train_valid_names[1L] # When early stopping is not activated, we compute the best iteration / score ourselves by # selecting the first metric and the first dataset if (record && length(non_train_valid_names) > 0L && is.na(env$best_score)) { # when using a custom eval function, the metric name is returned from the # function, so figure it out from record_evals if (!is.null(eval_functions[1L])) { first_metric <- names(booster$record_evals[[first_valid_name]])[1L] } else { first_metric <- booster$.__enclos_env__$private$eval_names[1L] } .find_best <- which.min if (isTRUE(env$eval_list[[1L]]$higher_better[1L])) { .find_best <- which.max } booster$best_iter <- unname( .find_best( unlist( booster$record_evals[[first_valid_name]][[first_metric]][[.EVAL_KEY()]] ) ) ) booster$best_score <- booster$record_evals[[first_valid_name]][[first_metric]][[.EVAL_KEY()]][[booster$best_iter]] } # Check for booster model conversion to predictor model if (reset_data) { # Store temporarily model data elsewhere booster_old <- list( best_iter = booster$best_iter , best_score = booster$best_score , record_evals = booster$record_evals ) # Reload model booster <- lgb.load(model_str = booster$save_model_to_string()) booster$best_iter <- booster_old$best_iter booster$best_score <- booster_old$best_score booster$record_evals <- booster_old$record_evals } if (serializable) { booster$save_raw() } return(booster) } ================================================ FILE: R-package/R/lightgbm.R ================================================ #' @name lgb_shared_params #' @title Shared parameter docs #' @description Parameter docs shared by \code{lgb.train}, \code{lgb.cv}, and \code{lightgbm} #' @param callbacks List of callback functions that are applied at each iteration. #' @param data a \code{lgb.Dataset} object, used for training. Some functions, such as \code{\link{lgb.cv}}, #' may allow you to pass other types of data like \code{matrix} and then separately supply #' \code{label} as a keyword argument. #' @param early_stopping_rounds int. Activates early stopping. When this parameter is non-null, #' training will stop if the evaluation of any metric on any validation set #' fails to improve for \code{early_stopping_rounds} consecutive boosting rounds. #' If training stops early, the returned model will have attribute \code{best_iter} #' set to the iteration number of the best iteration. #' @param eval evaluation function(s). This can be a character vector, function, or list with a mixture of #' strings and functions. #' #' \itemize{ #' \item{\bold{a. character vector}: #' If you provide a character vector to this argument, it should contain strings with valid #' evaluation metrics. #' See \href{https://lightgbm.readthedocs.io/en/latest/Parameters.html#metric}{ #' The "metric" section of the documentation} #' for a list of valid metrics. #' } #' \item{\bold{b. function}: #' You can provide a custom evaluation function. This #' should accept the keyword arguments \code{preds} and \code{dtrain} and should return a named #' list with three elements: #' \itemize{ #' \item{\code{name}: A string with the name of the metric, used for printing #' and storing results. #' } #' \item{\code{value}: A single number indicating the value of the metric for the #' given predictions and true values #' } #' \item{ #' \code{higher_better}: A boolean indicating whether higher values indicate a better fit. #' For example, this would be \code{FALSE} for metrics like MAE or RMSE. #' } #' } #' } #' \item{\bold{c. list}: #' If a list is given, it should only contain character vectors and functions. #' These should follow the requirements from the descriptions above. #' } #' } #' @param eval_freq evaluation output frequency, only effective when verbose > 0 and \code{valids} has been provided #' @param init_model path of model file or \code{lgb.Booster} object, will continue training from this model #' @param nrounds number of training rounds #' @param obj objective function, can be character or custom objective function. Examples include #' \code{regression}, \code{regression_l1}, \code{huber}, #' \code{binary}, \code{lambdarank}, \code{multiclass}, \code{multiclass} #' @param params a list of parameters. See \href{https://lightgbm.readthedocs.io/en/latest/Parameters.html}{ #' the "Parameters" section of the documentation} for a list of parameters and valid values. #' @param verbose verbosity for output, if <= 0 and \code{valids} has been provided, also will disable the #' printing of evaluation during training #' @param serializable whether to make the resulting objects serializable through functions such as #' \code{save} or \code{saveRDS} (see section "Model serialization"). #' @section Early Stopping: #' #' "early stopping" refers to stopping the training process if the model's performance on a given #' validation set does not improve for several consecutive iterations. #' #' If multiple arguments are given to \code{eval}, their order will be preserved. If you enable #' early stopping by setting \code{early_stopping_rounds} in \code{params}, by default all #' metrics will be considered for early stopping. #' #' If you want to only consider the first metric for early stopping, pass #' \code{first_metric_only = TRUE} in \code{params}. Note that if you also specify \code{metric} #' in \code{params}, that metric will be considered the "first" one. If you omit \code{metric}, #' a default metric will be used based on your choice for the parameter \code{obj} (keyword argument) #' or \code{objective} (passed into \code{params}). #' #' \bold{NOTE:} if using \code{boosting_type="dart"}, any early stopping configuration will be ignored #' and early stopping will not be performed. #' @section Model serialization: #' #' LightGBM model objects can be serialized and de-serialized through functions such as \code{save} #' or \code{saveRDS}, but similarly to libraries such as 'xgboost', serialization works a bit differently #' from typical R objects. In order to make models serializable in R, a copy of the underlying C++ object #' as serialized raw bytes is produced and stored in the R model object, and when this R object is #' de-serialized, the underlying C++ model object gets reconstructed from these raw bytes, but will only #' do so once some function that uses it is called, such as \code{predict}. In order to forcibly #' reconstruct the C++ object after deserialization (e.g. after calling \code{readRDS} or similar), one #' can use the function \link{lgb.restore_handle} (for example, if one makes predictions in parallel or in #' forked processes, it will be faster to restore the handle beforehand). #' #' Producing and keeping these raw bytes however uses extra memory, and if they are not required, #' it is possible to avoid producing them by passing `serializable=FALSE`. In such cases, these raw #' bytes can be added to the model on demand through function \link{lgb.make_serializable}. #' #' \emph{New in version 4.0.0} #' #' @keywords internal NULL #' @name lightgbm #' @title Train a LightGBM model #' @description High-level R interface to train a LightGBM model. Unlike \code{\link{lgb.train}}, this function #' is focused on compatibility with other statistics and machine learning interfaces in R. #' This focus on compatibility means that this interface may experience more frequent breaking API changes #' than \code{\link{lgb.train}}. #' For efficiency-sensitive applications, or for applications where breaking API changes across releases #' is very expensive, use \code{\link{lgb.train}}. #' @inheritParams lgb_shared_params #' @param label Vector of labels, used if \code{data} is not an \code{\link{lgb.Dataset}} #' @param weights Sample / observation weights for rows in the input data. If \code{NULL}, will assume that all #' observations / rows have the same importance / weight. #' #' \emph{Changed from 'weight', in version 4.0.0} #' #' @param objective Optimization objective (e.g. `"regression"`, `"binary"`, etc.). #' For a list of accepted objectives, see #' \href{https://lightgbm.readthedocs.io/en/latest/Parameters.html#objective}{ #' the "objective" item of the "Parameters" section of the documentation}. #' #' If passing \code{"auto"} and \code{data} is not of type \code{lgb.Dataset}, the objective will #' be determined according to what is passed for \code{label}:\itemize{ #' \item If passing a factor with two variables, will use objective \code{"binary"}. #' \item If passing a factor with more than two variables, will use objective \code{"multiclass"} #' (note that parameter \code{num_class} in this case will also be determined automatically from #' \code{label}). #' \item Otherwise (or if passing \code{lgb.Dataset} as input), will use objective \code{"regression"}. #' } #' #' \emph{New in version 4.0.0} #' #' @param init_score initial score is the base prediction lightgbm will boost from #' #' \emph{New in version 4.0.0} #' #' @param num_threads Number of parallel threads to use. For best speed, this should be set to the number of #' physical cores in the CPU - in a typical x86-64 machine, this corresponds to half the #' number of maximum threads. #' #' Be aware that using too many threads can result in speed degradation in smaller datasets #' (see the parameters documentation for more details). #' #' If passing zero, will use the default number of threads configured for OpenMP #' (typically controlled through an environment variable \code{OMP_NUM_THREADS}). #' #' If passing \code{NULL} (the default), will try to use the number of physical cores in the #' system, but be aware that getting the number of cores detected correctly requires package #' \code{RhpcBLASctl} to be installed. #' #' This parameter gets overridden by \code{num_threads} and its aliases under \code{params} #' if passed there. #' #' \emph{New in version 4.0.0} #' #' @param colnames Character vector of features. Only used if \code{data} is not an \code{\link{lgb.Dataset}}. #' @param categorical_feature categorical features. This can either be a character vector of feature #' names or an integer vector with the indices of the features (e.g. #' \code{c(1L, 10L)} to say "the first and tenth columns"). #' Only used if \code{data} is not an \code{\link{lgb.Dataset}}. #' #' @param ... Additional arguments passed to \code{\link{lgb.train}}. For example #' \itemize{ #' \item{\code{valids}: a list of \code{lgb.Dataset} objects, used for validation} #' \item{\code{obj}: objective function, can be character or custom objective function. Examples include #' \code{regression}, \code{regression_l1}, \code{huber}, #' \code{binary}, \code{lambdarank}, \code{multiclass}, \code{multiclass}} #' \item{\code{eval}: evaluation function, can be (a list of) character or custom eval function} #' \item{\code{record}: Boolean, TRUE will record iteration message to \code{booster$record_evals}} #' \item{\code{reset_data}: Boolean, setting it to TRUE (not the default value) will transform the booster model #' into a predictor model which frees up memory and the original datasets} #' } #' @inheritSection lgb_shared_params Early Stopping #' @return a trained \code{lgb.Booster} #' @export lightgbm <- function(data, label = NULL, weights = NULL, params = list(), nrounds = 100L, verbose = 1L, eval_freq = 1L, early_stopping_rounds = NULL, init_model = NULL, callbacks = list(), serializable = TRUE, objective = "auto", init_score = NULL, num_threads = NULL, colnames = NULL, categorical_feature = NULL, ...) { # validate inputs early to avoid unnecessary computation if (nrounds <= 0L) { stop("nrounds should be greater than zero") } if (is.null(num_threads)) { num_threads <- .get_default_num_threads() } params <- .check_wrapper_param( main_param_name = "num_threads" , params = params , alternative_kwarg_value = num_threads ) params <- .check_wrapper_param( main_param_name = "verbosity" , params = params , alternative_kwarg_value = verbose ) # Process factors as labels and auto-determine objective if (!.is_Dataset(data)) { data_processor <- DataProcessor$new() temp <- data_processor$process_label( label = label , objective = objective , params = params ) label <- temp$label objective <- temp$objective params <- temp$params rm(temp) } else { data_processor <- NULL if (objective == "auto") { objective <- "regression" } } # Set data to a temporary variable dtrain <- data # Check whether data is lgb.Dataset, if not then create lgb.Dataset manually if (!.is_Dataset(x = dtrain)) { dtrain <- lgb.Dataset( data = data , label = label , weight = weights , init_score = init_score , categorical_feature = categorical_feature , colnames = colnames ) } train_args <- list( "params" = params , "data" = dtrain , "nrounds" = nrounds , "obj" = objective , "verbose" = params[["verbosity"]] , "eval_freq" = eval_freq , "early_stopping_rounds" = early_stopping_rounds , "init_model" = init_model , "callbacks" = callbacks , "serializable" = serializable ) train_args <- append(train_args, list(...)) if (! "valids" %in% names(train_args)) { train_args[["valids"]] <- list() } # Train a model using the regular way bst <- do.call( what = lgb.train , args = train_args ) bst$data_processor <- data_processor return(bst) } #' @name agaricus.train #' @title Training part from Mushroom Data Set #' @description This data set is originally from the Mushroom data set, #' UCI Machine Learning Repository. #' This data set includes the following fields: #' #' \itemize{ #' \item{\code{label}: the label for each record} #' \item{\code{data}: a sparse Matrix of \code{dgCMatrix} class, with 126 columns.} #' } #' #' @references #' https://archive.ics.uci.edu/ml/datasets/Mushroom #' #' Bache, K. & Lichman, M. (2013). UCI Machine Learning Repository #' [https://archive.ics.uci.edu/ml]. Irvine, CA: University of California, #' School of Information and Computer Science. #' #' @docType data #' @keywords datasets #' @usage data(agaricus.train) #' @format A list containing a label vector, and a dgCMatrix object with 6513 #' rows and 127 variables NULL #' @name agaricus.test #' @title Test part from Mushroom Data Set #' @description This data set is originally from the Mushroom data set, #' UCI Machine Learning Repository. #' This data set includes the following fields: #' #' \itemize{ #' \item{\code{label}: the label for each record} #' \item{\code{data}: a sparse Matrix of \code{dgCMatrix} class, with 126 columns.} #' } #' @references #' https://archive.ics.uci.edu/ml/datasets/Mushroom #' #' Bache, K. & Lichman, M. (2013). UCI Machine Learning Repository #' [https://archive.ics.uci.edu/ml]. Irvine, CA: University of California, #' School of Information and Computer Science. #' #' @docType data #' @keywords datasets #' @usage data(agaricus.test) #' @format A list containing a label vector, and a dgCMatrix object with 1611 #' rows and 126 variables NULL #' @name bank #' @title Bank Marketing Data Set #' @description This data set is originally from the Bank Marketing data set, #' UCI Machine Learning Repository. #' #' It contains only the following: bank.csv with 10% of the examples and 17 inputs, #' randomly selected from 3 (older version of this dataset with less inputs). #' #' @references #' https://archive.ics.uci.edu/ml/datasets/Bank+Marketing #' #' S. Moro, P. Cortez and P. Rita. (2014) #' A Data-Driven Approach to Predict the Success of Bank Telemarketing. Decision Support Systems #' #' @docType data #' @keywords datasets #' @usage data(bank) #' @format A data.table with 4521 rows and 17 variables NULL # Various imports #' @import methods #' @importFrom Matrix Matrix #' @importFrom R6 R6Class #' @useDynLib lightgbm , .registration = TRUE NULL # Suppress false positive warnings from R CMD CHECK about # "unrecognized global variable" globalVariables(c( "." , ".N" , ".SD" , "abs_contribution" , "bar_color" , "Contribution" , "Cover" , "Feature" , "Frequency" , "Gain" , "internal_count" , "internal_value" , "leaf_index" , "leaf_parent" , "leaf_value" , "node_parent" , "split_feature" , "split_gain" , "split_index" , "tree_index" )) ================================================ FILE: R-package/R/metrics.R ================================================ # [description] List of metrics known to LightGBM. The most up to date list can be found # at https://lightgbm.readthedocs.io/en/latest/Parameters.html#metric-parameters # # [return] A named logical vector, where each key is a metric name and each value is a boolean. # TRUE if higher values of the metric are desirable, FALSE if lower values are desirable. # Note that only the 'main' metrics are stored here, not aliases, since only the 'main' metrics # are returned from the C++ side. For example, if you use `metric = "mse"` in your code, # the metric name `"l2"` will be returned. .METRICS_HIGHER_BETTER <- function() { return( c( "l1" = FALSE , "l2" = FALSE , "mape" = FALSE , "rmse" = FALSE , "quantile" = FALSE , "huber" = FALSE , "fair" = FALSE , "poisson" = FALSE , "gamma" = FALSE , "gamma_deviance" = FALSE , "tweedie" = FALSE , "ndcg" = TRUE , "map" = TRUE , "auc" = TRUE , "average_precision" = TRUE , "r2" = TRUE , "binary_logloss" = FALSE , "binary_error" = FALSE , "auc_mu" = TRUE , "multi_logloss" = FALSE , "multi_error" = FALSE , "cross_entropy" = FALSE , "cross_entropy_lambda" = FALSE , "kullback_leibler" = FALSE ) ) } ================================================ FILE: R-package/R/multithreading.R ================================================ #' @name setLGBMThreads #' @title Set maximum number of threads used by LightGBM #' @description LightGBM attempts to speed up many operations by using multi-threading. #' The number of threads used in those operations can be controlled via the #' \code{num_threads} parameter passed through \code{params} to functions like #' \link{lgb.train} and \link{lgb.Dataset}. However, some operations (like materializing #' a model from a text file) are done via code paths that don't explicitly accept thread-control #' configuration. #' #' Use this function to set the maximum number of threads LightGBM will use for such operations. #' #' This function affects all LightGBM operations in the same process. #' #' So, for example, if you call \code{setLGBMthreads(4)}, no other multi-threaded LightGBM #' operation in the same process will use more than 4 threads. #' #' Call \code{setLGBMthreads(-1)} to remove this limitation. #' @param num_threads maximum number of threads to be used by LightGBM in multi-threaded operations #' @return NULL #' @seealso \link{getLGBMthreads} #' @export setLGBMthreads <- function(num_threads) { .Call( LGBM_SetMaxThreads_R, num_threads ) return(invisible(NULL)) } #' @name getLGBMThreads #' @title Get default number of threads used by LightGBM #' @description LightGBM attempts to speed up many operations by using multi-threading. #' The number of threads used in those operations can be controlled via the #' \code{num_threads} parameter passed through \code{params} to functions like #' \link{lgb.train} and \link{lgb.Dataset}. However, some operations (like materializing #' a model from a text file) are done via code paths that don't explicitly accept thread-control #' configuration. #' #' Use this function to see the default number of threads LightGBM will use for such operations. #' @return number of threads as an integer. \code{-1} means that in situations where parameter \code{num_threads} is #' not explicitly supplied, LightGBM will choose a number of threads to use automatically. #' @seealso \link{setLGBMthreads} #' @export getLGBMthreads <- function() { out <- 0L .Call( LGBM_GetMaxThreads_R, out ) return(out) } ================================================ FILE: R-package/R/utils.R ================================================ .is_Booster <- function(x) { return(all(c("R6", "lgb.Booster") %in% class(x))) # nolint: class_equals. } .is_Dataset <- function(x) { return(all(c("R6", "lgb.Dataset") %in% class(x))) # nolint: class_equals. } .is_Predictor <- function(x) { return(all(c("R6", "lgb.Predictor") %in% class(x))) # nolint: class_equals. } .is_null_handle <- function(x) { if (is.null(x)) { return(TRUE) } return( isTRUE(.Call(LGBM_HandleIsNull_R, x)) ) } .params2str <- function(params) { if (!identical(class(params), "list")) { stop("params must be a list") } names(params) <- gsub(".", "_", names(params), fixed = TRUE) param_names <- names(params) ret <- list() # Perform key value join for (i in seq_along(params)) { # If a parameter has multiple values, join those values together with commas. # trimws() is necessary because format() will pad to make strings the same width val <- paste( trimws( format( x = unname(params[[i]]) , scientific = FALSE ) ) , collapse = "," ) if (nchar(val) <= 0L) next # Skip join # Join key value pair <- paste(c(param_names[[i]], val), collapse = "=") ret <- c(ret, pair) } if (length(ret) == 0L) { return("") } return(paste(ret, collapse = " ")) } # [description] # # Besides applying checks, this function # # 1. turns feature *names* into 1-based integer positions, then # 2. adds an extra list element with skipped features, then # 3. turns 1-based integer positions into 0-based positions, and finally # 4. collapses the values of each list element into a string like "[0, 1]". # .check_interaction_constraints <- function(interaction_constraints, column_names) { if (is.null(interaction_constraints)) { return(list()) } if (!identical(class(interaction_constraints), "list")) { stop("interaction_constraints must be a list") } column_indices <- seq_along(column_names) # Convert feature names to 1-based integer positions and apply checks for (j in seq_along(interaction_constraints)) { constraint <- interaction_constraints[[j]] if (is.character(constraint)) { constraint_indices <- match(constraint, column_names) } else if (is.numeric(constraint)) { constraint_indices <- as.integer(constraint) } else { stop("every element in interaction_constraints must be a character vector or numeric vector") } # Features outside range? bad <- !(constraint_indices %in% column_indices) if (any(bad)) { stop( "unknown feature(s) in interaction_constraints: " , toString(sQuote(constraint[bad], q = FALSE)) ) } interaction_constraints[[j]] <- constraint_indices } # Add missing features as new interaction set remaining_indices <- setdiff( column_indices, sort(unique(unlist(interaction_constraints))) ) if (length(remaining_indices) > 0L) { interaction_constraints <- c( interaction_constraints, list(remaining_indices) ) } # Turn indices 0-based and convert to string for (j in seq_along(interaction_constraints)) { interaction_constraints[[j]] <- paste0( "[", paste(interaction_constraints[[j]] - 1L, collapse = ","), "]" ) } return(interaction_constraints) } # [description] # Take any character values from eval and store them in params$metric. # This has to account for the fact that `eval` could be a character vector, # a function, a list of functions, or a list with a mix of strings and # functions .check_eval <- function(params, eval) { if (is.null(params$metric)) { params$metric <- list() } else if (is.character(params$metric)) { params$metric <- as.list(params$metric) } # if 'eval' is a character vector or list, find the character # elements and add them to 'metric' if (!is.function(eval)) { for (i in seq_along(eval)) { element <- eval[[i]] if (is.character(element)) { params$metric <- append(params$metric, element) } } } # If more than one character metric was given, then "None" should # not be included if (length(params$metric) > 1L) { params$metric <- Filter( f = function(metric) { !(metric %in% .NO_METRIC_STRINGS()) } , x = params$metric ) } # duplicate metrics should be filtered out params$metric <- as.list(unique(unlist(params$metric))) return(params) } # [description] # # Resolve differences between passed-in keyword arguments, parameters, # and parameter aliases. This function exists because some functions in the # package take in parameters through their own keyword arguments other than # the `params` list. # # If the same underlying parameter is provided multiple # ways, the first item in this list is used: # # 1. the main (non-alias) parameter found in `params` # 2. the alias with the highest priority found in `params` # 3. the keyword argument passed in # # For example, "num_iterations" can also be provided to lgb.train() # via keyword "nrounds". lgb.train() will choose one value for this parameter # based on the first match in this list: # # 1. params[["num_iterations]] # 2. the highest priority alias of "num_iterations" found in params # 3. the nrounds keyword argument # # If multiple aliases are found in `params` for the same parameter, they are # all removed before returning `params`. # # [return] # params with num_iterations set to the chosen value, and other aliases # of num_iterations removed .check_wrapper_param <- function(main_param_name, params, alternative_kwarg_value) { aliases <- .PARAMETER_ALIASES()[[main_param_name]] aliases_provided <- aliases[aliases %in% names(params)] aliases_provided <- aliases_provided[aliases_provided != main_param_name] # prefer the main parameter if (!is.null(params[[main_param_name]])) { for (param in aliases_provided) { params[[param]] <- NULL } return(params) } # if the main parameter wasn't provided, prefer the first alias if (length(aliases_provided) > 0L) { first_param <- aliases_provided[1L] params[[main_param_name]] <- params[[first_param]] for (param in aliases_provided) { params[[param]] <- NULL } return(params) } # if not provided in params at all, use the alternative value provided # through a keyword argument from lgb.train(), lgb.cv(), etc. params[[main_param_name]] <- alternative_kwarg_value return(params) } #' @importFrom parallel detectCores .get_default_num_threads <- function() { if (requireNamespace("RhpcBLASctl", quietly = TRUE)) { # nolint: undesirable_function. return(RhpcBLASctl::get_num_cores()) } else { msg <- "Optional package 'RhpcBLASctl' not found." cores <- 0L if (Sys.info()["sysname"] != "Linux") { cores <- parallel::detectCores(logical = FALSE) if (is.na(cores) || cores < 0L) { cores <- 0L } } if (cores == 0L) { msg <- paste(msg, "Will use default number of OpenMP threads.", sep = " ") } else { msg <- paste(msg, "Detection of CPU cores might not be accurate.", sep = " ") } warning(msg) return(cores) } } .equal_or_both_null <- function(a, b) { if (is.null(a)) { if (!is.null(b)) { return(FALSE) } return(TRUE) } else { if (is.null(b)) { return(FALSE) } return(a == b) } } ================================================ FILE: R-package/README.md ================================================ # LightGBM R-package [![CRAN Version](https://www.r-pkg.org/badges/version/lightgbm)](https://cran.r-project.org/package=lightgbm) [![Downloads](https://cranlogs.r-pkg.org/badges/grand-total/lightgbm)](https://cran.r-project.org/package=lightgbm) [![API Docs](https://readthedocs.org/projects/lightgbm/badge/?version=latest)](https://lightgbm.readthedocs.io/en/latest/R/reference/) ### Contents * [Installation](#installation) - [Installing the CRAN Package](#installing-the-cran-package) - [Installing from Source with CMake](#install) - [Installing a GPU-enabled Build](#installing-a-gpu-enabled-build) - [Installing Precompiled Binaries](#installing-precompiled-binaries) - [Installing from a Pre-compiled lib_lightgbm](#lib_lightgbm) * [Examples](#examples) * [Testing](#testing) - [Running the Tests](#running-the-tests) - [Code Coverage](#code-coverage) * [Updating Documentation](#updating-documentation) * [Preparing a CRAN Package](#preparing-a-cran-package) * [Known Issues](#known-issues) Installation ------------ For the easiest installation, go to ["Installing the CRAN package"](#installing-the-cran-package). If 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. To build a GPU-enabled version of the package, follow the steps in ["Installing a GPU-enabled Build"](#installing-a-gpu-enabled-build). If 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). When your package installation is done, you can check quickly if your LightGBM R-package is working by running the following: ```r library(lightgbm) data(agaricus.train, package='lightgbm') train <- agaricus.train dtrain <- lgb.Dataset(train$data, label = train$label) model <- lgb.cv( params = list( objective = "regression" , metric = "l2" ) , data = dtrain ) ``` ### Installing the CRAN package `{lightgbm}` is [available on CRAN](https://cran.r-project.org/package=lightgbm), and can be installed with the following R code. ```r install.packages("lightgbm", repos = "https://cran.r-project.org") ``` This 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. Each 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`. #### Custom Installation (Linux, Mac) The steps above should work on most systems, but users with highly-customized environments might want to change how R builds packages from source. To 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). For example, to use `gcc-14` instead of `clang` on macOS, you could use something like the following: ```make # ~/.R/Makevars CC=gcc-14 CC17=gcc-14 CXX=g++-14 CXX17=g++-14 ``` To check the values R is using, run the following: ```shell R CMD config --all ``` ### Installing from Source with CMake You need to install git and [CMake](https://cmake.org/) first. Note: 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). #### Windows Preparation NOTE: Windows users may need to run with administrator rights (either R or the command prompt, depending on the way you are installing this package). Installing a 64-bit version of [Rtools](https://cran.r-project.org/bin/windows/Rtools/) is mandatory. After 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. * `Rtools` - If you have `Rtools` 4.0, example: - `C:\rtools40\mingw64\bin` - `C:\rtools40\usr\bin` - If you have `Rtools` 4.2+, example: - `C:\rtools42\x86_64-w64-mingw32.static.posix\bin` - `C:\rtools42\usr\bin` - **NOTE**: this is e.g. `rtools43\` for R 4.3 * `CMake` - example: `C:\Program Files\CMake\bin` * `R` - example: `C:\Program Files\R\R-4.5.1\bin` NOTE: Two `Rtools` paths are required from `Rtools` 4.0 onwards because paths and the list of included software was changed in `Rtools` 4.0. NOTE: `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). #### Windows Toolchain Options A "toolchain" refers to the collection of software used to build the library. The R-package can be built with three different toolchains. **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.** **Visual Studio (default)** By default, the package will be built with [Visual Studio Build Tools](https://visualstudio.microsoft.com/downloads/). **MSYS2 (R 4.x)** If 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. If you want to force `LightGBM` to use MSYS2 (for any R version), pass `--use-msys2` to the installation script. ```shell Rscript build_r.R --use-msys2 ``` **MinGW** If you want to force `LightGBM` to use [MinGW](https://www.mingw-w64.org/) (for any R version), pass `--use-mingw` to the installation script. ```shell Rscript build_r.R --use-mingw ``` #### Mac OS Preparation You 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. ``` # replace 8 with version of gcc installed on your machine export CXX=/usr/local/bin/g++-8 CC=/usr/local/bin/gcc-8 ``` #### Install with CMake After following the "preparation" steps above for your operating system, build and install the R-package with the following commands: ```sh git clone --recursive https://github.com/lightgbm-org/LightGBM cd LightGBM Rscript build_r.R ``` The `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: - `--no-build-vignettes`: Skip building vignettes. - `-j[jobs]`: Number of threads to use when compiling LightGBM. E.g., `-j4` will try to compile 4 objects at a time. - by default, this script uses single-thread compilation - for best results, set `-j` to the number of physical CPUs - `--skip-install`: Build the package tarball, but do not install it. - `--use-gpu`: Build a GPU-enabled version of the library. - `--use-mingw`: Force the use of MinGW toolchain, regardless of R version. - `--use-msys2`: Force the use of MSYS2 toolchain, regardless of R version. Note: for the build with Visual Studio/VS Build Tools in Windows, you should use the Windows CMD or PowerShell. ### Installing a GPU-enabled Build You 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). After 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`. ```shell Rscript build_r.R --use-gpu ``` You 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. ```shell Rscript build_r.R \ --use-gpu \ --opencl-library=/usr/lib/x86_64-linux-gnu/libOpenCL.so \ --boost-librarydir=/usr/lib/x86_64-linux-gnu ``` The following options correspond to the [CMake FindBoost options](https://cmake.org/cmake/help/latest/module/FindBoost.html) by the same names. * `--boost-root` * `--boost-dir` * `--boost-include-dir` * `--boost-librarydir` The following options correspond to the [CMake FindOpenCL options](https://cmake.org/cmake/help/latest/module/FindOpenCL.html) by the same names. * `--opencl-include-dir` * `--opencl-library` ### Installing Precompiled Binaries Precompiled 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. ```r install.packages( "lightgbm" , type = "both" , repos = "https://cran.r-project.org" ) ``` These packages do not require compilation, so they will be faster and easier to install than packages that are built from source. CRAN does not prepare precompiled binaries for Linux, and as of this writing neither does this project. ### Installing from a Pre-compiled lib_lightgbm Previous 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. As 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). Examples -------- Please visit [demo](https://github.com/lightgbm-org/LightGBM/tree/master/R-package/demo): * [Basic walkthrough of wrappers](https://github.com/lightgbm-org/LightGBM/blob/master/R-package/demo/basic_walkthrough.R) * [Boosting from existing prediction](https://github.com/lightgbm-org/LightGBM/blob/master/R-package/demo/boost_from_prediction.R) * [Early Stopping](https://github.com/lightgbm-org/LightGBM/blob/master/R-package/demo/early_stopping.R) * [Cross Validation](https://github.com/lightgbm-org/LightGBM/blob/master/R-package/demo/cross_validation.R) * [Multiclass Training/Prediction](https://github.com/lightgbm-org/LightGBM/blob/master/R-package/demo/multiclass.R) * [Leaf (in)Stability](https://github.com/lightgbm-org/LightGBM/blob/master/R-package/demo/leaf_stability.R) * [Weight-Parameter Adjustment Relationship](https://github.com/lightgbm-org/LightGBM/blob/master/R-package/demo/weight_param.R) Testing ------- The 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. ### Running the Tests While developing the R-package, run the code below to run the unit tests. ```shell sh build-cran-package.sh \ --no-build-vignettes R CMD INSTALL --with-keep.source lightgbm*.tar.gz cd R-package/tests Rscript testthat.R ``` To 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). ```shell export LIGHTGBM_TEST_VERBOSITY=1 cd R-package/tests Rscript testthat.R ``` ### Code Coverage When 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. The 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). ```shell # Install sh build-cran-package.sh \ --no-build-vignettes # Get coverage Rscript -e " \ library(covr); coverage <- covr::package_coverage('./lightgbm_r', type = 'tests', quiet = FALSE); print(coverage); covr::report(coverage, file = file.path(getwd(), 'coverage.html'), browse = TRUE); " ``` Updating Documentation ---------------------- The R-package uses [`{roxygen2}`](https://CRAN.R-project.org/package=roxygen2) to generate its documentation. The generated `DESCRIPTION`, `NAMESPACE`, and `man/` files are checked into source control. To regenerate those files, run the following. ```shell Rscript \ --vanilla \ -e "install.packages('roxygen2', repos = 'https://cran.rstudio.com')" sh build-cran-package.sh --no-build-vignettes R CMD INSTALL \ --with-keep.source \ ./lightgbm_*.tar.gz cd R-package Rscript \ --vanilla \ -e "roxygen2::roxygenize(load = 'installed')" ``` Preparing a CRAN Package ------------------------ This section is primarily for maintainers, but may help users and contributors to understand the structure of the R-package. Most 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`. For more information on this approach, see ["Writing R Extensions"](https://cran.r-project.org/doc/manuals/r-release/R-exts.html#Configure-and-cleanup). ### Build a CRAN Package From the root of the repository, run the following. ```shell git submodule update --init --recursive sh build-cran-package.sh ``` This will create a file `lightgbm_${VERSION}.tar.gz`, where `VERSION` is the version of `LightGBM`. That script supports the following command-line options: - `--no-build-vignettes`: Skip building vignettes. - `--r-executable=[path-to-executable]`: Use an alternative build of R. ### Standard Installation from CRAN Package After building the package, install it with a command like the following: ```shell R CMD install lightgbm_*.tar.gz ``` ### Changing the CRAN Package A 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). This 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). **Linux or Mac** At build time, `configure` will be run and used to create a file `Makevars`, using `Makevars.in` as a template. 1. Edit `configure.ac`. 2. Create `configure` with `autoconf`. Do not edit it by hand. This file must be generated on Ubuntu 22.04. If you have an Ubuntu 22.04 environment available, run the provided script from the root of the `LightGBM` repository. ```shell ./R-package/recreate-configure.sh ``` 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. ```shell docker run \ --rm \ -v $(pwd):/opt/LightGBM \ -w /opt/LightGBM \ ubuntu:22.04 \ ./R-package/recreate-configure.sh ``` 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. 3. Edit `src/Makevars.in`. Alternatively, GitHub Actions can re-generate this file for you. 1. navigate to https://github.com/lightgbm-org/LightGBM/actions/workflows/r_configure.yml 2. click "Run workflow" (drop-down) 3. enter the branch from the pull request for the `pr-branch` input 4. click "Run workflow" (button) **Configuring for Windows** At build time, `configure.win` will be run and used to create a file `Makevars.win`, using `Makevars.win.in` as a template. 1. Edit `configure.win` directly. 2. Edit `src/Makevars.win.in`. ### Testing the CRAN Package `{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. #### Windows, Mac, and Linux ```shell sh build-cran-package.sh R CMD check --as-cran lightgbm_*.tar.gz ``` #### ASAN and UBSAN All 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). For more background, see * [this blog post](https://dirk.eddelbuettel.com/code/sanitizers.html) * [top-level CRAN documentation on these checks](https://cran.r-project.org/web/checks/check_issue_kinds.html) * [CRAN's configuration of these checks](https://www.stats.ox.ac.uk/pub/bdr/memtests/README.txt) You can replicate these checks locally using Docker. For more information on the image used for testing, see https://github.com/wch/r-debug. In the code below, environment variable `R_CUSTOMIZATION` should be set to one of two values. * `"san"` = replicates CRAN's `gcc-ASAN` and `gcc-UBSAN` checks * `"csan"` = replicates CRAN's `clang-ASAN` and `clang-UBSAN` checks ```shell docker run \ --rm \ -it \ -v $(pwd):/opt/LightGBM \ -w /opt/LightGBM \ --env R_CUSTOMIZATION=san \ wch1/r-debug:latest \ /bin/bash # install dependencies RDscript${R_CUSTOMIZATION} \ -e "install.packages(c('R6', 'data.table', 'jsonlite', 'knitr', 'markdown', 'Matrix', 'RhpcBLASctl', 'testthat'), repos = 'https://cran.r-project.org', Ncpus = parallel::detectCores())" # install lightgbm sh build-cran-package.sh --r-executable=RD${R_CUSTOMIZATION} RD${R_CUSTOMIZATION} \ CMD INSTALL lightgbm_*.tar.gz # run tests cd R-package/tests rm -f ./tests.log RDscript${R_CUSTOMIZATION} testthat.R >> tests.log 2>&1 # check that tests passed echo "test exit code: $?" tail -300 ./tests.log ``` #### Valgrind All 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/). You 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. ```shell docker run \ --rm \ -v $(pwd):/opt/LightGBM \ -w /opt/LightGBM \ -it \ wch1/r-debug RDscriptvalgrind -e "install.packages(c('R6', 'data.table', 'jsonlite', 'knitr', 'markdown', 'Matrix', 'RhpcBLASctl', 'testthat'), repos = 'https://cran.rstudio.com', Ncpus = parallel::detectCores())" sh build-cran-package.sh \ --r-executable=RDvalgrind RDvalgrind CMD INSTALL \ --preclean \ --install-tests \ lightgbm_*.tar.gz cd R-package/tests RDvalgrind \ --no-readline \ --vanilla \ -d "valgrind --tool=memcheck --leak-check=full --track-origins=yes" \ -f testthat.R \ 2>&1 \ | tee out.log \ | cat ``` These tests can also be triggered on a pull request branch, using GitHub Actions. 1. navigate to https://github.com/lightgbm-org/LightGBM/actions/workflows/r_valgrind.yml 2. click "Run workflow" (drop-down) 3. enter the branch from the pull request for the `pr-branch` input 4. enter the pull request ID for the `pr-number` input 5. click "Run workflow" (button) Or by using the GitHub CLI, using a command similar to this: ```shell gh workflow run \ --repo lightgbm-org/LightGBM \ r_valgrind.yml \ -f pr-branch=ci/fix-rerun-workflow \ -f pr-number=7072 ``` Known Issues ------------ For 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). ================================================ FILE: R-package/cleanup ================================================ #!/bin/sh rm -f src/Makevars ================================================ FILE: R-package/configure ================================================ #! /bin/sh # Guess values for system-dependent variables and create Makefiles. # Generated by GNU Autoconf 2.71 for lightgbm 4.6.0.99. # # # Copyright (C) 1992-1996, 1998-2017, 2020-2021 Free Software Foundation, # Inc. # # # This configure script is free software; the Free Software Foundation # gives unlimited permission to copy, distribute and modify it. ## -------------------- ## ## M4sh Initialization. ## ## -------------------- ## # Be more Bourne compatible DUALCASE=1; export DUALCASE # for MKS sh as_nop=: if test ${ZSH_VERSION+y} && (emulate sh) >/dev/null 2>&1 then : emulate sh NULLCMD=: # Pre-4.2 versions of Zsh do word splitting on ${1+"$@"}, which # is contrary to our usage. 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For more see: # * https://unconj.ca/blog/an-autoconf-primer-for-r-package-authors.html # * https://cran.r-project.org/doc/manuals/r-release/R-exts.html#Configure-and-cleanup AC_PREREQ(2.69) AC_INIT([lightgbm], [~~VERSION~~], [], [lightgbm], []) ########################### # find compiler and flags # ########################### AC_MSG_CHECKING([location of R]) AC_MSG_RESULT([${R_HOME}]) # set up CPP flags # find the compiler and compiler flags used by R. : ${R_HOME=`R HOME`} if test -z "${R_HOME}"; then echo "could not determine R_HOME" exit 1 fi CXX17=`"${R_HOME}/bin/R" CMD config CXX17` CXX17STD=`"${R_HOME}/bin/R" CMD config CXX17STD` CXX="${CXX17} ${CXX17STD}" CPPFLAGS=`"${R_HOME}/bin/R" CMD config CPPFLAGS` CXXFLAGS=`"${R_HOME}/bin/R" CMD config CXX17FLAGS` LDFLAGS=`"${R_HOME}/bin/R" CMD config LDFLAGS` AC_LANG(C++) # LightGBM-specific flags LGB_CPPFLAGS="" ######### # Eigen # ######### LGB_CPPFLAGS="${LGB_CPPFLAGS} -DEIGEN_MPL2_ONLY -DEIGEN_DONT_PARALLELIZE" ############### # MM_PREFETCH # ############### AC_MSG_CHECKING([whether MM_PREFETCH works]) ac_mmprefetch=no AC_LANG_CONFTEST( [ AC_LANG_PROGRAM( [[ #include ]], [[ int a = 0; _mm_prefetch(&a, _MM_HINT_NTA); return 0; ]] ) ] ) ${CXX} ${CPPFLAGS} ${CXXFLAGS} -o conftest conftest.cpp 2>/dev/null && ./conftest && ac_mmprefetch=yes AC_MSG_RESULT([${ac_mmprefetch}]) if test "${ac_mmprefetch}" = yes; then LGB_CPPFLAGS="${LGB_CPPFLAGS} -DMM_PREFETCH=1" fi ############ # MM_ALLOC # ############ AC_MSG_CHECKING([whether MM_MALLOC works]) ac_mm_malloc=no AC_LANG_CONFTEST( [ AC_LANG_PROGRAM( [[ #include ]], [[ char *a = (char*)_mm_malloc(8, 16); _mm_free(a); return 0; ]] ) ] ) ${CXX} ${CPPFLAGS} ${CXXFLAGS} -o conftest conftest.cpp 2>/dev/null && ./conftest && ac_mm_malloc=yes AC_MSG_RESULT([${ac_mm_malloc}]) if test "${ac_mm_malloc}" = yes; then LGB_CPPFLAGS="${LGB_CPPFLAGS} -DMM_MALLOC=1" fi ########## # OpenMP # ########## OPENMP_CXXFLAGS="" if test `uname -s` = "Linux" then OPENMP_CXXFLAGS="\$(SHLIB_OPENMP_CXXFLAGS)" fi if test `uname -s` = "Darwin" then OPENMP_CXXFLAGS='-Xclang -fopenmp' OPENMP_LIB='-lomp' # libomp 15.0+ from brew is keg-only (i.e. not symlinked into the standard paths search by the linker), # so need to search in other locations. # See https://github.com/Homebrew/homebrew-core/issues/112107#issuecomment-1278042927. # # If Homebrew is found and libomp was installed with it, this code adds the necessary # flags for the compiler to find libomp headers and for the linker to find libomp.dylib. HOMEBREW_LIBOMP_PREFIX="" if command -v brew >/dev/null 2>&1; then ac_brew_openmp=no AC_MSG_CHECKING([whether OpenMP was installed via Homebrew]) brew --prefix libomp >/dev/null 2>&1 && ac_brew_openmp=yes AC_MSG_RESULT([${ac_brew_openmp}]) if test "${ac_brew_openmp}" = yes; then HOMEBREW_LIBOMP_PREFIX=`brew --prefix libomp` OPENMP_CXXFLAGS="${OPENMP_CXXFLAGS} -I${HOMEBREW_LIBOMP_PREFIX}/include" OPENMP_LIB="${OPENMP_LIB} -L${HOMEBREW_LIBOMP_PREFIX}/lib" fi fi ac_pkg_openmp=no AC_MSG_CHECKING([whether OpenMP will work in a package]) AC_LANG_CONFTEST( [ AC_LANG_PROGRAM( [[ #include ]], [[ return (omp_get_max_threads() <= 1); ]] ) ] ) ${CXX} ${CPPFLAGS} ${CXXFLAGS} ${LDFLAGS} ${OPENMP_CXXFLAGS} ${OPENMP_LIB} -o conftest conftest.cpp 2>/dev/null && ./conftest && ac_pkg_openmp=yes # -Xclang is not portable (it is clang-specific) # if compilation above failed, try without that flag if test "${ac_pkg_openmp}" = no; then if test -f "./conftest"; then rm ./conftest fi OPENMP_CXXFLAGS="-fopenmp" ${CXX} ${CPPFLAGS} ${CXXFLAGS} ${LDFLAGS} ${OPENMP_CXXFLAGS} ${OPENMP_LIB} -o conftest conftest.cpp 2>/dev/null && ./conftest && ac_pkg_openmp=yes fi AC_MSG_RESULT([${ac_pkg_openmp}]) if test "${ac_pkg_openmp}" = no; then OPENMP_CXXFLAGS='' OPENMP_LIB='' echo '***********************************************************************************************' echo ' OpenMP is unavailable on this macOS system. LightGBM code will run single-threaded as a result.' echo ' To use all CPU cores for training jobs, you should install OpenMP by running' echo '' echo ' brew install libomp' echo '***********************************************************************************************' fi fi # substitute variables from this script into Makevars.in AC_SUBST(OPENMP_CXXFLAGS) AC_SUBST(OPENMP_LIB) AC_SUBST(LGB_CPPFLAGS) AC_CONFIG_FILES([src/Makevars]) # write out Autoconf output AC_OUTPUT ================================================ FILE: R-package/configure.win ================================================ # Script used to generate `Makevars.win` from `Makevars.win.in` # on Windows ########################### # find compiler and flags # ########################### R_EXE="${R_HOME}/bin${R_ARCH_BIN}/R" CXX17=`"${R_EXE}" CMD config CXX17` CXX17STD=`"${R_EXE}" CMD config CXX17STD` CXX="${CXX17} ${CXX17STD}" CXXFLAGS=`"${R_EXE}" CMD config CXX17FLAGS` CPPFLAGS=`"${R_EXE}" CMD config CPPFLAGS` # LightGBM-specific flags LGB_CPPFLAGS="" ######### # Eigen # ######### LGB_CPPFLAGS="${LGB_CPPFLAGS} -DEIGEN_MPL2_ONLY -DEIGEN_DONT_PARALLELIZE" ############### # MM_PREFETCH # ############### ac_mm_prefetch="no" cat > conftest.cpp < int main() { int a = 0; _mm_prefetch(&a, _MM_HINT_NTA); return 0; } EOL ${CXX} ${CXXFLAGS} ${CPPFLAGS} -o conftest conftest.cpp 2>/dev/null && ./conftest && ac_mm_prefetch="yes" rm -f ./conftest rm -f ./conftest.cpp echo "checking whether MM_PREFETCH works...${ac_mm_prefetch}" if test "${ac_mm_prefetch}" = "yes"; then LGB_CPPFLAGS="${LGB_CPPFLAGS} -DMM_PREFETCH=1" fi ############ # MM_ALLOC # ############ ac_mm_malloc="no" cat > conftest.cpp < int main() { char *a = (char*)_mm_malloc(8, 16); _mm_free(a); return 0; } EOL ${CXX} ${CXXFLAGS} ${CPPFLAGS} -o conftest conftest.cpp 2>/dev/null && ./conftest && ac_mm_malloc="yes" rm -f ./conftest rm -f ./conftest.cpp echo "checking whether MM_MALLOC works...${ac_mm_malloc}" if test "${ac_mm_malloc}" = "yes"; then LGB_CPPFLAGS="${LGB_CPPFLAGS} -DMM_MALLOC=1" fi ############# # INET_PTON # ############# ac_inet_pton="no" cat > conftest.cpp < int main() { int (*fptr)(int, const char*, void*); fptr = &inet_pton; return 0; } EOL ${CXX} ${CXXFLAGS} ${CPPFLAGS} -o conftest conftest.cpp 2>/dev/null && ./conftest && ac_inet_pton="yes" rm -f ./conftest rm -f ./conftest.cpp echo "checking whether INET_PTON works...${ac_inet_pton}" if test "${ac_inet_pton}" = "yes"; then LGB_CPPFLAGS="${LGB_CPPFLAGS} -DWIN_HAS_INET_PTON=1" fi # Generate Makevars.win from Makevars.win.in sed -e \ "s/@LGB_CPPFLAGS@/$LGB_CPPFLAGS/" \ < src/Makevars.win.in > src/Makevars.win ================================================ FILE: R-package/cran-comments.md ================================================ # CRAN Submission History ## v4.6.0 - Submission 1 - (February 13, 2025) ### CRAN response Accepted to CRAN ### Maintainer Notes This release fixed several issues reported by CRAN. Bashisms in `configure` ```text possible bashism in configure.ac line 63 (should be VAR="${VAR}foo"): LGB_CPPFLAGS+=" -DMM_PREFETCH=1" possible bashism in configure.ac line 89 (should be VAR="${VAR}foo"): LGB_CPPFLAGS+=" -DMM_MALLOC=1" ``` Compilation errors on GCC 15. ```text io/json11.cpp:97:28: error: 'uint8_t' does not name a type 97 | } else if (static_cast(ch) == 0xe2 && | ^~~~~~~ io/json11.cpp:97:28: note: 'uint8_t' is defined in header ''; this is probably fixable by adding '#include ' io/json11.cpp:98:28: error: 'uint8_t' does not name a type 98 | static_cast(value[i + 1]) == 0x80 && ``` This release contains fixes for those issues. ## v4.5.0 - Submission 1 - (July 25, 2024) ### CRAN response Accepted to CRAN ### Maintainer Notes This release was a response to a request from CRAN. On July 4, 2024, CRAN notified us that the following compiler warnings raised by `gcc` 14 needed to be fixed by August 3, 2024. ```text Result: WARN Found the following significant warnings: io/dense_bin.hpp:617:27: warning: template-id not allowed for constructor in C++20 [-Wtemplate-id-cdtor] io/multi_val_dense_bin.hpp:346:26: warning: template-id not allowed for constructor in C++20 [-Wtemplate-id-cdtor] io/multi_val_sparse_bin.hpp:433:36: warning: template-id not allowed for constructor in C++20 [-Wtemplate-id-cdtor] io/sparse_bin.hpp:785:19: warning: template-id not allowed for constructor in C++20 [-Wtemplate-id-cdtor] See ‘/data/gannet/ripley/R/packages/tests-devel/lightgbm.Rcheck/00install.out’ for details. ``` This release contains fixes for those issues. ## v4.4.0 - Submission 1 - (June 14, 2024) ### CRAN response Accepted to CRAN ### Maintainer Notes This was a standard release of `{lightgbm}`, not intended to fix any particular R-specific issues. ## v4.3.0 - Submission 1 - (January 18, 2024) ### CRAN response Accepted to CRAN ### Maintainer Notes This submission was put up in response to CRAN saying the package would be archived if the following warning was not fixed within 14 days. ```text /usr/local/clang-trunk/bin/../include/c++/v1/__fwd/string_view.h:22:41: warning: 'char_traits' is deprecated: char_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. It will be removed in LLVM 19, so please migrate off of it. [-Wdeprecated-declarations] ``` See https://github.com/lightgbm-org/LightGBM/issues/6264. ## v4.2.0 - Submission 1 - (December 7, 2023) ### CRAN response Accepted to CRAN ### Maintainer Notes This submission included many changes from the last 2 years, as well as fixes for a warning CRAN said could cause the package to be archived: https://github.com/lightgbm-org/LightGBM/issues/6221. ## v4.1.0 - not submitted v4.1.0 was not submitted to CRAN, because https://github.com/lightgbm-org/LightGBM/issues/5987 had not been resolved. ## v4.0.0 - Submission 2 - (July 19, 2023) ### CRAN response > Dear maintainer, > package lightgbm_4.0.0.tar.gz does not pass the incoming checks automatically. The logs linked from those messagges showed one issue remaining on Debian (0 on Windows). ```text * checking examples ... [7s/4s] NOTE Examples with CPU time > 2.5 times elapsed time user system elapsed ratio lgb.restore_handle 1.206 0.085 0.128 10.08 ``` ### Maintainer Notes Chose to document the issue and need for a fix in https://github.com/lightgbm-org/LightGBM/issues/5987, but not resubmit, to avoid annoying CRAN maintainers. ## v4.0.0 - Submission 1 - (July 16, 2023) ### CRAN response > Dear maintainer, > package lightgbm_4.0.0.tar.gz does not pass the incoming checks automatically. The logs linked from those messages showed the following issues from `R CMD check`. ```text * checking S3 generic/method consistency ... NOTE Mismatches for apparent methods not registered: merge: function(x, y, ...) merge.eval.string: function(env) format: function(x, ...) format.eval.string: function(eval_res, eval_err) See section 'Registering S3 methods' in the 'Writing R Extensions' manual. ``` ```text * checking examples ... [8s/4s] NOTE Examples with CPU time > 2.5 times elapsed time user system elapsed ratio lgb.restore_handle 1.819 0.128 0.165 11.8 ``` ### Maintainer Notes Attempted to fix these with https://github.com/lightgbm-org/LightGBM/pull/5988 and resubmitted. ## v3.3.5 - Submission 2 - (January 16, 2023) ### CRAN response > Reason was > > Flavor: r-devel-windows-x86_64 > Check: OOverall checktime, Result: NOTE > Overall checktime 14 min > 10 min > > but the maintainer cannot do much to reduce this, so I triggered revdep checks now. > Please reply to the archival message in case the issue is not fixable easily. > > Best, > Uwe Ligges ### Maintainer Notes This was technically not a "resubmission". We 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. ## v3.3.5 - Submission 1 - (January 11, 2023) ### CRAN response Archived without a response. ### Maintainer Notes Submitted with the following comment. > This submission contains {lightgbm} 3.3.5 > Per CRAN's policies, I am submitting it on behalf of the project's maintainer (Yu Shi), with his permission. > This submission includes patches to address the following warnings observed on the fedora and debian CRAN checks. > Found the following significant warnings: > io/json11.cpp:207:47: warning: unqualified call to 'std::move' [-Wunqualified-std-cast-call] > io/json11.cpp:216:51: warning: unqualified call to 'std::move' [-Wunqualified-std-cast-call] > io/json11.cpp:225:53: warning: unqualified call to 'std::move' [-Wunqualified-std-cast-call] > io/json11.cpp:268:60: warning: unqualified call to 'std::move' [-Wunqualified-std-cast-call] > io/json11.cpp:272:36: warning: unqualified call to 'std::move' [-Wunqualified-std-cast-call] > io/json11.cpp:276:37: warning: unqualified call to 'std::move' [-Wunqualified-std-cast-call] > io/json11.cpp:381:41: warning: unqualified call to 'std::move' [-Wunqualified-std-cast-call] > io/json11.cpp:150:39: warning: unqualified call to 'std::move' [-Wunqualified-std-cast-call] Thank you very much for your time and consideration. ## v3.3.4 - Submission 1 - (December 15, 2022) ### CRAN response Accepted to CRAN ### Maintainer Notes Submitted with the following comment: > This submission contains {lightgbm} 3.3.4 > Per CRAN's policies, I am submitting it on behalf of the project's maintainer (Yu Shi), with his permission. > This submission includes patches to address the following warnings observed on the fedora and debian CRAN checks. > > 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. > Thank you very much for your time and consideration. ## v3.3.3 - Submission 1 - (October 10, 2022) ### CRAN response Accepted to CRAN ### Maintainer Notes Submitted with the following comment: > This submission contains {lightgbm} 3.3.3. > 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). > This submission includes two patches: > * 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) > * modifications to allow compatibility with the RTools42 build toolchain (https://github.com/lightgbm-org/LightGBM/pull/5503) > Thank you very much for your time and consideration. ## v3.3.2 - Submission 1 - (January 7, 2022) ### CRAN response Accepted to CRAN on January 14, 2022. ### Maintainer Notes In this submission, we uploaded a patch that CRAN stuff provided us via e-mail. The full text of the e-mail from CRAN: ```text Dear maintainers, This concerns the CRAN packages Cairo cepreader gpboost httpuv ipaddress lightgbm proj4 prophet RcppCWB RcppParallel RDieHarder re2 redux rgeolocate RGtk2 tth udunits2 unrtf maintained by one of you: Andreas Blaette andreas.blaette@uni-due.de: RcppCWB David Hall david.hall.physics@gmail.com: ipaddress Dirk Eddelbuettel edd@debian.org: RDieHarder Fabio Sigrist fabiosigrist@gmail.com: gpboost Friedrich Leisch Friedrich.Leisch@R-project.org: tth Girish Palya girishji@gmail.com: re2 James Hiebert hiebert@uvic.ca: udunits2 Jari Oksanen jhoksane@gmail.com: cepreader Kevin Ushey kevin@rstudio.com: RcppParallel ORPHANED: RGtk2 Os Keyes ironholds@gmail.com: rgeolocate Rich FitzJohn rich.fitzjohn@gmail.com: redux Sean Taylor sjtz@pm.me: prophet Simon Urbanek simon.urbanek@r-project.org: proj4 Simon Urbanek Simon.Urbanek@r-project.org: Cairo Winston Chang winston@rstudio.com: httpuv Yu Shi yushi2@microsoft.com: lightgbm your packages need to be updated for R-devel/R 4.2 to work on Windows, following the recent switch to UCRT and Rtools42. Sorry for the group message, please feel free to respond individually regarding your package or ask specifically about what needs to be fixed. I've created patches for you, so please review them and fix your packages: https://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 You can apply them as follows tar xfz package_1.0.0.tar.gz wget https://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 patch --binary < package.diff These patches are currently automatically applied by R-devel on Windows at installation time, which makes most of your packages pass their checks (as OK or NOTE), but please check your results carefully and carefully review the patches. Usually these changes were because of newer GCC or newer MinGW in the toolchain, but some for other reasons, and some of them will definitely have to be improved so that the package keeps building also for older versions of R using Rtools40. We have only been testing the patches with UCRT (and Rtools42) on Windows. For more information, please see https://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 https://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 Once you add your patches/fix the issues, your package will probably show a warning during R CMD check (as patching would be attempted to be applied again). That's ok, at that point please let me know and I will remove my patch from the repository of automatically applied patches. If you end up just applying the patch as is, there is probably no need testing on your end, but you can do so using Winbuilder, r-hub, github actions (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). If you wanted to test locally on your Windows machine and do not have a UCRT version of R-devel yet, please uninstall your old version of R-devel, delete the old library used with that, install a new UCRT version of R-devel , and install Rtools42. You can keep Rtools40 installed if you need it with R 4.1 or earlier. Currently, the new R-devel can be downloaded from https://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 And Rtools42 from https://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 If you end up testing locally, you can use R_INSTALL_TIME_PATCHES environment variable to disable the automated patching, see the "howto" document above. That way you could also see what the original issue was causing. If you wanted to find libraries to link for yourself, e.g. in a newer version of your package, please look for "Using findLinkingOrder with Rtools42 (tiff package example)" in the "howto" document above. I created the patches for you manually before we finished this script, so you may be able to create a shorter version using it, but - it's probably not worth the effort. If you wanted to try in a virtual machine, but did not have a license, you can use also an automated setup of a free trial VM from https://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 (but that needs a very good and un-metered network connection to install) Please let us know if you have any questions. Thanks, Tomas & Uwe ``` ## v3.3.1 - Submission 1 - (October 27, 2021) ### CRAN response Accepted to CRAN on October 30, 2021. CRAN completed its checks and preparation of binaries on November 6, 2021. ### Maintainer Notes Submitted v3.3.1 to CRAN, with the following fixes for the issues that caused CRAN to reject v3.3.0 and archive the package: * https://github.com/lightgbm-org/LightGBM/pull/4673 * https://github.com/lightgbm-org/LightGBM/pull/4714 Submitted with the following comment: > This submission contains {lightgbm} 3.3.1. > 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). > {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). > Thank you very much for your time and consideration. Progress on the submission was tracked in https://github.com/lightgbm-org/LightGBM/issues/4713. ## v3.3.0 - Submission 1 - (October 8, 2021) ### CRAN response `{lightgbm}` was removed from CRAN entirely on October 25, 2021. On October 12, 2021, maintainers received the following message from CRAN (ripley@stats.ox.ac.uk): > Dear maintainer, > Please see the problems shown on https://cran.r-project.org/web/checks/check_results_lightgbm.html > Please correct before 2021-10-25 to safely retain your package on CRAN. > Do remember to look at the 'Additional issues'. > The CRAN Team We failed to produce a new submission prior to that date, so the package was removed entirely. See https://github.com/lightgbm-org/LightGBM/issues/4713 for additional background and links explaining the specific failed CRAN checks. ### Maintainer Notes In this submission, we attempted to switch the maintainer of the package (in the CRAN official sense) from Guolin Ke to Yu Shi. Did this by adding a note in the CRAN submission web form explaining Guolin's departure from Microsoft. ## v3.2.1 - Submission 1 - (April 12, 2021) ### CRAN response Accepted to CRAN. ### Maintainer Notes ## v3.2.0 - Submission 1 - (March 22, 2021) ### CRAN response Package 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. > Apparently your package fails its checks in a strict Latin-1* locale, e.g. under Linux using LANG=en_US.iso88591 (see the debian-clang results). > Please correct before 2021-04-21 to safely retain your package on CRAN. ### Maintainer Notes Submitted a version 3.2.1 to correct the errors noted. ## v3.1.1 - Submission 1 - (December 7, 2020) ### CRAN response Accepted to CRAN, December 8. ### Maintainer Notes Submitted a fix to 3.1.0 that skips some learning-to-rank tests on 32-bit Windows. ## v3.1.0 - Submission 1 - (November 15, 2020) ### CRAN response Accepted to CRAN, November 18. On 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. CRAN did not ask for a re-submission, but this was fixed in 3.1.1. ### Maintainer Notes This package was submitted with the following information in the "optional comments" box. ```text Hello, I'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. {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. We see two warnings from valgrind that we believe are not problematic. ==2063== Conditional jump or move depends on uninitialised value(s) ==2063== at 0x49CF138: gregexpr_Regexc (grep.c:2439) ==2063== by 0x49D1F13: do_regexpr (grep.c:3100) ==2063== by 0x49A0058: bcEval (eval.c:7121) ==2063== by 0x498B67F: Rf_eval (eval.c:727) ==2063== by 0x498E414: R_execClosure (eval.c:1895) ==2063== by 0x498E0C7: Rf_applyClosure (eval.c:1821) ==2063== by 0x499FC8C: bcEval (eval.c:7089) ==2063== by 0x498B67F: Rf_eval (eval.c:727) ==2063== by 0x498B1CB: forcePromise (eval.c:555) ==2063== by 0x49963AB: FORCE_PROMISE (eval.c:5142) ==2063== by 0x4996566: getvar (eval.c:5183) ==2063== by 0x499D1A5: bcEval (eval.c:6873) ==2063== Uninitialised value was created by a stack allocation ==2063== at 0x49CEC37: gregexpr_Regexc (grep.c:2369) This seems to be related to R itself and not any code in {lightgbm}. ==2063== 336 bytes in 1 blocks are possibly lost in loss record 153 of 2,709 ==2063== at 0x483DD99: calloc (in /usr/lib/x86_64-linux-gnu/valgrind/vgpreload_memcheck-amd64-linux.so) ==2063== by 0x40149CA: allocate_dtv (dl-tls.c:286) ==2063== by 0x40149CA: _dl_allocate_tls (dl-tls.c:532) ==2063== by 0x5702322: allocate_stack (allocatestack.c:622) ==2063== by 0x5702322: pthread_create@@GLIBC_2.2.5 (pthread_create.c:660) ==2063== by 0x56D0DDA: ??? (in /usr/lib/x86_64-linux-gnu/libgomp.so.1.0.0) ==2063== by 0x56C88E0: GOMP_parallel (in /usr/lib/x86_64-linux-gnu/libgomp.so.1.0.0) ==2063== by 0x1544D29C: LGBM_DatasetCreateFromCSC (c_api.cpp:1286) ==2063== by 0x1546F980: LGBM_DatasetCreateFromCSC_R (lightgbm_R.cpp:91) ==2063== by 0x4941E2F: R_doDotCall (dotcode.c:634) ==2063== by 0x494CCC6: do_dotcall (dotcode.c:1281) ==2063== by 0x499FB01: bcEval (eval.c:7078) ==2063== by 0x498B67F: Rf_eval (eval.c:727) ==2063== by 0x498E414: R_execClosure (eval.c:1895) We 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). We have also added automated tests with ASAN/UBSAN to our testing setup, and have checked the package on Solaris 10 and found no issues. Thanks for your time and consideration. ``` ## v3.0.0.2 - Submission 1 - (September 29, 2020) ### CRAN response First response was a message talking about failing checks on 3.0.0. ```text package lightgbm_3.0.0.2.tar.gz has been auto-processed. The auto-check found additional issues for the last version released on CRAN: gcc-UBSAN valgrind CRAN incoming checks do not test for these additional issues and you will need an appropriately instrumented build of R to reproduce these. Hence please reply-all and explain: Have these been fixed? Please correct before 2020-10-05 to safely retain your package on CRAN. There is still a valgrind error. This did not happen when tested on submission, but the tests did run until timeout at 4 hours. When you write illegally, corruption is common. Illegal writes are serious errors. ``` Then 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. The content of that frustrated message was regrettable and it does not need to be preserved forever in this file. ### Maintainer Notes The 3.0.0.x series is officially not making it to CRAN. We will wait until November, and try again. Detailed 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. ## v3.0.0.1 - Submission 1 - (September 24, 2020) ### CRAN response ```text Thanks, we see: Still lots of alignment errors, such as lightgbm.Rcheck/tests/testthat.Rout:io/dataset_loader.cpp:340:59: runtime error: reference binding to misaligned address 0x7f51fefad81e for type 'const value_type', which requires 4 byte alignment lightgbm.Rcheck/tests/testthat.Rout:/usr/include/c++/10/bits/stl_vector.h:1198:21: runtime 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 error: reference binding to misaligned address 0x7f51fefad81e for type 'const type', which requires 4 byte alignment lightgbm.Rcheck/tests/testthat.Rout:/usr/include/c++/10/bits/move.h:77:36: runtime 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/alloc_traits.h:512:17: runtime error: reference binding to misaligned address 0x7f51fefad81e for type 'const type', which requires 4 byte alignment Please fix and resubmit. ``` ### Maintainer Notes Ok, 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. Struggling 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. ## v3.0.0 - Submission 6 - (September 24, 2020) ### CRAN response Failing pre-checks. ### `R CMD check` results ```text * checking CRAN incoming feasibility ... WARNING Maintainer: ‘Guolin Ke ’ Insufficient package version (submitted: 3.0.0, existing: 3.0.0) Days since last update: 4 ``` ### Maintainer Notes Did 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! This can be fixed by just re-submitting but with the version changed from `3.0.0` to `3.0.0.1`. ## v3.0.0 - Submission 5 - (September 11, 2020) ### CRAN Response Accepted to CRAN! Please correct the problems below before 2020-10-05 to safely retain your package on CRAN: ```text checking installed package size ... NOTE installed size is 49.7Mb sub-directories of 1Mb or more: libs 49.1Mb "network/socket_wrapper.hpp", line 30: Error: Could not open include file. "network/socket_wrapper.hpp", line 216: Error: The type "ifaddrs" is incomplete. "network/socket_wrapper.hpp", line 217: Error: The type "ifaddrs" is incomplete. "network/socket_wrapper.hpp", line 220: Error: The type "ifaddrs" is incomplete. "network/socket_wrapper.hpp", line 222: Error: The type "ifaddrs" is incomplete. "network/socket_wrapper.hpp", line 214: Error: The function "getifaddrs" must have a prototype. "network/socket_wrapper.hpp", line 228: Error: The function "freeifaddrs" must have a prototype. "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*, ...)". 7 Error(s) and 1 Warning(s) detected. *** Error code 2 make: Fatal error: Command failed for target `network/linkers_socket.o' Current working directory /tmp/RtmpNfaavG/R.INSTALL40a84f70130a/lightgbm/src ERROR: compilation failed for package ‘lightgbm’ * removing ‘/home/ripley/R/Lib32/lightgbm’ ``` ### Maintainer Notes Added a patch that `psutil` has used to fix missing `ifaddrs.h` on Solaris 10: https://github.com/lightgbm-org/LightGBM/issues/629#issuecomment-665091451. ## v3.0.0 - Submission 4 - (September 4, 2020) ### CRAN Response > Thanks, if the running time is the only reason to wrap the examples in \donttest, please replace \donttest by \donttest (\donttest examples are not executed in the CRAN checks). > Please replace cat() by message() or warning() in your functions (except for print() and summary() functions). Messages and warnings can be suppressed if needed. > Missing Rd-tags: lightgbm/man/dimnames.lgb.Dataset.Rd: \value lightgbm/man/lgb.Dataset.construct.Rd: \value lightgbm/man/lgb.prepare.Rd: \value ... > Please add the tag and explain in detail the returned objects. ### Maintainer Notes Responded to CRAN with the following: All 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). All 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. All exported objects now have `\value{}` statements in their documentation files in `man/`. **We also replied directly to CRAN's feedback email** > Swetlana, > Thank you for your comments. I've just created a new submission that I believe addresses them. > Can you help us understand something? In your message you said "\donttest examples are not executed in the CRAN checks)", but in https://cran.r-project.org/doc/manuals/r-devel/NEWS.html we see the following: > > "`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." > Could you help us understand how both of those statements can be true? ## v3.0.0 - Submission 3 - (August 29, 2020) ### CRAN response * Please write references in the description of the DESCRIPTION file in the form - authors (year) doi:... - authors (year) arXiv:... - authors (year, ISBN:...) * if those are not available: authors (year) https:... with no space after 'doi:', 'arXiv:', 'https:' and angle brackets for auto-linking. * (If you want to add a title as well please put it in quotes: "Title") * \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 \donttest{} with \donttest{}. * Please do not modify the global environment (e.g. by using <<-) in your functions. This is not allowed by the CRAN policies. * 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. Please fix and resubmit ### Maintainer Notes Responded to CRAN with the following: The 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 All 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. lightgbm'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. We 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: > "`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." We 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. ## v3.0.0 - Submission 2 - (August 28, 2020) ### CRAN response Failing pre-checks. ### `R CMD check` results * Debian: 2 NOTEs ```text * checking CRAN incoming feasibility ... NOTE Maintainer: 'Guolin Ke ' New submission Possibly mis-spelled words in DESCRIPTION: Guolin (13:52) Ke (13:48) LightGBM (14:20) al (13:62) et (13:59) * checking top-level files ... NOTE Non-standard files/directories found at top level: 'docs' 'lightgbm-hex-logo.png' 'lightgbm-hex-logo.svg' ``` * Windows: 2 NOTEs ```text * checking CRAN incoming feasibility ... NOTE Maintainer: 'Guolin Ke ' New submission Possibly mis-spelled words in DESCRIPTION: Guolin (13:52) Ke (13:48) LightGBM (14:20) al (13:62) et (13:59) * checking top-level files ... NOTE Non-standard files/directories found at top level: 'docs' 'lightgbm-hex-logo.png' 'lightgbm-hex-logo.svg' ``` ### Maintainer Notes We should tell them the misspellings note is a false positive. For 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. ## v3.0.0 - Submission 1 - (August 24, 2020) NOTE: 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. ### CRAN response > Please only ship the CRAN template for the MIT license. > Is there some reference about the method you can add in the Description field in the form Authors (year) doi:.....? > Please fix and resubmit. ### `R CMD check` results * Debian: 1 NOTE ```text * checking CRAN incoming feasibility ... NOTE Maintainer: ‘Guolin Ke ’ New submission License components with restrictions and base license permitting such: MIT + file LICENSE ``` * Windows: 1 NOTE ```text * checking CRAN incoming feasibility ... NOTE Maintainer: 'Guolin Ke ' New submission License components with restrictions and base license permitting such: MIT + file LICENSE ``` ### Maintainer Notes Tried updating `LICENSE` file to this template: ```yaml YEAR: 2016 COPYRIGHT HOLDER: Microsoft Corporation ``` Added a citation and link for [the main paper](https://proceedings.neurips.cc/paper/2017/hash/6449f44a102fde848669bdd9eb6b76fa-Abstract.html) in `DESCRIPTION`. ## v3.0.0-1 - Submission 3 - (August 12, 2020) ### CRAN response Failing pre-checks. ### `R CMD check` results * Debian: 1 NOTE ```text * checking CRAN incoming feasibility ... NOTE Maintainer: ‘Guolin Ke ’ New submission License components with restrictions and base license permitting such: MIT + file LICENSE ``` * Windows: 1 ERROR, 1 NOTE ```text * checking CRAN incoming feasibility ... NOTE Maintainer: ‘Guolin Ke ’ New submission License components with restrictions and base license permitting such: MIT + file LICENSE ** running tests for arch 'i386' ... [9s] ERROR Running 'testthat.R' [8s] Running the tests in 'tests/testthat.R' failed. Complete output: > library(testthat) > library(lightgbm) Loading required package: R6 > > test_check( + package = "lightgbm" + , stop_on_failure = TRUE + , stop_on_warning = FALSE + ) -- 1. Error: predictions do not fail for integer input (@test_Predictor.R#7) -- lgb.Dataset.construct: cannot create Dataset handle Backtrace: 1. lightgbm::lgb.train(...) 2. data$construct() ``` ### Maintainer Notes The "checking CRAN incoming feasibility" NOTE can be safely ignored. It only shows up the first time you submit a package to CRAN. So 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. ## v3.0.0-1 - Submission 2 - (August 10, 2020) ### CRAN response Failing pre-checks. ### `R CMD check` results * Debian: 2 NOTEs ```text * checking CRAN incoming feasibility ... NOTE Maintainer: ‘Guolin Ke ’ New submission License components with restrictions and base license permitting such: MIT + file LICENSE Non-standard files/directories found at top level: ‘cran-comments.md’ ‘docs’ ``` * Windows: 1 ERROR, 2 NOTEs ```text * checking CRAN incoming feasibility ... NOTE Maintainer: 'Guolin Ke ' New submission License components with restrictions and base license permitting such: MIT + file LICENSE * checking top-level files ... NOTE Non-standard files/directories found at top level: 'cran-comments.md' 'docs' ** checking whether the package can be loaded ... ERROR Loading this package had a fatal error status code 1 Loading log: Error: package 'lightgbm' is not installed for 'arch = i386' Execution halted ``` ### Maintainer Notes Seems removing `Biarch` field didn't work. Noticed this in the install logs: > Warning: this package has a non-empty 'configure.win' file, so building only the main architecture Tried adding `Biarch: true` to `DESCRIPTION` to overcome this. NOTE 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`. ## v3.0.0-1 - Submission 1 - (August 9, 2020) ### CRAN response Failing pre-checks. ### `R CMD check` results * Debian: 1 NOTE ```text Possibly mis-spelled words in DESCRIPTION: LightGBM (12:88, 19:41, 20:60, 20:264) ``` * Windows: 1 ERROR, 1 NOTE ```text Possibly mis-spelled words in DESCRIPTION: LightGBM (12:88, 19:41, 20:60, 20:264) ** checking whether the package can be loaded ... ERROR Loading this package had a fatal error status code 1 Loading log: Error: package 'lightgbm' is not installed for 'arch = i386' Execution halted ``` ### Maintainer Notes Thought the issue on Windows was caused by `Biarch: false` in `DESCRIPTION`. Removed `Biarch` field. Thought the "misspellings" issue could be resolved by adding single quotes around LightGBM, like `'LightGBM'`. ================================================ FILE: R-package/demo/00Index ================================================ basic_walkthrough Basic feature walkthrough boost_from_prediction Boosting from existing prediction categorical_features_rules Categorical Feature Preparation with Rules cross_validation Cross Validation early_stopping Early Stop in training efficient_many_training Efficiency for Many Model Trainings multiclass Multiclass training/prediction multiclass_custom_objective Multiclass with Custom Objective Function leaf_stability Leaf (in)Stability example weight_param Weight-Parameter adjustment relationship ================================================ FILE: R-package/demo/basic_walkthrough.R ================================================ library(lightgbm) # We load in the agaricus dataset # In this example, we are aiming to predict whether a mushroom is edible data(agaricus.train, package = "lightgbm") data(agaricus.test, package = "lightgbm") train <- agaricus.train test <- agaricus.test # The loaded data is stored in sparseMatrix, and label is a numeric vector in {0,1} class(train$label) class(train$data) # Set parameters for model training train_params <- list( num_leaves = 4L , learning_rate = 1.0 , objective = "binary" , nthread = 2L ) #--------------------Basic Training using lightgbm---------------- # This is the basic usage of lightgbm you can put matrix in data field # Note: we are putting in sparse matrix here, lightgbm naturally handles sparse input # Use sparse matrix when your feature is sparse (e.g. when you are using one-hot encoding vector) print("Training lightgbm with sparseMatrix") bst <- lightgbm( data = train$data , params = train_params , label = train$label , nrounds = 2L ) # Alternatively, you can put in dense matrix, i.e. basic R-matrix print("Training lightgbm with Matrix") bst <- lightgbm( data = as.matrix(train$data) , params = train_params , label = train$label , nrounds = 2L ) # You can also put in lgb.Dataset object, which stores label, data and other meta datas needed for advanced features print("Training lightgbm with lgb.Dataset") dtrain <- lgb.Dataset( data = train$data , label = train$label ) bst <- lightgbm( data = dtrain , params = train_params , nrounds = 2L ) # Verbose = 0,1,2 print("Train lightgbm with verbose 0, no message") bst <- lightgbm( data = dtrain , params = train_params , nrounds = 2L , verbose = 0L ) print("Train lightgbm with verbose 1, print evaluation metric") bst <- lightgbm( data = dtrain , params = train_params , nrounds = 2L , verbose = 1L ) print("Train lightgbm with verbose 2, also print information about tree") bst <- lightgbm( data = dtrain , params = train_params , nrounds = 2L , verbose = 2L ) # You can also specify data as file path to a LibSVM/TCV/CSV format input # Since we do not have this file with us, the following line is just for illustration # bst <- lightgbm( # data = "agaricus.train.svm" # , num_leaves = 4L # , learning_rate = 1.0 # , nrounds = 2L # , objective = "binary" # ) #--------------------Basic prediction using lightgbm-------------- # You can do prediction using the following line # You can put in Matrix, sparseMatrix, or lgb.Dataset pred <- predict(bst, test$data) err <- mean(as.numeric(pred > 0.5) != test$label) print(paste("test-error=", err)) #--------------------Save and load models------------------------- # Save model to binary local file lgb.save(bst, "lightgbm.model") # Load binary model to R bst2 <- lgb.load("lightgbm.model") pred2 <- predict(bst2, test$data) # pred2 should be identical to pred print(paste("sum(abs(pred2-pred))=", sum(abs(pred2 - pred)))) #--------------------Advanced features --------------------------- # To use advanced features, we need to put data in lgb.Dataset dtrain <- lgb.Dataset(data = train$data, label = train$label, free_raw_data = FALSE) dtest <- lgb.Dataset.create.valid(dtrain, data = test$data, label = test$label) #--------------------Using validation set------------------------- # valids is a list of lgb.Dataset, each of them is tagged with name valids <- list(train = dtrain, test = dtest) # To train with valids, use lgb.train, which contains more advanced features # valids allows us to monitor the evaluation result on all data in the list print("Train lightgbm using lgb.train with valids") bst <- lgb.train( data = dtrain , params = train_params , nrounds = 2L , valids = valids ) # We can change evaluation metrics, or use multiple evaluation metrics print("Train lightgbm using lgb.train with valids, watch logloss and error") bst <- lgb.train( data = dtrain , params = train_params , nrounds = 2L , valids = valids , eval = c("binary_error", "binary_logloss") ) # lgb.Dataset can also be saved using lgb.Dataset.save lgb.Dataset.save(dtrain, "dtrain.buffer") # To load it in, simply call lgb.Dataset dtrain2 <- lgb.Dataset("dtrain.buffer") bst <- lgb.train( data = dtrain2 , params = train_params , nrounds = 2L , valids = valids ) # information can be extracted from lgb.Dataset using get_field() label <- get_field(dtest, "label") pred <- predict(bst, test$data) err <- as.numeric(sum(as.integer(pred > 0.5) != label)) / length(label) print(paste("test-error=", err)) ================================================ FILE: R-package/demo/boost_from_prediction.R ================================================ library(lightgbm) # Load in the agaricus dataset data(agaricus.train, package = "lightgbm") data(agaricus.test, package = "lightgbm") dtrain <- lgb.Dataset(agaricus.train$data, label = agaricus.train$label) dtest <- lgb.Dataset.create.valid(dtrain, data = agaricus.test$data, label = agaricus.test$label) valids <- list(eval = dtest, train = dtrain) #--------------------Advanced features --------------------------- # advanced: start from an initial base prediction print("Start running example to start from an initial prediction") # Train lightgbm for 1 round param <- list( num_leaves = 4L , learning_rate = 1.0 , nthread = 2L , objective = "binary" ) bst <- lgb.train(param, dtrain, 1L, valids = valids) # Note: we need the margin value instead of transformed prediction in set_init_score ptrain <- predict(bst, agaricus.train$data, type = "raw") ptest <- predict(bst, agaricus.test$data, type = "raw") # set the init_score property of dtrain and dtest # base margin is the base prediction we will boost from set_field(dtrain, "init_score", ptrain) set_field(dtest, "init_score", ptest) print("This is result of boost from initial prediction") bst <- lgb.train( params = param , data = dtrain , nrounds = 5L , valids = valids ) ================================================ FILE: R-package/demo/categorical_features_rules.R ================================================ # Here we are going to try training a model with categorical features # Load libraries library(data.table) library(lightgbm) # Load data and look at the structure # # Classes 'data.table' and 'data.frame': 4521 obs. of 17 variables: # $ age : int 30 33 35 30 59 35 36 39 41 43 ... # $ job : chr "unemployed" "services" "management" "management" ... # $ marital : chr "married" "married" "single" "married" ... # $ education: chr "primary" "secondary" "tertiary" "tertiary" ... # $ default : chr "no" "no" "no" "no" ... # $ balance : int 1787 4789 1350 1476 0 747 307 147 221 -88 ... # $ housing : chr "no" "yes" "yes" "yes" ... # $ loan : chr "no" "yes" "no" "yes" ... # $ contact : chr "cellular" "cellular" "cellular" "unknown" ... # $ day : int 19 11 16 3 5 23 14 6 14 17 ... # $ month : chr "oct" "may" "apr" "jun" ... # $ duration : int 79 220 185 199 226 141 341 151 57 313 ... # $ campaign : int 1 1 1 4 1 2 1 2 2 1 ... # $ pdays : int -1 339 330 -1 -1 176 330 -1 -1 147 ... # $ previous : int 0 4 1 0 0 3 2 0 0 2 ... # $ poutcome : chr "unknown" "failure" "failure" "unknown" ... # $ y : chr "no" "no" "no" "no" ... data(bank, package = "lightgbm") str(bank) # We are dividing the dataset into two: one train, one validation bank_train <- bank[1L:4000L, ] bank_test <- bank[4001L:4521L, ] # We must now transform the data to fit in LightGBM # For this task, we use lgb.convert_with_rules # The function transforms the data into a fittable data # # Classes 'data.table' and 'data.frame': 521 obs. of 17 variables: # $ age : int 53 36 58 26 34 55 55 34 41 38 ... # $ job : num 1 10 10 9 10 2 2 3 3 4 ... # $ marital : num 1 2 1 3 3 2 2 2 1 1 ... # $ education: num 2 2 2 2 2 1 2 3 2 2 ... # $ default : num 1 1 1 1 1 1 1 1 1 1 ... # $ balance : int 26 191 -123 -147 179 1086 471 105 1588 70 ... # $ housing : num 2 1 1 1 1 2 2 2 2 1 ... # $ loan : num 1 1 1 1 1 1 1 1 2 1 ... # $ contact : num 1 1 1 3 1 1 3 3 3 1 ... # $ day : int 7 31 5 4 19 6 30 28 20 27 ... # $ month : num 9 2 2 7 2 9 9 9 7 11 ... # $ duration : int 56 69 131 95 294 146 58 249 10 255 ... # $ campaign : int 1 1 2 2 3 1 2 2 8 3 ... # $ pdays : int 359 -1 -1 -1 -1 272 -1 -1 -1 148 ... # $ previous : int 1 0 0 0 0 2 0 0 0 1 ... # $ poutcome : num 1 4 4 4 4 1 4 4 4 3 ... # $ y : num 1 1 1 1 1 1 1 1 1 2 ... bank_rules <- lgb.convert_with_rules(data = bank_train) bank_train <- bank_rules$data bank_test <- lgb.convert_with_rules(data = bank_test, rules = bank_rules$rules)$data str(bank_test) # Remove 1 to label because it must be between 0 and 1 bank_train$y <- bank_train$y - 1L bank_test$y <- bank_test$y - 1L # Data input to LightGBM must be a matrix, without the label my_data_train <- as.matrix(bank_train[, 1L:16L, with = FALSE]) my_data_test <- as.matrix(bank_test[, 1L:16L, with = FALSE]) # Creating the LightGBM dataset with categorical features # The categorical features can be passed to lgb.train to not copy and paste a lot dtrain <- lgb.Dataset( data = my_data_train , label = bank_train$y , categorical_feature = c(2L, 3L, 4L, 5L, 7L, 8L, 9L, 11L, 16L) ) dtest <- lgb.Dataset.create.valid( dtrain , data = my_data_test , label = bank_test$y ) # We can now train a model params <- list( objective = "binary" , metric = "l2" , min_data = 1L , learning_rate = 0.1 , min_hessian = 1.0 , max_depth = 2L ) model <- lgb.train( params = params , data = dtrain , nrounds = 100L , valids = list(train = dtrain, valid = dtest) ) # Try to find split_feature: 11 # If you find it, it means it used a categorical feature in the first tree lgb.dump(model, num_iteration = 1L) ================================================ FILE: R-package/demo/cross_validation.R ================================================ library(lightgbm) # load in the agaricus dataset data(agaricus.train, package = "lightgbm") data(agaricus.test, package = "lightgbm") dtrain <- lgb.Dataset(agaricus.train$data, label = agaricus.train$label) dtest <- lgb.Dataset.create.valid(dtrain, data = agaricus.test$data, label = agaricus.test$label) nrounds <- 2L param <- list( num_leaves = 4L , learning_rate = 1.0 , objective = "binary" ) print("Running cross validation") # Do cross validation, this will print result out as # [iteration] metric_name:mean_value+std_value # std_value is standard deviation of the metric lgb.cv( param , dtrain , nrounds , nfold = 5L , eval = "binary_error" ) print("Running cross validation, disable standard deviation display") # do cross validation, this will print result out as # [iteration] metric_name:mean_value+std_value # std_value is standard deviation of the metric lgb.cv( param , dtrain , nrounds , nfold = 5L , eval = "binary_error" , showsd = FALSE ) # You can also do cross validation with customized loss function print("Running cross validation, with cutomsized loss function") logregobj <- function(preds, dtrain) { labels <- get_field(dtrain, "label") preds <- 1.0 / (1.0 + exp(-preds)) grad <- preds - labels hess <- preds * (1.0 - preds) return(list(grad = grad, hess = hess)) } # User-defined evaluation function returns a pair (metric_name, result, higher_better) # NOTE: when you do customized loss function, the default prediction value is margin # This may make built-in evaluation metric calculate wrong results # For example, we are doing logistic loss, the prediction is score before logistic transformation # Keep this in mind when you use the customization, and maybe you need write customized evaluation function evalerror <- function(preds, dtrain) { labels <- get_field(dtrain, "label") preds <- 1.0 / (1.0 + exp(-preds)) err <- as.numeric(sum(labels != (preds > 0.5))) / length(labels) return(list(name = "error", value = err, higher_better = FALSE)) } # train with customized objective lgb.cv( params = param , data = dtrain , nrounds = nrounds , obj = logregobj , eval = evalerror , nfold = 5L ) ================================================ FILE: R-package/demo/early_stopping.R ================================================ library(lightgbm) # Load in the agaricus dataset data(agaricus.train, package = "lightgbm") data(agaricus.test, package = "lightgbm") dtrain <- lgb.Dataset(agaricus.train$data, label = agaricus.train$label) dtest <- lgb.Dataset.create.valid(dtrain, data = agaricus.test$data, label = agaricus.test$label) # Note: for customized objective function, we leave objective as default # Note: what we are getting is margin value in prediction # You must know what you are doing param <- list( num_leaves = 4L , learning_rate = 1.0 ) valids <- list(eval = dtest) num_round <- 20L # User define objective function, given prediction, return gradient and second order gradient # This is loglikelihood loss logregobj <- function(preds, dtrain) { labels <- get_field(dtrain, "label") preds <- 1.0 / (1.0 + exp(-preds)) grad <- preds - labels hess <- preds * (1.0 - preds) return(list(grad = grad, hess = hess)) } # User-defined evaluation function returns a pair (metric_name, result, higher_better) # NOTE: when you do customized loss function, the default prediction value is margin # This may make built-in evaluation metric calculate wrong results # For example, we are doing logistic loss, the prediction is score before logistic transformation # The built-in evaluation error assumes input is after logistic transformation # Keep this in mind when you use the customization, and maybe you need write customized evaluation function evalerror <- function(preds, dtrain) { labels <- get_field(dtrain, "label") err <- as.numeric(sum(labels != (preds > 0.5))) / length(labels) return(list(name = "error", value = err, higher_better = FALSE)) } print("Start training with early Stopping setting") bst <- lgb.train( param , dtrain , num_round , valids , obj = logregobj , eval = evalerror , early_stopping_round = 3L ) ================================================ FILE: R-package/demo/efficient_many_training.R ================================================ # Efficient training means training without giving up too much RAM # In the case of many trainings (like 100+ models), RAM will be eaten very quickly # Therefore, it is essential to know a strategy to deal with such issue # More results can be found here: https://github.com/lightgbm-org/LightGBM/issues/879#issuecomment-326656580 # Quote: "@Laurae2 Thanks for nice easily reproducible example (unlike mine). # With reset=FALSE you get after 500 iterations (not 1000): OS reports 27GB usage, while R gc() reports 1.5GB. # Just doing reset=TRUE will already improve things: OS reports 4.6GB. # Doing reset=TRUE and calling gc() in the loop will have OS 1.3GB. Thanks for the latest tip." # Load library library(lightgbm) # Generate fictive data of size 1M x 100 set.seed(11111L) x_data <- matrix(rnorm(n = 100000000L, mean = 0.0, sd = 100.0), nrow = 1000000L, ncol = 100L) y_data <- rnorm(n = 1000000L, mean = 0.0, sd = 5.0) # Create lgb.Dataset for training data <- lgb.Dataset(x_data, label = y_data) data$construct() # Loop through a training of 1000 models, please check your RAM on your task manager # It MUST remain constant (if not increasing very slightly) gbm <- list() for (i in 1L:1000L) { print(i) gbm[[i]] <- lgb.train( params = list(objective = "regression") , data = data , 1L , reset_data = TRUE ) gc(verbose = FALSE) } ================================================ FILE: R-package/demo/leaf_stability.R ================================================ # We are going to look at how iterating too much might generate observation instability. # Obviously, we are in a controlled environment, without issues (real rules). # Do not do this in a real scenario. library(lightgbm) # define helper functions for creating plots # output of `RColorBrewer::brewer.pal(10, "RdYlGn")`, hardcooded here to avoid a dependency .diverging_palette <- c( "#A50026", "#D73027", "#F46D43", "#FDAE61", "#FEE08B" , "#D9EF8B", "#A6D96A", "#66BD63", "#1A9850", "#006837" ) .prediction_depth_plot <- function(df) { plot( x = df$X , y = df$Y , type = "p" , main = "Prediction Depth" , xlab = "Leaf Bin" , ylab = "Prediction Probability" , pch = 19L , col = .diverging_palette[df$binned + 1L] ) legend( "topright" , title = "bin" , legend = sort(unique(df$binned)) , pch = 19L , col = .diverging_palette[sort(unique(df$binned + 1L))] , cex = 0.7 ) } .prediction_depth_spread_plot <- function(df) { plot( x = df$binned , xlim = c(0L, 9L) , y = df$Z , type = "p" , main = "Prediction Depth Spread" , xlab = "Leaf Bin" , ylab = "Logloss" , pch = 19L , col = .diverging_palette[df$binned + 1L] ) legend( "topright" , title = "bin" , legend = sort(unique(df$binned)) , pch = 19L , col = .diverging_palette[sort(unique(df$binned + 1L))] , cex = 0.7 ) } .depth_density_plot <- function(df) { plot( x = density(df$Y) , xlim = c(min(df$Y), max(df$Y)) , type = "p" , main = "Depth Density" , xlab = "Prediction Probability" , ylab = "Bin Density" , pch = 19L , col = .diverging_palette[df$binned + 1L] ) legend( "topright" , title = "bin" , legend = sort(unique(df$binned)) , pch = 19L , col = .diverging_palette[sort(unique(df$binned + 1L))] , cex = 0.7 ) } # load some data data(agaricus.train, package = "lightgbm") train <- agaricus.train dtrain <- lgb.Dataset(train$data, label = train$label) data(agaricus.test, package = "lightgbm") test <- agaricus.test dtest <- lgb.Dataset.create.valid(dtrain, test$data, label = test$label) # setup parameters and we train a model params <- list( objective = "regression" , metric = "l2" , min_data = 1L , learning_rate = 0.1 , bagging_fraction = 0.1 , bagging_freq = 1L , bagging_seed = 1L ) valids <- list(test = dtest) model <- lgb.train( params , dtrain , 50L , valids ) # We create a data.frame with the following structure: # X = average leaf of the observation throughout all trees # Y = prediction probability (clamped to [1e-15, 1-1e-15]) # Z = logloss # binned = binned quantile of average leaf new_data <- data.frame( X = rowMeans(predict( model , agaricus.test$data , type = "leaf" )) , Y = pmin( pmax( predict(model, agaricus.test$data) , 1e-15 ) , 1.0 - 1e-15 ) ) new_data$Z <- -1.0 * (agaricus.test$label * log(new_data$Y) + (1L - agaricus.test$label) * log(1L - new_data$Y)) new_data$binned <- .bincode( x = new_data$X , breaks = quantile( x = new_data$X , probs = seq_len(9L) / 10.0 ) , right = TRUE , include.lowest = TRUE ) new_data$binned[is.na(new_data$binned)] <- 0L # We can check the binned content table(new_data$binned) # We can plot the binned content # On the second plot, we clearly notice the lower the bin (the lower the leaf value), the higher the loss # On the third plot, it is smooth! .prediction_depth_plot(df = new_data) .prediction_depth_spread_plot(df = new_data) .depth_density_plot(df = new_data) # Now, let's show with other parameters params <- list( objective = "regression" , metric = "l2" , min_data = 1L , learning_rate = 1.0 ) model2 <- lgb.train( params , dtrain , 100L , valids ) # We create the data structure, but for model2 new_data2 <- data.frame( X = rowMeans(predict( model2 , agaricus.test$data , type = "leaf" )) , Y = pmin( pmax( predict( model2 , agaricus.test$data ) , 1e-15 ) , 1.0 - 1e-15 ) ) new_data2$Z <- -1.0 * (agaricus.test$label * log(new_data2$Y) + (1L - agaricus.test$label) * log(1L - new_data2$Y)) new_data2$binned <- .bincode( x = new_data2$X , breaks = quantile( x = new_data2$X , probs = seq_len(9L) / 10.0 ) , right = TRUE , include.lowest = TRUE ) new_data2$binned[is.na(new_data2$binned)] <- 0L # We can check the binned content table(new_data2$binned) # We can plot the binned content # On the second plot, we clearly notice the lower the bin (the lower the leaf value), the higher the loss # On the third plot, it is clearly not smooth! We are severely overfitting the data, but the rules are # real thus it is not an issue # However, if the rules were not true, the loss would explode. .prediction_depth_plot(df = new_data2) .prediction_depth_spread_plot(df = new_data2) .depth_density_plot(df = new_data2) # Now, try with very severe overfitting params <- list( objective = "regression" , metric = "l2" , min_data = 1L , learning_rate = 1.0 ) model3 <- lgb.train( params , dtrain , 1000L , valids ) # We create the data structure, but for model3 new_data3 <- data.frame( X = rowMeans(predict( model3 , agaricus.test$data , type = "leaf" )) , Y = pmin( pmax( predict( model3 , agaricus.test$data ) , 1e-15 ) , 1.0 - 1e-15 ) ) new_data3$Z <- -1.0 * (agaricus.test$label * log(new_data3$Y) + (1L - agaricus.test$label) * log(1L - new_data3$Y)) new_data3$binned <- .bincode( x = new_data3$X , breaks = quantile( x = new_data3$X , probs = seq_len(9L) / 10.0 ) , right = TRUE , include.lowest = TRUE ) new_data3$binned[is.na(new_data3$binned)] <- 0L # We can check the binned content table(new_data3$binned) # We can plot the binned content # On the third plot, it is clearly not smooth! We are severely overfitting the data, but the rules # are real thus it is not an issue. # However, if the rules were not true, the loss would explode. See the sudden spikes? .depth_density_plot(df = new_data3) # Compare with our second model, the difference is severe. This is smooth. .depth_density_plot(df = new_data2) ================================================ FILE: R-package/demo/multiclass.R ================================================ library(lightgbm) # We load the default iris dataset shipped with R data(iris) # We must convert factors to numeric # They must be starting from number 0 to use multiclass # For instance: 0, 1, 2, 3, 4, 5... iris$Species <- as.numeric(as.factor(iris$Species)) - 1L # We cut the data set into 80% train and 20% validation # The 10 last samples of each class are for validation train <- as.matrix(iris[c(1L:40L, 51L:90L, 101L:140L), ]) test <- as.matrix(iris[c(41L:50L, 91L:100L, 141L:150L), ]) dtrain <- lgb.Dataset(data = train[, 1L:4L], label = train[, 5L]) dtest <- lgb.Dataset.create.valid(dtrain, data = test[, 1L:4L], label = test[, 5L]) valids <- list(test = dtest) # Method 1 of training params <- list( objective = "multiclass" , metric = "multi_error" , num_class = 3L , min_data = 1L , learning_rate = 1.0 ) model <- lgb.train( params , dtrain , 100L , valids , early_stopping_rounds = 10L ) # We can predict on test data, outputs a 90-length vector # Order: obs1 class1, obs1 class2, obs1 class3, obs2 class1, obs2 class2, obs2 class3... my_preds <- predict(model, test[, 1L:4L]) # Method 2 of training, identical params <- list( min_data = 1L , learning_rate = 1.0 , objective = "multiclass" , metric = "multi_error" , num_class = 3L ) model <- lgb.train( params , dtrain , 100L , valids , early_stopping_rounds = 10L ) # We can predict on test data, identical my_preds <- predict(model, test[, 1L:4L]) # A (30x3) matrix with the predictions # class1 class2 class3 # obs1 obs1 obs1 # obs2 obs2 obs2 # .... .... .... my_preds <- predict(model, test[, 1L:4L]) # We can also get the predicted scores before the Sigmoid/Softmax application my_preds <- predict(model, test[, 1L:4L], type = "raw") # We can also get the leaf index my_preds <- predict(model, test[, 1L:4L], type = "leaf") ================================================ FILE: R-package/demo/multiclass_custom_objective.R ================================================ library(lightgbm) # We load the default iris dataset shipped with R data(iris) # We must convert factors to numeric # They must be starting from number 0 to use multiclass # For instance: 0, 1, 2, 3, 4, 5... iris$Species <- as.numeric(as.factor(iris$Species)) - 1L # Create imbalanced training data (20, 30, 40 examples for classes 0, 1, 2) train <- as.matrix(iris[c(1L:20L, 51L:80L, 101L:140L), ]) # The 10 last samples of each class are for validation test <- as.matrix(iris[c(41L:50L, 91L:100L, 141L:150L), ]) dtrain <- lgb.Dataset(data = train[, 1L:4L], label = train[, 5L]) dtest <- lgb.Dataset.create.valid(dtrain, data = test[, 1L:4L], label = test[, 5L]) valids <- list(train = dtrain, test = dtest) # Method 1 of training with built-in multiclass objective # Note: need to turn off boost from average to match custom objective # (https://github.com/lightgbm-org/LightGBM/issues/1846) params <- list( min_data = 1L , learning_rate = 1.0 , num_class = 3L , boost_from_average = FALSE , metric = "multi_logloss" ) model_builtin <- lgb.train( params , dtrain , 100L , valids , early_stopping_rounds = 10L , obj = "multiclass" ) preds_builtin <- predict(model_builtin, test[, 1L:4L], type = "raw") probs_builtin <- exp(preds_builtin) / rowSums(exp(preds_builtin)) # Method 2 of training with custom objective function # User defined objective function, given prediction, return gradient and second order gradient custom_multiclass_obj <- function(preds, dtrain) { labels <- get_field(dtrain, "label") # preds is a matrix with rows corresponding to samples and columns corresponding to choices preds <- matrix(preds, nrow = length(labels)) # to prevent overflow, normalize preds by row preds <- preds - apply(preds, MARGIN = 1L, max) prob <- exp(preds) / rowSums(exp(preds)) # compute gradient grad <- prob subset_index <- as.matrix( data.frame( seq_along(labels) , labels + 1L , fix.empty.names = FALSE ) , nrow = length(labels) , dimnames = NULL ) grad[subset_index] <- grad[subset_index] - 1L # compute hessian (approximation) hess <- 2.0 * prob * (1.0 - prob) return(list(grad = grad, hess = hess)) } # define custom metric custom_multiclass_metric <- function(preds, dtrain) { labels <- get_field(dtrain, "label") preds <- matrix(preds, nrow = length(labels)) preds <- preds - apply(preds, 1L, max) prob <- exp(preds) / rowSums(exp(preds)) subset_index <- as.matrix( data.frame( seq_along(labels) , labels + 1L , fix.empty.names = FALSE ) , nrow = length(labels) , dimnames = NULL ) return(list( name = "error" , value = -mean(log(prob[subset_index])) , higher_better = FALSE )) } params <- list( min_data = 1L , learning_rate = 1.0 , num_class = 3L ) model_custom <- lgb.train( params , dtrain , 100L , valids , early_stopping_rounds = 10L , obj = custom_multiclass_obj , eval = custom_multiclass_metric ) preds_custom <- predict(model_custom, test[, 1L:4L], type = "raw") probs_custom <- exp(preds_custom) / rowSums(exp(preds_custom)) # compare predictions stopifnot(identical(probs_builtin, probs_custom)) stopifnot(identical(preds_builtin, preds_custom)) ================================================ FILE: R-package/demo/weight_param.R ================================================ # This demo R code is to provide a demonstration of hyperparameter adjustment # when scaling weights for appropriate learning # As with any optimizers, bad parameters can impair performance # Load library library(lightgbm) # We will train a model with the following scenarii: # - Run 1: sum of weights equal to 6513 (x 1e-5) without adjusted regularization (not learning) # - Run 2: sum of weights equal to 6513 (x 1e-5) adjusted regularization (learning) # - Run 3: sum of weights equal to 6513 with adjusted regularization (learning) # Setup small weights weights1 <- rep(1e-5, 6513L) weights2 <- rep(1e-5, 1611L) # Load data and create datasets data(agaricus.train, package = "lightgbm") train <- agaricus.train dtrain <- lgb.Dataset(train$data, label = train$label, weight = weights1) data(agaricus.test, package = "lightgbm") test <- agaricus.test dtest <- lgb.Dataset.create.valid(dtrain, test$data, label = test$label, weight = weights2) valids <- list(test = dtest) # Run 1: sum of weights equal to 6513 (x 1e-5) without adjusted regularization (not learning) # It cannot learn because regularization is too large! # min_sum_hessian alone is bigger than the sum of weights, thus you will never learn anything params <- list( objective = "regression" , metric = "l2" , device = "cpu" , min_sum_hessian = 10.0 , num_leaves = 7L , max_depth = 3L , nthread = 1L , min_data = 1L , learning_rate = 1.0 ) model <- lgb.train( params , dtrain , 50L , valids , early_stopping_rounds = 10L ) weight_loss <- as.numeric(model$record_evals$test$l2$eval) plot(weight_loss) # Shows how poor the learning was: a straight line! # Run 2: sum of weights equal to 6513 (x 1e-5) with adjusted regularization (learning) # Adjusted regularization just consisting in multiplicating results by 1e4 (x10000) # Notice how it learns, there is no issue as we adjusted regularization ourselves params <- list( objective = "regression" , metric = "l2" , device = "cpu" , min_sum_hessian = 1e-4 , num_leaves = 7L , max_depth = 3L , nthread = 1L , min_data = 1L , learning_rate = 1.0 ) model <- lgb.train( params , dtrain , 50L , valids , early_stopping_rounds = 10L ) small_weight_loss <- as.numeric(model$record_evals$test$l2$eval) plot(small_weight_loss) # It learns! # Run 3: sum of weights equal to 6513 with adjusted regularization (learning) dtrain <- lgb.Dataset(train$data, label = train$label) dtest <- lgb.Dataset.create.valid(dtrain, test$data, label = test$label) valids <- list(test = dtest) # Setup parameters and run model... params <- list( objective = "regression" , metric = "l2" , device = "cpu" , min_sum_hessian = 10.0 , num_leaves = 7L , max_depth = 3L , nthread = 1L , min_data = 1L , learning_rate = 1.0 ) model <- lgb.train( params , dtrain , 50L , valids , early_stopping_rounds = 10L ) large_weight_loss <- as.numeric(model$record_evals$test$l2$eval) plot(large_weight_loss) # It learns! # Do you want to compare the learning? They both converge. plot(small_weight_loss, large_weight_loss) curve(1.0 * x, from = 0L, to = 0.02, add = TRUE) ================================================ FILE: R-package/inst/Makevars ================================================ ================================================ FILE: R-package/inst/make-r-def.R ================================================ # [description] # Create a definition file (.def) from a .dll file, using objdump. # # [usage] # # Rscript make-r-def.R something.dll something.def # # [references] # * https://www.cs.colorado.edu/~main/cs1300/doc/mingwfaq.html args <- commandArgs(trailingOnly = TRUE) IN_DLL_FILE <- args[[1L]] OUT_DEF_FILE <- args[[2L]] DLL_BASE_NAME <- basename(IN_DLL_FILE) message(sprintf("Creating '%s' from '%s'", OUT_DEF_FILE, IN_DLL_FILE)) # system() will not raise an R exception if the process called # fails. Wrapping it here to get that behavior. # # system() introduces a lot of overhead, at least on Windows, # so trying processx if it is available .pipe_shell_command_to_stdout <- function(command, args, out_file) { has_processx <- suppressMessages({ suppressWarnings({ require("processx") # nolint: undesirable_function. }) }) if (has_processx) { p <- processx::process$new( command = command , args = args , stdout = out_file , windows_verbatim_args = FALSE ) invisible(p$wait()) } else { message(paste0( "Using system2() to run shell commands. Installing " , "'processx' with install.packages('processx') might " , "make this faster." )) # shQuote() is necessary here since one of the arguments # is a file-path to R.dll, which may have spaces. processx # does such quoting but system2() does not exit_code <- system2( command = command , args = shoQuote(args) , stdout = out_file ) if (exit_code != 0L) { stop(paste0("Command failed with exit code: ", exit_code)) } } return(invisible(NULL)) } # use objdump to dump all the symbols OBJDUMP_FILE <- "objdump-out.txt" .pipe_shell_command_to_stdout( command = "objdump" , args = c("-p", IN_DLL_FILE) , out_file = OBJDUMP_FILE ) objdump_results <- readLines(OBJDUMP_FILE) invisible(file.remove(OBJDUMP_FILE)) # Only one table in the objdump results matters for our purposes, # see https://www.cs.colorado.edu/~main/cs1300/doc/mingwfaq.html start_index <- which( grepl( pattern = "[Ordinal/Name Pointer] Table" # nolint: non_portable_path. , x = objdump_results , fixed = TRUE ) ) empty_lines <- which(objdump_results == "") end_of_table <- empty_lines[empty_lines > start_index][1L] # Read the contents of the table exported_symbols <- objdump_results[(start_index + 1L):end_of_table] exported_symbols <- gsub("\t", "", exported_symbols, fixed = TRUE) exported_symbols <- gsub(".*\\] ", "", exported_symbols) exported_symbols <- gsub(" ", "", exported_symbols, fixed = TRUE) # Write R.def file writeLines( text = c( paste0("LIBRARY \"", DLL_BASE_NAME, "\"") , "EXPORTS" , exported_symbols ) , con = OUT_DEF_FILE , sep = "\n" ) message(sprintf("Successfully created '%s'", OUT_DEF_FILE)) ================================================ FILE: R-package/man/agaricus.test.Rd ================================================ % Generated by roxygen2: do not edit by hand % Please edit documentation in R/lightgbm.R \docType{data} \name{agaricus.test} \alias{agaricus.test} \title{Test part from Mushroom Data Set} \format{ A list containing a label vector, and a dgCMatrix object with 1611 rows and 126 variables } \usage{ data(agaricus.test) } \description{ This data set is originally from the Mushroom data set, UCI Machine Learning Repository. This data set includes the following fields: \itemize{ \item{\code{label}: the label for each record} \item{\code{data}: a sparse Matrix of \code{dgCMatrix} class, with 126 columns.} } } \references{ https://archive.ics.uci.edu/ml/datasets/Mushroom Bache, K. & Lichman, M. (2013). UCI Machine Learning Repository [https://archive.ics.uci.edu/ml]. Irvine, CA: University of California, School of Information and Computer Science. } \keyword{datasets} ================================================ FILE: R-package/man/agaricus.train.Rd ================================================ % Generated by roxygen2: do not edit by hand % Please edit documentation in R/lightgbm.R \docType{data} \name{agaricus.train} \alias{agaricus.train} \title{Training part from Mushroom Data Set} \format{ A list containing a label vector, and a dgCMatrix object with 6513 rows and 127 variables } \usage{ data(agaricus.train) } \description{ This data set is originally from the Mushroom data set, UCI Machine Learning Repository. This data set includes the following fields: \itemize{ \item{\code{label}: the label for each record} \item{\code{data}: a sparse Matrix of \code{dgCMatrix} class, with 126 columns.} } } \references{ https://archive.ics.uci.edu/ml/datasets/Mushroom Bache, K. & Lichman, M. (2013). UCI Machine Learning Repository [https://archive.ics.uci.edu/ml]. Irvine, CA: University of California, School of Information and Computer Science. } \keyword{datasets} ================================================ FILE: R-package/man/bank.Rd ================================================ % Generated by roxygen2: do not edit by hand % Please edit documentation in R/lightgbm.R \docType{data} \name{bank} \alias{bank} \title{Bank Marketing Data Set} \format{ A data.table with 4521 rows and 17 variables } \usage{ data(bank) } \description{ This data set is originally from the Bank Marketing data set, UCI Machine Learning Repository. It contains only the following: bank.csv with 10% of the examples and 17 inputs, randomly selected from 3 (older version of this dataset with less inputs). } \references{ https://archive.ics.uci.edu/ml/datasets/Bank+Marketing S. Moro, P. Cortez and P. Rita. (2014) A Data-Driven Approach to Predict the Success of Bank Telemarketing. Decision Support Systems } \keyword{datasets} ================================================ FILE: R-package/man/dim.Rd ================================================ % Generated by roxygen2: do not edit by hand % Please edit documentation in R/lgb.Dataset.R \name{dim.lgb.Dataset} \alias{dim.lgb.Dataset} \title{Dimensions of an \code{lgb.Dataset}} \usage{ \method{dim}{lgb.Dataset}(x) } \arguments{ \item{x}{Object of class \code{lgb.Dataset}} } \value{ a vector of numbers of rows and of columns } \description{ Returns a vector of numbers of rows and of columns in an \code{lgb.Dataset}. } \details{ Note: since \code{nrow} and \code{ncol} internally use \code{dim}, they can also be directly used with an \code{lgb.Dataset} object. } \examples{ \donttest{ \dontshow{setLGBMthreads(2L)} \dontshow{data.table::setDTthreads(1L)} data(agaricus.train, package = "lightgbm") train <- agaricus.train dtrain <- lgb.Dataset(train$data, label = train$label) stopifnot(nrow(dtrain) == nrow(train$data)) stopifnot(ncol(dtrain) == ncol(train$data)) stopifnot(all(dim(dtrain) == dim(train$data))) } } ================================================ FILE: R-package/man/dimnames.lgb.Dataset.Rd ================================================ % Generated by roxygen2: do not edit by hand % Please edit documentation in R/lgb.Dataset.R \name{dimnames.lgb.Dataset} \alias{dimnames.lgb.Dataset} \alias{dimnames<-.lgb.Dataset} \title{Handling of column names of \code{lgb.Dataset}} \usage{ \method{dimnames}{lgb.Dataset}(x) \method{dimnames}{lgb.Dataset}(x) <- value } \arguments{ \item{x}{object of class \code{lgb.Dataset}} \item{value}{a list of two elements: the first one is ignored and the second one is column names} } \value{ A list with the dimension names of the dataset } \description{ Only column names are supported for \code{lgb.Dataset}, thus setting of row names would have no effect and returned row names would be NULL. } \details{ Generic \code{dimnames} methods are used by \code{colnames}. Since row names are irrelevant, it is recommended to use \code{colnames} directly. } \examples{ \donttest{ \dontshow{setLGBMthreads(2L)} \dontshow{data.table::setDTthreads(1L)} data(agaricus.train, package = "lightgbm") train <- agaricus.train dtrain <- lgb.Dataset(train$data, label = train$label) lgb.Dataset.construct(dtrain) dimnames(dtrain) colnames(dtrain) colnames(dtrain) <- make.names(seq_len(ncol(train$data))) print(dtrain, verbose = TRUE) } } ================================================ FILE: R-package/man/getLGBMThreads.Rd ================================================ % Generated by roxygen2: do not edit by hand % Please edit documentation in R/multithreading.R \name{getLGBMThreads} \alias{getLGBMThreads} \alias{getLGBMthreads} \title{Get default number of threads used by LightGBM} \usage{ getLGBMthreads() } \value{ number of threads as an integer. \code{-1} means that in situations where parameter \code{num_threads} is not explicitly supplied, LightGBM will choose a number of threads to use automatically. } \description{ LightGBM attempts to speed up many operations by using multi-threading. The number of threads used in those operations can be controlled via the \code{num_threads} parameter passed through \code{params} to functions like \link{lgb.train} and \link{lgb.Dataset}. However, some operations (like materializing a model from a text file) are done via code paths that don't explicitly accept thread-control configuration. Use this function to see the default number of threads LightGBM will use for such operations. } \seealso{ \link{setLGBMthreads} } ================================================ FILE: R-package/man/get_field.Rd ================================================ % Generated by roxygen2: do not edit by hand % Please edit documentation in R/lgb.Dataset.R \name{get_field} \alias{get_field} \alias{get_field.lgb.Dataset} \title{Get one attribute of a \code{lgb.Dataset}} \usage{ get_field(dataset, field_name) \method{get_field}{lgb.Dataset}(dataset, field_name) } \arguments{ \item{dataset}{Object of class \code{lgb.Dataset}} \item{field_name}{String with the name of the attribute to get. One of the following. \itemize{ \item \code{label}: label lightgbm learns from ; \item \code{weight}: to do a weight rescale ; \item{\code{group}: used for learning-to-rank tasks. An integer vector describing how to group rows together as ordered results from the same set of candidate results to be ranked. For example, if you have a 100-document dataset with \code{group = c(10, 20, 40, 10, 10, 10)}, that means that you have 6 groups, where the first 10 records are in the first group, records 11-30 are in the second group, etc.} \item \code{init_score}: initial score is the base prediction lightgbm will boost from. }} } \value{ requested attribute } \description{ Get one attribute of a \code{lgb.Dataset} } \examples{ \donttest{ \dontshow{setLGBMthreads(2L)} \dontshow{data.table::setDTthreads(1L)} data(agaricus.train, package = "lightgbm") train <- agaricus.train dtrain <- lgb.Dataset(train$data, label = train$label) lgb.Dataset.construct(dtrain) labels <- lightgbm::get_field(dtrain, "label") lightgbm::set_field(dtrain, "label", 1 - labels) labels2 <- lightgbm::get_field(dtrain, "label") stopifnot(all(labels2 == 1 - labels)) } } ================================================ FILE: R-package/man/lgb.Dataset.Rd ================================================ % Generated by roxygen2: do not edit by hand % Please edit documentation in R/lgb.Dataset.R \name{lgb.Dataset} \alias{lgb.Dataset} \title{Construct \code{lgb.Dataset} object} \usage{ lgb.Dataset( data, params = list(), reference = NULL, colnames = NULL, categorical_feature = NULL, free_raw_data = TRUE, label = NULL, weight = NULL, group = NULL, init_score = NULL ) } \arguments{ \item{data}{a \code{matrix} object, a \code{dgCMatrix} object, a character representing a path to a text file (CSV, TSV, or LibSVM), or a character representing a path to a binary \code{lgb.Dataset} file} \item{params}{a list of parameters. See \href{https://lightgbm.readthedocs.io/en/latest/Parameters.html#dataset-parameters}{ The "Dataset Parameters" section of the documentation} for a list of parameters and valid values.} \item{reference}{reference dataset. When LightGBM creates a Dataset, it does some preprocessing like binning continuous features into histograms. If you want to apply the same bin boundaries from an existing dataset to new \code{data}, pass that existing Dataset to this argument.} \item{colnames}{names of columns} \item{categorical_feature}{categorical features. This can either be a character vector of feature names or an integer vector with the indices of the features (e.g. \code{c(1L, 10L)} to say "the first and tenth columns").} \item{free_raw_data}{LightGBM constructs its data format, called a "Dataset", from tabular data. By default, that Dataset object on the R side does not keep a copy of the raw data. This reduces LightGBM's memory consumption, but it means that the Dataset object cannot be changed after it has been constructed. If you'd prefer to be able to change the Dataset object after construction, set \code{free_raw_data = FALSE}.} \item{label}{vector of labels to use as the target variable} \item{weight}{numeric vector of sample weights} \item{group}{used for learning-to-rank tasks. An integer vector describing how to group rows together as ordered results from the same set of candidate results to be ranked. For example, if you have a 100-document dataset with \code{group = c(10, 20, 40, 10, 10, 10)}, that means that you have 6 groups, where the first 10 records are in the first group, records 11-30 are in the second group, etc.} \item{init_score}{initial score is the base prediction lightgbm will boost from} } \value{ constructed dataset } \description{ LightGBM does not train on raw data. It discretizes continuous features into histogram bins, tries to combine categorical features, and automatically handles missing and The \code{Dataset} class handles that preprocessing, and holds that alternative representation of the input data. } \examples{ \donttest{ \dontshow{setLGBMthreads(2L)} \dontshow{data.table::setDTthreads(1L)} data(agaricus.train, package = "lightgbm") train <- agaricus.train dtrain <- lgb.Dataset(train$data, label = train$label) data_file <- tempfile(fileext = ".data") lgb.Dataset.save(dtrain, data_file) dtrain <- lgb.Dataset(data_file) lgb.Dataset.construct(dtrain) } } ================================================ FILE: R-package/man/lgb.Dataset.construct.Rd ================================================ % Generated by roxygen2: do not edit by hand % Please edit documentation in R/lgb.Dataset.R \name{lgb.Dataset.construct} \alias{lgb.Dataset.construct} \title{Construct Dataset explicitly} \usage{ lgb.Dataset.construct(dataset) } \arguments{ \item{dataset}{Object of class \code{lgb.Dataset}} } \value{ constructed dataset } \description{ Construct Dataset explicitly } \examples{ \donttest{ \dontshow{setLGBMthreads(2L)} \dontshow{data.table::setDTthreads(1L)} data(agaricus.train, package = "lightgbm") train <- agaricus.train dtrain <- lgb.Dataset(train$data, label = train$label) lgb.Dataset.construct(dtrain) } } ================================================ FILE: R-package/man/lgb.Dataset.create.valid.Rd ================================================ % Generated by roxygen2: do not edit by hand % Please edit documentation in R/lgb.Dataset.R \name{lgb.Dataset.create.valid} \alias{lgb.Dataset.create.valid} \title{Construct validation data} \usage{ lgb.Dataset.create.valid( dataset, data, label = NULL, weight = NULL, group = NULL, init_score = NULL, params = list() ) } \arguments{ \item{dataset}{\code{lgb.Dataset} object, training data} \item{data}{a \code{matrix} object, a \code{dgCMatrix} object, a character representing a path to a text file (CSV, TSV, or LibSVM), or a character representing a path to a binary \code{Dataset} file} \item{label}{vector of labels to use as the target variable} \item{weight}{numeric vector of sample weights} \item{group}{used for learning-to-rank tasks. An integer vector describing how to group rows together as ordered results from the same set of candidate results to be ranked. For example, if you have a 100-document dataset with \code{group = c(10, 20, 40, 10, 10, 10)}, that means that you have 6 groups, where the first 10 records are in the first group, records 11-30 are in the second group, etc.} \item{init_score}{initial score is the base prediction lightgbm will boost from} \item{params}{a list of parameters. See \href{https://lightgbm.readthedocs.io/en/latest/Parameters.html#dataset-parameters}{ The "Dataset Parameters" section of the documentation} for a list of parameters and valid values. If this is an empty list (the default), the validation Dataset will have the same parameters as the Dataset passed to argument \code{dataset}.} } \value{ constructed dataset } \description{ Construct validation data according to training data } \examples{ \donttest{ \dontshow{setLGBMthreads(2L)} \dontshow{data.table::setDTthreads(1L)} data(agaricus.train, package = "lightgbm") train <- agaricus.train dtrain <- lgb.Dataset(train$data, label = train$label) data(agaricus.test, package = "lightgbm") test <- agaricus.test dtest <- lgb.Dataset.create.valid(dtrain, test$data, label = test$label) # parameters can be changed between the training data and validation set, # for example to account for training data in a text file with a header row # and validation data in a text file without it train_file <- tempfile(pattern = "train_", fileext = ".csv") write.table( data.frame(y = rnorm(100L), x1 = rnorm(100L), x2 = rnorm(100L)) , file = train_file , sep = "," , col.names = TRUE , row.names = FALSE , quote = FALSE ) valid_file <- tempfile(pattern = "valid_", fileext = ".csv") write.table( data.frame(y = rnorm(100L), x1 = rnorm(100L), x2 = rnorm(100L)) , file = valid_file , sep = "," , col.names = FALSE , row.names = FALSE , quote = FALSE ) dtrain <- lgb.Dataset( data = train_file , params = list(has_header = TRUE) ) dtrain$construct() dvalid <- lgb.Dataset( data = valid_file , params = list(has_header = FALSE) ) dvalid$construct() } } ================================================ FILE: R-package/man/lgb.Dataset.save.Rd ================================================ % Generated by roxygen2: do not edit by hand % Please edit documentation in R/lgb.Dataset.R \name{lgb.Dataset.save} \alias{lgb.Dataset.save} \title{Save \code{lgb.Dataset} to a binary file} \usage{ lgb.Dataset.save(dataset, fname) } \arguments{ \item{dataset}{object of class \code{lgb.Dataset}} \item{fname}{object filename of output file} } \value{ the dataset you passed in } \description{ Please note that \code{init_score} is not saved in binary file. If you need it, please set it again after loading Dataset. } \examples{ \donttest{ \dontshow{setLGBMthreads(2L)} \dontshow{data.table::setDTthreads(1L)} data(agaricus.train, package = "lightgbm") train <- agaricus.train dtrain <- lgb.Dataset(train$data, label = train$label) lgb.Dataset.save(dtrain, tempfile(fileext = ".bin")) } } ================================================ FILE: R-package/man/lgb.Dataset.set.categorical.Rd ================================================ % Generated by roxygen2: do not edit by hand % Please edit documentation in R/lgb.Dataset.R \name{lgb.Dataset.set.categorical} \alias{lgb.Dataset.set.categorical} \title{Set categorical feature of \code{lgb.Dataset}} \usage{ lgb.Dataset.set.categorical(dataset, categorical_feature) } \arguments{ \item{dataset}{object of class \code{lgb.Dataset}} \item{categorical_feature}{categorical features. This can either be a character vector of feature names or an integer vector with the indices of the features (e.g. \code{c(1L, 10L)} to say "the first and tenth columns").} } \value{ the dataset you passed in } \description{ Set the categorical features of an \code{lgb.Dataset} object. Use this function to tell LightGBM which features should be treated as categorical. } \examples{ \donttest{ \dontshow{setLGBMthreads(2L)} \dontshow{data.table::setDTthreads(1L)} data(agaricus.train, package = "lightgbm") train <- agaricus.train dtrain <- lgb.Dataset(train$data, label = train$label) data_file <- tempfile(fileext = ".data") lgb.Dataset.save(dtrain, data_file) dtrain <- lgb.Dataset(data_file) lgb.Dataset.set.categorical(dtrain, 1L:2L) } } ================================================ FILE: R-package/man/lgb.Dataset.set.reference.Rd ================================================ % Generated by roxygen2: do not edit by hand % Please edit documentation in R/lgb.Dataset.R \name{lgb.Dataset.set.reference} \alias{lgb.Dataset.set.reference} \title{Set reference of \code{lgb.Dataset}} \usage{ lgb.Dataset.set.reference(dataset, reference) } \arguments{ \item{dataset}{object of class \code{lgb.Dataset}} \item{reference}{object of class \code{lgb.Dataset}} } \value{ the dataset you passed in } \description{ If you want to use validation data, you should set reference to training data } \examples{ \donttest{ \dontshow{setLGBMthreads(2L)} \dontshow{data.table::setDTthreads(1L)} # create training Dataset data(agaricus.train, package ="lightgbm") train <- agaricus.train dtrain <- lgb.Dataset(train$data, label = train$label) # create a validation Dataset, using dtrain as a reference data(agaricus.test, package = "lightgbm") test <- agaricus.test dtest <- lgb.Dataset(test$data, label = test$label) lgb.Dataset.set.reference(dtest, dtrain) } } ================================================ FILE: R-package/man/lgb.configure_fast_predict.Rd ================================================ % Generated by roxygen2: do not edit by hand % Please edit documentation in R/lgb.Booster.R \name{lgb.configure_fast_predict} \alias{lgb.configure_fast_predict} \title{Configure Fast Single-Row Predictions} \usage{ lgb.configure_fast_predict( model, csr = FALSE, start_iteration = NULL, num_iteration = NULL, type = "response", params = list() ) } \arguments{ \item{model}{LightGBM model object (class \code{lgb.Booster}). \bold{The object will be modified in-place}.} \item{csr}{Whether the prediction function is going to be called on sparse CSR inputs. If \code{FALSE}, will be assumed that predictions are going to be called on single-row regular R matrices.} \item{start_iteration}{int or None, optional (default=None) Start index of the iteration to predict. If None or <= 0, starts from the first iteration.} \item{num_iteration}{int or None, optional (default=None) Limit number of iterations in the prediction. If None, if the best iteration exists and start_iteration is None or <= 0, the best iteration is used; otherwise, all iterations from start_iteration are used. If <= 0, all iterations from start_iteration are used (no limits).} \item{type}{Type of prediction to output. Allowed types are:\itemize{ \item \code{"response"}: will output the predicted score according to the objective function being optimized (depending on the link function that the objective uses), after applying any necessary transformations - for example, for \code{objective="binary"}, it will output class probabilities. \item \code{"class"}: for classification objectives, will output the class with the highest predicted probability. For other objectives, will output the same as "response". Note that \code{"class"} is not a supported type for \link{lgb.configure_fast_predict} (see the documentation of that function for more details). \item \code{"raw"}: will output the non-transformed numbers (sum of predictions from boosting iterations' results) from which the "response" number is produced for a given objective function - for example, for \code{objective="binary"}, this corresponds to log-odds. For many objectives such as "regression", since no transformation is applied, the output will be the same as for "response". \item \code{"leaf"}: will output the index of the terminal node / leaf at which each observations falls in each tree in the model, outputted as integers, with one column per tree. \item \code{"contrib"}: will return the per-feature contributions for each prediction, including an intercept (each feature will produce one column). } Note that, if using custom objectives, types "class" and "response" will not be available and will default towards using "raw" instead. If the model was fit through function \link{lightgbm} and it was passed a factor as labels, passing the prediction type through \code{params} instead of through this argument might result in factor levels for classification objectives not being applied correctly to the resulting output. \emph{New in version 4.0.0}} \item{params}{a list of additional named parameters. See \href{https://lightgbm.readthedocs.io/en/latest/Parameters.html#predict-parameters}{ the "Predict Parameters" section of the documentation} for a list of parameters and valid values. Where these conflict with the values of keyword arguments to this function, the values in \code{params} take precedence.} } \value{ The same \code{model} that was passed as input, invisibly, with the desired configuration stored inside it and available to be used in future calls to \link{predict.lgb.Booster}. } \description{ Pre-configures a LightGBM model object to produce fast single-row predictions for a given input data type, prediction type, and parameters. } \details{ Calling this function multiple times with different parameters might not override the previous configuration and might trigger undefined behavior. Any saved configuration for fast predictions might be lost after making a single-row prediction of a different type than what was configured (except for types "response" and "class", which can be switched between each other at any time without losing the configuration). In some situations, setting a fast prediction configuration for one type of prediction might cause the prediction function to keep using that configuration for single-row predictions even if the requested type of prediction is different from what was configured. Note that this function will not accept argument \code{type="class"} - for such cases, one can pass \code{type="response"} to this function and then \code{type="class"} to the \code{predict} function - the fast configuration will not be lost or altered if the switch is between "response" and "class". The configuration does not survive de-serializations, so it has to be generated anew in every R process that is going to use it (e.g. if loading a model object through \code{readRDS}, whatever configuration was there previously will be lost). Requesting a different prediction type or passing parameters to \link{predict.lgb.Booster} will cause it to ignore the fast-predict configuration and take the slow route instead (but be aware that an existing configuration might not always be overridden by supplying different parameters or prediction type, so make sure to check that the output is what was expected when a prediction is to be made on a single row for something different than what is configured). Note that, if configuring a non-default prediction type (such as leaf indices), then that type must also be passed in the call to \link{predict.lgb.Booster} in order for it to use the configuration. This also applies for \code{start_iteration} and \code{num_iteration}, but \bold{the \code{params} list must be empty} in the call to \code{predict}. Predictions about feature contributions do not allow a fast route for CSR inputs, and as such, this function will produce an error if passing \code{csr=TRUE} and \code{type = "contrib"} together. } \examples{ \donttest{ \dontshow{setLGBMthreads(2L)} \dontshow{data.table::setDTthreads(1L)} library(lightgbm) data(mtcars) X <- as.matrix(mtcars[, -1L]) y <- mtcars[, 1L] dtrain <- lgb.Dataset(X, label = y, params = list(max_bin = 5L)) params <- list( min_data_in_leaf = 2L , num_threads = 2L ) model <- lgb.train( params = params , data = dtrain , obj = "regression" , nrounds = 5L , verbose = -1L ) lgb.configure_fast_predict(model) x_single <- X[11L, , drop = FALSE] predict(model, x_single) # Will not use it if the prediction to be made # is different from what was configured predict(model, x_single, type = "leaf") } } ================================================ FILE: R-package/man/lgb.convert_with_rules.Rd ================================================ % Generated by roxygen2: do not edit by hand % Please edit documentation in R/lgb.convert_with_rules.R \name{lgb.convert_with_rules} \alias{lgb.convert_with_rules} \title{Data preparator for LightGBM datasets with rules (integer)} \usage{ lgb.convert_with_rules(data, rules = NULL) } \arguments{ \item{data}{A data.frame or data.table to prepare.} \item{rules}{A set of rules from the data preparator, if already used. This should be an R list, where names are column names in \code{data} and values are named character vectors whose names are column values and whose values are new values to replace them with.} } \value{ A list with the cleaned dataset (\code{data}) and the rules (\code{rules}). Note that the data must be converted to a matrix format (\code{as.matrix}) for input in \code{lgb.Dataset}. } \description{ Attempts to prepare a clean dataset to prepare to put in a \code{lgb.Dataset}. Factor, character, and logical columns are converted to integer. Missing values in factors and characters will be filled with 0L. Missing values in logicals will be filled with -1L. This function returns and optionally takes in "rules" the describe exactly how to convert values in columns. Columns that contain only NA values will be converted by this function but will not show up in the returned \code{rules}. NOTE: In previous releases of LightGBM, this function was called \code{lgb.prepare_rules2}. } \examples{ \donttest{ data(iris) str(iris) new_iris <- lgb.convert_with_rules(data = iris) str(new_iris$data) data(iris) # Erase iris dataset iris$Species[1L] <- "NEW FACTOR" # Introduce junk factor (NA) # Use conversion using known rules # Unknown factors become 0, excellent for sparse datasets newer_iris <- lgb.convert_with_rules(data = iris, rules = new_iris$rules) # Unknown factor is now zero, perfect for sparse datasets newer_iris$data[1L, ] # Species became 0 as it is an unknown factor newer_iris$data[1L, 5L] <- 1.0 # Put back real initial value # Is the newly created dataset equal? YES! all.equal(new_iris$data, newer_iris$data) # Can we test our own rules? data(iris) # Erase iris dataset # We remapped values differently personal_rules <- list( Species = c( "setosa" = 3L , "versicolor" = 2L , "virginica" = 1L ) ) newest_iris <- lgb.convert_with_rules(data = iris, rules = personal_rules) str(newest_iris$data) # SUCCESS! } } ================================================ FILE: R-package/man/lgb.cv.Rd ================================================ % Generated by roxygen2: do not edit by hand % Please edit documentation in R/lgb.cv.R \name{lgb.cv} \alias{lgb.cv} \title{Main CV logic for LightGBM} \usage{ lgb.cv( params = list(), data, nrounds = 100L, nfold = 3L, obj = NULL, eval = NULL, verbose = 1L, record = TRUE, eval_freq = 1L, showsd = TRUE, stratified = TRUE, folds = NULL, init_model = NULL, early_stopping_rounds = NULL, callbacks = list(), reset_data = FALSE, serializable = TRUE, eval_train_metric = FALSE ) } \arguments{ \item{params}{a list of parameters. See \href{https://lightgbm.readthedocs.io/en/latest/Parameters.html}{ the "Parameters" section of the documentation} for a list of parameters and valid values.} \item{data}{a \code{lgb.Dataset} object, used for training. Some functions, such as \code{\link{lgb.cv}}, may allow you to pass other types of data like \code{matrix} and then separately supply \code{label} as a keyword argument.} \item{nrounds}{number of training rounds} \item{nfold}{the original dataset is randomly partitioned into \code{nfold} equal size subsamples.} \item{obj}{objective function, can be character or custom objective function. Examples include \code{regression}, \code{regression_l1}, \code{huber}, \code{binary}, \code{lambdarank}, \code{multiclass}, \code{multiclass}} \item{eval}{evaluation function(s). This can be a character vector, function, or list with a mixture of strings and functions. \itemize{ \item{\bold{a. character vector}: If you provide a character vector to this argument, it should contain strings with valid evaluation metrics. See \href{https://lightgbm.readthedocs.io/en/latest/Parameters.html#metric}{ The "metric" section of the documentation} for a list of valid metrics. } \item{\bold{b. function}: You can provide a custom evaluation function. This should accept the keyword arguments \code{preds} and \code{dtrain} and should return a named list with three elements: \itemize{ \item{\code{name}: A string with the name of the metric, used for printing and storing results. } \item{\code{value}: A single number indicating the value of the metric for the given predictions and true values } \item{ \code{higher_better}: A boolean indicating whether higher values indicate a better fit. For example, this would be \code{FALSE} for metrics like MAE or RMSE. } } } \item{\bold{c. list}: If a list is given, it should only contain character vectors and functions. These should follow the requirements from the descriptions above. } }} \item{verbose}{verbosity for output, if <= 0 and \code{valids} has been provided, also will disable the printing of evaluation during training} \item{record}{Boolean, TRUE will record iteration message to \code{booster$record_evals}} \item{eval_freq}{evaluation output frequency, only effective when verbose > 0 and \code{valids} has been provided} \item{showsd}{\code{boolean}, whether to show standard deviation of cross validation. This parameter defaults to \code{TRUE}. Setting it to \code{FALSE} can lead to a slight speedup by avoiding unnecessary computation.} \item{stratified}{a \code{boolean} indicating whether sampling of folds should be stratified by the values of outcome labels.} \item{folds}{\code{list} provides a possibility to use a list of pre-defined CV folds (each element must be a vector of test fold's indices). When folds are supplied, the \code{nfold} and \code{stratified} parameters are ignored.} \item{init_model}{path of model file or \code{lgb.Booster} object, will continue training from this model} \item{early_stopping_rounds}{int. Activates early stopping. When this parameter is non-null, training will stop if the evaluation of any metric on any validation set fails to improve for \code{early_stopping_rounds} consecutive boosting rounds. If training stops early, the returned model will have attribute \code{best_iter} set to the iteration number of the best iteration.} \item{callbacks}{List of callback functions that are applied at each iteration.} \item{reset_data}{Boolean, setting it to TRUE (not the default value) will transform the booster model into a predictor model which frees up memory and the original datasets} \item{serializable}{whether to make the resulting objects serializable through functions such as \code{save} or \code{saveRDS} (see section "Model serialization").} \item{eval_train_metric}{\code{boolean}, whether to add the cross validation results on the training data. This parameter defaults to \code{FALSE}. Setting it to \code{TRUE} will increase run time.} } \value{ a trained model \code{lgb.CVBooster}. } \description{ Cross validation logic used by LightGBM } \section{Early Stopping}{ "early stopping" refers to stopping the training process if the model's performance on a given validation set does not improve for several consecutive iterations. If multiple arguments are given to \code{eval}, their order will be preserved. If you enable early stopping by setting \code{early_stopping_rounds} in \code{params}, by default all metrics will be considered for early stopping. If you want to only consider the first metric for early stopping, pass \code{first_metric_only = TRUE} in \code{params}. Note that if you also specify \code{metric} in \code{params}, that metric will be considered the "first" one. If you omit \code{metric}, a default metric will be used based on your choice for the parameter \code{obj} (keyword argument) or \code{objective} (passed into \code{params}). \bold{NOTE:} if using \code{boosting_type="dart"}, any early stopping configuration will be ignored and early stopping will not be performed. } \examples{ \donttest{ \dontshow{setLGBMthreads(2L)} \dontshow{data.table::setDTthreads(1L)} data(agaricus.train, package = "lightgbm") train <- agaricus.train dtrain <- lgb.Dataset(train$data, label = train$label) params <- list( objective = "regression" , metric = "l2" , min_data = 1L , learning_rate = 1.0 , num_threads = 2L ) model <- lgb.cv( params = params , data = dtrain , nrounds = 5L , nfold = 3L ) } } ================================================ FILE: R-package/man/lgb.drop_serialized.Rd ================================================ % Generated by roxygen2: do not edit by hand % Please edit documentation in R/lgb.drop_serialized.R \name{lgb.drop_serialized} \alias{lgb.drop_serialized} \title{Drop serialized raw bytes in a LightGBM model object} \usage{ lgb.drop_serialized(model) } \arguments{ \item{model}{\code{lgb.Booster} object which was produced with `serializable=TRUE`.} } \value{ \code{lgb.Booster} (the same `model` object that was passed as input, as invisible). } \description{ If a LightGBM model object was produced with argument `serializable=TRUE`, the R object will keep a copy of the underlying C++ object as raw bytes, which can be used to reconstruct such object after getting serialized and de-serialized, but at the cost of extra memory usage. If these raw bytes are not needed anymore, they can be dropped through this function in order to save memory. Note that the object will be modified in-place. \emph{New in version 4.0.0} } \seealso{ \link{lgb.restore_handle}, \link{lgb.make_serializable}. } ================================================ FILE: R-package/man/lgb.dump.Rd ================================================ % Generated by roxygen2: do not edit by hand % Please edit documentation in R/lgb.Booster.R \name{lgb.dump} \alias{lgb.dump} \title{Dump LightGBM model to json} \usage{ lgb.dump(booster, num_iteration = NULL, start_iteration = 1L) } \arguments{ \item{booster}{Object of class \code{lgb.Booster}} \item{num_iteration}{Number of iterations to be dumped. NULL or <= 0 means use best iteration} \item{start_iteration}{Index (1-based) of the first boosting round to dump. For example, passing \code{start_iteration=5, num_iteration=3} for a regression model means "dump the fifth, sixth, and seventh tree" \emph{New in version 4.4.0}} } \value{ json format of model } \description{ Dump LightGBM model to json } \examples{ \donttest{ library(lightgbm) \dontshow{setLGBMthreads(2L)} \dontshow{data.table::setDTthreads(1L)} data(agaricus.train, package = "lightgbm") train <- agaricus.train dtrain <- lgb.Dataset(train$data, label = train$label) data(agaricus.test, package = "lightgbm") test <- agaricus.test dtest <- lgb.Dataset.create.valid(dtrain, test$data, label = test$label) params <- list( objective = "regression" , metric = "l2" , min_data = 1L , learning_rate = 1.0 , num_threads = 2L ) valids <- list(test = dtest) model <- lgb.train( params = params , data = dtrain , nrounds = 10L , valids = valids , early_stopping_rounds = 5L ) json_model <- lgb.dump(model) } } ================================================ FILE: R-package/man/lgb.get.eval.result.Rd ================================================ % Generated by roxygen2: do not edit by hand % Please edit documentation in R/lgb.Booster.R \name{lgb.get.eval.result} \alias{lgb.get.eval.result} \title{Get record evaluation result from booster} \usage{ lgb.get.eval.result( booster, data_name, eval_name, iters = NULL, is_err = FALSE ) } \arguments{ \item{booster}{Object of class \code{lgb.Booster}} \item{data_name}{Name of the dataset to return evaluation results for.} \item{eval_name}{Name of the evaluation metric to return results for.} \item{iters}{An integer vector of iterations you want to get evaluation results for. If NULL (the default), evaluation results for all iterations will be returned.} \item{is_err}{TRUE will return evaluation error instead} } \value{ numeric vector of evaluation result } \description{ Given a \code{lgb.Booster}, return evaluation results for a particular metric on a particular dataset. } \examples{ \donttest{ \dontshow{setLGBMthreads(2L)} \dontshow{data.table::setDTthreads(1L)} # train a regression model data(agaricus.train, package = "lightgbm") train <- agaricus.train dtrain <- lgb.Dataset(train$data, label = train$label) data(agaricus.test, package = "lightgbm") test <- agaricus.test dtest <- lgb.Dataset.create.valid(dtrain, test$data, label = test$label) params <- list( objective = "regression" , metric = "l2" , min_data = 1L , learning_rate = 1.0 , num_threads = 2L ) valids <- list(test = dtest) model <- lgb.train( params = params , data = dtrain , nrounds = 5L , valids = valids ) # Examine valid data_name values print(setdiff(names(model$record_evals), "start_iter")) # Examine valid eval_name values for dataset "test" print(names(model$record_evals[["test"]])) # Get L2 values for "test" dataset lgb.get.eval.result(model, "test", "l2") } } ================================================ FILE: R-package/man/lgb.importance.Rd ================================================ % Generated by roxygen2: do not edit by hand % Please edit documentation in R/lgb.importance.R \name{lgb.importance} \alias{lgb.importance} \title{Compute feature importance in a model} \usage{ lgb.importance(model, percentage = TRUE) } \arguments{ \item{model}{object of class \code{lgb.Booster}.} \item{percentage}{whether to show importance in relative percentage.} } \value{ For a tree model, a \code{data.table} with the following columns: \itemize{ \item{\code{Feature}: Feature names in the model.} \item{\code{Gain}: The total gain of this feature's splits.} \item{\code{Cover}: The number of observation related to this feature.} \item{\code{Frequency}: The number of times a feature split in trees.} } } \description{ Creates a \code{data.table} of feature importances in a model. } \examples{ \donttest{ \dontshow{setLGBMthreads(2L)} \dontshow{data.table::setDTthreads(1L)} data(agaricus.train, package = "lightgbm") train <- agaricus.train dtrain <- lgb.Dataset(train$data, label = train$label) params <- list( objective = "binary" , learning_rate = 0.1 , max_depth = -1L , min_data_in_leaf = 1L , min_sum_hessian_in_leaf = 1.0 , num_threads = 2L ) model <- lgb.train( params = params , data = dtrain , nrounds = 5L ) tree_imp1 <- lgb.importance(model, percentage = TRUE) tree_imp2 <- lgb.importance(model, percentage = FALSE) } } ================================================ FILE: R-package/man/lgb.interprete.Rd ================================================ % Generated by roxygen2: do not edit by hand % Please edit documentation in R/lgb.interprete.R \name{lgb.interprete} \alias{lgb.interprete} \title{Compute feature contribution of prediction} \usage{ lgb.interprete(model, data, idxset, num_iteration = NULL) } \arguments{ \item{model}{object of class \code{lgb.Booster}.} \item{data}{a matrix object or a dgCMatrix object.} \item{idxset}{an integer vector of indices of rows needed.} \item{num_iteration}{number of iteration want to predict with, NULL or <= 0 means use best iteration.} } \value{ For regression, binary classification and lambdarank model, a \code{list} of \code{data.table} with the following columns: \itemize{ \item{\code{Feature}: Feature names in the model.} \item{\code{Contribution}: The total contribution of this feature's splits.} } For multiclass classification, a \code{list} of \code{data.table} with the Feature column and Contribution columns to each class. } \description{ Computes feature contribution components of rawscore prediction. } \examples{ \donttest{ \dontshow{setLGBMthreads(2L)} \dontshow{data.table::setDTthreads(1L)} Logit <- function(x) log(x / (1.0 - x)) data(agaricus.train, package = "lightgbm") train <- agaricus.train dtrain <- lgb.Dataset(train$data, label = train$label) set_field( dataset = dtrain , field_name = "init_score" , data = rep(Logit(mean(train$label)), length(train$label)) ) data(agaricus.test, package = "lightgbm") test <- agaricus.test params <- list( objective = "binary" , learning_rate = 0.1 , max_depth = -1L , min_data_in_leaf = 1L , min_sum_hessian_in_leaf = 1.0 , num_threads = 2L ) model <- lgb.train( params = params , data = dtrain , nrounds = 3L ) tree_interpretation <- lgb.interprete(model, test$data, 1L:5L) } } ================================================ FILE: R-package/man/lgb.load.Rd ================================================ % Generated by roxygen2: do not edit by hand % Please edit documentation in R/lgb.Booster.R \name{lgb.load} \alias{lgb.load} \title{Load LightGBM model} \usage{ lgb.load(filename = NULL, model_str = NULL) } \arguments{ \item{filename}{path of model file} \item{model_str}{a str containing the model (as a \code{character} or \code{raw} vector)} } \value{ lgb.Booster } \description{ Load LightGBM takes in either a file path or model string. If both are provided, Load will default to loading from file } \examples{ \donttest{ \dontshow{setLGBMthreads(2L)} \dontshow{data.table::setDTthreads(1L)} data(agaricus.train, package = "lightgbm") train <- agaricus.train dtrain <- lgb.Dataset(train$data, label = train$label) data(agaricus.test, package = "lightgbm") test <- agaricus.test dtest <- lgb.Dataset.create.valid(dtrain, test$data, label = test$label) params <- list( objective = "regression" , metric = "l2" , min_data = 1L , learning_rate = 1.0 , num_threads = 2L ) valids <- list(test = dtest) model <- lgb.train( params = params , data = dtrain , nrounds = 5L , valids = valids , early_stopping_rounds = 3L ) model_file <- tempfile(fileext = ".txt") lgb.save(model, model_file) load_booster <- lgb.load(filename = model_file) model_string <- model$save_model_to_string(NULL) # saves best iteration load_booster_from_str <- lgb.load(model_str = model_string) } } ================================================ FILE: R-package/man/lgb.make_serializable.Rd ================================================ % Generated by roxygen2: do not edit by hand % Please edit documentation in R/lgb.make_serializable.R \name{lgb.make_serializable} \alias{lgb.make_serializable} \title{Make a LightGBM object serializable by keeping raw bytes} \usage{ lgb.make_serializable(model) } \arguments{ \item{model}{\code{lgb.Booster} object which was produced with `serializable=FALSE`.} } \value{ \code{lgb.Booster} (the same `model` object that was passed as input, as invisible). } \description{ If a LightGBM model object was produced with argument `serializable=FALSE`, the R object will not be serializable (e.g. cannot save and load with \code{saveRDS} and \code{readRDS}) as it will lack the raw bytes needed to reconstruct its underlying C++ object. This function can be used to forcibly produce those serialized raw bytes and make the object serializable. Note that the object will be modified in-place. \emph{New in version 4.0.0} } \seealso{ \link{lgb.restore_handle}, \link{lgb.drop_serialized}. } ================================================ FILE: R-package/man/lgb.model.dt.tree.Rd ================================================ % Generated by roxygen2: do not edit by hand % Please edit documentation in R/lgb.model.dt.tree.R \name{lgb.model.dt.tree} \alias{lgb.model.dt.tree} \title{Parse a LightGBM model json dump} \usage{ lgb.model.dt.tree(model, num_iteration = NULL, start_iteration = 1L) } \arguments{ \item{model}{object of class \code{lgb.Booster}.} \item{num_iteration}{Number of iterations to include. NULL or <= 0 means use best iteration.} \item{start_iteration}{Index (1-based) of the first boosting round to include in the output. For example, passing \code{start_iteration=5, num_iteration=3} for a regression model means "return information about the fifth, sixth, and seventh trees". \emph{New in version 4.4.0}} } \value{ A \code{data.table} with detailed information about model trees' nodes and leaves. The columns of the \code{data.table} are: \itemize{ \item{\code{tree_index}: ID of a tree in a model (integer)} \item{\code{split_index}: ID of a node in a tree (integer)} \item{\code{split_feature}: for a node, it's a feature name (character); for a leaf, it simply labels it as \code{"NA"}} \item{\code{node_parent}: ID of the parent node for current node (integer)} \item{\code{leaf_index}: ID of a leaf in a tree (integer)} \item{\code{leaf_parent}: ID of the parent node for current leaf (integer)} \item{\code{split_gain}: Split gain of a node} \item{\code{threshold}: Splitting threshold value of a node} \item{\code{decision_type}: Decision type of a node} \item{\code{default_left}: Determine how to handle NA value, TRUE -> Left, FALSE -> Right} \item{\code{internal_value}: Node value} \item{\code{internal_count}: The number of observation collected by a node} \item{\code{leaf_value}: Leaf value} \item{\code{leaf_count}: The number of observation collected by a leaf} } } \description{ Parse a LightGBM model json dump into a \code{data.table} structure. } \examples{ \donttest{ \dontshow{setLGBMthreads(2L)} \dontshow{data.table::setDTthreads(1L)} data(agaricus.train, package = "lightgbm") train <- agaricus.train dtrain <- lgb.Dataset(train$data, label = train$label) params <- list( objective = "binary" , learning_rate = 0.01 , num_leaves = 63L , max_depth = -1L , min_data_in_leaf = 1L , min_sum_hessian_in_leaf = 1.0 , num_threads = 2L ) model <- lgb.train(params, dtrain, 10L) tree_dt <- lgb.model.dt.tree(model) } } ================================================ FILE: R-package/man/lgb.plot.importance.Rd ================================================ % Generated by roxygen2: do not edit by hand % Please edit documentation in R/lgb.plot.importance.R \name{lgb.plot.importance} \alias{lgb.plot.importance} \title{Plot feature importance as a bar graph} \usage{ lgb.plot.importance( tree_imp, top_n = 10L, measure = "Gain", left_margin = 10L, cex = NULL ) } \arguments{ \item{tree_imp}{a \code{data.table} returned by \code{\link{lgb.importance}}.} \item{top_n}{maximal number of top features to include into the plot.} \item{measure}{the name of importance measure to plot, can be "Gain", "Cover" or "Frequency".} \item{left_margin}{(base R barplot) allows to adjust the left margin size to fit feature names.} \item{cex}{(base R barplot) passed as \code{cex.names} parameter to \code{\link[graphics]{barplot}}. Set a number smaller than 1.0 to make the bar labels smaller than R's default and values greater than 1.0 to make them larger.} } \value{ The \code{lgb.plot.importance} function creates a \code{barplot} and silently returns a processed data.table with \code{top_n} features sorted by defined importance. } \description{ Plot previously calculated feature importance: Gain, Cover and Frequency, as a bar graph. } \details{ The graph represents each feature as a horizontal bar of length proportional to the defined importance of a feature. Features are shown ranked in a decreasing importance order. } \examples{ \donttest{ \dontshow{setLGBMthreads(2L)} \dontshow{data.table::setDTthreads(1L)} data(agaricus.train, package = "lightgbm") train <- agaricus.train dtrain <- lgb.Dataset(train$data, label = train$label) params <- list( objective = "binary" , learning_rate = 0.1 , min_data_in_leaf = 1L , min_sum_hessian_in_leaf = 1.0 , num_threads = 2L ) model <- lgb.train( params = params , data = dtrain , nrounds = 5L ) tree_imp <- lgb.importance(model, percentage = TRUE) lgb.plot.importance(tree_imp, top_n = 5L, measure = "Gain") } } ================================================ FILE: R-package/man/lgb.plot.interpretation.Rd ================================================ % Generated by roxygen2: do not edit by hand % Please edit documentation in R/lgb.plot.interpretation.R \name{lgb.plot.interpretation} \alias{lgb.plot.interpretation} \title{Plot feature contribution as a bar graph} \usage{ lgb.plot.interpretation( tree_interpretation_dt, top_n = 10L, cols = 1L, left_margin = 10L, cex = NULL ) } \arguments{ \item{tree_interpretation_dt}{a \code{data.table} returned by \code{\link{lgb.interprete}}.} \item{top_n}{maximal number of top features to include into the plot.} \item{cols}{the column numbers of layout, will be used only for multiclass classification feature contribution.} \item{left_margin}{(base R barplot) allows to adjust the left margin size to fit feature names.} \item{cex}{(base R barplot) passed as \code{cex.names} parameter to \code{barplot}.} } \value{ The \code{lgb.plot.interpretation} function creates a \code{barplot}. } \description{ Plot previously calculated feature contribution as a bar graph. } \details{ The graph represents each feature as a horizontal bar of length proportional to the defined contribution of a feature. Features are shown ranked in a decreasing contribution order. } \examples{ \donttest{ \dontshow{setLGBMthreads(2L)} \dontshow{data.table::setDTthreads(1L)} Logit <- function(x) { log(x / (1.0 - x)) } data(agaricus.train, package = "lightgbm") labels <- agaricus.train$label dtrain <- lgb.Dataset( agaricus.train$data , label = labels ) set_field( dataset = dtrain , field_name = "init_score" , data = rep(Logit(mean(labels)), length(labels)) ) data(agaricus.test, package = "lightgbm") params <- list( objective = "binary" , learning_rate = 0.1 , max_depth = -1L , min_data_in_leaf = 1L , min_sum_hessian_in_leaf = 1.0 , num_threads = 2L ) model <- lgb.train( params = params , data = dtrain , nrounds = 5L ) tree_interpretation <- lgb.interprete( model = model , data = agaricus.test$data , idxset = 1L:5L ) lgb.plot.interpretation( tree_interpretation_dt = tree_interpretation[[1L]] , top_n = 3L ) } } ================================================ FILE: R-package/man/lgb.restore_handle.Rd ================================================ % Generated by roxygen2: do not edit by hand % Please edit documentation in R/lgb.restore_handle.R \name{lgb.restore_handle} \alias{lgb.restore_handle} \title{Restore the C++ component of a de-serialized LightGBM model} \usage{ lgb.restore_handle(model) } \arguments{ \item{model}{\code{lgb.Booster} object which was de-serialized and whose underlying C++ object and R handle need to be restored.} } \value{ \code{lgb.Booster} (the same `model` object that was passed as input, invisibly). } \description{ After a LightGBM model object is de-serialized through functions such as \code{save} or \code{saveRDS}, its underlying C++ object will be blank and needs to be restored to able to use it. Such object is restored automatically when calling functions such as \code{predict}, but this function can be used to forcibly restore it beforehand. Note that the object will be modified in-place. \emph{New in version 4.0.0} } \details{ Be aware that fast single-row prediction configurations are not restored through this function. If you wish to make fast single-row predictions using a \code{lgb.Booster} loaded this way, call \link{lgb.configure_fast_predict} on the loaded \code{lgb.Booster} object. } \examples{ \donttest{ library(lightgbm) \dontshow{setLGBMthreads(2L)} \dontshow{data.table::setDTthreads(1L)} data("agaricus.train") model <- lightgbm( agaricus.train$data , agaricus.train$label , params = list(objective = "binary") , nrounds = 5L , verbose = 0 , num_threads = 2L ) fname <- tempfile(fileext="rds") saveRDS(model, fname) model_new <- readRDS(fname) model_new$check_null_handle() lgb.restore_handle(model_new) model_new$check_null_handle() } } \seealso{ \link{lgb.make_serializable}, \link{lgb.drop_serialized}. } ================================================ FILE: R-package/man/lgb.save.Rd ================================================ % Generated by roxygen2: do not edit by hand % Please edit documentation in R/lgb.Booster.R \name{lgb.save} \alias{lgb.save} \title{Save LightGBM model} \usage{ lgb.save(booster, filename, num_iteration = NULL, start_iteration = 1L) } \arguments{ \item{booster}{Object of class \code{lgb.Booster}} \item{filename}{Saved filename} \item{num_iteration}{Number of iterations to save, NULL or <= 0 means use best iteration} \item{start_iteration}{Index (1-based) of the first boosting round to save. For example, passing \code{start_iteration=5, num_iteration=3} for a regression model means "save the fifth, sixth, and seventh tree" \emph{New in version 4.4.0}} } \value{ lgb.Booster } \description{ Save LightGBM model } \examples{ \donttest{ \dontshow{setLGBMthreads(2L)} \dontshow{data.table::setDTthreads(1L)} library(lightgbm) data(agaricus.train, package = "lightgbm") train <- agaricus.train dtrain <- lgb.Dataset(train$data, label = train$label) data(agaricus.test, package = "lightgbm") test <- agaricus.test dtest <- lgb.Dataset.create.valid(dtrain, test$data, label = test$label) params <- list( objective = "regression" , metric = "l2" , min_data = 1L , learning_rate = 1.0 , num_threads = 2L ) valids <- list(test = dtest) model <- lgb.train( params = params , data = dtrain , nrounds = 10L , valids = valids , early_stopping_rounds = 5L ) lgb.save(model, tempfile(fileext = ".txt")) } } ================================================ FILE: R-package/man/lgb.slice.Dataset.Rd ================================================ % Generated by roxygen2: do not edit by hand % Please edit documentation in R/lgb.Dataset.R \name{lgb.slice.Dataset} \alias{lgb.slice.Dataset} \title{Slice a dataset} \usage{ lgb.slice.Dataset(dataset, idxset) } \arguments{ \item{dataset}{Object of class \code{lgb.Dataset}} \item{idxset}{an integer vector of indices of rows needed} } \value{ constructed sub dataset } \description{ Get a new \code{lgb.Dataset} containing the specified rows of original \code{lgb.Dataset} object \emph{Renamed from} \code{slice()} \emph{in 4.4.0} } \examples{ \donttest{ \dontshow{setLGBMthreads(2L)} \dontshow{data.table::setDTthreads(1L)} data(agaricus.train, package = "lightgbm") train <- agaricus.train dtrain <- lgb.Dataset(train$data, label = train$label) dsub <- lgb.slice.Dataset(dtrain, seq_len(42L)) lgb.Dataset.construct(dsub) labels <- lightgbm::get_field(dsub, "label") } } ================================================ FILE: R-package/man/lgb.train.Rd ================================================ % Generated by roxygen2: do not edit by hand % Please edit documentation in R/lgb.train.R \name{lgb.train} \alias{lgb.train} \title{Main training logic for LightGBM} \usage{ lgb.train( params = list(), data, nrounds = 100L, valids = list(), obj = NULL, eval = NULL, verbose = 1L, record = TRUE, eval_freq = 1L, init_model = NULL, early_stopping_rounds = NULL, callbacks = list(), reset_data = FALSE, serializable = TRUE ) } \arguments{ \item{params}{a list of parameters. See \href{https://lightgbm.readthedocs.io/en/latest/Parameters.html}{ the "Parameters" section of the documentation} for a list of parameters and valid values.} \item{data}{a \code{lgb.Dataset} object, used for training. Some functions, such as \code{\link{lgb.cv}}, may allow you to pass other types of data like \code{matrix} and then separately supply \code{label} as a keyword argument.} \item{nrounds}{number of training rounds} \item{valids}{a list of \code{lgb.Dataset} objects, used for validation} \item{obj}{objective function, can be character or custom objective function. Examples include \code{regression}, \code{regression_l1}, \code{huber}, \code{binary}, \code{lambdarank}, \code{multiclass}, \code{multiclass}} \item{eval}{evaluation function(s). This can be a character vector, function, or list with a mixture of strings and functions. \itemize{ \item{\bold{a. character vector}: If you provide a character vector to this argument, it should contain strings with valid evaluation metrics. See \href{https://lightgbm.readthedocs.io/en/latest/Parameters.html#metric}{ The "metric" section of the documentation} for a list of valid metrics. } \item{\bold{b. function}: You can provide a custom evaluation function. This should accept the keyword arguments \code{preds} and \code{dtrain} and should return a named list with three elements: \itemize{ \item{\code{name}: A string with the name of the metric, used for printing and storing results. } \item{\code{value}: A single number indicating the value of the metric for the given predictions and true values } \item{ \code{higher_better}: A boolean indicating whether higher values indicate a better fit. For example, this would be \code{FALSE} for metrics like MAE or RMSE. } } } \item{\bold{c. list}: If a list is given, it should only contain character vectors and functions. These should follow the requirements from the descriptions above. } }} \item{verbose}{verbosity for output, if <= 0 and \code{valids} has been provided, also will disable the printing of evaluation during training} \item{record}{Boolean, TRUE will record iteration message to \code{booster$record_evals}} \item{eval_freq}{evaluation output frequency, only effective when verbose > 0 and \code{valids} has been provided} \item{init_model}{path of model file or \code{lgb.Booster} object, will continue training from this model} \item{early_stopping_rounds}{int. Activates early stopping. When this parameter is non-null, training will stop if the evaluation of any metric on any validation set fails to improve for \code{early_stopping_rounds} consecutive boosting rounds. If training stops early, the returned model will have attribute \code{best_iter} set to the iteration number of the best iteration.} \item{callbacks}{List of callback functions that are applied at each iteration.} \item{reset_data}{Boolean, setting it to TRUE (not the default value) will transform the booster model into a predictor model which frees up memory and the original datasets} \item{serializable}{whether to make the resulting objects serializable through functions such as \code{save} or \code{saveRDS} (see section "Model serialization").} } \value{ a trained booster model \code{lgb.Booster}. } \description{ Low-level R interface to train a LightGBM model. Unlike \code{\link{lightgbm}}, this function is focused on performance (e.g. speed, memory efficiency). It is also less likely to have breaking API changes in new releases than \code{\link{lightgbm}}. } \section{Early Stopping}{ "early stopping" refers to stopping the training process if the model's performance on a given validation set does not improve for several consecutive iterations. If multiple arguments are given to \code{eval}, their order will be preserved. If you enable early stopping by setting \code{early_stopping_rounds} in \code{params}, by default all metrics will be considered for early stopping. If you want to only consider the first metric for early stopping, pass \code{first_metric_only = TRUE} in \code{params}. Note that if you also specify \code{metric} in \code{params}, that metric will be considered the "first" one. If you omit \code{metric}, a default metric will be used based on your choice for the parameter \code{obj} (keyword argument) or \code{objective} (passed into \code{params}). \bold{NOTE:} if using \code{boosting_type="dart"}, any early stopping configuration will be ignored and early stopping will not be performed. } \examples{ \donttest{ \dontshow{setLGBMthreads(2L)} \dontshow{data.table::setDTthreads(1L)} data(agaricus.train, package = "lightgbm") train <- agaricus.train dtrain <- lgb.Dataset(train$data, label = train$label) data(agaricus.test, package = "lightgbm") test <- agaricus.test dtest <- lgb.Dataset.create.valid(dtrain, test$data, label = test$label) params <- list( objective = "regression" , metric = "l2" , min_data = 1L , learning_rate = 1.0 , num_threads = 2L ) valids <- list(test = dtest) model <- lgb.train( params = params , data = dtrain , nrounds = 5L , valids = valids , early_stopping_rounds = 3L ) } } ================================================ FILE: R-package/man/lgb_predict_shared_params.Rd ================================================ % Generated by roxygen2: do not edit by hand % Please edit documentation in R/lgb.Booster.R \name{lgb_predict_shared_params} \alias{lgb_predict_shared_params} \title{Shared prediction parameter docs} \arguments{ \item{type}{Type of prediction to output. Allowed types are:\itemize{ \item \code{"response"}: will output the predicted score according to the objective function being optimized (depending on the link function that the objective uses), after applying any necessary transformations - for example, for \code{objective="binary"}, it will output class probabilities. \item \code{"class"}: for classification objectives, will output the class with the highest predicted probability. For other objectives, will output the same as "response". Note that \code{"class"} is not a supported type for \link{lgb.configure_fast_predict} (see the documentation of that function for more details). \item \code{"raw"}: will output the non-transformed numbers (sum of predictions from boosting iterations' results) from which the "response" number is produced for a given objective function - for example, for \code{objective="binary"}, this corresponds to log-odds. For many objectives such as "regression", since no transformation is applied, the output will be the same as for "response". \item \code{"leaf"}: will output the index of the terminal node / leaf at which each observations falls in each tree in the model, outputted as integers, with one column per tree. \item \code{"contrib"}: will return the per-feature contributions for each prediction, including an intercept (each feature will produce one column). } Note that, if using custom objectives, types "class" and "response" will not be available and will default towards using "raw" instead. If the model was fit through function \link{lightgbm} and it was passed a factor as labels, passing the prediction type through \code{params} instead of through this argument might result in factor levels for classification objectives not being applied correctly to the resulting output. \emph{New in version 4.0.0}} \item{start_iteration}{int or None, optional (default=None) Start index of the iteration to predict. If None or <= 0, starts from the first iteration.} \item{num_iteration}{int or None, optional (default=None) Limit number of iterations in the prediction. If None, if the best iteration exists and start_iteration is None or <= 0, the best iteration is used; otherwise, all iterations from start_iteration are used. If <= 0, all iterations from start_iteration are used (no limits).} \item{params}{a list of additional named parameters. See \href{https://lightgbm.readthedocs.io/en/latest/Parameters.html#predict-parameters}{ the "Predict Parameters" section of the documentation} for a list of parameters and valid values. Where these conflict with the values of keyword arguments to this function, the values in \code{params} take precedence.} } \description{ Shared prediction parameter docs } \details{ This page contains shared documentation for prediction-related parameters used throughout the package. } \keyword{internal} ================================================ FILE: R-package/man/lgb_shared_dataset_params.Rd ================================================ % Generated by roxygen2: do not edit by hand % Please edit documentation in R/lgb.Dataset.R \name{lgb_shared_dataset_params} \alias{lgb_shared_dataset_params} \title{Shared Dataset parameter docs} \arguments{ \item{label}{vector of labels to use as the target variable} \item{weight}{numeric vector of sample weights} \item{init_score}{initial score is the base prediction lightgbm will boost from} \item{group}{used for learning-to-rank tasks. An integer vector describing how to group rows together as ordered results from the same set of candidate results to be ranked. For example, if you have a 100-document dataset with \code{group = c(10, 20, 40, 10, 10, 10)}, that means that you have 6 groups, where the first 10 records are in the first group, records 11-30 are in the second group, etc.} } \description{ Parameter docs for fields used in \code{lgb.Dataset} construction } \details{ This page contains shared documentation for dataset-related parameters used throughout the package. } \keyword{internal} ================================================ FILE: R-package/man/lgb_shared_params.Rd ================================================ % Generated by roxygen2: do not edit by hand % Please edit documentation in R/lightgbm.R \name{lgb_shared_params} \alias{lgb_shared_params} \title{Shared parameter docs} \arguments{ \item{callbacks}{List of callback functions that are applied at each iteration.} \item{data}{a \code{lgb.Dataset} object, used for training. Some functions, such as \code{\link{lgb.cv}}, may allow you to pass other types of data like \code{matrix} and then separately supply \code{label} as a keyword argument.} \item{early_stopping_rounds}{int. Activates early stopping. When this parameter is non-null, training will stop if the evaluation of any metric on any validation set fails to improve for \code{early_stopping_rounds} consecutive boosting rounds. If training stops early, the returned model will have attribute \code{best_iter} set to the iteration number of the best iteration.} \item{eval}{evaluation function(s). This can be a character vector, function, or list with a mixture of strings and functions. \itemize{ \item{\bold{a. character vector}: If you provide a character vector to this argument, it should contain strings with valid evaluation metrics. See \href{https://lightgbm.readthedocs.io/en/latest/Parameters.html#metric}{ The "metric" section of the documentation} for a list of valid metrics. } \item{\bold{b. function}: You can provide a custom evaluation function. This should accept the keyword arguments \code{preds} and \code{dtrain} and should return a named list with three elements: \itemize{ \item{\code{name}: A string with the name of the metric, used for printing and storing results. } \item{\code{value}: A single number indicating the value of the metric for the given predictions and true values } \item{ \code{higher_better}: A boolean indicating whether higher values indicate a better fit. For example, this would be \code{FALSE} for metrics like MAE or RMSE. } } } \item{\bold{c. list}: If a list is given, it should only contain character vectors and functions. These should follow the requirements from the descriptions above. } }} \item{eval_freq}{evaluation output frequency, only effective when verbose > 0 and \code{valids} has been provided} \item{init_model}{path of model file or \code{lgb.Booster} object, will continue training from this model} \item{nrounds}{number of training rounds} \item{obj}{objective function, can be character or custom objective function. Examples include \code{regression}, \code{regression_l1}, \code{huber}, \code{binary}, \code{lambdarank}, \code{multiclass}, \code{multiclass}} \item{params}{a list of parameters. See \href{https://lightgbm.readthedocs.io/en/latest/Parameters.html}{ the "Parameters" section of the documentation} for a list of parameters and valid values.} \item{verbose}{verbosity for output, if <= 0 and \code{valids} has been provided, also will disable the printing of evaluation during training} \item{serializable}{whether to make the resulting objects serializable through functions such as \code{save} or \code{saveRDS} (see section "Model serialization").} } \description{ Parameter docs shared by \code{lgb.train}, \code{lgb.cv}, and \code{lightgbm} } \section{Early Stopping}{ "early stopping" refers to stopping the training process if the model's performance on a given validation set does not improve for several consecutive iterations. If multiple arguments are given to \code{eval}, their order will be preserved. If you enable early stopping by setting \code{early_stopping_rounds} in \code{params}, by default all metrics will be considered for early stopping. If you want to only consider the first metric for early stopping, pass \code{first_metric_only = TRUE} in \code{params}. Note that if you also specify \code{metric} in \code{params}, that metric will be considered the "first" one. If you omit \code{metric}, a default metric will be used based on your choice for the parameter \code{obj} (keyword argument) or \code{objective} (passed into \code{params}). \bold{NOTE:} if using \code{boosting_type="dart"}, any early stopping configuration will be ignored and early stopping will not be performed. } \section{Model serialization}{ LightGBM model objects can be serialized and de-serialized through functions such as \code{save} or \code{saveRDS}, but similarly to libraries such as 'xgboost', serialization works a bit differently from typical R objects. In order to make models serializable in R, a copy of the underlying C++ object as serialized raw bytes is produced and stored in the R model object, and when this R object is de-serialized, the underlying C++ model object gets reconstructed from these raw bytes, but will only do so once some function that uses it is called, such as \code{predict}. In order to forcibly reconstruct the C++ object after deserialization (e.g. after calling \code{readRDS} or similar), one can use the function \link{lgb.restore_handle} (for example, if one makes predictions in parallel or in forked processes, it will be faster to restore the handle beforehand). Producing and keeping these raw bytes however uses extra memory, and if they are not required, it is possible to avoid producing them by passing `serializable=FALSE`. In such cases, these raw bytes can be added to the model on demand through function \link{lgb.make_serializable}. \emph{New in version 4.0.0} } \keyword{internal} ================================================ FILE: R-package/man/lightgbm.Rd ================================================ % Generated by roxygen2: do not edit by hand % Please edit documentation in R/lightgbm.R \name{lightgbm} \alias{lightgbm} \title{Train a LightGBM model} \usage{ lightgbm( data, label = NULL, weights = NULL, params = list(), nrounds = 100L, verbose = 1L, eval_freq = 1L, early_stopping_rounds = NULL, init_model = NULL, callbacks = list(), serializable = TRUE, objective = "auto", init_score = NULL, num_threads = NULL, colnames = NULL, categorical_feature = NULL, ... ) } \arguments{ \item{data}{a \code{lgb.Dataset} object, used for training. Some functions, such as \code{\link{lgb.cv}}, may allow you to pass other types of data like \code{matrix} and then separately supply \code{label} as a keyword argument.} \item{label}{Vector of labels, used if \code{data} is not an \code{\link{lgb.Dataset}}} \item{weights}{Sample / observation weights for rows in the input data. If \code{NULL}, will assume that all observations / rows have the same importance / weight. \emph{Changed from 'weight', in version 4.0.0}} \item{params}{a list of parameters. See \href{https://lightgbm.readthedocs.io/en/latest/Parameters.html}{ the "Parameters" section of the documentation} for a list of parameters and valid values.} \item{nrounds}{number of training rounds} \item{verbose}{verbosity for output, if <= 0 and \code{valids} has been provided, also will disable the printing of evaluation during training} \item{eval_freq}{evaluation output frequency, only effective when verbose > 0 and \code{valids} has been provided} \item{early_stopping_rounds}{int. Activates early stopping. When this parameter is non-null, training will stop if the evaluation of any metric on any validation set fails to improve for \code{early_stopping_rounds} consecutive boosting rounds. If training stops early, the returned model will have attribute \code{best_iter} set to the iteration number of the best iteration.} \item{init_model}{path of model file or \code{lgb.Booster} object, will continue training from this model} \item{callbacks}{List of callback functions that are applied at each iteration.} \item{serializable}{whether to make the resulting objects serializable through functions such as \code{save} or \code{saveRDS} (see section "Model serialization").} \item{objective}{Optimization objective (e.g. `"regression"`, `"binary"`, etc.). For a list of accepted objectives, see \href{https://lightgbm.readthedocs.io/en/latest/Parameters.html#objective}{ the "objective" item of the "Parameters" section of the documentation}. If passing \code{"auto"} and \code{data} is not of type \code{lgb.Dataset}, the objective will be determined according to what is passed for \code{label}:\itemize{ \item If passing a factor with two variables, will use objective \code{"binary"}. \item If passing a factor with more than two variables, will use objective \code{"multiclass"} (note that parameter \code{num_class} in this case will also be determined automatically from \code{label}). \item Otherwise (or if passing \code{lgb.Dataset} as input), will use objective \code{"regression"}. } \emph{New in version 4.0.0}} \item{init_score}{initial score is the base prediction lightgbm will boost from \emph{New in version 4.0.0}} \item{num_threads}{Number of parallel threads to use. For best speed, this should be set to the number of physical cores in the CPU - in a typical x86-64 machine, this corresponds to half the number of maximum threads. Be aware that using too many threads can result in speed degradation in smaller datasets (see the parameters documentation for more details). If passing zero, will use the default number of threads configured for OpenMP (typically controlled through an environment variable \code{OMP_NUM_THREADS}). If passing \code{NULL} (the default), will try to use the number of physical cores in the system, but be aware that getting the number of cores detected correctly requires package \code{RhpcBLASctl} to be installed. This parameter gets overridden by \code{num_threads} and its aliases under \code{params} if passed there. \emph{New in version 4.0.0}} \item{colnames}{Character vector of features. Only used if \code{data} is not an \code{\link{lgb.Dataset}}.} \item{categorical_feature}{categorical features. This can either be a character vector of feature names or an integer vector with the indices of the features (e.g. \code{c(1L, 10L)} to say "the first and tenth columns"). Only used if \code{data} is not an \code{\link{lgb.Dataset}}.} \item{...}{Additional arguments passed to \code{\link{lgb.train}}. For example \itemize{ \item{\code{valids}: a list of \code{lgb.Dataset} objects, used for validation} \item{\code{obj}: objective function, can be character or custom objective function. Examples include \code{regression}, \code{regression_l1}, \code{huber}, \code{binary}, \code{lambdarank}, \code{multiclass}, \code{multiclass}} \item{\code{eval}: evaluation function, can be (a list of) character or custom eval function} \item{\code{record}: Boolean, TRUE will record iteration message to \code{booster$record_evals}} \item{\code{reset_data}: Boolean, setting it to TRUE (not the default value) will transform the booster model into a predictor model which frees up memory and the original datasets} }} } \value{ a trained \code{lgb.Booster} } \description{ High-level R interface to train a LightGBM model. Unlike \code{\link{lgb.train}}, this function is focused on compatibility with other statistics and machine learning interfaces in R. This focus on compatibility means that this interface may experience more frequent breaking API changes than \code{\link{lgb.train}}. For efficiency-sensitive applications, or for applications where breaking API changes across releases is very expensive, use \code{\link{lgb.train}}. } \section{Early Stopping}{ "early stopping" refers to stopping the training process if the model's performance on a given validation set does not improve for several consecutive iterations. If multiple arguments are given to \code{eval}, their order will be preserved. If you enable early stopping by setting \code{early_stopping_rounds} in \code{params}, by default all metrics will be considered for early stopping. If you want to only consider the first metric for early stopping, pass \code{first_metric_only = TRUE} in \code{params}. Note that if you also specify \code{metric} in \code{params}, that metric will be considered the "first" one. If you omit \code{metric}, a default metric will be used based on your choice for the parameter \code{obj} (keyword argument) or \code{objective} (passed into \code{params}). \bold{NOTE:} if using \code{boosting_type="dart"}, any early stopping configuration will be ignored and early stopping will not be performed. } ================================================ FILE: R-package/man/predict.lgb.Booster.Rd ================================================ % Generated by roxygen2: do not edit by hand % Please edit documentation in R/lgb.Booster.R \name{predict.lgb.Booster} \alias{predict.lgb.Booster} \title{Predict method for LightGBM model} \usage{ \method{predict}{lgb.Booster}( object, newdata, type = "response", start_iteration = NULL, num_iteration = NULL, header = FALSE, params = list(), ... ) } \arguments{ \item{object}{Object of class \code{lgb.Booster}} \item{newdata}{a \code{matrix} object, a \code{dgCMatrix}, a \code{dgRMatrix} object, a \code{dsparseVector} object, or a character representing a path to a text file (CSV, TSV, or LibSVM). For sparse inputs, if predictions are only going to be made for a single row, it will be faster to use CSR format, in which case the data may be passed as either a single-row CSR matrix (class \code{dgRMatrix} from package \code{Matrix}) or as a sparse numeric vector (class \code{dsparseVector} from package \code{Matrix}). If single-row predictions are going to be performed frequently, it is recommended to pre-configure the model object for fast single-row sparse predictions through function \link{lgb.configure_fast_predict}. \emph{Changed from 'data', in version 4.0.0}} \item{type}{Type of prediction to output. Allowed types are:\itemize{ \item \code{"response"}: will output the predicted score according to the objective function being optimized (depending on the link function that the objective uses), after applying any necessary transformations - for example, for \code{objective="binary"}, it will output class probabilities. \item \code{"class"}: for classification objectives, will output the class with the highest predicted probability. For other objectives, will output the same as "response". Note that \code{"class"} is not a supported type for \link{lgb.configure_fast_predict} (see the documentation of that function for more details). \item \code{"raw"}: will output the non-transformed numbers (sum of predictions from boosting iterations' results) from which the "response" number is produced for a given objective function - for example, for \code{objective="binary"}, this corresponds to log-odds. For many objectives such as "regression", since no transformation is applied, the output will be the same as for "response". \item \code{"leaf"}: will output the index of the terminal node / leaf at which each observations falls in each tree in the model, outputted as integers, with one column per tree. \item \code{"contrib"}: will return the per-feature contributions for each prediction, including an intercept (each feature will produce one column). } Note that, if using custom objectives, types "class" and "response" will not be available and will default towards using "raw" instead. If the model was fit through function \link{lightgbm} and it was passed a factor as labels, passing the prediction type through \code{params} instead of through this argument might result in factor levels for classification objectives not being applied correctly to the resulting output. \emph{New in version 4.0.0}} \item{start_iteration}{int or None, optional (default=None) Start index of the iteration to predict. If None or <= 0, starts from the first iteration.} \item{num_iteration}{int or None, optional (default=None) Limit number of iterations in the prediction. If None, if the best iteration exists and start_iteration is None or <= 0, the best iteration is used; otherwise, all iterations from start_iteration are used. If <= 0, all iterations from start_iteration are used (no limits).} \item{header}{only used for prediction for text file. True if text file has header} \item{params}{a list of additional named parameters. See \href{https://lightgbm.readthedocs.io/en/latest/Parameters.html#predict-parameters}{ the "Predict Parameters" section of the documentation} for a list of parameters and valid values. Where these conflict with the values of keyword arguments to this function, the values in \code{params} take precedence.} \item{...}{ignored} } \value{ For prediction types that are meant to always return one output per observation (e.g. when predicting \code{type="response"} or \code{type="raw"} on a binary classification or regression objective), will return a vector with one element per row in \code{newdata}. For prediction types that are meant to return more than one output per observation (e.g. when predicting \code{type="response"} or \code{type="raw"} on a multi-class objective, or when predicting \code{type="leaf"}, regardless of objective), will return a matrix with one row per observation in \code{newdata} and one column per output. For \code{type="leaf"} predictions, will return a matrix with one row per observation in \code{newdata} and one column per tree. Note that for multiclass objectives, LightGBM trains one tree per class at each boosting iteration. That means that, for example, for a multiclass model with 3 classes, the leaf predictions for the first class can be found in columns 1, 4, 7, 10, etc. For \code{type="contrib"}, will return a matrix of SHAP values with one row per observation in \code{newdata} and columns corresponding to features. For regression, ranking, cross-entropy, and binary classification objectives, this matrix contains one column per feature plus a final column containing the Shapley base value. For multiclass objectives, this matrix will represent \code{num_classes} such matrices, in the order "feature contributions for first class, feature contributions for second class, feature contributions for third class, etc.". If the model was fit through function \link{lightgbm} and it was passed a factor as labels, predictions returned from this function will retain the factor levels (either as values for \code{type="class"}, or as column names for \code{type="response"} and \code{type="raw"} for multi-class objectives). Note that passing the requested prediction type under \code{params} instead of through \code{type} might result in the factor levels not being present in the output. } \description{ Predicted values based on class \code{lgb.Booster} \emph{New in version 4.0.0} } \details{ If the model object has been configured for fast single-row predictions through \link{lgb.configure_fast_predict}, this function will use the prediction parameters that were configured for it - as such, extra prediction parameters should not be passed here, otherwise the configuration will be ignored and the slow route will be taken. } \examples{ \donttest{ \dontshow{setLGBMthreads(2L)} \dontshow{data.table::setDTthreads(1L)} data(agaricus.train, package = "lightgbm") train <- agaricus.train dtrain <- lgb.Dataset(train$data, label = train$label) data(agaricus.test, package = "lightgbm") test <- agaricus.test dtest <- lgb.Dataset.create.valid(dtrain, test$data, label = test$label) params <- list( objective = "regression" , metric = "l2" , min_data = 1L , learning_rate = 1.0 , num_threads = 2L ) valids <- list(test = dtest) model <- lgb.train( params = params , data = dtrain , nrounds = 5L , valids = valids ) preds <- predict(model, test$data) # pass other prediction parameters preds <- predict( model, test$data, params = list( predict_disable_shape_check = TRUE ) ) } } ================================================ FILE: R-package/man/print.lgb.Booster.Rd ================================================ % Generated by roxygen2: do not edit by hand % Please edit documentation in R/lgb.Booster.R \name{print.lgb.Booster} \alias{print.lgb.Booster} \title{Print method for LightGBM model} \usage{ \method{print}{lgb.Booster}(x, ...) } \arguments{ \item{x}{Object of class \code{lgb.Booster}} \item{...}{Not used} } \value{ The same input \code{x}, returned as invisible. } \description{ Show summary information about a LightGBM model object (same as \code{summary}). \emph{New in version 4.0.0} } ================================================ FILE: R-package/man/setLGBMThreads.Rd ================================================ % Generated by roxygen2: do not edit by hand % Please edit documentation in R/multithreading.R \name{setLGBMThreads} \alias{setLGBMThreads} \alias{setLGBMthreads} \title{Set maximum number of threads used by LightGBM} \usage{ setLGBMthreads(num_threads) } \arguments{ \item{num_threads}{maximum number of threads to be used by LightGBM in multi-threaded operations} } \description{ LightGBM attempts to speed up many operations by using multi-threading. The number of threads used in those operations can be controlled via the \code{num_threads} parameter passed through \code{params} to functions like \link{lgb.train} and \link{lgb.Dataset}. However, some operations (like materializing a model from a text file) are done via code paths that don't explicitly accept thread-control configuration. Use this function to set the maximum number of threads LightGBM will use for such operations. This function affects all LightGBM operations in the same process. So, for example, if you call \code{setLGBMthreads(4)}, no other multi-threaded LightGBM operation in the same process will use more than 4 threads. Call \code{setLGBMthreads(-1)} to remove this limitation. } \seealso{ \link{getLGBMthreads} } ================================================ FILE: R-package/man/set_field.Rd ================================================ % Generated by roxygen2: do not edit by hand % Please edit documentation in R/lgb.Dataset.R \name{set_field} \alias{set_field} \alias{set_field.lgb.Dataset} \title{Set one attribute of a \code{lgb.Dataset} object} \usage{ set_field(dataset, field_name, data) \method{set_field}{lgb.Dataset}(dataset, field_name, data) } \arguments{ \item{dataset}{Object of class \code{lgb.Dataset}} \item{field_name}{String with the name of the attribute to set. One of the following. \itemize{ \item \code{label}: label lightgbm learns from ; \item \code{weight}: to do a weight rescale ; \item{\code{group}: used for learning-to-rank tasks. An integer vector describing how to group rows together as ordered results from the same set of candidate results to be ranked. For example, if you have a 100-document dataset with \code{group = c(10, 20, 40, 10, 10, 10)}, that means that you have 6 groups, where the first 10 records are in the first group, records 11-30 are in the second group, etc.} \item \code{init_score}: initial score is the base prediction lightgbm will boost from. }} \item{data}{The data for the field. See examples.} } \value{ The \code{lgb.Dataset} you passed in. } \description{ Set one attribute of a \code{lgb.Dataset} } \examples{ \donttest{ \dontshow{setLGBMthreads(2L)} \dontshow{data.table::setDTthreads(1L)} data(agaricus.train, package = "lightgbm") train <- agaricus.train dtrain <- lgb.Dataset(train$data, label = train$label) lgb.Dataset.construct(dtrain) labels <- lightgbm::get_field(dtrain, "label") lightgbm::set_field(dtrain, "label", 1 - labels) labels2 <- lightgbm::get_field(dtrain, "label") stopifnot(all.equal(labels2, 1 - labels)) } } ================================================ FILE: R-package/man/summary.lgb.Booster.Rd ================================================ % Generated by roxygen2: do not edit by hand % Please edit documentation in R/lgb.Booster.R \name{summary.lgb.Booster} \alias{summary.lgb.Booster} \title{Summary method for LightGBM model} \usage{ \method{summary}{lgb.Booster}(object, ...) } \arguments{ \item{object}{Object of class \code{lgb.Booster}} \item{...}{Not used} } \value{ The same input \code{object}, returned as invisible. } \description{ Show summary information about a LightGBM model object (same as \code{print}). \emph{New in version 4.0.0} } ================================================ FILE: R-package/pkgdown/_pkgdown.yml ================================================ template: params: bootswatch: cerulean site: root: '' title: LightGBM, Light Gradient Boosting Machine repo: url: home: https://github.com/lightgbm-org/LightGBM/ source: https://github.com/lightgbm-org/LightGBM/tree/master/R-package/ issue: https://github.com/lightgbm-org/LightGBM/issues/ user: https://github.com/ development: mode: unreleased authors: Yu Shi: href: https://github.com/shiyu1994 html: Yu Shi Guolin Ke: href: https://github.com/guolinke html: Guolin Ke Damien Soukhavong: href: https://github.com/Laurae2 html: Damien Soukhavong Yachen Yan: href: https://github.com/yanyachen html: Yachen Yan James Lamb: href: https://github.com/jameslamb html: James Lamb navbar: title: LightGBM type: default left: - icon: fa-reply fa-lg href: ../ - icon: fa-home fa-lg href: index.html - text: Articles href: articles/index.html - text: Reference href: reference/index.html right: - icon: fa-github fa-lg href: https://github.com/lightgbm-org/LightGBM/tree/master/R-package reference: - title: Datasets desc: Datasets included with the R-package contents: - '`agaricus.train`' - '`agaricus.test`' - '`bank`' - title: Data Input / Output desc: Data I/O required for LightGBM contents: - '`dim.lgb.Dataset`' - '`dimnames.lgb.Dataset`' - '`get_field`' - '`set_field`' - '`lgb.Dataset`' - '`lgb.Dataset.construct`' - '`lgb.Dataset.create.valid`' - '`lgb.Dataset.save`' - '`lgb.Dataset.set.categorical`' - '`lgb.Dataset.set.reference`' - '`lgb.convert_with_rules`' - '`lgb.slice.Dataset`' - title: Machine Learning desc: Train models with LightGBM and then use them to make predictions on new data contents: - '`lightgbm`' - '`lgb.train`' - '`predict.lgb.Booster`' - '`lgb.cv`' - '`lgb.configure_fast_predict`' - title: Saving / Loading Models desc: Save and load LightGBM models contents: - '`lgb.dump`' - '`lgb.save`' - '`lgb.load`' - '`lgb.model.dt.tree`' - '`lgb.drop_serialized`' - '`lgb.make_serializable`' - '`lgb.restore_handle`' - title: Model Interpretation desc: Analyze your models contents: - '`lgb.get.eval.result`' - '`lgb.importance`' - '`lgb.interprete`' - '`lgb.plot.importance`' - '`lgb.plot.interpretation`' - '`print.lgb.Booster`' - '`summary.lgb.Booster`' - title: Multithreading Control desc: Manage degree of parallelism used by LightGBM contents: - '`getLGBMThreads`' - '`setLGBMThreads`' ================================================ FILE: R-package/recreate-configure.sh ================================================ #!/bin/bash set -e -E -u -o pipefail # recreates 'configure' from 'configure.ac' # this script should run on Ubuntu 22.04 AUTOCONF_VERSION=$(cat R-package/AUTOCONF_UBUNTU_VERSION) # R packages cannot have versions like 3.0.0rc1, but # 3.0.0-1 is acceptable LGB_VERSION=$(sed "s/rc/-/g" < VERSION.txt) # this script changes configure.ac. Copying to a temporary file # so changes to configure.ac don't get committed in git TMP_CONFIGURE_AC=".configure.ac" echo "Creating 'configure' script with Autoconf ${AUTOCONF_VERSION}" apt update apt-get install \ --no-install-recommends \ -y \ autoconf="${AUTOCONF_VERSION}" cd R-package cp configure.ac ${TMP_CONFIGURE_AC} sed -i.bak -e "s/~~VERSION~~/${LGB_VERSION}/" ${TMP_CONFIGURE_AC} autoconf \ --output configure \ ${TMP_CONFIGURE_AC} \ || exit 1 rm ${TMP_CONFIGURE_AC} rm -r autom4te.cache || echo "no autoconf cache found" echo "done creating 'configure' script" ================================================ FILE: R-package/src/Makevars.in ================================================ CXX_STD = CXX17 PKGROOT=. LGB_CPPFLAGS = \ @LGB_CPPFLAGS@ \ -DUSE_SOCKET \ -DLGB_R_BUILD PKG_CPPFLAGS = \ -I$(PKGROOT)/include \ $(LGB_CPPFLAGS) PKG_CXXFLAGS = \ @OPENMP_CXXFLAGS@ \ -pthread PKG_LIBS = \ @OPENMP_CXXFLAGS@ \ @OPENMP_LIB@ \ -pthread OBJECTS = \ boosting/boosting.o \ boosting/gbdt.o \ boosting/gbdt_model_text.o \ boosting/gbdt_prediction.o \ boosting/prediction_early_stop.o \ boosting/sample_strategy.o \ io/bin.o \ io/config.o \ io/config_auto.o \ io/dataset.o \ io/dataset_loader.o \ io/file_io.o \ io/json11.o \ io/metadata.o \ io/parser.o \ io/train_share_states.o \ io/tree.o \ metric/dcg_calculator.o \ metric/metric.o \ objective/objective_function.o \ network/linker_topo.o \ network/linkers_mpi.o \ network/linkers_socket.o \ network/network.o \ treelearner/data_parallel_tree_learner.o \ treelearner/feature_histogram.o \ treelearner/feature_parallel_tree_learner.o \ treelearner/gpu_tree_learner.o \ treelearner/gradient_discretizer.o \ treelearner/linear_tree_learner.o \ treelearner/serial_tree_learner.o \ treelearner/tree_learner.o \ treelearner/voting_parallel_tree_learner.o \ utils/openmp_wrapper.o \ c_api.o \ lightgbm_R.o ================================================ FILE: R-package/src/Makevars.win.in ================================================ CXX_STD = CXX17 PKGROOT=. LGB_CPPFLAGS = \ @LGB_CPPFLAGS@ \ -DUSE_SOCKET \ -DLGB_R_BUILD PKG_CPPFLAGS = \ -I$(PKGROOT)/include \ $(LGB_CPPFLAGS) PKG_CXXFLAGS = \ ${SHLIB_OPENMP_CXXFLAGS} \ ${SHLIB_PTHREAD_FLAGS} PKG_LIBS = \ ${SHLIB_OPENMP_CXXFLAGS} \ ${SHLIB_PTHREAD_FLAGS} \ -lws2_32 \ -liphlpapi OBJECTS = \ boosting/boosting.o \ boosting/gbdt.o \ boosting/gbdt_model_text.o \ boosting/gbdt_prediction.o \ boosting/prediction_early_stop.o \ boosting/sample_strategy.o \ io/bin.o \ io/config.o \ io/config_auto.o \ io/dataset.o \ io/dataset_loader.o \ io/file_io.o \ io/json11.o \ io/metadata.o \ io/parser.o \ io/train_share_states.o \ io/tree.o \ metric/dcg_calculator.o \ metric/metric.o \ objective/objective_function.o \ network/linker_topo.o \ network/linkers_mpi.o \ network/linkers_socket.o \ network/network.o \ treelearner/data_parallel_tree_learner.o \ treelearner/feature_histogram.o \ treelearner/feature_parallel_tree_learner.o \ treelearner/gpu_tree_learner.o \ treelearner/gradient_discretizer.o \ treelearner/linear_tree_learner.o \ treelearner/serial_tree_learner.o \ treelearner/tree_learner.o \ treelearner/voting_parallel_tree_learner.o \ utils/openmp_wrapper.o \ c_api.o \ lightgbm_R.o ================================================ FILE: R-package/src/install.libs.R ================================================ # User options use_gpu <- FALSE make_args_from_build_script <- character(0L) # For Windows, the package will be built with Visual Studio # unless you set one of these to TRUE use_mingw <- FALSE use_msys2 <- FALSE if (use_mingw && use_msys2) { stop("Cannot use both MinGW and MSYS2. Please choose only one.") } if (.Machine$sizeof.pointer != 8L) { stop("LightGBM only supports 64-bit R, please check the version of R and Rtools.") } # Get some paths source_dir <- file.path(R_PACKAGE_SOURCE, "src", fsep = "/") build_dir <- file.path(source_dir, "build", fsep = "/") inst_dir <- file.path(R_PACKAGE_SOURCE, "inst", fsep = "/") # system() will not raise an R exception if the process called # fails. Wrapping it here to get that behavior. # # system() introduces a lot of overhead, at least on Windows, # so trying processx if it is available .run_shell_command <- function(cmd, args, strict = TRUE) { on_windows <- .Platform$OS.type == "windows" has_processx <- suppressMessages({ suppressWarnings({ require("processx") # nolint: undesirable_function, unused_import. }) }) if (has_processx && on_windows) { result <- processx::run( command = cmd , args = args , windows_verbatim_args = TRUE , error_on_status = FALSE , echo = TRUE ) exit_code <- result$status } else { if (on_windows) { message(paste0( "Using system() to run shell commands. Installing " , "'processx' with install.packages('processx') might " , "make this faster." )) } cmd <- paste0(cmd, " ", paste(args, collapse = " ")) exit_code <- system(cmd) } if (exit_code != 0L && isTRUE(strict)) { stop(paste0("Command failed with exit code: ", exit_code)) } return(invisible(exit_code)) } # try to generate Visual Studio build files .generate_vs_makefiles <- function(cmake_args) { vs_versions <- c( "Visual Studio 17 2022" , "Visual Studio 16 2019" , "Visual Studio 15 2017" ) working_vs_version <- NULL for (vs_version in vs_versions) { message(sprintf("Trying '%s'", vs_version)) # if the build directory is not empty, clean it if (file.exists("CMakeCache.txt")) { file.remove("CMakeCache.txt") } vs_cmake_args <- c( cmake_args , "-G" , shQuote(vs_version) , "-A" , "x64" ) exit_code <- .run_shell_command("cmake", c(vs_cmake_args, ".."), strict = FALSE) if (exit_code == 0L) { message(sprintf("Successfully created build files for '%s'", vs_version)) return(invisible(TRUE)) } } return(invisible(FALSE)) } # Move in CMakeLists.txt write_succeeded <- file.copy( file.path(inst_dir, "bin", "CMakeLists.txt") , "CMakeLists.txt" , overwrite = TRUE ) if (!write_succeeded) { stop("Copying CMakeLists.txt failed") } # Prepare building package dir.create( build_dir , recursive = TRUE , showWarnings = FALSE ) setwd(build_dir) use_visual_studio <- !(use_mingw || use_msys2) # If using MSVC to build, pull in the script used # to create R.def from R.dll if (WINDOWS && use_visual_studio) { write_succeeded <- file.copy( file.path(inst_dir, "make-r-def.R") , file.path(build_dir, "make-r-def.R") , overwrite = TRUE ) if (!write_succeeded) { stop("Copying make-r-def.R failed") } } # Prepare installation steps cmake_args <- c( "-D__BUILD_FOR_R=ON" # pass in R version, to help FindLibR find the R library , sprintf("-DCMAKE_R_VERSION='%s.%s'", R.Version()[["major"]], R.Version()[["minor"]]) # ensure CMake build respects how R is configured (`R CMD config SHLIB_EXT`) , sprintf("-DCMAKE_SHARED_LIBRARY_SUFFIX_CXX='%s'", SHLIB_EXT) ) build_cmd <- "make" build_args <- c("_lightgbm", make_args_from_build_script) lib_folder <- file.path(source_dir, fsep = "/") # add in command-line arguments # NOTE: build_r.R replaces the line below command_line_args <- NULL cmake_args <- c(cmake_args, command_line_args) WINDOWS_BUILD_TOOLS <- list( "MinGW" = c( build_tool = "mingw32-make.exe" , makefile_generator = "MinGW Makefiles" ) , "MSYS2" = c( build_tool = "make.exe" , makefile_generator = "MSYS Makefiles" ) ) if (use_mingw) { windows_toolchain <- "MinGW" } else if (use_msys2) { windows_toolchain <- "MSYS2" } else { # Rtools 4.0 moved from MinGW to MSYS toolchain. If user tries # Visual Studio install but that fails, fall back to the toolchain # supported in Rtools windows_toolchain <- "MSYS2" } windows_build_tool <- WINDOWS_BUILD_TOOLS[[windows_toolchain]][["build_tool"]] windows_makefile_generator <- WINDOWS_BUILD_TOOLS[[windows_toolchain]][["makefile_generator"]] if (use_gpu) { cmake_args <- c(cmake_args, "-DUSE_GPU=ON") } # the checks below might already run `cmake -G`. If they do, set this flag # to TRUE to avoid re-running it later makefiles_already_generated <- FALSE # Check if Windows installation (for gcc vs Visual Studio) if (WINDOWS) { if (!use_visual_studio) { message(sprintf("Trying to build with %s", windows_toolchain)) # Must build twice for Windows due sh.exe in Rtools cmake_args <- c(cmake_args, "-G", shQuote(windows_makefile_generator)) .run_shell_command("cmake", c(cmake_args, ".."), strict = FALSE) build_cmd <- windows_build_tool build_args <- c("_lightgbm", make_args_from_build_script) } else { visual_studio_succeeded <- .generate_vs_makefiles(cmake_args) if (!isTRUE(visual_studio_succeeded)) { warning(sprintf("Building with Visual Studio failed. Attempting with %s", windows_toolchain)) # Must build twice for Windows due sh.exe in Rtools cmake_args <- c(cmake_args, "-G", shQuote(windows_makefile_generator)) .run_shell_command("cmake", c(cmake_args, ".."), strict = FALSE) build_cmd <- windows_build_tool build_args <- c("_lightgbm", make_args_from_build_script) } else { build_cmd <- "cmake" build_args <- c("--build", ".", "--target", "_lightgbm", "--config", "Release") lib_folder <- file.path(source_dir, "Release", fsep = "/") makefiles_already_generated <- TRUE } } } else { .run_shell_command("cmake", c(cmake_args, "..")) makefiles_already_generated <- TRUE } # generate build files if (!makefiles_already_generated) { .run_shell_command("cmake", c(cmake_args, "..")) } # build the library message(paste0("Building lightgbm", SHLIB_EXT)) .run_shell_command(build_cmd, build_args) src <- file.path(lib_folder, paste0("lightgbm", SHLIB_EXT), fsep = "/") # Packages with install.libs.R need to copy some artifacts into the # expected places in the package structure. # see https://cran.r-project.org/doc/manuals/r-devel/R-exts.html#Package-subdirectories, # especially the paragraph on install.libs.R dest <- file.path(R_PACKAGE_DIR, paste0("libs", R_ARCH), fsep = "/") dir.create(dest, recursive = TRUE, showWarnings = FALSE) if (file.exists(src)) { message(paste0("Found library file: ", src, " to move to ", dest)) file.copy(src, dest, overwrite = TRUE) symbols_file <- file.path(source_dir, "symbols.rds") if (file.exists(symbols_file)) { file.copy(symbols_file, dest, overwrite = TRUE) } } else { stop(paste0("Cannot find lightgbm", SHLIB_EXT)) } # clean up the "build" directory if (dir.exists(build_dir)) { message("Removing 'build/' directory") unlink( x = build_dir , recursive = TRUE , force = TRUE ) } ================================================ FILE: R-package/src/lightgbm_R.cpp ================================================ /*! * Copyright (c) 2017-2026 Microsoft Corporation. All rights reserved. * Copyright (c) 2017-2026 The LightGBM developers. All rights reserved. * Licensed under the MIT License. See LICENSE file in the project root for license information. */ #include "lightgbm_R.h" #include #include #include #include #include #include #ifndef R_NO_REMAP #define R_NO_REMAP #endif #ifndef R_USE_C99_IN_CXX #define R_USE_C99_IN_CXX #endif #include #include #include #include #include #include #include #include #include #include R_altrep_class_t lgb_altrepped_char_vec; R_altrep_class_t lgb_altrepped_int_arr; R_altrep_class_t lgb_altrepped_dbl_arr; template void delete_cpp_array(SEXP R_ptr) { T *ptr_to_cpp_obj = static_cast(R_ExternalPtrAddr(R_ptr)); delete[] ptr_to_cpp_obj; R_ClearExternalPtr(R_ptr); } void delete_cpp_char_vec(SEXP R_ptr) { std::vector *ptr_to_cpp_obj = static_cast*>(R_ExternalPtrAddr(R_ptr)); delete ptr_to_cpp_obj; R_ClearExternalPtr(R_ptr); } // Note: MSVC has issues with Altrep classes, so they are disabled for it. // See: https://github.com/lightgbm-org/LightGBM/pull/6213#issuecomment-2111025768 #ifdef _MSC_VER # define LGB_NO_ALTREP #endif #ifndef LGB_NO_ALTREP SEXP make_altrepped_raw_vec(void *void_ptr) { std::unique_ptr> *ptr_to_cpp_vec = static_cast>*>(void_ptr); SEXP R_ptr = Rf_protect(R_MakeExternalPtr(nullptr, R_NilValue, R_NilValue)); SEXP R_raw = Rf_protect(R_new_altrep(lgb_altrepped_char_vec, R_NilValue, R_NilValue)); R_SetExternalPtrAddr(R_ptr, ptr_to_cpp_vec->get()); R_RegisterCFinalizerEx(R_ptr, delete_cpp_char_vec, TRUE); ptr_to_cpp_vec->release(); R_set_altrep_data1(R_raw, R_ptr); Rf_unprotect(2); return R_raw; } #else SEXP make_r_raw_vec(void *void_ptr) { std::unique_ptr> *ptr_to_cpp_vec = static_cast>*>(void_ptr); R_xlen_t len = ptr_to_cpp_vec->get()->size(); SEXP out = Rf_protect(Rf_allocVector(RAWSXP, len)); std::copy(ptr_to_cpp_vec->get()->begin(), ptr_to_cpp_vec->get()->end(), reinterpret_cast(RAW(out))); Rf_unprotect(1); return out; } #define make_altrepped_raw_vec make_r_raw_vec #endif std::vector* get_ptr_from_altrepped_raw(SEXP R_raw) { return static_cast*>(R_ExternalPtrAddr(R_altrep_data1(R_raw))); } R_xlen_t get_altrepped_raw_len(SEXP R_raw) { return get_ptr_from_altrepped_raw(R_raw)->size(); } const void* get_altrepped_raw_dataptr_or_null(SEXP R_raw) { return get_ptr_from_altrepped_raw(R_raw)->data(); } void* get_altrepped_raw_dataptr(SEXP R_raw, Rboolean writeable) { return get_ptr_from_altrepped_raw(R_raw)->data(); } #ifndef LGB_NO_ALTREP template R_altrep_class_t get_altrep_class_for_type() { if (std::is_same::value) { return lgb_altrepped_dbl_arr; } else { return lgb_altrepped_int_arr; } } #else template SEXPTYPE get_sexptype_class_for_type() { if (std::is_same::value) { return REALSXP; } else { return INTSXP; } } template T* get_r_vec_ptr(SEXP x) { if (std::is_same::value) { return static_cast(static_cast(REAL(x))); } else { return static_cast(static_cast(INTEGER(x))); } } #endif template struct arr_and_len { T *arr; int64_t len; }; #ifndef LGB_NO_ALTREP template SEXP make_altrepped_vec_from_arr(void *void_ptr) { T *arr = static_cast*>(void_ptr)->arr; uint64_t len = static_cast*>(void_ptr)->len; SEXP R_ptr = Rf_protect(R_MakeExternalPtr(nullptr, R_NilValue, R_NilValue)); SEXP R_len = Rf_protect(Rf_allocVector(REALSXP, 1)); SEXP R_vec = Rf_protect(R_new_altrep(get_altrep_class_for_type(), R_NilValue, R_NilValue)); REAL(R_len)[0] = static_cast(len); R_SetExternalPtrAddr(R_ptr, arr); R_RegisterCFinalizerEx(R_ptr, delete_cpp_array, TRUE); R_set_altrep_data1(R_vec, R_ptr); R_set_altrep_data2(R_vec, R_len); Rf_unprotect(3); return R_vec; } #else template SEXP make_R_vec_from_arr(void *void_ptr) { T *arr = static_cast*>(void_ptr)->arr; uint64_t len = static_cast*>(void_ptr)->len; SEXP out = Rf_protect(Rf_allocVector(get_sexptype_class_for_type(), len)); std::copy(arr, arr + len, get_r_vec_ptr(out)); Rf_unprotect(1); return out; } #define make_altrepped_vec_from_arr make_R_vec_from_arr #endif R_xlen_t get_altrepped_vec_len(SEXP R_vec) { return static_cast(Rf_asReal(R_altrep_data2(R_vec))); } const void* get_altrepped_vec_dataptr_or_null(SEXP R_vec) { return R_ExternalPtrAddr(R_altrep_data1(R_vec)); } void* get_altrepped_vec_dataptr(SEXP R_vec, Rboolean writeable) { return R_ExternalPtrAddr(R_altrep_data1(R_vec)); } #define COL_MAJOR (0) #define MAX_LENGTH_ERR_MSG 1024 char R_errmsg_buffer[MAX_LENGTH_ERR_MSG]; struct LGBM_R_ErrorClass { SEXP cont_token; }; void LGBM_R_save_exception_msg(const std::exception &err); void LGBM_R_save_exception_msg(const std::string &err); #define R_API_BEGIN() \ try { #define R_API_END() } \ catch(LGBM_R_ErrorClass &cont) { R_ContinueUnwind(cont.cont_token); } \ catch(std::exception& ex) { LGBM_R_save_exception_msg(ex); } \ catch(std::string& ex) { LGBM_R_save_exception_msg(ex); } \ catch(...) { Rf_error("unknown exception"); } \ Rf_error("%s", R_errmsg_buffer); \ return R_NilValue; /* <- won't be reached */ #define CHECK_CALL(x) \ if ((x) != 0) { \ throw std::runtime_error(LGBM_GetLastError()); \ } // These are helper functions to allow doing a stack unwind // after an R allocation error, which would trigger a long jump. void LGBM_R_save_exception_msg(const std::exception &err) { std::snprintf(R_errmsg_buffer, MAX_LENGTH_ERR_MSG, "%s\n", err.what()); } void LGBM_R_save_exception_msg(const std::string &err) { std::snprintf(R_errmsg_buffer, MAX_LENGTH_ERR_MSG, "%s\n", err.c_str()); } SEXP wrapped_R_string(void *len) { return Rf_allocVector(STRSXP, *(reinterpret_cast(len))); } SEXP wrapped_R_raw(void *len) { return Rf_allocVector(RAWSXP, *(reinterpret_cast(len))); } SEXP wrapped_R_int(void *len) { return Rf_allocVector(INTSXP, *(reinterpret_cast(len))); } SEXP wrapped_R_real(void *len) { return Rf_allocVector(REALSXP, *(reinterpret_cast(len))); } SEXP wrapped_Rf_mkChar(void *txt) { return Rf_mkChar(reinterpret_cast(txt)); } void throw_R_memerr(void *ptr_cont_token, Rboolean jump) { if (jump) { LGBM_R_ErrorClass err{*(reinterpret_cast(ptr_cont_token))}; throw err; } } SEXP safe_R_string(R_xlen_t len, SEXP *cont_token) { return R_UnwindProtect(wrapped_R_string, reinterpret_cast(&len), throw_R_memerr, cont_token, *cont_token); } SEXP safe_R_raw(R_xlen_t len, SEXP *cont_token) { return R_UnwindProtect(wrapped_R_raw, reinterpret_cast(&len), throw_R_memerr, cont_token, *cont_token); } SEXP safe_R_int(R_xlen_t len, SEXP *cont_token) { return R_UnwindProtect(wrapped_R_int, reinterpret_cast(&len), throw_R_memerr, cont_token, *cont_token); } SEXP safe_R_real(R_xlen_t len, SEXP *cont_token) { return R_UnwindProtect(wrapped_R_real, reinterpret_cast(&len), throw_R_memerr, cont_token, *cont_token); } SEXP safe_R_mkChar(char *txt, SEXP *cont_token) { return R_UnwindProtect(wrapped_Rf_mkChar, reinterpret_cast(txt), throw_R_memerr, cont_token, *cont_token); } using LightGBM::Common::Split; using LightGBM::Log; SEXP LGBM_HandleIsNull_R(SEXP handle) { return Rf_ScalarLogical(R_ExternalPtrAddr(handle) == NULL); } void _DatasetFinalizer(SEXP handle) { LGBM_DatasetFree_R(handle); } SEXP LGBM_NullBoosterHandleError_R() { Rf_error( "Attempting to use a Booster which no longer exists and/or cannot be restored. " "This can happen if the Booster's finalizer was called " "or if this Booster was saved through saveRDS() using 'serializable=FALSE'."); return R_NilValue; } void _AssertBoosterHandleNotNull(SEXP handle) { if (Rf_isNull(handle) || !R_ExternalPtrAddr(handle)) { LGBM_NullBoosterHandleError_R(); } } void _AssertDatasetHandleNotNull(SEXP handle) { if (Rf_isNull(handle) || !R_ExternalPtrAddr(handle)) { Rf_error( "Attempting to use a Dataset which no longer exists. " "This can happen if the Dataset's finalizer was called or if this Dataset was saved with saveRDS(). " "To avoid this error in the future, use lgb.Dataset.save() or Dataset$save_binary() to save lightgbm Datasets."); } } SEXP LGBM_DatasetCreateFromFile_R(SEXP filename, SEXP parameters, SEXP reference) { R_API_BEGIN(); SEXP ret = Rf_protect(R_MakeExternalPtr(nullptr, R_NilValue, R_NilValue)); DatasetHandle handle = nullptr; DatasetHandle ref = nullptr; if (!Rf_isNull(reference)) { ref = R_ExternalPtrAddr(reference); } const char* filename_ptr = CHAR(Rf_protect(Rf_asChar(filename))); const char* parameters_ptr = CHAR(Rf_protect(Rf_asChar(parameters))); CHECK_CALL(LGBM_DatasetCreateFromFile(filename_ptr, parameters_ptr, ref, &handle)); R_SetExternalPtrAddr(ret, handle); R_RegisterCFinalizerEx(ret, _DatasetFinalizer, TRUE); Rf_unprotect(3); return ret; R_API_END(); } SEXP LGBM_DatasetCreateFromCSC_R(SEXP indptr, SEXP indices, SEXP data, SEXP num_indptr, SEXP nelem, SEXP num_row, SEXP parameters, SEXP reference) { R_API_BEGIN(); SEXP ret = Rf_protect(R_MakeExternalPtr(nullptr, R_NilValue, R_NilValue)); const int* p_indptr = INTEGER(indptr); const int* p_indices = INTEGER(indices); const double* p_data = REAL(data); int64_t nindptr = static_cast(Rf_asInteger(num_indptr)); int64_t ndata = static_cast(Rf_asInteger(nelem)); int64_t nrow = static_cast(Rf_asInteger(num_row)); const char* parameters_ptr = CHAR(Rf_protect(Rf_asChar(parameters))); DatasetHandle handle = nullptr; DatasetHandle ref = nullptr; if (!Rf_isNull(reference)) { ref = R_ExternalPtrAddr(reference); } CHECK_CALL(LGBM_DatasetCreateFromCSC(p_indptr, C_API_DTYPE_INT32, p_indices, p_data, C_API_DTYPE_FLOAT64, nindptr, ndata, nrow, parameters_ptr, ref, &handle)); R_SetExternalPtrAddr(ret, handle); R_RegisterCFinalizerEx(ret, _DatasetFinalizer, TRUE); Rf_unprotect(2); return ret; R_API_END(); } SEXP LGBM_DatasetCreateFromMat_R(SEXP data, SEXP num_row, SEXP num_col, SEXP parameters, SEXP reference) { R_API_BEGIN(); SEXP ret = Rf_protect(R_MakeExternalPtr(nullptr, R_NilValue, R_NilValue)); int32_t nrow = static_cast(Rf_asInteger(num_row)); int32_t ncol = static_cast(Rf_asInteger(num_col)); double* p_mat = REAL(data); const char* parameters_ptr = CHAR(Rf_protect(Rf_asChar(parameters))); DatasetHandle handle = nullptr; DatasetHandle ref = nullptr; if (!Rf_isNull(reference)) { ref = R_ExternalPtrAddr(reference); } CHECK_CALL(LGBM_DatasetCreateFromMat(p_mat, C_API_DTYPE_FLOAT64, nrow, ncol, COL_MAJOR, parameters_ptr, ref, &handle)); R_SetExternalPtrAddr(ret, handle); R_RegisterCFinalizerEx(ret, _DatasetFinalizer, TRUE); Rf_unprotect(2); return ret; R_API_END(); } SEXP LGBM_DatasetGetSubset_R(SEXP handle, SEXP used_row_indices, SEXP len_used_row_indices, SEXP parameters) { R_API_BEGIN(); _AssertDatasetHandleNotNull(handle); SEXP ret = Rf_protect(R_MakeExternalPtr(nullptr, R_NilValue, R_NilValue)); int32_t len = static_cast(Rf_asInteger(len_used_row_indices)); std::unique_ptr idxvec(new int32_t[len]); // convert from one-based to zero-based index const int *used_row_indices_ = INTEGER(used_row_indices); #ifndef _MSC_VER #pragma omp simd #endif for (int32_t i = 0; i < len; ++i) { idxvec[i] = static_cast(used_row_indices_[i] - 1); } const char* parameters_ptr = CHAR(Rf_protect(Rf_asChar(parameters))); DatasetHandle res = nullptr; CHECK_CALL(LGBM_DatasetGetSubset(R_ExternalPtrAddr(handle), idxvec.get(), len, parameters_ptr, &res)); R_SetExternalPtrAddr(ret, res); R_RegisterCFinalizerEx(ret, _DatasetFinalizer, TRUE); Rf_unprotect(2); return ret; R_API_END(); } SEXP LGBM_DatasetSetFeatureNames_R(SEXP handle, SEXP feature_names) { R_API_BEGIN(); _AssertDatasetHandleNotNull(handle); auto vec_names = Split(CHAR(Rf_protect(Rf_asChar(feature_names))), '\t'); int len = static_cast(vec_names.size()); std::unique_ptr vec_sptr(new const char*[len]); for (int i = 0; i < len; ++i) { vec_sptr[i] = vec_names[i].c_str(); } CHECK_CALL(LGBM_DatasetSetFeatureNames(R_ExternalPtrAddr(handle), vec_sptr.get(), len)); Rf_unprotect(1); return R_NilValue; R_API_END(); } SEXP LGBM_DatasetGetFeatureNames_R(SEXP handle) { SEXP cont_token = Rf_protect(R_MakeUnwindCont()); R_API_BEGIN(); _AssertDatasetHandleNotNull(handle); SEXP feature_names; int len = 0; CHECK_CALL(LGBM_DatasetGetNumFeature(R_ExternalPtrAddr(handle), &len)); const size_t reserved_string_size = 256; std::vector> names(len); std::vector ptr_names(len); for (int i = 0; i < len; ++i) { names[i].resize(reserved_string_size); ptr_names[i] = names[i].data(); } int out_len; size_t required_string_size; CHECK_CALL( LGBM_DatasetGetFeatureNames( R_ExternalPtrAddr(handle), len, &out_len, reserved_string_size, &required_string_size, ptr_names.data())); // if any feature names were larger than allocated size, // allow for a larger size and try again if (required_string_size > reserved_string_size) { for (int i = 0; i < len; ++i) { names[i].resize(required_string_size); ptr_names[i] = names[i].data(); } CHECK_CALL( LGBM_DatasetGetFeatureNames( R_ExternalPtrAddr(handle), len, &out_len, required_string_size, &required_string_size, ptr_names.data())); } CHECK_EQ(len, out_len); feature_names = Rf_protect(safe_R_string(static_cast(len), &cont_token)); for (int i = 0; i < len; ++i) { SET_STRING_ELT(feature_names, i, safe_R_mkChar(ptr_names[i], &cont_token)); } Rf_unprotect(2); return feature_names; R_API_END(); } SEXP LGBM_DatasetSaveBinary_R(SEXP handle, SEXP filename) { R_API_BEGIN(); _AssertDatasetHandleNotNull(handle); const char* filename_ptr = CHAR(Rf_protect(Rf_asChar(filename))); CHECK_CALL(LGBM_DatasetSaveBinary(R_ExternalPtrAddr(handle), filename_ptr)); Rf_unprotect(1); return R_NilValue; R_API_END(); } SEXP LGBM_DatasetFree_R(SEXP handle) { R_API_BEGIN(); if (!Rf_isNull(handle) && R_ExternalPtrAddr(handle)) { CHECK_CALL(LGBM_DatasetFree(R_ExternalPtrAddr(handle))); R_ClearExternalPtr(handle); } return R_NilValue; R_API_END(); } SEXP LGBM_DatasetSetField_R(SEXP handle, SEXP field_name, SEXP field_data, SEXP num_element) { R_API_BEGIN(); _AssertDatasetHandleNotNull(handle); int len = Rf_asInteger(num_element); const char* name = CHAR(Rf_protect(Rf_asChar(field_name))); if (!strcmp("group", name) || !strcmp("query", name)) { CHECK_CALL(LGBM_DatasetSetField(R_ExternalPtrAddr(handle), name, INTEGER(field_data), len, C_API_DTYPE_INT32)); } else if (!strcmp("init_score", name)) { CHECK_CALL(LGBM_DatasetSetField(R_ExternalPtrAddr(handle), name, REAL(field_data), len, C_API_DTYPE_FLOAT64)); } else { std::unique_ptr vec(new float[len]); std::copy(REAL(field_data), REAL(field_data) + len, vec.get()); CHECK_CALL(LGBM_DatasetSetField(R_ExternalPtrAddr(handle), name, vec.get(), len, C_API_DTYPE_FLOAT32)); } Rf_unprotect(1); return R_NilValue; R_API_END(); } SEXP LGBM_DatasetGetField_R(SEXP handle, SEXP field_name, SEXP field_data) { R_API_BEGIN(); _AssertDatasetHandleNotNull(handle); const char* name = CHAR(Rf_protect(Rf_asChar(field_name))); int out_len = 0; int out_type = 0; const void* res; CHECK_CALL(LGBM_DatasetGetField(R_ExternalPtrAddr(handle), name, &out_len, &res, &out_type)); if (!strcmp("group", name) || !strcmp("query", name)) { auto p_data = reinterpret_cast(res); // convert from boundaries to size int *field_data_ = INTEGER(field_data); #ifndef _MSC_VER #pragma omp simd #endif for (int i = 0; i < out_len - 1; ++i) { field_data_[i] = p_data[i + 1] - p_data[i]; } } else if (!strcmp("init_score", name)) { auto p_data = reinterpret_cast(res); std::copy(p_data, p_data + out_len, REAL(field_data)); } else { auto p_data = reinterpret_cast(res); std::copy(p_data, p_data + out_len, REAL(field_data)); } Rf_unprotect(1); return R_NilValue; R_API_END(); } SEXP LGBM_DatasetGetFieldSize_R(SEXP handle, SEXP field_name, SEXP out) { R_API_BEGIN(); _AssertDatasetHandleNotNull(handle); const char* name = CHAR(Rf_protect(Rf_asChar(field_name))); int out_len = 0; int out_type = 0; const void* res; CHECK_CALL(LGBM_DatasetGetField(R_ExternalPtrAddr(handle), name, &out_len, &res, &out_type)); if (!strcmp("group", name) || !strcmp("query", name)) { out_len -= 1; } INTEGER(out)[0] = out_len; Rf_unprotect(1); return R_NilValue; R_API_END(); } SEXP LGBM_DatasetUpdateParamChecking_R(SEXP old_params, SEXP new_params) { R_API_BEGIN(); const char* old_params_ptr = CHAR(Rf_protect(Rf_asChar(old_params))); const char* new_params_ptr = CHAR(Rf_protect(Rf_asChar(new_params))); CHECK_CALL(LGBM_DatasetUpdateParamChecking(old_params_ptr, new_params_ptr)); Rf_unprotect(2); return R_NilValue; R_API_END(); } SEXP LGBM_DatasetGetNumData_R(SEXP handle, SEXP out) { R_API_BEGIN(); _AssertDatasetHandleNotNull(handle); int nrow; CHECK_CALL(LGBM_DatasetGetNumData(R_ExternalPtrAddr(handle), &nrow)); INTEGER(out)[0] = nrow; return R_NilValue; R_API_END(); } SEXP LGBM_DatasetGetNumFeature_R(SEXP handle, SEXP out) { R_API_BEGIN(); _AssertDatasetHandleNotNull(handle); int nfeature; CHECK_CALL(LGBM_DatasetGetNumFeature(R_ExternalPtrAddr(handle), &nfeature)); INTEGER(out)[0] = nfeature; return R_NilValue; R_API_END(); } SEXP LGBM_DatasetGetFeatureNumBin_R(SEXP handle, SEXP feature_idx, SEXP out) { R_API_BEGIN(); _AssertDatasetHandleNotNull(handle); int feature = Rf_asInteger(feature_idx); int nbins; CHECK_CALL(LGBM_DatasetGetFeatureNumBin(R_ExternalPtrAddr(handle), feature, &nbins)); INTEGER(out)[0] = nbins; return R_NilValue; R_API_END(); } // --- start Booster interfaces void _BoosterFinalizer(SEXP handle) { LGBM_BoosterFree_R(handle); } SEXP LGBM_BoosterFree_R(SEXP handle) { R_API_BEGIN(); if (!Rf_isNull(handle) && R_ExternalPtrAddr(handle)) { CHECK_CALL(LGBM_BoosterFree(R_ExternalPtrAddr(handle))); R_ClearExternalPtr(handle); } return R_NilValue; R_API_END(); } SEXP LGBM_BoosterCreate_R(SEXP train_data, SEXP parameters) { R_API_BEGIN(); _AssertDatasetHandleNotNull(train_data); SEXP ret = Rf_protect(R_MakeExternalPtr(nullptr, R_NilValue, R_NilValue)); const char* parameters_ptr = CHAR(Rf_protect(Rf_asChar(parameters))); BoosterHandle handle = nullptr; CHECK_CALL(LGBM_BoosterCreate(R_ExternalPtrAddr(train_data), parameters_ptr, &handle)); R_SetExternalPtrAddr(ret, handle); R_RegisterCFinalizerEx(ret, _BoosterFinalizer, TRUE); Rf_unprotect(2); return ret; R_API_END(); } SEXP LGBM_BoosterCreateFromModelfile_R(SEXP filename) { R_API_BEGIN(); SEXP ret = Rf_protect(R_MakeExternalPtr(nullptr, R_NilValue, R_NilValue)); int out_num_iterations = 0; const char* filename_ptr = CHAR(Rf_protect(Rf_asChar(filename))); BoosterHandle handle = nullptr; CHECK_CALL(LGBM_BoosterCreateFromModelfile(filename_ptr, &out_num_iterations, &handle)); R_SetExternalPtrAddr(ret, handle); R_RegisterCFinalizerEx(ret, _BoosterFinalizer, TRUE); Rf_unprotect(2); return ret; R_API_END(); } SEXP LGBM_BoosterLoadModelFromString_R(SEXP model_str) { R_API_BEGIN(); SEXP ret = Rf_protect(R_MakeExternalPtr(nullptr, R_NilValue, R_NilValue)); SEXP temp = NULL; int n_protected = 1; int out_num_iterations = 0; const char* model_str_ptr = nullptr; switch (TYPEOF(model_str)) { case RAWSXP: { model_str_ptr = reinterpret_cast(RAW(model_str)); break; } case CHARSXP: { model_str_ptr = reinterpret_cast(CHAR(model_str)); break; } case STRSXP: { temp = Rf_protect(STRING_ELT(model_str, 0)); n_protected++; model_str_ptr = reinterpret_cast(CHAR(temp)); } } BoosterHandle handle = nullptr; CHECK_CALL(LGBM_BoosterLoadModelFromString(model_str_ptr, &out_num_iterations, &handle)); R_SetExternalPtrAddr(ret, handle); R_RegisterCFinalizerEx(ret, _BoosterFinalizer, TRUE); Rf_unprotect(n_protected); return ret; R_API_END(); } SEXP LGBM_BoosterMerge_R(SEXP handle, SEXP other_handle) { R_API_BEGIN(); _AssertBoosterHandleNotNull(handle); _AssertBoosterHandleNotNull(other_handle); CHECK_CALL(LGBM_BoosterMerge(R_ExternalPtrAddr(handle), R_ExternalPtrAddr(other_handle))); return R_NilValue; R_API_END(); } SEXP LGBM_BoosterAddValidData_R(SEXP handle, SEXP valid_data) { R_API_BEGIN(); _AssertBoosterHandleNotNull(handle); _AssertDatasetHandleNotNull(valid_data); CHECK_CALL(LGBM_BoosterAddValidData(R_ExternalPtrAddr(handle), R_ExternalPtrAddr(valid_data))); return R_NilValue; R_API_END(); } SEXP LGBM_BoosterResetTrainingData_R(SEXP handle, SEXP train_data) { R_API_BEGIN(); _AssertBoosterHandleNotNull(handle); _AssertDatasetHandleNotNull(train_data); CHECK_CALL(LGBM_BoosterResetTrainingData(R_ExternalPtrAddr(handle), R_ExternalPtrAddr(train_data))); return R_NilValue; R_API_END(); } SEXP LGBM_BoosterResetParameter_R(SEXP handle, SEXP parameters) { R_API_BEGIN(); _AssertBoosterHandleNotNull(handle); const char* parameters_ptr = CHAR(Rf_protect(Rf_asChar(parameters))); CHECK_CALL(LGBM_BoosterResetParameter(R_ExternalPtrAddr(handle), parameters_ptr)); Rf_unprotect(1); return R_NilValue; R_API_END(); } SEXP LGBM_BoosterGetNumClasses_R(SEXP handle, SEXP out) { R_API_BEGIN(); _AssertBoosterHandleNotNull(handle); int num_class; CHECK_CALL(LGBM_BoosterGetNumClasses(R_ExternalPtrAddr(handle), &num_class)); INTEGER(out)[0] = num_class; return R_NilValue; R_API_END(); } SEXP LGBM_BoosterGetNumFeature_R(SEXP handle) { R_API_BEGIN(); _AssertBoosterHandleNotNull(handle); int out = 0; CHECK_CALL(LGBM_BoosterGetNumFeature(R_ExternalPtrAddr(handle), &out)); return Rf_ScalarInteger(out); R_API_END(); } SEXP LGBM_BoosterUpdateOneIter_R(SEXP handle) { R_API_BEGIN(); _AssertBoosterHandleNotNull(handle); int produced_empty_tree = 0; CHECK_CALL(LGBM_BoosterUpdateOneIter(R_ExternalPtrAddr(handle), &produced_empty_tree)); return R_NilValue; R_API_END(); } SEXP LGBM_BoosterUpdateOneIterCustom_R(SEXP handle, SEXP grad, SEXP hess, SEXP len) { R_API_BEGIN(); _AssertBoosterHandleNotNull(handle); int produced_empty_tree = 0; int int_len = Rf_asInteger(len); std::unique_ptr tgrad(new float[int_len]), thess(new float[int_len]); std::copy(REAL(grad), REAL(grad) + int_len, tgrad.get()); std::copy(REAL(hess), REAL(hess) + int_len, thess.get()); CHECK_CALL(LGBM_BoosterUpdateOneIterCustom(R_ExternalPtrAddr(handle), tgrad.get(), thess.get(), &produced_empty_tree)); return R_NilValue; R_API_END(); } SEXP LGBM_BoosterRollbackOneIter_R(SEXP handle) { R_API_BEGIN(); _AssertBoosterHandleNotNull(handle); CHECK_CALL(LGBM_BoosterRollbackOneIter(R_ExternalPtrAddr(handle))); return R_NilValue; R_API_END(); } SEXP LGBM_BoosterGetCurrentIteration_R(SEXP handle, SEXP out) { R_API_BEGIN(); _AssertBoosterHandleNotNull(handle); int out_iteration; CHECK_CALL(LGBM_BoosterGetCurrentIteration(R_ExternalPtrAddr(handle), &out_iteration)); INTEGER(out)[0] = out_iteration; return R_NilValue; R_API_END(); } SEXP LGBM_BoosterNumModelPerIteration_R(SEXP handle, SEXP out) { R_API_BEGIN(); _AssertBoosterHandleNotNull(handle); int models_per_iter; CHECK_CALL(LGBM_BoosterNumModelPerIteration(R_ExternalPtrAddr(handle), &models_per_iter)); INTEGER(out)[0] = models_per_iter; return R_NilValue; R_API_END(); } SEXP LGBM_BoosterNumberOfTotalModel_R(SEXP handle, SEXP out) { R_API_BEGIN(); _AssertBoosterHandleNotNull(handle); int total_models; CHECK_CALL(LGBM_BoosterNumberOfTotalModel(R_ExternalPtrAddr(handle), &total_models)); INTEGER(out)[0] = total_models; return R_NilValue; R_API_END(); } SEXP LGBM_BoosterGetUpperBoundValue_R(SEXP handle, SEXP out_result) { R_API_BEGIN(); _AssertBoosterHandleNotNull(handle); double* ptr_ret = REAL(out_result); CHECK_CALL(LGBM_BoosterGetUpperBoundValue(R_ExternalPtrAddr(handle), ptr_ret)); return R_NilValue; R_API_END(); } SEXP LGBM_BoosterGetLowerBoundValue_R(SEXP handle, SEXP out_result) { R_API_BEGIN(); _AssertBoosterHandleNotNull(handle); double* ptr_ret = REAL(out_result); CHECK_CALL(LGBM_BoosterGetLowerBoundValue(R_ExternalPtrAddr(handle), ptr_ret)); return R_NilValue; R_API_END(); } SEXP LGBM_BoosterGetEvalNames_R(SEXP handle) { SEXP cont_token = Rf_protect(R_MakeUnwindCont()); R_API_BEGIN(); _AssertBoosterHandleNotNull(handle); SEXP eval_names; int len; CHECK_CALL(LGBM_BoosterGetEvalCounts(R_ExternalPtrAddr(handle), &len)); const size_t reserved_string_size = 128; std::vector> names(len); std::vector ptr_names(len); for (int i = 0; i < len; ++i) { names[i].resize(reserved_string_size); ptr_names[i] = names[i].data(); } int out_len; size_t required_string_size; CHECK_CALL( LGBM_BoosterGetEvalNames( R_ExternalPtrAddr(handle), len, &out_len, reserved_string_size, &required_string_size, ptr_names.data())); // if any eval names were larger than allocated size, // allow for a larger size and try again if (required_string_size > reserved_string_size) { for (int i = 0; i < len; ++i) { names[i].resize(required_string_size); ptr_names[i] = names[i].data(); } CHECK_CALL( LGBM_BoosterGetEvalNames( R_ExternalPtrAddr(handle), len, &out_len, required_string_size, &required_string_size, ptr_names.data())); } CHECK_EQ(out_len, len); eval_names = Rf_protect(safe_R_string(static_cast(len), &cont_token)); for (int i = 0; i < len; ++i) { SET_STRING_ELT(eval_names, i, safe_R_mkChar(ptr_names[i], &cont_token)); } Rf_unprotect(2); return eval_names; R_API_END(); } SEXP LGBM_BoosterGetEval_R(SEXP handle, SEXP data_idx, SEXP out_result) { R_API_BEGIN(); _AssertBoosterHandleNotNull(handle); int len; CHECK_CALL(LGBM_BoosterGetEvalCounts(R_ExternalPtrAddr(handle), &len)); double* ptr_ret = REAL(out_result); int out_len; CHECK_CALL(LGBM_BoosterGetEval(R_ExternalPtrAddr(handle), Rf_asInteger(data_idx), &out_len, ptr_ret)); CHECK_EQ(out_len, len); return R_NilValue; R_API_END(); } SEXP LGBM_BoosterGetNumPredict_R(SEXP handle, SEXP data_idx, SEXP out) { R_API_BEGIN(); _AssertBoosterHandleNotNull(handle); int64_t len; CHECK_CALL(LGBM_BoosterGetNumPredict(R_ExternalPtrAddr(handle), Rf_asInteger(data_idx), &len)); INTEGER(out)[0] = static_cast(len); return R_NilValue; R_API_END(); } SEXP LGBM_BoosterGetPredict_R(SEXP handle, SEXP data_idx, SEXP out_result) { R_API_BEGIN(); _AssertBoosterHandleNotNull(handle); double* ptr_ret = REAL(out_result); int64_t out_len; CHECK_CALL(LGBM_BoosterGetPredict(R_ExternalPtrAddr(handle), Rf_asInteger(data_idx), &out_len, ptr_ret)); return R_NilValue; R_API_END(); } int GetPredictType(SEXP is_rawscore, SEXP is_leafidx, SEXP is_predcontrib) { int pred_type = C_API_PREDICT_NORMAL; if (Rf_asInteger(is_rawscore)) { pred_type = C_API_PREDICT_RAW_SCORE; } if (Rf_asInteger(is_leafidx)) { pred_type = C_API_PREDICT_LEAF_INDEX; } if (Rf_asInteger(is_predcontrib)) { pred_type = C_API_PREDICT_CONTRIB; } return pred_type; } SEXP LGBM_BoosterPredictForFile_R(SEXP handle, SEXP data_filename, SEXP data_has_header, SEXP is_rawscore, SEXP is_leafidx, SEXP is_predcontrib, SEXP start_iteration, SEXP num_iteration, SEXP parameter, SEXP result_filename) { R_API_BEGIN(); _AssertBoosterHandleNotNull(handle); const char* data_filename_ptr = CHAR(Rf_protect(Rf_asChar(data_filename))); const char* parameter_ptr = CHAR(Rf_protect(Rf_asChar(parameter))); const char* result_filename_ptr = CHAR(Rf_protect(Rf_asChar(result_filename))); int pred_type = GetPredictType(is_rawscore, is_leafidx, is_predcontrib); CHECK_CALL(LGBM_BoosterPredictForFile(R_ExternalPtrAddr(handle), data_filename_ptr, Rf_asInteger(data_has_header), pred_type, Rf_asInteger(start_iteration), Rf_asInteger(num_iteration), parameter_ptr, result_filename_ptr)); Rf_unprotect(3); return R_NilValue; R_API_END(); } SEXP LGBM_BoosterCalcNumPredict_R(SEXP handle, SEXP num_row, SEXP is_rawscore, SEXP is_leafidx, SEXP is_predcontrib, SEXP start_iteration, SEXP num_iteration, SEXP out_len) { R_API_BEGIN(); _AssertBoosterHandleNotNull(handle); int pred_type = GetPredictType(is_rawscore, is_leafidx, is_predcontrib); int64_t len = 0; CHECK_CALL(LGBM_BoosterCalcNumPredict(R_ExternalPtrAddr(handle), Rf_asInteger(num_row), pred_type, Rf_asInteger(start_iteration), Rf_asInteger(num_iteration), &len)); INTEGER(out_len)[0] = static_cast(len); return R_NilValue; R_API_END(); } SEXP LGBM_BoosterPredictForCSC_R(SEXP handle, SEXP indptr, SEXP indices, SEXP data, SEXP num_indptr, SEXP nelem, SEXP num_row, SEXP is_rawscore, SEXP is_leafidx, SEXP is_predcontrib, SEXP start_iteration, SEXP num_iteration, SEXP parameter, SEXP out_result) { R_API_BEGIN(); _AssertBoosterHandleNotNull(handle); int pred_type = GetPredictType(is_rawscore, is_leafidx, is_predcontrib); const int* p_indptr = INTEGER(indptr); const int32_t* p_indices = reinterpret_cast(INTEGER(indices)); const double* p_data = REAL(data); int64_t nindptr = static_cast(Rf_asInteger(num_indptr)); int64_t ndata = static_cast(Rf_asInteger(nelem)); int64_t nrow = static_cast(Rf_asInteger(num_row)); double* ptr_ret = REAL(out_result); int64_t out_len; const char* parameter_ptr = CHAR(Rf_protect(Rf_asChar(parameter))); CHECK_CALL(LGBM_BoosterPredictForCSC(R_ExternalPtrAddr(handle), p_indptr, C_API_DTYPE_INT32, p_indices, p_data, C_API_DTYPE_FLOAT64, nindptr, ndata, nrow, pred_type, Rf_asInteger(start_iteration), Rf_asInteger(num_iteration), parameter_ptr, &out_len, ptr_ret)); Rf_unprotect(1); return R_NilValue; R_API_END(); } SEXP LGBM_BoosterPredictForCSR_R(SEXP handle, SEXP indptr, SEXP indices, SEXP data, SEXP ncols, SEXP is_rawscore, SEXP is_leafidx, SEXP is_predcontrib, SEXP start_iteration, SEXP num_iteration, SEXP parameter, SEXP out_result) { R_API_BEGIN(); _AssertBoosterHandleNotNull(handle); int pred_type = GetPredictType(is_rawscore, is_leafidx, is_predcontrib); const char* parameter_ptr = CHAR(Rf_protect(Rf_asChar(parameter))); int64_t out_len; CHECK_CALL(LGBM_BoosterPredictForCSR(R_ExternalPtrAddr(handle), INTEGER(indptr), C_API_DTYPE_INT32, INTEGER(indices), REAL(data), C_API_DTYPE_FLOAT64, Rf_xlength(indptr), Rf_xlength(data), Rf_asInteger(ncols), pred_type, Rf_asInteger(start_iteration), Rf_asInteger(num_iteration), parameter_ptr, &out_len, REAL(out_result))); Rf_unprotect(1); return R_NilValue; R_API_END(); } SEXP LGBM_BoosterPredictForCSRSingleRow_R(SEXP handle, SEXP indices, SEXP data, SEXP ncols, SEXP is_rawscore, SEXP is_leafidx, SEXP is_predcontrib, SEXP start_iteration, SEXP num_iteration, SEXP parameter, SEXP out_result) { R_API_BEGIN(); _AssertBoosterHandleNotNull(handle); int pred_type = GetPredictType(is_rawscore, is_leafidx, is_predcontrib); const char* parameter_ptr = CHAR(Rf_protect(Rf_asChar(parameter))); int nnz = static_cast(Rf_xlength(data)); const int indptr[] = {0, nnz}; int64_t out_len; CHECK_CALL(LGBM_BoosterPredictForCSRSingleRow(R_ExternalPtrAddr(handle), indptr, C_API_DTYPE_INT32, INTEGER(indices), REAL(data), C_API_DTYPE_FLOAT64, 2, nnz, Rf_asInteger(ncols), pred_type, Rf_asInteger(start_iteration), Rf_asInteger(num_iteration), parameter_ptr, &out_len, REAL(out_result))); Rf_unprotect(1); return R_NilValue; R_API_END(); } void LGBM_FastConfigFree_wrapped(SEXP handle) { LGBM_FastConfigFree(static_cast(R_ExternalPtrAddr(handle))); } SEXP LGBM_BoosterPredictForCSRSingleRowFastInit_R(SEXP handle, SEXP ncols, SEXP is_rawscore, SEXP is_leafidx, SEXP is_predcontrib, SEXP start_iteration, SEXP num_iteration, SEXP parameter) { R_API_BEGIN(); _AssertBoosterHandleNotNull(handle); int pred_type = GetPredictType(is_rawscore, is_leafidx, is_predcontrib); SEXP ret = Rf_protect(R_MakeExternalPtr(nullptr, R_NilValue, R_NilValue)); const char* parameter_ptr = CHAR(Rf_protect(Rf_asChar(parameter))); FastConfigHandle out_fastConfig; CHECK_CALL(LGBM_BoosterPredictForCSRSingleRowFastInit(R_ExternalPtrAddr(handle), pred_type, Rf_asInteger(start_iteration), Rf_asInteger(num_iteration), C_API_DTYPE_FLOAT64, Rf_asInteger(ncols), parameter_ptr, &out_fastConfig)); R_SetExternalPtrAddr(ret, out_fastConfig); R_RegisterCFinalizerEx(ret, LGBM_FastConfigFree_wrapped, TRUE); Rf_unprotect(2); return ret; R_API_END(); } SEXP LGBM_BoosterPredictForCSRSingleRowFast_R(SEXP handle_fastConfig, SEXP indices, SEXP data, SEXP out_result) { R_API_BEGIN(); int nnz = static_cast(Rf_xlength(data)); const int indptr[] = {0, nnz}; int64_t out_len; CHECK_CALL(LGBM_BoosterPredictForCSRSingleRowFast(R_ExternalPtrAddr(handle_fastConfig), indptr, C_API_DTYPE_INT32, INTEGER(indices), REAL(data), 2, nnz, &out_len, REAL(out_result))); return R_NilValue; R_API_END(); } SEXP LGBM_BoosterPredictForMat_R(SEXP handle, SEXP data, SEXP num_row, SEXP num_col, SEXP is_rawscore, SEXP is_leafidx, SEXP is_predcontrib, SEXP start_iteration, SEXP num_iteration, SEXP parameter, SEXP out_result) { R_API_BEGIN(); _AssertBoosterHandleNotNull(handle); int pred_type = GetPredictType(is_rawscore, is_leafidx, is_predcontrib); int32_t nrow = static_cast(Rf_asInteger(num_row)); int32_t ncol = static_cast(Rf_asInteger(num_col)); const double* p_mat = REAL(data); double* ptr_ret = REAL(out_result); const char* parameter_ptr = CHAR(Rf_protect(Rf_asChar(parameter))); int64_t out_len; CHECK_CALL(LGBM_BoosterPredictForMat(R_ExternalPtrAddr(handle), p_mat, C_API_DTYPE_FLOAT64, nrow, ncol, COL_MAJOR, pred_type, Rf_asInteger(start_iteration), Rf_asInteger(num_iteration), parameter_ptr, &out_len, ptr_ret)); Rf_unprotect(1); return R_NilValue; R_API_END(); } struct SparseOutputPointers { void* indptr; int32_t* indices; void* data; SparseOutputPointers(void* indptr, int32_t* indices, void* data) : indptr(indptr), indices(indices), data(data) {} }; void delete_SparseOutputPointers(SparseOutputPointers *ptr) { LGBM_BoosterFreePredictSparse(ptr->indptr, ptr->indices, ptr->data, C_API_DTYPE_INT32, C_API_DTYPE_FLOAT64); delete ptr; } SEXP LGBM_BoosterPredictSparseOutput_R(SEXP handle, SEXP indptr, SEXP indices, SEXP data, SEXP is_csr, SEXP nrows, SEXP ncols, SEXP start_iteration, SEXP num_iteration, SEXP parameter) { SEXP cont_token = Rf_protect(R_MakeUnwindCont()); R_API_BEGIN(); _AssertBoosterHandleNotNull(handle); const char* out_names[] = {"indptr", "indices", "data", ""}; SEXP out = Rf_protect(Rf_mkNamed(VECSXP, out_names)); const char* parameter_ptr = CHAR(Rf_protect(Rf_asChar(parameter))); int64_t out_len[2]; void *out_indptr; int32_t *out_indices; void *out_data; CHECK_CALL(LGBM_BoosterPredictSparseOutput(R_ExternalPtrAddr(handle), INTEGER(indptr), C_API_DTYPE_INT32, INTEGER(indices), REAL(data), C_API_DTYPE_FLOAT64, Rf_xlength(indptr), Rf_xlength(data), Rf_asLogical(is_csr)? Rf_asInteger(ncols) : Rf_asInteger(nrows), C_API_PREDICT_CONTRIB, Rf_asInteger(start_iteration), Rf_asInteger(num_iteration), parameter_ptr, Rf_asLogical(is_csr)? C_API_MATRIX_TYPE_CSR : C_API_MATRIX_TYPE_CSC, out_len, &out_indptr, &out_indices, &out_data)); std::unique_ptr pointers_struct = { new SparseOutputPointers( out_indptr, out_indices, out_data), &delete_SparseOutputPointers }; arr_and_len indptr_str{static_cast(out_indptr), out_len[1]}; SET_VECTOR_ELT( out, 0, R_UnwindProtect(make_altrepped_vec_from_arr, static_cast(&indptr_str), throw_R_memerr, &cont_token, cont_token)); pointers_struct->indptr = nullptr; arr_and_len indices_str{static_cast(out_indices), out_len[0]}; SET_VECTOR_ELT( out, 1, R_UnwindProtect(make_altrepped_vec_from_arr, static_cast(&indices_str), throw_R_memerr, &cont_token, cont_token)); pointers_struct->indices = nullptr; arr_and_len data_str{static_cast(out_data), out_len[0]}; SET_VECTOR_ELT( out, 2, R_UnwindProtect(make_altrepped_vec_from_arr, static_cast(&data_str), throw_R_memerr, &cont_token, cont_token)); pointers_struct->data = nullptr; Rf_unprotect(3); return out; R_API_END(); } SEXP LGBM_BoosterPredictForMatSingleRow_R(SEXP handle, SEXP data, SEXP is_rawscore, SEXP is_leafidx, SEXP is_predcontrib, SEXP start_iteration, SEXP num_iteration, SEXP parameter, SEXP out_result) { R_API_BEGIN(); _AssertBoosterHandleNotNull(handle); int pred_type = GetPredictType(is_rawscore, is_leafidx, is_predcontrib); const char* parameter_ptr = CHAR(Rf_protect(Rf_asChar(parameter))); double* ptr_ret = REAL(out_result); int64_t out_len; CHECK_CALL(LGBM_BoosterPredictForMatSingleRow(R_ExternalPtrAddr(handle), REAL(data), C_API_DTYPE_FLOAT64, Rf_xlength(data), 1, pred_type, Rf_asInteger(start_iteration), Rf_asInteger(num_iteration), parameter_ptr, &out_len, ptr_ret)); Rf_unprotect(1); return R_NilValue; R_API_END(); } SEXP LGBM_BoosterPredictForMatSingleRowFastInit_R(SEXP handle, SEXP ncols, SEXP is_rawscore, SEXP is_leafidx, SEXP is_predcontrib, SEXP start_iteration, SEXP num_iteration, SEXP parameter) { R_API_BEGIN(); _AssertBoosterHandleNotNull(handle); int pred_type = GetPredictType(is_rawscore, is_leafidx, is_predcontrib); SEXP ret = Rf_protect(R_MakeExternalPtr(nullptr, R_NilValue, R_NilValue)); const char* parameter_ptr = CHAR(Rf_protect(Rf_asChar(parameter))); FastConfigHandle out_fastConfig; CHECK_CALL(LGBM_BoosterPredictForMatSingleRowFastInit(R_ExternalPtrAddr(handle), pred_type, Rf_asInteger(start_iteration), Rf_asInteger(num_iteration), C_API_DTYPE_FLOAT64, Rf_asInteger(ncols), parameter_ptr, &out_fastConfig)); R_SetExternalPtrAddr(ret, out_fastConfig); R_RegisterCFinalizerEx(ret, LGBM_FastConfigFree_wrapped, TRUE); Rf_unprotect(2); return ret; R_API_END(); } SEXP LGBM_BoosterPredictForMatSingleRowFast_R(SEXP handle_fastConfig, SEXP data, SEXP out_result) { R_API_BEGIN(); int64_t out_len; CHECK_CALL(LGBM_BoosterPredictForMatSingleRowFast(R_ExternalPtrAddr(handle_fastConfig), REAL(data), &out_len, REAL(out_result))); return R_NilValue; R_API_END(); } SEXP LGBM_BoosterSaveModel_R(SEXP handle, SEXP num_iteration, SEXP feature_importance_type, SEXP filename, SEXP start_iteration) { R_API_BEGIN(); _AssertBoosterHandleNotNull(handle); const char* filename_ptr = CHAR(Rf_protect(Rf_asChar(filename))); CHECK_CALL(LGBM_BoosterSaveModel(R_ExternalPtrAddr(handle), Rf_asInteger(start_iteration), Rf_asInteger(num_iteration), Rf_asInteger(feature_importance_type), filename_ptr)); Rf_unprotect(1); return R_NilValue; R_API_END(); } // Note: for some reason, MSVC crashes when an error is thrown here // if the buffer variable is defined as 'std::unique_ptr>', // but not if it is defined as ''. #ifndef _MSC_VER SEXP LGBM_BoosterSaveModelToString_R(SEXP handle, SEXP num_iteration, SEXP feature_importance_type, SEXP start_iteration) { SEXP cont_token = Rf_protect(R_MakeUnwindCont()); R_API_BEGIN(); _AssertBoosterHandleNotNull(handle); int64_t out_len = 0; int64_t buf_len = 1024 * 1024; int num_iter = Rf_asInteger(num_iteration); int start_iter = Rf_asInteger(start_iteration); int importance_type = Rf_asInteger(feature_importance_type); std::unique_ptr> inner_char_buf(new std::vector(buf_len)); CHECK_CALL(LGBM_BoosterSaveModelToString(R_ExternalPtrAddr(handle), start_iter, num_iter, importance_type, buf_len, &out_len, inner_char_buf->data())); inner_char_buf->resize(out_len); if (out_len > buf_len) { CHECK_CALL(LGBM_BoosterSaveModelToString(R_ExternalPtrAddr(handle), start_iter, num_iter, importance_type, out_len, &out_len, inner_char_buf->data())); } SEXP out = R_UnwindProtect(make_altrepped_raw_vec, &inner_char_buf, throw_R_memerr, &cont_token, cont_token); Rf_unprotect(1); return out; R_API_END(); } #else SEXP LGBM_BoosterSaveModelToString_R(SEXP handle, SEXP num_iteration, SEXP feature_importance_type, SEXP start_iteration) { SEXP cont_token = Rf_protect(R_MakeUnwindCont()); R_API_BEGIN(); _AssertBoosterHandleNotNull(handle); int64_t out_len = 0; int64_t buf_len = 1024 * 1024; int num_iter = Rf_asInteger(num_iteration); int start_iter = Rf_asInteger(start_iteration); int importance_type = Rf_asInteger(feature_importance_type); std::vector inner_char_buf(buf_len); CHECK_CALL(LGBM_BoosterSaveModelToString(R_ExternalPtrAddr(handle), start_iter, num_iter, importance_type, buf_len, &out_len, inner_char_buf.data())); SEXP model_str = Rf_protect(safe_R_raw(out_len, &cont_token)); // if the model string was larger than the initial buffer, call the function again, writing directly to the R object if (out_len > buf_len) { CHECK_CALL(LGBM_BoosterSaveModelToString(R_ExternalPtrAddr(handle), start_iter, num_iter, importance_type, out_len, &out_len, reinterpret_cast(RAW(model_str)))); } else { std::copy(inner_char_buf.begin(), inner_char_buf.begin() + out_len, reinterpret_cast(RAW(model_str))); } Rf_unprotect(2); return model_str; R_API_END(); } #endif SEXP LGBM_BoosterDumpModel_R(SEXP handle, SEXP num_iteration, SEXP feature_importance_type, SEXP start_iteration) { SEXP cont_token = Rf_protect(R_MakeUnwindCont()); R_API_BEGIN(); _AssertBoosterHandleNotNull(handle); SEXP model_str; int64_t out_len = 0; int64_t buf_len = 1024 * 1024; int num_iter = Rf_asInteger(num_iteration); int start_iter = Rf_asInteger(start_iteration); int importance_type = Rf_asInteger(feature_importance_type); std::vector inner_char_buf(buf_len); CHECK_CALL(LGBM_BoosterDumpModel(R_ExternalPtrAddr(handle), start_iter, num_iter, importance_type, buf_len, &out_len, inner_char_buf.data())); // if the model string was larger than the initial buffer, allocate a bigger buffer and try again if (out_len > buf_len) { inner_char_buf.resize(out_len); CHECK_CALL(LGBM_BoosterDumpModel(R_ExternalPtrAddr(handle), start_iter, num_iter, importance_type, out_len, &out_len, inner_char_buf.data())); } model_str = Rf_protect(safe_R_string(static_cast(1), &cont_token)); SET_STRING_ELT(model_str, 0, safe_R_mkChar(inner_char_buf.data(), &cont_token)); Rf_unprotect(2); return model_str; R_API_END(); } SEXP LGBM_DumpParamAliases_R() { SEXP cont_token = Rf_protect(R_MakeUnwindCont()); R_API_BEGIN(); SEXP aliases_str; int64_t out_len = 0; int64_t buf_len = 1024 * 1024; std::vector inner_char_buf(buf_len); CHECK_CALL(LGBM_DumpParamAliases(buf_len, &out_len, inner_char_buf.data())); // if aliases string was larger than the initial buffer, allocate a bigger buffer and try again if (out_len > buf_len) { inner_char_buf.resize(out_len); CHECK_CALL(LGBM_DumpParamAliases(out_len, &out_len, inner_char_buf.data())); } aliases_str = Rf_protect(safe_R_string(static_cast(1), &cont_token)); SET_STRING_ELT(aliases_str, 0, safe_R_mkChar(inner_char_buf.data(), &cont_token)); Rf_unprotect(2); return aliases_str; R_API_END(); } SEXP LGBM_BoosterGetLoadedParam_R(SEXP handle) { SEXP cont_token = Rf_protect(R_MakeUnwindCont()); R_API_BEGIN(); _AssertBoosterHandleNotNull(handle); SEXP params_str; int64_t out_len = 0; int64_t buf_len = 1024 * 1024; std::vector inner_char_buf(buf_len); CHECK_CALL(LGBM_BoosterGetLoadedParam(R_ExternalPtrAddr(handle), buf_len, &out_len, inner_char_buf.data())); // if aliases string was larger than the initial buffer, allocate a bigger buffer and try again if (out_len > buf_len) { inner_char_buf.resize(out_len); CHECK_CALL(LGBM_BoosterGetLoadedParam(R_ExternalPtrAddr(handle), out_len, &out_len, inner_char_buf.data())); } params_str = Rf_protect(safe_R_string(static_cast(1), &cont_token)); SET_STRING_ELT(params_str, 0, safe_R_mkChar(inner_char_buf.data(), &cont_token)); Rf_unprotect(2); return params_str; R_API_END(); } SEXP LGBM_GetMaxThreads_R(SEXP out) { R_API_BEGIN(); int num_threads; CHECK_CALL(LGBM_GetMaxThreads(&num_threads)); INTEGER(out)[0] = num_threads; return R_NilValue; R_API_END(); } SEXP LGBM_SetMaxThreads_R(SEXP num_threads) { R_API_BEGIN(); int new_num_threads = Rf_asInteger(num_threads); CHECK_CALL(LGBM_SetMaxThreads(new_num_threads)); return R_NilValue; R_API_END(); } // .Call() calls static const R_CallMethodDef CallEntries[] = { {"LGBM_HandleIsNull_R" , (DL_FUNC) &LGBM_HandleIsNull_R , 1}, {"LGBM_DatasetCreateFromFile_R" , (DL_FUNC) &LGBM_DatasetCreateFromFile_R , 3}, {"LGBM_DatasetCreateFromCSC_R" , (DL_FUNC) &LGBM_DatasetCreateFromCSC_R , 8}, {"LGBM_DatasetCreateFromMat_R" , (DL_FUNC) &LGBM_DatasetCreateFromMat_R , 5}, {"LGBM_DatasetGetSubset_R" , (DL_FUNC) &LGBM_DatasetGetSubset_R , 4}, {"LGBM_DatasetSetFeatureNames_R" , (DL_FUNC) &LGBM_DatasetSetFeatureNames_R , 2}, {"LGBM_DatasetGetFeatureNames_R" , (DL_FUNC) &LGBM_DatasetGetFeatureNames_R , 1}, {"LGBM_DatasetSaveBinary_R" , (DL_FUNC) &LGBM_DatasetSaveBinary_R , 2}, {"LGBM_DatasetFree_R" , (DL_FUNC) &LGBM_DatasetFree_R , 1}, {"LGBM_DatasetSetField_R" , (DL_FUNC) &LGBM_DatasetSetField_R , 4}, {"LGBM_DatasetGetFieldSize_R" , (DL_FUNC) &LGBM_DatasetGetFieldSize_R , 3}, {"LGBM_DatasetGetField_R" , (DL_FUNC) &LGBM_DatasetGetField_R , 3}, {"LGBM_DatasetUpdateParamChecking_R" , (DL_FUNC) &LGBM_DatasetUpdateParamChecking_R , 2}, {"LGBM_DatasetGetNumData_R" , (DL_FUNC) &LGBM_DatasetGetNumData_R , 2}, {"LGBM_DatasetGetNumFeature_R" , (DL_FUNC) &LGBM_DatasetGetNumFeature_R , 2}, {"LGBM_DatasetGetFeatureNumBin_R" , (DL_FUNC) &LGBM_DatasetGetFeatureNumBin_R , 3}, {"LGBM_BoosterCreate_R" , (DL_FUNC) &LGBM_BoosterCreate_R , 2}, {"LGBM_BoosterFree_R" , (DL_FUNC) &LGBM_BoosterFree_R , 1}, {"LGBM_BoosterCreateFromModelfile_R" , (DL_FUNC) &LGBM_BoosterCreateFromModelfile_R , 1}, {"LGBM_BoosterLoadModelFromString_R" , (DL_FUNC) &LGBM_BoosterLoadModelFromString_R , 1}, {"LGBM_BoosterMerge_R" , (DL_FUNC) &LGBM_BoosterMerge_R , 2}, {"LGBM_BoosterAddValidData_R" , (DL_FUNC) &LGBM_BoosterAddValidData_R , 2}, {"LGBM_BoosterResetTrainingData_R" , (DL_FUNC) &LGBM_BoosterResetTrainingData_R , 2}, {"LGBM_BoosterResetParameter_R" , (DL_FUNC) &LGBM_BoosterResetParameter_R , 2}, {"LGBM_BoosterGetNumClasses_R" , (DL_FUNC) &LGBM_BoosterGetNumClasses_R , 2}, {"LGBM_BoosterGetNumFeature_R" , (DL_FUNC) &LGBM_BoosterGetNumFeature_R , 1}, {"LGBM_BoosterGetLoadedParam_R" , (DL_FUNC) &LGBM_BoosterGetLoadedParam_R , 1}, {"LGBM_BoosterUpdateOneIter_R" , (DL_FUNC) &LGBM_BoosterUpdateOneIter_R , 1}, {"LGBM_BoosterUpdateOneIterCustom_R" , (DL_FUNC) &LGBM_BoosterUpdateOneIterCustom_R , 4}, {"LGBM_BoosterRollbackOneIter_R" , (DL_FUNC) &LGBM_BoosterRollbackOneIter_R , 1}, {"LGBM_BoosterGetCurrentIteration_R" , (DL_FUNC) &LGBM_BoosterGetCurrentIteration_R , 2}, {"LGBM_BoosterNumModelPerIteration_R" , (DL_FUNC) &LGBM_BoosterNumModelPerIteration_R , 2}, {"LGBM_BoosterNumberOfTotalModel_R" , (DL_FUNC) &LGBM_BoosterNumberOfTotalModel_R , 2}, {"LGBM_BoosterGetUpperBoundValue_R" , (DL_FUNC) &LGBM_BoosterGetUpperBoundValue_R , 2}, {"LGBM_BoosterGetLowerBoundValue_R" , (DL_FUNC) &LGBM_BoosterGetLowerBoundValue_R , 2}, {"LGBM_BoosterGetEvalNames_R" , (DL_FUNC) &LGBM_BoosterGetEvalNames_R , 1}, {"LGBM_BoosterGetEval_R" , (DL_FUNC) &LGBM_BoosterGetEval_R , 3}, {"LGBM_BoosterGetNumPredict_R" , (DL_FUNC) &LGBM_BoosterGetNumPredict_R , 3}, {"LGBM_BoosterGetPredict_R" , (DL_FUNC) &LGBM_BoosterGetPredict_R , 3}, {"LGBM_BoosterPredictForFile_R" , (DL_FUNC) &LGBM_BoosterPredictForFile_R , 10}, {"LGBM_BoosterCalcNumPredict_R" , (DL_FUNC) &LGBM_BoosterCalcNumPredict_R , 8}, {"LGBM_BoosterPredictForCSC_R" , (DL_FUNC) &LGBM_BoosterPredictForCSC_R , 14}, {"LGBM_BoosterPredictForCSR_R" , (DL_FUNC) &LGBM_BoosterPredictForCSR_R , 12}, {"LGBM_BoosterPredictForCSRSingleRow_R" , (DL_FUNC) &LGBM_BoosterPredictForCSRSingleRow_R , 11}, {"LGBM_BoosterPredictForCSRSingleRowFastInit_R", (DL_FUNC) &LGBM_BoosterPredictForCSRSingleRowFastInit_R, 8}, {"LGBM_BoosterPredictForCSRSingleRowFast_R" , (DL_FUNC) &LGBM_BoosterPredictForCSRSingleRowFast_R , 4}, {"LGBM_BoosterPredictSparseOutput_R" , (DL_FUNC) &LGBM_BoosterPredictSparseOutput_R , 10}, {"LGBM_BoosterPredictForMat_R" , (DL_FUNC) &LGBM_BoosterPredictForMat_R , 11}, {"LGBM_BoosterPredictForMatSingleRow_R" , (DL_FUNC) &LGBM_BoosterPredictForMatSingleRow_R , 9}, {"LGBM_BoosterPredictForMatSingleRowFastInit_R", (DL_FUNC) &LGBM_BoosterPredictForMatSingleRowFastInit_R, 8}, {"LGBM_BoosterPredictForMatSingleRowFast_R" , (DL_FUNC) &LGBM_BoosterPredictForMatSingleRowFast_R , 3}, {"LGBM_BoosterSaveModel_R" , (DL_FUNC) &LGBM_BoosterSaveModel_R , 5}, {"LGBM_BoosterSaveModelToString_R" , (DL_FUNC) &LGBM_BoosterSaveModelToString_R , 4}, {"LGBM_BoosterDumpModel_R" , (DL_FUNC) &LGBM_BoosterDumpModel_R , 4}, {"LGBM_NullBoosterHandleError_R" , (DL_FUNC) &LGBM_NullBoosterHandleError_R , 0}, {"LGBM_DumpParamAliases_R" , (DL_FUNC) &LGBM_DumpParamAliases_R , 0}, {"LGBM_GetMaxThreads_R" , (DL_FUNC) &LGBM_GetMaxThreads_R , 1}, {"LGBM_SetMaxThreads_R" , (DL_FUNC) &LGBM_SetMaxThreads_R , 1}, {NULL, NULL, 0} }; LIGHTGBM_C_EXPORT void R_init_lightgbm(DllInfo *dll); void R_init_lightgbm(DllInfo *dll) { R_registerRoutines(dll, NULL, CallEntries, NULL, NULL); R_useDynamicSymbols(dll, FALSE); #ifndef LGB_NO_ALTREP lgb_altrepped_char_vec = R_make_altraw_class("lgb_altrepped_char_vec", "lightgbm", dll); R_set_altrep_Length_method(lgb_altrepped_char_vec, get_altrepped_raw_len); R_set_altvec_Dataptr_method(lgb_altrepped_char_vec, get_altrepped_raw_dataptr); R_set_altvec_Dataptr_or_null_method(lgb_altrepped_char_vec, get_altrepped_raw_dataptr_or_null); lgb_altrepped_int_arr = R_make_altinteger_class("lgb_altrepped_int_arr", "lightgbm", dll); R_set_altrep_Length_method(lgb_altrepped_int_arr, get_altrepped_vec_len); R_set_altvec_Dataptr_method(lgb_altrepped_int_arr, get_altrepped_vec_dataptr); R_set_altvec_Dataptr_or_null_method(lgb_altrepped_int_arr, get_altrepped_vec_dataptr_or_null); lgb_altrepped_dbl_arr = R_make_altreal_class("lgb_altrepped_dbl_arr", "lightgbm", dll); R_set_altrep_Length_method(lgb_altrepped_dbl_arr, get_altrepped_vec_len); R_set_altvec_Dataptr_method(lgb_altrepped_dbl_arr, get_altrepped_vec_dataptr); R_set_altvec_Dataptr_or_null_method(lgb_altrepped_dbl_arr, get_altrepped_vec_dataptr_or_null); #endif } ================================================ FILE: R-package/src/lightgbm_R.h ================================================ /*! * Copyright (c) 2017-2026 Microsoft Corporation. All rights reserved. * Copyright (c) 2017-2026 The LightGBM developers. All rights reserved. * Licensed under the MIT License. See LICENSE file in the project root for license information. */ #ifndef LIGHTGBM_R_PACKAGE_SRC_LIGHTGBM_R_H_ #define LIGHTGBM_R_PACKAGE_SRC_LIGHTGBM_R_H_ #include #ifndef R_NO_REMAP #define R_NO_REMAP #endif #ifndef R_USE_C99_IN_CXX #define R_USE_C99_IN_CXX #endif #include /*! * \brief check if an R external pointer (like a Booster or Dataset handle) is a null pointer * \param handle handle for a Booster, Dataset, or Predictor * \return R logical, TRUE if the handle is a null pointer */ LIGHTGBM_C_EXPORT SEXP LGBM_HandleIsNull_R( SEXP handle ); /*! * \brief Throw a standardized error message when encountering a null Booster handle * \return No return, will throw an error */ LIGHTGBM_C_EXPORT SEXP LGBM_NullBoosterHandleError_R(); // --- start Dataset interface /*! * \brief load Dataset from file like the command_line LightGBM does * \param filename the name of the file * \param parameters additional parameters * \param reference used to align bin mapper with other Dataset, nullptr means not used * \return Dataset handle */ LIGHTGBM_C_EXPORT SEXP LGBM_DatasetCreateFromFile_R( SEXP filename, SEXP parameters, SEXP reference ); /*! * \brief create a Dataset from Compressed Sparse Column (CSC) format * \param indptr pointer to row headers * \param indices findex * \param data fvalue * \param num_indptr number of cols in the matrix + 1 * \param nelem number of nonzero elements in the matrix * \param num_row number of rows * \param parameters additional parameters * \param reference used to align bin mapper with other Dataset, nullptr means not used * \return Dataset handle */ LIGHTGBM_C_EXPORT SEXP LGBM_DatasetCreateFromCSC_R( SEXP indptr, SEXP indices, SEXP data, SEXP num_indptr, SEXP nelem, SEXP num_row, SEXP parameters, SEXP reference ); /*! * \brief create Dataset from dense matrix * \param data matrix data * \param num_row number of rows * \param num_col number columns * \param parameters additional parameters * \param reference used to align bin mapper with other Dataset, nullptr means not used * \return Dataset handle */ LIGHTGBM_C_EXPORT SEXP LGBM_DatasetCreateFromMat_R( SEXP data, SEXP num_row, SEXP num_col, SEXP parameters, SEXP reference ); /*! * \brief Create subset of a Dataset * \param handle handle of full Dataset * \param used_row_indices Indices used in subset * \param len_used_row_indices length of Indices used in subset * \param parameters additional parameters * \return Dataset handle */ LIGHTGBM_C_EXPORT SEXP LGBM_DatasetGetSubset_R( SEXP handle, SEXP used_row_indices, SEXP len_used_row_indices, SEXP parameters ); /*! * \brief save feature names to Dataset * \param handle handle * \param feature_names feature names * \return R character vector of feature names */ LIGHTGBM_C_EXPORT SEXP LGBM_DatasetSetFeatureNames_R( SEXP handle, SEXP feature_names ); /*! * \brief get feature names from Dataset * \param handle Dataset handle * \return an R character vector with feature names from the Dataset or NULL if no feature names */ LIGHTGBM_C_EXPORT SEXP LGBM_DatasetGetFeatureNames_R( SEXP handle ); /*! * \brief save Dataset to binary file * \param handle an instance of Dataset * \param filename file name * \return R NULL value */ LIGHTGBM_C_EXPORT SEXP LGBM_DatasetSaveBinary_R( SEXP handle, SEXP filename ); /*! * \brief free Dataset * \param handle an instance of Dataset * \return R NULL value */ LIGHTGBM_C_EXPORT SEXP LGBM_DatasetFree_R( SEXP handle ); /*! * \brief set vector to a content in info * Note: group and group_id only work for C_API_DTYPE_INT32 * label and weight only work for C_API_DTYPE_FLOAT32 * \param handle an instance of Dataset * \param field_name field name, can be label, weight, group, group_id * \param field_data pointer to vector * \param num_element number of element in field_data * \return R NULL value */ LIGHTGBM_C_EXPORT SEXP LGBM_DatasetSetField_R( SEXP handle, SEXP field_name, SEXP field_data, SEXP num_element ); /*! * \brief get size of info vector from Dataset * \param handle an instance of Dataset * \param field_name field name * \param out size of info vector from Dataset * \return R NULL value */ LIGHTGBM_C_EXPORT SEXP LGBM_DatasetGetFieldSize_R( SEXP handle, SEXP field_name, SEXP out ); /*! * \brief get info vector from Dataset * \param handle an instance of Dataset * \param field_name field name * \param field_data pointer to vector * \return R NULL value */ LIGHTGBM_C_EXPORT SEXP LGBM_DatasetGetField_R( SEXP handle, SEXP field_name, SEXP field_data ); /*! * \brief Raise errors for attempts to update Dataset parameters. * Some parameters cannot be updated after construction. * \param old_params Current Dataset parameters * \param new_params New Dataset parameters * \return R NULL value */ LIGHTGBM_C_EXPORT SEXP LGBM_DatasetUpdateParamChecking_R( SEXP old_params, SEXP new_params ); /*! * \brief get number of data. * \param handle the handle to the Dataset * \param out The address to hold number of data * \return R NULL value */ LIGHTGBM_C_EXPORT SEXP LGBM_DatasetGetNumData_R( SEXP handle, SEXP out ); /*! * \brief get number of features * \param handle the handle to the Dataset * \param out The output of number of features * \return R NULL value */ LIGHTGBM_C_EXPORT SEXP LGBM_DatasetGetNumFeature_R( SEXP handle, SEXP out ); /*! * \brief get number of bins for feature * \param handle the handle to the Dataset * \param feature the index of the feature * \param out The output of number of bins * \return R NULL value */ LIGHTGBM_C_EXPORT SEXP LGBM_DatasetGetFeatureNumBin_R( SEXP handle, SEXP feature, SEXP out ); // --- start Booster interfaces /*! * \brief create a new boosting learner * \param train_data training Dataset * \param parameters format: 'key1=value1 key2=value2' * \return Booster handle */ LIGHTGBM_C_EXPORT SEXP LGBM_BoosterCreate_R( SEXP train_data, SEXP parameters ); /*! * \brief free Booster * \param handle handle to be freed * \return R NULL value */ LIGHTGBM_C_EXPORT SEXP LGBM_BoosterFree_R( SEXP handle ); /*! * \brief load an existing Booster from model file * \param filename filename of model * \return Booster handle */ LIGHTGBM_C_EXPORT SEXP LGBM_BoosterCreateFromModelfile_R( SEXP filename ); /*! * \brief load an existing Booster from a string * \param model_str string containing the model * \return Booster handle */ LIGHTGBM_C_EXPORT SEXP LGBM_BoosterLoadModelFromString_R( SEXP model_str ); /*! * \brief Get parameters as JSON string. * \param handle Booster handle * \return R character vector (length=1) with parameters in JSON format */ LIGHTGBM_C_EXPORT SEXP LGBM_BoosterGetLoadedParam_R( SEXP handle ); /*! * \brief Merge model in two Boosters to first handle * \param handle handle primary Booster handle, will merge other handle to this * \param other_handle secondary Booster handle * \return R NULL value */ LIGHTGBM_C_EXPORT SEXP LGBM_BoosterMerge_R( SEXP handle, SEXP other_handle ); /*! * \brief Add new validation to Booster * \param handle Booster handle * \param valid_data validation Dataset * \return R NULL value */ LIGHTGBM_C_EXPORT SEXP LGBM_BoosterAddValidData_R( SEXP handle, SEXP valid_data ); /*! * \brief Reset training data for Booster * \param handle Booster handle * \param train_data training Dataset * \return R NULL value */ LIGHTGBM_C_EXPORT SEXP LGBM_BoosterResetTrainingData_R( SEXP handle, SEXP train_data ); /*! * \brief Reset config for current Booster * \param handle Booster handle * \param parameters format: 'key1=value1 key2=value2' * \return R NULL value */ LIGHTGBM_C_EXPORT SEXP LGBM_BoosterResetParameter_R( SEXP handle, SEXP parameters ); /*! * \brief Get number of classes * \param handle Booster handle * \param out number of classes * \return R NULL value */ LIGHTGBM_C_EXPORT SEXP LGBM_BoosterGetNumClasses_R( SEXP handle, SEXP out ); /*! * \brief Get number of features. * \param handle Booster handle * \return Total number of features, as R integer */ LIGHTGBM_C_EXPORT SEXP LGBM_BoosterGetNumFeature_R( SEXP handle ); /*! * \brief update the model in one round * \param handle Booster handle * \return R NULL value */ LIGHTGBM_C_EXPORT SEXP LGBM_BoosterUpdateOneIter_R( SEXP handle ); /*! * \brief update the model, by directly specifying gradient and second order gradient, * this can be used to support customized loss function * \param handle Booster handle * \param grad gradient statistics * \param hess second order gradient statistics * \param len length of grad/hess * \return R NULL value */ LIGHTGBM_C_EXPORT SEXP LGBM_BoosterUpdateOneIterCustom_R( SEXP handle, SEXP grad, SEXP hess, SEXP len ); /*! * \brief Rollback one iteration * \param handle Booster handle * \return R NULL value */ LIGHTGBM_C_EXPORT SEXP LGBM_BoosterRollbackOneIter_R( SEXP handle ); /*! * \brief Get iteration of current boosting rounds * \param handle Booster handle * \param out iteration of boosting rounds * \return R NULL value */ LIGHTGBM_C_EXPORT SEXP LGBM_BoosterGetCurrentIteration_R( SEXP handle, SEXP out ); /*! * \brief Get number of trees per iteration * \param handle Booster handle * \param out Number of trees per iteration * \return R NULL value */ LIGHTGBM_C_EXPORT SEXP LGBM_BoosterNumModelPerIteration_R( SEXP handle, SEXP out ); /*! * \brief Get total number of trees * \param handle Booster handle * \param out Total number of trees of Booster * \return R NULL value */ LIGHTGBM_C_EXPORT SEXP LGBM_BoosterNumberOfTotalModel_R( SEXP handle, SEXP out ); /*! * \brief Get model upper bound value. * \param handle Handle of Booster * \param[out] out_results Result pointing to max value * \return R NULL value */ LIGHTGBM_C_EXPORT SEXP LGBM_BoosterGetUpperBoundValue_R( SEXP handle, SEXP out_result ); /*! * \brief Get model lower bound value. * \param handle Handle of Booster * \param[out] out_results Result pointing to min value * \return R NULL value */ LIGHTGBM_C_EXPORT SEXP LGBM_BoosterGetLowerBoundValue_R( SEXP handle, SEXP out_result ); /*! * \brief Get names of eval metrics * \param handle Handle of booster * \return R character vector with names of eval metrics */ LIGHTGBM_C_EXPORT SEXP LGBM_BoosterGetEvalNames_R( SEXP handle ); /*! * \brief get evaluation for training data and validation data * \param handle Booster handle * \param data_idx 0:training data, 1: 1st valid data, 2:2nd valid data ... * \param out_result float array containing result * \return R NULL value */ LIGHTGBM_C_EXPORT SEXP LGBM_BoosterGetEval_R( SEXP handle, SEXP data_idx, SEXP out_result ); /*! * \brief Get number of prediction for training data and validation data * \param handle Booster handle * \param data_idx 0:training data, 1: 1st valid data, 2:2nd valid data ... * \param out size of predict * \return R NULL value */ LIGHTGBM_C_EXPORT SEXP LGBM_BoosterGetNumPredict_R( SEXP handle, SEXP data_idx, SEXP out ); /*! * \brief Get prediction for training data and validation data. * This can be used to support customized eval function * \param handle Booster handle * \param data_idx 0:training data, 1: 1st valid data, 2:2nd valid data ... * \param out_result, used to store predict result, should pre-allocate memory * \return R NULL value */ LIGHTGBM_C_EXPORT SEXP LGBM_BoosterGetPredict_R( SEXP handle, SEXP data_idx, SEXP out_result ); /*! * \brief make prediction for file * \param handle Booster handle * \param data_filename filename of data file * \param data_has_header data file has header or not * \param is_rawscore 1 to get raw predictions, before transformations like * converting to probabilities, 0 otherwise * \param is_leafidx 1 to get record of which leaf in each tree * observations fell into, 0 otherwise * \param is_predcontrib 1 to get feature contributions, 0 otherwise * \param start_iteration Start index of the iteration to predict * \param num_iteration number of iteration for prediction, <= 0 means no limit * \param parameter additional parameters * \param result_filename filename of file to write predictions to * \return R NULL value */ LIGHTGBM_C_EXPORT SEXP LGBM_BoosterPredictForFile_R( SEXP handle, SEXP data_filename, SEXP data_has_header, SEXP is_rawscore, SEXP is_leafidx, SEXP is_predcontrib, SEXP start_iteration, SEXP num_iteration, SEXP parameter, SEXP result_filename ); /*! * \brief Get number of prediction * \param handle Booster handle * \param num_row number of rows in input * \param is_rawscore 1 to get raw predictions, before transformations like * converting to probabilities, 0 otherwise * \param is_leafidx 1 to get record of which leaf in each tree * observations fell into, 0 otherwise * \param is_predcontrib 1 to get feature contributions, 0 otherwise * \param start_iteration Start index of the iteration to predict * \param num_iteration number of iteration for prediction, <= 0 means no limit * \param out_len length of prediction * \return R NULL value */ LIGHTGBM_C_EXPORT SEXP LGBM_BoosterCalcNumPredict_R( SEXP handle, SEXP num_row, SEXP is_rawscore, SEXP is_leafidx, SEXP is_predcontrib, SEXP start_iteration, SEXP num_iteration, SEXP out_len ); /*! * \brief make prediction for a new Dataset * Note: should pre-allocate memory for out_result, * for normal and raw score: its length is equal to num_class * num_data * for leaf index, its length is equal to num_class * num_data * num_iteration * for feature contributions, its length is equal to num_class * num_data * (num_features + 1) * \param handle Booster handle * \param indptr pointer to row headers * \param indices findex * \param data fvalue * \param num_indptr number of cols in the matrix + 1 * \param nelem number of non-zero elements in the matrix * \param num_row number of rows * \param is_rawscore 1 to get raw predictions, before transformations like * converting to probabilities, 0 otherwise * \param is_leafidx 1 to get record of which leaf in each tree * observations fell into, 0 otherwise * \param is_predcontrib 1 to get feature contributions, 0 otherwise * \param start_iteration start index of the iteration to predict * \param num_iteration number of iteration for prediction, <= 0 means no limit * \param parameter additional parameters * \param out_result prediction result * \return R NULL value */ LIGHTGBM_C_EXPORT SEXP LGBM_BoosterPredictForCSC_R( SEXP handle, SEXP indptr, SEXP indices, SEXP data, SEXP num_indptr, SEXP nelem, SEXP num_row, SEXP is_rawscore, SEXP is_leafidx, SEXP is_predcontrib, SEXP start_iteration, SEXP num_iteration, SEXP parameter, SEXP out_result ); /*! * \brief make prediction for a new Dataset * Note: should pre-allocate memory for out_result, * for normal and raw score: its length is equal to num_class * num_data * for leaf index, its length is equal to num_class * num_data * num_iteration * for feature contributions, its length is equal to num_class * num_data * (num_features + 1) * \param handle Booster handle * \param indptr array with the index pointer of the data in CSR format * \param indices array with the non-zero indices of the data in CSR format * \param data array with the non-zero values of the data in CSR format * \param ncols number of columns in the data * \param is_rawscore 1 to get raw predictions, before transformations like * converting to probabilities, 0 otherwise * \param is_leafidx 1 to get record of which leaf in each tree * observations fell into, 0 otherwise * \param is_predcontrib 1 to get feature contributions, 0 otherwise * \param start_iteration start index of the iteration to predict * \param num_iteration number of iteration for prediction, <= 0 means no limit * \param parameter additional parameters * \param out_result prediction result * \return R NULL value */ LIGHTGBM_C_EXPORT SEXP LGBM_BoosterPredictForCSR_R( SEXP handle, SEXP indptr, SEXP indices, SEXP data, SEXP ncols, SEXP is_rawscore, SEXP is_leafidx, SEXP is_predcontrib, SEXP start_iteration, SEXP num_iteration, SEXP parameter, SEXP out_result ); /*! * \brief make prediction for a single row of data * Note: should pre-allocate memory for out_result, * for normal and raw score: its length is equal to num_class * for leaf index, its length is equal to num_class * num_iteration * for feature contributions, its length is equal to num_class * (num_features + 1) * \param handle Booster handle * \param indices array corresponding to the indices of the columns with non-zero values of the row to predict on * \param data array corresponding to the non-zero values of row to predict on * \param ncols number of columns in the data * \param is_rawscore 1 to get raw predictions, before transformations like * converting to probabilities, 0 otherwise * \param is_leafidx 1 to get record of which leaf in each tree * observations fell into, 0 otherwise * \param is_predcontrib 1 to get feature contributions, 0 otherwise * \param start_iteration start index of the iteration to predict * \param num_iteration number of iteration for prediction, <= 0 means no limit * \param parameter additional parameters * \param out_result prediction result * \return R NULL value */ LIGHTGBM_C_EXPORT SEXP LGBM_BoosterPredictForCSRSingleRow_R( SEXP handle, SEXP indices, SEXP data, SEXP ncols, SEXP is_rawscore, SEXP is_leafidx, SEXP is_predcontrib, SEXP start_iteration, SEXP num_iteration, SEXP parameter, SEXP out_result ); /*! * \brief Initialize and return a fast configuration handle to use with ``LGBM_BoosterPredictForCSRSingleRowFast_R``. * \param handle Booster handle * \param ncols number columns in the data * \param is_rawscore 1 to get raw predictions, before transformations like * converting to probabilities, 0 otherwise * \param is_leafidx 1 to get record of which leaf in each tree * observations fell into, 0 otherwise * \param is_predcontrib 1 to get feature contributions, 0 otherwise * \param start_iteration start index of the iteration to predict * \param num_iteration number of iteration for prediction, <= 0 means no limit * \param parameter additional parameters * \return Fast configuration handle */ LIGHTGBM_C_EXPORT SEXP LGBM_BoosterPredictForCSRSingleRowFastInit_R( SEXP handle, SEXP ncols, SEXP is_rawscore, SEXP is_leafidx, SEXP is_predcontrib, SEXP start_iteration, SEXP num_iteration, SEXP parameter ); /*! * \brief make prediction for a single row of data * Note: should pre-allocate memory for out_result, * for normal and raw score: its length is equal to num_class * for leaf index, its length is equal to num_class * num_iteration * for feature contributions, its length is equal to num_class * (num_features + 1) * \param handle_fastConfig Fast configuration handle * \param indices array corresponding to the indices of the columns with non-zero values of the row to predict on * \param data array corresponding to the non-zero values of row to predict on * \param out_result prediction result * \return R NULL value */ LIGHTGBM_C_EXPORT SEXP LGBM_BoosterPredictForCSRSingleRowFast_R( SEXP handle_fastConfig, SEXP indices, SEXP data, SEXP out_result ); /*! * \brief make feature contribution prediction for a new Dataset * \param handle Booster handle * \param indptr array with the index pointer of the data in CSR or CSC format * \param indices array with the non-zero indices of the data in CSR or CSC format * \param data array with the non-zero values of the data in CSR or CSC format * \param is_csr whether the input data is in CSR format or not (pass FALSE for CSC) * \param nrows number of rows in the data * \param ncols number of columns in the data * \param start_iteration start index of the iteration to predict * \param num_iteration number of iteration for prediction, <= 0 means no limit * \param parameter additional parameters * \return An R list with entries "indptr", "indices", "data", constituting the * feature contributions in sparse format, in the same storage order as * the input data. */ LIGHTGBM_C_EXPORT SEXP LGBM_BoosterPredictSparseOutput_R( SEXP handle, SEXP indptr, SEXP indices, SEXP data, SEXP is_csr, SEXP nrows, SEXP ncols, SEXP start_iteration, SEXP num_iteration, SEXP parameter ); /*! * \brief make prediction for a new Dataset * Note: should pre-allocate memory for out_result, * for normal and raw score: its length is equal to num_class * num_data * for leaf index, its length is equal to num_class * num_data * num_iteration * for feature contributions, its length is equal to num_class * num_data * (num_features + 1) * \param handle Booster handle * \param data pointer to the data space * \param num_row number of rows * \param num_col number columns * \param is_rawscore 1 to get raw predictions, before transformations like * converting to probabilities, 0 otherwise * \param is_leafidx 1 to get record of which leaf in each tree * observations fell into, 0 otherwise * \param is_predcontrib 1 to get feature contributions, 0 otherwise * \param start_iteration start index of the iteration to predict * \param num_iteration number of iteration for prediction, <= 0 means no limit * \param parameter additional parameters * \param out_result prediction result * \return R NULL value */ LIGHTGBM_C_EXPORT SEXP LGBM_BoosterPredictForMat_R( SEXP handle, SEXP data, SEXP num_row, SEXP num_col, SEXP is_rawscore, SEXP is_leafidx, SEXP is_predcontrib, SEXP start_iteration, SEXP num_iteration, SEXP parameter, SEXP out_result ); /*! * \brief make prediction for a single row of data * Note: should pre-allocate memory for out_result, * for normal and raw score: its length is equal to num_class * for leaf index, its length is equal to num_class * num_iteration * for feature contributions, its length is equal to num_class * (num_features + 1) * \param handle Booster handle * \param data array corresponding to the row to predict on * \param is_rawscore 1 to get raw predictions, before transformations like * converting to probabilities, 0 otherwise * \param is_leafidx 1 to get record of which leaf in each tree * observations fell into, 0 otherwise * \param is_predcontrib 1 to get feature contributions, 0 otherwise * \param start_iteration start index of the iteration to predict * \param num_iteration number of iteration for prediction, <= 0 means no limit * \param parameter additional parameters * \param out_result prediction result * \return R NULL value */ LIGHTGBM_C_EXPORT SEXP LGBM_BoosterPredictForMatSingleRow_R( SEXP handle, SEXP data, SEXP is_rawscore, SEXP is_leafidx, SEXP is_predcontrib, SEXP start_iteration, SEXP num_iteration, SEXP parameter, SEXP out_result ); /*! * \brief Initialize and return a fast configuration handle to use with ``LGBM_BoosterPredictForMatSingleRowFast_R``. * \param handle Booster handle * \param ncols number columns in the data * \param is_rawscore 1 to get raw predictions, before transformations like * converting to probabilities, 0 otherwise * \param is_leafidx 1 to get record of which leaf in each tree * observations fell into, 0 otherwise * \param is_predcontrib 1 to get feature contributions, 0 otherwise * \param start_iteration start index of the iteration to predict * \param num_iteration number of iteration for prediction, <= 0 means no limit * \param parameter additional parameters * \return Fast configuration handle */ LIGHTGBM_C_EXPORT SEXP LGBM_BoosterPredictForMatSingleRowFastInit_R( SEXP handle, SEXP ncols, SEXP is_rawscore, SEXP is_leafidx, SEXP is_predcontrib, SEXP start_iteration, SEXP num_iteration, SEXP parameter ); /*! * \brief make prediction for a single row of data * Note: should pre-allocate memory for out_result, * for normal and raw score: its length is equal to num_class * for leaf index, its length is equal to num_class * num_iteration * for feature contributions, its length is equal to num_class * (num_features + 1) * \param handle_fastConfig Fast configuration handle * \param data array corresponding to the row to predict on * \param out_result prediction result * \return R NULL value */ LIGHTGBM_C_EXPORT SEXP LGBM_BoosterPredictForMatSingleRowFast_R( SEXP handle_fastConfig, SEXP data, SEXP out_result ); /*! * \brief save model into file * \param handle Booster handle * \param num_iteration, <= 0 means save all * \param feature_importance_type type of feature importance, 0: split, 1: gain * \param filename file name * \param start_iteration Starting iteration (0 based) * \return R NULL value */ LIGHTGBM_C_EXPORT SEXP LGBM_BoosterSaveModel_R( SEXP handle, SEXP num_iteration, SEXP feature_importance_type, SEXP filename, SEXP start_iteration ); /*! * \brief create string containing model * \param handle Booster handle * \param num_iteration, <= 0 means save all * \param feature_importance_type type of feature importance, 0: split, 1: gain * \param start_iteration Starting iteration (0 based) * \return R character vector (length=1) with model string */ LIGHTGBM_C_EXPORT SEXP LGBM_BoosterSaveModelToString_R( SEXP handle, SEXP num_iteration, SEXP feature_importance_type, SEXP start_iteration ); /*! * \brief dump model to JSON * \param handle Booster handle * \param num_iteration, <= 0 means save all * \param feature_importance_type type of feature importance, 0: split, 1: gain * \param start_iteration Index of starting iteration (0 based) * \return R character vector (length=1) with model JSON */ LIGHTGBM_C_EXPORT SEXP LGBM_BoosterDumpModel_R( SEXP handle, SEXP num_iteration, SEXP feature_importance_type, SEXP start_iteration ); /*! * \brief Dump parameter aliases to JSON * \return R character vector (length=1) with aliases JSON */ LIGHTGBM_C_EXPORT SEXP LGBM_DumpParamAliases_R(); /*! * \brief Get current maximum number of threads used by LightGBM routines in this process. * \param[out] out current maximum number of threads used by LightGBM. -1 means defaulting to omp_get_num_threads(). * \return R NULL value */ LIGHTGBM_C_EXPORT SEXP LGBM_GetMaxThreads_R( SEXP out ); /*! * \brief Set maximum number of threads used by LightGBM routines in this process. * \param num_threads maximum number of threads used by LightGBM. -1 means defaulting to omp_get_num_threads(). * \return R NULL value */ LIGHTGBM_C_EXPORT SEXP LGBM_SetMaxThreads_R( SEXP num_threads ); #endif // LIGHTGBM_R_PACKAGE_SRC_LIGHTGBM_R_H_ ================================================ FILE: R-package/tests/testthat/helper.R ================================================ # ref for this file: # # * https://r-pkgs.org/testing-design.html#testthat-helper-files # * https://r-pkgs.org/testing-design.html#testthat-setup-files # LightGBM-internal fix to comply with CRAN policy of only using up to 2 threads in tests and example. # # per https://cran.r-project.org/web/packages/policies.html # # > If running a package uses multiple threads/cores it must never use more than two simultaneously: # the check farm is a shared resource and will typically be running many checks simultaneously. # .LGB_MAX_THREADS <- 2L setLGBMthreads(.LGB_MAX_THREADS) # control data.table parallelism # ref: https://github.com/Rdatatable/data.table/issues/5658 data.table::setDTthreads(1L) # by default, how much should results in tests be allowed to differ from hard-coded expected numbers? .LGB_NUMERIC_TOLERANCE <- 1e-6 # are the tests running on Windows? .LGB_ON_WINDOWS <- .Platform$OS.type == "windows" .LGB_ON_32_BIT_WINDOWS <- .LGB_ON_WINDOWS && .Machine$sizeof.pointer != 8L # are the tests running in a UTF-8 locale? .LGB_UTF8_LOCALE <- all(endsWith( Sys.getlocale(category = "LC_CTYPE") , "UTF-8" )) # control how many loud LightGBM's logger is in tests .LGB_VERBOSITY <- as.integer( Sys.getenv("LIGHTGBM_TEST_VERBOSITY", "-1") ) # [description] # test that every element of 'x' is in 'y' # # testthat::expect_in() was added in {testthat} v3.1.19. # This is here to support a similar interface on older {testthat} versions. .expect_in <- function(x, y) { if (exists("expect_in")) { expect_in(x, y) } else { missing_items <- x[!(x %in% y)] if (length(missing_items) != 0L) { error_msg <- paste0("Some expected items not found: ", toString(missing_items)) stop(error_msg) } } } ================================================ FILE: R-package/tests/testthat/test_Predictor.R ================================================ library(Matrix) test_that("Predictor's finalizer should not fail", { X <- as.matrix(as.integer(iris[, "Species"]), ncol = 1L) y <- iris[["Sepal.Length"]] dtrain <- lgb.Dataset(X, label = y) bst <- lgb.train( data = dtrain , params = list( objective = "regression" , num_threads = .LGB_MAX_THREADS ) , verbose = .LGB_VERBOSITY , nrounds = 3L ) model_file <- tempfile(fileext = ".model") bst$save_model(filename = model_file) predictor <- Predictor$new(modelfile = model_file) expect_true(.is_Predictor(predictor)) expect_false(.is_null_handle(predictor$.__enclos_env__$private$handle)) predictor$.__enclos_env__$private$finalize() expect_true(.is_null_handle(predictor$.__enclos_env__$private$handle)) # calling finalize() a second time shouldn't cause any issues predictor$.__enclos_env__$private$finalize() expect_true(.is_null_handle(predictor$.__enclos_env__$private$handle)) }) test_that("predictions do not fail for integer input", { X <- as.matrix(as.integer(iris[, "Species"]), ncol = 1L) y <- iris[["Sepal.Length"]] dtrain <- lgb.Dataset(X, label = y) fit <- lgb.train( data = dtrain , params = list( objective = "regression" , num_threads = .LGB_MAX_THREADS ) , verbose = .LGB_VERBOSITY , nrounds = 3L ) X_double <- X[c(1L, 51L, 101L), , drop = FALSE] X_integer <- X_double storage.mode(X_double) <- "double" pred_integer <- predict(fit, X_integer) pred_double <- predict(fit, X_double) expect_equal(pred_integer, pred_double) }) test_that("start_iteration works correctly", { set.seed(708L) data(agaricus.train, package = "lightgbm") data(agaricus.test, package = "lightgbm") train <- agaricus.train test <- agaricus.test dtrain <- lgb.Dataset( agaricus.train$data , label = agaricus.train$label ) dtest <- lgb.Dataset.create.valid( dtrain , agaricus.test$data , label = agaricus.test$label ) bst <- lightgbm( data = as.matrix(train$data) , label = train$label , params = list( num_leaves = 4L , learning_rate = 0.6 , objective = "binary" , verbosity = .LGB_VERBOSITY , num_threads = .LGB_MAX_THREADS ) , nrounds = 50L , valids = list("test" = dtest) , early_stopping_rounds = 2L ) expect_true(.is_Booster(bst)) pred1 <- predict(bst, newdata = test$data, type = "raw") pred_contrib1 <- predict(bst, test$data, type = "contrib") pred2 <- rep(0.0, length(pred1)) pred_contrib2 <- rep(0.0, length(pred2)) step <- 11L end_iter <- 49L if (bst$best_iter != -1L) { end_iter <- bst$best_iter - 1L } start_iters <- seq(0L, end_iter, by = step) for (start_iter in start_iters) { n_iter <- min(c(end_iter - start_iter + 1L, step)) inc_pred <- predict(bst, test$data , start_iteration = start_iter , num_iteration = n_iter , type = "raw" ) inc_pred_contrib <- bst$predict(test$data , start_iteration = start_iter , num_iteration = n_iter , predcontrib = TRUE ) pred2 <- pred2 + inc_pred pred_contrib2 <- pred_contrib2 + inc_pred_contrib } expect_equal(pred2, pred1) expect_equal(pred_contrib2, pred_contrib1) pred_leaf1 <- predict(bst, test$data, type = "leaf") pred_leaf2 <- predict(bst, test$data, start_iteration = 0L, num_iteration = end_iter + 1L, type = "leaf") expect_equal(pred_leaf1, pred_leaf2) }) test_that("Feature contributions from sparse inputs produce sparse outputs", { data(mtcars) X <- as.matrix(mtcars[, -1L]) y <- as.numeric(mtcars[, 1L]) dtrain <- lgb.Dataset(X, label = y, params = list(max_bins = 5L)) bst <- lgb.train( data = dtrain , obj = "regression" , nrounds = 5L , verbose = .LGB_VERBOSITY , params = list(min_data_in_leaf = 5L, num_threads = .LGB_MAX_THREADS) ) pred_dense <- predict(bst, X, type = "contrib") Xcsc <- as(X, "CsparseMatrix") pred_csc <- predict(bst, Xcsc, type = "contrib") expect_s4_class(pred_csc, "dgCMatrix") expect_equal(unname(pred_dense), unname(as.matrix(pred_csc))) Xcsr <- as(X, "RsparseMatrix") pred_csr <- predict(bst, Xcsr, type = "contrib") expect_s4_class(pred_csr, "dgRMatrix") expect_equal(as(pred_csr, "CsparseMatrix"), pred_csc) Xspv <- as(X[1L, , drop = FALSE], "sparseVector") pred_spv <- predict(bst, Xspv, type = "contrib") expect_s4_class(pred_spv, "dsparseVector") expect_equal(Matrix::t(as(pred_spv, "CsparseMatrix")), unname(pred_csc[1L, , drop = FALSE])) }) test_that("Sparse feature contribution predictions do not take inputs with wrong number of columns", { data(mtcars) X <- as.matrix(mtcars[, -1L]) y <- as.numeric(mtcars[, 1L]) dtrain <- lgb.Dataset(X, label = y, params = list(max_bins = 5L)) bst <- lgb.train( data = dtrain , obj = "regression" , nrounds = 5L , verbose = .LGB_VERBOSITY , params = list(min_data_in_leaf = 5L, num_threads = .LGB_MAX_THREADS) ) X_wrong <- X[, c(1L:10L, 1L:10L)] X_wrong <- as(X_wrong, "CsparseMatrix") expect_error(predict(bst, X_wrong, type = "contrib"), regexp = "input data has 20 columns") X_wrong <- as(X_wrong, "RsparseMatrix") expect_error(predict(bst, X_wrong, type = "contrib"), regexp = "input data has 20 columns") X_wrong <- as(X_wrong, "CsparseMatrix") X_wrong <- X_wrong[, 1L:3L] expect_error(predict(bst, X_wrong, type = "contrib"), regexp = "input data has 3 columns") }) test_that("Feature contribution predictions do not take non-general CSR or CSC inputs", { set.seed(123L) y <- runif(25L) Dmat <- matrix(runif(625L), nrow = 25L, ncol = 25L) Dmat <- crossprod(Dmat) Dmat <- as(Dmat, "symmetricMatrix") SmatC <- as(Dmat, "sparseMatrix") SmatR <- as(SmatC, "RsparseMatrix") dtrain <- lgb.Dataset(as.matrix(Dmat), label = y, params = list(max_bins = 5L)) bst <- lgb.train( data = dtrain , obj = "regression" , nrounds = 5L , verbose = .LGB_VERBOSITY , params = list(min_data_in_leaf = 5L, num_threads = .LGB_MAX_THREADS) ) expect_error( predict(bst, SmatC, type = "contrib") , regexp = "Predictions on sparse inputs are only allowed for 'dsparseVector', 'dgRMatrix', 'dgCMatrix' - got: dsCMatrix" # nolint: line_length. ) expect_error( predict(bst, SmatR, type = "contrib") , regexp = "Predictions on sparse inputs are only allowed for 'dsparseVector', 'dgRMatrix', 'dgCMatrix' - got: dsRMatrix" # nolint: line_length. ) }) test_that("predict() params should override keyword argument for raw-score predictions", { data(agaricus.train, package = "lightgbm") X <- agaricus.train$data y <- agaricus.train$label bst <- lgb.train( data = lgb.Dataset( data = X , label = y , params = list( data_seed = 708L , min_data_in_bin = 5L ) ) , params = list( objective = "binary" , min_data_in_leaf = 1L , seed = 708L , num_threads = .LGB_MAX_THREADS ) , nrounds = 10L , verbose = .LGB_VERBOSITY ) # check that the predictions from predict.lgb.Booster() really look like raw score predictions preds_prob <- predict(bst, X) preds_raw_s3_keyword <- predict(bst, X, type = "raw") preds_prob_from_raw <- 1.0 / (1.0 + exp(-preds_raw_s3_keyword)) expect_equal(preds_prob, preds_prob_from_raw, tolerance = .LGB_NUMERIC_TOLERANCE) accuracy <- sum(as.integer(preds_prob_from_raw > 0.5) == y) / length(y) expect_equal(accuracy, 1.0) # should get the same results from Booster$predict() method preds_raw_r6_keyword <- bst$predict(X, rawscore = TRUE) expect_equal(preds_raw_s3_keyword, preds_raw_r6_keyword) # using a parameter alias of predict_raw_score should result in raw scores being returned aliases <- .PARAMETER_ALIASES()[["predict_raw_score"]] expect_true(length(aliases) > 1L) for (rawscore_alias in aliases) { params <- as.list( stats::setNames( object = TRUE , nm = rawscore_alias ) ) preds_raw_s3_param <- predict(bst, X, params = params) preds_raw_r6_param <- bst$predict(X, params = params) expect_equal(preds_raw_s3_keyword, preds_raw_s3_param) expect_equal(preds_raw_s3_keyword, preds_raw_r6_param) } }) test_that("predict() params should override keyword argument for leaf-index predictions", { data(mtcars) X <- as.matrix(mtcars[, which(names(mtcars) != "mpg")]) y <- as.numeric(mtcars[, "mpg"]) bst <- lgb.train( data = lgb.Dataset( data = X , label = y , params = list( min_data_in_bin = 1L , data_seed = 708L ) ) , params = list( objective = "regression" , min_data_in_leaf = 1L , seed = 708L , num_threads = .LGB_MAX_THREADS ) , nrounds = 10L , verbose = .LGB_VERBOSITY ) # check that predictions really look like leaf index predictions preds_leaf_s3_keyword <- predict(bst, X, type = "leaf") expect_true(is.matrix(preds_leaf_s3_keyword)) expect_equal(dim(preds_leaf_s3_keyword), c(nrow(X), bst$current_iter())) expect_true(min(preds_leaf_s3_keyword) >= 0L) trees_dt <- lgb.model.dt.tree(bst) max_leaf_by_tree_from_dt <- trees_dt[, .(idx = max(leaf_index, na.rm = TRUE)), by = tree_index]$idx max_leaf_by_tree_from_preds <- apply(preds_leaf_s3_keyword, 2L, max, na.rm = TRUE) expect_equal(max_leaf_by_tree_from_dt, max_leaf_by_tree_from_preds) # should get the same results from Booster$predict() method preds_leaf_r6_keyword <- bst$predict(X, predleaf = TRUE) expect_equal(preds_leaf_s3_keyword, preds_leaf_r6_keyword) # using a parameter alias of predict_leaf_index should result in leaf indices being returned aliases <- .PARAMETER_ALIASES()[["predict_leaf_index"]] expect_true(length(aliases) > 1L) for (predleaf_alias in aliases) { params <- as.list( stats::setNames( object = TRUE , nm = predleaf_alias ) ) preds_leaf_s3_param <- predict(bst, X, params = params) preds_leaf_r6_param <- bst$predict(X, params = params) expect_equal(preds_leaf_s3_keyword, preds_leaf_s3_param) expect_equal(preds_leaf_s3_keyword, preds_leaf_r6_param) } }) test_that("predict() params should override keyword argument for feature contributions", { data(mtcars) X <- as.matrix(mtcars[, which(names(mtcars) != "mpg")]) y <- as.numeric(mtcars[, "mpg"]) bst <- lgb.train( data = lgb.Dataset( data = X , label = y , params = list( min_data_in_bin = 1L , data_seed = 708L ) ) , params = list( objective = "regression" , min_data_in_leaf = 1L , seed = 708L , num_threads = .LGB_MAX_THREADS ) , nrounds = 10L , verbose = .LGB_VERBOSITY ) # check that predictions really look like feature contributions preds_contrib_s3_keyword <- predict(bst, X, type = "contrib") num_features <- ncol(X) shap_base_value <- unname(preds_contrib_s3_keyword[, ncol(preds_contrib_s3_keyword)]) expect_true(is.matrix(preds_contrib_s3_keyword)) expect_equal(dim(preds_contrib_s3_keyword), c(nrow(X), num_features + 1L)) expect_equal(length(unique(shap_base_value)), 1L) expect_equal(mean(y), shap_base_value[1L]) expect_equal(predict(bst, X), rowSums(preds_contrib_s3_keyword)) # should get the same results from Booster$predict() method preds_contrib_r6_keyword <- bst$predict(X, predcontrib = TRUE) expect_equal(preds_contrib_s3_keyword, preds_contrib_r6_keyword) # using a parameter alias of predict_contrib should result in feature contributions being returned aliases <- .PARAMETER_ALIASES()[["predict_contrib"]] expect_true(length(aliases) > 1L) for (predcontrib_alias in aliases) { params <- as.list( stats::setNames( object = TRUE , nm = predcontrib_alias ) ) preds_contrib_s3_param <- predict(bst, X, params = params) preds_contrib_r6_param <- bst$predict(X, params = params) expect_equal(preds_contrib_s3_keyword, preds_contrib_s3_param) expect_equal(preds_contrib_s3_keyword, preds_contrib_r6_param) } }) .expect_has_row_names <- function(pred, X) { if (is.vector(pred)) { rnames <- names(pred) } else { rnames <- row.names(pred) } expect_false(is.null(rnames)) expect_true(is.vector(rnames)) expect_true(length(rnames) > 0L) expect_equal(row.names(X), rnames) } .expect_doesnt_have_row_names <- function(pred) { if (is.vector(pred)) { expect_null(names(pred)) } else { expect_null(row.names(pred)) } } .check_all_row_name_expectations <- function(bst, X) { # dense matrix with row names pred <- predict(bst, X) .expect_has_row_names(pred, X) pred <- predict(bst, X, type = "raw") .expect_has_row_names(pred, X) pred <- predict(bst, X, type = "leaf") .expect_has_row_names(pred, X) pred <- predict(bst, X, type = "contrib") .expect_has_row_names(pred, X) # dense matrix without row names Xcopy <- X row.names(Xcopy) <- NULL pred <- predict(bst, Xcopy) .expect_doesnt_have_row_names(pred) # sparse matrix with row names Xcsc <- as(X, "CsparseMatrix") pred <- predict(bst, Xcsc) .expect_has_row_names(pred, Xcsc) pred <- predict(bst, Xcsc, type = "raw") .expect_has_row_names(pred, Xcsc) pred <- predict(bst, Xcsc, type = "leaf") .expect_has_row_names(pred, Xcsc) pred <- predict(bst, Xcsc, type = "contrib") .expect_has_row_names(pred, Xcsc) pred <- predict(bst, as(Xcsc, "RsparseMatrix"), type = "contrib") .expect_has_row_names(pred, Xcsc) # sparse matrix without row names Xcopy <- Xcsc row.names(Xcopy) <- NULL pred <- predict(bst, Xcopy) .expect_doesnt_have_row_names(pred) } test_that("predict() keeps row names from data (regression)", { data("mtcars") X <- as.matrix(mtcars[, -1L]) y <- as.numeric(mtcars[, 1L]) dtrain <- lgb.Dataset( X , label = y , params = list( max_bins = 5L , min_data_in_bin = 1L ) ) bst <- lgb.train( data = dtrain , obj = "regression" , nrounds = 5L , verbose = .LGB_VERBOSITY , params = list(min_data_in_leaf = 1L, num_threads = .LGB_MAX_THREADS) ) .check_all_row_name_expectations(bst, X) }) test_that("predict() keeps row names from data (binary classification)", { data(agaricus.train, package = "lightgbm") X <- as.matrix(agaricus.train$data) y <- agaricus.train$label row.names(X) <- paste0("rname", seq(1L, nrow(X))) dtrain <- lgb.Dataset(X, label = y, params = list(max_bins = 5L)) bst <- lgb.train( data = dtrain , obj = "binary" , nrounds = 5L , verbose = .LGB_VERBOSITY , params = list(num_threads = .LGB_MAX_THREADS) ) .check_all_row_name_expectations(bst, X) }) test_that("predict() keeps row names from data (multi-class classification)", { data(iris) y <- as.numeric(iris$Species) - 1.0 X <- as.matrix(iris[, names(iris) != "Species"]) row.names(X) <- paste0("rname", seq(1L, nrow(X))) dtrain <- lgb.Dataset(X, label = y, params = list(max_bins = 5L)) bst <- lgb.train( data = dtrain , obj = "multiclass" , params = list(num_class = 3L, num_threads = .LGB_MAX_THREADS) , nrounds = 5L , verbose = .LGB_VERBOSITY ) .check_all_row_name_expectations(bst, X) }) test_that("predictions for regression and binary classification are returned as vectors", { data(mtcars) X <- as.matrix(mtcars[, -1L]) y <- as.numeric(mtcars[, 1L]) dtrain <- lgb.Dataset( X , label = y , params = list( max_bins = 5L , min_data_in_bin = 1L ) ) model <- lgb.train( data = dtrain , obj = "regression" , nrounds = 5L , verbose = .LGB_VERBOSITY , params = list(min_data_in_leaf = 1L, num_threads = .LGB_MAX_THREADS) ) pred <- predict(model, X) expect_true(is.vector(pred)) expect_equal(length(pred), nrow(X)) pred <- predict(model, X, type = "raw") expect_true(is.vector(pred)) expect_equal(length(pred), nrow(X)) data(agaricus.train, package = "lightgbm") X <- agaricus.train$data y <- agaricus.train$label dtrain <- lgb.Dataset(X, label = y) model <- lgb.train( data = dtrain , obj = "binary" , nrounds = 5L , verbose = .LGB_VERBOSITY , params = list(num_threads = .LGB_MAX_THREADS) ) pred <- predict(model, X) expect_true(is.vector(pred)) expect_equal(length(pred), nrow(X)) pred <- predict(model, X, type = "raw") expect_true(is.vector(pred)) expect_equal(length(pred), nrow(X)) }) test_that("predictions for multiclass classification are returned as matrix", { data(iris) X <- as.matrix(iris[, -5L]) y <- as.numeric(iris$Species) - 1.0 dtrain <- lgb.Dataset(X, label = y) model <- lgb.train( data = dtrain , obj = "multiclass" , nrounds = 5L , verbose = .LGB_VERBOSITY , params = list(num_class = 3L, num_threads = .LGB_MAX_THREADS) ) pred <- predict(model, X) expect_true(is.matrix(pred)) expect_equal(nrow(pred), nrow(X)) expect_equal(ncol(pred), 3L) pred <- predict(model, X, type = "raw") expect_true(is.matrix(pred)) expect_equal(nrow(pred), nrow(X)) expect_equal(ncol(pred), 3L) }) test_that("Single-row predictions are identical to multi-row ones", { data(mtcars) X <- as.matrix(mtcars[, -1L]) y <- mtcars[, 1L] dtrain <- lgb.Dataset(X, label = y, params = list(max_bin = 5L)) params <- list(min_data_in_leaf = 2L, num_threads = .LGB_MAX_THREADS) model <- lgb.train( params = params , data = dtrain , obj = "regression" , nrounds = 5L , verbose = -1L ) x1 <- X[1L, , drop = FALSE] x11 <- X[11L, , drop = FALSE] x1_spv <- as(x1, "sparseVector") x11_spv <- as(x11, "sparseVector") x1_csr <- as(x1, "RsparseMatrix") x11_csr <- as(x11, "RsparseMatrix") pred_all <- predict(model, X) pred1_wo_config <- predict(model, x1) pred11_wo_config <- predict(model, x11) pred1_spv_wo_config <- predict(model, x1_spv) pred11_spv_wo_config <- predict(model, x11_spv) pred1_csr_wo_config <- predict(model, x1_csr) pred11_csr_wo_config <- predict(model, x11_csr) lgb.configure_fast_predict(model) pred1_w_config <- predict(model, x1) pred11_w_config <- predict(model, x11) model <- lgb.train( params = params , data = dtrain , obj = "regression" , nrounds = 5L , verbose = -1L ) lgb.configure_fast_predict(model, csr = TRUE) pred1_spv_w_config <- predict(model, x1_spv) pred11_spv_w_config <- predict(model, x11_spv) pred1_csr_w_config <- predict(model, x1_csr) pred11_csr_w_config <- predict(model, x11_csr) expect_equal(pred1_wo_config, pred_all[1L]) expect_equal(pred11_wo_config, pred_all[11L]) expect_equal(pred1_spv_wo_config, unname(pred_all[1L])) expect_equal(pred11_spv_wo_config, unname(pred_all[11L])) expect_equal(pred1_csr_wo_config, pred_all[1L]) expect_equal(pred11_csr_wo_config, pred_all[11L]) expect_equal(pred1_w_config, pred_all[1L]) expect_equal(pred11_w_config, pred_all[11L]) expect_equal(pred1_spv_w_config, unname(pred_all[1L])) expect_equal(pred11_spv_w_config, unname(pred_all[11L])) expect_equal(pred1_csr_w_config, pred_all[1L]) expect_equal(pred11_csr_w_config, pred_all[11L]) }) test_that("Fast-predict configuration accepts non-default prediction types", { data(mtcars) X <- as.matrix(mtcars[, -1L]) y <- mtcars[, 1L] dtrain <- lgb.Dataset(X, label = y, params = list(max_bin = 5L)) params <- list(min_data_in_leaf = 2L, num_threads = .LGB_MAX_THREADS) model <- lgb.train( params = params , data = dtrain , obj = "regression" , nrounds = 5L , verbose = -1L ) x1 <- X[1L, , drop = FALSE] x11 <- X[11L, , drop = FALSE] pred_all <- predict(model, X, type = "leaf") pred1_wo_config <- predict(model, x1, type = "leaf") pred11_wo_config <- predict(model, x11, type = "leaf") expect_equal(pred1_wo_config, pred_all[1L, , drop = FALSE]) expect_equal(pred11_wo_config, pred_all[11L, , drop = FALSE]) lgb.configure_fast_predict(model, type = "leaf") pred1_w_config <- predict(model, x1, type = "leaf") pred11_w_config <- predict(model, x11, type = "leaf") expect_equal(pred1_w_config, pred_all[1L, , drop = FALSE]) expect_equal(pred11_w_config, pred_all[11L, , drop = FALSE]) }) test_that("Fast-predict configuration does not block other prediction types", { data(mtcars) X <- as.matrix(mtcars[, -1L]) y <- mtcars[, 1L] dtrain <- lgb.Dataset(X, label = y, params = list(max_bin = 5L)) params <- list(min_data_in_leaf = 2L, num_threads = .LGB_MAX_THREADS) model <- lgb.train( params = params , data = dtrain , obj = "regression" , nrounds = 5L , verbose = -1L ) x1 <- X[1L, , drop = FALSE] x11 <- X[11L, , drop = FALSE] pred_all <- predict(model, X) pred_all_leaf <- predict(model, X, type = "leaf") lgb.configure_fast_predict(model) pred1_w_config <- predict(model, x1) pred11_w_config <- predict(model, x11) pred1_leaf_w_config <- predict(model, x1, type = "leaf") pred11_leaf_w_config <- predict(model, x11, type = "leaf") expect_equal(pred1_w_config, pred_all[1L]) expect_equal(pred11_w_config, pred_all[11L]) expect_equal(pred1_leaf_w_config, pred_all_leaf[1L, , drop = FALSE]) expect_equal(pred11_leaf_w_config, pred_all_leaf[11L, , drop = FALSE]) }) test_that("predict type='class' returns predicted class for classification objectives", { data(agaricus.train, package = "lightgbm") X <- as.matrix(agaricus.train$data) y <- agaricus.train$label dtrain <- lgb.Dataset(X, label = y, params = list(max_bins = 5L)) bst <- lgb.train( data = dtrain , obj = "binary" , nrounds = 5L , verbose = .LGB_VERBOSITY , params = list(num_threads = .LGB_MAX_THREADS) ) pred <- predict(bst, X, type = "class") expect_true(all(pred %in% c(0L, 1L))) data(iris) X <- as.matrix(iris[, -5L]) y <- as.numeric(iris$Species) - 1.0 dtrain <- lgb.Dataset(X, label = y) model <- lgb.train( data = dtrain , obj = "multiclass" , nrounds = 5L , verbose = .LGB_VERBOSITY , params = list(num_class = 3L, num_threads = .LGB_MAX_THREADS) ) pred <- predict(model, X, type = "class") expect_true(all(pred %in% c(0L, 1L, 2L))) }) test_that("predict type='class' returns values in the target's range for regression objectives", { data(agaricus.train, package = "lightgbm") X <- as.matrix(agaricus.train$data) y <- agaricus.train$label dtrain <- lgb.Dataset(X, label = y, params = list(max_bins = 5L)) bst <- lgb.train( data = dtrain , obj = "regression" , nrounds = 5L , verbose = .LGB_VERBOSITY , params = list(num_threads = .LGB_MAX_THREADS) ) pred <- predict(bst, X, type = "class") expect_true(!any(pred %in% c(0.0, 1.0))) }) ================================================ FILE: R-package/tests/testthat/test_basic.R ================================================ data(agaricus.train, package = "lightgbm") data(agaricus.test, package = "lightgbm") train <- agaricus.train test <- agaricus.test set.seed(708L) # [description] Every time this function is called, it adds 0.1 # to an accumulator then returns the current value. # This is used to mock the situation where an evaluation # metric increases every iteration ACCUMULATOR_NAME <- "INCREASING_METRIC_ACCUMULATOR" assign(x = ACCUMULATOR_NAME, value = 0.0, envir = .GlobalEnv) .increasing_metric <- function(preds, dtrain) { if (!exists(ACCUMULATOR_NAME, envir = .GlobalEnv)) { assign(ACCUMULATOR_NAME, 0.0, envir = .GlobalEnv) } assign( x = ACCUMULATOR_NAME , value = get(ACCUMULATOR_NAME, envir = .GlobalEnv) + 0.1 , envir = .GlobalEnv ) return(list( name = "increasing_metric" , value = get(ACCUMULATOR_NAME, envir = .GlobalEnv) , higher_better = TRUE )) } # [description] Evaluation function that always returns the # same value CONSTANT_METRIC_VALUE <- 0.2 .constant_metric <- function(preds, dtrain) { return(list( name = "constant_metric" , value = CONSTANT_METRIC_VALUE , higher_better = FALSE )) } # sample datasets to test early stopping DTRAIN_RANDOM_REGRESSION <- lgb.Dataset( data = as.matrix(rnorm(100L), ncol = 1L, drop = FALSE) , label = rnorm(100L) , params = list(num_threads = .LGB_MAX_THREADS) ) DVALID_RANDOM_REGRESSION <- lgb.Dataset( data = as.matrix(rnorm(50L), ncol = 1L, drop = FALSE) , label = rnorm(50L) , params = list(num_threads = .LGB_MAX_THREADS) ) DTRAIN_RANDOM_CLASSIFICATION <- lgb.Dataset( data = as.matrix(rnorm(120L), ncol = 1L, drop = FALSE) , label = sample(c(0L, 1L), size = 120L, replace = TRUE) , params = list(num_threads = .LGB_MAX_THREADS) ) DVALID_RANDOM_CLASSIFICATION <- lgb.Dataset( data = as.matrix(rnorm(37L), ncol = 1L, drop = FALSE) , label = sample(c(0L, 1L), size = 37L, replace = TRUE) , params = list(num_threads = .LGB_MAX_THREADS) ) test_that("train and predict binary classification", { nrounds <- 10L bst <- lightgbm( data = train$data , label = train$label , params = list( num_leaves = 5L , objective = "binary" , metric = "binary_error" , verbose = .LGB_VERBOSITY , num_threads = .LGB_MAX_THREADS ) , nrounds = nrounds , valids = list( "train" = lgb.Dataset( data = train$data , label = train$label ) ) ) expect_false(is.null(bst$record_evals)) record_results <- lgb.get.eval.result(bst, "train", "binary_error") expect_lt(min(record_results), 0.02) pred <- predict(bst, test$data) expect_equal(length(pred), 1611L) pred1 <- predict(bst, train$data, num_iteration = 1L) expect_equal(length(pred1), 6513L) err_pred1 <- sum((pred1 > 0.5) != train$label) / length(train$label) err_log <- record_results[1L] expect_lt(abs(err_pred1 - err_log), .LGB_NUMERIC_TOLERANCE) }) test_that("train and predict softmax", { set.seed(708L) X_mat <- as.matrix(iris[, -5L]) lb <- as.numeric(iris$Species) - 1L bst <- lightgbm( data = X_mat , label = lb , params = list( num_leaves = 4L , learning_rate = 0.05 , min_data = 20L , min_hessian = 10.0 , objective = "multiclass" , metric = "multi_error" , num_class = 3L , verbose = .LGB_VERBOSITY , num_threads = .LGB_MAX_THREADS ) , nrounds = 20L , valids = list( "train" = lgb.Dataset( data = X_mat , label = lb ) ) ) expect_false(is.null(bst$record_evals)) record_results <- lgb.get.eval.result(bst, "train", "multi_error") expect_lt(min(record_results), 0.06) pred <- predict(bst, as.matrix(iris[, -5L])) expect_equal(length(pred), nrow(iris) * 3L) }) test_that("use of multiple eval metrics works", { metrics <- list("binary_error", "auc", "binary_logloss") bst <- lightgbm( data = train$data , label = train$label , params = list( num_leaves = 4L , learning_rate = 1.0 , objective = "binary" , metric = metrics , verbose = .LGB_VERBOSITY , num_threads = .LGB_MAX_THREADS ) , nrounds = 10L , valids = list( "train" = lgb.Dataset( data = train$data , label = train$label , params = list(num_threads = .LGB_MAX_THREADS) ) ) ) expect_false(is.null(bst$record_evals)) expect_named( bst$record_evals[["train"]] , unlist(metrics) , ignore.order = FALSE , ignore.case = FALSE ) }) test_that("lgb.Booster.upper_bound() and lgb.Booster.lower_bound() work as expected for binary classification", { set.seed(708L) nrounds <- 10L bst <- lightgbm( data = train$data , label = train$label , params = list( num_leaves = 5L , objective = "binary" , metric = "binary_error" , verbose = .LGB_VERBOSITY , num_threads = .LGB_MAX_THREADS ) , nrounds = nrounds ) expect_true(abs(bst$lower_bound() - -1.590853) < .LGB_NUMERIC_TOLERANCE) expect_true(abs(bst$upper_bound() - 1.871015) < .LGB_NUMERIC_TOLERANCE) }) test_that("lgb.Booster.upper_bound() and lgb.Booster.lower_bound() work as expected for regression", { set.seed(708L) nrounds <- 10L bst <- lightgbm( data = train$data , label = train$label , params = list( num_leaves = 5L , objective = "regression" , metric = "l2" , verbose = .LGB_VERBOSITY , num_threads = .LGB_MAX_THREADS ) , nrounds = nrounds ) expect_true(abs(bst$lower_bound() - 0.1513859) < .LGB_NUMERIC_TOLERANCE) expect_true(abs(bst$upper_bound() - 0.9080349) < .LGB_NUMERIC_TOLERANCE) }) test_that("lightgbm() rejects negative or 0 value passed to nrounds", { dtrain <- lgb.Dataset(train$data, label = train$label) params <- list(objective = "regression", metric = "l2,l1", num_threads = .LGB_MAX_THREADS) for (nround_value in c(-10L, 0L)) { expect_error({ bst <- lightgbm( data = dtrain , params = params , nrounds = nround_value ) }, "nrounds should be greater than zero") } }) test_that("lightgbm() accepts nrounds as either a top-level argument or parameter", { nrounds <- 15L set.seed(708L) top_level_bst <- lightgbm( data = train$data , label = train$label , nrounds = nrounds , params = list( objective = "regression" , metric = "l2" , num_leaves = 5L , verbose = .LGB_VERBOSITY , num_threads = .LGB_MAX_THREADS ) ) set.seed(708L) param_bst <- lightgbm( data = train$data , label = train$label , params = list( objective = "regression" , metric = "l2" , num_leaves = 5L , nrounds = nrounds , verbose = .LGB_VERBOSITY , num_threads = .LGB_MAX_THREADS ) ) set.seed(708L) both_customized <- lightgbm( data = train$data , label = train$label , nrounds = 20L , params = list( objective = "regression" , metric = "l2" , num_leaves = 5L , nrounds = nrounds , verbose = .LGB_VERBOSITY , num_threads = .LGB_MAX_THREADS ) ) top_level_l2 <- top_level_bst$eval_train()[[1L]][["value"]] params_l2 <- param_bst$eval_train()[[1L]][["value"]] both_l2 <- both_customized$eval_train()[[1L]][["value"]] # check type just to be sure the subsetting didn't return a NULL expect_true(is.numeric(top_level_l2)) expect_true(is.numeric(params_l2)) expect_true(is.numeric(both_l2)) # check that model produces identical performance expect_identical(top_level_l2, params_l2) expect_identical(both_l2, params_l2) expect_identical(param_bst$current_iter(), top_level_bst$current_iter()) expect_identical(param_bst$current_iter(), both_customized$current_iter()) expect_identical(param_bst$current_iter(), nrounds) }) test_that("lightgbm() performs evaluation on validation sets if they are provided", { set.seed(708L) dvalid1 <- lgb.Dataset( data = train$data , label = train$label , params = list(num_threads = .LGB_MAX_THREADS) ) dvalid2 <- lgb.Dataset( data = train$data , label = train$label , params = list(num_threads = .LGB_MAX_THREADS) ) nrounds <- 10L bst <- lightgbm( data = train$data , label = train$label , params = list( num_leaves = 5L , objective = "binary" , metric = c( "binary_error" , "auc" ) , verbose = .LGB_VERBOSITY , num_threads = .LGB_MAX_THREADS ) , nrounds = nrounds , valids = list( "valid1" = dvalid1 , "valid2" = dvalid2 , "train" = lgb.Dataset( data = train$data , label = train$label , params = list(num_threads = .LGB_MAX_THREADS) ) ) ) expect_named( bst$record_evals , c("train", "valid1", "valid2", "start_iter") , ignore.order = TRUE , ignore.case = FALSE ) for (valid_name in c("train", "valid1", "valid2")) { eval_results <- bst$record_evals[[valid_name]][["binary_error"]] expect_length(eval_results[["eval"]], nrounds) } expect_true(abs(bst$record_evals[["train"]][["binary_error"]][["eval"]][[1L]] - 0.02226317) < .LGB_NUMERIC_TOLERANCE) expect_true(abs(bst$record_evals[["valid1"]][["binary_error"]][["eval"]][[1L]] - 0.02226317) < .LGB_NUMERIC_TOLERANCE) expect_true(abs(bst$record_evals[["valid2"]][["binary_error"]][["eval"]][[1L]] - 0.02226317) < .LGB_NUMERIC_TOLERANCE) }) test_that("training continuation works", { dtrain <- lgb.Dataset( train$data , label = train$label , free_raw_data = FALSE , params = list(num_threads = .LGB_MAX_THREADS) ) watchlist <- list(train = dtrain) param <- list( objective = "binary" , metric = "binary_logloss" , num_leaves = 5L , learning_rate = 1.0 , verbose = .LGB_VERBOSITY , num_threads = .LGB_MAX_THREADS ) # train for 10 consecutive iterations bst <- lgb.train(param, dtrain, nrounds = 10L, watchlist) err_bst <- lgb.get.eval.result(bst, "train", "binary_logloss", 10L) # train for 5 iterations, save, load, train for 5 more bst1 <- lgb.train(param, dtrain, nrounds = 5L, watchlist) model_file <- tempfile(fileext = ".model") lgb.save(bst1, model_file) bst2 <- lgb.train(param, dtrain, nrounds = 5L, watchlist, init_model = bst1) err_bst2 <- lgb.get.eval.result(bst2, "train", "binary_logloss", 10L) # evaluation metrics should be nearly identical for the model trained in 10 coonsecutive # iterations and the one trained in 5-then-5. expect_lt(abs(err_bst - err_bst2), 0.01) }) test_that("cv works", { dtrain <- lgb.Dataset(train$data, label = train$label) params <- list( objective = "regression" , metric = "l2,l1" , min_data = 1L , learning_rate = 1.0 , verbose = .LGB_VERBOSITY , num_threads = .LGB_MAX_THREADS ) bst <- lgb.cv( params , dtrain , 10L , nfold = 5L , early_stopping_rounds = 10L ) expect_false(is.null(bst$record_evals)) }) test_that("CVBooster$reset_parameter() works as expected", { dtrain <- lgb.Dataset(train$data, label = train$label) n_folds <- 2L cv_bst <- lgb.cv( params = list( objective = "regression" , min_data = 1L , num_leaves = 7L , verbose = .LGB_VERBOSITY , num_threads = .LGB_MAX_THREADS ) , data = dtrain , nrounds = 3L , nfold = n_folds ) expect_true(methods::is(cv_bst, "lgb.CVBooster")) expect_length(cv_bst$boosters, n_folds) for (bst in cv_bst$boosters) { expect_equal(bst[["booster"]]$params[["num_leaves"]], 7L) } cv_bst$reset_parameter(list(num_leaves = 11L)) for (bst in cv_bst$boosters) { expect_equal(bst[["booster"]]$params[["num_leaves"]], 11L) } }) test_that("lgb.cv() rejects negative or 0 value passed to nrounds", { dtrain <- lgb.Dataset(train$data, label = train$label, params = list(num_threads = 2L)) params <- list( objective = "regression" , metric = "l2,l1" , min_data = 1L , num_threads = .LGB_MAX_THREADS ) for (nround_value in c(-10L, 0L)) { expect_error({ bst <- lgb.cv( params , dtrain , nround_value , nfold = 5L ) }, "nrounds should be greater than zero") } }) test_that("lgb.cv() throws an informative error if 'data' is not an lgb.Dataset", { bad_values <- list( 4L , "hello" , list(a = TRUE, b = seq_len(10L)) , data.frame(x = seq_len(5L), y = seq_len(5L)) , data.table::data.table(x = seq_len(5L), y = seq_len(5L)) , matrix(data = seq_len(10L), 2L, 5L) ) for (val in bad_values) { expect_error({ bst <- lgb.cv( params = list( objective = "regression" , metric = "l2,l1" , min_data = 1L ) , data = val , 10L , nfold = 5L ) }, regexp = "lgb.cv: data must be an lgb.Dataset instance", fixed = TRUE) } }) test_that("lightgbm.cv() gives the correct best_score and best_iter for a metric where higher values are better", { set.seed(708L) dtrain <- lgb.Dataset( data = as.matrix(runif(n = 500L, min = 0.0, max = 15.0), drop = FALSE) , label = rep(c(0L, 1L), 250L) , params = list(num_threads = .LGB_MAX_THREADS) ) nrounds <- 10L cv_bst <- lgb.cv( data = dtrain , nfold = 5L , nrounds = nrounds , params = list( objective = "binary" , metric = "auc,binary_error" , learning_rate = 1.5 , num_leaves = 5L , verbose = .LGB_VERBOSITY , num_threads = .LGB_MAX_THREADS ) ) expect_true(methods::is(cv_bst, "lgb.CVBooster")) expect_named( cv_bst$record_evals , c("start_iter", "valid") , ignore.order = FALSE , ignore.case = FALSE ) auc_scores <- unlist(cv_bst$record_evals[["valid"]][["auc"]][["eval"]]) expect_length(auc_scores, nrounds) expect_identical(cv_bst$best_iter, which.max(auc_scores)) expect_identical(cv_bst$best_score, auc_scores[which.max(auc_scores)]) }) test_that("lgb.cv() fit on linearly-relatead data improves when using linear learners", { set.seed(708L) .new_dataset <- function() { X <- matrix(rnorm(1000L), ncol = 1L) return(lgb.Dataset( data = X , label = 2L * X + runif(nrow(X), 0L, 0.1) , params = list(num_threads = .LGB_MAX_THREADS) )) } params <- list( objective = "regression" , verbose = -1L , metric = "mse" , seed = 0L , num_leaves = 2L , num_threads = .LGB_MAX_THREADS ) dtrain <- .new_dataset() cv_bst <- lgb.cv( data = dtrain , nrounds = 10L , params = params , nfold = 5L ) expect_true(methods::is(cv_bst, "lgb.CVBooster")) dtrain <- .new_dataset() cv_bst_linear <- lgb.cv( data = dtrain , nrounds = 10L , params = utils::modifyList(params, list(linear_tree = TRUE)) , nfold = 5L ) expect_true(methods::is(cv_bst_linear, "lgb.CVBooster")) expect_true(cv_bst_linear$best_score < cv_bst$best_score) }) test_that("lgb.cv() respects showsd argument", { dtrain <- lgb.Dataset(train$data, label = train$label, params = list(num_threads = .LGB_MAX_THREADS)) params <- list( objective = "regression" , metric = "l2" , min_data = 1L , verbose = .LGB_VERBOSITY , num_threads = .LGB_MAX_THREADS ) nrounds <- 5L set.seed(708L) bst_showsd <- lgb.cv( params = params , data = dtrain , nrounds = nrounds , nfold = 3L , showsd = TRUE ) evals_showsd <- bst_showsd$record_evals[["valid"]][["l2"]] set.seed(708L) bst_no_showsd <- lgb.cv( params = params , data = dtrain , nrounds = nrounds , nfold = 3L , showsd = FALSE ) evals_no_showsd <- bst_no_showsd$record_evals[["valid"]][["l2"]] expect_equal( evals_showsd[["eval"]] , evals_no_showsd[["eval"]] ) expect_true(methods::is(evals_showsd[["eval_err"]], "list")) expect_equal(length(evals_showsd[["eval_err"]]), nrounds) expect_identical(evals_no_showsd[["eval_err"]], list()) }) test_that("lgb.cv() raises an informative error for unrecognized objectives", { dtrain <- lgb.Dataset( data = train$data , label = train$label , params = list(num_threads = .LGB_MAX_THREADS) ) expect_error({ capture.output({ bst <- lgb.cv( data = dtrain , params = list( objective_type = "not_a_real_objective" , verbosity = .LGB_VERBOSITY , num_threads = .LGB_MAX_THREADS ) ) }, type = "message") }, regexp = "Unknown objective type name: not_a_real_objective") }) test_that("lgb.cv() respects parameter aliases for objective", { nrounds <- 3L nfold <- 4L dtrain <- lgb.Dataset( data = train$data , label = train$label , params = list(num_threads = .LGB_MAX_THREADS) ) cv_bst <- lgb.cv( data = dtrain , params = list( num_leaves = 5L , application = "binary" , num_iterations = nrounds , verbose = .LGB_VERBOSITY , num_threads = .LGB_MAX_THREADS ) , nfold = nfold ) expect_equal(cv_bst$best_iter, nrounds) expect_named(cv_bst$record_evals[["valid"]], "binary_logloss") expect_length(cv_bst$record_evals[["valid"]][["binary_logloss"]][["eval"]], nrounds) expect_length(cv_bst$boosters, nfold) }) test_that("lgb.cv() prefers objective in params to keyword argument", { data("EuStockMarkets") cv_bst <- lgb.cv( data = lgb.Dataset( data = EuStockMarkets[, c("SMI", "CAC", "FTSE")] , label = EuStockMarkets[, "DAX"] , params = list(num_threads = .LGB_MAX_THREADS) ) , params = list( application = "regression_l1" , verbosity = .LGB_VERBOSITY , num_threads = .LGB_MAX_THREADS ) , nrounds = 5L , obj = "regression_l2" ) for (bst_list in cv_bst$boosters) { bst <- bst_list[["booster"]] expect_equal(bst$params$objective, "regression_l1") # NOTE: using save_model_to_string() since that is the simplest public API in the R-package # allowing access to the "objective" attribute of the Booster object on the C++ side model_txt_lines <- strsplit( x = bst$save_model_to_string() , split = "\n" , fixed = TRUE )[[1L]] expect_true(any(model_txt_lines == "objective=regression_l1")) expect_false(any(model_txt_lines == "objective=regression_l2")) } }) test_that("lgb.cv() respects parameter aliases for metric", { nrounds <- 3L nfold <- 4L dtrain <- lgb.Dataset( data = train$data , label = train$label , params = list(num_threads = .LGB_MAX_THREADS) ) cv_bst <- lgb.cv( data = dtrain , params = list( num_leaves = 5L , objective = "binary" , num_iterations = nrounds , metric_types = c("auc", "binary_logloss") , verbose = .LGB_VERBOSITY , num_threads = .LGB_MAX_THREADS ) , nfold = nfold ) expect_equal(cv_bst$best_iter, nrounds) expect_named(cv_bst$record_evals[["valid"]], c("auc", "binary_logloss")) expect_length(cv_bst$record_evals[["valid"]][["binary_logloss"]][["eval"]], nrounds) expect_length(cv_bst$record_evals[["valid"]][["auc"]][["eval"]], nrounds) expect_length(cv_bst$boosters, nfold) }) test_that("lgb.cv() respects eval_train_metric argument", { dtrain <- lgb.Dataset(train$data, label = train$label) params <- list( objective = "regression" , metric = "l2" , min_data = 1L , verbose = .LGB_VERBOSITY , num_threads = .LGB_MAX_THREADS ) nrounds <- 5L set.seed(708L) bst_train <- lgb.cv( params = params , data = dtrain , nrounds = nrounds , nfold = 3L , showsd = FALSE , eval_train_metric = TRUE ) set.seed(708L) bst_no_train <- lgb.cv( params = params , data = dtrain , nrounds = nrounds , nfold = 3L , showsd = FALSE , eval_train_metric = FALSE ) expect_equal( bst_train$record_evals[["valid"]][["l2"]] , bst_no_train$record_evals[["valid"]][["l2"]] ) expect_true("train" %in% names(bst_train$record_evals)) expect_false("train" %in% names(bst_no_train$record_evals)) expect_true(methods::is(bst_train$record_evals[["train"]][["l2"]][["eval"]], "list")) expect_equal( length(bst_train$record_evals[["train"]][["l2"]][["eval"]]) , nrounds ) }) test_that("lgb.train() works as expected with multiple eval metrics", { metrics <- c("binary_error", "auc", "binary_logloss") bst <- lgb.train( data = lgb.Dataset( train$data , label = train$label , params = list(num_threads = .LGB_MAX_THREADS) ) , nrounds = 10L , params = list( objective = "binary" , metric = metrics , learning_rate = 1.0 , verbose = .LGB_VERBOSITY , num_threads = .LGB_MAX_THREADS ) , valids = list( "train" = lgb.Dataset( train$data , label = train$label , params = list(num_threads = .LGB_MAX_THREADS) ) ) ) expect_false(is.null(bst$record_evals)) expect_named( bst$record_evals[["train"]] , unlist(metrics) , ignore.order = FALSE , ignore.case = FALSE ) }) test_that("lgb.train() raises an informative error for unrecognized objectives", { dtrain <- lgb.Dataset( data = train$data , label = train$label ) expect_error({ capture.output({ bst <- lgb.train( data = dtrain , params = list( objective_type = "not_a_real_objective" , verbosity = .LGB_VERBOSITY ) ) }, type = "message") }, regexp = "Unknown objective type name: not_a_real_objective") }) test_that("lgb.train() respects parameter aliases for objective", { nrounds <- 3L dtrain <- lgb.Dataset( data = train$data , label = train$label , params = list(num_threads = .LGB_MAX_THREADS) ) bst <- lgb.train( data = dtrain , params = list( num_leaves = 5L , application = "binary" , num_iterations = nrounds , verbose = .LGB_VERBOSITY , num_threads = .LGB_MAX_THREADS ) , valids = list( "the_training_data" = dtrain ) ) expect_named(bst$record_evals[["the_training_data"]], "binary_logloss") expect_length(bst$record_evals[["the_training_data"]][["binary_logloss"]][["eval"]], nrounds) expect_equal(bst$params[["objective"]], "binary") }) test_that("lgb.train() prefers objective in params to keyword argument", { data("EuStockMarkets") bst <- lgb.train( data = lgb.Dataset( data = EuStockMarkets[, c("SMI", "CAC", "FTSE")] , label = EuStockMarkets[, "DAX"] , params = list(num_threads = .LGB_MAX_THREADS) ) , params = list( loss = "regression_l1" , verbosity = .LGB_VERBOSITY , num_threads = .LGB_MAX_THREADS ) , nrounds = 5L , obj = "regression_l2" ) expect_equal(bst$params$objective, "regression_l1") # NOTE: using save_model_to_string() since that is the simplest public API in the R-package # allowing access to the "objective" attribute of the Booster object on the C++ side model_txt_lines <- strsplit( x = bst$save_model_to_string() , split = "\n" , fixed = TRUE )[[1L]] expect_true(any(model_txt_lines == "objective=regression_l1")) expect_false(any(model_txt_lines == "objective=regression_l2")) }) test_that("lgb.train() respects parameter aliases for metric", { nrounds <- 3L dtrain <- lgb.Dataset( data = train$data , label = train$label , params = list(num_threads = .LGB_MAX_THREADS) ) bst <- lgb.train( data = dtrain , params = list( num_leaves = 5L , objective = "binary" , num_iterations = nrounds , metric_types = c("auc", "binary_logloss") , verbose = .LGB_VERBOSITY , num_threads = .LGB_MAX_THREADS ) , valids = list( "train" = dtrain ) ) record_results <- bst$record_evals[["train"]] expect_equal(sort(names(record_results)), c("auc", "binary_logloss")) expect_length(record_results[["auc"]][["eval"]], nrounds) expect_length(record_results[["binary_logloss"]][["eval"]], nrounds) expect_equal(bst$params[["metric"]], list("auc", "binary_logloss")) }) test_that("lgb.train() rejects negative or 0 value passed to nrounds", { dtrain <- lgb.Dataset(train$data, label = train$label, params = list(num_threads = .LGB_MAX_THREADS)) params <- list( objective = "regression" , metric = "l2,l1" , verbose = .LGB_VERBOSITY , num_threads = .LGB_MAX_THREADS ) for (nround_value in c(-10L, 0L)) { expect_error({ bst <- lgb.train( params , dtrain , nround_value ) }, "nrounds should be greater than zero") } }) test_that("lgb.train() accepts nrounds as either a top-level argument or parameter", { nrounds <- 15L set.seed(708L) top_level_bst <- lgb.train( data = lgb.Dataset( train$data , label = train$label , params = list(num_threads = .LGB_MAX_THREADS) ) , nrounds = nrounds , params = list( objective = "regression" , metric = "l2" , num_leaves = 5L , verbose = .LGB_VERBOSITY , num_threads = .LGB_MAX_THREADS ) ) set.seed(708L) param_bst <- lgb.train( data = lgb.Dataset( train$data , label = train$label , params = list(num_threads = .LGB_MAX_THREADS) ) , params = list( objective = "regression" , metric = "l2" , num_leaves = 5L , nrounds = nrounds , verbose = .LGB_VERBOSITY ) ) set.seed(708L) both_customized <- lgb.train( data = lgb.Dataset( train$data , label = train$label , params = list(num_threads = .LGB_MAX_THREADS) ) , nrounds = 20L , params = list( objective = "regression" , metric = "l2" , num_leaves = 5L , nrounds = nrounds , verbose = .LGB_VERBOSITY , num_threads = .LGB_MAX_THREADS ) ) top_level_l2 <- top_level_bst$eval_train()[[1L]][["value"]] params_l2 <- param_bst$eval_train()[[1L]][["value"]] both_l2 <- both_customized$eval_train()[[1L]][["value"]] # check type just to be sure the subsetting didn't return a NULL expect_true(is.numeric(top_level_l2)) expect_true(is.numeric(params_l2)) expect_true(is.numeric(both_l2)) # check that model produces identical performance expect_identical(top_level_l2, params_l2) expect_identical(both_l2, params_l2) expect_identical(param_bst$current_iter(), top_level_bst$current_iter()) expect_identical(param_bst$current_iter(), both_customized$current_iter()) expect_identical(param_bst$current_iter(), nrounds) }) test_that("lgb.train() throws an informative error if 'data' is not an lgb.Dataset", { bad_values <- list( 4L , "hello" , list(a = TRUE, b = seq_len(10L)) , data.frame(x = seq_len(5L), y = seq_len(5L)) , data.table::data.table(x = seq_len(5L), y = seq_len(5L)) , matrix(data = seq_len(10L), 2L, 5L) ) for (val in bad_values) { expect_error({ bst <- lgb.train( params = list( objective = "regression" , metric = "l2,l1" , verbose = .LGB_VERBOSITY ) , data = val , 10L ) }, regexp = "data must be an lgb.Dataset instance", fixed = TRUE) } }) test_that("lgb.train() throws an informative error if 'valids' is not a list of lgb.Dataset objects", { valids <- list( "valid1" = data.frame(x = rnorm(5L), y = rnorm(5L)) , "valid2" = data.frame(x = rnorm(5L), y = rnorm(5L)) ) expect_error({ bst <- lgb.train( params = list( objective = "regression" , metric = "l2,l1" , verbose = .LGB_VERBOSITY ) , data = lgb.Dataset(train$data, label = train$label) , 10L , valids = valids ) }, regexp = "valids must be a list of lgb.Dataset elements") }) test_that("lgb.train() errors if 'valids' is a list of lgb.Dataset objects but some do not have names", { valids <- list( "valid1" = lgb.Dataset(matrix(rnorm(10L), 5L, 2L)) , lgb.Dataset(matrix(rnorm(10L), 2L, 5L)) ) expect_error({ bst <- lgb.train( params = list( objective = "regression" , metric = "l2,l1" , verbose = .LGB_VERBOSITY ) , data = lgb.Dataset(train$data, label = train$label) , 10L , valids = valids ) }, regexp = "each element of valids must have a name") }) test_that("lgb.train() throws an informative error if 'valids' contains lgb.Dataset objects but none have names", { valids <- list( lgb.Dataset(matrix(rnorm(10L), 5L, 2L)) , lgb.Dataset(matrix(rnorm(10L), 2L, 5L)) ) expect_error({ bst <- lgb.train( params = list( objective = "regression" , metric = "l2,l1" , verbose = .LGB_VERBOSITY ) , data = lgb.Dataset(train$data, label = train$label) , 10L , valids = valids ) }, regexp = "each element of valids must have a name") }) test_that("lgb.train() works with force_col_wise and force_row_wise", { set.seed(1234L) nrounds <- 10L dtrain <- lgb.Dataset( train$data , label = train$label , params = list(num_threads = .LGB_MAX_THREADS) ) params <- list( objective = "binary" , metric = "binary_error" , force_col_wise = TRUE , verbose = .LGB_VERBOSITY , num_threads = .LGB_MAX_THREADS ) bst_col_wise <- lgb.train( params = params , data = dtrain , nrounds = nrounds ) params <- list( objective = "binary" , metric = "binary_error" , force_row_wise = TRUE , verbose = .LGB_VERBOSITY , num_threads = .LGB_MAX_THREADS ) bst_row_wise <- lgb.train( params = params , data = dtrain , nrounds = nrounds ) expected_error <- 0.003070782 expect_equal(bst_col_wise$eval_train()[[1L]][["value"]], expected_error) expect_equal(bst_row_wise$eval_train()[[1L]][["value"]], expected_error) # check some basic details of the boosters just to be sure force_col_wise # and force_row_wise are not causing any weird side effects for (bst in list(bst_row_wise, bst_col_wise)) { expect_equal(bst$current_iter(), nrounds) parsed_model <- jsonlite::fromJSON(bst$dump_model()) expect_equal(parsed_model$objective, "binary sigmoid:1") expect_false(parsed_model$average_output) } }) test_that("lgb.train() works as expected with sparse features", { set.seed(708L) num_obs <- 70000L trainDF <- data.frame( y = sample(c(0L, 1L), size = num_obs, replace = TRUE) , x = sample(c(1.0:10.0, rep(NA_real_, 50L)), size = num_obs, replace = TRUE) ) dtrain <- lgb.Dataset( data = as.matrix(trainDF[["x"]], drop = FALSE) , label = trainDF[["y"]] , params = list(num_threads = .LGB_MAX_THREADS) ) nrounds <- 1L bst <- lgb.train( params = list( objective = "binary" , min_data = 1L , min_data_in_bin = 1L , verbose = .LGB_VERBOSITY , num_threads = .LGB_MAX_THREADS ) , data = dtrain , nrounds = nrounds ) expect_true(.is_Booster(bst)) expect_equal(bst$current_iter(), nrounds) parsed_model <- jsonlite::fromJSON(bst$dump_model()) expect_equal(parsed_model$objective, "binary sigmoid:1") expect_false(parsed_model$average_output) expected_error <- 0.6931268 expect_true(abs(bst$eval_train()[[1L]][["value"]] - expected_error) < .LGB_NUMERIC_TOLERANCE) }) test_that("lgb.train() works with early stopping for classification", { trainDF <- data.frame( "feat1" = rep(c(5.0, 10.0), 500L) , "target" = rep(c(0L, 1L), 500L) ) validDF <- data.frame( "feat1" = rep(c(5.0, 10.0), 50L) , "target" = rep(c(0L, 1L), 50L) ) dtrain <- lgb.Dataset( data = as.matrix(trainDF[["feat1"]], drop = FALSE) , label = trainDF[["target"]] , params = list(num_threads = .LGB_MAX_THREADS) ) dvalid <- lgb.Dataset( data = as.matrix(validDF[["feat1"]], drop = FALSE) , label = validDF[["target"]] , params = list(num_threads = .LGB_MAX_THREADS) ) nrounds <- 10L ################################ # train with no early stopping # ################################ bst <- lgb.train( params = list( objective = "binary" , metric = "binary_error" , verbose = .LGB_VERBOSITY , num_threads = .LGB_MAX_THREADS ) , data = dtrain , nrounds = nrounds , valids = list( "valid1" = dvalid ) ) # a perfect model should be trivial to obtain, but all 10 rounds # should happen expect_equal(bst$best_score, 0.0) expect_equal(bst$best_iter, 1L) expect_equal(length(bst$record_evals[["valid1"]][["binary_error"]][["eval"]]), nrounds) ############################# # train with early stopping # ############################# early_stopping_rounds <- 5L bst <- lgb.train( params = list( objective = "binary" , metric = "binary_error" , early_stopping_rounds = early_stopping_rounds , verbose = .LGB_VERBOSITY , num_threads = .LGB_MAX_THREADS ) , data = dtrain , nrounds = nrounds , valids = list( "valid1" = dvalid ) ) # a perfect model should be trivial to obtain, and only 6 rounds # should have happen (1 with improvement, 5 consecutive with no improvement) expect_equal(bst$best_score, 0.0) expect_equal(bst$best_iter, 1L) expect_equal( length(bst$record_evals[["valid1"]][["binary_error"]][["eval"]]) , early_stopping_rounds + 1L ) }) test_that("lgb.train() treats early_stopping_rounds<=0 as disabling early stopping", { set.seed(708L) trainDF <- data.frame( "feat1" = rep(c(5.0, 10.0), 500L) , "target" = rep(c(0L, 1L), 500L) ) validDF <- data.frame( "feat1" = rep(c(5.0, 10.0), 50L) , "target" = rep(c(0L, 1L), 50L) ) dtrain <- lgb.Dataset( data = as.matrix(trainDF[["feat1"]], drop = FALSE) , label = trainDF[["target"]] , params = list(num_threads = .LGB_MAX_THREADS) ) dvalid <- lgb.Dataset( data = as.matrix(validDF[["feat1"]], drop = FALSE) , label = validDF[["target"]] , params = list(num_threads = .LGB_MAX_THREADS) ) nrounds <- 5L for (value in c(-5L, 0L)) { #----------------------------# # passed as keyword argument # #----------------------------# bst <- lgb.train( params = list( objective = "binary" , metric = "binary_error" , verbose = .LGB_VERBOSITY , num_threads = .LGB_MAX_THREADS ) , data = dtrain , nrounds = nrounds , valids = list( "valid1" = dvalid ) , early_stopping_rounds = value ) # a perfect model should be trivial to obtain, but all 10 rounds # should happen expect_equal(bst$best_score, 0.0) expect_equal(bst$best_iter, 1L) expect_equal(length(bst$record_evals[["valid1"]][["binary_error"]][["eval"]]), nrounds) #---------------------------# # passed as parameter alias # #---------------------------# bst <- lgb.train( params = list( objective = "binary" , metric = "binary_error" , n_iter_no_change = value , verbose = .LGB_VERBOSITY , num_threads = .LGB_MAX_THREADS ) , data = dtrain , nrounds = nrounds , valids = list( "valid1" = dvalid ) ) # a perfect model should be trivial to obtain, but all 10 rounds # should happen expect_equal(bst$best_score, 0.0) expect_equal(bst$best_iter, 1L) expect_equal(length(bst$record_evals[["valid1"]][["binary_error"]][["eval"]]), nrounds) } }) test_that("lgb.train() works with early stopping for classification with a metric that should be maximized", { set.seed(708L) dtrain <- lgb.Dataset( data = train$data , label = train$label , params = list(num_threads = .LGB_MAX_THREADS) ) dvalid <- lgb.Dataset( data = test$data , label = test$label , params = list(num_threads = .LGB_MAX_THREADS) ) nrounds <- 10L ############################# # train with early stopping # ############################# early_stopping_rounds <- 5L # the harsh max_depth guarantees that AUC improves over at least the first few iterations bst_auc <- lgb.train( params = list( objective = "binary" , metric = "auc" , max_depth = 3L , early_stopping_rounds = early_stopping_rounds , verbose = .LGB_VERBOSITY , num_threads = .LGB_MAX_THREADS ) , data = dtrain , nrounds = nrounds , valids = list( "valid1" = dvalid ) ) bst_binary_error <- lgb.train( params = list( objective = "binary" , metric = "binary_error" , max_depth = 3L , early_stopping_rounds = early_stopping_rounds , verbose = .LGB_VERBOSITY , num_threads = .LGB_MAX_THREADS ) , data = dtrain , nrounds = nrounds , valids = list( "valid1" = dvalid ) ) # early stopping should have been hit for binary_error (higher_better = FALSE) eval_info <- bst_binary_error$.__enclos_env__$private$get_eval_info() expect_identical(eval_info, "binary_error") expect_identical( unname(bst_binary_error$.__enclos_env__$private$higher_better_inner_eval) , FALSE ) expect_identical(bst_binary_error$best_iter, 1L) expect_identical(bst_binary_error$current_iter(), early_stopping_rounds + 1L) expect_true(abs(bst_binary_error$best_score - 0.01613904) < .LGB_NUMERIC_TOLERANCE) # early stopping should not have been hit for AUC (higher_better = TRUE) eval_info <- bst_auc$.__enclos_env__$private$get_eval_info() expect_identical(eval_info, "auc") expect_identical( unname(bst_auc$.__enclos_env__$private$higher_better_inner_eval) , TRUE ) expect_identical(bst_auc$best_iter, 9L) expect_identical(bst_auc$current_iter(), nrounds) expect_true(abs(bst_auc$best_score - 0.9999969) < .LGB_NUMERIC_TOLERANCE) }) test_that("lgb.train() works with early stopping for regression", { set.seed(708L) trainDF <- data.frame( "feat1" = rep(c(10.0, 100.0), 500L) , "target" = rep(c(-50.0, 50.0), 500L) ) validDF <- data.frame( "feat1" = rep(50.0, 4L) , "target" = rep(50.0, 4L) ) dtrain <- lgb.Dataset( data = as.matrix(trainDF[["feat1"]], drop = FALSE) , label = trainDF[["target"]] , params = list(num_threads = .LGB_MAX_THREADS) ) dvalid <- lgb.Dataset( data = as.matrix(validDF[["feat1"]], drop = FALSE) , label = validDF[["target"]] , params = list(num_threads = .LGB_MAX_THREADS) ) nrounds <- 10L ################################ # train with no early stopping # ################################ bst <- lgb.train( params = list( objective = "regression" , metric = "rmse" , verbose = .LGB_VERBOSITY , num_threads = .LGB_MAX_THREADS ) , data = dtrain , nrounds = nrounds , valids = list( "valid1" = dvalid ) ) # the best possible model should come from the first iteration, but # all 10 training iterations should happen expect_equal(bst$best_score, 55.0) expect_equal(bst$best_iter, 1L) expect_equal(length(bst$record_evals[["valid1"]][["rmse"]][["eval"]]), nrounds) ############################# # train with early stopping # ############################# early_stopping_rounds <- 5L bst <- lgb.train( params = list( objective = "regression" , metric = "rmse" , early_stopping_rounds = early_stopping_rounds , verbose = .LGB_VERBOSITY , num_threads = .LGB_MAX_THREADS ) , data = dtrain , nrounds = nrounds , valids = list( "valid1" = dvalid ) ) # the best model should be from the first iteration, and only 6 rounds # should have happen (1 with improvement, 5 consecutive with no improvement) expect_equal(bst$best_score, 55.0) expect_equal(bst$best_iter, 1L) expect_equal( length(bst$record_evals[["valid1"]][["rmse"]][["eval"]]) , early_stopping_rounds + 1L ) }) test_that("lgb.train() does not stop early if early_stopping_rounds is not given", { set.seed(708L) increasing_metric_starting_value <- get( ACCUMULATOR_NAME , envir = .GlobalEnv ) nrounds <- 10L metrics <- list( .constant_metric , .increasing_metric ) bst <- lgb.train( params = list( objective = "regression" , metric = "None" , verbose = .LGB_VERBOSITY , num_threads = .LGB_MAX_THREADS ) , data = DTRAIN_RANDOM_REGRESSION , nrounds = nrounds , valids = list("valid1" = DVALID_RANDOM_REGRESSION) , eval = metrics ) # Only the two functions provided to "eval" should have been evaluated expect_equal(length(bst$record_evals[["valid1"]]), 2L) # all 10 iterations should have happen, and the best_iter should be # the first one (based on constant_metric) best_iter <- 1L expect_equal(bst$best_iter, best_iter) # best_score should be taken from the first metric expect_equal( bst$best_score , bst$record_evals[["valid1"]][["constant_metric"]][["eval"]][[best_iter]] ) # early stopping should not have happened. Even though constant_metric # had 9 consecutive iterations with no improvement, it is ignored because of # first_metric_only = TRUE expect_equal( length(bst$record_evals[["valid1"]][["constant_metric"]][["eval"]]) , nrounds ) expect_equal( length(bst$record_evals[["valid1"]][["increasing_metric"]][["eval"]]) , nrounds ) }) test_that("If first_metric_only is not given or is FALSE, lgb.train() decides to stop early based on all metrics", { set.seed(708L) early_stopping_rounds <- 3L param_variations <- list( list( objective = "regression" , metric = "None" , early_stopping_rounds = early_stopping_rounds , verbose = .LGB_VERBOSITY , num_threads = .LGB_MAX_THREADS ) , list( objective = "regression" , metric = "None" , early_stopping_rounds = early_stopping_rounds , first_metric_only = FALSE , verbose = .LGB_VERBOSITY , num_threads = .LGB_MAX_THREADS ) ) for (params in param_variations) { nrounds <- 10L bst <- lgb.train( params = params , data = DTRAIN_RANDOM_REGRESSION , nrounds = nrounds , valids = list( "valid1" = DVALID_RANDOM_REGRESSION ) , eval = list( .increasing_metric , .constant_metric ) ) # Only the two functions provided to "eval" should have been evaluated expect_equal(length(bst$record_evals[["valid1"]]), 2L) # early stopping should have happened, and should have stopped early_stopping_rounds + 1 rounds in # because constant_metric never improves # # the best iteration should be the last one, because increasing_metric was first # and gets better every iteration best_iter <- early_stopping_rounds + 1L expect_equal(bst$best_iter, best_iter) # best_score should be taken from "increasing_metric" because it was first expect_equal( bst$best_score , bst$record_evals[["valid1"]][["increasing_metric"]][["eval"]][[best_iter]] ) # early stopping should not have happened. even though increasing_metric kept # getting better, early stopping should have happened because "constant_metric" # did not improve expect_equal( length(bst$record_evals[["valid1"]][["constant_metric"]][["eval"]]) , early_stopping_rounds + 1L ) expect_equal( length(bst$record_evals[["valid1"]][["increasing_metric"]][["eval"]]) , early_stopping_rounds + 1L ) } }) test_that("If first_metric_only is TRUE, lgb.train() decides to stop early based on only the first metric", { set.seed(708L) nrounds <- 10L early_stopping_rounds <- 3L increasing_metric_starting_value <- get(ACCUMULATOR_NAME, envir = .GlobalEnv) bst <- lgb.train( params = list( objective = "regression" , metric = "None" , early_stopping_rounds = early_stopping_rounds , first_metric_only = TRUE , verbose = .LGB_VERBOSITY , num_threads = .LGB_MAX_THREADS ) , data = DTRAIN_RANDOM_REGRESSION , nrounds = nrounds , valids = list( "valid1" = DVALID_RANDOM_REGRESSION ) , eval = list( .increasing_metric , .constant_metric ) ) # Only the two functions provided to "eval" should have been evaluated expect_equal(length(bst$record_evals[["valid1"]]), 2L) # all 10 iterations should happen, and the best_iter should be the final one expect_equal(bst$best_iter, nrounds) # best_score should be taken from "increasing_metric" expect_equal( bst$best_score , increasing_metric_starting_value + 0.1 * nrounds ) # early stopping should not have happened. Even though constant_metric # had 9 consecutive iterations with no improvement, it is ignored because of # first_metric_only = TRUE expect_equal( length(bst$record_evals[["valid1"]][["constant_metric"]][["eval"]]) , nrounds ) expect_equal( length(bst$record_evals[["valid1"]][["increasing_metric"]][["eval"]]) , nrounds ) }) test_that("lgb.train() works when a mixture of functions and strings are passed to eval", { set.seed(708L) nrounds <- 10L increasing_metric_starting_value <- get(ACCUMULATOR_NAME, envir = .GlobalEnv) bst <- lgb.train( params = list( objective = "regression" , metric = "None" , verbose = .LGB_VERBOSITY , num_threads = .LGB_MAX_THREADS ) , data = DTRAIN_RANDOM_REGRESSION , nrounds = nrounds , valids = list( "valid1" = DVALID_RANDOM_REGRESSION ) , eval = list( .increasing_metric , "rmse" , .constant_metric , "l2" ) ) # all 4 metrics should have been used expect_named( bst$record_evals[["valid1"]] , expected = c("rmse", "l2", "increasing_metric", "constant_metric") , ignore.order = TRUE , ignore.case = FALSE ) # the difference metrics shouldn't have been mixed up with each other results <- bst$record_evals[["valid1"]] expect_true(abs(results[["rmse"]][["eval"]][[1L]] - 1.105012) < .LGB_NUMERIC_TOLERANCE) expect_true(abs(results[["l2"]][["eval"]][[1L]] - 1.221051) < .LGB_NUMERIC_TOLERANCE) expected_increasing_metric <- increasing_metric_starting_value + 0.1 expect_true( abs( results[["increasing_metric"]][["eval"]][[1L]] - expected_increasing_metric ) < .LGB_NUMERIC_TOLERANCE ) expect_true(abs(results[["constant_metric"]][["eval"]][[1L]] - CONSTANT_METRIC_VALUE) < .LGB_NUMERIC_TOLERANCE) }) test_that("lgb.train() works when a list of strings or a character vector is passed to eval", { # testing list and character vector, as well as length-1 and length-2 eval_variations <- list( c("binary_error", "binary_logloss") , "binary_logloss" , list("binary_error", "binary_logloss") , list("binary_logloss") ) for (eval_variation in eval_variations) { set.seed(708L) nrounds <- 10L increasing_metric_starting_value <- get(ACCUMULATOR_NAME, envir = .GlobalEnv) bst <- lgb.train( params = list( objective = "binary" , metric = "None" , verbose = .LGB_VERBOSITY , num_threads = .LGB_MAX_THREADS ) , data = DTRAIN_RANDOM_CLASSIFICATION , nrounds = nrounds , valids = list( "valid1" = DVALID_RANDOM_CLASSIFICATION ) , eval = eval_variation ) # both metrics should have been used expect_named( bst$record_evals[["valid1"]] , expected = unlist(eval_variation) , ignore.order = TRUE , ignore.case = FALSE ) # the difference metrics shouldn't have been mixed up with each other results <- bst$record_evals[["valid1"]] if ("binary_error" %in% unlist(eval_variation)) { expect_true(abs(results[["binary_error"]][["eval"]][[1L]] - 0.4864865) < .LGB_NUMERIC_TOLERANCE) } if ("binary_logloss" %in% unlist(eval_variation)) { expect_true(abs(results[["binary_logloss"]][["eval"]][[1L]] - 0.6932548) < .LGB_NUMERIC_TOLERANCE) } } }) test_that("lgb.train() works when you specify both 'metric' and 'eval' with strings", { set.seed(708L) nrounds <- 10L increasing_metric_starting_value <- get(ACCUMULATOR_NAME, envir = .GlobalEnv) bst <- lgb.train( params = list( objective = "binary" , metric = "binary_error" , verbose = .LGB_VERBOSITY , num_threads = .LGB_MAX_THREADS ) , data = DTRAIN_RANDOM_CLASSIFICATION , nrounds = nrounds , valids = list( "valid1" = DVALID_RANDOM_CLASSIFICATION ) , eval = "binary_logloss" ) # both metrics should have been used expect_named( bst$record_evals[["valid1"]] , expected = c("binary_error", "binary_logloss") , ignore.order = TRUE , ignore.case = FALSE ) # the difference metrics shouldn't have been mixed up with each other results <- bst$record_evals[["valid1"]] expect_true(abs(results[["binary_error"]][["eval"]][[1L]] - 0.4864865) < .LGB_NUMERIC_TOLERANCE) expect_true(abs(results[["binary_logloss"]][["eval"]][[1L]] - 0.6932548) < .LGB_NUMERIC_TOLERANCE) }) test_that("lgb.train() works when you give a function for eval", { set.seed(708L) nrounds <- 10L increasing_metric_starting_value <- get(ACCUMULATOR_NAME, envir = .GlobalEnv) bst <- lgb.train( params = list( objective = "binary" , metric = "None" , verbose = .LGB_VERBOSITY , num_threads = .LGB_MAX_THREADS ) , data = DTRAIN_RANDOM_CLASSIFICATION , nrounds = nrounds , valids = list( "valid1" = DVALID_RANDOM_CLASSIFICATION ) , eval = .constant_metric ) # the difference metrics shouldn't have been mixed up with each other results <- bst$record_evals[["valid1"]] expect_true(abs(results[["constant_metric"]][["eval"]][[1L]] - CONSTANT_METRIC_VALUE) < .LGB_NUMERIC_TOLERANCE) }) test_that("lgb.train() works with early stopping for regression with a metric that should be minimized", { set.seed(708L) trainDF <- data.frame( "feat1" = rep(c(10.0, 100.0), 500L) , "target" = rep(c(-50.0, 50.0), 500L) ) validDF <- data.frame( "feat1" = rep(50.0, 4L) , "target" = rep(50.0, 4L) ) dtrain <- lgb.Dataset( data = as.matrix(trainDF[["feat1"]], drop = FALSE) , label = trainDF[["target"]] , params = list(num_threads = .LGB_MAX_THREADS) ) dvalid <- lgb.Dataset( data = as.matrix(validDF[["feat1"]], drop = FALSE) , label = validDF[["target"]] , params = list(num_threads = .LGB_MAX_THREADS) ) nrounds <- 10L ############################# # train with early stopping # ############################# early_stopping_rounds <- 5L bst <- lgb.train( params = list( objective = "regression" , metric = c( "mape" , "rmse" , "mae" ) , min_data_in_bin = 5L , early_stopping_rounds = early_stopping_rounds , verbose = .LGB_VERBOSITY , num_threads = .LGB_MAX_THREADS ) , data = dtrain , nrounds = nrounds , valids = list( "valid1" = dvalid ) ) # the best model should be from the first iteration, and only 6 rounds # should have happened (1 with improvement, 5 consecutive with no improvement) expect_equal(bst$best_score, 1.1) expect_equal(bst$best_iter, 1L) expect_equal( length(bst$record_evals[["valid1"]][["mape"]][["eval"]]) , early_stopping_rounds + 1L ) # Booster should understand that all three of these metrics should be minimized eval_info <- bst$.__enclos_env__$private$get_eval_info() expect_identical(eval_info, c("mape", "rmse", "l1")) expect_identical( unname(bst$.__enclos_env__$private$higher_better_inner_eval) , rep(FALSE, 3L) ) }) test_that("lgb.train() supports non-ASCII feature names", { # content below is equivalent to # # feature_names <- c("F_零", "F_一", "F_二", "F_三") # # but using rawToChar() to avoid weird issues when {testthat} # sources files and converts their encodings prior to evaluating the code feature_names <- c( rawToChar(as.raw(c(0x46, 0x5f, 0xe9, 0x9b, 0xb6))) , rawToChar(as.raw(c(0x46, 0x5f, 0xe4, 0xb8, 0x80))) , rawToChar(as.raw(c(0x46, 0x5f, 0xe4, 0xba, 0x8c))) , rawToChar(as.raw(c(0x46, 0x5f, 0xe4, 0xb8, 0x89))) ) dtrain <- lgb.Dataset( data = matrix(rnorm(400L), ncol = 4L) , label = rnorm(100L) , params = list(num_threads = .LGB_MAX_THREADS) , colnames = feature_names ) bst <- lgb.train( data = dtrain , nrounds = 5L , obj = "regression" , params = list( metric = "rmse" , verbose = .LGB_VERBOSITY , num_threads = .LGB_MAX_THREADS ) ) expect_true(.is_Booster(bst)) dumped_model <- jsonlite::fromJSON(bst$dump_model()) # UTF-8 strings are not well-supported on Windows # * https://developer.r-project.org/Blog/public/2020/05/02/utf-8-support-on-windows/ # * https://developer.r-project.org/Blog/public/2020/07/30/windows/utf-8-build-of-r-and-cran-packages/index.html if (.LGB_UTF8_LOCALE && !.LGB_ON_WINDOWS) { expect_identical( dumped_model[["feature_names"]] , feature_names ) } else { expect_identical( dumped_model[["feature_names"]] , iconv(feature_names, to = "UTF-8") ) } }) test_that("lgb.train() works with integer, double, and numeric data", { data(mtcars) X <- as.matrix(mtcars[, -1L]) y <- mtcars[, 1L, drop = TRUE] expected_mae <- 4.263667 for (data_mode in c("numeric", "double", "integer")) { mode(X) <- data_mode nrounds <- 10L bst <- lightgbm( data = X , label = y , params = list( objective = "regression" , min_data_in_bin = 1L , min_data_in_leaf = 1L , learning_rate = 0.01 , seed = 708L , verbose = .LGB_VERBOSITY ) , nrounds = nrounds ) # should have trained for 10 iterations and found splits modelDT <- lgb.model.dt.tree(bst) expect_equal(modelDT[, max(tree_index)], nrounds - 1L) expect_gt(nrow(modelDT), nrounds * 3L) # should have achieved expected performance preds <- predict(bst, X) mae <- mean(abs(y - preds)) expect_true(abs(mae - expected_mae) < .LGB_NUMERIC_TOLERANCE) } }) test_that("lgb.train() updates params based on keyword arguments", { dtrain <- lgb.Dataset( data = matrix(rnorm(400L), ncol = 4L) , label = rnorm(100L) , params = list(num_threads = .LGB_MAX_THREADS) ) # defaults from keyword arguments should be used if not specified in params invisible( capture.output({ bst <- lgb.train( data = dtrain , obj = "regression" , params = list(num_threads = .LGB_MAX_THREADS) ) }) ) expect_equal(bst$params[["verbosity"]], 1L) expect_equal(bst$params[["num_iterations"]], 100L) # main param names should be preferred to keyword arguments invisible( capture.output({ bst <- lgb.train( data = dtrain , obj = "regression" , params = list( "verbosity" = 5L , "num_iterations" = 2L , num_threads = .LGB_MAX_THREADS ) ) }) ) expect_equal(bst$params[["verbosity"]], 5L) expect_equal(bst$params[["num_iterations"]], 2L) # aliases should be preferred to keyword arguments, and converted to main parameter name invisible( capture.output({ bst <- lgb.train( data = dtrain , obj = "regression" , params = list( "verbose" = 5L , "num_boost_round" = 2L , num_threads = .LGB_MAX_THREADS ) ) }) ) expect_equal(bst$params[["verbosity"]], 5L) expect_false("verbose" %in% bst$params) expect_equal(bst$params[["num_iterations"]], 2L) expect_false("num_boost_round" %in% bst$params) }) test_that("when early stopping is not activated, best_iter and best_score come from valids and not training data", { set.seed(708L) trainDF <- data.frame( "feat1" = rep(c(10.0, 100.0), 500L) , "target" = rep(c(-50.0, 50.0), 500L) ) validDF <- data.frame( "feat1" = rep(50.0, 4L) , "target" = rep(50.0, 4L) ) dtrain <- lgb.Dataset( data = as.matrix(trainDF[["feat1"]], drop = FALSE) , label = trainDF[["target"]] , params = list(num_threads = .LGB_MAX_THREADS) ) dvalid1 <- lgb.Dataset( data = as.matrix(validDF[["feat1"]], drop = FALSE) , label = validDF[["target"]] , params = list(num_threads = .LGB_MAX_THREADS) ) dvalid2 <- lgb.Dataset( data = as.matrix(validDF[1L:10L, "feat1"], drop = FALSE) , label = validDF[1L:10L, "target"] , params = list(num_threads = .LGB_MAX_THREADS) ) nrounds <- 10L train_params <- list( objective = "regression" , metric = "rmse" , learning_rate = 1.5 , num_leaves = 5L , verbose = .LGB_VERBOSITY , num_threads = .LGB_MAX_THREADS ) # example 1: two valids, neither are the training data bst <- lgb.train( data = dtrain , nrounds = nrounds , valids = list( "valid1" = dvalid1 , "valid2" = dvalid2 ) , params = train_params ) expect_named( bst$record_evals , c("start_iter", "valid1", "valid2") , ignore.order = FALSE , ignore.case = FALSE ) rmse_scores <- unlist(bst$record_evals[["valid1"]][["rmse"]][["eval"]]) expect_length(rmse_scores, nrounds) expect_identical(bst$best_iter, which.min(rmse_scores)) expect_identical(bst$best_score, rmse_scores[which.min(rmse_scores)]) # example 2: train first (called "train") and two valids bst <- lgb.train( data = dtrain , nrounds = nrounds , valids = list( "train" = dtrain , "valid1" = dvalid1 , "valid2" = dvalid2 ) , params = train_params ) expect_named( bst$record_evals , c("start_iter", "train", "valid1", "valid2") , ignore.order = FALSE , ignore.case = FALSE ) rmse_scores <- unlist(bst$record_evals[["valid1"]][["rmse"]][["eval"]]) expect_length(rmse_scores, nrounds) expect_identical(bst$best_iter, which.min(rmse_scores)) expect_identical(bst$best_score, rmse_scores[which.min(rmse_scores)]) # example 3: train second (called "train") and two valids bst <- lgb.train( data = dtrain , nrounds = nrounds , valids = list( "valid1" = dvalid1 , "train" = dtrain , "valid2" = dvalid2 ) , params = train_params ) # note that "train" still ends up as the first one expect_named( bst$record_evals , c("start_iter", "train", "valid1", "valid2") , ignore.order = FALSE , ignore.case = FALSE ) rmse_scores <- unlist(bst$record_evals[["valid1"]][["rmse"]][["eval"]]) expect_length(rmse_scores, nrounds) expect_identical(bst$best_iter, which.min(rmse_scores)) expect_identical(bst$best_score, rmse_scores[which.min(rmse_scores)]) # example 4: train third (called "train") and two valids bst <- lgb.train( data = dtrain , nrounds = nrounds , valids = list( "valid1" = dvalid1 , "valid2" = dvalid2 , "train" = dtrain ) , params = train_params ) # note that "train" still ends up as the first one expect_named( bst$record_evals , c("start_iter", "train", "valid1", "valid2") , ignore.order = FALSE , ignore.case = FALSE ) rmse_scores <- unlist(bst$record_evals[["valid1"]][["rmse"]][["eval"]]) expect_length(rmse_scores, nrounds) expect_identical(bst$best_iter, which.min(rmse_scores)) expect_identical(bst$best_score, rmse_scores[which.min(rmse_scores)]) # example 5: train second (called "something-random-we-would-not-hardcode") and two valids bst <- lgb.train( data = dtrain , nrounds = nrounds , valids = list( "valid1" = dvalid1 , "something-random-we-would-not-hardcode" = dtrain , "valid2" = dvalid2 ) , params = train_params ) # note that "something-random-we-would-not-hardcode" was recognized as the training # data even though it isn't named "train" expect_named( bst$record_evals , c("start_iter", "something-random-we-would-not-hardcode", "valid1", "valid2") , ignore.order = FALSE , ignore.case = FALSE ) rmse_scores <- unlist(bst$record_evals[["valid1"]][["rmse"]][["eval"]]) expect_length(rmse_scores, nrounds) expect_identical(bst$best_iter, which.min(rmse_scores)) expect_identical(bst$best_score, rmse_scores[which.min(rmse_scores)]) # example 6: the only valid supplied is the training data bst <- lgb.train( data = dtrain , nrounds = nrounds , valids = list( "train" = dtrain ) , params = train_params ) expect_identical(bst$best_iter, -1L) expect_identical(bst$best_score, NA_real_) }) test_that("lightgbm.train() gives the correct best_score and best_iter for a metric where higher values are better", { set.seed(708L) trainDF <- data.frame( "feat1" = runif(n = 500L, min = 0.0, max = 15.0) , "target" = rep(c(0L, 1L), 500L) ) validDF <- data.frame( "feat1" = runif(n = 50L, min = 0.0, max = 15.0) , "target" = rep(c(0L, 1L), 50L) ) dtrain <- lgb.Dataset( data = as.matrix(trainDF[["feat1"]], drop = FALSE) , label = trainDF[["target"]] , params = list(num_threads = .LGB_MAX_THREADS) ) dvalid1 <- lgb.Dataset( data = as.matrix(validDF[1L:25L, "feat1"], drop = FALSE) , label = validDF[1L:25L, "target"] , params = list(num_threads = .LGB_MAX_THREADS) ) nrounds <- 10L bst <- lgb.train( data = dtrain , nrounds = nrounds , valids = list( "valid1" = dvalid1 , "something-random-we-would-not-hardcode" = dtrain ) , params = list( objective = "binary" , metric = "auc" , learning_rate = 1.5 , num_leaves = 5L , verbose = .LGB_VERBOSITY , num_threads = .LGB_MAX_THREADS ) ) # note that "something-random-we-would-not-hardcode" was recognized as the training # data even though it isn't named "train" expect_named( bst$record_evals , c("start_iter", "something-random-we-would-not-hardcode", "valid1") , ignore.order = FALSE , ignore.case = FALSE ) auc_scores <- unlist(bst$record_evals[["valid1"]][["auc"]][["eval"]]) expect_length(auc_scores, nrounds) expect_identical(bst$best_iter, which.max(auc_scores)) expect_identical(bst$best_score, auc_scores[which.max(auc_scores)]) }) test_that("using lightgbm() without early stopping, best_iter and best_score come from valids and not training data", { set.seed(708L) # example: train second (called "something-random-we-would-not-hardcode"), two valids, # and a metric where higher values are better ("auc") trainDF <- data.frame( "feat1" = runif(n = 500L, min = 0.0, max = 15.0) , "target" = rep(c(0L, 1L), 500L) ) validDF <- data.frame( "feat1" = runif(n = 50L, min = 0.0, max = 15.0) , "target" = rep(c(0L, 1L), 50L) ) dtrain <- lgb.Dataset( data = as.matrix(trainDF[["feat1"]], drop = FALSE) , label = trainDF[["target"]] , params = list(num_threads = .LGB_MAX_THREADS) ) dvalid1 <- lgb.Dataset( data = as.matrix(validDF[1L:25L, "feat1"], drop = FALSE) , label = validDF[1L:25L, "target"] , params = list(num_threads = .LGB_MAX_THREADS) ) dvalid2 <- lgb.Dataset( data = as.matrix(validDF[26L:50L, "feat1"], drop = FALSE) , label = validDF[26L:50L, "target"] , params = list(num_threads = .LGB_MAX_THREADS) ) nrounds <- 10L bst <- lightgbm( data = dtrain , nrounds = nrounds , valids = list( "valid1" = dvalid1 , "something-random-we-would-not-hardcode" = dtrain , "valid2" = dvalid2 ) , params = list( objective = "binary" , metric = "auc" , learning_rate = 1.5 , num_leaves = 5L , num_threads = .LGB_MAX_THREADS ) , verbose = -7L ) # when verbose <= 0 is passed to lightgbm(), 'valids' is passed through to lgb.train() # untouched. If you set verbose to > 0, the training data will still be first but called "train" expect_named( bst$record_evals , c("start_iter", "something-random-we-would-not-hardcode", "valid1", "valid2") , ignore.order = FALSE , ignore.case = FALSE ) auc_scores <- unlist(bst$record_evals[["valid1"]][["auc"]][["eval"]]) expect_length(auc_scores, nrounds) expect_identical(bst$best_iter, which.max(auc_scores)) expect_identical(bst$best_score, auc_scores[which.max(auc_scores)]) }) test_that("lgb.cv() works when you specify both 'metric' and 'eval' with strings", { set.seed(708L) nrounds <- 10L nfolds <- 4L increasing_metric_starting_value <- get(ACCUMULATOR_NAME, envir = .GlobalEnv) bst <- lgb.cv( params = list( objective = "binary" , metric = "binary_error" , verbose = .LGB_VERBOSITY , num_threads = .LGB_MAX_THREADS ) , data = DTRAIN_RANDOM_CLASSIFICATION , nrounds = nrounds , nfold = nfolds , eval = "binary_logloss" ) # both metrics should have been used expect_named( bst$record_evals[["valid"]] , expected = c("binary_error", "binary_logloss") , ignore.order = TRUE , ignore.case = FALSE ) # the difference metrics shouldn't have been mixed up with each other results <- bst$record_evals[["valid"]] expect_true(abs(results[["binary_error"]][["eval"]][[1L]] - 0.5005654) < .LGB_NUMERIC_TOLERANCE) expect_true(abs(results[["binary_logloss"]][["eval"]][[1L]] - 0.7011232) < .LGB_NUMERIC_TOLERANCE) # all boosters should have been created expect_length(bst$boosters, nfolds) }) test_that("lgb.cv() works when you give a function for eval", { set.seed(708L) nrounds <- 10L nfolds <- 3L increasing_metric_starting_value <- get(ACCUMULATOR_NAME, envir = .GlobalEnv) bst <- lgb.cv( params = list( objective = "binary" , metric = "None" , verbose = .LGB_VERBOSITY , num_threads = .LGB_MAX_THREADS ) , data = DTRAIN_RANDOM_CLASSIFICATION , nfold = nfolds , nrounds = nrounds , eval = .constant_metric ) # the difference metrics shouldn't have been mixed up with each other results <- bst$record_evals[["valid"]] expect_true(abs(results[["constant_metric"]][["eval"]][[1L]] - CONSTANT_METRIC_VALUE) < .LGB_NUMERIC_TOLERANCE) expect_named(results, "constant_metric") }) test_that("If first_metric_only is TRUE, lgb.cv() decides to stop early based on only the first metric", { set.seed(708L) nrounds <- 10L nfolds <- 5L early_stopping_rounds <- 3L increasing_metric_starting_value <- get(ACCUMULATOR_NAME, envir = .GlobalEnv) bst <- lgb.cv( params = list( objective = "regression" , metric = "None" , early_stopping_rounds = early_stopping_rounds , first_metric_only = TRUE , verbose = .LGB_VERBOSITY , num_threads = .LGB_MAX_THREADS ) , data = DTRAIN_RANDOM_REGRESSION , nfold = nfolds , nrounds = nrounds , eval = list( .increasing_metric , .constant_metric ) ) # Only the two functions provided to "eval" should have been evaluated expect_named(bst$record_evals[["valid"]], c("increasing_metric", "constant_metric")) # all 10 iterations should happen, and the best_iter should be the final one expect_equal(bst$best_iter, nrounds) # best_score should be taken from "increasing_metric" # # this expected value looks magical and confusing, but it's because # evaluation metrics are averaged over all folds. # # consider 5-fold CV with a metric that adds 0.1 to a global accumulator # each time it's called # # * iter 1: [0.1, 0.2, 0.3, 0.4, 0.5] (mean = 0.3) # * iter 2: [0.6, 0.7, 0.8, 0.9, 1.0] (mean = 1.3) # * iter 3: [1.1, 1.2, 1.3, 1.4, 1.5] (mean = 1.8) # cv_value <- increasing_metric_starting_value + mean(seq_len(nfolds) / 10.0) + (nrounds - 1L) * 0.1 * nfolds expect_equal(bst$best_score, cv_value) # early stopping should not have happened. Even though constant_metric # had 9 consecutive iterations with no improvement, it is ignored because of # first_metric_only = TRUE expect_equal( length(bst$record_evals[["valid"]][["constant_metric"]][["eval"]]) , nrounds ) expect_equal( length(bst$record_evals[["valid"]][["increasing_metric"]][["eval"]]) , nrounds ) }) test_that("early stopping works with lgb.cv()", { set.seed(708L) nrounds <- 10L nfolds <- 5L early_stopping_rounds <- 3L increasing_metric_starting_value <- get(ACCUMULATOR_NAME, envir = .GlobalEnv) bst <- lgb.cv( params = list( objective = "regression" , metric = "None" , early_stopping_rounds = early_stopping_rounds , first_metric_only = TRUE , verbose = .LGB_VERBOSITY , num_threads = .LGB_MAX_THREADS ) , data = DTRAIN_RANDOM_REGRESSION , nfold = nfolds , nrounds = nrounds , eval = list( .constant_metric , .increasing_metric ) ) # only the two functions provided to "eval" should have been evaluated expect_named(bst$record_evals[["valid"]], c("constant_metric", "increasing_metric")) # best_iter should be based on the first metric. Since constant_metric # never changes, its first iteration was the best oone expect_equal(bst$best_iter, 1L) # best_score should be taken from the first metric expect_equal(bst$best_score, 0.2) # early stopping should have happened, since constant_metric was the first # one passed to eval and it will not improve over consecutive iterations # # note that this test is identical to the previous one, but with the # order of the eval metrics switched expect_equal( length(bst$record_evals[["valid"]][["constant_metric"]][["eval"]]) , early_stopping_rounds + 1L ) expect_equal( length(bst$record_evals[["valid"]][["increasing_metric"]][["eval"]]) , early_stopping_rounds + 1L ) # every booster's predict method should use best_iter as num_iteration in predict random_data <- as.matrix(rnorm(10L), ncol = 1L, drop = FALSE) for (x in bst$boosters) { expect_equal(x$booster$best_iter, bst$best_iter) expect_gt(x$booster$current_iter(), bst$best_iter) preds_iter <- predict(x$booster, random_data, num_iteration = bst$best_iter) preds_no_iter <- predict(x$booster, random_data) expect_equal(preds_iter, preds_no_iter) } }) test_that("lgb.cv() respects changes to logging verbosity", { dtrain <- lgb.Dataset( data = train$data , label = train$label , params = list(num_threads = .LGB_MAX_THREADS) ) # (verbose = 1) should be INFO and WARNING level logs lgb_cv_logs <- capture.output({ cv_bst <- lgb.cv( params = list(num_threads = .LGB_MAX_THREADS) , nfold = 2L , nrounds = 5L , data = dtrain , obj = "binary" , verbose = 1L ) }) expect_true(any(grepl("[LightGBM] [Info]", lgb_cv_logs, fixed = TRUE))) expect_true(any(grepl("[LightGBM] [Warning]", lgb_cv_logs, fixed = TRUE))) # (verbose = 0) should be WARNING level logs only lgb_cv_logs <- capture.output({ cv_bst <- lgb.cv( params = list(num_threads = .LGB_MAX_THREADS) , nfold = 2L , nrounds = 5L , data = dtrain , obj = "binary" , verbose = 0L ) }) expect_false(any(grepl("[LightGBM] [Info]", lgb_cv_logs, fixed = TRUE))) expect_true(any(grepl("[LightGBM] [Warning]", lgb_cv_logs, fixed = TRUE))) # (verbose = -1) no logs lgb_cv_logs <- capture.output({ cv_bst <- lgb.cv( params = list(num_threads = .LGB_MAX_THREADS) , nfold = 2L , nrounds = 5L , data = dtrain , obj = "binary" , verbose = -1L ) }) # NOTE: this is not length(lgb_cv_logs) == 0 because lightgbm's # dependencies might print other messages expect_false(any(grepl("[LightGBM] [Info]", lgb_cv_logs, fixed = TRUE))) expect_false(any(grepl("[LightGBM] [Warning]", lgb_cv_logs, fixed = TRUE))) }) test_that("lgb.cv() updates params based on keyword arguments", { dtrain <- lgb.Dataset( data = matrix(rnorm(400L), ncol = 4L) , label = rnorm(100L) , params = list(num_threads = .LGB_MAX_THREADS) ) # defaults from keyword arguments should be used if not specified in params invisible( capture.output({ cv_bst <- lgb.cv( data = dtrain , obj = "regression" , params = list(num_threads = .LGB_MAX_THREADS) , nfold = 2L ) }) ) for (bst in cv_bst$boosters) { bst_params <- bst[["booster"]]$params expect_equal(bst_params[["verbosity"]], 1L) expect_equal(bst_params[["num_iterations"]], 100L) } # main param names should be preferred to keyword arguments invisible( capture.output({ cv_bst <- lgb.cv( data = dtrain , obj = "regression" , params = list( "verbosity" = 5L , "num_iterations" = 2L , num_threads = .LGB_MAX_THREADS ) , nfold = 2L ) }) ) for (bst in cv_bst$boosters) { bst_params <- bst[["booster"]]$params expect_equal(bst_params[["verbosity"]], 5L) expect_equal(bst_params[["num_iterations"]], 2L) } # aliases should be preferred to keyword arguments, and converted to main parameter name invisible( capture.output({ cv_bst <- lgb.cv( data = dtrain , obj = "regression" , params = list( "verbose" = 5L , "num_boost_round" = 2L , num_threads = .LGB_MAX_THREADS ) , nfold = 2L ) }) ) for (bst in cv_bst$boosters) { bst_params <- bst[["booster"]]$params expect_equal(bst_params[["verbosity"]], 5L) expect_false("verbose" %in% bst_params) expect_equal(bst_params[["num_iterations"]], 2L) expect_false("num_boost_round" %in% bst_params) } }) test_that("lgb.train() fit on linearly-relatead data improves when using linear learners", { set.seed(708L) .new_dataset <- function() { X <- matrix(rnorm(100L), ncol = 1L) return(lgb.Dataset( data = X , label = 2L * X + runif(nrow(X), 0L, 0.1) , params = list(num_threads = .LGB_MAX_THREADS) )) } params <- list( objective = "regression" , verbose = .LGB_VERBOSITY , metric = "mse" , seed = 0L , num_leaves = 2L , num_threads = .LGB_MAX_THREADS ) dtrain <- .new_dataset() bst <- lgb.train( data = dtrain , nrounds = 10L , params = params , valids = list("train" = dtrain) ) expect_true(.is_Booster(bst)) dtrain <- .new_dataset() bst_linear <- lgb.train( data = dtrain , nrounds = 10L , params = utils::modifyList(params, list(linear_tree = TRUE)) , valids = list("train" = dtrain) ) expect_true(.is_Booster(bst_linear)) bst_last_mse <- bst$record_evals[["train"]][["l2"]][["eval"]][[10L]] bst_lin_last_mse <- bst_linear$record_evals[["train"]][["l2"]][["eval"]][[10L]] expect_true(bst_lin_last_mse < bst_last_mse) }) test_that("lgb.train() with linear learner fails already-constructed dataset with linear=false", { set.seed(708L) params <- list( objective = "regression" , verbose = .LGB_VERBOSITY , metric = "mse" , seed = 0L , num_leaves = 2L , num_threads = .LGB_MAX_THREADS ) dtrain <- lgb.Dataset( data = matrix(rnorm(100L), ncol = 1L) , label = rnorm(100L) , params = list(num_threads = .LGB_MAX_THREADS) ) dtrain$construct() expect_error({ capture.output({ bst_linear <- lgb.train( data = dtrain , nrounds = 10L , params = utils::modifyList(params, list(linear_tree = TRUE)) ) }, type = "message") }, regexp = "Cannot change linear_tree after constructed Dataset handle") }) test_that("lgb.train() works with linear learners even if Dataset has missing values", { set.seed(708L) .new_dataset <- function() { values <- rnorm(100L) values[sample(seq_along(values), size = 10L)] <- NA_real_ X <- matrix( data = sample(values, size = 100L) , ncol = 1L ) return(lgb.Dataset( data = X , label = 2L * X + runif(nrow(X), 0L, 0.1) , params = list(num_threads = .LGB_MAX_THREADS) )) } params <- list( objective = "regression" , verbose = .LGB_VERBOSITY , metric = "mse" , seed = 0L , num_leaves = 2L , num_threads = .LGB_MAX_THREADS ) dtrain <- .new_dataset() bst <- lgb.train( data = dtrain , nrounds = 10L , params = params , valids = list("train" = dtrain) ) expect_true(.is_Booster(bst)) dtrain <- .new_dataset() bst_linear <- lgb.train( data = dtrain , nrounds = 10L , params = utils::modifyList(params, list(linear_tree = TRUE)) , valids = list("train" = dtrain) ) expect_true(.is_Booster(bst_linear)) bst_last_mse <- bst$record_evals[["train"]][["l2"]][["eval"]][[10L]] bst_lin_last_mse <- bst_linear$record_evals[["train"]][["l2"]][["eval"]][[10L]] expect_true(bst_lin_last_mse < bst_last_mse) }) test_that("lgb.train() works with linear learners, bagging, and a Dataset that has missing values", { set.seed(708L) .new_dataset <- function() { values <- rnorm(100L) values[sample(seq_along(values), size = 10L)] <- NA_real_ X <- matrix( data = sample(values, size = 100L) , ncol = 1L ) return(lgb.Dataset( data = X , label = 2L * X + runif(nrow(X), 0L, 0.1) , params = list(num_threads = .LGB_MAX_THREADS) )) } params <- list( objective = "regression" , verbose = .LGB_VERBOSITY , metric = "mse" , seed = 0L , num_leaves = 2L , bagging_freq = 1L , subsample = 0.8 , num_threads = .LGB_MAX_THREADS ) dtrain <- .new_dataset() bst <- lgb.train( data = dtrain , nrounds = 10L , params = params , valids = list("train" = dtrain) ) expect_true(.is_Booster(bst)) dtrain <- .new_dataset() bst_linear <- lgb.train( data = dtrain , nrounds = 10L , params = utils::modifyList(params, list(linear_tree = TRUE)) , valids = list("train" = dtrain) ) expect_true(.is_Booster(bst_linear)) bst_last_mse <- bst$record_evals[["train"]][["l2"]][["eval"]][[10L]] bst_lin_last_mse <- bst_linear$record_evals[["train"]][["l2"]][["eval"]][[10L]] expect_true(bst_lin_last_mse < bst_last_mse) }) test_that("lgb.train() works with linear learners and data where a feature has only 1 non-NA value", { set.seed(708L) .new_dataset <- function() { values <- c(rnorm(100L), rep(NA_real_, 100L)) values[118L] <- rnorm(1L) X <- matrix( data = values , ncol = 2L ) return(lgb.Dataset( data = X , label = 2L * X[, 1L] + runif(nrow(X), 0L, 0.1) , params = list( feature_pre_filter = FALSE , num_threads = .LGB_MAX_THREADS ) )) } params <- list( objective = "regression" , verbose = -1L , metric = "mse" , seed = 0L , num_leaves = 2L , num_threads = .LGB_MAX_THREADS ) dtrain <- .new_dataset() bst_linear <- lgb.train( data = dtrain , nrounds = 10L , params = utils::modifyList(params, list(linear_tree = TRUE)) ) expect_true(.is_Booster(bst_linear)) }) test_that("lgb.train() works with linear learners when Dataset has categorical features", { set.seed(708L) .new_dataset <- function() { X <- matrix(numeric(200L), nrow = 100L, ncol = 2L) X[, 1L] <- rnorm(100L) X[, 2L] <- sample(seq_len(4L), size = 100L, replace = TRUE) return(lgb.Dataset( data = X , label = 2L * X[, 1L] + runif(nrow(X), 0L, 0.1) , params = list(num_threads = .LGB_MAX_THREADS) )) } params <- list( objective = "regression" , verbose = -1L , metric = "mse" , seed = 0L , num_leaves = 2L , categorical_feature = 1L , num_threads = .LGB_MAX_THREADS ) dtrain <- .new_dataset() bst <- lgb.train( data = dtrain , nrounds = 10L , params = params , valids = list("train" = dtrain) ) expect_true(.is_Booster(bst)) dtrain <- .new_dataset() bst_linear <- lgb.train( data = dtrain , nrounds = 10L , params = utils::modifyList(params, list(linear_tree = TRUE)) , valids = list("train" = dtrain) ) expect_true(.is_Booster(bst_linear)) bst_last_mse <- bst$record_evals[["train"]][["l2"]][["eval"]][[10L]] bst_lin_last_mse <- bst_linear$record_evals[["train"]][["l2"]][["eval"]][[10L]] expect_true(bst_lin_last_mse < bst_last_mse) }) test_that("lgb.train() throws an informative error if interaction_constraints is not a list", { dtrain <- lgb.Dataset(train$data, label = train$label) params <- list(objective = "regression", interaction_constraints = "[1,2],[3]") expect_error({ bst <- lightgbm( data = dtrain , params = params , nrounds = 2L ) }, "interaction_constraints must be a list") }) test_that(paste0("lgb.train() throws an informative error if the members of interaction_constraints ", "are not character or numeric vectors"), { dtrain <- lgb.Dataset(train$data, label = train$label) params <- list(objective = "regression", interaction_constraints = list(list(1L, 2L), list(3L))) expect_error({ bst <- lightgbm( data = dtrain , params = params , nrounds = 2L ) }, "every element in interaction_constraints must be a character vector or numeric vector") }) test_that("lgb.train() throws an informative error if interaction_constraints contains a too large index", { dtrain <- lgb.Dataset(train$data, label = train$label) params <- list(objective = "regression", interaction_constraints = list(c(1L, ncol(train$data) + 1L:2L), 3L)) expect_error( lightgbm(data = dtrain, params = params, nrounds = 2L) , "unknown feature(s) in interaction_constraints: '127', '128'" , fixed = TRUE ) }) test_that(paste0("lgb.train() gives same result when interaction_constraints is specified as a list of ", "character vectors, numeric vectors, or a combination"), { set.seed(1L) dtrain <- lgb.Dataset(train$data, label = train$label, params = list(num_threads = .LGB_MAX_THREADS)) params <- list( objective = "regression" , interaction_constraints = list(c(1L, 2L), 3L) , verbose = .LGB_VERBOSITY , num_threads = .LGB_MAX_THREADS ) bst <- lightgbm( data = dtrain , params = params , nrounds = 2L ) pred1 <- bst$predict(test$data) cnames <- colnames(train$data) params <- list( objective = "regression" , interaction_constraints = list(c(cnames[[1L]], cnames[[2L]]), cnames[[3L]]) , verbose = .LGB_VERBOSITY , num_threads = .LGB_MAX_THREADS ) bst <- lightgbm( data = dtrain , params = params , nrounds = 2L ) pred2 <- bst$predict(test$data) params <- list( objective = "regression" , interaction_constraints = list(c(cnames[[1L]], cnames[[2L]]), 3L) , verbose = .LGB_VERBOSITY , num_threads = .LGB_MAX_THREADS ) bst <- lightgbm( data = dtrain , params = params , nrounds = 2L ) pred3 <- bst$predict(test$data) expect_equal(pred1, pred2) expect_equal(pred2, pred3) }) test_that(paste0("lgb.train() gives same results when using interaction_constraints and specifying colnames"), { set.seed(1L) dtrain <- lgb.Dataset( train$data , label = train$label , params = list(num_threads = .LGB_MAX_THREADS) ) params <- list( objective = "regression" , interaction_constraints = list(c(1L, 2L), 3L) , verbose = .LGB_VERBOSITY , num_threads = .LGB_MAX_THREADS ) bst <- lightgbm( data = dtrain , params = params , nrounds = 2L ) pred1 <- bst$predict(test$data) new_colnames <- paste0(colnames(train$data), "_x") dtrain$set_colnames(new_colnames) params <- list( objective = "regression" , interaction_constraints = list(c(new_colnames[1L], new_colnames[2L]), new_colnames[3L]) , verbose = .LGB_VERBOSITY , num_threads = .LGB_MAX_THREADS ) bst <- lightgbm( data = dtrain , params = params , nrounds = 2L ) pred2 <- bst$predict(test$data) expect_equal(pred1, pred2) }) test_that("Interaction constraints add missing features correctly as new group", { dtrain <- lgb.Dataset( train$data[, 1L:6L] # Pick only some columns , label = train$label , params = list(num_threads = .LGB_MAX_THREADS) ) list_of_constraints <- list( list(3L, 1L:2L) , list("cap-shape=convex", c("cap-shape=bell", "cap-shape=conical")) ) for (constraints in list_of_constraints) { params <- list( objective = "regression" , interaction_constraints = constraints , verbose = .LGB_VERBOSITY , num_threads = .LGB_MAX_THREADS ) bst <- lightgbm(data = dtrain, params = params, nrounds = 10L) expected_list <- list("[2]", "[0,1]", "[3,4,5]") expect_equal(bst$params$interaction_constraints, expected_list) expected_string <- "[interaction_constraints: [2],[0,1],[3,4,5]]" expect_true( grepl(expected_string, bst$save_model_to_string(), fixed = TRUE) ) } }) .generate_trainset_for_monotone_constraints_tests <- function(x3_to_categorical) { n_samples <- 3000L x1_positively_correlated_with_y <- runif(n = n_samples, min = 0.0, max = 1.0) x2_negatively_correlated_with_y <- runif(n = n_samples, min = 0.0, max = 1.0) x3_negatively_correlated_with_y <- runif(n = n_samples, min = 0.0, max = 1.0) if (x3_to_categorical) { x3_negatively_correlated_with_y <- as.integer(x3_negatively_correlated_with_y / 0.01) categorical_features <- "feature_3" } else { categorical_features <- NULL } X <- matrix( data = c( x1_positively_correlated_with_y , x2_negatively_correlated_with_y , x3_negatively_correlated_with_y ) , ncol = 3L ) zs <- rnorm(n = n_samples, mean = 0.0, sd = 0.01) scales <- 10.0 * (runif(n = 6L, min = 0.0, max = 1.0) + 0.5) y <- ( scales[1L] * x1_positively_correlated_with_y + sin(scales[2L] * pi * x1_positively_correlated_with_y) - scales[3L] * x2_negatively_correlated_with_y - cos(scales[4L] * pi * x2_negatively_correlated_with_y) - scales[5L] * x3_negatively_correlated_with_y - cos(scales[6L] * pi * x3_negatively_correlated_with_y) + zs ) return(lgb.Dataset( data = X , label = y , categorical_feature = categorical_features , free_raw_data = FALSE , colnames = c("feature_1", "feature_2", "feature_3") , params = list(num_threads = .LGB_MAX_THREADS) )) } .is_increasing <- function(y) { return(all(diff(y) >= 0.0)) } .is_decreasing <- function(y) { return(all(diff(y) <= 0.0)) } .is_non_monotone <- function(y) { return(any(diff(y) < 0.0) & any(diff(y) > 0.0)) } # R equivalent of numpy.linspace() .linspace <- function(start_val, stop_val, num) { weights <- (seq_len(num) - 1L) / (num - 1L) return(start_val + weights * (stop_val - start_val)) } .is_correctly_constrained <- function(learner, x3_to_categorical) { iterations <- 10L n <- 1000L variable_x <- .linspace(0L, 1L, n) fixed_xs_values <- .linspace(0L, 1L, n) for (i in seq_len(iterations)) { fixed_x <- fixed_xs_values[i] * rep(1.0, n) monotonically_increasing_x <- matrix( data = c(variable_x, fixed_x, fixed_x) , ncol = 3L ) monotonically_increasing_y <- predict( learner , monotonically_increasing_x ) monotonically_decreasing_x <- matrix( data = c(fixed_x, variable_x, fixed_x) , ncol = 3L ) monotonically_decreasing_y <- predict( learner , monotonically_decreasing_x ) if (x3_to_categorical) { non_monotone_data <- c( fixed_x , fixed_x , as.integer(variable_x / 0.01) ) } else { non_monotone_data <- c(fixed_x, fixed_x, variable_x) } non_monotone_x <- matrix( data = non_monotone_data , ncol = 3L ) non_monotone_y <- predict( learner , non_monotone_x ) if (!(.is_increasing(monotonically_increasing_y) && .is_decreasing(monotonically_decreasing_y) && .is_non_monotone(non_monotone_y) )) { return(FALSE) } } return(TRUE) } for (x3_to_categorical in c(TRUE, FALSE)) { set.seed(708L) dtrain <- .generate_trainset_for_monotone_constraints_tests( x3_to_categorical = x3_to_categorical ) for (monotone_constraints_method in c("basic", "intermediate", "advanced")) { test_msg <- paste0( "lgb.train() supports monotone constraints (" , "categoricals=" , x3_to_categorical , ", method=" , monotone_constraints_method , ")" ) test_that(test_msg, { params <- list( min_data = 20L , num_leaves = 20L , monotone_constraints = c(1L, -1L, 0L) , monotone_constraints_method = monotone_constraints_method , use_missing = FALSE , verbose = .LGB_VERBOSITY , num_threads = .LGB_MAX_THREADS ) constrained_model <- lgb.train( params = params , data = dtrain , obj = "regression_l2" , nrounds = 100L ) expect_true({ .is_correctly_constrained( learner = constrained_model , x3_to_categorical = x3_to_categorical ) }) }) } } test_that("lightgbm() accepts objective as function argument and under params", { bst1 <- lightgbm( data = train$data , label = train$label , params = list(objective = "regression_l1", num_threads = .LGB_MAX_THREADS) , nrounds = 5L , verbose = .LGB_VERBOSITY ) expect_equal(bst1$params$objective, "regression_l1") model_txt_lines <- strsplit( x = bst1$save_model_to_string() , split = "\n" , fixed = TRUE )[[1L]] expect_true(any(model_txt_lines == "objective=regression_l1")) expect_false(any(model_txt_lines == "objective=regression_l2")) bst2 <- lightgbm( data = train$data , label = train$label , objective = "regression_l1" , nrounds = 5L , verbose = .LGB_VERBOSITY ) expect_equal(bst2$params$objective, "regression_l1") model_txt_lines <- strsplit( x = bst2$save_model_to_string() , split = "\n" , fixed = TRUE )[[1L]] expect_true(any(model_txt_lines == "objective=regression_l1")) expect_false(any(model_txt_lines == "objective=regression_l2")) }) test_that("lightgbm() prioritizes objective under params over objective as function argument", { bst1 <- lightgbm( data = train$data , label = train$label , objective = "regression" , params = list(objective = "regression_l1", num_threads = .LGB_MAX_THREADS) , nrounds = 5L , verbose = .LGB_VERBOSITY ) expect_equal(bst1$params$objective, "regression_l1") model_txt_lines <- strsplit( x = bst1$save_model_to_string() , split = "\n" , fixed = TRUE )[[1L]] expect_true(any(model_txt_lines == "objective=regression_l1")) expect_false(any(model_txt_lines == "objective=regression_l2")) bst2 <- lightgbm( data = train$data , label = train$label , objective = "regression" , params = list(loss = "regression_l1", num_threads = .LGB_MAX_THREADS) , nrounds = 5L , verbose = .LGB_VERBOSITY ) expect_equal(bst2$params$objective, "regression_l1") model_txt_lines <- strsplit( x = bst2$save_model_to_string() , split = "\n" , fixed = TRUE )[[1L]] expect_true(any(model_txt_lines == "objective=regression_l1")) expect_false(any(model_txt_lines == "objective=regression_l2")) }) test_that("lightgbm() accepts init_score as function argument", { bst1 <- lightgbm( data = train$data , label = train$label , objective = "binary" , nrounds = 5L , verbose = .LGB_VERBOSITY , params = list(num_threads = .LGB_MAX_THREADS) ) pred1 <- predict(bst1, train$data, type = "raw") bst2 <- lightgbm( data = train$data , label = train$label , init_score = pred1 , objective = "binary" , nrounds = 5L , verbose = .LGB_VERBOSITY , params = list(num_threads = .LGB_MAX_THREADS) ) pred2 <- predict(bst2, train$data, type = "raw") expect_true(any(pred1 != pred2)) }) test_that("lightgbm() defaults to 'regression' objective if objective not otherwise provided", { bst <- lightgbm( data = train$data , label = train$label , nrounds = 5L , verbose = .LGB_VERBOSITY , params = list(num_threads = .LGB_MAX_THREADS) ) expect_equal(bst$params$objective, "regression") model_txt_lines <- strsplit( x = bst$save_model_to_string() , split = "\n" , fixed = TRUE )[[1L]] expect_true(any(model_txt_lines == "objective=regression")) expect_false(any(model_txt_lines == "objective=regression_l1")) }) test_that("lightgbm() accepts 'num_threads' as either top-level argument or under params", { bst <- lightgbm( data = train$data , label = train$label , nrounds = 5L , verbose = .LGB_VERBOSITY , num_threads = 1L ) expect_equal(bst$params$num_threads, 1L) model_txt_lines <- strsplit( x = bst$save_model_to_string() , split = "\n" , fixed = TRUE )[[1L]] expect_true(any(grepl("[num_threads: 1]", model_txt_lines, fixed = TRUE))) bst <- lightgbm( data = train$data , label = train$label , nrounds = 5L , verbose = .LGB_VERBOSITY , params = list(num_threads = 1L) ) expect_equal(bst$params$num_threads, 1L) model_txt_lines <- strsplit( x = bst$save_model_to_string() , split = "\n" , fixed = TRUE )[[1L]] expect_true(any(grepl("[num_threads: 1]", model_txt_lines, fixed = TRUE))) bst <- lightgbm( data = train$data , label = train$label , nrounds = 5L , verbose = .LGB_VERBOSITY , num_threads = 10L , params = list(num_threads = 1L) ) expect_equal(bst$params$num_threads, 1L) model_txt_lines <- strsplit( x = bst$save_model_to_string() , split = "\n" , fixed = TRUE )[[1L]] expect_true(any(grepl("[num_threads: 1]", model_txt_lines, fixed = TRUE))) }) test_that("lightgbm() accepts 'weight' and 'weights'", { data(mtcars) X <- as.matrix(mtcars[, -1L]) y <- as.numeric(mtcars[, 1L]) w <- rep(1.0, nrow(X)) model <- lightgbm( X , y , weights = w , obj = "regression" , nrounds = 5L , verbose = .LGB_VERBOSITY , params = list( min_data_in_bin = 1L , min_data_in_leaf = 1L , num_threads = .LGB_MAX_THREADS ) ) expect_equal(model$.__enclos_env__$private$train_set$get_field("weight"), w) # Avoid a bad CRAN check due to partial argument matches lgb_args <- list( X , y , weight = w , obj = "regression" , nrounds = 5L , verbose = -1L ) model <- do.call(lightgbm, lgb_args) expect_equal(model$.__enclos_env__$private$train_set$get_field("weight"), w) }) .assert_has_expected_logs <- function(log_txt, lgb_info, lgb_warn, early_stopping, valid_eval_msg) { expect_identical( object = any(grepl("[LightGBM] [Info]", log_txt, fixed = TRUE)) , expected = lgb_info ) expect_identical( object = any(grepl("[LightGBM] [Warning]", log_txt, fixed = TRUE)) , expected = lgb_warn ) expect_identical( object = any(grepl("Will train until there is no improvement in 5 rounds", log_txt, fixed = TRUE)) , expected = early_stopping ) expect_identical( object = any(grepl("Did not meet early stopping", log_txt, fixed = TRUE)) , expected = early_stopping ) expect_identical( object = any(grepl("valid's auc\\:[0-9]+", log_txt)) , expected = valid_eval_msg ) } .assert_has_expected_record_evals <- function(fitted_model) { record_evals <- fitted_model$record_evals expect_equal(record_evals$start_iter, 1L) if (inherits(fitted_model, "lgb.CVBooster")) { expected_valid_auc <- c(0.979056, 0.9844697, 0.9900813, 0.9908026, 0.9935588) } else { expected_valid_auc <- c(0.9805752, 0.9805752, 0.9934957, 0.9934957, 0.9949372) } expect_equal( object = unlist(record_evals[["valid"]][["auc"]][["eval"]]) , expected = expected_valid_auc , tolerance = .LGB_NUMERIC_TOLERANCE ) expect_named(record_evals, c("start_iter", "valid"), ignore.order = TRUE, ignore.case = FALSE) expect_equal(record_evals[["valid"]][["auc"]][["eval_err"]], list()) } .train_for_verbosity_test <- function(train_function, verbose_kwarg, verbose_param) { set.seed(708L) nrounds <- 5L params <- list( num_leaves = 5L , objective = "binary" , metric = "auc" , early_stopping_round = nrounds , num_threads = .LGB_MAX_THREADS # include a nonsense parameter just to trigger a WARN-level log , nonsense_param = 1.0 ) if (!is.null(verbose_param)) { params[["verbose"]] <- verbose_param } train_kwargs <- list( params = params , nrounds = nrounds ) if (!is.null(verbose_kwarg)) { train_kwargs[["verbose"]] <- verbose_kwarg } function_name <- deparse(substitute(train_function)) if (function_name == "lgb.train") { train_kwargs[["data"]] <- lgb.Dataset( data = train$data , label = train$label , params = list(num_threads = .LGB_MAX_THREADS) ) train_kwargs[["valids"]] <- list( "valid" = lgb.Dataset(data = test$data, label = test$label) ) } else if (function_name == "lightgbm") { train_kwargs[["data"]] <- train$data train_kwargs[["label"]] <- train$label train_kwargs[["valids"]] <- list( "valid" = lgb.Dataset(data = test$data, label = test$label) ) } else if (function_name == "lgb.cv") { train_kwargs[["data"]] <- lgb.Dataset( data = train$data , label = train$label ) train_kwargs[["nfold"]] <- 3L train_kwargs[["showsd"]] <- FALSE } log_txt <- capture.output({ bst <- do.call( what = train_function , args = train_kwargs ) }) return(list(booster = bst, logs = log_txt)) } test_that("lgb.train() only prints eval metrics when expected to", { # regardless of value passed to keyword argument 'verbose', value in params # should take precedence for (verbose_keyword_arg in c(-5L, -1L, 0L, 1L, 5L)) { # (verbose = -1) should not be any logs, should be record evals out <- .train_for_verbosity_test( train_function = lgb.train , verbose_kwarg = verbose_keyword_arg , verbose_param = -1L ) .assert_has_expected_logs( log_txt = out[["logs"]] , lgb_info = FALSE , lgb_warn = FALSE , early_stopping = FALSE , valid_eval_msg = FALSE ) .assert_has_expected_record_evals( fitted_model = out[["booster"]] ) # (verbose = 0) should be only WARN-level LightGBM logs out <- .train_for_verbosity_test( train_function = lgb.train , verbose_kwarg = verbose_keyword_arg , verbose_param = 0L ) .assert_has_expected_logs( log_txt = out[["logs"]] , lgb_info = FALSE , lgb_warn = TRUE , early_stopping = FALSE , valid_eval_msg = FALSE ) .assert_has_expected_record_evals( fitted_model = out[["booster"]] ) # (verbose > 0) should be INFO- and WARN-level LightGBM logs, and record eval messages out <- .train_for_verbosity_test( train_function = lgb.train , verbose_kwarg = verbose_keyword_arg , verbose_param = 1L ) .assert_has_expected_logs( log_txt = out[["logs"]] , lgb_info = TRUE , lgb_warn = TRUE , early_stopping = TRUE , valid_eval_msg = TRUE ) .assert_has_expected_record_evals( fitted_model = out[["booster"]] ) } # if verbosity isn't specified in `params`, changing keyword argument `verbose` should # alter what messages are printed # (verbose = -1) should not be any logs, should be record evals out <- .train_for_verbosity_test( train_function = lgb.train , verbose_kwarg = -1L , verbose_param = NULL ) .assert_has_expected_logs( log_txt = out[["logs"]] , lgb_info = FALSE , lgb_warn = FALSE , early_stopping = FALSE , valid_eval_msg = FALSE ) .assert_has_expected_record_evals( fitted_model = out[["booster"]] ) # (verbose = 0) should be only WARN-level LightGBM logs out <- .train_for_verbosity_test( train_function = lgb.train , verbose_kwarg = 0L , verbose_param = NULL ) .assert_has_expected_logs( log_txt = out[["logs"]] , lgb_info = FALSE , lgb_warn = TRUE , early_stopping = FALSE , valid_eval_msg = FALSE ) .assert_has_expected_record_evals( fitted_model = out[["booster"]] ) # (verbose > 0) should be INFO- and WARN-level LightGBM logs, and record eval messages out <- .train_for_verbosity_test( train_function = lgb.train , verbose_kwarg = 1L , verbose_param = NULL ) .assert_has_expected_logs( log_txt = out[["logs"]] , lgb_info = TRUE , lgb_warn = TRUE , early_stopping = TRUE , valid_eval_msg = TRUE ) .assert_has_expected_record_evals( fitted_model = out[["booster"]] ) }) test_that("lightgbm() only prints eval metrics when expected to", { # regardless of value passed to keyword argument 'verbose', value in params # should take precedence for (verbose_keyword_arg in c(-5L, -1L, 0L, 1L, 5L)) { # (verbose = -1) should not be any logs, train should not be in valids out <- .train_for_verbosity_test( train_function = lightgbm , verbose_kwarg = verbose_keyword_arg , verbose_param = -1L ) .assert_has_expected_logs( log_txt = out[["logs"]] , lgb_info = FALSE , lgb_warn = FALSE , early_stopping = FALSE , valid_eval_msg = FALSE ) .assert_has_expected_record_evals( fitted_model = out[["booster"]] ) # (verbose = 0) should be only WARN-level LightGBM logs, train should not be in valids out <- .train_for_verbosity_test( train_function = lightgbm , verbose_kwarg = verbose_keyword_arg , verbose_param = 0L ) .assert_has_expected_logs( log_txt = out[["logs"]] , lgb_info = FALSE , lgb_warn = TRUE , early_stopping = FALSE , valid_eval_msg = FALSE ) .assert_has_expected_record_evals( fitted_model = out[["booster"]] ) # (verbose > 0) should be INFO- and WARN-level LightGBM logs, and record eval messages, and # train should be in valids out <- .train_for_verbosity_test( train_function = lightgbm , verbose_kwarg = verbose_keyword_arg , verbose_param = 1L ) .assert_has_expected_logs( log_txt = out[["logs"]] , lgb_info = TRUE , lgb_warn = TRUE , early_stopping = TRUE , valid_eval_msg = TRUE ) .assert_has_expected_record_evals( fitted_model = out[["booster"]] ) } # if verbosity isn't specified in `params`, changing keyword argument `verbose` should # alter what messages are printed # (verbose = -1) should not be any logs, train should not be in valids out <- .train_for_verbosity_test( train_function = lightgbm , verbose_kwarg = -1L , verbose_param = NULL ) .assert_has_expected_logs( log_txt = out[["logs"]] , lgb_info = FALSE , lgb_warn = FALSE , early_stopping = FALSE , valid_eval_msg = FALSE ) .assert_has_expected_record_evals( fitted_model = out[["booster"]] ) # (verbose = 0) should be only WARN-level LightGBM logs, train should not be in valids out <- .train_for_verbosity_test( train_function = lightgbm , verbose_kwarg = 0L , verbose_param = NULL ) .assert_has_expected_logs( log_txt = out[["logs"]] , lgb_info = FALSE , lgb_warn = TRUE , early_stopping = FALSE , valid_eval_msg = FALSE ) .assert_has_expected_record_evals( fitted_model = out[["booster"]] ) # (verbose > 0) should be INFO- and WARN-level LightGBM logs, and record eval messages, and # train should be in valids out <- .train_for_verbosity_test( train_function = lightgbm , verbose_kwarg = 1L , verbose_param = NULL ) .assert_has_expected_logs( log_txt = out[["logs"]] , lgb_info = TRUE , lgb_warn = TRUE , early_stopping = TRUE , valid_eval_msg = TRUE ) .assert_has_expected_record_evals( fitted_model = out[["booster"]] ) }) test_that("lgb.cv() only prints eval metrics when expected to", { # regardless of value passed to keyword argument 'verbose', value in params # should take precedence for (verbose_keyword_arg in c(-5L, -1L, 0L, 1L, 5L)) { # (verbose = -1) should not be any logs, should be record evals out <- .train_for_verbosity_test( verbose_kwarg = verbose_keyword_arg , verbose_param = -1L , train_function = lgb.cv ) .assert_has_expected_logs( log_txt = out[["logs"]] , lgb_info = FALSE , lgb_warn = FALSE , early_stopping = FALSE , valid_eval_msg = FALSE ) .assert_has_expected_record_evals( fitted_model = out[["booster"]] ) # (verbose = 0) should be only WARN-level LightGBM logs out <- .train_for_verbosity_test( verbose_kwarg = verbose_keyword_arg , verbose_param = 0L , train_function = lgb.cv ) .assert_has_expected_logs( log_txt = out[["logs"]] , lgb_info = FALSE , lgb_warn = TRUE , early_stopping = FALSE , valid_eval_msg = FALSE ) .assert_has_expected_record_evals( fitted_model = out[["booster"]] ) # (verbose > 0) should be INFO- and WARN-level LightGBM logs, and record eval messages out <- .train_for_verbosity_test( verbose_kwarg = verbose_keyword_arg , verbose_param = 1L , train_function = lgb.cv ) .assert_has_expected_logs( log_txt = out[["logs"]] , lgb_info = TRUE , lgb_warn = TRUE , early_stopping = TRUE , valid_eval_msg = TRUE ) .assert_has_expected_record_evals( fitted_model = out[["booster"]] ) } # if verbosity isn't specified in `params`, changing keyword argument `verbose` should # alter what messages are printed # (verbose = -1) should not be any logs, should be record evals out <- .train_for_verbosity_test( verbose_kwarg = verbose_keyword_arg , verbose_param = -1L , train_function = lgb.cv ) .assert_has_expected_logs( log_txt = out[["logs"]] , lgb_info = FALSE , lgb_warn = FALSE , early_stopping = FALSE , valid_eval_msg = FALSE ) .assert_has_expected_record_evals( fitted_model = out[["booster"]] ) # (verbose = 0) should be only WARN-level LightGBM logs out <- .train_for_verbosity_test( verbose_kwarg = verbose_keyword_arg , verbose_param = 0L , train_function = lgb.cv ) .assert_has_expected_logs( log_txt = out[["logs"]] , lgb_info = FALSE , lgb_warn = TRUE , early_stopping = FALSE , valid_eval_msg = FALSE ) .assert_has_expected_record_evals( fitted_model = out[["booster"]] ) # (verbose > 0) should be INFO- and WARN-level LightGBM logs, and record eval messages out <- .train_for_verbosity_test( verbose_kwarg = verbose_keyword_arg , verbose_param = 1L , train_function = lgb.cv ) .assert_has_expected_logs( log_txt = out[["logs"]] , lgb_info = TRUE , lgb_warn = TRUE , early_stopping = TRUE , valid_eval_msg = TRUE ) .assert_has_expected_record_evals( fitted_model = out[["booster"]] ) }) test_that("lightgbm() changes objective='auto' appropriately", { # Regression data("mtcars") y <- mtcars$mpg x <- as.matrix(mtcars[, -1L]) model <- lightgbm(x, y, objective = "auto", verbose = .LGB_VERBOSITY, nrounds = 5L, num_threads = .LGB_MAX_THREADS) expect_equal(model$params$objective, "regression") model_txt_lines <- strsplit( x = model$save_model_to_string() , split = "\n" , fixed = TRUE )[[1L]] expect_true(any(grepl("objective=regression", model_txt_lines, fixed = TRUE))) expect_false(any(grepl("objective=regression_l1", model_txt_lines, fixed = TRUE))) # Binary classification x <- train$data y <- factor(train$label) model <- lightgbm(x, y, objective = "auto", verbose = .LGB_VERBOSITY, nrounds = 5L, num_threads = .LGB_MAX_THREADS) expect_equal(model$params$objective, "binary") model_txt_lines <- strsplit( x = model$save_model_to_string() , split = "\n" , fixed = TRUE )[[1L]] expect_true(any(grepl("objective=binary", model_txt_lines, fixed = TRUE))) # Multi-class classification data("iris") y <- factor(iris$Species) x <- as.matrix(iris[, -5L]) model <- lightgbm(x, y, objective = "auto", verbose = .LGB_VERBOSITY, nrounds = 5L, num_threads = .LGB_MAX_THREADS) expect_equal(model$params$objective, "multiclass") expect_equal(model$params$num_class, 3L) model_txt_lines <- strsplit( x = model$save_model_to_string() , split = "\n" , fixed = TRUE )[[1L]] expect_true(any(grepl("objective=multiclass", model_txt_lines, fixed = TRUE))) }) test_that("lightgbm() determines number of classes for non-default multiclass objectives", { data("iris") y <- factor(iris$Species) x <- as.matrix(iris[, -5L]) model <- lightgbm( x , y , objective = "multiclassova" , verbose = .LGB_VERBOSITY , nrounds = 5L , num_threads = .LGB_MAX_THREADS ) expect_equal(model$params$objective, "multiclassova") expect_equal(model$params$num_class, 3L) model_txt_lines <- strsplit( x = model$save_model_to_string() , split = "\n" , fixed = TRUE )[[1L]] expect_true(any(grepl("objective=multiclassova", model_txt_lines, fixed = TRUE))) }) test_that("lightgbm() doesn't accept binary classification with non-binary factors", { data("iris") y <- factor(iris$Species) x <- as.matrix(iris[, -5L]) expect_error({ lightgbm(x, y, objective = "binary", verbose = .LGB_VERBOSITY, nrounds = 5L, num_threads = .LGB_MAX_THREADS) }, regexp = "Factors with >2 levels as labels only allowed for multi-class objectives") }) test_that("lightgbm() doesn't accept multi-class classification with binary factors", { data("iris") y <- as.character(iris$Species) y[y == "setosa"] <- "versicolor" y <- factor(y) x <- as.matrix(iris[, -5L]) expect_error({ lightgbm(x, y, objective = "multiclass", verbose = .LGB_VERBOSITY, nrounds = 5L, num_threads = .LGB_MAX_THREADS) }, regexp = "Two-level factors as labels only allowed for objective='binary'") }) test_that("lightgbm() model predictions retain factor levels for multiclass classification", { data("iris") y <- factor(iris$Species) x <- as.matrix(iris[, -5L]) model <- lightgbm(x, y, objective = "auto", verbose = .LGB_VERBOSITY, nrounds = 5L, num_threads = .LGB_MAX_THREADS) pred <- predict(model, x, type = "class") expect_true(is.factor(pred)) expect_equal(levels(pred), levels(y)) pred <- predict(model, x, type = "response") expect_equal(colnames(pred), levels(y)) pred <- predict(model, x, type = "raw") expect_equal(colnames(pred), levels(y)) }) test_that("lightgbm() model predictions retain factor levels for binary classification", { data("iris") y <- as.character(iris$Species) y[y == "setosa"] <- "versicolor" y <- factor(y) x <- as.matrix(iris[, -5L]) model <- lightgbm(x, y, objective = "auto", verbose = .LGB_VERBOSITY, nrounds = 5L, num_threads = .LGB_MAX_THREADS) pred <- predict(model, x, type = "class") expect_true(is.factor(pred)) expect_equal(levels(pred), levels(y)) pred <- predict(model, x, type = "response") expect_true(is.vector(pred)) expect_true(is.numeric(pred)) expect_false(any(pred %in% y)) pred <- predict(model, x, type = "raw") expect_true(is.vector(pred)) expect_true(is.numeric(pred)) expect_false(any(pred %in% y)) }) test_that("lightgbm() accepts named categorical_features", { data(mtcars) y <- mtcars$mpg x <- as.matrix(mtcars[, -1L]) model <- lightgbm( x , y , categorical_feature = "cyl" , verbose = .LGB_VERBOSITY , nrounds = 5L , num_threads = .LGB_MAX_THREADS ) expect_true(length(model$params$categorical_feature) > 0L) }) test_that("lightgbm() correctly sets objective when passing lgb.Dataset as input", { data(mtcars) y <- mtcars$mpg x <- as.matrix(mtcars[, -1L]) ds <- lgb.Dataset(x, label = y) model <- lightgbm( ds , objective = "auto" , verbose = .LGB_VERBOSITY , nrounds = 5L , num_threads = .LGB_MAX_THREADS ) expect_equal(model$params$objective, "regression") }) test_that("Evaluation metrics aren't printed as a single-element vector", { log_txt <- capture_output({ data(mtcars) y <- mtcars$mpg x <- as.matrix(mtcars[, -1L]) cv_result <- lgb.cv( data = lgb.Dataset(x, label = y) , params = list( objective = "regression" , metric = "l2" , min_data_in_leaf = 5L , max_depth = 3L , num_threads = .LGB_MAX_THREADS ) , nrounds = 2L , nfold = 3L , verbose = 1L , eval_train_metric = TRUE ) }) expect_false(grepl("[1] \"[1]", log_txt, fixed = TRUE)) }) ================================================ FILE: R-package/tests/testthat/test_custom_objective.R ================================================ data(agaricus.train, package = "lightgbm") data(agaricus.test, package = "lightgbm") dtrain <- lgb.Dataset(agaricus.train$data, label = agaricus.train$label) dtest <- lgb.Dataset(agaricus.test$data, label = agaricus.test$label) watchlist <- list(eval = dtest, train = dtrain) logregobj <- function(preds, dtrain) { labels <- get_field(dtrain, "label") preds <- 1.0 / (1.0 + exp(-preds)) grad <- preds - labels hess <- preds * (1.0 - preds) return(list(grad = grad, hess = hess)) } # User-defined evaluation function returns a pair (metric_name, result, higher_better) # NOTE: when you do customized loss function, the default prediction value is margin # This may make built-in evaluation metric calculate wrong results # Keep this in mind when you use the customization, and maybe you need write customized evaluation function evalerror <- function(preds, dtrain) { labels <- get_field(dtrain, "label") preds <- 1.0 / (1.0 + exp(-preds)) err <- as.numeric(sum(labels != (preds > 0.5))) / length(labels) return(list( name = "error" , value = err , higher_better = FALSE )) } param <- list( num_leaves = 8L , learning_rate = 1.0 , objective = logregobj , metric = "auc" , verbose = .LGB_VERBOSITY , num_threads = .LGB_MAX_THREADS ) num_round <- 10L test_that("custom objective works", { bst <- lgb.train(param, dtrain, num_round, watchlist, eval = evalerror) expect_false(is.null(bst$record_evals)) }) test_that("using a custom objective, custom eval, and no other metrics works", { set.seed(708L) bst <- lgb.train( params = list( num_leaves = 8L , learning_rate = 1.0 , verbose = .LGB_VERBOSITY , num_threads = .LGB_MAX_THREADS ) , data = dtrain , nrounds = 4L , valids = watchlist , obj = logregobj , eval = evalerror ) expect_false(is.null(bst$record_evals)) expect_equal(bst$best_iter, 4L) expect_true(abs(bst$best_score - 0.000621) < .LGB_NUMERIC_TOLERANCE) eval_results <- bst$eval_valid(feval = evalerror)[[1L]] expect_true(eval_results[["data_name"]] == "eval") expect_true(abs(eval_results[["value"]] - 0.0006207325) < .LGB_NUMERIC_TOLERANCE) expect_true(eval_results[["name"]] == "error") expect_false(eval_results[["higher_better"]]) }) test_that("using a custom objective that returns wrong shape grad or hess raises an informative error", { bad_grad <- function(preds, dtrain) { return(list(grad = numeric(0L), hess = rep(1.0, length(preds)))) } bad_hess <- function(preds, dtrain) { return(list(grad = rep(1.0, length(preds)), hess = numeric(0L))) } params <- list(num_leaves = 3L, verbose = .LGB_VERBOSITY) expect_error({ lgb.train(params = params, data = dtrain, obj = bad_grad) }, sprintf("Expected custom objective function to return grad with length %d, got 0.", nrow(dtrain))) expect_error({ lgb.train(params = params, data = dtrain, obj = bad_hess) }, sprintf("Expected custom objective function to return hess with length %d, got 0.", nrow(dtrain))) }) ================================================ FILE: R-package/tests/testthat/test_dataset.R ================================================ data(agaricus.train, package = "lightgbm") train_data <- agaricus.train$data[seq_len(1000L), ] train_label <- agaricus.train$label[seq_len(1000L)] data(agaricus.test, package = "lightgbm") test_data <- agaricus.test$data[1L:100L, ] test_label <- agaricus.test$label[1L:100L] test_that("lgb.Dataset: basic construction, saving, loading", { # from sparse matrix dtest1 <- lgb.Dataset( test_data , label = test_label , params = list( verbose = .LGB_VERBOSITY ) ) # from dense matrix dtest2 <- lgb.Dataset(as.matrix(test_data), label = test_label) expect_equal(get_field(dtest1, "label"), get_field(dtest2, "label")) # save to a local file tmp_file <- tempfile("lgb.Dataset_") lgb.Dataset.save(dtest1, tmp_file) # read from a local file dtest3 <- lgb.Dataset( tmp_file , params = list( verbose = .LGB_VERBOSITY ) ) lgb.Dataset.construct(dtest3) unlink(tmp_file) expect_equal(get_field(dtest1, "label"), get_field(dtest3, "label")) }) test_that("lgb.Dataset: get_field & set_field", { dtest <- lgb.Dataset(test_data) dtest$construct() set_field(dtest, "label", test_label) labels <- get_field(dtest, "label") expect_equal(test_label, get_field(dtest, "label")) expect_true(length(get_field(dtest, "weight")) == 0L) expect_true(length(get_field(dtest, "init_score")) == 0L) # any other label should error expect_error( set_field(dtest, "asdf", test_label) , regexp = "Dataset$set_field(): field_name must be one of the following: 'label', 'weight', 'init_score', 'group'" # nolint: line_length. , fixed = TRUE ) }) test_that("lgb.Dataset: slice, dim", { dtest <- lgb.Dataset(test_data, label = test_label) lgb.Dataset.construct(dtest) expect_equal(dim(dtest), dim(test_data)) dsub1 <- lgb.slice.Dataset(dtest, seq_len(42L)) lgb.Dataset.construct(dsub1) expect_equal(nrow(dsub1), 42L) expect_equal(ncol(dsub1), ncol(test_data)) }) test_that("Dataset$set_reference() on a constructed Dataset fails if raw data has been freed", { dtrain <- lgb.Dataset(train_data, label = train_label) dtrain$construct() dtest <- lgb.Dataset(test_data, label = test_label) dtest$construct() expect_error({ dtest$set_reference(dtrain) }, regexp = "cannot set reference after freeing raw data") }) test_that("Dataset$set_reference() fails if reference is not a Dataset", { dtrain <- lgb.Dataset( train_data , label = train_label , free_raw_data = FALSE ) expect_error({ dtrain$set_reference(reference = data.frame(x = rnorm(10L))) }, regexp = "Can only use lgb.Dataset as a reference") # passing NULL when the Dataset already has a reference raises an error dtest <- lgb.Dataset( test_data , label = test_label , free_raw_data = FALSE ) dtrain$set_reference(dtest) expect_error({ dtrain$set_reference(reference = NULL) }, regexp = "Can only use lgb.Dataset as a reference") }) test_that("Dataset$set_reference() setting reference to the same Dataset has no side effects", { dtrain <- lgb.Dataset( train_data , label = train_label , free_raw_data = FALSE , categorical_feature = c(2L, 3L) ) dtrain$construct() cat_features_before <- dtrain$.__enclos_env__$private$categorical_feature colnames_before <- dtrain$get_colnames() predictor_before <- dtrain$.__enclos_env__$private$predictor dtrain$set_reference(dtrain) expect_identical( cat_features_before , dtrain$.__enclos_env__$private$categorical_feature ) expect_identical( colnames_before , dtrain$get_colnames() ) expect_identical( predictor_before , dtrain$.__enclos_env__$private$predictor ) }) test_that("Dataset$set_reference() updates categorical_feature, colnames, and predictor", { dtrain <- lgb.Dataset( train_data , label = train_label , free_raw_data = FALSE , categorical_feature = c(2L, 3L) ) dtrain$construct() bst <- Booster$new( train_set = dtrain , params = list(verbose = -1L, num_threads = .LGB_MAX_THREADS) ) dtrain$.__enclos_env__$private$predictor <- bst$to_predictor() test_original_feature_names <- paste0("feature_col_", seq_len(ncol(test_data))) dtest <- lgb.Dataset( test_data , label = test_label , free_raw_data = FALSE , colnames = test_original_feature_names ) dtest$construct() # at this point, dtest should not have categorical_feature expect_null(dtest$.__enclos_env__$private$predictor) expect_null(dtest$.__enclos_env__$private$categorical_feature) expect_identical( dtest$get_colnames() , test_original_feature_names ) dtest$set_reference(dtrain) # after setting reference to dtrain, those attributes should have dtrain's values expect_true(methods::is( dtest$.__enclos_env__$private$predictor , "lgb.Predictor" )) expect_identical( dtest$.__enclos_env__$private$predictor$.__enclos_env__$private$handle , dtrain$.__enclos_env__$private$predictor$.__enclos_env__$private$handle ) expect_identical( dtest$.__enclos_env__$private$categorical_feature , dtrain$.__enclos_env__$private$categorical_feature ) expect_identical( dtest$get_colnames() , dtrain$get_colnames() ) expect_false( identical(dtest$get_colnames(), test_original_feature_names) ) }) test_that("lgb.Dataset: colnames", { dtest <- lgb.Dataset(test_data, label = test_label) expect_equal(colnames(dtest), colnames(test_data)) lgb.Dataset.construct(dtest) expect_equal(colnames(dtest), colnames(test_data)) expect_error({ colnames(dtest) <- "asdf" }, regexp = "can't assign '1' colnames to an lgb.Dataset with '126' columns") new_names <- make.names(seq_len(ncol(test_data))) expect_silent({ colnames(dtest) <- new_names }) expect_equal(colnames(dtest), new_names) }) test_that("lgb.Dataset: nrow is correct for a very sparse matrix", { nr <- 1000L x <- Matrix::rsparsematrix(nr, 100L, density = 0.0005) # we want it very sparse, so that last rows are empty expect_lt(max(x@i), nr) dtest <- lgb.Dataset(x) expect_equal(dim(dtest), dim(x)) }) test_that("lgb.Dataset: Dataset should be able to construct from matrix and return non-null handle", { rawData <- matrix(runif(1000L), ncol = 10L) ref_handle <- NULL handle <- .Call( LGBM_DatasetCreateFromMat_R , rawData , nrow(rawData) , ncol(rawData) , lightgbm:::.params2str(params = list()) , ref_handle ) expect_true(methods::is(handle, "externalptr")) expect_false(is.null(handle)) .Call(LGBM_DatasetFree_R, handle) handle <- NULL }) test_that("cpp errors should be raised as proper R errors", { testthat::skip_if( Sys.getenv("COMPILER", "") == "MSVC" , message = "Skipping on Visual Studio" ) data(agaricus.train, package = "lightgbm") train <- agaricus.train dtrain <- lgb.Dataset( train$data , label = train$label , init_score = seq_len(10L) ) expect_error({ capture.output({ dtrain$construct() }, type = "message") }, regexp = "Initial score size doesn't match data size") }) test_that("lgb.Dataset$set_field() should convert 'group' to integer", { ds <- lgb.Dataset( data = matrix(rnorm(100L), nrow = 50L, ncol = 2L) , label = sample(c(0L, 1L), size = 50L, replace = TRUE) ) ds$construct() current_group <- ds$get_field("group") expect_null(current_group) group_as_numeric <- rep(25.0, 2L) ds$set_field("group", group_as_numeric) expect_identical(ds$get_field("group"), as.integer(group_as_numeric)) }) test_that("lgb.Dataset should throw an error if 'reference' is provided but of the wrong format", { data(agaricus.test, package = "lightgbm") test_data <- agaricus.test$data[1L:100L, ] test_label <- agaricus.test$label[1L:100L] # Try to trick lgb.Dataset() into accepting bad input expect_error({ dtest <- lgb.Dataset( data = test_data , label = test_label , reference = data.frame(x = seq_len(10L), y = seq_len(10L)) ) }, regexp = "reference must be a") }) test_that("Dataset$new() should throw an error if 'predictor' is provided but of the wrong format", { data(agaricus.test, package = "lightgbm") test_data <- agaricus.test$data[1L:100L, ] test_label <- agaricus.test$label[1L:100L] expect_error({ dtest <- Dataset$new( data = test_data , label = test_label , predictor = data.frame(x = seq_len(10L), y = seq_len(10L)) ) }, regexp = "predictor must be a", fixed = TRUE) }) test_that("Dataset$get_params() successfully returns parameters if you passed them", { # note that this list uses one "main" parameter (feature_pre_filter) and one that # is an alias (is_sparse), to check that aliases are handled correctly params <- list( "feature_pre_filter" = TRUE , "is_sparse" = FALSE ) ds <- lgb.Dataset( test_data , label = test_label , params = params ) returned_params <- ds$get_params() expect_identical(class(returned_params), "list") expect_identical(length(params), length(returned_params)) expect_identical(sort(names(params)), sort(names(returned_params))) for (param_name in names(params)) { expect_identical(params[[param_name]], returned_params[[param_name]]) } }) test_that("Dataset$get_params() ignores irrelevant parameters", { params <- list( "feature_pre_filter" = TRUE , "is_sparse" = FALSE , "nonsense_parameter" = c(1.0, 2.0, 5.0) ) ds <- lgb.Dataset( test_data , label = test_label , params = params ) returned_params <- ds$get_params() expect_false("nonsense_parameter" %in% names(returned_params)) }) test_that("Dataset$update_parameters() does nothing for empty inputs", { ds <- lgb.Dataset( test_data , label = test_label ) initial_params <- ds$get_params() expect_identical(initial_params, list()) # update_params() should return "self" so it can be chained res <- ds$update_params( params = list() ) expect_true(.is_Dataset(res)) new_params <- ds$get_params() expect_identical(new_params, initial_params) }) test_that("Dataset$update_params() works correctly for recognized Dataset parameters", { ds <- lgb.Dataset( test_data , label = test_label ) initial_params <- ds$get_params() expect_identical(initial_params, list()) new_params <- list( "data_random_seed" = 708L , "enable_bundle" = FALSE ) res <- ds$update_params( params = new_params ) expect_true(.is_Dataset(res)) updated_params <- ds$get_params() for (param_name in names(new_params)) { expect_identical(new_params[[param_name]], updated_params[[param_name]]) } }) test_that("Dataset's finalizer should not fail on an already-finalized Dataset", { dtest <- lgb.Dataset( data = test_data , label = test_label ) expect_true(.is_null_handle(dtest$.__enclos_env__$private$handle)) dtest$construct() expect_false(.is_null_handle(dtest$.__enclos_env__$private$handle)) dtest$.__enclos_env__$private$finalize() expect_true(.is_null_handle(dtest$.__enclos_env__$private$handle)) # calling finalize() a second time shouldn't cause any issues dtest$.__enclos_env__$private$finalize() expect_true(.is_null_handle(dtest$.__enclos_env__$private$handle)) }) test_that("lgb.Dataset: should be able to run lgb.train() immediately after using lgb.Dataset() on a file", { dtest <- lgb.Dataset( data = test_data , label = test_label , params = list( verbose = .LGB_VERBOSITY ) ) tmp_file <- tempfile(pattern = "lgb.Dataset_") lgb.Dataset.save( dataset = dtest , fname = tmp_file ) # read from a local file dtest_read_in <- lgb.Dataset(data = tmp_file) param <- list( objective = "binary" , metric = "binary_logloss" , num_leaves = 5L , learning_rate = 1.0 , verbose = .LGB_VERBOSITY , num_threads = .LGB_MAX_THREADS ) # should be able to train right away bst <- lgb.train( params = param , data = dtest_read_in ) expect_true(.is_Booster(x = bst)) }) test_that("lgb.Dataset: should be able to run lgb.cv() immediately after using lgb.Dataset() on a file", { dtest <- lgb.Dataset( data = test_data , label = test_label , params = list( verbosity = .LGB_VERBOSITY ) ) tmp_file <- tempfile(pattern = "lgb.Dataset_") lgb.Dataset.save( dataset = dtest , fname = tmp_file ) # read from a local file dtest_read_in <- lgb.Dataset(data = tmp_file) param <- list( objective = "binary" , metric = "binary_logloss" , num_leaves = 5L , learning_rate = 1.0 , num_iterations = 5L , verbosity = .LGB_VERBOSITY , num_threads = .LGB_MAX_THREADS ) # should be able to train right away bst <- lgb.cv( params = param , data = dtest_read_in ) expect_true(methods::is(bst, "lgb.CVBooster")) }) test_that("lgb.Dataset: should be able to be used in lgb.cv() when constructed with categorical feature indices", { data("mtcars") y <- mtcars$mpg x <- as.matrix(mtcars[, -1L]) categorical_feature <- which(names(mtcars) %in% c("cyl", "vs", "am", "gear", "carb")) - 1L dtrain <- lgb.Dataset( data = x , label = y , categorical_feature = categorical_feature , free_raw_data = TRUE , params = list(num_threads = .LGB_MAX_THREADS) ) # constructing the Dataset frees the raw data dtrain$construct() params <- list( objective = "regression" , num_leaves = 2L , verbose = .LGB_VERBOSITY , num_threads = .LGB_MAX_THREADS ) # cv should reuse the same categorical features without checking the indices bst <- lgb.cv(params = params, data = dtrain, stratified = FALSE, nrounds = 1L) expect_equal( unlist(bst$boosters[[1L]]$booster$params$categorical_feature) , categorical_feature - 1L # 0-based ) }) test_that("lgb.Dataset: should be able to use and retrieve long feature names", { # set one feature to a value longer than the default buffer size used # in LGBM_DatasetGetFeatureNames_R feature_names <- names(iris) long_name <- strrep("a", 1000L) feature_names[1L] <- long_name names(iris) <- feature_names # check that feature name survived the trip from R to C++ and back dtrain <- lgb.Dataset( data = as.matrix(iris[, -5L]) , label = as.numeric(iris$Species) - 1L ) dtrain$construct() col_names <- dtrain$get_colnames() expect_equal(col_names[1L], long_name) expect_equal(nchar(col_names[1L]), 1000L) }) test_that("lgb.Dataset: should be able to create a Dataset from a text file with a header", { train_file <- tempfile(pattern = "train_", fileext = ".csv") write.table( data.frame(y = rnorm(100L), x1 = rnorm(100L), x2 = rnorm(100L)) , file = train_file , sep = "," , col.names = TRUE , row.names = FALSE , quote = FALSE ) dtrain <- lgb.Dataset( data = train_file , params = list( header = TRUE , verbosity = .LGB_VERBOSITY ) ) dtrain$construct() expect_identical(dtrain$get_colnames(), c("x1", "x2")) expect_identical(dtrain$get_params(), list(header = TRUE)) expect_identical(dtrain$dim(), c(100L, 2L)) }) test_that("lgb.Dataset: should be able to create a Dataset from a text file without a header", { train_file <- tempfile(pattern = "train_", fileext = ".csv") write.table( data.frame(y = rnorm(100L), x1 = rnorm(100L), x2 = rnorm(100L)) , file = train_file , sep = "," , col.names = FALSE , row.names = FALSE , quote = FALSE ) dtrain <- lgb.Dataset( data = train_file , params = list( header = FALSE , verbosity = .LGB_VERBOSITY ) ) dtrain$construct() expect_identical(dtrain$get_colnames(), c("Column_0", "Column_1")) expect_identical(dtrain$get_params(), list(header = FALSE)) expect_identical(dtrain$dim(), c(100L, 2L)) }) test_that("Dataset: method calls on a Dataset with a null handle should raise an informative error and not segfault", { data(agaricus.train, package = "lightgbm") train <- agaricus.train dtrain <- lgb.Dataset(train$data, label = train$label) dtrain$construct() dvalid <- dtrain$create_valid( data = train$data[seq_len(100L), ] , label = train$label[seq_len(100L)] ) dvalid$construct() tmp_file <- tempfile(fileext = ".rds") saveRDS(dtrain, tmp_file) rm(dtrain) dtrain <- readRDS(tmp_file) expect_error({ dtrain$construct() }, regexp = "Attempting to create a Dataset without any raw data") expect_error({ dtrain$dim() }, regexp = "cannot get dimensions before dataset has been constructed") expect_error({ dtrain$get_colnames() }, regexp = "cannot get column names before dataset has been constructed") expect_error({ dtrain$get_feature_num_bin(1L) }, regexp = "Cannot get number of bins in feature before constructing Dataset.") expect_error({ dtrain$save_binary(fname = tempfile(fileext = ".bin")) }, regexp = "Attempting to create a Dataset without any raw data") expect_error({ dtrain$set_categorical_feature(categorical_feature = 1L) }, regexp = "cannot set categorical feature after freeing raw data") expect_error({ dtrain$set_reference(reference = dvalid) }, regexp = "cannot set reference after freeing raw data") tmp_valid_file <- tempfile(fileext = ".rds") saveRDS(dvalid, tmp_valid_file) rm(dvalid) dvalid <- readRDS(tmp_valid_file) dtrain <- lgb.Dataset( train$data , label = train$label , free_raw_data = FALSE ) dtrain$construct() expect_error({ dtrain$set_reference(reference = dvalid) }, regexp = "cannot get column names before dataset has been constructed") }) test_that("lgb.Dataset$get_feature_num_bin() works", { raw_df <- data.frame( all_random = runif(100L) , two_vals = rep(c(1.0, 2.0), 50L) , three_vals = c(rep(c(0.0, 1.0, 2.0), 33L), 0.0) , two_vals_plus_missing = c(rep(c(1.0, 2.0), 49L), NA_real_, NA_real_) , all_zero = rep(0.0, 100L) , categorical = sample.int(2L, 100L, replace = TRUE) ) n_features <- ncol(raw_df) raw_mat <- data.matrix(raw_df) min_data_in_bin <- 2L ds <- lgb.Dataset( raw_mat , params = list(min_data_in_bin = min_data_in_bin) , categorical_feature = n_features ) ds$construct() expected_num_bins <- c( 100L %/% min_data_in_bin + 1L # extra bin for zero , 3L # 0, 1, 2 , 3L # 0, 1, 2 , 4L # 0, 1, 2 + NA , 0L # unused , 3L # 1, 2 + NA ) actual_num_bins <- sapply(1L:n_features, ds$get_feature_num_bin) expect_identical(actual_num_bins, expected_num_bins) # test using defined feature names bins_by_name <- sapply(colnames(raw_mat), ds$get_feature_num_bin) expect_identical(unname(bins_by_name), expected_num_bins) # test using default feature names no_names_mat <- raw_mat colnames(no_names_mat) <- NULL ds_no_names <- lgb.Dataset( no_names_mat , params = list(min_data_in_bin = min_data_in_bin) , categorical_feature = n_features ) ds_no_names$construct() default_names <- lapply( X = seq(1L, ncol(raw_mat)) , FUN = function(i) { sprintf("Column_%d", i - 1L) } ) bins_by_default_name <- sapply(default_names, ds_no_names$get_feature_num_bin) expect_identical(bins_by_default_name, expected_num_bins) }) test_that("lgb.Dataset can be constructed with categorical features and without colnames", { # check that dataset can be constructed raw_mat <- matrix(rep(c(0L, 1L), 50L), ncol = 1L) ds <- lgb.Dataset(raw_mat, categorical_feature = 1L)$construct() sparse_mat <- as(raw_mat, "dgCMatrix") ds2 <- lgb.Dataset(sparse_mat, categorical_feature = 1L)$construct() # check that the column names are the default ones expect_equal(ds$.__enclos_env__$private$colnames, "Column_0") expect_equal(ds2$.__enclos_env__$private$colnames, "Column_0") # check for error when index is greater than the number of columns expect_error({ lgb.Dataset(raw_mat, categorical_feature = 2L)$construct() }, regexp = "supplied a too large value in categorical_feature: 2 but only 1 features") }) test_that("lgb.Dataset.slice fails with a categorical feature index greater than the number of features", { data <- matrix(runif(100L), nrow = 50L, ncol = 2L) ds <- lgb.Dataset(data = data, categorical_feature = 3L) subset <- ds$slice(1L:20L) expect_error({ subset$construct() }, regexp = "supplied a too large value in categorical_feature: 3 but only 2 features") }) ================================================ FILE: R-package/tests/testthat/test_learning_to_rank.R ================================================ test_that("learning-to-rank with lgb.train() works as expected", { set.seed(708L) data(agaricus.train, package = "lightgbm") # just keep a few features,to generate an model with imperfect fit train <- agaricus.train train_data <- train$data[1L:6000L, 1L:20L] dtrain <- lgb.Dataset( train_data , label = train$label[1L:6000L] , group = rep(150L, 40L) ) ndcg_at <- "1,2,3" eval_names <- paste0("ndcg@", strsplit(ndcg_at, ",", fixed = TRUE)[[1L]]) params <- list( objective = "lambdarank" , metric = "ndcg" , ndcg_at = ndcg_at , lambdarank_truncation_level = 3L , learning_rate = 0.001 , verbose = .LGB_VERBOSITY , num_threads = .LGB_MAX_THREADS ) model <- lgb.train( params = params , data = dtrain , nrounds = 10L ) expect_true(.is_Booster(model)) dumped_model <- jsonlite::fromJSON( model$dump_model() ) expect_equal(dumped_model[["objective"]], "lambdarank") expect_equal(dumped_model[["max_feature_idx"]], ncol(train_data) - 1L) # check that evaluation results make sense (0.0 < nDCG < 1.0) eval_results <- model$eval_train() expect_equal(length(eval_results), length(eval_names)) for (result in eval_results) { expect_true(result[["value"]] > 0.0) expect_true(result[["value"]] < 1.0) expect_true(result[["higher_better"]]) expect_identical(result[["data_name"]], "training") } expect_identical( sapply( X = eval_results , FUN = function(x) { x$name } ) , eval_names ) expect_equal(eval_results[[1L]][["value"]], 0.775) if (!.LGB_ON_32_BIT_WINDOWS) { expect_true(abs(eval_results[[2L]][["value"]] - 0.745986) < .LGB_NUMERIC_TOLERANCE) expect_true(abs(eval_results[[3L]][["value"]] - 0.7351959) < .LGB_NUMERIC_TOLERANCE) } }) test_that("learning-to-rank with lgb.cv() works as expected", { testthat::skip_if( .LGB_ON_32_BIT_WINDOWS , message = "Skipping on 32-bit Windows" ) set.seed(708L) data(agaricus.train, package = "lightgbm") # just keep a few features,to generate an model with imperfect fit train <- agaricus.train train_data <- train$data[1L:6000L, 1L:20L] dtrain <- lgb.Dataset( train_data , label = train$label[1L:6000L] , group = rep(150L, 40L) ) ndcg_at <- "1,2,3" eval_names <- paste0("ndcg@", strsplit(ndcg_at, ",", fixed = TRUE)[[1L]]) params <- list( objective = "lambdarank" , metric = "ndcg" , ndcg_at = ndcg_at , lambdarank_truncation_level = 3L , label_gain = "0,1,3" , min_data = 1L , learning_rate = 0.01 , verbose = .LGB_VERBOSITY , num_threads = .LGB_MAX_THREADS ) nfold <- 4L nrounds <- 10L cv_bst <- lgb.cv( params = params , data = dtrain , nrounds = nrounds , nfold = nfold ) expect_true(methods::is(cv_bst, "lgb.CVBooster")) expect_equal(length(cv_bst$boosters), nfold) # "valid" should contain results for each metric eval_results <- cv_bst$record_evals[["valid"]] eval_names <- c("ndcg@1", "ndcg@2", "ndcg@3") expect_identical(names(eval_results), eval_names) # check that best score and iter make sense (0.0 < nDCG < 1.0) best_iter <- cv_bst$best_iter best_score <- cv_bst$best_score expect_true(best_iter > 0L) expect_true(best_iter <= nrounds) expect_true(best_score > 0.0) expect_true(best_score < 1.0) expect_true(abs(best_score - 0.75) < .LGB_NUMERIC_TOLERANCE) # best_score should be set for the first metric first_metric <- eval_names[[1L]] expect_equal(best_score, eval_results[[first_metric]][["eval"]][[best_iter]]) for (eval_name in eval_names) { results_for_this_metric <- eval_results[[eval_name]] # each set of metrics should have eval and eval_err expect_identical(names(results_for_this_metric), c("eval", "eval_err")) # there should be one "eval" and "eval_err" per round expect_equal(length(results_for_this_metric[["eval"]]), nrounds) expect_equal(length(results_for_this_metric[["eval_err"]]), nrounds) # check that evaluation results make sense (0.0 < nDCG < 1.0) all_evals <- unlist(results_for_this_metric[["eval"]]) expect_true(all(all_evals > 0.0 & all_evals < 1.0)) } # first and last value of each metric should be as expected ndcg1_values <- c(0.675, 0.725, 0.65, 0.725, 0.75, 0.725, 0.75, 0.725, 0.75, 0.75) expect_true(all(abs(unlist(eval_results[["ndcg@1"]][["eval"]]) - ndcg1_values) < .LGB_NUMERIC_TOLERANCE)) ndcg2_values <- c( 0.6556574, 0.6669721, 0.6306574, 0.6476294, 0.6629581, 0.6476294, 0.6629581, 0.6379581, 0.7113147, 0.6823008 ) expect_true(all(abs(unlist(eval_results[["ndcg@2"]][["eval"]]) - ndcg2_values) < .LGB_NUMERIC_TOLERANCE)) ndcg3_values <- c( 0.6484639, 0.6571238, 0.6469279, 0.6540516, 0.6481857, 0.6481857, 0.6481857, 0.6466496, 0.7027939, 0.6629898 ) expect_true(all(abs(unlist(eval_results[["ndcg@3"]][["eval"]]) - ndcg3_values) < .LGB_NUMERIC_TOLERANCE)) # check details of each booster for (bst in cv_bst$boosters) { dumped_model <- jsonlite::fromJSON( bst$booster$dump_model() ) expect_equal(dumped_model[["objective"]], "lambdarank") expect_equal(dumped_model[["max_feature_idx"]], ncol(train_data) - 1L) } }) ================================================ FILE: R-package/tests/testthat/test_lgb.Booster.R ================================================ test_that("Booster's finalizer should not fail", { X <- as.matrix(as.integer(iris[, "Species"]), ncol = 1L) y <- iris[["Sepal.Length"]] dtrain <- lgb.Dataset(X, label = y) bst <- lgb.train( data = dtrain , params = list( objective = "regression" , num_threads = .LGB_MAX_THREADS ) , verbose = .LGB_VERBOSITY , nrounds = 3L ) expect_true(.is_Booster(bst)) expect_false(.is_null_handle(bst$.__enclos_env__$private$handle)) bst$.__enclos_env__$private$finalize() expect_true(.is_null_handle(bst$.__enclos_env__$private$handle)) # calling finalize() a second time shouldn't cause any issues bst$.__enclos_env__$private$finalize() expect_true(.is_null_handle(bst$.__enclos_env__$private$handle)) }) test_that("lgb.get.eval.result() should throw an informative error if booster is not an lgb.Booster", { bad_inputs <- list( matrix(1.0:10.0, 2L, 5L) , TRUE , c("a", "b") , NA , 10L , lgb.Dataset( data = matrix(1.0:10.0, 2L, 5L) , params = list() ) ) for (bad_input in bad_inputs) { expect_error({ lgb.get.eval.result( booster = bad_input , data_name = "test" , eval_name = "l2" ) }, regexp = "Can only use", fixed = TRUE) } }) test_that("lgb.get.eval.result() should throw an informative error for incorrect data_name", { data(agaricus.train, package = "lightgbm") data(agaricus.test, package = "lightgbm") dtrain <- lgb.Dataset( agaricus.train$data , label = agaricus.train$label ) model <- lgb.train( params = list( objective = "regression" , metric = "l2" , min_data = 1L , learning_rate = 1.0 , verbose = .LGB_VERBOSITY , num_threads = .LGB_MAX_THREADS ) , data = dtrain , nrounds = 5L , valids = list( "test" = lgb.Dataset.create.valid( dtrain , agaricus.test$data , label = agaricus.test$label ) ) ) expect_error({ eval_results <- lgb.get.eval.result( booster = model , data_name = "testing" , eval_name = "l2" ) }, regexp = "Only the following datasets exist in record evals: [test]", fixed = TRUE) }) test_that("lgb.get.eval.result() should throw an informative error for incorrect eval_name", { data(agaricus.train, package = "lightgbm") data(agaricus.test, package = "lightgbm") dtrain <- lgb.Dataset( agaricus.train$data , label = agaricus.train$label ) model <- lgb.train( params = list( objective = "regression" , metric = "l2" , min_data = 1L , learning_rate = 1.0 , verbose = .LGB_VERBOSITY ) , data = dtrain , nrounds = 5L , valids = list( "test" = lgb.Dataset.create.valid( dtrain , agaricus.test$data , label = agaricus.test$label ) ) ) expect_error({ eval_results <- lgb.get.eval.result( booster = model , data_name = "test" , eval_name = "l1" ) }, regexp = "Only the following eval_names exist for dataset.*\\: \\[l2\\]", fixed = FALSE) }) test_that("lgb.load() gives the expected error messages given different incorrect inputs", { set.seed(708L) data(agaricus.train, package = "lightgbm") data(agaricus.test, package = "lightgbm") train <- agaricus.train test <- agaricus.test bst <- lightgbm( data = as.matrix(train$data) , label = train$label , params = list( objective = "binary" , num_leaves = 4L , learning_rate = 1.0 , verbose = .LGB_VERBOSITY ) , nrounds = 2L ) # you have to give model_str or filename expect_error({ lgb.load() }, regexp = "either filename or model_str must be given") expect_error({ lgb.load(filename = NULL, model_str = NULL) }, regexp = "either filename or model_str must be given") # if given, filename should be a string that points to an existing file model_file <- tempfile(fileext = ".model") expect_error({ lgb.load(filename = list(model_file)) }, regexp = "filename should be character") file_to_check <- paste0("a.model") while (file.exists(file_to_check)) { file_to_check <- paste0("a", file_to_check) } expect_error({ lgb.load(filename = file_to_check) }, regexp = "passed to filename does not exist") # if given, model_str should be a string expect_error({ lgb.load(model_str = c(4.0, 5.0, 6.0)) }, regexp = "lgb.load: model_str should be a character/raw vector") }) test_that("Loading a Booster from a text file works", { set.seed(708L) data(agaricus.train, package = "lightgbm") data(agaricus.test, package = "lightgbm") train <- agaricus.train test <- agaricus.test params <- list( num_leaves = 4L , boosting = "rf" , bagging_fraction = 0.8 , bagging_freq = 1L , boost_from_average = FALSE , categorical_feature = c(1L, 2L) , interaction_constraints = list(1L:2L, 3L, 4L:ncol(train$data)) , feature_contri = rep(0.5, ncol(train$data)) , metric = c("mape", "average_precision") , learning_rate = 1.0 , objective = "binary" , verbosity = .LGB_VERBOSITY , num_threads = .LGB_MAX_THREADS ) bst <- lightgbm( data = as.matrix(train$data) , label = train$label , params = params , nrounds = 2L ) expect_true(.is_Booster(bst)) pred <- predict(bst, test$data) model_file <- tempfile(fileext = ".model") lgb.save(bst, model_file) # finalize the booster and destroy it so you know we aren't cheating bst$.__enclos_env__$private$finalize() expect_null(bst$.__enclos_env__$private$handle) rm(bst) bst2 <- lgb.load( filename = model_file ) pred2 <- predict(bst2, test$data) expect_identical(pred, pred2) # check that the parameters are loaded correctly expect_equal(bst2$params[names(params)], params) }) test_that("boosters with linear models at leaves can be written to text file and re-loaded successfully", { X <- matrix(rnorm(100L), ncol = 1L) labels <- 2L * X + runif(nrow(X), 0L, 0.1) dtrain <- lgb.Dataset( data = X , label = labels ) params <- list( objective = "regression" , verbose = -1L , metric = "mse" , seed = 0L , num_leaves = 2L , num_threads = .LGB_MAX_THREADS ) bst <- lgb.train( data = dtrain , nrounds = 10L , params = params , verbose = .LGB_VERBOSITY ) expect_true(.is_Booster(bst)) # save predictions, then write the model to a file and destroy it in R preds <- predict(bst, X) model_file <- tempfile(fileext = ".model") lgb.save(bst, model_file) bst$.__enclos_env__$private$finalize() expect_null(bst$.__enclos_env__$private$handle) rm(bst) # load the booster and make predictions...should be the same bst2 <- lgb.load( filename = model_file ) preds2 <- predict(bst2, X) expect_identical(preds, preds2) }) test_that("Loading a Booster from a string works", { set.seed(708L) data(agaricus.train, package = "lightgbm") data(agaricus.test, package = "lightgbm") train <- agaricus.train test <- agaricus.test bst <- lightgbm( data = as.matrix(train$data) , label = train$label , params = list( num_leaves = 4L , learning_rate = 1.0 , objective = "binary" , verbose = .LGB_VERBOSITY , num_threads = .LGB_MAX_THREADS ) , nrounds = 2L ) expect_true(.is_Booster(bst)) pred <- predict(bst, test$data) model_string <- bst$save_model_to_string() # finalize the booster and destroy it so you know we aren't cheating bst$.__enclos_env__$private$finalize() expect_null(bst$.__enclos_env__$private$handle) rm(bst) bst2 <- lgb.load( model_str = model_string ) pred2 <- predict(bst2, test$data) expect_identical(pred, pred2) }) test_that("Saving a large model to string should work", { set.seed(708L) data(agaricus.train, package = "lightgbm") train <- agaricus.train bst <- lightgbm( data = as.matrix(train$data) , label = train$label , params = list( num_leaves = 100L , learning_rate = 0.01 , objective = "binary" , num_threads = .LGB_MAX_THREADS ) , nrounds = 500L , verbose = .LGB_VERBOSITY ) pred <- predict(bst, train$data) pred_leaf_indx <- predict(bst, train$data, type = "leaf") pred_raw_score <- predict(bst, train$data, type = "raw") model_string <- bst$save_model_to_string() # make sure this test is still producing a model bigger than the default # buffer size used in LGBM_BoosterSaveModelToString_R expect_gt(nchar(model_string), 1024L * 1024L) # finalize the booster and destroy it so you know we aren't cheating bst$.__enclos_env__$private$finalize() expect_null(bst$.__enclos_env__$private$handle) rm(bst) # make sure a new model can be created from this string, and that it # produces expected results bst2 <- lgb.load( model_str = model_string ) pred2 <- predict(bst2, train$data) pred2_leaf_indx <- predict(bst2, train$data, type = "leaf") pred2_raw_score <- predict(bst2, train$data, type = "raw") expect_identical(pred, pred2) expect_identical(pred_leaf_indx, pred2_leaf_indx) expect_identical(pred_raw_score, pred2_raw_score) }) test_that("Saving a large model to JSON should work", { set.seed(708L) data(agaricus.train, package = "lightgbm") train <- agaricus.train bst <- lightgbm( data = as.matrix(train$data) , label = train$label , params = list( num_leaves = 100L , learning_rate = 0.01 , objective = "binary" , num_threads = .LGB_MAX_THREADS ) , nrounds = 200L , verbose = .LGB_VERBOSITY ) model_json <- bst$dump_model() # make sure this test is still producing a model bigger than the default # buffer size used in LGBM_BoosterDumpModel_R expect_gt(nchar(model_json), 1024L * 1024L) # check that it is valid JSON that looks like a LightGBM model model_list <- jsonlite::fromJSON(model_json) expect_equal(model_list[["objective"]], "binary sigmoid:1") }) test_that("If a string and a file are both passed to lgb.load() the file is used model_str is totally ignored", { set.seed(708L) data(agaricus.train, package = "lightgbm") data(agaricus.test, package = "lightgbm") train <- agaricus.train test <- agaricus.test bst <- lightgbm( data = as.matrix(train$data) , label = train$label , params = list( num_leaves = 4L , learning_rate = 1.0 , objective = "binary" , verbose = .LGB_VERBOSITY , num_threads = .LGB_MAX_THREADS ) , nrounds = 2L ) expect_true(.is_Booster(bst)) pred <- predict(bst, test$data) model_file <- tempfile(fileext = ".model") lgb.save(bst, model_file) # finalize the booster and destroy it so you know we aren't cheating bst$.__enclos_env__$private$finalize() expect_null(bst$.__enclos_env__$private$handle) rm(bst) bst2 <- lgb.load( filename = model_file , model_str = 4.0 ) pred2 <- predict(bst2, test$data) expect_identical(pred, pred2) }) test_that("Creating a Booster from a Dataset should work", { set.seed(708L) data(agaricus.train, package = "lightgbm") data(agaricus.test, package = "lightgbm") dtrain <- lgb.Dataset( agaricus.train$data , label = agaricus.train$label ) bst <- Booster$new( params = list( objective = "binary" , verbose = .LGB_VERBOSITY , num_threads = .LGB_MAX_THREADS ), train_set = dtrain ) expect_true(.is_Booster(bst)) expect_equal(bst$current_iter(), 0L) expect_true(is.na(bst$best_score)) expect_true(all(bst$predict(agaricus.train$data) == 0.5)) }) test_that("Creating a Booster from a Dataset with an existing predictor should work", { set.seed(708L) data(agaricus.train, package = "lightgbm") nrounds <- 2L bst <- lightgbm( data = as.matrix(agaricus.train$data) , label = agaricus.train$label , params = list( num_leaves = 4L , learning_rate = 1.0 , objective = "binary" , verbose = .LGB_VERBOSITY , num_threads = .LGB_MAX_THREADS ) , nrounds = nrounds ) data(agaricus.test, package = "lightgbm") dtest <- Dataset$new( data = agaricus.test$data , label = agaricus.test$label , predictor = bst$to_predictor() ) bst_from_ds <- Booster$new( train_set = dtest , params = list( verbose = .LGB_VERBOSITY , num_threads = .LGB_MAX_THREADS ) ) expect_true(.is_Booster(bst)) expect_equal(bst$current_iter(), nrounds) expect_equal(bst$eval_train()[[1L]][["value"]], 0.1115352) expect_true(.is_Booster(bst_from_ds)) expect_equal(bst_from_ds$current_iter(), nrounds) expect_equal(bst_from_ds$eval_train()[[1L]][["value"]], 5.65704892) dumped_model <- jsonlite::fromJSON(bst$dump_model()) }) test_that("Booster$eval() should work on a Dataset stored in a binary file", { set.seed(708L) data(agaricus.train, package = "lightgbm") train <- agaricus.train dtrain <- lgb.Dataset(train$data, label = train$label) bst <- lgb.train( params = list( objective = "regression" , metric = "l2" , num_leaves = 4L , verbose = .LGB_VERBOSITY , num_threads = .LGB_MAX_THREADS ) , data = dtrain , nrounds = 2L ) data(agaricus.test, package = "lightgbm") test <- agaricus.test dtest <- lgb.Dataset.create.valid( dataset = dtrain , data = test$data , label = test$label ) dtest$construct() eval_in_mem <- bst$eval( data = dtest , name = "test" ) test_file <- tempfile(pattern = "lgb.Dataset_") lgb.Dataset.save( dataset = dtest , fname = test_file ) rm(dtest) eval_from_file <- bst$eval( data = lgb.Dataset( data = test_file , params = list(verbose = .LGB_VERBOSITY, num_threads = .LGB_MAX_THREADS) )$construct() , name = "test" ) expect_true(abs(eval_in_mem[[1L]][["value"]] - 0.1744423) < .LGB_NUMERIC_TOLERANCE) # refer to https://github.com/lightgbm-org/LightGBM/issues/4680 if (isTRUE(.LGB_ON_WINDOWS)) { expect_equal(eval_in_mem, eval_from_file) } else { expect_identical(eval_in_mem, eval_from_file) } }) test_that("Booster$rollback_one_iter() should work as expected", { set.seed(708L) data(agaricus.train, package = "lightgbm") data(agaricus.test, package = "lightgbm") train <- agaricus.train test <- agaricus.test nrounds <- 5L bst <- lightgbm( data = as.matrix(train$data) , label = train$label , params = list( num_leaves = 4L , learning_rate = 1.0 , objective = "binary" , verbose = .LGB_VERBOSITY , num_threads = .LGB_MAX_THREADS ) , nrounds = nrounds ) expect_equal(bst$current_iter(), nrounds) expect_true(.is_Booster(bst)) logloss <- bst$eval_train()[[1L]][["value"]] expect_equal(logloss, 0.01904786) x <- bst$rollback_one_iter() # rollback_one_iter() should return a booster and modify the original # booster in place expect_true(.is_Booster(x)) expect_equal(bst$current_iter(), nrounds - 1L) # score should now come from the model as of 4 iterations logloss <- bst$eval_train()[[1L]][["value"]] expect_equal(logloss, 0.027915146) }) test_that("Booster$update() passing a train_set works as expected", { set.seed(708L) data(agaricus.train, package = "lightgbm") nrounds <- 2L # train with 2 rounds and then update bst <- lightgbm( data = as.matrix(agaricus.train$data) , label = agaricus.train$label , params = list( num_leaves = 4L , learning_rate = 1.0 , objective = "binary" , verbose = .LGB_VERBOSITY , num_threads = .LGB_MAX_THREADS ) , nrounds = nrounds ) expect_true(.is_Booster(bst)) expect_equal(bst$current_iter(), nrounds) bst$update( train_set = Dataset$new( data = agaricus.train$data , label = agaricus.train$label , params = list(verbose = .LGB_VERBOSITY) ) ) expect_true(.is_Booster(bst)) expect_equal(bst$current_iter(), nrounds + 1L) # train with 3 rounds directly bst2 <- lightgbm( data = as.matrix(agaricus.train$data) , label = agaricus.train$label , params = list( num_leaves = 4L , learning_rate = 1.0 , objective = "binary" , verbose = .LGB_VERBOSITY , num_threads = .LGB_MAX_THREADS ) , nrounds = nrounds + 1L ) expect_true(.is_Booster(bst2)) expect_equal(bst2$current_iter(), nrounds + 1L) # model with 2 rounds + 1 update should be identical to 3 rounds expect_equal(bst2$eval_train()[[1L]][["value"]], 0.04806585) expect_equal(bst$eval_train()[[1L]][["value"]], bst2$eval_train()[[1L]][["value"]]) }) test_that("Booster$update() throws an informative error if you provide a non-Dataset to update()", { set.seed(708L) data(agaricus.train, package = "lightgbm") nrounds <- 2L # train with 2 rounds and then update bst <- lightgbm( data = as.matrix(agaricus.train$data) , label = agaricus.train$label , params = list( num_leaves = 4L , learning_rate = 1.0 , objective = "binary" , verbose = .LGB_VERBOSITY , num_threads = .LGB_MAX_THREADS ) , nrounds = nrounds ) expect_error({ bst$update( train_set = data.frame(x = rnorm(10L)) ) }, regexp = "lgb.Booster.update: Only can use lgb.Dataset", fixed = TRUE) }) test_that("Booster$num_trees_per_iter() works as expected", { set.seed(708L) X <- data.matrix(iris[2L:4L]) y_reg <- iris[, 1L] y_binary <- as.integer(y_reg > median(y_reg)) y_class <- as.integer(iris[, 5L]) - 1L num_class <- 3L nrounds <- 10L # Regression and binary probabilistic classification (1 iteration = 1 tree) fit_reg <- lgb.train( params = list( objective = "mse" , verbose = .LGB_VERBOSITY , num_threads = .LGB_MAX_THREADS ) , data = lgb.Dataset(X, label = y_reg) , nrounds = nrounds ) fit_binary <- lgb.train( params = list( objective = "binary" , verbose = .LGB_VERBOSITY , num_threads = .LGB_MAX_THREADS ) , data = lgb.Dataset(X, label = y_binary) , nrounds = nrounds ) # Multiclass probabilistic classification (1 iteration = num_class trees) fit_class <- lgb.train( params = list( objective = "multiclass" , verbose = .LGB_VERBOSITY , num_threads = .LGB_MAX_THREADS , num_class = num_class ) , data = lgb.Dataset(X, label = y_class) , nrounds = nrounds ) expect_equal(fit_reg$num_trees_per_iter(), 1L) expect_equal(fit_binary$num_trees_per_iter(), 1L) expect_equal(fit_class$num_trees_per_iter(), num_class) }) test_that("Booster$num_trees() and $num_iter() works (no early stopping)", { set.seed(708L) X <- data.matrix(iris[2L:4L]) y_reg <- iris[, 1L] y_binary <- as.integer(y_reg > median(y_reg)) y_class <- as.integer(iris[, 5L]) - 1L num_class <- 3L nrounds <- 10L # Regression and binary probabilistic classification (1 iteration = 1 tree) fit_reg <- lgb.train( params = list( objective = "mse" , verbose = .LGB_VERBOSITY , num_threads = .LGB_MAX_THREADS ) , data = lgb.Dataset(X, label = y_reg) , nrounds = nrounds ) fit_binary <- lgb.train( params = list( objective = "binary" , verbose = .LGB_VERBOSITY , num_threads = .LGB_MAX_THREADS ) , data = lgb.Dataset(X, label = y_binary) , nrounds = nrounds ) # Multiclass probabilistic classification (1 iteration = num_class trees) fit_class <- lgb.train( params = list( objective = "multiclass" , verbose = .LGB_VERBOSITY , num_threads = .LGB_MAX_THREADS , num_class = num_class ) , data = lgb.Dataset(X, label = y_class) , nrounds = nrounds ) expect_equal(fit_reg$num_trees(), nrounds) expect_equal(fit_binary$num_trees(), nrounds) expect_equal(fit_class$num_trees(), num_class * nrounds) expect_equal(fit_reg$num_iter(), nrounds) expect_equal(fit_binary$num_iter(), nrounds) expect_equal(fit_class$num_iter(), nrounds) }) test_that("Booster$num_trees() and $num_iter() work (with early stopping)", { set.seed(708L) X <- data.matrix(iris[2L:4L]) y_reg <- iris[, 1L] y_binary <- as.integer(y_reg > median(y_reg)) y_class <- as.integer(iris[, 5L]) - 1L train_ix <- c(1L:40L, 51L:90L, 101L:140L) X_train <- X[train_ix, ] X_valid <- X[-train_ix, ] num_class <- 3L nrounds <- 1000L early_stopping <- 2L # Regression and binary probabilistic classification (1 iteration = 1 tree) fit_reg <- lgb.train( params = list( objective = "mse" , verbose = .LGB_VERBOSITY , num_threads = .LGB_MAX_THREADS ) , data = lgb.Dataset(X_train, label = y_reg[train_ix]) , valids = list(valid = lgb.Dataset(X_valid, label = y_reg[-train_ix])) , nrounds = nrounds , early_stopping_round = early_stopping ) fit_binary <- lgb.train( params = list( objective = "binary" , verbose = .LGB_VERBOSITY , num_threads = .LGB_MAX_THREADS ) , data = lgb.Dataset(X_train, label = y_binary[train_ix]) , valids = list(valid = lgb.Dataset(X_valid, label = y_binary[-train_ix])) , nrounds = nrounds , early_stopping_round = early_stopping ) # Multiclass probabilistic classification (1 iteration = num_class trees) fit_class <- lgb.train( params = list( objective = "multiclass" , verbose = .LGB_VERBOSITY , num_threads = .LGB_MAX_THREADS , num_class = num_class ) , data = lgb.Dataset(X_train, label = y_class[train_ix]) , valids = list(valid = lgb.Dataset(X_valid, label = y_class[-train_ix])) , nrounds = nrounds , early_stopping_round = early_stopping ) expected_trees_reg <- fit_reg$best_iter + early_stopping expected_trees_binary <- fit_binary$best_iter + early_stopping expected_trees_class <- (fit_class$best_iter + early_stopping) * num_class expect_equal(fit_reg$num_trees(), expected_trees_reg) expect_equal(fit_binary$num_trees(), expected_trees_binary) expect_equal(fit_class$num_trees(), expected_trees_class) expect_equal(fit_reg$num_iter(), expected_trees_reg) expect_equal(fit_binary$num_iter(), expected_trees_binary) expect_equal(fit_class$num_iter(), expected_trees_class / num_class) }) test_that("Booster should store parameters and Booster$reset_parameter() should update them", { data(agaricus.train, package = "lightgbm") dtrain <- lgb.Dataset( agaricus.train$data , label = agaricus.train$label ) # testing that this works for some cases that could break it: # - multiple metrics # - using "metric", "boosting", "num_class" in params params <- list( objective = "multiclass" , max_depth = 4L , bagging_fraction = 0.8 , metric = c("multi_logloss", "multi_error") , boosting = "gbdt" , num_class = 5L , verbose = .LGB_VERBOSITY , num_threads = .LGB_MAX_THREADS ) bst <- Booster$new( params = params , train_set = dtrain ) expect_identical(bst$params, params) params[["bagging_fraction"]] <- 0.9 ret_bst <- bst$reset_parameter(params = params) expect_identical(ret_bst$params, params) expect_identical(bst$params, params) }) test_that("Booster$params should include dataset params, before and after Booster$reset_parameter()", { data(agaricus.train, package = "lightgbm") dtrain <- lgb.Dataset( agaricus.train$data , label = agaricus.train$label , params = list( max_bin = 17L ) ) params <- list( objective = "binary" , max_depth = 4L , bagging_fraction = 0.8 , verbose = .LGB_VERBOSITY , num_threads = .LGB_MAX_THREADS ) bst <- Booster$new( params = params , train_set = dtrain ) expect_identical( bst$params , list( objective = "binary" , max_depth = 4L , bagging_fraction = 0.8 , verbose = .LGB_VERBOSITY , num_threads = .LGB_MAX_THREADS , max_bin = 17L ) ) params[["bagging_fraction"]] <- 0.9 ret_bst <- bst$reset_parameter(params = params) expected_params <- list( objective = "binary" , max_depth = 4L , bagging_fraction = 0.9 , verbose = .LGB_VERBOSITY , num_threads = .LGB_MAX_THREADS , max_bin = 17L ) expect_identical(ret_bst$params, expected_params) expect_identical(bst$params, expected_params) }) test_that("Saving a model with different feature importance types works", { set.seed(708L) data(agaricus.train, package = "lightgbm") train <- agaricus.train bst <- lightgbm( data = as.matrix(train$data) , label = train$label , params = list( num_leaves = 4L , learning_rate = 1.0 , objective = "binary" , verbose = .LGB_VERBOSITY , num_threads = .LGB_MAX_THREADS ) , nrounds = 2L ) expect_true(.is_Booster(bst)) .feat_importance_from_string <- function(model_string) { file_lines <- strsplit(model_string, "\n", fixed = TRUE)[[1L]] start_indx <- which(file_lines == "feature_importances:") + 1L blank_line_indices <- which(file_lines == "") end_indx <- blank_line_indices[blank_line_indices > start_indx][1L] - 1L importances <- file_lines[start_indx: end_indx] return(importances) } GAIN_IMPORTANCE <- 1L model_string <- bst$save_model_to_string(feature_importance_type = GAIN_IMPORTANCE) expect_equal( .feat_importance_from_string(model_string) , c( "odor=none=4010" , "stalk-root=club=1163" , "stalk-root=rooted=573" , "stalk-surface-above-ring=silky=450" , "spore-print-color=green=397" , "gill-color=buff=281" ) ) SPLIT_IMPORTANCE <- 0L model_string <- bst$save_model_to_string(feature_importance_type = SPLIT_IMPORTANCE) expect_equal( .feat_importance_from_string(model_string) , c( "odor=none=1" , "gill-color=buff=1" , "stalk-root=club=1" , "stalk-root=rooted=1" , "stalk-surface-above-ring=silky=1" , "spore-print-color=green=1" ) ) }) test_that("Saving a model with unknown importance type fails", { set.seed(708L) data(agaricus.train, package = "lightgbm") train <- agaricus.train bst <- lightgbm( data = as.matrix(train$data) , label = train$label , params = list( num_leaves = 4L , learning_rate = 1.0 , objective = "binary" , verbose = .LGB_VERBOSITY , num_threads = .LGB_MAX_THREADS ) , nrounds = 2L ) expect_true(.is_Booster(bst)) UNSUPPORTED_IMPORTANCE <- 2L expect_error({ capture.output({ model_string <- bst$save_model_to_string( feature_importance_type = UNSUPPORTED_IMPORTANCE ) }, type = "message") }, "Unknown importance type") }) .params_from_model_string <- function(model_str) { file_lines <- strsplit(model_str, "\n", fixed = TRUE)[[1L]] start_indx <- which(file_lines == "parameters:") + 1L blank_line_indices <- which(file_lines == "") end_indx <- blank_line_indices[blank_line_indices > start_indx][1L] - 1L params <- file_lines[start_indx: end_indx] return(params) } test_that("all parameters are stored correctly with save_model_to_string()", { dtrain <- lgb.Dataset( data = matrix(rnorm(500L), nrow = 100L) , label = rnorm(100L) ) bst <- lgb.train( params = list( objective = "mape" , metric = c("l2", "mae") , num_threads = .LGB_MAX_THREADS , seed = 708L , data_sample_strategy = "bagging" , sub_row = 0.8234 ) , data = dtrain , nrounds = 3L , verbose = .LGB_VERBOSITY ) # entries whose values should reflect params passed to lgb.train() non_default_param_entries <- c( "[objective: mape]" # 'l1' was passed in with alias 'mae' , "[metric: l2,l1]" , "[data_sample_strategy: bagging]" , "[seed: 708]" # this was passed in with alias 'sub_row' , "[bagging_fraction: 0.8234]" , "[num_iterations: 3]" ) # entries with default values of params default_param_entries <- c( "[boosting: gbdt]" , "[tree_learner: serial]" , "[device_type: cpu]" , "[data: ]" , "[valid: ]" , "[learning_rate: 0.1]" , "[num_leaves: 31]" , sprintf("[num_threads: %i]", .LGB_MAX_THREADS) , "[deterministic: 0]" , "[histogram_pool_size: -1]" , "[max_depth: -1]" , "[min_data_in_leaf: 20]" , "[min_sum_hessian_in_leaf: 0.001]" , "[pos_bagging_fraction: 1]" , "[neg_bagging_fraction: 1]" , "[bagging_freq: 0]" , "[bagging_seed: 15415]" , "[feature_fraction: 1]" , "[feature_fraction_bynode: 1]" , "[feature_fraction_seed: 32671]" , "[extra_trees: 0]" , "[extra_seed: 6642]" , "[early_stopping_round: 0]" , "[early_stopping_min_delta: 0]" , "[first_metric_only: 0]" , "[max_delta_step: 0]" , "[lambda_l1: 0]" , "[lambda_l2: 0]" , "[linear_lambda: 0]" , "[min_gain_to_split: 0]" , "[drop_rate: 0.1]" , "[max_drop: 50]" , "[skip_drop: 0.5]" , "[xgboost_dart_mode: 0]" , "[uniform_drop: 0]" , "[drop_seed: 20623]" , "[top_rate: 0.2]" , "[other_rate: 0.1]" , "[min_data_per_group: 100]" , "[max_cat_threshold: 32]" , "[cat_l2: 10]" , "[cat_smooth: 10]" , "[max_cat_to_onehot: 4]" , "[top_k: 20]" , "[monotone_constraints: ]" , "[monotone_constraints_method: basic]" , "[monotone_penalty: 0]" , "[feature_contri: ]" , "[forcedsplits_filename: ]" , "[force_col_wise: 0]" , "[force_row_wise: 0]" , "[refit_decay_rate: 0.9]" , "[cegb_tradeoff: 1]" , "[cegb_penalty_split: 0]" , "[cegb_penalty_feature_lazy: ]" , "[cegb_penalty_feature_coupled: ]" , "[path_smooth: 0]" , "[interaction_constraints: ]" , sprintf("[verbosity: %i]", .LGB_VERBOSITY) , "[saved_feature_importance_type: 0]" , "[use_quantized_grad: 0]" , "[num_grad_quant_bins: 4]" , "[quant_train_renew_leaf: 0]" , "[stochastic_rounding: 1]" , "[linear_tree: 0]" , "[max_bin: 255]" , "[max_bin_by_feature: ]" , "[min_data_in_bin: 3]" , "[bin_construct_sample_cnt: 200000]" , "[data_random_seed: 2350]" , "[is_enable_sparse: 1]" , "[enable_bundle: 1]" , "[use_missing: 1]" , "[zero_as_missing: 0]" , "[feature_pre_filter: 1]" , "[pre_partition: 0]" , "[two_round: 0]" , "[header: 0]" , "[label_column: ]" , "[weight_column: ]" , "[group_column: ]" , "[ignore_column: ]" , "[categorical_feature: ]" , "[forcedbins_filename: ]" , "[precise_float_parser: 0]" , "[parser_config_file: ]" , "[objective_seed: 4309]" , "[num_class: 1]" , "[is_unbalance: 0]" , "[scale_pos_weight: 1]" , "[sigmoid: 1]" , "[boost_from_average: 1]" , "[reg_sqrt: 0]" , "[alpha: 0.9]" , "[fair_c: 1]" , "[poisson_max_delta_step: 0.7]" , "[tweedie_variance_power: 1.5]" , "[lambdarank_truncation_level: 30]" , "[lambdarank_norm: 1]" , "[label_gain: ]" , "[lambdarank_position_bias_regularization: 0]" , "[eval_at: ]" , "[multi_error_top_k: 1]" , "[auc_mu_weights: ]" , "[num_machines: 1]" , "[local_listen_port: 12400]" , "[time_out: 120]" , "[machine_list_filename: ]" , "[machines: ]" , "[gpu_platform_id: -1]" , "[gpu_device_id: -1]" , "[gpu_use_dp: 0]" , "[num_gpu: 1]" ) all_param_entries <- c(non_default_param_entries, default_param_entries) # parameters should match what was passed from the R-package model_str <- bst$save_model_to_string() params_in_file <- .params_from_model_string(model_str = model_str) .expect_in(all_param_entries, params_in_file) # early stopping should be off by default expect_equal(sum(startsWith(params_in_file, "[early_stopping_round:")), 1L) expect_equal(sum(params_in_file == "[early_stopping_round: 0]"), 1L) # since save_model_to_string() is used when serializing with saveRDS(), check that parameters all # roundtrip saveRDS()/loadRDS() successfully rds_file <- tempfile() saveRDS(bst, rds_file) bst_rds <- readRDS(rds_file) model_str <- bst_rds$save_model_to_string() params_in_file <- .params_from_model_string(model_str = model_str) .expect_in(all_param_entries, params_in_file) }) test_that("early_stopping, num_iterations are stored correctly in model string even with aliases", { dtrain <- lgb.Dataset( data = matrix(rnorm(500L), nrow = 100L) , label = rnorm(100L) ) dvalid <- lgb.Dataset( data = matrix(rnorm(500L), nrow = 100L) , label = rnorm(100L) ) # num_iterations values (all different) num_iterations <- 4L num_boost_round <- 2L n_iter <- 3L nrounds_kwarg <- 6L # early_stopping_round values (all different) early_stopping_round <- 2L early_stopping_round_kwarg <- 3L n_iter_no_change <- 4L params <- list( objective = "regression" , metric = "l2" , num_boost_round = num_boost_round , num_iterations = num_iterations , n_iter = n_iter , early_stopping_round = early_stopping_round , n_iter_no_change = n_iter_no_change , num_threads = .LGB_MAX_THREADS ) bst <- lgb.train( params = params , data = dtrain , nrounds = nrounds_kwarg , early_stopping_rounds = early_stopping_round_kwarg , valids = list( "random_valid" = dvalid ) , verbose = .LGB_VERBOSITY ) model_str <- bst$save_model_to_string() params_in_file <- .params_from_model_string(model_str = model_str) # parameters should match what was passed from the R-package, and the "main" (non-alias) # params values in `params` should be preferred to keyword argumentts or aliases expect_equal(sum(startsWith(params_in_file, "[num_iterations:")), 1L) expect_equal(sum(params_in_file == sprintf("[num_iterations: %s]", num_iterations)), 1L) expect_equal(sum(startsWith(params_in_file, "[early_stopping_round:")), 1L) expect_equal(sum(params_in_file == sprintf("[early_stopping_round: %s]", early_stopping_round)), 1L) # none of the aliases shouold have been written to the model file expect_equal(sum(startsWith(params_in_file, "[num_boost_round:")), 0L) expect_equal(sum(startsWith(params_in_file, "[n_iter:")), 0L) expect_equal(sum(startsWith(params_in_file, "[n_iter_no_change:")), 0L) }) test_that("Booster: method calls Booster with a null handle should raise an informative error and not segfault", { data(agaricus.train, package = "lightgbm") train <- agaricus.train dtrain <- lgb.Dataset(train$data, label = train$label) bst <- lgb.train( params = list( objective = "regression" , metric = "l2" , num_leaves = 8L , num_threads = .LGB_MAX_THREADS ) , data = dtrain , verbose = .LGB_VERBOSITY , nrounds = 5L , valids = list( train = dtrain ) , serializable = FALSE ) tmp_file <- tempfile(fileext = ".rds") saveRDS(bst, tmp_file) rm(bst) bst <- readRDS(tmp_file) .expect_booster_error <- function(object) { error_regexp <- "Attempting to use a Booster which no longer exists" expect_error(object, regexp = error_regexp) } .expect_booster_error({ bst$current_iter() }) .expect_booster_error({ bst$dump_model() }) .expect_booster_error({ bst$eval(data = dtrain, name = "valid") }) .expect_booster_error({ bst$eval_train() }) .expect_booster_error({ bst$lower_bound() }) .expect_booster_error({ bst$predict(data = train$data[seq_len(5L), ]) }) .expect_booster_error({ bst$reset_parameter(params = list(learning_rate = 0.123)) }) .expect_booster_error({ bst$rollback_one_iter() }) .expect_booster_error({ bst$save_raw() }) .expect_booster_error({ bst$save_model(filename = tempfile(fileext = ".model")) }) .expect_booster_error({ bst$save_model_to_string() }) .expect_booster_error({ bst$update() }) .expect_booster_error({ bst$upper_bound() }) predictor <- bst$to_predictor() .expect_booster_error({ predictor$current_iter() }) .expect_booster_error({ predictor$predict(data = train$data[seq_len(5L), ]) }) }) test_that("Booster$new() using a Dataset with a null handle should raise an informative error and not segfault", { data(agaricus.train, package = "lightgbm") train <- agaricus.train dtrain <- lgb.Dataset(train$data, label = train$label) dtrain$construct() tmp_file <- tempfile(fileext = ".bin") saveRDS(dtrain, tmp_file) rm(dtrain) dtrain <- readRDS(tmp_file) expect_error({ bst <- Booster$new( train_set = dtrain , params = list( verbose = .LGB_VERBOSITY ) ) }, regexp = "Attempting to create a Dataset without any raw data") }) test_that("Booster$new() raises informative errors for malformed inputs", { data(agaricus.train, package = "lightgbm") train <- agaricus.train dtrain <- lgb.Dataset(train$data, label = train$label) # no inputs expect_error({ Booster$new() }, regexp = "lgb.Booster: Need at least either training dataset, model file, or model_str") # unrecognized objective expect_error({ capture.output({ Booster$new( params = list(objective = "not_a_real_objective") , train_set = dtrain ) }, type = "message") }, regexp = "Unknown objective type name: not_a_real_objective") # train_set is not a Dataset expect_error({ Booster$new( train_set = data.table::data.table(rnorm(1L:10L)) ) }, regexp = "lgb.Booster: Can only use lgb.Dataset as training data") # model file isn't a string expect_error({ Booster$new( modelfile = list() ) }, regexp = "lgb.Booster: Can only use a string as model file path") # model file doesn't exist expect_error({ capture.output({ Booster$new( params = list() , modelfile = "file-that-does-not-exist.model" ) }, type = "message") }, regexp = "Could not open file-that-does-not-exist.model") # model file doesn't contain a valid LightGBM model model_file <- tempfile(fileext = ".model") writeLines( text = c("make", "good", "predictions") , con = model_file ) expect_error({ capture.output({ Booster$new( params = list() , modelfile = model_file ) }, type = "message") }, regexp = "Unknown model format or submodel type in model file") # malformed model string expect_error({ capture.output({ Booster$new( params = list() , model_str = "a\nb\n" ) }, type = "message") }, regexp = "Model file doesn't specify the number of classes") # model string isn't character or raw expect_error({ Booster$new( model_str = numeric() ) }, regexp = "lgb.Booster: Can only use a character/raw vector as model_str") }) # this is almost identical to the test above it, but for lgb.cv(). A lot of code # is duplicated between lgb.train() and lgb.cv(), and this will catch cases where # one is updated and the other isn't test_that("lgb.cv() correctly handles passing through params to the model file", { dtrain <- lgb.Dataset( data = matrix(rnorm(500L), nrow = 100L) , label = rnorm(100L) ) # num_iterations values (all different) num_iterations <- 4L num_boost_round <- 2L n_iter <- 3L nrounds_kwarg <- 6L # early_stopping_round values (all different) early_stopping_round <- 2L early_stopping_round_kwarg <- 3L n_iter_no_change <- 4L params <- list( objective = "regression" , metric = "l2" , num_boost_round = num_boost_round , num_iterations = num_iterations , n_iter = n_iter , early_stopping_round = early_stopping_round , n_iter_no_change = n_iter_no_change , verbose = .LGB_VERBOSITY , num_threads = .LGB_MAX_THREADS ) cv_bst <- lgb.cv( params = params , data = dtrain , nrounds = nrounds_kwarg , early_stopping_rounds = early_stopping_round_kwarg , nfold = 3L , verbose = .LGB_VERBOSITY ) for (bst in cv_bst$boosters) { model_str <- bst[["booster"]]$save_model_to_string() params_in_file <- .params_from_model_string(model_str = model_str) # parameters should match what was passed from the R-package, and the "main" (non-alias) # params values in `params` should be preferred to keyword argumentts or aliases expect_equal(sum(startsWith(params_in_file, "[num_iterations:")), 1L) expect_equal(sum(params_in_file == sprintf("[num_iterations: %s]", num_iterations)), 1L) expect_equal(sum(startsWith(params_in_file, "[early_stopping_round:")), 1L) expect_equal(sum(params_in_file == sprintf("[early_stopping_round: %s]", early_stopping_round)), 1L) # none of the aliases shouold have been written to the model file expect_equal(sum(startsWith(params_in_file, "[num_boost_round:")), 0L) expect_equal(sum(startsWith(params_in_file, "[n_iter:")), 0L) expect_equal(sum(startsWith(params_in_file, "[n_iter_no_change:")), 0L) } }) test_that("params (including dataset params) should be stored in .rds file for Booster", { data(agaricus.train, package = "lightgbm") dtrain <- lgb.Dataset( agaricus.train$data , label = agaricus.train$label , params = list( max_bin = 17L ) ) params <- list( objective = "binary" , max_depth = 4L , bagging_fraction = 0.8 , verbose = .LGB_VERBOSITY , num_threads = .LGB_MAX_THREADS ) bst <- Booster$new( params = params , train_set = dtrain ) bst_file <- tempfile(fileext = ".rds") saveRDS(bst, file = bst_file) bst_from_file <- readRDS(file = bst_file) expect_identical( bst_from_file$params , list( objective = "binary" , max_depth = 4L , bagging_fraction = 0.8 , verbose = .LGB_VERBOSITY , num_threads = .LGB_MAX_THREADS , max_bin = 17L ) ) }) test_that("Handle is automatically restored when calling predict", { data(agaricus.train, package = "lightgbm") bst <- lightgbm( agaricus.train$data , agaricus.train$label , nrounds = 5L , obj = "binary" , params = list( verbose = .LGB_VERBOSITY ) , num_threads = .LGB_MAX_THREADS ) bst_file <- tempfile(fileext = ".rds") saveRDS(bst, file = bst_file) bst_from_file <- readRDS(file = bst_file) pred_before <- predict(bst, agaricus.train$data) pred_after <- predict(bst_from_file, agaricus.train$data) expect_equal(pred_before, pred_after) }) test_that("boosters with linear models at leaves can be written to RDS and re-loaded successfully", { X <- matrix(rnorm(100L), ncol = 1L) labels <- 2L * X + runif(nrow(X), 0L, 0.1) dtrain <- lgb.Dataset( data = X , label = labels ) params <- list( objective = "regression" , verbose = .LGB_VERBOSITY , metric = "mse" , seed = 0L , num_leaves = 2L , num_threads = .LGB_MAX_THREADS ) bst <- lgb.train( data = dtrain , nrounds = 10L , params = params ) expect_true(.is_Booster(bst)) # save predictions, then write the model to a file and destroy it in R preds <- predict(bst, X) model_file <- tempfile(fileext = ".rds") saveRDS(bst, file = model_file) bst$.__enclos_env__$private$finalize() expect_null(bst$.__enclos_env__$private$handle) rm(bst) # load the booster and make predictions...should be the same bst2 <- readRDS(file = model_file) preds2 <- predict(bst2, X) expect_identical(preds, preds2) }) .have_same_handle <- function(model, other_model) { expect_equal( model$.__enclos_env__$private$handle , other_model$.__enclos_env__$private$handle ) } .has_expected_content_for_fitted_model <- function(printed_txt) { expect_true(any(startsWith(printed_txt, "LightGBM Model"))) expect_true(any(startsWith(printed_txt, "Fitted to dataset"))) } .has_expected_content_for_finalized_model <- function(printed_txt) { expect_true(any(printed_txt == "LightGBM Model")) expect_true(any(grepl("Booster handle is invalid", printed_txt, fixed = TRUE))) } .check_methods_work <- function(model) { #--- should work for fitted models --- # # print() log_txt <- capture.output({ ret <- print(model) }) .have_same_handle(ret, model) .has_expected_content_for_fitted_model(log_txt) # show() log_txt <- capture.output({ ret <- show(model) }) expect_null(ret) .has_expected_content_for_fitted_model(log_txt) # summary() log_txt <- capture.output({ ret <- summary(model) }) .have_same_handle(ret, model) .has_expected_content_for_fitted_model(log_txt) #--- should not fail for finalized models ---# model$.__enclos_env__$private$finalize() # print() log_txt <- capture.output({ ret <- print(model) }) .has_expected_content_for_finalized_model(log_txt) # show() .have_same_handle(ret, model) log_txt <- capture.output({ ret <- show(model) }) expect_null(ret) .has_expected_content_for_finalized_model(log_txt) # summary() log_txt <- capture.output({ ret <- summary(model) }) .have_same_handle(ret, model) .has_expected_content_for_finalized_model(log_txt) } test_that("Booster's print, show, and summary work correctly for built-in objectives", { data("mtcars") model <- lgb.train( params = list( objective = "regression" , min_data_in_leaf = 1L , num_threads = .LGB_MAX_THREADS ) , data = lgb.Dataset( as.matrix(mtcars[, -1L]) , label = mtcars$mpg , params = list( min_data_in_bin = 1L ) ) , verbose = .LGB_VERBOSITY , nrounds = 5L ) .check_methods_work(model) data("iris") model <- lgb.train( params = list(objective = "multiclass", num_class = 3L, num_threads = .LGB_MAX_THREADS) , data = lgb.Dataset( as.matrix(iris[, -5L]) , label = as.numeric(factor(iris$Species)) - 1.0 ) , verbose = .LGB_VERBOSITY , nrounds = 5L ) .check_methods_work(model) }) test_that("Booster's print, show, and summary work correctly for custom objective", { .logregobj <- function(preds, dtrain) { labels <- get_field(dtrain, "label") preds <- 1.0 / (1.0 + exp(-preds)) grad <- preds - labels hess <- preds * (1.0 - preds) return(list(grad = grad, hess = hess)) } .evalerror <- function(preds, dtrain) { labels <- get_field(dtrain, "label") preds <- 1.0 / (1.0 + exp(-preds)) err <- as.numeric(sum(labels != (preds > 0.5))) / length(labels) return(list( name = "error" , value = err , higher_better = FALSE )) } data("iris") model <- lgb.train( data = lgb.Dataset( as.matrix(iris[, -5L]) , label = as.numeric(iris$Species == "virginica") ) , obj = .logregobj , eval = .evalerror , verbose = .LGB_VERBOSITY , nrounds = 5L , params = list(num_threads = .LGB_MAX_THREADS) ) .check_methods_work(model) }) test_that("Booster's print, show, and summary work correctly when objective is not provided", { data("iris") model <- lgb.train( data = lgb.Dataset( as.matrix(iris[, seq_len(3L)]) , label = iris[, 4L] ) , verbose = .LGB_VERBOSITY , nrounds = 5L , params = list(num_threads = .LGB_MAX_THREADS) ) log_txt <- capture.output(print(model)) expect_true(any(log_txt == "Objective: (default)")) .check_methods_work(model) }) test_that("LGBM_BoosterGetNumFeature_R returns correct outputs", { data("mtcars") model <- lgb.train( params = list( objective = "regression" , min_data_in_leaf = 1L , num_threads = .LGB_MAX_THREADS ) , data = lgb.Dataset( as.matrix(mtcars[, -1L]) , label = mtcars$mpg , params = list( min_data_in_bin = 1L ) ) , verbose = .LGB_VERBOSITY , nrounds = 5L ) ncols <- .Call(LGBM_BoosterGetNumFeature_R, model$.__enclos_env__$private$handle) expect_equal(ncols, ncol(mtcars) - 1L) data("iris") model <- lgb.train( params = list(objective = "multiclass", num_class = 3L) , data = lgb.Dataset( as.matrix(iris[, -5L]) , label = as.numeric(factor(iris$Species)) - 1.0 ) , verbose = .LGB_VERBOSITY , nrounds = 5L ) ncols <- .Call(LGBM_BoosterGetNumFeature_R, model$.__enclos_env__$private$handle) expect_equal(ncols, ncol(iris) - 1L) }) # Helper function that creates a fitted model with nrounds boosting rounds .get_test_model <- function(nrounds) { set.seed(1L) data(agaricus.train, package = "lightgbm") train <- agaricus.train bst <- lightgbm( data = as.matrix(train$data) , label = train$label , params = list(objective = "binary", num_threads = .LGB_MAX_THREADS) , nrounds = nrounds , verbose = .LGB_VERBOSITY ) return(bst) } # Simplified version of lgb.model.dt.tree() .get_trees_from_dump <- function(x) { parsed <- jsonlite::fromJSON( txt = x , simplifyVector = TRUE , simplifyDataFrame = FALSE , simplifyMatrix = FALSE , flatten = FALSE ) return(lapply(parsed$tree_info, FUN = .single_tree_parse)) } test_that("num_iteration and start_iteration work for lgb.dump()", { bst <- .get_test_model(5L) first2 <- .get_trees_from_dump(lgb.dump(bst, num_iteration = 2L)) last3 <- .get_trees_from_dump( lgb.dump(bst, num_iteration = 3L, start_iteration = 3L) ) all5 <- .get_trees_from_dump(lgb.dump(bst)) too_many <- .get_trees_from_dump(lgb.dump(bst, num_iteration = 10L)) expect_equal( data.table::rbindlist(c(first2, last3)), data.table::rbindlist(all5) ) expect_equal(too_many, all5) }) test_that("num_iteration and start_iteration work for lgb.save()", { .get_n_trees <- function(x) { return(length(.get_trees_from_dump(lgb.dump(x)))) } .save_and_load <- function(bst, ...) { model_file <- tempfile(fileext = ".model") lgb.save(bst, model_file, ...) return(lgb.load(model_file)) } bst <- .get_test_model(5L) n_first2 <- .get_n_trees(.save_and_load(bst, num_iteration = 2L)) n_last3 <- .get_n_trees( .save_and_load(bst, num_iteration = 3L, start_iteration = 3L) ) n_all5 <- .get_n_trees(.save_and_load(bst)) n_too_many <- .get_n_trees(.save_and_load(bst, num_iteration = 10L)) expect_equal(n_first2, 2L) expect_equal(n_last3, 3L) expect_equal(n_all5, 5L) expect_equal(n_too_many, 5L) }) test_that("num_iteration and start_iteration work for save_model_to_string()", { .get_n_trees_from_string <- function(x) { return(sum(gregexpr("Tree=", x, fixed = TRUE)[[1L]] > 0L)) } bst <- .get_test_model(5L) n_first2 <- .get_n_trees_from_string( bst$save_model_to_string(num_iteration = 2L) ) n_last3 <- .get_n_trees_from_string( bst$save_model_to_string(num_iteration = 3L, start_iteration = 3L) ) n_all5 <- .get_n_trees_from_string(bst$save_model_to_string()) n_too_many <- .get_n_trees_from_string( bst$save_model_to_string(num_iteration = 10L) ) expect_equal(n_first2, 2L) expect_equal(n_last3, 3L) expect_equal(n_all5, 5L) expect_equal(n_too_many, 5L) }) ================================================ FILE: R-package/tests/testthat/test_lgb.convert_with_rules.R ================================================ test_that("lgb.convert_with_rules() rejects inputs that are not a data.table or data.frame", { bad_inputs <- list( matrix(1.0:10.0, 2L, 5L) , TRUE , c("a", "b") , NA , 10L , lgb.Dataset( data = matrix(1.0:10.0, 2L, 5L) , params = list() ) ) for (bad_input in bad_inputs) { expect_error({ conversion_result <- lgb.convert_with_rules(bad_input) }, regexp = "lgb.convert_with_rules: you provided", fixed = TRUE) } }) test_that("lgb.convert_with_rules() should work correctly for a dataset with only character columns", { testDF <- data.frame( col1 = c("a", "b", "c") , col2 = c("green", "green", "red") , stringsAsFactors = FALSE ) testDT <- data.table::as.data.table(testDF) for (input_data in list(testDF, testDT)) { conversion_result <- lgb.convert_with_rules(input_data) # dataset should have been converted to integer converted_dataset <- conversion_result[["data"]] expect_identical(class(input_data), class(converted_dataset)) expect_identical(class(converted_dataset[["col1"]]), "integer") expect_identical(class(converted_dataset[["col2"]]), "integer") expect_identical(converted_dataset[["col1"]], c(1L, 2L, 3L)) expect_identical(converted_dataset[["col2"]], c(1L, 1L, 2L)) # rules should be returned and correct rules <- conversion_result$rules expect_true(methods::is(rules, "list")) expect_length(rules, ncol(input_data)) expect_identical(rules[["col1"]], c("a" = 1L, "b" = 2L, "c" = 3L)) expect_identical(rules[["col2"]], c("green" = 1L, "red" = 2L)) } }) test_that("lgb.convert_with_rules() should work correctly for a dataset with only factor columns", { testDF <- data.frame( col1 = as.factor(c("a", "b", "c")) , col2 = as.factor(c("green", "green", "red")) , stringsAsFactors = FALSE ) testDT <- data.table::as.data.table(testDF) for (input_data in list(testDF, testDT)) { conversion_result <- lgb.convert_with_rules(input_data) # dataset should have been converted to integer converted_dataset <- conversion_result[["data"]] expect_identical(class(input_data), class(converted_dataset)) expect_identical(class(converted_dataset[["col1"]]), "integer") expect_identical(class(converted_dataset[["col2"]]), "integer") expect_identical(converted_dataset[["col1"]], c(1L, 2L, 3L)) expect_identical(converted_dataset[["col2"]], c(1L, 1L, 2L)) # rules should be returned and correct rules <- conversion_result$rules expect_true(methods::is(rules, "list")) expect_length(rules, ncol(input_data)) expect_identical(rules[["col1"]], c("a" = 1L, "b" = 2L, "c" = 3L)) expect_identical(rules[["col2"]], c("green" = 1L, "red" = 2L)) } }) test_that("lgb.convert_with_rules() should not change a dataset with only integer columns", { testDF <- data.frame( col1 = 11L:15L , col2 = 16L:20L , stringsAsFactors = FALSE ) testDT <- data.table::as.data.table(testDF) for (input_data in list(testDF, testDT)) { conversion_result <- lgb.convert_with_rules(input_data) # dataset should have been converted to integer converted_dataset <- conversion_result[["data"]] expect_identical(converted_dataset, input_data) # rules should be returned and correct rules <- conversion_result$rules expect_identical(rules, list()) } }) test_that("lgb.convert_with_rules() should work correctly for a dataset with numeric, factor, and character columns", { testDF <- data.frame( character_col = c("a", "b", "c") , numeric_col = c(1.0, 9.0, 10.0) , factor_col = as.factor(c("n", "n", "y")) , stringsAsFactors = FALSE ) testDT <- data.table::as.data.table(testDF) for (input_data in list(testDF, testDT)) { conversion_result <- lgb.convert_with_rules(input_data) # dataset should have been converted to numeric converted_dataset <- conversion_result[["data"]] expect_identical(class(input_data), class(converted_dataset)) expect_identical(class(converted_dataset[["character_col"]]), "integer") expect_identical(class(converted_dataset[["factor_col"]]), "integer") expect_identical(converted_dataset[["character_col"]], c(1L, 2L, 3L)) expect_identical(converted_dataset[["factor_col"]], c(1L, 1L, 2L)) # rules should be returned and correct rules <- conversion_result$rules expect_true(methods::is(rules, "list")) expect_length(rules, 2L) expect_identical(rules[["character_col"]], c("a" = 1L, "b" = 2L, "c" = 3L)) expect_identical(rules[["factor_col"]], c("n" = 1L, "y" = 2L)) # today, lgb.convert_with_rules() does not convert numeric columns expect_identical(class(converted_dataset[["numeric_col"]]), "numeric") expect_identical(converted_dataset[["numeric_col"]], c(1.0, 9.0, 10.0)) } }) test_that("lgb.convert_with_rules() should convert missing values to the expected value", { testDF <- data.frame( character_col = c("a", NA_character_, "c") , na_col = rep(NA, 3L) , na_real_col = rep(NA_real_, 3L) , na_int_col = rep(NA_integer_, 3L) , na_character_col = rep(NA_character_, 3L) , numeric_col = c(1.0, 9.0, NA_real_) , factor_col = as.factor(c("n", "n", "y")) , integer_col = c(1L, 9L, NA_integer_) , stringsAsFactors = FALSE ) testDT <- data.table::as.data.table(testDF) for (input_data in list(testDF, testDT)) { conversion_result <- lgb.convert_with_rules(input_data) # dataset should have been converted to integer converted_dataset <- conversion_result[["data"]] expect_identical(class(input_data), class(converted_dataset)) expect_identical(class(converted_dataset[["character_col"]]), "integer") expect_identical(converted_dataset[["character_col"]], c(1L, 0L, 2L)) # does not try to fill 0s in for already-integer columns expect_identical(class(converted_dataset[["integer_col"]]), "integer") expect_identical(converted_dataset[["integer_col"]], c(1L, 9L, NA_integer_)) expect_identical(class(converted_dataset[["na_int_col"]]), "integer") expect_identical(converted_dataset[["na_int_col"]], rep(NA_integer_, nrow(converted_dataset))) expect_identical(class(converted_dataset[["factor_col"]]), "integer") expect_identical(converted_dataset[["factor_col"]], c(1L, 1L, 2L)) # NAs in character columns should be converted to 0 expect_identical(class(converted_dataset[["na_character_col"]]), "integer") expect_identical(converted_dataset[["na_character_col"]], rep(0L, nrow(converted_dataset))) # logical should be converted to integer expect_identical(class(converted_dataset[["na_col"]]), "integer") expect_identical(converted_dataset[["na_col"]], rep(-1L, 3L)) # lgb.convert_with_rules() should not convert numeric columns to integer expect_identical(class(converted_dataset[["na_real_col"]]), "numeric") expect_identical(converted_dataset[["na_real_col"]], rep(NA_real_, nrow(converted_dataset))) expect_identical(class(converted_dataset[["numeric_col"]]), "numeric") expect_identical(converted_dataset[["numeric_col"]], c(1.0, 9.0, NA_real_)) # rules should be returned and correct rules <- conversion_result$rules expect_true(methods::is(rules, "list")) expect_length(rules, 3L) expect_identical(rules[["character_col"]], c("a" = 1L, "c" = 2L)) expect_identical(rules[["factor_col"]], c("n" = 1L, "y" = 2L)) expect_identical(rules[["na_col"]], stats::setNames(c(0L, 1L), c(FALSE, TRUE))) } }) test_that("lgb.convert_with_rules() should work correctly if you provide your own well-formed rules", { testDF <- data.frame( character_col = c("a", NA_character_, "c", "a", "a", "c") , na_col = rep(NA, 6L) , na_real_col = rep(NA_real_, 6L) , na_int_col = rep(NA_integer_, 6L) , na_character_col = rep(NA_character_, 6L) , numeric_col = c(1.0, 9.0, NA_real_, 10.0, 11.0, 12.0) , factor_col = as.factor(c("n", "n", "y", "y", "n", "n")) , integer_col = c(1L, 9L, NA_integer_, 1L, 1L, 1L) , stringsAsFactors = FALSE ) testDT <- data.table::as.data.table(testDF) # value used by lgb.convert_with_rules() when it encounters a categorical value that # is not in the provided rules UNKNOWN_FACTOR_VALUE <- 0L UNKNOWN_LOGICAL_VALUE <- -1L for (input_data in list(testDF, testDT)) { custom_rules <- list( "character_col" = c( "a" = 5L , "c" = -10L ) , "factor_col" = c( "n" = 65L , "y" = 66L ) ) conversion_result <- lgb.convert_with_rules( data = input_data , rules = custom_rules ) # dataset should have been converted to integer converted_dataset <- conversion_result[["data"]] expect_identical(class(input_data), class(converted_dataset)) expect_identical(class(converted_dataset[["character_col"]]), "integer") expect_identical(converted_dataset[["character_col"]], c(5L, UNKNOWN_FACTOR_VALUE, -10L, 5L, 5L, -10L)) expect_identical(class(converted_dataset[["factor_col"]]), "integer") expect_identical(converted_dataset[["factor_col"]], c(65L, 65L, 66L, 66L, 65L, 65L)) # columns not specified in rules are not going to be converted, unless they are all NA for (col in c("na_real_col", "na_int_col", "numeric_col", "integer_col")) { expect_identical(converted_dataset[[col]], input_data[[col]]) } # non-numeric/integer columns that are all NA should have been filled in expect_identical(converted_dataset[["na_col"]], rep(UNKNOWN_LOGICAL_VALUE, 6L)) expect_identical(converted_dataset[["na_character_col"]], rep(UNKNOWN_FACTOR_VALUE, 6L)) # the rules you passed in should be returned unchanged rules <- conversion_result$rules expect_identical(rules, custom_rules) } }) test_that("lgb.convert_with_rules() should modify data.tables in-place", { testDT <- data.table::data.table( character_col = c("a", NA_character_, "c") , na_col = rep(NA, 3L) , na_real_col = rep(NA_real_, 3L) , na_int_col = rep(NA_integer_, 3L) , na_character_col = rep(NA_character_, 3L) , numeric_col = c(1.0, 9.0, NA_real_) , factor_col = as.factor(c("n", "n", "y")) , integer_col = c(1L, 9L, NA_integer_) ) conversion_result <- lgb.convert_with_rules(testDT) resultDT <- conversion_result[["data"]] expect_identical(resultDT, testDT) }) ================================================ FILE: R-package/tests/testthat/test_lgb.importance.R ================================================ test_that("lgb.importance() should reject bad inputs", { bad_inputs <- list( .Machine$integer.max , Inf , -Inf , NA , NA_real_ , -10L:10L , list(c("a", "b", "c")) , data.frame( x = rnorm(20L) , y = sample( x = c(1L, 2L) , size = 20L , replace = TRUE ) ) , data.table::data.table( x = rnorm(20L) , y = sample( x = c(1L, 2L) , size = 20L , replace = TRUE ) ) , lgb.Dataset( data = matrix(rnorm(100L), ncol = 2L) , label = matrix(sample(c(0L, 1L), 50L, replace = TRUE)) ) , "lightgbm.model" ) for (input in bad_inputs) { expect_error({ lgb.importance(input) }, regexp = "'model' has to be an object of class lgb\\.Booster") } }) ================================================ FILE: R-package/tests/testthat/test_lgb.interprete.R ================================================ .sigmoid <- function(x) { 1.0 / (1.0 + exp(-x)) } .logit <- function(x) { log(x / (1.0 - x)) } test_that("lgb.interprete works as expected for binary classification", { data(agaricus.train, package = "lightgbm") train <- agaricus.train dtrain <- lgb.Dataset(train$data, label = train$label) set_field( dataset = dtrain , field_name = "init_score" , data = rep( .logit(mean(train$label)) , length(train$label) ) ) data(agaricus.test, package = "lightgbm") test <- agaricus.test params <- list( objective = "binary" , learning_rate = 0.01 , num_leaves = 63L , max_depth = -1L , min_data_in_leaf = 1L , min_sum_hessian_in_leaf = 1.0 , verbose = .LGB_VERBOSITY , num_threads = .LGB_MAX_THREADS ) model <- lgb.train( params = params , data = dtrain , nrounds = 3L ) num_trees <- 5L tree_interpretation <- lgb.interprete( model = model , data = test$data , idxset = seq_len(num_trees) ) expect_identical(class(tree_interpretation), "list") expect_true(length(tree_interpretation) == num_trees) expect_null(names(tree_interpretation)) expect_true(all( sapply( X = tree_interpretation , FUN = function(treeDT) { checks <- c( data.table::is.data.table(treeDT) , identical(names(treeDT), c("Feature", "Contribution")) , is.character(treeDT[, Feature]) , is.numeric(treeDT[, Contribution]) ) return(all(checks)) } ) )) }) test_that("lgb.intereprete works as expected for multiclass classification", { data(iris) # We must convert factors to numeric # They must be starting from number 0 to use multiclass # For instance: 0, 1, 2, 3, 4, 5... iris$Species <- as.numeric(as.factor(iris$Species)) - 1L # Create imbalanced training data (20, 30, 40 examples for classes 0, 1, 2) train <- as.matrix(iris[c(1L:20L, 51L:80L, 101L:140L), ]) # The 10 last samples of each class are for validation test <- as.matrix(iris[c(41L:50L, 91L:100L, 141L:150L), ]) dtrain <- lgb.Dataset(data = train[, 1L:4L], label = train[, 5L]) dtest <- lgb.Dataset.create.valid(dtrain, data = test[, 1L:4L], label = test[, 5L]) params <- list( objective = "multiclass" , metric = "multi_logloss" , num_class = 3L , learning_rate = 0.00001 , min_data = 1L , verbose = .LGB_VERBOSITY , num_threads = .LGB_MAX_THREADS ) model <- lgb.train( params = params , data = dtrain , nrounds = 3L ) num_trees <- 5L tree_interpretation <- lgb.interprete( model = model , data = test[, 1L:4L] , idxset = seq_len(num_trees) ) expect_identical(class(tree_interpretation), "list") expect_true(length(tree_interpretation) == num_trees) expect_null(names(tree_interpretation)) expect_true(all( sapply( X = tree_interpretation , FUN = function(treeDT) { checks <- c( data.table::is.data.table(treeDT) , identical(names(treeDT), c("Feature", "Class 0", "Class 1", "Class 2")) , is.character(treeDT[, Feature]) , is.numeric(treeDT[, `Class 0`]) , is.numeric(treeDT[, `Class 1`]) , is.numeric(treeDT[, `Class 2`]) ) return(all(checks)) } ) )) }) ================================================ FILE: R-package/tests/testthat/test_lgb.model.dt.tree.R ================================================ NROUNDS <- 10L MAX_DEPTH <- 3L N <- nrow(iris) X <- data.matrix(iris[2L:4L]) FEAT <- colnames(X) NCLASS <- nlevels(iris[, 5L]) model_reg <- lgb.train( params = list( objective = "regression" , num_threads = .LGB_MAX_THREADS , max.depth = MAX_DEPTH ) , data = lgb.Dataset(X, label = iris[, 1L]) , verbose = .LGB_VERBOSITY , nrounds = NROUNDS ) model_binary <- lgb.train( params = list( objective = "binary" , num_threads = .LGB_MAX_THREADS , max.depth = MAX_DEPTH ) , data = lgb.Dataset(X, label = iris[, 5L] == "setosa") , verbose = .LGB_VERBOSITY , nrounds = NROUNDS ) model_multiclass <- lgb.train( params = list( objective = "multiclass" , num_threads = .LGB_MAX_THREADS , max.depth = MAX_DEPTH , num_classes = NCLASS ) , data = lgb.Dataset(X, label = as.integer(iris[, 5L]) - 1L) , verbose = .LGB_VERBOSITY , nrounds = NROUNDS ) model_rank <- lgb.train( params = list( objective = "lambdarank" , num_threads = .LGB_MAX_THREADS , max.depth = MAX_DEPTH , lambdarank_truncation_level = 3L ) , data = lgb.Dataset( X , label = as.integer(iris[, 1L] > 5.8) , group = rep(10L, times = 15L) ) , verbose = .LGB_VERBOSITY , nrounds = NROUNDS ) models <- list( reg = model_reg , bin = model_binary , multi = model_multiclass , rank = model_rank ) for (model_name in names(models)) { model <- models[[model_name]] expected_n_trees <- NROUNDS if (model_name == "multi") { expected_n_trees <- NROUNDS * NCLASS } df <- as.data.frame(lgb.model.dt.tree(model)) df_list <- split(df, f = df$tree_index, drop = TRUE) df_leaf <- df[!is.na(df$leaf_index), ] df_internal <- df[is.na(df$leaf_index), ] test_that("lgb.model.dt.tree() returns the right number of trees", { expect_equal(length(unique(df$tree_index)), expected_n_trees) }) test_that("num_iteration can return less trees", { expect_equal( length(unique(lgb.model.dt.tree(model, num_iteration = 2L)$tree_index)) , 2L * (if (model_name == "multi") NCLASS else 1L) ) }) test_that("Tree index from lgb.model.dt.tree() is in 0:(NROUNS-1)", { expect_equal(unique(df$tree_index), (0L:(expected_n_trees - 1L))) }) test_that("Depth calculated from lgb.model.dt.tree() respects max.depth", { expect_true(max(df$depth) <= MAX_DEPTH) }) test_that("Each tree from lgb.model.dt.tree() has single root node", { expect_equal( unname(sapply(df_list, function(df) sum(df$depth == 0L))) , rep(1L, expected_n_trees) ) }) test_that("Each tree from lgb.model.dt.tree() has two depth 1 nodes", { expect_equal( unname(sapply(df_list, function(df) sum(df$depth == 1L))) , rep(2L, expected_n_trees) ) }) test_that("leaves from lgb.model.dt.tree() do not have split info", { internal_node_cols <- c( "split_index" , "split_feature" , "split_gain" , "threshold" , "decision_type" , "default_left" , "internal_value" , "internal_count" ) expect_true(all(is.na(df_leaf[internal_node_cols]))) }) test_that("leaves from lgb.model.dt.tree() have valid leaf info", { expect_true(all(df_leaf$leaf_index %in% 0L:(2.0^MAX_DEPTH - 1.0))) expect_true(all(is.finite(df_leaf$leaf_value))) expect_true(all(df_leaf$leaf_count > 0L & df_leaf$leaf_count <= N)) }) test_that("non-leaves from lgb.model.dt.tree() do not have leaf info", { leaf_node_cols <- c( "leaf_index", "leaf_parent", "leaf_value", "leaf_count" ) expect_true(all(is.na(df_internal[leaf_node_cols]))) }) test_that("non-leaves from lgb.model.dt.tree() have valid split info", { expect_true( all( sapply( split(df_internal, df_internal$tree_index), function(x) all(x$split_index %in% 0L:(nrow(x) - 1L)) ) ) ) expect_true(all(df_internal$split_feature %in% FEAT)) num_cols <- c("split_gain", "threshold", "internal_value") expect_true(all(is.finite(unlist(df_internal[, num_cols])))) # range of decision type? expect_true(all(df_internal$default_left %in% c(TRUE, FALSE))) counts <- df_internal$internal_count expect_true(all(counts > 1L & counts <= N)) }) } test_that("num_iteration and start_iteration work as expected", { set.seed(1L) data(agaricus.train, package = "lightgbm") train <- agaricus.train bst <- lightgbm( data = as.matrix(train$data) , label = train$label , params = list(objective = "binary", num_threads = .LGB_MAX_THREADS) , nrounds = 5L , verbose = .LGB_VERBOSITY ) first2 <- lgb.model.dt.tree(bst, num_iteration = 2L) last3 <- lgb.model.dt.tree(bst, num_iteration = 3L, start_iteration = 3L) all5 <- lgb.model.dt.tree(bst) too_many <- lgb.model.dt.tree(bst, num_iteration = 10L) expect_equal(data.table::rbindlist(list(first2, last3)), all5) expect_equal(too_many, all5) # Check tree indices expect_equal(unique(first2[["tree_index"]]), 0L:1L) expect_equal(unique(last3[["tree_index"]]), 2L:4L) expect_equal(unique(all5[["tree_index"]]), 0L:4L) }) ================================================ FILE: R-package/tests/testthat/test_lgb.plot.importance.R ================================================ test_that("lgb.plot.importance() should run without error for well-formed inputs", { data(agaricus.train, package = "lightgbm") train <- agaricus.train dtrain <- lgb.Dataset(train$data, label = train$label) params <- list( objective = "binary" , learning_rate = 0.01 , num_leaves = 63L , max_depth = -1L , min_data_in_leaf = 1L , min_sum_hessian_in_leaf = 1.0 , verbosity = .LGB_VERBOSITY , num_threads = .LGB_MAX_THREADS ) model <- lgb.train(params, dtrain, 3L) tree_imp <- lgb.importance(model, percentage = TRUE) # Check that there are no plots present before plotting expect_null(dev.list()) args_no_cex <- list( "tree_imp" = tree_imp , top_n = 10L , measure = "Gain" ) args_cex <- args_no_cex args_cex[["cex"]] <- 0.75 for (arg_list in list(args_no_cex, args_cex)) { resDT <- do.call( what = lgb.plot.importance , args = arg_list ) # Check that lgb.plot.importance() returns the data.table of the plotted data expect_true(data.table::is.data.table(resDT)) expect_named(resDT, c("Feature", "Gain", "Cover", "Frequency")) # Check that a plot was produced expect_false(is.null(dev.list())) # remove all plots dev.off() expect_null(dev.list()) } }) ================================================ FILE: R-package/tests/testthat/test_lgb.plot.interpretation.R ================================================ .sigmoid <- function(x) { 1.0 / (1.0 + exp(-x)) } .logit <- function(x) { log(x / (1.0 - x)) } test_that("lgb.plot.interpretation works as expected for binary classification", { data(agaricus.train, package = "lightgbm") train <- agaricus.train dtrain <- lgb.Dataset(train$data, label = train$label) set_field( dataset = dtrain , field_name = "init_score" , data = rep( .logit(mean(train$label)) , length(train$label) ) ) data(agaricus.test, package = "lightgbm") test <- agaricus.test params <- list( objective = "binary" , learning_rate = 0.01 , num_leaves = 63L , max_depth = -1L , min_data_in_leaf = 1L , min_sum_hessian_in_leaf = 1.0 , verbosity = .LGB_VERBOSITY , num_threads = .LGB_MAX_THREADS ) model <- lgb.train( params = params , data = dtrain , nrounds = 3L ) num_trees <- 5L tree_interpretation <- lgb.interprete( model = model , data = test$data , idxset = seq_len(num_trees) ) expect_true({ lgb.plot.interpretation( tree_interpretation_dt = tree_interpretation[[1L]] , top_n = 5L ) TRUE }) # should also work when you explicitly pass cex plot_res <- lgb.plot.interpretation( tree_interpretation_dt = tree_interpretation[[1L]] , top_n = 5L , cex = 0.95 ) expect_null(plot_res) }) test_that("lgb.plot.interpretation works as expected for multiclass classification", { data(iris) # We must convert factors to numeric # They must be starting from number 0 to use multiclass # For instance: 0, 1, 2, 3, 4, 5... iris$Species <- as.numeric(as.factor(iris$Species)) - 1L # Create imbalanced training data (20, 30, 40 examples for classes 0, 1, 2) train <- as.matrix(iris[c(1L:20L, 51L:80L, 101L:140L), ]) # The 10 last samples of each class are for validation test <- as.matrix(iris[c(41L:50L, 91L:100L, 141L:150L), ]) dtrain <- lgb.Dataset(data = train[, 1L:4L], label = train[, 5L]) dtest <- lgb.Dataset.create.valid(dtrain, data = test[, 1L:4L], label = test[, 5L]) params <- list( objective = "multiclass" , metric = "multi_logloss" , num_class = 3L , learning_rate = 0.00001 , min_data = 1L , num_threads = .LGB_MAX_THREADS ) model <- lgb.train( params = params , data = dtrain , nrounds = 3L , verbose = .LGB_VERBOSITY ) num_trees <- 5L tree_interpretation <- lgb.interprete( model = model , data = test[, 1L:4L] , idxset = seq_len(num_trees) ) plot_res <- lgb.plot.interpretation( tree_interpretation_dt = tree_interpretation[[1L]] , top_n = 5L ) expect_null(plot_res) }) ================================================ FILE: R-package/tests/testthat/test_metrics.R ================================================ test_that(".METRICS_HIGHER_BETTER() should be well formed", { metrics <- .METRICS_HIGHER_BETTER() metric_names <- names(.METRICS_HIGHER_BETTER()) # should be a logical vector expect_true(is.logical(metrics)) # no metrics should be repeated expect_true(length(unique(metric_names)) == length(metrics)) # should not be any NAs expect_false(anyNA(metrics)) }) ================================================ FILE: R-package/tests/testthat/test_multithreading.R ================================================ test_that("getLGBMthreads() and setLGBMthreads() work as expected", { # works with integer input ret <- setLGBMthreads(2L) expect_null(ret) expect_equal(getLGBMthreads(), 2L) # works with float input ret <- setLGBMthreads(1.0) expect_null(ret) expect_equal(getLGBMthreads(), 1L) # setting to any negative number sets max threads to -1 ret <- setLGBMthreads(-312L) expect_null(ret) expect_equal(getLGBMthreads(), -1L) }) ================================================ FILE: R-package/tests/testthat/test_parameters.R ================================================ data(agaricus.train, package = "lightgbm") data(agaricus.test, package = "lightgbm") train <- agaricus.train test <- agaricus.test test_that("Feature penalties work properly", { # Fit a series of models with varying penalty on most important variable var_name <- "odor=none" var_index <- which(train$data@Dimnames[[2L]] == var_name) bst <- lapply(seq(1.0, 0.0, by = -0.1), function(x) { feature_penalties <- rep(1.0, ncol(train$data)) feature_penalties[var_index] <- x lightgbm( data = train$data , label = train$label , params = list( num_leaves = 5L , learning_rate = 0.05 , objective = "binary" , feature_penalty = paste(feature_penalties, collapse = ",") , metric = "binary_error" , num_threads = .LGB_MAX_THREADS ) , nrounds = 5L , verbose = -1L ) }) var_gain <- lapply(bst, function(x) lgb.importance(x)[Feature == var_name, Gain]) var_cover <- lapply(bst, function(x) lgb.importance(x)[Feature == var_name, Cover]) var_freq <- lapply(bst, function(x) lgb.importance(x)[Feature == var_name, Frequency]) # Ensure that feature gain, cover, and frequency decreases with stronger penalties expect_true(all(diff(unlist(var_gain)) <= 0.0)) expect_true(all(diff(unlist(var_cover)) <= 0.0)) expect_true(all(diff(unlist(var_freq)) <= 0.0)) expect_lt(min(diff(unlist(var_gain))), 0.0) expect_lt(min(diff(unlist(var_cover))), 0.0) expect_lt(min(diff(unlist(var_freq))), 0.0) # Ensure that feature is not used when feature_penalty = 0 expect_length(var_gain[[length(var_gain)]], 0L) }) test_that(".PARAMETER_ALIASES() returns a named list of character vectors, where names are unique", { param_aliases <- .PARAMETER_ALIASES() expect_identical(class(param_aliases), "list") expect_true(length(param_aliases) > 100L) expect_true(is.character(names(param_aliases))) expect_true(is.character(param_aliases[["boosting"]])) expect_true(is.character(param_aliases[["early_stopping_round"]])) expect_true(is.character(param_aliases[["num_iterations"]])) expect_true(is.character(param_aliases[["pre_partition"]])) expect_true(length(names(param_aliases)) == length(param_aliases)) expect_true(all(sapply(param_aliases, is.character))) expect_true(length(unique(names(param_aliases))) == length(param_aliases)) expect_equal(sort(param_aliases[["task"]]), c("task", "task_type")) expect_equal(param_aliases[["bagging_fraction"]], c("bagging_fraction", "bagging", "sub_row", "subsample")) }) test_that(".PARAMETER_ALIASES() uses the internal session cache", { cache_key <- "PARAMETER_ALIASES" # clear cache, so this test isn't reliant on the order unit tests are run in if (exists(cache_key, where = .lgb_session_cache_env)) { rm(list = cache_key, envir = .lgb_session_cache_env) } expect_false(exists(cache_key, where = .lgb_session_cache_env)) # check that result looks correct for at least one parameter iter_aliases <- .PARAMETER_ALIASES()[["num_iterations"]] expect_true(is.character(iter_aliases)) expect_true(all(c("num_round", "nrounds") %in% iter_aliases)) # patch the cache to check that .PARAMETER_ALIASES() checks it assign( x = cache_key , value = list(num_iterations = c("test", "other_test")) , envir = .lgb_session_cache_env ) iter_aliases <- .PARAMETER_ALIASES()[["num_iterations"]] expect_equal(iter_aliases, c("test", "other_test")) # re-set cache so this doesn't interfere with other unit tests if (exists(cache_key, where = .lgb_session_cache_env)) { rm(list = cache_key, envir = .lgb_session_cache_env) } expect_false(exists(cache_key, where = .lgb_session_cache_env)) }) test_that("training should warn if you use 'dart' boosting with early stopping", { for (boosting_param in .PARAMETER_ALIASES()[["boosting"]]) { params <- list( num_leaves = 5L , learning_rate = 0.05 , objective = "binary" , metric = "binary_error" , num_threads = .LGB_MAX_THREADS ) params[[boosting_param]] <- "dart" # warning: early stopping requested expect_warning({ result <- lightgbm( data = train$data , label = train$label , params = params , nrounds = 2L , verbose = .LGB_VERBOSITY , early_stopping_rounds = 1L ) }, regexp = "Early stopping is not available in 'dart' mode") # no warning: early stopping not requested expect_silent({ result <- lightgbm( data = train$data , label = train$label , params = params , nrounds = 2L , verbose = .LGB_VERBOSITY , early_stopping_rounds = NULL ) }) } }) test_that("lgb.cv() should warn if you use 'dart' boosting with early stopping", { for (boosting_param in .PARAMETER_ALIASES()[["boosting"]]) { params <- list( num_leaves = 5L , objective = "binary" , metric = "binary_error" , num_threads = .LGB_MAX_THREADS ) params[[boosting_param]] <- "dart" # warning: early stopping requested expect_warning({ result <- lgb.cv( data = lgb.Dataset( data = train$data , label = train$label ) , params = params , nrounds = 2L , verbose = .LGB_VERBOSITY , early_stopping_rounds = 1L ) }, regexp = "Early stopping is not available in 'dart' mode") # no warning: early stopping not requested expect_silent({ result <- lgb.cv( data = lgb.Dataset( data = train$data , label = train$label ) , params = params , nrounds = 2L , verbose = .LGB_VERBOSITY , early_stopping_rounds = NULL ) }) } }) ================================================ FILE: R-package/tests/testthat/test_utils.R ================================================ test_that(".params2str() works as expected for empty lists", { out_str <- .params2str( params = list() ) expect_identical(class(out_str), "character") expect_equal(out_str, "") }) test_that(".params2str() works as expected for a key in params with multiple different-length elements", { metrics <- c("a", "ab", "abc", "abcdefg") params <- list( objective = "magic" , metric = metrics , nrounds = 10L , learning_rate = 0.0000001 ) out_str <- .params2str( params = params ) expect_identical(class(out_str), "character") expect_identical( out_str , "objective=magic metric=a,ab,abc,abcdefg nrounds=10 learning_rate=0.0000001" ) }) test_that(".params2str() passes through duplicated params", { out_str <- .params2str( params = list( objective = "regression" , bagging_fraction = 0.8 , bagging_fraction = 0.5 # nolint: duplicate_argument. ) ) expect_equal(out_str, "objective=regression bagging_fraction=0.8 bagging_fraction=0.5") }) test_that(".check_eval works as expected with no metric", { params <- .check_eval( params = list(device = "cpu") , eval = "binary_error" ) expect_named(params, c("device", "metric")) expect_identical(params[["metric"]], list("binary_error")) }) test_that(".check_eval adds eval to metric in params", { params <- .check_eval( params = list(metric = "auc") , eval = "binary_error" ) expect_named(params, "metric") expect_identical(params[["metric"]], list("auc", "binary_error")) }) test_that(".check_eval adds eval to metric in params if two evaluation names are provided", { params <- .check_eval( params = list(metric = "auc") , eval = c("binary_error", "binary_logloss") ) expect_named(params, "metric") expect_identical(params[["metric"]], list("auc", "binary_error", "binary_logloss")) }) test_that(".check_eval adds eval to metric in params if a list is provided", { params <- .check_eval( params = list(metric = "auc") , eval = list("binary_error", "binary_logloss") ) expect_named(params, "metric") expect_identical(params[["metric"]], list("auc", "binary_error", "binary_logloss")) }) test_that(".check_eval drops duplicate metrics and preserves order", { params <- .check_eval( params = list(metric = "l1") , eval = list("l2", "rmse", "l1", "rmse") ) expect_named(params, "metric") expect_identical(params[["metric"]], list("l1", "l2", "rmse")) }) test_that(".check_wrapper_param() uses passed-in keyword arg if no alias found in params", { kwarg_val <- sample(seq_len(100L), size = 1L) params <- .check_wrapper_param( main_param_name = "num_iterations" , params = list() , alternative_kwarg_value = kwarg_val ) expect_equal(params[["num_iterations"]], kwarg_val) }) test_that(".check_wrapper_param() prefers main parameter to alias and keyword arg", { num_iterations <- sample(seq_len(100L), size = 1L) kwarg_val <- sample(seq_len(100L), size = 1L) params <- .check_wrapper_param( main_param_name = "num_iterations" , params = list( num_iterations = num_iterations , num_tree = sample(seq_len(100L), size = 1L) , n_estimators = sample(seq_len(100L), size = 1L) ) , alternative_kwarg_value = kwarg_val ) expect_equal(params[["num_iterations"]], num_iterations) # aliases should be removed expect_identical(params, list(num_iterations = num_iterations)) }) test_that(".check_wrapper_param() prefers alias to keyword arg", { n_estimators <- sample(seq_len(100L), size = 1L) num_tree <- sample(seq_len(100L), size = 1L) kwarg_val <- sample(seq_len(100L), size = 1L) params <- .check_wrapper_param( main_param_name = "num_iterations" , params = list( num_tree = num_tree , n_estimators = n_estimators ) , alternative_kwarg_value = kwarg_val ) expect_equal(params[["num_iterations"]], num_tree) expect_identical(params, list(num_iterations = num_tree)) # switching the order shouldn't switch which one is chosen params2 <- .check_wrapper_param( main_param_name = "num_iterations" , params = list( n_estimators = n_estimators , num_tree = num_tree ) , alternative_kwarg_value = kwarg_val ) expect_equal(params2[["num_iterations"]], num_tree) expect_identical(params2, list(num_iterations = num_tree)) }) test_that(".equal_or_both_null produces expected results", { expect_true(.equal_or_both_null(NULL, NULL)) expect_false(.equal_or_both_null(1.0, NULL)) expect_false(.equal_or_both_null(NULL, 1.0)) expect_true(.equal_or_both_null(1.0, 1.0)) expect_true(.equal_or_both_null(1.0, 1L)) expect_false(.equal_or_both_null(NA, NULL)) expect_false(.equal_or_both_null(NULL, NA)) expect_false(.equal_or_both_null(10.0, 1L)) expect_true(.equal_or_both_null(0L, 0L)) }) test_that(".check_interaction_constraints() adds skipped features", { ref <- letters[1L:5L] ic_num <- list(1L, c(2L, 3L)) ic_char <- list("a", c("b", "c")) expected <- list("[0]", "[1,2]", "[3,4]") ic_checked_num <- .check_interaction_constraints( interaction_constraints = ic_num, column_names = ref ) ic_checked_char <- .check_interaction_constraints( interaction_constraints = ic_char, column_names = ref ) expect_equal(ic_checked_num, expected) expect_equal(ic_checked_char, expected) }) ================================================ FILE: R-package/tests/testthat/test_weighted_loss.R ================================================ test_that("Gamma regression reacts on 'weight'", { n <- 100L set.seed(87L) X <- matrix(runif(2L * n), ncol = 2L) y <- X[, 1L] + X[, 2L] + runif(n) X_pred <- X[1L:5L, ] params <- list(objective = "gamma", num_threads = .LGB_MAX_THREADS) # Unweighted dtrain <- lgb.Dataset(X, label = y) bst <- lgb.train( params = params , data = dtrain , nrounds = 4L , verbose = .LGB_VERBOSITY ) pred_unweighted <- predict(bst, X_pred) # Constant weight 1 dtrain <- lgb.Dataset( X , label = y , weight = rep(1.0, n) ) bst <- lgb.train( params = params , data = dtrain , nrounds = 4L , verbose = .LGB_VERBOSITY ) pred_weighted_1 <- predict(bst, X_pred) # Constant weight 2 dtrain <- lgb.Dataset( X , label = y , weight = rep(2.0, n) ) bst <- lgb.train( params = params , data = dtrain , nrounds = 4L , verbose = .LGB_VERBOSITY ) pred_weighted_2 <- predict(bst, X_pred) # Non-constant weights dtrain <- lgb.Dataset( X , label = y , weight = seq(0.0, 1.0, length.out = n) ) bst <- lgb.train( params = params , data = dtrain , nrounds = 4L , verbose = .LGB_VERBOSITY ) pred_weighted <- predict(bst, X_pred) expect_equal(pred_unweighted, pred_weighted_1) expect_equal(pred_weighted_1, pred_weighted_2) expect_false(all(pred_unweighted == pred_weighted)) }) ================================================ FILE: R-package/tests/testthat.R ================================================ library(testthat) library(lightgbm) # nolint: unused_import. test_check( package = "lightgbm" , stop_on_failure = TRUE , stop_on_warning = FALSE , reporter = testthat::SummaryReporter$new() ) ================================================ FILE: R-package/vignettes/basic_walkthrough.Rmd ================================================ --- title: "Basic Walkthrough" description: > This vignette describes how to train a LightGBM model for binary classification. output: markdown::html_format: options: toc: true number_sections: true vignette: > %\VignetteIndexEntry{Basic Walkthrough} %\VignetteEngine{knitr::knitr} %\VignetteEncoding{UTF-8} --- ```{r, include = FALSE} knitr::opts_chunk$set( collapse = TRUE , comment = "#>" , warning = FALSE , message = FALSE ) ``` ## Introduction Welcome to the world of [LightGBM](https://lightgbm.readthedocs.io/en/latest/), a highly efficient gradient boosting implementation (Ke et al. 2017). ```{r} library(lightgbm) ``` ```{r, include=FALSE} # limit number of threads used, to be respectful of CRAN's resources when it checks this vignette data.table::setDTthreads(1L) setLGBMthreads(2L) ``` This vignette will guide you through its basic usage. It will show how to build a simple binary classification model based on a subset of the `bank` dataset (Moro, Cortez, and Rita 2014). You will use the two input features "age" and "balance" to predict whether a client has subscribed a term deposit. ## The dataset The dataset looks as follows. ```{r} data(bank, package = "lightgbm") bank[1L:5L, c("y", "age", "balance")] # Distribution of the response table(bank$y) ``` ## Training the model The R-package of LightGBM offers two functions to train a model: - `lgb.train()`: This is the main training logic. It offers full flexibility but requires a `Dataset` object created by the `lgb.Dataset()` function. - `lightgbm()`: Simpler, but less flexible. Data can be passed without having to bother with `lgb.Dataset()`. ### Using the `lightgbm()` function In a first step, you need to convert data to numeric. Afterwards, you are ready to fit the model by the `lightgbm()` function. ```{r} # Numeric response and feature matrix y <- as.numeric(bank$y == "yes") X <- data.matrix(bank[, c("age", "balance")]) # Train fit <- lightgbm( data = X , label = y , params = list( num_leaves = 4L , learning_rate = 1.0 , objective = "binary" ) , nrounds = 10L , verbose = -1L ) # Result summary(predict(fit, X)) ``` It seems to have worked! And the predictions are indeed probabilities between 0 and 1. ### Using the `lgb.train()` function Alternatively, 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. ```{r} # Data interface dtrain <- lgb.Dataset(X, label = y) # Parameters params <- list( objective = "binary" , num_leaves = 4L , learning_rate = 1.0 ) # Train fit <- lgb.train( params , data = dtrain , nrounds = 10L , verbose = -1L ) ``` Try it out! If stuck, visit LightGBM's [documentation](https://lightgbm.readthedocs.io/en/latest/R/index.html) for more details. ```{r, echo = FALSE, results = "hide"} # Cleanup if (file.exists("lightgbm.model")) { file.remove("lightgbm.model") } ``` ## References Ke, 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). Moro, Sérgio, Paulo Cortez, and Paulo Rita. 2014. "A Data-Driven Approach to Predict the Success of Bank Telemarketing." Decision Support Systems 62: 22–31. ================================================ FILE: README.md ================================================ > [!NOTE] > This project moved from `Microsoft/LightGBM` to `lightgbm-org/LightGBM` in March 2026. > This repository is still the official LightGBM source code, managed by the same maintainers (including the creator of LightGBM). > For details, see https://github.com/lightgbm-org/LightGBM/issues/7187 Light Gradient Boosting Machine =============================== [![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) [![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) [![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) [![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) [![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) [![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) [![Appveyor Build Status](https://ci.appveyor.com/api/projects/status/1ys5ot401m0fep6l/branch/master?svg=true)](https://ci.appveyor.com/project/guolinke/lightgbm/branch/master) [![Documentation Status](https://readthedocs.org/projects/lightgbm/badge/?version=latest)](https://lightgbm.readthedocs.io/) [![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) [![License](https://img.shields.io/github/license/lightgbm-org/lightgbm.svg)](https://github.com/lightgbm-org/LightGBM/blob/master/LICENSE) [![EffVer Versioning](https://img.shields.io/badge/version_scheme-EffVer-0097a7)](https://jacobtomlinson.dev/effver) [![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) [![Python Versions](https://img.shields.io/pypi/pyversions/lightgbm.svg?logo=python&logoColor=white)](https://pypi.org/project/lightgbm) [![PyPI Version](https://img.shields.io/pypi/v/lightgbm.svg?logo=pypi&logoColor=white)](https://pypi.org/project/lightgbm) [![conda Version](https://img.shields.io/conda/vn/conda-forge/lightgbm?logo=conda-forge&logoColor=white&label=conda)](https://anaconda.org/conda-forge/lightgbm) [![CRAN Version](https://www.r-pkg.org/badges/version/lightgbm)](https://cran.r-project.org/package=lightgbm) [![NuGet Version](https://img.shields.io/nuget/v/lightgbm?logo=nuget&logoColor=white)](https://www.nuget.org/packages/LightGBM) [![Winget Version](https://img.shields.io/winget/v/Microsoft.LightGBM)](https://github.com/microsoft/winget-pkgs/tree/master/manifests/m/Microsoft/LightGBM) LightGBM is a gradient boosting framework that uses tree based learning algorithms. It is designed to be distributed and efficient with the following advantages: - Faster training speed and higher efficiency. - Lower memory usage. - Better accuracy. - Support of parallel, distributed, and GPU learning. - Capable of handling large-scale data. For further details, please refer to [Features](https://github.com/lightgbm-org/LightGBM/blob/master/docs/Features.rst). Benefiting 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. [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. Get Started and Documentation ----------------------------- Our 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. Next you may want to read: - [**Examples**](https://github.com/lightgbm-org/LightGBM/tree/master/examples) showing command line usage of common tasks. - [**Features**](https://github.com/lightgbm-org/LightGBM/blob/master/docs/Features.rst) and algorithms supported by LightGBM. - [**Parameters**](https://github.com/lightgbm-org/LightGBM/blob/master/docs/Parameters.rst) is an exhaustive list of customization you can make. - [**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. - [**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/)). - [**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)). - [**Understanding LightGBM Parameters (and How to Tune Them using Neptune)**](https://neptune.ai/blog/lightgbm-parameters-guide). Documentation for contributors: - [**How we update readthedocs.io**](https://github.com/lightgbm-org/LightGBM/blob/master/docs/README.rst). - Check out the [**Development Guide**](https://github.com/lightgbm-org/LightGBM/blob/master/docs/Development-Guide.rst). News ---- Please refer to changelogs at [GitHub releases](https://github.com/lightgbm-org/LightGBM/releases) page. External (Unofficial) Repositories ---------------------------------- Projects listed here offer alternative ways to use LightGBM. They are not maintained or officially endorsed by the `LightGBM` development team. JPMML (Java PMML converter): https://github.com/jpmml/jpmml-lightgbm Nyoka (Python PMML converter): https://github.com/SoftwareAG/nyoka Treelite (model compiler for efficient deployment): https://github.com/dmlc/treelite lleaves (LLVM-based model compiler for efficient inference): https://github.com/siboehm/lleaves Hummingbird (model compiler into tensor computations): https://github.com/microsoft/hummingbird GBNet (use `LightGBM` as a [PyTorch Module](https://docs.pytorch.org/docs/stable/generated/torch.nn.Module.html)): https://github.com/mthorrell/gbnet cuML Forest Inference Library (GPU-accelerated inference): https://github.com/rapidsai/cuml daal4py (Intel CPU-accelerated inference): https://github.com/intel/scikit-learn-intelex/tree/master/daal4py m2cgen (model appliers for various languages): https://github.com/BayesWitnesses/m2cgen leaves (Go model applier): https://github.com/dmitryikh/leaves ONNXMLTools (ONNX converter): https://github.com/onnx/onnxmltools SHAP (model output explainer): https://github.com/slundberg/shap Shapash (model visualization and interpretation): https://github.com/MAIF/shapash dtreeviz (decision tree visualization and model interpretation): https://github.com/parrt/dtreeviz supertree (interactive visualization of decision trees): https://github.com/mljar/supertree SynapseML (LightGBM on Spark): https://github.com/microsoft/SynapseML Kubeflow Fairing (LightGBM on Kubernetes): https://github.com/kubeflow/fairing Kubeflow Operator (LightGBM on Kubernetes): https://github.com/kubeflow/xgboost-operator lightgbm_ray (LightGBM on Ray): https://github.com/ray-project/lightgbm_ray Ray (distributed computing framework): https://github.com/ray-project/ray Mars (LightGBM on Mars): https://github.com/mars-project/mars ML.NET (.NET/C#-package): https://github.com/dotnet/machinelearning LightGBM.NET (.NET/C#-package): https://github.com/rca22/LightGBM.Net LightGBM Ruby (Ruby gem): https://github.com/ankane/lightgbm-ruby LightGBM4j (Java high-level binding): https://github.com/metarank/lightgbm4j LightGBM4J (JVM interface for LightGBM written in Scala): https://github.com/seek-oss/lightgbm4j Julia-package: https://github.com/IQVIA-ML/LightGBM.jl lightgbm3 (Rust binding): https://github.com/Mottl/lightgbm3-rs MLServer (inference server for LightGBM): https://github.com/SeldonIO/MLServer MLflow (experiment tracking, model monitoring framework): https://github.com/mlflow/mlflow FLAML (AutoML library for hyperparameter optimization): https://github.com/microsoft/FLAML MLJAR AutoML (AutoML on tabular data): https://github.com/mljar/mljar-supervised Optuna (hyperparameter optimization framework): https://github.com/optuna/optuna LightGBMLSS (probabilistic modelling with LightGBM): https://github.com/StatMixedML/LightGBMLSS mlforecast (time series forecasting with LightGBM): https://github.com/Nixtla/mlforecast skforecast (time series forecasting with LightGBM): https://github.com/JoaquinAmatRodrigo/skforecast `{bonsai}` (R `{parsnip}`-compliant interface): https://github.com/tidymodels/bonsai `{mlr3extralearners}` (R `{mlr3}`-compliant interface): https://github.com/mlr-org/mlr3extralearners lightgbm-transform (feature transformation binding): https://github.com/lightgbm-org/LightGBM-transform `postgresml` (LightGBM training and prediction in SQL, via a Postgres extension): https://github.com/postgresml/postgresml `pyodide` (run `lightgbm` Python-package in a web browser): https://github.com/pyodide/pyodide `vaex-ml` (Python DataFrame library with its own interface to LightGBM): https://github.com/vaexio/vaex Support ------- - Ask a question [on Stack Overflow with the `lightgbm` tag](https://stackoverflow.com/questions/ask?tags=lightgbm), we monitor this for new questions. - Open **bug reports** and **feature requests** on [GitHub issues](https://github.com/lightgbm-org/LightGBM/issues). How to Contribute ----------------- Check [CONTRIBUTING](https://github.com/lightgbm-org/LightGBM/blob/master/CONTRIBUTING.md) page. Microsoft Open Source Code of Conduct ------------------------------------- This 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. Reference Papers ---------------- Yu 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. 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](https://proceedings.neurips.cc/paper/2017/hash/6449f44a102fde848669bdd9eb6b76fa-Abstract.html)". Advances in Neural Information Processing Systems 30 (NIPS 2017), pp. 3149-3157. Qi 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. Huan Zhang, Si Si and Cho-Jui Hsieh. "[GPU Acceleration for Large-scale Tree Boosting](https://arxiv.org/abs/1706.08359)". SysML Conference, 2018. License ------- This project is licensed under the terms of the MIT license. See [LICENSE](https://github.com/lightgbm-org/LightGBM/blob/master/LICENSE) for additional details. ================================================ FILE: SECURITY.md ================================================ ## Security Microsoft 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/). If 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. ## Reporting Security Issues **Please do not report security vulnerabilities through public GitHub issues.** Instead, please report them to the Microsoft Security Response Center (MSRC) at [https://msrc.microsoft.com/create-report](https://aka.ms/opensource/security/create-report). If 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). You 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. 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Please visit our [Microsoft Bug Bounty Program](https://aka.ms/opensource/security/bounty) page for more details about our active programs. ## Preferred Languages We prefer all communications to be in English. ## Policy Microsoft follows the principle of [Coordinated Vulnerability Disclosure](https://aka.ms/opensource/security/cvd). ================================================ FILE: VERSION.txt ================================================ 4.6.0.99 ================================================ FILE: biome.json ================================================ { "root": false, "vcs": { "enabled": true, "clientKind": "git", "useIgnoreFile": true }, "files": { "includes": [ "**", "!build", "!external_libs", "!lightgbm-python", "!lightgbm_r" ] }, "formatter": { "enabled": true, "expand": "always", "useEditorconfig": true, "lineWidth": 120 }, "assist": { "enabled": true, "actions": { "recommended": true } }, "linter": { "enabled": true, "domains": { "project": "all" }, "rules": { "recommended": true } } } ================================================ FILE: build-cran-package.sh ================================================ #!/bin/sh # [description] # Prepare a source distribution of the R-package # to be submitted to CRAN. # # [arguments] # # --r-executable Customize the R executable used by `R CMD build`. # Useful if building the R-package in an environment with # non-standard builds of R, such as those provided in # https://github.com/wch/r-debug. # # --no-build-vignettes Pass this flag to skip creating vignettes. # You might want to do this to avoid installing # vignette-only dependencies, or to avoid # portability issues. # # [usage] # # # default usage # sh build-cran-package.sh # # # custom R build # sh build-cran-package.sh --r-executable=RDvalgrind # # # skip vignette building # sh build-cran-package.sh --no-build-vignettes set -e -u # Default values of arguments BUILD_VIGNETTES=true LGB_R_EXECUTABLE=R while [ $# -gt 0 ]; do case "$1" in --r-executable=*) LGB_R_EXECUTABLE="${1#*=}" ;; --no-build-vignettes*) BUILD_VIGNETTES=false ;; *) echo "invalid argument '${1}'" exit 1 ;; esac shift done echo "Building lightgbm with R executable: ${LGB_R_EXECUTABLE}" ORIG_WD="$(pwd)" TEMP_R_DIR="$(pwd)/lightgbm_r" if test -d "${TEMP_R_DIR}"; then rm -r "${TEMP_R_DIR}" fi mkdir -p "${TEMP_R_DIR}" CURRENT_DATE=$(date +'%Y-%m-%d') # R packages cannot have versions like 3.0.0rc1, but # 3.0.0-1 is acceptable LGB_VERSION=$(head -1 ./VERSION.txt | sed "s/rc/-/g") # move relevant files cp -R R-package/* "${TEMP_R_DIR}" cp -R include "${TEMP_R_DIR}/src/" cp -R src/* "${TEMP_R_DIR}/src/" if ${BUILD_VIGNETTES} ; then cp docs/logo/LightGBM_logo_black_text.svg "${TEMP_R_DIR}/vignettes/" fi cp \ external_libs/fast_double_parser/include/fast_double_parser.h \ "${TEMP_R_DIR}/src/include/LightGBM/utils" mkdir -p "${TEMP_R_DIR}/src/include/LightGBM/utils/fmt" cp \ external_libs/fmt/include/fmt/*.h \ "${TEMP_R_DIR}/src/include/LightGBM/utils/fmt" # including only specific files from Eigen, to keep the R-package # small and avoid redistributing code with licenses incompatible with # LightGBM's license EIGEN_R_DIR="${TEMP_R_DIR}/src/include/Eigen" mkdir -p "${EIGEN_R_DIR}" modules="Cholesky Core Dense Eigenvalues Geometry Householder Jacobi LU QR SVD" for eigen_module in ${modules}; do cp "external_libs/eigen/Eigen/${eigen_module}" "${EIGEN_R_DIR}/${eigen_module}" if [ "${eigen_module}" != "Dense" ]; then mkdir -p "${EIGEN_R_DIR}/src/${eigen_module}/" cp -R "external_libs/eigen/Eigen/src/${eigen_module}"/* "${EIGEN_R_DIR}/src/${eigen_module}/" fi done mkdir -p "${EIGEN_R_DIR}/src/misc" cp -R external_libs/eigen/Eigen/src/misc/* "${EIGEN_R_DIR}/src/misc/" mkdir -p "${EIGEN_R_DIR}/src/plugins" cp -R external_libs/eigen/Eigen/src/plugins/* "${EIGEN_R_DIR}/src/plugins/" cd "${TEMP_R_DIR}" # Remove files not needed for CRAN echo "Removing files not needed for CRAN" rm src/install.libs.R rm -r inst/ rm -r pkgdown/ rm cran-comments.md rm AUTOCONF_UBUNTU_VERSION rm recreate-configure.sh # files only used by the lightgbm CLI aren't needed for # the R-package rm src/application/application.cpp rm src/include/LightGBM/application.h rm src/main.cpp # configure.ac and DESCRIPTION have placeholders for version # and date so they don't have to be updated manually sed -i.bak -e "s/~~VERSION~~/${LGB_VERSION}/" configure.ac sed -i.bak -e "s/~~VERSION~~/${LGB_VERSION}/" DESCRIPTION sed -i.bak -e "s/~~DATE~~/${CURRENT_DATE}/" DESCRIPTION # Remove 'region', 'endregion', and 'warning' pragmas. # This won't change the correctness of the code. CRAN does # not allow you to use compiler flag '-Wno-unknown-pragmas' or # pragmas that suppress warnings. echo "Removing unknown pragmas in headers" find . \( -name '*.h' -o -name '*.hpp' -o -name '*.cpp' \) -exec \ sed \ -i.bak \ -e 's/^.*#pragma clang diagnostic.*$//' \ -e 's/^.*#pragma diag_suppress.*$//' \ -e 's/^.*#pragma GCC diagnostic.*$//' \ -e 's/^.*#pragma region.*$//' \ -e 's/^.*#pragma endregion.*$//' \ -e 's/^.*#pragma warning.*$//' \ {} + # 'processx' is listed as a 'Suggests' dependency in DESCRIPTION # because it is used in install.libs.R, a file that is not # included in the CRAN distribution of the package sed \ -i.bak \ '/processx/d' \ DESCRIPTION echo "Cleaning sed backup files" find . -name '*.bak' -exec rm {} \; cd "${ORIG_WD}" if ${BUILD_VIGNETTES} ; then "${LGB_R_EXECUTABLE}" CMD build \ --keep-empty-dirs \ lightgbm_r echo "removing object files created by vignettes" rm -rf ./_tmp mkdir _tmp TARBALL_NAME="lightgbm_${LGB_VERSION}.tar.gz" mv "${TARBALL_NAME}" _tmp/ echo "untarring ${TARBALL_NAME}" cd _tmp tar -xf "${TARBALL_NAME}" > /dev/null 2>&1 rm -f "${TARBALL_NAME}" echo "done untarring ${TARBALL_NAME}" # Object files are left behind from compiling the library to generate vignettes. # Approaches like using tar --exclude=*.so to exclude them are not portable # (for example, don't work with some versions of tar on Windows). # # Removing them manually here removes the need to use tar --exclude. # # For background, see https://github.com/lightgbm-org/LightGBM/pull/3946#pullrequestreview-799415812. rm -f ./lightgbm/src/*.o rm -f ./lightgbm/src/boosting/*.o rm -f ./lightgbm/src/io/*.o rm -f ./lightgbm/src/metric/*.o rm -f ./lightgbm/src/network/*.o rm -f ./lightgbm/src/objective/*.o rm -f ./lightgbm/src/treelearner/*.o rm -f ./lightgbm/src/utils/*.o echo "re-tarring ${TARBALL_NAME}" # --no-xattrs is the default in GNU tar but not some distributions of BSD tar. # Enable it here to avoid errors on macOS. # ref: https://stackoverflow.com/a/74373784/3986677 tar \ -cz \ --no-xattrs \ -f "${TARBALL_NAME}" \ lightgbm \ > /dev/null 2>&1 mv "${TARBALL_NAME}" ../ cd .. echo "Done creating ${TARBALL_NAME}" rm -rf ./_tmp else "${LGB_R_EXECUTABLE}" CMD build \ --keep-empty-dirs \ --no-build-vignettes \ lightgbm_r fi echo "Done building R-package" ================================================ FILE: build-python.sh ================================================ #!/bin/sh # [description] # # Prepare a source distribution (sdist) or built distribution (wheel) # of the Python-package, and optionally install it. # # [usage] # # # build sdist and put it in dist/ # sh ./build-python.sh sdist # # # build wheel and put it in dist/ # sh ./build-python.sh bdist_wheel [OPTIONS] # # # compile lib_lightgbm and install the Python-package wrapping it # sh ./build-python.sh install [OPTIONS] # # # install the Python-package using a pre-compiled lib_lightgbm # # (assumes lib_lightgbm.{dll,so} is located at the root of the repo) # sh ./build-python.sh install --precompile # # [options] # # --boost-dir=FILEPATH # Directory with Boost package configuration file. # --boost-include-dir=FILEPATH # Directory containing Boost headers. # --boost-librarydir=FILEPATH # Preferred Boost library directory. # --boost-root=FILEPATH # Boost preferred installation prefix. # --opencl-include-dir=FILEPATH # OpenCL include directory. # --opencl-library=FILEPATH # Path to OpenCL library. # --bit32 # Compile 32-bit version. # --cuda # Compile CUDA version. # --gpu # Compile GPU version. # --integrated-opencl # Compile integrated OpenCL version. # --mingw # Compile with MinGW. # --mpi # Compile MPI version. # --no-isolation # Assume all build and install dependencies are already installed, # don't go to the internet to get them. # --nomp # Compile version without OpenMP support. # --precompile # Use precompiled library. # Only used with 'install' command. # --rocm # Compile ROCm version. # --time-costs # Compile version that outputs time costs for different internal routines. # --user # Install into user-specific instead of global site-packages directory. # Only used with 'install' command. set -e -u echo "[INFO] building lightgbm" # Default values of arguments INSTALL="false" BUILD_SDIST="false" BUILD_WHEEL="false" PIP_INSTALL_ARGS="" BUILD_ARGS="" PRECOMPILE="false" while [ $# -gt 0 ]; do case "$1" in ############################ # sub-commands of setup.py # ############################ install) INSTALL="true" ;; sdist) BUILD_SDIST="true" ;; bdist_wheel) BUILD_WHEEL="true" ;; ############################ # customized library paths # ############################ --boost-dir|--boost-dir=*) if echo "$1" | grep -q '^*=*$'; then shift; fi BOOST_DIR="${1#*=}" BUILD_ARGS="${BUILD_ARGS} --config-setting=cmake.define.Boost_DIR='${BOOST_DIR}'" ;; --boost-include-dir|--boost-include-dir=*) if echo "$1" | grep -q '^*=*$'; then shift; fi BOOST_INCLUDE_DIR="${1#*=}" BUILD_ARGS="${BUILD_ARGS} --config-setting=cmake.define.Boost_INCLUDE_DIR='${BOOST_INCLUDE_DIR}'" ;; --boost-librarydir|--boost-librarydir=*) if echo "$1" | grep -q '^*=*$'; then shift; fi BOOST_LIBRARY_DIR="${1#*=}" BUILD_ARGS="${BUILD_ARGS} --config-setting=cmake.define.BOOST_LIBRARYDIR='${BOOST_LIBRARY_DIR}'" ;; --boost-root|--boost-root=*) if echo "$1" | grep -q '^*=*$'; then shift; fi BOOST_ROOT="${1#*=}" BUILD_ARGS="${BUILD_ARGS} --config-setting=cmake.define.Boost_ROOT='${BOOST_ROOT}'" ;; --opencl-include-dir|--opencl-include-dir=*) if echo "$1" | grep -q '^*=*$'; then shift; fi OPENCL_INCLUDE_DIR="${1#*=}" BUILD_ARGS="${BUILD_ARGS} --config-setting=cmake.define.OpenCL_INCLUDE_DIR='${OPENCL_INCLUDE_DIR}'" ;; --opencl-library|--opencl-library=*) if echo "$1" | grep -q '^*=*$'; then shift; fi OPENCL_LIBRARY="${1#*=}" BUILD_ARGS="${BUILD_ARGS} --config-setting=cmake.define.OpenCL_LIBRARY='${OPENCL_LIBRARY}'" ;; ######### # flags # ######### --bit32) echo "[INFO] Attempting to build 32-bit version of LightGBM, which is only supported on Windows with Visual Studio." BUILD_ARGS="${BUILD_ARGS} --config-setting=cmake.args=-AWin32" ;; --cuda) BUILD_ARGS="${BUILD_ARGS} --config-setting=cmake.define.USE_CUDA=ON" ;; --rocm) BUILD_ARGS="${BUILD_ARGS} --config-setting=cmake.define.USE_ROCM=ON" ;; --gpu) BUILD_ARGS="${BUILD_ARGS} --config-setting=cmake.define.USE_GPU=ON" ;; --integrated-opencl) BUILD_ARGS="${BUILD_ARGS} --config-setting=cmake.define.__INTEGRATE_OPENCL=ON" ;; --mingw) # ref: https://stackoverflow.com/a/45104058/3986677 BUILD_ARGS="${BUILD_ARGS} --config-setting=cmake.define.CMAKE_SH=CMAKE_SH-NOTFOUND" BUILD_ARGS="${BUILD_ARGS} --config-setting=cmake.args=-G'MinGW Makefiles'" ;; --mpi) BUILD_ARGS="${BUILD_ARGS} --config-setting=cmake.define.USE_MPI=ON" ;; --no-isolation) BUILD_ARGS="${BUILD_ARGS} --no-isolation" PIP_INSTALL_ARGS="${PIP_INSTALL_ARGS} --no-build-isolation" ;; --nomp) BUILD_ARGS="${BUILD_ARGS} --config-setting=cmake.define.USE_OPENMP=OFF" ;; --precompile) PRECOMPILE="true" ;; --time-costs) BUILD_ARGS="${BUILD_ARGS} --config-setting=cmake.define.USE_TIMETAG=ON" ;; --user) PIP_INSTALL_ARGS="${PIP_INSTALL_ARGS} --user" ;; *) echo "[ERROR] invalid argument '${1}'. Aborting" exit 1 ;; esac shift done pip install --prefer-binary 'build>=0.10.0' # create a new directory that just contains the files needed # to build the Python-package create_isolated_source_dir() { rm -rf \ ./lightgbm-python \ ./lightgbm \ ./python-package/build \ ./python-package/build_cpp \ ./python-package/compile \ ./python-package/dist \ ./python-package/lightgbm.egg-info cp -R ./python-package ./lightgbm-python cp LICENSE ./lightgbm-python/ cp VERSION.txt ./lightgbm-python/lightgbm/VERSION.txt cp -R ./cmake ./lightgbm-python cp CMakeLists.txt ./lightgbm-python cp -R ./include ./lightgbm-python cp -R ./src ./lightgbm-python cp -R ./swig ./lightgbm-python # include only specific files from external_libs, to keep the package # small and avoid redistributing code with licenses incompatible with # LightGBM's license ###################### # fast_double_parser # ###################### mkdir -p ./lightgbm-python/external_libs/fast_double_parser cp \ external_libs/fast_double_parser/CMakeLists.txt \ ./lightgbm-python/external_libs/fast_double_parser/CMakeLists.txt cp \ external_libs/fast_double_parser/LICENSE* \ ./lightgbm-python/external_libs/fast_double_parser/ mkdir -p ./lightgbm-python/external_libs/fast_double_parser/include/ cp \ external_libs/fast_double_parser/include/fast_double_parser.h \ ./lightgbm-python/external_libs/fast_double_parser/include/ ####### # fmt # ####### mkdir -p ./lightgbm-python/external_libs/fmt cp \ external_libs/fast_double_parser/CMakeLists.txt \ ./lightgbm-python/external_libs/fmt/CMakeLists.txt cp \ external_libs/fmt/LICENSE* \ ./lightgbm-python/external_libs/fmt/ mkdir -p ./lightgbm-python/external_libs/fmt/include/fmt cp \ external_libs/fmt/include/fmt/*.h \ ./lightgbm-python/external_libs/fmt/include/fmt/ ######### # Eigen # ######### mkdir -p ./lightgbm-python/external_libs/eigen/Eigen cp \ external_libs/eigen/CMakeLists.txt \ ./lightgbm-python/external_libs/eigen/CMakeLists.txt modules="Cholesky Core Dense Eigenvalues Geometry Householder Jacobi LU QR SVD" for eigen_module in ${modules}; do cp \ "external_libs/eigen/Eigen/${eigen_module}" \ "./lightgbm-python/external_libs/eigen/Eigen/${eigen_module}" if [ "${eigen_module}" != "Dense" ]; then mkdir -p "./lightgbm-python/external_libs/eigen/Eigen/src/${eigen_module}/" cp \ -R \ "external_libs/eigen/Eigen/src/${eigen_module}"/* \ "./lightgbm-python/external_libs/eigen/Eigen/src/${eigen_module}/" fi done mkdir -p ./lightgbm-python/external_libs/eigen/Eigen/misc cp \ -R \ external_libs/eigen/Eigen/src/misc \ ./lightgbm-python/external_libs/eigen/Eigen/src/misc/ mkdir -p ./lightgbm-python/external_libs/eigen/Eigen/plugins cp \ -R \ external_libs/eigen/Eigen/src/plugins \ ./lightgbm-python/external_libs/eigen/Eigen/src/plugins/ ################### # compute (Boost) # ################### mkdir -p ./lightgbm-python/external_libs/compute cp \ -R \ external_libs/compute/include \ ./lightgbm-python/external_libs/compute/include/ } create_isolated_source_dir cd ./lightgbm-python # if 'install' was passed, choose the type of package to build and install if test "${INSTALL}" = true; then if test "${PRECOMPILE}" = true; then BUILD_SDIST=false BUILD_WHEEL=true BUILD_ARGS="" rm -rf \ ./cmake \ ./CMakeLists.txt \ ./external_libs \ ./include \ ./src \ ./swig # avoid trying to recompile, just use hatchling and copy in relevant files sed -i.bak -e '/start:build-system/,/end:build-system/d' pyproject.toml # replace build backend configuration cat >> ./pyproject.toml <=1.27.0"] build-backend = "hatchling.build" [tool.hatch.build.targets.wheel] # do not consider .gitignore when choosing files to include / exclude ignore-vcs = true packages = ["lightgbm"] EOF mkdir -p ./lightgbm/lib if test -f ../lib_lightgbm.so; then echo "[INFO] found pre-compiled lib_lightgbm.so" cp ../lib_lightgbm.so ./lightgbm/lib/lib_lightgbm.so elif test -f ../lib_lightgbm.dylib; then echo "[INFO] found pre-compiled lib_lightgbm.dylib" cp ../lib_lightgbm.dylib ./lightgbm/lib/lib_lightgbm.dylib elif test -f ../lib_lightgbm.dll; then echo "[INFO] found pre-compiled lib_lightgbm.dll" cp ../lib_lightgbm.dll ./lightgbm/lib/lib_lightgbm.dll elif test -f ../Release/lib_lightgbm.dll; then echo "[INFO] found pre-compiled Release/lib_lightgbm.dll" cp ../Release/lib_lightgbm.dll ./lightgbm/lib/lib_lightgbm.dll elif test -f ../windows/x64/DLL/lib_lightgbm.dll; then echo "[INFO] found pre-compiled windows/x64/DLL/lib_lightgbm.dll" cp ../windows/x64/DLL/lib_lightgbm.dll ./lightgbm/lib/lib_lightgbm.dll cp ../windows/x64/DLL/lib_lightgbm.lib ./lightgbm/lib/lib_lightgbm.lib elif test -f ../windows/x64/Debug_DLL/lib_lightgbm.dll; then echo "[INFO] found pre-compiled windows/x64/Debug_DLL/lib_lightgbm.dll" cp ../windows/x64/Debug_DLL/lib_lightgbm.dll ./lightgbm/lib/lib_lightgbm.dll cp ../windows/x64/Debug_DLL/lib_lightgbm.lib ./lightgbm/lib/lib_lightgbm.lib else echo "[ERROR] cannot find pre-compiled library. Aborting" exit 1 fi rm -f ./*.bak fi # at this point, if 'install' was passed but the package type wasn't indicated, prefer wheel if test "${BUILD_SDIST}" = false && test "${BUILD_WHEEL}" = false; then echo "[INFO] 'install' passed but no package type ('bdist_wheel', 'sdist') chosen. Defaulting to 'bdist_wheel'." BUILD_SDIST="false" BUILD_WHEEL="true" fi fi if test "${BUILD_SDIST}" = true; then echo "[INFO] --- building sdist ---" rm -f ../dist/*.tar.gz # use xargs to work with args that contain whitespaces # note that empty echo string leads to that xargs doesn't run the command # in some implementations of xargs # ref: https://stackoverflow.com/a/8296746 echo "--sdist --outdir ../dist ${BUILD_ARGS} ." | xargs python -m build fi if test "${BUILD_WHEEL}" = true; then echo "[INFO] --- building wheel ---" rm -f ../dist/*.whl || true # use xargs to work with args that contain whitespaces # note that empty echo string leads to that xargs doesn't run the command # in some implementations of xargs # ref: https://stackoverflow.com/a/8296746 echo "--wheel --outdir ../dist ${BUILD_ARGS} ." | xargs python -m build fi if test "${INSTALL}" = true; then echo "[INFO] --- installing lightgbm ---" cd .. if test "${BUILD_WHEEL}" = true; then PACKAGE_FILE="$(echo dist/lightgbm*.whl)" else PACKAGE_FILE="$(echo dist/lightgbm*.tar.gz)" fi # shellcheck disable=SC2086 pip install \ ${PIP_INSTALL_ARGS} \ --force-reinstall \ --no-cache-dir \ --no-deps \ "${PACKAGE_FILE}" fi echo "[INFO] cleaning up" rm -rf ./lightgbm-python ================================================ FILE: build_r.R ================================================ # For macOS users who have decided to use gcc # (replace 8 with version of gcc installed on your machine) # NOTE: your gcc / g++ from Homebrew is probably in /usr/local/bin #export CXX=/usr/local/bin/g++-8 CC=/usr/local/bin/gcc-8 # Sys.setenv("CXX" = "/usr/local/bin/g++-8") # Sys.setenv("CC" = "/usr/local/bin/gcc-8") args <- commandArgs(trailingOnly = TRUE) INSTALL_AFTER_BUILD <- !("--skip-install" %in% args) TEMP_R_DIR <- file.path(getwd(), "lightgbm_r") TEMP_SOURCE_DIR <- file.path(TEMP_R_DIR, "src") # [description] # Parse the content of commandArgs() into a structured # list. This returns a list with two sections. # * "flags" = a character of vector of flags like "--use-gpu" # * "keyword_args" = a named character vector, where names # refer to options and values are the option values. For # example, c("--boost-librarydir" = "/usr/lib/x86_64-linux-gnu") .parse_args <- function(args) { out_list <- list( "flags" = character(0L) , "keyword_args" = character(0L) , "make_args" = character(0L) ) for (arg in args) { if (any(grepl("^\\-j[0-9]+", arg))) { # nolint: non_portable_path. out_list[["make_args"]] <- arg } else if (any(grepl("=", arg, fixed = TRUE))) { split_arg <- strsplit(arg, "=", fixed = TRUE)[[1L]] arg_name <- split_arg[[1L]] arg_value <- split_arg[[2L]] out_list[["keyword_args"]][[arg_name]] <- arg_value } else { out_list[["flags"]] <- c(out_list[["flags"]], arg) } } return(out_list) } parsed_args <- .parse_args(args) SKIP_VIGNETTES <- "--no-build-vignettes" %in% parsed_args[["flags"]] USING_GPU <- "--use-gpu" %in% parsed_args[["flags"]] USING_MINGW <- "--use-mingw" %in% parsed_args[["flags"]] USING_MSYS2 <- "--use-msys2" %in% parsed_args[["flags"]] # this maps command-line arguments to defines passed into CMake, ARGS_TO_DEFINES <- c( "--boost-root" = "-DBOOST_ROOT" , "--boost-dir" = "-DBoost_DIR" , "--boost-include-dir" = "-DBoost_INCLUDE_DIR" , "--boost-librarydir" = "-DBOOST_LIBRARYDIR" , "--opencl-include-dir" = "-DOpenCL_INCLUDE_DIR" , "--opencl-library" = "-DOpenCL_LIBRARY" ) recognized_args <- c( "--no-build-vignettes" , "--skip-install" , "--use-gpu" , "--use-mingw" , "--use-msys2" , names(ARGS_TO_DEFINES) ) given_args <- c( parsed_args[["flags"]] , names(parsed_args[["keyword_args"]]) ) unrecognized_args <- setdiff(given_args, recognized_args) if (length(unrecognized_args) > 0L) { msg <- paste0( "Unrecognized arguments: " , toString(unrecognized_args) ) stop(msg) } # [description] Replace statements in install.libs.R code based on # command-line flags .replace_flag <- function(variable_name, value, content) { out <- gsub( pattern = paste0(variable_name, " <-.*") , replacement = paste0(variable_name, " <- ", as.character(value)) , x = content ) return(out) } install_libs_content <- readLines( file.path("R-package", "src", "install.libs.R") ) install_libs_content <- .replace_flag("use_gpu", USING_GPU, install_libs_content) install_libs_content <- .replace_flag("use_mingw", USING_MINGW, install_libs_content) install_libs_content <- .replace_flag("use_msys2", USING_MSYS2, install_libs_content) # set up extra flags based on keyword arguments keyword_args <- parsed_args[["keyword_args"]] if (length(keyword_args) > 0L) { cmake_args_to_add <- NULL for (i in seq_along(keyword_args)) { arg_name <- names(keyword_args)[[i]] define_name <- ARGS_TO_DEFINES[[arg_name]] arg_value <- shQuote(normalizePath(keyword_args[[arg_name]], winslash = "/")) cmake_args_to_add <- c(cmake_args_to_add, paste0(define_name, "=", arg_value)) } install_libs_content <- gsub( pattern = paste0("command_line_args <- NULL") , replacement = paste0( "command_line_args <- c(\'" , paste(cmake_args_to_add, collapse = "', '") , "')" ) , x = install_libs_content , fixed = TRUE ) } # if provided, set '-j' in 'make' commands in install.libs.R if (length(parsed_args[["make_args"]]) > 0L) { install_libs_content <- gsub( pattern = "make_args_from_build_script <- character(0L)" , replacement = paste0( "make_args_from_build_script <- c(\"" , paste(parsed_args[["make_args"]], collapse = "\", \"") , "\")" ) , x = install_libs_content , fixed = TRUE ) } # R returns FALSE (not a non-zero exit code) if a file copy operation # breaks. Let's fix that .handle_result <- function(res) { if (!all(res)) { stop("Copying files failed!") } return(invisible(NULL)) } # system() will not raise an R exception if the process called # fails. Wrapping it here to get that behavior. # # system() introduces a lot of overhead, at least on Windows, # so trying processx if it is available .run_shell_command <- function(cmd, args, strict = TRUE) { on_windows <- .Platform$OS.type == "windows" has_processx <- suppressMessages({ suppressWarnings({ require("processx") # nolint: undesirable_function, unused_import. }) }) if (has_processx && on_windows) { result <- processx::run( command = cmd , args = args , windows_verbatim_args = TRUE , error_on_status = FALSE , echo = TRUE ) exit_code <- result$status } else { if (on_windows) { message(paste0( "Using system() to run shell commands. Installing " , "'processx' with install.packages('processx') might " , "make this faster." )) } cmd <- paste0(cmd, " ", paste(args, collapse = " ")) exit_code <- system(cmd) } if (exit_code != 0L && isTRUE(strict)) { stop(paste0("Command failed with exit code: ", exit_code)) } return(invisible(exit_code)) } # Make a new temporary folder to work in unlink(x = TEMP_R_DIR, recursive = TRUE) dir.create(TEMP_R_DIR) # copy in the relevant files result <- file.copy( from = "R-package/./" , to = sprintf("%s/", TEMP_R_DIR) , recursive = TRUE , overwrite = TRUE ) .handle_result(result) # overwrite src/install.libs.R with new content based on command-line flags writeLines( text = install_libs_content , con = file.path(TEMP_SOURCE_DIR, "install.libs.R") ) # Add blank Makevars files result <- file.copy( from = file.path(TEMP_R_DIR, "inst", "Makevars") , to = file.path(TEMP_SOURCE_DIR, "Makevars") , overwrite = TRUE ) .handle_result(result) result <- file.copy( from = file.path(TEMP_R_DIR, "inst", "Makevars.win") , to = file.path(TEMP_SOURCE_DIR, "Makevars.win") , overwrite = TRUE ) .handle_result(result) result <- file.copy( from = "include/" , to = sprintf("%s/", TEMP_SOURCE_DIR) , recursive = TRUE , overwrite = TRUE ) .handle_result(result) result <- file.copy( from = "src/" , to = sprintf("%s/", TEMP_SOURCE_DIR) , recursive = TRUE , overwrite = TRUE ) .handle_result(result) EIGEN_R_DIR <- file.path(TEMP_SOURCE_DIR, "include", "Eigen") dir.create(EIGEN_R_DIR) eigen_modules <- c( "Cholesky" , "Core" , "Dense" , "Eigenvalues" , "Geometry" , "Householder" , "Jacobi" , "LU" , "QR" , "SVD" ) for (eigen_module in eigen_modules) { result <- file.copy( from = file.path("external_libs", "eigen", "Eigen", eigen_module) , to = EIGEN_R_DIR , recursive = FALSE , overwrite = TRUE ) .handle_result(result) } dir.create(file.path(EIGEN_R_DIR, "src")) for (eigen_module in c(eigen_modules, "misc", "plugins")) { if (eigen_module == "Dense") { next } module_dir <- file.path(EIGEN_R_DIR, "src", eigen_module) dir.create(module_dir, recursive = TRUE) result <- file.copy( from = sprintf("%s/", file.path("external_libs", "eigen", "Eigen", "src", eigen_module)) , to = sprintf("%s/", file.path(EIGEN_R_DIR, "src")) , recursive = TRUE , overwrite = TRUE ) .handle_result(result) } .replace_pragmas <- function(filepath) { pragma_patterns <- c( "^.*#pragma clang diagnostic.*$" , "^.*#pragma diag_suppress.*$" , "^.*#pragma GCC diagnostic.*$" , "^.*#pragma region.*$" , "^.*#pragma endregion.*$" , "^.*#pragma warning.*$" ) content <- readLines(filepath) for (pragma_pattern in pragma_patterns) { content <- content[!grepl(pragma_pattern, content)] } writeLines(content, filepath) } # remove pragmas that suppress warnings, to appease R CMD check .replace_pragmas( file.path(EIGEN_R_DIR, "src", "Core", "arch", "SSE", "Complex.h") ) .replace_pragmas( file.path(EIGEN_R_DIR, "src", "Core", "util", "DisableStupidWarnings.h") ) result <- file.copy( from = "CMakeLists.txt" , to = file.path(TEMP_R_DIR, "inst", "bin/") , overwrite = TRUE ) .handle_result(result) # remove CRAN-specific files result <- file.remove( file.path(TEMP_R_DIR, "cleanup") , file.path(TEMP_R_DIR, "configure") , file.path(TEMP_R_DIR, "configure.ac") , file.path(TEMP_R_DIR, "configure.win") , file.path(TEMP_SOURCE_DIR, "Makevars.in") , file.path(TEMP_SOURCE_DIR, "Makevars.win.in") ) .handle_result(result) #------------# # submodules # #------------# EXTERNAL_LIBS_R_DIR <- file.path(TEMP_SOURCE_DIR, "external_libs") dir.create(EXTERNAL_LIBS_R_DIR) for (submodule in list.dirs( path = "external_libs" , full.names = FALSE , recursive = FALSE )) { # compute/ is a submodule with boost, only needed if # building the R-package with GPU support; # eigen/ has a special treatment due to licensing aspects if ((submodule == "compute" && !USING_GPU) || submodule == "eigen") { next } result <- file.copy( from = sprintf("%s/", file.path("external_libs", submodule)) , to = sprintf("%s/", EXTERNAL_LIBS_R_DIR) , recursive = TRUE , overwrite = TRUE ) .handle_result(result) } # copy files into the place CMake expects CMAKE_MODULES_R_DIR <- file.path(TEMP_SOURCE_DIR, "cmake", "modules") dir.create(CMAKE_MODULES_R_DIR, recursive = TRUE) result <- file.copy( from = file.path("cmake", "modules", "FindLibR.cmake") , to = sprintf("%s/", CMAKE_MODULES_R_DIR) , overwrite = TRUE ) .handle_result(result) for (src_file in c("lightgbm_R.cpp", "lightgbm_R.h")) { result <- file.copy( from = file.path(TEMP_SOURCE_DIR, src_file) , to = file.path(TEMP_SOURCE_DIR, "src", src_file) , overwrite = TRUE ) .handle_result(result) result <- file.remove( file.path(TEMP_SOURCE_DIR, src_file) ) .handle_result(result) } result <- file.copy( from = file.path("R-package", "inst", "make-r-def.R") , to = file.path(TEMP_R_DIR, "inst", "bin/") , overwrite = TRUE ) .handle_result(result) # R packages cannot have versions like 3.0.0rc1, but # 3.0.0-1 is acceptable LGB_VERSION <- readLines("VERSION.txt")[1L] LGB_VERSION <- gsub( pattern = "rc" , replacement = "-" , x = LGB_VERSION , fixed = TRUE ) # DESCRIPTION has placeholders for version # and date so it doesn't have to be updated manually DESCRIPTION_FILE <- file.path(TEMP_R_DIR, "DESCRIPTION") description_contents <- readLines(DESCRIPTION_FILE) description_contents <- gsub( pattern = "~~VERSION~~" , replacement = LGB_VERSION , x = description_contents , fixed = TRUE ) description_contents <- gsub( pattern = "~~DATE~~" , replacement = as.character(Sys.Date()) , x = description_contents , fixed = TRUE ) writeLines(description_contents, DESCRIPTION_FILE) # NOTE: --keep-empty-dirs is necessary to keep the deep paths expected # by CMake while also meeting the CRAN req to create object files # on demand r_build_args <- c("CMD", "build", TEMP_R_DIR, "--keep-empty-dirs") if (isTRUE(SKIP_VIGNETTES)) { r_build_args <- c(r_build_args, "--no-build-vignettes") } .run_shell_command("R", r_build_args) # Install the package version <- gsub( pattern = "Version: ", replacement = "", x = grep( pattern = "Version: " , x = readLines(con = file.path(TEMP_R_DIR, "DESCRIPTION")) , value = TRUE , fixed = TRUE ) , fixed = TRUE ) tarball <- file.path(getwd(), sprintf("lightgbm_%s.tar.gz", version)) install_cmd <- "R" install_args <- c("CMD", "INSTALL", "--no-multiarch", "--with-keep.source", tarball) if (INSTALL_AFTER_BUILD) { .run_shell_command(install_cmd, install_args) } else { cmd <- paste0(install_cmd, " ", paste(install_args, collapse = " ")) print(sprintf("Skipping installation. Install the package with command '%s'", cmd)) } ================================================ FILE: cmake/IntegratedOpenCL.cmake ================================================ set(BUILD_SHARED_LIBS OFF CACHE BOOL "" FORCE) set(BOOST_VERSION_DOT "1.74") string(REPLACE "." "_" BOOST_VERSION_UNDERSCORE ${BOOST_VERSION_DOT}) set(OPENCL_HEADER_REPOSITORY "https://github.com/KhronosGroup/OpenCL-Headers.git") set(OPENCL_HEADER_TAG "1b2a1850f410aaaaeaa56cead5a179b5aea4918e") set(OPENCL_LOADER_REPOSITORY "https://github.com/KhronosGroup/OpenCL-ICD-Loader.git") set(OPENCL_LOADER_TAG "98ca71fb9f8484f1cd1999f55224bf9e8d18693b") set(BOOST_REPOSITORY "https://github.com/boostorg/boost.git") set(BOOST_TAG "boost-${BOOST_VERSION_DOT}.0") # Build Independent OpenCL library include(FetchContent) # lint_cmake: -readability/wonkycase FetchContent_Declare(OpenCL-Headers GIT_REPOSITORY ${OPENCL_HEADER_REPOSITORY} GIT_TAG ${OPENCL_HEADER_TAG}) FetchContent_GetProperties(OpenCL-Headers) # lint_cmake: +readability/wonkycase if(NOT OpenCL-Headers_POPULATED) # lint_cmake: -readability/wonkycase FetchContent_MakeAvailable(OpenCL-Headers) # lint_cmake: +readability/wonkycase message(STATUS "Populated OpenCL Headers") endif() set(OPENCL_ICD_LOADER_HEADERS_DIR ${opencl-headers_SOURCE_DIR} CACHE PATH "") # for OpenCL ICD Loader set(OpenCL_INCLUDE_DIR ${opencl-headers_SOURCE_DIR} CACHE PATH "") # for Boost::Compute # lint_cmake: -readability/wonkycase FetchContent_Declare( # lint_cmake: +readability/wonkycase OpenCL-ICD-Loader GIT_REPOSITORY ${OPENCL_LOADER_REPOSITORY} GIT_TAG ${OPENCL_LOADER_TAG} EXCLUDE_FROM_ALL ) # lint_cmake: -readability/wonkycase FetchContent_GetProperties(OpenCL-ICD-Loader) # lint_cmake: +readability/wonkycase if(NOT OpenCL-ICD-Loader_POPULATED) # lint_cmake: -readability/wonkycase FetchContent_MakeAvailable(OpenCL-ICD-Loader) # lint_cmake: +readability/wonkycase if(WIN32) set(USE_DYNAMIC_VCXX_RUNTIME ON) endif() message(STATUS "Populated OpenCL ICD Loader") endif() list(APPEND INTEGRATED_OPENCL_INCLUDES ${OPENCL_ICD_LOADER_HEADERS_DIR}) list(APPEND INTEGRATED_OPENCL_DEFINITIONS CL_TARGET_OPENCL_VERSION=120) if(WIN32) list( APPEND INTEGRATED_OPENCL_LIBRARIES ${opencl-icd-loader_BINARY_DIR}/Release/OpenCL.lib cfgmgr32.lib runtimeobject.lib ) else() list( APPEND INTEGRATED_OPENCL_LIBRARIES ${opencl-icd-loader_BINARY_DIR}/libOpenCL.a ) set_property(TARGET OpenCL PROPERTY POSITION_INDEPENDENT_CODE ON) endif() # Build Independent Boost libraries include(ExternalProject) include(ProcessorCount) # lint_cmake: -readability/wonkycase ProcessorCount(J) # lint_cmake: +readability/wonkycase set(BOOST_BASE "${PROJECT_BINARY_DIR}/Boost") set(BOOST_INCLUDE "${BOOST_BASE}/source" CACHE PATH "") set(BOOST_LIBRARY "${BOOST_BASE}/source/stage/lib" CACHE PATH "") if(WIN32) if(MSVC) # references: # # * range of MSVC versions: https://learn.microsoft.com/en-us/cpp/overview/compiler-versions # * MSVC toolchain IDs: not sure... # comments like https://learn.microsoft.com/en-us/answers/questions/769911/visual-studio-2019-build-tools-v143 # if(${MSVC_VERSION} GREATER 1929) set(MSVC_TOOLCHAIN_ID "143") elseif(${MSVC_VERSION} GREATER 1919) set(MSVC_TOOLCHAIN_ID "142") elseif(${MSVC_VERSION} GREATER 1909) set(MSVC_TOOLCHAIN_ID "141") else() message(FATAL_ERROR "Unsupported MSVC version number: ${MSVC_VERSION}") endif() list( APPEND BOOST_BUILD_BYPRODUCTS ${BOOST_LIBRARY}/libboost_filesystem-vc${MSVC_TOOLCHAIN_ID}-mt-x64-${BOOST_VERSION_UNDERSCORE}.lib ${BOOST_LIBRARY}/libboost_system-vc${MSVC_TOOLCHAIN_ID}-mt-x64-${BOOST_VERSION_UNDERSCORE}.lib ${BOOST_LIBRARY}/libboost_chrono-vc${MSVC_TOOLCHAIN_ID}-mt-x64-${BOOST_VERSION_UNDERSCORE}.lib ) else() message(FATAL_ERROR "Integrated OpenCL build is not yet available for MinGW") endif() set(BOOST_BOOTSTRAP "${BOOST_BASE}/source/bootstrap.bat") set(BOOST_BUILD "${BOOST_BASE}/source/b2.exe") set(BOOST_FLAGS "") else() set(BOOST_BOOTSTRAP "${BOOST_BASE}/source/bootstrap.sh") set(BOOST_BUILD "${BOOST_BASE}/source/b2") set(BOOST_FLAGS "-fPIC") list( APPEND BOOST_BUILD_BYPRODUCTS ${BOOST_LIBRARY}/libboost_filesystem.a ${BOOST_LIBRARY}/libboost_system.a ${BOOST_LIBRARY}/libboost_chrono.a ) endif() list( APPEND BOOST_SUBMODULES "libs/algorithm" "libs/align" "libs/any" "libs/array" "libs/assert" "libs/bind" "libs/chrono" "libs/compute" "libs/concept_check" "libs/config" "libs/container" "libs/container_hash" "libs/core" "libs/detail" "libs/filesystem" "libs/foreach" "libs/format" "libs/function" "libs/function_types" "libs/fusion" "libs/headers" "libs/integer" "libs/io" "libs/iterator" "libs/lexical_cast" "libs/math" "libs/move" "libs/mpl" "libs/multi_index" "libs/numeric/conversion" "libs/optional" "libs/predef" "libs/preprocessor" "libs/property_tree" "libs/range" "libs/ratio" "libs/serialization" "libs/smart_ptr" "libs/static_assert" "libs/system" "libs/throw_exception" "libs/tuple" "libs/typeof" "libs/type_index" "libs/type_traits" "libs/utility" "libs/uuid" "libs/winapi" "tools/boost_install" "tools/build" ) # lint_cmake: -readability/wonkycase ExternalProject_Add( # lint_cmake: +readability/wonkycase Boost TMP_DIR "${BOOST_BASE}/tmp" STAMP_DIR "${BOOST_BASE}/stamp" DOWNLOAD_DIR "${BOOST_BASE}/download" SOURCE_DIR "${BOOST_BASE}/source" BINARY_DIR "${BOOST_BASE}/source" INSTALL_DIR "${BOOST_BASE}/install" GIT_REPOSITORY ${BOOST_REPOSITORY} GIT_TAG ${BOOST_TAG} GIT_SUBMODULES ${BOOST_SUBMODULES} GIT_SHALLOW ON UPDATE_COMMAND "" PATCH_COMMAND "" CONFIGURE_COMMAND ${BOOST_BOOTSTRAP} BUILD_COMMAND ${BOOST_BUILD} -sBOOST_ROOT=${BOOST_BASE}/source -a -q -j ${J} --with-headers --with-chrono --with-filesystem --with-system link=static runtime-link=shared variant=release threading=multi cxxflags="${BOOST_FLAGS}" INSTALL_COMMAND "" # BUILD_BYPRODUCTS is necessary to support 'Ninja' builds. # ref: # - https://cmake.org/cmake/help/latest/module/ExternalProject.html # - https://stackoverflow.com/a/65803911/3986677 BUILD_BYPRODUCTS ${BOOST_BUILD_BYPRODUCTS} ) list(APPEND INTEGRATED_OPENCL_INCLUDES ${BOOST_INCLUDE}) list(APPEND INTEGRATED_OPENCL_LIBRARIES ${BOOST_BUILD_BYPRODUCTS}) set(BUILD_SHARED_LIBS ON CACHE BOOL "" FORCE) ================================================ FILE: cmake/Sanitizer.cmake ================================================ # Set appropriate compiler and linker flags for sanitizers. # # Usage of this module: # enable_sanitizers("address;leak") # Add flags macro(enable_sanitizer sanitizer) if(${sanitizer} MATCHES "address") set(SAN_COMPILE_FLAGS "${SAN_COMPILE_FLAGS} -fsanitize=address") elseif(${sanitizer} MATCHES "thread") set(SAN_COMPILE_FLAGS "${SAN_COMPILE_FLAGS} -fsanitize=thread") elseif(${sanitizer} MATCHES "leak") set(SAN_COMPILE_FLAGS "${SAN_COMPILE_FLAGS} -fsanitize=leak") elseif(${sanitizer} MATCHES "undefined") set(SAN_COMPILE_FLAGS "${SAN_COMPILE_FLAGS} -fsanitize=undefined -fno-sanitize-recover=undefined") else() message(FATAL_ERROR "Sanitizer ${sanitizer} not supported.") endif() endmacro() macro(enable_sanitizers SANITIZERS) # Check sanitizers compatibility. foreach(_san ${SANITIZERS}) string(TOLOWER ${_san} _san) if(_san MATCHES "thread") if(${_use_other_sanitizers}) message(FATAL_ERROR "thread sanitizer is not compatible with ${_san} sanitizer.") endif() set(_use_thread_sanitizer 1) else() if(${_use_thread_sanitizer}) message(FATAL_ERROR "${_san} sanitizer is not compatible with thread sanitizer.") endif() set(_use_other_sanitizers 1) endif() endforeach() message(STATUS "Sanitizers: ${SANITIZERS}") foreach(_san ${SANITIZERS}) string(TOLOWER ${_san} _san) enable_sanitizer(${_san}) endforeach() message(STATUS "Sanitizers compile flags: ${SAN_COMPILE_FLAGS}") set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} ${SAN_COMPILE_FLAGS}") set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} ${SAN_COMPILE_FLAGS}") endmacro() ================================================ FILE: cmake/modules/FindLibR.cmake ================================================ # CMake module used to find the location of R's # dll and header files. # # Borrows heavily from xgboost's R package: # # * https://github.com/dmlc/xgboost/blob/master/cmake/modules/FindLibR.cmake # # Defines the following: # LIBR_FOUND # LIBR_HOME # LIBR_EXECUTABLE # LIBR_MSVC_CORE_LIBRARY # LIBR_INCLUDE_DIRS # LIBR_LIBS_DIR # LIBR_CORE_LIBRARY # and a CMake function to create R.lib for MSVC # lint_cmake: -convention/filename if(NOT R_ARCH) if("${CMAKE_SIZEOF_VOID_P}" STREQUAL "4") set(R_ARCH "i386") else() set(R_ARCH "x64") endif() endif() if(NOT ("${R_ARCH}" STREQUAL "x64")) message(FATAL_ERROR "LightGBM's R-package currently only supports 64-bit operating systems") endif() # Creates R.lib and R.def in the build directory for linking with MSVC # https://docs.microsoft.com/en-us/cpp/build/reference/link-input-files?redirectedfrom=MSDN&view=vs-2019 function(create_rlib_for_msvc) message(STATUS "Creating R.lib and R.def") # various checks and warnings if(NOT WIN32 OR NOT MSVC) message(FATAL_ERROR "create_rlib_for_msvc() can only be used with MSVC") endif() if(NOT EXISTS "${LIBR_CORE_LIBRARY}") message(FATAL_ERROR "LIBR_CORE_LIBRARY, '${LIBR_CORE_LIBRARY}', not found") endif() find_program(DLLTOOL_EXE dlltool) if(NOT DLLTOOL_EXE) message(FATAL_ERROR "dlltool.exe not found!\nDo you have Rtools installed with its MinGW's bin/ in PATH?") endif() set(LIBR_MSVC_CORE_LIBRARY "${CMAKE_CURRENT_BINARY_DIR}/R.lib" CACHE PATH "R.lib filepath") get_filename_component( LIBR_RSCRIPT_EXECUTABLE_DIR ${LIBR_EXECUTABLE} DIRECTORY ) set(LIBR_RSCRIPT_EXECUTABLE "${LIBR_RSCRIPT_EXECUTABLE_DIR}/Rscript") execute_process( COMMAND ${LIBR_RSCRIPT_EXECUTABLE} "${CMAKE_CURRENT_BINARY_DIR}/make-r-def.R" "${LIBR_CORE_LIBRARY}" "${CMAKE_CURRENT_BINARY_DIR}/R.def" ) execute_process( COMMAND ${DLLTOOL_EXE} "--input-def" "${CMAKE_CURRENT_BINARY_DIR}/R.def" "--output-lib" "${LIBR_MSVC_CORE_LIBRARY}" ) endfunction() # R version information is used to search for R's libraries in # the registry on Windows. Since this code is orchestrated by # an R script (src/install.libs.R), that script uses R's built-ins to # find the version of R and pass it through as a CMake variable if(CMAKE_R_VERSION) message(STATUS "R version passed into FindLibR.cmake: ${CMAKE_R_VERSION}") elseif(WIN32) message( FATAL_ERROR "Expected CMAKE_R_VERSION to be passed in on Windows but none was provided. Check src/install.libs.R" ) endif() if(NOT LIBR_EXECUTABLE) find_program( LIBR_EXECUTABLE NAMES R R.exe ) # CRAN may run RD CMD CHECK instead of R CMD CHECK, # which can lead to this infamous error: # 'R' should not be used without a path -- see par. 1.6 of the manual if(LIBR_EXECUTABLE MATCHES ".*\\.Rcheck.*") unset(LIBR_EXECUTABLE CACHE) endif() # ignore the R bundled with R.app on Mac, since that is GUI-only if(LIBR_EXECUTABLE MATCHES ".+R\\.app.*") unset(LIBR_EXECUTABLE CACHE) endif() endif() # Find R executable unless it has been provided directly or already found if(NOT LIBR_EXECUTABLE) if(APPLE) find_library(LIBR_LIBRARIES R) if(LIBR_LIBRARIES MATCHES ".*\\.framework") set(LIBR_HOME "${LIBR_LIBRARIES}/Resources") set(LIBR_EXECUTABLE "${LIBR_HOME}/R") else() get_filename_component(_LIBR_LIBRARIES "${LIBR_LIBRARIES}" REALPATH) get_filename_component(_LIBR_LIBRARIES_DIR "${_LIBR_LIBRARIES}" DIRECTORY) set(LIBR_EXECUTABLE "${_LIBR_LIBRARIES_DIR}/../bin/R") endif() elseif(UNIX) # attempt to find R executable if(NOT LIBR_EXECUTABLE) find_program( LIBR_EXECUTABLE NO_DEFAULT_PATH HINTS "${CMAKE_CURRENT_BINARY_DIR}" "/usr/bin" "/usr/lib/" "/usr/local/bin/" NAMES R ) endif() # Windows else() # if R executable not available, query R_HOME path from registry if(NOT LIBR_HOME) # Try to find R's location in the registry # ref: https://cran.r-project.org/bin/windows/base/rw-FAQ.html#Does-R-use-the-Registry_003f get_filename_component( LIBR_HOME "[HKEY_LOCAL_MACHINE\\SOFTWARE\\R-core\\R\\${CMAKE_R_VERSION};InstallPath]" ABSOLUTE ) endif() if(NOT LIBR_HOME) get_filename_component( LIBR_HOME "[HKEY_CURRENT_USER\\SOFTWARE\\R-core\\R\\${CMAKE_R_VERSION};InstallPath]" ABSOLUTE ) endif() if(NOT LIBR_HOME) message( FATAL_ERROR "Unable to locate R executable.\ \nEither add its location to PATH or provide it through the LIBR_EXECUTABLE CMake variable" ) endif() # set exe location based on R_ARCH set(LIBR_EXECUTABLE "${LIBR_HOME}/bin/${R_ARCH}/R.exe") endif() if(NOT LIBR_EXECUTABLE) message( FATAL_ERROR "Unable to locate R executable.\ \nEither add its location to PATH or provide it through the LIBR_EXECUTABLE CMake variable" ) endif() endif() # ask R for the home path execute_process( COMMAND ${LIBR_EXECUTABLE} "--slave" "--vanilla" "-e" "cat(normalizePath(R.home(), winslash='/'))" OUTPUT_VARIABLE LIBR_HOME ) # ask R for the include dir execute_process( COMMAND ${LIBR_EXECUTABLE} "--slave" "--vanilla" "-e" "cat(normalizePath(R.home('include'), winslash='/'))" OUTPUT_VARIABLE LIBR_INCLUDE_DIRS ) # ask R for the lib dir execute_process( COMMAND ${LIBR_EXECUTABLE} "--slave" "--vanilla" "-e" "cat(normalizePath(R.home('lib'), winslash='/'))" OUTPUT_VARIABLE LIBR_LIBS_DIR ) set(LIBR_HOME ${LIBR_HOME} CACHE PATH "R home directory") set(LIBR_EXECUTABLE ${LIBR_EXECUTABLE} CACHE PATH "R executable") set(LIBR_INCLUDE_DIRS ${LIBR_INCLUDE_DIRS} CACHE PATH "R include directory") set(LIBR_LIBS_DIR ${LIBR_LIBS_DIR} CACHE PATH "Where R stores vendored third-party libraries") # where is R.so / R.dll / libR.so likely to be found? set( LIBR_PATH_HINTS "${CMAKE_CURRENT_BINARY_DIR}" "${LIBR_HOME}/lib" "${LIBR_HOME}/bin/${R_ARCH}" "${LIBR_HOME}/bin" "${LIBR_LIBRARIES}" ) # look for the core R library find_library( LIBR_CORE_LIBRARY NAMES R R.dll HINTS ${LIBR_PATH_HINTS} ) # starting from CMake 3.17, find_library() will not find .dll files by default # https://cmake.org/cmake/help/v3.17/release/3.17.html#other-changes if(WIN32 AND NOT LIBR_CORE_LIBRARY) find_file( LIBR_CORE_LIBRARY NAME R.dll HINTS ${LIBR_PATH_HINTS} ) endif() set(LIBR_CORE_LIBRARY ${LIBR_CORE_LIBRARY} CACHE PATH "R core shared library") if(WIN32 AND MSVC) # create a local R.lib import library for R.dll if it doesn't exist if(NOT EXISTS "${CMAKE_CURRENT_BINARY_DIR}/R.lib") create_rlib_for_msvc() endif() endif() # define find requirements include(FindPackageHandleStandardArgs) if(WIN32 AND MSVC) # lint_cmake: -package/stdargs find_package_handle_standard_args( # lint_cmake: +package/stdargs LibR DEFAULT_MSG LIBR_HOME LIBR_EXECUTABLE LIBR_INCLUDE_DIRS LIBR_LIBS_DIR LIBR_CORE_LIBRARY LIBR_MSVC_CORE_LIBRARY ) else() # lint_cmake: -package/stdargs find_package_handle_standard_args( # lint_cmake: +package/stdargs LibR DEFAULT_MSG LIBR_HOME LIBR_EXECUTABLE LIBR_INCLUDE_DIRS LIBR_LIBS_DIR LIBR_CORE_LIBRARY ) endif() ================================================ FILE: cmake/modules/FindNCCL.cmake ================================================ # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # # Tries to find NCCL headers and libraries. # # Usage of this module as follows: # # find_package(NCCL) # # Variables used by this module, they can change the default behaviour and need # to be set before calling find_package: # # NCCL_ROOT - When set, this path is inspected instead of standard library # locations as the root of the NCCL installation. # The environment variable NCCL_ROOT overrides this variable. # # This module defines # NCCL_FOUND, whether nccl has been found # NCCL_INCLUDE_DIR, directory containing header # NCCL_LIBRARY, directory containing nccl library # NCCL_LIB_NAME, nccl library name # USE_NCCL_LIB_PATH, when set, NCCL_LIBRARY path is also inspected for the # location of the nccl library. This would disable # switching between static and shared. # # This module assumes that the user has already called find_package(CUDA) if(NCCL_LIBRARY) if(NOT USE_NCCL_LIB_PATH) # Don't cache NCCL_LIBRARY to enable switching between static and shared. unset(NCCL_LIBRARY CACHE) endif() endif() if(BUILD_WITH_SHARED_NCCL) # libnccl.so set(NCCL_LIB_NAME nccl) else() # libnccl_static.a set(NCCL_LIB_NAME nccl_static) endif() find_path(NCCL_INCLUDE_DIR NAMES nccl.h PATHS $ENV{NCCL_ROOT}/include ${NCCL_ROOT}/include) find_library(NCCL_LIBRARY NAMES ${NCCL_LIB_NAME} PATHS $ENV{NCCL_ROOT}/lib/ ${NCCL_ROOT}/lib) message(STATUS "Using nccl library: ${NCCL_LIBRARY}") include(FindPackageHandleStandardArgs) find_package_handle_standard_args(NCCL DEFAULT_MSG NCCL_INCLUDE_DIR NCCL_LIBRARY) mark_as_advanced( NCCL_INCLUDE_DIR NCCL_LIBRARY ) ================================================ FILE: docker/README.md ================================================ # Using LightGBM via Docker This directory contains `Dockerfile`s to make it easy to build and run LightGBM via [Docker](https://www.docker.com/). These builds of LightGBM all train on the CPU. For GPU-enabled builds, see [the gpu/ directory](./gpu). ## Installing Docker Follow the general installation instructions [on the Docker site](https://docs.docker.com/install/): * [macOS](https://docs.docker.com/docker-for-mac/install/) * [Ubuntu](https://docs.docker.com/install/linux/docker-ce/ubuntu/) * [Windows](https://docs.docker.com/docker-for-windows/install/) ## Using CLI Version of LightGBM via Docker Build an image with the LightGBM CLI. ```shell mkdir lightgbm-docker cd lightgbm-docker wget https://raw.githubusercontent.com/lightgbm-org/LightGBM/master/docker/dockerfile-cli docker build \ -t lightgbm-cli \ -f dockerfile-cli \ . ``` Once that completes, the built image can be used to run the CLI in a container. To try it out, run the following. ```shell # configure the CLI cat << EOF > train.conf task = train objective = binary data = binary.train num_trees = 10 output_model = LightGBM-CLI-model.txt EOF # get training data curl -O https://raw.githubusercontent.com/lightgbm-org/LightGBM/master/examples/binary_classification/binary.train # train, and save model to a text file docker run \ --rm \ --volume "${PWD}":/opt/training \ --workdir /opt/training \ lightgbm-cli \ config=train.conf ``` After this runs, a LightGBM model can be found at `LightGBM-CLI-model.txt`. For more details on how to configure and use the LightGBM CLI, see https://lightgbm.readthedocs.io/en/latest/Quick-Start.html. ## Running the Python-package Container Build an image with the LightGBM Python-package installed. ```shell mkdir lightgbm-docker cd lightgbm-docker wget https://raw.githubusercontent.com/lightgbm-org/LightGBM/master/docker/dockerfile-python docker build \ -t lightgbm-python \ -f dockerfile-python \ . ``` Once that completes, the built image can be used to run LightGBM's Python-package in a container. Run the following to produce a model using the Python-package. ```shell # get training data curl -O https://raw.githubusercontent.com/lightgbm-org/LightGBM/master/examples/binary_classification/binary.train # create training script cat << EOF > train.py import lightgbm as lgb import numpy as np params = { "objective": "binary", "num_trees": 10 } bst = lgb.train( train_set=lgb.Dataset("binary.train"), params=params ) bst.save_model("LightGBM-python-model.txt") EOF # run training in a container docker run \ --rm \ --volume "${PWD}":/opt/training \ --workdir /opt/training \ lightgbm-python \ python train.py ``` After this runs, a LightGBM model can be found at `LightGBM-python-model.txt`. Or run an interactive Python session in a container. ```shell docker run \ --rm \ --volume "${PWD}":/opt/training \ --workdir /opt/training \ -it lightgbm-python \ python ``` ## Running the R-package Container Build an image with the LightGBM R-package installed. ```shell mkdir lightgbm-docker cd lightgbm-docker wget https://raw.githubusercontent.com/lightgbm-org/LightGBM/master/docker/dockerfile-r docker build \ -t lightgbm-r \ -f dockerfile-r \ . ``` Once that completes, the built image can be used to run LightGBM's R-package in a container. Run the following to produce a model using the R-package. ```shell # get training data curl -O https://raw.githubusercontent.com/lightgbm-org/LightGBM/master/examples/binary_classification/binary.train # create training script cat << EOF > train.R library(lightgbm) params <- list( objective = "binary" , num_trees = 10L ) bst <- lgb.train( data = lgb.Dataset("binary.train"), params = params ) lgb.save(bst, "LightGBM-R-model.txt") EOF # run training in a container docker run \ --rm \ --volume "${PWD}":/opt/training \ --workdir /opt/training \ lightgbm-r \ Rscript train.R ``` After this runs, a LightGBM model can be found at `LightGBM-R-model.txt`. Run the following to get an interactive R session in a container. ```shell docker run \ --rm \ -it lightgbm-r \ R ``` To use [RStudio](https://www.rstudio.com/products/rstudio/), an interactive development environment, run the following. ```shell docker run \ --rm \ --env PASSWORD="lightgbm" \ -p 8787:8787 \ lightgbm-r ``` Then navigate to `localhost:8787` in your local web browser, and log in with username `rstudio` and password `lightgbm`. To target a different R version, pass any [valid rocker/verse tag](https://hub.docker.com/r/rocker/verse/tags) to `docker build`. For example, to test LightGBM with R 4.5: ```shell docker build \ -t lightgbm-r-45 \ -f dockerfile-r \ --build-arg R_VERSION=4.5 \ . ``` ================================================ FILE: docker/dockerfile-cli ================================================ FROM ubuntu:20.04 ENV \ DEBIAN_FRONTEND=noninteractive \ LANG=C.UTF-8 \ LC_ALL=C.UTF-8 RUN apt-get update -y && \ apt-get install -y --no-install-recommends \ ca-certificates \ curl \ build-essential \ gcc \ g++ \ git \ libomp-dev && \ rm -rf /var/lib/apt/lists/* RUN curl -L -o cmake.sh https://github.com/Kitware/CMake/releases/download/v3.29.2/cmake-3.29.2-linux-x86_64.sh && \ chmod +x cmake.sh && \ sh ./cmake.sh --prefix=/usr/local --skip-license && \ rm cmake.sh RUN git clone \ --recursive \ --branch stable \ --depth 1 \ https://github.com/lightgbm-org/LightGBM && \ cd ./LightGBM && \ cmake -B build -S . && \ cmake --build build -j4 && \ cmake --install build && \ cd "${HOME}" && \ rm -rf LightGBM ENTRYPOINT ["lightgbm"] ================================================ FILE: docker/dockerfile-python ================================================ FROM ubuntu:20.04 ARG CONDA_DIR=/opt/miniforge ENV \ DEBIAN_FRONTEND=noninteractive \ LANG=C.UTF-8 \ LC_ALL=C.UTF-8 \ PATH=$CONDA_DIR/bin:$PATH RUN apt-get update && \ apt-get install -y --no-install-recommends \ ca-certificates \ cmake \ build-essential \ gcc \ g++ \ curl \ git \ libomp-dev && \ # python environment curl -sL https://github.com/conda-forge/miniforge/releases/latest/download/Miniforge3-Linux-x86_64.sh -o miniforge.sh && \ /bin/bash miniforge.sh -f -b -p $CONDA_DIR && \ export PATH="$CONDA_DIR/bin:$PATH" && \ conda config --set always_yes yes --set changeps1 no && \ # lightgbm conda install -q -y numpy scipy scikit-learn pandas && \ git clone --recursive --branch stable --depth 1 https://github.com/lightgbm-org/LightGBM && \ cd ./LightGBM && \ sh ./build-python.sh install && \ # clean apt-get autoremove -y && apt-get clean && \ conda clean -a -y && \ rm -rf /usr/local/src/* ================================================ FILE: docker/dockerfile-r ================================================ ARG R_VERSION=latest FROM rocker/verse:${R_VERSION} RUN apt-get update && \ apt-get install -y --no-install-recommends \ build-essential \ libomp-dev && \ git clone \ --recursive \ --branch stable \ --depth 1 https://github.com/lightgbm-org/LightGBM && \ cd ./LightGBM && \ sh build-cran-package.sh --no-build-vignettes && \ R CMD INSTALL ./lightgbm_*.tar.gz && \ cd .. && \ rm -rf ./LightGBM ================================================ FILE: docker/gpu/README.md ================================================ # Tiny Distroless Dockerfile for LightGBM GPU CLI-only Version `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. --- # Small Dockerfile for LightGBM GPU CLI-only Version `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. --- # Dockerfile for LightGBM GPU Version with Python `dockerfile.gpu` - A docker file with LightGBM utilizing nvidia-docker. The file is based on the `nvidia/cuda:8.0-cudnn5-devel` image. LightGBM can be utilized in GPU and CPU modes and via Python. ## Contents - LightGBM (cpu + gpu) - Python (conda) + scikit-learn, notebooks, pandas, matplotlib Running the container starts a Jupyter Notebook at `localhost:8888`. Jupyter password: `keras`. ## Requirements Requires docker and [nvidia-docker](https://github.com/NVIDIA/nvidia-docker) on host machine. ## Quickstart ### Build Docker Image ```sh mkdir lightgbm-docker cd lightgbm-docker wget https://raw.githubusercontent.com/lightgbm-org/LightGBM/master/docker/gpu/dockerfile.gpu docker build -f dockerfile.gpu -t lightgbm-gpu . ``` ### Run Image ```sh nvidia-docker run --rm -d --name lightgbm-gpu -p 8888:8888 -v /home:/home lightgbm-gpu ``` ### Attach with Command Line Access (if required) ```sh docker exec -it lightgbm-gpu bash ``` ### Jupyter Notebook ```sh localhost:8888 ``` ================================================ FILE: docker/gpu/dockerfile-cli-only-distroless.gpu ================================================ # Copyright (c) 2020 The Rector and Visitors of the University of Virginia # # Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated # documentation files (the "Software"), to deal in the Software without restriction, including without # limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the # Software, and to permit persons to whom the Software is furnished to do so, subject to the following # conditions: # # The above copyright notice and this permission notice shall be included in all copies or substantial portions # of the Software. # # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED # TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL # THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF # CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER # DEALINGS IN THE SOFTWARE. FROM nvidia/opencl:devel-ubuntu18.04 AS build ARG DEBIAN_FRONTEND=noninteractive ARG OPENCL_LIBRARIES=/usr/lib/x86_64-linux-gnu ARG OPENCL_INCLUDE_DIR=/usr/include/CL # SYSTEM RUN apt-get update && apt-get install -y --no-install-recommends \ build-essential \ git \ ca-certificates \ libglib2.0-0 \ libxext6 \ libsm6 \ libxrender1 \ cmake \ libboost-dev \ libboost-system-dev \ libboost-filesystem-dev \ gcc \ g++ && \ rm -rf /var/lib/apt/lists/* # LightGBM WORKDIR /opt RUN git clone --recursive --branch stable --depth 1 https://github.com/lightgbm-org/LightGBM && \ cd LightGBM && \ cmake -B build -S . -DUSE_GPU=1 -DOpenCL_LIBRARY=${OPENCL_LIBRARIES}/libOpenCL.so.1 -DOpenCL_INCLUDE_DIR=$OPENCL_INCLUDE_DIR && \ OPENCL_HEADERS=$OPENCL_INCLUDE_DIR LIBOPENCL=$OPENCL_LIBRARIES cmake --build build FROM gcr.io/distroless/cc-debian10 COPY --from=build \ /opt/LightGBM/lightgbm \ /opt/LightGBM/lib_lightgbm.so \ /opt/LightGBM/ COPY --from=build \ /usr/lib/x86_64-linux-gnu/libOpenCL.so.1 \ /usr/lib/x86_64-linux-gnu/libboost_filesystem.so.1.65.1 \ /usr/lib/x86_64-linux-gnu/libboost_system.so.1.65.1 \ /usr/lib/x86_64-linux-gnu/libgomp.so.1 \ /usr/lib/x86_64-linux-gnu/libstdc++.so.6 \ /usr/lib/x86_64-linux-gnu/ COPY --from=build \ /lib/x86_64-linux-gnu/libm.so.6 \ /lib/x86_64-linux-gnu/libgcc_s.so.1 \ /lib/x86_64-linux-gnu/libpthread.so.0 \ /lib/x86_64-linux-gnu/libc.so.6 \ /lib/x86_64-linux-gnu/libdl.so.2 \ /lib/x86_64-linux-gnu/ COPY --from=build \ /lib64/ld-linux-x86-64.so.2 \ /lib64/ COPY --from=build /etc/OpenCL/vendors/nvidia.icd /etc/OpenCL/vendors/nvidia.icd ENV PATH /opt/LightGBM:${PATH} ENV LANG C.UTF-8 ENV LC_ALL C.UTF-8 ENTRYPOINT ["lightgbm"] ================================================ FILE: docker/gpu/dockerfile-cli-only.gpu ================================================ # Copyright (c) 2020 The Rector and Visitors of the University of Virginia # # Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated # documentation files (the "Software"), to deal in the Software without restriction, including without # limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the # Software, and to permit persons to whom the Software is furnished to do so, subject to the following # conditions: # # The above copyright notice and this permission notice shall be included in all copies or substantial portions # of the Software. # # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED # TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL # THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF # CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER # DEALINGS IN THE SOFTWARE. FROM nvidia/opencl:devel AS build ARG DEBIAN_FRONTEND=noninteractive ARG OPENCL_LIBRARIES=/usr/lib/x86_64-linux-gnu ARG OPENCL_INCLUDE_DIR=/usr/include/CL # SYSTEM RUN apt-get update && apt-get install -y --no-install-recommends \ build-essential \ git \ ca-certificates \ libglib2.0-0 \ libxext6 \ libsm6 \ libxrender1 \ cmake \ libboost-dev \ libboost-system-dev \ libboost-filesystem-dev \ gcc \ g++ && \ rm -rf /var/lib/apt/lists/* # LightGBM WORKDIR /opt RUN git clone --recursive --branch stable --depth 1 https://github.com/lightgbm-org/LightGBM && \ cd LightGBM && \ cmake -B build -S . -DUSE_GPU=1 -DOpenCL_LIBRARY=${OPENCL_LIBRARIES}/libOpenCL.so.1 -DOpenCL_INCLUDE_DIR=$OPENCL_INCLUDE_DIR && \ OPENCL_HEADERS=$OPENCL_INCLUDE_DIR LIBOPENCL=$OPENCL_LIBRARIES cmake --build build FROM nvidia/opencl:runtime RUN apt-get update && apt-get install -y --no-install-recommends \ libxext6 \ libsm6 \ libxrender1 \ libboost-system-dev \ libboost-filesystem-dev \ gcc \ g++ && \ rm -rf /var/lib/apt/lists/* COPY --from=build \ /opt/LightGBM/lightgbm \ /opt/LightGBM/lib_lightgbm.so \ /opt/LightGBM/ ENV PATH /opt/LightGBM:${PATH} ENV LANG C.UTF-8 ENV LC_ALL C.UTF-8 ENTRYPOINT ["lightgbm"] ================================================ FILE: docker/gpu/dockerfile.gpu ================================================ FROM nvidia/cuda:8.0-cudnn5-devel ################################################################################################################# # Global ################################################################################################################# # apt-get to skip any interactive post-install configuration steps with DEBIAN_FRONTEND=noninteractive and apt-get install -y ENV LANG=C.UTF-8 LC_ALL=C.UTF-8 ARG DEBIAN_FRONTEND=noninteractive ################################################################################################################# # Global Path Setting ################################################################################################################# ENV CUDA_HOME /usr/local/cuda ENV LD_LIBRARY_PATH ${LD_LIBRARY_PATH}:${CUDA_HOME}/lib64 ENV LD_LIBRARY_PATH ${LD_LIBRARY_PATH}:/usr/local/lib ENV OPENCL_LIBRARIES /usr/local/cuda/lib64 ENV OPENCL_INCLUDE_DIR /usr/local/cuda/include ################################################################################################################# # TINI ################################################################################################################# # Install tini ENV TINI_VERSION v0.14.0 ADD https://github.com/krallin/tini/releases/download/${TINI_VERSION}/tini /tini RUN chmod +x /tini ################################################################################################################# # SYSTEM ################################################################################################################# # update: downloads the package lists from the repositories and "updates" them to get information on the newest versions of packages and their # dependencies. It will do this for all repositories and PPAs. RUN apt-get update && \ apt-get install -y --no-install-recommends \ build-essential \ curl \ bzip2 \ ca-certificates \ libglib2.0-0 \ libxext6 \ libsm6 \ libxrender1 \ git \ vim \ mercurial \ subversion \ cmake \ libboost-dev \ libboost-system-dev \ libboost-filesystem-dev \ gcc \ g++ # Add OpenCL ICD files for LightGBM RUN mkdir -p /etc/OpenCL/vendors && \ echo "libnvidia-opencl.so.1" > /etc/OpenCL/vendors/nvidia.icd ################################################################################################################# # CONDA ################################################################################################################# ARG CONDA_DIR=/opt/miniforge # add to path ENV PATH $CONDA_DIR/bin:$PATH # Install miniforge RUN echo "export PATH=$CONDA_DIR/bin:"'$PATH' > /etc/profile.d/conda.sh && \ curl -sL https://github.com/conda-forge/miniforge/releases/latest/download/Miniforge3-Linux-x86_64.sh -o ~/miniforge.sh && \ /bin/bash ~/miniforge.sh -b -p $CONDA_DIR && \ rm ~/miniforge.sh RUN conda config --set always_yes yes --set changeps1 no && \ conda create -y -q -n py3 numpy scipy scikit-learn jupyter notebook ipython pandas matplotlib ################################################################################################################# # LightGBM ################################################################################################################# RUN cd /usr/local/src && mkdir lightgbm && cd lightgbm && \ git clone --recursive --branch stable --depth 1 https://github.com/lightgbm-org/LightGBM && \ cd LightGBM && \ cmake -B build -S . -DUSE_GPU=1 -DOpenCL_LIBRARY=/usr/local/cuda/lib64/libOpenCL.so -DOpenCL_INCLUDE_DIR=/usr/local/cuda/include/ && \ OPENCL_HEADERS=/usr/local/cuda-8.0/targets/x86_64-linux/include LIBOPENCL=/usr/local/cuda-8.0/targets/x86_64-linux/lib cmake --build build ENV PATH /usr/local/src/lightgbm/LightGBM:${PATH} RUN /bin/bash -c "source activate py3 && cd /usr/local/src/lightgbm/LightGBM && sh ./build-python.sh install --precompile && source deactivate" ################################################################################################################# # System CleanUp ################################################################################################################# # apt-get autoremove: used to remove packages that were automatically installed to satisfy dependencies for some package and that are no more needed. # 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 # have to be downloaded again if you reinstall them. RUN apt-get autoremove -y && apt-get clean && \ rm -rf /var/lib/apt/lists/* && \ conda clean -a -y ################################################################################################################# # JUPYTER ################################################################################################################# # password: keras # password key: --NotebookApp.password='sha1:98b767162d34:8da1bc3c75a0f29145769edc977375a373407824' # Add a notebook profile. RUN mkdir -p -m 700 ~/.jupyter/ && \ echo "c.NotebookApp.ip = '*'" >> ~/.jupyter/jupyter_notebook_config.py VOLUME /home WORKDIR /home # IPython EXPOSE 8888 ENTRYPOINT [ "/tini", "--" ] CMD /bin/bash -c "source activate py3 && jupyter notebook --allow-root --no-browser --NotebookApp.password='sha1:98b767162d34:8da1bc3c75a0f29145769edc977375a373407824' && source deactivate" ================================================ FILE: docs/.lychee.toml ================================================ verbose = "info" no_progress = false cache = false scheme = ["http", "https", "file"] include_mail = false include_fragments = true no_ignore = true insecure = false require_https = true accept = ["100..=103", "200..=299"] user_agent = "curl/7.88.1" header = {"User-Agent" = "curl/7.88.1"} timeout = 30 retry_wait_time = 10 max_concurrency = 10 # remove anchors from GitHub URLs to overcome https://github.com/lycheeverse/lychee/issues/1729 remap = [ '(?P^https://github\.com)/(?P.*)#(?P.*)$ $host/$path/', ] exclude = [ '^https://www\.swig\.org/download\.html$', '^https://proceedings\.neurips\.cc/.*', '^https://www\.amd\.com/en/support\.html$', '^https://www\.jstor\.org/stable/2281952$', '^https://dl\.acm\.org/doi/10\.1145/3298689\.3347033$', '^https://packages\.ubuntu\.com/search.*', '^https://stackoverflow\.com/.*', '^https://.*\.stackexchange\.com/.*', ] exclude_path = [ "(^|/)docs/.*\\.rst", ] ================================================ FILE: docs/Advanced-Topics.rst ================================================ Advanced Topics =============== Missing Value Handle -------------------- - LightGBM enables the missing value handle by default. Disable it by setting ``use_missing=false``. - LightGBM uses NA (NaN) to represent missing values by default. Change it to use zero by setting ``zero_as_missing=true``. - When ``zero_as_missing=false`` (default), the unrecorded values in sparse matrices (and LightSVM) are treated as zeros. - When ``zero_as_missing=true``, NA and zeros (including unrecorded values in sparse matrices (and LightSVM)) are treated as missing. Categorical Feature Support --------------------------- - LightGBM offers good accuracy with integer-encoded categorical features. LightGBM applies `Fisher (1958) `_ to find the optimal split over categories as `described here <./Features.rst#optimal-split-for-categorical-features>`_. This often performs better than one-hot encoding. - Use ``categorical_feature`` to specify the categorical features. Refer to the parameter ``categorical_feature`` in `Parameters <./Parameters.rst#categorical_feature>`__. - 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) less than ``Int32.MaxValue`` (2147483647). It is best to use a contiguous range of integers started from zero. Floating point numbers in categorical features will be rounded towards 0. - Use ``min_data_per_group``, ``cat_smooth`` to deal with over-fitting (when ``#data`` is small or ``#category`` is large). - For a categorical feature with high cardinality (``#category`` is large), it often works best to treat the feature as numeric, either by simply ignoring the categorical interpretation of the integers or by embedding the categories in a low-dimensional numeric space. LambdaRank ---------- - 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). - Use ``label_gain`` to set the gain(weight) of ``int`` label. - Use ``lambdarank_truncation_level`` to truncate the max DCG. Cost Efficient Gradient Boosting -------------------------------- `Cost Efficient Gradient Boosting `_ (CEGB) makes it possible to penalise boosting based on the cost of obtaining feature values. CEGB penalises learning in the following ways: - Each time a tree is split, a penalty of ``cegb_penalty_split`` is applied. - 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. - 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. Each of the penalties above is scaled by ``cegb_tradeoff``. Using this parameter, it is possible to change the overall strength of the CEGB penalties by changing only one parameter. Parameters Tuning ----------------- - Refer to `Parameters Tuning <./Parameters-Tuning.rst>`__. .. _Parallel Learning: Distributed Learning -------------------- - Refer to `Distributed Learning Guide <./Parallel-Learning-Guide.rst>`__. GPU Support ----------- - Refer to `GPU Tutorial <./GPU-Tutorial.rst>`__ and `GPU Targets <./GPU-Targets.rst>`__. Support for Position Bias Treatment ------------------------------------ Often 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. LightGBM can make use of positional data. For 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. LightGBM could be told to account for the position bias from results being "above the fold" by providing a ``positions`` array encoded as follows: :: 0 0 0 1 1 0 0 0 1 ... Where ``0 = "above the fold"`` and ``1 = "requires scrolling"``. The specific values are not important, as long as they are consistent across all observations in the training data. An encoding like ``100 = "above the fold"`` and ``17 = "requires scrolling"`` would result in exactly the same trained model. In that way, ``positions`` in LightGBM's API are similar to a categorical feature. Just 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. Unlike a categorical feature, however, ``positions`` are used to adjust the target to reduce the bias in predictions made by the trained model. The 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. In 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. Currently, 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. During 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. Similar 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 `_). In LightGBM, we adapt this idea to general pairwise Lerarning-to-Rank with arbitrary ordinal relevance labels. Besides, 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. ================================================ FILE: docs/C-API.rst ================================================ C API ===== .. doxygenfile:: c_api.h ================================================ FILE: docs/Development-Guide.rst ================================================ Development Guide ================= Algorithms ---------- Refer to `Features <./Features.rst>`__ for understanding of important algorithms used in LightGBM. Classes and Code Structure -------------------------- Important Classes ~~~~~~~~~~~~~~~~~ +-------------------------+----------------------------------------------------------------------------------------+ | Class | Description | +=========================+========================================================================================+ | ``Application`` | The entrance of application, including training and prediction logic | +-------------------------+----------------------------------------------------------------------------------------+ | ``Bin`` | Data structure used for storing feature discrete values (converted from float values) | +-------------------------+----------------------------------------------------------------------------------------+ | ``Boosting`` | Boosting interface (GBDT, DART, etc.) | +-------------------------+----------------------------------------------------------------------------------------+ | ``Config`` | Stores parameters and configurations | +-------------------------+----------------------------------------------------------------------------------------+ | ``Dataset`` | Stores information of dataset | +-------------------------+----------------------------------------------------------------------------------------+ | ``DatasetLoader`` | Used to construct dataset | +-------------------------+----------------------------------------------------------------------------------------+ | ``FeatureGroup`` | Stores the data of feature, could be multiple features | +-------------------------+----------------------------------------------------------------------------------------+ | ``Metric`` | Evaluation metrics | +-------------------------+----------------------------------------------------------------------------------------+ | ``Network`` | Network interfaces and communication algorithms | +-------------------------+----------------------------------------------------------------------------------------+ | ``ObjectiveFunction`` | Objective functions used to train | +-------------------------+----------------------------------------------------------------------------------------+ | ``Tree`` | Stores information of tree model | +-------------------------+----------------------------------------------------------------------------------------+ | ``TreeLearner`` | Used to learn trees | +-------------------------+----------------------------------------------------------------------------------------+ Code Structure ~~~~~~~~~~~~~~ +---------------------+------------------------------------------------------------------------------------------------------------------------------------+ | Path | Description | +=====================+====================================================================================================================================+ | ./include | Header files | +---------------------+------------------------------------------------------------------------------------------------------------------------------------+ | ./include/utils | Some common functions | +---------------------+------------------------------------------------------------------------------------------------------------------------------------+ | ./src/application | Implementations of training and prediction logic | +---------------------+------------------------------------------------------------------------------------------------------------------------------------+ | ./src/boosting | Implementations of Boosting | +---------------------+------------------------------------------------------------------------------------------------------------------------------------+ | ./src/io | Implementations of IO related classes, including ``Bin``, ``Config``, ``Dataset``, ``DatasetLoader``, ``Feature`` and ``Tree`` | +---------------------+------------------------------------------------------------------------------------------------------------------------------------+ | ./src/metric | Implementations of metrics | +---------------------+------------------------------------------------------------------------------------------------------------------------------------+ | ./src/network | Implementations of network functions | +---------------------+------------------------------------------------------------------------------------------------------------------------------------+ | ./src/objective | Implementations of objective functions | +---------------------+------------------------------------------------------------------------------------------------------------------------------------+ | ./src/treelearner | Implementations of tree learners | +---------------------+------------------------------------------------------------------------------------------------------------------------------------+ Documents API ------------- Refer to `docs README <./README.rst>`__. C API ----- Refer to `C API <./C-API.rst>`__ or the comments in `c\_api.h `__ file, from which the documentation is generated. Tests ----- C++ unit tests are located in the ``./tests/cpp_tests`` folder and written with the help of Google Test framework. To 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. It is highly recommended to build tests with `sanitizers <./Installation-Guide.rst#sanitizers>`__. High Level Language Package --------------------------- See the implementations at `Python-package `__ and `R-package `__. Questions --------- Refer to `FAQ <./FAQ.rst>`__. Also feel free to open `issues `__ if you met problems. ================================================ FILE: docs/Experiments.rst ================================================ Experiments =========== Comparison Experiment --------------------- For the detailed experiment scripts and output logs, please refer to this `repo`_. History ^^^^^^^ 08 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.) 27 Feb, 2017: first version. Data ^^^^ We used 5 datasets to conduct our comparison experiments. Details of data are listed in the following table: +-----------+-----------------------+---------------------------------------------------------------------------------+-------------+----------+----------------------------------------------+ | Data | Task | Link | #Train\_Set | #Feature | Comments | +===========+=======================+=================================================================================+=============+==========+==============================================+ | Higgs | Binary classification | `link `__ | 10,500,000 | 28 | last 500,000 samples were used as test set | +-----------+-----------------------+---------------------------------------------------------------------------------+-------------+----------+----------------------------------------------+ | Yahoo LTR | Learning to rank | `link `__ | 473,134 | 700 | set1.train as train, set1.test as test | +-----------+-----------------------+---------------------------------------------------------------------------------+-------------+----------+----------------------------------------------+ | MS LTR | Learning to rank | `link `__ | 2,270,296 | 137 | {S1,S2,S3} as train set, {S5} as test set | +-----------+-----------------------+---------------------------------------------------------------------------------+-------------+----------+----------------------------------------------+ | Expo | Binary classification | `link `__ | 11,000,000 | 700 | last 1,000,000 samples were used as test set | +-----------+-----------------------+---------------------------------------------------------------------------------+-------------+----------+----------------------------------------------+ | Allstate | Binary classification | `link `__ | 13,184,290 | 4228 | last 1,000,000 samples were used as test set | +-----------+-----------------------+---------------------------------------------------------------------------------+-------------+----------+----------------------------------------------+ Environment ^^^^^^^^^^^ We ran all experiments on a single Linux server (Azure ND24s) with the following specifications: +------------------+-----------------+---------------------+ | OS | CPU | Memory | +==================+=================+=====================+ | Ubuntu 16.04 LTS | 2 \* E5-2690 v4 | 448GB | +------------------+-----------------+---------------------+ Baseline ^^^^^^^^ We used `xgboost`_ as a baseline. Both xgboost and LightGBM were built with OpenMP support. Settings ^^^^^^^^ We set up total 3 settings for experiments. The parameters of these settings are: 1. xgboost: .. code:: text eta = 0.1 max_depth = 8 num_round = 500 nthread = 16 tree_method = exact min_child_weight = 100 2. xgboost\_hist (using histogram based algorithm): .. code:: text eta = 0.1 num_round = 500 nthread = 16 min_child_weight = 100 tree_method = hist grow_policy = lossguide max_depth = 0 max_leaves = 255 3. LightGBM: .. code:: text learning_rate = 0.1 num_leaves = 255 num_trees = 500 num_threads = 16 min_data_in_leaf = 0 min_sum_hessian_in_leaf = 100 xgboost grows trees depth-wise and controls model complexity by ``max_depth``. LightGBM uses a leaf-wise algorithm instead and controls model complexity by ``num_leaves``. So 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``. Other parameters are default values. Result ^^^^^^ Speed ''''' We compared speed using only the training task without any test or metric output. We didn't count the time for IO. For the ranking tasks, since XGBoost and LightGBM implement different ranking objective functions, we used ``regression`` objective for speed benchmark, for the fair comparison. The following table is the comparison of time cost: +-----------+-----------+---------------+---------------+ | Data | xgboost | xgboost\_hist | LightGBM | +===========+===========+===============+===============+ | Higgs | 3794.34 s | 165.575 s | **130.094 s** | +-----------+-----------+---------------+---------------+ | Yahoo LTR | 674.322 s | 131.462 s | **76.229 s** | +-----------+-----------+---------------+---------------+ | MS LTR | 1251.27 s | 98.386 s | **70.417 s** | +-----------+-----------+---------------+---------------+ | Expo | 1607.35 s | 137.65 s | **62.607 s** | +-----------+-----------+---------------+---------------+ | Allstate | 2867.22 s | 315.256 s | **148.231 s** | +-----------+-----------+---------------+---------------+ LightGBM ran faster than xgboost on all experiment data sets. Accuracy '''''''' We computed all accuracy metrics only on the test data set. +-----------+-----------------+----------+-------------------+--------------+ | Data | Metric | xgboost | xgboost\_hist | LightGBM | +===========+=================+==========+===================+==============+ | Higgs | AUC | 0.839593 | 0.845314 | **0.845724** | +-----------+-----------------+----------+-------------------+--------------+ | Yahoo LTR | NDCG\ :sub:`1` | 0.719748 | 0.720049 | **0.732981** | | +-----------------+----------+-------------------+--------------+ | | NDCG\ :sub:`3` | 0.717813 | 0.722573 | **0.735689** | | +-----------------+----------+-------------------+--------------+ | | NDCG\ :sub:`5` | 0.737849 | 0.740899 | **0.75352** | | +-----------------+----------+-------------------+--------------+ | | NDCG\ :sub:`10` | 0.78089 | 0.782957 | **0.793498** | +-----------+-----------------+----------+-------------------+--------------+ | MS LTR | NDCG\ :sub:`1` | 0.483956 | 0.485115 | **0.517767** | | +-----------------+----------+-------------------+--------------+ | | NDCG\ :sub:`3` | 0.467951 | 0.47313 | **0.501063** | | +-----------------+----------+-------------------+--------------+ | | NDCG\ :sub:`5` | 0.472476 | 0.476375 | **0.504648** | | +-----------------+----------+-------------------+--------------+ | | NDCG\ :sub:`10` | 0.492429 | 0.496553 | **0.524252** | +-----------+-----------------+----------+-------------------+--------------+ | Expo | AUC | 0.756713 | 0.776224 | **0.776935** | +-----------+-----------------+----------+-------------------+--------------+ | Allstate | AUC | 0.607201 | **0.609465** | 0.609072 | +-----------+-----------------+----------+-------------------+--------------+ Memory Consumption '''''''''''''''''' We monitored RES while running training task. And we set ``two_round=true`` (this will increase data-loading time and reduce peak memory usage but not affect training speed or accuracy) in LightGBM to reduce peak memory usage. +-----------+---------+---------------+--------------------+--------------------+ | Data | xgboost | xgboost\_hist | LightGBM (col-wise)|LightGBM (row-wise) | +===========+=========+===============+====================+====================+ | Higgs | 4.853GB | 7.335GB | **0.897GB** | 1.401GB | +-----------+---------+---------------+--------------------+--------------------+ | Yahoo LTR | 1.907GB | 4.023GB | **1.741GB** | 2.161GB | +-----------+---------+---------------+--------------------+--------------------+ | MS LTR | 5.469GB | 7.491GB | **0.940GB** | 1.296GB | +-----------+---------+---------------+--------------------+--------------------+ | Expo | 1.553GB | 2.606GB | **0.555GB** | 0.711GB | +-----------+---------+---------------+--------------------+--------------------+ | Allstate | 6.237GB | 12.090GB | **1.116GB** | 1.755GB | +-----------+---------+---------------+--------------------+--------------------+ Parallel Experiment ------------------- History ^^^^^^^ 27 Feb, 2017: first version. Data ^^^^ We used a terabyte click log dataset to conduct parallel experiments. Details are listed in following table: +--------+-----------------------+---------+---------------+----------+ | Data | Task | Link | #Data | #Feature | +========+=======================+=========+===============+==========+ | Criteo | Binary classification | `link`_ | 1,700,000,000 | 67 | +--------+-----------------------+---------+---------------+----------+ This data contains 13 integer features and 26 categorical features for 24 days of click logs. We statisticized the click-through rate (CTR) and count for these 26 categorical features from the first ten days. Then we used next ten days' data, after replacing the categorical features by the corresponding CTR and count, as training data. The processed training data have a total of 1.7 billions records and 67 features. Environment ^^^^^^^^^^^ We ran our experiments on 16 Windows servers with the following specifications: +---------------------+-----------------+---------------------+-------------------------------------------+ | OS | CPU | Memory | Network Adapter | +=====================+=================+=====================+===========================================+ | Windows Server 2012 | 2 \* E5-2670 v2 | DDR3 1600Mhz, 256GB | Mellanox ConnectX-3, 54Gbps, RDMA support | +---------------------+-----------------+---------------------+-------------------------------------------+ Settings ^^^^^^^^ .. code:: text learning_rate = 0.1 num_leaves = 255 num_trees = 100 num_thread = 16 tree_learner = data We used data parallel here because this data is large in ``#data`` but small in ``#feature``. Other parameters were default values. Results ^^^^^^^ +----------+---------------+---------------------------+ | #Machine | Time per Tree | Memory Usage(per Machine) | +==========+===============+===========================+ | 1 | 627.8 s | 176GB | +----------+---------------+---------------------------+ | 2 | 311 s | 87GB | +----------+---------------+---------------------------+ | 4 | 156 s | 43GB | +----------+---------------+---------------------------+ | 8 | 80 s | 22GB | +----------+---------------+---------------------------+ | 16 | 42 s | 11GB | +----------+---------------+---------------------------+ The results show that LightGBM achieves a linear speedup with distributed learning. GPU Experiments --------------- Refer to `GPU Performance <./GPU-Performance.rst>`__. .. _repo: https://github.com/guolinke/boosting_tree_benchmarks .. _xgboost: https://github.com/dmlc/xgboost .. _link: https://ailab.criteo.com/download-criteo-1tb-click-logs-dataset/ ================================================ FILE: docs/FAQ.rst ================================================ .. role:: raw-html(raw) :format: html LightGBM FAQ ############ .. contents:: LightGBM Frequently Asked Questions :depth: 1 :local: :backlinks: none ------ Please post questions, feature requests, and bug reports at https://github.com/lightgbm-org/LightGBM/issues. This project is mostly maintained by volunteers, so please be patient. If your request is time-sensitive or more than a month goes by without a response, please tag the maintainers below for help. - `@guolinke `__ **Guolin Ke** - `@shiyu1994 `__ **Yu Shi** - `@jameslamb `__ **James Lamb** - `@jmoralez `__ **José Morales** -------------- General LightGBM Questions ========================== .. contents:: :local: :backlinks: none 1. Where do I find more details about LightGBM parameters? ---------------------------------------------------------- Take a look at `Parameters <./Parameters.rst>`__. 2. On datasets with millions of features, training does not start (or starts after a very long time). ----------------------------------------------------------------------------------------------------- Use a smaller value for ``bin_construct_sample_cnt`` and a larger value for ``min_data``. 3. When running LightGBM on a large dataset, my computer runs out of RAM. ------------------------------------------------------------------------- **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), lower ``num_leaves`` or lower ``max_bin`` (see `lightgbm-org/LightGBM#562 `__). 4. I am using Windows. Should I use Visual Studio or MinGW for compiling LightGBM? ---------------------------------------------------------------------------------- Visual Studio `performs best for LightGBM `__. 5. When using LightGBM GPU, I cannot reproduce results over several runs. ------------------------------------------------------------------------- This is normal and expected behaviour, but you may try to use ``gpu_use_dp = true`` for reproducibility (see `lightgbm-org/LightGBM#560 `__). You may also use the CPU version. 6. Bagging is not reproducible when changing the number of threads. ------------------------------------------------------------------- :raw-html:`` LightGBM bagging is multithreaded, so its output depends on the number of threads used. There is `no workaround currently `__. :raw-html:`` Starting from `#2804 `__ bagging result doesn't depend on the number of threads. So this issue should be solved in the latest version. 7. I tried to use Random Forest mode, and LightGBM crashes! ----------------------------------------------------------- This is expected behaviour for arbitrary parameters. To enable Random Forest, you must use ``bagging_fraction`` and ``feature_fraction`` different from 1, along with a ``bagging_freq``. `This thread `__ includes an example. 8. CPU usage is low (like 10%) in Windows when using LightGBM on very large datasets with many-core systems. ------------------------------------------------------------------------------------------------------------ Please use `Visual Studio `__ as it may be `10x faster than MinGW `__ especially for very large trees. 9. 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. ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- .. code-block:: console [LightGBM] [Warning] Met negative value in categorical features, will convert it to NaN [LightGBM] [Warning] There are no meaningful features, as all feature values are constant. The column you're trying to pass via ``categorical_feature`` likely contains very large values. Categorical features in LightGBM are limited by int32 range, so you cannot pass values that are greater than ``Int32.MaxValue`` (2147483647) as categorical features (see `lightgbm-org/LightGBM#1359 `__). You should convert them to integers ranging from zero to the number of categories first. 10. LightGBM crashes randomly with the error like: ``Initializing libiomp5.dylib, but found libomp.dylib already initialized.`` ------------------------------------------------------------------------------------------------------------------------------- .. code-block:: console OMP: Error #15: Initializing libiomp5.dylib, but found libomp.dylib already initialized. 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/. **Possible Cause**: This error means that you have multiple OpenMP libraries installed on your machine and they conflict with each other. (File extensions in the error message may differ depending on the operating system). If 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. In 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 ``$CONDA_PREFIX/lib``. **Solution**: Assuming you are using macOS with Homebrew, the command which overwrites OpenMP library files in the current active Conda environment with symlinks to the system-wide library ones installed by Homebrew: .. code-block:: bash ln -sf `ls -d "$(brew --cellar libomp)"/*/lib`/* $CONDA_PREFIX/lib The described above fix worked fine before the release of OpenMP 8.0.0 version. Starting from 8.0.0 version, Homebrew formula for OpenMP includes ``-DLIBOMP_INSTALL_ALIASES=OFF`` option which leads to that the fix doesn't work anymore. However, you can create symlinks to library aliases manually: .. code-block:: bash for LIBOMP_ALIAS in libgomp.dylib libiomp5.dylib libomp.dylib; do sudo ln -sf "$(brew --cellar libomp)"/*/lib/libomp.dylib $CONDA_PREFIX/lib/$LIBOMP_ALIAS; done Another workaround would be removing MKL optimizations from Conda's packages completely: .. code-block:: bash conda install nomkl If this is not your case, then you should find conflicting OpenMP library installations on your own and leave only one of them. 11. LightGBM hangs when multithreading (OpenMP) and using forking in Linux at the same time. -------------------------------------------------------------------------------------------- Use ``nthreads=1`` to disable multithreading of LightGBM. There is a bug with OpenMP which hangs forked sessions with multithreading activated. A more expensive solution is to use new processes instead of using fork, however, keep in mind it is creating new processes where you have to copy memory and load libraries (example: if you want to fork 16 times your current process, then you will require to make 16 copies of your dataset in memory) (see `lightgbm-org/LightGBM#1789 `__). An alternative, if multithreading is really necessary inside the forked sessions, would be to compile LightGBM with Intel toolchain. Intel compilers are unaffected by this bug. For C/C++ users, any OpenMP feature cannot be used before the fork happens. If an OpenMP feature is used before the fork happens (example: using OpenMP for forking), OpenMP will hang inside the forked sessions. Use new processes instead and copy memory as required by creating new processes instead of forking (or, use Intel compilers). Cloud platform container services may cause LightGBM to hang, if they use Linux fork to run multiple containers on a single instance. For example, LightGBM hangs in AWS Batch array jobs, which `use the ECS agent `__ to manage multiple running jobs. Setting ``nthreads=1`` mitigates the issue. 12. Why is early stopping not enabled by default in LightGBM? ------------------------------------------------------------- Early 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. In ``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. The 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. 13. Does LightGBM support direct loading data from zero-based or one-based LibSVM format file? ---------------------------------------------------------------------------------------------- LightGBM supports loading data from zero-based LibSVM format file directly. 14. Why CMake cannot find the compiler when compiling LightGBM with MinGW? -------------------------------------------------------------------------- .. code-block:: bash CMake Error: CMAKE_C_COMPILER not set, after EnableLanguage CMake Error: CMAKE_CXX_COMPILER not set, after EnableLanguage This 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. See `lightgbm-org/LightGBM#3060 `__ for more details. 15. Where can I find LightGBM's logo to use it in my presentation? ------------------------------------------------------------------ You can find LightGBM's logo in different file formats and resolutions `here `__. 16. LightGBM crashes randomly or operating system hangs during or after running LightGBM. ----------------------------------------------------------------------------------------- **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``. If you are using any Python-package that depends on ``threadpoolctl``, you also may see the following warning in your logs in this case: .. code-block:: console /root/miniconda/envs/test-env/lib/python3.8/site-packages/threadpoolctl.py:546: RuntimeWarning: Found Intel OpenMP ('libiomp') and LLVM OpenMP ('libomp') loaded at the same time. Both libraries are known to be incompatible and this can cause random crashes or deadlocks on Linux when loaded in the same Python program. Using threadpoolctl may cause crashes or deadlocks. For more information and possible workarounds, please see https://github.com/joblib/threadpoolctl/blob/master/multiple_openmp.md Detailed description of conflicts between multiple OpenMP instances is provided in the `following document `__. **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. If this is not your case, then you should find conflicting OpenMP library installations on your own and leave only one of them. 17. Loading LightGBM fails like: ``cannot allocate memory in static TLS block`` ------------------------------------------------------------------------------- When loading LightGBM, you may encounter errors like the following. .. code-block:: console lib/libgomp.so.1: cannot allocate memory in static TLS block This most commonly happens on aarch64 Linux systems. ``gcc``'s OpenMP library (``libgomp.so``) tries to allocate a small amount of static thread-local storage ("TLS") when it's dynamically loaded. That error can happen when the loader isn't able to find a large enough block of memory. On aarch64 Linux, processes and loaded libraries share the same pool of static TLS, which makes such failures more likely. See these discussions: * https://bugzilla.redhat.com/show_bug.cgi?id=1722181#c6 * https://gcc.gcc.gnu.narkive.com/vOXMQqLA/failure-to-dlopen-libgomp-due-to-static-tls-data If you are experiencing this issue when using the ``lightgbm`` Python-package, try upgrading to at least ``v4.6.0``. For older versions of the Python-package, or for other LightGBM APIs, this issue can often be avoided by loading ``libgomp.so.1``. That can be done directly by setting environment variable ``LD_PRELOAD``, like this: .. code-block:: console export LD_PRELOAD=/root/miniconda3/envs/test-env/lib/libgomp.so.1 It can also be done indirectly by changing the order that other libraries are loaded into processes, which varies by programming language and application type. For more details, see these discussions: * https://github.com/lightgbm-org/LightGBM/pull/6654#issuecomment-2352014275 * https://github.com/lightgbm-org/LightGBM/issues/6509 * https://maskray.me/blog/2021-02-14-all-about-thread-local-storage * https://bugzilla.redhat.com/show_bug.cgi?id=1722181#c6 ------ R-package ========= .. contents:: :local: :backlinks: none 1. Any training command using LightGBM does not work after an error occurred during the training of a previous LightGBM model. ------------------------------------------------------------------------------------------------------------------------------ In 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 `__. That is no longer necessary as of ``v3.3.0``, and function ``lgb.unloader()`` has since been removed from the R-package. 2. I used ``setinfo()``, tried to print my ``lgb.Dataset``, and now the R console froze! ---------------------------------------------------------------------------------------- As of at least LightGBM v3.3.0, this issue has been resolved and printing a ``Dataset`` object does not cause the console to freeze. In older versions, avoid printing the ``Dataset`` after calling ``setinfo()``. As of LightGBM v4.0.0, ``setinfo()`` has been replaced by a new method, ``set_field()``. 3. ``error in data.table::data.table()...argument 2 is NULL``. -------------------------------------------------------------- If 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. 4. ``package/dependency ‘Matrix’ is not available ...`` ------------------------------------------------------- In April 2024, ``Matrix==1.7-0`` was published to CRAN. That version had a floor of ``R (>=4.4.0)``. ``{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. .. code-block:: text package ‘Matrix’ is not available for this version of R To fix that without upgrading to R 4.4.0 or greater, manually install an older version of ``{Matrix}``. .. code-block:: R install.packages('https://cran.r-project.org/src/contrib/Archive/Matrix/Matrix_1.6-5.tar.gz', repos = NULL) ------ Python-package ============== .. contents:: :local: :backlinks: none 1. ``Error: setup script specifies an absolute path`` when installing from GitHub using ``python setup.py install``. -------------------------------------------------------------------------------------------------------------------- .. note:: As of v4.0.0, ``lightgbm`` does not support directly invoking ``setup.py``. This answer refers only to versions of ``lightgbm`` prior to v4.0.0. .. code-block:: console error: Error: setup script specifies an absolute path: /Users/Microsoft/LightGBM/python-package/lightgbm/../../lib_lightgbm.so setup() arguments must *always* be /-separated paths relative to the setup.py directory, *never* absolute paths. This error should be solved in latest version. If you still meet this error, try to remove ``lightgbm.egg-info`` folder in your Python-package and reinstall, or check `this thread on stackoverflow `__. 2. Error messages: ``Cannot ... before construct dataset``. ----------------------------------------------------------- I see error messages like... .. code-block:: console Cannot get/set label/weight/init_score/group/num_data/num_feature before construct dataset but I've already constructed a dataset by some code like: .. code-block:: python train = lightgbm.Dataset(X_train, y_train) or error messages like .. code-block:: console Cannot set predictor/reference/categorical feature after freed raw data, set free_raw_data=False when construct Dataset to avoid this. **Solution**: Because LightGBM constructs bin mappers to build trees, and train and valid Datasets within one Booster share the same bin mappers, categorical features and feature names etc., the Dataset objects are constructed when constructing a Booster. If you set ``free_raw_data=True`` (default), the raw data (with Python data struct) will be freed. So, if you want to: - get label (or weight/init\_score/group/data) before constructing a dataset, it's same as get ``self.label``; - set label (or weight/init\_score/group) before constructing a dataset, it's same as ``self.label=some_label_array``; - get num\_data (or num\_feature) before constructing a dataset, you can get data with ``self.data``. 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``; - set predictor (or reference/categorical feature) after constructing a dataset, you should set ``free_raw_data=False`` or init a Dataset object with the same raw data. 3. I encounter segmentation faults (segfaults) randomly after installing LightGBM from PyPI using ``pip install lightgbm``. --------------------------------------------------------------------------------------------------------------------------- We 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. However, sometimes it's just impossible to guarantee the possibility of usage of LightGBM in any specific environment (see `lightgbm-org/LightGBM#1743 `__). Therefore, the first thing you should try in case of segfaults is **compiling from the source** using ``pip install --no-binary lightgbm lightgbm``. For the OS-specific prerequisites see https://github.com/lightgbm-org/LightGBM/blob/master/python-package/README.rst. Also, 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. 4. I would like to install LightGBM from conda. What channel should I choose? ----------------------------------------------------------------------------- We strongly recommend installation from the ``conda-forge`` channel and not from the ``default`` one. For some specific examples, see `this comment `__. In addition, as of ``lightgbm==4.4.0``, the ``conda-forge`` package automatically supports CUDA-based GPU acceleration. 5. How do I subclass ``scikit-learn`` estimators? ------------------------------------------------- For ``lightgbm <= 4.5.0``, copy all of the constructor arguments from the corresponding ``lightgbm`` class into the constructor of your custom estimator. For later versions, just ensure that the constructor of your custom estimator calls ``super().__init__()``. Consider the example below, which implements a regressor that allows creation of truncated predictions. This pattern will work with ``lightgbm > 4.5.0``. .. code-block:: python import numpy as np from lightgbm import LGBMRegressor from sklearn.datasets import make_regression class TruncatedRegressor(LGBMRegressor): def __init__(self, **kwargs): super().__init__(**kwargs) def predict(self, X, max_score: float = np.inf): preds = super().predict(X) np.clip(preds, a_min=None, a_max=max_score, out=preds) return preds X, y = make_regression(n_samples=1_000, n_features=4) reg_trunc = TruncatedRegressor().fit(X, y) preds = reg_trunc.predict(X) print(f"mean: {preds.mean():.2f}, max: {preds.max():.2f}") # mean: -6.81, max: 345.10 preds_trunc = reg_trunc.predict(X, max_score=preds.mean()) print(f"mean: {preds_trunc.mean():.2f}, max: {preds_trunc.max():.2f}") # mean: -56.50, max: -6.81 ================================================ FILE: docs/Features.rst ================================================ Features ======== This 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. Optimization in Speed and Memory Usage -------------------------------------- Many 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. LightGBM 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: - **Reduced cost of calculating the gain for each split** - Pre-sort-based algorithms have time complexity ``O(#data)`` - 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``. - **Use histogram subtraction for further speedup** - To get one leaf's histograms in a binary tree, use the histogram subtraction of its parent and its neighbor - 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)``) - **Reduce memory usage** - Replaces continuous values with discrete bins. If ``#bins`` is small, can use small data type, e.g. uint8\_t, to store training data - No need to store additional information for pre-sorting feature values - **Reduce communication cost for distributed learning** Sparse Optimization ------------------- - Need only ``O(2 * #non_zero_data)`` to construct histogram for sparse features Optimization in Accuracy ------------------------ Leaf-wise (Best-first) Tree Growth ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Most decision tree learning algorithms grow trees by level (depth)-wise, like the following image: .. image:: ./_static/images/level-wise.png :align: center :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. LightGBM grows trees leaf-wise (best-first)\ `[7] <#references>`__. It will choose the leaf with max delta loss to grow. Holding ``#leaf`` fixed, leaf-wise algorithms tend to achieve lower loss than level-wise algorithms. Leaf-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. .. image:: ./_static/images/leaf-wise.png :align: center :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. Optimal Split for Categorical Features ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ It 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. Instead 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. But there is an efficient solution for regression trees\ `[8] <#references>`__. It needs about ``O(k * log(k))`` to find the optimal partition. The basic idea is to sort the categories according to the training objective at each split. More 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. Optimization in Network Communication ------------------------------------- It only needs to use some collective communication algorithms, like "All reduce", "All gather" and "Reduce scatter", in distributed learning of LightGBM. LightGBM implements state-of-the-art algorithms\ `[9] <#references>`__. These collective communication algorithms can provide much better performance than point-to-point communication. .. _Optimization in Parallel Learning: Optimization in Distributed Learning ------------------------------------ LightGBM provides the following distributed learning algorithms. Feature Parallel ~~~~~~~~~~~~~~~~ Traditional Algorithm ^^^^^^^^^^^^^^^^^^^^^ Feature parallel aims to parallelize the "Find Best Split" in the decision tree. The procedure of traditional feature parallel is: 1. Partition data vertically (different machines have different feature set). 2. Workers find local best split point {feature, threshold} on local feature set. 3. Communicate local best splits with each other and get the best one. 4. Worker with best split to perform split, then send the split result of data to other workers. 5. Other workers split data according to received data. The shortcomings of traditional feature parallel: - Has computation overhead, since it cannot speed up "split", whose time complexity is ``O(#data)``. Thus, feature parallel cannot speed up well when ``#data`` is large. - Need communication of split result, which costs about ``O(#data / 8)`` (one bit for one data). Feature Parallel in LightGBM ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Since 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. Thus, LightGBM doesn't need to communicate for split result of data since every worker knows how to split data. And ``#data`` won't be larger, so it is reasonable to hold the full data in every machine. The procedure of feature parallel in LightGBM: 1. Workers find local best split point {feature, threshold} on local feature set. 2. Communicate local best splits with each other and get the best one. 3. Perform best split. However, this feature parallel algorithm still suffers from computation overhead for "split" when ``#data`` is large. So it will be better to use data parallel when ``#data`` is large. Data Parallel ~~~~~~~~~~~~~ Traditional Algorithm ^^^^^^^^^^^^^^^^^^^^^ Data parallel aims to parallelize the whole decision learning. The procedure of data parallel is: 1. Partition data horizontally. 2. Workers use local data to construct local histograms. 3. Merge global histograms from all local histograms. 4. Find best split from merged global histograms, then perform splits. The shortcomings of traditional data parallel: - High communication cost. If using point-to-point communication algorithm, communication cost for one machine is about ``O(#machine * #feature * #bin)``. 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>`__). Data Parallel in LightGBM ^^^^^^^^^^^^^^^^^^^^^^^^^ We reduce communication cost of data parallel in LightGBM: 1. Instead of "Merge global histograms from all local histograms", LightGBM uses "Reduce Scatter" to merge histograms of different (non-overlapping) features for different workers. Then workers find the local best split on local merged histograms and sync up the global best split. 2. As aforementioned, LightGBM uses histogram subtraction to speed up training. Based on this, we can communicate histograms only for one leaf, and get its neighbor's histograms by subtraction as well. All things considered, data parallel in LightGBM has time complexity ``O(0.5 * #feature * #bin)``. Voting Parallel ~~~~~~~~~~~~~~~ Voting parallel further reduces the communication cost in `Data Parallel <#data-parallel>`__ to constant cost. It uses two-stage voting to reduce the communication cost of feature histograms\ `[10] <#references>`__. GPU Support ----------- Thanks `@huanzhang12 `__ for contributing this feature. Please read `[11] <#references>`__ to get more details. - `GPU Installation <./Installation-Guide.rst#build-gpu-version>`__ - `GPU Tutorial <./GPU-Tutorial.rst>`__ Applications and Metrics ------------------------ LightGBM supports the following applications: - regression, the objective function is L2 loss - binary classification, the objective function is logloss - multi classification - cross-entropy, the objective function is logloss and supports training on non-binary labels - LambdaRank, the objective function is LambdaRank with NDCG LightGBM supports the following metrics: - L1 loss - L2 loss - Log loss - Classification error rate - AUC - NDCG - MAP - Multi-class log loss - Multi-class error rate - AUC-mu ``(new in v3.0.0)`` - Average precision ``(new in v3.1.0)`` - Fair - Huber - Poisson - Quantile - MAPE - Kullback-Leibler - Gamma - Tweedie For more details, please refer to `Parameters <./Parameters.rst#metric-parameters>`__. Other Features -------------- - Limit ``max_depth`` of tree while grows tree leaf-wise - `DART `__ - L1/L2 regularization - Bagging - Column (feature) sub-sample - Continued train with input GBDT model - Continued train with the input score file - Weighted training - Validation metric output during training - Multiple validation data - Multiple metrics - Early stopping (both training and prediction) - Prediction for leaf index For more details, please refer to `Parameters <./Parameters.rst>`__. References ---------- [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. [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. [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. [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. [5] Machado, F. P. "Communication and memory efficient parallel decision tree construction." (2003). [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). [7] Shi, Haijian. "Best-first decision tree learning." Diss. The University of Waikato, 2007. [8] Walter D. Fisher. "`On Grouping for Maximum Homogeneity`_." Journal of the American Statistical Association. Vol. 53, No. 284 (Dec., 1958), pp. 789-798. [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. [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. [11] Huan Zhang, Si Si and Cho-Jui Hsieh. "`GPU Acceleration for Large-scale Tree Boosting`_." SysML Conference, 2018. .. _LightGBM\: A Highly Efficient Gradient Boosting Decision Tree: https://proceedings.neurips.cc/paper/2017/hash/6449f44a102fde848669bdd9eb6b76fa-Abstract.html .. _On Grouping for Maximum Homogeneity: https://www.jstor.org/stable/2281952 .. _Optimization of collective communication operations in MPICH: https://www.mpich.org/2012/10/24/optimization-of-collective-communication-operations-in-mpich/ .. _A Communication-Efficient Parallel Algorithm for Decision Tree: https://proceedings.neurips.cc/paper/2016/hash/10a5ab2db37feedfdeaab192ead4ac0e-Abstract.html .. _GPU Acceleration for Large-scale Tree Boosting: https://arxiv.org/abs/1706.08359 ================================================ FILE: docs/GPU-Performance.rst ================================================ GPU Tuning Guide and Performance Comparison =========================================== How It Works? ------------- In LightGBM, the main computation cost during training is building the feature histograms. We use an efficient algorithm on GPU to accelerate this process. The implementation is highly modular, and works for all learning tasks (classification, ranking, regression, etc). GPU acceleration also works in distributed learning settings. GPU algorithm implementation is based on OpenCL and can work with a wide range of GPUs. Supported Hardware ------------------ We target AMD Graphics Core Next (GCN) architecture and NVIDIA Maxwell and Pascal architectures. Most AMD GPUs released after 2012 and NVIDIA GPUs released after 2014 should be supported. We have tested the GPU implementation on the following GPUs: - AMD RX 480 with AMDGPU-pro driver 16.60 on Ubuntu 16.10 - AMD R9 280X (aka Radeon HD 7970) with fglrx driver 15.302.2301 on Ubuntu 16.10 - NVIDIA GTX 1080 with driver 375.39 and CUDA 8.0 on Ubuntu 16.10 - NVIDIA Titan X (Pascal) with driver 367.48 and CUDA 8.0 on Ubuntu 16.04 - NVIDIA Tesla M40 with driver 375.39 and CUDA 7.5 on Ubuntu 16.04 Using the following hardware is discouraged: - 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. - AMD VLIW4-based GPUs, including Radeon HD 6xxx series and earlier GPUs. These GPUs have been discontinued for years and are rarely seen nowadays. How to Achieve Good Speedup on GPU ---------------------------------- #. 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. If you have multiple GPUs, make sure to set ``gpu_platform_id`` and ``gpu_device_id`` to use the desired GPU. Also make sure your system is idle (especially when using a shared computer) to get accuracy performance measurements. #. 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. If you have categorical features, use the ``categorical_column`` option and input them into LightGBM directly; do not convert them into one-hot variables. #. To get good speedup with GPU, it is suggested to use a smaller number of bins. 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. 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. #. Try to use single precision training (``gpu_use_dp=false``) when possible, because most GPUs (especially NVIDIA consumer GPUs) have poor double-precision performance. Performance Comparison ---------------------- We evaluate the training performance of GPU acceleration on the following datasets: +-----------+----------------+----------+------------+-----------+------------+ | Data | Task | Link | #Examples | #Features | Comments | +===========+================+==========+============+===========+============+ | Higgs | Binary | `link1`_ | 10,500,000 | 28 | use last | | | classification | | | | 500,000 | | | | | | | samples | | | | | | | as test | | | | | | | set | +-----------+----------------+----------+------------+-----------+------------+ | Epsilon | Binary | `link2`_ | 400,000 | 2,000 | use the | | | classification | | | | provided | | | | | | | test set | +-----------+----------------+----------+------------+-----------+------------+ | Bosch | Binary | `link3`_ | 1,000,000 | 968 | use the | | | classification | | | | provided | | | | | | | test set | +-----------+----------------+----------+------------+-----------+------------+ | Yahoo LTR | Learning to | `link4`_ | 473,134 | 700 | set1.train | | | rank | | | | as train, | | | | | | | set1.test | | | | | | | as test | +-----------+----------------+----------+------------+-----------+------------+ | MS LTR | Learning to | `link5`_ | 2,270,296 | 137 | {S1,S2,S3} | | | rank | | | | as train | | | | | | | set, {S5} | | | | | | | as test | | | | | | | set | +-----------+----------------+----------+------------+-----------+------------+ | Expo | Binary | `link6`_ | 11,000,000 | 700 | use last | | | classification | | | | 1,000,000 | | | (Categorical) | | | | as test | | | | | | | set | +-----------+----------------+----------+------------+-----------+------------+ We used the following hardware to evaluate the performance of LightGBM GPU training. Our CPU reference is **a high-end dual socket Haswell-EP Xeon server with 28 cores**; GPUs include a budget GPU (RX 480) and a mainstream (GTX 1080) GPU installed on the same server. It is worth mentioning that **the GPUs used are not the best GPUs in the market**; if 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. +--------------------------------+----------------+------------------+---------------+ | Hardware | Peak FLOPS | Peak Memory BW | Cost (MSRP) | +================================+================+==================+===============+ | AMD Radeon RX 480 | 5,161 GFLOPS | 256 GB/s | $199 | +--------------------------------+----------------+------------------+---------------+ | NVIDIA GTX 1080 | 8,228 GFLOPS | 320 GB/s | $499 | +--------------------------------+----------------+------------------+---------------+ | 2x Xeon E5-2683v3 (28 cores) | 1,792 GFLOPS | 133 GB/s | $3,692 | +--------------------------------+----------------+------------------+---------------+ During benchmarking on CPU we used only 28 physical cores of the CPU, and did not use hyper-threading cores, because we found that using too many threads actually makes performance worse. The following shows the training configuration we used: :: max_bin = 63 num_leaves = 255 num_iterations = 500 learning_rate = 0.1 tree_learner = serial task = train is_training_metric = false min_data_in_leaf = 1 min_sum_hessian_in_leaf = 100 ndcg_eval_at = 1,3,5,10 device = gpu gpu_platform_id = 0 gpu_device_id = 0 num_thread = 28 We 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``. For all GPU training we vary the max number of bins (255, 63 and 15). The GPU implementation is from commit `0bb4a82`_ of LightGBM, when the GPU support was just merged in. The following table lists the accuracy on test set that CPU and GPU learner can achieve after 500 iterations. GPU with the same number of bins can achieve a similar level of accuracy as on the CPU, despite using single precision arithmetic. For most datasets, using 63 bins is sufficient. +---------------------------+----------------+---------------+---------------+----------------+---------------+---------------+ | | CPU 255 bins | CPU 63 bins | CPU 15 bins | GPU 255 bins | GPU 63 bins | GPU 15 bins | +===========================+================+===============+===============+================+===============+===============+ | Higgs AUC | 0.845612 | 0.845239 | 0.841066 | 0.845612 | 0.845209 | 0.840748 | +---------------------------+----------------+---------------+---------------+----------------+---------------+---------------+ | Epsilon AUC | 0.950243 | 0.949952 | 0.948365 | 0.950057 | 0.949876 | 0.948365 | +---------------------------+----------------+---------------+---------------+----------------+---------------+---------------+ | Yahoo-LTR NDCG\ :sub:`1` | 0.730824 | 0.730165 | 0.729647 | 0.730936 | 0.732257 | 0.73114 | +---------------------------+----------------+---------------+---------------+----------------+---------------+---------------+ | Yahoo-LTR NDCG\ :sub:`3` | 0.738687 | 0.737243 | 0.736445 | 0.73698 | 0.739474 | 0.735868 | +---------------------------+----------------+---------------+---------------+----------------+---------------+---------------+ | Yahoo-LTR NDCG\ :sub:`5` | 0.756609 | 0.755729 | 0.754607 | 0.756206 | 0.757007 | 0.754203 | +---------------------------+----------------+---------------+---------------+----------------+---------------+---------------+ | Yahoo-LTR NDCG\ :sub:`10` | 0.79655 | 0.795827 | 0.795273 | 0.795894 | 0.797302 | 0.795584 | +---------------------------+----------------+---------------+---------------+----------------+---------------+---------------+ | Expo AUC | 0.776217 | 0.771566 | 0.743329 | 0.776285 | 0.77098 | 0.744078 | +---------------------------+----------------+---------------+---------------+----------------+---------------+---------------+ | MS-LTR NDCG\ :sub:`1` | 0.521265 | 0.521392 | 0.518653 | 0.521789 | 0.522163 | 0.516388 | +---------------------------+----------------+---------------+---------------+----------------+---------------+---------------+ | MS-LTR NDCG\ :sub:`3` | 0.503153 | 0.505753 | 0.501697 | 0.503886 | 0.504089 | 0.501691 | +---------------------------+----------------+---------------+---------------+----------------+---------------+---------------+ | MS-LTR NDCG\ :sub:`5` | 0.509236 | 0.510391 | 0.507193 | 0.509861 | 0.510095 | 0.50663 | +---------------------------+----------------+---------------+---------------+----------------+---------------+---------------+ | MS-LTR NDCG\ :sub:`10` | 0.527835 | 0.527304 | 0.524603 | 0.528009 | 0.527059 | 0.524722 | +---------------------------+----------------+---------------+---------------+----------------+---------------+---------------+ | Bosch AUC | 0.718115 | 0.721791 | 0.716677 | 0.717184 | 0.724761 | 0.717005 | +---------------------------+----------------+---------------+---------------+----------------+---------------+---------------+ We record the wall clock time after 500 iterations, as shown in the figure below: .. image:: ./_static/images/gpu-performance-comparison.png :align: center :target: ./_static/images/gpu-performance-comparison.png :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. When 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. On CPU, using a smaller bin size only marginally improves performance, sometimes even slows down training, like in Higgs (we can reproduce the same slowdown on two different machines, with different GCC versions). We found that GPU can achieve impressive acceleration on large and dense datasets like Higgs and Epsilon. Even on smaller and sparse datasets, a *budget* GPU can still compete and be faster than a 28-core Haswell server. Memory Usage ------------ The next table shows GPU memory usage reported by ``nvidia-smi`` during training with 63 bins. We can see that even the largest dataset just uses about 1 GB of GPU memory, indicating that our GPU implementation can scale to huge datasets over 10x larger than Bosch or Epsilon. Also, we can observe that generally a larger dataset (using more GPU memory, like Epsilon or Bosch) has better speedup, because the overhead of invoking GPU functions becomes significant when the dataset is small. +-------------------------+---------+-----------+---------+----------+--------+-------------+ | Datasets | Higgs | Epsilon | Bosch | MS-LTR | Expo | Yahoo-LTR | +=========================+=========+===========+=========+==========+========+=============+ | GPU Memory Usage (MB) | 611 | 901 | 1067 | 413 | 405 | 291 | +-------------------------+---------+-----------+---------+----------+--------+-------------+ Further Reading --------------- You can find more details about the GPU algorithm and benchmarks in the following article: Huan Zhang, Si Si and Cho-Jui Hsieh. `GPU Acceleration for Large-scale Tree Boosting`_. SysML Conference, 2018. .. _link1: https://archive.ics.uci.edu/dataset/280/higgs .. _link2: https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/binary.html .. _link3: https://www.kaggle.com/c/bosch-production-line-performance/data .. _link4: https://proceedings.mlr.press/v14/chapelle11a.html .. _link5: https://www.microsoft.com/en-us/research/project/mslr/ .. _link6: https://community.amstat.org/jointscsg-section/dataexpo/dataexpo2009 .. _0bb4a82: https://github.com/lightgbm-org/LightGBM/commit/0bb4a82 .. _GPU Acceleration for Large-scale Tree Boosting: https://arxiv.org/abs/1706.08359 ================================================ FILE: docs/GPU-Targets.rst ================================================ GPU SDK Correspondence and Device Targeting Table ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ GPU Targets Table ================= OpenCL is a universal massively parallel programming framework that targets multiple backends (GPU, CPU, FPGA, etc). Basically, to use a device from a vendor, you have to install drivers from that specific vendor. Intel's and AMD's OpenCL runtime also include x86 CPU target support. NVIDIA's OpenCL runtime only supports NVIDIA GPU (no CPU support). In general, OpenCL CPU backends are quite slow, and should be used for testing and debugging only. You can find below a table of correspondence: +---------------------------+-----------------+-----------------+-----------------+--------------+ | SDK | CPU Intel/AMD | GPU Intel | GPU AMD | GPU NVIDIA | +===========================+=================+=================+=================+==============+ | `Intel SDK for OpenCL`_ | Supported | Supported | Not Supported | Not Supported| +---------------------------+-----------------+-----------------+-----------------+--------------+ | AMD APP SDK \* | Supported | Not Supported | Supported | Not Supported| +---------------------------+-----------------+-----------------+-----------------+--------------+ | `PoCL`_ | Supported | Not Supported | Supported | Not Supported| +---------------------------+-----------------+-----------------+-----------------+--------------+ | `NVIDIA CUDA Toolkit`_ | Not Supported | Not Supported | Not Supported | Supported | +---------------------------+-----------------+-----------------+-----------------+--------------+ Legend: \* 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`_). -------------- Query OpenCL Devices in Your System =================================== Your 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. On 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. On Linux, OpenCL devices can be listed using the ``clinfo`` command. On Ubuntu, you can install ``clinfo`` by executing ``sudo apt-get install clinfo``. Examples =============== We provide test R code below, but you can use the language of your choice with the examples of your choices: .. code:: r library(lightgbm) data(agaricus.train, package = "lightgbm") train <- agaricus.train train$data[, 1] <- 1:6513 dtrain <- lgb.Dataset(train$data, label = train$label) data(agaricus.test, package = "lightgbm") test <- agaricus.test dtest <- lgb.Dataset.create.valid(dtrain, test$data, label = test$label) valids <- list(test = dtest) params <- list(objective = "regression", metric = "rmse", device = "gpu", gpu_platform_id = 0, gpu_device_id = 0, nthread = 1, boost_from_average = FALSE, num_tree_per_iteration = 10, max_bin = 32) model <- lgb.train(params, dtrain, 2, valids, min_data = 1, learning_rate = 1, early_stopping_rounds = 10) Make 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. Example of using GPU (``gpu_platform_id = 0`` and ``gpu_device_id = 0`` in our system): .. code:: r > params <- list(objective = "regression", + metric = "rmse", + device = "gpu", + gpu_platform_id = 0, + gpu_device_id = 0, + nthread = 1, + boost_from_average = FALSE, + num_tree_per_iteration = 10, + max_bin = 32) > model <- lgb.train(params, + dtrain, + 2, + valids, + min_data = 1, + learning_rate = 1, + early_stopping_rounds = 10) [LightGBM] [Info] This is the GPU trainer!! [LightGBM] [Info] Total Bins 232 [LightGBM] [Info] Number of data: 6513, number of used features: 116 [LightGBM] [Info] Using GPU Device: Oland, Vendor: Advanced Micro Devices, Inc. [LightGBM] [Info] Compiling OpenCL Kernel with 16 bins... [LightGBM] [Info] GPU programs have been built [LightGBM] [Info] Size of histogram bin entry: 12 [LightGBM] [Info] 40 dense feature groups (0.12 MB) transferred to GPU in 0.004211 secs. 76 sparse feature groups. [LightGBM] [Info] No further splits with positive gain, best gain: -inf [LightGBM] [Info] Trained a tree with leaves=16 and depth=8 [1]: test's rmse:1.10643e-17 [LightGBM] [Info] No further splits with positive gain, best gain: -inf [LightGBM] [Info] Trained a tree with leaves=7 and depth=5 [2]: test's rmse:0 Running 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. Example 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. .. code:: r > params <- list(objective = "regression", + metric = "rmse", + device = "gpu", + gpu_platform_id = 0, + gpu_device_id = 1, + nthread = 1, + boost_from_average = FALSE, + num_tree_per_iteration = 10, + max_bin = 32) > model <- lgb.train(params, + dtrain, + 2, + valids, + min_data = 1, + learning_rate = 1, + early_stopping_rounds = 10) [LightGBM] [Info] This is the GPU trainer!! [LightGBM] [Info] Total Bins 232 [LightGBM] [Info] Number of data: 6513, number of used features: 116 [LightGBM] [Info] Using requested OpenCL platform 0 device 1 [LightGBM] [Info] Using GPU Device: Intel(R) Core(TM) i7-4600U CPU @ 2.10GHz, Vendor: GenuineIntel [LightGBM] [Info] Compiling OpenCL Kernel with 16 bins... [LightGBM] [Info] GPU programs have been built [LightGBM] [Info] Size of histogram bin entry: 12 [LightGBM] [Info] 40 dense feature groups (0.12 MB) transferred to GPU in 0.004540 secs. 76 sparse feature groups. [LightGBM] [Info] No further splits with positive gain, best gain: -inf [LightGBM] [Info] Trained a tree with leaves=16 and depth=8 [1]: test's rmse:1.10643e-17 [LightGBM] [Info] No further splits with positive gain, best gain: -inf [LightGBM] [Info] Trained a tree with leaves=7 and depth=5 [2]: test's rmse:0 Known issues: - 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. - 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. .. _Intel SDK for OpenCL: https://software.intel.com/en-us/articles/opencl-drivers .. _ROCm: https://rocmdocs.amd.com/en/latest/ .. _for Linux: https://github.com/lightgbm-org/LightGBM/releases/download/v2.0.12/AMD-APP-SDKInstaller-v3.0.130.136-GA-linux64.tar.bz2 .. _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 .. _NVIDIA CUDA Toolkit: https://developer.nvidia.com/cuda-downloads .. _clinfo: https://github.com/Oblomov/clinfo .. _GPUCapsViewer: https://www.ozone3d.net/gpu_caps_viewer/ .. _PoCL: https://portablecl.org/ ================================================ FILE: docs/GPU-Tutorial.rst ================================================ LightGBM GPU Tutorial ===================== The purpose of this document is to give you a quick step-by-step tutorial on GPU training. We will use the GPU instance on `Microsoft Azure cloud computing platform`_ for demonstration, but you can use any machine with modern AMD or NVIDIA GPUs. GPU Setup --------- You 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) and select Ubuntu 16.04 LTS as the operating system. For 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. Don't use the ``NC`` type instance as the GPUs (K80) are based on an older architecture (Kepler). First we need to install minimal NVIDIA drivers and OpenCL development environment: :: sudo apt-get update sudo apt-get install --no-install-recommends nvidia-375 sudo apt-get install --no-install-recommends nvidia-opencl-icd-375 nvidia-opencl-dev opencl-headers After installing the drivers you need to restart the server. :: sudo init 6 After about 30 seconds, the server should be up again. If 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``. Build LightGBM -------------- Now install necessary building tools and dependencies: :: sudo apt-get install --no-install-recommends git cmake build-essential libboost-dev libboost-system-dev libboost-filesystem-dev The ``NV6`` GPU instance has a 320 GB ultra-fast SSD mounted at ``/mnt``. Let's use it as our workspace (skip this if you are using your own machine): :: sudo mkdir -p /mnt/workspace sudo chown $(whoami):$(whoami) /mnt/workspace cd /mnt/workspace Now we are ready to checkout LightGBM and compile it with GPU support: :: git clone --recursive https://github.com/lightgbm-org/LightGBM cd LightGBM cmake -B build -S . -DUSE_GPU=1   # if you have installed NVIDIA CUDA to a customized location, you should specify paths to OpenCL headers and library like the following: # cmake -B build -S . -DUSE_GPU=1 -DOpenCL_LIBRARY=/usr/local/cuda/lib64/libOpenCL.so -DOpenCL_INCLUDE_DIR=/usr/local/cuda/include/ cmake --build build -j$(nproc) You will see two binaries are generated, ``lightgbm`` and ``lib_lightgbm.so``. If 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. Install Python Interface (optional) ----------------------------------- If you want to use the Python interface of LightGBM, you can install it now (along with some necessary Python-package dependencies): :: sudo apt-get -y install python3-pip python3-venv sudo -H pip install numpy scipy scikit-learn -U sudo sh ./build-python.sh install --precompile You need to set an additional parameter ``"device" : "gpu"`` (along with your other options like ``learning_rate``, ``num_leaves``, etc) to use GPU in Python. You can read our `Python-package Examples`_ for more information on how to use the Python interface. Dataset Preparation ------------------- Using the following commands to prepare the Higgs dataset: :: git clone https://github.com/guolinke/boosting_tree_benchmarks.git cd boosting_tree_benchmarks/data wget "https://archive.ics.uci.edu/ml/machine-learning-databases/00280/HIGGS.csv.gz" gunzip HIGGS.csv.gz python higgs2libsvm.py cd ../.. ln -s boosting_tree_benchmarks/data/higgs.train ln -s boosting_tree_benchmarks/data/higgs.test Now we create a configuration file for LightGBM by running the following commands (please copy the entire block and run it as a whole): :: cat > lightgbm_gpu.conf <> lightgbm_gpu.conf GPU is enabled in the configuration file we just created by setting ``device=gpu``. In 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. You 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. Run Your First Learning Task on GPU ----------------------------------- Now we are ready to start GPU training! First we want to verify the GPU works correctly. Run the following command to train on GPU, and take a note of the AUC after 50 iterations: :: ./lightgbm config=lightgbm_gpu.conf data=higgs.train valid=higgs.test objective=binary metric=auc Now train the same dataset on CPU using the following command. You should observe a similar AUC: :: ./lightgbm config=lightgbm_gpu.conf data=higgs.train valid=higgs.test objective=binary metric=auc device=cpu Now we can make a speed test on GPU without calculating AUC after each iteration. :: ./lightgbm config=lightgbm_gpu.conf data=higgs.train objective=binary metric=auc Speed test on CPU: :: ./lightgbm config=lightgbm_gpu.conf data=higgs.train objective=binary metric=auc device=cpu You should observe over three times speedup on this GPU. The GPU acceleration can be used on other tasks/metrics (regression, multi-class classification, ranking, etc) as well. For example, we can train the Higgs dataset on GPU as a regression task: :: ./lightgbm config=lightgbm_gpu.conf data=higgs.train objective=regression_l2 metric=l2 Also, you can compare the training speed with CPU: :: ./lightgbm config=lightgbm_gpu.conf data=higgs.train objective=regression_l2 metric=l2 device=cpu Further Reading --------------- - `GPU Tuning Guide and Performance Comparison <./GPU-Performance.rst>`__ - `GPU SDK Correspondence and Device Targeting Table <./GPU-Targets.rst>`__ Reference --------- Please kindly cite the following article in your publications if you find the GPU acceleration useful: Huan Zhang, Si Si and Cho-Jui Hsieh. "`GPU Acceleration for Large-scale Tree Boosting`_." SysML Conference, 2018. .. _Microsoft Azure cloud computing platform: https://azure.microsoft.com/ .. _AMDGPU-Pro: https://www.amd.com/en/support.html .. _Python-package Examples: https://github.com/lightgbm-org/LightGBM/tree/master/examples/python-guide .. _GPU Acceleration for Large-scale Tree Boosting: https://arxiv.org/abs/1706.08359 .. _clinfo: https://github.com/Oblomov/clinfo ================================================ FILE: docs/GPU-Windows.rst ================================================ The content of this document was very outdated and is no longer available to avoid any misleadings. Starting from the ``3.2.0`` version LightGBM Python packages have been having built-in support of training on GPU devices. ================================================ FILE: docs/Installation-Guide.rst ================================================ Installation Guide ================== Versioning ~~~~~~~~~~ LightGBM releases use a 3-part version number, with this format: .. code:: {major}.{minor}.{patch} This version follows a scheme called Intended Effort Versioning ("Effver" for short). Changes to a component of the version indicate how much effort it will likely take to update code using a previous version. * ``major`` = updating will require significant effort * ``minor`` = some effort * ``patch`` = no or very little effort This means that **new minor versions can contain breaking changes**, but these are typically small or limited to less-frequently-used parts of the project. When built from source on an unreleased commit, this version takes the following form: .. code:: {major}.{minor}.{patch}.99 That ``.99`` is added to ensure that a version built from an unreleased commit is considered "newer" than all previous releases, and "older" than all future releases. .. _nightly-builds: To download such artifacts, run the following from the root of this repository. .. code:: sh bash .ci/download-artifacts.sh ${COMMIT_ID} Where `COMMIT_ID` is the full commit SHA pointing to a commit on ``master``. The artifacts can then be found in the ``release-artifacts/`` directory. For more details on why LightGBM uses EffVer instead of other schemes like semantic versioning, see https://jacobtomlinson.dev/effver/. General Installation Notes ~~~~~~~~~~~~~~~~~~~~~~~~~~ All instructions below are aimed at compiling the 64-bit version of LightGBM. It is worth compiling the 32-bit version only in very rare special cases involving environmental limitations. The 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. By default, instructions below will use **VS Build Tools** or **make** tool to compile the code. It it possible to use `Ninja`_ tool instead of make on all platforms, but VS Build Tools cannot be replaced with Ninja. You can add ``-G Ninja`` to CMake flags to use Ninja. By default, instructions below will produce a shared library file and an executable file with command-line interface. You can add ``-DBUILD_CLI=OFF`` to CMake flags to disable the executable compilation. If you need to build a static library instead of a shared one, you can add ``-DBUILD_STATIC_LIB=ON`` to CMake flags. By default, instructions below will place header files into system-wide folder. You can add ``-DINSTALL_HEADERS=OFF`` to CMake flags to disable headers installation. By default, on macOS, CMake is looking into Homebrew standard folders for finding dependencies (e.g. OpenMP). You can add ``-DUSE_HOMEBREW_FALLBACK=OFF`` to CMake flags to disable this behaviour. Users who want to perform benchmarking can make LightGBM output time costs for different internal routines by adding ``-DUSE_TIMETAG=ON`` to CMake flags. It is possible to build LightGBM in debug mode. In this mode all compiler optimizations are disabled and LightGBM performs more checks internally. To 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. .. _sanitizers: In addition to the debug mode, LightGBM can be built with compiler sanitizers. To enable them add ``-DUSE_SANITIZER=ON -DENABLED_SANITIZERS="address;leak;undefined"`` to CMake flags. These values refer to the following supported sanitizers: - ``address`` - AddressSanitizer (ASan); - ``leak`` - LeakSanitizer (LSan); - ``undefined`` - UndefinedBehaviorSanitizer (UBSan); - ``thread`` - ThreadSanitizer (TSan). Please note, that ThreadSanitizer cannot be used together with other sanitizers. For more info and additional sanitizers' parameters please refer to the `following docs`_. It is very useful to build `C++ unit tests <#build-c-unit-tests>`__ with sanitizers. .. contents:: **Contents** :depth: 1 :local: :backlinks: none Windows ~~~~~~~ On Windows, LightGBM can be built using - **Visual Studio**; - **CMake** and **VS Build Tools**; - **CMake** and **MinGW**. Visual Studio (or VS Build Tools) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ With GUI ******** 1. Install `Visual Studio`_. 2. Navigate to one of the releases at https://github.com/lightgbm-org/LightGBM/releases, download ``LightGBM-complete_source_code_zip.zip``, and unzip it. 3. Go to ``LightGBM-complete_source_code_zip/windows`` folder. 4. 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)``. If you have errors about **Platform Toolset**, go to ``Project`` -> ``Properties`` -> ``Configuration Properties`` -> ``General`` and select the toolset installed on your machine. If you have errors about **Windows SDK Version**, go to ``Project`` -> ``Properties`` -> ``Configuration Properties`` -> ``General`` and select the SDK installed on your machine. The ``.exe`` file will be in ``LightGBM-complete_source_code_zip/windows/x64/Release`` folder. The ``.dll`` file will be in ``LightGBM-complete_source_code_zip/windows/x64/DLL`` folder. From Command Line ***************** 1. Install `Git for Windows`_, `CMake`_ and `VS Build Tools`_ (**VS Build Tools** is not needed if **Visual Studio** is already installed). 2. Run the following commands: .. code:: console git clone --recursive https://github.com/lightgbm-org/LightGBM cd LightGBM cmake -B build -S . -A x64 cmake --build build --target ALL_BUILD --config Release The ``.exe`` and ``.dll`` files will be in ``LightGBM/Release`` folder. MinGW-w64 ^^^^^^^^^ 1. Install `Git for Windows`_, `CMake`_ and `MinGW-w64`_. 2. Run the following commands: .. code:: console git clone --recursive https://github.com/lightgbm-org/LightGBM cd LightGBM cmake -B build -S . -G "MinGW Makefiles" cmake --build build -j4 The ``.exe`` and ``.dll`` files will be in ``LightGBM/`` folder. **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. It is recommended that you use **Visual Studio** since it has better multithreading efficiency in **Windows** for many-core systems (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>`__). Linux ~~~~~ On Linux, LightGBM can be built using - **CMake** and **gcc**; - **CMake** and **Clang**. After compilation the executable and ``.so`` files will be in ``LightGBM/`` folder. gcc ^^^ 1. Install `CMake`_ and **gcc**. 2. Run the following commands: .. code:: sh git clone --recursive https://github.com/lightgbm-org/LightGBM cd LightGBM cmake -B build -S . cmake --build build -j4 Clang ^^^^^ 1. Install `CMake`_, **Clang** and **OpenMP**. 2. Run the following commands: .. code:: sh git clone --recursive https://github.com/lightgbm-org/LightGBM cd LightGBM export CXX=clang++-14 CC=clang-14 # replace "14" with version of Clang installed on your machine cmake -B build -S . cmake --build build -j4 macOS ~~~~~ On macOS, LightGBM can be installed using - **Homebrew**; - **MacPorts**; or can be built using - **CMake** and **Apple Clang**; - **CMake** and **gcc**. Install Using ``Homebrew`` ^^^^^^^^^^^^^^^^^^^^^^^^^^ .. code:: sh brew install lightgbm Refer to https://formulae.brew.sh/formula/lightgbm for more details. Install Using ``MacPorts`` ^^^^^^^^^^^^^^^^^^^^^^^^^^ .. code:: sh sudo port install LightGBM Refer to https://ports.macports.org/port/LightGBM for more details. **Note**: Port for LightGBM is not maintained by LightGBM's maintainers. Build from GitHub ^^^^^^^^^^^^^^^^^ After compilation the executable and ``.dylib`` files will be in ``LightGBM/`` folder. Apple Clang *********** 1. Install `CMake`_ and **OpenMP**: .. code:: sh brew install cmake libomp 2. Run the following commands: .. code:: sh git clone --recursive https://github.com/lightgbm-org/LightGBM cd LightGBM cmake -B build -S . cmake --build build -j4 gcc *** 1. Install `CMake`_ and **gcc**: .. code:: sh brew install cmake gcc 2. Run the following commands: .. code:: sh git clone --recursive https://github.com/lightgbm-org/LightGBM cd LightGBM export CXX=g++-7 CC=gcc-7 # replace "7" with version of gcc installed on your machine cmake -B build -S . cmake --build build -j4 Docker ~~~~~~ Refer to `Docker folder `__. Build Threadless Version (not Recommended) ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ The default build version of LightGBM is based on OpenMP. You can build LightGBM without OpenMP support but it is **strongly not recommended**. Windows ^^^^^^^ On Windows, a version of LightGBM without OpenMP support can be built using - **Visual Studio**; - **CMake** and **VS Build Tools**; - **CMake** and **MinGW**. Visual Studio (or VS Build Tools) ********************************* With GUI -------- 1. Install `Visual Studio`_. 2. Navigate to one of the releases at https://github.com/lightgbm-org/LightGBM/releases, download ``LightGBM-complete_source_code_zip.zip``, and unzip it. 3. Go to ``LightGBM-complete_source_code_zip/windows`` folder. 4. Open ``LightGBM.sln`` file with **Visual Studio**, choose ``Release`` configuration if you need executable file or ``DLL`` configuration if you need shared library. 5. Go to ``Project`` -> ``Properties`` -> ``Configuration Properties`` -> ``C/C++`` -> ``Language`` and change the ``OpenMP Support`` property to ``No (/openmp-)``. 6. Get back to the project's main screen and click ``Build`` -> ``Build Solution (Ctrl+Shift+B)``. If you have errors about **Platform Toolset**, go to ``Project`` -> ``Properties`` -> ``Configuration Properties`` -> ``General`` and select the toolset installed on your machine. If you have errors about **Windows SDK Version**, go to ``Project`` -> ``Properties`` -> ``Configuration Properties`` -> ``General`` and select the SDK installed on your machine. The ``.exe`` file will be in ``LightGBM-complete_source_code_zip/windows/x64/Release`` folder. The ``.dll`` file will be in ``LightGBM-complete_source_code_zip/windows/x64/DLL`` folder. From Command Line ----------------- 1. Install `Git for Windows`_, `CMake`_ and `VS Build Tools`_ (**VS Build Tools** is not needed if **Visual Studio** is already installed). 2. Run the following commands: .. code:: console git clone --recursive https://github.com/lightgbm-org/LightGBM cd LightGBM cmake -B build -S . -A x64 -DUSE_OPENMP=OFF cmake --build build --target ALL_BUILD --config Release The ``.exe`` and ``.dll`` files will be in ``LightGBM/Release`` folder. MinGW-w64 ********* 1. Install `Git for Windows`_, `CMake`_ and `MinGW-w64`_. 2. Run the following commands: .. code:: console git clone --recursive https://github.com/lightgbm-org/LightGBM cd LightGBM cmake -B build -S . -G "MinGW Makefiles" -DUSE_OPENMP=OFF cmake --build build -j4 The ``.exe`` and ``.dll`` files will be in ``LightGBM/`` folder. **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. Linux ^^^^^ On Linux, a version of LightGBM without OpenMP support can be built using - **CMake** and **gcc**; - **CMake** and **Clang**. After compilation the executable and ``.so`` files will be in ``LightGBM/`` folder. gcc *** 1. Install `CMake`_ and **gcc**. 2. Run the following commands: .. code:: sh git clone --recursive https://github.com/lightgbm-org/LightGBM cd LightGBM cmake -B build -S . -DUSE_OPENMP=OFF cmake --build build -j4 Clang ***** 1. Install `CMake`_ and **Clang**. 2. Run the following commands: .. code:: sh git clone --recursive https://github.com/lightgbm-org/LightGBM cd LightGBM export CXX=clang++-14 CC=clang-14 # replace "14" with version of Clang installed on your machine cmake -B build -S . -DUSE_OPENMP=OFF cmake --build build -j4 macOS ^^^^^ On macOS, a version of LightGBM without OpenMP support can be built using - **CMake** and **Apple Clang**; - **CMake** and **gcc**. After compilation the executable and ``.dylib`` files will be in ``LightGBM/`` folder. Apple Clang *********** 1. Install `CMake`_: .. code:: sh brew install cmake 2. Run the following commands: .. code:: sh git clone --recursive https://github.com/lightgbm-org/LightGBM cd LightGBM cmake -B build -S . -DUSE_OPENMP=OFF cmake --build build -j4 gcc *** 1. Install `CMake`_ and **gcc**: .. code:: sh brew install cmake gcc 2. Run the following commands: .. code:: sh git clone --recursive https://github.com/lightgbm-org/LightGBM cd LightGBM export CXX=g++-7 CC=gcc-7 # replace "7" with version of gcc installed on your machine cmake -B build -S . -DUSE_OPENMP=OFF cmake --build build -j4 Build MPI Version ~~~~~~~~~~~~~~~~~ The default build version of LightGBM is based on socket. LightGBM also supports MPI. `MPI`_ is a high performance communication approach with `RDMA`_ support. If you need to run a distributed learning application with high performance communication, you can build the LightGBM with MPI support. Windows ^^^^^^^ On Windows, an MPI version of LightGBM can be built using - **MS MPI** and **Visual Studio**; - **MS MPI**, **CMake** and **VS Build Tools**. **Note**: Building MPI version by **MinGW** is not supported due to the miss of MPI library in it. With GUI ******** 1. You need to install `MS MPI`_ first. Both ``msmpisdk.msi`` and ``msmpisetup.exe`` are needed. 2. Install `Visual Studio`_. 3. Navigate to one of the releases at https://github.com/lightgbm-org/LightGBM/releases, download ``LightGBM-complete_source_code_zip.zip``, and unzip it. 4. Go to ``LightGBM-complete_source_code_zip/windows`` folder. 5. Open ``LightGBM.sln`` file with **Visual Studio**, choose ``Release_mpi`` configuration and click ``Build`` -> ``Build Solution (Ctrl+Shift+B)``. If you have errors about **Platform Toolset**, go to ``Project`` -> ``Properties`` -> ``Configuration Properties`` -> ``General`` and select the toolset installed on your machine. If you have errors about **Windows SDK Version**, go to ``Project`` -> ``Properties`` -> ``Configuration Properties`` -> ``General`` and select the SDK installed on your machine. The ``.exe`` file will be in ``LightGBM-complete_source_code_zip/windows/x64/Release_mpi`` folder. From Command Line ***************** 1. You need to install `MS MPI`_ first. Both ``msmpisdk.msi`` and ``msmpisetup.exe`` are needed. 2. Install `Git for Windows`_, `CMake`_ and `VS Build Tools`_ (**VS Build Tools** is not needed if **Visual Studio** is already installed). 3. Run the following commands: .. code:: console git clone --recursive https://github.com/lightgbm-org/LightGBM cd LightGBM cmake -B build -S . -A x64 -DUSE_MPI=ON cmake --build build --target ALL_BUILD --config Release The ``.exe`` and ``.dll`` files will be in ``LightGBM/Release`` folder. Linux ^^^^^ On Linux, an MPI version of LightGBM can be built using - **CMake**, **gcc** and **Open MPI**; - **CMake**, **Clang** and **Open MPI**. After compilation the executable and ``.so`` files will be in ``LightGBM/`` folder. gcc *** 1. Install `CMake`_, **gcc** and `Open MPI`_. 2. Run the following commands: .. code:: sh git clone --recursive https://github.com/lightgbm-org/LightGBM cd LightGBM cmake -B build -S . -DUSE_MPI=ON cmake --build build -j4 Clang ***** 1. Install `CMake`_, **Clang**, **OpenMP** and `Open MPI`_. 2. Run the following commands: .. code:: sh git clone --recursive https://github.com/lightgbm-org/LightGBM cd LightGBM export CXX=clang++-14 CC=clang-14 # replace "14" with version of Clang installed on your machine cmake -B build -S . -DUSE_MPI=ON cmake --build build -j4 macOS ^^^^^ On macOS, an MPI version of LightGBM can be built using - **CMake**, **Open MPI** and **Apple Clang**; - **CMake**, **Open MPI** and **gcc**. After compilation the executable and ``.dylib`` files will be in ``LightGBM/`` folder. Apple Clang *********** 1. Install `CMake`_, **OpenMP** and `Open MPI`_: .. code:: sh brew install cmake libomp open-mpi 2. Run the following commands: .. code:: sh git clone --recursive https://github.com/lightgbm-org/LightGBM cd LightGBM cmake -B build -S . -DUSE_MPI=ON cmake --build build -j4 gcc *** 1. Install `CMake`_, `Open MPI`_ and **gcc**: .. code:: sh brew install cmake open-mpi gcc 2. Run the following commands: .. code:: sh git clone --recursive https://github.com/lightgbm-org/LightGBM cd LightGBM export CXX=g++-7 CC=gcc-7 # replace "7" with version of gcc installed on your machine cmake -B build -S . -DUSE_MPI=ON cmake --build build -j4 Build GPU Version ~~~~~~~~~~~~~~~~~ Windows ^^^^^^^ On Windows, a GPU version of LightGBM (``device_type=gpu``) can be built using - **OpenCL**, **Boost**, **CMake** and **VS Build Tools**; - **OpenCL**, **Boost**, **CMake** and **MinGW**. If you use **MinGW**, the build procedure is similar to the build on Linux. Following procedure is for the **MSVC** (Microsoft Visual C++) build. 1. Install `Git for Windows`_, `CMake`_ and `VS Build Tools`_ (**VS Build Tools** is not needed if **Visual Studio** is installed). 2. Install **OpenCL** for Windows. The installation depends on the brand (NVIDIA, AMD, Intel) of your GPU card. - For running on Intel, get `Intel SDK for OpenCL`_. - For running on AMD, get AMD APP SDK. - For running on NVIDIA, get `CUDA Toolkit`_. Further reading and correspondence table: `GPU SDK Correspondence and Device Targeting Table <./GPU-Targets.rst>`__. 3. Install `Boost Binaries`_. **Note**: Match your Visual C++ version: Visual Studio 2017 -> ``msvc-14.1-64.exe``, Visual Studio 2019 -> ``msvc-14.2-64.exe``, Visual Studio 2022 -> ``msvc-14.3-64.exe``. 4. Run the following commands: .. code:: console git clone --recursive https://github.com/lightgbm-org/LightGBM cd LightGBM 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 # if you have installed NVIDIA CUDA to a customized location, you should specify paths to OpenCL headers and library like the following: # 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" cmake --build build --target ALL_BUILD --config Release **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). The ``.exe`` and ``.dll`` files will be in ``LightGBM/Release`` folder. Linux ^^^^^ On Linux, a GPU version of LightGBM (``device_type=gpu``) can be built using - **CMake**, **OpenCL**, **Boost** and **gcc**; - **CMake**, **OpenCL**, **Boost** and **Clang**. **OpenCL** headers and libraries are usually provided by GPU manufacture. The generic OpenCL ICD packages (for example, Debian packages ``ocl-icd-libopencl1``, ``ocl-icd-opencl-dev``, ``pocl-opencl-icd``) can also be used. Required **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``. After compilation the executable and ``.so`` files will be in ``LightGBM/`` folder. gcc *** 1. Install `CMake`_, **gcc**, **OpenCL** and **Boost**. 2. Run the following commands: .. code:: sh git clone --recursive https://github.com/lightgbm-org/LightGBM cd LightGBM cmake -B build -S . -DUSE_GPU=ON # if you have installed NVIDIA CUDA to a customized location, you should specify paths to OpenCL headers and library like the following: # cmake -B build -S . -DUSE_GPU=ON -DOpenCL_LIBRARY=/usr/local/cuda/lib64/libOpenCL.so -DOpenCL_INCLUDE_DIR=/usr/local/cuda/include/ cmake --build build -j4 Clang ***** 1. Install `CMake`_, **Clang**, **OpenMP**, **OpenCL** and **Boost**. 2. Run the following commands: .. code:: sh git clone --recursive https://github.com/lightgbm-org/LightGBM cd LightGBM export CXX=clang++-14 CC=clang-14 # replace "14" with version of Clang installed on your machine cmake -B build -S . -DUSE_GPU=ON # if you have installed NVIDIA CUDA to a customized location, you should specify paths to OpenCL headers and library like the following: # cmake -B build -S . -DUSE_GPU=ON -DOpenCL_LIBRARY=/usr/local/cuda/lib64/libOpenCL.so -DOpenCL_INCLUDE_DIR=/usr/local/cuda/include/ cmake --build build -j4 macOS ^^^^^ The GPU version is not supported on macOS. Docker ^^^^^^ Refer to `GPU Docker folder `__. Build CUDA Version ~~~~~~~~~~~~~~~~~~ The `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. The 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. Use this version in Linux environments with an NVIDIA GPU with compute capability 6.0 or higher. Windows ^^^^^^^ The CUDA version is not supported on Windows. Use the `GPU version <#build-gpu-version>`__ (``device_type=gpu``) for GPU acceleration on Windows. Linux ^^^^^ On Linux, a CUDA version of LightGBM can be built using - **CMake**, **gcc** and **CUDA**; - **CMake**, **Clang** and **CUDA**. Please refer to `this detailed guide`_ for **CUDA** libraries installation. After compilation the executable and ``.so`` files will be in ``LightGBM/`` folder. gcc *** 1. Install `CMake`_, **gcc** and **CUDA**. 2. Run the following commands: .. code:: sh git clone --recursive https://github.com/lightgbm-org/LightGBM cd LightGBM cmake -B build -S . -DUSE_CUDA=ON cmake --build build -j4 Clang ***** 1. Install `CMake`_, **Clang**, **OpenMP** and **CUDA**. 2. Run the following commands: .. code:: sh git clone --recursive https://github.com/lightgbm-org/LightGBM cd LightGBM export CXX=clang++-14 CC=clang-14 # replace "14" with version of Clang installed on your machine cmake -B build -S . -DUSE_CUDA=ON cmake --build build -j4 macOS ^^^^^ The CUDA version is not supported on macOS. Build ROCm Version ~~~~~~~~~~~~~~~~~~ The `original GPU version <#build-gpu-version>`__ of LightGBM (``device_type=gpu``) is based on OpenCL. The 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. Windows ^^^^^^^ The ROCm version is not supported on Windows. Use the `GPU version <#build-gpu-version>`__ (``device_type=gpu``) for GPU acceleration on Windows. Linux ^^^^^ On Linux, a ROCm version of LightGBM can be built using - **CMake**, **gcc** and **ROCm**; - **CMake**, **Clang** and **ROCm**. Please refer to `the ROCm docs`_ for **ROCm** libraries installation. After compilation the executable and ``.so`` files will be in ``LightGBM/`` folder. gcc *** 1. Install `CMake`_, **gcc** and **ROCm**. 2. Run the following commands: .. code:: sh git clone --recursive https://github.com/lightgbm-org/LightGBM cd LightGBM cmake -B build -S . -DUSE_ROCM=ON cmake --build build -j4 Clang ***** 1. Install `CMake`_, **Clang**, **OpenMP** and **ROCm**. 2. Run the following commands: .. code:: sh git clone --recursive https://github.com/lightgbm-org/LightGBM cd LightGBM export CXX=clang++-14 CC=clang-14 # replace "14" with version of Clang installed on your machine cmake -B build -S . -DUSE_ROCM=ON cmake --build build -j4 macOS ^^^^^ The ROCm version is not supported on macOS. Build Java Wrapper ~~~~~~~~~~~~~~~~~~ Using the following instructions you can generate a JAR file containing the LightGBM `C API <./Development-Guide.rst#c-api>`__ wrapped by **SWIG**. After compilation the ``.jar`` file will be in ``LightGBM/build`` folder. Windows ^^^^^^^ On Windows, a Java wrapper of LightGBM can be built using - **Java**, **SWIG**, **CMake** and **VS Build Tools**; - **Java**, **SWIG**, **CMake** and **MinGW**. VS Build Tools ************** 1. Install `Git for Windows`_, `CMake`_ and `VS Build Tools`_ (**VS Build Tools** is not needed if **Visual Studio** is already installed). 2. Install `SWIG`_ and **Java** (also make sure that ``JAVA_HOME`` environment variable is set properly). 3. Run the following commands: .. code:: console git clone --recursive https://github.com/lightgbm-org/LightGBM cd LightGBM cmake -B build -S . -A x64 -DUSE_SWIG=ON cmake --build build --target ALL_BUILD --config Release MinGW-w64 ********* 1. Install `Git for Windows`_, `CMake`_ and `MinGW-w64`_. 2. Install `SWIG`_ and **Java** (also make sure that ``JAVA_HOME`` environment variable is set properly). 3. Run the following commands: .. code:: console git clone --recursive https://github.com/lightgbm-org/LightGBM cd LightGBM cmake -B build -S . -G "MinGW Makefiles" -DUSE_SWIG=ON cmake --build build -j4 **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. It is recommended to use **VS Build Tools (Visual Studio)** since it has better multithreading efficiency in **Windows** for many-core systems (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>`__). Linux ^^^^^ On Linux, a Java wrapper of LightGBM can be built using - **CMake**, **gcc**, **Java** and **SWIG**; - **CMake**, **Clang**, **Java** and **SWIG**. gcc *** 1. Install `CMake`_, **gcc**, `SWIG`_ and **Java** (also make sure that ``JAVA_HOME`` environment variable is set properly). 2. Run the following commands: .. code:: sh git clone --recursive https://github.com/lightgbm-org/LightGBM cd LightGBM cmake -B build -S . -DUSE_SWIG=ON cmake --build build -j4 Clang ***** 1. Install `CMake`_, **Clang**, **OpenMP**, `SWIG`_ and **Java** (also make sure that ``JAVA_HOME`` environment variable is set properly). 2. Run the following commands: .. code:: sh git clone --recursive https://github.com/lightgbm-org/LightGBM cd LightGBM export CXX=clang++-14 CC=clang-14 # replace "14" with version of Clang installed on your machine cmake -B build -S . -DUSE_SWIG=ON cmake --build build -j4 macOS ^^^^^ On macOS, a Java wrapper of LightGBM can be built using - **CMake**, **Java**, **SWIG** and **Apple Clang**; - **CMake**, **Java**, **SWIG** and **gcc**. Apple Clang *********** 1. Install `CMake`_, **Java** (also make sure that ``JAVA_HOME`` environment variable is set properly), `SWIG`_ and **OpenMP**: .. code:: sh brew install cmake openjdk swig libomp export JAVA_HOME="$(brew --prefix openjdk)/libexec/openjdk.jdk/Contents/Home/" 2. Run the following commands: .. code:: sh git clone --recursive https://github.com/lightgbm-org/LightGBM cd LightGBM cmake -B build -S . -DUSE_SWIG=ON cmake --build build -j4 gcc *** 1. Install `CMake`_, **Java** (also make sure that ``JAVA_HOME`` environment variable is set properly), `SWIG`_ and **gcc**: .. code:: sh brew install cmake openjdk swig gcc export JAVA_HOME="$(brew --prefix openjdk)/libexec/openjdk.jdk/Contents/Home/" 2. Run the following commands: .. code:: sh git clone --recursive https://github.com/lightgbm-org/LightGBM cd LightGBM export CXX=g++-7 CC=gcc-7 # replace "7" with version of gcc installed on your machine cmake -B build -S . -DUSE_SWIG=ON cmake --build build -j4 Build Python-package ~~~~~~~~~~~~~~~~~~~~ Refer to `Python-package folder `__. Build R-package ~~~~~~~~~~~~~~~ Refer to `R-package folder `__. Build C++ Unit Tests ~~~~~~~~~~~~~~~~~~~~ Windows ^^^^^^^ On Windows, C++ unit tests of LightGBM can be built using - **CMake** and **VS Build Tools**; - **CMake** and **MinGW**. VS Build Tools ************** 1. Install `Git for Windows`_, `CMake`_ and `VS Build Tools`_ (**VS Build Tools** is not needed if **Visual Studio** is already installed). 2. Run the following commands: .. code:: console git clone --recursive https://github.com/lightgbm-org/LightGBM cd LightGBM cmake -B build -S . -A x64 -DBUILD_CPP_TEST=ON cmake --build build --target testlightgbm --config Debug The ``.exe`` file will be in ``LightGBM/Debug`` folder. MinGW-w64 ********* 1. Install `Git for Windows`_, `CMake`_ and `MinGW-w64`_. 2. Run the following commands: .. code:: console git clone --recursive https://github.com/lightgbm-org/LightGBM cd LightGBM cmake -B build -S . -G "MinGW Makefiles" -DBUILD_CPP_TEST=ON cmake --build build --target testlightgbm -j4 The ``.exe`` file will be in ``LightGBM/`` folder. **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. Linux ^^^^^ On Linux, a C++ unit tests of LightGBM can be built using - **CMake** and **gcc**; - **CMake** and **Clang**. After compilation the executable file will be in ``LightGBM/`` folder. gcc *** 1. Install `CMake`_ and **gcc**. 2. Run the following commands: .. code:: sh git clone --recursive https://github.com/lightgbm-org/LightGBM cd LightGBM cmake -B build -S . -DBUILD_CPP_TEST=ON cmake --build build --target testlightgbm -j4 Clang ***** 1. Install `CMake`_, **Clang** and **OpenMP**. 2. Run the following commands: .. code:: sh git clone --recursive https://github.com/lightgbm-org/LightGBM cd LightGBM export CXX=clang++-14 CC=clang-14 # replace "14" with version of Clang installed on your machine cmake -B build -S . -DBUILD_CPP_TEST=ON cmake --build build --target testlightgbm -j4 macOS ^^^^^ On macOS, a C++ unit tests of LightGBM can be built using - **CMake** and **Apple Clang**; - **CMake** and **gcc**. After compilation the executable file will be in ``LightGBM/`` folder. Apple Clang *********** 1. Install `CMake`_ and **OpenMP**: .. code:: sh brew install cmake libomp 2. Run the following commands: .. code:: sh git clone --recursive https://github.com/lightgbm-org/LightGBM cd LightGBM cmake -B build -S . -DBUILD_CPP_TEST=ON cmake --build build --target testlightgbm -j4 gcc *** 1. Install `CMake`_ and **gcc**: .. code:: sh brew install cmake gcc 2. Run the following commands: .. code:: sh git clone --recursive https://github.com/lightgbm-org/LightGBM cd LightGBM export CXX=g++-7 CC=gcc-7 # replace "7" with version of gcc installed on your machine cmake -B build -S . -DBUILD_CPP_TEST=ON cmake --build build --target testlightgbm -j4 .. _Visual Studio: https://visualstudio.microsoft.com/downloads/ .. _Git for Windows: https://git-scm.com/download/win .. _CMake: https://cmake.org/ .. _VS Build Tools: https://visualstudio.microsoft.com/downloads/ .. _MinGW-w64: https://www.mingw-w64.org/downloads/ .. _MPI: https://en.wikipedia.org/wiki/Message_Passing_Interface .. _RDMA: https://en.wikipedia.org/wiki/Remote_direct_memory_access .. _MS MPI: https://learn.microsoft.com/en-us/message-passing-interface/microsoft-mpi-release-notes .. _Open MPI: https://www.open-mpi.org/ .. _Intel SDK for OpenCL: https://software.intel.com/en-us/articles/opencl-drivers .. _CUDA Toolkit: https://developer.nvidia.com/cuda-downloads .. _Boost Binaries: https://sourceforge.net/projects/boost/files/boost-binaries/ .. _SWIG: https://www.swig.org/download.html .. _this detailed guide: https://docs.nvidia.com/cuda/cuda-installation-guide-linux/index.html .. _the ROCm docs: https://rocm.docs.amd.com/projects/install-on-linux/en/latest/ .. _following docs: https://github.com/google/sanitizers/wiki .. _Ninja: https://ninja-build.org ================================================ FILE: docs/Key-Events.md ================================================ The content of this document was very outdated and is no longer available to avoid any misleadings. ================================================ FILE: docs/Makefile ================================================ # Minimal makefile for Sphinx documentation # # You can set these variables from the command line. SPHINXOPTS = -W SPHINXBUILD = sphinx-build SPHINXPROJ = LightGBM SOURCEDIR = . BUILDDIR = _build # Put it first so that "make" without argument is like "make help". help: @$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) .PHONY: help Makefile # Catch-all target: route all unknown targets to Sphinx using the new # "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS). %: Makefile @$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) ================================================ FILE: docs/Parallel-Learning-Guide.rst ================================================ Distributed Learning Guide ========================== .. _Parallel Learning Guide: This guide describes distributed learning in LightGBM. Distributed learning allows the use of multiple machines to produce a single model. Follow the `Quick Start <./Quick-Start.rst>`__ to know how to use LightGBM first. How Distributed LightGBM Works ------------------------------ This section describes how distributed learning in LightGBM works. To learn how to do this in various programming languages and frameworks, please see `Integrations <#integrations>`__. Choose Appropriate Parallel Algorithm ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ LightGBM provides 3 distributed learning algorithms now. +--------------------+---------------------------+ | Parallel Algorithm | How to Use | +====================+===========================+ | Data parallel | ``tree_learner=data`` | +--------------------+---------------------------+ | Feature parallel | ``tree_learner=feature`` | +--------------------+---------------------------+ | Voting parallel | ``tree_learner=voting`` | +--------------------+---------------------------+ These algorithms are suited for different scenarios, which is listed in the following table: +-------------------------+-------------------+-----------------+ | | #data is small | #data is large | +=========================+===================+=================+ | **#feature is small** | Feature Parallel | Data Parallel | +-------------------------+-------------------+-----------------+ | **#feature is large** | Feature Parallel | Voting Parallel | +-------------------------+-------------------+-----------------+ More details about these parallel algorithms can be found in `optimization in distributed learning <./Features.rst#optimization-in-distributed-learning>`__. Integrations ------------ This 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>`__. Apache Spark ^^^^^^^^^^^^ Apache Spark users can use `SynapseML`_ for machine learning workflows with LightGBM. This project is not maintained by LightGBM's maintainers. See `this SynapseML example`_ for additional information on using LightGBM on Spark. .. note:: ``SynapseML`` is not maintained by LightGBM's maintainers. Bug reports or feature requests should be directed to https://github.com/microsoft/SynapseML/issues. Dask ^^^^ .. versionadded:: 3.2.0 LightGBM's Python-package supports distributed learning via `Dask`_. This integration is maintained by LightGBM's maintainers. .. warning:: Dask integration is only tested on macOS and Linux. Dask Examples ''''''''''''' For sample code using ``lightgbm.dask``, see `these Dask examples`_. Training with Dask '''''''''''''''''' This section contains detailed information on performing LightGBM distributed training using Dask. Configuring the Dask Cluster **************************** **Allocating Threads** When 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. If you do not have other significant processes competing with Dask for resources, just accept the default ``nthreads`` from your chosen ``dask.distributed`` cluster. .. code:: python from distributed import Client, LocalCluster cluster = LocalCluster(n_workers=3) client = Client(cluster) **Managing Memory** Use 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. `At 60% of memory load, [Dask will] spill least recently used data to disk` To reduce the risk of hitting memory limits, consider restarting each worker process before running any data loading or training code. .. code:: python client.restart() Setting Up Training Data ************************* The 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. .. image:: ./_static/images/dask-initial-setup.svg :align: center :width: 600px :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. :target: ./_static/images/dask-initial-setup.svg While 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. .. image:: ./_static/images/dask-concat.svg :align: center :width: 600px :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. :target: ./_static/images/dask-concat.svg When setting up data partitioning for LightGBM training with Dask, try to follow these suggestions: * ensure that each worker in the cluster has some of the training data * try to give each worker roughly the same amount of data, especially if your dataset is small * 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 Using a Specific Dask Client **************************** In 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. However, 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. LightGBM's Dask estimators support setting an attribute ``client`` to control the client that is used. .. code:: python import lightgbm as lgb from distributed import Client, LocalCluster cluster = LocalCluster() client = Client(cluster) # option 1: keyword argument in constructor dask_model = lgb.DaskLGBMClassifier(client=client) # option 2: set_params() after construction dask_model = lgb.DaskLGBMClassifier() dask_model.set_params(client=client) Using Specific Ports ******************** At 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. If 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. **Option 1: provide a specific list of addresses and ports** LightGBM 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. For example, consider the case where you are running one Dask worker process on each of the following IP addresses: .. code:: text 10.0.1.0 10.0.2.0 10.0.3.0 You could edit your firewall rules to allow traffic on one additional port on each of these hosts, then provide ``machines`` directly. .. code:: python import lightgbm as lgb machines = "10.0.1.0:12401,10.0.2.0:12402,10.0.3.0:15000" dask_model = lgb.DaskLGBMRegressor(machines=machines) If 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. .. code:: python import lightgbm as lgb machines = ",".join([ "10.0.1.0:16000", "10.0.1.0:16001", "10.0.2.0:16000", "10.0.2.0:16001", ]) dask_model = lgb.DaskLGBMRegressor(machines=machines) .. warning:: 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: * any of the ports mentioned in ``machines`` are not open when training begins * some partitions of the training data are held by machines that that are not present in ``machines`` * some machines mentioned in ``machines`` do not hold any of the training data **Option 2: specify one port to use on every worker** If 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. For example, consider the case where you are running one Dask worker process on each of the following IP addresses: .. code:: text 10.0.1.0 10.0.2.0 10.0.3.0 You 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``. .. code:: python import lightgbm as lgb dask_model = lgb.DaskLGBMRegressor(local_listen_port=12400) .. warning:: 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: * the port ``local_listen_port`` is not open on any of the worker hosts * any machine has multiple Dask worker processes running on it Using Custom Objective Functions with Dask ****************************************** .. versionadded:: 4.0.0 It is possible to customize the boosting process by providing a custom objective function written in Python. See the Dask API's documentation for details on how to implement such functions. .. warning:: Custom objective functions used with ``lightgbm.dask`` will be called by each worker process on only that worker's local data. Follow the example below to use a custom implementation of the ``regression_l2`` objective. .. code:: python import dask.array as da import lightgbm as lgb import numpy as np from distributed import Client, LocalCluster cluster = LocalCluster(n_workers=2) client = Client(cluster) X = da.random.random((1000, 10), (500, 10)) y = da.random.random((1000,), (500,)) def custom_l2_obj(y_true, y_pred): grad = y_pred - y_true hess = np.ones(len(y_true)) return grad, hess dask_model = lgb.DaskLGBMRegressor( objective=custom_l2_obj ) dask_model.fit(X, y) Prediction with Dask '''''''''''''''''''' The 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. See `the Dask prediction example`_ for some sample code that shows how to perform Dask-based prediction. For 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. Saving Dask Models '''''''''''''''''' After training with Dask, you have several options for saving a fitted model. **Option 1: pickle the Dask estimator** LightGBM's Dask estimators can be pickled directly with ``cloudpickle``, ``joblib``, or ``pickle``. .. code:: python import dask.array as da import pickle import lightgbm as lgb from distributed import Client, LocalCluster cluster = LocalCluster(n_workers=2) client = Client(cluster) X = da.random.random((1000, 10), (500, 10)) y = da.random.random((1000,), (500,)) dask_model = lgb.DaskLGBMRegressor() dask_model.fit(X, y) with open("dask-model.pkl", "wb") as f: pickle.dump(dask_model, f) A model saved this way can then later be loaded with whichever serialization library you used to save it. .. code:: python import pickle with open("dask-model.pkl", "rb") as f: dask_model = pickle.load(f) .. note:: 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)``. **Option 2: pickle the sklearn estimator** The 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. .. code:: python import dask.array as da import joblib import lightgbm as lgb from distributed import Client, LocalCluster cluster = LocalCluster(n_workers=2) client = Client(cluster) X = da.random.random((1000, 10), (500, 10)) y = da.random.random((1000,), (500,)) dask_model = lgb.DaskLGBMRegressor() dask_model.fit(X, y) # convert to sklearn equivalent sklearn_model = dask_model.to_local() print(type(sklearn_model)) #> lightgbm.sklearn.LGBMRegressor joblib.dump(sklearn_model, "sklearn-model.joblib") A model saved this way can then later be loaded with whichever serialization library you used to save it. .. code:: python import joblib sklearn_model = joblib.load("sklearn-model.joblib") **Option 3: save the LightGBM Booster** The lowest-level model object in LightGBM is the ``lightgbm.Booster``. After training, you can extract a Booster from the Dask estimator. .. code:: python import dask.array as da import lightgbm as lgb from distributed import Client, LocalCluster cluster = LocalCluster(n_workers=2) client = Client(cluster) X = da.random.random((1000, 10), (500, 10)) y = da.random.random((1000,), (500,)) dask_model = lgb.DaskLGBMRegressor() dask_model.fit(X, y) # get underlying Booster object bst = dask_model.booster_ From the point forward, you can use any of the following methods to save the Booster: * serialize with ``cloudpickle``, ``joblib``, or ``pickle`` * ``bst.dump_model()``: dump the model to a dictionary which could be written out as JSON * ``bst.model_to_string()``: dump the model to a string in memory * ``bst.save_model()``: write the output of ``bst.model_to_string()`` to a text file Kubeflow ^^^^^^^^ Kubeflow users can also use the `Kubeflow XGBoost Operator`_ for machine learning workflows with LightGBM. You can see `this example`_ for more details. Kubeflow integrations for LightGBM are not maintained by LightGBM's maintainers. .. note:: 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. LightGBM CLI ^^^^^^^^^^^^ .. _Build Parallel Version: Preparation ''''''''''' By default, distributed learning with LightGBM uses socket-based communication. If you need to build distributed version with MPI support, please refer to `Installation Guide <./Installation-Guide.rst#build-mpi-version>`__. Socket Version ************** It 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, and change firewall rules to allow income of this port (12345). Then write these IP and ports in one file (assume ``mlist.txt``), like following: .. code:: text machine1_ip 12345 machine2_ip 12345 MPI Version *********** It needs to collect IP (or hostname) of all machines that want to run distributed learning in. Then write these IP in one file (assume ``mlist.txt``) like following: .. code:: text machine1_ip machine2_ip **Note**: For Windows users, need to start "smpd" to start MPI service. More details can be found `here`_. Run Distributed Learning '''''''''''''''''''''''' .. _Run Parallel Learning: Socket Version ************** 1. Edit following parameters in config file: ``tree_learner=your_parallel_algorithm``, edit ``your_parallel_algorithm`` (e.g. feature/data) here. ``num_machines=your_num_machines``, edit ``your_num_machines`` (e.g. 4) here. ``machine_list_file=mlist.txt``, ``mlist.txt`` is created in `Preparation section <#preparation>`__. ``local_listen_port=12345``, ``12345`` is allocated in `Preparation section <#preparation>`__. 2. Copy data file, executable file, config file and ``mlist.txt`` to all machines. 3. Run following command on all machines, you need to change ``your_config_file`` to real config file. For Windows: ``lightgbm.exe config=your_config_file`` For Linux: ``./lightgbm config=your_config_file`` MPI Version *********** 1. Edit following parameters in config file: ``tree_learner=your_parallel_algorithm``, edit ``your_parallel_algorithm`` (e.g. feature/data) here. ``num_machines=your_num_machines``, edit ``your_num_machines`` (e.g. 4) here. 2. Copy data file, executable file, config file and ``mlist.txt`` to all machines. **Note**: MPI needs to be run in the **same path on all machines**. 3. Run following command on one machine (not need to run on all machines), need to change ``your_config_file`` to real config file. For Windows: .. code:: console mpiexec.exe /machinefile mlist.txt lightgbm.exe config=your_config_file For Linux: .. code:: console mpiexec --machinefile mlist.txt ./lightgbm config=your_config_file Example ''''''' - `A simple distributed learning example`_ Ray ^^^ `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. For the Ray Train API, see `the Ray documentation`_ for usage examples. For the lightgbm_ray project, see `the lightgbm_ray documentation`_ for usage examples. .. note:: ``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. Mars ^^^^ `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``. See `the mars documentation`_ for usage examples. .. note:: ``Mars`` is not maintained by LightGBM's maintainers. Bug reports or feature requests should be directed to https://github.com/mars-project/mars/issues. .. _Dask: https://docs.dask.org/en/latest/ .. _SynapseML: https://aka.ms/spark .. _this SynapseML example: https://github.com/microsoft/SynapseML/tree/master/docs/Explore%20Algorithms/LightGBM .. _the Dask Array documentation: https://docs.dask.org/en/latest/array.html .. _the Dask DataFrame documentation: https://docs.dask.org/en/latest/dataframe.html .. _the Dask prediction example: https://github.com/lightgbm-org/LightGBM/blob/master/examples/python-guide/dask/prediction.py .. _the Dask worker documentation: https://distributed.dask.org/en/stable/worker-memory.html .. _the metrics functions from dask-ml: https://ml.dask.org/modules/api.html#dask-ml-metrics-metrics .. _these Dask examples: https://github.com/lightgbm-org/LightGBM/tree/master/examples/python-guide/dask .. _Kubeflow XGBoost Operator: https://github.com/kubeflow/xgboost-operator .. _this example: https://github.com/kubeflow/xgboost-operator/tree/master/config/samples/lightgbm-dist .. _here: https://www.youtube.com/watch?v=iqzXhp5TxUY .. _A simple distributed learning example: https://github.com/lightgbm-org/LightGBM/tree/master/examples/parallel_learning .. _lightgbm_ray: https://github.com/ray-project/lightgbm_ray .. _Ray: https://www.ray.io/ .. _the lightgbm_ray documentation: https://docs.ray.io/en/latest/tune/api_docs/integration.html#lightgbm-tune-integration-lightgbm .. _the Ray documentation: https://docs.ray.io/en/latest/train/api/api.html#lightgbm .. _Mars: https://mars-project.readthedocs.io/en/latest/ .. _the mars documentation: https://mars-project.readthedocs.io/en/latest/user_guide/learn/lightgbm.html ================================================ FILE: docs/Parameters-Tuning.rst ================================================ Parameters Tuning ================= This page contains parameters tuning guides for different scenarios. **List of other helpful links** - `Parameters <./Parameters.rst>`__ - `Python API <./Python-API.rst>`__ - `FLAML`_ for automated hyperparameter tuning - `Optuna`_ for automated hyperparameter tuning Tune Parameters for the Leaf-wise (Best-first) Tree --------------------------------------------------- LightGBM 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. Compared with depth-wise growth, the leaf-wise algorithm can converge much faster. However, the leaf-wise growth may be over-fitting if not used with the appropriate parameters. To get good results using a leaf-wise tree, these are some important parameters: 1. ``num_leaves``. This is the main parameter to control the complexity of the tree model. Theoretically, we can set ``num_leaves = 2^(max_depth)`` to obtain the same number of leaves as depth-wise tree. However, this simple conversion is not good in practice. 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. Thus, when trying to tune the ``num_leaves``, we should let it be smaller than ``2^(max_depth)``. For example, when the ``max_depth=7`` the depth-wise tree can get good accuracy, but setting ``num_leaves`` to ``127`` may cause over-fitting, and setting it to ``70`` or ``80`` may get better accuracy than depth-wise. 2. ``min_data_in_leaf``. This is a very important parameter to prevent over-fitting in a leaf-wise tree. Its optimal value depends on the number of training samples and ``num_leaves``. Setting it to a large value can avoid growing too deep a tree, but may cause under-fitting. In practice, setting it to hundreds or thousands is enough for a large dataset. 3. ``max_depth``. You also can use ``max_depth`` to limit the tree depth explicitly. If you set ``max_depth``, also explicitly set ``num_leaves`` to some value ``<= 2^max_depth``. For Faster Speed ---------------- Add More Computational Resources '''''''''''''''''''''''''''''''' On 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. You might be able to achieve faster training by moving to a machine with more available CPU cores. Using distributed (multi-machine) training might also reduce training time. See the `Distributed Learning Guide <./Parallel-Learning-Guide.rst>`_ for details. Use a GPU-enabled version of LightGBM ''''''''''''''''''''''''''''''''''''' You might find that training is faster using a GPU-enabled build of LightGBM. See the `GPU Tutorial <./GPU-Tutorial.rst>`__ for details. Grow Shallower Trees '''''''''''''''''''' The 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. The suggestions below will speed up training, but might hurt training accuracy. Decrease ``max_depth`` ********************** This 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. Decrease ``num_leaves`` *********************** LightGBM 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. .. image:: ./_static/images/leaf-wise.png :align: center :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. Because 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. Increase ``min_gain_to_split`` ****************************** When 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. Increase ``min_data_in_leaf`` and ``min_sum_hessian_in_leaf`` ************************************************************* Depending 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. This 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. - ``min_data_in_leaf``: Minimum number of observations that must fall into a tree node for it to be added. - ``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. Grow Less Trees ''''''''''''''' Decrease ``num_iterations`` *************************** The ``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". If 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``. Choosing 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. Decrease ``num_iterations`` to reduce training time. Use Early Stopping ****************** If 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. That "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". Set ``early_stopping_round`` and provide a validation set to possibly reduce training time. Consider Fewer Splits ''''''''''''''''''''' The 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. The suggestions below will speed up training, but might hurt training accuracy. Enable Feature Pre-Filtering When Creating Dataset ************************************************** By default, when a LightGBM ``Dataset`` object is constructed, some features will be filtered out based on the value of ``min_data_in_leaf``. For 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. Instead of reconsidering this feature and then ignoring it every iteration, LightGBM filters this feature out at before training, when the ``Dataset`` is constructed. If this default behavior has been overridden by setting ``feature_pre_filter=False``, set ``feature_pre_filter=True`` to reduce training time. Decrease ``max_bin`` or ``max_bin_by_feature`` When Creating Dataset ******************************************************************** LightGBM 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. ``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``. Reduce ``max_bin`` or ``max_bin_by_feature`` to reduce training time. Increase ``min_data_in_bin`` When Creating Dataset ************************************************** Some 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``. Increase ``min_data_in_bin`` to reduce training time. Decrease ``feature_fraction`` ***************************** By 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. Decrease ``feature_fraction`` to reduce training time. Decrease ``max_cat_threshold`` ****************************** LightGBM 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. Decrease ``max_cat_threshold`` to reduce training time. Use Less Data ''''''''''''' Use Bagging *********** By 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". Set ``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". Decrease ``bagging_fraction`` to reduce training time. Save Constructed Datasets with ``save_binary`` '''''''''''''''''''''''''''''''''''''''''''''' This 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. For Better Accuracy ------------------- - Use large ``max_bin`` (may be slower) - Use small ``learning_rate`` with large ``num_iterations`` - Use large ``num_leaves`` (may cause over-fitting) - Use bigger training data - Try ``dart`` Deal with Over-fitting ---------------------- - Use small ``max_bin`` - Use small ``num_leaves`` - Use ``min_data_in_leaf`` and ``min_sum_hessian_in_leaf`` - Use bagging by set ``bagging_fraction`` and ``bagging_freq`` - Use feature sub-sampling by set ``feature_fraction`` - Use bigger training data - Try ``lambda_l1``, ``lambda_l2`` and ``min_gain_to_split`` for regularization - Try ``max_depth`` to avoid growing deep tree - Try ``extra_trees`` - Try increasing ``path_smooth`` .. _Optuna: https://medium.com/optuna/lightgbm-tuner-new-optuna-integration-for-hyperparameter-optimization-8b7095e99258 .. _FLAML: https://github.com/microsoft/FLAML ================================================ FILE: docs/Parameters.rst ================================================ .. List of parameters is auto generated by LightGBM\.ci\parameter-generator.py from LightGBM\include\LightGBM\config.h file. .. role:: raw-html(raw) :format: html Parameters ========== This page contains descriptions of all parameters in LightGBM. **List of other helpful links** - `Python API <./Python-API.rst>`__ - `Parameters Tuning <./Parameters-Tuning.rst>`__ Parameters Format ----------------- Parameters are merged together in the following order (later items overwrite earlier ones): 1. LightGBM's default values 2. special files for ``weight``, ``init_score``, ``query``, and ``positions`` (see `Others <#others>`__) 3. (CLI only) configuration in a file passed like ``config=train.conf`` 4. (CLI only) configuration passed via the command line 5. (Python, R) special keyword arguments to some functions (e.g. ``num_boost_round`` in ``train()``) 6. (Python, R) ``params`` function argument (including ``**kwargs`` in Python and ``...`` in R) 7. (C API) ``parameters`` or ``params`` function argument Many parameters have "aliases", alternative names which refer to the same configuration. Where a mix of the primary parameter name and aliases are given, the primary parameter name is always preferred to any aliases. For example, in Python: .. code-block:: python # use learning rate of 0.07, because 'learning_rate' # is the primary parameter name lgb.train( params={ "learning_rate": 0.07, "shrinkage_rate": 0.12 }, train_set=dtrain ) Where multiple aliases are given, and the primary parameter name is not, the first alias appearing in the lists returned by ``Config::parameter2aliases()`` in the C++ library is used. Those lists are hard-coded in a fairly arbitrary way... wherever possible, avoid relying on this behavior. For example, in Python: .. code-block:: python # use learning rate of 0.12, LightGBM has a hard-coded preference for 'shrinkage_rate' # over any other aliases, and 'learning_rate' is not provided lgb.train( params={ "eta": 0.19, "shrinkage_rate": 0.12 }, train_set=dtrain ) **CLI** The parameters format is ``key1=value1 key2=value2 ...``. Parameters can be set both in config file and command line. By using command line, parameters should not have spaces before and after ``=``. By using config files, one line can only contain one parameter. You can use ``#`` to comment. **Python** Any parameters that accept multiple values should be passed as a Python list. .. code-block:: python params = { "monotone_constraints": [-1, 0, 1] } **R** Any parameters that accept multiple values should be passed as an R list. .. code-block:: r params <- list( monotone_constraints = c(-1, 0, 1) ) .. start params list Core Parameters --------------- - ``config`` :raw-html:`🔗︎`, default = ``""``, type = string, aliases: ``config_file`` - path of config file - **Note**: can be used only in CLI version - ``task`` :raw-html:`🔗︎`, default = ``train``, type = enum, options: ``train``, ``predict``, ``convert_model``, ``refit``, aliases: ``task_type`` - ``train``, for training, aliases: ``training`` - ``predict``, for prediction, aliases: ``prediction``, ``test`` - ``convert_model``, for converting model file into if-else format, see more information in `Convert Parameters <#convert-parameters>`__ - ``refit``, for refitting existing models with new data, aliases: ``refit_tree`` - ``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 - **Note**: can be used only in CLI version; for language-specific packages you can use the correspondent functions - ``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`` - regression application - ``regression``, L2 loss, aliases: ``regression_l2``, ``l2``, ``mean_squared_error``, ``mse``, ``l2_root``, ``root_mean_squared_error``, ``rmse`` - ``regression_l1``, L1 loss, aliases: ``l1``, ``mean_absolute_error``, ``mae`` - ``huber``, `Huber loss `__ - ``fair``, `Fair loss `__ - ``poisson``, `Poisson regression `__ - ``quantile``, `Quantile regression `__ - ``mape``, `MAPE loss `__, aliases: ``mean_absolute_percentage_error`` - ``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 `__ - ``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 `__ - binary classification application - ``binary``, binary `log loss `__ classification (or logistic regression) - requires labels in {0, 1}; see ``cross-entropy`` application for general probability labels in [0, 1] - multi-class classification application - ``multiclass``, `softmax `__ objective function, aliases: ``softmax`` - ``multiclassova``, `One-vs-All `__ binary objective function, aliases: ``multiclass_ova``, ``ova``, ``ovr`` - ``num_class`` should be set as well - cross-entropy application - ``cross_entropy``, objective function for cross-entropy (with optional linear weights), aliases: ``xentropy`` - ``cross_entropy_lambda``, alternative parameterization of cross-entropy, aliases: ``xentlambda`` - label is anything in interval [0, 1] - ranking application - ``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`` - ``rank_xendcg``, `XE_NDCG_MART `__ ranking objective function, aliases: ``xendcg``, ``xe_ndcg``, ``xe_ndcg_mart``, ``xendcg_mart`` - ``rank_xendcg`` is faster than and achieves the similar performance as ``lambdarank`` - label should be ``int`` type, and larger number represents the higher relevance (e.g. 0:bad, 1:fair, 2:good, 3:perfect) - custom objective function (gradients and hessians not computed directly by LightGBM) - ``custom`` - must be passed through parameters explicitly in the C API - **Note**: cannot be used in CLI version - ``boosting`` :raw-html:`🔗︎`, default = ``gbdt``, type = enum, options: ``gbdt``, ``rf``, ``dart``, aliases: ``boosting_type``, ``boost`` - ``gbdt``, traditional Gradient Boosting Decision Tree, aliases: ``gbrt`` - ``rf``, Random Forest, aliases: ``random_forest`` - ``dart``, `Dropouts meet Multiple Additive Regression Trees `__ - **Note**: internally, LightGBM uses ``gbdt`` mode for the first ``1 / learning_rate`` iterations - ``data_sample_strategy`` :raw-html:`🔗︎`, default = ``bagging``, type = enum, options: ``bagging``, ``goss`` - ``bagging``, Randomly Bagging Sampling - **Note**: ``bagging`` is only effective when ``bagging_freq > 0`` and ``bagging_fraction < 1.0`` - ``goss``, Gradient-based One-Side Sampling - *New in version 4.0.0* - ``data`` :raw-html:`🔗︎`, default = ``""``, type = string, aliases: ``train``, ``train_data``, ``train_data_file``, ``data_filename`` - path of training data, LightGBM will train from this data - **Note**: can be used only in CLI version - ``valid`` :raw-html:`🔗︎`, default = ``""``, type = string, aliases: ``test``, ``valid_data``, ``valid_data_file``, ``test_data``, ``test_data_file``, ``valid_filenames`` - path(s) of validation/test data, LightGBM will output metrics for these data - support multiple validation data, separated by ``,`` - **Note**: can be used only in CLI version - ``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`` - number of boosting iterations - **Note**: internally, LightGBM constructs ``num_class * num_iterations`` trees for multi-class classification problems - ``learning_rate`` :raw-html:`🔗︎`, default = ``0.1``, type = double, aliases: ``shrinkage_rate``, ``eta``, constraints: ``learning_rate > 0.0`` - shrinkage rate - in ``dart``, it also affects on normalization weights of dropped trees - ``num_leaves`` :raw-html:`🔗︎`, default = ``31``, type = int, aliases: ``num_leaf``, ``max_leaves``, ``max_leaf``, ``max_leaf_nodes``, constraints: ``1 < num_leaves <= 131072`` - max number of leaves in one tree - ``tree_learner`` :raw-html:`🔗︎`, default = ``serial``, type = enum, options: ``serial``, ``feature``, ``data``, ``voting``, aliases: ``tree``, ``tree_type``, ``tree_learner_type`` - ``serial``, single machine tree learner - ``feature``, feature parallel tree learner, aliases: ``feature_parallel`` - ``data``, data parallel tree learner, aliases: ``data_parallel`` - ``voting``, voting parallel tree learner, aliases: ``voting_parallel`` - refer to `Distributed Learning Guide <./Parallel-Learning-Guide.rst>`__ to get more details - ``num_threads`` :raw-html:`🔗︎`, default = ``0``, type = int, aliases: ``num_thread``, ``nthread``, ``nthreads``, ``n_jobs`` - used only in ``train``, ``prediction`` and ``refit`` tasks or in correspondent functions of language-specific packages - number of threads for LightGBM - ``0`` means default number of threads in OpenMP - 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) - 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) - be aware a task manager or any similar CPU monitoring tool might report that cores not being fully utilized. **This is normal** - for distributed learning, do not use all CPU cores because this will cause poor performance for the network communication - **Note**: please **don't** change this during training, especially when running multiple jobs simultaneously by external packages, otherwise it may cause undesirable errors - ``device_type`` :raw-html:`🔗︎`, default = ``cpu``, type = enum, options: ``cpu``, ``gpu``, ``cuda``, aliases: ``device`` - device for the tree learning - ``cpu`` supports all LightGBM functionality and is portable across the widest range of operating systems and hardware - ``cuda`` offers faster training than ``gpu`` or ``cpu``, but only works on GPUs supporting CUDA or ROCm - ``gpu`` can be faster than ``cpu`` and works on a wider range of GPUs than CUDA - **Note**: it is recommended to use the smaller ``max_bin`` (e.g. 63) to get the better speed up - **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 - **Note**: refer to `Installation Guide <./Installation-Guide.rst>`__ to build LightGBM with GPU, CUDA, or ROCm support - ``seed`` :raw-html:`🔗︎`, default = ``None``, type = int, aliases: ``random_seed``, ``random_state`` - this seed is used to generate other seeds, e.g. ``data_random_seed``, ``feature_fraction_seed``, etc. - by default, this seed is unused in favor of default values of other seeds - this seed has lower priority in comparison with other seeds, which means that it will be overridden, if you set other seeds explicitly - ``deterministic`` :raw-html:`🔗︎`, default = ``false``, type = bool - used only with ``cpu`` device type - setting this to ``true`` should ensure the stable results when using the same data and the same parameters (and different ``num_threads``) - 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 - you can `raise issues `__ in LightGBM GitHub repo when you meet the unstable results - **Note**: setting this to ``true`` may slow down the training - **Note**: to avoid potential instability due to numerical issues, please set ``force_col_wise=true`` or ``force_row_wise=true`` when setting ``deterministic=true`` Learning Control Parameters --------------------------- - ``force_col_wise`` :raw-html:`🔗︎`, default = ``false``, type = bool - used only with ``cpu`` device type - set this to ``true`` to force col-wise histogram building - enabling this is recommended when: - the number of columns is large, or the total number of bins is large - ``num_threads`` is large, e.g. ``> 20`` - you want to reduce memory cost - **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 - **Note**: this parameter cannot be used at the same time with ``force_row_wise``, choose only one of them - ``force_row_wise`` :raw-html:`🔗︎`, default = ``false``, type = bool - used only with ``cpu`` device type - set this to ``true`` to force row-wise histogram building - enabling this is recommended when: - the number of data points is large, and the total number of bins is relatively small - ``num_threads`` is relatively small, e.g. ``<= 16`` - you want to use small ``bagging_fraction`` or ``goss`` sample strategy to speed up - **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`` - **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 - **Note**: this parameter cannot be used at the same time with ``force_col_wise``, choose only one of them - ``histogram_pool_size`` :raw-html:`🔗︎`, default = ``-1.0``, type = double, aliases: ``hist_pool_size`` - max cache size in MB for historical histogram - ``< 0`` means no limit - ``max_depth`` :raw-html:`🔗︎`, default = ``-1``, type = int - limit the max depth for tree model. This is used to deal with over-fitting when ``#data`` is small. Tree still grows leaf-wise - ``<= 0`` means no limit - ``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`` - minimal number of data in one leaf. Can be used to deal with over-fitting - **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 - ``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`` - minimal sum hessian in one leaf. Like ``min_data_in_leaf``, it can be used to deal with over-fitting - ``bagging_fraction`` :raw-html:`🔗︎`, default = ``1.0``, type = double, aliases: ``sub_row``, ``subsample``, ``bagging``, constraints: ``0.0 < bagging_fraction <= 1.0`` - like ``feature_fraction``, but this will randomly select part of data without resampling - can be used to speed up training - can be used to deal with over-fitting - **Note**: to enable bagging, ``bagging_freq`` should be set to a non zero value as well - ``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`` - used only in ``binary`` application - used for imbalanced binary classification problem, will randomly sample ``#pos_samples * pos_bagging_fraction`` positive samples in bagging - should be used together with ``neg_bagging_fraction`` - set this to ``1.0`` to disable - **Note**: to enable this, you need to set ``bagging_freq`` and ``neg_bagging_fraction`` as well - **Note**: if both ``pos_bagging_fraction`` and ``neg_bagging_fraction`` are set to ``1.0``, balanced bagging is disabled - **Note**: if balanced bagging is enabled, ``bagging_fraction`` will be ignored - ``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`` - used only in ``binary`` application - used for imbalanced binary classification problem, will randomly sample ``#neg_samples * neg_bagging_fraction`` negative samples in bagging - should be used together with ``pos_bagging_fraction`` - set this to ``1.0`` to disable - **Note**: to enable this, you need to set ``bagging_freq`` and ``pos_bagging_fraction`` as well - **Note**: if both ``pos_bagging_fraction`` and ``neg_bagging_fraction`` are set to ``1.0``, balanced bagging is disabled - **Note**: if balanced bagging is enabled, ``bagging_fraction`` will be ignored - ``bagging_freq`` :raw-html:`🔗︎`, default = ``0``, type = int, aliases: ``subsample_freq`` - frequency for bagging - ``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 - **Note**: bagging is only effective when ``0.0 < bagging_fraction < 1.0`` - ``bagging_seed`` :raw-html:`🔗︎`, default = ``3``, type = int, aliases: ``bagging_fraction_seed`` - random seed for bagging - ``bagging_by_query`` :raw-html:`🔗︎`, default = ``false``, type = bool - whether to do bagging sample by query - *New in version 4.6.0* - ``feature_fraction`` :raw-html:`🔗︎`, default = ``1.0``, type = double, aliases: ``sub_feature``, ``colsample_bytree``, constraints: ``0.0 < feature_fraction <= 1.0`` - 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 - can be used to speed up training - can be used to deal with over-fitting - ``feature_fraction_bynode`` :raw-html:`🔗︎`, default = ``1.0``, type = double, aliases: ``sub_feature_bynode``, ``colsample_bynode``, constraints: ``0.0 < feature_fraction_bynode <= 1.0`` - 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 - can be used to deal with over-fitting - **Note**: unlike ``feature_fraction``, this cannot speed up training - **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`` - ``feature_fraction_seed`` :raw-html:`🔗︎`, default = ``2``, type = int - random seed for ``feature_fraction`` - ``extra_trees`` :raw-html:`🔗︎`, default = ``false``, type = bool, aliases: ``extra_tree`` - use extremely randomized trees - if set to ``true``, when evaluating node splits LightGBM will check only one randomly-chosen threshold for each feature - can be used to speed up training - can be used to deal with over-fitting - ``extra_seed`` :raw-html:`🔗︎`, default = ``6``, type = int - random seed for selecting thresholds when ``extra_trees`` is true - ``early_stopping_round`` :raw-html:`🔗︎`, default = ``0``, type = int, aliases: ``early_stopping_rounds``, ``early_stopping``, ``n_iter_no_change`` - will stop training if one metric of one validation data doesn't improve in last ``early_stopping_round`` rounds - ``<= 0`` means disable - can be used to speed up training - ``early_stopping_min_delta`` :raw-html:`🔗︎`, default = ``0.0``, type = double, constraints: ``early_stopping_min_delta >= 0.0`` - 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 - *New in version 4.4.0* - ``first_metric_only`` :raw-html:`🔗︎`, default = ``false``, type = bool - LightGBM allows you to provide multiple evaluation metrics. Set this to ``true``, if you want to use only the first metric for early stopping - ``max_delta_step`` :raw-html:`🔗︎`, default = ``0.0``, type = double, aliases: ``max_tree_output``, ``max_leaf_output`` - used to limit the max output of tree leaves - ``<= 0`` means no constraint - the final max output of leaves is ``learning_rate * max_delta_step`` - ``lambda_l1`` :raw-html:`🔗︎`, default = ``0.0``, type = double, aliases: ``reg_alpha``, ``l1_regularization``, constraints: ``lambda_l1 >= 0.0`` - L1 regularization - ``lambda_l2`` :raw-html:`🔗︎`, default = ``0.0``, type = double, aliases: ``reg_lambda``, ``lambda``, ``l2_regularization``, constraints: ``lambda_l2 >= 0.0`` - L2 regularization - ``linear_lambda`` :raw-html:`🔗︎`, default = ``0.0``, type = double, constraints: ``linear_lambda >= 0.0`` - linear tree regularization, corresponds to the parameter ``lambda`` in Eq. 3 of `Gradient Boosting with Piece-Wise Linear Regression Trees `__ - ``min_gain_to_split`` :raw-html:`🔗︎`, default = ``0.0``, type = double, aliases: ``min_split_gain``, constraints: ``min_gain_to_split >= 0.0`` - the minimal gain to perform split - can be used to speed up training - ``drop_rate`` :raw-html:`🔗︎`, default = ``0.1``, type = double, aliases: ``rate_drop``, constraints: ``0.0 <= drop_rate <= 1.0`` - used only in ``dart`` - dropout rate: a fraction of previous trees to drop during the dropout - ``max_drop`` :raw-html:`🔗︎`, default = ``50``, type = int - used only in ``dart`` - max number of dropped trees during one boosting iteration - ``<=0`` means no limit - ``skip_drop`` :raw-html:`🔗︎`, default = ``0.5``, type = double, constraints: ``0.0 <= skip_drop <= 1.0`` - used only in ``dart`` - probability of skipping the dropout procedure during a boosting iteration - ``xgboost_dart_mode`` :raw-html:`🔗︎`, default = ``false``, type = bool - used only in ``dart`` - set this to ``true``, if you want to use XGBoost DART mode - ``uniform_drop`` :raw-html:`🔗︎`, default = ``false``, type = bool - used only in ``dart`` - set this to ``true``, if you want to use uniform drop - ``drop_seed`` :raw-html:`🔗︎`, default = ``4``, type = int - used only in ``dart`` - random seed to choose dropping models - ``top_rate`` :raw-html:`🔗︎`, default = ``0.2``, type = double, constraints: ``0.0 <= top_rate <= 1.0`` - used only in ``goss`` - the retain ratio of large gradient data - ``other_rate`` :raw-html:`🔗︎`, default = ``0.1``, type = double, constraints: ``0.0 <= other_rate <= 1.0`` - used only in ``goss`` - the retain ratio of small gradient data - ``min_data_per_group`` :raw-html:`🔗︎`, default = ``100``, type = int, constraints: ``min_data_per_group > 0`` - used for the categorical features - minimal number of data per categorical group - ``max_cat_threshold`` :raw-html:`🔗︎`, default = ``32``, type = int, constraints: ``max_cat_threshold > 0`` - used for the categorical features - 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 - can be used to speed up training - ``cat_l2`` :raw-html:`🔗︎`, default = ``10.0``, type = double, constraints: ``cat_l2 >= 0.0`` - used for the categorical features - L2 regularization in categorical split - ``cat_smooth`` :raw-html:`🔗︎`, default = ``10.0``, type = double, constraints: ``cat_smooth >= 0.0`` - used for the categorical features - this can reduce the effect of noises in categorical features, especially for categories with few data - ``max_cat_to_onehot`` :raw-html:`🔗︎`, default = ``4``, type = int, constraints: ``max_cat_to_onehot > 0`` - used for the categorical features - when number of categories of one feature smaller than or equal to ``max_cat_to_onehot``, one-vs-other split algorithm will be used - ``top_k`` :raw-html:`🔗︎`, default = ``20``, type = int, aliases: ``topk``, constraints: ``top_k > 0`` - used only in ``voting`` tree learner, refer to `Voting parallel <./Parallel-Learning-Guide.rst#choose-appropriate-parallel-algorithm>`__ - set this to larger value for more accurate result, but it will slow down the training speed - ``monotone_constraints`` :raw-html:`🔗︎`, default = ``None``, type = multi-int, aliases: ``mc``, ``monotone_constraint``, ``monotonic_cst`` - used for constraints of monotonic features - ``1`` means increasing, ``-1`` means decreasing, ``0`` means non-constraint - 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 - ``monotone_constraints_method`` :raw-html:`🔗︎`, default = ``basic``, type = enum, options: ``basic``, ``intermediate``, ``advanced``, aliases: ``monotone_constraining_method``, ``mc_method`` - used only if ``monotone_constraints`` is set - monotone constraints method - ``basic``, the most basic monotone constraints method. It does not slow down the training speed at all, but over-constrains the predictions - ``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 - ``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 - ``monotone_penalty`` :raw-html:`🔗︎`, default = ``0.0``, type = double, aliases: ``monotone_splits_penalty``, ``ms_penalty``, ``mc_penalty``, constraints: ``monotone_penalty >= 0.0`` - used only if ``monotone_constraints`` is set - `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 - if ``0.0`` (the default), no penalization is applied - ``feature_contri`` :raw-html:`🔗︎`, default = ``None``, type = multi-double, aliases: ``feature_contrib``, ``fc``, ``fp``, ``feature_penalty`` - 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 - you need to specify all features in order - ``forcedsplits_filename`` :raw-html:`🔗︎`, default = ``""``, type = string, aliases: ``fs``, ``forced_splits_filename``, ``forced_splits_file``, ``forced_splits`` - path to a ``.json`` file that specifies splits to force at the top of every decision tree before best-first learning commences - ``.json`` file can be arbitrarily nested, and each split contains ``feature``, ``threshold`` fields, as well as ``left`` and ``right`` fields representing subsplits - categorical splits are forced in a one-hot fashion, with ``left`` representing the split containing the feature value and ``right`` representing other values - **Note**: the forced split logic will be ignored, if the split makes gain worse - see `this file `__ as an example - ``refit_decay_rate`` :raw-html:`🔗︎`, default = ``0.9``, type = double, constraints: ``0.0 <= refit_decay_rate <= 1.0`` - 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 - used only in ``refit`` task in CLI version or as argument in ``refit`` function in language-specific package - ``cegb_tradeoff`` :raw-html:`🔗︎`, default = ``1.0``, type = double, constraints: ``cegb_tradeoff >= 0.0`` - cost-effective gradient boosting multiplier for all penalties - ``cegb_penalty_split`` :raw-html:`🔗︎`, default = ``0.0``, type = double, constraints: ``cegb_penalty_split >= 0.0`` - cost-effective gradient-boosting penalty for splitting a node - ``cegb_penalty_feature_lazy`` :raw-html:`🔗︎`, default = ``0,0,...,0``, type = multi-double - cost-effective gradient boosting penalty for using a feature - applied per data point - ``cegb_penalty_feature_coupled`` :raw-html:`🔗︎`, default = ``0,0,...,0``, type = multi-double - cost-effective gradient boosting penalty for using a feature - applied once per forest - ``path_smooth`` :raw-html:`🔗︎`, default = ``0``, type = double, constraints: ``path_smooth >= 0.0`` - controls smoothing applied to tree nodes - helps prevent overfitting on leaves with few samples - if ``0.0`` (the default), no smoothing is applied - if ``path_smooth > 0`` then ``min_data_in_leaf`` must be at least ``2`` - larger values give stronger regularization - 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 - 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 - ``interaction_constraints`` :raw-html:`🔗︎`, default = ``""``, type = string - controls which features can appear in the same branch - by default interaction constraints are disabled, to enable them you can specify - for CLI, lists separated by commas, e.g. ``[0,1,2],[2,3]`` - for Python-package, list of lists, e.g. ``[[0, 1, 2], [2, 3]]`` - 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. - any two features can only appear in the same branch only if there exists a constraint containing both features - ``verbosity`` :raw-html:`🔗︎`, default = ``1``, type = int, aliases: ``verbose`` - controls the level of LightGBM's verbosity - ``< 0``: Fatal, ``= 0``: Error (Warning), ``= 1``: Info, ``> 1``: Debug - ``input_model`` :raw-html:`🔗︎`, default = ``""``, type = string, aliases: ``model_input``, ``model_in`` - filename of input model - for ``prediction`` task, this model will be applied to prediction data - for ``train`` task, training will be continued from this model - **Note**: can be used only in CLI version - ``output_model`` :raw-html:`🔗︎`, default = ``LightGBM_model.txt``, type = string, aliases: ``model_output``, ``model_out`` - filename of output model in training - **Note**: can be used only in CLI version - ``saved_feature_importance_type`` :raw-html:`🔗︎`, default = ``0``, type = int - the feature importance type in the saved model file - ``0``: count-based feature importance (numbers of splits are counted); ``1``: gain-based feature importance (values of gain are counted) - **Note**: can be used only in CLI version - ``snapshot_freq`` :raw-html:`🔗︎`, default = ``-1``, type = int, aliases: ``save_period`` - frequency of saving model file snapshot - set this to positive value to enable this function. For example, the model file will be snapshotted at each iteration if ``snapshot_freq=1`` - **Note**: can be used only in CLI version - ``use_quantized_grad`` :raw-html:`🔗︎`, default = ``false``, type = bool - whether to use gradient quantization when training - enabling this will discretize (quantize) the gradients and hessians into bins of ``num_grad_quant_bins`` - with quantized training, most arithmetics in the training process will be integer operations - gradient quantization can accelerate training, with little accuracy drop in most cases - **Note**: works only with ``cpu`` and ``cuda`` device type - *New in version 4.0.0* - ``num_grad_quant_bins`` :raw-html:`🔗︎`, default = ``4``, type = int - used only if ``use_quantized_grad=true`` - number of bins to quantization gradients and hessians - with more bins, the quantized training will be closer to full precision training - **Note**: works only with ``cpu`` and ``cuda`` device type - *New in version 4.0.0* - ``quant_train_renew_leaf`` :raw-html:`🔗︎`, default = ``false``, type = bool - used only if ``use_quantized_grad=true`` - whether to renew the leaf values with original gradients when quantized training - renewing is very helpful for good quantized training accuracy for ranking objectives - **Note**: works only with ``cpu`` and ``cuda`` device type - *New in version 4.0.0* - ``stochastic_rounding`` :raw-html:`🔗︎`, default = ``true``, type = bool - used only if ``use_quantized_grad=true`` - whether to use stochastic rounding in gradient quantization - **Note**: works only with ``cpu`` and ``cuda`` device type - *New in version 4.0.0* IO Parameters ------------- Dataset Parameters ~~~~~~~~~~~~~~~~~~ - ``linear_tree`` :raw-html:`🔗︎`, default = ``false``, type = bool, aliases: ``linear_trees`` - fit piecewise linear gradient boosting tree - tree splits are chosen in the usual way, but the model at each leaf is linear instead of constant - the linear model at each leaf includes all the numerical features in that leaf's branch - the first tree has constant leaf values - categorical features are used for splits as normal but are not used in the linear models - 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 - it is recommended to rescale data before training so that features have similar mean and standard deviation - **Note**: works only with ``cpu``, ``gpu`` device type and ``serial`` tree learner - **Note**: ``regression_l1`` objective is not supported with linear tree boosting - **Note**: setting ``linear_tree=true`` significantly increases the memory use of LightGBM - **Note**: if you specify ``monotone_constraints``, constraints will be enforced when choosing the split points, but not when fitting the linear models on leaves - ``max_bin`` :raw-html:`🔗︎`, default = ``255``, type = int, aliases: ``max_bins``, constraints: ``max_bin > 1`` - max number of bins that feature values will be bucketed in - small number of bins may reduce training accuracy but may increase general power (deal with over-fitting) - LightGBM will auto compress memory according to ``max_bin``. For example, LightGBM will use ``uint8_t`` for feature value if ``max_bin=255`` - ``max_bin_by_feature`` :raw-html:`🔗︎`, default = ``None``, type = multi-int - max number of bins for each feature - if not specified, will use ``max_bin`` for all features - ``min_data_in_bin`` :raw-html:`🔗︎`, default = ``3``, type = int, constraints: ``min_data_in_bin > 0`` - minimal number of data inside one bin - use this to avoid one-data-one-bin (potential over-fitting) - ``bin_construct_sample_cnt`` :raw-html:`🔗︎`, default = ``200000``, type = int, aliases: ``subsample_for_bin``, constraints: ``bin_construct_sample_cnt > 0`` - number of data that sampled to construct feature discrete bins - setting this to larger value will give better training result, but may increase data loading time - set this to larger value if data is very sparse - **Note**: don't set this to small values, otherwise, you may encounter unexpected errors and poor accuracy - ``data_random_seed`` :raw-html:`🔗︎`, default = ``1``, type = int, aliases: ``data_seed`` - random seed for sampling data to construct histogram bins - ``is_enable_sparse`` :raw-html:`🔗︎`, default = ``true``, type = bool, aliases: ``is_sparse``, ``enable_sparse``, ``sparse`` - used to enable/disable sparse optimization - ``enable_bundle`` :raw-html:`🔗︎`, default = ``true``, type = bool, aliases: ``is_enable_bundle``, ``bundle`` - set this to ``false`` to disable Exclusive Feature Bundling (EFB), which is described in `LightGBM: A Highly Efficient Gradient Boosting Decision Tree `__ - **Note**: disabling this may cause the slow training speed for sparse datasets - ``use_missing`` :raw-html:`🔗︎`, default = ``true``, type = bool - set this to ``false`` to disable the special handle of missing value - ``zero_as_missing`` :raw-html:`🔗︎`, default = ``false``, type = bool - set this to ``true`` to treat all zero as missing values (including the unshown values in LibSVM / sparse matrices) - set this to ``false`` to use ``na`` for representing missing values - ``feature_pre_filter`` :raw-html:`🔗︎`, default = ``true``, type = bool - set this to ``true`` (the default) to tell LightGBM to ignore the features that are unsplittable based on ``min_data_in_leaf`` - 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 - **Note**: setting this to ``false`` may slow down the training - ``pre_partition`` :raw-html:`🔗︎`, default = ``false``, type = bool, aliases: ``is_pre_partition`` - used for distributed learning (excluding the ``feature_parallel`` mode) - ``true`` if training data are pre-partitioned, and different machines use different partitions - ``two_round`` :raw-html:`🔗︎`, default = ``false``, type = bool, aliases: ``two_round_loading``, ``use_two_round_loading`` - set this to ``true`` if data file is too big to fit in memory - 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 - **Note**: works only in case of loading data directly from text file - ``header`` :raw-html:`🔗︎`, default = ``false``, type = bool, aliases: ``has_header`` - set this to ``true`` if input data has header - **Note**: works only in case of loading data directly from text file - ``label_column`` :raw-html:`🔗︎`, default = ``""``, type = int or string, aliases: ``label`` - used to specify the label column - use number for index, e.g. ``label=0`` means column\_0 is the label - add a prefix ``name:`` for column name, e.g. ``label=name:is_click`` - if omitted, the first column in the training data is used as the label - **Note**: works only in case of loading data directly from text file - ``weight_column`` :raw-html:`🔗︎`, default = ``""``, type = int or string, aliases: ``weight`` - used to specify the weight column - use number for index, e.g. ``weight=0`` means column\_0 is the weight - add a prefix ``name:`` for column name, e.g. ``weight=name:weight`` - **Note**: works only in case of loading data directly from text file - **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`` - **Note**: weights should be non-negative - ``group_column`` :raw-html:`🔗︎`, default = ``""``, type = int or string, aliases: ``group``, ``group_id``, ``query_column``, ``query``, ``query_id`` - used to specify the query/group id column - use number for index, e.g. ``query=0`` means column\_0 is the query id - add a prefix ``name:`` for column name, e.g. ``query=name:query_id`` - **Note**: works only in case of loading data directly from text file - **Note**: data should be grouped by query\_id, for more information, see `Query Data <#query-data>`__ - **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`` - ``ignore_column`` :raw-html:`🔗︎`, default = ``""``, type = multi-int or string, aliases: ``ignore_feature``, ``blacklist`` - used to specify some ignoring columns in training - use number for index, e.g. ``ignore_column=0,1,2`` means column\_0, column\_1 and column\_2 will be ignored - add a prefix ``name:`` for column name, e.g. ``ignore_column=name:c1,c2,c3`` means c1, c2 and c3 will be ignored - **Note**: works only in case of loading data directly from text file - **Note**: index starts from ``0`` and it doesn't count the label column when passing type is ``int`` - **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 - ``categorical_feature`` :raw-html:`🔗︎`, default = ``""``, type = multi-int or string, aliases: ``cat_feature``, ``categorical_column``, ``cat_column``, ``categorical_features`` - used to specify categorical features - use number for index, e.g. ``categorical_feature=0,1,2`` means column\_0, column\_1 and column\_2 are categorical features - add a prefix ``name:`` for column name, e.g. ``categorical_feature=name:c1,c2,c3`` means c1, c2 and c3 are categorical features - **Note**: all values will be cast to ``int32`` (integer codes will be extracted from pandas categoricals in the Python-package) - **Note**: index starts from ``0`` and it doesn't count the label column when passing type is ``int`` - **Note**: all values should be less than ``Int32.MaxValue`` (2147483647) - **Note**: using large values could be memory consuming. Tree decision rule works best when categorical features are presented by consecutive integers starting from zero - **Note**: all negative values will be treated as **missing values** - **Note**: the output cannot be monotonically constrained with respect to a categorical feature - **Note**: floating point numbers in categorical features will be rounded towards 0 - ``forcedbins_filename`` :raw-html:`🔗︎`, default = ``""``, type = string - path to a ``.json`` file that specifies bin upper bounds for some or all features - ``.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) - see `this file `__ as an example - ``save_binary`` :raw-html:`🔗︎`, default = ``false``, type = bool, aliases: ``is_save_binary``, ``is_save_binary_file`` - if ``true``, LightGBM will save the dataset (including validation data) to a binary file. This speed ups the data loading for the next time - **Note**: ``init_score`` is not saved in binary file - **Note**: can be used only in CLI version; for language-specific packages you can use the correspondent function - ``precise_float_parser`` :raw-html:`🔗︎`, default = ``false``, type = bool - use precise floating point number parsing for text parser (e.g. CSV, TSV, LibSVM input) - **Note**: setting this to ``true`` may lead to much slower text parsing - ``parser_config_file`` :raw-html:`🔗︎`, default = ``""``, type = string - path to a ``.json`` file that specifies customized parser initialized configuration - see `lightgbm-transform `__ for usage examples - **Note**: ``lightgbm-transform`` is not maintained by LightGBM's maintainers. Bug reports or feature requests should go to `issues page `__ - *New in version 4.0.0* Predict Parameters ~~~~~~~~~~~~~~~~~~ - ``start_iteration_predict`` :raw-html:`🔗︎`, default = ``0``, type = int - used only in ``prediction`` task - used to specify from which iteration to start the prediction - ``<= 0`` means from the first iteration - ``num_iteration_predict`` :raw-html:`🔗︎`, default = ``-1``, type = int - used only in ``prediction`` task - used to specify how many trained iterations will be used in prediction - ``<= 0`` means no limit - ``predict_raw_score`` :raw-html:`🔗︎`, default = ``false``, type = bool, aliases: ``is_predict_raw_score``, ``predict_rawscore``, ``raw_score`` - used only in ``prediction`` task - set this to ``true`` to predict only the raw scores - set this to ``false`` to predict transformed scores - ``predict_leaf_index`` :raw-html:`🔗︎`, default = ``false``, type = bool, aliases: ``is_predict_leaf_index``, ``leaf_index`` - used only in ``prediction`` task - set this to ``true`` to predict with leaf index of all trees - ``predict_contrib`` :raw-html:`🔗︎`, default = ``false``, type = bool, aliases: ``is_predict_contrib``, ``contrib`` - used only in ``prediction`` task - set this to ``true`` to estimate `SHAP values `__, which represent how each feature contributes to each prediction - produces ``#features + 1`` values where the last value is the expected value of the model output over the training data - **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 `__ - **Note**: unlike the shap package, with ``predict_contrib`` we return a matrix with an extra column, where the last column is the expected value - **Note**: this feature is not implemented for linear trees - ``predict_disable_shape_check`` :raw-html:`🔗︎`, default = ``false``, type = bool - used only in ``prediction`` task - 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 - 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 - 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 - **Note**: be very careful setting this parameter to ``true`` - ``pred_early_stop`` :raw-html:`🔗︎`, default = ``false``, type = bool - used only in ``prediction`` task - used only in ``classification`` and ``ranking`` applications - used only for predicting normal or raw scores - if ``true``, will use early-stopping to speed up the prediction. May affect the accuracy - **Note**: cannot be used with ``rf`` boosting type or custom objective function - ``pred_early_stop_freq`` :raw-html:`🔗︎`, default = ``10``, type = int - used only in ``prediction`` task and if ``pred_early_stop=true`` - the frequency of checking early-stopping prediction - ``pred_early_stop_margin`` :raw-html:`🔗︎`, default = ``10.0``, type = double - used only in ``prediction`` task and if ``pred_early_stop=true`` - the threshold of margin in early-stopping prediction - ``output_result`` :raw-html:`🔗︎`, default = ``LightGBM_predict_result.txt``, type = string, aliases: ``predict_result``, ``prediction_result``, ``predict_name``, ``prediction_name``, ``pred_name``, ``name_pred`` - used only in ``prediction`` task - filename of prediction result - **Note**: can be used only in CLI version Convert Parameters ~~~~~~~~~~~~~~~~~~ - ``convert_model_language`` :raw-html:`🔗︎`, default = ``""``, type = string - used only in ``convert_model`` task - only ``cpp`` is supported yet; for conversion model to other languages consider using `m2cgen `__ utility - if ``convert_model_language`` is set and ``task=train``, the model will be also converted - **Note**: can be used only in CLI version - ``convert_model`` :raw-html:`🔗︎`, default = ``gbdt_prediction.cpp``, type = string, aliases: ``convert_model_file`` - used only in ``convert_model`` task - output filename of converted model - **Note**: can be used only in CLI version Objective Parameters -------------------- - ``objective_seed`` :raw-html:`🔗︎`, default = ``5``, type = int - used only in ``rank_xendcg`` objective - random seed for objectives, if random process is needed - ``num_class`` :raw-html:`🔗︎`, default = ``1``, type = int, aliases: ``num_classes``, constraints: ``num_class > 0`` - used only in ``multi-class`` classification application - ``is_unbalance`` :raw-html:`🔗︎`, default = ``false``, type = bool, aliases: ``unbalance``, ``unbalanced_sets`` - used only in ``binary`` and ``multiclassova`` applications - set this to ``true`` if training data are unbalanced - **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 - **Note**: this parameter cannot be used at the same time with ``scale_pos_weight``, choose only **one** of them - ``scale_pos_weight`` :raw-html:`🔗︎`, default = ``1.0``, type = double, constraints: ``scale_pos_weight > 0.0`` - used only in ``binary`` and ``multiclassova`` applications - weight of labels with positive class - **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 - **Note**: this parameter cannot be used at the same time with ``is_unbalance``, choose only **one** of them - ``sigmoid`` :raw-html:`🔗︎`, default = ``1.0``, type = double, constraints: ``sigmoid > 0.0`` - used only in ``binary`` and ``multiclassova`` classification and in ``lambdarank`` applications - parameter for the sigmoid function - ``boost_from_average`` :raw-html:`🔗︎`, default = ``true``, type = bool - used only in ``regression``, ``binary``, ``multiclassova`` and ``cross-entropy`` applications - adjusts initial score to the mean of labels for faster convergence - ``reg_sqrt`` :raw-html:`🔗︎`, default = ``false``, type = bool - used only in ``regression`` application - used to fit ``sqrt(label)`` instead of original values and prediction result will be also automatically converted to ``prediction^2`` - might be useful in case of large-range labels - ``alpha`` :raw-html:`🔗︎`, default = ``0.9``, type = double, constraints: ``alpha > 0.0`` - used only in ``huber`` and ``quantile`` ``regression`` applications - parameter for `Huber loss `__ and `Quantile regression `__ - ``fair_c`` :raw-html:`🔗︎`, default = ``1.0``, type = double, constraints: ``fair_c > 0.0`` - used only in ``fair`` ``regression`` application - parameter for `Fair loss `__ - ``poisson_max_delta_step`` :raw-html:`🔗︎`, default = ``0.7``, type = double, constraints: ``poisson_max_delta_step > 0.0`` - used only in ``poisson`` ``regression`` application - parameter for `Poisson regression `__ to safeguard optimization - ``tweedie_variance_power`` :raw-html:`🔗︎`, default = ``1.5``, type = double, constraints: ``1.0 <= tweedie_variance_power < 2.0`` - used only in ``tweedie`` ``regression`` application - used to control the variance of the tweedie distribution - set this closer to ``2`` to shift towards a **Gamma** distribution - set this closer to ``1`` to shift towards a **Poisson** distribution - ``lambdarank_truncation_level`` :raw-html:`🔗︎`, default = ``30``, type = int, constraints: ``lambdarank_truncation_level > 0`` - used only in ``lambdarank`` application - controls the number of top-results to focus on during training, refer to "truncation level" in the Sec. 3 of `LambdaMART paper `__ - 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** - ``lambdarank_norm`` :raw-html:`🔗︎`, default = ``true``, type = bool - used only in ``lambdarank`` application - set this to ``true`` to normalize the lambdas for different queries, and improve the performance for unbalanced data - set this to ``false`` to enforce the original lambdarank algorithm - ``label_gain`` :raw-html:`🔗︎`, default = ``0,1,3,7,15,31,63,...,2^30-1``, type = multi-double - used only in ``lambdarank`` application - relevant gain for labels. For example, the gain of label ``2`` is ``3`` in case of default label gains - separate by ``,`` - ``lambdarank_position_bias_regularization`` :raw-html:`🔗︎`, default = ``0.0``, type = double, constraints: ``lambdarank_position_bias_regularization >= 0.0`` - used only in ``lambdarank`` application when positional information is provided and position bias is modeled - larger values reduce the inferred position bias factors - *New in version 4.1.0* Metric Parameters ----------------- - ``metric`` :raw-html:`🔗︎`, default = ``""``, type = multi-enum, aliases: ``metrics``, ``metric_types`` - metric(s) to be evaluated on the evaluation set(s) - ``""`` (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) - ``"None"`` (string, **not** a ``None`` value) means that no metric will be registered, aliases: ``na``, ``null``, ``custom`` - ``l1``, absolute loss, aliases: ``mean_absolute_error``, ``mae``, ``regression_l1`` - ``l2``, square loss, aliases: ``mean_squared_error``, ``mse``, ``regression_l2``, ``regression`` - ``rmse``, root square loss, aliases: ``root_mean_squared_error``, ``l2_root`` - ``quantile``, `Quantile regression `__ - ``mape``, `MAPE loss `__, aliases: ``mean_absolute_percentage_error`` - ``huber``, `Huber loss `__ - ``fair``, `Fair loss `__ - ``poisson``, negative log-likelihood for `Poisson regression `__ - ``gamma``, negative log-likelihood for **Gamma** regression - ``gamma_deviance``, residual deviance for **Gamma** regression - ``tweedie``, negative log-likelihood for **Tweedie** regression - ``ndcg``, `NDCG `__, aliases: ``lambdarank``, ``rank_xendcg``, ``xendcg``, ``xe_ndcg``, ``xe_ndcg_mart``, ``xendcg_mart`` - ``map``, `MAP `__, aliases: ``mean_average_precision`` - ``auc``, `AUC `__ - ``average_precision``, `average precision score `__ - ``r2``, `R-squared `__ - ``binary_logloss``, `log loss `__, aliases: ``binary`` - ``binary_error``, for one sample: ``0`` for correct classification, ``1`` for error classification - ``auc_mu``, `AUC-mu `__ - ``multi_logloss``, log loss for multi-class classification, aliases: ``multiclass``, ``softmax``, ``multiclassova``, ``multiclass_ova``, ``ova``, ``ovr`` - ``multi_error``, error rate for multi-class classification - ``cross_entropy``, cross-entropy (with optional linear weights), aliases: ``xentropy`` - ``cross_entropy_lambda``, "intensity-weighted" cross-entropy, aliases: ``xentlambda`` - ``kullback_leibler``, `Kullback-Leibler divergence `__, aliases: ``kldiv`` - support multiple metrics, separated by ``,`` - ``metric_freq`` :raw-html:`🔗︎`, default = ``1``, type = int, aliases: ``output_freq``, constraints: ``metric_freq > 0`` - frequency for metric output - **Note**: can be used only in CLI version - ``is_provide_training_metric`` :raw-html:`🔗︎`, default = ``false``, type = bool, aliases: ``training_metric``, ``is_training_metric``, ``train_metric`` - set this to ``true`` to output metric result over training dataset - **Note**: can be used only in CLI version - ``eval_at`` :raw-html:`🔗︎`, default = ``1,2,3,4,5``, type = multi-int, aliases: ``ndcg_eval_at``, ``ndcg_at``, ``map_eval_at``, ``map_at`` - used only with ``ndcg`` and ``map`` metrics - `NDCG `__ and `MAP `__ evaluation positions, separated by ``,`` - ``multi_error_top_k`` :raw-html:`🔗︎`, default = ``1``, type = int, constraints: ``multi_error_top_k > 0`` - used only with ``multi_error`` metric - threshold for top-k multi-error metric - the error on each sample is ``0`` if the true class is among the top ``multi_error_top_k`` predictions, and ``1`` otherwise - 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 - when ``multi_error_top_k=1`` this is equivalent to the usual multi-error metric - ``auc_mu_weights`` :raw-html:`🔗︎`, default = ``None``, type = multi-double - used only with ``auc_mu`` metric - list representing flattened matrix (in row-major order) giving loss weights for classification errors - list should have ``n * n`` elements, where ``n`` is the number of classes - the matrix co-ordinate ``[i, j]`` should correspond to the ``i * n + j``-th element of the list - if not specified, will use equal weights for all classes Network Parameters ------------------ - ``num_machines`` :raw-html:`🔗︎`, default = ``1``, type = int, aliases: ``num_machine``, constraints: ``num_machines > 0`` - the number of machines for distributed learning application - this parameter is needed to be set in both **socket** and **MPI** versions - ``local_listen_port`` :raw-html:`🔗︎`, default = ``12400 (random for Dask-package)``, type = int, aliases: ``local_port``, ``port``, constraints: ``local_listen_port > 0`` - TCP listen port for local machines - **Note**: don't forget to allow this port in firewall settings before training - ``time_out`` :raw-html:`🔗︎`, default = ``120``, type = int, constraints: ``time_out > 0`` - socket time-out in minutes - ``machine_list_filename`` :raw-html:`🔗︎`, default = ``""``, type = string, aliases: ``machine_list_file``, ``machine_list``, ``mlist`` - path of file that lists machines for this distributed learning application - each line contains one IP and one port for one machine. The format is ``ip port`` (space as a separator) - **Note**: can be used only in CLI version - ``machines`` :raw-html:`🔗︎`, default = ``""``, type = string, aliases: ``workers``, ``nodes`` - list of machines in the following format: ``ip1:port1,ip2:port2`` GPU Parameters -------------- - ``gpu_platform_id`` :raw-html:`🔗︎`, default = ``-1``, type = int - used only with ``gpu`` device type - OpenCL platform ID. Usually each GPU vendor exposes one OpenCL platform - ``-1`` means the system-wide default platform - **Note**: refer to `GPU Targets <./GPU-Targets.rst#query-opencl-devices-in-your-system>`__ for more details - ``gpu_device_id`` :raw-html:`🔗︎`, default = ``-1``, type = int - OpenCL device ID in the specified platform or CUDA device ID. Each GPU in the selected platform has a unique device ID - ``-1`` means the default device in the selected platform - in multi-GPU case (``num_gpu>1``) means ID of the master GPU - **Note**: refer to `GPU Targets <./GPU-Targets.rst#query-opencl-devices-in-your-system>`__ for more details - ``gpu_device_id_list`` :raw-html:`🔗︎`, default = ``""``, type = string - list of CUDA device IDs - **Note**: can be used only in CUDA implementation (``device_type="cuda"``) and when ``num_gpu>1`` - if empty, the devices with the smallest IDs will be used - ``gpu_use_dp`` :raw-html:`🔗︎`, default = ``false``, type = bool - set this to ``true`` to use double precision math on GPU (by default single precision is used) - **Note**: can be used only in OpenCL implementation (``device_type="gpu"``), in CUDA implementation only double precision is currently supported - ``num_gpu`` :raw-html:`🔗︎`, default = ``1``, type = int, constraints: ``num_gpu > 0`` - number of GPUs used for training in this node - **Note**: can be used only in CUDA implementation (``device_type="cuda"``) - if ``0``, only 1 GPU will be used - used in both single-machine and distributed learning applications - in distributed learning application, each machine can use different number of GPUs .. end params list Others ------ Continued Training with Input Score ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ LightGBM supports continued training with initial scores. It uses an additional file to store these initial scores, like the following: :: 0.5 -0.1 0.9 ... It means the initial score of the first data row is ``0.5``, second is ``-0.1``, and so on. The initial score file corresponds with data file line by line, and has per score per line. If 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. In this case, LightGBM will auto load initial score file if it exists. If 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``. Weight Data ~~~~~~~~~~~ LightGBM supports weighted training. It uses an additional file to store weight data, like the following: :: 1.0 0.5 0.8 ... It means the weight of the first data row is ``1.0``, second is ``0.5``, and so on. Weights should be non-negative. The weight file corresponds with data file line by line, and has per weight per line. And 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. In this case, LightGBM will load the weight file automatically if it exists. Also, you can include weight column in your data file. Please refer to the ``weight_column`` `parameter <#weight_column>`__ in above. Query Data ~~~~~~~~~~ For learning to rank, it needs query information for training data. LightGBM uses an additional file to store query data, like the following: :: 27 18 67 ... For wrapper libraries like in Python and R, this information can also be provided as an array-like via the Dataset parameter ``group``. :: [27, 18, 67, ...] For 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. **Note**: data should be ordered by the query. If 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. In this case, LightGBM will load the query file automatically if it exists. Also, you can include query/group id column in your data file. Please refer to the ``group_column`` `parameter <#group_column>`__ in above. ================================================ FILE: docs/Python-API.rst ================================================ Python API ========== .. currentmodule:: lightgbm Data Structure API ------------------ .. autosummary:: :toctree: pythonapi/ Dataset Booster CVBooster Sequence Training API ------------ .. autosummary:: :toctree: pythonapi/ train cv Scikit-learn API ---------------- .. autosummary:: :toctree: pythonapi/ LGBMModel LGBMClassifier LGBMRegressor LGBMRanker Dask API -------- .. versionadded:: 3.2.0 .. autosummary:: :toctree: pythonapi/ DaskLGBMClassifier DaskLGBMRegressor DaskLGBMRanker Callbacks --------- .. autosummary:: :toctree: pythonapi/ early_stopping log_evaluation record_evaluation reset_parameter Plotting -------- .. autosummary:: :toctree: pythonapi/ plot_importance plot_split_value_histogram plot_metric plot_tree create_tree_digraph Utilities --------- .. autosummary:: :toctree: pythonapi/ register_logger ================================================ FILE: docs/Python-Intro.rst ================================================ Python-package Introduction =========================== This document gives a basic walk-through of LightGBM Python-package. **List of other helpful links** - `Python Examples `__ - `Python API <./Python-API.rst>`__ - `Parameters Tuning <./Parameters-Tuning.rst>`__ Install ------- The preferred way to install LightGBM is via pip: :: pip install lightgbm Refer to `Python-package`_ folder for the detailed installation guide. To verify your installation, try to ``import lightgbm`` in Python: :: import lightgbm as lgb Data Interface -------------- The LightGBM Python module can load data from: - LibSVM (zero-based) / TSV / CSV format text file - NumPy 2D array(s), pandas DataFrame, pyarrow Table, SciPy sparse matrix - LightGBM binary file - LightGBM ``Sequence`` object(s) The data is stored in a ``Dataset`` object. Many of the examples in this page use functionality from ``numpy``. To run the examples, be sure to import ``numpy`` in your session. .. code:: python import numpy as np **To load a LibSVM (zero-based) text file or a LightGBM binary file into Dataset:** .. code:: python train_data = lgb.Dataset('train.svm.bin') **To load a numpy array into Dataset:** .. code:: python rng = np.random.default_rng() data = rng.uniform(size=(500, 10)) # 500 entities, each contains 10 features label = rng.integers(low=0, high=2, size=(500, )) # binary target train_data = lgb.Dataset(data, label=label) **To load a scipy.sparse.csr\_matrix array into Dataset:** .. code:: python import scipy csr = scipy.sparse.csr_matrix((dat, (row, col))) train_data = lgb.Dataset(csr) **Load from Sequence objects:** We can implement ``Sequence`` interface to read binary files. The following example shows reading HDF5 file with ``h5py``. .. code:: python import h5py class HDFSequence(lgb.Sequence): def __init__(self, hdf_dataset, batch_size): self.data = hdf_dataset self.batch_size = batch_size def __getitem__(self, idx): return self.data[idx] def __len__(self): return len(self.data) f = h5py.File('train.hdf5', 'r') train_data = lgb.Dataset(HDFSequence(f['X'], 8192), label=f['Y'][:]) Features of using ``Sequence`` interface: - Data sampling uses random access, thus does not go through the whole dataset - Reading data in batch, thus saves memory when constructing ``Dataset`` object - Supports creating ``Dataset`` from multiple data files Please refer to ``Sequence`` `API doc <./Python-API.rst#data-structure-api>`__. `dataset_from_multi_hdf5.py `__ is a detailed example. **Saving Dataset into a LightGBM binary file will make loading faster:** .. code:: python train_data = lgb.Dataset('train.svm.txt') train_data.save_binary('train.bin') **Create validation data:** .. code:: python validation_data = train_data.create_valid('validation.svm') or .. code:: python validation_data = lgb.Dataset('validation.svm', reference=train_data) In LightGBM, the validation data should be aligned with training data. **Specific feature names and categorical features:** .. code:: python train_data = lgb.Dataset(data, label=label, feature_name=['c1', 'c2', 'c3'], categorical_feature=['c3']) LightGBM can use categorical features as input directly. It doesn't need to convert to one-hot encoding, and is much faster than one-hot encoding (about 8x speed-up). **Note**: You should convert your categorical features to ``int`` type before you construct ``Dataset``. **Weights can be set when needed:** .. code:: python rng = np.random.default_rng() w = rng.uniform(size=(500, )) train_data = lgb.Dataset(data, label=label, weight=w) or .. code:: python train_data = lgb.Dataset(data, label=label) rng = np.random.default_rng() w = rng.uniform(size=(500, )) train_data.set_weight(w) And you can use ``Dataset.set_init_score()`` to set initial score, and ``Dataset.set_group()`` to set group/query data for ranking tasks. **Memory efficient usage:** The ``Dataset`` object in LightGBM is very memory-efficient, it only needs to save discrete bins. However, Numpy/Array/Pandas object is memory expensive. If you are concerned about your memory consumption, you can save memory by: 1. Set ``free_raw_data=True`` (default is ``True``) when constructing the ``Dataset`` 2. Explicitly set ``raw_data=None`` after the ``Dataset`` has been constructed 3. Call ``gc`` Setting Parameters ------------------ LightGBM can use a dictionary to set `Parameters <./Parameters.rst>`__. For instance: - Booster parameters: .. code:: python param = {'num_leaves': 31, 'objective': 'binary'} param['metric'] = 'auc' - You can also specify multiple eval metrics: .. code:: python param['metric'] = ['auc', 'binary_logloss'] Training -------- Training a model requires a parameter list and data set: .. code:: python num_round = 10 bst = lgb.train(param, train_data, num_round, valid_sets=[validation_data]) After training, the model can be saved: .. code:: python bst.save_model('model.txt') The trained model can also be dumped to JSON format: .. code:: python json_model = bst.dump_model() A saved model can be loaded: .. code:: python bst = lgb.Booster(model_file='model.txt') # init model CV -- Training with 5-fold CV: .. code:: python lgb.cv(param, train_data, num_round, nfold=5) Early Stopping -------------- If you have a validation set, you can use early stopping to find the optimal number of boosting rounds. Early 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: .. code:: python bst = lgb.train(param, train_data, num_round, valid_sets=valid_sets, callbacks=[lgb.early_stopping(stopping_rounds=5)]) bst.save_model('model.txt', num_iteration=bst.best_iteration) The model will train until the validation score stops improving. Validation score needs to improve at least every ``stopping_rounds`` to continue training. The 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. Note that ``train()`` will return a model from the best iteration. This works with both metrics to minimize (L2, log loss, etc.) and to maximize (NDCG, AUC, etc.). Note that if you specify more than one evaluation metric, all of them will be used for early stopping. However, 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. Prediction ---------- A model that has been trained or loaded can perform predictions on datasets: .. code:: python # 7 entities, each contains 10 features rng = np.random.default_rng() data = rng.uniform(size=(7, 10)) ypred = bst.predict(data) If early stopping is enabled during training, you can get predictions from the best iteration with ``bst.best_iteration``: .. code:: python ypred = bst.predict(data, num_iteration=bst.best_iteration) .. _Python-package: https://github.com/lightgbm-org/LightGBM/tree/master/python-package ================================================ FILE: docs/Quick-Start.rst ================================================ Quick Start =========== This is a quick start guide for LightGBM CLI version. Follow the `Installation Guide <./Installation-Guide.rst>`__ to install LightGBM first. **List of other helpful links** - `Parameters <./Parameters.rst>`__ - `Parameters Tuning <./Parameters-Tuning.rst>`__ - `Python-package Quick Start <./Python-Intro.rst>`__ - `Python API <./Python-API.rst>`__ Training Data Format -------------------- LightGBM supports input data files with `CSV`_, `TSV`_ and `LibSVM`_ (zero-based) formats. Files could be both with and without `headers <./Parameters.rst#header>`__. `Label column <./Parameters.rst#label_column>`__ could be specified both by index and by name. Some columns could be `ignored <./Parameters.rst#ignore_column>`__. Categorical Feature Support ~~~~~~~~~~~~~~~~~~~~~~~~~~~ LightGBM can use categorical features directly (without one-hot encoding). The experiment on `Expo data`_ shows about 8x speed-up compared with one-hot encoding. For the setting details, please refer to the ``categorical_feature`` `parameter <./Parameters.rst#categorical_feature>`__. Weight and Query/Group Data ~~~~~~~~~~~~~~~~~~~~~~~~~~~ LightGBM also supports weighted training, it needs an additional `weight data <./Parameters.rst#weight-data>`__. And it needs an additional `query data <./Parameters.rst#query-data>`_ for ranking task. Also, `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. Parameters Quick Look --------------------- The parameters format is ``key1=value1 key2=value2 ...``. Parameters can be set both in config file and command line. If one parameter appears in both command line and config file, LightGBM will use the parameter from the command line. The most important parameters which new users should take a look at are located into `Core Parameters <./Parameters.rst#core-parameters>`__ and the top of `Learning Control Parameters <./Parameters.rst#learning-control-parameters>`__ sections of the full detailed list of `LightGBM's parameters <./Parameters.rst>`__. Run LightGBM ------------ :: lightgbm config=your_config_file other_args ... Parameters 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. For example, the following command line will keep ``num_trees=10`` and ignore the same parameter in the config file. :: lightgbm config=train.conf num_trees=10 Examples -------- - `Binary Classification `__ - `Regression `__ - `Lambdarank `__ - `Distributed Learning `__ .. _CSV: https://en.wikipedia.org/wiki/Comma-separated_values .. _TSV: https://en.wikipedia.org/wiki/Tab-separated_values .. _LibSVM: https://www.csie.ntu.edu.tw/~cjlin/libsvm/ .. _Expo data: https://community.amstat.org/jointscsg-section/dataexpo/dataexpo2009 ================================================ FILE: docs/README.rst ================================================ Documentation ============= Documentation for LightGBM is generated using `Sphinx `__ and `Breathe `__, which works on top of `Doxygen `__ output. List of parameters and their descriptions in `Parameters.rst <./Parameters.rst>`__ is generated automatically from comments in `config file `__ by `this script `__. After each commit on ``master``, documentation is updated and published to `Read the Docs `__. Build ----- It is not necessary to re-build this documentation while modifying LightGBM's source code. The HTML files generated using ``Sphinx`` are not checked into source control. However, you may want to build them locally during development to test changes. Docker ^^^^^^ The most reliable way to build the documentation locally is with Docker, using `the same images Read the Docs uses `_. Run the following from the root of this repository to pull the relevant image and run a container locally. .. code:: sh docker run \ --rm \ --user=0 \ -v $(pwd):/opt/LightGBM \ --env C_API=true \ --env CONDA=/opt/miniforge \ --env READTHEDOCS=true \ --workdir=/opt/LightGBM/docs \ --entrypoint="" \ readthedocs/build:ubuntu-24.04-2024.06.17 \ /bin/bash build-docs.sh When that code completes, open ``docs/_build/html/index.html`` in your browser. .. note:: 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. Without Docker ^^^^^^^^^^^^^^ You can build the documentation locally without Docker. Just install Doxygen and run in ``docs`` folder .. code:: sh pip install breathe sphinx 'sphinx_rtd_theme>=0.5' make html Note that this will not build the R documentation. Consider using common R utilities for documentation generation, if you need it. Or use the Docker-based approach described above to build the R documentation locally. Optionally, you may also install ``scikit-learn`` and get richer documentation for the classes in ``Scikit-learn API``. If 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: .. code:: sh pip install sphinx 'sphinx_rtd_theme>=0.5' export C_API=NO || set C_API=NO make html ================================================ FILE: docs/_static/js/script.js ================================================ $(() => { /* Use wider container for the page content */ $(".wy-nav-content").each(function () { this.style.setProperty("max-width", "none", "important"); }); /* List each class property item on a new line https://github.com/lightgbm-org/LightGBM/issues/5073 */ if (window.location.pathname.toLocaleLowerCase().indexOf("pythonapi") !== -1) { $(".py.property").each(function () { this.style.setProperty("display", "inline", "important"); }); } /* Collapse specified sections in the installation guide */ if (window.location.pathname.toLocaleLowerCase().indexOf("installation-guide") !== -1) { $( '', ).appendTo("body"); const collapsible = [ "#build-threadless-version-not-recommended", "#build-mpi-version", "#build-gpu-version", "#build-cuda-version", "#build-rocm-version", "#build-java-wrapper", "#build-python-package", "#build-r-package", "#build-c-unit-tests", ]; $.each(collapsible, (_, val) => { const header = `${val} > :header:first`; const content = `${val} :not(:header:first)`; $(header).addClass("closed"); $(content).hide(); $(header).click(() => { $(header).toggleClass("closed opened"); $(content).slideToggle(0); }); }); /* Uncollapse parent sections when nested section is specified in the URL or before navigate to it from navbar */ function uncollapse(section) { section.parents().each((_, val) => { $(val).children(".closed").click(); }); } uncollapse($(window.location.hash)); $(".wy-menu.wy-menu-vertical li a.reference.internal").click(function () { uncollapse($($(this).attr("href"))); }); } }); ================================================ FILE: docs/build-docs.sh ================================================ #!/bin/bash set -e -E -u -o pipefail rm -f ./_FIRST_RUN.flag export PATH="${CONDA}/bin:${PATH}" curl \ -sL \ -o "${HOME}/miniforge.sh" \ https://github.com/conda-forge/miniforge/releases/latest/download/Miniforge3-Linux-x86_64.sh /bin/bash "${HOME}/miniforge.sh" -b -p "${CONDA}" conda config --set always_yes yes --set changeps1 no conda update -q -y conda conda env create \ --name docs-env \ --file env.yml || exit 1 # shellcheck disable=SC1091 source activate docs-env make clean html || exit 1 echo "Done building docs. Open docs/_build/html/index.html in a web browser to view them." ================================================ FILE: docs/conf.py ================================================ #!/usr/bin/env python3 # -*- coding: utf-8 -*- # # LightGBM documentation build configuration file, created by # sphinx-quickstart on Thu May 4 14:30:58 2017. # # This file is execfile()d with the current directory set to its # containing dir. # # Note that not all possible configuration values are present in this # autogenerated file. # # All configuration values have a default; values that are commented out # serve to show the default. # If extensions (or modules to document with autodoc) are in another directory, # add these directories to sys.path here. If the directory is relative to the # documentation root, use os.path.abspath to make it absolute. """Sphinx configuration file.""" import datetime import os import sys from pathlib import Path from re import compile from shutil import copytree from subprocess import PIPE, Popen from typing import Any, List import sphinx from docutils.nodes import reference from docutils.parsers.rst import Directive from docutils.transforms import Transform from sphinx.application import Sphinx from sphinx.errors import VersionRequirementError CURR_PATH = Path(__file__).absolute().parent LIB_PATH = CURR_PATH.parent / "python-package" sys.path.insert(0, str(LIB_PATH)) INTERNAL_REF_REGEX = compile(r"(?P\.\/.+)(?P\.rst)(?P$|#)") RTD_R_REF_REGEX = compile(r"(?Phttps://.+/)(?Platest)(?P/R/reference/)") class InternalRefTransform(Transform): """Replaces '.rst' with '.html' in all internal links like './[Something].rst[#anchor]'.""" default_priority = 210 """Numerical priority of this transform, 0 through 999.""" def apply(self, **kwargs: Any) -> None: """Apply the transform to the document tree.""" for section in self.document.traverse(reference): if section.get("refuri") is not None: section["refuri"] = INTERNAL_REF_REGEX.sub(r"\g.html\g", section["refuri"]) class IgnoredDirective(Directive): """Stub for unknown directives.""" has_content = True def run(self) -> List: """Do nothing.""" return [] # -- General configuration ------------------------------------------------ os.environ["LIGHTGBM_BUILD_DOC"] = "True" C_API = os.environ.get("C_API", "").lower().strip() != "no" RTD = bool(os.environ.get("READTHEDOCS", "")) RTD_VERSION = os.environ.get("READTHEDOCS_VERSION", "stable") # If your documentation needs a minimal Sphinx version, state it here. needs_sphinx = "2.1.0" # Due to sphinx.ext.napoleon, autodoc_typehints if needs_sphinx > sphinx.__version__: message = f"This project needs at least Sphinx v{needs_sphinx}" raise VersionRequirementError(message) # Add any Sphinx extension module names here, as strings. They can be # extensions coming with Sphinx (named 'sphinx.ext.*') or your custom # ones. extensions = [ "sphinx.ext.autodoc", "sphinx.ext.autosummary", "sphinx.ext.todo", "sphinx.ext.viewcode", "sphinx.ext.napoleon", "sphinx.ext.intersphinx", ] autodoc_default_flags = ["members", "inherited-members", "show-inheritance"] autodoc_default_options = { "members": True, "inherited-members": True, "show-inheritance": True, } # mock out modules autodoc_mock_imports = [ "dask", "dask.distributed", "graphviz", "matplotlib", "numpy", "pandas", "scipy", "scipy.sparse", ] try: import sklearn # noqa: F401 except ImportError: autodoc_mock_imports.append("sklearn") # hide type hints in API docs autodoc_typehints = "none" # Generate autosummary pages. Output should be set with: `:toctree: pythonapi/` autosummary_generate = ["Python-API.rst"] # Only the class' docstring is inserted. autoclass_content = "class" # If true, `todo` and `todoList` produce output, else they produce nothing. todo_include_todos = False # The master toctree document. master_doc = "index" # General information about the project. project = "LightGBM" copyright = f"{datetime.datetime.now().year}, Microsoft Corporation" author = "Microsoft Corporation" # The name of an image file (relative to this directory) to place at the top # of the sidebar. html_logo = str(CURR_PATH / "logo" / "LightGBM_logo_grey_text.svg") # The name of an image file (relative to this directory) to use as a favicon of # the docs. This file should be a Windows icon file (.ico) being 16x16 or 32x32 # pixels large. html_favicon = str(CURR_PATH / "_static" / "images" / "favicon.ico") # The version info for the project you're documenting, acts as replacement for # |version| and |release|, also used in various other places throughout the # built documents. # The short X.Y version. version = (CURR_PATH.parent / "VERSION.txt").read_text(encoding="utf-8").strip().replace("rc", "-rc") # The full version, including alpha/beta/rc tags. release = version # The language for content autogenerated by Sphinx. Refer to documentation # for a list of supported languages. # # This is also used if you do content translation via gettext catalogs. # Usually you set "language" from the command line for these cases. language = "en" # List of patterns, relative to source directory, that match files and # directories to ignore when looking for source files. # This patterns also effect to html_static_path and html_extra_path exclude_patterns = ["_build", "Thumbs.db", ".DS_Store"] # The name of the Pygments (syntax highlighting) style to use. pygments_style = "default" # -- Configuration for C API docs generation ------------------------------ if C_API: extensions.extend( [ "breathe", ] ) breathe_projects = {"LightGBM": str(CURR_PATH / "doxyoutput" / "xml")} breathe_default_project = "LightGBM" breathe_domain_by_extension = { "h": "c", } breathe_show_define_initializer = True c_id_attributes = ["LIGHTGBM_C_EXPORT"] # -- Options for HTML output ---------------------------------------------- # The theme to use for HTML and HTML Help pages. See the documentation for # a list of builtin themes. html_theme = "sphinx_rtd_theme" # Theme options are theme-specific and customize the look and feel of a theme # further. For a list of options available for each theme, see the # documentation. html_theme_options = { "includehidden": False, "logo_only": True, } # Add any paths that contain custom static files (such as style sheets) here, # relative to this directory. They are copied after the builtin static files, # so a file named "default.css" will overwrite the builtin "default.css". html_static_path = ["_static"] # -- Options for HTMLHelp output ------------------------------------------ # Output file base name for HTML help builder. htmlhelp_basename = "LightGBMdoc" # -- Options for LaTeX output --------------------------------------------- # The name of an image file (relative to this directory) to place at the top of # the title page. latex_logo = str(CURR_PATH / "logo" / "LightGBM_logo_black_text_small.png") # intersphinx configuration intersphinx_mapping = { "sklearn": ("https://scikit-learn.org/stable/", None), } def generate_doxygen_xml(app: Sphinx) -> None: """Generate XML documentation for C API by Doxygen. Parameters ---------- app : sphinx.application.Sphinx The application object representing the Sphinx process. """ doxygen_args = [ f"INPUT={CURR_PATH.parent / 'include' / 'LightGBM' / 'c_api.h'}", f"OUTPUT_DIRECTORY={CURR_PATH / 'doxyoutput'}", "GENERATE_HTML=NO", "GENERATE_LATEX=NO", "GENERATE_XML=YES", "XML_OUTPUT=xml", "XML_PROGRAMLISTING=YES", r'ALIASES="rst=\verbatim embed:rst:leading-asterisk"', r'ALIASES+="endrst=\endverbatim"', "ENABLE_PREPROCESSING=YES", "MACRO_EXPANSION=YES", "EXPAND_ONLY_PREDEF=NO", "SKIP_FUNCTION_MACROS=NO", "PREDEFINED=__cplusplus", "SORT_BRIEF_DOCS=YES", "WARN_AS_ERROR=YES", ] doxygen_input = "\n".join(doxygen_args) doxygen_input = bytes(doxygen_input, "utf-8") (CURR_PATH / "doxyoutput").mkdir(parents=True, exist_ok=True) try: # Warning! The following code can cause buffer overflows on RTD. # Consider suppressing output completely if RTD project silently fails. # Refer to https://github.com/svenevs/exhale # /blob/fe7644829057af622e467bb529db6c03a830da99/exhale/deploy.py#L99-L111 process = Popen(["doxygen", "-"], stdin=PIPE, stdout=PIPE, stderr=PIPE) stdout, stderr = process.communicate(doxygen_input) output = "\n".join([i.decode("utf-8") for i in (stdout, stderr) if i is not None]) if process.returncode != 0: raise RuntimeError(output) print(output) except BaseException as e: raise Exception(f"An error has occurred while executing Doxygen\n{e}") def generate_r_docs(app: Sphinx) -> None: """Generate documentation for R-package. Parameters ---------- app : sphinx.application.Sphinx The application object representing the Sphinx process. """ commands = f""" export TAR=/bin/tar cd {CURR_PATH.parent} export R_LIBS="$CONDA_PREFIX/lib/R/library" sh build-cran-package.sh || exit 1 R CMD INSTALL --with-keep.source lightgbm_*.tar.gz || exit 1 cp -R \ {CURR_PATH.parent / "R-package" / "pkgdown"} \ {CURR_PATH.parent / "lightgbm_r" / "pkgdown"} cd {CURR_PATH.parent / "lightgbm_r"} Rscript -e "roxygen2::roxygenize(load = 'installed')" || exit 1 Rscript -e "pkgdown::build_site( \ lazy = FALSE \ , install = FALSE \ , devel = FALSE \ , examples = TRUE \ , run_dont_run = TRUE \ , seed = 42L \ , preview = FALSE \ , new_process = TRUE \ ) " || exit 1 cd {CURR_PATH.parent} """ try: print("Building R-package documentation") # Warning! The following code can cause buffer overflows on RTD. # Consider suppressing output completely if RTD project silently fails. # Refer to https://github.com/svenevs/exhale # /blob/fe7644829057af622e467bb529db6c03a830da99/exhale/deploy.py#L99-L111 process = Popen(["/bin/bash"], stdin=PIPE, stdout=PIPE, stderr=PIPE, universal_newlines=True) stdout, stderr = process.communicate(commands) output = "\n".join([i for i in (stdout, stderr) if i is not None]) if process.returncode != 0: raise RuntimeError(output) print(output) print("Done building R-package documentation") except BaseException as e: raise Exception(f"An error has occurred while generating documentation for R-package\n{e}") def replace_reference_to_r_docs(app: Sphinx) -> None: """Make reference to R-package documentation point to the actual version. Parameters ---------- app : sphinx.application.Sphinx The application object representing the Sphinx process. """ index_doc_path = CURR_PATH / "index.rst" with open(index_doc_path, "r+t", encoding="utf-8") as index_doc: content = index_doc.read() content = RTD_R_REF_REGEX.sub(rf"\g{RTD_VERSION}\g", content) index_doc.seek(0) index_doc.write(content) def setup(app: Sphinx) -> None: """Add new elements at Sphinx initialization time. Parameters ---------- app : sphinx.application.Sphinx The application object representing the Sphinx process. """ first_run = not (CURR_PATH / "_FIRST_RUN.flag").exists() if first_run and RTD: (CURR_PATH / "_FIRST_RUN.flag").touch() if C_API: app.connect("builder-inited", generate_doxygen_xml) else: app.add_directive("doxygenfile", IgnoredDirective) if RTD: # build R docs only on Read the Docs site if first_run: app.connect("builder-inited", generate_r_docs) app.connect( "build-finished", lambda app, _: copytree(CURR_PATH.parent / "lightgbm_r" / "docs", Path(app.outdir) / "R") ) app.connect("builder-inited", replace_reference_to_r_docs) app.add_transform(InternalRefTransform) add_js_file = getattr(app, "add_js_file", False) or app.add_javascript add_js_file("js/script.js") ================================================ FILE: docs/env.yml ================================================ name: docs-env channels: - nodefaults - conda-forge dependencies: - breathe>=4.36 - doxygen>=1.13.2 - python=3.12 - r-base>=4.5.1 - r-data.table=1.17.8 - r-jsonlite=2.0.0 - r-knitr=1.50 - r-markdown=2.0 - r-matrix=1.7_4 - r-pkgdown=2.1.3 - r-roxygen2=7.3.3 # skipping scikit-learn 1.7.1 because of the problems described in # https://github.com/lightgbm-org/LightGBM/issues/6978 - scikit-learn>=1.6.1,!=1.7.1 - sphinx>=8.1.3 - sphinx_rtd_theme>=3.0.1 ================================================ FILE: docs/gcc-Tips.rst ================================================ The content of this document was very outdated and is no longer available to avoid any misleadings. ================================================ FILE: docs/index.rst ================================================ .. LightGBM documentation master file, created by sphinx-quickstart on Thu May 4 14:30:58 2017. You can adapt this file completely to your liking, but it should at least contain the root `toctree` directive. .. image:: ./logo/LightGBM_logo_black_text.svg :align: center :width: 600 :alt: Light Gradient Boosting Machine logo. | Welcome to LightGBM's documentation! ==================================== **LightGBM** is a gradient boosting framework that uses tree based learning algorithms. It is designed to be distributed and efficient with the following advantages: - Faster training speed and higher efficiency. - Lower memory usage. - Better accuracy. - Support of parallel, distributed, and GPU learning. - Capable of handling large-scale data. For more details, please refer to `Features <./Features.rst>`__. .. toctree:: :maxdepth: 1 :caption: Contents: Installation Guide Quick Start Python Quick Start Features Experiments Parameters Parameters Tuning C API Python API R API Distributed Learning Guide GPU Tutorial Advanced Topics FAQ Development Guide .. toctree:: :hidden: GPU-Performance GPU-Targets GPU-Windows gcc-Tips README Indices and Tables ================== * :ref:`genindex` ================================================ FILE: docs/make.bat ================================================ @ECHO OFF pushd %~dp0 REM Command file for Sphinx documentation if "%SPHINXBUILD%" == "" ( set SPHINXBUILD=sphinx-build ) set SOURCEDIR=. set BUILDDIR=_build set SPHINXPROJ=LightGBM set SPHINXOPTS=-W if "%1" == "" goto help %SPHINXBUILD% >NUL 2>NUL if errorlevel 9009 ( echo. echo.The 'sphinx-build' command was not found. Make sure you have Sphinx echo.installed, then set the SPHINXBUILD environment variable to point echo.to the full path of the 'sphinx-build' executable. Alternatively you echo.may add the Sphinx directory to PATH. echo. echo.If you don't have Sphinx installed, grab it from echo.https://www.sphinx-doc.org/ exit /b 1 ) %SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% goto end :help %SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% :end popd ================================================ FILE: examples/README.md ================================================ Examples ======== You can learn how to use LightGBM by these examples. Comments 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). Machine Learning Challenge Winning Solutions ============================================ **LightGBM is used in many winning solutions, but this table is updated very infrequently.** | Place         | Competition   | Solution | Date | |---------|:------------- | --------- | -----| | 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 | | 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 | | 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 | | 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 | | 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 | | 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 | | 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 | | 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 | | 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 | | 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 | | 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 | | 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 | | 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 | | 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 | | 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 | | 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 | | 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 | | 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 | | 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 | | 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 | | 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 | | 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 | | 2nd | [Kuzushiji Recognition](https://www.kaggle.com/c/kuzushiji-recognition) | [link](https://www.kaggle.com/c/kuzushiji-recognition/discussion/112712) | 2019.10 | | 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 | | 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 | | 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 | | 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 | | 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 | | 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 | | 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 | | 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 | | 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 | | 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 | | 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 | | 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 | | 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 | | 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 | | 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 | | 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 | | 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 | | 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 | | 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 | | 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 | | 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 | | 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 | | 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 | | 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 | | 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| | 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 | | 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 | | 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 | | 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 | | 1st | [CIKM2017 AnalytiCup - Lazada Product Title Quality Challenge](https://cikm2017.org/CIKM_AnalytiCup_task3.html) | [link](https://arxiv.org/abs/1804.01000) | 2017.9 | | 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 | | 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 | | 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) | - | | 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 | | 1st | [The 1st Di-Tech Competitions](https://web.archive.org/web/20170311212917/https://research.xiaojukeji.com/competition/main.action?competitionId=DiTech2016) | - | 2016.7 | ================================================ FILE: examples/binary_classification/README.md ================================================ Binary Classification Example ============================= Here is an example for LightGBM to run binary classification task. ***You must follow the [installation instructions](https://lightgbm.readthedocs.io/en/latest/Installation-Guide.html) for the following commands to work. The `lightgbm` binary must be built and available at the root of this project.*** Training -------- Run the following command in this folder: ```bash "../../lightgbm" config=train.conf ``` Prediction ---------- You should finish training first. Run the following command in this folder: ```bash "../../lightgbm" config=predict.conf ``` ================================================ FILE: examples/binary_classification/binary.test ================================================ 1 0.644 0.247 -0.447 0.862 0.374 0.854 -1.126 -0.790 2.173 1.015 -0.201 1.400 0.000 1.575 1.807 1.607 0.000 1.585 -0.190 -0.744 3.102 0.958 1.061 0.980 0.875 0.581 0.905 0.796 0 0.385 1.800 1.037 1.044 0.349 1.502 -0.966 1.734 0.000 0.966 -1.960 -0.249 0.000 1.501 0.465 -0.354 2.548 0.834 -0.440 0.638 3.102 0.695 0.909 0.981 0.803 0.813 1.149 1.116 0 1.214 -0.166 0.004 0.505 1.434 0.628 -1.174 -1.230 1.087 0.579 -1.047 -0.118 0.000 0.835 0.340 1.234 2.548 0.711 -1.383 1.355 0.000 0.848 0.911 1.043 0.931 1.058 0.744 0.696 1 0.420 1.111 0.137 1.516 -1.657 0.854 0.623 1.605 1.087 1.511 -1.297 0.251 0.000 0.872 -0.368 -0.721 0.000 0.543 0.731 1.424 3.102 1.597 1.282 1.105 0.730 0.148 1.231 1.234 0 0.897 -1.703 -1.306 1.022 -0.729 0.836 0.859 -0.333 2.173 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1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 ================================================ FILE: examples/binary_classification/forced_splits.json ================================================ { "feature": 25, "threshold": 1.3, "left": { "feature": 26, "threshold": 0.85 }, "right": { "feature": 26, "threshold": 0.85 } } ================================================ FILE: examples/binary_classification/predict.conf ================================================ task = predict data = binary.test input_model= LightGBM_model.txt ================================================ FILE: examples/binary_classification/train.conf ================================================ # task type, support train and predict task = train # boosting type, support gbdt for now, alias: boosting, boost boosting_type = gbdt # application type, support following application # regression , regression task # binary , binary classification task # lambdarank , LambdaRank task # alias: application, app objective = binary # eval metrics, support multi metric, delimited by ',' , support following metrics # l1 # l2 , default metric for regression # ndcg , default metric for lambdarank # auc # binary_logloss , default metric for binary # binary_error metric = binary_logloss,auc # frequency for metric output metric_freq = 1 # true if need output metric for training data, alias: tranining_metric, train_metric is_training_metric = true # column in data to use as label label_column = 0 # number of bins for feature bucket, 255 is a recommend setting, it can save memories, and also has good accuracy. max_bin = 255 # training data # if existing weight file, should name to "binary.train.weight" # alias: train_data, train data = binary.train # validation data, support multi validation data, separated by ',' # if existing weight file, should name to "binary.test.weight" # alias: valid, test, test_data, valid_data = binary.test # number of trees(iterations), alias: num_tree, num_iteration, num_iterations, num_round, num_rounds num_trees = 100 # shrinkage rate , alias: shrinkage_rate learning_rate = 0.1 # number of leaves for one tree, alias: num_leaf num_leaves = 63 # type of tree learner, support following types: # serial , single machine version # feature , use feature parallel to train # data , use data parallel to train # voting , use voting based parallel to train # alias: tree tree_learner = serial # number of threads for multi-threading. One thread will use each CPU. The default is the CPU count. # num_threads = 8 # feature sub-sample, will random select 80% feature to train on each iteration # alias: sub_feature feature_fraction = 0.8 # Support bagging (data sub-sample), will perform bagging every 5 iterations bagging_freq = 5 # Bagging fraction, will random select 80% data on bagging # alias: sub_row bagging_fraction = 0.8 # minimal number data for one leaf, use this to deal with over-fit # alias : min_data_per_leaf, min_data min_data_in_leaf = 50 # minimal sum Hessians for one leaf, use this to deal with over-fit min_sum_hessian_in_leaf = 5.0 # save memory and faster speed for sparse feature, alias: is_sparse is_enable_sparse = true # when data is bigger than memory size, set this to true. otherwise set false will have faster speed # alias: two_round_loading, two_round use_two_round_loading = false # true if need to save data to binary file and application will auto load data from binary file next time # alias: is_save_binary, save_binary is_save_binary_file = false # output model file output_model = LightGBM_model.txt # support continuous train from trained gbdt model # input_model= trained_model.txt # output prediction file for predict task # output_result= prediction.txt # number of machines in distributed training, alias: num_machine num_machines = 1 # local listening port in distributed training, alias: local_port local_listen_port = 12400 # machines list file for distributed training, alias: mlist machine_list_file = mlist.txt # force splits # forced_splits = forced_splits.json ================================================ FILE: examples/binary_classification/train_linear.conf ================================================ # task type, support train and predict task = train # boosting type, support gbdt for now, alias: boosting, boost boosting_type = gbdt # application type, support following application # regression , regression task # binary , binary classification task # lambdarank , LambdaRank task # alias: application, app objective = binary linear_tree = true # eval metrics, support multi metric, delimited by ',' , support following metrics # l1 # l2 , default metric for regression # ndcg , default metric for lambdarank # auc # binary_logloss , default metric for binary # binary_error metric = binary_logloss,auc # frequency for metric output metric_freq = 1 # true if need output metric for training data, alias: tranining_metric, train_metric is_training_metric = true # number of bins for feature bucket, 255 is a recommend setting, it can save memories, and also has good accuracy. max_bin = 255 # training data # if existing weight file, should name to "binary.train.weight" # alias: train_data, train data = binary.train # validation data, support multi validation data, separated by ',' # if existing weight file, should name to "binary.test.weight" # alias: valid, test, test_data, valid_data = binary.test # number of trees(iterations), alias: num_tree, num_iteration, num_iterations, num_round, num_rounds num_trees = 100 # shrinkage rate , alias: shrinkage_rate learning_rate = 0.1 # number of leaves for one tree, alias: num_leaf num_leaves = 63 # type of tree learner, support following types: # serial , single machine version # feature , use feature parallel to train # data , use data parallel to train # voting , use voting based parallel to train # alias: tree tree_learner = serial # number of threads for multi-threading. One thread will use each CPU. The default is set to CPU count. # num_threads = 8 # feature sub-sample, will random select 80% feature to train on each iteration # alias: sub_feature feature_fraction = 0.8 # Support bagging (data sub-sample), will perform bagging every 5 iterations bagging_freq = 5 # Bagging fraction, will random select 80% data on bagging # alias: sub_row bagging_fraction = 0.8 # minimal number data for one leaf, use this to deal with over-fit # alias : min_data_per_leaf, min_data min_data_in_leaf = 50 # minimal sum Hessians for one leaf, use this to deal with over-fit min_sum_hessian_in_leaf = 5.0 # save memory and faster speed for sparse feature, alias: is_sparse is_enable_sparse = true # when data is bigger than memory size, set this to true. otherwise set false will have faster speed # alias: two_round_loading, two_round use_two_round_loading = false # true if need to save data to binary file and application will auto load data from binary file next time # alias: is_save_binary, save_binary is_save_binary_file = false # output model file output_model = LightGBM_model.txt # support continuous train from trained gbdt model # input_model= trained_model.txt # output prediction file for predict task # output_result= prediction.txt # number of machines in distributed training, alias: num_machine num_machines = 1 # local listening port in distributed training, alias: local_port local_listen_port = 12400 # machines list file for distributed training, alias: mlist machine_list_file = mlist.txt # force splits # forced_splits = forced_splits.json ================================================ FILE: examples/lambdarank/README.md ================================================ LambdaRank Example ================== Here is an example for LightGBM to run LambdaRank task. ***You must follow the [installation instructions](https://lightgbm.readthedocs.io/en/latest/Installation-Guide.html) for the following commands to work. The `lightgbm` binary must be built and available at the root of this project.*** Training -------- Run the following command in this folder: ```bash "../../lightgbm" config=train.conf ``` Prediction ---------- You should finish training first. Run the following command in this folder: ```bash "../../lightgbm" config=predict.conf ``` Data Format ----------- To learn more about the query format used in this example, check out the [query data format](https://lightgbm.readthedocs.io/en/latest/Parameters.html#query-data). ================================================ FILE: examples/lambdarank/predict.conf ================================================ task = predict data = rank.test input_model= LightGBM_model.txt ================================================ FILE: examples/lambdarank/rank.test ================================================ 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 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271:0.35 276:0.19 282:0.88 285:0.62 297:0.36 299:0.88 300:0.25 2 1:0.74 6:0.85 7:0.81 8:0.64 12:0.20 17:0.47 20:0.78 21:0.40 27:1.00 28:0.59 32:0.78 34:0.78 36:0.40 37:0.62 39:0.59 74:0.58 76:0.85 77:0.70 78:0.92 81:0.68 91:0.53 100:0.87 111:0.90 114:0.82 121:0.97 126:0.54 135:0.66 145:0.50 150:0.73 152:0.77 155:0.67 161:0.88 167:0.61 169:0.83 175:0.91 176:0.73 178:0.55 181:0.86 186:0.92 187:0.39 189:0.88 192:0.59 195:0.62 201:0.74 204:0.89 208:0.64 215:0.67 216:0.27 222:0.35 230:0.87 233:0.76 235:0.60 238:0.84 241:0.54 244:0.83 253:0.65 257:0.84 259:0.21 261:0.81 267:0.23 271:0.35 277:0.87 282:0.86 290:0.82 297:0.36 2 1:0.74 6:0.89 7:0.78 8:0.84 9:0.80 12:0.20 17:0.28 20:0.96 21:0.47 27:0.14 28:0.59 30:0.95 34:0.19 36:0.76 37:0.67 39:0.59 41:0.97 43:0.35 55:0.45 66:0.64 69:0.57 74:0.63 78:0.93 81:0.66 83:0.77 91:0.94 96:0.85 98:0.13 100:0.94 108:0.44 111:0.93 114:0.80 120:0.86 122:0.67 123:0.42 126:0.54 127:0.08 129:0.05 135:0.44 140:0.93 145:0.58 146:0.21 147:0.26 149:0.27 150:0.78 151:0.59 152:0.89 153:0.48 154:0.26 155:0.67 159:0.10 161:0.88 162:0.49 163:0.90 164:0.93 165:0.80 167:0.86 169:0.92 172:0.16 173:0.49 175:0.75 176:0.73 177:0.05 178:0.52 179:0.11 181:0.86 186:0.93 189:0.90 190:0.77 191:0.96 192:0.59 201:0.74 202:0.96 208:0.64 212:0.95 215:0.63 216:0.53 220:0.91 222:0.35 230:0.81 233:0.76 235:0.68 238:0.89 241:0.91 242:0.82 243:0.69 244:0.61 247:0.12 248:0.74 253:0.84 255:0.79 256:0.94 257:0.65 260:0.86 261:0.93 265:0.14 266:0.12 267:0.95 268:0.93 271:0.35 276:0.19 277:0.69 285:0.62 290:0.80 297:0.36 300:0.08 0 1:0.74 12:0.20 17:0.53 21:0.54 27:0.45 28:0.59 30:0.38 34:0.85 36:0.20 37:0.50 39:0.59 43:0.29 55:0.71 66:0.29 69:0.70 70:0.63 74:0.77 81:0.59 91:0.18 98:0.44 108:0.77 114:0.77 122:0.57 123:0.76 124:0.73 126:0.54 127:0.32 129:0.59 133:0.64 135:0.65 145:0.49 146:0.26 147:0.32 149:0.38 150:0.67 154:0.75 155:0.67 158:0.86 159:0.45 161:0.88 163:0.90 167:0.52 172:0.27 173:0.69 176:0.73 177:0.38 178:0.55 179:0.73 181:0.86 187:0.68 192:0.59 201:0.74 208:0.64 212:0.42 215:0.58 216:0.77 222:0.35 235:0.74 241:0.24 242:0.29 243:0.34 245:0.56 247:0.60 253:0.67 254:0.74 259:0.21 265:0.46 266:0.54 267:0.28 271:0.35 276:0.39 279:0.86 283:0.67 290:0.77 297:0.36 300:0.43 0 1:0.74 8:0.58 9:0.80 12:0.20 17:0.38 20:0.79 21:0.13 27:0.35 28:0.59 30:0.45 34:0.34 36:0.93 37:0.49 39:0.59 43:0.29 55:0.45 66:0.77 69:0.63 70:0.33 74:0.73 78:0.86 81:0.86 91:0.25 96:0.74 98:0.30 100:0.73 104:0.90 106:0.81 108:0.48 111:0.83 114:0.92 117:0.86 120:0.76 122:0.41 123:0.46 126:0.54 127:0.12 129:0.05 135:0.78 140:0.80 146:0.52 147:0.58 149:0.19 150:0.88 151:0.69 152:0.77 153:0.77 154:0.79 155:0.67 158:0.87 159:0.18 161:0.88 162:0.86 164:0.69 167:0.50 169:0.72 172:0.37 173:0.48 176:0.73 177:0.38 179:0.25 181:0.86 186:0.86 187:0.87 190:0.77 192:0.59 201:0.74 202:0.54 206:0.81 208:0.64 212:0.52 215:0.84 216:0.84 222:0.35 232:0.98 235:0.22 241:0.15 242:0.24 243:0.79 247:0.47 248:0.65 253:0.79 254:0.74 255:0.79 256:0.58 259:0.21 260:0.77 261:0.80 265:0.32 266:0.27 267:0.44 268:0.63 271:0.35 276:0.30 279:0.86 283:0.82 285:0.62 290:0.92 297:0.36 300:0.25 2 1:0.74 7:0.81 9:0.80 12:0.20 17:0.38 21:0.40 27:1.00 28:0.59 32:0.78 34:0.52 36:0.34 37:0.62 39:0.59 74:0.58 76:0.85 77:0.70 81:0.68 91:0.54 96:0.70 114:0.82 120:0.57 121:0.97 126:0.54 135:0.51 140:0.45 145:0.50 150:0.73 151:0.54 153:0.48 155:0.67 161:0.88 162:0.86 164:0.57 167:0.71 175:0.91 176:0.73 181:0.86 187:0.39 192:0.59 195:0.64 201:0.74 202:0.36 204:0.89 208:0.64 215:0.67 216:0.27 220:0.91 222:0.35 230:0.87 233:0.76 235:0.60 241:0.69 244:0.83 248:0.53 253:0.66 255:0.79 256:0.45 257:0.84 259:0.21 260:0.57 267:0.36 268:0.46 271:0.35 277:0.87 282:0.86 285:0.62 290:0.82 297:0.36 0 1:0.74 9:0.80 11:0.57 12:0.20 17:0.76 21:0.13 27:0.44 28:0.59 30:0.70 32:0.78 34:0.76 36:0.39 37:0.86 39:0.59 43:0.84 55:0.45 66:0.83 69:0.63 70:0.62 74:0.96 77:0.99 81:0.84 83:0.77 85:0.85 91:0.60 96:0.89 98:0.86 101:0.76 106:0.81 108:0.67 114:0.92 117:0.86 120:0.82 122:0.57 123:0.64 124:0.39 126:0.54 127:0.60 129:0.59 133:0.47 135:0.79 140:0.45 145:0.76 146:0.31 147:0.38 149:0.76 150:0.90 151:0.75 153:0.84 154:0.80 155:0.67 159:0.66 161:0.88 162:0.83 164:0.83 165:0.88 167:0.53 172:0.90 173:0.54 176:0.73 177:0.38 179:0.27 181:0.86 187:0.21 192:0.59 195:0.84 201:0.74 202:0.75 206:0.81 208:0.64 212:0.59 215:0.84 216:0.62 220:0.74 222:0.35 232:0.87 235:0.31 241:0.27 242:0.54 243:0.14 245:0.86 247:0.60 248:0.88 253:0.93 255:0.79 256:0.79 259:0.21 260:0.82 265:0.86 266:0.63 267:0.69 268:0.81 271:0.35 276:0.84 282:0.86 285:0.62 290:0.92 297:0.36 300:0.84 2 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 4 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 34:0.71 36:0.85 39:0.51 43:0.58 45:0.87 46:0.96 62:0.96 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 3 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 2 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 99:0.95 101:0.65 107:0.94 108:0.59 110:0.94 114:0.92 120:0.65 122:0.65 123:0.56 124:0.48 125:0.99 126:0.51 127:0.87 129:0.05 133:0.43 135:0.26 137:0.92 139:0.72 140:0.45 146:0.66 147:0.42 149:0.65 150:0.58 151:0.76 153:0.48 154:0.58 155:0.64 159:0.36 160:0.96 162:0.86 164:0.56 165:0.81 170:0.82 172:0.45 173:0.89 174:0.83 175:0.75 176:0.70 177:0.38 179:0.83 190:0.77 192:0.97 196:0.90 197:0.88 201:0.63 202:0.36 208:0.61 212:0.23 215:0.84 216:0.10 220:0.62 222:0.35 235:0.31 239:0.87 241:0.86 242:0.24 243:0.33 244:0.61 245:0.60 247:0.74 248:0.70 253:0.75 255:0.73 256:0.45 257:0.84 260:0.66 265:0.79 266:0.38 267:0.23 268:0.46 271:0.38 276:0.42 277:0.69 284:0.88 285:0.60 289:0.95 290:0.92 297:0.36 300:0.33 3 1:0.65 7:0.69 10:0.84 11:0.52 12:0.86 17:0.47 18:0.83 21:0.22 27:0.72 29:0.86 30:0.60 32:0.72 34:0.69 36:0.65 39:0.51 43:0.77 44:0.92 45:0.88 46:0.98 64:0.77 66:0.90 69:0.17 70:0.51 71:0.57 74:0.83 76:0.85 77:0.89 81:0.68 83:0.69 86:0.88 91:0.46 97:0.99 98:0.95 99:0.95 101:0.65 107:0.96 108:0.14 110:0.96 114:0.88 122:0.65 123:0.13 125:0.88 126:0.51 127:0.68 129:0.05 135:0.54 139:0.90 145:0.98 146:0.74 147:0.78 149:0.85 150:0.56 154:0.69 155:0.50 158:0.81 159:0.31 160:0.88 165:0.81 170:0.82 172:0.73 173:0.38 174:0.79 176:0.70 177:0.88 178:0.55 179:0.60 192:0.49 195:0.57 197:0.83 201:0.63 208:0.61 212:0.90 215:0.79 216:0.09 219:0.94 222:0.35 230:0.71 233:0.76 235:0.42 239:0.83 241:0.84 242:0.24 243:0.91 244:0.61 247:0.84 253:0.69 259:0.21 265:0.95 266:0.27 267:0.23 271:0.38 276:0.59 279:0.81 282:0.80 283:0.67 289:0.95 290:0.88 292:0.88 297:0.36 300:0.43 1 10:0.97 11:0.52 12:0.86 17:0.38 18:0.70 21:0.78 27:0.54 29:0.86 30:0.13 34:0.49 36:0.64 37:0.72 39:0.51 43:0.19 45:0.81 46:0.93 55:0.52 66:0.12 69:0.73 70:0.98 71:0.57 74:0.66 86:0.82 91:0.44 98:0.25 101:0.65 107:0.94 108:0.56 110:0.94 122:0.94 123:0.84 124:0.94 125:0.88 127:0.75 129:0.05 133:0.94 135:0.81 139:0.78 146:0.28 147:0.05 149:0.19 154:0.71 159:0.84 160:0.88 170:0.82 172:0.48 173:0.52 174:0.94 177:0.05 178:0.55 179:0.44 187:0.39 197:0.94 212:0.42 216:0.32 222:0.35 235:0.12 239:0.96 241:0.07 242:0.14 243:0.08 245:0.69 247:0.90 253:0.49 254:0.74 257:0.93 259:0.21 265:0.15 266:0.92 267:0.44 271:0.38 276:0.73 277:0.87 289:0.91 300:0.94 0 1:0.67 10:0.98 11:0.81 12:0.86 17:0.55 18:0.79 21:0.22 27:0.64 29:0.86 30:0.84 32:0.80 34:0.34 36:0.90 37:0.49 39:0.51 43:0.88 44:0.93 45:0.85 46:0.94 60:0.87 64:0.77 66:0.51 69:0.51 70:0.37 71:0.57 74:0.85 75:0.99 76:0.85 77:0.89 81:0.79 83:0.91 85:0.85 86:0.90 91:0.35 98:0.38 101:0.87 102:0.98 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39:0.98 43:0.64 44:0.93 45:0.81 66:0.63 69:0.33 70:0.40 71:0.89 74:0.73 77:0.70 81:0.31 83:0.60 86:0.85 91:0.58 97:0.98 98:0.36 99:0.83 101:0.52 108:0.26 114:0.54 122:0.51 123:0.25 124:0.45 126:0.44 127:0.27 129:0.05 133:0.43 135:0.42 139:0.86 144:0.85 145:0.50 146:0.51 147:0.57 149:0.40 150:0.51 154:0.85 155:0.58 158:0.78 159:0.47 165:0.70 172:0.41 173:0.26 176:0.66 177:0.70 178:0.55 179:0.72 187:0.68 192:0.59 195:0.82 201:0.68 208:0.54 212:0.61 215:0.36 216:0.40 222:0.20 235:0.95 241:0.10 242:0.33 243:0.68 245:0.54 247:0.55 253:0.42 254:0.92 259:0.21 265:0.38 266:0.38 267:0.65 271:0.47 276:0.26 279:0.82 282:0.86 283:0.78 290:0.54 297:0.36 300:0.33 0 8:0.66 11:0.91 17:0.18 18:0.88 21:0.59 22:0.93 27:0.21 29:0.77 30:0.40 31:0.90 34:0.42 36:0.47 37:0.74 39:0.98 43:0.34 45:0.81 47:0.93 48:0.91 55:0.59 64:0.77 66:0.60 69:0.27 70:0.84 71:0.89 74:0.66 79:0.91 81:0.24 86:0.84 91:0.49 98:0.75 101:0.52 108:0.93 122:0.77 123:0.93 124:0.66 126:0.26 127:0.95 129:0.59 133:0.74 135:0.24 137:0.90 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21:0.40 27:0.71 28:0.89 29:0.71 30:0.55 32:0.67 34:0.78 36:0.61 37:0.56 39:0.51 43:0.90 44:0.88 45:0.93 55:0.52 66:0.99 69:0.19 70:0.55 71:0.94 74:0.89 76:0.94 77:0.99 81:0.59 83:0.60 85:0.72 86:0.74 91:0.37 96:0.73 97:0.94 98:0.96 101:0.87 104:0.79 106:0.81 108:0.15 114:0.72 117:0.86 120:0.61 123:0.15 126:0.44 127:0.73 129:0.05 135:0.49 138:0.95 139:0.76 140:0.45 145:0.77 146:0.92 147:0.94 149:0.88 150:0.60 151:0.65 153:0.48 154:0.89 155:0.58 158:0.75 159:0.55 161:0.96 162:0.86 164:0.68 167:0.50 172:0.97 173:0.23 176:0.60 177:0.70 179:0.17 181:0.48 187:0.21 190:0.95 192:0.57 195:0.72 201:0.63 202:0.53 204:0.85 206:0.81 208:0.54 212:0.93 215:0.63 216:0.05 220:0.91 222:0.35 230:0.71 232:0.70 233:0.66 235:0.68 241:0.15 242:0.70 243:0.99 247:0.76 248:0.65 253:0.75 255:0.71 256:0.58 259:0.21 260:0.61 265:0.96 266:0.27 267:0.18 268:0.62 271:0.54 276:0.93 279:0.81 282:0.75 283:0.82 285:0.56 286:0.99 290:0.72 297:0.36 298:0.99 300:0.70 0 1:0.69 7:0.69 8:0.72 11:0.45 12:0.51 17:0.90 18:0.74 21:0.13 27:0.32 28:0.89 29:0.71 30:0.76 32:0.67 34:0.28 36:0.34 37:0.64 39:0.51 43:0.40 44:0.88 45:0.83 55:0.52 64:0.77 66:0.93 69:0.15 70:0.42 71:0.94 74:0.77 76:0.94 77:0.99 81:0.83 83:0.60 86:0.75 91:0.37 98:0.93 99:0.83 101:0.52 108:0.12 114:0.88 117:0.86 122:0.77 123:0.12 126:0.54 127:0.59 129:0.05 135:0.51 139:0.76 145:0.97 146:0.73 147:0.77 149:0.42 150:0.86 154:0.54 155:0.56 159:0.29 161:0.96 165:0.70 167:0.52 172:0.82 173:0.37 176:0.63 177:0.70 178:0.55 179:0.43 181:0.48 187:0.21 191:0.70 192:0.55 195:0.60 201:0.68 204:0.80 208:0.64 212:0.88 215:0.84 216:0.16 222:0.35 230:0.71 232:0.86 233:0.76 235:0.52 241:0.27 242:0.70 243:0.94 244:0.83 247:0.79 253:0.68 254:0.74 259:0.21 265:0.93 266:0.27 267:0.23 271:0.54 276:0.72 282:0.75 290:0.88 297:0.36 300:0.55 1 1:0.59 6:0.82 7:0.66 8:0.70 11:0.45 12:0.51 17:0.98 18:0.73 20:0.86 21:0.77 27:0.52 28:0.89 29:0.71 30:0.27 32:0.64 34:0.42 36:0.32 39:0.51 43:0.57 45:0.73 48:0.91 55:0.52 64:0.77 66:0.77 69:0.73 70:0.83 71:0.94 74:0.67 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29:0.71 30:0.88 32:0.67 34:0.37 36:0.22 37:0.47 39:0.51 43:0.91 44:0.88 45:0.87 48:0.91 55:0.52 64:0.77 66:0.98 69:0.44 70:0.29 71:0.94 74:0.85 76:0.85 77:0.99 81:0.76 83:0.91 85:0.72 86:0.85 91:0.04 97:0.89 98:0.95 99:0.95 101:0.87 108:0.34 114:0.69 123:0.33 126:0.54 127:0.64 129:0.05 135:0.26 139:0.88 145:1.00 146:0.93 147:0.94 149:0.87 150:0.73 154:0.91 155:0.64 158:0.77 159:0.56 161:0.96 165:0.70 167:0.57 172:0.96 173:0.40 176:0.62 177:0.70 178:0.55 179:0.18 181:0.48 187:0.39 192:0.58 195:0.73 201:0.68 204:0.85 208:0.64 212:0.93 215:0.55 216:0.36 219:0.91 222:0.35 230:0.73 233:0.66 235:0.97 241:0.47 242:0.53 243:0.99 247:0.88 253:0.64 265:0.95 266:0.27 267:0.19 271:0.54 276:0.92 279:0.80 281:0.86 282:0.75 283:0.82 290:0.69 292:0.95 297:0.36 300:0.70 0 12:0.51 17:0.38 21:0.22 27:0.45 28:0.83 30:0.87 32:0.72 34:0.68 36:0.17 37:0.50 39:0.66 43:0.30 55:0.71 66:0.86 69:0.68 70:0.26 74:0.44 81:0.60 91:0.18 98:0.88 108:0.52 123:0.50 124:0.38 126:0.32 127:0.49 129:0.05 133:0.39 135:0.61 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238:0.86 241:0.83 242:0.63 243:0.37 245:0.46 246:0.97 247:0.30 248:0.80 253:0.85 255:0.79 256:0.64 259:0.21 260:0.74 261:0.77 265:0.50 266:0.27 267:0.59 268:0.67 271:0.35 276:0.23 279:0.86 282:0.88 283:0.71 285:0.62 287:0.98 289:0.98 290:0.95 297:0.36 299:0.88 300:0.18 4 1:0.74 6:0.98 8:0.97 9:0.80 11:0.78 12:0.37 17:0.12 20:0.99 27:0.07 28:0.56 30:0.70 34:0.52 36:0.46 37:0.99 39:0.79 41:0.99 43:0.95 46:0.99 60:0.95 66:0.88 69:0.15 70:0.39 74:0.87 78:0.89 81:0.95 83:0.90 85:0.93 91:0.98 96:0.99 98:0.98 100:0.99 101:0.85 104:0.58 107:0.99 108:0.12 110:0.99 111:0.98 114:0.98 120:0.97 122:0.57 123:0.12 125:0.98 126:0.54 127:0.86 129:0.05 131:0.95 135:0.47 138:0.99 140:0.45 144:0.76 146:0.76 147:0.80 149:0.91 150:0.98 151:1.00 152:0.99 153:0.98 154:0.95 155:0.67 157:0.91 158:0.92 159:0.32 160:1.00 161:0.45 162:0.73 164:0.96 165:0.92 166:0.91 167:0.97 169:0.99 172:0.65 173:0.37 176:0.73 177:0.38 179:0.72 181:0.96 182:0.88 186:0.89 189:0.99 191:0.96 192:0.59 201:0.74 202:0.93 208:0.64 212:0.35 215:0.96 216:0.59 222:0.60 227:0.98 229:0.93 231:0.87 232:0.77 235:0.05 238:0.99 241:0.90 242:0.62 243:0.88 246:0.97 247:0.39 248:0.99 253:0.99 255:0.79 256:0.94 259:0.21 260:0.97 261:1.00 265:0.98 266:0.38 267:0.79 268:0.95 271:0.35 276:0.52 279:0.86 283:0.82 285:0.62 287:0.98 289:0.99 290:0.98 297:0.36 300:0.33 2 1:0.74 6:0.84 8:0.88 9:0.80 11:0.78 12:0.37 17:0.45 20:0.85 21:0.05 27:0.50 28:0.56 30:0.61 34:0.39 36:0.62 37:0.37 39:0.79 41:0.90 43:0.84 46:1.00 60:0.87 66:0.89 69:0.55 70:0.63 74:0.88 78:0.75 81:0.91 83:0.86 85:0.93 91:0.83 96:0.85 98:0.87 100:0.78 101:0.85 104:0.58 107:1.00 108:0.42 110:0.99 111:0.74 114:0.95 120:0.76 122:0.43 123:0.40 125:0.99 126:0.54 127:0.40 129:0.05 131:0.95 135:0.37 140:0.45 144:0.76 146:0.63 147:0.68 149:0.91 150:0.96 151:0.89 152:0.95 153:0.87 154:0.81 155:0.67 157:0.91 158:0.87 159:0.23 160:0.98 161:0.45 162:0.68 164:0.75 165:0.90 166:0.91 167:0.95 169:0.77 172:0.68 173:0.76 176:0.73 177:0.38 179:0.58 181:0.96 182:0.87 186:0.76 192:0.59 201:0.74 202:0.67 208:0.64 212:0.91 215:0.91 216:0.21 220:0.62 222:0.60 227:0.98 229:0.93 231:0.87 232:0.77 235:0.12 241:0.84 242:0.57 243:0.89 246:0.97 247:0.47 248:0.83 253:0.89 255:0.79 256:0.64 259:0.21 260:0.77 261:0.81 265:0.87 266:0.23 267:0.28 268:0.69 271:0.35 276:0.56 279:0.86 283:0.71 285:0.62 287:0.98 289:0.99 290:0.95 297:0.36 300:0.55 2 1:0.74 6:0.84 8:0.70 9:0.80 11:0.78 12:0.37 17:0.66 20:0.90 21:0.05 27:0.50 28:0.56 30:0.61 34:0.44 36:0.62 37:0.37 39:0.79 41:0.82 43:0.84 46:1.00 66:0.89 69:0.55 70:0.63 74:0.86 78:0.76 81:0.91 83:0.79 85:0.93 91:0.80 96:0.78 98:0.87 100:0.79 101:0.85 104:0.58 107:1.00 108:0.42 110:0.99 111:0.76 114:0.95 120:0.66 122:0.43 123:0.40 125:1.00 126:0.54 127:0.40 129:0.05 131:0.94 135:0.37 140:0.45 144:0.76 146:0.63 147:0.68 149:0.91 150:0.96 151:0.79 152:0.95 153:0.69 154:0.81 155:0.67 157:0.91 159:0.23 160:0.96 161:0.45 162:0.86 164:0.62 165:0.90 166:0.91 167:0.93 169:0.77 172:0.68 173:0.76 176:0.73 177:0.38 179:0.58 181:0.96 182:0.87 186:0.77 187:0.21 189:0.88 192:0.59 201:0.74 202:0.46 208:0.64 212:0.91 215:0.91 216:0.21 220:0.62 222:0.60 227:0.98 229:0.93 231:0.87 232:0.77 235:0.12 238:0.89 241:0.80 242:0.57 243:0.89 246:0.97 247:0.47 248:0.72 253:0.84 255:0.79 256:0.45 259:0.21 260:0.67 261:0.81 265:0.87 266:0.23 267:0.44 268:0.55 271:0.35 276:0.56 285:0.62 287:0.98 289:0.98 290:0.95 297:0.36 300:0.55 0 1:0.74 9:0.80 11:0.78 12:0.37 17:0.40 21:0.64 27:0.45 28:0.56 30:0.58 32:0.80 34:0.68 36:0.19 37:0.50 39:0.79 41:0.82 43:0.82 46:0.95 60:0.87 66:0.18 69:0.70 70:0.60 74:0.78 77:0.70 81:0.57 83:0.83 85:0.85 91:0.23 96:0.68 98:0.19 101:0.77 107:0.98 108:0.53 110:0.98 114:0.72 120:0.54 122:0.43 123:0.81 124:0.73 125:0.97 126:0.54 127:0.36 129:0.81 131:0.94 133:0.67 135:0.63 140:0.45 144:0.76 145:0.50 146:0.23 147:0.29 149:0.75 150:0.72 151:0.49 153:0.48 154:0.77 155:0.67 157:0.85 158:0.87 159:0.48 160:0.96 161:0.45 162:0.86 163:0.88 164:0.57 165:0.70 166:0.91 167:0.67 172:0.21 173:0.68 176:0.73 177:0.38 179:0.82 181:0.96 182:0.87 187:0.68 192:0.59 195:0.73 201:0.74 202:0.36 208:0.64 212:0.37 215:0.53 216:0.77 220:0.97 222:0.60 227:0.98 229:0.93 231:0.87 235:0.80 241:0.35 242:0.58 243:0.46 245:0.51 246:0.97 247:0.35 248:0.49 253:0.66 255:0.79 256:0.45 259:0.21 260:0.55 265:0.17 266:0.47 267:0.36 268:0.46 271:0.35 276:0.28 279:0.86 282:0.88 283:0.71 285:0.62 287:0.98 289:0.98 290:0.72 297:0.36 299:0.88 300:0.43 2 1:0.74 7:0.81 8:0.84 9:0.80 11:0.78 12:0.37 17:0.62 20:0.75 21:0.30 27:0.50 28:0.56 30:0.45 32:0.80 34:0.64 36:0.68 37:0.60 39:0.79 41:0.88 43:0.88 46:0.98 60:0.95 66:0.36 69:0.53 70:0.72 74:0.97 77:0.89 78:0.83 81:0.78 83:0.84 85:0.99 91:0.20 96:0.76 98:0.66 100:0.86 101:0.84 104:0.84 106:0.81 107:1.00 108:0.95 110:0.99 111:0.83 114:0.86 117:0.86 120:0.64 121:0.90 122:0.43 123:0.94 124:0.90 125:0.99 126:0.54 127:0.87 129:0.95 131:0.94 133:0.91 135:0.42 140:0.45 144:0.76 145:0.65 146:0.85 147:0.88 149:0.98 150:0.86 151:0.72 152:0.74 153:0.72 154:0.86 155:0.67 157:0.94 158:0.92 159:0.92 160:0.97 161:0.45 162:0.86 164:0.69 165:0.84 166:0.91 167:0.55 169:0.84 172:0.93 173:0.18 176:0.73 177:0.38 179:0.16 181:0.96 182:0.87 186:0.83 187:0.39 192:0.59 195:0.98 201:0.74 202:0.53 204:0.89 206:0.81 208:0.64 212:0.55 215:0.72 216:0.86 220:0.74 222:0.60 227:0.98 229:0.93 230:0.87 231:0.87 232:0.89 233:0.76 235:0.42 241:0.01 242:0.51 243:0.32 245:0.95 246:0.97 247:0.67 248:0.69 253:0.84 255:0.79 256:0.58 259:0.21 260:0.65 261:0.75 265:0.66 266:0.91 267:0.44 268:0.62 271:0.35 276:0.95 279:0.86 282:0.88 283:0.82 285:0.62 287:0.98 289:0.98 290:0.86 297:0.36 299:0.97 300:0.70 0 1:0.74 9:0.80 11:0.78 12:0.37 17:0.22 27:0.32 28:0.56 30:0.95 32:0.80 34:0.66 36:0.52 37:0.67 39:0.79 41:0.88 43:0.90 46:0.99 66:0.54 69:0.44 74:0.58 77:0.70 81:0.95 83:0.81 85:0.72 91:0.63 96:0.82 98:0.19 101:0.77 104:0.80 106:0.81 107:0.93 108:0.34 110:0.93 114:0.98 120:0.71 123:0.33 125:0.99 126:0.54 127:0.10 129:0.05 131:0.94 135:0.37 140:0.45 144:0.76 145:0.39 146:0.13 147:0.18 149:0.51 150:0.98 151:0.87 153:0.48 154:0.89 155:0.67 157:0.79 159:0.08 160:0.97 161:0.45 162:0.86 164:0.67 165:0.92 166:0.91 167:0.75 172:0.10 173:0.91 176:0.73 177:0.38 179:0.28 181:0.96 182:0.87 187:0.39 192:0.59 195:0.53 201:0.74 202:0.50 206:0.81 208:0.64 215:0.96 216:0.47 222:0.60 227:0.98 229:0.93 231:0.87 235:0.05 241:0.63 243:0.63 246:0.97 248:0.79 253:0.83 255:0.79 256:0.58 259:0.21 260:0.72 265:0.21 267:0.91 268:0.59 271:0.35 282:0.88 285:0.62 287:0.98 289:0.98 290:0.98 297:0.36 299:0.88 300:0.08 3 1:0.74 6:0.98 8:0.98 9:0.80 11:0.78 12:0.37 17:0.67 20:0.81 27:0.07 28:0.56 30:0.66 34:0.74 36:0.40 37:0.99 39:0.79 41:0.98 43:0.87 46:0.98 60:0.87 66:0.36 69:0.44 70:0.53 74:0.83 78:0.88 81:0.95 83:0.89 85:0.90 91:0.90 96:0.96 98:0.62 100:0.98 101:0.83 104:0.58 107:0.99 108:0.65 110:0.98 111:0.96 114:0.98 120:0.94 122:0.57 123:0.63 124:0.60 125:0.99 126:0.54 127:0.66 129:0.59 131:0.95 133:0.47 135:0.47 140:0.45 144:0.76 146:0.45 147:0.51 149:0.84 150:0.98 151:0.99 152:0.99 153:0.97 154:0.85 155:0.67 157:0.90 158:0.92 159:0.35 160:0.99 161:0.45 162:0.80 164:0.91 165:0.92 166:0.91 167:0.90 169:0.99 172:0.37 173:0.56 176:0.73 177:0.38 179:0.78 181:0.96 182:0.88 186:0.88 187:0.39 189:0.99 191:0.94 192:0.59 201:0.74 202:0.85 208:0.64 212:0.22 215:0.96 216:0.59 222:0.60 227:0.98 229:0.93 231:0.87 232:0.77 235:0.05 238:0.99 241:0.77 242:0.61 243:0.48 245:0.67 246:0.97 247:0.39 248:0.96 253:0.97 255:0.79 256:0.88 259:0.21 260:0.94 261:1.00 265:0.63 266:0.54 267:0.19 268:0.89 271:0.35 276:0.43 279:0.86 283:0.82 285:0.62 287:0.98 289:0.99 290:0.98 297:0.36 300:0.43 2 6:0.78 8:0.58 9:0.80 12:0.37 17:0.09 20:0.86 21:0.05 25:0.88 27:0.51 28:0.56 30:0.95 34:0.80 36:0.92 37:0.23 39:0.79 41:0.82 43:0.45 46:0.88 55:0.71 66:0.77 69:0.05 70:0.13 78:0.75 81:0.88 83:0.77 91:0.83 96:0.84 98:0.96 100:0.77 104:0.58 107:0.95 108:0.05 110:0.96 111:0.74 120:0.89 123:0.05 125:0.96 126:0.32 127:0.69 129:0.05 131:0.94 135:0.34 140:0.93 144:0.76 146:0.61 147:0.66 149:0.43 150:0.74 151:0.92 152:0.81 153:0.85 154:0.34 155:0.67 157:0.61 159:0.22 160:0.96 161:0.45 162:0.73 164:0.85 166:0.84 167:0.97 169:0.75 172:0.37 173:0.35 175:0.75 177:0.05 179:0.93 181:0.96 182:0.87 186:0.76 189:0.81 191:0.92 192:0.59 202:0.78 208:0.64 212:0.52 215:0.91 216:0.23 220:0.62 222:0.60 227:0.98 229:0.93 231:0.87 235:0.05 238:0.83 241:0.92 242:0.82 243:0.79 244:0.61 246:0.61 247:0.12 248:0.81 253:0.77 255:0.79 256:0.79 257:0.65 259:0.21 260:0.89 261:0.77 265:0.96 266:0.27 267:0.82 268:0.82 271:0.35 276:0.33 277:0.69 283:0.71 285:0.62 287:0.98 289:0.98 297:0.36 300:0.13 2 1:0.74 6:0.82 8:0.66 9:0.80 11:0.78 12:0.37 17:0.88 20:0.88 21:0.05 27:0.71 28:0.56 30:0.89 32:0.80 34:0.47 36:0.51 37:0.65 39:0.79 41:0.82 43:0.79 46:0.97 66:0.47 69:0.76 70:0.20 74:0.65 77:0.70 78:0.74 81:0.91 83:0.79 85:0.85 91:0.83 96:0.77 98:0.48 100:0.78 101:0.80 104:0.58 107:0.97 108:0.70 110:0.97 111:0.74 114:0.95 120:0.65 122:0.43 123:0.69 124:0.52 125:1.00 126:0.54 127:0.29 129:0.59 131:0.94 133:0.47 135:0.90 140:0.45 144:0.76 145:0.69 146:0.13 147:0.18 149:0.68 150:0.96 151:0.77 152:0.87 153:0.48 154:0.72 155:0.67 157:0.86 159:0.28 160:0.96 161:0.45 162:0.86 163:0.81 164:0.57 165:0.90 166:0.91 167:0.92 169:0.75 172:0.21 173:0.87 176:0.73 177:0.38 179:0.88 181:0.96 182:0.87 186:0.75 187:0.21 192:0.59 195:0.58 201:0.74 202:0.36 208:0.64 212:0.30 215:0.91 216:0.21 220:0.62 222:0.60 227:0.98 229:0.93 231:0.87 232:0.77 235:0.12 241:0.79 242:0.63 243:0.37 245:0.46 246:0.97 247:0.30 248:0.71 253:0.80 255:0.79 256:0.45 259:0.21 260:0.66 261:0.77 265:0.50 266:0.27 267:0.65 268:0.46 271:0.35 276:0.23 282:0.88 285:0.62 287:0.98 289:0.98 290:0.95 297:0.36 299:0.88 300:0.18 1 1:0.74 6:0.87 7:0.81 8:0.78 9:0.80 11:0.78 12:0.37 17:0.45 20:0.83 21:0.30 27:0.12 28:0.56 30:0.33 34:0.82 36:0.39 37:0.81 39:0.79 41:0.93 43:0.59 46:0.95 55:0.59 60:0.87 66:0.44 69:0.93 70:0.64 74:0.86 78:0.75 81:0.78 83:0.88 85:0.91 91:0.33 96:0.92 98:0.43 100:0.78 101:0.77 107:0.99 108:0.89 110:0.98 111:0.75 114:0.86 120:0.86 121:0.90 122:0.67 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26:0.94 27:0.45 28:0.91 29:0.91 30:0.45 34:0.83 36:0.29 37:0.50 39:0.29 43:0.76 58:0.93 66:0.69 69:0.82 70:0.25 71:0.57 74:0.49 85:0.72 86:0.72 91:0.11 98:0.15 101:0.87 108:0.67 122:0.57 123:0.65 127:0.09 129:0.05 135:0.88 139:0.70 141:0.91 145:0.67 146:0.22 147:0.28 149:0.50 154:0.67 159:0.10 161:0.87 167:0.57 172:0.21 173:0.83 177:0.70 178:0.55 179:0.12 181:0.55 187:0.68 199:0.94 212:0.61 216:0.77 222:0.60 223:0.93 224:0.93 234:0.91 235:0.98 241:0.35 242:0.63 243:0.73 247:0.30 253:0.40 259:0.21 265:0.16 266:0.17 267:0.28 271:0.38 274:0.91 276:0.20 300:0.18 2 1:0.66 6:0.90 7:0.81 8:0.80 9:0.79 11:0.57 12:0.37 17:0.57 18:0.87 20:0.93 21:0.30 26:0.99 27:0.35 28:0.91 29:0.91 30:0.45 31:0.95 32:0.80 34:0.52 36:0.43 37:0.43 39:0.29 41:0.92 43:0.95 44:0.93 55:0.45 58:0.99 60:0.95 64:0.77 66:0.74 69:0.19 70:0.69 71:0.57 74:0.90 77:0.89 78:0.96 79:0.95 81:0.79 83:0.60 85:0.93 86:0.96 91:0.62 96:0.87 98:0.81 100:0.96 101:0.87 104:0.54 108:0.15 111:0.95 114:0.86 120:0.78 121:0.90 122:0.57 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58:0.98 60:0.99 64:0.77 66:0.51 69:0.54 70:0.62 71:0.57 74:0.98 77:0.89 79:0.90 81:0.85 83:0.60 85:0.99 86:0.99 91:0.10 96:0.74 98:0.73 101:0.87 104:0.95 106:0.81 108:0.80 114:0.86 117:0.86 120:0.62 121:0.99 122:0.57 123:0.79 124:0.74 126:0.54 127:0.76 129:0.89 133:0.78 135:0.77 137:0.90 139:1.00 140:0.45 141:1.00 145:0.87 146:0.75 147:0.79 149:1.00 150:0.85 151:0.68 153:0.48 154:0.86 155:0.65 158:0.86 159:0.82 161:0.87 162:0.86 164:0.57 165:0.93 167:0.51 172:0.96 173:0.32 176:0.73 177:0.70 179:0.14 181:0.55 187:0.21 192:0.87 195:0.93 196:0.95 199:0.99 201:0.67 202:0.36 204:0.85 206:0.81 208:0.64 212:0.86 215:0.72 216:0.89 219:0.95 220:0.74 222:0.60 223:0.99 224:0.99 230:0.87 232:0.96 233:0.76 234:0.98 235:0.95 241:0.07 242:0.37 243:0.31 245:0.98 247:0.82 248:0.64 253:0.82 255:0.73 256:0.45 259:0.21 260:0.63 262:0.93 265:0.73 266:0.54 267:0.59 268:0.46 271:0.38 274:0.97 276:0.95 279:0.82 281:0.91 282:0.88 283:0.67 284:0.89 285:0.62 290:0.86 292:0.88 297:0.36 299:0.97 300:0.84 2 1:0.66 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234:0.98 235:0.80 241:0.35 242:0.24 243:0.86 245:0.92 247:0.93 248:0.77 253:0.92 255:0.79 256:0.58 259:0.21 260:0.79 262:0.93 265:0.89 266:0.75 267:0.84 268:0.59 271:0.38 274:0.97 276:0.95 279:0.82 281:0.91 283:0.82 284:0.89 285:0.62 290:0.80 292:0.95 297:0.36 300:0.84 2 1:0.68 6:0.92 7:0.81 8:0.76 9:0.79 11:0.75 12:0.37 17:0.66 18:0.94 20:0.87 21:0.13 26:0.99 27:0.72 28:0.91 29:0.91 30:0.31 31:0.94 32:0.80 34:0.79 36:0.84 39:0.29 41:0.82 43:0.95 44:0.93 45:0.73 58:0.98 60:0.87 64:0.77 66:0.47 69:0.15 70:0.85 71:0.57 74:0.94 77:0.89 78:0.92 79:0.93 81:0.82 83:0.60 85:0.95 86:0.98 91:0.44 96:0.80 98:0.64 100:0.88 101:0.87 104:0.58 108:0.12 111:0.90 114:0.92 120:0.69 121:0.90 122:0.57 123:0.12 124:0.75 126:0.54 127:0.88 129:0.81 133:0.74 135:0.58 137:0.94 139:0.98 140:0.45 141:1.00 145:0.69 146:0.82 147:0.85 149:0.97 150:0.78 151:0.86 152:0.82 153:0.48 154:0.95 155:0.66 158:0.92 159:0.71 161:0.87 162:0.86 164:0.57 165:0.93 167:0.85 169:0.86 172:0.80 173:0.15 176:0.73 177:0.70 179:0.33 181:0.55 186:0.92 189:0.93 192:0.87 195:0.82 196:0.98 199:0.98 201:0.65 202:0.36 204:0.84 208:0.64 212:0.58 215:0.84 216:0.04 219:0.95 222:0.60 223:0.99 224:0.99 230:0.87 232:0.77 233:0.76 234:0.99 235:0.52 238:0.83 241:0.79 242:0.32 243:0.59 245:0.88 247:0.88 248:0.77 253:0.88 255:0.78 256:0.45 259:0.21 260:0.70 261:0.87 265:0.65 266:0.54 267:0.23 268:0.46 271:0.38 274:0.97 276:0.82 279:0.80 281:0.91 282:0.88 283:0.82 284:0.89 285:0.62 290:0.92 292:0.95 297:0.36 299:0.97 300:0.70 0 1:0.67 6:0.87 7:0.81 8:0.74 9:0.80 11:0.75 12:0.37 17:0.57 18:0.80 20:0.98 21:0.22 22:0.93 26:0.99 27:0.37 28:0.91 29:0.91 30:0.26 31:0.98 32:0.80 34:0.45 36:0.98 37:0.62 39:0.29 41:0.94 43:0.89 44:0.93 45:0.89 47:0.93 48:0.91 58:0.99 60:0.97 64:0.77 66:0.96 69:0.47 70:0.77 71:0.57 74:0.87 77:0.70 78:0.92 79:0.98 81:0.81 83:0.91 85:0.85 86:0.88 91:0.54 96:0.95 97:0.89 98:0.97 100:0.89 101:0.87 104:0.94 106:0.81 108:0.36 111:0.90 114:0.90 117:0.86 120:0.90 121:0.90 122:0.75 123:0.34 126:0.54 127:0.76 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64:0.77 66:0.85 69:0.23 70:0.89 71:0.57 74:0.94 78:0.92 79:0.98 81:0.76 83:0.60 85:0.97 86:0.98 91:0.46 96:0.93 97:0.89 98:0.89 100:0.92 101:0.97 104:0.84 106:0.81 108:0.18 111:0.91 114:0.80 117:0.86 120:0.89 121:0.97 122:0.57 123:0.18 124:0.39 126:0.54 127:0.73 129:0.59 133:0.61 135:0.47 137:0.98 139:0.98 140:0.45 141:1.00 146:0.99 147:0.99 149:0.96 150:0.60 151:0.96 152:0.90 153:0.88 154:0.95 155:0.67 158:0.92 159:0.87 161:0.87 162:0.70 164:0.85 165:0.93 167:0.57 169:0.90 172:0.97 173:0.10 176:0.73 177:0.70 179:0.15 181:0.55 186:0.92 187:0.39 189:0.92 192:0.87 196:0.99 199:0.99 201:0.62 202:0.78 204:0.85 206:0.81 208:0.64 212:0.67 215:0.63 216:0.95 219:0.95 220:0.74 222:0.60 223:0.99 224:0.99 230:0.87 232:0.89 233:0.76 234:0.98 235:0.80 238:0.87 241:0.32 242:0.24 243:0.86 245:0.92 247:0.93 248:0.92 253:0.96 255:0.94 256:0.79 259:0.21 260:0.89 261:0.91 262:0.93 265:0.89 266:0.75 267:0.88 268:0.81 271:0.38 274:0.99 276:0.94 279:0.82 281:0.91 283:0.82 284:0.97 285:0.62 290:0.80 292:0.95 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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 2 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 ================================================ FILE: examples/lambdarank/rank.train.query ================================================ 1 13 5 8 19 12 18 5 14 13 8 9 16 11 21 14 21 9 14 11 20 18 13 20 22 22 13 17 10 13 12 13 13 23 18 13 20 12 22 14 13 23 13 14 14 5 13 15 14 14 16 16 15 21 22 10 22 18 25 16 12 12 15 15 25 13 9 12 8 16 25 19 24 12 16 10 16 9 17 15 7 9 15 14 16 17 8 17 12 18 23 10 12 12 4 14 12 15 27 16 20 13 19 13 17 17 16 12 15 14 14 19 12 23 18 16 9 23 11 15 8 10 10 16 11 15 22 16 17 23 16 22 17 14 12 14 20 15 17 15 15 22 9 21 9 17 16 15 13 13 15 14 18 21 14 17 15 14 16 12 17 19 16 11 18 11 13 14 9 16 15 16 25 9 13 22 16 18 20 14 11 9 16 19 19 11 11 13 14 14 13 16 6 21 16 12 16 11 24 12 10 ================================================ FILE: examples/lambdarank/train.conf ================================================ # task type, support train and predict task = train # boosting type, support gbdt for now, alias: boosting, boost boosting_type = gbdt # application type, support following application # regression , regression task # binary , binary classification task # lambdarank , LambdaRank task # alias: application, app objective = lambdarank # eval metrics, support multi metric, delimited by ',' , support following metrics # l1 # l2 , default metric for regression # ndcg , default metric for lambdarank # auc # binary_logloss , default metric for binary # binary_error metric = ndcg # evaluation position for ndcg metric, alias : ndcg_at ndcg_eval_at = 1,3,5 # frequency for metric output metric_freq = 1 # true if need output metric for training data, alias: tranining_metric, train_metric is_training_metric = true # column in data to use as label label_column = 0 # number of bins for feature bucket, 255 is a recommend setting, it can save memories, and also has good accuracy. max_bin = 255 # training data # if existing weight file, should name to "rank.train.weight" # if existing query file, should name to "rank.train.query" # alias: train_data, train data = rank.train # validation data, support multi validation data, separated by ',' # if existing weight file, should name to "rank.test.weight" # if existing query file, should name to "rank.test.query" # alias: valid, test, test_data, valid_data = rank.test # number of trees(iterations), alias: num_tree, num_iteration, num_iterations, num_round, num_rounds num_trees = 100 # shrinkage rate , alias: shrinkage_rate learning_rate = 0.1 # number of leaves for one tree, alias: num_leaf num_leaves = 31 # type of tree learner, support following types: # serial , single machine version # feature , use feature parallel to train # data , use data parallel to train # voting , use voting based parallel to train # alias: tree tree_learner = serial # number of threads for multi-threading. One thread will use one CPU, default is set to #cpu. # num_threads = 8 # feature sub-sample, will random select 80% feature to train on each iteration # alias: sub_feature feature_fraction = 1.0 # Support bagging (data sub-sample), will perform bagging every 5 iterations bagging_freq = 1 # Bagging fraction, will random select 80% data on bagging # alias: sub_row bagging_fraction = 0.9 # minimal number data for one leaf, use this to deal with over-fit # alias : min_data_per_leaf, min_data min_data_in_leaf = 50 # minimal sum Hessians for one leaf, use this to deal with over-fit min_sum_hessian_in_leaf = 5.0 # save memory and faster speed for sparse feature, alias: is_sparse is_enable_sparse = true # when data is bigger than memory size, set this to true. otherwise set false will have faster speed # alias: two_round_loading, two_round use_two_round_loading = false # true if need to save data to binary file and application will auto load data from binary file next time # alias: is_save_binary, save_binary is_save_binary_file = false # output model file output_model = LightGBM_model.txt # support continuous train from trained gbdt model # input_model= trained_model.txt # output prediction file for predict task # output_result= prediction.txt # number of machines in distributed training, alias: num_machine num_machines = 1 # local listening port in distributed training, alias: local_port local_listen_port = 12400 # machines list file for distributed training, alias: mlist machine_list_file = mlist.txt ================================================ FILE: examples/multiclass_classification/README.md ================================================ Multiclass Classification Example ================================= Here is an example for LightGBM to run multiclass classification task. ***You must follow the [installation instructions](https://lightgbm.readthedocs.io/en/latest/Installation-Guide.html) for the following commands to work. The `lightgbm` binary must be built and available at the root of this project.*** Training -------- Run the following command in this folder: ```bash "../../lightgbm" config=train.conf ``` Prediction ---------- You should finish training first. Run the following command in this folder: ```bash "../../lightgbm" config=predict.conf ``` ================================================ FILE: examples/multiclass_classification/multiclass.test ================================================ 1 0.109 1.261 -0.274 2.605 0.472 -0.429 -0.983 1.000 -0.095 -1.219 -0.369 -0.312 -0.840 1.281 -0.618 -0.532 -0.132 0.443 0.028 2.201 0.044 1.671 0.660 -0.114 0.574 0.276 0.680 -0.670 4 -0.294 -0.659 -0.569 2.351 0.775 -0.339 -2.436 -1.704 0.086 -0.291 -1.152 -0.132 -0.745 -0.641 -0.173 -1.016 -1.386 -1.354 -0.405 -1.583 2.190 0.933 0.720 -0.885 -0.848 -1.416 1.838 1.343 2 0.194 1.849 -1.486 0.190 0.331 -1.080 -2.175 1.073 0.371 -1.200 0.022 0.784 -1.400 -0.130 -1.631 -0.166 0.156 0.944 0.360 -0.081 -0.736 0.534 -1.664 0.267 -0.813 0.871 0.924 -0.935 0 0.261 -0.391 -0.138 -0.822 0.774 -0.146 -0.738 -0.121 1.735 0.335 1.076 -1.612 -0.197 -1.000 -0.519 -0.767 1.289 0.717 -0.755 -0.465 0.363 0.891 1.041 0.635 1.814 0.427 -0.445 -0.815 2 0.547 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-0.678 -1.534 -1.162 -0.096 -1.220 -0.172 -0.655 -0.001 -0.510 -0.036 0.603 -0.958 0.281 -0.852 -0.006 -0.082 0.356 0.185 2 -1.054 -0.648 -0.313 -0.185 -2.438 -0.753 -0.554 1.891 0.227 -0.798 -0.439 0.425 -0.378 -0.585 -0.553 0.480 -1.382 -1.087 -0.928 -1.104 -0.824 0.897 1.036 0.272 0.215 -0.670 -2.081 0.144 3 -0.366 -0.458 0.955 0.153 -0.653 0.491 0.768 -0.240 -0.212 1.715 -0.821 -0.540 -0.463 1.699 -1.977 0.822 -0.029 1.813 0.356 -2.151 1.611 -0.236 -2.041 -0.405 0.036 -1.049 -1.531 0.297 1 -0.667 0.409 0.347 -0.646 0.840 0.636 0.611 1.529 0.616 -0.121 -0.084 -1.738 -0.779 1.293 1.202 0.457 0.118 -1.107 -1.467 0.129 0.376 -1.018 -0.107 1.273 0.387 0.097 -0.726 1.867 3 0.288 -0.115 -1.091 -1.181 0.583 0.659 1.190 -0.169 -0.471 -1.831 -0.013 -1.395 -0.574 -0.336 -1.027 1.381 2.484 1.461 1.420 0.475 0.957 0.803 -0.412 1.226 -0.567 1.180 0.867 -0.181 3 1.227 -0.900 0.683 -0.624 -2.269 0.932 0.772 1.485 -1.463 0.164 -0.223 1.144 -1.389 -0.253 0.094 0.742 0.415 -0.032 -0.311 -0.089 0.752 -0.826 -0.234 -0.234 -2.086 -1.956 -1.491 -0.213 0 0.491 -0.126 0.420 -0.837 -0.604 -0.734 -0.565 1.287 0.726 -1.077 0.437 -0.391 -0.682 -1.486 0.972 -1.364 -0.052 -0.946 0.776 -0.334 -0.134 -0.101 0.303 0.013 -0.043 -0.518 -1.575 -1.119 2 -0.316 1.946 0.839 -1.289 -1.518 1.871 0.366 0.986 1.648 -0.735 0.454 0.343 -0.768 -0.742 0.548 -1.308 -0.535 0.398 0.057 1.000 0.078 0.469 -0.653 0.051 0.513 -0.186 -1.679 1.098 2 -0.010 -0.555 0.570 2.541 -1.295 1.338 -0.411 -0.683 0.818 -0.256 -1.286 -0.024 0.137 -0.040 0.086 -0.396 0.922 0.266 -1.657 -1.771 1.562 0.832 0.263 -0.503 0.389 0.995 0.194 0.654 ================================================ FILE: examples/multiclass_classification/predict.conf ================================================ task = predict data = multiclass.test input_model= LightGBM_model.txt ================================================ FILE: examples/multiclass_classification/train.conf ================================================ # task type, support train and predict task = train # boosting type, support gbdt for now, alias: boosting, boost boosting_type = gbdt # application type, support following application # regression , regression task # binary , binary classification task # lambdarank , LambdaRank task # multiclass # alias: application, app objective = multiclass # eval metrics, support multi metric, delimited by ',' , support following metrics # l1 # l2 , default metric for regression # ndcg , default metric for lambdarank # auc # binary_logloss , default metric for binary # binary_error # multi_logloss # multi_error # auc_mu metric = multi_logloss,auc_mu # AUC-mu weights; the matrix of loss weights below is passed in parameter auc_mu_weights as a list # 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 auc_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 # number of class, for multiclass classification num_class = 5 # frequency for metric output metric_freq = 1 # true if need output metric for training data, alias: tranining_metric, train_metric is_training_metric = true # column in data to use as label label_column = 0 # number of bins for feature bucket, 255 is a recommend setting, it can save memories, and also has good accuracy. max_bin = 255 # training data # if existing weight file, should name to "regression.train.weight" # alias: train_data, train data = multiclass.train # valid data valid_data = multiclass.test # round for early stopping early_stopping = 10 # number of trees(iterations), alias: num_tree, num_iteration, num_iterations, num_round, num_rounds num_trees = 100 # shrinkage rate , alias: shrinkage_rate learning_rate = 0.05 # number of leaves for one tree, alias: num_leaf num_leaves = 31 ================================================ FILE: examples/parallel_learning/README.md ================================================ Distributed Learning Example ============================ Here is an example for LightGBM to perform distributed learning for 2 machines. 1. Edit [mlist.txt](./mlist.txt): write the ip of these 2 machines that you want to run application on. ``` machine1_ip 12400 machine2_ip 12400 ``` 2. Copy this folder and executable file to these 2 machines that you want to run application on. 3. Run command in this folder on both 2 machines: ```"./lightgbm" config=train.conf``` This distributed learning example is based on socket. LightGBM also supports distributed learning based on MPI. For 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). ================================================ FILE: examples/parallel_learning/binary.test ================================================ 1 0.644 0.247 -0.447 0.862 0.374 0.854 -1.126 -0.790 2.173 1.015 -0.201 1.400 0.000 1.575 1.807 1.607 0.000 1.585 -0.190 -0.744 3.102 0.958 1.061 0.980 0.875 0.581 0.905 0.796 0 0.385 1.800 1.037 1.044 0.349 1.502 -0.966 1.734 0.000 0.966 -1.960 -0.249 0.000 1.501 0.465 -0.354 2.548 0.834 -0.440 0.638 3.102 0.695 0.909 0.981 0.803 0.813 1.149 1.116 0 1.214 -0.166 0.004 0.505 1.434 0.628 -1.174 -1.230 1.087 0.579 -1.047 -0.118 0.000 0.835 0.340 1.234 2.548 0.711 -1.383 1.355 0.000 0.848 0.911 1.043 0.931 1.058 0.744 0.696 1 0.420 1.111 0.137 1.516 -1.657 0.854 0.623 1.605 1.087 1.511 -1.297 0.251 0.000 0.872 -0.368 -0.721 0.000 0.543 0.731 1.424 3.102 1.597 1.282 1.105 0.730 0.148 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1.182 1.024 1.036 0.901 1 1.020 -0.172 0.051 0.827 1.679 0.850 0.651 -0.794 0.000 1.380 -0.696 1.451 2.215 1.563 -0.844 0.068 0.000 1.259 0.749 -1.442 0.000 0.891 0.735 1.266 0.985 0.908 0.987 0.886 0 0.631 0.317 -0.817 0.249 -0.317 0.541 -0.592 1.415 0.000 0.835 -1.181 0.111 2.215 1.423 1.171 -1.178 1.274 1.342 -1.150 0.793 0.000 0.869 0.892 0.976 1.174 2.141 1.345 1.076 1 0.567 1.648 -0.361 0.958 1.051 0.952 0.926 -0.732 2.173 0.832 0.466 1.667 0.000 1.031 1.290 0.131 2.548 1.429 1.008 1.331 0.000 0.678 1.012 0.988 0.947 0.916 0.891 0.781 0 1.134 -0.303 -0.806 1.270 -1.358 0.720 0.140 0.842 0.000 0.410 0.097 -0.101 0.000 0.310 1.538 1.343 0.000 0.576 1.156 -1.669 3.102 0.868 0.746 0.984 0.866 0.484 0.709 0.735 1 0.758 0.682 -0.444 0.938 -1.632 1.179 -0.084 0.624 0.000 0.880 0.535 0.412 0.000 1.320 0.587 1.483 2.548 2.797 0.726 -1.090 1.551 0.934 0.869 1.024 0.698 1.089 0.735 0.650 1 1.095 -0.733 0.372 0.939 0.191 1.186 0.014 -1.148 2.173 0.896 -0.208 1.243 0.000 0.647 -2.172 -0.622 0.000 0.612 0.423 1.064 3.102 1.923 1.222 1.002 1.687 0.853 1.124 1.103 1 0.433 0.852 -1.542 1.366 0.726 1.085 -0.534 -1.507 0.000 0.814 -0.632 -1.026 2.215 1.652 -1.256 -0.104 0.000 1.415 0.489 0.883 3.102 0.897 0.972 0.990 0.604 1.151 0.947 1.158 0 0.416 -1.352 0.839 0.854 1.269 0.978 -0.442 -1.513 2.173 0.898 2.369 0.484 0.000 0.764 0.045 -0.462 2.548 1.698 -0.947 -0.406 0.000 4.819 2.601 0.983 0.871 0.915 1.898 1.486 0 0.535 1.359 1.126 1.322 -1.572 1.203 1.087 -1.301 1.087 1.973 -0.763 0.410 2.215 1.053 -0.945 -0.083 0.000 0.593 -2.137 -0.134 0.000 0.789 0.844 0.992 0.815 3.348 1.654 1.385 0 1.719 0.561 1.517 0.784 -1.006 0.942 0.932 0.153 0.000 0.345 0.832 1.035 0.000 0.795 0.857 -1.473 2.548 0.811 0.179 -0.663 3.102 0.866 0.917 1.229 0.758 0.469 0.615 0.697 1 1.040 -0.803 1.637 0.264 -0.814 1.819 0.055 0.353 0.000 1.541 -0.934 -0.811 2.215 1.701 -0.112 -1.387 0.000 1.762 -0.267 1.163 3.102 1.301 1.135 0.979 0.914 1.534 1.142 1.018 ================================================ FILE: examples/parallel_learning/predict.conf ================================================ task = predict data = binary.test input_model= LightGBM_model.txt ================================================ FILE: examples/parallel_learning/train.conf ================================================ # task type, support train and predict task = train # boosting type, support gbdt for now, alias: boosting, boost boosting_type = gbdt # application type, support following application # regression , regression task # binary , binary classification task # lambdarank , LambdaRank task # alias: application, app objective = binary # eval metrics, support multi metric, delimited by ',' , support following metrics # l1 # l2 , default metric for regression # ndcg , default metric for lambdarank # auc # binary_logloss , default metric for binary # binary_error metric = binary_logloss,auc # frequency for metric output metric_freq = 1 # true if need output metric for training data, alias: tranining_metric, train_metric is_training_metric = true # column in data to use as label label_column = 0 # number of bins for feature bucket, 255 is a recommend setting, it can save memories, and also has good accuracy. max_bin = 255 # training data # if existing weight file, should name to "binary.train.weight" # alias: train_data, train data = binary.train # validation data, support multi validation data, separated by ',' # if existing weight file, should name to "binary.test.weight" # alias: valid, test, test_data, valid_data = binary.test # number of trees(iterations), alias: num_tree, num_iteration, num_iterations, num_round, num_rounds num_trees = 100 # shrinkage rate , alias: shrinkage_rate learning_rate = 0.1 # number of leaves for one tree, alias: num_leaf num_leaves = 63 # type of tree learner, support following types: # serial , single machine version # feature , use feature parallel to train # data , use data parallel to train # voting , use voting based parallel to train # alias: tree tree_learner = feature # number of threads for multi-threading. One thread will use each CPU. The default is the CPU count. # num_threads = 8 # feature sub-sample, will random select 80% feature to train on each iteration # alias: sub_feature feature_fraction = 0.8 # Support bagging (data sub-sample), will perform bagging every 5 iterations bagging_freq = 5 # Bagging fraction, will random select 80% data on bagging # alias: sub_row bagging_fraction = 0.8 # minimal number data for one leaf, use this to deal with over-fit # alias : min_data_per_leaf, min_data min_data_in_leaf = 50 # minimal sum Hessians for one leaf, use this to deal with over-fit min_sum_hessian_in_leaf = 5.0 # save memory and faster speed for sparse feature, alias: is_sparse is_enable_sparse = true # when data is bigger than memory size, set this to true. otherwise set false will have faster speed # alias: two_round_loading, two_round use_two_round_loading = false # true if need to save data to binary file and application will auto load data from binary file next time # alias: is_save_binary, save_binary is_save_binary_file = false # output model file output_model = LightGBM_model.txt # support continuous train from trained gbdt model # input_model= trained_model.txt # output prediction file for predict task # output_result= prediction.txt # number of machines in parallel training, alias: num_machine num_machines = 2 # local listening port in parallel training, alias: local_port local_listen_port = 12400 # machines list file for parallel training, alias: mlist machine_list_file = mlist.txt ================================================ FILE: examples/python-guide/README.md ================================================ Python-package Examples ======================= Here is an example for LightGBM to use Python-package. You should install LightGBM [Python-package](https://github.com/lightgbm-org/LightGBM/tree/master/python-package) first. You 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: ``` pip install scikit-learn pandas matplotlib scipy -U ``` Now you can run examples in this folder, for example: ``` python simple_example.py ``` Examples include: - [`dask/`](./dask): examples using Dask for distributed training - [simple_example.py](https://github.com/lightgbm-org/LightGBM/blob/master/examples/python-guide/simple_example.py) - Construct Dataset - Basic train and predict - Eval during training - Early stopping - Save model to file - [sklearn_example.py](https://github.com/lightgbm-org/LightGBM/blob/master/examples/python-guide/sklearn_example.py) - Create data for learning with sklearn interface - Basic train and predict with sklearn interface - Feature importances with sklearn interface - Self-defined eval metric with sklearn interface - Find best parameters for the model with sklearn's GridSearchCV - [advanced_example.py](https://github.com/lightgbm-org/LightGBM/blob/master/examples/python-guide/advanced_example.py) - Construct Dataset - Set feature names - Directly use categorical features without one-hot encoding - Save model to file - Dump model to JSON format - Get feature names - Get feature importances - Load model to predict - Dump and load model with pickle - Load model file to continue training - Change learning rates during training - Change any parameters during training - Self-defined objective function - Self-defined eval metric - Callback function - [logistic_regression.py](https://github.com/lightgbm-org/LightGBM/blob/master/examples/python-guide/logistic_regression.py) - Use objective `xentropy` or `binary` - Use `xentropy` with binary labels or probability labels - Use `binary` only with binary labels - Compare speed of `xentropy` versus `binary` - [plot_example.py](https://github.com/lightgbm-org/LightGBM/blob/master/examples/python-guide/plot_example.py) - Construct Dataset - Train and record eval results for further plotting - Plot metrics recorded during training - Plot feature importances - Plot split value histogram - Plot one specified tree - Plot one specified tree with Graphviz - [dataset_from_multi_hdf5.py](https://github.com/lightgbm-org/LightGBM/blob/master/examples/python-guide/dataset_from_multi_hdf5.py) - Construct Dataset from multiple HDF5 files - Avoid loading all data into memory ================================================ FILE: examples/python-guide/advanced_example.py ================================================ # coding: utf-8 import copy import json import pickle from pathlib import Path import numpy as np import pandas as pd from sklearn.metrics import roc_auc_score import lightgbm as lgb print("Loading data...") # load or create your dataset binary_example_dir = Path(__file__).absolute().parents[1] / "binary_classification" df_train = pd.read_csv(str(binary_example_dir / "binary.train"), header=None, sep="\t") df_test = pd.read_csv(str(binary_example_dir / "binary.test"), header=None, sep="\t") W_train = pd.read_csv(str(binary_example_dir / "binary.train.weight"), header=None)[0] W_test = pd.read_csv(str(binary_example_dir / "binary.test.weight"), header=None)[0] y_train = df_train[0] y_test = df_test[0] X_train = df_train.drop(0, axis=1) X_test = df_test.drop(0, axis=1) num_train, num_feature = X_train.shape # generate feature names feature_name = [f"feature_{col}" for col in range(num_feature)] # create dataset for lightgbm # if you want to re-use data, remember to set free_raw_data=False lgb_train = lgb.Dataset( X_train, y_train, weight=W_train, feature_name=feature_name, categorical_feature=[21], free_raw_data=False ) lgb_eval = lgb.Dataset(X_test, y_test, reference=lgb_train, weight=W_test, free_raw_data=False) # specify your configurations as a dict params = { "boosting_type": "gbdt", "objective": "binary", "metric": "binary_logloss", "num_leaves": 31, "learning_rate": 0.05, "feature_fraction": 0.9, "bagging_fraction": 0.8, "bagging_freq": 5, "verbose": 0, } print("Starting training...") # feature_name and categorical_feature gbm = lgb.train( params, lgb_train, num_boost_round=10, valid_sets=lgb_train, # eval training data ) print("Finished first 10 rounds...") # check feature name print(f"7th feature name is: {lgb_train.feature_name[6]}") print("Saving model...") # save model to file gbm.save_model("model.txt") print("Dumping model to JSON...") # dump model to JSON (and save to file) model_json = gbm.dump_model() with open("model.json", "w+") as f: json.dump(model_json, f, indent=4) # feature names print(f"Feature names: {gbm.feature_name()}") # feature importances print(f"Feature importances: {list(gbm.feature_importance())}") print("Loading model to predict...") # load model to predict bst = lgb.Booster(model_file="model.txt") # can only predict with the best iteration (or the saving iteration) y_pred = bst.predict(X_test) # eval with loaded model auc_loaded_model = roc_auc_score(y_test, y_pred) print(f"The ROC AUC of loaded model's prediction is: {auc_loaded_model}") print("Dumping and loading model with pickle...") # dump model with pickle with open("model.pkl", "wb") as fout: pickle.dump(gbm, fout) # load model with pickle to predict with open("model.pkl", "rb") as fin: pkl_bst = pickle.load(fin) # can predict with any iteration when loaded in pickle way y_pred = pkl_bst.predict(X_test, num_iteration=7) # eval with loaded model auc_pickled_model = roc_auc_score(y_test, y_pred) print(f"The ROC AUC of pickled model's prediction is: {auc_pickled_model}") # continue training # init_model accepts: # 1. model file name # 2. Booster() gbm = lgb.train(params, lgb_train, num_boost_round=10, init_model="model.txt", valid_sets=lgb_eval) print("Finished 10 - 20 rounds with model file...") # decay learning rates # reset_parameter callback accepts: # 1. list with length = num_boost_round # 2. function(curr_iter) gbm = lgb.train( params, lgb_train, num_boost_round=10, init_model=gbm, valid_sets=lgb_eval, callbacks=[lgb.reset_parameter(learning_rate=lambda iter: 0.05 * (0.99**iter))], ) print("Finished 20 - 30 rounds with decay learning rates...") # change other parameters during training gbm = lgb.train( params, lgb_train, num_boost_round=10, init_model=gbm, valid_sets=lgb_eval, callbacks=[lgb.reset_parameter(bagging_fraction=[0.7] * 5 + [0.6] * 5)], ) print("Finished 30 - 40 rounds with changing bagging_fraction...") # self-defined objective function # f(preds: array, train_data: Dataset) -> grad: array, hess: array # log likelihood loss def loglikelihood(preds, train_data): labels = train_data.get_label() preds = 1.0 / (1.0 + np.exp(-preds)) grad = preds - labels hess = preds * (1.0 - preds) return grad, hess # self-defined eval metric # f(preds: array, train_data: Dataset) -> name: str, eval_result: float, is_higher_better: bool # binary error # NOTE: when you do customized loss function, the default prediction value is margin # This may make built-in evaluation metric calculate wrong results # For example, we are doing log likelihood loss, the prediction is score before logistic transformation # Keep this in mind when you use the customization def binary_error(preds, train_data): labels = train_data.get_label() preds = 1.0 / (1.0 + np.exp(-preds)) return "error", np.mean(labels != (preds > 0.5)), False # Pass custom objective function through params params_custom_obj = copy.deepcopy(params) params_custom_obj["objective"] = loglikelihood gbm = lgb.train( params_custom_obj, lgb_train, num_boost_round=10, init_model=gbm, feval=binary_error, valid_sets=lgb_eval ) print("Finished 40 - 50 rounds with self-defined objective function and eval metric...") # another self-defined eval metric # f(preds: array, train_data: Dataset) -> name: str, eval_result: float, is_higher_better: bool # accuracy # NOTE: when you do customized loss function, the default prediction value is margin # This may make built-in evaluation metric calculate wrong results # For example, we are doing log likelihood loss, the prediction is score before logistic transformation # Keep this in mind when you use the customization def accuracy(preds, train_data): labels = train_data.get_label() preds = 1.0 / (1.0 + np.exp(-preds)) return "accuracy", np.mean(labels == (preds > 0.5)), True # Pass custom objective function through params params_custom_obj = copy.deepcopy(params) params_custom_obj["objective"] = loglikelihood gbm = lgb.train( params_custom_obj, lgb_train, num_boost_round=10, init_model=gbm, feval=[binary_error, accuracy], valid_sets=lgb_eval, ) print("Finished 50 - 60 rounds with self-defined objective function and multiple self-defined eval metrics...") print("Starting a new training job...") # callback def reset_metrics(): def callback(env): lgb_eval_new = lgb.Dataset(X_test, y_test, reference=lgb_train) if env.iteration - env.begin_iteration == 5: print("Add a new valid dataset at iteration 5...") env.model.add_valid(lgb_eval_new, "new_valid") callback.before_iteration = True callback.order = 0 return callback gbm = lgb.train(params, lgb_train, num_boost_round=10, valid_sets=lgb_train, callbacks=[reset_metrics()]) print("Finished first 10 rounds with callback function...") ================================================ FILE: examples/python-guide/dask/README.md ================================================ Dask Examples ============= This directory contains examples of machine learning workflows with LightGBM and [Dask](https://dask.org/). Before running this code, see [the installation instructions for the Dask-package](https://github.com/lightgbm-org/LightGBM/tree/master/python-package#install-dask-package). After installing the package and its dependencies, any of the examples here can be run with a command like this: ```shell python binary-classification.py ``` The examples listed below contain minimal code showing how to train LightGBM models using Dask. **Training** * [binary-classification.py](./binary-classification.py) * [multiclass-classification.py](./multiclass-classification.py) * [ranking.py](./ranking.py) * [regression.py](./regression.py) **Prediction** * [prediction.py](./prediction.py) ================================================ FILE: examples/python-guide/dask/binary-classification.py ================================================ import dask.array as da from distributed import Client, LocalCluster from sklearn.datasets import make_blobs import lightgbm as lgb if __name__ == "__main__": print("loading data") X, y = make_blobs(n_samples=1000, n_features=50, centers=2) print("initializing a Dask cluster") cluster = LocalCluster() client = Client(cluster) print("created a Dask LocalCluster") print("distributing training data on the Dask cluster") dX = da.from_array(X, chunks=(100, 50)) dy = da.from_array(y, chunks=(100,)) print("beginning training") dask_model = lgb.DaskLGBMClassifier(n_estimators=10) dask_model.fit(dX, dy) assert dask_model.fitted_ print("done training") ================================================ FILE: examples/python-guide/dask/multiclass-classification.py ================================================ import dask.array as da from distributed import Client, LocalCluster from sklearn.datasets import make_blobs import lightgbm as lgb if __name__ == "__main__": print("loading data") X, y = make_blobs(n_samples=1000, n_features=50, centers=3) print("initializing a Dask cluster") cluster = LocalCluster(n_workers=2) client = Client(cluster) print("created a Dask LocalCluster") print("distributing training data on the Dask cluster") dX = da.from_array(X, chunks=(100, 50)) dy = da.from_array(y, chunks=(100,)) print("beginning training") dask_model = lgb.DaskLGBMClassifier(n_estimators=10) dask_model.fit(dX, dy) assert dask_model.fitted_ print("done training") ================================================ FILE: examples/python-guide/dask/prediction.py ================================================ import dask.array as da from distributed import Client, LocalCluster from sklearn.datasets import make_regression from sklearn.metrics import mean_squared_error import lightgbm as lgb if __name__ == "__main__": print("loading data") X, y = make_regression(n_samples=1000, n_features=50) print("initializing a Dask cluster") cluster = LocalCluster(n_workers=2) client = Client(cluster) print("created a Dask LocalCluster") print("distributing training data on the Dask cluster") dX = da.from_array(X, chunks=(100, 50)) dy = da.from_array(y, chunks=(100,)) print("beginning training") dask_model = lgb.DaskLGBMRegressor(n_estimators=10) dask_model.fit(dX, dy) assert dask_model.fitted_ print("done training") print("predicting on the training data") preds = dask_model.predict(dX) # the code below uses sklearn.metrics, but this requires pulling all of the # predictions and target values back from workers to the client # # for larger datasets, consider the metrics from dask-ml instead # https://ml.dask.org/modules/api.html#dask-ml-metrics-metrics print("computing MSE") preds_local = preds.compute() actuals_local = dy.compute() mse = mean_squared_error(actuals_local, preds_local) print(f"MSE: {mse}") ================================================ FILE: examples/python-guide/dask/ranking.py ================================================ from pathlib import Path import dask.array as da import numpy as np from distributed import Client, LocalCluster from sklearn.datasets import load_svmlight_file import lightgbm as lgb if __name__ == "__main__": print("loading data") rank_example_dir = Path(__file__).absolute().parents[2] / "lambdarank" X, y = load_svmlight_file(str(rank_example_dir / "rank.train")) group = np.loadtxt(str(rank_example_dir / "rank.train.query")) print("initializing a Dask cluster") cluster = LocalCluster(n_workers=2) client = Client(cluster) print("created a Dask LocalCluster") print("distributing training data on the Dask cluster") # split training data into two partitions rows_in_part1 = int(np.sum(group[:100])) rows_in_part2 = X.shape[0] - rows_in_part1 num_features = X.shape[1] # make this array dense because we're splitting across # a sparse boundary to partition the data X = X.toarray() dX = da.from_array(x=X, chunks=[(rows_in_part1, rows_in_part2), (num_features,)]) dy = da.from_array( x=y, chunks=[ (rows_in_part1, rows_in_part2), ], ) dg = da.from_array(x=group, chunks=[(100, group.size - 100)]) print("beginning training") dask_model = lgb.DaskLGBMRanker(n_estimators=10) dask_model.fit(dX, dy, group=dg) assert dask_model.fitted_ print("done training") ================================================ FILE: examples/python-guide/dask/regression.py ================================================ import dask.array as da from distributed import Client, LocalCluster from sklearn.datasets import make_regression import lightgbm as lgb if __name__ == "__main__": print("loading data") X, y = make_regression(n_samples=1000, n_features=50) print("initializing a Dask cluster") cluster = LocalCluster(n_workers=2) client = Client(cluster) print("created a Dask LocalCluster") print("distributing training data on the Dask cluster") dX = da.from_array(X, chunks=(100, 50)) dy = da.from_array(y, chunks=(100,)) print("beginning training") dask_model = lgb.DaskLGBMRegressor(n_estimators=10) dask_model.fit(dX, dy) assert dask_model.fitted_ print("done training") ================================================ FILE: examples/python-guide/dataset_from_multi_hdf5.py ================================================ from pathlib import Path import h5py import numpy as np import pandas as pd import lightgbm as lgb class HDFSequence(lgb.Sequence): def __init__(self, hdf_dataset, batch_size): """ Construct a sequence object from HDF5 with required interface. Parameters ---------- hdf_dataset : h5py.Dataset Dataset in HDF5 file. batch_size : int Size of a batch. When reading data to construct lightgbm Dataset, each read reads batch_size rows. """ # We can also open HDF5 file once and get access to self.data = hdf_dataset self.batch_size = batch_size def __getitem__(self, idx): return self.data[idx] def __len__(self): return len(self.data) def create_dataset_from_multiple_hdf(input_flist, batch_size): data = [] ylist = [] for f in input_flist: f = h5py.File(f, "r") data.append(HDFSequence(f["X"], batch_size)) ylist.append(f["Y"][:]) params = { "bin_construct_sample_cnt": 200000, "max_bin": 255, } y = np.concatenate(ylist) dataset = lgb.Dataset(data, label=y, params=params) # With binary dataset created, we can use either Python API or cmdline version to train. # # Note: in order to create exactly the same dataset with the one created in simple_example.py, we need # to modify simple_example.py to pass numpy array instead of pandas DataFrame to Dataset constructor. # The reason is that DataFrame column names will be used in Dataset. For a DataFrame with Int64Index # as columns, Dataset will use column names like ["0", "1", "2", ...]. While for numpy array, column names # are using the default one assigned in C++ code (dataset_loader.cpp), like ["Column_0", "Column_1", ...]. dataset.save_binary("regression.train.from_hdf.bin") def save2hdf(input_data, fname, batch_size): """Store numpy array to HDF5 file. Please note chunk size settings in the implementation for I/O performance optimization. """ with h5py.File(fname, "w") as f: for name, data in input_data.items(): nrow, ncol = data.shape if ncol == 1: # Y has a single column and we read it in single shot. So store it as an 1-d array. chunk = (nrow,) data = data.values.flatten() else: # We use random access for data sampling when creating LightGBM Dataset from Sequence. # When accessing any element in a HDF5 chunk, it's read entirely. # To save I/O for sampling, we should keep number of total chunks much larger than sample count. # Here we are just creating a chunk size that matches with batch_size. # # Also note that the data is stored in row major order to avoid extra copy when passing to # lightgbm Dataset. chunk = (batch_size, ncol) f.create_dataset(name, data=data, chunks=chunk, compression="lzf") def generate_hdf(input_fname, output_basename, batch_size): # Save to 2 HDF5 files for demonstration. df = pd.read_csv(input_fname, header=None, sep="\t") mid = len(df) // 2 df1 = df.iloc[:mid] df2 = df.iloc[mid:] # We can store multiple datasets inside a single HDF5 file. # Separating X and Y for choosing best chunk size for data loading. fname1 = f"{output_basename}1.h5" fname2 = f"{output_basename}2.h5" save2hdf({"Y": df1.iloc[:, :1], "X": df1.iloc[:, 1:]}, fname1, batch_size) save2hdf({"Y": df2.iloc[:, :1], "X": df2.iloc[:, 1:]}, fname2, batch_size) return [fname1, fname2] def main(): batch_size = 64 output_basename = "regression" hdf_files = generate_hdf( str(Path(__file__).absolute().parents[1] / "regression" / "regression.train"), output_basename, batch_size ) create_dataset_from_multiple_hdf(hdf_files, batch_size=batch_size) if __name__ == "__main__": main() ================================================ FILE: examples/python-guide/logistic_regression.py ================================================ # coding: utf-8 """Comparison of `binary` and `xentropy` objectives. BLUF: The `xentropy` objective does logistic regression and generalizes to the case where labels are probabilistic (i.e. numbers between 0 and 1). Details: Both `binary` and `xentropy` minimize the log loss and use `boost_from_average = TRUE` by default. Possibly the only difference between them with default settings is that `binary` may achieve a slight speed improvement by assuming that the labels are binary instead of probabilistic. """ import time import numpy as np import pandas as pd from scipy.special import expit import lightgbm as lgb ################# # Simulate some binary data with a single categorical and # single continuous predictor rng = np.random.default_rng(seed=0) N = 1000 X = pd.DataFrame({"continuous": range(N), "categorical": np.repeat([0, 1, 2, 3, 4], N / 5)}) CATEGORICAL_EFFECTS = [-1, -1, -2, -2, 2] LINEAR_TERM = np.array( [-0.5 + 0.01 * X["continuous"][k] + CATEGORICAL_EFFECTS[X["categorical"][k]] for k in range(X.shape[0])] ) + rng.normal(loc=0, scale=1, size=X.shape[0]) TRUE_PROB = expit(LINEAR_TERM) Y = rng.binomial(n=1, p=TRUE_PROB, size=N) DATA = { "X": X, "probability_labels": TRUE_PROB, "binary_labels": Y, "lgb_with_binary_labels": lgb.Dataset(X, Y), "lgb_with_probability_labels": lgb.Dataset(X, TRUE_PROB), } ################# # Set up a couple of utilities for our experiments def log_loss(preds, labels): """Logarithmic loss with non-necessarily-binary labels.""" log_likelihood = np.sum(labels * np.log(preds)) / len(preds) return -log_likelihood def experiment(objective, label_type, data): """Measure performance of an objective. Parameters ---------- objective : {'binary', 'xentropy'} Objective function. label_type : {'binary', 'probability'} Type of the label. data : dict Data for training. Returns ------- result : dict Experiment summary stats. """ nrounds = 5 lgb_data = data[f"lgb_with_{label_type}_labels"] params = {"objective": objective, "feature_fraction": 1, "bagging_fraction": 1, "verbose": -1, "seed": 123} time_zero = time.time() gbm = lgb.train(params, lgb_data, num_boost_round=nrounds) y_fitted = gbm.predict(data["X"]) y_true = data[f"{label_type}_labels"] duration = time.time() - time_zero return {"time": duration, "correlation": np.corrcoef(y_fitted, y_true)[0, 1], "logloss": log_loss(y_fitted, y_true)} ################# # Observe the behavior of `binary` and `xentropy` objectives print("Performance of `binary` objective with binary labels:") print(experiment("binary", label_type="binary", data=DATA)) print("Performance of `xentropy` objective with binary labels:") print(experiment("xentropy", label_type="binary", data=DATA)) print("Performance of `xentropy` objective with probability labels:") print(experiment("xentropy", label_type="probability", data=DATA)) # Trying this throws an error on non-binary values of y: # experiment('binary', label_type='probability', DATA) # The speed of `binary` is not drastically different than # `xentropy`. `xentropy` runs faster than `binary` in many cases, although # there are reasons to suspect that `binary` should run faster when the # label is an integer instead of a float K = 10 A = [experiment("binary", label_type="binary", data=DATA)["time"] for k in range(K)] B = [experiment("xentropy", label_type="binary", data=DATA)["time"] for k in range(K)] print(f"Best `binary` time: {min(A)}") print(f"Best `xentropy` time: {min(B)}") ================================================ FILE: examples/python-guide/notebooks/interactive_plot_example.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Load libraries" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "ExecuteTime": { "end_time": "2018-10-25T22:13:39.152526Z", "start_time": "2018-10-25T22:13:37.503680Z" } }, "outputs": [], "source": [ "from pathlib import Path\n", "\n", "import matplotlib.pyplot as plt\n", "import pandas as pd\n", "\n", "import lightgbm as lgb\n", "\n", "%matplotlib inline\n", "\n", "try:\n", " # To enable interactive mode you should install ipywidgets\n", " # https://github.com/jupyter-widgets/ipywidgets\n", " from ipywidgets import SelectMultiple, interact\n", "\n", " INTERACTIVE = True\n", "except ImportError:\n", " INTERACTIVE = False" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Load data" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "ExecuteTime": { "end_time": "2018-10-25T22:13:39.238695Z", "start_time": "2018-10-25T22:13:39.160165Z" } }, "outputs": [], "source": [ "regression_example_dir = Path().absolute().parents[1] / \"regression\"\n", "df_train = pd.read_csv(str(regression_example_dir / \"regression.train\"), header=None, sep=\"\\t\")\n", "df_test = pd.read_csv(str(regression_example_dir / \"regression.test\"), header=None, sep=\"\\t\")\n", "\n", "y_train = df_train[0]\n", "y_test = df_test[0]\n", "X_train = df_train.drop(0, axis=1)\n", "X_test = df_test.drop(0, axis=1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Create Dataset object for LightGBM" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "lgb_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", ")\n", "lgb_test = lgb.Dataset(X_test, y_test, reference=lgb_train)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Configuration dictionary" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "ExecuteTime": { "end_time": "2018-10-25T22:13:39.243104Z", "start_time": "2018-10-25T22:13:39.240578Z" } }, "outputs": [], "source": [ "params = {\"num_leaves\": 5, \"metric\": [\"l1\", \"l2\"], \"verbose\": -1}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Training" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "ExecuteTime": { "end_time": "2018-10-25T22:13:39.336630Z", "start_time": "2018-10-25T22:13:39.246006Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[10]\ttraining's l1: 0.457448\ttraining's l2: 0.217995\tvalid_1's l1: 0.456464\tvalid_1's l2: 0.21641\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", "[30]\ttraining's l1: 0.421302\ttraining's l2: 0.197421\tvalid_1's l1: 0.417019\tvalid_1's l2: 0.192514\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", "[50]\ttraining's l1: 0.403695\ttraining's l2: 0.189593\tvalid_1's l1: 0.398997\tvalid_1's l2: 0.183688\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", "[70]\ttraining's l1: 0.394876\ttraining's l2: 0.184982\tvalid_1's l1: 0.389805\tvalid_1's l2: 0.178803\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", "[90]\ttraining's l1: 0.388101\ttraining's l2: 0.180817\tvalid_1's l1: 0.384404\tvalid_1's l2: 0.175775\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" ] } ], "source": [ "evals_result = {} # to record eval results for plotting\n", "gbm = 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", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Plot metrics recorded during training" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "ExecuteTime": { "end_time": "2018-10-25T22:13:39.809203Z", "start_time": "2018-10-25T22:13:39.338985Z" } }, "outputs": [], "source": [ "def render_metric(metric_name):\n", " lgb.plot_metric(evals_result, metric=metric_name, figsize=(10, 5))\n", " plt.show()" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "if INTERACTIVE:\n", " # create widget to switch between metrics\n", " interact(render_metric, metric_name=params[\"metric\"])\n", "else:\n", " render_metric(params[\"metric\"][0])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Plot feature importances" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "ExecuteTime": { "end_time": "2018-10-25T22:13:39.958548Z", "start_time": "2018-10-25T22:13:39.811530Z" } }, "outputs": [], "source": [ "def render_plot_importance(importance_type, max_features=10, ignore_zero=True, precision=3):\n", " lgb.plot_importance(\n", " gbm,\n", " importance_type=importance_type,\n", " max_num_features=max_features,\n", " ignore_zero=ignore_zero,\n", " figsize=(12, 8),\n", " precision=precision,\n", " )\n", " plt.show()" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "if INTERACTIVE:\n", " # create widget for interactive feature importance plot\n", " interact(\n", " render_plot_importance,\n", " importance_type=[\"split\", \"gain\"],\n", " max_features=(1, X_train.shape[-1]),\n", " precision=(0, 10),\n", " )\n", "else:\n", " render_plot_importance(importance_type=\"split\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Plot split value histogram" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "def render_histogram(feature):\n", " lgb.plot_split_value_histogram(gbm, feature=feature, bins=\"auto\", figsize=(10, 5))\n", " plt.show()" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "if INTERACTIVE:\n", " # create widget for interactive split value histogram\n", " interact(render_histogram, feature=gbm.feature_name())\n", "else:\n", " render_histogram(feature=\"f26\")" ] }, { "cell_type": "markdown", "metadata": { "ExecuteTime": { "end_time": "2018-10-25T22:13:40.027803Z", "start_time": "2018-10-25T22:13:39.960713Z" } }, "source": [ "## Plot trees" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "def render_tree(tree_index, show_info, precision=3):\n", " show_info = None if \"None\" in show_info else show_info\n", " return lgb.create_tree_digraph(gbm, tree_index=tree_index, show_info=show_info, precision=precision)" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "scrolled": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "if INTERACTIVE:\n", " # create widget to switch between trees and control info in nodes\n", " interact(\n", " render_tree,\n", " tree_index=(0, gbm.num_trees() - 1),\n", " show_info=SelectMultiple( # allow multiple values to be selected\n", " options=[\n", " \"None\",\n", " \"split_gain\",\n", " \"internal_value\",\n", " \"internal_count\",\n", " \"internal_weight\",\n", " \"leaf_count\",\n", " \"leaf_weight\",\n", " \"data_percentage\",\n", " ],\n", " value=[\"None\"],\n", " ),\n", " precision=(0, 10),\n", " )\n", " tree = None\n", "else:\n", " tree = render_tree(53, [\"None\"])\n", "tree" ] } ], "metadata": { "hide_input": false, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.11.7" }, "varInspector": { "cols": { "lenName": 16, "lenType": 16, "lenVar": 40 }, "kernels_config": { "python": { "delete_cmd_postfix": "", "delete_cmd_prefix": "del ", "library": "var_list.py", "varRefreshCmd": "print(var_dic_list())" }, "r": { "delete_cmd_postfix": ") ", "delete_cmd_prefix": "rm(", "library": "var_list.r", "varRefreshCmd": "cat(var_dic_list()) " } }, "types_to_exclude": [ "module", "function", "builtin_function_or_method", "instance", "_Feature" ], "window_display": false } }, "nbformat": 4, "nbformat_minor": 2 } ================================================ FILE: examples/python-guide/plot_example.py ================================================ # coding: utf-8 from pathlib import Path import pandas as pd import lightgbm as lgb if lgb.compat.MATPLOTLIB_INSTALLED: import matplotlib.pyplot as plt else: raise ImportError("You need to install matplotlib and restart your session for plot_example.py.") print("Loading data...") # load or create your dataset regression_example_dir = Path(__file__).absolute().parents[1] / "regression" df_train = pd.read_csv(str(regression_example_dir / "regression.train"), header=None, sep="\t") df_test = pd.read_csv(str(regression_example_dir / "regression.test"), header=None, sep="\t") y_train = df_train[0] y_test = df_test[0] X_train = df_train.drop(0, axis=1) X_test = df_test.drop(0, axis=1) # create dataset for lightgbm lgb_train = lgb.Dataset( X_train, y_train, feature_name=[f"f{i + 1}" for i in range(X_train.shape[-1])], categorical_feature=[21], ) lgb_test = lgb.Dataset(X_test, y_test, reference=lgb_train) # specify your configurations as a dict params = {"num_leaves": 5, "metric": ("l1", "l2"), "verbose": 0} evals_result = {} # to record eval results for plotting print("Starting training...") # train gbm = lgb.train( params, lgb_train, num_boost_round=100, valid_sets=[lgb_train, lgb_test], callbacks=[lgb.log_evaluation(10), lgb.record_evaluation(evals_result)], ) print("Plotting metrics recorded during training...") ax = lgb.plot_metric(evals_result, metric="l1") plt.show() print("Plotting feature importances...") ax = lgb.plot_importance(gbm, max_num_features=10) plt.show() print("Plotting split value histogram...") ax = lgb.plot_split_value_histogram(gbm, feature="f26", bins="auto") plt.show() print("Plotting 54th tree...") # one tree use categorical feature to split ax = lgb.plot_tree(gbm, tree_index=53, figsize=(15, 15), show_info=["split_gain"]) plt.show() print("Plotting 54th tree with graphviz...") graph = lgb.create_tree_digraph(gbm, tree_index=53, name="Tree54") graph.render(view=True) ================================================ FILE: examples/python-guide/simple_example.py ================================================ # coding: utf-8 from pathlib import Path import pandas as pd from sklearn.metrics import mean_squared_error import lightgbm as lgb print("Loading data...") # load or create your dataset regression_example_dir = Path(__file__).absolute().parents[1] / "regression" df_train = pd.read_csv(str(regression_example_dir / "regression.train"), header=None, sep="\t") df_test = pd.read_csv(str(regression_example_dir / "regression.test"), header=None, sep="\t") y_train = df_train[0] y_test = df_test[0] X_train = df_train.drop(0, axis=1) X_test = df_test.drop(0, axis=1) # create dataset for lightgbm lgb_train = lgb.Dataset(X_train, y_train) lgb_eval = lgb.Dataset(X_test, y_test, reference=lgb_train) # specify your configurations as a dict params = { "boosting_type": "gbdt", "objective": "regression", "metric": {"l2", "l1"}, "num_leaves": 31, "learning_rate": 0.05, "feature_fraction": 0.9, "bagging_fraction": 0.8, "bagging_freq": 5, "verbose": 0, } print("Starting training...") # train gbm = lgb.train( params, lgb_train, num_boost_round=20, valid_sets=lgb_eval, callbacks=[lgb.early_stopping(stopping_rounds=5)] ) print("Saving model...") # save model to file gbm.save_model("model.txt") print("Starting predicting...") # predict y_pred = gbm.predict(X_test, num_iteration=gbm.best_iteration) # eval rmse_test = mean_squared_error(y_test, y_pred) ** 0.5 print(f"The RMSE of prediction is: {rmse_test}") ================================================ FILE: examples/python-guide/sklearn_example.py ================================================ # coding: utf-8 from pathlib import Path import numpy as np import pandas as pd from sklearn.metrics import mean_squared_error from sklearn.model_selection import GridSearchCV import lightgbm as lgb print("Loading data...") # load or create your dataset regression_example_dir = Path(__file__).absolute().parents[1] / "regression" df_train = pd.read_csv(str(regression_example_dir / "regression.train"), header=None, sep="\t") df_test = pd.read_csv(str(regression_example_dir / "regression.test"), header=None, sep="\t") y_train = df_train[0] y_test = df_test[0] X_train = df_train.drop(0, axis=1) X_test = df_test.drop(0, axis=1) print("Starting training...") # train gbm = lgb.LGBMRegressor(num_leaves=31, learning_rate=0.05, n_estimators=20) gbm.fit(X_train, y_train, eval_X=(X_test,), eval_y=(y_test,), eval_metric="l1", callbacks=[lgb.early_stopping(5)]) print("Starting predicting...") # predict y_pred = gbm.predict(X_test, num_iteration=gbm.best_iteration_) # eval rmse_test = mean_squared_error(y_test, y_pred) ** 0.5 print(f"The RMSE of prediction is: {rmse_test}") # feature importances print(f"Feature importances: {list(gbm.feature_importances_)}") # self-defined eval metric # f(y_true: array, y_pred: array) -> name: str, eval_result: float, is_higher_better: bool # Root Mean Squared Logarithmic Error (RMSLE) def rmsle(y_true, y_pred): return "RMSLE", np.sqrt(np.mean(np.power(np.log1p(y_pred) - np.log1p(y_true), 2))), False print("Starting training with custom eval function...") # train gbm.fit(X_train, y_train, eval_X=(X_test,), eval_y=(y_test,), eval_metric=rmsle, callbacks=[lgb.early_stopping(5)]) # another self-defined eval metric # f(y_true: array, y_pred: array) -> name: str, eval_result: float, is_higher_better: bool # Relative Absolute Error (RAE) def rae(y_true, y_pred): return "RAE", np.sum(np.abs(y_pred - y_true)) / np.sum(np.abs(np.mean(y_true) - y_true)), False print("Starting training with multiple custom eval functions...") # train gbm.fit( X_train, y_train, eval_X=(X_test,), eval_y=(y_test,), eval_metric=[rmsle, rae], callbacks=[lgb.early_stopping(5)] ) print("Starting predicting...") # predict y_pred = gbm.predict(X_test, num_iteration=gbm.best_iteration_) # eval rmsle_test = rmsle(y_test, y_pred)[1] rae_test = rae(y_test, y_pred)[1] print(f"The RMSLE of prediction is: {rmsle_test}") print(f"The RAE of prediction is: {rae_test}") # other scikit-learn modules estimator = lgb.LGBMRegressor(num_leaves=31) param_grid = {"learning_rate": [0.01, 0.1, 1], "n_estimators": [20, 40]} gbm = GridSearchCV(estimator, param_grid, cv=3) gbm.fit(X_train, y_train) print(f"Best parameters found by grid search are: {gbm.best_params_}") ================================================ FILE: examples/regression/README.md ================================================ Regression Example ================== Here is an example for LightGBM to run regression task. ***You must follow the [installation instructions](https://lightgbm.readthedocs.io/en/latest/Installation-Guide.html) for the following commands to work. The `lightgbm` binary must be built and available at the root of this project.*** Training -------- Run the following command in this folder: ```bash "../../lightgbm" config=train.conf ``` Prediction ---------- You should finish training first. Run the following command in this folder: ```bash "../../lightgbm" config=predict.conf ``` ================================================ FILE: examples/regression/forced_bins.json ================================================ [ { "feature": 0, "bin_upper_bound": [ 0.3, 0.35, 0.4 ] }, { "feature": 1, "bin_upper_bound": [ -0.1, -0.15, -0.2 ] } ] ================================================ FILE: examples/regression/forced_bins2.json ================================================ [ { "feature": 0, "bin_upper_bound": [ 0.19, 0.39, 0.59, 0.79 ] } ] ================================================ FILE: examples/regression/predict.conf ================================================ task = predict data = regression.test input_model= LightGBM_model.txt ================================================ FILE: examples/regression/regression.test ================================================ 1 0.644 0.247 -0.447 0.862 0.374 0.854 -1.126 -0.790 2.173 1.015 -0.201 1.400 0.000 1.575 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0.616 0.000 1.358 0.636 -1.721 2.548 1.464 0.855 0.691 0.000 0.999 1.027 0.981 0.719 0.868 1.053 0.982 1 0.764 -0.039 -1.021 0.735 -0.583 1.349 0.644 0.974 2.173 0.366 0.499 -1.700 0.000 0.328 0.885 -1.018 2.548 0.498 -0.895 -0.876 0.000 0.618 1.026 0.979 0.660 0.818 1.056 0.825 1 0.674 1.048 -1.549 1.030 -0.231 1.428 0.094 1.159 1.087 0.581 0.258 -0.894 0.000 0.557 1.282 -0.225 0.000 0.777 -0.364 0.440 3.102 0.722 0.698 1.071 1.327 0.738 0.900 0.818 1 0.865 0.412 1.511 1.103 -0.849 0.940 0.372 0.295 0.000 1.051 1.211 1.603 2.215 1.065 0.944 -0.097 0.000 0.762 -0.000 -1.141 3.102 0.868 0.886 1.150 0.882 0.735 0.895 0.827 0 0.577 1.174 -0.049 0.676 1.238 0.518 -0.340 -1.677 0.000 0.588 0.596 -0.265 2.215 0.521 -0.443 0.503 2.548 0.466 1.110 -1.192 0.000 0.773 0.773 0.982 0.575 0.507 0.548 0.536 1 1.043 0.347 -1.555 1.751 -1.009 0.621 -0.731 0.187 2.173 0.756 0.709 0.739 0.000 0.466 -0.459 1.640 0.000 0.692 0.460 0.434 1.551 0.891 0.916 0.985 0.805 0.503 0.807 0.762 0 1.167 0.346 1.669 0.806 0.657 1.014 0.861 -0.300 2.173 0.668 -1.183 1.498 0.000 0.836 0.724 0.627 2.548 1.646 1.766 -0.994 0.000 3.654 2.091 1.062 1.144 0.852 1.445 1.187 0 0.738 1.166 0.550 1.749 1.125 0.518 -2.507 -0.079 0.000 0.570 2.009 -0.984 0.000 1.234 2.294 -1.675 0.000 1.541 0.400 -0.447 3.102 0.805 0.644 0.988 1.211 0.133 0.769 0.838 0 0.756 0.655 1.156 1.404 -1.559 0.774 -1.715 0.350 0.000 1.094 0.691 -0.786 2.215 1.153 -1.164 1.272 2.548 0.435 -0.812 -0.480 0.000 0.694 0.821 0.990 0.949 1.804 1.233 1.155 1 0.906 -1.266 -1.600 0.320 0.493 0.449 -0.417 0.235 1.087 0.566 -1.789 -0.858 0.000 1.260 -0.577 -1.597 2.548 1.353 0.593 0.662 0.000 0.808 0.978 0.988 0.704 0.939 0.676 0.642 1 2.672 0.822 0.926 0.589 0.806 0.830 0.373 -0.601 0.000 0.542 2.024 -0.923 0.000 0.742 0.124 -0.071 0.000 0.947 0.033 -1.593 1.551 1.093 0.762 0.979 0.909 0.684 0.764 0.725 1 1.448 0.084 1.032 0.540 -0.388 0.478 0.655 0.683 0.000 0.838 0.233 -1.134 2.215 1.386 0.995 -0.519 2.548 0.959 0.283 -1.701 0.000 0.882 0.980 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-0.701 0.869 1.274 0.463 1.245 -0.738 0.000 1.470 0.981 0.987 0.788 0.773 0.889 0.771 1 0.841 0.378 -0.212 0.727 -1.375 0.923 0.411 -1.478 0.000 1.128 1.205 0.007 2.215 0.496 -0.741 1.082 0.000 0.677 1.695 -1.014 0.000 0.930 0.905 0.987 0.797 0.737 0.826 0.722 0 0.584 -1.556 0.625 1.531 0.713 0.577 -1.233 -1.165 1.087 0.474 -1.495 1.552 0.000 1.159 -1.297 -0.514 2.548 0.366 2.416 0.056 0.000 0.270 0.646 0.997 1.071 0.572 0.862 0.716 0 0.883 -0.139 0.696 1.162 -0.065 0.789 1.363 1.520 2.173 0.872 0.291 -1.461 0.000 1.164 -0.175 -0.550 2.548 0.396 -0.195 0.220 0.000 0.791 0.902 0.988 0.752 1.542 1.111 0.906 0 1.037 1.273 -1.294 1.058 0.496 1.203 -0.131 0.938 1.087 0.594 0.269 1.259 0.000 0.935 -2.331 -0.562 0.000 2.043 1.311 -0.121 3.102 2.499 2.110 1.450 0.957 2.091 2.122 1.884 0 1.536 1.372 0.723 0.252 -0.832 1.002 1.644 -0.141 2.173 1.200 1.526 -1.259 2.215 1.571 1.369 -1.722 0.000 2.244 2.348 0.190 0.000 1.121 1.014 0.987 0.899 1.366 1.121 0.978 1 0.859 -0.860 0.895 1.772 0.283 1.075 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0.811 0.828 0.863 1 0.670 -2.150 0.565 0.605 1.695 0.787 -0.437 0.553 0.000 1.003 -0.345 -1.156 2.215 0.737 0.943 -1.494 2.548 0.994 -1.036 0.125 0.000 0.967 1.045 0.990 0.964 0.741 0.910 0.846 1 1.032 -0.507 -1.220 1.039 1.153 1.510 0.177 0.232 2.173 1.022 2.516 -1.134 0.000 0.940 0.647 1.359 2.548 0.764 -0.578 -0.997 0.000 2.865 1.785 1.209 1.409 1.323 1.690 1.552 1 0.643 0.086 -1.641 0.362 -0.030 0.770 2.702 1.529 0.000 1.633 -1.075 -0.288 2.215 1.243 -0.219 -0.335 2.548 3.611 0.022 1.205 0.000 0.817 1.278 0.985 0.913 0.679 1.273 1.012 1 1.483 -1.157 0.931 0.865 -0.763 1.774 0.917 -0.014 0.000 0.958 -0.218 1.465 0.000 1.495 0.265 -1.541 2.548 2.171 -0.638 -1.288 1.551 3.173 2.185 1.567 1.100 0.824 1.622 1.564 0 0.533 -0.374 -1.210 0.965 1.034 0.494 -2.378 -0.537 0.000 0.739 0.737 -0.186 0.000 0.679 1.471 -0.815 0.000 1.569 0.610 0.685 3.102 0.755 0.861 0.986 0.973 0.724 0.892 0.907 0 0.429 -1.663 -0.629 0.495 0.883 0.546 -0.122 -1.022 0.000 0.575 2.019 1.451 0.000 0.851 0.291 -0.590 0.000 1.203 -0.232 0.523 3.102 0.761 0.836 0.979 0.646 0.290 0.566 0.565 1 1.347 -1.055 0.461 0.372 1.661 1.341 -2.340 -1.606 0.000 1.293 -0.799 -0.795 2.215 1.690 -1.193 -0.172 2.548 1.634 -0.826 1.147 0.000 0.848 1.151 0.988 1.005 0.922 0.956 0.823 0 1.914 -1.517 0.373 0.144 -0.907 1.880 0.584 -0.992 2.173 0.566 0.833 1.743 0.000 1.093 -1.474 0.779 0.000 0.958 -0.684 0.889 3.102 0.939 1.208 0.983 2.438 1.780 1.587 1.580 1 0.497 -0.833 1.149 1.709 0.131 0.971 0.756 -1.622 0.000 0.396 0.344 -0.548 0.000 0.408 2.066 0.218 0.000 0.901 0.577 1.156 3.102 0.840 1.001 1.014 0.883 0.655 0.872 0.793 1 0.754 -0.001 1.460 0.978 0.473 0.530 2.321 -1.417 0.000 0.637 1.522 -0.595 0.000 0.814 0.115 -0.694 2.548 1.186 0.358 0.740 1.551 0.968 1.065 0.992 0.599 0.731 0.846 0.966 1 0.606 0.238 0.945 1.266 -1.351 0.997 0.316 -0.192 1.087 0.615 0.053 1.523 0.000 0.848 -0.327 -0.248 0.000 1.253 -1.008 1.471 0.000 0.775 0.536 1.067 1.051 0.666 0.821 0.747 0 0.500 1.415 -1.067 0.200 1.704 0.748 -1.263 1.115 0.000 1.002 -0.583 -0.082 1.107 1.301 -0.112 -0.788 2.548 1.054 -1.600 -1.432 0.000 1.108 1.267 0.995 0.679 0.778 0.993 0.924 1 1.135 -0.308 0.574 0.275 -0.845 0.382 2.606 -1.453 0.000 0.494 -1.318 -1.453 1.107 0.886 -1.125 -0.441 2.548 1.093 -0.005 0.222 0.000 0.845 0.739 0.988 0.685 0.558 0.562 0.545 1 0.349 1.255 -0.985 0.646 -0.931 0.801 0.927 -0.820 0.000 1.319 -0.162 0.855 0.000 0.517 -1.108 0.861 0.000 0.786 0.203 0.366 3.102 0.696 0.686 0.987 0.648 0.440 0.465 0.582 1 1.631 0.190 -0.315 1.076 0.096 0.758 0.498 -1.246 2.173 0.645 1.546 1.183 0.000 1.556 0.483 1.021 1.274 0.788 0.971 -1.275 0.000 0.769 0.836 1.002 1.203 1.204 1.033 0.965 0 2.055 -0.883 0.806 1.140 0.667 1.059 -1.429 -0.809 2.173 0.482 -1.413 0.191 0.000 0.879 -1.058 1.620 2.548 1.301 -0.448 -1.242 0.000 0.856 0.873 0.973 0.871 0.993 1.137 0.957 0 1.559 -1.783 0.402 0.418 -0.779 0.772 0.259 1.646 2.173 0.789 1.013 -1.347 0.000 0.587 -0.185 0.296 2.548 0.424 2.461 -0.731 0.000 0.925 1.005 0.989 1.602 0.811 1.047 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0.266 -1.226 0.000 0.913 0.686 0.986 0.914 0.526 0.663 0.719 0 0.786 0.991 -1.721 0.926 -0.158 0.557 0.261 1.297 2.173 0.786 1.550 -0.360 0.000 0.449 -1.112 1.008 2.548 0.722 0.231 -1.056 0.000 0.903 1.032 1.167 0.934 0.532 0.804 0.738 0 1.107 0.538 -1.297 0.803 0.959 1.191 0.950 -0.308 2.173 1.461 0.697 1.326 2.215 0.535 -0.241 0.550 0.000 0.648 0.322 -0.673 0.000 0.618 0.872 1.169 1.152 1.946 1.063 0.844 0 0.662 -0.450 1.677 0.496 0.276 0.896 1.570 1.189 0.000 1.231 -0.736 -0.532 2.215 0.789 1.958 -1.401 0.000 0.930 0.939 0.452 0.000 0.919 0.868 0.987 0.956 0.832 1.268 1.000 1 0.608 -1.260 0.484 0.594 -1.028 1.014 1.378 -1.740 0.000 1.258 0.629 -0.073 2.215 0.611 1.707 -0.218 0.000 0.779 -0.187 1.037 0.000 1.177 0.939 0.987 1.062 0.686 1.176 1.136 1 0.321 -1.394 1.010 0.690 -0.991 0.462 1.260 -1.023 0.000 0.537 0.456 -0.572 0.000 1.028 -0.680 0.418 1.274 1.492 0.419 1.103 3.102 0.874 0.951 0.988 0.745 0.835 0.877 0.771 0 0.904 0.816 0.585 0.300 -0.584 0.869 0.916 -0.872 2.173 0.412 -1.497 1.436 0.000 0.541 2.124 -1.282 0.000 0.588 -0.628 0.910 3.102 2.502 1.437 0.985 0.927 1.039 1.082 0.907 1 0.451 -0.318 1.440 1.212 -1.287 3.069 -1.133 -0.251 0.000 1.661 1.728 1.537 0.000 1.819 -2.332 1.036 0.000 1.707 -0.030 -1.591 3.102 0.873 0.887 0.998 0.833 0.617 0.845 1.348 0 0.396 -1.290 -0.945 0.868 1.010 0.486 0.698 -1.297 1.087 0.348 -1.152 0.163 0.000 0.664 -0.224 0.595 0.000 1.279 0.797 -0.363 3.102 0.456 0.861 0.986 1.763 0.631 1.368 1.044 1 0.925 1.663 1.353 0.569 -1.333 0.441 -1.412 1.673 0.000 1.047 0.328 -0.853 0.000 0.904 -0.381 0.772 0.000 1.952 0.582 0.294 1.551 0.922 1.193 0.992 0.649 0.492 0.714 0.801 1 1.289 0.147 -1.295 1.515 -1.500 1.028 -0.945 1.042 0.000 1.408 0.797 -0.004 1.107 0.929 -0.670 -0.277 2.548 0.565 0.408 -1.587 0.000 1.223 1.092 0.969 1.650 1.085 1.159 1.147 1 0.384 -2.007 -1.648 0.629 -0.075 0.800 -0.170 -0.212 1.087 0.811 -1.081 1.690 2.215 0.926 -0.686 -1.119 0.000 1.390 0.568 0.971 0.000 1.541 1.166 0.992 0.764 1.307 0.949 0.810 1 0.937 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================================================ FILE: examples/regression/train.conf ================================================ # task type, support train and predict task = train # boosting type, support gbdt for now, alias: boosting, boost boosting_type = gbdt # application type, support following application # regression , regression task # binary , binary classification task # lambdarank , lambdarank task # alias: application, app objective = regression # eval metrics, support multi metric, delimited by ',' , support following metrics # l1 # l2 , default metric for regression # ndcg , default metric for lambdarank # auc # binary_logloss , default metric for binary # binary_error metric = l2 # frequency for metric output metric_freq = 1 # true if need output metric for training data, alias: tranining_metric, train_metric is_training_metric = true # column in data to use as label label_column = 0 # number of bins for feature bucket, 255 is a recommend setting, it can save memories, and also has good accuracy. max_bin = 255 # forced bin thresholds # forcedbins_filename = forced_bins.json # training data # if existing weight file, should name to "regression.train.weight" # alias: train_data, train data = regression.train # validation data, support multi validation data, separated by ',' # if existing weight file, should name to "regression.test.weight" # alias: valid, test, test_data, valid_data = regression.test # number of trees(iterations), alias: num_tree, num_iteration, num_iterations, num_round, num_rounds num_trees = 100 # shrinkage rate , alias: shrinkage_rate learning_rate = 0.05 # number of leaves for one tree, alias: num_leaf num_leaves = 31 # type of tree learner, support following types: # serial , single machine version # feature , use feature parallel to train # data , use data parallel to train # voting , use voting based parallel to train # alias: tree tree_learner = serial # number of threads for multi-threading. One thread will use one CPU, default is set to #cpu. # num_threads = 8 # feature sub-sample, will random select 80% feature to train on each iteration # alias: sub_feature feature_fraction = 0.9 # Support bagging (data sub-sample), will perform bagging every 5 iterations bagging_freq = 5 # Bagging fraction, will random select 80% data on bagging # alias: sub_row bagging_fraction = 0.8 # minimal number data for one leaf, use this to deal with over-fit # alias : min_data_per_leaf, min_data min_data_in_leaf = 100 # minimal sum hessians for one leaf, use this to deal with over-fit min_sum_hessian_in_leaf = 5.0 # save memory and faster speed for sparse feature, alias: is_sparse is_enable_sparse = true # when data is bigger than memory size, set this to true. otherwise set false will have faster speed # alias: two_round_loading, two_round use_two_round_loading = false # true if need to save data to binary file and application will auto load data from binary file next time # alias: is_save_binary, save_binary is_save_binary_file = false # output model file output_model = LightGBM_model.txt # support continuous train from trained gbdt model # input_model= trained_model.txt # output prediction file for predict task # output_result= prediction.txt # number of machines in distributed training, alias: num_machine num_machines = 1 # local listening port in distributed training, alias: local_port local_listen_port = 12400 # machines list file for distributed training, alias: mlist machine_list_file = mlist.txt ================================================ FILE: examples/xendcg/README.md ================================================ XE_NDCG Ranking Example ======================= Here is an example for LightGBM to train a ranking model with the [XE_NDCG loss](https://arxiv.org/abs/1911.09798). ***You must follow the [installation instructions](https://lightgbm.readthedocs.io/en/latest/Installation-Guide.html) for the following commands to work. The `lightgbm` binary must be built and available at the root of this project.*** Training -------- Run the following command in this folder: ```bash "../../lightgbm" config=train.conf ``` Prediction ---------- You should finish training first. Run the following command in this folder: ```bash "../../lightgbm" config=predict.conf ``` Data Format ----------- To learn more about the query format used in this example, check out the [query data format](https://lightgbm.readthedocs.io/en/latest/Parameters.html#query-data). ================================================ FILE: examples/xendcg/predict.conf ================================================ task = predict data = rank.test input_model= LightGBM_model.txt ================================================ FILE: examples/xendcg/rank.test ================================================ 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 3 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 2 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 0 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 2 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 1 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 2 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 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34:0.74 36:0.73 37:0.69 39:0.53 41:0.97 43:0.82 60:1.00 66:0.74 69:0.69 70:0.57 74:0.98 77:0.70 81:0.86 83:0.83 85:0.98 91:0.53 96:0.91 98:0.73 101:0.85 104:0.99 106:0.81 108:0.52 114:0.92 117:0.86 120:0.84 121:0.90 122:0.57 123:0.50 124:0.42 126:0.54 127:0.28 129:0.59 133:0.47 135:0.30 138:0.96 140:0.45 144:0.85 145:0.40 146:0.95 147:0.96 149:0.97 150:0.92 151:0.99 153:0.95 154:0.77 155:0.67 158:0.92 159:0.71 161:0.85 162:0.68 164:0.86 165:0.88 167:0.51 172:0.91 173:0.48 176:0.73 177:0.38 179:0.17 181:0.64 187:0.87 192:0.59 195:0.93 201:0.74 202:0.81 204:0.89 206:0.81 208:0.64 212:0.55 215:0.84 216:0.80 222:0.76 230:0.87 232:0.99 233:0.76 235:0.22 241:0.56 242:0.54 243:0.77 245:0.93 247:0.47 248:0.90 253:0.94 255:0.79 256:0.83 259:0.21 260:0.84 265:0.74 266:0.80 267:0.73 268:0.84 271:0.38 276:0.86 279:0.86 282:0.88 283:0.82 285:0.62 290:0.92 297:0.36 299:0.88 300:0.55 2 1:0.74 6:0.93 7:0.76 8:0.88 9:0.80 11:0.88 12:0.37 17:0.34 20:0.86 21:0.74 27:0.16 28:0.92 30:0.20 32:0.80 34:0.33 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242:0.16 243:0.58 244:0.61 245:0.91 246:0.92 247:0.88 248:0.79 253:0.84 254:0.84 255:0.94 256:0.64 259:0.21 261:0.96 264:0.98 265:0.68 266:0.84 267:0.59 268:0.65 271:0.89 275:0.96 276:0.81 279:0.81 281:0.86 282:0.75 284:0.94 285:0.61 287:0.94 290:0.88 292:0.95 294:0.98 295:0.99 300:0.70 0 1:0.67 7:0.64 8:0.58 9:0.73 10:0.89 11:0.88 12:0.51 17:0.34 18:0.79 21:0.05 22:0.93 23:0.96 27:0.58 29:0.62 30:0.87 31:0.92 32:0.71 33:0.97 34:0.86 36:0.96 37:0.36 39:0.51 43:0.57 44:0.92 45:0.93 47:0.93 56:0.97 62:0.97 64:0.77 66:0.83 69:0.79 70:0.36 71:0.79 74:0.56 75:0.97 77:0.70 79:0.92 80:0.98 81:0.76 82:0.99 83:0.69 86:0.73 88:0.98 91:0.44 96:0.78 97:0.99 98:0.62 99:0.99 101:0.70 102:0.97 108:0.63 114:0.92 117:0.86 122:0.91 123:0.60 124:0.37 126:0.54 127:0.30 128:0.97 129:0.05 131:0.61 133:0.36 135:0.75 137:0.92 139:0.81 140:0.45 144:0.92 145:0.76 146:0.59 147:0.05 149:0.65 150:0.60 151:0.77 153:0.69 154:0.46 155:0.58 157:0.87 158:0.77 159:0.31 162:0.86 165:0.88 166:0.94 168:0.97 170:0.90 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117:0.86 122:0.91 123:0.82 124:0.64 126:0.54 127:0.52 129:0.05 131:0.61 133:0.66 135:0.98 139:0.85 144:0.92 145:0.74 146:0.35 147:0.42 149:0.55 150:0.56 154:0.61 155:0.54 157:0.90 158:0.77 159:0.63 165:0.88 166:0.94 170:0.90 172:0.89 173:0.46 174:0.86 176:0.63 177:0.99 178:0.55 179:0.21 182:0.93 187:0.87 192:0.56 193:0.95 195:0.83 197:0.89 201:0.66 204:0.81 208:0.64 212:0.90 216:0.64 219:0.91 222:0.48 226:0.98 227:0.61 229:0.61 230:0.64 231:0.90 232:0.94 233:0.76 235:0.74 236:0.94 239:0.94 241:0.07 242:0.50 243:0.28 244:0.61 245:0.94 246:0.92 247:0.84 253:0.63 254:0.80 259:0.21 262:0.93 264:0.98 265:0.53 266:0.63 267:0.76 271:0.89 275:0.94 276:0.86 277:0.95 279:0.81 281:0.86 282:0.75 287:0.61 290:0.85 291:0.97 292:0.95 294:0.98 295:0.99 300:0.70 0 1:0.64 7:0.64 10:0.89 11:0.86 12:0.51 17:0.77 18:0.81 21:0.47 27:0.25 29:0.62 30:0.87 32:0.65 34:0.88 36:0.41 37:0.83 39:0.51 43:0.05 44:0.88 45:0.93 56:0.97 64:0.77 66:0.92 69:0.64 70:0.32 71:0.79 74:0.47 77:0.70 80:0.98 81:0.73 82:0.98 83:0.69 86:0.73 88:0.98 91:0.20 98:0.85 99:0.99 101:0.64 102:0.95 104:0.76 108:0.49 114:0.74 122:0.67 123:0.46 126:0.54 127:0.36 129:0.05 131:0.61 135:0.56 139:0.74 144:0.92 145:0.52 146:0.75 147:0.79 149:0.05 150:0.54 154:0.17 155:0.57 157:0.95 159:0.31 165:0.79 166:0.94 170:0.90 172:0.79 173:0.74 174:0.89 176:0.62 177:0.99 178:0.55 179:0.41 182:0.93 187:0.68 192:0.87 193:0.95 195:0.61 197:0.92 201:0.66 204:0.84 208:0.64 212:0.69 215:0.63 216:0.40 222:0.48 227:0.61 229:0.61 230:0.64 231:0.90 232:0.86 233:0.66 235:0.84 236:0.92 239:0.90 241:0.21 242:0.24 243:0.92 244:0.93 246:0.92 247:0.60 253:0.55 254:0.93 259:0.21 264:0.98 265:0.85 266:0.38 267:0.52 271:0.89 275:0.96 276:0.66 281:0.86 282:0.74 287:0.61 290:0.74 294:0.97 297:0.36 300:0.43 0 1:0.67 7:0.64 8:0.79 9:0.75 10:0.90 11:0.88 12:0.51 17:0.85 18:0.82 21:0.30 22:0.93 23:0.97 27:0.67 29:0.62 30:0.33 31:0.93 33:0.97 34:0.40 36:0.89 39:0.51 43:0.62 45:0.96 47:0.93 56:0.97 62:0.97 64:0.77 66:0.07 69:0.27 70:0.97 71:0.79 74:0.52 75:0.97 79:0.92 80:0.99 81:0.75 82:0.98 83:0.69 86:0.76 88:0.99 91:0.12 96:0.79 97:1.00 98:0.28 99:1.00 101:0.97 102:0.94 108:0.99 114:0.82 117:0.86 122:0.90 123:0.98 124:0.97 126:0.54 127:1.00 128:0.97 129:0.59 131:0.96 133:0.97 135:0.34 137:0.93 139:0.77 140:0.45 143:0.99 144:0.92 145:0.58 146:0.67 147:0.72 149:0.55 150:0.58 151:0.80 153:0.72 154:0.52 155:0.65 157:0.90 158:0.76 159:0.97 162:0.76 165:0.88 166:0.94 168:0.97 170:0.90 172:0.70 173:0.06 174:0.89 176:0.63 177:0.99 179:0.25 182:0.94 187:0.21 190:0.89 192:0.99 193:0.96 195:1.00 196:0.92 197:0.88 201:0.67 202:0.58 204:0.81 205:0.95 208:0.64 212:0.55 216:0.03 219:0.91 220:0.62 222:0.48 226:0.98 227:0.95 229:0.97 230:0.64 231:0.91 232:0.70 233:0.76 235:0.88 236:0.94 239:0.92 241:0.30 242:0.14 243:0.32 244:0.83 245:0.82 246:0.92 247:0.98 248:0.73 253:0.77 255:0.78 256:0.58 259:0.21 262:0.93 264:0.98 265:0.15 266:0.98 267:0.23 268:0.62 271:0.89 275:0.97 276:0.88 277:0.99 279:0.81 281:0.91 284:0.93 285:0.61 287:0.94 290:0.82 291:0.97 292:0.88 294:0.98 295:0.99 300:0.98 0 1:0.66 8:0.64 9:0.74 10:0.92 11:0.88 12:0.51 17:0.22 18:0.81 21:0.13 23:0.95 27:0.35 29:0.62 30:0.75 31:0.92 34:0.26 36:0.84 37:0.43 39:0.51 43:0.28 44:0.88 45:0.99 56:0.97 62:0.97 64:0.77 66:0.23 69:0.89 70:0.56 71:0.79 74:0.58 75:0.97 77:0.70 79:0.91 80:0.98 81:0.75 82:0.99 83:0.69 86:0.79 88:0.99 91:0.44 96:0.77 97:0.99 98:0.34 99:0.99 101:0.97 102:0.96 108:0.78 114:0.88 122:0.91 123:0.76 124:0.90 126:0.54 127:0.62 128:0.97 129:0.59 131:0.96 133:0.88 135:1.00 137:0.92 139:0.82 140:0.80 143:0.99 144:0.92 145:0.83 146:0.69 147:0.05 149:0.73 150:0.58 151:0.75 153:0.48 154:0.22 155:0.65 157:0.96 158:0.77 159:0.83 162:0.49 165:0.88 166:0.94 168:0.97 170:0.90 172:0.78 173:0.77 174:0.95 176:0.63 177:0.05 178:0.42 179:0.21 182:0.94 187:0.68 190:0.98 191:0.73 192:0.99 193:0.95 195:0.94 196:0.93 197:0.92 201:0.66 202:0.64 204:0.82 205:0.95 208:0.64 212:0.68 216:0.70 219:0.91 220:0.62 222:0.48 226:0.98 227:0.95 229:0.97 231:0.91 235:0.68 236:0.94 239:0.94 241:0.41 242:0.21 243:0.06 244:0.61 245:0.91 246:0.92 247:0.90 248:0.69 253:0.76 254:0.96 255:0.78 256:0.45 257:0.99 259:0.21 264:0.98 265:0.36 266:0.80 267:0.59 268:0.46 271:0.89 275:0.97 276:0.88 279:0.81 281:0.86 282:0.75 284:0.88 285:0.61 287:0.94 290:0.88 292:0.95 294:0.98 295:0.99 300:0.84 0 1:0.56 10:0.82 11:0.91 12:0.51 17:0.34 18:0.78 21:0.64 27:0.45 29:0.62 30:0.66 32:0.62 34:0.71 36:0.24 37:0.50 39:0.51 43:0.22 45:0.89 64:0.77 66:0.30 69:0.69 70:0.61 71:0.79 74:0.32 81:0.36 83:0.54 86:0.64 91:0.12 98:0.49 99:0.83 101:0.87 108:0.86 114:0.63 122:0.67 123:0.51 124:0.59 126:0.26 127:0.40 129:0.05 131:0.61 133:0.39 135:0.74 139:0.63 144:0.92 145:0.49 146:0.67 147:0.72 149:0.25 150:0.34 154:0.14 155:0.46 157:0.79 159:0.58 165:0.63 166:0.61 170:0.90 172:0.48 173:0.63 174:0.79 176:0.59 177:0.99 178:0.55 179:0.60 182:0.73 187:0.68 192:0.44 195:0.81 197:0.82 201:0.54 208:0.49 212:0.57 216:0.77 222:0.48 227:0.61 229:0.61 231:0.65 235:0.80 239:0.82 241:0.18 242:0.33 243:0.48 244:0.97 245:0.75 246:0.61 247:0.74 253:0.48 259:0.21 264:0.98 265:0.17 266:0.63 267:0.52 271:0.89 275:0.91 276:0.40 287:0.61 290:0.63 294:0.93 300:0.55 0 1:0.66 10:0.88 11:0.88 12:0.51 17:0.55 18:0.71 21:0.13 27:0.54 29:0.62 30:0.60 34:0.97 36:0.76 37:0.41 39:0.51 43:0.09 45:0.85 56:0.97 64:0.77 66:0.69 69:0.51 70:0.67 71:0.79 74:0.46 75:0.97 81:0.75 83:0.69 86:0.72 91:0.31 97:0.98 98:0.69 99:0.99 101:0.87 108:0.39 114:0.88 122:0.41 123:0.38 124:0.42 126:0.54 127:0.43 129:0.05 131:0.61 133:0.36 135:0.96 139:0.73 144:0.92 145:0.59 146:0.61 147:0.66 149:0.11 150:0.58 154:0.60 155:0.51 157:0.87 158:0.77 159:0.34 165:0.88 166:0.94 170:0.90 172:0.54 173:0.61 174:0.79 176:0.63 177:0.99 178:0.55 179:0.70 182:0.93 187:0.87 192:0.55 197:0.84 201:0.66 208:0.64 212:0.42 216:0.55 219:0.91 222:0.48 226:0.98 227:0.61 229:0.61 231:0.90 235:0.68 236:0.94 239:0.89 241:0.32 242:0.12 243:0.73 244:0.61 245:0.59 246:0.92 247:0.82 253:0.62 254:0.90 259:0.21 264:0.98 265:0.69 266:0.38 267:0.88 271:0.89 275:0.93 276:0.45 279:0.79 287:0.61 290:0.88 292:0.95 294:0.97 295:0.99 300:0.55 0 1:0.61 7:0.64 8:0.89 9:0.73 10:0.92 11:0.88 12:0.51 17:0.73 18:0.78 21:0.59 23:0.95 27:0.40 29:0.62 30:0.56 31:0.91 34:0.98 36:0.88 37:0.88 39:0.51 43:0.05 44:0.88 45:0.92 56:0.97 62:0.97 64:0.77 66:0.91 69:0.64 70:0.62 71:0.79 74:0.58 75:0.97 77:0.70 79:0.91 80:0.98 81:0.65 82:0.99 83:0.69 86:0.79 88:0.98 91:0.48 96:0.77 97:0.99 98:0.83 99:0.99 101:0.64 102:0.96 108:0.49 114:0.71 122:0.91 123:0.47 126:0.54 127:0.33 128:0.97 129:0.05 131:0.61 135:0.84 137:0.91 139:0.82 140:0.90 144:0.92 145:0.77 146:0.80 147:0.83 149:0.05 150:0.46 151:0.75 153:0.48 154:0.50 155:0.57 157:0.93 158:0.76 159:0.36 162:0.51 165:0.79 166:0.94 168:0.97 170:0.90 172:0.74 173:0.70 174:0.88 176:0.63 177:0.99 178:0.50 179:0.46 182:0.93 187:0.39 190:0.99 191:0.87 192:0.87 193:0.95 195:0.69 196:0.93 197:0.90 201:0.62 202:0.60 204:0.81 205:0.95 208:0.64 212:0.71 216:0.41 219:0.91 220:0.62 222:0.48 226:0.96 227:0.61 229:0.61 230:0.64 231:0.90 232:0.87 233:0.76 235:0.97 236:0.94 239:0.93 241:0.37 242:0.24 243:0.91 244:0.83 246:0.92 247:0.67 248:0.69 253:0.72 254:0.74 255:0.72 256:0.45 259:0.21 264:0.98 265:0.83 266:0.47 267:0.44 268:0.46 271:0.89 275:0.95 276:0.60 279:0.81 281:0.86 282:0.75 284:0.87 285:0.50 287:0.61 290:0.71 292:0.88 294:0.98 295:0.98 300:0.55 0 1:0.63 8:0.61 10:0.87 11:0.86 12:0.51 17:0.24 18:0.95 20:0.92 21:0.47 27:0.20 29:0.62 30:0.70 34:0.36 36:0.70 37:0.68 39:0.51 43:0.08 44:0.86 45:0.97 56:0.97 64:0.77 66:0.97 69:0.69 70:0.55 71:0.79 74:0.51 75:0.95 77:0.89 78:0.96 80:0.98 81:0.67 82:0.98 83:0.69 86:0.76 88:0.98 91:0.23 97:0.97 98:0.86 99:0.99 100:0.73 101:0.64 102:0.94 108:0.52 111:0.93 114:0.72 122:0.91 123:0.50 126:0.54 127:0.37 129:0.05 131:0.61 135:0.85 139:0.77 144:0.92 145:0.72 146:0.89 147:0.91 149:0.32 150:0.50 152:0.95 154:0.16 155:0.57 157:0.94 158:0.73 159:0.48 165:0.79 166:0.94 169:0.72 170:0.90 172:0.93 173:0.67 174:0.88 176:0.60 177:0.99 178:0.55 179:0.19 182:0.93 186:0.96 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227:0.61 229:0.61 231:0.94 232:0.97 235:0.42 236:0.97 239:0.93 241:0.27 242:0.63 243:0.73 246:0.96 247:0.30 253:0.66 259:0.21 262:0.93 265:0.18 266:0.12 267:0.23 271:0.32 276:0.23 279:0.80 283:0.67 287:0.61 290:0.92 291:0.97 292:0.88 297:0.36 300:0.08 1 1:0.68 8:0.76 10:0.99 11:0.75 12:0.20 17:0.85 18:0.92 21:0.05 22:0.93 27:0.31 29:0.94 30:0.09 33:0.97 34:0.40 36:0.95 37:0.68 39:0.36 43:0.88 45:0.88 47:0.93 64:0.77 66:0.86 69:0.35 70:0.95 71:0.86 74:0.87 75:0.99 77:0.70 81:0.83 83:0.69 86:0.95 91:0.25 97:0.94 98:0.89 99:0.95 101:0.65 102:0.94 104:0.98 106:0.81 108:0.27 114:0.95 117:0.86 122:0.94 123:0.26 124:0.38 126:0.54 127:0.82 129:0.05 131:0.61 133:0.62 135:0.97 139:0.94 144:0.76 145:0.65 146:0.99 147:0.99 149:0.85 150:0.82 154:0.87 155:0.53 157:0.87 158:0.86 159:0.87 165:0.81 166:0.96 170:0.90 172:0.96 173:0.14 174:0.99 176:0.73 177:0.88 178:0.55 179:0.19 182:0.87 187:0.39 192:0.55 195:0.95 197:0.99 201:0.67 202:0.46 206:0.81 208:0.64 212:0.60 215:0.91 216:0.57 219:0.95 222:0.97 226:0.96 227:0.61 229:0.61 231:0.94 232:0.99 235:0.31 236:0.97 239:0.99 241:0.43 242:0.18 243:0.87 245:0.87 246:0.96 247:0.98 253:0.74 262:0.93 265:0.89 266:0.75 267:0.91 271:0.32 276:0.92 279:0.80 282:0.75 283:0.67 287:0.61 290:0.95 291:0.97 292:0.88 297:0.36 300:0.84 2 1:0.68 7:0.70 8:0.71 9:0.74 10:0.97 11:0.75 12:0.20 17:0.06 18:0.83 21:0.13 23:0.96 27:0.19 29:0.94 30:0.74 31:0.90 34:0.79 36:0.75 37:0.73 39:0.36 43:0.96 45:0.90 62:0.98 64:0.77 66:0.77 69:0.15 70:0.53 71:0.86 74:0.85 75:0.99 79:0.90 81:0.81 83:0.69 86:0.92 91:0.17 96:0.74 97:0.94 98:0.76 99:0.95 101:0.65 102:0.94 108:0.59 114:0.92 120:0.62 122:0.94 123:0.57 124:0.41 126:0.54 127:0.56 128:0.96 129:0.59 131:0.98 133:0.47 135:0.70 137:0.90 139:0.93 140:0.80 144:0.76 145:0.67 146:0.18 147:0.23 149:0.92 150:0.77 151:0.69 153:0.48 154:0.96 155:0.65 157:0.89 158:0.86 159:0.40 162:0.51 164:0.57 165:0.81 166:0.96 168:0.96 170:0.90 172:0.81 173:0.27 174:0.94 176:0.73 177:0.88 178:0.42 179:0.41 182:0.88 187:0.87 192:0.98 193:0.98 195:0.63 196:0.94 197:0.96 201:0.67 202:0.60 204:0.83 205:0.95 208:0.64 212:0.90 215:0.84 216:0.81 219:0.95 220:0.74 222:0.97 226:0.96 227:0.99 229:0.93 230:0.73 231:0.94 233:0.66 235:0.42 236:0.97 239:0.98 241:0.12 242:0.18 243:0.19 245:0.81 246:0.96 247:0.94 248:0.64 253:0.80 255:0.74 256:0.45 259:0.21 260:0.63 265:0.76 266:0.47 267:0.18 268:0.46 271:0.32 276:0.70 279:0.80 281:0.86 283:0.67 284:0.89 285:0.62 287:0.97 290:0.92 292:0.88 297:0.36 300:0.70 1 1:0.58 10:0.94 11:0.75 12:0.20 17:0.67 18:0.84 21:0.47 22:0.93 27:0.19 29:0.94 30:0.45 33:0.97 34:0.90 36:0.43 37:0.73 39:0.36 43:0.11 45:0.81 47:0.93 48:0.91 64:0.77 66:0.30 69:0.37 70:0.46 71:0.86 74:0.57 81:0.51 83:0.69 86:0.87 91:0.40 97:0.89 98:0.19 99:0.83 101:0.65 108:0.29 122:0.65 123:0.28 124:0.78 126:0.32 127:0.47 129:0.59 131:0.61 133:0.73 135:0.90 139:0.84 144:0.76 145:0.61 146:0.25 147:0.31 149:0.09 150:0.46 154:0.74 155:0.52 157:0.83 158:0.77 159:0.45 165:0.65 166:0.96 170:0.90 172:0.32 173:0.39 174:0.86 177:0.88 178:0.55 179:0.74 182:0.87 187:0.98 192:0.46 193:0.98 197:0.89 201:0.55 204:0.82 208:0.61 212:0.58 216:0.81 222:0.97 227:0.61 229:0.61 231:0.94 235:0.60 236:0.94 239:0.93 241:0.27 242:0.22 243:0.48 245:0.56 246:0.96 247:0.67 253:0.45 254:0.74 259:0.21 262:0.93 265:0.20 266:0.47 267:0.69 271:0.32 276:0.40 279:0.76 281:0.91 283:0.82 287:0.61 291:0.97 300:0.33 2 1:0.66 7:0.81 8:0.77 10:0.98 11:0.75 12:0.20 17:0.92 18:0.93 21:0.30 22:0.93 27:0.34 29:0.94 30:0.36 32:0.80 33:0.97 34:0.76 36:0.99 37:0.64 39:0.36 43:0.83 44:0.93 45:0.92 47:0.93 48:0.91 64:0.77 66:0.60 69:0.37 70:0.85 71:0.86 74:0.83 75:0.99 77:0.89 81:0.77 83:0.69 86:0.93 91:0.18 97:0.99 98:0.85 99:0.95 101:0.65 102:0.98 104:0.99 106:0.81 108:0.29 114:0.86 117:0.86 122:0.94 123:0.28 124:0.51 126:0.54 127:0.46 129:0.59 131:0.61 133:0.47 135:0.93 139:0.96 144:0.76 145:0.89 146:0.95 147:0.96 149:0.85 150:0.67 154:0.79 155:0.57 157:0.92 158:0.86 159:0.69 165:0.81 166:0.96 170:0.90 172:0.87 173:0.23 174:0.98 176:0.73 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165:0.81 166:0.96 170:0.90 172:0.87 173:0.52 174:0.97 176:0.73 177:0.88 178:0.55 179:0.18 182:0.87 187:0.39 192:0.86 193:0.99 195:0.91 197:0.96 201:0.65 204:0.85 206:0.81 208:0.64 212:0.91 215:0.72 216:0.67 222:0.97 227:0.61 229:0.61 230:0.83 231:0.94 232:0.95 233:0.66 235:0.60 236:0.97 239:0.99 241:0.07 242:0.36 243:0.18 245:0.97 246:0.96 247:0.84 253:0.68 259:0.21 262:0.93 265:0.50 266:0.75 267:0.36 271:0.32 276:0.87 281:0.86 282:0.88 287:0.61 290:0.86 291:0.97 297:0.36 300:0.84 2 1:0.64 7:0.75 10:0.96 11:0.75 12:0.20 17:0.84 18:0.82 21:0.47 22:0.93 27:0.58 29:0.94 30:0.74 32:0.78 33:0.97 34:0.67 36:0.46 37:0.71 39:0.36 43:0.19 44:0.93 45:0.95 47:0.93 64:0.77 66:0.23 69:0.84 70:0.39 71:0.86 74:0.74 76:0.85 77:0.70 81:0.74 83:0.69 86:0.89 91:0.15 97:0.89 98:0.39 99:0.95 101:0.65 102:0.98 104:0.99 106:0.81 108:0.89 114:0.80 117:0.86 122:0.93 123:0.89 124:0.87 126:0.54 127:0.68 129:0.93 131:0.61 133:0.87 135:0.21 139:0.91 144:0.76 145:0.77 146:0.47 147:0.05 149:0.09 150:0.60 154:0.52 155:0.53 157:0.92 158:0.76 159:0.84 165:0.81 166:0.96 170:0.90 172:0.63 173:0.66 174:0.94 176:0.73 177:0.05 178:0.55 179:0.34 182:0.87 187:0.68 192:0.55 193:0.99 195:0.94 197:0.91 201:0.64 204:0.80 206:0.81 208:0.64 212:0.30 215:0.63 216:0.49 222:0.97 227:0.61 229:0.61 230:0.77 231:0.94 232:0.99 233:0.65 235:0.74 236:0.97 239:0.97 241:0.52 242:0.33 243:0.07 245:0.83 246:0.96 247:0.55 253:0.64 254:0.74 257:0.93 259:0.21 262:0.93 265:0.41 266:0.84 267:0.44 271:0.32 276:0.78 279:0.75 281:0.86 282:0.86 283:0.67 287:0.61 290:0.80 291:0.97 297:0.36 300:0.55 0 1:0.64 9:0.75 10:0.93 11:0.75 12:0.20 17:0.81 18:0.96 21:0.47 22:0.93 23:0.98 27:0.61 29:0.94 30:0.29 31:0.93 33:0.97 37:0.41 39:0.36 43:0.74 44:0.85 45:0.87 47:0.93 55:0.71 62:0.98 64:0.77 66:0.68 69:0.84 70:0.74 71:0.86 74:0.62 75:0.99 79:0.92 81:0.74 83:0.69 86:0.85 91:0.23 96:0.79 97:0.94 98:0.74 99:0.95 101:0.65 102:0.94 104:0.96 106:0.81 108:0.80 114:0.80 117:0.86 120:0.67 122:0.92 123:0.78 124:0.48 126:0.54 127:0.34 128:0.97 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29:0.71 30:0.88 32:0.67 34:0.37 36:0.22 37:0.47 39:0.51 43:0.91 44:0.88 45:0.87 48:0.91 55:0.52 64:0.77 66:0.98 69:0.44 70:0.29 71:0.94 74:0.85 76:0.85 77:0.99 81:0.76 83:0.91 85:0.72 86:0.85 91:0.04 97:0.89 98:0.95 99:0.95 101:0.87 108:0.34 114:0.69 123:0.33 126:0.54 127:0.64 129:0.05 135:0.26 139:0.88 145:1.00 146:0.93 147:0.94 149:0.87 150:0.73 154:0.91 155:0.64 158:0.77 159:0.56 161:0.96 165:0.70 167:0.57 172:0.96 173:0.40 176:0.62 177:0.70 178:0.55 179:0.18 181:0.48 187:0.39 192:0.58 195:0.73 201:0.68 204:0.85 208:0.64 212:0.93 215:0.55 216:0.36 219:0.91 222:0.35 230:0.73 233:0.66 235:0.97 241:0.47 242:0.53 243:0.99 247:0.88 253:0.64 265:0.95 266:0.27 267:0.19 271:0.54 276:0.92 279:0.80 281:0.86 282:0.75 283:0.82 290:0.69 292:0.95 297:0.36 300:0.70 0 12:0.51 17:0.38 21:0.22 27:0.45 28:0.83 30:0.87 32:0.72 34:0.68 36:0.17 37:0.50 39:0.66 43:0.30 55:0.71 66:0.86 69:0.68 70:0.26 74:0.44 81:0.60 91:0.18 98:0.88 108:0.52 123:0.50 124:0.38 126:0.32 127:0.49 129:0.05 133:0.39 135:0.61 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23:0.98 26:0.98 27:0.66 29:0.77 30:0.75 31:0.94 34:0.73 36:0.97 37:0.96 39:0.78 43:0.17 45:0.93 58:0.99 62:0.97 64:0.77 66:0.95 69:0.15 70:0.48 71:0.81 74:0.26 79:0.94 81:0.28 83:0.60 86:0.65 89:0.99 91:0.82 98:0.97 101:0.52 108:0.12 122:0.98 123:0.12 126:0.22 127:0.74 128:0.96 129:0.05 131:0.86 135:0.76 137:0.94 139:0.63 140:0.45 141:0.99 143:0.99 146:0.81 147:0.84 149:0.14 150:0.31 151:0.57 153:0.48 154:0.28 155:0.55 157:0.61 159:0.37 162:0.69 166:0.80 168:0.96 170:0.79 172:0.86 173:0.31 174:0.94 177:0.99 179:0.39 182:0.61 190:0.99 191:0.87 192:0.56 196:0.88 197:0.96 199:0.97 202:0.72 205:0.98 208:0.54 212:0.80 216:0.24 220:0.91 222:0.90 223:0.98 224:0.98 225:0.97 227:0.89 229:0.61 231:0.75 234:0.97 235:0.31 239:0.92 240:0.95 241:0.80 242:0.18 243:0.95 244:0.93 246:0.61 247:0.91 248:0.60 253:0.23 254:0.80 255:0.70 256:0.73 259:0.21 264:0.98 265:0.97 266:0.27 267:0.19 268:0.75 274:0.98 275:0.97 276:0.77 284:0.96 285:0.50 287:0.61 294:0.98 300:0.55 0 1:0.60 8:0.98 9:0.70 10:0.98 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28:0.56 29:0.62 30:0.95 31:0.99 34:0.64 36:0.58 37:0.84 39:0.99 41:0.98 43:0.66 48:0.91 55:0.45 60:0.95 64:0.77 66:0.92 69:0.81 70:0.13 71:0.89 74:0.62 78:0.92 79:0.99 81:0.81 83:0.91 85:0.72 86:0.69 91:0.97 96:0.95 98:0.80 100:0.94 101:0.87 108:0.65 111:0.92 114:0.98 120:0.95 121:0.90 123:0.63 124:0.36 126:0.54 127:0.82 129:0.05 133:0.37 135:0.18 137:0.99 139:0.85 140:0.90 145:0.67 146:0.78 147:0.50 149:0.70 150:0.88 151:0.98 152:0.90 153:0.93 154:0.55 155:0.67 158:0.92 159:0.48 161:0.69 162:0.51 164:0.95 165:0.93 167:0.96 169:0.91 172:0.95 173:0.88 176:0.73 177:0.05 178:0.50 179:0.22 181:0.48 186:0.92 189:0.96 191:0.97 192:0.87 196:0.98 201:0.93 202:0.96 204:0.89 208:0.64 212:0.93 215:0.96 216:0.59 219:0.95 220:0.82 222:0.25 230:0.87 233:0.76 235:0.12 238:0.90 241:0.91 242:0.82 243:0.08 245:0.83 247:0.35 248:0.95 253:0.95 255:0.95 256:0.95 257:0.84 259:0.21 260:0.94 261:0.93 265:0.80 266:0.54 267:0.52 268:0.95 271:0.66 276:0.88 279:0.86 281:0.91 283:0.82 284:0.99 285:0.62 290:0.98 292:0.95 297:0.36 300:0.55 2 1:0.74 6:0.90 7:0.81 8:0.88 9:0.80 11:0.57 12:0.86 17:0.28 18:0.86 20:0.98 21:0.22 27:0.29 28:0.56 29:0.62 30:0.41 31:1.00 32:0.68 34:0.80 36:0.73 37:0.63 39:0.99 41:0.99 43:0.76 44:0.90 48:0.98 55:0.59 60:0.87 64:0.77 66:0.54 69:0.77 70:0.51 71:0.89 74:0.64 78:0.92 79:1.00 81:0.65 83:0.91 85:0.72 86:0.90 91:0.98 96:0.96 98:0.69 100:0.94 101:0.87 108:0.61 111:0.92 114:0.71 120:0.95 121:0.90 122:0.43 123:0.58 124:0.50 126:0.54 127:0.54 129:0.05 133:0.43 135:0.20 137:1.00 139:0.94 140:0.45 145:0.79 146:0.56 147:0.37 149:0.54 150:0.79 151:1.00 152:0.90 153:0.99 154:0.68 155:0.67 158:0.87 159:0.32 161:0.69 162:0.56 164:0.93 165:0.93 167:0.95 169:0.92 172:0.48 173:0.91 176:0.73 177:0.38 179:0.73 181:0.48 186:0.92 189:0.92 191:0.97 192:0.87 195:0.59 196:1.00 201:0.93 202:0.97 204:0.89 208:0.64 212:0.72 215:0.51 216:0.64 219:0.95 222:0.25 228:0.99 230:0.87 233:0.76 235:0.52 238:0.90 241:0.89 242:0.19 243:0.36 245:0.66 247:0.74 248:0.96 253:0.96 255:0.95 256:1.00 257:0.65 259:0.21 260:0.94 261:0.94 265:0.70 266:0.27 267:0.28 268:0.97 271:0.66 276:0.45 279:0.86 281:0.91 283:0.82 284:1.00 285:0.62 290:0.71 292:0.95 297:0.36 300:0.33 1 1:0.74 6:0.81 7:0.81 8:0.94 9:0.80 12:0.86 17:0.49 20:0.76 21:0.05 27:0.56 28:0.56 29:0.62 30:0.95 31:0.95 32:0.80 34:0.84 36:0.73 37:0.84 39:0.99 41:0.88 43:0.10 44:0.93 48:0.98 55:0.45 64:0.77 66:0.54 69:0.81 71:0.89 74:0.59 77:0.70 78:0.85 79:0.94 81:0.80 83:0.91 91:0.88 96:0.83 98:0.15 100:0.81 104:0.76 108:0.66 111:0.83 114:0.89 120:0.82 121:0.90 123:0.63 126:0.54 127:0.09 129:0.05 135:0.28 137:0.95 139:0.82 140:0.45 145:0.65 146:0.19 147:0.24 149:0.08 150:0.87 151:0.82 152:0.75 153:0.72 154:0.09 155:0.67 159:0.10 161:0.69 162:0.72 164:0.77 165:0.93 167:0.97 169:0.79 172:0.10 173:0.96 175:0.91 176:0.73 177:0.05 179:0.22 181:0.48 186:0.85 189:0.83 190:0.89 191:0.86 192:0.87 195:0.58 196:0.94 201:0.93 202:0.72 204:0.89 208:0.64 215:0.78 216:0.13 220:0.62 222:0.25 230:0.87 233:0.76 235:0.31 238:0.82 241:0.95 243:0.63 244:0.83 248:0.80 253:0.82 255:0.95 256:0.73 257:0.84 259:0.21 260:0.80 261:0.76 265:0.16 267:0.36 268:0.75 271:0.66 277:0.87 281:0.91 282:0.88 284:0.94 285:0.62 290:0.89 297:0.36 299:0.88 300:0.08 2 1:0.60 6:0.80 7:0.75 8:0.60 9:0.80 10:0.95 11:0.46 12:0.86 17:0.82 18:0.97 20:0.92 21:0.54 23:1.00 25:0.88 26:0.95 27:0.43 28:0.76 29:0.86 30:0.62 31:1.00 32:0.67 34:0.78 36:0.81 37:0.26 39:0.99 41:0.90 43:0.66 44:0.88 45:0.83 48:1.00 58:1.00 62:1.00 64:0.77 66:0.61 69:0.27 70:0.66 71:0.92 74:0.41 75:0.98 78:0.96 79:1.00 81:0.60 83:0.91 86:0.97 89:0.98 91:0.85 96:0.96 97:0.99 98:0.80 99:0.83 100:0.92 101:0.47 108:0.21 111:0.93 120:0.97 122:0.91 123:0.20 124:0.50 126:0.51 127:0.94 128:1.00 129:0.59 133:0.46 135:0.19 137:1.00 139:0.98 140:0.96 141:0.98 145:0.94 146:0.72 147:0.76 149:0.82 150:0.49 151:0.96 152:0.92 153:0.93 154:0.65 155:0.67 159:0.43 161:0.90 162:0.86 164:0.96 165:0.65 167:0.81 168:1.00 169:0.88 170:0.77 172:0.80 173:0.37 174:0.89 175:0.75 177:0.70 179:0.41 181:0.50 186:0.96 189:0.82 191:0.73 192:0.99 193:0.99 195:0.64 196:1.00 197:0.92 199:1.00 201:0.64 202:0.91 204:0.82 205:0.99 208:0.64 212:0.84 215:0.58 216:0.29 219:0.94 220:0.62 222:0.21 223:0.95 224:0.99 225:0.99 226:0.98 228:0.99 230:0.77 233:0.76 234:0.99 235:0.88 236:0.95 238:0.84 239:0.96 240:0.99 241:0.82 242:0.27 243:0.68 244:0.61 245:0.90 247:0.84 248:0.91 253:0.93 255:1.00 256:0.93 257:0.65 259:0.21 260:0.97 261:0.93 265:0.80 266:0.47 267:0.17 268:0.95 271:0.66 274:1.00 276:0.76 277:0.69 279:0.81 281:0.91 283:0.82 284:1.00 285:0.62 292:0.95 297:0.36 300:0.70 2 1:0.66 6:0.84 8:0.67 10:0.97 11:0.75 12:0.86 17:0.64 18:0.92 20:0.89 21:0.47 22:0.93 26:0.95 27:0.42 28:0.76 29:0.86 30:0.74 32:0.80 33:0.97 34:0.31 36:0.34 37:0.63 39:0.99 43:0.77 44:0.93 45:0.91 47:0.93 58:0.93 64:0.77 66:0.82 69:0.80 70:0.33 71:0.92 74:0.54 77:0.70 78:0.82 81:0.74 83:0.91 85:0.72 86:0.95 89:0.99 91:0.12 98:0.69 100:0.80 101:0.96 102:0.98 104:0.90 106:0.81 108:0.63 111:0.80 114:0.80 122:0.93 123:0.61 124:0.38 126:0.54 127:0.26 129:0.05 133:0.39 135:0.20 139:0.94 141:0.91 145:0.59 146:0.79 147:0.18 149:0.84 150:0.54 152:0.84 154:0.69 155:0.51 159:0.43 161:0.90 165:0.93 167:0.53 169:0.77 170:0.77 172:0.79 173:0.79 174:0.94 176:0.73 177:0.38 178:0.55 179:0.30 181:0.50 186:0.82 187:0.87 189:0.86 192:0.50 195:0.74 197:0.96 199:0.98 201:0.66 206:0.81 208:0.64 212:0.83 215:0.63 216:0.68 219:0.91 222:0.21 223:0.94 224:0.93 225:0.98 234:0.91 235:0.80 236:0.97 238:0.82 239:0.97 240:0.95 241:0.37 242:0.44 243:0.13 245:0.71 247:0.76 253:0.59 257:0.84 259:0.21 261:0.82 262:0.93 265:0.69 266:0.38 267:0.23 271:0.66 274:0.91 276:0.70 282:0.88 283:0.82 290:0.80 291:0.97 292:0.95 297:0.36 299:0.88 300:0.33 2 1:0.64 7:0.69 9:0.71 10:0.99 11:0.46 12:0.86 17:0.53 18:0.97 21:0.30 26:0.95 27:0.51 28:0.76 29:0.86 30:0.73 31:0.88 32:0.80 34:0.36 36:0.35 37:0.72 39:0.99 43:0.63 44:0.93 45:0.95 48:0.97 55:0.59 58:0.98 64:0.77 66:0.74 69:0.15 70:0.45 71:0.92 74:0.47 77:0.70 79:0.88 81:0.62 83:0.69 86:0.98 89:0.99 91:0.22 96:0.71 98:0.80 99:0.95 101:0.47 102:0.98 108:0.12 114:0.84 120:0.58 122:0.76 123:0.12 124:0.43 126:0.51 127:0.63 129:0.05 133:0.37 135:0.82 137:0.88 139:0.97 140:0.45 141:0.98 145:0.61 146:0.91 147:0.93 149:0.46 150:0.47 151:0.58 153:0.48 154:0.48 155:0.57 159:0.67 161:0.90 162:0.86 164:0.56 165:0.81 167:0.58 170:0.77 172:0.93 173:0.15 174:0.96 175:0.75 176:0.70 177:0.70 179:0.21 181:0.50 187:0.39 190:0.77 192:0.86 195:0.83 196:0.93 197:0.97 199:0.99 201:0.62 202:0.36 208:0.61 212:0.89 215:0.72 216:0.59 220:0.87 222:0.21 223:0.95 224:0.99 225:0.98 228:0.99 230:0.71 233:0.76 234:0.98 235:0.52 239:0.99 240:0.95 241:0.54 242:0.31 243:0.77 244:0.61 245:0.95 247:0.86 248:0.56 253:0.62 254:0.92 255:0.70 256:0.45 257:0.65 259:0.21 260:0.58 265:0.80 266:0.54 267:0.44 268:0.46 271:0.66 274:0.97 276:0.89 277:0.69 281:0.91 282:0.88 284:0.88 285:0.60 290:0.84 297:0.36 299:0.88 300:0.70 2 6:0.97 7:0.75 8:0.90 9:0.72 10:0.96 11:0.57 12:0.86 17:0.75 18:0.89 20:0.82 22:0.93 26:0.95 27:0.19 28:0.76 29:0.86 30:0.73 31:0.97 32:0.67 33:0.97 34:0.59 36:0.47 37:0.84 39:0.99 43:0.73 44:0.88 45:0.93 47:0.93 48:0.98 58:0.99 64:0.77 66:0.61 69:0.40 70:0.40 71:0.92 74:0.56 75:0.97 78:0.90 79:0.97 81:0.46 83:0.91 86:0.91 89:0.99 91:0.42 96:0.73 97:0.97 98:0.76 100:0.93 101:0.87 104:0.86 106:0.81 108:0.74 111:0.90 120:0.81 122:0.93 123:0.72 124:0.58 126:0.32 127:0.80 129:0.05 133:0.65 135:0.68 137:0.97 139:0.93 140:0.95 141:0.98 145:0.54 146:0.37 147:0.44 149:0.76 150:0.46 151:0.63 152:0.78 153:0.83 154:0.66 155:0.64 158:0.81 159:0.76 161:0.90 162:0.75 163:0.81 164:0.74 167:0.54 169:0.91 170:0.77 172:0.77 173:0.25 174:0.95 177:0.88 179:0.44 181:0.50 186:0.90 187:0.68 189:0.98 190:0.77 191:0.73 192:0.87 193:0.99 195:0.88 196:0.98 197:0.95 199:0.99 201:0.60 202:0.65 204:0.83 206:0.81 208:0.64 212:0.48 215:0.96 216:0.59 219:0.94 222:0.21 223:0.95 224:1.00 225:0.97 226:0.98 230:0.77 233:0.76 234:1.00 235:0.05 238:0.93 239:0.97 240:0.95 241:0.43 242:0.41 243:0.29 244:0.61 245:0.82 247:0.82 248:0.61 253:0.54 255:0.73 256:0.80 259:0.21 260:0.82 261:0.84 262:0.93 265:0.76 266:0.71 267:0.52 268:0.69 271:0.66 274:0.99 276:0.73 277:0.69 279:0.80 281:0.91 283:0.82 284:0.95 285:0.60 291:0.97 292:0.95 297:0.36 300:0.55 2 1:0.59 7:0.75 8:0.89 9:0.70 10:0.95 11:0.52 12:0.86 17:0.85 18:0.89 21:0.40 22:0.93 23:0.96 26:0.95 27:0.35 28:0.76 29:0.86 30:0.75 31:0.88 32:0.72 33:0.97 34:0.23 36:0.29 37:0.79 39:0.99 43:0.32 44:0.92 45:0.92 47:0.93 48:0.98 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 2 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 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253:0.29 259:0.21 265:0.24 266:0.12 267:0.59 271:0.63 274:0.91 276:0.12 287:0.61 300:0.13 0 6:0.93 8:0.60 9:0.80 12:0.79 17:0.53 20:0.78 21:0.78 26:0.98 27:0.51 28:0.47 29:0.91 31:0.96 34:0.40 36:0.39 37:0.94 39:0.88 41:0.90 58:0.99 71:0.56 78:0.78 79:0.96 83:0.91 91:0.67 96:0.85 100:0.73 111:0.77 120:0.75 131:0.85 135:0.47 137:0.96 140:0.97 141:0.99 144:0.57 151:0.87 152:0.89 153:0.48 155:0.67 157:0.61 161:0.68 162:0.50 164:0.72 166:0.61 167:0.85 169:0.81 175:0.97 178:0.53 181:0.60 182:0.80 186:0.79 187:0.39 191:0.92 192:0.87 199:0.95 202:0.81 208:0.64 216:0.43 220:0.62 222:0.35 223:0.98 224:0.98 227:0.88 229:0.89 231:0.68 234:0.97 241:0.72 244:0.93 246:0.61 248:0.83 253:0.79 255:0.95 256:0.73 257:0.93 259:0.21 260:0.76 261:0.82 267:0.44 268:0.73 271:0.63 274:0.98 277:0.95 284:0.95 285:0.62 287:0.91 2 1:0.74 6:0.99 8:0.94 9:0.80 11:0.85 12:0.79 17:0.06 18:0.82 20:0.81 21:0.13 26:0.98 27:0.19 28:0.47 29:0.91 30:0.91 31:0.95 34:0.74 36:0.56 37:0.96 39:0.88 41:0.82 43:0.96 45:0.66 58:0.98 64:0.77 66:0.19 69:0.12 70:0.24 71:0.56 74:0.75 78:0.83 79:0.94 81:0.75 83:0.91 85:0.90 86:0.92 91:0.72 96:0.80 98:0.25 100:0.95 101:0.87 104:0.58 108:0.54 111:0.90 114:0.79 120:0.69 122:0.57 123:0.63 124:0.81 126:0.54 127:0.69 129:0.89 131:0.84 133:0.78 135:0.47 137:0.95 139:0.89 140:0.45 141:0.99 144:0.57 146:0.17 147:0.22 149:0.90 150:0.84 151:0.87 152:0.99 153:0.48 154:0.96 155:0.67 157:0.85 159:0.39 161:0.68 162:0.62 164:0.57 165:0.93 166:0.78 167:0.76 169:1.00 172:0.32 173:0.28 176:0.73 177:0.70 179:0.68 181:0.60 182:0.80 186:0.83 187:0.21 189:0.99 191:0.75 192:0.87 196:0.96 199:0.96 201:0.93 202:0.60 208:0.64 212:0.57 215:0.61 216:0.34 222:0.35 223:0.98 224:0.99 227:0.88 229:0.88 231:0.68 232:0.80 234:0.98 235:0.31 238:0.98 241:0.63 242:0.44 243:0.25 245:0.66 246:0.89 247:0.55 248:0.77 253:0.83 255:0.95 256:0.58 259:0.21 260:0.70 261:1.00 265:0.24 266:0.27 267:0.28 268:0.59 271:0.63 274:0.97 276:0.51 284:0.91 285:0.62 287:0.91 290:0.79 297:0.36 300:0.33 1 1:0.74 8:0.59 11:0.64 12:0.79 17:0.11 18:0.79 21:0.13 26:0.98 27:0.19 28:0.47 29:0.91 30:0.86 34:0.42 36:0.56 37:0.96 39:0.88 43:0.96 58:0.93 64:0.77 66:0.26 69:0.12 70:0.19 71:0.56 74:0.55 81:0.75 83:0.91 85:0.85 86:0.86 91:0.58 98:0.50 101:0.87 104:0.58 108:0.10 114:0.79 122:0.57 123:0.10 124:0.74 126:0.54 127:0.91 129:0.81 131:0.61 133:0.67 135:0.70 139:0.82 141:0.91 144:0.57 146:0.63 147:0.68 149:0.77 150:0.84 154:0.96 155:0.67 157:0.81 159:0.63 161:0.68 163:0.75 165:0.93 166:0.78 167:0.72 172:0.27 173:0.17 176:0.73 177:0.70 178:0.55 179:0.83 181:0.60 182:0.79 187:0.21 192:0.87 199:0.95 201:0.93 202:0.36 208:0.64 212:0.23 215:0.61 216:0.34 222:0.35 223:0.98 224:0.93 227:0.61 229:0.61 231:0.68 232:0.80 234:0.91 235:0.31 241:0.47 242:0.42 243:0.45 245:0.59 246:0.89 247:0.60 253:0.56 259:0.21 265:0.52 266:0.63 267:0.91 271:0.63 274:0.91 276:0.38 287:0.61 290:0.79 297:0.36 300:0.18 0 1:0.74 7:0.81 9:0.80 11:0.64 12:0.79 17:0.59 18:0.91 21:0.40 22:0.93 26:0.98 27:0.68 28:0.47 29:0.91 30:0.68 31:0.86 32:0.80 34:0.76 36:0.35 37:0.37 39:0.88 41:0.82 43:0.91 44:0.93 47:0.93 58:0.98 60:0.87 64:0.77 66:0.86 69:0.44 70:0.41 71:0.56 74:0.81 77:0.89 79:0.86 81:0.56 83:0.91 85:0.85 86:0.92 91:0.38 96:0.68 98:0.80 101:0.87 104:0.95 106:0.81 108:0.34 114:0.62 117:0.86 120:0.55 121:0.97 122:0.86 123:0.33 126:0.54 127:0.29 129:0.05 131:0.84 135:0.74 137:0.86 139:0.96 140:0.45 141:0.99 144:0.57 145:0.70 146:0.61 147:0.67 149:0.85 150:0.75 151:0.50 153:0.48 154:0.90 155:0.67 157:0.82 158:0.91 159:0.23 161:0.68 162:0.86 164:0.57 165:0.93 166:0.78 167:0.65 172:0.61 173:0.61 176:0.73 177:0.70 179:0.58 181:0.60 182:0.80 187:0.39 192:0.87 195:0.58 196:0.90 199:0.97 201:0.93 202:0.36 204:0.89 206:0.81 208:0.64 212:0.59 215:0.42 216:0.40 219:0.95 220:0.91 222:0.35 223:0.98 224:0.99 227:0.88 229:0.88 230:0.86 231:0.68 232:0.97 233:0.73 234:0.98 235:0.60 241:0.15 242:0.54 243:0.87 246:0.89 247:0.55 248:0.49 253:0.50 255:0.95 256:0.45 259:0.21 260:0.55 262:0.93 265:0.80 266:0.27 267:0.23 268:0.46 271:0.63 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238:0.86 241:0.83 242:0.63 243:0.37 245:0.46 246:0.97 247:0.30 248:0.80 253:0.85 255:0.79 256:0.64 259:0.21 260:0.74 261:0.77 265:0.50 266:0.27 267:0.59 268:0.67 271:0.35 276:0.23 279:0.86 282:0.88 283:0.71 285:0.62 287:0.98 289:0.98 290:0.95 297:0.36 299:0.88 300:0.18 4 1:0.74 6:0.98 8:0.97 9:0.80 11:0.78 12:0.37 17:0.12 20:0.99 27:0.07 28:0.56 30:0.70 34:0.52 36:0.46 37:0.99 39:0.79 41:0.99 43:0.95 46:0.99 60:0.95 66:0.88 69:0.15 70:0.39 74:0.87 78:0.89 81:0.95 83:0.90 85:0.93 91:0.98 96:0.99 98:0.98 100:0.99 101:0.85 104:0.58 107:0.99 108:0.12 110:0.99 111:0.98 114:0.98 120:0.97 122:0.57 123:0.12 125:0.98 126:0.54 127:0.86 129:0.05 131:0.95 135:0.47 138:0.99 140:0.45 144:0.76 146:0.76 147:0.80 149:0.91 150:0.98 151:1.00 152:0.99 153:0.98 154:0.95 155:0.67 157:0.91 158:0.92 159:0.32 160:1.00 161:0.45 162:0.73 164:0.96 165:0.92 166:0.91 167:0.97 169:0.99 172:0.65 173:0.37 176:0.73 177:0.38 179:0.72 181:0.96 182:0.88 186:0.89 189:0.99 191:0.96 192:0.59 201:0.74 202:0.93 208:0.64 212:0.35 215:0.96 216:0.59 222:0.60 227:0.98 229:0.93 231:0.87 232:0.77 235:0.05 238:0.99 241:0.90 242:0.62 243:0.88 246:0.97 247:0.39 248:0.99 253:0.99 255:0.79 256:0.94 259:0.21 260:0.97 261:1.00 265:0.98 266:0.38 267:0.79 268:0.95 271:0.35 276:0.52 279:0.86 283:0.82 285:0.62 287:0.98 289:0.99 290:0.98 297:0.36 300:0.33 2 1:0.74 6:0.84 8:0.88 9:0.80 11:0.78 12:0.37 17:0.45 20:0.85 21:0.05 27:0.50 28:0.56 30:0.61 34:0.39 36:0.62 37:0.37 39:0.79 41:0.90 43:0.84 46:1.00 60:0.87 66:0.89 69:0.55 70:0.63 74:0.88 78:0.75 81:0.91 83:0.86 85:0.93 91:0.83 96:0.85 98:0.87 100:0.78 101:0.85 104:0.58 107:1.00 108:0.42 110:0.99 111:0.74 114:0.95 120:0.76 122:0.43 123:0.40 125:0.99 126:0.54 127:0.40 129:0.05 131:0.95 135:0.37 140:0.45 144:0.76 146:0.63 147:0.68 149:0.91 150:0.96 151:0.89 152:0.95 153:0.87 154:0.81 155:0.67 157:0.91 158:0.87 159:0.23 160:0.98 161:0.45 162:0.68 164:0.75 165:0.90 166:0.91 167:0.95 169:0.77 172:0.68 173:0.76 176:0.73 177:0.38 179:0.58 181:0.96 182:0.87 186:0.76 192:0.59 201:0.74 202:0.67 208:0.64 212:0.91 215:0.91 216:0.21 220:0.62 222:0.60 227:0.98 229:0.93 231:0.87 232:0.77 235:0.12 241:0.84 242:0.57 243:0.89 246:0.97 247:0.47 248:0.83 253:0.89 255:0.79 256:0.64 259:0.21 260:0.77 261:0.81 265:0.87 266:0.23 267:0.28 268:0.69 271:0.35 276:0.56 279:0.86 283:0.71 285:0.62 287:0.98 289:0.99 290:0.95 297:0.36 300:0.55 2 1:0.74 6:0.84 8:0.70 9:0.80 11:0.78 12:0.37 17:0.66 20:0.90 21:0.05 27:0.50 28:0.56 30:0.61 34:0.44 36:0.62 37:0.37 39:0.79 41:0.82 43:0.84 46:1.00 66:0.89 69:0.55 70:0.63 74:0.86 78:0.76 81:0.91 83:0.79 85:0.93 91:0.80 96:0.78 98:0.87 100:0.79 101:0.85 104:0.58 107:1.00 108:0.42 110:0.99 111:0.76 114:0.95 120:0.66 122:0.43 123:0.40 125:1.00 126:0.54 127:0.40 129:0.05 131:0.94 135:0.37 140:0.45 144:0.76 146:0.63 147:0.68 149:0.91 150:0.96 151:0.79 152:0.95 153:0.69 154:0.81 155:0.67 157:0.91 159:0.23 160:0.96 161:0.45 162:0.86 164:0.62 165:0.90 166:0.91 167:0.93 169:0.77 172:0.68 173:0.76 176:0.73 177:0.38 179:0.58 181:0.96 182:0.87 186:0.77 187:0.21 189:0.88 192:0.59 201:0.74 202:0.46 208:0.64 212:0.91 215:0.91 216:0.21 220:0.62 222:0.60 227:0.98 229:0.93 231:0.87 232:0.77 235:0.12 238:0.89 241:0.80 242:0.57 243:0.89 246:0.97 247:0.47 248:0.72 253:0.84 255:0.79 256:0.45 259:0.21 260:0.67 261:0.81 265:0.87 266:0.23 267:0.44 268:0.55 271:0.35 276:0.56 285:0.62 287:0.98 289:0.98 290:0.95 297:0.36 300:0.55 0 1:0.74 9:0.80 11:0.78 12:0.37 17:0.40 21:0.64 27:0.45 28:0.56 30:0.58 32:0.80 34:0.68 36:0.19 37:0.50 39:0.79 41:0.82 43:0.82 46:0.95 60:0.87 66:0.18 69:0.70 70:0.60 74:0.78 77:0.70 81:0.57 83:0.83 85:0.85 91:0.23 96:0.68 98:0.19 101:0.77 107:0.98 108:0.53 110:0.98 114:0.72 120:0.54 122:0.43 123:0.81 124:0.73 125:0.97 126:0.54 127:0.36 129:0.81 131:0.94 133:0.67 135:0.63 140:0.45 144:0.76 145:0.50 146:0.23 147:0.29 149:0.75 150:0.72 151:0.49 153:0.48 154:0.77 155:0.67 157:0.85 158:0.87 159:0.48 160:0.96 161:0.45 162:0.86 163:0.88 164:0.57 165:0.70 166:0.91 167:0.67 172:0.21 173:0.68 176:0.73 177:0.38 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265:0.25 266:0.38 267:0.36 271:0.21 274:0.91 276:0.34 282:0.87 290:0.65 297:0.36 300:0.55 4 1:0.74 6:1.00 8:0.96 9:0.80 11:0.64 12:0.20 17:0.30 18:0.73 20:0.99 21:0.59 26:1.00 27:1.00 29:0.62 30:0.33 31:0.97 34:0.49 36:0.88 37:0.73 39:0.37 43:0.74 45:0.66 58:1.00 64:0.77 66:0.32 69:0.85 70:0.49 71:0.97 74:0.39 78:0.96 79:0.97 81:0.45 83:0.60 86:0.80 91:0.82 96:0.91 98:0.55 99:0.83 100:1.00 101:0.52 104:0.58 108:0.41 111:1.00 114:0.58 120:0.78 123:0.39 124:0.61 126:0.54 127:0.36 129:0.05 133:0.43 135:0.58 137:0.97 139:0.77 140:0.45 141:1.00 146:0.22 147:0.57 149:0.51 150:0.67 151:0.94 152:0.99 153:0.82 154:0.64 155:0.67 159:0.33 162:0.81 164:0.86 165:0.70 169:1.00 172:0.21 173:0.96 176:0.73 177:0.38 179:0.85 186:0.96 189:1.00 192:0.87 196:0.94 199:0.98 201:0.93 202:0.80 208:0.64 212:0.19 215:0.39 216:0.16 222:0.76 223:1.00 224:0.99 234:0.98 235:0.80 238:1.00 241:0.89 242:0.19 243:0.49 245:0.54 247:0.55 248:0.85 253:0.86 255:0.95 256:0.85 257:0.65 259:0.21 260:0.79 261:1.00 265:0.57 266:0.47 267:0.91 268:0.85 271:0.21 274:0.99 276:0.29 277:0.69 284:0.98 285:0.62 290:0.58 297:0.36 300:0.33 0 8:0.99 11:0.64 12:0.20 17:0.04 18:0.70 21:0.13 26:0.95 27:0.47 29:0.62 30:0.76 34:0.36 36:0.83 37:0.64 39:0.37 43:0.60 45:0.66 55:0.71 58:0.93 66:0.77 69:0.63 70:0.29 71:0.97 74:0.36 81:0.58 86:0.73 91:0.88 98:0.76 101:0.52 104:0.76 108:0.48 120:0.64 123:0.46 126:0.26 127:0.25 129:0.05 135:0.47 139:0.70 140:0.45 141:0.91 146:0.57 147:0.63 149:0.33 150:0.43 151:0.53 153:0.83 154:0.49 159:0.21 162:0.80 164:0.82 172:0.37 173:0.80 177:0.70 179:0.81 190:0.89 199:0.94 202:0.79 212:0.73 215:0.61 216:0.31 220:0.91 222:0.76 223:0.94 224:0.93 234:0.91 235:0.12 241:0.90 242:0.24 243:0.79 244:0.61 247:0.47 248:0.55 253:0.29 256:0.84 259:0.21 260:0.62 265:0.76 266:0.23 267:0.28 268:0.84 271:0.21 274:0.91 276:0.29 283:0.78 285:0.47 297:0.36 300:0.25 0 11:0.64 12:0.20 17:0.64 18:0.96 21:0.78 26:0.99 27:0.67 29:0.62 30:0.13 34:0.47 36:0.47 37:0.84 39:0.37 43:0.06 45:0.93 58:0.93 66:0.12 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268:0.46 276:0.80 277:0.69 279:0.86 281:0.91 282:0.87 283:0.78 284:0.89 285:0.62 290:0.71 292:0.91 297:0.36 300:0.84 1 1:0.66 9:0.79 11:0.82 12:0.06 17:0.20 18:0.78 21:0.40 27:0.11 28:1.00 29:0.77 30:0.88 31:0.95 32:0.72 34:0.93 36:0.44 37:0.89 39:0.63 43:0.76 44:0.92 45:0.83 64:0.77 66:0.16 69:0.82 70:0.33 71:0.85 74:0.64 77:0.70 79:0.95 81:0.65 83:0.91 85:0.72 86:0.85 91:0.58 96:0.85 98:0.18 99:0.83 101:0.87 108:0.80 114:0.82 120:0.76 122:0.57 123:0.79 124:0.86 126:0.54 127:0.31 129:0.93 131:0.85 133:0.84 135:0.85 137:0.95 139:0.85 140:0.87 144:0.76 145:0.77 146:0.18 147:0.23 149:0.79 150:0.56 151:0.86 153:0.48 154:0.67 155:0.66 157:0.82 159:0.59 161:0.57 162:0.50 164:0.66 165:0.70 166:0.79 167:0.67 172:0.32 173:0.74 176:0.70 177:0.70 178:0.48 179:0.49 181:0.55 182:0.78 187:0.39 190:0.77 192:0.87 195:0.87 196:0.95 201:0.63 202:0.70 208:0.64 212:0.37 215:0.67 216:0.90 220:0.62 222:0.35 227:0.92 229:0.87 231:0.69 235:0.60 241:0.44 242:0.45 243:0.23 245:0.66 246:0.91 247:0.35 248:0.83 253:0.84 255:0.79 256:0.58 259:0.21 260:0.76 265:0.19 266:0.54 267:0.73 268:0.59 271:0.78 276:0.55 282:0.80 284:0.92 285:0.62 287:0.93 290:0.82 297:0.36 300:0.43 2 1:0.66 7:0.64 8:0.58 9:0.75 11:0.78 12:0.06 17:0.16 18:0.83 20:0.74 21:0.30 22:0.93 27:0.18 28:1.00 29:0.77 30:0.45 31:0.92 32:0.80 34:0.63 36:0.29 37:0.46 39:0.63 41:0.88 43:0.92 44:0.93 47:0.93 60:0.87 64:0.77 66:0.10 69:0.37 70:0.48 71:0.85 74:0.81 77:0.70 78:0.75 79:0.92 81:0.74 83:0.60 85:0.91 86:0.92 91:0.35 96:0.78 98:0.18 100:0.73 101:0.87 104:0.89 106:0.81 108:0.83 111:0.74 114:0.86 117:0.86 120:0.66 122:0.57 123:0.68 124:0.86 126:0.54 127:0.56 129:0.93 131:0.85 133:0.84 135:0.98 137:0.92 139:0.95 140:0.45 144:0.76 145:0.76 146:0.18 147:0.23 149:0.89 150:0.56 151:0.77 152:0.74 153:0.48 154:0.92 155:0.65 157:0.82 158:0.92 159:0.65 161:0.57 162:0.86 164:0.67 165:0.93 166:0.79 167:0.55 169:0.77 172:0.32 173:0.27 176:0.73 177:0.70 179:0.63 181:0.55 182:0.78 186:0.76 187:0.95 192:0.87 195:0.82 196:0.96 201:0.64 202:0.50 204:0.82 206:0.81 208:0.64 212:0.39 215:0.72 216:0.75 219:0.95 220:0.62 222:0.35 227:0.92 229:0.88 230:0.64 231:0.69 232:0.91 233:0.66 235:0.60 241:0.03 242:0.51 243:0.24 245:0.64 246:0.91 247:0.47 248:0.71 253:0.82 255:0.77 256:0.58 259:0.21 260:0.67 261:0.74 262:0.93 265:0.20 266:0.54 267:0.59 268:0.59 271:0.78 276:0.53 279:0.80 281:0.86 282:0.88 283:0.82 284:0.92 285:0.62 287:0.93 290:0.86 292:0.95 297:0.36 299:0.88 300:0.43 0 11:0.82 12:0.06 17:0.30 18:0.75 21:0.30 27:0.45 28:1.00 29:0.77 30:0.27 34:0.81 36:0.21 37:0.50 39:0.63 43:0.75 45:0.73 66:0.62 69:0.68 70:0.91 71:0.85 74:0.64 81:0.30 85:0.88 86:0.86 91:0.20 98:0.58 101:0.87 108:0.52 123:0.50 124:0.64 126:0.26 127:0.46 129:0.05 131:0.61 133:0.73 135:0.93 139:0.83 144:0.76 145:0.47 146:0.78 147:0.82 149:0.85 150:0.33 154:0.66 157:0.82 159:0.64 161:0.57 166:0.73 167:0.70 172:0.67 173:0.59 177:0.70 178:0.55 179:0.51 181:0.55 182:0.75 187:0.68 212:0.42 215:0.72 216:0.77 222:0.35 227:0.61 229:0.61 231:0.67 235:0.31 241:0.55 242:0.30 243:0.68 245:0.66 246:0.61 247:0.67 253:0.48 259:0.21 265:0.59 266:0.80 267:0.36 271:0.78 276:0.62 283:0.67 287:0.61 297:0.36 300:0.70 1 1:0.69 7:0.64 9:0.79 11:0.56 12:0.06 17:0.97 18:0.67 21:0.05 27:0.08 28:1.00 29:0.77 30:0.55 31:0.94 32:0.80 34:0.66 36:0.40 37:0.69 39:0.63 43:0.23 44:0.93 45:0.83 64:0.77 66:0.18 69:0.96 70:0.72 71:0.85 74:0.57 76:0.85 77:0.70 79:0.93 81:0.72 83:0.91 86:0.72 91:0.31 96:0.80 98:0.34 99:0.83 101:0.52 104:0.58 108:0.65 114:0.95 120:0.69 122:0.77 123:0.62 124:0.88 126:0.54 127:0.67 129:0.81 131:0.85 133:0.86 135:0.73 137:0.94 139:0.84 140:0.45 144:0.76 145:0.40 146:0.28 147:0.74 149:0.53 150:0.64 151:0.86 153:0.48 154:0.15 155:0.66 157:0.81 159:0.84 161:0.57 162:0.86 164:0.56 165:0.70 166:0.79 167:0.76 172:0.45 173:0.97 176:0.70 177:0.38 179:0.50 181:0.55 182:0.78 187:0.39 190:0.77 192:0.87 195:0.94 196:0.94 201:0.66 202:0.36 204:0.81 208:0.64 212:0.39 215:0.91 216:0.08 222:0.35 227:0.92 229:0.87 230:0.65 231:0.69 232:0.75 233:0.76 235:0.22 241:0.67 242:0.53 243:0.39 244:0.61 245:0.73 246:0.92 247:0.74 248:0.77 253:0.81 255:0.78 256:0.45 257:0.65 259:0.21 260:0.70 265:0.36 266:0.71 267:0.44 268:0.46 271:0.78 276:0.63 277:0.69 281:0.91 282:0.88 284:0.88 285:0.62 287:0.93 290:0.95 297:0.36 299:0.88 300:0.70 3 1:0.66 7:0.75 9:0.80 11:0.82 12:0.06 17:0.36 18:0.92 21:0.30 22:0.93 27:0.21 28:1.00 29:0.77 30:0.87 31:0.98 34:0.49 36:0.86 37:0.74 39:0.63 41:0.90 43:0.83 45:0.87 47:0.93 48:0.91 64:0.77 66:0.45 69:0.15 70:0.46 71:0.85 74:0.81 79:0.98 81:0.66 83:0.91 85:0.72 86:0.95 91:0.56 96:0.94 97:0.98 98:0.47 99:0.83 101:0.87 104:0.84 106:0.81 108:0.64 114:0.84 117:0.86 120:0.89 122:0.57 123:0.62 124:0.58 126:0.54 127:0.24 129:0.59 131:0.85 133:0.46 135:0.18 137:0.98 139:0.95 140:0.96 144:0.76 146:0.49 147:0.55 149:0.92 150:0.58 151:0.86 153:0.92 154:0.79 155:0.67 157:0.84 158:0.82 159:0.38 161:0.57 162:0.64 164:0.84 165:0.70 166:0.79 167:0.71 172:0.63 173:0.19 176:0.70 177:0.70 179:0.30 181:0.55 182:0.78 187:0.39 192:0.87 196:0.99 201:0.64 202:0.80 204:0.84 206:0.81 208:0.64 212:0.69 215:0.72 216:0.95 219:0.94 222:0.35 227:0.92 229:0.88 230:0.77 231:0.69 232:0.88 233:0.76 235:0.52 241:0.57 242:0.43 243:0.48 245:0.85 246:0.92 247:0.55 248:0.86 253:0.95 255:0.95 256:0.81 259:0.21 260:0.89 262:0.93 265:0.49 266:0.27 267:0.65 268:0.81 271:0.78 276:0.65 279:0.82 281:0.91 283:0.82 284:0.97 285:0.62 287:0.94 290:0.84 292:0.95 297:0.36 300:0.70 2 1:0.69 6:0.84 7:0.64 8:0.72 9:0.80 11:0.56 12:0.06 17:0.97 18:0.59 20:0.94 21:0.05 27:0.08 28:1.00 29:0.77 30:0.21 31:0.98 32:0.80 34:0.22 36:0.32 37:0.69 39:0.63 41:0.82 43:0.22 44:0.93 45:0.66 55:0.59 64:0.77 66:0.36 69:0.96 70:0.88 71:0.85 74:0.53 76:0.85 77:0.70 78:0.76 79:0.98 81:0.72 83:0.91 86:0.65 91:0.85 96:0.92 98:0.37 99:0.83 100:0.79 101:0.52 104:0.58 108:0.65 111:0.75 114:0.95 120:0.86 122:0.77 123:0.62 124:0.77 126:0.54 127:0.66 129:0.05 131:0.85 133:0.73 135:0.56 137:0.98 139:0.81 140:0.45 144:0.76 145:0.40 146:0.43 147:0.74 149:0.29 150:0.64 151:0.93 152:0.94 153:0.78 154:0.15 155:0.67 157:0.75 159:0.81 161:0.57 162:0.78 164:0.81 165:0.70 166:0.79 167:0.96 169:0.83 172:0.45 173:0.99 176:0.70 177:0.38 179:0.69 181:0.55 182:0.78 186:0.76 189:0.87 191:0.81 192:0.87 195:0.93 196:0.96 201:0.66 202:0.71 204:0.81 208:0.64 212:0.35 215:0.91 216:0.08 222:0.35 227:0.92 229:0.88 230:0.65 231:0.69 232:0.75 233:0.76 235:0.22 238:0.91 241:0.98 242:0.75 243:0.51 244:0.61 245:0.62 246:0.92 247:0.55 248:0.83 253:0.90 255:0.94 256:0.75 257:0.65 259:0.21 260:0.86 261:0.86 265:0.39 266:0.80 267:0.44 268:0.76 271:0.78 276:0.47 277:0.69 281:0.91 282:0.88 284:0.97 285:0.62 287:0.94 290:0.95 297:0.36 299:0.88 300:0.70 1 1:0.66 11:0.78 12:0.06 17:0.40 18:0.71 21:0.30 27:0.45 28:1.00 29:0.77 30:0.62 34:0.85 36:0.20 37:0.50 39:0.63 43:0.82 60:0.87 64:0.77 66:0.75 69:0.69 70:0.23 71:0.85 74:0.63 81:0.74 83:0.60 85:0.80 86:0.82 91:0.14 98:0.19 101:0.87 108:0.52 114:0.86 122:0.57 123:0.50 126:0.54 127:0.10 129:0.05 131:0.61 135:0.80 139:0.85 144:0.76 145:0.49 146:0.27 147:0.33 149:0.75 150:0.56 154:0.77 155:0.51 157:0.78 158:0.86 159:0.12 161:0.57 165:0.93 166:0.79 167:0.58 172:0.32 173:0.69 176:0.73 177:0.70 178:0.55 179:0.14 181:0.55 182:0.77 187:0.68 192:0.48 201:0.64 208:0.64 212:0.84 215:0.72 216:0.77 219:0.95 222:0.35 227:0.61 229:0.61 231:0.68 235:0.60 241:0.12 242:0.63 243:0.77 246:0.91 247:0.35 253:0.64 259:0.21 265:0.21 266:0.17 267:0.28 271:0.78 276:0.22 279:0.80 283:0.67 287:0.61 290:0.86 292:0.88 297:0.36 300:0.18 2 1:0.66 6:0.96 8:0.97 9:0.79 12:0.06 17:0.64 20:0.77 21:0.30 27:0.16 28:1.00 29:0.77 31:0.94 34:0.85 36:0.51 37:0.91 39:0.63 64:0.77 71:0.85 74:0.41 78:0.74 79:0.93 81:0.74 83:0.91 91:0.37 96:0.80 97:0.89 100:0.78 104:0.98 111:0.74 114:0.86 120:0.69 126:0.54 131:0.85 135:0.60 137:0.94 139:0.70 140:0.45 144:0.76 150:0.56 151:0.86 152:0.75 153:0.48 155:0.66 157:0.61 158:0.82 161:0.57 162:0.86 164:0.56 165:0.93 166:0.79 167:0.74 169:0.75 175:0.97 176:0.73 181:0.55 182:0.78 186:0.75 187:0.95 189:0.97 190:0.77 192:0.87 196:0.91 201:0.64 202:0.36 208:0.64 215:0.72 216:0.44 219:0.94 222:0.35 227:0.92 229:0.87 231:0.69 232:0.98 235:0.60 238:0.91 241:0.64 244:0.97 246:0.91 248:0.77 253:0.79 255:0.78 256:0.45 257:0.93 259:0.21 260:0.70 261:0.74 267:0.36 268:0.46 271:0.78 277:0.95 279:0.80 283:0.82 284:0.88 285:0.62 287:0.93 290:0.86 292:0.95 297:0.36 1 1:0.64 6:0.89 7:0.75 8:0.76 9:0.70 11:0.82 12:0.06 17:0.38 18:0.73 20:0.75 21:0.54 27:0.16 28:1.00 29:0.77 30:0.91 31:0.87 32:0.72 34:0.75 36:0.33 37:0.85 39:0.63 43:0.62 44:0.92 45:0.77 48:0.99 64:0.77 66:0.33 69:0.85 70:0.22 71:0.85 74:0.63 77:0.89 78:0.76 79:0.86 81:0.62 83:0.91 85:0.72 86:0.80 91:0.27 96:0.69 98:0.17 99:0.83 100:0.73 101:0.87 108:0.79 111:0.75 114:0.76 120:0.55 122:0.57 123:0.78 124:0.72 126:0.54 127:0.20 129:0.81 131:0.85 133:0.67 135:0.88 137:0.87 139:0.85 140:0.90 144:0.76 145:0.91 146:0.17 147:0.22 149:0.70 150:0.52 151:0.50 152:0.81 153:0.48 154:0.50 155:0.64 157:0.81 159:0.40 161:0.57 162:0.49 164:0.56 165:0.70 166:0.79 167:0.70 169:0.75 172:0.32 173:0.81 176:0.70 177:0.70 178:0.50 179:0.52 181:0.55 182:0.78 186:0.77 187:0.68 190:0.77 191:0.70 192:0.58 195:0.84 196:0.89 201:0.62 202:0.65 204:0.85 208:0.64 212:0.39 215:0.58 216:0.81 220:0.96 222:0.35 227:0.92 229:0.87 230:0.77 231:0.69 233:0.66 235:0.74 241:0.53 242:0.55 243:0.33 245:0.58 246:0.91 247:0.35 248:0.50 253:0.59 255:0.69 256:0.45 259:0.21 260:0.55 261:0.75 265:0.18 266:0.27 267:0.36 268:0.46 271:0.78 276:0.35 281:0.86 282:0.80 284:0.88 285:0.62 287:0.93 290:0.76 297:0.36 300:0.25 1 1:0.68 7:0.81 11:0.56 12:0.06 17:0.47 18:0.97 21:0.13 27:0.03 28:1.00 29:0.77 30:0.93 32:0.72 34:0.96 36:0.93 37:0.98 39:0.63 43:0.74 44:0.92 45:0.97 64:0.77 66:0.51 69:0.61 70:0.29 71:0.85 74:0.94 77:0.70 81:0.70 83:0.91 86:0.98 91:0.38 97:0.94 98:0.74 99:0.83 101:0.52 104:0.93 108:0.73 114:0.92 121:1.00 122:0.57 123:0.72 124:0.56 126:0.54 127:0.54 129:0.59 131:0.61 133:0.46 135:0.73 139:0.99 144:0.76 145:0.45 146:0.62 147:0.67 149:0.98 150:0.61 154:0.64 155:0.65 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1:0.64 6:0.96 7:0.81 8:0.92 9:0.80 11:0.75 12:0.37 17:0.03 18:0.75 20:0.98 21:0.47 26:0.99 27:0.17 28:0.91 29:0.91 30:0.54 31:1.00 32:0.80 34:0.86 36:0.76 37:0.62 39:0.29 41:0.99 43:0.80 44:0.93 45:0.81 58:1.00 60:0.99 64:0.77 66:0.88 69:0.73 70:0.56 71:0.57 74:0.83 77:0.70 78:0.98 79:1.00 81:0.76 83:0.91 85:0.80 86:0.82 91:0.89 96:0.99 97:0.98 98:0.79 100:0.99 101:0.87 108:0.56 111:0.99 114:0.80 120:0.98 121:0.90 122:0.75 123:0.53 126:0.54 127:0.27 129:0.05 135:0.28 137:1.00 138:0.99 139:0.92 140:0.45 141:1.00 145:0.44 146:0.73 147:0.77 149:0.78 150:0.60 151:0.99 152:0.94 153:0.96 154:0.74 155:0.67 158:0.92 159:0.29 161:0.87 162:0.78 164:0.97 165:0.93 167:0.84 169:0.99 172:0.67 173:0.81 176:0.73 177:0.70 179:0.49 181:0.55 186:0.98 187:0.21 189:0.97 191:0.80 192:0.87 195:0.62 196:1.00 199:1.00 201:0.62 202:0.94 204:0.84 208:0.64 212:0.75 215:0.63 216:0.92 219:0.95 220:0.74 222:0.60 223:0.99 224:0.98 230:0.87 233:0.76 234:0.98 235:0.80 238:0.96 241:0.78 242:0.33 243:0.89 247:0.74 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26:0.94 27:0.45 28:0.91 29:0.91 30:0.45 34:0.83 36:0.29 37:0.50 39:0.29 43:0.76 58:0.93 66:0.69 69:0.82 70:0.25 71:0.57 74:0.49 85:0.72 86:0.72 91:0.11 98:0.15 101:0.87 108:0.67 122:0.57 123:0.65 127:0.09 129:0.05 135:0.88 139:0.70 141:0.91 145:0.67 146:0.22 147:0.28 149:0.50 154:0.67 159:0.10 161:0.87 167:0.57 172:0.21 173:0.83 177:0.70 178:0.55 179:0.12 181:0.55 187:0.68 199:0.94 212:0.61 216:0.77 222:0.60 223:0.93 224:0.93 234:0.91 235:0.98 241:0.35 242:0.63 243:0.73 247:0.30 253:0.40 259:0.21 265:0.16 266:0.17 267:0.28 271:0.38 274:0.91 276:0.20 300:0.18 2 1:0.66 6:0.90 7:0.81 8:0.80 9:0.79 11:0.57 12:0.37 17:0.57 18:0.87 20:0.93 21:0.30 26:0.99 27:0.35 28:0.91 29:0.91 30:0.45 31:0.95 32:0.80 34:0.52 36:0.43 37:0.43 39:0.29 41:0.92 43:0.95 44:0.93 55:0.45 58:0.99 60:0.95 64:0.77 66:0.74 69:0.19 70:0.69 71:0.57 74:0.90 77:0.89 78:0.96 79:0.95 81:0.79 83:0.60 85:0.93 86:0.96 91:0.62 96:0.87 98:0.81 100:0.96 101:0.87 104:0.54 108:0.15 111:0.95 114:0.86 120:0.78 121:0.90 122:0.57 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181:0.55 186:0.92 189:0.93 192:0.87 195:0.82 196:0.98 199:0.98 201:0.65 202:0.36 204:0.84 208:0.64 212:0.58 215:0.84 216:0.04 219:0.95 222:0.60 223:0.99 224:0.99 230:0.87 232:0.77 233:0.76 234:0.99 235:0.52 238:0.83 241:0.79 242:0.32 243:0.59 245:0.88 247:0.88 248:0.77 253:0.88 255:0.78 256:0.45 259:0.21 260:0.70 261:0.87 265:0.65 266:0.54 267:0.23 268:0.46 271:0.38 274:0.97 276:0.82 279:0.80 281:0.91 282:0.88 283:0.82 284:0.89 285:0.62 290:0.92 292:0.95 297:0.36 299:0.97 300:0.70 0 1:0.67 6:0.87 7:0.81 8:0.74 9:0.80 11:0.75 12:0.37 17:0.57 18:0.80 20:0.98 21:0.22 22:0.93 26:0.99 27:0.37 28:0.91 29:0.91 30:0.26 31:0.98 32:0.80 34:0.45 36:0.98 37:0.62 39:0.29 41:0.94 43:0.89 44:0.93 45:0.89 47:0.93 48:0.91 58:0.99 60:0.97 64:0.77 66:0.96 69:0.47 70:0.77 71:0.57 74:0.87 77:0.70 78:0.92 79:0.98 81:0.81 83:0.91 85:0.85 86:0.88 91:0.54 96:0.95 97:0.89 98:0.97 100:0.89 101:0.87 104:0.94 106:0.81 108:0.36 111:0.90 114:0.90 117:0.86 120:0.90 121:0.90 122:0.75 123:0.34 126:0.54 127:0.76 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64:0.77 66:0.85 69:0.23 70:0.89 71:0.57 74:0.94 78:0.92 79:0.98 81:0.76 83:0.60 85:0.97 86:0.98 91:0.46 96:0.93 97:0.89 98:0.89 100:0.92 101:0.97 104:0.84 106:0.81 108:0.18 111:0.91 114:0.80 117:0.86 120:0.89 121:0.97 122:0.57 123:0.18 124:0.39 126:0.54 127:0.73 129:0.59 133:0.61 135:0.47 137:0.98 139:0.98 140:0.45 141:1.00 146:0.99 147:0.99 149:0.96 150:0.60 151:0.96 152:0.90 153:0.88 154:0.95 155:0.67 158:0.92 159:0.87 161:0.87 162:0.70 164:0.85 165:0.93 167:0.57 169:0.90 172:0.97 173:0.10 176:0.73 177:0.70 179:0.15 181:0.55 186:0.92 187:0.39 189:0.92 192:0.87 196:0.99 199:0.99 201:0.62 202:0.78 204:0.85 206:0.81 208:0.64 212:0.67 215:0.63 216:0.95 219:0.95 220:0.74 222:0.60 223:0.99 224:0.99 230:0.87 232:0.89 233:0.76 234:0.98 235:0.80 238:0.87 241:0.32 242:0.24 243:0.86 245:0.92 247:0.93 248:0.92 253:0.96 255:0.94 256:0.79 259:0.21 260:0.89 261:0.91 262:0.93 265:0.89 266:0.75 267:0.88 268:0.81 271:0.38 274:0.99 276:0.94 279:0.82 281:0.91 283:0.82 284:0.97 285:0.62 290:0.80 292:0.95 297:0.36 300:0.84 2 1:0.69 6:0.90 8:0.68 9:0.71 11:0.75 12:0.37 17:0.22 18:0.97 20:0.86 26:0.99 27:0.70 28:0.91 29:0.91 30:0.19 34:0.47 36:0.97 37:0.32 39:0.29 43:0.85 45:0.95 58:0.93 60:0.87 64:0.77 66:0.54 69:0.61 70:0.93 71:0.57 74:0.90 78:0.87 81:0.85 83:0.60 85:0.95 86:0.97 91:0.31 96:0.72 98:0.76 100:0.86 101:0.87 104:0.90 106:0.81 108:0.79 111:0.86 114:0.98 117:0.86 120:0.59 122:0.57 123:0.78 124:0.83 126:0.54 127:0.72 129:0.81 133:0.89 135:0.73 139:0.97 140:0.45 141:0.91 146:0.77 147:0.80 149:0.96 150:0.86 151:0.57 152:0.81 153:0.48 154:0.81 155:0.58 158:0.92 159:0.91 161:0.87 162:0.76 164:0.68 165:0.93 167:0.57 169:0.84 172:0.96 173:0.25 176:0.73 177:0.70 179:0.14 181:0.55 186:0.87 187:0.39 189:0.91 190:0.95 192:0.57 199:0.99 201:0.67 202:0.58 206:0.81 208:0.64 212:0.51 215:0.96 216:0.35 219:0.95 220:0.91 222:0.60 223:0.99 224:0.93 232:0.93 234:0.91 235:0.31 238:0.82 241:0.35 242:0.19 243:0.31 245:0.95 247:0.91 248:0.58 253:0.79 255:0.70 256:0.58 259:0.21 260:0.59 261:0.84 265:0.76 266:0.94 267:0.28 268:0.62 271:0.38 274:0.91 276:0.95 279:0.80 283:0.82 285:0.56 290:0.98 292:0.95 297:0.36 300:0.84 1 1:0.64 7:0.81 11:0.82 12:0.37 17:0.51 18:0.96 21:0.47 22:0.93 26:0.99 27:0.50 28:0.91 29:0.91 30:0.21 32:0.80 34:0.80 36:0.88 37:0.60 39:0.29 43:0.95 44:0.93 45:0.93 47:0.93 58:0.93 60:0.95 64:0.77 66:0.72 69:0.27 70:0.89 71:0.57 74:0.96 77:0.70 81:0.76 83:0.60 85:0.98 86:0.98 91:0.07 97:0.89 98:0.88 101:0.97 104:0.84 106:0.81 108:0.21 114:0.80 117:0.86 121:0.90 122:0.57 123:0.20 124:0.46 126:0.54 127:0.88 129:0.59 133:0.61 135:0.61 139:0.99 141:0.91 145:0.70 146:0.99 147:0.99 149:0.98 150:0.60 154:0.95 155:0.57 158:0.92 159:0.89 161:0.87 165:0.93 167:0.50 172:0.98 173:0.10 176:0.73 177:0.70 178:0.55 179:0.14 181:0.55 187:0.39 192:0.56 195:0.97 199:0.99 201:0.62 204:0.84 206:0.81 208:0.64 212:0.60 215:0.63 216:0.86 219:0.95 222:0.60 223:0.99 224:0.93 230:0.87 232:0.89 233:0.76 234:0.91 235:0.80 241:0.03 242:0.24 243:0.75 245:0.98 247:0.93 253:0.72 259:0.21 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204:0.82 206:0.81 208:0.64 212:0.70 215:0.72 216:0.66 219:0.95 222:0.60 223:0.99 224:0.98 230:0.71 233:0.66 234:0.98 235:0.68 238:0.83 241:0.35 242:0.24 243:0.15 245:0.89 247:0.94 248:0.89 253:0.93 255:0.94 256:0.79 257:0.84 259:0.21 260:0.84 261:0.87 265:0.70 266:0.75 267:0.73 268:0.80 271:0.38 274:0.99 276:0.86 279:0.82 281:0.86 282:0.88 283:0.82 284:0.97 285:0.62 290:0.86 292:0.95 297:0.36 299:0.88 300:0.84 1 1:0.62 6:0.86 8:0.71 9:0.75 10:0.97 11:0.86 12:0.92 17:0.69 18:0.92 20:0.87 21:0.47 22:0.93 23:0.99 27:0.30 28:0.96 29:0.86 30:0.52 31:0.95 33:0.97 34:0.37 36:0.18 37:0.71 39:0.55 41:0.82 43:0.76 44:0.88 45:0.98 47:0.93 48:0.91 62:0.99 64:0.77 66:0.45 69:0.95 70:0.81 71:0.81 74:0.86 75:0.98 76:0.85 78:0.99 79:0.94 81:0.61 83:0.91 85:0.80 86:0.92 91:0.11 96:0.84 97:0.98 98:0.56 99:0.95 100:0.98 101:1.00 102:0.96 104:0.58 108:0.93 111:0.98 114:0.78 117:0.86 120:0.79 122:0.82 123:0.93 124:0.87 126:0.51 127:0.96 128:0.98 129:0.59 131:0.84 133:0.89 135:0.71 137:0.95 139:0.92 140:0.97 144:0.76 145:0.67 146:0.88 147:0.80 149:0.89 150:0.47 151:0.77 152:0.86 153:0.89 154:0.20 155:0.66 157:0.86 158:0.81 159:0.89 161:0.60 162:0.55 164:0.75 165:0.81 166:0.77 167:0.45 168:0.98 169:0.95 170:0.82 172:0.95 173:0.88 174:0.97 176:0.70 177:0.70 178:0.42 179:0.15 181:0.66 182:0.80 186:0.99 187:0.87 189:0.88 192:0.98 195:0.96 196:0.97 197:0.96 201:0.60 202:0.81 205:0.98 208:0.64 212:0.77 216:0.29 219:0.94 220:0.62 222:0.35 226:0.98 227:0.91 229:0.89 231:0.67 232:0.95 235:0.68 236:0.95 238:0.92 239:0.97 242:0.15 243:0.22 245:0.96 246:0.93 247:0.99 248:0.70 253:0.88 255:0.78 256:0.80 257:0.65 259:0.21 260:0.79 261:0.94 262:0.93 265:0.57 266:0.89 267:0.23 268:0.79 271:0.16 276:0.96 279:0.81 281:0.91 283:0.82 284:0.97 285:0.62 287:0.95 290:0.78 291:0.97 292:0.95 300:0.84 2 1:0.67 6:0.85 8:0.64 9:0.76 10:0.93 11:0.84 12:0.92 17:0.30 18:0.83 20:0.88 21:0.22 22:0.93 23:0.97 27:0.35 28:0.96 29:0.86 30:0.33 31:0.94 33:0.97 34:0.58 36:0.43 37:0.48 39:0.55 41:0.88 43:0.85 45:0.89 47:0.93 60:0.95 62:0.98 64:0.77 66:0.45 69:0.62 70:0.96 71:0.81 74:0.78 75:0.99 78:0.90 79:0.94 81:0.79 83:0.91 85:0.72 86:0.84 91:0.17 96:0.82 98:0.35 100:0.88 101:0.96 104:0.94 106:0.81 108:0.47 111:0.88 114:0.90 117:0.86 120:0.68 123:0.45 124:0.93 126:0.54 127:0.85 128:0.97 129:0.05 131:0.84 133:0.95 135:0.93 137:0.94 139:0.85 140:0.87 144:0.76 146:0.84 147:0.86 149:0.79 150:0.62 151:0.81 152:0.95 153:0.77 154:0.81 155:0.66 157:0.76 158:0.86 159:0.91 161:0.60 162:0.52 164:0.69 165:0.93 166:0.77 167:0.46 168:0.97 169:0.85 170:0.82 172:0.75 173:0.27 174:0.86 176:0.73 177:0.88 178:0.48 179:0.39 181:0.66 182:0.80 186:0.90 187:0.87 189:0.85 192:0.99 196:0.95 197:0.89 201:0.66 202:0.72 205:0.97 206:0.81 208:0.64 212:0.39 215:0.79 216:0.64 219:0.95 220:0.62 222:0.35 226:0.96 227:0.91 229:0.89 231:0.67 232:0.97 235:0.52 236:0.97 238:0.83 239:0.95 241:0.03 242:0.14 243:0.58 245:0.71 246:0.94 247:0.96 248:0.75 253:0.85 255:0.79 256:0.64 259:0.21 260:0.69 261:0.90 262:0.93 265:0.37 266:0.97 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98:0.30 104:0.90 106:0.81 108:0.86 114:0.66 117:0.86 120:0.92 122:0.43 123:0.44 124:0.74 126:0.54 127:0.43 129:0.59 131:0.99 133:0.76 135:0.85 138:0.95 140:0.45 144:0.57 145:0.99 146:0.48 147:0.54 149:0.90 150:0.74 151:0.97 153:0.90 154:0.82 155:0.67 157:0.61 158:0.84 159:0.63 162:0.83 164:0.89 166:0.97 172:0.63 173:0.48 176:0.56 177:0.38 179:0.42 182:0.96 187:0.87 192:0.59 195:0.82 201:0.74 202:0.81 206:0.81 208:0.64 212:0.84 215:0.63 216:0.88 222:0.76 227:0.92 229:0.98 231:0.96 232:0.83 235:0.52 241:0.44 242:0.33 243:0.46 245:0.76 246:0.61 247:0.67 248:0.97 253:0.97 255:0.79 256:0.86 260:0.92 265:0.32 266:0.63 267:0.69 268:0.86 271:0.38 276:0.65 279:0.86 282:0.83 283:0.82 285:0.62 286:0.99 287:0.90 290:0.66 297:0.36 300:0.70 1 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 1 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 3 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 2 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 3 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 0 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 0 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 2 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 2 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 2 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 2 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 ================================================ FILE: examples/xendcg/rank.train.query ================================================ 1 13 5 8 19 12 18 5 14 13 8 9 16 11 21 14 21 9 14 11 20 18 13 20 22 22 13 17 10 13 12 13 13 23 18 13 20 12 22 14 13 23 13 14 14 5 13 15 14 14 16 16 15 21 22 10 22 18 25 16 12 12 15 15 25 13 9 12 8 16 25 19 24 12 16 10 16 9 17 15 7 9 15 14 16 17 8 17 12 18 23 10 12 12 4 14 12 15 27 16 20 13 19 13 17 17 16 12 15 14 14 19 12 23 18 16 9 23 11 15 8 10 10 16 11 15 22 16 17 23 16 22 17 14 12 14 20 15 17 15 15 22 9 21 9 17 16 15 13 13 15 14 18 21 14 17 15 14 16 12 17 19 16 11 18 11 13 14 9 16 15 16 25 9 13 22 16 18 20 14 11 9 16 19 19 11 11 13 14 14 13 16 6 21 16 12 16 11 24 12 10 ================================================ FILE: examples/xendcg/train.conf ================================================ # task type, support train and predict task = train # boosting type, support gbdt for now, alias: boosting, boost boosting_type = gbdt # application type, support following application # regression , regression task # binary , binary classification task # lambdarank , LambdaRank task # alias: application, app objective = rank_xendcg # eval metrics, support multi metric, delimited by ',' , support following metrics # l1 # l2 , default metric for regression # ndcg , default metric for lambdarank # auc # binary_logloss , default metric for binary # binary_error metric = ndcg # evaluation position for ndcg metric, alias : ndcg_at ndcg_eval_at = 1,3,5 # frequency for metric output metric_freq = 1 # true if need output metric for training data, alias: tranining_metric, train_metric is_training_metric = true # column in data to use as label label_column = 0 # number of bins for feature bucket, 255 is a recommend setting, it can save memories, and also has good accuracy. max_bin = 255 # training data # if existing weight file, should name to "rank.train.weight" # if existing query file, should name to "rank.train.query" # alias: train_data, train data = rank.train # validation data, support multi validation data, separated by ',' # if existing weight file, should name to "rank.test.weight" # if existing query file, should name to "rank.test.query" # alias: valid, test, test_data, valid_data = rank.test # number of trees(iterations), alias: num_tree, num_iteration, num_iterations, num_round, num_rounds num_trees = 100 # shrinkage rate , alias: shrinkage_rate learning_rate = 0.1 # number of leaves for one tree, alias: num_leaf num_leaves = 31 # type of tree learner, support following types: # serial , single machine version # feature , use feature parallel to train # data , use data parallel to train # voting , use voting based parallel to train # alias: tree tree_learner = serial # Set num_threads and objective_seed for stable unit-tests. Comment out otherwise. num_threads = 1 objective_seed = 1025 # feature sub-sample, will random select 80% feature to train on each iteration # alias: sub_feature feature_fraction = 1.0 # Support bagging (data sub-sample), will perform bagging every 5 iterations bagging_freq = 1 # Bagging fraction, will random select 80% data on bagging # alias: sub_row bagging_fraction = 0.9 # minimal number data for one leaf, use this to deal with over-fit # alias : min_data_per_leaf, min_data min_data_in_leaf = 50 # minimal sum Hessians for one leaf, use this to deal with over-fit min_sum_hessian_in_leaf = 5.0 # save memory and faster speed for sparse feature, alias: is_sparse is_enable_sparse = true # when data is bigger than memory size, set this to true. otherwise set false will have faster speed # alias: two_round_loading, two_round use_two_round_loading = false # true if need to save data to binary file and application will auto load data from binary file next time # alias: is_save_binary, save_binary is_save_binary_file = false # output model file output_model = LightGBM_model.txt # support continuous train from trained gbdt model # input_model= trained_model.txt # output prediction file for predict task # output_result= prediction.txt # number of machines in distributed training, alias: num_machine num_machines = 1 # local listening port in distributed training, alias: local_port local_listen_port = 12400 # machines list file for distributed training, alias: mlist machine_list_file = mlist.txt ================================================ FILE: include/LightGBM/application.h ================================================ /*! * Copyright (c) 2016-2026 Microsoft Corporation. All rights reserved. * Copyright (c) 2016-2026 The LightGBM developers. All rights reserved. * Licensed under the MIT License. See LICENSE file in the project root for license information. */ #ifndef LIGHTGBM_INCLUDE_LIGHTGBM_APPLICATION_H_ #define LIGHTGBM_INCLUDE_LIGHTGBM_APPLICATION_H_ #include #include #include #include namespace LightGBM { class DatasetLoader; class Dataset; class Boosting; class ObjectiveFunction; class Metric; /*! * \brief The main entrance of LightGBM. this application has two tasks: * Train and Predict. * Train task will train a new model * Predict task will predict the scores of test data using existing model, * and save the score to disk. */ class Application { public: Application(int argc, char** argv); /*! \brief Destructor */ ~Application(); /*! \brief To call this function to run application*/ inline void Run(); private: /*! \brief Load parameters from command line and config file*/ void LoadParameters(int argc, char** argv); /*! \brief Load data, including training data and validation data*/ void LoadData(); /*! \brief Initialization before training*/ void InitTrain(); /*! \brief Main Training logic */ void Train(); /*! \brief Initializations before prediction */ void InitPredict(); /*! \brief Main predicting logic */ void Predict(); /*! \brief Main Convert model logic */ void ConvertModel(); /*! \brief All configs */ Config config_; /*! \brief Training data */ std::unique_ptr train_data_; /*! \brief Validation data */ std::vector> valid_datas_; /*! \brief Metric for training data */ std::vector> train_metric_; /*! \brief Metrics for validation data */ std::vector>> valid_metrics_; /*! \brief Boosting object */ std::unique_ptr boosting_; /*! \brief Training objective function */ std::unique_ptr objective_fun_; }; inline void Application::Run() { if (config_.task == TaskType::kPredict || config_.task == TaskType::KRefitTree) { InitPredict(); Predict(); } else if (config_.task == TaskType::kConvertModel) { ConvertModel(); } else { InitTrain(); Train(); } } } // namespace LightGBM #endif // LIGHTGBM_INCLUDE_LIGHTGBM_APPLICATION_H_ ================================================ FILE: include/LightGBM/arrow.h ================================================ /*! * Copyright (c) 2023-2026 Microsoft Corporation. All rights reserved. * Copyright (c) 2023-2026 The LightGBM developers. All rights reserved. * Licensed under the MIT License. See LICENSE file in the project root for license information. * * Author: Oliver Borchert */ #ifndef LIGHTGBM_INCLUDE_LIGHTGBM_ARROW_H_ #define LIGHTGBM_INCLUDE_LIGHTGBM_ARROW_H_ #ifdef __cplusplus #include #include #include #include #include #include #include #include #include #endif /* -------------------------------------- C DATA INTERFACE ------------------------------------- */ // The C data interface is taken from // https://arrow.apache.org/docs/format/CDataInterface.html#structure-definitions // and is available under Apache License 2.0 (https://www.apache.org/licenses/LICENSE-2.0). #ifdef __cplusplus extern "C" { #endif #define ARROW_FLAG_DICTIONARY_ORDERED 1 #define ARROW_FLAG_NULLABLE 2 #define ARROW_FLAG_MAP_KEYS_SORTED 4 struct ArrowSchema { // Array type description const char* format; const char* name; const char* metadata; int64_t flags; int64_t n_children; struct ArrowSchema** children; struct ArrowSchema* dictionary; // Release callback void (*release)(struct ArrowSchema*); // Opaque producer-specific data void* private_data; }; struct ArrowArray { // Array data description int64_t length; int64_t null_count; int64_t offset; int64_t n_buffers; int64_t n_children; const void** buffers; struct ArrowArray** children; struct ArrowArray* dictionary; // Release callback void (*release)(struct ArrowArray*); // Opaque producer-specific data void* private_data; }; #ifdef __cplusplus } #endif /* --------------------------------------------------------------------------------------------- */ /* CHUNKED ARRAY */ /* --------------------------------------------------------------------------------------------- */ #ifdef __cplusplus namespace LightGBM { /** * @brief Arrow array-like container for a list of Arrow arrays. */ class ArrowChunkedArray { /* List of length `n` for `n` chunks containing the individual Arrow arrays. */ std::vector chunks_; /* Schema for all chunks. */ const ArrowSchema* schema_; /* List of length `n + 1` for `n` chunks containing the offsets for each chunk. */ std::vector chunk_offsets_; /* Indicator whether this chunked array needs to call the arrays' release callbacks. NOTE: This is MUST only be set to `true` if this chunked array is not part of a `ArrowTable` as children arrays may not be released by the consumer (see below). */ const bool releases_arrow_; inline void construct_chunk_offsets() { chunk_offsets_.reserve(chunks_.size() + 1); chunk_offsets_.emplace_back(0); for (size_t k = 0; k < chunks_.size(); ++k) { chunk_offsets_.emplace_back(chunks_[k]->length + chunk_offsets_.back()); } } public: /** * @brief Construct a new Arrow Chunked Array object. * * @param chunks A list with the chunks. * @param schema The schema for all chunks. */ inline ArrowChunkedArray(std::vector chunks, const ArrowSchema* schema) : releases_arrow_(false) { chunks_ = chunks; schema_ = schema; construct_chunk_offsets(); } /** * @brief Construct a new Arrow Chunked Array object. * * @param n_chunks The number of chunks. * @param chunks A C-style array containing the chunks. * @param schema The schema for all chunks. */ inline ArrowChunkedArray(int64_t n_chunks, const struct ArrowArray* chunks, const struct ArrowSchema* schema) : releases_arrow_(true) { chunks_.reserve(n_chunks); for (int64_t k = 0; k < n_chunks; ++k) { if (chunks[k].length == 0) continue; chunks_.push_back(&chunks[k]); } schema_ = schema; construct_chunk_offsets(); } ~ArrowChunkedArray() { if (!releases_arrow_) { return; } for (size_t i = 0; i < chunks_.size(); ++i) { auto chunk = chunks_[i]; if (chunk->release) { chunk->release(const_cast(chunk)); } } if (schema_->release) { schema_->release(const_cast(schema_)); } } /** * @brief Get the length of the chunked array. * This method returns the cumulative length of all chunks. * Complexity: O(1) * * @return int64_t The number of elements in the chunked array. */ inline int64_t get_length() const { return chunk_offsets_.back(); } /* ----------------------------------------- ITERATOR ---------------------------------------- */ template class Iterator { using getter_fn = std::function; /* Reference to the chunked array that this iterator iterates over. */ const ArrowChunkedArray& array_; /* Function to fetch the value at a certain index from a single chunk. */ getter_fn get_; /* The chunk the iterator currently points to. */ int64_t ptr_chunk_; /* The index inside the current chunk that the iterator points to. */ int64_t ptr_offset_; public: using iterator_category = std::random_access_iterator_tag; using difference_type = int64_t; using value_type = T; using pointer = value_type*; using reference = value_type&; /** * @brief Construct a new Iterator object. * * @param array Reference to the chunked array to iterator over. * @param get Function to fetch the value at a certain index from a single chunk. * @param ptr_chunk The index of the chunk to whose first index the iterator points to. */ Iterator(const ArrowChunkedArray& array, getter_fn get, int64_t ptr_chunk); T operator*() const; template T operator[](I idx) const; Iterator& operator++(); Iterator& operator--(); Iterator& operator+=(int64_t c); template friend bool operator==(const Iterator& a, const Iterator& b); template friend bool operator!=(const Iterator& a, const Iterator& b); template friend int64_t operator-(const Iterator& a, const Iterator& b); }; /** * @brief Obtain an iterator to the beginning of the chunked array. * * @tparam T The value type of the iterator. May be any primitive type. * @return Iterator The iterator. */ template inline Iterator begin() const; /** * @brief Obtain an iterator to the beginning of the chunked array. * * @tparam T The value type of the iterator. May be any primitive type. * @return Iterator The iterator. */ template inline Iterator end() const; template friend int64_t operator-(const Iterator& a, const Iterator& b); }; /** * @brief Arrow container for a list of chunked arrays. */ class ArrowTable { std::vector columns_; const int64_t n_chunks_; const ArrowArray* chunks_ptr_; const ArrowSchema* schema_ptr_; public: /** * @brief Construct a new Arrow Table object. * * @param n_chunks The number of chunks. * @param chunks A C-style array containing the chunks. * @param schema The schema for all chunks. */ inline ArrowTable(int64_t n_chunks, const ArrowArray* chunks, const ArrowSchema* schema) : n_chunks_(n_chunks), chunks_ptr_(chunks), schema_ptr_(schema) { columns_.reserve(schema->n_children); for (int64_t j = 0; j < schema->n_children; ++j) { std::vector children_chunks; children_chunks.reserve(n_chunks); for (int64_t k = 0; k < n_chunks; ++k) { if (chunks[k].length == 0) continue; children_chunks.push_back(chunks[k].children[j]); } columns_.emplace_back(children_chunks, schema->children[j]); } } ~ArrowTable() { // As consumer of the Arrow array, the Arrow table must release all Arrow arrays it receives // as well as the schema. As per the specification, children arrays are released by the // producer. See: // https://arrow.apache.org/docs/format/CDataInterface.html#release-callback-semantics-for-consumers for (int64_t i = 0; i < n_chunks_; ++i) { auto chunk = &chunks_ptr_[i]; if (chunk->release) { chunk->release(const_cast(chunk)); } } if (schema_ptr_->release) { schema_ptr_->release(const_cast(schema_ptr_)); } } /** * @brief Get the number of rows in the table. * * @return int64_t The number of rows. */ inline int64_t get_num_rows() const { return columns_.front().get_length(); } /** * @brief Get the number of columns of this table. * * @return int64_t The column count. */ inline int64_t get_num_columns() const { return columns_.size(); } /** * @brief Get the column at a particular index. * * @param idx The index of the column, must me in the range `[0, num_columns)`. * @return const ArrowChunkedArray& The chunked array for the child at the provided index. */ inline const ArrowChunkedArray& get_column(size_t idx) const { return this->columns_[idx]; } }; } // namespace LightGBM #include "arrow.tpp" #endif /* __cplusplus */ #endif // LIGHTGBM_INCLUDE_LIGHTGBM_ARROW_H_ ================================================ FILE: include/LightGBM/arrow.tpp ================================================ #include #ifndef ARROW_TPP_ #define ARROW_TPP_ namespace LightGBM { /** * @brief Obtain a function to access an index from an Arrow array. * * @tparam T The return type of the function, must be a primitive type. * @param dtype The Arrow format string describing the datatype of the Arrow array. * @return std::function The index accessor function. */ template std::function get_index_accessor(const char* dtype); /* ---------------------------------- ITERATOR INITIALIZATION ---------------------------------- */ template inline ArrowChunkedArray::Iterator ArrowChunkedArray::begin() const { return ArrowChunkedArray::Iterator(*this, get_index_accessor(schema_->format), 0); } template inline ArrowChunkedArray::Iterator ArrowChunkedArray::end() const { return ArrowChunkedArray::Iterator(*this, get_index_accessor(schema_->format), chunk_offsets_.size() - 1); } /* ---------------------------------- ITERATOR IMPLEMENTATION ---------------------------------- */ template ArrowChunkedArray::Iterator::Iterator(const ArrowChunkedArray& array, getter_fn get, int64_t ptr_chunk) : array_(array), get_(get), ptr_chunk_(ptr_chunk) { this->ptr_offset_ = 0; } template T ArrowChunkedArray::Iterator::operator*() const { auto chunk = array_.chunks_[ptr_chunk_]; return get_(chunk, ptr_offset_); } template template T ArrowChunkedArray::Iterator::operator[](I idx) const { auto it = std::lower_bound(array_.chunk_offsets_.begin(), array_.chunk_offsets_.end(), idx, [](int64_t a, int64_t b) { return a <= b; }); auto chunk_idx = std::distance(array_.chunk_offsets_.begin() + 1, it); auto chunk = array_.chunks_[chunk_idx]; auto ptr_offset = static_cast(idx) - array_.chunk_offsets_[chunk_idx]; return get_(chunk, ptr_offset); } template ArrowChunkedArray::Iterator& ArrowChunkedArray::Iterator::operator++() { if (ptr_offset_ + 1 >= array_.chunks_[ptr_chunk_]->length) { ptr_offset_ = 0; ptr_chunk_++; } else { ptr_offset_++; } return *this; } template ArrowChunkedArray::Iterator& ArrowChunkedArray::Iterator::operator--() { if (ptr_offset_ == 0) { ptr_chunk_--; ptr_offset_ = array_.chunks_[ptr_chunk_]->length - 1; } else { ptr_chunk_--; } return *this; } template ArrowChunkedArray::Iterator& ArrowChunkedArray::Iterator::operator+=(int64_t c) { while (ptr_offset_ + c >= array_.chunks_[ptr_chunk_]->length) { c -= array_.chunks_[ptr_chunk_]->length - ptr_offset_; ptr_offset_ = 0; ptr_chunk_++; } ptr_offset_ += c; return *this; } template bool operator==(const ArrowChunkedArray::Iterator& a, const ArrowChunkedArray::Iterator& b) { return a.ptr_chunk_ == b.ptr_chunk_ && a.ptr_offset_ == b.ptr_offset_; } template bool operator!=(const ArrowChunkedArray::Iterator& a, const ArrowChunkedArray::Iterator& b) { return a.ptr_chunk_ != b.ptr_chunk_ || a.ptr_offset_ != b.ptr_offset_; } template int64_t operator-(const ArrowChunkedArray::Iterator& a, const ArrowChunkedArray::Iterator& b) { auto full_offset_a = a.array_.chunk_offsets_[a.ptr_chunk_] + a.ptr_offset_; auto full_offset_b = b.array_.chunk_offsets_[b.ptr_chunk_] + b.ptr_offset_; return full_offset_a - full_offset_b; } /* --------------------------------------- INDEX ACCESSOR -------------------------------------- */ /** * @brief The value of "no value" for a primitive type. * * @tparam T The type for which the missing value is defined. * @return T The missing value. */ template inline T arrow_primitive_missing_value() { return 0; } template <> inline double arrow_primitive_missing_value() { return std::numeric_limits::quiet_NaN(); } template <> inline float arrow_primitive_missing_value() { return std::numeric_limits::quiet_NaN(); } template struct ArrayIndexAccessor { V operator()(const ArrowArray* array, size_t idx) { auto buffer_idx = idx + array->offset; // For primitive types, buffer at idx 0 provides validity, buffer at idx 1 data, see: // https://arrow.apache.org/docs/format/Columnar.html#buffer-listing-for-each-layout auto validity = static_cast(array->buffers[0]); // Take return value from data buffer conditional on the validity of the index: // - The structure of validity bitmasks is taken from here: // https://arrow.apache.org/docs/format/Columnar.html#validity-bitmaps // - If the bitmask is NULL, all indices are valid if (validity == nullptr || (validity[buffer_idx / 8] & (1 << (buffer_idx % 8)))) { // In case the index is valid, we take it from the data buffer auto data = static_cast(array->buffers[1]); return static_cast(data[buffer_idx]); } // In case the index is not valid, we return a default value return arrow_primitive_missing_value(); } }; template struct ArrayIndexAccessor { V operator()(const ArrowArray* array, size_t idx) { // Custom implementation for booleans as values are bit-packed: // https://arrow.apache.org/docs/cpp/api/datatype.html#_CPPv4N5arrow4Type4type4BOOLE auto buffer_idx = idx + array->offset; auto validity = static_cast(array->buffers[0]); if (validity == nullptr || (validity[buffer_idx / 8] & (1 << (buffer_idx % 8)))) { // In case the index is valid, we have to take the appropriate bit from the buffer auto data = static_cast(array->buffers[1]); auto value = (data[buffer_idx / 8] & (1 << (buffer_idx % 8))) >> (buffer_idx % 8); return static_cast(value); } return arrow_primitive_missing_value(); } }; template std::function get_index_accessor(const char* dtype) { // Mapping obtained from: // https://arrow.apache.org/docs/format/CDataInterface.html#data-type-description-format-strings switch (dtype[0]) { case 'c': return ArrayIndexAccessor(); case 'C': return ArrayIndexAccessor(); case 's': return ArrayIndexAccessor(); case 'S': return ArrayIndexAccessor(); case 'i': return ArrayIndexAccessor(); case 'I': return ArrayIndexAccessor(); case 'l': return ArrayIndexAccessor(); case 'L': return ArrayIndexAccessor(); case 'f': return ArrayIndexAccessor(); case 'g': return ArrayIndexAccessor(); case 'b': return ArrayIndexAccessor(); default: throw std::invalid_argument("unsupported Arrow datatype"); } } } // namespace LightGBM #endif ================================================ FILE: include/LightGBM/bin.h ================================================ /*! * Copyright (c) 2016-2026 Microsoft Corporation. All rights reserved. * Copyright (c) 2016-2026 The LightGBM developers. All rights reserved. * Licensed under the MIT License. See LICENSE file in the project root for license information. */ #ifndef LIGHTGBM_INCLUDE_LIGHTGBM_BIN_H_ #define LIGHTGBM_INCLUDE_LIGHTGBM_BIN_H_ #include #include #include #include #include #include #include #include #include #include namespace LightGBM { enum BinType { NumericalBin, CategoricalBin }; enum MissingType { None, Zero, NaN }; typedef double hist_t; typedef int32_t int_hist_t; typedef uint64_t hist_cnt_t; // check at compile time static_assert(sizeof(hist_t) == sizeof(hist_cnt_t), "Histogram entry size is not correct"); const size_t kHistEntrySize = 2 * sizeof(hist_t); const size_t kInt32HistEntrySize = 2 * sizeof(int_hist_t); const size_t kInt16HistEntrySize = 2 * sizeof(int16_t); const int kHistOffset = 2; const double kSparseThreshold = 0.7; #define GET_GRAD(hist, i) hist[(i) << 1] #define GET_HESS(hist, i) hist[((i) << 1) + 1] inline static void HistogramSumReducer(const char* src, char* dst, int type_size, comm_size_t len) { comm_size_t used_size = 0; const hist_t* p1; hist_t* p2; while (used_size < len) { // convert p1 = reinterpret_cast(src); p2 = reinterpret_cast(dst); *p2 += *p1; src += type_size; dst += type_size; used_size += type_size; } } inline static void Int32HistogramSumReducer(const char* src, char* dst, int type_size, comm_size_t len) { const int64_t* src_ptr = reinterpret_cast(src); int64_t* dst_ptr = reinterpret_cast(dst); const comm_size_t steps = (len + (type_size * 2) - 1) / (type_size * 2); #pragma omp parallel for schedule(static) num_threads(OMP_NUM_THREADS()) for (comm_size_t i = 0; i < steps; ++i) { dst_ptr[i] += src_ptr[i]; } } inline static void Int16HistogramSumReducer(const char* src, char* dst, int type_size, comm_size_t len) { const int32_t* src_ptr = reinterpret_cast(src); int32_t* dst_ptr = reinterpret_cast(dst); const comm_size_t steps = (len + (type_size * 2) - 1) / (type_size * 2); #pragma omp parallel for schedule(static) num_threads(OMP_NUM_THREADS()) for (comm_size_t i = 0; i < steps; ++i) { dst_ptr[i] += src_ptr[i]; } } /*! \brief This class used to convert feature values into bin, * and store some meta information for bin*/ class BinMapper { public: BinMapper(); BinMapper(const BinMapper& other); explicit BinMapper(const void* memory); ~BinMapper(); bool CheckAlign(const BinMapper& other) const { if (num_bin_ != other.num_bin_) { return false; } if (missing_type_ != other.missing_type_) { return false; } if (bin_type_ == BinType::NumericalBin) { for (int i = 0; i < num_bin_; ++i) { if (bin_upper_bound_[i] != other.bin_upper_bound_[i]) { return false; } } } else { for (int i = 0; i < num_bin_; i++) { if (bin_2_categorical_[i] != other.bin_2_categorical_[i]) { return false; } } } return true; } /*! \brief Get number of bins */ inline int num_bin() const { return num_bin_; } /*! \brief Missing Type */ inline MissingType missing_type() const { return missing_type_; } /*! \brief True if bin is trivial (contains only one bin) */ inline bool is_trivial() const { return is_trivial_; } /*! \brief Sparsity of this bin ( num_zero_bins / num_data ) */ inline double sparse_rate() const { return sparse_rate_; } /*! * \brief Save binary data to file * \param file File want to write */ void SaveBinaryToFile(BinaryWriter* writer) const; /*! * \brief Mapping bin into feature value * \param bin * \return Feature value of this bin */ inline double BinToValue(uint32_t bin) const { if (bin_type_ == BinType::NumericalBin) { return bin_upper_bound_[bin]; } else { return bin_2_categorical_[bin]; } } /*! * \brief Maximum categorical value * \return Maximum categorical value for categorical features, 0 for numerical features */ inline int MaxCatValue() const { if (bin_2_categorical_.size() == 0) { return 0; } int max_cat_value = bin_2_categorical_[0]; for (size_t i = 1; i < bin_2_categorical_.size(); ++i) { if (bin_2_categorical_[i] > max_cat_value) { max_cat_value = bin_2_categorical_[i]; } } return max_cat_value; } /*! * \brief Get sizes in byte of this object */ size_t SizesInByte() const; /*! * \brief Mapping feature value into bin * \param value * \return bin for this feature value */ inline uint32_t ValueToBin(double value) const; /*! * \brief Get the default bin when value is 0 * \return default bin */ inline uint32_t GetDefaultBin() const { return default_bin_; } inline uint32_t GetMostFreqBin() const { return most_freq_bin_; } /*! * \brief Construct feature value to bin mapper according feature values * \param values (Sampled) values of this feature, Note: not include zero. * \param num_values number of values. * \param total_sample_cnt number of total sample count, equal with values.size() + num_zeros * \param max_bin The maximal number of bin * \param min_data_in_bin min number of data in one bin * \param min_split_data * \param pre_filter * \param bin_type Type of this bin * \param use_missing True to enable missing value handle * \param zero_as_missing True to use zero as missing value * \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) */ 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, bool use_missing, bool zero_as_missing, const std::vector& forced_upper_bounds); /*! * \brief Serializing this object to buffer * \param buffer The destination */ void CopyTo(char* buffer) const; /*! * \brief Deserializing this object from buffer * \param buffer The source */ void CopyFrom(const char* buffer); /*! * \brief Get bin types */ inline BinType bin_type() const { return bin_type_; } /*! * \brief Get bin info */ inline std::string bin_info_string() const { if (bin_type_ == BinType::CategoricalBin) { return Common::Join(bin_2_categorical_, ":"); } else { std::stringstream str_buf; str_buf << std::setprecision(std::numeric_limits::digits10 + 2); str_buf << '[' << min_val_ << ':' << max_val_ << ']'; return str_buf.str(); } } private: /*! \brief Number of bins */ int num_bin_; MissingType missing_type_; /*! \brief Store upper bound for each bin */ std::vector bin_upper_bound_; /*! \brief True if this feature is trivial */ bool is_trivial_; /*! \brief Sparse rate of this bins( num_bin0/num_data ) */ double sparse_rate_; /*! \brief Type of this bin */ BinType bin_type_; /*! \brief Mapper from categorical to bin */ std::unordered_map categorical_2_bin_; /*! \brief Mapper from bin to categorical */ std::vector bin_2_categorical_; /*! \brief minimal feature value */ double min_val_; /*! \brief maximum feature value */ double max_val_; /*! \brief bin value of feature value 0 */ uint32_t default_bin_; uint32_t most_freq_bin_; }; /*! \brief Iterator for one bin column */ class BinIterator { public: /*! * \brief Get bin data on specific row index * \param idx Index of this data * \return Bin data */ virtual uint32_t Get(data_size_t idx) = 0; virtual uint32_t RawGet(data_size_t idx) = 0; virtual void Reset(data_size_t idx) = 0; virtual ~BinIterator() = default; }; /*! * \brief Interface for bin data. This class will store bin data for one feature. * unlike OrderedBin, this class will store data by original order. * Note that it may cause cache misses when construct histogram, * but it doesn't need to re-order operation, So it will be faster than OrderedBin for dense feature */ class Bin { public: /*! \brief virtual destructor */ virtual ~Bin() {} /*! * \brief Initialize for pushing. By default, no action needed. * \param num_thread The number of external threads that will be calling the push APIs * \param omp_max_threads The maximum number of OpenMP threads to allocate for */ virtual void InitStreaming(uint32_t /*num_thread*/, int32_t /*omp_max_threads*/) { } /*! * \brief Push one record * \param tid Thread id * \param idx Index of record * \param value bin value of record */ virtual void Push(int tid, data_size_t idx, uint32_t value) = 0; virtual void CopySubrow(const Bin* full_bin, const data_size_t* used_indices, data_size_t num_used_indices) = 0; /*! * \brief Get bin iterator of this bin for specific feature * \param min_bin min_bin of current used feature * \param max_bin max_bin of current used feature * \param most_freq_bin * \return Iterator of this bin */ virtual BinIterator* GetIterator(uint32_t min_bin, uint32_t max_bin, uint32_t most_freq_bin) const = 0; /*! * \brief Save binary data to file * \param file File want to write */ virtual void SaveBinaryToFile(BinaryWriter* writer) const = 0; /*! * \brief Load from memory * \param memory * \param local_used_indices */ virtual void LoadFromMemory(const void* memory, const std::vector& local_used_indices) = 0; /*! * \brief Get sizes in byte of this object */ virtual size_t SizesInByte() const = 0; /*! \brief Number of all data */ virtual data_size_t num_data() const = 0; /*! \brief Get data pointer */ virtual void* get_data() = 0; virtual void ReSize(data_size_t num_data) = 0; /*! * \brief Construct histogram of this feature, * Note: We use ordered_gradients and ordered_hessians to improve cache hit chance * The naive solution is using gradients[data_indices[i]] for data_indices[i] to get gradients, which is not cache friendly, since the access of memory is not continuous. * ordered_gradients and ordered_hessians are preprocessed, and they are re-ordered by data_indices. * Ordered_gradients[i] is aligned with data_indices[i]'s gradients (same for ordered_hessians). * \param data_indices Used data indices in current leaf * \param start start index in data_indices * \param end end index in data_indices * \param ordered_gradients Pointer to gradients, the data_indices[i]-th data's gradient is ordered_gradients[i] * \param ordered_hessians Pointer to hessians, the data_indices[i]-th data's hessian is ordered_hessians[i] * \param out Output Result */ virtual void ConstructHistogram( const data_size_t* data_indices, data_size_t start, data_size_t end, const score_t* ordered_gradients, const score_t* ordered_hessians, hist_t* out) const = 0; virtual void ConstructHistogram(data_size_t start, data_size_t end, const score_t* ordered_gradients, const score_t* ordered_hessians, hist_t* out) const = 0; virtual void ConstructHistogramInt8( const data_size_t* data_indices, data_size_t start, data_size_t end, const score_t* ordered_gradients, const score_t* ordered_hessians, hist_t* out) const = 0; virtual void ConstructHistogramInt8(data_size_t start, data_size_t end, const score_t* ordered_gradients, const score_t* ordered_hessians, hist_t* out) const = 0; virtual void ConstructHistogramInt16( const data_size_t* data_indices, data_size_t start, data_size_t end, const score_t* ordered_gradients, const score_t* ordered_hessians, hist_t* out) const = 0; virtual void ConstructHistogramInt16(data_size_t start, data_size_t end, const score_t* ordered_gradients, const score_t* ordered_hessians, hist_t* out) const = 0; virtual void ConstructHistogramInt32( const data_size_t* data_indices, data_size_t start, data_size_t end, const score_t* ordered_gradients, const score_t* ordered_hessians, hist_t* out) const = 0; virtual void ConstructHistogramInt32(data_size_t start, data_size_t end, const score_t* ordered_gradients, const score_t* ordered_hessians, hist_t* out) const = 0; /*! * \brief Construct histogram of this feature, * Note: We use ordered_gradients and ordered_hessians to improve cache hit chance * The naive solution is using gradients[data_indices[i]] for data_indices[i] to get gradients, which is not cache friendly, since the access of memory is not continuous. * ordered_gradients and ordered_hessians are preprocessed, and they are re-ordered by data_indices. * Ordered_gradients[i] is aligned with data_indices[i]'s gradients (same for ordered_hessians). * \param data_indices Used data indices in current leaf * \param start start index in data_indices * \param end end index in data_indices * \param ordered_gradients Pointer to gradients, the data_indices[i]-th data's gradient is ordered_gradients[i] * \param out Output Result */ virtual void ConstructHistogram(const data_size_t* data_indices, data_size_t start, data_size_t end, const score_t* ordered_gradients, hist_t* out) const = 0; virtual void ConstructHistogram(data_size_t start, data_size_t end, const score_t* ordered_gradients, hist_t* out) const = 0; virtual void ConstructHistogramInt8(const data_size_t* data_indices, data_size_t start, data_size_t end, const score_t* ordered_gradients, hist_t* out) const = 0; virtual void ConstructHistogramInt8(data_size_t start, data_size_t end, const score_t* ordered_gradients, hist_t* out) const = 0; virtual void ConstructHistogramInt16(const data_size_t* data_indices, data_size_t start, data_size_t end, const score_t* ordered_gradients, hist_t* out) const = 0; virtual void ConstructHistogramInt16(data_size_t start, data_size_t end, const score_t* ordered_gradients, hist_t* out) const = 0; virtual void ConstructHistogramInt32(const data_size_t* data_indices, data_size_t start, data_size_t end, const score_t* ordered_gradients, hist_t* out) const = 0; virtual void ConstructHistogramInt32(data_size_t start, data_size_t end, const score_t* ordered_gradients, hist_t* out) const = 0; virtual data_size_t Split(uint32_t min_bin, uint32_t max_bin, uint32_t default_bin, uint32_t most_freq_bin, MissingType missing_type, bool default_left, uint32_t threshold, const data_size_t* data_indices, data_size_t cnt, data_size_t* lte_indices, data_size_t* gt_indices) const = 0; virtual data_size_t SplitCategorical( uint32_t min_bin, uint32_t max_bin, uint32_t most_freq_bin, const uint32_t* threshold, int num_threshold, const data_size_t* data_indices, data_size_t cnt, data_size_t* lte_indices, data_size_t* gt_indices) const = 0; virtual data_size_t Split(uint32_t max_bin, uint32_t default_bin, uint32_t most_freq_bin, MissingType missing_type, bool default_left, uint32_t threshold, const data_size_t* data_indices, data_size_t cnt, data_size_t* lte_indices, data_size_t* gt_indices) const = 0; virtual data_size_t SplitCategorical( uint32_t max_bin, uint32_t most_freq_bin, const uint32_t* threshold, int num_threshold, const data_size_t* data_indices, data_size_t cnt, data_size_t* lte_indices, data_size_t* gt_indices) const = 0; /*! * \brief After pushed all feature data, call this could have better refactor for bin data */ virtual void FinishLoad() = 0; /*! * \brief Create object for bin data of one feature, used for dense feature * \param num_data Total number of data * \param num_bin Number of bin * \return The bin data object */ static Bin* CreateDenseBin(data_size_t num_data, int num_bin); /*! * \brief Create object for bin data of one feature, used for sparse feature * \param num_data Total number of data * \param num_bin Number of bin * \return The bin data object */ static Bin* CreateSparseBin(data_size_t num_data, int num_bin); /*! * \brief Deep copy the bin */ virtual Bin* Clone() = 0; virtual const void* GetColWiseData(uint8_t* bit_type, bool* is_sparse, std::vector* bin_iterator, const int num_threads) const = 0; virtual const void* GetColWiseData(uint8_t* bit_type, bool* is_sparse, BinIterator** bin_iterator) const = 0; }; class MultiValBin { public: virtual ~MultiValBin() {} virtual data_size_t num_data() const = 0; virtual int32_t num_bin() const = 0; virtual double num_element_per_row() const = 0; virtual const std::vector& offsets() const = 0; virtual void PushOneRow(int tid, data_size_t idx, const std::vector& values) = 0; virtual void CopySubrow(const MultiValBin* full_bin, const data_size_t* used_indices, data_size_t num_used_indices) = 0; virtual MultiValBin* CreateLike(data_size_t num_data, int num_bin, int num_feature, double estimate_element_per_row, const std::vector& offsets) const = 0; virtual void CopySubcol(const MultiValBin* full_bin, const std::vector& used_feature_index, const std::vector& lower, const std::vector& upper, const std::vector& delta) = 0; virtual void ReSize(data_size_t num_data, int num_bin, int num_feature, double estimate_element_per_row, const std::vector& offsets) = 0; virtual void CopySubrowAndSubcol( const MultiValBin* full_bin, const data_size_t* used_indices, data_size_t num_used_indices, const std::vector& used_feature_index, const std::vector& lower, const std::vector& upper, const std::vector& delta) = 0; virtual void ConstructHistogram(const data_size_t* data_indices, data_size_t start, data_size_t end, const score_t* gradients, const score_t* hessians, hist_t* out) const = 0; virtual void ConstructHistogram(data_size_t start, data_size_t end, const score_t* gradients, const score_t* hessians, hist_t* out) const = 0; virtual void ConstructHistogramOrdered(const data_size_t* data_indices, data_size_t start, data_size_t end, const score_t* ordered_gradients, const score_t* ordered_hessians, hist_t* out) const = 0; virtual void ConstructHistogramInt32(const data_size_t* data_indices, data_size_t start, data_size_t end, const score_t* gradients, const score_t* hessians, hist_t* out) const = 0; virtual void ConstructHistogramInt32(data_size_t start, data_size_t end, const score_t* gradients, const score_t* hessians, hist_t* out) const = 0; virtual void ConstructHistogramOrderedInt32(const data_size_t* data_indices, data_size_t start, data_size_t end, const score_t* ordered_gradients, const score_t* ordered_hessians, hist_t* out) const = 0; virtual void ConstructHistogramInt16(const data_size_t* data_indices, data_size_t start, data_size_t end, const score_t* gradients, const score_t* hessians, hist_t* out) const = 0; virtual void ConstructHistogramInt16(data_size_t start, data_size_t end, const score_t* gradients, const score_t* hessians, hist_t* out) const = 0; virtual void ConstructHistogramOrderedInt16(const data_size_t* data_indices, data_size_t start, data_size_t end, const score_t* ordered_gradients, const score_t* ordered_hessians, hist_t* out) const = 0; virtual void ConstructHistogramInt8(const data_size_t* data_indices, data_size_t start, data_size_t end, const score_t* gradients, const score_t* hessians, hist_t* out) const = 0; virtual void ConstructHistogramInt8(data_size_t start, data_size_t end, const score_t* gradients, const score_t* hessians, hist_t* out) const = 0; virtual void ConstructHistogramOrderedInt8(const data_size_t* data_indices, data_size_t start, data_size_t end, const score_t* ordered_gradients, const score_t* ordered_hessians, hist_t* out) const = 0; virtual void FinishLoad() = 0; virtual bool IsSparse() = 0; static MultiValBin* CreateMultiValBin(data_size_t num_data, int num_bin, int num_feature, double sparse_rate, const std::vector& offsets); static MultiValBin* CreateMultiValDenseBin(data_size_t num_data, int num_bin, int num_feature, const std::vector& offsets); static MultiValBin* CreateMultiValSparseBin(data_size_t num_data, int num_bin, double estimate_element_per_row); static constexpr double multi_val_bin_sparse_threshold = 0.25f; virtual MultiValBin* Clone() = 0; #ifdef USE_CUDA virtual const void* GetRowWiseData(uint8_t* bit_type, size_t* total_size, bool* is_sparse, const void** out_data_ptr, uint8_t* data_ptr_bit_type) const = 0; #endif // USE_CUDA }; inline uint32_t BinMapper::ValueToBin(double value) const { if (std::isnan(value)) { if (bin_type_ == BinType::CategoricalBin) { return 0; } else if (missing_type_ == MissingType::NaN) { return num_bin_ - 1; } else { value = 0.0f; } } if (bin_type_ == BinType::NumericalBin) { // binary search to find bin int l = 0; int r = num_bin_ - 1; if (missing_type_ == MissingType::NaN) { r -= 1; } while (l < r) { int m = (r + l - 1) / 2; if (value <= bin_upper_bound_[m]) { r = m; } else { l = m + 1; } } return l; } else { int int_value = static_cast(value); // convert negative value to NaN bin if (int_value < 0) { return 0; } if (categorical_2_bin_.count(int_value)) { return categorical_2_bin_.at(int_value); } else { return 0; } } } } // namespace LightGBM #endif // LIGHTGBM_INCLUDE_LIGHTGBM_BIN_H_ ================================================ FILE: include/LightGBM/boosting.h ================================================ /*! * Copyright (c) 2016-2026 Microsoft Corporation. All rights reserved. * Copyright (c) 2016-2026 The LightGBM developers. All rights reserved. * Licensed under the MIT License. See LICENSE file in the project root for license information. */ #ifndef LIGHTGBM_INCLUDE_LIGHTGBM_BOOSTING_H_ #define LIGHTGBM_INCLUDE_LIGHTGBM_BOOSTING_H_ #include #include #include #include #include #include namespace LightGBM { /*! \brief forward declaration */ class Dataset; class ObjectiveFunction; class Metric; struct PredictionEarlyStopInstance; /*! * \brief The interface for Boosting */ class LIGHTGBM_EXPORT Boosting { public: /*! \brief virtual destructor */ virtual ~Boosting() {} /*! * \brief Initialization logic * \param config Configs for boosting * \param train_data Training data * \param objective_function Training objective function * \param training_metrics Training metric */ virtual void Init( const Config* config, const Dataset* train_data, const ObjectiveFunction* objective_function, const std::vector& training_metrics) = 0; /*! * \brief Merge model from other boosting object Will insert to the front of current boosting object * \param other */ virtual void MergeFrom(const Boosting* other) = 0; /*! * \brief Shuffle Existing Models */ virtual void ShuffleModels(int start_iter, int end_iter) = 0; virtual void ResetTrainingData(const Dataset* train_data, const ObjectiveFunction* objective_function, const std::vector& training_metrics) = 0; virtual void ResetConfig(const Config* config) = 0; /*! * \brief Add a validation data * \param valid_data Validation data * \param valid_metrics Metric for validation data */ virtual void AddValidDataset(const Dataset* valid_data, const std::vector& valid_metrics) = 0; virtual void Train(int snapshot_freq, const std::string& model_output_path) = 0; /*! * \brief Update the tree output by new training data */ virtual void RefitTree(const int* tree_leaf_prediction, const size_t nrow, const size_t ncol) = 0; /*! * \brief Training logic * \param gradients nullptr for using default objective, otherwise use self-defined boosting * \param hessians nullptr for using default objective, otherwise use self-defined boosting * \return True if cannot train anymore */ virtual bool TrainOneIter(const score_t* gradients, const score_t* hessians) = 0; /*! * \brief Rollback one iteration */ virtual void RollbackOneIter() = 0; /*! * \brief return current iteration */ virtual int GetCurrentIteration() const = 0; /*! * \brief Get evaluation result at data_idx data * \param data_idx 0: training data, 1: 1st validation data * \return evaluation result */ virtual std::vector GetEvalAt(int data_idx) const = 0; /*! * \brief Get current training score * \param out_len length of returned score * \return training score */ virtual const double* GetTrainingScore(int64_t* out_len) = 0; /*! * \brief Get prediction result at data_idx data * \param data_idx 0: training data, 1: 1st validation data * \return out_len length of returned score */ virtual int64_t GetNumPredictAt(int data_idx) const = 0; /*! * \brief Get prediction result at data_idx data * \param data_idx 0: training data, 1: 1st validation data * \param result used to store prediction result, should allocate memory before call this function * \param out_len length of returned score */ virtual void GetPredictAt(int data_idx, double* result, int64_t* out_len) = 0; virtual int NumPredictOneRow(int start_iteration, int num_iteration, bool is_pred_leaf, bool is_pred_contrib) const = 0; /*! * \brief Prediction for one record, not sigmoid transform * \param feature_values Feature value on this record * \param output Prediction result for this record * \param early_stop Early stopping instance. If nullptr, no early stopping is applied and all models are evaluated. */ virtual void PredictRaw(const double* features, double* output, const PredictionEarlyStopInstance* early_stop) const = 0; virtual void PredictRawByMap(const std::unordered_map& features, double* output, const PredictionEarlyStopInstance* early_stop) const = 0; /*! * \brief Prediction for one record, sigmoid transformation will be used if needed * \param feature_values Feature value on this record * \param output Prediction result for this record * \param early_stop Early stopping instance. If nullptr, no early stopping is applied and all models are evaluated. */ virtual void Predict(const double* features, double* output, const PredictionEarlyStopInstance* early_stop) const = 0; virtual void PredictByMap(const std::unordered_map& features, double* output, const PredictionEarlyStopInstance* early_stop) const = 0; /*! * \brief Prediction for one record with leaf index * \param feature_values Feature value on this record * \param output Prediction result for this record */ virtual void PredictLeafIndex( const double* features, double* output) const = 0; virtual void PredictLeafIndexByMap( const std::unordered_map& features, double* output) const = 0; /*! * \brief Feature contributions for the model's prediction of one record * \param feature_values Feature value on this record * \param output Prediction result for this record */ virtual void PredictContrib(const double* features, double* output) const = 0; virtual void PredictContribByMap(const std::unordered_map& features, std::vector>* output) const = 0; /*! * \brief Dump model to json format string * \param start_iteration The model will be saved start from * \param num_iteration Number of iterations that want to dump, -1 means dump all * \param feature_importance_type Type of feature importance, 0: split, 1: gain * \return Json format string of model */ virtual std::string DumpModel(int start_iteration, int num_iteration, int feature_importance_type) const = 0; /*! * \brief Translate model to if-else statement * \param num_iteration Number of iterations that want to translate, -1 means translate all * \return if-else format codes of model */ virtual std::string ModelToIfElse(int num_iteration) const = 0; /*! * \brief Translate model to if-else statement * \param num_iteration Number of iterations that want to translate, -1 means translate all * \param filename Filename that want to save to * \return is_finish Is training finished or not */ virtual bool SaveModelToIfElse(int num_iteration, const char* filename) const = 0; /*! * \brief Save model to file * \param start_iteration The model will be saved start from * \param num_iterations Number of model that want to save, -1 means save all * \param feature_importance_type Type of feature importance, 0: split, 1: gain * \param filename Filename that want to save to * \return true if succeeded */ virtual bool SaveModelToFile(int start_iteration, int num_iterations, int feature_importance_type, const char* filename) const = 0; /*! * \brief Save model to string * \param start_iteration The model will be saved start from * \param num_iterations Number of model that want to save, -1 means save all * \param feature_importance_type Type of feature importance, 0: split, 1: gain * \return Non-empty string if succeeded */ virtual std::string SaveModelToString(int start_iteration, int num_iterations, int feature_importance_type) const = 0; /*! * \brief Restore from a serialized string * \param buffer The content of model * \param len The length of buffer * \return true if succeeded */ virtual bool LoadModelFromString(const char* buffer, size_t len) = 0; /*! * \brief Calculate feature importances * \param num_iteration Number of model that want to use for feature importance, -1 means use all * \param importance_type: 0 for split, 1 for gain * \return vector of feature_importance */ virtual std::vector FeatureImportance(int num_iteration, int importance_type) const = 0; /*! * \brief Calculate upper bound value * \return max possible value */ virtual double GetUpperBoundValue() const = 0; /*! * \brief Calculate lower bound value * \return min possible value */ virtual double GetLowerBoundValue() const = 0; /*! * \brief Get max feature index of this model * \return Max feature index of this model */ virtual int MaxFeatureIdx() const = 0; /*! * \brief Get feature names of this model * \return Feature names of this model */ virtual std::vector FeatureNames() const = 0; /*! * \brief Get index of label column * \return index of label column */ virtual int LabelIdx() const = 0; /*! * \brief Get number of weak sub-models * \return Number of weak sub-models */ virtual int NumberOfTotalModel() const = 0; /*! * \brief Get number of models per iteration * \return Number of models per iteration */ virtual int NumModelPerIteration() const = 0; /*! * \brief Get number of classes * \return Number of classes */ virtual int NumberOfClasses() const = 0; /*! \brief The prediction should be accurate or not. True will disable early stopping for prediction. */ virtual bool NeedAccuratePrediction() const = 0; /*! * \brief Initial work for the prediction * \param start_iteration Start index of the iteration to predict * \param num_iteration number of used iteration * \param is_pred_contrib */ virtual void InitPredict(int start_iteration, int num_iteration, bool is_pred_contrib) = 0; /*! * \brief Name of submodel */ virtual const char* SubModelName() const = 0; Boosting() = default; /*! \brief Disable copy */ Boosting& operator=(const Boosting&) = delete; /*! \brief Disable copy */ Boosting(const Boosting&) = delete; static bool LoadFileToBoosting(Boosting* boosting, const char* filename); /*! * \brief Create boosting object * \param type Type of boosting * \param format Format of model * \param config config for boosting * \param filename name of model file, if existing will continue to train from this model * \param device_type type of device, can be cpu, gpu or cuda * \param num_gpu number of GPUs to use * \return The boosting object */ static Boosting* CreateBoosting(const std::string& type, const char* filename, const std::string& device_type, const int num_gpu); virtual std::string GetLoadedParam() const = 0; virtual bool IsLinear() const { return false; } virtual std::string ParserConfigStr() const = 0; }; class GBDTBase : public Boosting { public: virtual double GetLeafValue(int tree_idx, int leaf_idx) const = 0; virtual void SetLeafValue(int tree_idx, int leaf_idx, double val) = 0; }; } // namespace LightGBM #endif // LIGHTGBM_INCLUDE_LIGHTGBM_BOOSTING_H_ ================================================ FILE: include/LightGBM/c_api.h ================================================ /*! * \file c_api.h * \copyright Copyright (c) 2016-2026 Microsoft Corporation. All rights reserved. * Copyright (c) 2016-2026 The LightGBM developers. All rights reserved. * Licensed under the MIT License. See LICENSE file in the project root for license information. * \note * To avoid type conversion on large data, the most of our exposed interface supports both float32 and float64, * except the following: * 1. gradient and Hessian; * 2. current score for training and validation data. * . * The reason is that they are called frequently, and the type conversion on them may be time-cost. */ #ifndef LIGHTGBM_INCLUDE_LIGHTGBM_C_API_H_ #define LIGHTGBM_INCLUDE_LIGHTGBM_C_API_H_ #include #include #ifdef __cplusplus #include #include #include #else #include #include #include #endif typedef void* DatasetHandle; /*!< \brief Handle of dataset. */ typedef void* BoosterHandle; /*!< \brief Handle of booster. */ typedef void* FastConfigHandle; /*!< \brief Handle of FastConfig. */ typedef void* ByteBufferHandle; /*!< \brief Handle of ByteBuffer. */ #define C_API_DTYPE_FLOAT32 (0) /*!< \brief float32 (single precision float). */ #define C_API_DTYPE_FLOAT64 (1) /*!< \brief float64 (double precision float). */ #define C_API_DTYPE_INT32 (2) /*!< \brief int32. */ #define C_API_DTYPE_INT64 (3) /*!< \brief int64. */ #define C_API_PREDICT_NORMAL (0) /*!< \brief Normal prediction, with transform (if needed). */ #define C_API_PREDICT_RAW_SCORE (1) /*!< \brief Predict raw score. */ #define C_API_PREDICT_LEAF_INDEX (2) /*!< \brief Predict leaf index. */ #define C_API_PREDICT_CONTRIB (3) /*!< \brief Predict feature contributions (SHAP values). */ #define C_API_MATRIX_TYPE_CSR (0) /*!< \brief CSR sparse matrix type. */ #define C_API_MATRIX_TYPE_CSC (1) /*!< \brief CSC sparse matrix type. */ #define C_API_FEATURE_IMPORTANCE_SPLIT (0) /*!< \brief Split type of feature importance. */ #define C_API_FEATURE_IMPORTANCE_GAIN (1) /*!< \brief Gain type of feature importance. */ /*! * \brief Get string message of the last error. * \return Error information */ LIGHTGBM_C_EXPORT const char* LGBM_GetLastError(); /*! * \brief Dump all parameter names with their aliases to JSON. * \param buffer_len String buffer length, if ``buffer_len < out_len``, you should re-allocate buffer * \param[out] out_len Actual output length * \param[out] out_str JSON format string of parameters, should pre-allocate memory * \return 0 when succeed, -1 when failure happens */ LIGHTGBM_C_EXPORT int LGBM_DumpParamAliases(int64_t buffer_len, int64_t* out_len, char* out_str); /*! * \brief Register a callback function for log redirecting. * \param callback The callback function to register * \return 0 when succeed, -1 when failure happens */ LIGHTGBM_C_EXPORT int LGBM_RegisterLogCallback(void (*callback)(const char*)); /*! * \brief Get number of samples based on parameters and total number of rows of data. * \param num_total_row Number of total rows * \param parameters Additional parameters, namely, ``bin_construct_sample_cnt`` is used to calculate returned value * \param[out] out Number of samples. This value is used to pre-allocate memory to hold sample indices when calling ``LGBM_SampleIndices`` * \return 0 when succeed, -1 when failure happens */ LIGHTGBM_C_EXPORT int LGBM_GetSampleCount(int32_t num_total_row, const char* parameters, int* out); /*! * \brief Create sample indices for total number of rows. * \note * You should pre-allocate memory for ``out``, you can get its length by ``LGBM_GetSampleCount``. * \param num_total_row Number of total rows * \param parameters Additional parameters, namely, ``bin_construct_sample_cnt`` and ``data_random_seed`` are used to produce the output * \param[out] out Created indices, type is int32_t * \param[out] out_len Number of indices * \return 0 when succeed, -1 when failure happens */ LIGHTGBM_C_EXPORT int LGBM_SampleIndices(int32_t num_total_row, const char* parameters, void* out, int32_t* out_len); /*! * \brief Get a ByteBuffer value at an index. * \param handle Handle of byte buffer to be read * \param index Index of value to return * \param[out] out_val Byte value at index to return * \return 0 when succeed, -1 when failure happens */ LIGHTGBM_C_EXPORT int LGBM_ByteBufferGetAt(ByteBufferHandle handle, int32_t index, uint8_t* out_val); /*! * \brief Free space for byte buffer. * \param handle Handle of byte buffer to be freed * \return 0 when succeed, -1 when failure happens */ LIGHTGBM_C_EXPORT int LGBM_ByteBufferFree(ByteBufferHandle handle); /* --- start Dataset interface */ /*! * \brief Load dataset from file (like LightGBM CLI version does). * \param filename The name of the file * \param parameters Additional parameters * \param reference Used to align bin mapper with other dataset, nullptr means isn't used * \param[out] out A loaded dataset * \return 0 when succeed, -1 when failure happens */ LIGHTGBM_C_EXPORT int LGBM_DatasetCreateFromFile(const char* filename, const char* parameters, const DatasetHandle reference, DatasetHandle* out); /*! * \brief Allocate the space for dataset and bucket feature bins according to sampled data. * \param sample_data Sampled data, grouped by the column * \param sample_indices Indices of sampled data * \param ncol Number of columns * \param num_per_col Size of each sampling column * \param num_sample_row Number of sampled rows * \param num_local_row Total number of rows local to machine * \param num_dist_row Number of total distributed rows * \param parameters Additional parameters * \param[out] out Created dataset * \return 0 when succeed, -1 when failure happens */ LIGHTGBM_C_EXPORT int LGBM_DatasetCreateFromSampledColumn(double** sample_data, int** sample_indices, int32_t ncol, const int* num_per_col, int32_t num_sample_row, int32_t num_local_row, int64_t num_dist_row, const char* parameters, DatasetHandle* out); /*! * \brief Allocate the space for dataset and bucket feature bins according to reference dataset. * \param reference Used to align bin mapper with other dataset * \param num_total_row Number of total rows * \param[out] out Created dataset * \return 0 when succeed, -1 when failure happens */ LIGHTGBM_C_EXPORT int LGBM_DatasetCreateByReference(const DatasetHandle reference, int64_t num_total_row, DatasetHandle* out); /*! * \brief Initialize the Dataset for streaming. * \param dataset Handle of dataset * \param has_weights Whether the dataset has Metadata weights * \param has_init_scores Whether the dataset has Metadata initial scores * \param has_queries Whether the dataset has Metadata queries/groups * \param nclasses Number of initial score classes * \param nthreads Number of external threads that will use the PushRows APIs * \param omp_max_threads Maximum number of OpenMP threads (-1 for default) * \return 0 when succeed, -1 when failure happens */ LIGHTGBM_C_EXPORT int LGBM_DatasetInitStreaming(DatasetHandle dataset, int32_t has_weights, int32_t has_init_scores, int32_t has_queries, int32_t nclasses, int32_t nthreads, int32_t omp_max_threads); /*! * \brief Allocate the space for dataset and bucket feature bins according to serialized reference dataset. * \param ref_buffer A binary representation of the dataset schema (feature groups, bins, etc.) * \param ref_buffer_size The size of the reference array in bytes * \param num_row Number of total rows the dataset will contain * \param num_classes Number of classes (will be used only in case of multiclass and specifying initial scores) * \param parameters Additional parameters * \param[out] out Created dataset * \return 0 when succeed, -1 when failure happens */ LIGHTGBM_C_EXPORT int LGBM_DatasetCreateFromSerializedReference(const void* ref_buffer, int32_t ref_buffer_size, int64_t num_row, int32_t num_classes, const char* parameters, DatasetHandle* out); /*! * \brief Push data to existing dataset, if ``nrow + start_row == num_total_row``, will call ``dataset->FinishLoad``. * \param dataset Handle of dataset * \param data Pointer to the data space * \param data_type Type of ``data`` pointer, can be ``C_API_DTYPE_FLOAT32`` or ``C_API_DTYPE_FLOAT64`` * \param nrow Number of rows * \param ncol Number of columns * \param start_row Row start index * \return 0 when succeed, -1 when failure happens */ LIGHTGBM_C_EXPORT int LGBM_DatasetPushRows(DatasetHandle dataset, const void* data, int data_type, int32_t nrow, int32_t ncol, int32_t start_row); /*! * \brief Push data to existing dataset. * The general flow for a streaming scenario is: * 1. create Dataset "schema" (e.g. ``LGBM_DatasetCreateFromSampledColumn``) * 2. init them for thread-safe streaming (``LGBM_DatasetInitStreaming``) * 3. push data (``LGBM_DatasetPushRowsWithMetadata`` or ``LGBM_DatasetPushRowsByCSRWithMetadata``) * 4. call ``LGBM_DatasetMarkFinished`` * \param dataset Handle of dataset * \param data Pointer to the data space * \param data_type Type of ``data`` pointer, can be ``C_API_DTYPE_FLOAT32`` or ``C_API_DTYPE_FLOAT64`` * \param nrow Number of rows * \param ncol Number of feature columns * \param start_row Row start index, i.e., the index at which to start inserting data * \param label Pointer to array with nrow labels * \param weight Optional pointer to array with nrow weights * \param init_score Optional pointer to array with nrow*nclasses initial scores, in column format * \param query Optional pointer to array with nrow query values * \param tid The id of the calling thread, from 0...N-1 threads * \return 0 when succeed, -1 when failure happens */ LIGHTGBM_C_EXPORT int LGBM_DatasetPushRowsWithMetadata(DatasetHandle dataset, const void* data, int data_type, int32_t nrow, int32_t ncol, int32_t start_row, const float* label, const float* weight, const double* init_score, const int32_t* query, int32_t tid); /*! * \brief Push data to existing dataset, if ``nrow + start_row == num_total_row``, will call ``dataset->FinishLoad``. * \param dataset Handle of dataset * \param indptr Pointer to row headers * \param indptr_type Type of ``indptr``, can be ``C_API_DTYPE_INT32`` or ``C_API_DTYPE_INT64`` * \param indices Pointer to column indices * \param data Pointer to the data space * \param data_type Type of ``data`` pointer, can be ``C_API_DTYPE_FLOAT32`` or ``C_API_DTYPE_FLOAT64`` * \param nindptr Number of rows in the matrix + 1 * \param nelem Number of nonzero elements in the matrix * \param num_col Number of columns * \param start_row Row start index * \return 0 when succeed, -1 when failure happens */ LIGHTGBM_C_EXPORT int LGBM_DatasetPushRowsByCSR(DatasetHandle dataset, const void* indptr, int indptr_type, const int32_t* indices, const void* data, int data_type, int64_t nindptr, int64_t nelem, int64_t num_col, int64_t start_row); /*! * \brief Push CSR data to existing dataset. (See ``LGBM_DatasetPushRowsWithMetadata`` for more details.) * \param dataset Handle of dataset * \param indptr Pointer to row headers * \param indptr_type Type of ``indptr``, can be ``C_API_DTYPE_INT32`` or ``C_API_DTYPE_INT64`` * \param indices Pointer to column indices * \param data Pointer to the data space * \param data_type Type of ``data`` pointer, can be ``C_API_DTYPE_FLOAT32`` or ``C_API_DTYPE_FLOAT64`` * \param nindptr Number of rows in the matrix + 1 * \param nelem Number of nonzero elements in the matrix * \param start_row Row start index * \param label Pointer to array with nindptr-1 labels * \param weight Optional pointer to array with nindptr-1 weights * \param init_score Optional pointer to array with (nindptr-1)*nclasses initial scores, in column format * \param query Optional pointer to array with nindptr-1 query values * \param tid The id of the calling thread, from 0...N-1 threads * \return 0 when succeed, -1 when failure happens */ LIGHTGBM_C_EXPORT int LGBM_DatasetPushRowsByCSRWithMetadata(DatasetHandle dataset, const void* indptr, int indptr_type, const int32_t* indices, const void* data, int data_type, int64_t nindptr, int64_t nelem, int64_t start_row, const float* label, const float* weight, const double* init_score, const int32_t* query, int32_t tid); /*! * \brief Set whether or not the Dataset waits for a manual MarkFinished call or calls FinishLoad on itself automatically. * Set to 1 for streaming scenario, and use ``LGBM_DatasetMarkFinished`` to manually finish the Dataset. * \param dataset Handle of dataset * \param wait Whether to wait or not (1 or 0) * \return 0 when succeed, -1 when failure happens */ LIGHTGBM_C_EXPORT int LGBM_DatasetSetWaitForManualFinish(DatasetHandle dataset, int wait); /*! * \brief Mark the Dataset as complete by calling ``dataset->FinishLoad``. * \param dataset Handle of dataset * \return 0 when succeed, -1 when failure happens */ LIGHTGBM_C_EXPORT int LGBM_DatasetMarkFinished(DatasetHandle dataset); /*! * \brief Create a dataset from CSR format. * \param indptr Pointer to row headers * \param indptr_type Type of ``indptr``, can be ``C_API_DTYPE_INT32`` or ``C_API_DTYPE_INT64`` * \param indices Pointer to column indices * \param data Pointer to the data space * \param data_type Type of ``data`` pointer, can be ``C_API_DTYPE_FLOAT32`` or ``C_API_DTYPE_FLOAT64`` * \param nindptr Number of rows in the matrix + 1 * \param nelem Number of nonzero elements in the matrix * \param num_col Number of columns * \param parameters Additional parameters * \param reference Used to align bin mapper with other dataset, nullptr means isn't used * \param[out] out Created dataset * \return 0 when succeed, -1 when failure happens */ LIGHTGBM_C_EXPORT int LGBM_DatasetCreateFromCSR(const void* indptr, int indptr_type, const int32_t* indices, const void* data, int data_type, int64_t nindptr, int64_t nelem, int64_t num_col, const char* parameters, const DatasetHandle reference, DatasetHandle* out); /*! * \brief Create a dataset from CSR format through callbacks. * \param get_row_funptr Pointer to ``std::function>& ret)>`` * (called for every row and expected to clear and fill ``ret``) * \param num_rows Number of rows * \param num_col Number of columns * \param parameters Additional parameters * \param reference Used to align bin mapper with other dataset, nullptr means isn't used * \param[out] out Created dataset * \return 0 when succeed, -1 when failure happens */ LIGHTGBM_C_EXPORT int LGBM_DatasetCreateFromCSRFunc(void* get_row_funptr, int num_rows, int64_t num_col, const char* parameters, const DatasetHandle reference, DatasetHandle* out); /*! * \brief Create a dataset from CSC format. * \param col_ptr Pointer to column headers * \param col_ptr_type Type of ``col_ptr``, can be ``C_API_DTYPE_INT32`` or ``C_API_DTYPE_INT64`` * \param indices Pointer to row indices * \param data Pointer to the data space * \param data_type Type of ``data`` pointer, can be ``C_API_DTYPE_FLOAT32`` or ``C_API_DTYPE_FLOAT64`` * \param ncol_ptr Number of columns in the matrix + 1 * \param nelem Number of nonzero elements in the matrix * \param num_row Number of rows * \param parameters Additional parameters * \param reference Used to align bin mapper with other dataset, nullptr means isn't used * \param[out] out Created dataset * \return 0 when succeed, -1 when failure happens */ LIGHTGBM_C_EXPORT int LGBM_DatasetCreateFromCSC(const void* col_ptr, int col_ptr_type, const int32_t* indices, const void* data, int data_type, int64_t ncol_ptr, int64_t nelem, int64_t num_row, const char* parameters, const DatasetHandle reference, DatasetHandle* out); /*! * \brief Create dataset from dense matrix. * \param data Pointer to the data space * \param data_type Type of ``data`` pointer, can be ``C_API_DTYPE_FLOAT32`` or ``C_API_DTYPE_FLOAT64`` * \param nrow Number of rows * \param ncol Number of columns * \param is_row_major 1 for row-major, 0 for column-major * \param parameters Additional parameters * \param reference Used to align bin mapper with other dataset, nullptr means isn't used * \param[out] out Created dataset * \return 0 when succeed, -1 when failure happens */ LIGHTGBM_C_EXPORT int LGBM_DatasetCreateFromMat(const void* data, int data_type, int32_t nrow, int32_t ncol, int is_row_major, const char* parameters, const DatasetHandle reference, DatasetHandle* out); /*! * \brief Create dataset from array of dense matrices. * \param nmat Number of dense matrices * \param data Pointer to the data space * \param data_type Type of ``data`` pointer, can be ``C_API_DTYPE_FLOAT32`` or ``C_API_DTYPE_FLOAT64`` * \param nrow Number of rows * \param ncol Number of columns * \param is_row_major Pointer to the data layouts. 1 for row-major, 0 for column-major * \param parameters Additional parameters * \param reference Used to align bin mapper with other dataset, nullptr means isn't used * \param[out] out Created dataset * \return 0 when succeed, -1 when failure happens */ LIGHTGBM_C_EXPORT int LGBM_DatasetCreateFromMats(int32_t nmat, const void** data, int data_type, int32_t* nrow, int32_t ncol, int* is_row_major, const char* parameters, const DatasetHandle reference, DatasetHandle* out); /*! * \brief Create dataset from Arrow. * \param n_chunks The number of Arrow arrays passed to this function * \param chunks Pointer to the list of Arrow arrays * \param schema Pointer to the schema of all Arrow arrays * \param parameters Additional parameters * \param reference Used to align bin mapper with other dataset, nullptr means isn't used * \param[out] out Created dataset * \return 0 when succeed, -1 when failure happens */ LIGHTGBM_C_EXPORT int LGBM_DatasetCreateFromArrow(int64_t n_chunks, const struct ArrowArray* chunks, const struct ArrowSchema* schema, const char* parameters, const DatasetHandle reference, DatasetHandle *out); /*! * \brief Create subset of a data. * \param handle Handle of full dataset * \param used_row_indices Indices used in subset * \param num_used_row_indices Length of ``used_row_indices`` * \param parameters Additional parameters * \param[out] out Subset of data * \return 0 when succeed, -1 when failure happens */ LIGHTGBM_C_EXPORT int LGBM_DatasetGetSubset(const DatasetHandle handle, const int32_t* used_row_indices, int32_t num_used_row_indices, const char* parameters, DatasetHandle* out); /*! * \brief Save feature names to dataset. * \param handle Handle of dataset * \param feature_names Feature names * \param num_feature_names Number of feature names * \return 0 when succeed, -1 when failure happens */ LIGHTGBM_C_EXPORT int LGBM_DatasetSetFeatureNames(DatasetHandle handle, const char** feature_names, int num_feature_names); /*! * \brief Get feature names of dataset. * \param handle Handle of dataset * \param len Number of ``char*`` pointers stored at ``out_strs``. * If smaller than the max size, only this many strings are copied * \param[out] num_feature_names Number of feature names * \param buffer_len Size of pre-allocated strings. * Content is copied up to ``buffer_len - 1`` and null-terminated * \param[out] out_buffer_len String sizes required to do the full string copies * \param[out] feature_names Feature names, should pre-allocate memory * \return 0 when succeed, -1 when failure happens */ LIGHTGBM_C_EXPORT int LGBM_DatasetGetFeatureNames(DatasetHandle handle, const int len, int* num_feature_names, const size_t buffer_len, size_t* out_buffer_len, char** feature_names); /*! * \brief Free space for dataset. * \param handle Handle of dataset to be freed * \return 0 when succeed, -1 when failure happens */ LIGHTGBM_C_EXPORT int LGBM_DatasetFree(DatasetHandle handle); /*! * \brief Save dataset to binary file. * \param handle Handle of dataset * \param filename The name of the file * \return 0 when succeed, -1 when failure happens */ LIGHTGBM_C_EXPORT int LGBM_DatasetSaveBinary(DatasetHandle handle, const char* filename); /*! * \brief Create a dataset schema representation as a binary byte array (excluding data). * \param handle Handle of dataset * \param[out] out The output byte array * \param[out] out_len The length of the output byte array (returned for convenience) * \return 0 when succeed, -1 when failure happens */ LIGHTGBM_C_EXPORT int LGBM_DatasetSerializeReferenceToBinary(DatasetHandle handle, ByteBufferHandle* out, int32_t* out_len); /*! * \brief Save dataset to text file, intended for debugging use only. * \param handle Handle of dataset * \param filename The name of the file * \return 0 when succeed, -1 when failure happens */ LIGHTGBM_C_EXPORT int LGBM_DatasetDumpText(DatasetHandle handle, const char* filename); /*! * \brief Set vector to a content in info. * \note * - \a group only works for ``C_API_DTYPE_INT32``; * - \a label and \a weight only work for ``C_API_DTYPE_FLOAT32``; * - \a init_score only works for ``C_API_DTYPE_FLOAT64``. * \param handle Handle of dataset * \param field_name Field name, can be \a label, \a weight, \a init_score, \a group * \param field_data Pointer to data vector * \param num_element Number of elements in ``field_data`` * \param type Type of ``field_data`` pointer, can be ``C_API_DTYPE_INT32``, ``C_API_DTYPE_FLOAT32`` or ``C_API_DTYPE_FLOAT64`` * \return 0 when succeed, -1 when failure happens */ LIGHTGBM_C_EXPORT int LGBM_DatasetSetField(DatasetHandle handle, const char* field_name, const void* field_data, int num_element, int type); /*! * \brief Set vector to a content in info. * \note * - \a group converts input datatype into ``int32``; * - \a label and \a weight convert input datatype into ``float32``; * - \a init_score converts input datatype into ``float64``. * \param handle Handle of dataset * \param field_name Field name, can be \a label, \a weight, \a init_score, \a group * \param n_chunks The number of Arrow arrays passed to this function * \param chunks Pointer to the list of Arrow arrays * \param schema Pointer to the schema of all Arrow arrays * \return 0 when succeed, -1 when failure happens */ LIGHTGBM_C_EXPORT int LGBM_DatasetSetFieldFromArrow(DatasetHandle handle, const char* field_name, int64_t n_chunks, const struct ArrowArray* chunks, const struct ArrowSchema* schema); /*! * \brief Get info vector from dataset. * \param handle Handle of dataset * \param field_name Field name * \param[out] out_len Used to set result length * \param[out] out_ptr Pointer to the result * \param[out] out_type Type of result pointer, can be ``C_API_DTYPE_INT32``, ``C_API_DTYPE_FLOAT32`` or ``C_API_DTYPE_FLOAT64`` * \return 0 when succeed, -1 when failure happens */ LIGHTGBM_C_EXPORT int LGBM_DatasetGetField(DatasetHandle handle, const char* field_name, int* out_len, const void** out_ptr, int* out_type); /*! * \brief Raise errors for attempts to update dataset parameters. * \param old_parameters Current dataset parameters * \param new_parameters New dataset parameters * \return 0 when succeed, -1 when failure happens */ LIGHTGBM_C_EXPORT int LGBM_DatasetUpdateParamChecking(const char* old_parameters, const char* new_parameters); /*! * \brief Get number of data points. * \param handle Handle of dataset * \param[out] out The address to hold number of data points * \return 0 when succeed, -1 when failure happens */ LIGHTGBM_C_EXPORT int LGBM_DatasetGetNumData(DatasetHandle handle, int* out); /*! * \brief Get number of features. * \param handle Handle of dataset * \param[out] out The address to hold number of features * \return 0 when succeed, -1 when failure happens */ LIGHTGBM_C_EXPORT int LGBM_DatasetGetNumFeature(DatasetHandle handle, int* out); /*! * \brief Get number of bins for feature. * \param handle Handle of dataset * \param feature Index of the feature * \param[out] out The address to hold number of bins * \return 0 when succeed, -1 when failure happens */ LIGHTGBM_C_EXPORT int LGBM_DatasetGetFeatureNumBin(DatasetHandle handle, int feature, int* out); /*! * \brief Add features from ``source`` to ``target``. * \param target The handle of the dataset to add features to * \param source The handle of the dataset to take features from * \return 0 when succeed, -1 when failure happens */ LIGHTGBM_C_EXPORT int LGBM_DatasetAddFeaturesFrom(DatasetHandle target, DatasetHandle source); /* --- start Booster interfaces */ /*! * \brief Get int representing whether booster is fitting linear trees. * \param handle Handle of booster * \param[out] out The address to hold linear trees indicator * \return 0 when succeed, -1 when failure happens */ LIGHTGBM_C_EXPORT int LGBM_BoosterGetLinear(BoosterHandle handle, int* out); /*! * \brief Create a new boosting learner. * \param train_data Training dataset * \param parameters Parameters in format 'key1=value1 key2=value2' * \param[out] out Handle of created booster * \return 0 when succeed, -1 when failure happens */ LIGHTGBM_C_EXPORT int LGBM_BoosterCreate(const DatasetHandle train_data, const char* parameters, BoosterHandle* out); /*! * \brief Load an existing booster from model file. * \param filename Filename of model * \param[out] out_num_iterations Number of iterations of this booster * \param[out] out Handle of created booster * \return 0 when succeed, -1 when failure happens */ LIGHTGBM_C_EXPORT int LGBM_BoosterCreateFromModelfile(const char* filename, int* out_num_iterations, BoosterHandle* out); /*! * \brief Load an existing booster from string. * \param model_str Model string * \param[out] out_num_iterations Number of iterations of this booster * \param[out] out Handle of created booster * \return 0 when succeed, -1 when failure happens */ LIGHTGBM_C_EXPORT int LGBM_BoosterLoadModelFromString(const char* model_str, int* out_num_iterations, BoosterHandle* out); /*! * \brief Get parameters as JSON string. * \param handle Handle of booster * \param buffer_len Allocated space for string * \param[out] out_len Actual size of string * \param[out] out_str JSON string containing parameters * \return 0 when succeed, -1 when failure happens */ LIGHTGBM_C_EXPORT int LGBM_BoosterGetLoadedParam(BoosterHandle handle, int64_t buffer_len, int64_t* out_len, char* out_str); /*! * \brief Free space for booster. * \param handle Handle of booster to be freed * \return 0 when succeed, -1 when failure happens */ LIGHTGBM_C_EXPORT int LGBM_BoosterFree(BoosterHandle handle); /*! * \brief Shuffle models. * \param handle Handle of booster * \param start_iter The first iteration that will be shuffled * \param end_iter The last iteration that will be shuffled * \return 0 when succeed, -1 when failure happens */ LIGHTGBM_C_EXPORT int LGBM_BoosterShuffleModels(BoosterHandle handle, int start_iter, int end_iter); /*! * \brief Merge model from ``other_handle`` into ``handle``. * \param handle Handle of booster, will merge another booster into this one * \param other_handle Other handle of booster * \return 0 when succeed, -1 when failure happens */ LIGHTGBM_C_EXPORT int LGBM_BoosterMerge(BoosterHandle handle, BoosterHandle other_handle); /*! * \brief Add new validation data to booster. * \param handle Handle of booster * \param valid_data Validation dataset * \return 0 when succeed, -1 when failure happens */ LIGHTGBM_C_EXPORT int LGBM_BoosterAddValidData(BoosterHandle handle, const DatasetHandle valid_data); /*! * \brief Reset training data for booster. * \param handle Handle of booster * \param train_data Training dataset * \return 0 when succeed, -1 when failure happens */ LIGHTGBM_C_EXPORT int LGBM_BoosterResetTrainingData(BoosterHandle handle, const DatasetHandle train_data); /*! * \brief Reset config for booster. * \param handle Handle of booster * \param parameters Parameters in format 'key1=value1 key2=value2' * \return 0 when succeed, -1 when failure happens */ LIGHTGBM_C_EXPORT int LGBM_BoosterResetParameter(BoosterHandle handle, const char* parameters); /*! * \brief Get number of classes. * \param handle Handle of booster * \param[out] out_len Number of classes * \return 0 when succeed, -1 when failure happens */ LIGHTGBM_C_EXPORT int LGBM_BoosterGetNumClasses(BoosterHandle handle, int* out_len); /*! * \brief Update the model for one iteration. * \param handle Handle of booster * \param[out] produced_empty_tree 1 means the tree(s) produced by this iteration did not have any splits. * This usually means that training is "finished" (calling this function again will not change the model's predictions). * However, that is not always the case. * For example, if you have added any randomness (like column sampling by setting ``feature_fraction_bynode < 1.0``), * it is possible that another call to this function would produce a non-empty tree. * \return 0 when succeed, -1 when failure happens */ LIGHTGBM_C_EXPORT int LGBM_BoosterUpdateOneIter(BoosterHandle handle, int* produced_empty_tree); /*! * \brief Refit the tree model using the new data (online learning). * \param handle Handle of booster * \param leaf_preds Pointer to predicted leaf indices * \param nrow Number of rows of ``leaf_preds`` * \param ncol Number of columns of ``leaf_preds`` * \return 0 when succeed, -1 when failure happens */ LIGHTGBM_C_EXPORT int LGBM_BoosterRefit(BoosterHandle handle, const int32_t* leaf_preds, int32_t nrow, int32_t ncol); /*! * \brief Update the model by specifying gradient and Hessian directly * (this can be used to support customized loss functions). * \note * The length of the arrays referenced by ``grad`` and ``hess`` must be equal to * ``num_class * num_train_data``, this is not verified by the library, the caller must ensure this. * \param handle Handle of booster * \param grad The first order derivative (gradient) statistics * \param hess The second order derivative (Hessian) statistics * \param[out] produced_empty_tree 1 means the tree(s) produced by this iteration did not have any splits. * This usually means that training is "finished" (calling this function again will not change the model's predictions). * However, that is not always the case. * For example, if you have added any randomness (like column sampling by setting ``feature_fraction_bynode < 1.0``), * it is possible that another call to this function would produce a non-empty tree. * \return 0 when succeed, -1 when failure happens */ LIGHTGBM_C_EXPORT int LGBM_BoosterUpdateOneIterCustom(BoosterHandle handle, const float* grad, const float* hess, int* produced_empty_tree); /*! * \brief Rollback one iteration. * \param handle Handle of booster * \return 0 when succeed, -1 when failure happens */ LIGHTGBM_C_EXPORT int LGBM_BoosterRollbackOneIter(BoosterHandle handle); /*! * \brief Get index of the current boosting iteration. * \param handle Handle of booster * \param[out] out_iteration Index of the current boosting iteration * \return 0 when succeed, -1 when failure happens */ LIGHTGBM_C_EXPORT int LGBM_BoosterGetCurrentIteration(BoosterHandle handle, int* out_iteration); /*! * \brief Get number of trees per iteration. * \param handle Handle of booster * \param[out] out_tree_per_iteration Number of trees per iteration * \return 0 when succeed, -1 when failure happens */ LIGHTGBM_C_EXPORT int LGBM_BoosterNumModelPerIteration(BoosterHandle handle, int* out_tree_per_iteration); /*! * \brief Get number of weak sub-models. * \param handle Handle of booster * \param[out] out_models Number of weak sub-models * \return 0 when succeed, -1 when failure happens */ LIGHTGBM_C_EXPORT int LGBM_BoosterNumberOfTotalModel(BoosterHandle handle, int* out_models); /*! * \brief Get number of evaluation metrics. * \param handle Handle of booster * \param[out] out_len Total number of evaluation metrics * \return 0 when succeed, -1 when failure happens */ LIGHTGBM_C_EXPORT int LGBM_BoosterGetEvalCounts(BoosterHandle handle, int* out_len); /*! * \brief Get names of evaluation metrics. * \param handle Handle of booster * \param len Number of ``char*`` pointers stored at ``out_strs``. * If smaller than the max size, only this many strings are copied * \param[out] out_len Total number of evaluation metrics * \param buffer_len Size of pre-allocated strings. * Content is copied up to ``buffer_len - 1`` and null-terminated * \param[out] out_buffer_len String sizes required to do the full string copies * \param[out] out_strs Names of evaluation metrics, should pre-allocate memory * \return 0 when succeed, -1 when failure happens */ LIGHTGBM_C_EXPORT int LGBM_BoosterGetEvalNames(BoosterHandle handle, const int len, int* out_len, const size_t buffer_len, size_t* out_buffer_len, char** out_strs); /*! * \brief Get names of features. * \param handle Handle of booster * \param len Number of ``char*`` pointers stored at ``out_strs``. * If smaller than the max size, only this many strings are copied * \param[out] out_len Total number of features * \param buffer_len Size of pre-allocated strings. * Content is copied up to ``buffer_len - 1`` and null-terminated * \param[out] out_buffer_len String sizes required to do the full string copies * \param[out] out_strs Names of features, should pre-allocate memory * \return 0 when succeed, -1 when failure happens */ LIGHTGBM_C_EXPORT int LGBM_BoosterGetFeatureNames(BoosterHandle handle, const int len, int* out_len, const size_t buffer_len, size_t* out_buffer_len, char** out_strs); /*! * \brief Check that the feature names of the data match the ones used to train the booster. * \param handle Handle of booster * \param data_names Array with the feature names in the data * \param data_num_features Number of features in the data * \return 0 when succeed, -1 when failure happens */ LIGHTGBM_C_EXPORT int LGBM_BoosterValidateFeatureNames(BoosterHandle handle, const char** data_names, int data_num_features); /*! * \brief Get number of features. * \param handle Handle of booster * \param[out] out_len Total number of features * \return 0 when succeed, -1 when failure happens */ LIGHTGBM_C_EXPORT int LGBM_BoosterGetNumFeature(BoosterHandle handle, int* out_len); /*! * \brief Get evaluation for training data and validation data. * \note * 1. You should call ``LGBM_BoosterGetEvalNames`` first to get the names of evaluation metrics. * 2. You should pre-allocate memory for ``out_results``, you can get its length by ``LGBM_BoosterGetEvalCounts``. * \param handle Handle of booster * \param data_idx Index of data, 0: training data, 1: 1st validation data, 2: 2nd validation data and so on * \param[out] out_len Length of output result * \param[out] out_results Array with evaluation results * \return 0 when succeed, -1 when failure happens */ LIGHTGBM_C_EXPORT int LGBM_BoosterGetEval(BoosterHandle handle, int data_idx, int* out_len, double* out_results); /*! * \brief Get number of predictions for training data and validation data * (this can be used to support customized evaluation functions). * \param handle Handle of booster * \param data_idx Index of data, 0: training data, 1: 1st validation data, 2: 2nd validation data and so on * \param[out] out_len Number of predictions * \return 0 when succeed, -1 when failure happens */ LIGHTGBM_C_EXPORT int LGBM_BoosterGetNumPredict(BoosterHandle handle, int data_idx, int64_t* out_len); /*! * \brief Get prediction for training data and validation data. * \note * You should pre-allocate memory for ``out_result``, its length is equal to ``num_class * num_data``. * \param handle Handle of booster * \param data_idx Index of data, 0: training data, 1: 1st validation data, 2: 2nd validation data and so on * \param[out] out_len Length of output result * \param[out] out_result Pointer to array with predictions * \return 0 when succeed, -1 when failure happens */ LIGHTGBM_C_EXPORT int LGBM_BoosterGetPredict(BoosterHandle handle, int data_idx, int64_t* out_len, double* out_result); /*! * \brief Make prediction for file. * \param handle Handle of booster * \param data_filename Filename of file with data * \param data_has_header Whether file has header or not * \param predict_type What should be predicted * - ``C_API_PREDICT_NORMAL``: normal prediction, with transform (if needed); * - ``C_API_PREDICT_RAW_SCORE``: raw score; * - ``C_API_PREDICT_LEAF_INDEX``: leaf index; * - ``C_API_PREDICT_CONTRIB``: feature contributions (SHAP values) * \param start_iteration Start index of the iteration to predict * \param num_iteration Number of iterations for prediction, <= 0 means no limit * \param parameter Other parameters for prediction, e.g. early stopping for prediction * \param result_filename Filename of result file in which predictions will be written * \return 0 when succeed, -1 when failure happens */ LIGHTGBM_C_EXPORT int LGBM_BoosterPredictForFile(BoosterHandle handle, const char* data_filename, int data_has_header, int predict_type, int start_iteration, int num_iteration, const char* parameter, const char* result_filename); /*! * \brief Get number of predictions. * \param handle Handle of booster * \param num_row Number of rows * \param predict_type What should be predicted * - ``C_API_PREDICT_NORMAL``: normal prediction, with transform (if needed); * - ``C_API_PREDICT_RAW_SCORE``: raw score; * - ``C_API_PREDICT_LEAF_INDEX``: leaf index; * - ``C_API_PREDICT_CONTRIB``: feature contributions (SHAP values) * \param start_iteration Start index of the iteration to predict * \param num_iteration Number of iterations for prediction, <= 0 means no limit * \param[out] out_len Length of prediction * \return 0 when succeed, -1 when failure happens */ LIGHTGBM_C_EXPORT int LGBM_BoosterCalcNumPredict(BoosterHandle handle, int num_row, int predict_type, int start_iteration, int num_iteration, int64_t* out_len); /*! * \brief Release FastConfig object. * * \param fastConfig Handle to the FastConfig object acquired with a ``*FastInit()`` method. * \return 0 when it succeeds, -1 when failure happens */ LIGHTGBM_C_EXPORT int LGBM_FastConfigFree(FastConfigHandle fastConfig); /*! * \brief Make prediction for a new dataset in CSR format. * \note * You should pre-allocate memory for ``out_result``: * - for normal and raw score, its length is equal to ``num_class * num_data``; * - for leaf index, its length is equal to ``num_class * num_data * num_iteration``; * - for feature contributions, its length is equal to ``num_class * num_data * (num_feature + 1)``. * \param handle Handle of booster * \param indptr Pointer to row headers * \param indptr_type Type of ``indptr``, can be ``C_API_DTYPE_INT32`` or ``C_API_DTYPE_INT64`` * \param indices Pointer to column indices * \param data Pointer to the data space * \param data_type Type of ``data`` pointer, can be ``C_API_DTYPE_FLOAT32`` or ``C_API_DTYPE_FLOAT64`` * \param nindptr Number of rows in the matrix + 1 * \param nelem Number of nonzero elements in the matrix * \param num_col Number of columns * \param predict_type What should be predicted * - ``C_API_PREDICT_NORMAL``: normal prediction, with transform (if needed); * - ``C_API_PREDICT_RAW_SCORE``: raw score; * - ``C_API_PREDICT_LEAF_INDEX``: leaf index; * - ``C_API_PREDICT_CONTRIB``: feature contributions (SHAP values) * \param start_iteration Start index of the iteration to predict * \param num_iteration Number of iterations for prediction, <= 0 means no limit * \param parameter Other parameters for prediction, e.g. early stopping for prediction * \param[out] out_len Length of output result * \param[out] out_result Pointer to array with predictions * \return 0 when succeed, -1 when failure happens */ LIGHTGBM_C_EXPORT int LGBM_BoosterPredictForCSR(BoosterHandle handle, const void* indptr, int indptr_type, const int32_t* indices, const void* data, int data_type, int64_t nindptr, int64_t nelem, int64_t num_col, int predict_type, int start_iteration, int num_iteration, const char* parameter, int64_t* out_len, double* out_result); /*! * \brief Make sparse prediction for a new dataset in CSR or CSC format. Currently only used for feature contributions. * \note * The outputs are pre-allocated, as they can vary for each invocation, but the shape should be the same: * - for feature contributions, the shape of sparse matrix will be ``num_class * num_data * (num_feature + 1)``. * The output indptr_type for the sparse matrix will be the same as the given input indptr_type. * Call ``LGBM_BoosterFreePredictSparse`` to deallocate resources. * \param handle Handle of booster * \param indptr Pointer to row headers for CSR or column headers for CSC * \param indptr_type Type of ``indptr``, can be ``C_API_DTYPE_INT32`` or ``C_API_DTYPE_INT64`` * \param indices Pointer to column indices for CSR or row indices for CSC * \param data Pointer to the data space * \param data_type Type of ``data`` pointer, can be ``C_API_DTYPE_FLOAT32`` or ``C_API_DTYPE_FLOAT64`` * \param nindptr Number of entries in ``indptr`` * \param nelem Number of nonzero elements in the matrix * \param num_col_or_row Number of columns for CSR or number of rows for CSC * \param predict_type What should be predicted, only feature contributions supported currently * - ``C_API_PREDICT_CONTRIB``: feature contributions (SHAP values) * \param start_iteration Start index of the iteration to predict * \param num_iteration Number of iterations for prediction, <= 0 means no limit * \param parameter Other parameters for prediction, e.g. early stopping for prediction * \param matrix_type Type of matrix input and output, can be ``C_API_MATRIX_TYPE_CSR`` or ``C_API_MATRIX_TYPE_CSC`` * \param[out] out_len Length of output data and output indptr (pointer to an array with two entries where to write them) * \param[out] out_indptr Pointer to output row headers for CSR or column headers for CSC * \param[out] out_indices Pointer to sparse column indices for CSR or row indices for CSC * \param[out] out_data Pointer to sparse data space * \return 0 when succeed, -1 when failure happens */ LIGHTGBM_C_EXPORT int LGBM_BoosterPredictSparseOutput(BoosterHandle handle, const void* indptr, int indptr_type, const int32_t* indices, const void* data, int data_type, int64_t nindptr, int64_t nelem, int64_t num_col_or_row, int predict_type, int start_iteration, int num_iteration, const char* parameter, int matrix_type, int64_t* out_len, void** out_indptr, int32_t** out_indices, void** out_data); /*! * \brief Method corresponding to ``LGBM_BoosterPredictSparseOutput`` to free the allocated data. * \param indptr Pointer to output row headers or column headers to be deallocated * \param indices Pointer to sparse indices to be deallocated * \param data Pointer to sparse data space to be deallocated * \param indptr_type Type of ``indptr``, can be ``C_API_DTYPE_INT32`` or ``C_API_DTYPE_INT64`` * \param data_type Type of ``data`` pointer, can be ``C_API_DTYPE_FLOAT32`` or ``C_API_DTYPE_FLOAT64`` * \return 0 when succeed, -1 when failure happens */ LIGHTGBM_C_EXPORT int LGBM_BoosterFreePredictSparse(void* indptr, int32_t* indices, void* data, int indptr_type, int data_type); /*! * \brief Make prediction for a new dataset in CSR format. This method re-uses the internal predictor structure * from previous calls and is optimized for single row invocation. * \note * You should pre-allocate memory for ``out_result``: * - for normal and raw score, its length is equal to ``num_class * num_data``; * - for leaf index, its length is equal to ``num_class * num_data * num_iteration``; * - for feature contributions, its length is equal to ``num_class * num_data * (num_feature + 1)``. * \param handle Handle of booster * \param indptr Pointer to row headers * \param indptr_type Type of ``indptr``, can be ``C_API_DTYPE_INT32`` or ``C_API_DTYPE_INT64`` * \param indices Pointer to column indices * \param data Pointer to the data space * \param data_type Type of ``data`` pointer, can be ``C_API_DTYPE_FLOAT32`` or ``C_API_DTYPE_FLOAT64`` * \param nindptr Number of rows in the matrix + 1 * \param nelem Number of nonzero elements in the matrix * \param num_col Number of columns * \param predict_type What should be predicted * - ``C_API_PREDICT_NORMAL``: normal prediction, with transform (if needed); * - ``C_API_PREDICT_RAW_SCORE``: raw score; * - ``C_API_PREDICT_LEAF_INDEX``: leaf index; * - ``C_API_PREDICT_CONTRIB``: feature contributions (SHAP values) * \param start_iteration Start index of the iteration to predict * \param num_iteration Number of iterations for prediction, <= 0 means no limit * \param parameter Other parameters for prediction, e.g. early stopping for prediction * \param[out] out_len Length of output result * \param[out] out_result Pointer to array with predictions * \return 0 when succeed, -1 when failure happens */ LIGHTGBM_C_EXPORT int LGBM_BoosterPredictForCSRSingleRow(BoosterHandle handle, const void* indptr, int indptr_type, const int32_t* indices, const void* data, int data_type, int64_t nindptr, int64_t nelem, int64_t num_col, int predict_type, int start_iteration, int num_iteration, const char* parameter, int64_t* out_len, double* out_result); /*! * \brief Initialize and return a ``FastConfigHandle`` for use with ``LGBM_BoosterPredictForCSRSingleRowFast``. * * Release the ``FastConfig`` by passing its handle to ``LGBM_FastConfigFree`` when no longer needed. * * \param handle Booster handle * \param predict_type What should be predicted * - ``C_API_PREDICT_NORMAL``: normal prediction, with transform (if needed); * - ``C_API_PREDICT_RAW_SCORE``: raw score; * - ``C_API_PREDICT_LEAF_INDEX``: leaf index; * - ``C_API_PREDICT_CONTRIB``: feature contributions (SHAP values) * \param start_iteration Start index of the iteration to predict * \param num_iteration Number of iterations for prediction, <= 0 means no limit * \param data_type Type of ``data`` pointer, can be ``C_API_DTYPE_FLOAT32`` or ``C_API_DTYPE_FLOAT64`` * \param num_col Number of columns * \param parameter Other parameters for prediction, e.g. early stopping for prediction * \param[out] out_fastConfig FastConfig object with which you can call ``LGBM_BoosterPredictForCSRSingleRowFast`` * \return 0 when it succeeds, -1 when failure happens */ LIGHTGBM_C_EXPORT int LGBM_BoosterPredictForCSRSingleRowFastInit(BoosterHandle handle, const int predict_type, const int start_iteration, const int num_iteration, const int data_type, const int64_t num_col, const char* parameter, FastConfigHandle *out_fastConfig); /*! * \brief Faster variant of ``LGBM_BoosterPredictForCSRSingleRow``. * * Score single rows after setup with ``LGBM_BoosterPredictForCSRSingleRowFastInit``. * * By removing the setup steps from this call extra optimizations can be made like * initializing the config only once, instead of once per call. * * \note * Setting up the number of threads is only done once at ``LGBM_BoosterPredictForCSRSingleRowFastInit`` * instead of at each prediction. * If you use a different number of threads in other calls, you need to start the setup process over, * or that number of threads will be used for these calls as well. * * \note * You should pre-allocate memory for ``out_result``: * - for normal and raw score, its length is equal to ``num_class * num_data``; * - for leaf index, its length is equal to ``num_class * num_data * num_iteration``; * - for feature contributions, its length is equal to ``num_class * num_data * (num_feature + 1)``. * * \param fastConfig_handle FastConfig object handle returned by ``LGBM_BoosterPredictForCSRSingleRowFastInit`` * \param indptr Pointer to row headers * \param indptr_type Type of ``indptr``, can be ``C_API_DTYPE_INT32`` or ``C_API_DTYPE_INT64`` * \param indices Pointer to column indices * \param data Pointer to the data space * \param nindptr Number of rows in the matrix + 1 * \param nelem Number of nonzero elements in the matrix * \param[out] out_len Length of output result * \param[out] out_result Pointer to array with predictions * \return 0 when succeed, -1 when failure happens */ LIGHTGBM_C_EXPORT int LGBM_BoosterPredictForCSRSingleRowFast(FastConfigHandle fastConfig_handle, const void* indptr, const int indptr_type, const int32_t* indices, const void* data, const int64_t nindptr, const int64_t nelem, int64_t* out_len, double* out_result); /*! * \brief Make prediction for a new dataset in CSC format. * \note * You should pre-allocate memory for ``out_result``: * - for normal and raw score, its length is equal to ``num_class * num_data``; * - for leaf index, its length is equal to ``num_class * num_data * num_iteration``; * - for feature contributions, its length is equal to ``num_class * num_data * (num_feature + 1)``. * \param handle Handle of booster * \param col_ptr Pointer to column headers * \param col_ptr_type Type of ``col_ptr``, can be ``C_API_DTYPE_INT32`` or ``C_API_DTYPE_INT64`` * \param indices Pointer to row indices * \param data Pointer to the data space * \param data_type Type of ``data`` pointer, can be ``C_API_DTYPE_FLOAT32`` or ``C_API_DTYPE_FLOAT64`` * \param ncol_ptr Number of columns in the matrix + 1 * \param nelem Number of nonzero elements in the matrix * \param num_row Number of rows * \param predict_type What should be predicted * - ``C_API_PREDICT_NORMAL``: normal prediction, with transform (if needed); * - ``C_API_PREDICT_RAW_SCORE``: raw score; * - ``C_API_PREDICT_LEAF_INDEX``: leaf index; * - ``C_API_PREDICT_CONTRIB``: feature contributions (SHAP values) * \param start_iteration Start index of the iteration to predict * \param num_iteration Number of iteration for prediction, <= 0 means no limit * \param parameter Other parameters for prediction, e.g. early stopping for prediction * \param[out] out_len Length of output result * \param[out] out_result Pointer to array with predictions * \return 0 when succeed, -1 when failure happens */ LIGHTGBM_C_EXPORT int LGBM_BoosterPredictForCSC(BoosterHandle handle, const void* col_ptr, int col_ptr_type, const int32_t* indices, const void* data, int data_type, int64_t ncol_ptr, int64_t nelem, int64_t num_row, int predict_type, int start_iteration, int num_iteration, const char* parameter, int64_t* out_len, double* out_result); /*! * \brief Make prediction for a new dataset. * \note * You should pre-allocate memory for ``out_result``: * - for normal and raw score, its length is equal to ``num_class * num_data``; * - for leaf index, its length is equal to ``num_class * num_data * num_iteration``; * - for feature contributions, its length is equal to ``num_class * num_data * (num_feature + 1)``. * \param handle Handle of booster * \param data Pointer to the data space * \param data_type Type of ``data`` pointer, can be ``C_API_DTYPE_FLOAT32`` or ``C_API_DTYPE_FLOAT64`` * \param nrow Number of rows * \param ncol Number of columns * \param is_row_major 1 for row-major, 0 for column-major * \param predict_type What should be predicted * - ``C_API_PREDICT_NORMAL``: normal prediction, with transform (if needed); * - ``C_API_PREDICT_RAW_SCORE``: raw score; * - ``C_API_PREDICT_LEAF_INDEX``: leaf index; * - ``C_API_PREDICT_CONTRIB``: feature contributions (SHAP values) * \param start_iteration Start index of the iteration to predict * \param num_iteration Number of iteration for prediction, <= 0 means no limit * \param parameter Other parameters for prediction, e.g. early stopping for prediction * \param[out] out_len Length of output result * \param[out] out_result Pointer to array with predictions * \return 0 when succeed, -1 when failure happens */ LIGHTGBM_C_EXPORT int LGBM_BoosterPredictForMat(BoosterHandle handle, const void* data, int data_type, int32_t nrow, int32_t ncol, int is_row_major, int predict_type, int start_iteration, int num_iteration, const char* parameter, int64_t* out_len, double* out_result); /*! * \brief Make prediction for a new dataset. This method re-uses the internal predictor structure * from previous calls and is optimized for single row invocation. * \note * You should pre-allocate memory for ``out_result``: * - for normal and raw score, its length is equal to ``num_class * num_data``; * - for leaf index, its length is equal to ``num_class * num_data * num_iteration``; * - for feature contributions, its length is equal to ``num_class * num_data * (num_feature + 1)``. * \param handle Handle of booster * \param data Pointer to the data space * \param data_type Type of ``data`` pointer, can be ``C_API_DTYPE_FLOAT32`` or ``C_API_DTYPE_FLOAT64`` * \param ncol Number columns * \param is_row_major 1 for row-major, 0 for column-major * \param predict_type What should be predicted * - ``C_API_PREDICT_NORMAL``: normal prediction, with transform (if needed); * - ``C_API_PREDICT_RAW_SCORE``: raw score; * - ``C_API_PREDICT_LEAF_INDEX``: leaf index; * - ``C_API_PREDICT_CONTRIB``: feature contributions (SHAP values) * \param start_iteration Start index of the iteration to predict * \param num_iteration Number of iteration for prediction, <= 0 means no limit * \param parameter Other parameters for prediction, e.g. early stopping for prediction * \param[out] out_len Length of output result * \param[out] out_result Pointer to array with predictions * \return 0 when succeed, -1 when failure happens */ LIGHTGBM_C_EXPORT int LGBM_BoosterPredictForMatSingleRow(BoosterHandle handle, const void* data, int data_type, int ncol, int is_row_major, int predict_type, int start_iteration, int num_iteration, const char* parameter, int64_t* out_len, double* out_result); /*! * \brief Initialize and return a ``FastConfigHandle`` for use with ``LGBM_BoosterPredictForMatSingleRowFast``. * * Release the ``FastConfig`` by passing its handle to ``LGBM_FastConfigFree`` when no longer needed. * * \param handle Booster handle * \param predict_type What should be predicted * - ``C_API_PREDICT_NORMAL``: normal prediction, with transform (if needed); * - ``C_API_PREDICT_RAW_SCORE``: raw score; * - ``C_API_PREDICT_LEAF_INDEX``: leaf index; * - ``C_API_PREDICT_CONTRIB``: feature contributions (SHAP values) * \param start_iteration Start index of the iteration to predict * \param num_iteration Number of iterations for prediction, <= 0 means no limit * \param data_type Type of ``data`` pointer, can be ``C_API_DTYPE_FLOAT32`` or ``C_API_DTYPE_FLOAT64`` * \param ncol Number of columns * \param parameter Other parameters for prediction, e.g. early stopping for prediction * \param[out] out_fastConfig FastConfig object with which you can call ``LGBM_BoosterPredictForMatSingleRowFast`` * \return 0 when it succeeds, -1 when failure happens */ LIGHTGBM_C_EXPORT int LGBM_BoosterPredictForMatSingleRowFastInit(BoosterHandle handle, const int predict_type, const int start_iteration, const int num_iteration, const int data_type, const int32_t ncol, const char* parameter, FastConfigHandle *out_fastConfig); /*! * \brief Faster variant of ``LGBM_BoosterPredictForMatSingleRow``. * * Score a single row after setup with ``LGBM_BoosterPredictForMatSingleRowFastInit``. * * By removing the setup steps from this call extra optimizations can be made like * initializing the config only once, instead of once per call. * * \note * Setting up the number of threads is only done once at ``LGBM_BoosterPredictForMatSingleRowFastInit`` * instead of at each prediction. * If you use a different number of threads in other calls, you need to start the setup process over, * or that number of threads will be used for these calls as well. * * \param fastConfig_handle FastConfig object handle returned by ``LGBM_BoosterPredictForMatSingleRowFastInit`` * \param data Single-row array data (no other way than row-major form). * \param[out] out_len Length of output result * \param[out] out_result Pointer to array with predictions * \return 0 when it succeeds, -1 when failure happens */ LIGHTGBM_C_EXPORT int LGBM_BoosterPredictForMatSingleRowFast(FastConfigHandle fastConfig_handle, const void* data, int64_t* out_len, double* out_result); /*! * \brief Make prediction for a new dataset presented in a form of array of pointers to rows. * \note * You should pre-allocate memory for ``out_result``: * - for normal and raw score, its length is equal to ``num_class * num_data``; * - for leaf index, its length is equal to ``num_class * num_data * num_iteration``; * - for feature contributions, its length is equal to ``num_class * num_data * (num_feature + 1)``. * \param handle Handle of booster * \param data Pointer to the data space * \param data_type Type of ``data`` pointer, can be ``C_API_DTYPE_FLOAT32`` or ``C_API_DTYPE_FLOAT64`` * \param nrow Number of rows * \param ncol Number columns * \param predict_type What should be predicted * - ``C_API_PREDICT_NORMAL``: normal prediction, with transform (if needed); * - ``C_API_PREDICT_RAW_SCORE``: raw score; * - ``C_API_PREDICT_LEAF_INDEX``: leaf index; * - ``C_API_PREDICT_CONTRIB``: feature contributions (SHAP values) * \param start_iteration Start index of the iteration to predict * \param num_iteration Number of iteration for prediction, <= 0 means no limit * \param parameter Other parameters for prediction, e.g. early stopping for prediction * \param[out] out_len Length of output result * \param[out] out_result Pointer to array with predictions * \return 0 when succeed, -1 when failure happens */ LIGHTGBM_C_EXPORT int LGBM_BoosterPredictForMats(BoosterHandle handle, const void** data, int data_type, int32_t nrow, int32_t ncol, int predict_type, int start_iteration, int num_iteration, const char* parameter, int64_t* out_len, double* out_result); /*! * \brief Make prediction for a new dataset. * \note * You should pre-allocate memory for ``out_result``: * - for normal and raw score, its length is equal to ``num_class * num_data``; * - for leaf index, its length is equal to ``num_class * num_data * num_iteration``; * - for feature contributions, its length is equal to ``num_class * num_data * (num_feature + 1)``. * \param handle Handle of booster * \param n_chunks The number of Arrow arrays passed to this function * \param chunks Pointer to the list of Arrow arrays * \param schema Pointer to the schema of all Arrow arrays * \param predict_type What should be predicted * - ``C_API_PREDICT_NORMAL``: normal prediction, with transform (if needed); * - ``C_API_PREDICT_RAW_SCORE``: raw score; * - ``C_API_PREDICT_LEAF_INDEX``: leaf index; * - ``C_API_PREDICT_CONTRIB``: feature contributions (SHAP values) * \param start_iteration Start index of the iteration to predict * \param num_iteration Number of iteration for prediction, <= 0 means no limit * \param parameter Other parameters for prediction, e.g. early stopping for prediction * \param[out] out_len Length of output result * \param[out] out_result Pointer to array with predictions * \return 0 when succeed, -1 when failure happens */ LIGHTGBM_C_EXPORT int LGBM_BoosterPredictForArrow(BoosterHandle handle, int64_t n_chunks, const struct ArrowArray* chunks, const struct ArrowSchema* schema, int predict_type, int start_iteration, int num_iteration, const char* parameter, int64_t* out_len, double* out_result); /*! * \brief Save model into file. * \param handle Handle of booster * \param start_iteration Start index of the iteration that should be saved * \param num_iteration Index of the iteration that should be saved, <= 0 means save all * \param feature_importance_type Type of feature importance, can be ``C_API_FEATURE_IMPORTANCE_SPLIT`` or ``C_API_FEATURE_IMPORTANCE_GAIN`` * \param filename The name of the file * \return 0 when succeed, -1 when failure happens */ LIGHTGBM_C_EXPORT int LGBM_BoosterSaveModel(BoosterHandle handle, int start_iteration, int num_iteration, int feature_importance_type, const char* filename); /*! * \brief Save model to string. * \param handle Handle of booster * \param start_iteration Start index of the iteration that should be saved * \param num_iteration Index of the iteration that should be saved, <= 0 means save all * \param feature_importance_type Type of feature importance, can be ``C_API_FEATURE_IMPORTANCE_SPLIT`` or ``C_API_FEATURE_IMPORTANCE_GAIN`` * \param buffer_len String buffer length, if ``buffer_len < out_len``, you should re-allocate buffer * \param[out] out_len Actual output length * \param[out] out_str String of model, should pre-allocate memory * \return 0 when succeed, -1 when failure happens */ LIGHTGBM_C_EXPORT int LGBM_BoosterSaveModelToString(BoosterHandle handle, int start_iteration, int num_iteration, int feature_importance_type, int64_t buffer_len, int64_t* out_len, char* out_str); /*! * \brief Dump model to JSON. * \param handle Handle of booster * \param start_iteration Start index of the iteration that should be dumped * \param num_iteration Index of the iteration that should be dumped, <= 0 means dump all * \param feature_importance_type Type of feature importance, can be ``C_API_FEATURE_IMPORTANCE_SPLIT`` or ``C_API_FEATURE_IMPORTANCE_GAIN`` * \param buffer_len String buffer length, if ``buffer_len < out_len``, you should re-allocate buffer * \param[out] out_len Actual output length * \param[out] out_str JSON format string of model, should pre-allocate memory * \return 0 when succeed, -1 when failure happens */ LIGHTGBM_C_EXPORT int LGBM_BoosterDumpModel(BoosterHandle handle, int start_iteration, int num_iteration, int feature_importance_type, int64_t buffer_len, int64_t* out_len, char* out_str); /*! * \brief Get leaf value. * \param handle Handle of booster * \param tree_idx Index of tree * \param leaf_idx Index of leaf * \param[out] out_val Output result from the specified leaf * \return 0 when succeed, -1 when failure happens */ LIGHTGBM_C_EXPORT int LGBM_BoosterGetLeafValue(BoosterHandle handle, int tree_idx, int leaf_idx, double* out_val); /*! * \brief Set leaf value. * \param handle Handle of booster * \param tree_idx Index of tree * \param leaf_idx Index of leaf * \param val Leaf value * \return 0 when succeed, -1 when failure happens */ LIGHTGBM_C_EXPORT int LGBM_BoosterSetLeafValue(BoosterHandle handle, int tree_idx, int leaf_idx, double val); /*! * \brief Get model feature importance. * \param handle Handle of booster * \param num_iteration Number of iterations for which feature importance is calculated, <= 0 means use all * \param importance_type Method of importance calculation: * - ``C_API_FEATURE_IMPORTANCE_SPLIT``: result contains numbers of times the feature is used in a model; * - ``C_API_FEATURE_IMPORTANCE_GAIN``: result contains total gains of splits which use the feature * \param[out] out_results Result array with feature importance * \return 0 when succeed, -1 when failure happens */ LIGHTGBM_C_EXPORT int LGBM_BoosterFeatureImportance(BoosterHandle handle, int num_iteration, int importance_type, double* out_results); /*! * \brief Get model upper bound value. * \param handle Handle of booster * \param[out] out_results Result pointing to max value * \return 0 when succeed, -1 when failure happens */ LIGHTGBM_C_EXPORT int LGBM_BoosterGetUpperBoundValue(BoosterHandle handle, double* out_results); /*! * \brief Get model lower bound value. * \param handle Handle of booster * \param[out] out_results Result pointing to min value * \return 0 when succeed, -1 when failure happens */ LIGHTGBM_C_EXPORT int LGBM_BoosterGetLowerBoundValue(BoosterHandle handle, double* out_results); /*! * \brief Initialize the network. * \param machines List of machines in format 'ip1:port1,ip2:port2' * \param local_listen_port TCP listen port for local machines * \param listen_time_out Socket time-out in minutes * \param num_machines Total number of machines * \return 0 when succeed, -1 when failure happens */ LIGHTGBM_C_EXPORT int LGBM_NetworkInit(const char* machines, int local_listen_port, int listen_time_out, int num_machines); /*! * \brief Finalize the network. * \return 0 when succeed, -1 when failure happens */ LIGHTGBM_C_EXPORT int LGBM_NetworkFree(); /*! * \brief Initialize the network with external collective functions. * \param num_machines Total number of machines * \param rank Rank of local machine * \param reduce_scatter_ext_fun The external reduce-scatter function * \param allgather_ext_fun The external allgather function * \return 0 when succeed, -1 when failure happens */ LIGHTGBM_C_EXPORT int LGBM_NetworkInitWithFunctions(int num_machines, int rank, void* reduce_scatter_ext_fun, void* allgather_ext_fun); /*! * \brief Set maximum number of threads used by LightGBM routines in this process. * \param num_threads maximum number of threads used by LightGBM. -1 means defaulting to omp_get_num_threads(). * \return 0 when succeed, -1 when failure happens */ LIGHTGBM_C_EXPORT int LGBM_SetMaxThreads(int num_threads); /*! * \brief Get current maximum number of threads used by LightGBM routines in this process. * \param[out] out current maximum number of threads used by LightGBM. -1 means defaulting to omp_get_num_threads(). * \return 0 when succeed, -1 when failure happens */ LIGHTGBM_C_EXPORT int LGBM_GetMaxThreads(int* out); #if !defined(__cplusplus) && (!defined(__STDC__) || (__STDC_VERSION__ < 199901L)) /*! \brief Inline specifier no-op in C using standards before C99. */ #define INLINE_FUNCTION #else /*! \brief Inline specifier. */ #define INLINE_FUNCTION inline #endif #if !defined(__cplusplus) && (!defined(__STDC__) || (__STDC_VERSION__ < 201112L)) /*! \brief Thread local specifier no-op in C using standards before C11. */ #define THREAD_LOCAL #elif !defined(__cplusplus) /*! \brief Thread local specifier. */ #define THREAD_LOCAL _Thread_local #elif defined(_MSC_VER) /*! \brief Thread local specifier. */ #define THREAD_LOCAL __declspec(thread) #else /*! \brief Thread local specifier. */ #define THREAD_LOCAL thread_local #endif /*! * \brief Handle of error message. * \return Error message */ static char* LastErrorMsg() { static THREAD_LOCAL char err_msg[512] = "Everything is fine"; return err_msg; } #ifdef _MSC_VER #pragma warning(disable : 4996) #endif /*! * \brief Set string message of the last error. * \note * This will call unsafe ``sprintf`` when compiled using C standards before C99. * \param msg Error message */ INLINE_FUNCTION void LGBM_SetLastError(const char* msg) { #if !defined(__cplusplus) && (!defined(__STDC__) || (__STDC_VERSION__ < 199901L)) sprintf(LastErrorMsg(), "%s", msg); /* NOLINT(runtime/printf) */ #else const int err_buf_len = 512; snprintf(LastErrorMsg(), err_buf_len, "%s", msg); #endif } #endif /* LIGHTGBM_INCLUDE_LIGHTGBM_C_API_H_ */ ================================================ FILE: include/LightGBM/config.h ================================================ /*! * Copyright (c) 2016-2026 Microsoft Corporation. All rights reserved. * Copyright (c) 2016-2026 The LightGBM developers. All rights reserved. * Licensed under the MIT License. See LICENSE file in the project root for license information. * * \note * - desc and descl2 fields must be written in reStructuredText format; * - nested sections can be placed only at the bottom of parent's section; * - [no-automatically-extract] * - do not automatically extract this parameter into a Config property with the same name in Config::GetMembersFromString(). Use if: * - specialized extraction logic for this param exists in Config::GetMembersFromString() * - [no-save] * - this param should not be saved into a model text representation via Config::SaveMembersToString(). Use if: * - param is only used by the CLI (especially the "predict" and "convert_model" tasks) * - param is related to LightGBM writing files (e.g. "output_model", "save_binary") */ #ifndef LIGHTGBM_INCLUDE_LIGHTGBM_CONFIG_H_ #define LIGHTGBM_INCLUDE_LIGHTGBM_CONFIG_H_ #include #include #include #include #include #include #include #include #include #include #include namespace LightGBM { /*! \brief Types of tasks */ enum TaskType { kTrain, kPredict, kConvertModel, KRefitTree, kSaveBinary }; const int kDefaultNumLeaves = 31; struct Config { public: Config() {} explicit Config(std::unordered_map parameters_map) { Set(parameters_map); } std::string ToString() const; /*! * \brief Get string value by specific name of key * \param params Store the key and value for params * \param name Name of key * \param out Value will assign to out if key exists * \return True if key exists */ inline static bool GetString( const std::unordered_map& params, const std::string& name, std::string* out); /*! * \brief Get int value by specific name of key * \param params Store the key and value for params * \param name Name of key * \param out Value will assign to out if key exists * \return True if key exists */ inline static bool GetInt( const std::unordered_map& params, const std::string& name, int* out); /*! * \brief Get double value by specific name of key * \param params Store the key and value for params * \param name Name of key * \param out Value will assign to out if key exists * \return True if key exists */ inline static bool GetDouble( const std::unordered_map& params, const std::string& name, double* out); /*! * \brief Get bool value by specific name of key * \param params Store the key and value for params * \param name Name of key * \param out Value will assign to out if key exists * \return True if key exists */ inline static bool GetBool( const std::unordered_map& params, const std::string& name, bool* out); /*! * \brief Sort aliases by length and then alphabetically * \param x Alias 1 * \param y Alias 2 * \return true if x has higher priority than y */ inline static bool SortAlias(const std::string& x, const std::string& y); static void KeepFirstValues(const std::unordered_map>& params, std::unordered_map* out); static void KV2Map(std::unordered_map>* params, const char* kv); static void SetVerbosity(const std::unordered_map>& params); static std::unordered_map Str2Map(const char* parameters); #ifndef __NVCC__ #pragma region Parameters #pragma region Core Parameters #endif // __NVCC__ // [no-automatically-extract] // [no-save] // alias = config_file // desc = path of config file // desc = **Note**: can be used only in CLI version std::string config = ""; // [no-automatically-extract] // [no-save] // type = enum // default = train // options = train, predict, convert_model, refit // alias = task_type // desc = ``train``, for training, aliases: ``training`` // desc = ``predict``, for prediction, aliases: ``prediction``, ``test`` // desc = ``convert_model``, for converting model file into if-else format, see more information in `Convert Parameters <#convert-parameters>`__ // desc = ``refit``, for refitting existing models with new data, aliases: ``refit_tree`` // 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 // desc = **Note**: can be used only in CLI version; for language-specific packages you can use the correspondent functions TaskType task = TaskType::kTrain; // [no-automatically-extract] // [no-save] // type = enum // options = regression, regression_l1, huber, fair, poisson, quantile, mape, gamma, tweedie, binary, multiclass, multiclassova, cross_entropy, cross_entropy_lambda, lambdarank, rank_xendcg // alias = objective_type, app, application, loss // desc = regression application // descl2 = ``regression``, L2 loss, aliases: ``regression_l2``, ``l2``, ``mean_squared_error``, ``mse``, ``l2_root``, ``root_mean_squared_error``, ``rmse`` // descl2 = ``regression_l1``, L1 loss, aliases: ``l1``, ``mean_absolute_error``, ``mae`` // descl2 = ``huber``, `Huber loss `__ // descl2 = ``fair``, `Fair loss `__ // descl2 = ``poisson``, `Poisson regression `__ // descl2 = ``quantile``, `Quantile regression `__ // descl2 = ``mape``, `MAPE loss `__, aliases: ``mean_absolute_percentage_error`` // 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 `__ // 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 `__ // desc = binary classification application // descl2 = ``binary``, binary `log loss `__ classification (or logistic regression) // descl2 = requires labels in {0, 1}; see ``cross-entropy`` application for general probability labels in [0, 1] // desc = multi-class classification application // descl2 = ``multiclass``, `softmax `__ objective function, aliases: ``softmax`` // descl2 = ``multiclassova``, `One-vs-All `__ binary objective function, aliases: ``multiclass_ova``, ``ova``, ``ovr`` // descl2 = ``num_class`` should be set as well // desc = cross-entropy application // descl2 = ``cross_entropy``, objective function for cross-entropy (with optional linear weights), aliases: ``xentropy`` // descl2 = ``cross_entropy_lambda``, alternative parameterization of cross-entropy, aliases: ``xentlambda`` // descl2 = label is anything in interval [0, 1] // desc = ranking application // 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`` // descl2 = ``rank_xendcg``, `XE_NDCG_MART `__ ranking objective function, aliases: ``xendcg``, ``xe_ndcg``, ``xe_ndcg_mart``, ``xendcg_mart`` // descl2 = ``rank_xendcg`` is faster than and achieves the similar performance as ``lambdarank`` // descl2 = label should be ``int`` type, and larger number represents the higher relevance (e.g. 0:bad, 1:fair, 2:good, 3:perfect) // desc = custom objective function (gradients and hessians not computed directly by LightGBM) // descl2 = ``custom`` // descl2 = must be passed through parameters explicitly in the C API // descl2 = **Note**: cannot be used in CLI version std::string objective = "regression"; // [no-automatically-extract] // [no-save] // type = enum // alias = boosting_type, boost // options = gbdt, rf, dart // desc = ``gbdt``, traditional Gradient Boosting Decision Tree, aliases: ``gbrt`` // desc = ``rf``, Random Forest, aliases: ``random_forest`` // desc = ``dart``, `Dropouts meet Multiple Additive Regression Trees `__ // descl2 = **Note**: internally, LightGBM uses ``gbdt`` mode for the first ``1 / learning_rate`` iterations std::string boosting = "gbdt"; // [no-automatically-extract] // type = enum // options = bagging, goss // desc = ``bagging``, Randomly Bagging Sampling // descl2 = **Note**: ``bagging`` is only effective when ``bagging_freq > 0`` and ``bagging_fraction < 1.0`` // desc = ``goss``, Gradient-based One-Side Sampling // desc = *New in version 4.0.0* std::string data_sample_strategy = "bagging"; // alias = train, train_data, train_data_file, data_filename // desc = path of training data, LightGBM will train from this data // desc = **Note**: can be used only in CLI version std::string data = ""; // alias = test, valid_data, valid_data_file, test_data, test_data_file, valid_filenames // default = "" // desc = path(s) of validation/test data, LightGBM will output metrics for these data // desc = support multiple validation data, separated by ``,`` // desc = **Note**: can be used only in CLI version std::vector valid; // alias = num_iteration, n_iter, num_tree, num_trees, num_round, num_rounds, nrounds, num_boost_round, n_estimators, max_iter // check = >=0 // desc = number of boosting iterations // desc = **Note**: internally, LightGBM constructs ``num_class * num_iterations`` trees for multi-class classification problems int num_iterations = 100; // alias = shrinkage_rate, eta // check = >0.0 // desc = shrinkage rate // desc = in ``dart``, it also affects on normalization weights of dropped trees double learning_rate = 0.1; // default = 31 // alias = num_leaf, max_leaves, max_leaf, max_leaf_nodes // check = >1 // check = <=131072 // desc = max number of leaves in one tree int num_leaves = kDefaultNumLeaves; // [no-automatically-extract] // [no-save] // type = enum // options = serial, feature, data, voting // alias = tree, tree_type, tree_learner_type // desc = ``serial``, single machine tree learner // desc = ``feature``, feature parallel tree learner, aliases: ``feature_parallel`` // desc = ``data``, data parallel tree learner, aliases: ``data_parallel`` // desc = ``voting``, voting parallel tree learner, aliases: ``voting_parallel`` // desc = refer to `Distributed Learning Guide <./Parallel-Learning-Guide.rst>`__ to get more details std::string tree_learner = "serial"; // alias = num_thread, nthread, nthreads, n_jobs // desc = used only in ``train``, ``prediction`` and ``refit`` tasks or in correspondent functions of language-specific packages // desc = number of threads for LightGBM // desc = ``0`` means default number of threads in OpenMP // 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) // 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) // desc = be aware a task manager or any similar CPU monitoring tool might report that cores not being fully utilized. **This is normal** // desc = for distributed learning, do not use all CPU cores because this will cause poor performance for the network communication // desc = **Note**: please **don't** change this during training, especially when running multiple jobs simultaneously by external packages, otherwise it may cause undesirable errors int num_threads = 0; // [no-automatically-extract] // [no-save] // type = enum // options = cpu, gpu, cuda // alias = device // desc = device for the tree learning // desc = ``cpu`` supports all LightGBM functionality and is portable across the widest range of operating systems and hardware // desc = ``cuda`` offers faster training than ``gpu`` or ``cpu``, but only works on GPUs supporting CUDA or ROCm // desc = ``gpu`` can be faster than ``cpu`` and works on a wider range of GPUs than CUDA // desc = **Note**: it is recommended to use the smaller ``max_bin`` (e.g. 63) to get the better speed up // 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 // desc = **Note**: refer to `Installation Guide <./Installation-Guide.rst>`__ to build LightGBM with GPU, CUDA, or ROCm support std::string device_type = "cpu"; // [no-automatically-extract] // alias = random_seed, random_state // default = None // desc = this seed is used to generate other seeds, e.g. ``data_random_seed``, ``feature_fraction_seed``, etc. // desc = by default, this seed is unused in favor of default values of other seeds // desc = this seed has lower priority in comparison with other seeds, which means that it will be overridden, if you set other seeds explicitly int seed = 0; // desc = used only with ``cpu`` device type // desc = setting this to ``true`` should ensure the stable results when using the same data and the same parameters (and different ``num_threads``) // 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 // desc = you can `raise issues `__ in LightGBM GitHub repo when you meet the unstable results // desc = **Note**: setting this to ``true`` may slow down the training // 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`` bool deterministic = false; #ifndef __NVCC__ #pragma endregion #pragma region Learning Control Parameters #endif // __NVCC__ // desc = used only with ``cpu`` device type // desc = set this to ``true`` to force col-wise histogram building // desc = enabling this is recommended when: // descl2 = the number of columns is large, or the total number of bins is large // descl2 = ``num_threads`` is large, e.g. ``> 20`` // descl2 = you want to reduce memory cost // 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 // desc = **Note**: this parameter cannot be used at the same time with ``force_row_wise``, choose only one of them bool force_col_wise = false; // desc = used only with ``cpu`` device type // desc = set this to ``true`` to force row-wise histogram building // desc = enabling this is recommended when: // descl2 = the number of data points is large, and the total number of bins is relatively small // descl2 = ``num_threads`` is relatively small, e.g. ``<= 16`` // descl2 = you want to use small ``bagging_fraction`` or ``goss`` sample strategy to speed up // 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`` // 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 // desc = **Note**: this parameter cannot be used at the same time with ``force_col_wise``, choose only one of them bool force_row_wise = false; // alias = hist_pool_size // desc = max cache size in MB for historical histogram // desc = ``< 0`` means no limit double histogram_pool_size = -1.0; // 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 // desc = ``<= 0`` means no limit int max_depth = -1; // alias = min_data_per_leaf, min_data, min_child_samples, min_samples_leaf // check = >=0 // desc = minimal number of data in one leaf. Can be used to deal with over-fitting // 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 int min_data_in_leaf = 20; // alias = min_sum_hessian_per_leaf, min_sum_hessian, min_hessian, min_child_weight // check = >=0.0 // desc = minimal sum hessian in one leaf. Like ``min_data_in_leaf``, it can be used to deal with over-fitting double min_sum_hessian_in_leaf = 1e-3; // alias = sub_row, subsample, bagging // check = >0.0 // check = <=1.0 // desc = like ``feature_fraction``, but this will randomly select part of data without resampling // desc = can be used to speed up training // desc = can be used to deal with over-fitting // desc = **Note**: to enable bagging, ``bagging_freq`` should be set to a non zero value as well double bagging_fraction = 1.0; // alias = pos_sub_row, pos_subsample, pos_bagging // check = >0.0 // check = <=1.0 // desc = used only in ``binary`` application // desc = used for imbalanced binary classification problem, will randomly sample ``#pos_samples * pos_bagging_fraction`` positive samples in bagging // desc = should be used together with ``neg_bagging_fraction`` // desc = set this to ``1.0`` to disable // desc = **Note**: to enable this, you need to set ``bagging_freq`` and ``neg_bagging_fraction`` as well // desc = **Note**: if both ``pos_bagging_fraction`` and ``neg_bagging_fraction`` are set to ``1.0``, balanced bagging is disabled // desc = **Note**: if balanced bagging is enabled, ``bagging_fraction`` will be ignored double pos_bagging_fraction = 1.0; // alias = neg_sub_row, neg_subsample, neg_bagging // check = >0.0 // check = <=1.0 // desc = used only in ``binary`` application // desc = used for imbalanced binary classification problem, will randomly sample ``#neg_samples * neg_bagging_fraction`` negative samples in bagging // desc = should be used together with ``pos_bagging_fraction`` // desc = set this to ``1.0`` to disable // desc = **Note**: to enable this, you need to set ``bagging_freq`` and ``pos_bagging_fraction`` as well // desc = **Note**: if both ``pos_bagging_fraction`` and ``neg_bagging_fraction`` are set to ``1.0``, balanced bagging is disabled // desc = **Note**: if balanced bagging is enabled, ``bagging_fraction`` will be ignored double neg_bagging_fraction = 1.0; // alias = subsample_freq // desc = frequency for bagging // 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 // desc = **Note**: bagging is only effective when ``0.0 < bagging_fraction < 1.0`` int bagging_freq = 0; // alias = bagging_fraction_seed // desc = random seed for bagging int bagging_seed = 3; // desc = whether to do bagging sample by query // desc = *New in version 4.6.0* bool bagging_by_query = false; // alias = sub_feature, colsample_bytree // check = >0.0 // check = <=1.0 // 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 // desc = can be used to speed up training // desc = can be used to deal with over-fitting double feature_fraction = 1.0; // alias = sub_feature_bynode, colsample_bynode // check = >0.0 // check = <=1.0 // 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 // desc = can be used to deal with over-fitting // desc = **Note**: unlike ``feature_fraction``, this cannot speed up training // 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`` double feature_fraction_bynode = 1.0; // desc = random seed for ``feature_fraction`` int feature_fraction_seed = 2; // alias = extra_tree // desc = use extremely randomized trees // desc = if set to ``true``, when evaluating node splits LightGBM will check only one randomly-chosen threshold for each feature // desc = can be used to speed up training // desc = can be used to deal with over-fitting bool extra_trees = false; // desc = random seed for selecting thresholds when ``extra_trees`` is true int extra_seed = 6; // alias = early_stopping_rounds, early_stopping, n_iter_no_change // desc = will stop training if one metric of one validation data doesn't improve in last ``early_stopping_round`` rounds // desc = ``<= 0`` means disable // desc = can be used to speed up training int early_stopping_round = 0; // check = >=0.0 // 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 // desc = *New in version 4.4.0* double early_stopping_min_delta = 0.0; // 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 bool first_metric_only = false; // alias = max_tree_output, max_leaf_output // desc = used to limit the max output of tree leaves // desc = ``<= 0`` means no constraint // desc = the final max output of leaves is ``learning_rate * max_delta_step`` double max_delta_step = 0.0; // alias = reg_alpha, l1_regularization // check = >=0.0 // desc = L1 regularization double lambda_l1 = 0.0; // alias = reg_lambda, lambda, l2_regularization // check = >=0.0 // desc = L2 regularization double lambda_l2 = 0.0; // check = >=0.0 // desc = linear tree regularization, corresponds to the parameter ``lambda`` in Eq. 3 of `Gradient Boosting with Piece-Wise Linear Regression Trees `__ double linear_lambda = 0.0; // alias = min_split_gain // check = >=0.0 // desc = the minimal gain to perform split // desc = can be used to speed up training double min_gain_to_split = 0.0; // alias = rate_drop // check = >=0.0 // check = <=1.0 // desc = used only in ``dart`` // desc = dropout rate: a fraction of previous trees to drop during the dropout double drop_rate = 0.1; // desc = used only in ``dart`` // desc = max number of dropped trees during one boosting iteration // desc = ``<=0`` means no limit int max_drop = 50; // check = >=0.0 // check = <=1.0 // desc = used only in ``dart`` // desc = probability of skipping the dropout procedure during a boosting iteration double skip_drop = 0.5; // desc = used only in ``dart`` // desc = set this to ``true``, if you want to use XGBoost DART mode bool xgboost_dart_mode = false; // desc = used only in ``dart`` // desc = set this to ``true``, if you want to use uniform drop bool uniform_drop = false; // desc = used only in ``dart`` // desc = random seed to choose dropping models int drop_seed = 4; // check = >=0.0 // check = <=1.0 // desc = used only in ``goss`` // desc = the retain ratio of large gradient data double top_rate = 0.2; // check = >=0.0 // check = <=1.0 // desc = used only in ``goss`` // desc = the retain ratio of small gradient data double other_rate = 0.1; // check = >0 // desc = used for the categorical features // desc = minimal number of data per categorical group int min_data_per_group = 100; // check = >0 // desc = used for the categorical features // 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 // desc = can be used to speed up training int max_cat_threshold = 32; // check = >=0.0 // desc = used for the categorical features // desc = L2 regularization in categorical split double cat_l2 = 10.0; // check = >=0.0 // desc = used for the categorical features // desc = this can reduce the effect of noises in categorical features, especially for categories with few data double cat_smooth = 10.0; // check = >0 // desc = used for the categorical features // 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 int max_cat_to_onehot = 4; // alias = topk // check = >0 // desc = used only in ``voting`` tree learner, refer to `Voting parallel <./Parallel-Learning-Guide.rst#choose-appropriate-parallel-algorithm>`__ // desc = set this to larger value for more accurate result, but it will slow down the training speed int top_k = 20; // type = multi-int // alias = mc, monotone_constraint, monotonic_cst // default = None // desc = used for constraints of monotonic features // desc = ``1`` means increasing, ``-1`` means decreasing, ``0`` means non-constraint // 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 std::vector monotone_constraints; // type = enum // alias = monotone_constraining_method, mc_method // options = basic, intermediate, advanced // desc = used only if ``monotone_constraints`` is set // desc = monotone constraints method // descl2 = ``basic``, the most basic monotone constraints method. It does not slow down the training speed at all, but over-constrains the predictions // 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 // 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 std::string monotone_constraints_method = "basic"; // alias = monotone_splits_penalty, ms_penalty, mc_penalty // check = >=0.0 // desc = used only if ``monotone_constraints`` is set // 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 // desc = if ``0.0`` (the default), no penalization is applied double monotone_penalty = 0.0; // type = multi-double // alias = feature_contrib, fc, fp, feature_penalty // default = None // 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 // desc = you need to specify all features in order std::vector feature_contri; // alias = fs, forced_splits_filename, forced_splits_file, forced_splits // desc = path to a ``.json`` file that specifies splits to force at the top of every decision tree before best-first learning commences // desc = ``.json`` file can be arbitrarily nested, and each split contains ``feature``, ``threshold`` fields, as well as ``left`` and ``right`` fields representing subsplits // desc = categorical splits are forced in a one-hot fashion, with ``left`` representing the split containing the feature value and ``right`` representing other values // desc = **Note**: the forced split logic will be ignored, if the split makes gain worse // desc = see `this file `__ as an example std::string forcedsplits_filename = ""; // check = >=0.0 // check = <=1.0 // 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 // desc = used only in ``refit`` task in CLI version or as argument in ``refit`` function in language-specific package double refit_decay_rate = 0.9; // check = >=0.0 // desc = cost-effective gradient boosting multiplier for all penalties double cegb_tradeoff = 1.0; // check = >=0.0 // desc = cost-effective gradient-boosting penalty for splitting a node double cegb_penalty_split = 0.0; // type = multi-double // default = 0,0,...,0 // desc = cost-effective gradient boosting penalty for using a feature // desc = applied per data point std::vector cegb_penalty_feature_lazy; // type = multi-double // default = 0,0,...,0 // desc = cost-effective gradient boosting penalty for using a feature // desc = applied once per forest std::vector cegb_penalty_feature_coupled; // check = >= 0.0 // desc = controls smoothing applied to tree nodes // desc = helps prevent overfitting on leaves with few samples // desc = if ``0.0`` (the default), no smoothing is applied // desc = if ``path_smooth > 0`` then ``min_data_in_leaf`` must be at least ``2`` // desc = larger values give stronger regularization // 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 // 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 double path_smooth = 0; // desc = controls which features can appear in the same branch // desc = by default interaction constraints are disabled, to enable them you can specify // descl2 = for CLI, lists separated by commas, e.g. ``[0,1,2],[2,3]`` // descl2 = for Python-package, list of lists, e.g. ``[[0, 1, 2], [2, 3]]`` // 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. // desc = any two features can only appear in the same branch only if there exists a constraint containing both features std::string interaction_constraints = ""; // alias = verbose // desc = controls the level of LightGBM's verbosity // desc = ``< 0``: Fatal, ``= 0``: Error (Warning), ``= 1``: Info, ``> 1``: Debug int verbosity = 1; // [no-save] // alias = model_input, model_in // desc = filename of input model // desc = for ``prediction`` task, this model will be applied to prediction data // desc = for ``train`` task, training will be continued from this model // desc = **Note**: can be used only in CLI version std::string input_model = ""; // [no-save] // alias = model_output, model_out // desc = filename of output model in training // desc = **Note**: can be used only in CLI version std::string output_model = "LightGBM_model.txt"; // desc = the feature importance type in the saved model file // desc = ``0``: count-based feature importance (numbers of splits are counted); ``1``: gain-based feature importance (values of gain are counted) // desc = **Note**: can be used only in CLI version int saved_feature_importance_type = 0; // [no-save] // alias = save_period // desc = frequency of saving model file snapshot // 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`` // desc = **Note**: can be used only in CLI version int snapshot_freq = -1; // desc = whether to use gradient quantization when training // desc = enabling this will discretize (quantize) the gradients and hessians into bins of ``num_grad_quant_bins`` // desc = with quantized training, most arithmetics in the training process will be integer operations // desc = gradient quantization can accelerate training, with little accuracy drop in most cases // desc = **Note**: works only with ``cpu`` and ``cuda`` device type // desc = *New in version 4.0.0* bool use_quantized_grad = false; // desc = used only if ``use_quantized_grad=true`` // desc = number of bins to quantization gradients and hessians // desc = with more bins, the quantized training will be closer to full precision training // desc = **Note**: works only with ``cpu`` and ``cuda`` device type // desc = *New in version 4.0.0* int num_grad_quant_bins = 4; // desc = used only if ``use_quantized_grad=true`` // desc = whether to renew the leaf values with original gradients when quantized training // desc = renewing is very helpful for good quantized training accuracy for ranking objectives // desc = **Note**: works only with ``cpu`` and ``cuda`` device type // desc = *New in version 4.0.0* bool quant_train_renew_leaf = false; // desc = used only if ``use_quantized_grad=true`` // desc = whether to use stochastic rounding in gradient quantization // desc = **Note**: works only with ``cpu`` and ``cuda`` device type // desc = *New in version 4.0.0* bool stochastic_rounding = true; #ifndef __NVCC__ #pragma endregion #pragma region IO Parameters #pragma region Dataset Parameters #endif // __NVCC__ // alias = linear_trees // desc = fit piecewise linear gradient boosting tree // desc = tree splits are chosen in the usual way, but the model at each leaf is linear instead of constant // desc = the linear model at each leaf includes all the numerical features in that leaf's branch // desc = the first tree has constant leaf values // desc = categorical features are used for splits as normal but are not used in the linear models // 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 // desc = it is recommended to rescale data before training so that features have similar mean and standard deviation // desc = **Note**: works only with ``cpu``, ``gpu`` device type and ``serial`` tree learner // desc = **Note**: ``regression_l1`` objective is not supported with linear tree boosting // desc = **Note**: setting ``linear_tree=true`` significantly increases the memory use of LightGBM // 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 bool linear_tree = false; // alias = max_bins // check = >1 // desc = max number of bins that feature values will be bucketed in // desc = small number of bins may reduce training accuracy but may increase general power (deal with over-fitting) // desc = LightGBM will auto compress memory according to ``max_bin``. For example, LightGBM will use ``uint8_t`` for feature value if ``max_bin=255`` int max_bin = 255; // type = multi-int // default = None // desc = max number of bins for each feature // desc = if not specified, will use ``max_bin`` for all features std::vector max_bin_by_feature; // check = >0 // desc = minimal number of data inside one bin // desc = use this to avoid one-data-one-bin (potential over-fitting) int min_data_in_bin = 3; // alias = subsample_for_bin // check = >0 // desc = number of data that sampled to construct feature discrete bins // desc = setting this to larger value will give better training result, but may increase data loading time // desc = set this to larger value if data is very sparse // desc = **Note**: don't set this to small values, otherwise, you may encounter unexpected errors and poor accuracy int bin_construct_sample_cnt = 200000; // alias = data_seed // desc = random seed for sampling data to construct histogram bins int data_random_seed = 1; // alias = is_sparse, enable_sparse, sparse // desc = used to enable/disable sparse optimization bool is_enable_sparse = true; // alias = is_enable_bundle, bundle // desc = set this to ``false`` to disable Exclusive Feature Bundling (EFB), which is described in `LightGBM: A Highly Efficient Gradient Boosting Decision Tree `__ // desc = **Note**: disabling this may cause the slow training speed for sparse datasets bool enable_bundle = true; // desc = set this to ``false`` to disable the special handle of missing value bool use_missing = true; // desc = set this to ``true`` to treat all zero as missing values (including the unshown values in LibSVM / sparse matrices) // desc = set this to ``false`` to use ``na`` for representing missing values bool zero_as_missing = false; // desc = set this to ``true`` (the default) to tell LightGBM to ignore the features that are unsplittable based on ``min_data_in_leaf`` // 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 // desc = **Note**: setting this to ``false`` may slow down the training bool feature_pre_filter = true; // alias = is_pre_partition // desc = used for distributed learning (excluding the ``feature_parallel`` mode) // desc = ``true`` if training data are pre-partitioned, and different machines use different partitions bool pre_partition = false; // alias = two_round_loading, use_two_round_loading // desc = set this to ``true`` if data file is too big to fit in memory // 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 // desc = **Note**: works only in case of loading data directly from text file bool two_round = false; // alias = has_header // desc = set this to ``true`` if input data has header // desc = **Note**: works only in case of loading data directly from text file bool header = false; // type = int or string // alias = label // desc = used to specify the label column // desc = use number for index, e.g. ``label=0`` means column\_0 is the label // desc = add a prefix ``name:`` for column name, e.g. ``label=name:is_click`` // desc = if omitted, the first column in the training data is used as the label // desc = **Note**: works only in case of loading data directly from text file std::string label_column = ""; // type = int or string // alias = weight // desc = used to specify the weight column // desc = use number for index, e.g. ``weight=0`` means column\_0 is the weight // desc = add a prefix ``name:`` for column name, e.g. ``weight=name:weight`` // desc = **Note**: works only in case of loading data directly from text file // 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`` // desc = **Note**: weights should be non-negative std::string weight_column = ""; // type = int or string // alias = group, group_id, query_column, query, query_id // desc = used to specify the query/group id column // desc = use number for index, e.g. ``query=0`` means column\_0 is the query id // desc = add a prefix ``name:`` for column name, e.g. ``query=name:query_id`` // desc = **Note**: works only in case of loading data directly from text file // desc = **Note**: data should be grouped by query\_id, for more information, see `Query Data <#query-data>`__ // 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`` std::string group_column = ""; // type = multi-int or string // alias = ignore_feature, blacklist // desc = used to specify some ignoring columns in training // desc = use number for index, e.g. ``ignore_column=0,1,2`` means column\_0, column\_1 and column\_2 will be ignored // desc = add a prefix ``name:`` for column name, e.g. ``ignore_column=name:c1,c2,c3`` means c1, c2 and c3 will be ignored // desc = **Note**: works only in case of loading data directly from text file // desc = **Note**: index starts from ``0`` and it doesn't count the label column when passing type is ``int`` // 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 std::string ignore_column = ""; // type = multi-int or string // alias = cat_feature, categorical_column, cat_column, categorical_features // desc = used to specify categorical features // desc = use number for index, e.g. ``categorical_feature=0,1,2`` means column\_0, column\_1 and column\_2 are categorical features // desc = add a prefix ``name:`` for column name, e.g. ``categorical_feature=name:c1,c2,c3`` means c1, c2 and c3 are categorical features // desc = **Note**: all values will be cast to ``int32`` (integer codes will be extracted from pandas categoricals in the Python-package) // desc = **Note**: index starts from ``0`` and it doesn't count the label column when passing type is ``int`` // desc = **Note**: all values should be less than ``Int32.MaxValue`` (2147483647) // 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 // desc = **Note**: all negative values will be treated as **missing values** // desc = **Note**: the output cannot be monotonically constrained with respect to a categorical feature // desc = **Note**: floating point numbers in categorical features will be rounded towards 0 std::string categorical_feature = ""; // desc = path to a ``.json`` file that specifies bin upper bounds for some or all features // 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) // desc = see `this file `__ as an example std::string forcedbins_filename = ""; // [no-save] // alias = is_save_binary, is_save_binary_file // 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 // desc = **Note**: ``init_score`` is not saved in binary file // desc = **Note**: can be used only in CLI version; for language-specific packages you can use the correspondent function bool save_binary = false; // desc = use precise floating point number parsing for text parser (e.g. CSV, TSV, LibSVM input) // desc = **Note**: setting this to ``true`` may lead to much slower text parsing bool precise_float_parser = false; // desc = path to a ``.json`` file that specifies customized parser initialized configuration // desc = see `lightgbm-transform `__ for usage examples // desc = **Note**: ``lightgbm-transform`` is not maintained by LightGBM's maintainers. Bug reports or feature requests should go to `issues page `__ // desc = *New in version 4.0.0* std::string parser_config_file = ""; #ifndef __NVCC__ #pragma endregion #pragma region Predict Parameters #endif // __NVCC__ // [no-save] // desc = used only in ``prediction`` task // desc = used to specify from which iteration to start the prediction // desc = ``<= 0`` means from the first iteration int start_iteration_predict = 0; // [no-save] // desc = used only in ``prediction`` task // desc = used to specify how many trained iterations will be used in prediction // desc = ``<= 0`` means no limit int num_iteration_predict = -1; // [no-save] // alias = is_predict_raw_score, predict_rawscore, raw_score // desc = used only in ``prediction`` task // desc = set this to ``true`` to predict only the raw scores // desc = set this to ``false`` to predict transformed scores bool predict_raw_score = false; // [no-save] // alias = is_predict_leaf_index, leaf_index // desc = used only in ``prediction`` task // desc = set this to ``true`` to predict with leaf index of all trees bool predict_leaf_index = false; // [no-save] // alias = is_predict_contrib, contrib // desc = used only in ``prediction`` task // desc = set this to ``true`` to estimate `SHAP values `__, which represent how each feature contributes to each prediction // desc = produces ``#features + 1`` values where the last value is the expected value of the model output over the training data // 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 `__ // 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 // desc = **Note**: this feature is not implemented for linear trees bool predict_contrib = false; // [no-save] // desc = used only in ``prediction`` task // 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 // 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 // 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 // desc = **Note**: be very careful setting this parameter to ``true`` bool predict_disable_shape_check = false; // [no-save] // desc = used only in ``prediction`` task // desc = used only in ``classification`` and ``ranking`` applications // desc = used only for predicting normal or raw scores // desc = if ``true``, will use early-stopping to speed up the prediction. May affect the accuracy // desc = **Note**: cannot be used with ``rf`` boosting type or custom objective function bool pred_early_stop = false; // [no-save] // desc = used only in ``prediction`` task and if ``pred_early_stop=true`` // desc = the frequency of checking early-stopping prediction int pred_early_stop_freq = 10; // [no-save] // desc = used only in ``prediction`` task and if ``pred_early_stop=true`` // desc = the threshold of margin in early-stopping prediction double pred_early_stop_margin = 10.0; // [no-save] // alias = predict_result, prediction_result, predict_name, prediction_name, pred_name, name_pred // desc = used only in ``prediction`` task // desc = filename of prediction result // desc = **Note**: can be used only in CLI version std::string output_result = "LightGBM_predict_result.txt"; #ifndef __NVCC__ #pragma endregion #pragma region Convert Parameters #endif // __NVCC__ // [no-save] // desc = used only in ``convert_model`` task // desc = only ``cpp`` is supported yet; for conversion model to other languages consider using `m2cgen `__ utility // desc = if ``convert_model_language`` is set and ``task=train``, the model will be also converted // desc = **Note**: can be used only in CLI version std::string convert_model_language = ""; // [no-save] // alias = convert_model_file // desc = used only in ``convert_model`` task // desc = output filename of converted model // desc = **Note**: can be used only in CLI version std::string convert_model = "gbdt_prediction.cpp"; #ifndef __NVCC__ #pragma endregion #pragma endregion #pragma region Objective Parameters #endif // __NVCC__ // desc = used only in ``rank_xendcg`` objective // desc = random seed for objectives, if random process is needed int objective_seed = 5; // check = >0 // alias = num_classes // desc = used only in ``multi-class`` classification application int num_class = 1; // alias = unbalance, unbalanced_sets // desc = used only in ``binary`` and ``multiclassova`` applications // desc = set this to ``true`` if training data are unbalanced // 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 // desc = **Note**: this parameter cannot be used at the same time with ``scale_pos_weight``, choose only **one** of them bool is_unbalance = false; // check = >0.0 // desc = used only in ``binary`` and ``multiclassova`` applications // desc = weight of labels with positive class // 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 // desc = **Note**: this parameter cannot be used at the same time with ``is_unbalance``, choose only **one** of them double scale_pos_weight = 1.0; // check = >0.0 // desc = used only in ``binary`` and ``multiclassova`` classification and in ``lambdarank`` applications // desc = parameter for the sigmoid function double sigmoid = 1.0; // desc = used only in ``regression``, ``binary``, ``multiclassova`` and ``cross-entropy`` applications // desc = adjusts initial score to the mean of labels for faster convergence bool boost_from_average = true; // desc = used only in ``regression`` application // desc = used to fit ``sqrt(label)`` instead of original values and prediction result will be also automatically converted to ``prediction^2`` // desc = might be useful in case of large-range labels bool reg_sqrt = false; // check = >0.0 // desc = used only in ``huber`` and ``quantile`` ``regression`` applications // desc = parameter for `Huber loss `__ and `Quantile regression `__ double alpha = 0.9; // check = >0.0 // desc = used only in ``fair`` ``regression`` application // desc = parameter for `Fair loss `__ double fair_c = 1.0; // check = >0.0 // desc = used only in ``poisson`` ``regression`` application // desc = parameter for `Poisson regression `__ to safeguard optimization double poisson_max_delta_step = 0.7; // check = >=1.0 // check = <2.0 // desc = used only in ``tweedie`` ``regression`` application // desc = used to control the variance of the tweedie distribution // desc = set this closer to ``2`` to shift towards a **Gamma** distribution // desc = set this closer to ``1`` to shift towards a **Poisson** distribution double tweedie_variance_power = 1.5; // check = >0 // desc = used only in ``lambdarank`` application // desc = controls the number of top-results to focus on during training, refer to "truncation level" in the Sec. 3 of `LambdaMART paper `__ // 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** int lambdarank_truncation_level = 30; // desc = used only in ``lambdarank`` application // desc = set this to ``true`` to normalize the lambdas for different queries, and improve the performance for unbalanced data // desc = set this to ``false`` to enforce the original lambdarank algorithm bool lambdarank_norm = true; // type = multi-double // default = 0,1,3,7,15,31,63,...,2^30-1 // desc = used only in ``lambdarank`` application // desc = relevant gain for labels. For example, the gain of label ``2`` is ``3`` in case of default label gains // desc = separate by ``,`` std::vector label_gain; // check = >=0.0 // desc = used only in ``lambdarank`` application when positional information is provided and position bias is modeled // desc = larger values reduce the inferred position bias factors // desc = *New in version 4.1.0* double lambdarank_position_bias_regularization = 0.0; #ifndef __NVCC__ #pragma endregion #pragma region Metric Parameters #endif // __NVCC__ // [no-automatically-extract] // [no-save] // alias = metrics, metric_types // default = "" // type = multi-enum // desc = metric(s) to be evaluated on the evaluation set(s) // 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) // descl2 = ``"None"`` (string, **not** a ``None`` value) means that no metric will be registered, aliases: ``na``, ``null``, ``custom`` // descl2 = ``l1``, absolute loss, aliases: ``mean_absolute_error``, ``mae``, ``regression_l1`` // descl2 = ``l2``, square loss, aliases: ``mean_squared_error``, ``mse``, ``regression_l2``, ``regression`` // descl2 = ``rmse``, root square loss, aliases: ``root_mean_squared_error``, ``l2_root`` // descl2 = ``quantile``, `Quantile regression `__ // descl2 = ``mape``, `MAPE loss `__, aliases: ``mean_absolute_percentage_error`` // descl2 = ``huber``, `Huber loss `__ // descl2 = ``fair``, `Fair loss `__ // descl2 = ``poisson``, negative log-likelihood for `Poisson regression `__ // descl2 = ``gamma``, negative log-likelihood for **Gamma** regression // descl2 = ``gamma_deviance``, residual deviance for **Gamma** regression // descl2 = ``tweedie``, negative log-likelihood for **Tweedie** regression // descl2 = ``ndcg``, `NDCG `__, aliases: ``lambdarank``, ``rank_xendcg``, ``xendcg``, ``xe_ndcg``, ``xe_ndcg_mart``, ``xendcg_mart`` // descl2 = ``map``, `MAP `__, aliases: ``mean_average_precision`` // descl2 = ``auc``, `AUC `__ // descl2 = ``average_precision``, `average precision score `__ // descl2 = ``r2``, `R-squared `__ // descl2 = ``binary_logloss``, `log loss `__, aliases: ``binary`` // descl2 = ``binary_error``, for one sample: ``0`` for correct classification, ``1`` for error classification // descl2 = ``auc_mu``, `AUC-mu `__ // descl2 = ``multi_logloss``, log loss for multi-class classification, aliases: ``multiclass``, ``softmax``, ``multiclassova``, ``multiclass_ova``, ``ova``, ``ovr`` // descl2 = ``multi_error``, error rate for multi-class classification // descl2 = ``cross_entropy``, cross-entropy (with optional linear weights), aliases: ``xentropy`` // descl2 = ``cross_entropy_lambda``, "intensity-weighted" cross-entropy, aliases: ``xentlambda`` // descl2 = ``kullback_leibler``, `Kullback-Leibler divergence `__, aliases: ``kldiv`` // desc = support multiple metrics, separated by ``,`` std::vector metric; // [no-save] // check = >0 // alias = output_freq // desc = frequency for metric output // desc = **Note**: can be used only in CLI version int metric_freq = 1; // [no-save] // alias = training_metric, is_training_metric, train_metric // desc = set this to ``true`` to output metric result over training dataset // desc = **Note**: can be used only in CLI version bool is_provide_training_metric = false; // type = multi-int // default = 1,2,3,4,5 // alias = ndcg_eval_at, ndcg_at, map_eval_at, map_at // desc = used only with ``ndcg`` and ``map`` metrics // desc = `NDCG `__ and `MAP `__ evaluation positions, separated by ``,`` std::vector eval_at; // check = >0 // desc = used only with ``multi_error`` metric // desc = threshold for top-k multi-error metric // desc = the error on each sample is ``0`` if the true class is among the top ``multi_error_top_k`` predictions, and ``1`` otherwise // 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 // desc = when ``multi_error_top_k=1`` this is equivalent to the usual multi-error metric int multi_error_top_k = 1; // type = multi-double // default = None // desc = used only with ``auc_mu`` metric // desc = list representing flattened matrix (in row-major order) giving loss weights for classification errors // desc = list should have ``n * n`` elements, where ``n`` is the number of classes // desc = the matrix co-ordinate ``[i, j]`` should correspond to the ``i * n + j``-th element of the list // desc = if not specified, will use equal weights for all classes std::vector auc_mu_weights; #ifndef __NVCC__ #pragma endregion #pragma region Network Parameters #endif // __NVCC__ // check = >0 // alias = num_machine // desc = the number of machines for distributed learning application // desc = this parameter is needed to be set in both **socket** and **MPI** versions int num_machines = 1; // check = >0 // default = 12400 (random for Dask-package) // alias = local_port, port // desc = TCP listen port for local machines // desc = **Note**: don't forget to allow this port in firewall settings before training int local_listen_port = 12400; // check = >0 // desc = socket time-out in minutes int time_out = 120; // alias = machine_list_file, machine_list, mlist // desc = path of file that lists machines for this distributed learning application // desc = each line contains one IP and one port for one machine. The format is ``ip port`` (space as a separator) // desc = **Note**: can be used only in CLI version std::string machine_list_filename = ""; // alias = workers, nodes // desc = list of machines in the following format: ``ip1:port1,ip2:port2`` std::string machines = ""; #ifndef __NVCC__ #pragma endregion #pragma region GPU Parameters #endif // __NVCC__ // desc = used only with ``gpu`` device type // desc = OpenCL platform ID. Usually each GPU vendor exposes one OpenCL platform // desc = ``-1`` means the system-wide default platform // desc = **Note**: refer to `GPU Targets <./GPU-Targets.rst#query-opencl-devices-in-your-system>`__ for more details int gpu_platform_id = -1; // desc = OpenCL device ID in the specified platform or CUDA device ID. Each GPU in the selected platform has a unique device ID // desc = ``-1`` means the default device in the selected platform // desc = in multi-GPU case (``num_gpu>1``) means ID of the master GPU // desc = **Note**: refer to `GPU Targets <./GPU-Targets.rst#query-opencl-devices-in-your-system>`__ for more details int gpu_device_id = -1; // desc = list of CUDA device IDs // desc = **Note**: can be used only in CUDA implementation (``device_type="cuda"``) and when ``num_gpu>1`` // desc = if empty, the devices with the smallest IDs will be used std::string gpu_device_id_list = ""; // desc = set this to ``true`` to use double precision math on GPU (by default single precision is used) // desc = **Note**: can be used only in OpenCL implementation (``device_type="gpu"``), in CUDA implementation only double precision is currently supported bool gpu_use_dp = false; // check = >0 // desc = number of GPUs used for training in this node // desc = **Note**: can be used only in CUDA implementation (``device_type="cuda"``) // desc = if ``0``, only 1 GPU will be used // desc = used in both single-machine and distributed learning applications // desc = in distributed learning application, each machine can use different number of GPUs int num_gpu = 1; #ifndef __NVCC__ #pragma endregion #pragma endregion #endif // __NVCC__ size_t file_load_progress_interval_bytes = size_t(10) * 1024 * 1024 * 1024; bool is_parallel = false; bool is_data_based_parallel = false; LIGHTGBM_EXPORT void Set(const std::unordered_map& params); static const std::unordered_map& alias_table(); static const std::unordered_map>& parameter2aliases(); static const std::unordered_set& parameter_set(); std::vector> auc_mu_weights_matrix; std::vector> interaction_constraints_vector; static const std::unordered_map& ParameterTypes(); static const std::string DumpAliases(); private: void CheckParamConflict(const std::unordered_map& params); void GetMembersFromString(const std::unordered_map& params); std::string SaveMembersToString() const; void GetAucMuWeights(); void GetInteractionConstraints(); }; inline bool Config::GetString( const std::unordered_map& params, const std::string& name, std::string* out) { if (params.count(name) > 0 && !params.at(name).empty()) { *out = params.at(name); return true; } return false; } inline bool Config::GetInt( const std::unordered_map& params, const std::string& name, int* out) { if (params.count(name) > 0 && !params.at(name).empty()) { if (!Common::AtoiAndCheck(params.at(name).c_str(), out)) { Log::Fatal("Parameter %s should be of type int, got \"%s\"", name.c_str(), params.at(name).c_str()); } return true; } return false; } inline bool Config::GetDouble( const std::unordered_map& params, const std::string& name, double* out) { if (params.count(name) > 0 && !params.at(name).empty()) { if (!Common::AtofAndCheck(params.at(name).c_str(), out)) { Log::Fatal("Parameter %s should be of type double, got \"%s\"", name.c_str(), params.at(name).c_str()); } return true; } return false; } inline bool Config::GetBool( const std::unordered_map& params, const std::string& name, bool* out) { if (params.count(name) > 0 && !params.at(name).empty()) { std::string value = params.at(name); std::transform(value.begin(), value.end(), value.begin(), [](unsigned char c){ return std::tolower(c); }); if (value == std::string("false") || value == std::string("-")) { *out = false; } else if (value == std::string("true") || value == std::string("+")) { *out = true; } else { Log::Fatal("Parameter %s should be \"true\"/\"+\" or \"false\"/\"-\", got \"%s\"", name.c_str(), params.at(name).c_str()); } return true; } return false; } inline bool Config::SortAlias(const std::string& x, const std::string& y) { return x.size() < y.size() || (x.size() == y.size() && x < y); } struct ParameterAlias { static void KeyAliasTransform(std::unordered_map* params) { std::unordered_map tmp_map; for (const auto& pair : *params) { auto alias = Config::alias_table().find(pair.first); if (alias != Config::alias_table().end()) { // found alias auto alias_set = tmp_map.find(alias->second); if (alias_set != tmp_map.end()) { // alias already set if (Config::SortAlias(alias_set->second, pair.first)) { Log::Warning("%s is set with %s=%s, %s=%s will be ignored. Current value: %s=%s", alias->second.c_str(), alias_set->second.c_str(), params->at(alias_set->second).c_str(), pair.first.c_str(), pair.second.c_str(), alias->second.c_str(), params->at(alias_set->second).c_str()); } else { Log::Warning("%s is set with %s=%s, will be overridden by %s=%s. Current value: %s=%s", alias->second.c_str(), alias_set->second.c_str(), params->at(alias_set->second).c_str(), pair.first.c_str(), pair.second.c_str(), alias->second.c_str(), pair.second.c_str()); tmp_map[alias->second] = pair.first; } } else { // alias not set tmp_map.emplace(alias->second, pair.first); } } else if (Config::parameter_set().find(pair.first) == Config::parameter_set().end()) { Log::Warning("Unknown parameter: %s", pair.first.c_str()); } } for (const auto& pair : tmp_map) { auto alias = params->find(pair.first); if (alias == params->end()) { // not find params->emplace(pair.first, params->at(pair.second)); params->erase(pair.second); } else { Log::Warning("%s is set=%s, %s=%s will be ignored. Current value: %s=%s", pair.first.c_str(), alias->second.c_str(), pair.second.c_str(), params->at(pair.second).c_str(), pair.first.c_str(), alias->second.c_str()); } } } }; inline std::string ParseObjectiveAlias(const std::string& type) { if (type == std::string("regression") || type == std::string("regression_l2") || type == std::string("mean_squared_error") || type == std::string("mse") || type == std::string("l2") || type == std::string("l2_root") || type == std::string("root_mean_squared_error") || type == std::string("rmse")) { return "regression"; } else if (type == std::string("regression_l1") || type == std::string("mean_absolute_error") || type == std::string("l1") || type == std::string("mae")) { return "regression_l1"; } else if (type == std::string("multiclass") || type == std::string("softmax")) { return "multiclass"; } else if (type == std::string("multiclassova") || type == std::string("multiclass_ova") || type == std::string("ova") || type == std::string("ovr")) { return "multiclassova"; } else if (type == std::string("xentropy") || type == std::string("cross_entropy")) { return "cross_entropy"; } else if (type == std::string("xentlambda") || type == std::string("cross_entropy_lambda")) { return "cross_entropy_lambda"; } else if (type == std::string("mean_absolute_percentage_error") || type == std::string("mape")) { return "mape"; } else if (type == std::string("rank_xendcg") || type == std::string("xendcg") || type == std::string("xe_ndcg") || type == std::string("xe_ndcg_mart") || type == std::string("xendcg_mart")) { return "rank_xendcg"; } else if (type == std::string("none") || type == std::string("null") || type == std::string("custom") || type == std::string("na")) { return "custom"; } return type; } inline std::string ParseMetricAlias(const std::string& type) { if (type == std::string("regression") || type == std::string("regression_l2") || type == std::string("l2") || type == std::string("mean_squared_error") || type == std::string("mse")) { return "l2"; } else if (type == std::string("l2_root") || type == std::string("root_mean_squared_error") || type == std::string("rmse")) { return "rmse"; } else if (type == std::string("regression_l1") || type == std::string("l1") || type == std::string("mean_absolute_error") || type == std::string("mae")) { return "l1"; } else if (type == std::string("binary_logloss") || type == std::string("binary")) { return "binary_logloss"; } else if (type == std::string("ndcg") || type == std::string("lambdarank") || type == std::string("rank_xendcg") || type == std::string("xendcg") || type == std::string("xe_ndcg") || type == std::string("xe_ndcg_mart") || type == std::string("xendcg_mart")) { return "ndcg"; } else if (type == std::string("map") || type == std::string("mean_average_precision")) { return "map"; } 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")) { return "multi_logloss"; } else if (type == std::string("xentropy") || type == std::string("cross_entropy")) { return "cross_entropy"; } else if (type == std::string("xentlambda") || type == std::string("cross_entropy_lambda")) { return "cross_entropy_lambda"; } else if (type == std::string("kldiv") || type == std::string("kullback_leibler")) { return "kullback_leibler"; } else if (type == std::string("mean_absolute_percentage_error") || type == std::string("mape")) { return "mape"; } else if (type == std::string("none") || type == std::string("null") || type == std::string("custom") || type == std::string("na")) { return "custom"; } return type; } } // namespace LightGBM #endif // LIGHTGBM_INCLUDE_LIGHTGBM_CONFIG_H_ ================================================ FILE: include/LightGBM/cuda/cuda_algorithms.hpp ================================================ /*! * Copyright (c) 2021-2026 Microsoft Corporation. All rights reserved. * Copyright (c) 2021-2026 The LightGBM developers. All rights reserved. * Licensed under the MIT License. See LICENSE file in the project root for license information. * Modifications Copyright(C) 2023 Advanced Micro Devices, Inc. All rights reserved. */ #ifndef LIGHTGBM_INCLUDE_LIGHTGBM_CUDA_CUDA_ALGORITHMS_HPP_ #define LIGHTGBM_INCLUDE_LIGHTGBM_CUDA_CUDA_ALGORITHMS_HPP_ #ifdef USE_CUDA #ifndef USE_ROCM #include #include #endif #include #include #include #include #include #include #define GLOBAL_PREFIX_SUM_BLOCK_SIZE (1024) #define BITONIC_SORT_NUM_ELEMENTS (1024) #define BITONIC_SORT_DEPTH (11) #define BITONIC_SORT_QUERY_ITEM_BLOCK_SIZE (10) namespace LightGBM { template __device__ __forceinline__ T ShufflePrefixSum(T value, T* shared_mem_buffer) { const uint32_t mask = 0xffffffff; const uint32_t warpLane = threadIdx.x % warpSize; const uint32_t warpID = threadIdx.x / warpSize; const uint32_t num_warp = blockDim.x / warpSize; for (uint32_t offset = 1; offset < warpSize; offset <<= 1) { const T other_value = __shfl_up_sync(mask, value, offset); if (warpLane >= offset) { value += other_value; } } if (warpLane == warpSize - 1) { shared_mem_buffer[warpID] = value; } __syncthreads(); if (warpID == 0) { T warp_sum = (warpLane < num_warp ? shared_mem_buffer[warpLane] : 0); for (uint32_t offset = 1; offset < warpSize; offset <<= 1) { const T other_warp_sum = __shfl_up_sync(mask, warp_sum, offset); if (warpLane >= offset) { warp_sum += other_warp_sum; } } shared_mem_buffer[warpLane] = warp_sum; } __syncthreads(); const T warp_base = warpID == 0 ? 0 : shared_mem_buffer[warpID - 1]; return warp_base + value; } template __device__ __forceinline__ T ShufflePrefixSumExclusive(T value, T* shared_mem_buffer) { const uint32_t mask = 0xffffffff; const uint32_t warpLane = threadIdx.x % warpSize; const uint32_t warpID = threadIdx.x / warpSize; const uint32_t num_warp = blockDim.x / warpSize; for (uint32_t offset = 1; offset < warpSize; offset <<= 1) { const T other_value = __shfl_up_sync(mask, value, offset); if (warpLane >= offset) { value += other_value; } } if (warpLane == warpSize - 1) { shared_mem_buffer[warpID] = value; } __syncthreads(); if (warpID == 0) { T warp_sum = (warpLane < num_warp ? shared_mem_buffer[warpLane] : 0); for (uint32_t offset = 1; offset < warpSize; offset <<= 1) { const T other_warp_sum = __shfl_up_sync(mask, warp_sum, offset); if (warpLane >= offset) { warp_sum += other_warp_sum; } } shared_mem_buffer[warpLane] = warp_sum; } __syncthreads(); const T warp_base = warpID == 0 ? 0 : shared_mem_buffer[warpID - 1]; const T inclusive_result = warp_base + value; if (threadIdx.x % warpSize == warpSize - 1) { shared_mem_buffer[warpLane] = inclusive_result; } __syncthreads(); T exclusive_result = __shfl_up_sync(mask, inclusive_result, 1); if (threadIdx.x == 0) { exclusive_result = 0; } else if (threadIdx.x % warpSize == 0) { exclusive_result = shared_mem_buffer[warpLane - 1]; } return exclusive_result; } template void ShufflePrefixSumGlobal(T* values, size_t len, T* block_prefix_sum_buffer); template void GlobalInclusiveArgPrefixSum(const INDEX_T* sorted_indices, const VAL_T* in_values, REDUCE_T* out_values, REDUCE_T* block_buffer, size_t n); template __device__ __forceinline__ T ShuffleReduceSumWarp(T value, const data_size_t len) { if (len > 0) { const uint32_t mask = 0xffffffff; for (int offset = warpSize / 2; offset > 0; offset >>= 1) { value += __shfl_down_sync(mask, value, offset); } } return value; } // reduce values from an 1-dimensional block (block size must be no greater than 1024) template __device__ __forceinline__ T ShuffleReduceSum(T value, T* shared_mem_buffer, const size_t len) { const uint32_t warpLane = threadIdx.x % warpSize; const uint32_t warpID = threadIdx.x / warpSize; const data_size_t warp_len = min(static_cast(warpSize), static_cast(len) - static_cast(warpID * warpSize)); value = ShuffleReduceSumWarp(value, warp_len); if (warpLane == 0) { shared_mem_buffer[warpID] = value; } __syncthreads(); const data_size_t num_warp = static_cast((len + warpSize - 1) / warpSize); if (warpID == 0) { value = (warpLane < num_warp ? shared_mem_buffer[warpLane] : 0); value = ShuffleReduceSumWarp(value, num_warp); } return value; } template __device__ __forceinline__ T ShuffleReduceMaxWarp(T value, const data_size_t len) { if (len > 0) { const uint32_t mask = 0xffffffff; for (int offset = warpSize / 2; offset > 0; offset >>= 1) { value = max(value, __shfl_down_sync(mask, value, offset)); } } return value; } // reduce values from an 1-dimensional block (block size must be no greater than 1024) template __device__ __forceinline__ T ShuffleReduceMax(T value, T* shared_mem_buffer, const size_t len) { const uint32_t warpLane = threadIdx.x % warpSize; const uint32_t warpID = threadIdx.x / warpSize; const data_size_t warp_len = min(static_cast(warpSize), static_cast(len) - static_cast(warpID * warpSize)); value = ShuffleReduceMaxWarp(value, warp_len); if (warpLane == 0) { shared_mem_buffer[warpID] = value; } __syncthreads(); const data_size_t num_warp = static_cast((len + warpSize - 1) / warpSize); if (warpID == 0) { value = (warpLane < num_warp ? shared_mem_buffer[warpLane] : 0); value = ShuffleReduceMaxWarp(value, num_warp); } return value; } // calculate prefix sum values within an 1-dimensional block in global memory, exclusively template __device__ __forceinline__ void GlobalMemoryPrefixSum(T* array, const size_t len) { const size_t num_values_per_thread = (len + blockDim.x - 1) / blockDim.x; const size_t start = threadIdx.x * num_values_per_thread; const size_t end = min(start + num_values_per_thread, len); T thread_sum = 0; for (size_t index = start; index < end; ++index) { thread_sum += array[index]; } __shared__ T shared_mem[WARPSIZE]; const T thread_base = ShufflePrefixSumExclusive(thread_sum, shared_mem); if (start < end) { array[start] += thread_base; } for (size_t index = start + 1; index < end; ++index) { array[index] += array[index - 1]; } } template __device__ __forceinline__ T ShuffleReduceMinWarp(T value, const data_size_t len) { if (len > 0) { const uint32_t mask = (0xffffffff >> (warpSize - len)); for (int offset = warpSize / 2; offset > 0; offset >>= 1) { const T other_value = __shfl_down_sync(mask, value, offset); value = (other_value < value) ? other_value : value; } } return value; } // reduce values from an 1-dimensional block (block size must be no greater than 1024) template __device__ __forceinline__ T ShuffleReduceMin(T value, T* shared_mem_buffer, const size_t len) { const uint32_t warpLane = threadIdx.x % warpSize; const uint32_t warpID = threadIdx.x / warpSize; const data_size_t warp_len = min(static_cast(warpSize), static_cast(len) - static_cast(warpID * warpSize)); value = ShuffleReduceMinWarp(value, warp_len); if (warpLane == 0) { shared_mem_buffer[warpID] = value; } __syncthreads(); const data_size_t num_warp = static_cast((len + warpSize - 1) / warpSize); if (warpID == 0) { value = (warpLane < num_warp ? shared_mem_buffer[warpLane] : shared_mem_buffer[0]); value = ShuffleReduceMinWarp(value, num_warp); } return value; } template void ShuffleReduceMinGlobal(const VAL_T* values, size_t n, REDUCE_T* block_buffer); template __device__ __forceinline__ void BitonicArgSort_1024(const VAL_T* scores, INDEX_T* indices, const INDEX_T num_items) { INDEX_T depth = 1; INDEX_T num_items_aligend = 1; INDEX_T num_items_ref = num_items - 1; while (num_items_ref > 0) { num_items_ref >>= 1; num_items_aligend <<= 1; ++depth; } for (INDEX_T outer_depth = depth - 1; outer_depth >= 1; --outer_depth) { const INDEX_T outer_segment_length = 1 << (depth - outer_depth); const INDEX_T outer_segment_index = threadIdx.x / outer_segment_length; const bool ascending = ASCENDING ? (outer_segment_index % 2 == 0) : (outer_segment_index % 2 > 0); for (INDEX_T inner_depth = outer_depth; inner_depth < depth; ++inner_depth) { const INDEX_T segment_length = 1 << (depth - inner_depth); const INDEX_T half_segment_length = segment_length >> 1; const INDEX_T half_segment_index = threadIdx.x / half_segment_length; if (threadIdx.x < num_items_aligend) { if (half_segment_index % 2 == 0) { const INDEX_T index_to_compare = threadIdx.x + half_segment_length; if ((scores[indices[threadIdx.x]] > scores[indices[index_to_compare]]) == ascending) { const INDEX_T index = indices[threadIdx.x]; indices[threadIdx.x] = indices[index_to_compare]; indices[index_to_compare] = index; } } } __syncthreads(); } } } template __device__ __forceinline__ void BitonicArgSort_2048(const VAL_T* scores, INDEX_T* indices) { for (INDEX_T base = 0; base < 2048; base += 1024) { for (INDEX_T outer_depth = 10; outer_depth >= 1; --outer_depth) { const INDEX_T outer_segment_length = 1 << (11 - outer_depth); const INDEX_T outer_segment_index = threadIdx.x / outer_segment_length; const bool ascending = ((base == 0) ^ ASCENDING) ? (outer_segment_index % 2 > 0) : (outer_segment_index % 2 == 0); for (INDEX_T inner_depth = outer_depth; inner_depth < 11; ++inner_depth) { const INDEX_T segment_length = 1 << (11 - inner_depth); const INDEX_T half_segment_length = segment_length >> 1; const INDEX_T half_segment_index = threadIdx.x / half_segment_length; if (half_segment_index % 2 == 0) { const INDEX_T index_to_compare = threadIdx.x + half_segment_length + base; if ((scores[indices[threadIdx.x + base]] > scores[indices[index_to_compare]]) == ascending) { const INDEX_T index = indices[threadIdx.x + base]; indices[threadIdx.x + base] = indices[index_to_compare]; indices[index_to_compare] = index; } } __syncthreads(); } } } const unsigned int index_to_compare = threadIdx.x + 1024; if (scores[indices[index_to_compare]] > scores[indices[threadIdx.x]]) { const INDEX_T temp_index = indices[index_to_compare]; indices[index_to_compare] = indices[threadIdx.x]; indices[threadIdx.x] = temp_index; } __syncthreads(); for (INDEX_T base = 0; base < 2048; base += 1024) { for (INDEX_T inner_depth = 1; inner_depth < 11; ++inner_depth) { const INDEX_T segment_length = 1 << (11 - inner_depth); const INDEX_T half_segment_length = segment_length >> 1; const INDEX_T half_segment_index = threadIdx.x / half_segment_length; if (half_segment_index % 2 == 0) { const INDEX_T index_to_compare = threadIdx.x + half_segment_length + base; if (scores[indices[threadIdx.x + base]] < scores[indices[index_to_compare]]) { const INDEX_T index = indices[threadIdx.x + base]; indices[threadIdx.x + base] = indices[index_to_compare]; indices[index_to_compare] = index; } } __syncthreads(); } } } template __device__ void BitonicArgSortDevice(const VAL_T* values, INDEX_T* indices, const int len) { __shared__ VAL_T shared_values[BLOCK_DIM]; __shared__ INDEX_T shared_indices[BLOCK_DIM]; int len_to_shift = len - 1; int max_depth = 1; while (len_to_shift > 0) { len_to_shift >>= 1; ++max_depth; } const int num_blocks = (len + static_cast(BLOCK_DIM) - 1) / static_cast(BLOCK_DIM); for (int block_index = 0; block_index < num_blocks; ++block_index) { const int this_index = block_index * static_cast(BLOCK_DIM) + static_cast(threadIdx.x); if (this_index < len) { shared_values[threadIdx.x] = values[this_index]; shared_indices[threadIdx.x] = this_index; } else { shared_indices[threadIdx.x] = len; } __syncthreads(); for (int depth = max_depth - 1; depth > max_depth - static_cast(MAX_DEPTH); --depth) { const int segment_length = (1 << (max_depth - depth)); const int segment_index = this_index / segment_length; const bool ascending = ASCENDING ? (segment_index % 2 == 0) : (segment_index % 2 == 1); { const int half_segment_length = (segment_length >> 1); const int half_segment_index = this_index / half_segment_length; const int num_total_segment = (len + segment_length - 1) / segment_length; const int offset = (segment_index == num_total_segment - 1 && ascending == ASCENDING) ? (num_total_segment * segment_length - len) : 0; if (half_segment_index % 2 == 0) { const int segment_start = segment_index * segment_length; if (this_index >= offset + segment_start) { const int other_index = static_cast(threadIdx.x) + half_segment_length - offset; const INDEX_T this_data_index = shared_indices[threadIdx.x]; const INDEX_T other_data_index = shared_indices[other_index]; const VAL_T this_value = shared_values[threadIdx.x]; const VAL_T other_value = shared_values[other_index]; if (other_data_index < len && (this_value > other_value) == ascending) { shared_indices[threadIdx.x] = other_data_index; shared_indices[other_index] = this_data_index; shared_values[threadIdx.x] = other_value; shared_values[other_index] = this_value; } } } __syncthreads(); } for (int inner_depth = depth + 1; inner_depth < max_depth; ++inner_depth) { const int half_segment_length = (1 << (max_depth - inner_depth - 1)); const int half_segment_index = this_index / half_segment_length; if (half_segment_index % 2 == 0) { const int other_index = static_cast(threadIdx.x) + half_segment_length; const INDEX_T this_data_index = shared_indices[threadIdx.x]; const INDEX_T other_data_index = shared_indices[other_index]; const VAL_T this_value = shared_values[threadIdx.x]; const VAL_T other_value = shared_values[other_index]; if (other_data_index < len && (this_value > other_value) == ascending) { shared_indices[threadIdx.x] = other_data_index; shared_indices[other_index] = this_data_index; shared_values[threadIdx.x] = other_value; shared_values[other_index] = this_value; } } __syncthreads(); } } if (this_index < len) { indices[this_index] = shared_indices[threadIdx.x]; } __syncthreads(); } for (int depth = max_depth - static_cast(MAX_DEPTH); depth >= 1; --depth) { const int segment_length = (1 << (max_depth - depth)); { const int num_total_segment = (len + segment_length - 1) / segment_length; const int half_segment_length = (segment_length >> 1); for (int block_index = 0; block_index < num_blocks; ++block_index) { const int this_index = block_index * static_cast(BLOCK_DIM) + static_cast(threadIdx.x); const int segment_index = this_index / segment_length; const int half_segment_index = this_index / half_segment_length; const bool ascending = ASCENDING ? (segment_index % 2 == 0) : (segment_index % 2 == 1); const int offset = (segment_index == num_total_segment - 1 && ascending == ASCENDING) ? (num_total_segment * segment_length - len) : 0; if (half_segment_index % 2 == 0) { const int segment_start = segment_index * segment_length; if (this_index >= offset + segment_start) { const int other_index = this_index + half_segment_length - offset; if (other_index < len) { const INDEX_T this_data_index = indices[this_index]; const INDEX_T other_data_index = indices[other_index]; const VAL_T this_value = values[this_data_index]; const VAL_T other_value = values[other_data_index]; if ((this_value > other_value) == ascending) { indices[this_index] = other_data_index; indices[other_index] = this_data_index; } } } } } __syncthreads(); } for (int inner_depth = depth + 1; inner_depth <= max_depth - static_cast(MAX_DEPTH); ++inner_depth) { const int half_segment_length = (1 << (max_depth - inner_depth - 1)); for (int block_index = 0; block_index < num_blocks; ++block_index) { const int this_index = block_index * static_cast(BLOCK_DIM) + static_cast(threadIdx.x); const int segment_index = this_index / segment_length; const int half_segment_index = this_index / half_segment_length; const bool ascending = ASCENDING ? (segment_index % 2 == 0) : (segment_index % 2 == 1); if (half_segment_index % 2 == 0) { const int other_index = this_index + half_segment_length; if (other_index < len) { const INDEX_T this_data_index = indices[this_index]; const INDEX_T other_data_index = indices[other_index]; const VAL_T this_value = values[this_data_index]; const VAL_T other_value = values[other_data_index]; if ((this_value > other_value) == ascending) { indices[this_index] = other_data_index; indices[other_index] = this_data_index; } } } __syncthreads(); } } for (int block_index = 0; block_index < num_blocks; ++block_index) { const int this_index = block_index * static_cast(BLOCK_DIM) + static_cast(threadIdx.x); const int segment_index = this_index / segment_length; const bool ascending = ASCENDING ? (segment_index % 2 == 0) : (segment_index % 2 == 1); if (this_index < len) { const INDEX_T index = indices[this_index]; shared_values[threadIdx.x] = values[index]; shared_indices[threadIdx.x] = index; } else { shared_indices[threadIdx.x] = len; } __syncthreads(); for (int inner_depth = max_depth - static_cast(MAX_DEPTH) + 1; inner_depth < max_depth; ++inner_depth) { const int half_segment_length = (1 << (max_depth - inner_depth - 1)); const int half_segment_index = this_index / half_segment_length; if (half_segment_index % 2 == 0) { const int other_index = static_cast(threadIdx.x) + half_segment_length; const INDEX_T this_data_index = shared_indices[threadIdx.x]; const INDEX_T other_data_index = shared_indices[other_index]; const VAL_T this_value = shared_values[threadIdx.x]; const VAL_T other_value = shared_values[other_index]; if (other_data_index < len && (this_value > other_value) == ascending) { shared_indices[threadIdx.x] = other_data_index; shared_indices[other_index] = this_data_index; shared_values[threadIdx.x] = other_value; shared_values[other_index] = this_value; } } __syncthreads(); } if (this_index < len) { indices[this_index] = shared_indices[threadIdx.x]; } __syncthreads(); } } } void BitonicArgSortItemsGlobal( const double* scores, const int num_queries, const data_size_t* cuda_query_boundaries, data_size_t* out_indices); template void BitonicArgSortGlobal(const VAL_T* values, INDEX_T* indices, const size_t len); template void ShuffleReduceSumGlobal(const VAL_T* values, size_t n, REDUCE_T* block_buffer); template void ShuffleReduceDotProdGlobal(const VAL_T* values1, const VAL_T* values2, size_t n, REDUCE_T* block_buffer); template __device__ void ShuffleSortedPrefixSumDevice(const VAL_T* in_values, const INDEX_T* sorted_indices, REDUCE_VAL_T* out_values, const INDEX_T num_data) { __shared__ REDUCE_VAL_T shared_buffer[WARPSIZE]; const INDEX_T num_data_per_thread = (num_data + static_cast(blockDim.x) - 1) / static_cast(blockDim.x); const INDEX_T start = num_data_per_thread * static_cast(threadIdx.x); const INDEX_T end = min(start + num_data_per_thread, num_data); REDUCE_VAL_T thread_sum = 0; for (INDEX_T index = start; index < end; ++index) { thread_sum += static_cast(in_values[sorted_indices[index]]); } __syncthreads(); thread_sum = ShufflePrefixSumExclusive(thread_sum, shared_buffer); const REDUCE_VAL_T thread_base = shared_buffer[threadIdx.x]; for (INDEX_T index = start; index < end; ++index) { out_values[index] = thread_base + static_cast(in_values[sorted_indices[index]]); } __syncthreads(); } template __global__ void PercentileGlobalKernel(const VAL_T* values, const WEIGHT_T* weights, const INDEX_T* sorted_indices, const WEIGHT_REDUCE_T* weights_prefix_sum, const double alpha, const INDEX_T len, VAL_T* out_value) { if (!USE_WEIGHT) { const double float_pos = (1.0f - alpha) * len; const INDEX_T pos = static_cast(float_pos); if (pos < 1) { *out_value = values[sorted_indices[0]]; } else if (pos >= len) { *out_value = values[sorted_indices[len - 1]]; } else { const double bias = float_pos - static_cast(pos); const VAL_T v1 = values[sorted_indices[pos - 1]]; const VAL_T v2 = values[sorted_indices[pos]]; *out_value = static_cast(v1 - (v1 - v2) * bias); } } else { const WEIGHT_REDUCE_T threshold = weights_prefix_sum[len - 1] * (1.0f - alpha); __shared__ INDEX_T pos; if (threadIdx.x == 0) { pos = len; } __syncthreads(); for (INDEX_T index = static_cast(threadIdx.x); index < len; index += static_cast(blockDim.x)) { if (weights_prefix_sum[index] > threshold && (index == 0 || weights_prefix_sum[index - 1] <= threshold)) { pos = index; } } __syncthreads(); pos = min(pos, len - 1); if (pos == 0 || pos == len - 1) { *out_value = values[pos]; } const VAL_T v1 = values[sorted_indices[pos - 1]]; const VAL_T v2 = values[sorted_indices[pos]]; *out_value = static_cast(v1 - (v1 - v2) * (threshold - weights_prefix_sum[pos - 1]) / (weights_prefix_sum[pos] - weights_prefix_sum[pos - 1])); } } template void PercentileGlobal(const VAL_T* values, const WEIGHT_T* weights, INDEX_T* indices, WEIGHT_REDUCE_T* weights_prefix_sum, WEIGHT_REDUCE_T* weights_prefix_sum_buffer, const double alpha, const INDEX_T len, VAL_T* cuda_out_value) { if (len <= 1) { CopyFromCUDADeviceToCUDADevice(cuda_out_value, values, 1, __FILE__, __LINE__); } BitonicArgSortGlobal(values, indices, len); SynchronizeCUDADevice(__FILE__, __LINE__); if (USE_WEIGHT) { GlobalInclusiveArgPrefixSum(indices, weights, weights_prefix_sum, weights_prefix_sum_buffer, static_cast(len)); } SynchronizeCUDADevice(__FILE__, __LINE__); PercentileGlobalKernel<<<1, GLOBAL_PREFIX_SUM_BLOCK_SIZE>>>(values, weights, indices, weights_prefix_sum, alpha, len, cuda_out_value); SynchronizeCUDADevice(__FILE__, __LINE__); } template __device__ VAL_T PercentileDevice(const VAL_T* values, const WEIGHT_T* weights, INDEX_T* indices, REDUCE_WEIGHT_T* weights_prefix_sum, const double alpha, const INDEX_T len) { if (len <= 1) { return values[0]; } if (!USE_WEIGHT) { BitonicArgSortDevice(values, indices, len); const double float_pos = (1.0f - alpha) * len; const INDEX_T pos = static_cast(float_pos); if (pos < 1) { return values[indices[0]]; } else if (pos >= len) { return values[indices[len - 1]]; } else { const double bias = float_pos - pos; const VAL_T v1 = values[indices[pos - 1]]; const VAL_T v2 = values[indices[pos]]; return static_cast(v1 - (v1 - v2) * bias); } } else { BitonicArgSortDevice(values, indices, len); ShuffleSortedPrefixSumDevice(weights, indices, weights_prefix_sum, len); const REDUCE_WEIGHT_T threshold = weights_prefix_sum[len - 1] * (1.0f - alpha); __shared__ INDEX_T pos; if (threadIdx.x == 0) { pos = len; } __syncthreads(); for (INDEX_T index = static_cast(threadIdx.x); index < len; index += static_cast(blockDim.x)) { if (weights_prefix_sum[index] > threshold && (index == 0 || weights_prefix_sum[index - 1] <= threshold)) { pos = index; } } __syncthreads(); pos = min(pos, len - 1); if (pos == 0 || pos == len - 1) { return values[pos]; } const VAL_T v1 = values[indices[pos - 1]]; const VAL_T v2 = values[indices[pos]]; return static_cast(v1 - (v1 - v2) * (threshold - weights_prefix_sum[pos - 1]) / (weights_prefix_sum[pos] - weights_prefix_sum[pos - 1])); } } } // namespace LightGBM #endif // USE_CUDA #endif // LIGHTGBM_INCLUDE_LIGHTGBM_CUDA_CUDA_ALGORITHMS_HPP_ ================================================ FILE: include/LightGBM/cuda/cuda_column_data.hpp ================================================ /*! * Copyright (c) 2021-2026 Microsoft Corporation. All rights reserved. * Copyright (c) 2021-2026 The LightGBM developers. All rights reserved. * Licensed under the MIT License. See LICENSE file in the project root for license information. */ #ifndef LIGHTGBM_INCLUDE_LIGHTGBM_CUDA_CUDA_COLUMN_DATA_HPP_ #define LIGHTGBM_INCLUDE_LIGHTGBM_CUDA_CUDA_COLUMN_DATA_HPP_ #ifdef USE_CUDA #include #include #include #include #include #include #include namespace LightGBM { class CUDAColumnData { public: CUDAColumnData(const data_size_t num_data, const int gpu_device_id); ~CUDAColumnData(); void Init(const int num_columns, const std::vector& column_data, const std::vector& column_bin_iterator, const std::vector& column_bit_type, const std::vector& feature_max_bin, const std::vector& feature_min_bin, const std::vector& feature_offset, const std::vector& feature_most_freq_bin, const std::vector& feature_default_bin, const std::vector& feature_missing_is_zero, const std::vector& feature_missing_is_na, const std::vector& feature_mfb_is_zero, const std::vector& feature_mfb_is_na, const std::vector& feature_to_column); const uint8_t* GetColumnData(const int column_index) const { return data_by_column_[column_index]->RawData(); } void CopySubrow(const CUDAColumnData* full_set, const data_size_t* used_indices, const data_size_t num_used_indices); uint8_t* const* cuda_data_by_column() const { return cuda_data_by_column_.RawData(); } uint32_t feature_min_bin(const int feature_index) const { return feature_min_bin_[feature_index]; } uint32_t feature_max_bin(const int feature_index) const { return feature_max_bin_[feature_index]; } uint32_t feature_offset(const int feature_index) const { return feature_offset_[feature_index]; } uint32_t feature_most_freq_bin(const int feature_index) const { return feature_most_freq_bin_[feature_index]; } uint32_t feature_default_bin(const int feature_index) const { return feature_default_bin_[feature_index]; } uint8_t feature_missing_is_zero(const int feature_index) const { return feature_missing_is_zero_[feature_index]; } uint8_t feature_missing_is_na(const int feature_index) const { return feature_missing_is_na_[feature_index]; } uint8_t feature_mfb_is_zero(const int feature_index) const { return feature_mfb_is_zero_[feature_index]; } uint8_t feature_mfb_is_na(const int feature_index) const { return feature_mfb_is_na_[feature_index]; } const uint32_t* cuda_feature_min_bin() const { return cuda_feature_min_bin_.RawData(); } const uint32_t* cuda_feature_max_bin() const { return cuda_feature_max_bin_.RawData(); } const uint32_t* cuda_feature_offset() const { return cuda_feature_offset_.RawData(); } const uint32_t* cuda_feature_most_freq_bin() const { return cuda_feature_most_freq_bin_.RawData(); } const uint32_t* cuda_feature_default_bin() const { return cuda_feature_default_bin_.RawData(); } const uint8_t* cuda_feature_missing_is_zero() const { return cuda_feature_missing_is_zero_.RawData(); } const uint8_t* cuda_feature_missing_is_na() const { return cuda_feature_missing_is_na_.RawData(); } const uint8_t* cuda_feature_mfb_is_zero() const { return cuda_feature_mfb_is_zero_.RawData(); } const uint8_t* cuda_feature_mfb_is_na() const { return cuda_feature_mfb_is_na_.RawData(); } const int* cuda_feature_to_column() const { return cuda_feature_to_column_.RawData(); } const uint8_t* cuda_column_bit_type() const { return cuda_column_bit_type_.RawData(); } int feature_to_column(const int feature_index) const { return feature_to_column_[feature_index]; } uint8_t column_bit_type(const int column_index) const { return column_bit_type_[column_index]; } private: template void InitOneColumnData(const void* in_column_data, BinIterator* bin_iterator, CUDAVector* out_column_data_pointer); void LaunchCopySubrowKernel(uint8_t* const* in_cuda_data_by_column); void InitColumnMetaInfo(); void ResizeWhenCopySubrow(const data_size_t num_used_indices); std::vector GetDataByColumnPointers(const std::vector>>& data_by_column) const { std::vector data_by_column_pointers(data_by_column.size(), nullptr); for (size_t i = 0; i < data_by_column.size(); ++i) { data_by_column_pointers[i] = reinterpret_cast(data_by_column[i]->RawData()); } return data_by_column_pointers; } int gpu_device_id_; int num_threads_; data_size_t num_data_; int num_columns_; std::vector column_bit_type_; std::vector feature_min_bin_; std::vector feature_max_bin_; std::vector feature_offset_; std::vector feature_most_freq_bin_; std::vector feature_default_bin_; std::vector feature_missing_is_zero_; std::vector feature_missing_is_na_; std::vector feature_mfb_is_zero_; std::vector feature_mfb_is_na_; CUDAVector cuda_data_by_column_; std::vector feature_to_column_; std::vector>> data_by_column_; CUDAVector cuda_column_bit_type_; CUDAVector cuda_feature_min_bin_; CUDAVector cuda_feature_max_bin_; CUDAVector cuda_feature_offset_; CUDAVector cuda_feature_most_freq_bin_; CUDAVector cuda_feature_default_bin_; CUDAVector cuda_feature_missing_is_zero_; CUDAVector cuda_feature_missing_is_na_; CUDAVector cuda_feature_mfb_is_zero_; CUDAVector cuda_feature_mfb_is_na_; CUDAVector cuda_feature_to_column_; // used when bagging with subset CUDAVector cuda_used_indices_; data_size_t num_used_indices_; data_size_t cur_subset_buffer_size_; }; } // namespace LightGBM #endif // USE_CUDA #endif // LIGHTGBM_INCLUDE_LIGHTGBM_CUDA_CUDA_COLUMN_DATA_HPP_ ================================================ FILE: include/LightGBM/cuda/cuda_metadata.hpp ================================================ /*! * Copyright (c) 2021-2026 Microsoft Corporation. All rights reserved. * Copyright (c) 2021-2026 The LightGBM developers. All rights reserved. * Licensed under the MIT License. See LICENSE file in the project root for license information. */ #ifndef LIGHTGBM_INCLUDE_LIGHTGBM_CUDA_CUDA_METADATA_HPP_ #define LIGHTGBM_INCLUDE_LIGHTGBM_CUDA_CUDA_METADATA_HPP_ #ifdef USE_CUDA #include #include #include namespace LightGBM { class CUDAMetadata { public: explicit CUDAMetadata(const int gpu_device_id); ~CUDAMetadata(); void Init(const std::vector& label, const std::vector& weight, const std::vector& query_boundaries, const std::vector& query_weights, const std::vector& init_score); void SetLabel(const label_t* label, data_size_t len); void SetWeights(const label_t* weights, data_size_t len); void SetQuery(const data_size_t* query, const label_t* query_weights, data_size_t num_queries); void SetInitScore(const double* init_score, data_size_t len); const label_t* cuda_label() const { return cuda_label_.RawData(); } const label_t* cuda_weights() const { return cuda_weights_.RawData(); } const data_size_t* cuda_query_boundaries() const { return cuda_query_boundaries_.RawData(); } const label_t* cuda_query_weights() const { return cuda_query_weights_.RawData(); } private: CUDAVector cuda_label_; CUDAVector cuda_weights_; CUDAVector cuda_query_boundaries_; CUDAVector cuda_query_weights_; CUDAVector cuda_init_score_; }; } // namespace LightGBM #endif // USE_CUDA #endif // LIGHTGBM_INCLUDE_LIGHTGBM_CUDA_CUDA_METADATA_HPP_ ================================================ FILE: include/LightGBM/cuda/cuda_metric.hpp ================================================ /*! * Copyright (c) 2021-2026 Microsoft Corporation. All rights reserved. * Copyright (c) 2021-2026 The LightGBM developers. All rights reserved. * Licensed under the MIT License. See LICENSE file in the project root for * license information. */ #ifndef LIGHTGBM_INCLUDE_LIGHTGBM_CUDA_CUDA_METRIC_HPP_ #define LIGHTGBM_INCLUDE_LIGHTGBM_CUDA_CUDA_METRIC_HPP_ #ifdef USE_CUDA #include #include namespace LightGBM { template class CUDAMetricInterface: public HOST_METRIC { public: explicit CUDAMetricInterface(const Config& config): HOST_METRIC(config) { cuda_labels_ = nullptr; cuda_weights_ = nullptr; const int gpu_device_id = config.gpu_device_id >= 0 ? config.gpu_device_id : 0; SetCUDADevice(gpu_device_id, __FILE__, __LINE__); } void Init(const Metadata& metadata, data_size_t num_data) override { HOST_METRIC::Init(metadata, num_data); cuda_labels_ = metadata.cuda_metadata()->cuda_label(); cuda_weights_ = metadata.cuda_metadata()->cuda_weights(); } bool IsCUDAMetric() const { return true; } protected: const label_t* cuda_labels_; const label_t* cuda_weights_; }; } // namespace LightGBM #endif // USE_CUDA #endif // LIGHTGBM_INCLUDE_LIGHTGBM_CUDA_CUDA_METRIC_HPP_ ================================================ FILE: include/LightGBM/cuda/cuda_nccl_topology.hpp ================================================ /*! * Copyright (c) 2023-2026 Microsoft Corporation. All rights reserved. * Copyright (c) 2023-2026 The LightGBM developers. All rights reserved. * Licensed under the MIT License. See LICENSE file in the project root for license information. */ #ifndef LIGHTGBM_INCLUDE_LIGHTGBM_CUDA_CUDA_NCCL_TOPOLOGY_HPP_ #define LIGHTGBM_INCLUDE_LIGHTGBM_CUDA_CUDA_NCCL_TOPOLOGY_HPP_ #ifdef USE_CUDA #ifdef USE_ROCM #include #else #include #endif #include #include #include #include #include #include #include #include #include namespace LightGBM { class NCCLTopology { public: 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) { num_gpu_ = num_gpu; master_gpu_device_id_ = master_gpu_device_id; global_num_data_ = global_num_data; int max_num_gpu = 0; CUDASUCCESS_OR_FATAL(cudaGetDeviceCount(&max_num_gpu)); if (gpu_device_id_list != std::string("")) { std::set gpu_id_set; std::vector gpu_list_str = Common::Split(gpu_device_id_list.c_str(), ","); for (const auto& gpu_str : gpu_list_str) { int gpu_id = 0; Common::Atoi(gpu_str.c_str(), &gpu_id); if (gpu_id < 0 || gpu_id >= max_num_gpu) { Log::Warning("Invalid GPU device ID %d in gpu_device_list is ignored.", gpu_id); } else { gpu_id_set.insert(gpu_id); } } for (const int gpu_id : gpu_id_set) { gpu_list_.push_back(gpu_id); } } if (!gpu_list_.empty() && num_gpu_ != static_cast(gpu_list_.size())) { Log::Warning("num_gpu = %d is different from the number of valid device IDs in gpu_device_list (%d), using %d GPUs instead.", \ num_gpu_, static_cast(gpu_list_.size()), static_cast(gpu_list_.size())); num_gpu_ = static_cast(gpu_list_.size()); } if (!gpu_list_.empty()) { bool check_master_gpu = false; for (int i = 0; i < static_cast(gpu_list_.size()); ++i) { const int gpu_id = gpu_list_[i]; if (gpu_id == master_gpu_device_id_) { check_master_gpu = true; master_gpu_index_ = i; break; } } if (!check_master_gpu) { Log::Warning("Master GPU index not in gpu_device_list. Using %d as the master GPU instead.", gpu_list_[0]); master_gpu_device_id_ = gpu_list_[0]; master_gpu_index_ = 0; } } else { if (num_gpu_ <= 0) { num_gpu_ = 1; } else if (num_gpu_ > max_num_gpu) { Log::Warning("Only %d GPUs available, using num_gpu = %d.", max_num_gpu, max_num_gpu); num_gpu_ = max_num_gpu; } if (master_gpu_device_id_ < 0 || master_gpu_device_id_ >= num_gpu_) { Log::Warning("Invalid gpu_device_id = %d for master GPU index, using gpu_device_id = 0 instead.", master_gpu_device_id_); master_gpu_device_id_ = 0; master_gpu_index_ = 0; } for (int i = 0; i < num_gpu_; ++i) { gpu_list_.push_back(i); } } Log::Info("Using GPU devices %s, and local master GPU device %d.", Common::Join(gpu_list_, ",").c_str(), master_gpu_device_id_); const int num_threads = OMP_NUM_THREADS(); if (num_gpu_ > num_threads) { Log::Fatal("Number of GPUs %d is greater than the number of threads %d. Please use more threads.", num_gpu_, num_threads); } host_threads_.resize(num_gpu_); } ~NCCLTopology() {} void InitNCCL() { nccl_gpu_rank_.resize(num_gpu_, -1); nccl_communicators_.resize(num_gpu_); ncclUniqueId nccl_unique_id; if (Network::num_machines() == 1 || Network::rank() == 0) { NCCLCHECK(ncclGetUniqueId(&nccl_unique_id)); } if (Network::num_machines() > 1) { std::vector output_buffer(Network::num_machines()); Network::Allgather( reinterpret_cast(&nccl_unique_id), sizeof(ncclUniqueId) / sizeof(char), reinterpret_cast(output_buffer.data())); if (Network::rank() > 0) { nccl_unique_id = output_buffer[0]; } } if (Network::num_machines() > 1) { node_rank_offset_.resize(Network::num_machines() + 1, 0); Network::Allgather( reinterpret_cast(&num_gpu_), sizeof(int) / sizeof(char), reinterpret_cast(node_rank_offset_.data() + 1)); for (int rank = 1; rank < Network::num_machines() + 1; ++rank) { node_rank_offset_[rank] += node_rank_offset_[rank - 1]; } CHECK_EQ(node_rank_offset_[Network::rank() + 1] - node_rank_offset_[Network::rank()], num_gpu_); NCCLCHECK(ncclGroupStart()); for (int gpu_index = 0; gpu_index < num_gpu_; ++gpu_index) { SetCUDADevice(gpu_list_[gpu_index], __FILE__, __LINE__); nccl_gpu_rank_[gpu_index] = gpu_index + node_rank_offset_[Network::rank()]; NCCLCHECK(ncclCommInitRank(&nccl_communicators_[gpu_index], node_rank_offset_.back(), nccl_unique_id, nccl_gpu_rank_[gpu_index])); } NCCLCHECK(ncclGroupEnd()); } else { NCCLCHECK(ncclGroupStart()); for (int gpu_index = 0; gpu_index < num_gpu_; ++gpu_index) { SetCUDADevice(gpu_list_[gpu_index], __FILE__, __LINE__); nccl_gpu_rank_[gpu_index] = gpu_index; NCCLCHECK(ncclCommInitRank(&nccl_communicators_[gpu_index], num_gpu_, nccl_unique_id, gpu_index)); } NCCLCHECK(ncclGroupEnd()); } // return to master gpu device CUDASUCCESS_OR_FATAL(cudaSetDevice(master_gpu_device_id_)); } template void RunPerDevice(const std::vector>& objs, const std::function& func) { #pragma omp parallel for schedule(static) num_threads(num_gpu_) for (int i = 0; i < num_gpu_; ++i) { CUDASUCCESS_OR_FATAL(cudaSetDevice(gpu_list_[i])); func(objs[i].get()); } CUDASUCCESS_OR_FATAL(cudaSetDevice(master_gpu_device_id_)); } template void InitPerDevice(std::vector>* vec) { vec->resize(num_gpu_); #pragma omp parallel for schedule(static) num_threads(num_gpu_) for (int i = 0; i < num_gpu_; ++i) { CUDASUCCESS_OR_FATAL(cudaSetDevice(gpu_list_[i])); RET_T* nccl_info = new RET_T(); nccl_info->SetNCCLInfo(nccl_communicators_[i], nccl_gpu_rank_[i], i, gpu_list_[i], global_num_data_); vec->operator[](i).reset(nccl_info); } CUDASUCCESS_OR_FATAL(cudaSetDevice(master_gpu_device_id_)); } template void DispatchPerDevice(std::vector>* objs, const std::function& func) { for (int i = 0; i < num_gpu_; ++i) { host_threads_[i] = std::thread([this, i, &func, objs] () { CUDASUCCESS_OR_FATAL(cudaSetDevice(gpu_list_[i])) func(objs->operator[](i).get()); }); } for (int i = 0; i < num_gpu_; ++i) { host_threads_[i].join(); } CUDASUCCESS_OR_FATAL(cudaSetDevice(master_gpu_device_id_)); } template void RunOnMasterDevice(const std::vector>& objs, const std::function& func) { CUDASUCCESS_OR_FATAL(cudaSetDevice(master_gpu_device_id_)); func(objs[master_gpu_index_].get()); } template void RunOnNonMasterDevice(const std::vector>& objs, const std::function& func) { for (int i = 0; i < num_gpu_; ++i) { if (i != master_gpu_index_) { CUDASUCCESS_OR_FATAL(cudaSetDevice(gpu_list_[i])); func(objs[i].get()); } } CUDASUCCESS_OR_FATAL(cudaSetDevice(master_gpu_device_id_)); } int num_gpu() const { return num_gpu_; } int master_gpu_index() const { return master_gpu_index_; } int master_gpu_device_id() const { return master_gpu_device_id_; } const std::vector& gpu_list() const { return gpu_list_; } private: int num_gpu_; int master_gpu_index_; int master_gpu_device_id_; std::vector gpu_list_; data_size_t global_num_data_; ncclUniqueId nccl_unique_id_; std::vector node_rank_offset_; std::vector nccl_gpu_rank_; std::vector nccl_communicators_; std::vector host_threads_; }; } // namespace LightGBM #endif // USE_CUDA #endif // LIGHTGBM_INCLUDE_LIGHTGBM_CUDA_CUDA_NCCL_TOPOLOGY_HPP_ ================================================ FILE: include/LightGBM/cuda/cuda_objective_function.hpp ================================================ /*! * Copyright (c) 2021-2026 Microsoft Corporation. All rights reserved. * Copyright (c) 2026-2026 The LightGBM developers. All rights reserved. * Licensed under the MIT License. See LICENSE file in the project root for * license information. */ #ifndef LIGHTGBM_INCLUDE_LIGHTGBM_CUDA_CUDA_OBJECTIVE_FUNCTION_HPP_ #define LIGHTGBM_INCLUDE_LIGHTGBM_CUDA_CUDA_OBJECTIVE_FUNCTION_HPP_ #ifdef USE_CUDA #include #include #include #include #include namespace LightGBM { template class CUDAObjectiveInterface: public HOST_OBJECTIVE, public NCCLInfo { public: explicit CUDAObjectiveInterface(const Config& config): HOST_OBJECTIVE(config) { if (config.num_gpu <= 1) { const int gpu_device_id = config.gpu_device_id >= 0 ? config.gpu_device_id : 0; SetCUDADevice(gpu_device_id, __FILE__, __LINE__); } } explicit CUDAObjectiveInterface(const std::vector& strs): HOST_OBJECTIVE(strs) {} void Init(const Metadata& metadata, data_size_t num_data) { HOST_OBJECTIVE::Init(metadata, num_data); cuda_labels_ = metadata.cuda_metadata()->cuda_label(); cuda_weights_ = metadata.cuda_metadata()->cuda_weights(); } void SetNCCLInfo( ncclComm_t nccl_communicator, int nccl_gpu_rank, int local_gpu_rank, int gpu_device_id, data_size_t global_num_data) override { NCCLInfo::SetNCCLInfo(nccl_communicator, nccl_gpu_rank, local_gpu_rank, gpu_device_id, global_num_data); } virtual const double* ConvertOutputCUDA(const data_size_t num_data, const double* input, double* output) const { return LaunchConvertOutputCUDAKernel(num_data, input, output); } double BoostFromScore(int class_id) const override { return LaunchCalcInitScoreKernel(class_id); } bool IsCUDAObjective() const override { return true; } void GetGradients(const double* scores, score_t* gradients, score_t* hessians) const override { LaunchGetGradientsKernel(scores, gradients, hessians); SynchronizeCUDADevice(__FILE__, __LINE__); } 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 { LaunchGetGradientsKernel(scores, gradients, hessians); SynchronizeCUDADevice(__FILE__, __LINE__); } void RenewTreeOutputCUDA(const double* score, const data_size_t* data_indices_in_leaf, const data_size_t* num_data_in_leaf, const data_size_t* data_start_in_leaf, const int num_leaves, double* leaf_value) const override { global_timer.Start("CUDAObjectiveInterface::LaunchRenewTreeOutputCUDAKernel"); LaunchRenewTreeOutputCUDAKernel(score, data_indices_in_leaf, num_data_in_leaf, data_start_in_leaf, num_leaves, leaf_value); SynchronizeCUDADevice(__FILE__, __LINE__); global_timer.Stop("CUDAObjectiveInterface::LaunchRenewTreeOutputCUDAKernel"); } protected: virtual void LaunchGetGradientsKernel(const double* scores, score_t* gradients, score_t* hessians) const = 0; virtual double LaunchCalcInitScoreKernel(const int class_id) const { return HOST_OBJECTIVE::BoostFromScore(class_id); } virtual const double* LaunchConvertOutputCUDAKernel(const data_size_t /*num_data*/, const double* input, double* /*output*/) const { return input; } virtual void LaunchRenewTreeOutputCUDAKernel( const double* /*score*/, const data_size_t* /*data_indices_in_leaf*/, const data_size_t* /*num_data_in_leaf*/, const data_size_t* /*data_start_in_leaf*/, const int /*num_leaves*/, double* /*leaf_value*/) const {} const label_t* cuda_labels_; const label_t* cuda_weights_; }; } // namespace LightGBM #endif // USE_CUDA #endif // LIGHTGBM_INCLUDE_LIGHTGBM_CUDA_CUDA_OBJECTIVE_FUNCTION_HPP_ ================================================ FILE: include/LightGBM/cuda/cuda_random.hpp ================================================ /*! * Copyright (c) 2021-2026 Microsoft Corporation. All rights reserved. * Copyright (c) 2021-2026 The LightGBM developers. All rights reserved. * Licensed under the MIT License. See LICENSE file in the project root for license information. */ #ifndef LIGHTGBM_INCLUDE_LIGHTGBM_CUDA_CUDA_RANDOM_HPP_ #define LIGHTGBM_INCLUDE_LIGHTGBM_CUDA_CUDA_RANDOM_HPP_ #ifdef USE_CUDA #ifndef USE_ROCM #include #include #endif namespace LightGBM { /*! * \brief A wrapper for random generator */ class CUDARandom { public: /*! * \brief Set specific seed */ __device__ void SetSeed(int seed) { x = seed; } /*! * \brief Generate random integer, int16 range. [0, 65536] * \param lower_bound lower bound * \param upper_bound upper bound * \return The random integer between [lower_bound, upper_bound) */ __device__ inline int NextShort(int lower_bound, int upper_bound) { return (RandInt16()) % (upper_bound - lower_bound) + lower_bound; } /*! * \brief Generate random integer, int32 range * \param lower_bound lower bound * \param upper_bound upper bound * \return The random integer between [lower_bound, upper_bound) */ __device__ inline int NextInt(int lower_bound, int upper_bound) { return (RandInt32()) % (upper_bound - lower_bound) + lower_bound; } /*! * \brief Generate random float data * \return The random float between [0.0, 1.0) */ __device__ inline float NextFloat() { // get random float in [0,1) return static_cast(RandInt16()) / (32768.0f); } private: __device__ inline int RandInt16() { x = (214013 * x + 2531011); return static_cast((x >> 16) & 0x7FFF); } __device__ inline int RandInt32() { x = (214013 * x + 2531011); return static_cast(x & 0x7FFFFFFF); } unsigned int x = 123456789; }; } // namespace LightGBM #endif // USE_CUDA #endif // LIGHTGBM_INCLUDE_LIGHTGBM_CUDA_CUDA_RANDOM_HPP_ ================================================ FILE: include/LightGBM/cuda/cuda_rocm_interop.h ================================================ /*! * Copyright(C) 2023 Advanced Micro Devices, Inc. All rights reserved. */ #ifndef LIGHTGBM_INCLUDE_LIGHTGBM_CUDA_CUDA_ROCM_INTEROP_H_ #define LIGHTGBM_INCLUDE_LIGHTGBM_CUDA_CUDA_ROCM_INTEROP_H_ #ifdef USE_CUDA #if defined(__HIP_PLATFORM_AMD__) // ROCm doesn't have atomicAdd_block, but it should be semantically the same as atomicAdd #define atomicAdd_block atomicAdd // hipify #include #define cudaDeviceProp hipDeviceProp_t #define cudaDeviceSynchronize hipDeviceSynchronize #define cudaError_t hipError_t #define cudaFree hipFree #define cudaFreeHost hipFreeHost #define cudaGetDevice hipGetDevice #define cudaGetDeviceCount hipGetDeviceCount #define cudaGetDeviceProperties hipGetDeviceProperties #define cudaGetErrorName hipGetErrorName #define cudaGetErrorString hipGetErrorString #define cudaGetLastError hipGetLastError #define cudaHostAlloc hipHostAlloc #define cudaHostAllocPortable hipHostAllocPortable #define cudaMalloc hipMalloc #define cudaMemcpy hipMemcpy #define cudaMemcpyAsync hipMemcpyAsync #define cudaMemcpyDeviceToDevice hipMemcpyDeviceToDevice #define cudaMemcpyDeviceToHost hipMemcpyDeviceToHost #define cudaMemcpyHostToDevice hipMemcpyHostToDevice #define cudaMemoryTypeHost hipMemoryTypeHost #define cudaMemset hipMemset #define cudaPointerAttributes hipPointerAttribute_t #define cudaPointerGetAttributes hipPointerGetAttributes #define cudaSetDevice hipSetDevice #define cudaStreamCreate hipStreamCreate #define cudaStreamDestroy hipStreamDestroy #define cudaStreamSynchronize hipStreamSynchronize #define cudaStream_t hipStream_t #define cudaSuccess hipSuccess // ROCm 7.0 did add __shfl_down_sync et al, but the following hack still works. // Since mask is full 0xffffffff, we can use __shfl_down instead. #define __shfl_down_sync(mask, val, offset) __shfl_down(val, offset) #define __shfl_up_sync(mask, val, offset) __shfl_up(val, offset) // warpSize is only allowed for device code. // HIP header used to define warpSize as a constexpr that was either 32 or 64 // depending on the target device, and then always set it to 64 for host code. static inline constexpr int WARP_SIZE_INTERNAL() { #if defined(__GFX9__) return 64; #else // __GFX9__ return 32; #endif // __GFX9__ } #define WARPSIZE (WARP_SIZE_INTERNAL()) #else // __HIP_PLATFORM_AMD__ // CUDA warpSize is not a constexpr, but always 32 #define WARPSIZE 32 #endif // defined(__HIP_PLATFORM_AMD__) || defined(__HIP__) #endif // USE_CUDA #endif // LIGHTGBM_INCLUDE_LIGHTGBM_CUDA_CUDA_ROCM_INTEROP_H_ ================================================ FILE: include/LightGBM/cuda/cuda_row_data.hpp ================================================ /*! * Copyright (c) 2021-2026 Microsoft Corporation. All rights reserved. * Copyright (c) 2021-2026 The LightGBM developers. All rights reserved. * Licensed under the MIT License. See LICENSE file in the project root for license information. */ #ifndef LIGHTGBM_INCLUDE_LIGHTGBM_CUDA_CUDA_ROW_DATA_HPP_ #define LIGHTGBM_INCLUDE_LIGHTGBM_CUDA_CUDA_ROW_DATA_HPP_ #ifdef USE_CUDA #include #include #include #include #include #include #include #include #define COPY_SUBROW_BLOCK_SIZE_ROW_DATA (1024) #if CUDART_VERSION == 10000 #define DP_SHARED_HIST_SIZE (5176) #else #define DP_SHARED_HIST_SIZE (6144) #endif #define SP_SHARED_HIST_SIZE (DP_SHARED_HIST_SIZE * 2) namespace LightGBM { class CUDARowData { public: CUDARowData(const Dataset* train_data, const TrainingShareStates* train_share_state, const int gpu_device_id, const bool gpu_use_dp); ~CUDARowData(); void Init(const Dataset* train_data, TrainingShareStates* train_share_state); void CopySubrow(const CUDARowData* full_set, const data_size_t* used_indices, const data_size_t num_used_indices); void CopySubcol(const CUDARowData* full_set, const std::vector& is_feature_used, const Dataset* train_data); void CopySubrowAndSubcol(const CUDARowData* full_set, const data_size_t* used_indices, const data_size_t num_used_indices, const std::vector& is_feature_used, const Dataset* train_data); template const BIN_TYPE* GetBin() const; template const PTR_TYPE* GetPartitionPtr() const; template const PTR_TYPE* GetRowPtr() const; int NumLargeBinPartition() const { return static_cast(large_bin_partitions_.size()); } int num_feature_partitions() const { return num_feature_partitions_; } int max_num_column_per_partition() const { return max_num_column_per_partition_; } bool is_sparse() const { return is_sparse_; } uint8_t bit_type() const { return bit_type_; } uint8_t row_ptr_bit_type() const { return row_ptr_bit_type_; } const int* cuda_feature_partition_column_index_offsets() const { return cuda_feature_partition_column_index_offsets_.RawData(); } const uint32_t* cuda_column_hist_offsets() const { return cuda_column_hist_offsets_.RawData(); } const uint32_t* cuda_partition_hist_offsets() const { return cuda_partition_hist_offsets_.RawData(); } int shared_hist_size() const { return shared_hist_size_; } private: void DivideCUDAFeatureGroups(const Dataset* train_data, TrainingShareStates* share_state); template void GetDenseDataPartitioned(const BIN_TYPE* row_wise_data, std::vector* partitioned_data); template void GetSparseDataPartitioned(const BIN_TYPE* row_wise_data, const ROW_PTR_TYPE* row_ptr, std::vector>* partitioned_data, std::vector>* partitioned_row_ptr, std::vector* partition_ptr); template void InitSparseData(const BIN_TYPE* host_data, const ROW_PTR_TYPE* host_row_ptr, CUDAVector* cuda_data, CUDAVector* cuda_row_ptr, CUDAVector* cuda_partition_ptr); /*! \brief number of threads to use */ int num_threads_; /*! \brief number of training data */ data_size_t num_data_; /*! \brief number of bins of all features */ int num_total_bin_; /*! \brief number of feature groups in dataset */ int num_feature_group_; /*! \brief number of features in dataset */ int num_feature_; /*! \brief number of bits used to store each bin value */ uint8_t bit_type_; /*! \brief number of bits used to store each row pointer value */ uint8_t row_ptr_bit_type_; /*! \brief is sparse row wise data */ bool is_sparse_; /*! \brief start column index of each feature partition */ std::vector feature_partition_column_index_offsets_; /*! \brief histogram offset of each column */ std::vector column_hist_offsets_; /*! \brief hisotgram offset of each partition */ std::vector partition_hist_offsets_; /*! \brief maximum number of columns among all feature partitions */ int max_num_column_per_partition_; /*! \brief number of partitions */ int num_feature_partitions_; /*! \brief used when bagging with subset, number of used indices */ data_size_t num_used_indices_; /*! \brief used when bagging with subset, number of total elements */ uint64_t num_total_elements_; /*! \brief used when bagging with column subset, the size of maximum number of feature partitions */ int cur_num_feature_partition_buffer_size_; /*! \brief CUDA device ID */ int gpu_device_id_; /*! \brief index of partitions with large bins that its histogram cannot fit into shared memory, each large bin partition contains a single column */ std::vector large_bin_partitions_; /*! \brief index of partitions with small bins */ std::vector small_bin_partitions_; /*! \brief shared memory size used by histogram */ int shared_hist_size_; /*! \brief whether to use double precision in histograms per block */ bool gpu_use_dp_; // CUDA memory /*! \brief row-wise data stored in CUDA, 8 bits */ CUDAVector cuda_data_uint8_t_; /*! \brief row-wise data stored in CUDA, 16 bits */ CUDAVector cuda_data_uint16_t_; /*! \brief row-wise data stored in CUDA, 32 bits */ CUDAVector cuda_data_uint32_t_; /*! \brief row pointer stored in CUDA, 16 bits */ CUDAVector cuda_row_ptr_uint16_t_; /*! \brief row pointer stored in CUDA, 32 bits */ CUDAVector cuda_row_ptr_uint32_t_; /*! \brief row pointer stored in CUDA, 64 bits */ CUDAVector cuda_row_ptr_uint64_t_; /*! \brief partition bin offsets, 16 bits */ CUDAVector cuda_partition_ptr_uint16_t_; /*! \brief partition bin offsets, 32 bits */ CUDAVector cuda_partition_ptr_uint32_t_; /*! \brief partition bin offsets, 64 bits */ CUDAVector cuda_partition_ptr_uint64_t_; /*! \brief start column index of each feature partition */ CUDAVector cuda_feature_partition_column_index_offsets_; /*! \brief histogram offset of each column */ CUDAVector cuda_column_hist_offsets_; /*! \brief hisotgram offset of each partition */ CUDAVector cuda_partition_hist_offsets_; /*! \brief block buffer when calculating prefix sum */ CUDAVector cuda_block_buffer_uint16_t_; /*! \brief block buffer when calculating prefix sum */ CUDAVector cuda_block_buffer_uint32_t_; /*! \brief block buffer when calculating prefix sum */ CUDAVector cuda_block_buffer_uint64_t_; }; } // namespace LightGBM #endif // USE_CUDA #endif // LIGHTGBM_INCLUDE_LIGHTGBM_CUDA_CUDA_ROW_DATA_HPP_ ================================================ FILE: include/LightGBM/cuda/cuda_split_info.hpp ================================================ /*! * Copyright (c) 2021-2026 Microsoft Corporation. All rights reserved. * Copyright (c) 2022-2026 The LightGBM developers. All rights reserved. * Licensed under the MIT License. See LICENSE file in the project root for * license information. * Modifications Copyright(C) 2023 Advanced Micro Devices, Inc. All rights reserved. */ #ifndef LIGHTGBM_INCLUDE_LIGHTGBM_CUDA_CUDA_SPLIT_INFO_HPP_ #define LIGHTGBM_INCLUDE_LIGHTGBM_CUDA_CUDA_SPLIT_INFO_HPP_ #ifdef USE_CUDA #include namespace LightGBM { class CUDASplitInfo { public: bool is_valid; int leaf_index; double gain; int inner_feature_index; uint32_t threshold; bool default_left; double left_sum_gradients; double left_sum_hessians; int64_t left_sum_of_gradients_hessians; data_size_t left_count; double left_gain; double left_value; double right_sum_gradients; double right_sum_hessians; int64_t right_sum_of_gradients_hessians; data_size_t right_count; double right_gain; double right_value; int num_cat_threshold = 0; uint32_t* cat_threshold = nullptr; int* cat_threshold_real = nullptr; __host__ __device__ CUDASplitInfo() { num_cat_threshold = 0; cat_threshold = nullptr; cat_threshold_real = nullptr; } __host__ __device__ ~CUDASplitInfo() { if (num_cat_threshold > 0) { if (cat_threshold != nullptr) { CUDASUCCESS_OR_FATAL(cudaFree(cat_threshold)); } if (cat_threshold_real != nullptr) { CUDASUCCESS_OR_FATAL(cudaFree(cat_threshold_real)); } } } __host__ __device__ CUDASplitInfo& operator=(const CUDASplitInfo& other) { is_valid = other.is_valid; leaf_index = other.leaf_index; gain = other.gain; inner_feature_index = other.inner_feature_index; threshold = other.threshold; default_left = other.default_left; left_sum_gradients = other.left_sum_gradients; left_sum_hessians = other.left_sum_hessians; left_count = other.left_count; left_gain = other.left_gain; left_value = other.left_value; right_sum_gradients = other.right_sum_gradients; right_sum_hessians = other.right_sum_hessians; right_count = other.right_count; right_gain = other.right_gain; right_value = other.right_value; num_cat_threshold = other.num_cat_threshold; if (num_cat_threshold > 0 && cat_threshold == nullptr) { cat_threshold = new uint32_t[num_cat_threshold]; } if (num_cat_threshold > 0 && cat_threshold_real == nullptr) { cat_threshold_real = new int[num_cat_threshold]; } if (num_cat_threshold > 0) { if (other.cat_threshold != nullptr) { for (int i = 0; i < num_cat_threshold; ++i) { cat_threshold[i] = other.cat_threshold[i]; } } if (other.cat_threshold_real != nullptr) { for (int i = 0; i < num_cat_threshold; ++i) { cat_threshold_real[i] = other.cat_threshold_real[i]; } } } return *this; } }; } // namespace LightGBM #endif // USE_CUDA #endif // LIGHTGBM_INCLUDE_LIGHTGBM_CUDA_CUDA_SPLIT_INFO_HPP_ ================================================ FILE: include/LightGBM/cuda/cuda_tree.hpp ================================================ /*! * Copyright (c) 2021-2026 Microsoft Corporation. All rights reserved. * Copyright (c) 2021-2026 The LightGBM developers. All rights reserved. * Licensed under the MIT License. See LICENSE file in the project root for license information. */ #ifndef LIGHTGBM_INCLUDE_LIGHTGBM_CUDA_CUDA_TREE_HPP_ #define LIGHTGBM_INCLUDE_LIGHTGBM_CUDA_CUDA_TREE_HPP_ #ifdef USE_CUDA #include #include #include #include namespace LightGBM { __device__ void SetDecisionTypeCUDA(int8_t* decision_type, bool input, int8_t mask); __device__ void SetMissingTypeCUDA(int8_t* decision_type, int8_t input); __device__ bool GetDecisionTypeCUDA(int8_t decision_type, int8_t mask); __device__ int8_t GetMissingTypeCUDA(int8_t decision_type); __device__ bool IsZeroCUDA(double fval); class CUDATree : public Tree { public: /*! * \brief Constructor * \param max_leaves The number of max leaves * \param track_branch_features Whether to keep track of ancestors of leaf nodes * \param is_linear Whether the tree has linear models at each leaf */ explicit CUDATree(int max_leaves, bool track_branch_features, bool is_linear, const int gpu_device_id, const bool has_categorical_feature); explicit CUDATree(const Tree* host_tree); ~CUDATree() noexcept; int Split(const int leaf_index, const int real_feature_index, const double real_threshold, const MissingType missing_type, const CUDASplitInfo* cuda_split_info); int SplitCategorical( const int leaf_index, const int real_feature_index, const MissingType missing_type, const CUDASplitInfo* cuda_split_info, uint32_t* cuda_bitset, size_t cuda_bitset_len, uint32_t* cuda_bitset_inner, size_t cuda_bitset_inner_len); /*! * \brief Adding prediction value of this tree model to scores * \param data The dataset * \param num_data Number of total data * \param score Will add prediction to score */ void AddPredictionToScore(const Dataset* data, data_size_t num_data, double* score) const override; /*! * \brief Adding prediction value of this tree model to scores * \param data The dataset * \param used_data_indices Indices of used data * \param num_data Number of total data * \param score Will add prediction to score */ void AddPredictionToScore(const Dataset* data, const data_size_t* used_data_indices, data_size_t num_data, double* score) const override; inline void AsConstantTree(double val, int count) override; const int* cuda_leaf_parent() const { return cuda_leaf_parent_.RawData(); } const int* cuda_left_child() const { return cuda_left_child_.RawData(); } const int* cuda_right_child() const { return cuda_right_child_.RawData(); } const int* cuda_split_feature_inner() const { return cuda_split_feature_inner_.RawData(); } const int* cuda_split_feature() const { return cuda_split_feature_.RawData(); } const uint32_t* cuda_threshold_in_bin() const { return cuda_threshold_in_bin_.RawData(); } const double* cuda_threshold() const { return cuda_threshold_.RawData(); } const int8_t* cuda_decision_type() const { return cuda_decision_type_.RawData(); } const double* cuda_leaf_value() const { return cuda_leaf_value_.RawData(); } double* cuda_leaf_value_ref() { return cuda_leaf_value_.RawData(); } inline void Shrinkage(double rate) override; inline void AddBias(double val) override; void ToHost(); void SyncLeafOutputFromHostToCUDA(); void SyncLeafOutputFromCUDAToHost(); private: void InitCUDAMemory(); void InitCUDA(); void LaunchSplitKernel(const int leaf_index, const int real_feature_index, const double real_threshold, const MissingType missing_type, const CUDASplitInfo* cuda_split_info); void LaunchSplitCategoricalKernel( const int leaf_index, const int real_feature_index, const MissingType missing_type, const CUDASplitInfo* cuda_split_info, size_t cuda_bitset_len, size_t cuda_bitset_inner_len); void LaunchAddPredictionToScoreKernel(const Dataset* data, const data_size_t* used_data_indices, data_size_t num_data, double* score) const; void LaunchShrinkageKernel(const double rate); void LaunchAddBiasKernel(const double val); void RecordBranchFeatures(const int left_leaf_index, const int right_leaf_index, const int real_feature_index); CUDAVector cuda_left_child_; CUDAVector cuda_right_child_; CUDAVector cuda_split_feature_inner_; CUDAVector cuda_split_feature_; CUDAVector cuda_leaf_depth_; CUDAVector cuda_leaf_parent_; CUDAVector cuda_threshold_in_bin_; CUDAVector cuda_threshold_; CUDAVector cuda_internal_weight_; CUDAVector cuda_internal_value_; CUDAVector cuda_decision_type_; CUDAVector cuda_leaf_value_; CUDAVector cuda_leaf_count_; CUDAVector cuda_leaf_weight_; CUDAVector cuda_internal_count_; CUDAVector cuda_split_gain_; CUDAVector cuda_bitset_; CUDAVector cuda_bitset_inner_; CUDAVector cuda_cat_boundaries_; CUDAVector cuda_cat_boundaries_inner_; cudaStream_t cuda_stream_; const int num_threads_per_block_add_prediction_to_score_; }; } // namespace LightGBM #endif // USE_CUDA #endif // LIGHTGBM_INCLUDE_LIGHTGBM_CUDA_CUDA_TREE_HPP_ ================================================ FILE: include/LightGBM/cuda/cuda_utils.hu ================================================ /*! * Copyright (c) 2020-2021 IBM Corporation, Microsoft Corporation. All rights reserved. * Copyright (c) 2020-2026 Microsoft Corporation. All rights reserved. * Copyright (c) 2020-2026 The LightGBM developers. All rights reserved. * Licensed under the MIT License. See LICENSE file in the project root for license information. */ #ifndef LIGHTGBM_CUDA_CUDA_UTILS_H_ #define LIGHTGBM_CUDA_CUDA_UTILS_H_ #ifdef USE_CUDA #if defined(USE_ROCM) #include #include #else #include #include #include #endif #include #include #include #include #include #include namespace LightGBM { typedef unsigned long long atomic_add_long_t; #define CUDASUCCESS_OR_FATAL(ans) { gpuAssert((ans), __FILE__, __LINE__); } inline void gpuAssert(cudaError_t code, const char *file, int line, bool abort = true) { if (code != cudaSuccess) { LightGBM::Log::Fatal("[CUDA] %s %s %d\n", cudaGetErrorString(code), file, line); if (abort) exit(code); } } #define CUDASUCCESS_OR_FATAL_OUTER(ans) { gpuAssert((ans), file, line); } #define NCCLCHECK(cmd) do { \ ncclResult_t r = cmd; \ if (r!= ncclSuccess) { \ printf("Failed, NCCL error %s:%d '%s'\n", \ __FILE__,__LINE__,ncclGetErrorString(r)); \ exit(EXIT_FAILURE); \ } \ } while(0) void SetCUDADevice(int gpu_device_id, const char* file, int line); int GetCUDADevice(const char* file, int line); template void AllocateCUDAMemory(T** out_ptr, size_t size, const char* file, const int line) { void* tmp_ptr = nullptr; CUDASUCCESS_OR_FATAL_OUTER(cudaMalloc(&tmp_ptr, size * sizeof(T))); *out_ptr = reinterpret_cast(tmp_ptr); } template void CopyFromHostToCUDADevice(T* dst_ptr, const T* src_ptr, size_t size, const char* file, const int line) { void* void_dst_ptr = reinterpret_cast(dst_ptr); const void* void_src_ptr = reinterpret_cast(src_ptr); size_t size_in_bytes = size * sizeof(T); CUDASUCCESS_OR_FATAL_OUTER(cudaMemcpy(void_dst_ptr, void_src_ptr, size_in_bytes, cudaMemcpyHostToDevice)); } template void InitCUDAMemoryFromHostMemory(T** dst_ptr, const T* src_ptr, size_t size, const char* file, const int line) { AllocateCUDAMemory(dst_ptr, size, file, line); CopyFromHostToCUDADevice(*dst_ptr, src_ptr, size, file, line); } template void CopyFromCUDADeviceToHost(T* dst_ptr, const T* src_ptr, size_t size, const char* file, const int line) { void* void_dst_ptr = reinterpret_cast(dst_ptr); const void* void_src_ptr = reinterpret_cast(src_ptr); size_t size_in_bytes = size * sizeof(T); CUDASUCCESS_OR_FATAL_OUTER(cudaMemcpy(void_dst_ptr, void_src_ptr, size_in_bytes, cudaMemcpyDeviceToHost)); } template void CopyFromCUDADeviceToHostAsync(T* dst_ptr, const T* src_ptr, size_t size, cudaStream_t stream, const char* file, const int line) { void* void_dst_ptr = reinterpret_cast(dst_ptr); const void* void_src_ptr = reinterpret_cast(src_ptr); size_t size_in_bytes = size * sizeof(T); CUDASUCCESS_OR_FATAL_OUTER(cudaMemcpyAsync(void_dst_ptr, void_src_ptr, size_in_bytes, cudaMemcpyDeviceToHost, stream)); } template void CopyFromCUDADeviceToCUDADevice(T* dst_ptr, const T* src_ptr, size_t size, const char* file, const int line) { void* void_dst_ptr = reinterpret_cast(dst_ptr); const void* void_src_ptr = reinterpret_cast(src_ptr); size_t size_in_bytes = size * sizeof(T); CUDASUCCESS_OR_FATAL_OUTER(cudaMemcpy(void_dst_ptr, void_src_ptr, size_in_bytes, cudaMemcpyDeviceToDevice)); } template void CopyFromCUDADeviceToCUDADeviceAsync(T* dst_ptr, const T* src_ptr, size_t size, const char* file, const int line) { void* void_dst_ptr = reinterpret_cast(dst_ptr); const void* void_src_ptr = reinterpret_cast(src_ptr); size_t size_in_bytes = size * sizeof(T); CUDASUCCESS_OR_FATAL_OUTER(cudaMemcpyAsync(void_dst_ptr, void_src_ptr, size_in_bytes, cudaMemcpyDeviceToDevice)); } void SynchronizeCUDADevice(const char* file, const int line); void SynchronizeCUDAStream(cudaStream_t cuda_stream, const char* file, const int line); template void SetCUDAMemory(T* dst_ptr, int value, size_t size, const char* file, const int line) { CUDASUCCESS_OR_FATAL_OUTER(cudaMemset(reinterpret_cast(dst_ptr), value, size * sizeof(T))); SynchronizeCUDADevice(file, line); } template void DeallocateCUDAMemory(T** ptr, const char* file, const int line) { if (*ptr != nullptr) { CUDASUCCESS_OR_FATAL_OUTER(cudaFree(reinterpret_cast(*ptr))); *ptr = nullptr; } } void PrintLastCUDAError(); template class CUDAVector { public: CUDAVector() { size_ = 0; data_ = nullptr; } explicit CUDAVector(size_t size) { size_ = size; AllocateCUDAMemory(&data_, size_, __FILE__, __LINE__); } void Resize(size_t size) { if (size == size_) { return; } if (size == 0) { Clear(); return; } T* new_data = nullptr; AllocateCUDAMemory(&new_data, size, __FILE__, __LINE__); if (size_ > 0 && data_ != nullptr) { const size_t size_for_old_content = std::min(size_, size); CopyFromCUDADeviceToCUDADevice(new_data, data_, size_for_old_content, __FILE__, __LINE__); } DeallocateCUDAMemory(&data_, __FILE__, __LINE__); data_ = new_data; size_ = size; } void InitFromHostVector(const std::vector& host_vector) { Resize(host_vector.size()); CopyFromHostToCUDADevice(data_, host_vector.data(), host_vector.size(), __FILE__, __LINE__); } void InitFromHostMemory(const T* host_memory, size_t len) { Resize(len); CopyFromHostToCUDADevice(data_, host_memory, len, __FILE__, __LINE__); } void Clear() { if (size_ > 0 && data_ != nullptr) { DeallocateCUDAMemory(&data_, __FILE__, __LINE__); } size_ = 0; } void PushBack(const T* values, size_t len) { T* new_data = nullptr; AllocateCUDAMemory(&new_data, size_ + len, __FILE__, __LINE__); if (size_ > 0 && data_ != nullptr) { CopyFromCUDADeviceToCUDADevice(new_data, data_, size_, __FILE__, __LINE__); } CopyFromCUDADeviceToCUDADevice(new_data + size_, values, len, __FILE__, __LINE__); DeallocateCUDAMemory(&data_, __FILE__, __LINE__); size_ += len; data_ = new_data; } size_t Size() const { return size_; } ~CUDAVector() { DeallocateCUDAMemory(&data_, __FILE__, __LINE__); } std::vector ToHost() { std::vector host_vector(size_); if (size_ > 0 && data_ != nullptr) { CopyFromCUDADeviceToHost(host_vector.data(), data_, size_, __FILE__, __LINE__); } return host_vector; } T* RawData() const { return data_; } void SetValue(int value) { SetCUDAMemory(data_, value, size_, __FILE__, __LINE__); } const T* RawDataReadOnly() const { return data_; } T* MoveTo() { size_ = 0; T* old_data = data_; data_ = nullptr; return old_data; } template void MoveFrom(CUDAVector& other, size_t new_size) { data_ = reinterpret_cast(other.MoveTo()); size_ = new_size; } private: T* data_; size_t size_; }; template static __device__ T SafeLog(T x) { if (x > 0) { return std::log(x); } else { return -INFINITY; } } class NCCLInfo { public: NCCLInfo() { nccl_communicator_ = nullptr; nccl_gpu_rank_ = -1; local_gpu_rank_ = -1; gpu_device_id_ = -1; num_gpu_in_node_ = 0; global_num_data_ = 0; } virtual void SetNCCLInfo( ncclComm_t nccl_communicator, int nccl_gpu_rank, int local_gpu_rank, int gpu_device_id, data_size_t global_num_data) { nccl_communicator_ = nccl_communicator; nccl_gpu_rank_ = nccl_gpu_rank; local_gpu_rank_ = local_gpu_rank; gpu_device_id_ = gpu_device_id; global_num_data_ = global_num_data; } protected: ncclComm_t nccl_communicator_ = nullptr; int nccl_gpu_rank_ = -1; int local_gpu_rank_ = -1; int gpu_device_id_ = -1; int num_gpu_in_node_ = 0; data_size_t global_num_data_ = 0; }; cudaStream_t CUDAStreamCreate(); void CUDAStreamDestroy(cudaStream_t cuda_stream); void NCCLGroupStart(); void NCCLGroupEnd(); template void NCCLAllReduce(const T* send_buffer, T* recv_buffer, size_t count, ncclDataType_t datatype, ncclRedOp_t op, ncclComm_t comm, cudaStream_t stream) { NCCLCHECK(ncclAllReduce(reinterpret_cast(send_buffer), reinterpret_cast(recv_buffer), count, datatype, op, comm, stream)); } template void NCCLAllReduce(const T* send_buffer, T* recv_buffer, size_t count, ncclDataType_t datatype, ncclRedOp_t op, ncclComm_t comm) { cudaStream_t nccl_stream; CUDASUCCESS_OR_FATAL(cudaStreamCreate(&nccl_stream)); NCCLCHECK(ncclAllReduce(reinterpret_cast(send_buffer), reinterpret_cast(recv_buffer), count, datatype, op, comm, nccl_stream)); CUDASUCCESS_OR_FATAL(cudaStreamSynchronize(nccl_stream)); CUDASUCCESS_OR_FATAL(cudaStreamDestroy(nccl_stream)); } template T NCCLAllReduce(T send_value, ncclDataType_t datatype, ncclRedOp_t op, ncclComm_t comm, cudaStream_t stream) { CUDAVector send_buffer(1); CopyFromHostToCUDADevice(send_buffer.RawData(), &send_value, 1, __FILE__, __LINE__); NCCLAllReduce(send_buffer.RawDataReadOnly(), send_buffer.RawData(), 1, datatype, op, comm, stream); T recv_value = 0; CopyFromCUDADeviceToHost(&recv_value, send_buffer.RawDataReadOnly(), 1, __FILE__, __LINE__); return recv_value; } template T NCCLAllReduce(T send_value, ncclDataType_t datatype, ncclRedOp_t op, ncclComm_t comm) { CUDAVector send_buffer(1); CopyFromHostToCUDADevice(send_buffer.RawData(), &send_value, 1, __FILE__, __LINE__); NCCLAllReduce(send_buffer.RawDataReadOnly(), send_buffer.RawData(), 1, datatype, op, comm); T recv_value = 0; CopyFromCUDADeviceToHost(&recv_value, send_buffer.RawDataReadOnly(), 1, __FILE__, __LINE__); return recv_value; } } // namespace LightGBM #endif // USE_CUDA #endif // LIGHTGBM_CUDA_CUDA_UTILS_H_ ================================================ FILE: include/LightGBM/cuda/vector_cudahost.h ================================================ /*! * Copyright (c) 2020-2021 IBM Corporation, Microsoft Corporation. All rights reserved. * Copyright (c) 2020-2026 Microsoft Corporation. All rights reserved. * Copyright (c) 2020-2026 The LightGBM developers. All rights reserved. * Licensed under the MIT License. See LICENSE file in the project root for license information. * Modifications Copyright(C) 2023 Advanced Micro Devices, Inc. All rights reserved. */ #ifndef LIGHTGBM_INCLUDE_LIGHTGBM_CUDA_VECTOR_CUDAHOST_H_ #define LIGHTGBM_INCLUDE_LIGHTGBM_CUDA_VECTOR_CUDAHOST_H_ #include #ifdef USE_CUDA #ifndef USE_ROCM #include #include #endif // USE_ROCM #include #endif // USE_CUDA #include enum LGBM_Device { lgbm_device_cpu, lgbm_device_gpu, lgbm_device_cuda }; enum Use_Learner { use_cpu_learner, use_gpu_learner, use_cuda_learner }; namespace LightGBM { class LGBM_config_ { public: static int current_device; // Default: lgbm_device_cpu static int current_learner; // Default: use_cpu_learner }; template struct CHAllocator { typedef T value_type; CHAllocator() {} template CHAllocator(const CHAllocator& other); T* allocate(std::size_t n) { T* ptr; if (n == 0) return NULL; n = SIZE_ALIGNED(n); #ifdef USE_CUDA if (LGBM_config_::current_device == lgbm_device_cuda) { cudaError_t ret = cudaHostAlloc(reinterpret_cast(&ptr), n*sizeof(T), cudaHostAllocPortable); if (ret != cudaSuccess) { Log::Warning("Defaulting to malloc in CHAllocator!!!"); ptr = reinterpret_cast(_mm_malloc(n*sizeof(T), 16)); } } else { ptr = reinterpret_cast(_mm_malloc(n*sizeof(T), 16)); } #else ptr = reinterpret_cast(_mm_malloc(n*sizeof(T), 16)); #endif return ptr; } void deallocate(T* p, std::size_t n) { (void)n; // UNUSED if (p == NULL) return; #ifdef USE_CUDA if (LGBM_config_::current_device == lgbm_device_cuda) { cudaPointerAttributes attributes; CUDASUCCESS_OR_FATAL(cudaPointerGetAttributes(&attributes, p)); #if CUDA_VERSION >= 10000 || defined(USE_ROCM) if ((attributes.type == cudaMemoryTypeHost) && (attributes.devicePointer != NULL)) { CUDASUCCESS_OR_FATAL(cudaFreeHost(p)); } #else if ((attributes.memoryType == cudaMemoryTypeHost) && (attributes.devicePointer != NULL)) { CUDASUCCESS_OR_FATAL(cudaFreeHost(p)); } #endif } else { _mm_free(p); } #else _mm_free(p); #endif } }; template bool operator==(const CHAllocator&, const CHAllocator&); template bool operator!=(const CHAllocator&, const CHAllocator&); } // namespace LightGBM #endif // LIGHTGBM_INCLUDE_LIGHTGBM_CUDA_VECTOR_CUDAHOST_H_ ================================================ FILE: include/LightGBM/dataset.h ================================================ /*! * Copyright (c) 2016-2026 Microsoft Corporation. All rights reserved. * Copyright (c) 2016-2026 The LightGBM developers. All rights reserved. * Licensed under the MIT License. See LICENSE file in the project root for license information. */ #ifndef LIGHTGBM_INCLUDE_LIGHTGBM_DATASET_H_ #define LIGHTGBM_INCLUDE_LIGHTGBM_DATASET_H_ #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include namespace LightGBM { /*! \brief forward declaration */ class DatasetLoader; /*! * \brief This class is used to store some meta(non-feature) data for training data, * e.g. labels, weights, initial scores, query level information. * * Some details: * 1. Label, used for training. * 2. Weights, weighs of records, optional * 3. Query Boundaries, necessary for LambdaRank. * The documents of i-th query is in [ query_boundaries[i], query_boundaries[i+1] ) * 4. Query Weights, auto calculate by weights and query_boundaries(if both of them are existed) * the weight for i-th query is sum(query_boundaries[i] , .., query_boundaries[i+1]) / (query_boundaries[i + 1] - query_boundaries[i+1]) * 5. Initial score. optional. if existing, the model will boost from this score, otherwise will start from 0. */ class Metadata { public: /*! * \brief Null constructor */ Metadata(); /*! * \brief Initialization will load query level information, since it is need for sampling data * \param data_filename Filename of data */ void Init(const char* data_filename); /*! * \brief init as subset * \param metadata Filename of data * \param used_indices * \param num_used_indices */ void Init(const Metadata& metadata, const data_size_t* used_indices, data_size_t num_used_indices); /*! * \brief Initial with binary memory * \param memory Pointer to memory */ void LoadFromMemory(const void* memory); /*! \brief Destructor */ ~Metadata(); /*! * \brief Initial work, will allocate space for label, weight (if exists) and query (if exists) * \param num_data Number of training data * \param weight_idx Index of weight column, < 0 means doesn't exists * \param query_idx Index of query id column, < 0 means doesn't exists */ void Init(data_size_t num_data, int weight_idx, int query_idx); /*! * \brief Allocate space for label, weight (if exists), initial score (if exists) and query (if exists) * \param num_data Number of data * \param reference Reference metadata */ void InitByReference(data_size_t num_data, const Metadata* reference); /*! * \brief Allocate space for label, weight (if exists), initial score (if exists) and query (if exists) * \param num_data Number of data rows * \param has_weights Whether the metadata has weights * \param has_init_scores Whether the metadata has initial scores * \param has_queries Whether the metadata has queries * \param nclasses Number of classes for initial scores */ void Init(data_size_t num_data, int32_t has_weights, int32_t has_init_scores, int32_t has_queries, int32_t nclasses); /*! * \brief Partition label by used indices * \param used_indices Indices of local used */ void PartitionLabel(const std::vector& used_indices); /*! * \brief Partition meta data according to local used indices if need * \param num_all_data Number of total training data, including other machines' data on distributed learning * \param used_data_indices Indices of local used training data */ void CheckOrPartition(data_size_t num_all_data, const std::vector& used_data_indices); void SetLabel(const label_t* label, data_size_t len); void SetLabel(const ArrowChunkedArray& array); void SetWeights(const label_t* weights, data_size_t len); void SetWeights(const ArrowChunkedArray& array); void SetQuery(const data_size_t* query, data_size_t len); void SetQuery(const ArrowChunkedArray& array); void SetPosition(const data_size_t* position, data_size_t len); /*! * \brief Set initial scores * \param init_score Initial scores, this class will manage memory for init_score. */ void SetInitScore(const double* init_score, data_size_t len); void SetInitScore(const ArrowChunkedArray& array); /*! * \brief Save binary data to file * \param file File want to write */ void SaveBinaryToFile(BinaryWriter* writer) const; /*! * \brief Get sizes in byte of this object */ size_t SizesInByte() const; /*! * \brief Get pointer of label * \return Pointer of label */ inline const label_t* label() const { return label_.data(); } /*! * \brief Set label for one record * \param idx Index of this record * \param value Label value of this record */ inline void SetLabelAt(data_size_t idx, label_t value) { label_[idx] = value; } /*! * \brief Set Weight for one record * \param idx Index of this record * \param value Weight value of this record */ inline void SetWeightAt(data_size_t idx, label_t value) { weights_[idx] = value; } /*! * \brief Set initial scores for one record. Note that init_score might have multiple columns and is stored in column format. * \param idx Index of this record * \param values Initial score values for this record, one per class */ inline void SetInitScoreAt(data_size_t idx, const double* values) { const auto nclasses = num_init_score_classes(); const double* val_ptr = values; for (int i = idx; i < nclasses * num_data_; i += num_data_, ++val_ptr) { init_score_[i] = *val_ptr; } } /*! * \brief Set Query Id for one record * \param idx Index of this record * \param value Query Id value of this record */ inline void SetQueryAt(data_size_t idx, data_size_t value) { queries_[idx] = static_cast(value); } /*! \brief Load initial scores from file */ void LoadInitialScore(const std::string& data_filename); /*! * \brief Insert data from a given data to the current data at a specified index * \param start_index The target index to begin the insertion * \param count Number of records to insert * \param labels Pointer to label data * \param weights Pointer to weight data, or null * \param init_scores Pointer to init-score data, or null * \param queries Pointer to query data, or null */ void InsertAt(data_size_t start_index, data_size_t count, const float* labels, const float* weights, const double* init_scores, const int32_t* queries); /*! * \brief Perform any extra operations after all data has been loaded */ void FinishLoad(); /*! * \brief Get weights, if not exists, will return nullptr * \return Pointer of weights */ inline const label_t* weights() const { if (!weights_.empty()) { return weights_.data(); } else { return nullptr; } } /*! * \brief Get positions, if does not exist then return nullptr * \return Pointer of positions */ inline const data_size_t* positions() const { if (!positions_.empty()) { return positions_.data(); } else { return nullptr; } } /*! * \brief Get position IDs, if does not exist then return nullptr * \return Pointer of position IDs */ inline const std::string* position_ids() const { if (!position_ids_.empty()) { return position_ids_.data(); } else { return nullptr; } } /*! * \brief Get Number of different position IDs * \return number of different position IDs */ inline size_t num_position_ids() const { return position_ids_.size(); } /*! * \brief Get data boundaries on queries, if not exists, will return nullptr * we assume data will order by query, * the interval of [query_boundaris[i], query_boundaris[i+1]) * is the data indices for query i. * \return Pointer of data boundaries on queries */ inline const data_size_t* query_boundaries() const { if (!query_boundaries_.empty()) { return query_boundaries_.data(); } else { return nullptr; } } /*! * \brief Get Number of queries * \return Number of queries */ inline data_size_t num_queries() const { return num_queries_; } /*! * \brief Get weights for queries, if not exists, will return nullptr * \return Pointer of weights for queries */ inline const label_t* query_weights() const { if (!query_weights_.empty()) { return query_weights_.data(); } else { return nullptr; } } /*! * \brief Get initial scores, if not exists, will return nullptr * \return Pointer of initial scores */ inline const double* init_score() const { if (!init_score_.empty()) { return init_score_.data(); } else { return nullptr; } } /*! * \brief Get size of initial scores */ inline int64_t num_init_score() const { return num_init_score_; } /*! * \brief Get number of classes */ inline int32_t num_init_score_classes() const { if (num_data_ && num_init_score_) { return static_cast(num_init_score_ / num_data_); } return 1; } /*! \brief Disable copy */ Metadata& operator=(const Metadata&) = delete; /*! \brief Disable copy */ Metadata(const Metadata&) = delete; #ifdef USE_CUDA CUDAMetadata* cuda_metadata() const { return cuda_metadata_.get(); } void CreateCUDAMetadata(const int gpu_device_id); #endif // USE_CUDA private: /*! \brief Load wights from file */ void LoadWeights(); /*! \brief Load positions from file */ void LoadPositions(); /*! \brief Load query boundaries from file */ void LoadQueryBoundaries(); /*! \brief Calculate query weights from queries */ void CalculateQueryWeights(); /*! \brief Calculate query boundaries from queries */ void CalculateQueryBoundaries(); /*! \brief Insert labels at the given index */ void InsertLabels(const label_t* labels, data_size_t start_index, data_size_t len); /*! \brief Set labels from pointers to the first element and the end of an iterator. */ template void SetLabelsFromIterator(It first, It last); /*! \brief Insert weights at the given index */ void InsertWeights(const label_t* weights, data_size_t start_index, data_size_t len); /*! \brief Set weights from pointers to the first element and the end of an iterator. */ template void SetWeightsFromIterator(It first, It last); /*! \brief Insert initial scores at the given index */ void InsertInitScores(const double* init_scores, data_size_t start_index, data_size_t len, data_size_t source_size); /*! \brief Set init scores from pointers to the first element and the end of an iterator. */ template void SetInitScoresFromIterator(It first, It last); /*! \brief Insert queries at the given index */ void InsertQueries(const data_size_t* queries, data_size_t start_index, data_size_t len); /*! \brief Set queries from pointers to the first element and the end of an iterator. */ template void SetQueriesFromIterator(It first, It last); /*! \brief Filename of current data */ std::string data_filename_; /*! \brief Number of data */ data_size_t num_data_; /*! \brief Number of weights, used to check correct weight file */ data_size_t num_weights_; /*! \brief Number of positions, used to check correct position file */ data_size_t num_positions_; /*! \brief Label data */ std::vector label_; /*! \brief Weights data */ std::vector weights_; /*! \brief Positions data */ std::vector positions_; /*! \brief Position identifiers */ std::vector position_ids_; /*! \brief Query boundaries */ std::vector query_boundaries_; /*! \brief Query weights */ std::vector query_weights_; /*! \brief Number of queries */ data_size_t num_queries_; /*! \brief Number of Initial score, used to check correct weight file */ int64_t num_init_score_; /*! \brief Initial score */ std::vector init_score_; /*! \brief Queries data */ std::vector queries_; /*! \brief mutex for threading safe call */ std::mutex mutex_; bool weight_load_from_file_; bool position_load_from_file_; bool query_load_from_file_; bool init_score_load_from_file_; #ifdef USE_CUDA std::unique_ptr cuda_metadata_; #endif // USE_CUDA }; /*! \brief Interface for Parser */ class Parser { public: typedef const char* (*AtofFunc)(const char* p, double* out); /*! \brief Default constructor */ Parser() {} /*! * \brief Constructor for customized parser. The constructor accepts content not path because need to save/load the config along with model string */ explicit Parser(std::string) {} /*! \brief virtual destructor */ virtual ~Parser() {} /*! * \brief Parse one line with label * \param str One line record, string format, should end with '\0' * \param out_features Output columns, store in (column_idx, values) * \param out_label Label will store to this if exists */ virtual void ParseOneLine(const char* str, std::vector>* out_features, double* out_label) const = 0; virtual int NumFeatures() const = 0; /*! * \brief Create an object of parser, will auto choose the format depend on file * \param filename One Filename of data * \param header whether input file contains header * \param num_features Pass num_features of this data file if you know, <=0 means don't know * \param label_idx index of label column * \param precise_float_parser using precise floating point number parsing if true * \return Object of parser */ static Parser* CreateParser(const char* filename, bool header, int num_features, int label_idx, bool precise_float_parser); /*! * \brief Create an object of parser, could use customized parser, or auto choose the format depend on file * \param filename One Filename of data * \param header whether input file contains header * \param num_features Pass num_features of this data file if you know, <=0 means don't know * \param label_idx index of label column * \param precise_float_parser using precise floating point number parsing if true * \param parser_config_str Customized parser config content * \return Object of parser */ static Parser* CreateParser(const char* filename, bool header, int num_features, int label_idx, bool precise_float_parser, std::string parser_config_str); /*! * \brief Generate parser config str used for custom parser initialization, may save values of label id and header * \param filename One Filename of data * \param parser_config_filename One Filename of parser config * \param header whether input file contains header * \param label_idx index of label column * \return Parser config str */ static std::string GenerateParserConfigStr(const char* filename, const char* parser_config_filename, bool header, int label_idx); }; /*! \brief Interface for parser factory, used by customized parser */ class ParserFactory { private: ParserFactory() {} std::map> object_map_; public: ~ParserFactory() {} static ParserFactory& getInstance(); void Register(std::string class_name, std::function objc); Parser* getObject(std::string class_name, std::string config_str); }; /*! \brief Interface for parser reflector, used by customized parser */ class ParserReflector { public: ParserReflector(std::string class_name, std::function objc) { ParserFactory::getInstance().Register(class_name, objc); } virtual ~ParserReflector() {} }; /*! \brief The main class of data set, * which are used to training or validation */ class Dataset { public: friend DatasetLoader; LIGHTGBM_EXPORT Dataset(); LIGHTGBM_EXPORT Dataset(data_size_t num_data); void Construct( std::vector>* bin_mappers, int num_total_features, const std::vector>& forced_bins, int** sample_non_zero_indices, double** sample_values, const int* num_per_col, int num_sample_col, size_t total_sample_cnt, const Config& io_config); /*! \brief Destructor */ LIGHTGBM_EXPORT ~Dataset(); /*! * \brief Initialize from the given reference * \param num_data Number of data * \param reference Reference dataset */ LIGHTGBM_EXPORT void InitByReference(data_size_t num_data, const Dataset* reference) { metadata_.InitByReference(num_data, &reference->metadata()); } LIGHTGBM_EXPORT void InitStreaming(data_size_t num_data, int32_t has_weights, int32_t has_init_scores, int32_t has_queries, int32_t nclasses, int32_t nthreads, int32_t omp_max_threads) { // Initialize optional max thread count with either parameter or OMP setting if (omp_max_threads > 0) { omp_max_threads_ = omp_max_threads; } else if (omp_max_threads_ <= 0) { omp_max_threads_ = OMP_NUM_THREADS(); } metadata_.Init(num_data, has_weights, has_init_scores, has_queries, nclasses); for (int i = 0; i < num_groups_; ++i) { feature_groups_[i]->InitStreaming(nthreads, omp_max_threads_); } } LIGHTGBM_EXPORT bool CheckAlign(const Dataset& other) const { if (num_features_ != other.num_features_) { return false; } if (num_total_features_ != other.num_total_features_) { return false; } if (label_idx_ != other.label_idx_) { return false; } for (int i = 0; i < num_features_; ++i) { if (!FeatureBinMapper(i)->CheckAlign(*(other.FeatureBinMapper(i)))) { return false; } } return true; } inline void FinishOneRow(int tid, data_size_t row_idx, const std::vector& is_feature_added) { if (is_finish_load_) { return; } for (auto fidx : feature_need_push_zeros_) { if (is_feature_added[fidx]) { continue; } const int group = feature2group_[fidx]; const int sub_feature = feature2subfeature_[fidx]; feature_groups_[group]->PushData(tid, sub_feature, row_idx, 0.0f); } } inline void PushOneValue(int tid, data_size_t row_idx, size_t col_idx, double value) { if (this->is_finish_load_) return; auto feature_idx = this->used_feature_map_[col_idx]; if (feature_idx >= 0) { auto group = this->feature2group_[feature_idx]; auto sub_feature = this->feature2subfeature_[feature_idx]; this->feature_groups_[group]->PushData(tid, sub_feature, row_idx, value); if (this->has_raw_) { auto feat_ind = numeric_feature_map_[feature_idx]; if (feat_ind >= 0) { raw_data_[feat_ind][row_idx] = static_cast(value); } } } } inline void PushOneRow(int tid, data_size_t row_idx, const std::vector& feature_values) { for (size_t i = 0; i < feature_values.size() && i < static_cast(num_total_features_); ++i) { this->PushOneValue(tid, row_idx, i, feature_values[i]); } } inline void PushOneRow(int tid, data_size_t row_idx, const std::vector>& feature_values) { if (is_finish_load_) { return; } std::vector is_feature_added(num_features_, false); for (auto& inner_data : feature_values) { if (inner_data.first >= num_total_features_) { continue; } int feature_idx = used_feature_map_[inner_data.first]; if (feature_idx >= 0) { is_feature_added[feature_idx] = true; const int group = feature2group_[feature_idx]; const int sub_feature = feature2subfeature_[feature_idx]; feature_groups_[group]->PushData(tid, sub_feature, row_idx, inner_data.second); if (has_raw_) { int feat_ind = numeric_feature_map_[feature_idx]; if (feat_ind >= 0) { raw_data_[feat_ind][row_idx] = static_cast(inner_data.second); } } } } FinishOneRow(tid, row_idx, is_feature_added); } inline void PushOneData(int tid, data_size_t row_idx, int group, int feature_idx, int sub_feature, double value) { feature_groups_[group]->PushData(tid, sub_feature, row_idx, value); if (has_raw_) { int feat_ind = numeric_feature_map_[feature_idx]; if (feat_ind >= 0) { raw_data_[feat_ind][row_idx] = static_cast(value); } } } inline void InsertMetadataAt(data_size_t start_index, data_size_t count, const label_t* labels, const label_t* weights, const double* init_scores, const data_size_t* queries) { metadata_.InsertAt(start_index, count, labels, weights, init_scores, queries); } inline int RealFeatureIndex(int fidx) const { return real_feature_idx_[fidx]; } inline int InnerFeatureIndex(int col_idx) const { return used_feature_map_[col_idx]; } inline int Feature2Group(int feature_idx) const { return feature2group_[feature_idx]; } inline int Feature2SubFeature(int feature_idx) const { return feature2subfeature_[feature_idx]; } inline uint64_t GroupBinBoundary(int group_idx) const { return group_bin_boundaries_[group_idx]; } inline uint64_t NumTotalBin() const { return group_bin_boundaries_.back(); } inline std::vector ValidFeatureIndices() const { std::vector ret; for (int i = 0; i < num_total_features_; ++i) { if (used_feature_map_[i] >= 0) { ret.push_back(i); } } return ret; } void ReSize(data_size_t num_data); void CopySubrow(const Dataset* fullset, const data_size_t* used_indices, data_size_t num_used_indices, bool need_meta_data); 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); MultiValBin* GetMultiBinFromSparseFeatures(const std::vector& offsets) const; MultiValBin* GetMultiBinFromAllFeatures(const std::vector& offsets) const; template TrainingShareStates* GetShareStates( score_t* gradients, score_t* hessians, const std::vector& is_feature_used, bool is_constant_hessian, bool force_col_wise, bool force_row_wise, const int num_grad_quant_bins) const; LIGHTGBM_EXPORT void FinishLoad(); bool SetFieldFromArrow(const char* field_name, const ArrowChunkedArray& ca); LIGHTGBM_EXPORT bool SetFloatField(const char* field_name, const float* field_data, data_size_t num_element); LIGHTGBM_EXPORT bool SetDoubleField(const char* field_name, const double* field_data, data_size_t num_element); LIGHTGBM_EXPORT bool SetIntField(const char* field_name, const int* field_data, data_size_t num_element); LIGHTGBM_EXPORT bool GetFloatField(const char* field_name, data_size_t* out_len, const float** out_ptr); LIGHTGBM_EXPORT bool GetDoubleField(const char* field_name, data_size_t* out_len, const double** out_ptr); LIGHTGBM_EXPORT bool GetIntField(const char* field_name, data_size_t* out_len, const int** out_ptr); /*! * \brief Save current dataset into binary file, will save to "filename.bin" */ LIGHTGBM_EXPORT void SaveBinaryFile(const char* bin_filename); /*! * \brief Serialize the overall Dataset definition/schema to a binary buffer (i.e., without data) */ LIGHTGBM_EXPORT void SerializeReference(ByteBuffer* out); LIGHTGBM_EXPORT void DumpTextFile(const char* text_filename); LIGHTGBM_EXPORT void CopyFeatureMapperFrom(const Dataset* dataset); LIGHTGBM_EXPORT void CreateValid(const Dataset* dataset); void InitTrain(const std::vector& is_feature_used, TrainingShareStates* share_state) const; template void ConstructHistogramsInner(const std::vector& is_feature_used, const data_size_t* data_indices, data_size_t num_data, const score_t* gradients, const score_t* hessians, score_t* ordered_gradients, score_t* ordered_hessians, TrainingShareStates* share_state, hist_t* hist_data) const; template void ConstructHistogramsMultiVal(const data_size_t* data_indices, data_size_t num_data, const score_t* gradients, const score_t* hessians, TrainingShareStates* share_state, hist_t* hist_data) const; template inline void ConstructHistograms( const std::vector& is_feature_used, const data_size_t* data_indices, data_size_t num_data, const score_t* gradients, const score_t* hessians, score_t* ordered_gradients, score_t* ordered_hessians, TrainingShareStates* share_state, hist_t* hist_data) const { if (num_data <= 0) { return; } bool use_indices = data_indices != nullptr && (num_data < num_data_); if (share_state->is_constant_hessian) { if (use_indices) { ConstructHistogramsInner( is_feature_used, data_indices, num_data, gradients, hessians, ordered_gradients, ordered_hessians, share_state, hist_data); } else { ConstructHistogramsInner( is_feature_used, data_indices, num_data, gradients, hessians, ordered_gradients, ordered_hessians, share_state, hist_data); } } else { if (use_indices) { ConstructHistogramsInner( is_feature_used, data_indices, num_data, gradients, hessians, ordered_gradients, ordered_hessians, share_state, hist_data); } else { ConstructHistogramsInner( is_feature_used, data_indices, num_data, gradients, hessians, ordered_gradients, ordered_hessians, share_state, hist_data); } } } void FixHistogram(int feature_idx, double sum_gradient, double sum_hessian, hist_t* data) const; template void FixHistogramInt(int feature_idx, int64_t sum_gradient_and_hessian, hist_t* data) const; inline data_size_t Split(int feature, const uint32_t* threshold, int num_threshold, bool default_left, const data_size_t* data_indices, data_size_t cnt, data_size_t* lte_indices, data_size_t* gt_indices) const { const int group = feature2group_[feature]; const int sub_feature = feature2subfeature_[feature]; return feature_groups_[group]->Split( sub_feature, threshold, num_threshold, default_left, data_indices, cnt, lte_indices, gt_indices); } inline int SubFeatureBinOffset(int i) const { const int sub_feature = feature2subfeature_[i]; if (sub_feature == 0) { return 1; } else { return 0; } } inline int FeatureNumBin(int i) const { const int group = feature2group_[i]; const int sub_feature = feature2subfeature_[i]; return feature_groups_[group]->bin_mappers_[sub_feature]->num_bin(); } inline int FeatureGroupNumBin(int group) const { return feature_groups_[group]->num_total_bin_; } inline const BinMapper* FeatureBinMapper(int i) const { const int group = feature2group_[i]; const int sub_feature = feature2subfeature_[i]; return feature_groups_[group]->bin_mappers_[sub_feature].get(); } inline const Bin* FeatureGroupBin(int group) const { return feature_groups_[group]->bin_data_.get(); } inline BinIterator* FeatureIterator(int i) const { const int group = feature2group_[i]; const int sub_feature = feature2subfeature_[i]; return feature_groups_[group]->SubFeatureIterator(sub_feature); } inline BinIterator* FeatureGroupIterator(int group) const { return feature_groups_[group]->FeatureGroupIterator(); } inline bool IsMultiGroup(int i) const { return feature_groups_[i]->is_multi_val_; } inline size_t FeatureGroupSizesInByte(int group) const { return feature_groups_[group]->FeatureGroupSizesInByte(); } inline void* FeatureGroupData(int group) const { return feature_groups_[group]->FeatureGroupData(); } const void* GetColWiseData( const int feature_group_index, const int sub_feature_index, uint8_t* bit_type, bool* is_sparse, std::vector* bin_iterator, const int num_threads) const; const void* GetColWiseData( const int feature_group_index, const int sub_feature_index, uint8_t* bit_type, bool* is_sparse, BinIterator** bin_iterator) const; inline double RealThreshold(int i, uint32_t threshold) const { const int group = feature2group_[i]; const int sub_feature = feature2subfeature_[i]; return feature_groups_[group]->bin_mappers_[sub_feature]->BinToValue(threshold); } // given a real threshold, find the closest threshold bin inline uint32_t BinThreshold(int i, double threshold_double) const { const int group = feature2group_[i]; const int sub_feature = feature2subfeature_[i]; return feature_groups_[group]->bin_mappers_[sub_feature]->ValueToBin(threshold_double); } inline int MaxRealCatValue(int i) const { const int group = feature2group_[i]; const int sub_feature = feature2subfeature_[i]; return feature_groups_[group]->bin_mappers_[sub_feature]->MaxCatValue(); } /*! * \brief Get meta data pointer * \return Pointer of meta data */ inline const Metadata& metadata() const { return metadata_; } /*! \brief Get Number of used features */ inline int num_features() const { return num_features_; } /*! \brief Get number of numeric features */ inline int num_numeric_features() const { return num_numeric_features_; } /*! \brief Get Number of feature groups */ inline int num_feature_groups() const { return num_groups_;} /*! \brief Get Number of total features */ inline int num_total_features() const { return num_total_features_; } /*! \brief Get the index of label column */ inline int label_idx() const { return label_idx_; } /*! \brief Get names of current data set */ inline const std::vector& feature_names() const { return feature_names_; } /*! \brief Get content of parser config file */ inline const std::string parser_config_str() const { return parser_config_str_; } inline void set_feature_names(const std::vector& feature_names) { if (feature_names.size() != static_cast(num_total_features_)) { Log::Fatal("Size of feature_names error, should equal with total number of features"); } feature_names_ = std::vector(feature_names); std::unordered_set feature_name_set; // replace ' ' in feature_names with '_' bool spaceInFeatureName = false; for (auto& feature_name : feature_names_) { // check JSON if (!Common::CheckAllowedJSON(feature_name)) { Log::Fatal("Do not support special JSON characters in feature name."); } if (feature_name.find(' ') != std::string::npos) { spaceInFeatureName = true; std::replace(feature_name.begin(), feature_name.end(), ' ', '_'); } if (feature_name_set.count(feature_name) > 0) { Log::Fatal("Feature (%s) appears more than one time.", feature_name.c_str()); } feature_name_set.insert(feature_name); } if (spaceInFeatureName) { Log::Warning("Found whitespace in feature_names, replace with underlines"); } } inline std::vector feature_infos() const { std::vector bufs; for (int i = 0; i < num_total_features_; ++i) { int fidx = used_feature_map_[i]; if (fidx < 0) { bufs.push_back("none"); } else { const auto bin_mapper = FeatureBinMapper(fidx); bufs.push_back(bin_mapper->bin_info_string()); } } return bufs; } /*! \brief Get Number of data */ inline data_size_t num_data() const { return num_data_; } /*! \brief Get whether FinishLoad is automatically called when pushing last row. */ inline bool wait_for_manual_finish() const { return wait_for_manual_finish_; } /*! \brief Get the maximum number of OpenMP threads to allocate for. */ inline int omp_max_threads() const { return omp_max_threads_; } /*! \brief Set whether the Dataset is finished automatically when last row is pushed or with a manual * MarkFinished API call. Set to true for thread-safe streaming and/or if will be coalesced later. * FinishLoad should not be called on any Dataset that will be coalesced. */ inline void set_wait_for_manual_finish(bool value) { std::lock_guard lock(mutex_); wait_for_manual_finish_ = value; } /*! \brief Disable copy */ Dataset& operator=(const Dataset&) = delete; /*! \brief Disable copy */ Dataset(const Dataset&) = delete; void AddFeaturesFrom(Dataset* other); /*! \brief Get has_raw_ */ inline bool has_raw() const { return has_raw_; } /*! \brief Set has_raw_ */ inline void SetHasRaw(bool has_raw) { has_raw_ = has_raw; } /*! \brief Resize raw_data_ */ inline void ResizeRaw(int num_rows) { if (static_cast(raw_data_.size()) > num_numeric_features_) { raw_data_.resize(num_numeric_features_); } for (size_t i = 0; i < raw_data_.size(); ++i) { raw_data_[i].resize(num_rows); } int curr_size = static_cast(raw_data_.size()); for (int i = curr_size; i < num_numeric_features_; ++i) { raw_data_.push_back(std::vector(num_rows, 0)); } } /*! \brief Get pointer to raw_data_ feature */ inline const float* raw_index(int feat_ind) const { return raw_data_[numeric_feature_map_[feat_ind]].data(); } inline uint32_t feature_max_bin(const int inner_feature_index) const { const int feature_group_index = Feature2Group(inner_feature_index); const int sub_feature_index = feature2subfeature_[inner_feature_index]; return feature_groups_[feature_group_index]->feature_max_bin(sub_feature_index); } inline uint32_t feature_min_bin(const int inner_feature_index) const { const int feature_group_index = Feature2Group(inner_feature_index); const int sub_feature_index = feature2subfeature_[inner_feature_index]; return feature_groups_[feature_group_index]->feature_min_bin(sub_feature_index); } #ifdef USE_CUDA const CUDAColumnData* cuda_column_data() const { return cuda_column_data_.get(); } #endif // USE_CUDA private: void SerializeHeader(BinaryWriter* serializer); size_t GetSerializedHeaderSize(); void CreateCUDAColumnData(); void CopySubrowHostPart(const Dataset* fullset, const data_size_t* used_indices, data_size_t num_used_indices, bool need_meta_data); std::string data_filename_; /*! \brief Store used features */ std::vector> feature_groups_; /*! \brief Mapper from real feature index to used index*/ std::vector used_feature_map_; /*! \brief Number of used features*/ int num_features_; /*! \brief Number of total features*/ int num_total_features_; /*! \brief Number of total data*/ data_size_t num_data_; /*! \brief Store some label level data*/ Metadata metadata_; /*! \brief index of label column */ int label_idx_ = 0; /*! \brief store feature names */ std::vector feature_names_; /*! \brief serialized versions */ static const int kSerializedReferenceVersionLength; static const char* serialized_reference_version; static const char* binary_file_token; static const char* binary_serialized_reference_token; int num_groups_; std::vector real_feature_idx_; std::vector feature2group_; std::vector feature2subfeature_; std::vector group_bin_boundaries_; std::vector group_feature_start_; std::vector group_feature_cnt_; bool is_finish_load_; int max_bin_; std::vector max_bin_by_feature_; std::vector> forced_bin_bounds_; int bin_construct_sample_cnt_; int min_data_in_bin_; bool use_missing_; bool zero_as_missing_; std::vector feature_need_push_zeros_; std::vector> raw_data_; bool wait_for_manual_finish_; int omp_max_threads_ = -1; bool has_raw_; /*! map feature (inner index) to its index in the list of numeric (non-categorical) features */ std::vector numeric_feature_map_; int num_numeric_features_; std::string device_type_; int gpu_device_id_; /*! \brief mutex for threading safe call */ std::mutex mutex_; #ifdef USE_CUDA std::unique_ptr cuda_column_data_; #endif // USE_CUDA std::string parser_config_str_; }; } // namespace LightGBM #endif // LIGHTGBM_INCLUDE_LIGHTGBM_DATASET_H_ ================================================ FILE: include/LightGBM/dataset_loader.h ================================================ /*! * Copyright (c) 2016-2026 Microsoft Corporation. All rights reserved. * Copyright (c) 2016-2026 The LightGBM developers. All rights reserved. * Licensed under the MIT License. See LICENSE file in the project root for license information. */ #ifndef LIGHTGBM_INCLUDE_LIGHTGBM_DATASET_LOADER_H_ #define LIGHTGBM_INCLUDE_LIGHTGBM_DATASET_LOADER_H_ #include #include #include #include #include namespace LightGBM { class DatasetLoader { public: LIGHTGBM_EXPORT DatasetLoader(const Config& io_config, const PredictFunction& predict_fun, int num_class, const char* filename); LIGHTGBM_EXPORT ~DatasetLoader(); LIGHTGBM_EXPORT Dataset* LoadFromFile(const char* filename, int rank, int num_machines); LIGHTGBM_EXPORT Dataset* LoadFromFile(const char* filename) { return LoadFromFile(filename, 0, 1); } LIGHTGBM_EXPORT Dataset* LoadFromFileAlignWithOtherDataset(const char* filename, const Dataset* train_data); LIGHTGBM_EXPORT Dataset* LoadFromSerializedReference(const char* buffer, size_t buffer_size, data_size_t num_data, int32_t num_classes); LIGHTGBM_EXPORT Dataset* ConstructFromSampleData(double** sample_values, int** sample_indices, int num_col, const int* num_per_col, size_t total_sample_size, data_size_t num_local_data, int64_t num_dist_data); /*! \brief Disable copy */ DatasetLoader& operator=(const DatasetLoader&) = delete; /*! \brief Disable copy */ DatasetLoader(const DatasetLoader&) = delete; static std::vector> GetForcedBins(std::string forced_bins_path, int num_total_features, const std::unordered_set& categorical_features); private: void LoadHeaderFromMemory(Dataset* dataset, const char* buffer); Dataset* LoadFromBinFile(const char* data_filename, const char* bin_filename, int rank, int num_machines, int* num_global_data, std::vector* used_data_indices); void SetHeader(const char* filename); void CheckDataset(const Dataset* dataset, bool is_load_from_binary); std::vector LoadTextDataToMemory(const char* filename, const Metadata& metadata, int rank, int num_machines, int* num_global_data, std::vector* used_data_indices); std::vector SampleTextDataFromMemory(const std::vector& data); std::vector SampleTextDataFromFile(const char* filename, const Metadata& metadata, int rank, int num_machines, int* num_global_data, std::vector* used_data_indices); void ConstructBinMappersFromTextData(int rank, int num_machines, const std::vector& sample_data, const Parser* parser, Dataset* dataset); /*! \brief Extract local features from memory */ void ExtractFeaturesFromMemory(std::vector* text_data, const Parser* parser, Dataset* dataset); /*! \brief Extract local features from file */ void ExtractFeaturesFromFile(const char* filename, const Parser* parser, const std::vector& used_data_indices, Dataset* dataset); /*! \brief Check can load from binary file */ std::string CheckCanLoadFromBin(const char* filename); /*! \brief Check the number of bins for categorical features. * The number of bins for categorical features may exceed the configured maximum value. * Log warnings when such cases happen. * * \param bin_mappers the bin_mappers of all features * \param max_bin max_bin from Config * \param max_bin_by_feature max_bin_by_feature from Config */ void CheckCategoricalFeatureNumBin(const std::vector>& bin_mappers, const int max_bin, const std::vector& max_bin_by_feature) const; const Config& config_; /*! \brief Random generator*/ Random random_; /*! \brief prediction function for initial model */ const PredictFunction predict_fun_; /*! \brief number of classes */ int num_class_; /*! \brief index of label column */ int label_idx_; /*! \brief index of weight column */ int weight_idx_; /*! \brief index of group column */ int group_idx_; /*! \brief Mapper from real feature index to used index*/ std::unordered_set ignore_features_; /*! \brief store feature names */ std::vector feature_names_; /*! \brief Mapper from real feature index to used index*/ std::unordered_set categorical_features_; /*! \brief Whether to store raw feature values */ bool store_raw_; }; } // namespace LightGBM #endif // LIGHTGBM_INCLUDE_LIGHTGBM_DATASET_LOADER_H_ ================================================ FILE: include/LightGBM/export.h ================================================ /*! * Copyright (c) 2017-2026 Microsoft Corporation. All rights reserved. * Copyright (c) 2017-2026 The LightGBM developers. All rights reserved. * Licensed under the MIT License. See LICENSE file in the project root for license information. */ #ifndef LIGHTGBM_INCLUDE_LIGHTGBM_EXPORT_H_ #define LIGHTGBM_INCLUDE_LIGHTGBM_EXPORT_H_ /** Macros for exporting symbols in MSVC/GCC/CLANG **/ #ifdef __cplusplus #define LIGHTGBM_EXTERN_C extern "C" #else #define LIGHTGBM_EXTERN_C #endif #ifdef _MSC_VER #define LIGHTGBM_EXPORT __declspec(dllexport) #define LIGHTGBM_C_EXPORT LIGHTGBM_EXTERN_C __declspec(dllexport) #else #define LIGHTGBM_EXPORT #define LIGHTGBM_C_EXPORT LIGHTGBM_EXTERN_C #endif #endif // LIGHTGBM_INCLUDE_LIGHTGBM_EXPORT_H_ ================================================ FILE: include/LightGBM/feature_group.h ================================================ /*! * Copyright (c) 2017-2026 Microsoft Corporation. All rights reserved. * Copyright (c) 2017-2026 The LightGBM developers. All rights reserved. * Licensed under the MIT License. See LICENSE file in the project root for * license information. */ #ifndef LIGHTGBM_INCLUDE_LIGHTGBM_FEATURE_GROUP_H_ #define LIGHTGBM_INCLUDE_LIGHTGBM_FEATURE_GROUP_H_ #include #include #include #include #include #include #include namespace LightGBM { class Dataset; class DatasetLoader; struct TrainingShareStates; class MultiValBinWrapper; /*! \brief Using to store data and providing some operations on one feature * group*/ class FeatureGroup { public: friend Dataset; friend DatasetLoader; friend TrainingShareStates; friend MultiValBinWrapper; /*! * \brief Constructor * \param num_feature number of features of this group * \param bin_mappers Bin mapper for features * \param num_data Total number of data * \param is_enable_sparse True if enable sparse feature */ FeatureGroup(int num_feature, int8_t is_multi_val, std::vector>* bin_mappers, data_size_t num_data, int group_id) : num_feature_(num_feature), is_multi_val_(is_multi_val > 0), is_sparse_(false) { CHECK_EQ(static_cast(bin_mappers->size()), num_feature); auto& ref_bin_mappers = *bin_mappers; double sum_sparse_rate = 0.0f; for (int i = 0; i < num_feature_; ++i) { bin_mappers_.emplace_back(ref_bin_mappers[i].release()); sum_sparse_rate += bin_mappers_.back()->sparse_rate(); } sum_sparse_rate /= num_feature_; int offset = 1; is_dense_multi_val_ = false; if (sum_sparse_rate < MultiValBin::multi_val_bin_sparse_threshold && is_multi_val_) { // use dense multi val bin offset = 0; is_dense_multi_val_ = true; } // use bin at zero to store most_freq_bin only when not using dense multi val bin num_total_bin_ = offset; // however, we should force to leave one bin, if dense multi val bin is the first bin // and its first feature has most freq bin > 0 if (group_id == 0 && num_feature_ > 0 && is_dense_multi_val_ && bin_mappers_[0]->GetMostFreqBin() > 0) { num_total_bin_ = 1; } bin_offsets_.emplace_back(num_total_bin_); for (int i = 0; i < num_feature_; ++i) { auto num_bin = bin_mappers_[i]->num_bin(); if (bin_mappers_[i]->GetMostFreqBin() == 0) { num_bin -= offset; } num_total_bin_ += num_bin; bin_offsets_.emplace_back(num_total_bin_); } CreateBinData(num_data, is_multi_val_, true, false); } FeatureGroup(const FeatureGroup& other, int num_data) { num_feature_ = other.num_feature_; is_multi_val_ = other.is_multi_val_; is_dense_multi_val_ = other.is_dense_multi_val_; is_sparse_ = other.is_sparse_; num_total_bin_ = other.num_total_bin_; bin_offsets_ = other.bin_offsets_; bin_mappers_.reserve(other.bin_mappers_.size()); for (auto& bin_mapper : other.bin_mappers_) { bin_mappers_.emplace_back(new BinMapper(*bin_mapper)); } CreateBinData(num_data, is_multi_val_, !is_sparse_, is_sparse_); } FeatureGroup(std::vector>* bin_mappers, data_size_t num_data) : num_feature_(1), is_multi_val_(false) { CHECK_EQ(static_cast(bin_mappers->size()), 1); // use bin at zero to store default_bin num_total_bin_ = 1; is_dense_multi_val_ = false; bin_offsets_.emplace_back(num_total_bin_); auto& ref_bin_mappers = *bin_mappers; for (int i = 0; i < num_feature_; ++i) { bin_mappers_.emplace_back(ref_bin_mappers[i].release()); auto num_bin = bin_mappers_[i]->num_bin(); if (bin_mappers_[i]->GetMostFreqBin() == 0) { num_bin -= 1; } num_total_bin_ += num_bin; bin_offsets_.emplace_back(num_total_bin_); } CreateBinData(num_data, false, false, false); } /*! * \brief Constructor from memory when data is present * \param memory Pointer of memory * \param num_all_data Number of global data * \param local_used_indices Local used indices, empty means using all data * \param group_id Id of group */ FeatureGroup(const void* memory, data_size_t num_all_data, const std::vector& local_used_indices, int group_id) { // Load the definition schema first const char* memory_ptr = LoadDefinitionFromMemory(memory, group_id); // Allocate memory for the data data_size_t num_data = num_all_data; if (!local_used_indices.empty()) { num_data = static_cast(local_used_indices.size()); } AllocateBins(num_data); // Now load the actual data if (is_multi_val_) { for (int i = 0; i < num_feature_; ++i) { multi_bin_data_[i]->LoadFromMemory(memory_ptr, local_used_indices); memory_ptr += multi_bin_data_[i]->SizesInByte(); } } else { bin_data_->LoadFromMemory(memory_ptr, local_used_indices); } } /*! * \brief Constructor from definition in memory (without data) * \param memory Pointer of memory * \param local_used_indices Local used indices, empty means using all data */ FeatureGroup(const void* memory, data_size_t num_data, int group_id) { LoadDefinitionFromMemory(memory, group_id); AllocateBins(num_data); } /*! \brief Destructor */ ~FeatureGroup() {} /*! * \brief Load the overall definition of the feature group from binary serialized data * \param memory Pointer of memory * \param group_id Id of group */ const char* LoadDefinitionFromMemory(const void* memory, int group_id) { const char* memory_ptr = reinterpret_cast(memory); // get is_sparse is_multi_val_ = *(reinterpret_cast(memory_ptr)); memory_ptr += VirtualFileWriter::AlignedSize(sizeof(is_multi_val_)); is_dense_multi_val_ = *(reinterpret_cast(memory_ptr)); memory_ptr += VirtualFileWriter::AlignedSize(sizeof(is_dense_multi_val_)); is_sparse_ = *(reinterpret_cast(memory_ptr)); memory_ptr += VirtualFileWriter::AlignedSize(sizeof(is_sparse_)); num_feature_ = *(reinterpret_cast(memory_ptr)); memory_ptr += VirtualFileWriter::AlignedSize(sizeof(num_feature_)); // get bin mapper(s) bin_mappers_.clear(); for (int i = 0; i < num_feature_; ++i) { bin_mappers_.emplace_back(new BinMapper(memory_ptr)); memory_ptr += bin_mappers_[i]->SizesInByte(); } bin_offsets_.clear(); int offset = 1; if (is_dense_multi_val_) { offset = 0; } // use bin at zero to store most_freq_bin only when not using dense multi val bin num_total_bin_ = offset; // however, we should force to leave one bin, if dense multi val bin is the first bin // and its first feature has most freq bin > 0 if (group_id == 0 && num_feature_ > 0 && is_dense_multi_val_ && bin_mappers_[0]->GetMostFreqBin() > 0) { num_total_bin_ = 1; } bin_offsets_.emplace_back(num_total_bin_); for (int i = 0; i < num_feature_; ++i) { auto num_bin = bin_mappers_[i]->num_bin(); if (bin_mappers_[i]->GetMostFreqBin() == 0) { num_bin -= offset; } num_total_bin_ += num_bin; bin_offsets_.emplace_back(num_total_bin_); } return memory_ptr; } /*! * \brief Allocate the bins * \param num_all_data Number of global data */ inline void AllocateBins(data_size_t num_data) { if (is_multi_val_) { for (int i = 0; i < num_feature_; ++i) { int addi = bin_mappers_[i]->GetMostFreqBin() == 0 ? 0 : 1; if (bin_mappers_[i]->sparse_rate() >= kSparseThreshold) { multi_bin_data_.emplace_back(Bin::CreateSparseBin(num_data, bin_mappers_[i]->num_bin() + addi)); } else { multi_bin_data_.emplace_back(Bin::CreateDenseBin(num_data, bin_mappers_[i]->num_bin() + addi)); } } } else { if (is_sparse_) { bin_data_.reset(Bin::CreateSparseBin(num_data, num_total_bin_)); } else { bin_data_.reset(Bin::CreateDenseBin(num_data, num_total_bin_)); } } } /*! * \brief Initialize for pushing in a streaming fashion. By default, no action needed. * \param num_thread The number of external threads that will be calling the push APIs * \param omp_max_threads The maximum number of OpenMP threads to allocate for */ void InitStreaming(int32_t num_thread, int32_t omp_max_threads) { if (is_multi_val_) { for (int i = 0; i < num_feature_; ++i) { multi_bin_data_[i]->InitStreaming(num_thread, omp_max_threads); } } else { bin_data_->InitStreaming(num_thread, omp_max_threads); } } /*! * \brief Push one record, will auto convert to bin and push to bin data * \param tid Thread id * \param sub_feature_idx Index of the subfeature * \param line_idx Index of record * \param value feature value of record */ inline void PushData(int tid, int sub_feature_idx, data_size_t line_idx, double value) { uint32_t bin = bin_mappers_[sub_feature_idx]->ValueToBin(value); if (bin == bin_mappers_[sub_feature_idx]->GetMostFreqBin()) { return; } if (bin_mappers_[sub_feature_idx]->GetMostFreqBin() == 0) { bin -= 1; } if (is_multi_val_) { multi_bin_data_[sub_feature_idx]->Push(tid, line_idx, bin + 1); } else { bin += bin_offsets_[sub_feature_idx]; bin_data_->Push(tid, line_idx, bin); } } void ReSize(int num_data) { if (!is_multi_val_) { bin_data_->ReSize(num_data); } else { for (int i = 0; i < num_feature_; ++i) { multi_bin_data_[i]->ReSize(num_data); } } } inline void CopySubrow(const FeatureGroup* full_feature, const data_size_t* used_indices, data_size_t num_used_indices) { if (!is_multi_val_) { bin_data_->CopySubrow(full_feature->bin_data_.get(), used_indices, num_used_indices); } else { for (int i = 0; i < num_feature_; ++i) { multi_bin_data_[i]->CopySubrow(full_feature->multi_bin_data_[i].get(), used_indices, num_used_indices); } } } inline void CopySubrowByCol(const FeatureGroup* full_feature, const data_size_t* used_indices, data_size_t num_used_indices, int fidx) { if (!is_multi_val_) { bin_data_->CopySubrow(full_feature->bin_data_.get(), used_indices, num_used_indices); } else { multi_bin_data_[fidx]->CopySubrow(full_feature->multi_bin_data_[fidx].get(), used_indices, num_used_indices); } } void AddFeaturesFrom(const FeatureGroup* other, int group_id) { CHECK(is_multi_val_); CHECK(other->is_multi_val_); // every time when new features are added, we need to reconsider sparse or dense double sum_sparse_rate = 0.0f; for (int i = 0; i < num_feature_; ++i) { sum_sparse_rate += bin_mappers_[i]->sparse_rate(); } for (int i = 0; i < other->num_feature_; ++i) { sum_sparse_rate += other->bin_mappers_[i]->sparse_rate(); } sum_sparse_rate /= (num_feature_ + other->num_feature_); int offset = 1; is_dense_multi_val_ = false; if (sum_sparse_rate < MultiValBin::multi_val_bin_sparse_threshold && is_multi_val_) { // use dense multi val bin offset = 0; is_dense_multi_val_ = true; } bin_offsets_.clear(); num_total_bin_ = offset; // however, we should force to leave one bin, if dense multi val bin is the first bin // and its first feature has most freq bin > 0 if (group_id == 0 && num_feature_ > 0 && is_dense_multi_val_ && bin_mappers_[0]->GetMostFreqBin() > 0) { num_total_bin_ = 1; } bin_offsets_.emplace_back(num_total_bin_); for (int i = 0; i < num_feature_; ++i) { auto num_bin = bin_mappers_[i]->num_bin(); if (bin_mappers_[i]->GetMostFreqBin() == 0) { num_bin -= offset; } num_total_bin_ += num_bin; bin_offsets_.emplace_back(num_total_bin_); } for (int i = 0; i < other->num_feature_; ++i) { const auto& other_bin_mapper = other->bin_mappers_[i]; bin_mappers_.emplace_back(new BinMapper(*other_bin_mapper)); auto num_bin = other_bin_mapper->num_bin(); if (other_bin_mapper->GetMostFreqBin() == 0) { num_bin -= offset; } num_total_bin_ += num_bin; bin_offsets_.emplace_back(num_total_bin_); multi_bin_data_.emplace_back(other->multi_bin_data_[i]->Clone()); } num_feature_ += other->num_feature_; } inline BinIterator* SubFeatureIterator(int sub_feature) { uint32_t most_freq_bin = bin_mappers_[sub_feature]->GetMostFreqBin(); if (!is_multi_val_) { uint32_t min_bin = bin_offsets_[sub_feature]; uint32_t max_bin = bin_offsets_[sub_feature + 1] - 1; return bin_data_->GetIterator(min_bin, max_bin, most_freq_bin); } else { int addi = bin_mappers_[sub_feature]->GetMostFreqBin() == 0 ? 0 : 1; uint32_t min_bin = 1; uint32_t max_bin = bin_mappers_[sub_feature]->num_bin() - 1 + addi; return multi_bin_data_[sub_feature]->GetIterator(min_bin, max_bin, most_freq_bin); } } inline void FinishLoad() { if (is_multi_val_) { OMP_INIT_EX(); #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(guided) for (int i = 0; i < num_feature_; ++i) { OMP_LOOP_EX_BEGIN(); multi_bin_data_[i]->FinishLoad(); OMP_LOOP_EX_END(); } OMP_THROW_EX(); } else { bin_data_->FinishLoad(); } } inline BinIterator* FeatureGroupIterator() { if (is_multi_val_) { return nullptr; } uint32_t min_bin = bin_offsets_[0]; uint32_t max_bin = bin_offsets_.back() - 1; uint32_t most_freq_bin = 0; return bin_data_->GetIterator(min_bin, max_bin, most_freq_bin); } inline size_t FeatureGroupSizesInByte() { return bin_data_->SizesInByte(); } inline void* FeatureGroupData() { if (is_multi_val_) { return nullptr; } return bin_data_->get_data(); } inline data_size_t Split(int sub_feature, const uint32_t* threshold, int num_threshold, bool default_left, const data_size_t* data_indices, data_size_t cnt, data_size_t* lte_indices, data_size_t* gt_indices) const { uint32_t default_bin = bin_mappers_[sub_feature]->GetDefaultBin(); uint32_t most_freq_bin = bin_mappers_[sub_feature]->GetMostFreqBin(); if (!is_multi_val_) { uint32_t min_bin = bin_offsets_[sub_feature]; uint32_t max_bin = bin_offsets_[sub_feature + 1] - 1; if (bin_mappers_[sub_feature]->bin_type() == BinType::NumericalBin) { auto missing_type = bin_mappers_[sub_feature]->missing_type(); if (num_feature_ == 1) { return bin_data_->Split(max_bin, default_bin, most_freq_bin, missing_type, default_left, *threshold, data_indices, cnt, lte_indices, gt_indices); } else { return bin_data_->Split(min_bin, max_bin, default_bin, most_freq_bin, missing_type, default_left, *threshold, data_indices, cnt, lte_indices, gt_indices); } } else { if (num_feature_ == 1) { return bin_data_->SplitCategorical(max_bin, most_freq_bin, threshold, num_threshold, data_indices, cnt, lte_indices, gt_indices); } else { return bin_data_->SplitCategorical( min_bin, max_bin, most_freq_bin, threshold, num_threshold, data_indices, cnt, lte_indices, gt_indices); } } } else { int addi = bin_mappers_[sub_feature]->GetMostFreqBin() == 0 ? 0 : 1; uint32_t max_bin = bin_mappers_[sub_feature]->num_bin() - 1 + addi; if (bin_mappers_[sub_feature]->bin_type() == BinType::NumericalBin) { auto missing_type = bin_mappers_[sub_feature]->missing_type(); return multi_bin_data_[sub_feature]->Split( max_bin, default_bin, most_freq_bin, missing_type, default_left, *threshold, data_indices, cnt, lte_indices, gt_indices); } else { return multi_bin_data_[sub_feature]->SplitCategorical( max_bin, most_freq_bin, threshold, num_threshold, data_indices, cnt, lte_indices, gt_indices); } } } /*! * \brief From bin to feature value * \param bin * \return FeatureGroup value of this bin */ inline double BinToValue(int sub_feature_idx, uint32_t bin) const { return bin_mappers_[sub_feature_idx]->BinToValue(bin); } /*! * \brief Write to binary stream * \param writer Writer * \param include_data Whether to write data (true) or just header information (false) */ void SerializeToBinary(BinaryWriter* writer, bool include_data = true) const { writer->AlignedWrite(&is_multi_val_, sizeof(is_multi_val_)); writer->AlignedWrite(&is_dense_multi_val_, sizeof(is_dense_multi_val_)); writer->AlignedWrite(&is_sparse_, sizeof(is_sparse_)); writer->AlignedWrite(&num_feature_, sizeof(num_feature_)); for (int i = 0; i < num_feature_; ++i) { bin_mappers_[i]->SaveBinaryToFile(writer); } if (include_data) { if (is_multi_val_) { for (int i = 0; i < num_feature_; ++i) { multi_bin_data_[i]->SaveBinaryToFile(writer); } } else { bin_data_->SaveBinaryToFile(writer); } } } /*! * \brief Get sizes in byte of this object */ size_t SizesInByte(bool include_data = true) const { size_t ret = VirtualFileWriter::AlignedSize(sizeof(is_multi_val_)) + VirtualFileWriter::AlignedSize(sizeof(is_dense_multi_val_)) + VirtualFileWriter::AlignedSize(sizeof(is_sparse_)) + VirtualFileWriter::AlignedSize(sizeof(num_feature_)); for (int i = 0; i < num_feature_; ++i) { ret += bin_mappers_[i]->SizesInByte(); } if (include_data) { if (!is_multi_val_) { ret += bin_data_->SizesInByte(); } else { for (int i = 0; i < num_feature_; ++i) { ret += multi_bin_data_[i]->SizesInByte(); } } } return ret; } /*! \brief Disable copy */ FeatureGroup& operator=(const FeatureGroup&) = delete; /*! \brief Deep copy */ FeatureGroup(const FeatureGroup& other, bool should_handle_dense_mv, int group_id) { num_feature_ = other.num_feature_; is_multi_val_ = other.is_multi_val_; is_dense_multi_val_ = other.is_dense_multi_val_; is_sparse_ = other.is_sparse_; num_total_bin_ = other.num_total_bin_; bin_offsets_ = other.bin_offsets_; bin_mappers_.reserve(other.bin_mappers_.size()); for (auto& bin_mapper : other.bin_mappers_) { bin_mappers_.emplace_back(new BinMapper(*bin_mapper)); } if (!is_multi_val_) { bin_data_.reset(other.bin_data_->Clone()); } else { multi_bin_data_.clear(); for (int i = 0; i < num_feature_; ++i) { multi_bin_data_.emplace_back(other.multi_bin_data_[i]->Clone()); } } if (should_handle_dense_mv && is_dense_multi_val_ && group_id > 0) { // this feature group was the first feature group, but now no longer is, // so we need to eliminate its special empty bin for multi val dense bin if (bin_mappers_[0]->GetMostFreqBin() > 0 && bin_offsets_[0] == 1) { for (size_t i = 0; i < bin_offsets_.size(); ++i) { bin_offsets_[i] -= 1; } num_total_bin_ -= 1; } } } const void* GetColWiseData(const int sub_feature_index, uint8_t* bit_type, bool* is_sparse, std::vector* bin_iterator, const int num_threads) const { if (sub_feature_index >= 0) { CHECK(is_multi_val_); return multi_bin_data_[sub_feature_index]->GetColWiseData(bit_type, is_sparse, bin_iterator, num_threads); } else { CHECK(!is_multi_val_); return bin_data_->GetColWiseData(bit_type, is_sparse, bin_iterator, num_threads); } } const void* GetColWiseData(const int sub_feature_index, uint8_t* bit_type, bool* is_sparse, BinIterator** bin_iterator) const { if (sub_feature_index >= 0) { CHECK(is_multi_val_); return multi_bin_data_[sub_feature_index]->GetColWiseData(bit_type, is_sparse, bin_iterator); } else { CHECK(!is_multi_val_); return bin_data_->GetColWiseData(bit_type, is_sparse, bin_iterator); } } uint32_t feature_max_bin(const int sub_feature_index) { if (!is_multi_val_) { return bin_offsets_[sub_feature_index + 1] - 1; } else { int addi = bin_mappers_[sub_feature_index]->GetMostFreqBin() == 0 ? 0 : 1; return bin_mappers_[sub_feature_index]->num_bin() - 1 + addi; } } uint32_t feature_min_bin(const int sub_feature_index) { if (!is_multi_val_) { return bin_offsets_[sub_feature_index]; } else { return 1; } } private: void CreateBinData(int num_data, bool is_multi_val, bool force_dense, bool force_sparse) { if (is_multi_val) { multi_bin_data_.clear(); for (int i = 0; i < num_feature_; ++i) { int addi = bin_mappers_[i]->GetMostFreqBin() == 0 ? 0 : 1; if (bin_mappers_[i]->sparse_rate() >= kSparseThreshold) { multi_bin_data_.emplace_back(Bin::CreateSparseBin( num_data, bin_mappers_[i]->num_bin() + addi)); } else { multi_bin_data_.emplace_back( Bin::CreateDenseBin(num_data, bin_mappers_[i]->num_bin() + addi)); } } is_multi_val_ = true; } else { if (force_sparse || (!force_dense && num_feature_ == 1 && bin_mappers_[0]->sparse_rate() >= kSparseThreshold)) { is_sparse_ = true; bin_data_.reset(Bin::CreateSparseBin(num_data, num_total_bin_)); } else { is_sparse_ = false; bin_data_.reset(Bin::CreateDenseBin(num_data, num_total_bin_)); } is_multi_val_ = false; } } /*! \brief Number of features */ int num_feature_; /*! \brief Bin mapper for sub features */ std::vector> bin_mappers_; /*! \brief Bin offsets for sub features */ std::vector bin_offsets_; /*! \brief Bin data of this feature */ std::unique_ptr bin_data_; std::vector> multi_bin_data_; /*! \brief True if this feature is sparse */ bool is_multi_val_; bool is_dense_multi_val_; bool is_sparse_; int num_total_bin_; }; } // namespace LightGBM #endif // LIGHTGBM_INCLUDE_LIGHTGBM_FEATURE_GROUP_H_ ================================================ FILE: include/LightGBM/meta.h ================================================ /*! * Copyright (c) 2016-2026 Microsoft Corporation. All rights reserved. * Copyright (c) 2016-2026 The LightGBM developers. All rights reserved. * Licensed under the MIT License. See LICENSE file in the project root for license information. */ #ifndef LIGHTGBM_INCLUDE_LIGHTGBM_META_H_ #define LIGHTGBM_INCLUDE_LIGHTGBM_META_H_ #include #include #include #include #include #include #include #if (defined(_MSC_VER) && (defined(_M_IX86) || defined(_M_AMD64))) || defined(__INTEL_COMPILER) || MM_PREFETCH #include #define PREFETCH_T0(addr) _mm_prefetch(reinterpret_cast(addr), _MM_HINT_T0) #elif defined(__GNUC__) #define PREFETCH_T0(addr) __builtin_prefetch(reinterpret_cast(addr), 0, 3) #else #define PREFETCH_T0(addr) do {} while (0) #endif namespace LightGBM { /*! \brief Type of data size, it is better to use signed type*/ typedef int32_t data_size_t; // Enable following macro to use double for score_t // #define SCORE_T_USE_DOUBLE // Enable following macro to use double for label_t // #define LABEL_T_USE_DOUBLE /*! \brief Type of score, and gradients */ #ifdef SCORE_T_USE_DOUBLE typedef double score_t; #else typedef float score_t; #endif /*! \brief Type of metadata, include weight and label */ #ifdef LABEL_T_USE_DOUBLE typedef double label_t; #else typedef float label_t; #endif const score_t kMinScore = -std::numeric_limits::infinity(); const score_t kMaxScore = std::numeric_limits::infinity(); const score_t kEpsilon = 1e-15f; const double kZeroThreshold = 1e-35f; typedef int32_t comm_size_t; using PredictFunction = std::function>&, double* output)>; using PredictSparseFunction = std::function>&, std::vector>* output)>; typedef void(*ReduceFunction)(const char* input, char* output, int type_size, comm_size_t array_size); typedef void(*ReduceScatterFunction)(char* input, comm_size_t input_size, int type_size, const comm_size_t* block_start, const comm_size_t* block_len, int num_block, char* output, comm_size_t output_size, const ReduceFunction& reducer); typedef void(*AllgatherFunction)(char* input, comm_size_t input_size, const comm_size_t* block_start, const comm_size_t* block_len, int num_block, char* output, comm_size_t output_size); #define NO_SPECIFIC (-1) const int kAlignedSize = 32; #define SIZE_ALIGNED(t) ((t) + kAlignedSize - 1) / kAlignedSize * kAlignedSize // Refer to https://docs.microsoft.com/en-us/cpp/error-messages/compiler-warnings/compiler-warning-level-4-c4127?view=vs-2019 #ifdef _MSC_VER #pragma warning(disable : 4127) #endif } // namespace LightGBM #endif // LIGHTGBM_INCLUDE_LIGHTGBM_META_H_ ================================================ FILE: include/LightGBM/metric.h ================================================ /*! * Copyright (c) 2016-2026 Microsoft Corporation. All rights reserved. * Copyright (c) 2016-2026 The LightGBM developers. All rights reserved. * Licensed under the MIT License. See LICENSE file in the project root for license information. */ #ifndef LIGHTGBM_INCLUDE_LIGHTGBM_METRIC_H_ #define LIGHTGBM_INCLUDE_LIGHTGBM_METRIC_H_ #include #include #include #include #include #include #include #include namespace LightGBM { /*! * \brief The interface of metric. * Metric is used to calculate metric result */ class Metric { public: /*! \brief virtual destructor */ virtual ~Metric() {} /*! * \brief Initialize * \param test_name Specific name for this metric, will output on log * \param metadata Label data * \param num_data Number of data */ virtual void Init(const Metadata& metadata, data_size_t num_data) = 0; virtual const std::vector& GetName() const = 0; virtual double factor_to_bigger_better() const = 0; /*! * \brief Calculating and printing metric result * \param score Current prediction score */ virtual std::vector Eval(const double* score, const ObjectiveFunction* objective) const = 0; Metric() = default; /*! \brief Disable copy */ Metric& operator=(const Metric&) = delete; /*! \brief Disable copy */ Metric(const Metric&) = delete; /*! * \brief Create object of metrics * \param type Specific type of metric * \param config Config for metric */ LIGHTGBM_EXPORT static Metric* CreateMetric(const std::string& type, const Config& config); /*! * \brief Whether boosting is done on CUDA */ virtual bool IsCUDAMetric() const { return false; } }; /*! * \brief Static class, used to calculate DCG score */ class DCGCalculator { public: static void DefaultEvalAt(std::vector* eval_at); static void DefaultLabelGain(std::vector* label_gain); /*! * \brief Initial logic * \param label_gain Gain for labels, default is 2^i - 1 */ static void Init(const std::vector& label_gain); /*! * \brief Calculate the DCG score at multi position * \param ks The positions to evaluate * \param label Pointer of label * \param score Pointer of score * \param num_data Number of data * \param out Output result */ static void CalDCG(const std::vector& ks, const label_t* label, const double* score, data_size_t num_data, std::vector* out); /*! * \brief Calculate the Max DCG score at position k * \param k The position want to eval at * \param label Pointer of label * \param num_data Number of data * \return The max DCG score */ static double CalMaxDCGAtK(data_size_t k, const label_t* label, data_size_t num_data); /*! * \brief Check the metadata for NDCG and LambdaRank * \param metadata Metadata * \param num_queries Number of queries */ static void CheckMetadata(const Metadata& metadata, data_size_t num_queries); /*! * \brief Check the label range for NDCG and LambdaRank * \param label Pointer of label * \param num_data Number of data */ static void CheckLabel(const label_t* label, data_size_t num_data); /*! * \brief Calculate the Max DCG score at multi position * \param ks The positions want to eval at * \param label Pointer of label * \param num_data Number of data * \param out Output result */ static void CalMaxDCG(const std::vector& ks, const label_t* label, data_size_t num_data, std::vector* out); /*! * \brief Get discount score of position k * \param k The position * \return The discount of this position */ inline static double GetDiscount(data_size_t k) { return discount_[k]; } private: /*! \brief store gains for different label */ static std::vector label_gain_; /*! \brief store discount score for different position */ static std::vector discount_; /*! \brief max position for eval */ static const data_size_t kMaxPosition; }; } // namespace LightGBM #endif // LIGHTGBM_INCLUDE_LIGHTGBM_METRIC_H_ ================================================ FILE: include/LightGBM/network.h ================================================ /*! * Copyright (c) 2016-2026 Microsoft Corporation. All rights reserved. * Copyright (c) 2016-2026 The LightGBM developers. All rights reserved. * Licensed under the MIT License. See LICENSE file in the project root for license information. */ #ifndef LIGHTGBM_INCLUDE_LIGHTGBM_NETWORK_H_ #define LIGHTGBM_INCLUDE_LIGHTGBM_NETWORK_H_ #include #include #include #include #include #include namespace LightGBM { /*! \brief forward declaration */ class Linkers; /*! \brief The network structure for all_gather */ class BruckMap { public: /*! \brief The communication times for one all gather operation */ int k; /*! \brief in_ranks[i] means the incoming rank on i-th communication */ std::vector in_ranks; /*! \brief out_ranks[i] means the out rank on i-th communication */ std::vector out_ranks; BruckMap(); explicit BruckMap(int n); /*! * \brief Create the object of bruck map * \param rank Rank of this machine * \param num_machines The total number of machines * \return The object of bruck map */ static BruckMap Construct(int rank, int num_machines); }; /*! * \brief node type on recursive halving algorithm * When number of machines is not power of 2, need group machines into power of 2 group. * And we can let each group has at most 2 machines. * if the group only has 1 machine. this machine is the normal node * if the group has 2 machines, this group will have two type of nodes, one is the leader. * leader will represent this group and communication with others. */ enum RecursiveHalvingNodeType { Normal, // normal node, 1 group only have 1 machine GroupLeader, // leader of group when number of machines in this group is 2. Other // non-leader machines in group }; /*! \brief Network structure for recursive halving algorithm */ class RecursiveHalvingMap { public: /*! \brief Communication times for one recursive halving algorithm */ int k; /*! \brief Node type */ RecursiveHalvingNodeType type; bool is_power_of_2; int neighbor; /*! \brief ranks[i] means the machines that will communicate with on i-th communication*/ std::vector ranks; /*! \brief send_block_start[i] means send block start index at i-th communication*/ std::vector send_block_start; /*! \brief send_block_start[i] means send block size at i-th communication*/ std::vector send_block_len; /*! \brief send_block_start[i] means recv block start index at i-th communication*/ std::vector recv_block_start; /*! \brief send_block_start[i] means recv block size at i-th communication*/ std::vector recv_block_len; RecursiveHalvingMap(); RecursiveHalvingMap(int k, RecursiveHalvingNodeType _type, bool _is_power_of_2); /*! * \brief Create the object of recursive halving map * \param rank Rank of this machine * \param num_machines The total number of machines * \return The object of recursive halving map */ static RecursiveHalvingMap Construct(int rank, int num_machines); }; /*! \brief A static class that contains some collective communication algorithm */ class Network { public: /*! * \brief Initialize * \param config Config of network setting */ static void Init(Config config); /*! * \brief Initialize */ static void Init(int num_machines, int rank, ReduceScatterFunction reduce_scatter_ext_fun, AllgatherFunction allgather_ext_fun); /*! \brief Free this static class */ static void Dispose(); /*! \brief Get rank of this machine */ static int rank(); /*! \brief Get total number of machines */ static int num_machines(); /*! * \brief Perform all_reduce. if data size is small, will perform AllreduceByAllGather, else with call ReduceScatter followed allgather * \param input Input data * \param input_size The size of input data * \param type_size The size of one object in the reduce function * \param output Output result * \param reducer Reduce function */ static void Allreduce(char* input, comm_size_t input_size, int type_size, char* output, const ReduceFunction& reducer); /*! * \brief Perform all_reduce by using all_gather. it can be use to reduce communication time when data is small * \param input Input data * \param input_size The size of input data * \param type_size The size of one object in the reduce function * \param output Output result * \param reducer Reduce function */ static void AllreduceByAllGather(char* input, comm_size_t input_size, int type_size, char* output, const ReduceFunction& reducer); /*! * \brief Performing all_gather by using Bruck algorithm. Communication times is O(log(n)), and communication cost is O(send_size * number_machine) * It can be used when all nodes have same input size. * \param input Input data * \param send_size The size of input data * \param output Output result */ static void Allgather(char* input, comm_size_t send_size, char* output); /*! * \brief Performing all_gather by using Bruck algorithm. Communication times is O(log(n)), and communication cost is O(all_size) * It can be used when nodes have different input size. * \param input Input data * \param block_start The block start for different machines * \param block_len The block size for different machines * \param output Output result * \param all_size The size of output data */ static void Allgather(char* input, const comm_size_t* block_start, const comm_size_t* block_len, char* output, comm_size_t all_size); /*! * \brief Perform reduce scatter by using recursive halving algorithm. Communication times is O(log(n)), and communication cost is O(input_size) * \param input Input data * \param input_size The size of input data * \param type_size The size of one object in the reduce function * \param block_start The block start for different machines * \param block_len The block size for different machines * \param output Output result * \param output_size size of output data * \param reducer Reduce function */ static void ReduceScatter(char* input, comm_size_t input_size, int type_size, const comm_size_t* block_start, const comm_size_t* block_len, char* output, comm_size_t output_size, const ReduceFunction& reducer); template static T GlobalSyncUpByMin(T local) { T global = local; Allreduce(reinterpret_cast(&local), sizeof(local), sizeof(local), reinterpret_cast(&global), [] (const char* src, char* dst, int type_size, comm_size_t len) { comm_size_t used_size = 0; const T *p1; T *p2; while (used_size < len) { p1 = reinterpret_cast(src); p2 = reinterpret_cast(dst); if (*p1 < *p2) { std::memcpy(dst, src, type_size); } src += type_size; dst += type_size; used_size += type_size; } }); return global; } template static T GlobalSyncUpByMax(T local) { T global = local; Allreduce(reinterpret_cast(&local), sizeof(local), sizeof(local), reinterpret_cast(&global), [] (const char* src, char* dst, int type_size, comm_size_t len) { comm_size_t used_size = 0; const T *p1; T *p2; while (used_size < len) { p1 = reinterpret_cast(src); p2 = reinterpret_cast(dst); if (*p1 > *p2) { std::memcpy(dst, src, type_size); } src += type_size; dst += type_size; used_size += type_size; } }); return global; } template static T GlobalSyncUpBySum(T local) { T global = (T)0; Allreduce(reinterpret_cast(&local), sizeof(local), sizeof(local), reinterpret_cast(&global), [](const char* src, char* dst, int type_size, comm_size_t len) { comm_size_t used_size = 0; const T* p1; T* p2; while (used_size < len) { p1 = reinterpret_cast(src); p2 = reinterpret_cast(dst); *p2 += *p1; src += type_size; dst += type_size; used_size += type_size; } }); return static_cast(global); } template static T GlobalSyncUpByMean(T local) { return static_cast(GlobalSyncUpBySum(local) / num_machines_); } template static std::vector GlobalSum(std::vector* local) { std::vector global(local->size(), 0); Allreduce(reinterpret_cast(local->data()), static_cast(sizeof(T) * local->size()), sizeof(T), reinterpret_cast(global.data()), [](const char* src, char* dst, int type_size, comm_size_t len) { comm_size_t used_size = 0; const T *p1; T *p2; while (used_size < len) { p1 = reinterpret_cast(src); p2 = reinterpret_cast(dst); *p2 += *p1; src += type_size; dst += type_size; used_size += type_size; } }); return global; } template static std::vector GlobalArray(T local) { std::vector global(num_machines_, 0); int type_size = sizeof(T); std::vector block_start(num_machines_); std::vector block_len(num_machines_, type_size); for (int i = 1; i < num_machines_; ++i) { block_start[i] = block_start[i - 1] + block_len[i - 1]; } Allgather(reinterpret_cast(&local), block_start.data(), block_len.data(), reinterpret_cast(global.data()), type_size*num_machines_); return global; } private: static void AllgatherBruck(char* input, const comm_size_t* block_start, const comm_size_t* block_len, char* output, comm_size_t all_size); static void AllgatherRecursiveDoubling(char* input, const comm_size_t* block_start, const comm_size_t* block_len, char* output, comm_size_t all_size); static void AllgatherRing(char* input, const comm_size_t* block_start, const comm_size_t* block_len, char* output, comm_size_t all_size); static void ReduceScatterRecursiveHalving(char* input, comm_size_t input_size, int type_size, const comm_size_t* block_start, const comm_size_t* block_len, char* output, comm_size_t output_size, const ReduceFunction& reducer); static void ReduceScatterRing(char* input, comm_size_t input_size, int type_size, const comm_size_t* block_start, const comm_size_t* block_len, char* output, comm_size_t output_size, const ReduceFunction& reducer); /*! \brief Number of all machines */ static THREAD_LOCAL int num_machines_; /*! \brief Rank of local machine */ static THREAD_LOCAL int rank_; /*! \brief The network interface, provide send/recv functions */ static THREAD_LOCAL std::unique_ptr linkers_; /*! \brief Bruck map for all gather algorithm*/ static THREAD_LOCAL BruckMap bruck_map_; /*! \brief Recursive halving map for reduce scatter */ static THREAD_LOCAL RecursiveHalvingMap recursive_halving_map_; /*! \brief Buffer to store block start index */ static THREAD_LOCAL std::vector block_start_; /*! \brief Buffer to store block size */ static THREAD_LOCAL std::vector block_len_; /*! \brief Buffer */ static THREAD_LOCAL std::vector buffer_; /*! \brief Size of buffer_ */ static THREAD_LOCAL comm_size_t buffer_size_; /*! \brief Funcs*/ static THREAD_LOCAL ReduceScatterFunction reduce_scatter_ext_fun_; static THREAD_LOCAL AllgatherFunction allgather_ext_fun_; }; } // namespace LightGBM #endif // LIGHTGBM_INCLUDE_LIGHTGBM_NETWORK_H_ ================================================ FILE: include/LightGBM/objective_function.h ================================================ /*! * Copyright (c) 2016-2026 Microsoft Corporation. All rights reserved. * Copyright (c) 2016-2026 The LightGBM developers. All rights reserved. * Licensed under the MIT License. See LICENSE file in the project root for license information. */ #ifndef LIGHTGBM_INCLUDE_LIGHTGBM_OBJECTIVE_FUNCTION_H_ #define LIGHTGBM_INCLUDE_LIGHTGBM_OBJECTIVE_FUNCTION_H_ #include #include #include #include #include namespace LightGBM { /*! * \brief The interface of Objective Function. */ class ObjectiveFunction { public: /*! \brief virtual destructor */ virtual ~ObjectiveFunction() {} /*! * \brief Initialize * \param metadata Label data * \param num_data Number of data */ virtual void Init(const Metadata& metadata, data_size_t num_data) = 0; /*! * \brief calculating first order derivative of loss function * \param score prediction score in this round * \gradients Output gradients * \hessians Output hessians */ virtual void GetGradients(const double* score, score_t* gradients, score_t* hessians) const = 0; /*! * \brief calculating first order derivative of loss function, used only for bagging by query in lambdarank * \param score prediction score in this round * \param num_sampled_queries number of in-bag queries * \param sampled_query_indices indices of in-bag queries * \gradients Output gradients * \hessians Output hessians */ virtual void GetGradientsWithSampledQueries(const double* score, const data_size_t /*num_sampled_queries*/, const data_size_t* /*sampled_query_indices*/, score_t* gradients, score_t* hessians) const { GetGradients(score, gradients, hessians); } virtual const char* GetName() const = 0; virtual bool IsConstantHessian() const { return false; } virtual bool IsRenewTreeOutput() const { return false; } virtual double RenewTreeOutput(double ori_output, std::function, const data_size_t*, const data_size_t*, data_size_t) const { return ori_output; } virtual void RenewTreeOutputCUDA(const double* /*score*/, const data_size_t* /*data_indices_in_leaf*/, const data_size_t* /*num_data_in_leaf*/, const data_size_t* /*data_start_in_leaf*/, const int /*num_leaves*/, double* /*leaf_value*/) const {} virtual double BoostFromScore(int /*class_id*/) const { return 0.0; } virtual bool ClassNeedTrain(int /*class_id*/) const { return true; } virtual bool SkipEmptyClass() const { return false; } virtual int NumModelPerIteration() const { return 1; } virtual int NumPredictOneRow() const { return 1; } /*! \brief The prediction should be accurate or not. True will disable early stopping for prediction. */ virtual bool NeedAccuratePrediction() const { return true; } /*! \brief Return the number of positive samples. Return 0 if no binary classification tasks.*/ virtual data_size_t NumPositiveData() const { return 0; } virtual void ConvertOutput(const double* input, double* output) const { output[0] = input[0]; } virtual std::string ToString() const = 0; ObjectiveFunction() = default; /*! \brief Disable copy */ ObjectiveFunction& operator=(const ObjectiveFunction&) = delete; /*! \brief Disable copy */ ObjectiveFunction(const ObjectiveFunction&) = delete; /*! * \brief Create object of objective function * \param type Specific type of objective function * \param config Config for objective function */ LIGHTGBM_EXPORT static ObjectiveFunction* CreateObjectiveFunction(const std::string& type, const Config& config); /*! * \brief Load objective function from string object */ LIGHTGBM_EXPORT static ObjectiveFunction* CreateObjectiveFunction(const std::string& str); /*! * \brief Whether boosting is done on CUDA */ virtual bool IsCUDAObjective() const { return false; } #ifdef USE_CUDA /*! * \brief Convert output for CUDA version */ virtual const double* ConvertOutputCUDA(data_size_t /*num_data*/, const double* input, double* /*output*/) const { return input; } virtual bool NeedConvertOutputCUDA () const { return false; } virtual void SetNCCLInfo( ncclComm_t /*nccl_communicator*/, int /*nccl_gpu_rank*/, int /*local_gpu_rank*/, int /*gpu_device_id*/, data_size_t /*global_num_data*/) {} /*! * \brief Create object of objective function on CUDA * \param type Specific type of objective function * \param config Config for objective function */ LIGHTGBM_EXPORT static ObjectiveFunction* CreateObjectiveFunctionCUDA(const std::string& type, const Config& config); #endif // USE_CUDA }; } // namespace LightGBM #endif // LIGHTGBM_INCLUDE_LIGHTGBM_OBJECTIVE_FUNCTION_H_ ================================================ FILE: include/LightGBM/prediction_early_stop.h ================================================ /*! * Copyright (c) 2017-2026 Microsoft Corporation. All rights reserved. * Copyright (c) 2017-2026 The LightGBM developers. All rights reserved. * Licensed under the MIT License. See LICENSE file in the project root for license information. */ #ifndef LIGHTGBM_INCLUDE_LIGHTGBM_PREDICTION_EARLY_STOP_H_ #define LIGHTGBM_INCLUDE_LIGHTGBM_PREDICTION_EARLY_STOP_H_ #include #include #include namespace LightGBM { struct PredictionEarlyStopInstance { /// Callback function type for early stopping. /// Takes current prediction and number of elements in prediction /// @returns true if prediction should stop according to criterion using FunctionType = std::function; FunctionType callback_function; // callback function itself int round_period; // call callback_function every `runPeriod` iterations }; struct PredictionEarlyStopConfig { int round_period; double margin_threshold; }; /// Create an early stopping algorithm of type `type`, with given round_period and margin threshold LIGHTGBM_EXPORT PredictionEarlyStopInstance CreatePredictionEarlyStopInstance(const std::string& type, const PredictionEarlyStopConfig& config); } // namespace LightGBM #endif // LIGHTGBM_INCLUDE_LIGHTGBM_PREDICTION_EARLY_STOP_H_ ================================================ FILE: include/LightGBM/sample_strategy.h ================================================ /*! * Copyright (c) 2021-2026 Microsoft Corporation. All rights reserved. * Copyright (c) 2021-2026 The LightGBM developers. All rights reserved. * Licensed under the MIT License. See LICENSE file in the project root for license information. */ #ifndef LIGHTGBM_INCLUDE_LIGHTGBM_SAMPLE_STRATEGY_H_ #define LIGHTGBM_INCLUDE_LIGHTGBM_SAMPLE_STRATEGY_H_ #include #include #include #include #include #include #include #include #include #include namespace LightGBM { class SampleStrategy { public: SampleStrategy() : balanced_bagging_(false), bagging_runner_(0, bagging_rand_block_), need_resize_gradients_(false) {} virtual ~SampleStrategy() {} static SampleStrategy* CreateSampleStrategy(const Config* config, const Dataset* train_data, const ObjectiveFunction* objective_function, int num_tree_per_iteration); virtual void Bagging(int iter, TreeLearner* tree_learner, score_t* gradients, score_t* hessians) = 0; virtual void ResetSampleConfig(const Config* config, bool is_change_dataset) = 0; bool is_use_subset() const { return is_use_subset_; } data_size_t bag_data_cnt() const { return bag_data_cnt_; } std::vector>& bag_data_indices() { return bag_data_indices_; } #ifdef USE_CUDA CUDAVector& cuda_bag_data_indices() { return cuda_bag_data_indices_; } #endif // USE_CUDA void UpdateObjectiveFunction(const ObjectiveFunction* objective_function) { objective_function_ = objective_function; } void UpdateTrainingData(const Dataset* train_data) { train_data_ = train_data; num_data_ = train_data->num_data(); } virtual bool IsHessianChange() const = 0; bool NeedResizeGradients() const { return need_resize_gradients_; } virtual data_size_t num_sampled_queries() const { return 0; } virtual const data_size_t* sampled_query_indices() const { return nullptr; } protected: const Config* config_; const Dataset* train_data_; const ObjectiveFunction* objective_function_; std::vector> bag_data_indices_; data_size_t bag_data_cnt_; data_size_t num_data_; int num_tree_per_iteration_; std::unique_ptr tmp_subset_; bool is_use_subset_; bool balanced_bagging_; const int bagging_rand_block_ = 1024; std::vector bagging_rands_; ParallelPartitionRunner bagging_runner_; /*! \brief whether need to resize the gradient vectors */ bool need_resize_gradients_; #ifdef USE_CUDA /*! \brief Buffer for bag_data_indices_ on GPU, used only with cuda */ CUDAVector cuda_bag_data_indices_; #endif // USE_CUDA }; } // namespace LightGBM #endif // LIGHTGBM_INCLUDE_LIGHTGBM_SAMPLE_STRATEGY_H_ ================================================ FILE: include/LightGBM/train_share_states.h ================================================ /*! * Copyright (c) 2016-2026 Microsoft Corporation. All rights reserved. * Copyright (c) 2016-2026 The LightGBM developers. All rights reserved. * Licensed under the MIT License. See LICENSE file in the project root for license information. */ #ifndef LIGHTGBM_INCLUDE_LIGHTGBM_TRAIN_SHARE_STATES_H_ #define LIGHTGBM_INCLUDE_LIGHTGBM_TRAIN_SHARE_STATES_H_ #include #include #include #include #include #include #include #include namespace LightGBM { class MultiValBinWrapper { public: MultiValBinWrapper(MultiValBin* bin, data_size_t num_data, const std::vector& feature_groups_contained, const int num_grad_quant_bins); bool IsSparse() { if (multi_val_bin_ != nullptr) { return multi_val_bin_->IsSparse(); } return false; } void InitTrain(const std::vector& group_feature_start, const std::vector>& feature_groups, const std::vector& is_feature_used, const data_size_t* bagging_use_indices, data_size_t bagging_indices_cnt); template void HistMove(const std::vector>& hist_buf); template void HistMerge(std::vector>* hist_buf); void ResizeHistBuf(std::vector>* hist_buf, MultiValBin* sub_multi_val_bin, hist_t* origin_hist_data); template void ConstructHistograms(const data_size_t* data_indices, data_size_t num_data, const score_t* gradients, const score_t* hessians, std::vector>* hist_buf, hist_t* origin_hist_data) { const auto cur_multi_val_bin = (is_use_subcol_ || is_use_subrow_) ? multi_val_bin_subset_.get() : multi_val_bin_.get(); if (cur_multi_val_bin != nullptr) { global_timer.Start("Dataset::sparse_bin_histogram"); n_data_block_ = 1; data_block_size_ = num_data; Threading::BlockInfo(num_threads_, num_data, min_block_size_, &n_data_block_, &data_block_size_); ResizeHistBuf(hist_buf, cur_multi_val_bin, origin_hist_data); const int inner_hist_bits = (data_block_size_ * num_grad_quant_bins_ < 256 && HIST_BITS == 16) ? 8 : HIST_BITS; OMP_INIT_EX(); #pragma omp parallel for schedule(static) num_threads(num_threads_) for (int block_id = 0; block_id < n_data_block_; ++block_id) { OMP_LOOP_EX_BEGIN(); data_size_t start = block_id * data_block_size_; data_size_t end = std::min(start + data_block_size_, num_data); if (inner_hist_bits == 8) { ConstructHistogramsForBlock( cur_multi_val_bin, start, end, data_indices, gradients, hessians, block_id, hist_buf); } else { ConstructHistogramsForBlock( cur_multi_val_bin, start, end, data_indices, gradients, hessians, block_id, hist_buf); } OMP_LOOP_EX_END(); } OMP_THROW_EX(); global_timer.Stop("Dataset::sparse_bin_histogram"); global_timer.Start("Dataset::sparse_bin_histogram_merge"); if (inner_hist_bits == 8) { HistMerge(hist_buf); } else { HistMerge(hist_buf); } global_timer.Stop("Dataset::sparse_bin_histogram_merge"); global_timer.Start("Dataset::sparse_bin_histogram_move"); if (inner_hist_bits == 8) { HistMove(*hist_buf); } else { HistMove(*hist_buf); } global_timer.Stop("Dataset::sparse_bin_histogram_move"); } } template void ConstructHistogramsForBlock(const MultiValBin* sub_multi_val_bin, data_size_t start, data_size_t end, const data_size_t* data_indices, const score_t* gradients, const score_t* hessians, int block_id, std::vector>* hist_buf) { if (USE_QUANT_GRAD) { if (HIST_BITS == 8) { int8_t* hist_buf_ptr = reinterpret_cast(hist_buf->data()); int8_t* data_ptr = hist_buf_ptr + static_cast(num_bin_aligned_) * block_id * 2; std::memset(reinterpret_cast(data_ptr), 0, num_bin_ * kInt8HistBufferEntrySize); if (USE_INDICES) { if (ORDERED) { sub_multi_val_bin->ConstructHistogramOrderedInt8(data_indices, start, end, gradients, hessians, reinterpret_cast(data_ptr)); } else { sub_multi_val_bin->ConstructHistogramInt8(data_indices, start, end, gradients, hessians, reinterpret_cast(data_ptr)); } } else { sub_multi_val_bin->ConstructHistogramInt8(start, end, gradients, hessians, reinterpret_cast(data_ptr)); } } else if (HIST_BITS == 16) { int16_t* data_ptr = reinterpret_cast(origin_hist_data_); int16_t* hist_buf_ptr = reinterpret_cast(hist_buf->data()); if (block_id == 0) { if (is_use_subcol_) { data_ptr = hist_buf_ptr + hist_buf->size() - 2 * static_cast(num_bin_aligned_); } } else { data_ptr = hist_buf_ptr + static_cast(num_bin_aligned_) * (block_id - 1) * 2; } std::memset(reinterpret_cast(data_ptr), 0, num_bin_ * kInt16HistBufferEntrySize); if (USE_INDICES) { if (ORDERED) { sub_multi_val_bin->ConstructHistogramOrderedInt16(data_indices, start, end, gradients, hessians, reinterpret_cast(data_ptr)); } else { sub_multi_val_bin->ConstructHistogramInt16(data_indices, start, end, gradients, hessians, reinterpret_cast(data_ptr)); } } else { sub_multi_val_bin->ConstructHistogramInt16(start, end, gradients, hessians, reinterpret_cast(data_ptr)); } } else { int32_t* data_ptr = reinterpret_cast(origin_hist_data_); int32_t* hist_buf_ptr = reinterpret_cast(hist_buf->data()); if (block_id == 0) { if (is_use_subcol_) { data_ptr = hist_buf_ptr + hist_buf->size() - 2 * static_cast(num_bin_aligned_); } } else { data_ptr = hist_buf_ptr + static_cast(num_bin_aligned_) * (block_id - 1) * 2; } std::memset(reinterpret_cast(data_ptr), 0, num_bin_ * kInt32HistBufferEntrySize); if (USE_INDICES) { if (ORDERED) { sub_multi_val_bin->ConstructHistogramOrderedInt32(data_indices, start, end, gradients, hessians, reinterpret_cast(data_ptr)); } else { sub_multi_val_bin->ConstructHistogramInt32(data_indices, start, end, gradients, hessians, reinterpret_cast(data_ptr)); } } else { sub_multi_val_bin->ConstructHistogramInt32(start, end, gradients, hessians, reinterpret_cast(data_ptr)); } } } else { hist_t* data_ptr = origin_hist_data_; if (block_id == 0) { if (is_use_subcol_) { data_ptr = hist_buf->data() + hist_buf->size() - 2 * static_cast(num_bin_aligned_); } } else { data_ptr = hist_buf->data() + static_cast(num_bin_aligned_) * (block_id - 1) * 2; } std::memset(reinterpret_cast(data_ptr), 0, num_bin_ * kHistBufferEntrySize); if (USE_INDICES) { if (ORDERED) { sub_multi_val_bin->ConstructHistogramOrdered(data_indices, start, end, gradients, hessians, data_ptr); } else { sub_multi_val_bin->ConstructHistogram(data_indices, start, end, gradients, hessians, data_ptr); } } else { sub_multi_val_bin->ConstructHistogram(start, end, gradients, hessians, data_ptr); } } } void CopyMultiValBinSubset(const std::vector& group_feature_start, const std::vector>& feature_groups, const std::vector& is_feature_used, const data_size_t* bagging_use_indices, data_size_t bagging_indices_cnt); void SetUseSubrow(bool is_use_subrow) { is_use_subrow_ = is_use_subrow; } void SetSubrowCopied(bool is_subrow_copied) { is_subrow_copied_ = is_subrow_copied; } #ifdef USE_CUDA const void* GetRowWiseData( uint8_t* bit_type, size_t* total_size, bool* is_sparse, const void** out_data_ptr, uint8_t* data_ptr_bit_type) const { if (multi_val_bin_ == nullptr) { *bit_type = 0; *total_size = 0; *is_sparse = false; return nullptr; } else { return multi_val_bin_->GetRowWiseData(bit_type, total_size, is_sparse, out_data_ptr, data_ptr_bit_type); } } #endif // USE_CUDA private: bool is_use_subcol_ = false; bool is_use_subrow_ = false; bool is_subrow_copied_ = false; std::unique_ptr multi_val_bin_; std::unique_ptr multi_val_bin_subset_; std::vector hist_move_src_; std::vector hist_move_dest_; std::vector hist_move_size_; const std::vector feature_groups_contained_; int num_threads_; int num_bin_; int num_bin_aligned_; int n_data_block_; int data_block_size_; int min_block_size_; int num_data_; int num_grad_quant_bins_; hist_t* origin_hist_data_; const size_t kHistBufferEntrySize = 2 * sizeof(hist_t); const size_t kInt32HistBufferEntrySize = 2 * sizeof(int32_t); const size_t kInt16HistBufferEntrySize = 2 * sizeof(int16_t); const size_t kInt8HistBufferEntrySize = 2 * sizeof(int8_t); }; struct TrainingShareStates { int num_threads = 0; bool is_col_wise = true; bool is_constant_hessian = true; const data_size_t* bagging_use_indices; data_size_t bagging_indices_cnt; TrainingShareStates() { multi_val_bin_wrapper_.reset(nullptr); } int num_hist_total_bin() { return num_hist_total_bin_; } const std::vector& feature_hist_offsets() const { return feature_hist_offsets_; } #ifdef USE_CUDA const std::vector& column_hist_offsets() const { return column_hist_offsets_; } #endif // USE_CUDA bool IsSparseRowwise() { return (multi_val_bin_wrapper_ != nullptr && multi_val_bin_wrapper_->IsSparse()); } void SetMultiValBin(MultiValBin* bin, data_size_t num_data, const std::vector>& feature_groups, bool dense_only, bool sparse_only, const int num_grad_quant_bins); void CalcBinOffsets(const std::vector>& feature_groups, std::vector* offsets, bool is_col_wise); void InitTrain(const std::vector& group_feature_start, const std::vector>& feature_groups, const std::vector& is_feature_used) { if (multi_val_bin_wrapper_ != nullptr) { multi_val_bin_wrapper_->InitTrain(group_feature_start, feature_groups, is_feature_used, bagging_use_indices, bagging_indices_cnt); } } template void ConstructHistograms(const data_size_t* data_indices, data_size_t num_data, const score_t* gradients, const score_t* hessians, hist_t* hist_data) { if (multi_val_bin_wrapper_ != nullptr) { multi_val_bin_wrapper_->ConstructHistograms( data_indices, num_data, gradients, hessians, &hist_buf_, hist_data); } } void SetUseSubrow(bool is_use_subrow) { if (multi_val_bin_wrapper_ != nullptr) { multi_val_bin_wrapper_->SetUseSubrow(is_use_subrow); } } void SetSubrowCopied(bool is_subrow_copied) { if (multi_val_bin_wrapper_ != nullptr) { multi_val_bin_wrapper_->SetSubrowCopied(is_subrow_copied); } } #ifdef USE_CUDA const void* GetRowWiseData(uint8_t* bit_type, size_t* total_size, bool* is_sparse, const void** out_data_ptr, uint8_t* data_ptr_bit_type) { if (multi_val_bin_wrapper_ != nullptr) { return multi_val_bin_wrapper_->GetRowWiseData(bit_type, total_size, is_sparse, out_data_ptr, data_ptr_bit_type); } else { *bit_type = 0; *total_size = 0; *is_sparse = false; return nullptr; } } #endif // USE_CUDA private: std::vector feature_hist_offsets_; #ifdef USE_CUDA std::vector column_hist_offsets_; #endif // USE_CUDA int num_hist_total_bin_ = 0; std::unique_ptr multi_val_bin_wrapper_; std::vector> hist_buf_; int num_total_bin_ = 0; double num_elements_per_row_ = 0.0f; }; } // namespace LightGBM #endif // LIGHTGBM_INCLUDE_LIGHTGBM_TRAIN_SHARE_STATES_H_ ================================================ FILE: include/LightGBM/tree.h ================================================ /*! * Copyright (c) 2016-2026 Microsoft Corporation. All rights reserved. * Copyright (c) 2016-2026 The LightGBM developers. All rights reserved. * Licensed under the MIT License. See LICENSE file in the project root for license information. */ #ifndef LIGHTGBM_INCLUDE_LIGHTGBM_TREE_H_ #define LIGHTGBM_INCLUDE_LIGHTGBM_TREE_H_ #include #include #include #include #include #include #include #include namespace LightGBM { #define kCategoricalMask (1) #define kDefaultLeftMask (2) /*! * \brief Tree model */ class Tree { public: /*! * \brief Constructor * \param max_leaves The number of max leaves * \param track_branch_features Whether to keep track of ancestors of leaf nodes * \param is_linear Whether the tree has linear models at each leaf */ explicit Tree(int max_leaves, bool track_branch_features, bool is_linear); /*! * \brief Constructor, from a string * \param str Model string * \param used_len used count of str */ Tree(const char* str, size_t* used_len); virtual ~Tree() noexcept = default; /*! * \brief Performing a split on tree leaves. * \param leaf Index of leaf to be split * \param feature Index of feature; the converted index after removing useless features * \param real_feature Index of feature, the original index on data * \param threshold_bin Threshold(bin) of split * \param threshold_double Threshold on feature value * \param left_value Model Left child output * \param right_value Model Right child output * \param left_cnt Count of left child * \param right_cnt Count of right child * \param left_weight Weight of left child * \param right_weight Weight of right child * \param gain Split gain * \param missing_type missing type * \param default_left default direction for missing value * \return The index of new leaf. */ int Split(int leaf, int feature, int real_feature, uint32_t threshold_bin, double threshold_double, double left_value, double right_value, int left_cnt, int right_cnt, double left_weight, double right_weight, float gain, MissingType missing_type, bool default_left); /*! * \brief Performing a split on tree leaves, with categorical feature * \param leaf Index of leaf to be split * \param feature Index of feature; the converted index after removing useless features * \param real_feature Index of feature, the original index on data * \param threshold_bin Threshold(bin) of split, use bitset to represent * \param num_threshold_bin size of threshold_bin * \param threshold Thresholds of real feature value, use bitset to represent * \param num_threshold size of threshold * \param left_value Model Left child output * \param right_value Model Right child output * \param left_cnt Count of left child * \param right_cnt Count of right child * \param left_weight Weight of left child * \param right_weight Weight of right child * \param gain Split gain * \return The index of new leaf. */ int SplitCategorical(int leaf, int feature, int real_feature, const uint32_t* threshold_bin, int num_threshold_bin, const uint32_t* threshold, int num_threshold, double left_value, double right_value, int left_cnt, int right_cnt, double left_weight, double right_weight, float gain, MissingType missing_type); /*! \brief Get the output of one leaf */ inline double LeafOutput(int leaf) const { return leaf_value_[leaf]; } /*! \brief Set the output of one leaf */ inline void SetLeafOutput(int leaf, double output) { leaf_value_[leaf] = MaybeRoundToZero(output); } /*! * \brief Adding prediction value of this tree model to scores * \param data The dataset * \param num_data Number of total data * \param score Will add prediction to score */ virtual void AddPredictionToScore(const Dataset* data, data_size_t num_data, double* score) const; /*! * \brief Adding prediction value of this tree model to scores * \param data The dataset * \param used_data_indices Indices of used data * \param num_data Number of total data * \param score Will add prediction to score */ virtual void AddPredictionToScore(const Dataset* data, const data_size_t* used_data_indices, data_size_t num_data, double* score) const; /*! * \brief Get upper bound leaf value of this tree model */ double GetUpperBoundValue() const; /*! * \brief Get lower bound leaf value of this tree model */ double GetLowerBoundValue() const; /*! * \brief Prediction on one record * \param feature_values Feature value of this record * \return Prediction result */ inline double Predict(const double* feature_values) const; inline double PredictByMap(const std::unordered_map& feature_values) const; inline int PredictLeafIndex(const double* feature_values) const; inline int PredictLeafIndexByMap(const std::unordered_map& feature_values) const; inline void PredictContrib(const double* feature_values, int num_features, double* output); inline void PredictContribByMap(const std::unordered_map& feature_values, int num_features, std::unordered_map* output); /*! \brief Get Number of leaves*/ inline int num_leaves() const { return num_leaves_; } /*! \brief Get depth of specific leaf*/ inline int leaf_depth(int leaf_idx) const { return leaf_depth_[leaf_idx]; } /*! \brief Get parent of specific leaf*/ inline int leaf_parent(int leaf_idx) const {return leaf_parent_[leaf_idx]; } /*! \brief Get feature of specific split (original feature index)*/ inline int split_feature(int split_idx) const { return split_feature_[split_idx]; } /*! \brief Get feature of specific split*/ inline int split_feature_inner(int split_idx) const { return split_feature_inner_[split_idx]; } /*! \brief Get features on leaf's branch*/ inline std::vector branch_features(int leaf) const { return branch_features_[leaf]; } inline double split_gain(int split_idx) const { return split_gain_[split_idx]; } inline double internal_value(int node_idx) const { return internal_value_[node_idx]; } inline bool IsNumericalSplit(int node_idx) const { return !GetDecisionType(decision_type_[node_idx], kCategoricalMask); } inline int left_child(int node_idx) const { return left_child_[node_idx]; } inline int right_child(int node_idx) const { return right_child_[node_idx]; } inline uint32_t threshold_in_bin(int node_idx) const { return threshold_in_bin_[node_idx]; } /*! \brief Get the number of data points that fall at or below this node*/ inline int data_count(int node) const { return node >= 0 ? internal_count_[node] : leaf_count_[~node]; } /*! * \brief Shrinkage for the tree's output * shrinkage rate (a.k.a learning rate) is used to tune the training process * \param rate The factor of shrinkage */ virtual inline void Shrinkage(double rate) { #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static, 1024) if (num_leaves_ >= 2048) for (int i = 0; i < num_leaves_ - 1; ++i) { leaf_value_[i] = MaybeRoundToZero(leaf_value_[i] * rate); internal_value_[i] = MaybeRoundToZero(internal_value_[i] * rate); if (is_linear_) { leaf_const_[i] = MaybeRoundToZero(leaf_const_[i] * rate); for (size_t j = 0; j < leaf_coeff_[i].size(); ++j) { leaf_coeff_[i][j] = MaybeRoundToZero(leaf_coeff_[i][j] * rate); } } } leaf_value_[num_leaves_ - 1] = MaybeRoundToZero(leaf_value_[num_leaves_ - 1] * rate); if (is_linear_) { leaf_const_[num_leaves_ - 1] = MaybeRoundToZero(leaf_const_[num_leaves_ - 1] * rate); for (size_t j = 0; j < leaf_coeff_[num_leaves_ - 1].size(); ++j) { leaf_coeff_[num_leaves_ - 1][j] = MaybeRoundToZero(leaf_coeff_[num_leaves_ - 1][j] * rate); } } shrinkage_ *= rate; } inline double shrinkage() const { return shrinkage_; } virtual inline void AddBias(double val) { #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static, 1024) if (num_leaves_ >= 2048) for (int i = 0; i < num_leaves_ - 1; ++i) { leaf_value_[i] = MaybeRoundToZero(leaf_value_[i] + val); internal_value_[i] = MaybeRoundToZero(internal_value_[i] + val); } leaf_value_[num_leaves_ - 1] = MaybeRoundToZero(leaf_value_[num_leaves_ - 1] + val); if (is_linear_) { #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static, 1024) if (num_leaves_ >= 2048) for (int i = 0; i < num_leaves_ - 1; ++i) { leaf_const_[i] = MaybeRoundToZero(leaf_const_[i] + val); } leaf_const_[num_leaves_ - 1] = MaybeRoundToZero(leaf_const_[num_leaves_ - 1] + val); } // force to 1.0 shrinkage_ = 1.0f; } virtual inline void AsConstantTree(double val, int count = 0) { num_leaves_ = 1; shrinkage_ = 1.0f; leaf_value_[0] = val; if (is_linear_) { leaf_const_[0] = val; } leaf_count_[0] = count; } /*! \brief Serialize this object to string*/ std::string ToString() const; /*! \brief Serialize this object to json*/ std::string ToJSON() const; /*! \brief Serialize linear model of tree node to json*/ std::string LinearModelToJSON(int index) const; /*! \brief Serialize this object to if-else statement*/ std::string ToIfElse(int index, bool predict_leaf_index) const; inline static bool IsZero(double fval) { return (fval >= -kZeroThreshold && fval <= kZeroThreshold); } inline static double MaybeRoundToZero(double fval) { return IsZero(fval) ? 0 : fval; } inline static bool GetDecisionType(int8_t decision_type, int8_t mask) { return (decision_type & mask) > 0; } inline static void SetDecisionType(int8_t* decision_type, bool input, int8_t mask) { if (input) { (*decision_type) |= mask; } else { (*decision_type) &= (127 - mask); } } inline static int8_t GetMissingType(int8_t decision_type) { return (decision_type >> 2) & 3; } inline static void SetMissingType(int8_t* decision_type, int8_t input) { (*decision_type) &= 3; (*decision_type) |= (input << 2); } void RecomputeMaxDepth(); int NextLeafId() const { return num_leaves_; } /*! \brief Get the linear model constant term (bias) of one leaf */ inline double LeafConst(int leaf) const { return leaf_const_[leaf]; } /*! \brief Get the linear model coefficients of one leaf */ inline std::vector LeafCoeffs(int leaf) const { return leaf_coeff_[leaf]; } /*! \brief Get the linear model features of one leaf */ inline std::vector LeafFeaturesInner(int leaf) const {return leaf_features_inner_[leaf]; } /*! \brief Get the linear model features of one leaf */ inline std::vector LeafFeatures(int leaf) const {return leaf_features_[leaf]; } /*! \brief Set the linear model coefficients on one leaf */ inline void SetLeafCoeffs(int leaf, const std::vector& output) { leaf_coeff_[leaf].resize(output.size()); for (size_t i = 0; i < output.size(); ++i) { leaf_coeff_[leaf][i] = MaybeRoundToZero(output[i]); } } /*! \brief Set the linear model constant term (bias) on one leaf */ inline void SetLeafConst(int leaf, double output) { leaf_const_[leaf] = MaybeRoundToZero(output); } /*! \brief Set the linear model features on one leaf */ inline void SetLeafFeaturesInner(int leaf, const std::vector& features) { leaf_features_inner_[leaf] = features; } /*! \brief Set the linear model features on one leaf */ inline void SetLeafFeatures(int leaf, const std::vector& features) { leaf_features_[leaf] = features; } inline bool is_linear() const { return is_linear_; } #ifdef USE_CUDA inline bool is_cuda_tree() const { return is_cuda_tree_; } #endif // USE_CUDA inline void SetIsLinear(bool is_linear) { is_linear_ = is_linear; } protected: std::string NumericalDecisionIfElse(int node) const; std::string CategoricalDecisionIfElse(int node) const; inline int NumericalDecision(double fval, int node) const { uint8_t missing_type = GetMissingType(decision_type_[node]); if (std::isnan(fval) && missing_type != MissingType::NaN) { fval = 0.0f; } if ((missing_type == MissingType::Zero && IsZero(fval)) || (missing_type == MissingType::NaN && std::isnan(fval))) { if (GetDecisionType(decision_type_[node], kDefaultLeftMask)) { return left_child_[node]; } else { return right_child_[node]; } } if (fval <= threshold_[node]) { return left_child_[node]; } else { return right_child_[node]; } } inline int NumericalDecisionInner(uint32_t fval, int node, uint32_t default_bin, uint32_t max_bin) const { uint8_t missing_type = GetMissingType(decision_type_[node]); if ((missing_type == MissingType::Zero && fval == default_bin) || (missing_type == MissingType::NaN && fval == max_bin)) { if (GetDecisionType(decision_type_[node], kDefaultLeftMask)) { return left_child_[node]; } else { return right_child_[node]; } } if (fval <= threshold_in_bin_[node]) { return left_child_[node]; } else { return right_child_[node]; } } inline int CategoricalDecision(double fval, int node) const { int int_fval; if (std::isnan(fval)) { return right_child_[node]; } else { int_fval = static_cast(fval); if (int_fval < 0) { return right_child_[node]; } } int cat_idx = static_cast(threshold_[node]); if (Common::FindInBitset(cat_threshold_.data() + cat_boundaries_[cat_idx], cat_boundaries_[cat_idx + 1] - cat_boundaries_[cat_idx], int_fval)) { return left_child_[node]; } return right_child_[node]; } inline int CategoricalDecisionInner(uint32_t fval, int node) const { int cat_idx = static_cast(threshold_in_bin_[node]); if (Common::FindInBitset(cat_threshold_inner_.data() + cat_boundaries_inner_[cat_idx], cat_boundaries_inner_[cat_idx + 1] - cat_boundaries_inner_[cat_idx], fval)) { return left_child_[node]; } return right_child_[node]; } inline int Decision(double fval, int node) const { if (GetDecisionType(decision_type_[node], kCategoricalMask)) { return CategoricalDecision(fval, node); } else { return NumericalDecision(fval, node); } } inline int DecisionInner(uint32_t fval, int node, uint32_t default_bin, uint32_t max_bin) const { if (GetDecisionType(decision_type_[node], kCategoricalMask)) { return CategoricalDecisionInner(fval, node); } else { return NumericalDecisionInner(fval, node, default_bin, max_bin); } } inline void Split(int leaf, int feature, int real_feature, double left_value, double right_value, int left_cnt, int right_cnt, double left_weight, double right_weight, float gain); /*! * \brief Find leaf index of which record belongs by features * \param feature_values Feature value of this record * \return Leaf index */ inline int GetLeaf(const double* feature_values) const; inline int GetLeafByMap(const std::unordered_map& feature_values) const; /*! \brief Serialize one node to json*/ std::string NodeToJSON(int index) const; /*! \brief Serialize one node to if-else statement*/ std::string NodeToIfElse(int index, bool predict_leaf_index) const; std::string NodeToIfElseByMap(int index, bool predict_leaf_index) const; double ExpectedValue() const; /*! \brief This is used fill in leaf_depth_ after reloading a model*/ inline void RecomputeLeafDepths(int node = 0, int depth = 0); /*! * \brief Used by TreeSHAP for data we keep about our decision path */ struct PathElement { int feature_index; double zero_fraction; double one_fraction; // note that pweight is included for convenience and is not tied with the other attributes, // the pweight of the i'th path element is the permutation weight of paths with i-1 ones in them double pweight; PathElement() {} PathElement(int i, double z, double o, double w) : feature_index(i), zero_fraction(z), one_fraction(o), pweight(w) {} }; /*! \brief Polynomial time algorithm for SHAP values (arXiv:1706.06060)*/ void TreeSHAP(const double *feature_values, double *phi, int node, int unique_depth, PathElement *parent_unique_path, double parent_zero_fraction, double parent_one_fraction, int parent_feature_index) const; void TreeSHAPByMap(const std::unordered_map& feature_values, std::unordered_map* phi, int node, int unique_depth, PathElement *parent_unique_path, double parent_zero_fraction, double parent_one_fraction, int parent_feature_index) const; /*! \brief Extend our decision path with a fraction of one and zero extensions for TreeSHAP*/ static void ExtendPath(PathElement *unique_path, int unique_depth, double zero_fraction, double one_fraction, int feature_index); /*! \brief Undo a previous extension of the decision path for TreeSHAP*/ static void UnwindPath(PathElement *unique_path, int unique_depth, int path_index); /*! determine what the total permutation weight would be if we unwound a previous extension in the decision path*/ static double UnwoundPathSum(const PathElement *unique_path, int unique_depth, int path_index); /*! \brief Number of max leaves*/ int max_leaves_; /*! \brief Number of current leaves*/ int num_leaves_; // following values used for non-leaf node /*! \brief A non-leaf node's left child */ std::vector left_child_; /*! \brief A non-leaf node's right child */ std::vector right_child_; /*! \brief A non-leaf node's split feature */ std::vector split_feature_inner_; /*! \brief A non-leaf node's split feature, the original index */ std::vector split_feature_; /*! \brief A non-leaf node's split threshold in bin */ std::vector threshold_in_bin_; /*! \brief A non-leaf node's split threshold in feature value */ std::vector threshold_; int num_cat_; std::vector cat_boundaries_inner_; std::vector cat_threshold_inner_; std::vector cat_boundaries_; std::vector cat_threshold_; /*! \brief Store the information for categorical feature handle and missing value handle. */ std::vector decision_type_; /*! \brief A non-leaf node's split gain */ std::vector split_gain_; // used for leaf node /*! \brief The parent of leaf */ std::vector leaf_parent_; /*! \brief Output of leaves */ std::vector leaf_value_; /*! \brief weight of leaves */ std::vector leaf_weight_; /*! \brief DataCount of leaves */ std::vector leaf_count_; /*! \brief Output of non-leaf nodes */ std::vector internal_value_; /*! \brief weight of non-leaf nodes */ std::vector internal_weight_; /*! \brief DataCount of non-leaf nodes */ std::vector internal_count_; /*! \brief Depth for leaves */ std::vector leaf_depth_; /*! \brief whether to keep track of ancestor nodes for each leaf (only needed when feature interactions are restricted) */ bool track_branch_features_; /*! \brief Features on leaf's branch, original index */ std::vector> branch_features_; double shrinkage_; int max_depth_; /*! \brief Tree has linear model at each leaf */ bool is_linear_; /*! \brief coefficients of linear models on leaves */ std::vector> leaf_coeff_; /*! \brief constant term (bias) of linear models on leaves */ std::vector leaf_const_; /* \brief features used in leaf linear models; indexing is relative to num_total_features_ */ std::vector> leaf_features_; /* \brief features used in leaf linear models; indexing is relative to used_features_ */ std::vector> leaf_features_inner_; #ifdef USE_CUDA /*! \brief Marks whether this tree is a CUDATree */ bool is_cuda_tree_; #endif // USE_CUDA }; inline void Tree::Split(int leaf, int feature, int real_feature, double left_value, double right_value, int left_cnt, int right_cnt, double left_weight, double right_weight, float gain) { int new_node_idx = num_leaves_ - 1; // update parent info int parent = leaf_parent_[leaf]; if (parent >= 0) { // if cur node is left child if (left_child_[parent] == ~leaf) { left_child_[parent] = new_node_idx; } else { right_child_[parent] = new_node_idx; } } // add new node split_feature_inner_[new_node_idx] = feature; split_feature_[new_node_idx] = real_feature; split_gain_[new_node_idx] = gain; // add two new leaves left_child_[new_node_idx] = ~leaf; right_child_[new_node_idx] = ~num_leaves_; // update new leaves leaf_parent_[leaf] = new_node_idx; leaf_parent_[num_leaves_] = new_node_idx; // save current leaf value to internal node before change internal_weight_[new_node_idx] = left_weight + right_weight; internal_value_[new_node_idx] = leaf_value_[leaf]; internal_count_[new_node_idx] = left_cnt + right_cnt; leaf_value_[leaf] = std::isnan(left_value) ? 0.0f : left_value; leaf_weight_[leaf] = left_weight; leaf_count_[leaf] = left_cnt; leaf_value_[num_leaves_] = std::isnan(right_value) ? 0.0f : right_value; leaf_weight_[num_leaves_] = right_weight; leaf_count_[num_leaves_] = right_cnt; // update leaf depth leaf_depth_[num_leaves_] = leaf_depth_[leaf] + 1; leaf_depth_[leaf]++; if (track_branch_features_) { branch_features_[num_leaves_] = branch_features_[leaf]; branch_features_[num_leaves_].push_back(split_feature_[new_node_idx]); branch_features_[leaf].push_back(split_feature_[new_node_idx]); } } inline double Tree::Predict(const double* feature_values) const { if (is_linear_) { int leaf = (num_leaves_ > 1) ? GetLeaf(feature_values) : 0; double output = leaf_const_[leaf]; bool nan_found = false; for (size_t i = 0; i < leaf_features_[leaf].size(); ++i) { int feat_raw = leaf_features_[leaf][i]; double feat_val = feature_values[feat_raw]; if (std::isnan(feat_val)) { nan_found = true; break; } else { output += leaf_coeff_[leaf][i] * feat_val; } } if (nan_found) { return LeafOutput(leaf); } else { return output; } } else { if (num_leaves_ > 1) { int leaf = GetLeaf(feature_values); return LeafOutput(leaf); } else { return leaf_value_[0]; } } } inline double Tree::PredictByMap(const std::unordered_map& feature_values) const { if (is_linear_) { int leaf = (num_leaves_ > 1) ? GetLeafByMap(feature_values) : 0; double output = leaf_const_[leaf]; bool nan_found = false; for (size_t i = 0; i < leaf_features_[leaf].size(); ++i) { int feat = leaf_features_[leaf][i]; auto val_it = feature_values.find(feat); if (val_it != feature_values.end()) { double feat_val = val_it->second; if (std::isnan(feat_val)) { nan_found = true; break; } else { output += leaf_coeff_[leaf][i] * feat_val; } } } if (nan_found) { return LeafOutput(leaf); } else { return output; } } else { if (num_leaves_ > 1) { int leaf = GetLeafByMap(feature_values); return LeafOutput(leaf); } else { return leaf_value_[0]; } } } inline int Tree::PredictLeafIndex(const double* feature_values) const { if (num_leaves_ > 1) { int leaf = GetLeaf(feature_values); return leaf; } else { return 0; } } inline int Tree::PredictLeafIndexByMap(const std::unordered_map& feature_values) const { if (num_leaves_ > 1) { int leaf = GetLeafByMap(feature_values); return leaf; } else { return 0; } } inline void Tree::PredictContrib(const double* feature_values, int num_features, double* output) { output[num_features] += ExpectedValue(); // Run the recursion with preallocated space for the unique path data if (num_leaves_ > 1) { CHECK_GE(max_depth_, 0); const int max_path_len = max_depth_ + 1; std::vector unique_path_data(max_path_len*(max_path_len + 1) / 2); TreeSHAP(feature_values, output, 0, 0, unique_path_data.data(), 1, 1, -1); } } inline void Tree::PredictContribByMap(const std::unordered_map& feature_values, int num_features, std::unordered_map* output) { (*output)[num_features] += ExpectedValue(); // Run the recursion with preallocated space for the unique path data if (num_leaves_ > 1) { CHECK_GE(max_depth_, 0); const int max_path_len = max_depth_ + 1; std::vector unique_path_data(max_path_len*(max_path_len + 1) / 2); TreeSHAPByMap(feature_values, output, 0, 0, unique_path_data.data(), 1, 1, -1); } } inline void Tree::RecomputeLeafDepths(int node, int depth) { if (node == 0) leaf_depth_.resize(num_leaves()); if (node < 0) { leaf_depth_[~node] = depth; } else { RecomputeLeafDepths(left_child_[node], depth + 1); RecomputeLeafDepths(right_child_[node], depth + 1); } } inline int Tree::GetLeaf(const double* feature_values) const { int node = 0; if (num_cat_ > 0) { while (node >= 0) { node = Decision(feature_values[split_feature_[node]], node); } } else { while (node >= 0) { node = NumericalDecision(feature_values[split_feature_[node]], node); } } return ~node; } inline int Tree::GetLeafByMap(const std::unordered_map& feature_values) const { int node = 0; if (num_cat_ > 0) { while (node >= 0) { node = Decision(feature_values.count(split_feature_[node]) > 0 ? feature_values.at(split_feature_[node]) : 0.0f, node); } } else { while (node >= 0) { node = NumericalDecision(feature_values.count(split_feature_[node]) > 0 ? feature_values.at(split_feature_[node]) : 0.0f, node); } } return ~node; } } // namespace LightGBM #endif // LIGHTGBM_INCLUDE_LIGHTGBM_TREE_H_ ================================================ FILE: include/LightGBM/tree_learner.h ================================================ /*! * Copyright (c) 2016-2026 Microsoft Corporation. All rights reserved. * Copyright (c) 2016-2026 The LightGBM developers. All rights reserved. * Licensed under the MIT License. See LICENSE file in the project root for license information. */ #ifndef LIGHTGBM_INCLUDE_LIGHTGBM_TREE_LEARNER_H_ #define LIGHTGBM_INCLUDE_LIGHTGBM_TREE_LEARNER_H_ #include #include #include #include #include namespace LightGBM { using json11_internal_lightgbm::Json; /*! \brief forward declaration */ class Tree; class Dataset; class ObjectiveFunction; /*! * \brief Interface for tree learner */ class TreeLearner { public: /*! \brief virtual destructor */ virtual ~TreeLearner() {} /*! * \brief Initialize tree learner with training dataset * \param train_data The used training data * \param is_constant_hessian True if all hessians share the same value */ virtual void Init(const Dataset* train_data, bool is_constant_hessian) = 0; /*! Initialise some temporary storage, only needed for the linear tree; needs to be a method of TreeLearner since we call it in GBDT::RefitTree */ virtual void InitLinear(const Dataset* /*train_data*/, const int /*max_leaves*/) {} virtual void ResetIsConstantHessian(bool is_constant_hessian) = 0; virtual void ResetTrainingData(const Dataset* train_data, bool is_constant_hessian) = 0; /*! * \brief Reset tree configs * \param config config of tree */ virtual void ResetConfig(const Config* config) = 0; /*! * \brief Reset boosting_on_gpu_ * \param boosting_on_gpu flag for boosting on GPU */ virtual void ResetBoostingOnGPU(const bool /*boosting_on_gpu*/) {} virtual void SetForcedSplit(const Json* forced_split_json) = 0; /*! * \brief training tree model on dataset * \param gradients The first order gradients * \param hessians The second order gradients * \param is_first_tree If linear tree learning is enabled, first tree needs to be handled differently * \return A trained tree */ virtual Tree* Train(const score_t* gradients, const score_t* hessians, bool is_first_tree) = 0; /*! * \brief use an existing tree to fit the new gradients and hessians. */ virtual Tree* FitByExistingTree(const Tree* old_tree, const score_t* gradients, const score_t* hessians) const = 0; virtual Tree* FitByExistingTree(const Tree* old_tree, const std::vector& leaf_pred, const score_t* gradients, const score_t* hessians) const = 0; /*! * \brief Set bagging data * \param subset subset of bagging * \param used_indices Used data indices * \param num_data Number of used data */ virtual void SetBaggingData(const Dataset* subset, const data_size_t* used_indices, data_size_t num_data) = 0; /*! * \brief Using last trained tree to predict score then adding to out_score; * \param out_score output score */ virtual void AddPredictionToScore(const Tree* tree, double* out_score) const = 0; virtual void RenewTreeOutput(Tree* tree, const ObjectiveFunction* obj, std::function residual_getter, data_size_t total_num_data, const data_size_t* bag_indices, data_size_t bag_cnt, const double* train_score) const = 0; TreeLearner() = default; /*! \brief Disable copy */ TreeLearner& operator=(const TreeLearner&) = delete; /*! \brief Disable copy */ TreeLearner(const TreeLearner&) = delete; /*! * \brief Create object of tree learner * \param learner_type Type of tree learner * \param device_type Type of tree learner * \param booster_type Type of boosting * \param config config of tree */ static TreeLearner* CreateTreeLearner(const std::string& learner_type, const std::string& device_type, const Config* config, const bool boosting_on_cuda); }; } // namespace LightGBM #endif // LIGHTGBM_INCLUDE_LIGHTGBM_TREE_LEARNER_H_ ================================================ FILE: include/LightGBM/utils/array_args.h ================================================ /*! * Copyright (c) 2016-2026 Microsoft Corporation. All rights reserved. * Copyright (c) 2020-2026 The LightGBM developers. All rights reserved. * Licensed under the MIT License. See LICENSE file in the project root for license information. */ #ifndef LIGHTGBM_INCLUDE_LIGHTGBM_UTILS_ARRAY_ARGS_H_ #define LIGHTGBM_INCLUDE_LIGHTGBM_UTILS_ARRAY_ARGS_H_ #include #include #include #include #include namespace LightGBM { /*! * \brief Contains some operation for an array, e.g. ArgMax, TopK. */ template class ArrayArgs { public: inline static size_t ArgMaxMT(const std::vector& array) { int num_threads = OMP_NUM_THREADS(); std::vector arg_maxs(num_threads, 0); int n_blocks = Threading::For( 0, array.size(), 1024, [&array, &arg_maxs](int i, size_t start, size_t end) { size_t arg_max = start; for (size_t j = start + 1; j < end; ++j) { if (array[j] > array[arg_max]) { arg_max = j; } } arg_maxs[i] = arg_max; }); size_t ret = arg_maxs[0]; for (int i = 1; i < n_blocks; ++i) { if (array[arg_maxs[i]] > array[ret]) { ret = arg_maxs[i]; } } return ret; } inline static size_t ArgMax(const std::vector& array) { if (array.empty()) { return 0; } if (array.size() > 1024) { return ArgMaxMT(array); } else { size_t arg_max = 0; for (size_t i = 1; i < array.size(); ++i) { if (array[i] > array[arg_max]) { arg_max = i; } } return arg_max; } } inline static size_t ArgMin(const std::vector& array) { if (array.empty()) { return 0; } size_t arg_min = 0; for (size_t i = 1; i < array.size(); ++i) { if (array[i] < array[arg_min]) { arg_min = i; } } return arg_min; } inline static size_t ArgMax(const VAL_T* array, size_t n) { if (n <= 0) { return 0; } size_t arg_max = 0; for (size_t i = 1; i < n; ++i) { if (array[i] > array[arg_max]) { arg_max = i; } } return arg_max; } inline static size_t ArgMin(const VAL_T* array, size_t n) { if (n <= 0) { return 0; } size_t arg_min = 0; for (size_t i = 1; i < n; ++i) { if (array[i] < array[arg_min]) { arg_min = i; } } return arg_min; } inline static void Partition(std::vector* arr, int start, int end, int* l, int* r) { int i = start - 1; int j = end - 1; int p = i; int q = j; if (start >= end - 1) { *l = start - 1; *r = end; return; } std::vector& ref = *arr; VAL_T v = ref[end - 1]; for (;;) { while (ref[++i] > v) {} while (v > ref[--j]) { if (j == start) { break; } } if (i >= j) { break; } std::swap(ref[i], ref[j]); if (ref[i] == v) { p++; std::swap(ref[p], ref[i]); } if (v == ref[j]) { q--; std::swap(ref[j], ref[q]); } } std::swap(ref[i], ref[end - 1]); j = i - 1; i = i + 1; for (int k = start; k <= p; k++, j--) { std::swap(ref[k], ref[j]); } for (int k = end - 2; k >= q; k--, i++) { std::swap(ref[i], ref[k]); } *l = j; *r = i; } // Note: k refer to index here. e.g. k=0 means get the max number. inline static int ArgMaxAtK(std::vector* arr, int start, int end, int k) { if (start >= end - 1) { return start; } int l = start; int r = end - 1; Partition(arr, start, end, &l, &r); // if find or all elements are the same. if ((k > l && k < r) || (l == start - 1 && r == end - 1)) { return k; } else if (k <= l) { return ArgMaxAtK(arr, start, l + 1, k); } else { return ArgMaxAtK(arr, r, end, k); } } // Note: k is 1-based here. e.g. k=3 means get the top-3 numbers. inline static void MaxK(const std::vector& array, int k, std::vector* out) { out->clear(); if (k <= 0) { return; } for (auto val : array) { out->push_back(val); } if (static_cast(k) >= array.size()) { return; } ArgMaxAtK(out, 0, static_cast(out->size()), k - 1); out->erase(out->begin() + k, out->end()); } inline static void Assign(std::vector* array, VAL_T t, size_t n) { array->resize(n); for (size_t i = 0; i < array->size(); ++i) { (*array)[i] = t; } } inline static bool CheckAllZero(const std::vector& array) { for (size_t i = 0; i < array.size(); ++i) { if (array[i] != VAL_T(0)) { return false; } } return true; } inline static bool CheckAll(const std::vector& array, VAL_T t) { for (size_t i = 0; i < array.size(); ++i) { if (array[i] != t) { return false; } } return true; } }; } // namespace LightGBM #endif // LIGHTGBM_INCLUDE_LIGHTGBM_UTILS_ARRAY_ARGS_H_ ================================================ FILE: include/LightGBM/utils/binary_writer.h ================================================ /*! * Copyright (c) 2022-2026 Microsoft Corporation. All rights reserved. * Copyright (c) 2022-2026 The LightGBM developers. All rights reserved. * Licensed under the MIT License. See LICENSE file in the project root for license information. */ #ifndef LIGHTGBM_INCLUDE_LIGHTGBM_UTILS_BINARY_WRITER_H_ #define LIGHTGBM_INCLUDE_LIGHTGBM_UTILS_BINARY_WRITER_H_ #include #include namespace LightGBM { /*! * \brief An interface for serializing binary data to a buffer */ struct BinaryWriter { /*! * \brief Append data to this binary target * \param data Buffer to write from * \param bytes Number of bytes to write from buffer * \return Number of bytes written */ virtual size_t Write(const void* data, size_t bytes) = 0; /*! * \brief Append data to this binary target aligned on a given byte size boundary * \param data Buffer to write from * \param bytes Number of bytes to write from buffer * \param alignment The size of bytes to align to in whole increments * \return Number of bytes written */ size_t AlignedWrite(const void* data, size_t bytes, size_t alignment = 8) { auto ret = Write(data, bytes); if (bytes % alignment != 0) { size_t padding = AlignedSize(bytes, alignment) - bytes; std::vector tmp(padding, 0); ret += Write(tmp.data(), padding); } return ret; } /*! * \brief The aligned size of a buffer length. * \param bytes The number of bytes in a buffer * \param alignment The size of bytes to align to in whole increments * \return Number of aligned bytes */ static size_t AlignedSize(size_t bytes, size_t alignment = 8) { if (bytes % alignment == 0) { return bytes; } else { return bytes / alignment * alignment + alignment; } } }; } // namespace LightGBM #endif // LIGHTGBM_INCLUDE_LIGHTGBM_UTILS_BINARY_WRITER_H_ ================================================ FILE: include/LightGBM/utils/byte_buffer.h ================================================ /*! * Copyright (c) 2022-2026 Microsoft Corporation. All rights reserved. * Copyright (c) 2022-2026 The LightGBM developers. All rights reserved. * Licensed under the MIT License. See LICENSE file in the project root for license information. */ #ifndef LIGHTGBM_INCLUDE_LIGHTGBM_UTILS_BYTE_BUFFER_H_ #define LIGHTGBM_INCLUDE_LIGHTGBM_UTILS_BYTE_BUFFER_H_ #include #include #include #include #include #include #include #include #include namespace LightGBM { /*! * \brief An implementation for serializing binary data to an auto-expanding memory buffer */ struct ByteBuffer final : public BinaryWriter { ByteBuffer() {} explicit ByteBuffer(size_t initial_size) { buffer_.reserve(initial_size); } size_t Write(const void* data, size_t bytes) { const char* mem_ptr = static_cast(data); for (size_t i = 0; i < bytes; ++i) { buffer_.push_back(mem_ptr[i]); } return bytes; } LIGHTGBM_EXPORT void Reserve(size_t capacity) { buffer_.reserve(capacity); } LIGHTGBM_EXPORT size_t GetSize() { return buffer_.size(); } LIGHTGBM_EXPORT char GetAt(size_t index) { return buffer_.at(index); } LIGHTGBM_EXPORT char* Data() { return buffer_.data(); } private: std::vector buffer_; }; } // namespace LightGBM #endif // LIGHTGBM_INCLUDE_LIGHTGBM_UTILS_BYTE_BUFFER_H_ ================================================ FILE: include/LightGBM/utils/chunked_array.hpp ================================================ /*! * Copyright (c) 2021-2026 Microsoft Corporation. All rights reserved. * Copyright (c) 2021-2026 The LightGBM developers. All rights reserved. * Licensed under the MIT License. See LICENSE file in the project root for license information. * * Author: Alberto Ferreira */ #ifndef LIGHTGBM_INCLUDE_LIGHTGBM_UTILS_CHUNKED_ARRAY_HPP_ #define LIGHTGBM_INCLUDE_LIGHTGBM_UTILS_CHUNKED_ARRAY_HPP_ #include #include #include #include #include namespace LightGBM { /** * Container that manages a dynamic array of fixed-length chunks. * * The class also takes care of allocation & release of the underlying * memory. It can be used with either a high or low-level API. * * The high-level API allocates chunks as needed, manages addresses automatically and keeps * track of number of inserted elements, but is not thread-safe (this is ok as usually input is a streaming iterator). * For parallel input sources the low-level API must be used. * * Note: When using this for `LGBM_DatasetCreateFromMats` use a * chunk_size multiple of #num_cols for your dataset, so each chunk * contains "complete" instances. * * === High-level insert API intro === * * The easiest way to use is: * 0. ChunkedArray(chunk_size) # Choose appropriate size * 1. add(value) # as many times as you want (will generate chunks as needed) * 2. data() or void_data() # retrieves a T** or void** pointer (useful for `LGBM_DatasetCreateFromMats`). * * Useful query methods (all O(1)): * - get_add_count() # total count of added elements. * - get_chunks_count() # how many chunks are currently allocated. * - get_current_chunk_added_count() # for the last add() chunk, how many items there are. * - get_chunk_size() # get constant chunk_size from constructor call. * * With those you can generate int32_t sizes[]. Last chunk can be smaller than chunk_size, so, for any i: * - sizes[i class ChunkedArray { public: explicit ChunkedArray(size_t chunk_size) : _chunk_size(chunk_size), _last_chunk_idx(0), _last_idx_in_last_chunk(0) { if (chunk_size == 0) { Log::Fatal("ChunkedArray chunk size must be larger than 0!"); } new_chunk(); } ~ChunkedArray() { release(); } /** * Adds a value to the chunks sequentially. * If the last chunk is full it creates a new one and appends to it. * * @param value value to insert. */ void add(T value) { if (!within_bounds(_last_chunk_idx, _last_idx_in_last_chunk)) { new_chunk(); ++_last_chunk_idx; _last_idx_in_last_chunk = 0; } CHECK_EQ(setitem(_last_chunk_idx, _last_idx_in_last_chunk, value), 0); ++_last_idx_in_last_chunk; } /** * @return Number of add() calls. */ size_t get_add_count() const { return _last_chunk_idx * _chunk_size + _last_idx_in_last_chunk; } /** * @return Number of allocated chunks. */ size_t get_chunks_count() const { return _chunks.size(); } /** * @return Number of elemends add()'ed in the last chunk. */ size_t get_last_chunk_add_count() const { return _last_idx_in_last_chunk; } /** * Getter for the chunk size set at the constructor. * * @return Return the size of chunks. */ size_t get_chunk_size() const { return _chunk_size; } /** * Returns the pointer to the raw chunks data. * * @return T** pointer to raw data. */ T **data() noexcept { return _chunks.data(); } /** * Returns the pointer to the raw chunks data, but cast to void**. * This is so ``LGBM_DatasetCreateFromMats`` accepts it. * * @return void** pointer to raw data. */ void **data_as_void() noexcept { return reinterpret_cast(_chunks.data()); } /** * Coalesces (copies chunked data) to a contiguous array of the same type. * It assumes that ``other`` has enough space to receive that data. * * @param other array with elements T of size >= this->get_add_count(). * @param all_valid_addresses * If true exports values from all valid addresses independently of add() count. * Otherwise, exports only up to `get_add_count()` addresses. */ void coalesce_to(T *other, bool all_valid_addresses = false) const { const size_t full_chunks = this->get_chunks_count() - 1; // Copy full chunks: size_t i = 0; for (size_t chunk = 0; chunk < full_chunks; ++chunk) { T* chunk_ptr = _chunks[chunk]; for (size_t in_chunk_idx = 0; in_chunk_idx < _chunk_size; ++in_chunk_idx) { other[i++] = chunk_ptr[in_chunk_idx]; } } // Copy filled values from last chunk only: const size_t last_chunk_elems_to_copy = all_valid_addresses ? _chunk_size : this->get_last_chunk_add_count(); T* chunk_ptr = _chunks[full_chunks]; for (size_t in_chunk_idx = 0; in_chunk_idx < last_chunk_elems_to_copy; ++in_chunk_idx) { other[i++] = chunk_ptr[in_chunk_idx]; } } /** * Return value from array of chunks. * * @param chunk_index index of the chunk * @param index_within_chunk index within chunk * @param on_fail_value sentinel value. If out of bounds returns that value. * * @return pointer or nullptr if index is out of bounds. */ T getitem(size_t chunk_index, size_t index_within_chunk, T on_fail_value) const noexcept { if (within_bounds(chunk_index, index_within_chunk)) return _chunks[chunk_index][index_within_chunk]; else return on_fail_value; } /** * Sets the value at a specific address in one of the chunks. * * @param chunk_index index of the chunk * @param index_within_chunk index within chunk * @param value value to store * * @return 0 = success, -1 = out of bounds access. */ int setitem(size_t chunk_index, size_t index_within_chunk, T value) noexcept { if (within_bounds(chunk_index, index_within_chunk)) { _chunks[chunk_index][index_within_chunk] = value; return 0; } else { return -1; } } /** * To reset storage call this. * Will release existing resources and prepare for reuse. */ void clear() noexcept { release(); new_chunk(); } /** * Deletes all the allocated chunks. * Do not use container after this! See ``clear()`` instead. */ void release() noexcept { std::for_each(_chunks.begin(), _chunks.end(), [](T* c) { delete[] c; }); _chunks.clear(); _chunks.shrink_to_fit(); _last_chunk_idx = 0; _last_idx_in_last_chunk = 0; } /** * As the array is dynamic, checks whether a given address is currently within bounds. * * @param chunk_index index of the chunk * @param index_within_chunk index within that chunk * @return true if that chunk is already allocated and index_within_chunk < chunk size. */ inline bool within_bounds(size_t chunk_index, size_t index_within_chunk) const { return (chunk_index < _chunks.size()) && (index_within_chunk < _chunk_size); } /** * Adds a new chunk to the array of chunks. Not thread-safe. */ void new_chunk() { _chunks.push_back(new (std::nothrow) T[_chunk_size]); // Check memory allocation success: if (!_chunks[_chunks.size() - 1]) { release(); Log::Fatal("Memory exhausted! Cannot allocate new ChunkedArray chunk."); } } private: const size_t _chunk_size; std::vector _chunks; // For the add() interface & some of the get_*() queries: size_t _last_chunk_idx; // #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #define FMT_HEADER_ONLY #include "fast_double_parser.h" #include "fmt/format.h" #ifdef _MSC_VER #include #pragma intrinsic(_BitScanReverse) #endif #if defined(_MSC_VER) #include #elif MM_MALLOC #include // https://gcc.gnu.org/onlinedocs/cpp/Common-Predefined-Macros.html // https://www.oreilly.com/library/view/mac-os-x/0596003560/ch05s01s02.html #elif defined(__GNUC__) && defined(HAVE_MALLOC_H) #include #define _mm_malloc(a, b) memalign(b, a) #define _mm_free(a) free(a) #else #include #define _mm_malloc(a, b) malloc(a) #define _mm_free(a) free(a) #endif namespace LightGBM { namespace Common { using json11_internal_lightgbm::Json; /*! * Imbues the stream with the C locale. */ static void C_stringstream(std::stringstream &ss) { ss.imbue(std::locale::classic()); } inline static std::string Trim(std::string str) { if (str.empty()) { return str; } str.erase(str.find_last_not_of(" \f\n\r\t\v") + 1); str.erase(0, str.find_first_not_of(" \f\n\r\t\v")); return str; } inline static std::string RemoveQuotationSymbol(std::string str) { if (str.empty()) { return str; } str.erase(str.find_last_not_of("'\"") + 1); str.erase(0, str.find_first_not_of("'\"")); return str; } inline static bool StartsWith(const std::string& str, const std::string prefix) { if (str.substr(0, prefix.size()) == prefix) { return true; } else { return false; } } inline static std::vector Split(const char* c_str, char delimiter) { std::vector ret; std::string str(c_str); size_t i = 0; size_t pos = 0; while (pos < str.length()) { if (str[pos] == delimiter) { if (i < pos) { ret.push_back(str.substr(i, pos - i)); } ++pos; i = pos; } else { ++pos; } } if (i < pos) { ret.push_back(str.substr(i)); } return ret; } inline static std::vector SplitBrackets(const char* c_str, char left_delimiter, char right_delimiter) { std::vector ret; std::string str(c_str); size_t i = 0; size_t pos = 0; bool open = false; while (pos < str.length()) { if (str[pos] == left_delimiter) { open = true; ++pos; i = pos; } else if (str[pos] == right_delimiter && open) { if (i < pos) { ret.push_back(str.substr(i, pos - i)); } open = false; ++pos; } else { ++pos; } } return ret; } inline static std::vector SplitLines(const char* c_str) { std::vector ret; std::string str(c_str); size_t i = 0; size_t pos = 0; while (pos < str.length()) { if (str[pos] == '\n' || str[pos] == '\r') { if (i < pos) { ret.push_back(str.substr(i, pos - i)); } // skip the line endings while (str[pos] == '\n' || str[pos] == '\r') ++pos; // new begin i = pos; } else { ++pos; } } if (i < pos) { ret.push_back(str.substr(i)); } return ret; } inline static std::vector Split(const char* c_str, const char* delimiters) { std::vector ret; std::string str(c_str); size_t i = 0; size_t pos = 0; while (pos < str.length()) { bool met_delimiters = false; for (int j = 0; delimiters[j] != '\0'; ++j) { if (str[pos] == delimiters[j]) { met_delimiters = true; break; } } if (met_delimiters) { if (i < pos) { ret.push_back(str.substr(i, pos - i)); } ++pos; i = pos; } else { ++pos; } } if (i < pos) { ret.push_back(str.substr(i)); } return ret; } inline static std::string GetFromParserConfig(std::string config_str, std::string key) { // parser config should follow json format. std::string err; Json config_json = Json::parse(config_str, &err); if (!err.empty()) { Log::Fatal("Invalid parser config: %s. Please check if follow json format.", err.c_str()); } return config_json[key].string_value(); } inline static std::string SaveToParserConfig(std::string config_str, std::string key, std::string value) { std::string err; Json config_json = Json::parse(config_str, &err); if (!err.empty()) { Log::Fatal("Invalid parser config: %s. Please check if follow json format.", err.c_str()); } CHECK(config_json.is_object()); std::map config_map = config_json.object_items(); config_map.insert(std::pair(key, Json(value))); return Json(config_map).dump(); } template inline static const char* Atoi(const char* p, T* out) { int sign; T value; while (*p == ' ') { ++p; } sign = 1; if (*p == '-') { sign = -1; ++p; } else if (*p == '+') { ++p; } for (value = 0; *p >= '0' && *p <= '9'; ++p) { value = value * 10 + (*p - '0'); } *out = static_cast(sign * value); while (*p == ' ') { ++p; } return p; } template inline static double Pow(T base, int power) { if (power < 0) { return 1.0 / Pow(base, -power); } else if (power == 0) { return 1; } else if (power % 2 == 0) { return Pow(base*base, power / 2); } else if (power % 3 == 0) { return Pow(base*base*base, power / 3); } else { return base * Pow(base, power - 1); } } inline static const char* Atof(const char* p, double* out) { int frac; double sign, value, scale; *out = NAN; // Skip leading white space, if any. while (*p == ' ') { ++p; } // Get sign, if any. sign = 1.0; if (*p == '-') { sign = -1.0; ++p; } else if (*p == '+') { ++p; } // is a number if ((*p >= '0' && *p <= '9') || *p == '.' || *p == 'e' || *p == 'E') { // Get digits before decimal point or exponent, if any. for (value = 0.0; *p >= '0' && *p <= '9'; ++p) { value = value * 10.0 + (*p - '0'); } // Get digits after decimal point, if any. if (*p == '.') { double right = 0.0; int nn = 0; ++p; while (*p >= '0' && *p <= '9') { right = (*p - '0') + right * 10.0; ++nn; ++p; } value += right / Pow(10.0, nn); } // Handle exponent, if any. frac = 0; scale = 1.0; if ((*p == 'e') || (*p == 'E')) { uint32_t expon; // Get sign of exponent, if any. ++p; if (*p == '-') { frac = 1; ++p; } else if (*p == '+') { ++p; } // Get digits of exponent, if any. for (expon = 0; *p >= '0' && *p <= '9'; ++p) { expon = expon * 10 + (*p - '0'); } if (expon > 308) expon = 308; // Calculate scaling factor. while (expon >= 50) { scale *= 1E50; expon -= 50; } while (expon >= 8) { scale *= 1E8; expon -= 8; } while (expon > 0) { scale *= 10.0; expon -= 1; } } // Return signed and scaled floating point result. *out = sign * (frac ? (value / scale) : (value * scale)); } else { size_t cnt = 0; while (*(p + cnt) != '\0' && *(p + cnt) != ' ' && *(p + cnt) != '\t' && *(p + cnt) != ',' && *(p + cnt) != '\n' && *(p + cnt) != '\r' && *(p + cnt) != ':') { ++cnt; } if (cnt > 0) { std::string tmp_str(p, cnt); std::transform(tmp_str.begin(), tmp_str.end(), tmp_str.begin(), [](unsigned char c){ return std::tolower(c); }); if (tmp_str == std::string("na") || tmp_str == std::string("nan") || tmp_str == std::string("null")) { *out = NAN; } else if (tmp_str == std::string("inf") || tmp_str == std::string("infinity")) { *out = sign * 1e308; } else { Log::Fatal("Unknown token %s in data file", tmp_str.c_str()); } p += cnt; } } while (*p == ' ') { ++p; } return p; } // Use fast_double_parse and strtod (if parse failed) to parse double. inline static const char* AtofPrecise(const char* p, double* out) { const char* end = fast_double_parser::parse_number(p, out); if (end != nullptr) { return end; } // Rare path: Not in RFC 7159 format. Possible "inf", "nan", etc. Fallback to standard library: char* end2; errno = 0; // This is Required before calling strtod. *out = std::strtod(p, &end2); // strtod is locale aware. if (end2 == p) { Log::Fatal("no conversion to double for: %s", p); } if (errno == ERANGE) { Log::Warning("convert to double got underflow or overflow: %s", p); } return end2; } inline static bool AtoiAndCheck(const char* p, int* out) { const char* after = Atoi(p, out); if (*after != '\0') { return false; } return true; } inline static bool AtofAndCheck(const char* p, double* out) { const char* after = Atof(p, out); if (*after != '\0') { return false; } return true; } inline static const char* SkipSpaceAndTab(const char* p) { while (*p == ' ' || *p == '\t') { ++p; } return p; } inline static const char* SkipReturn(const char* p) { while (*p == '\n' || *p == '\r' || *p == ' ') { ++p; } return p; } template inline static std::vector ArrayCast(const std::vector& arr) { std::vector ret(arr.size()); for (size_t i = 0; i < arr.size(); ++i) { ret[i] = static_cast(arr[i]); } return ret; } template struct __StringToTHelper { T operator()(const std::string& str) const { T ret = 0; Atoi(str.c_str(), &ret); return ret; } }; template struct __StringToTHelper { T operator()(const std::string& str) const { return static_cast(std::stod(str)); } }; template inline static std::vector StringToArray(const std::string& str, char delimiter) { std::vector strs = Split(str.c_str(), delimiter); std::vector ret; ret.reserve(strs.size()); __StringToTHelper::value> helper; for (const auto& s : strs) { ret.push_back(helper(s)); } return ret; } template inline static std::vector> StringToArrayofArrays( const std::string& str, char left_bracket, char right_bracket, char delimiter) { std::vector strs = SplitBrackets(str.c_str(), left_bracket, right_bracket); std::vector> ret; for (const auto& s : strs) { ret.push_back(StringToArray(s, delimiter)); } return ret; } template inline static std::vector StringToArray(const std::string& str, int n) { if (n == 0) { return std::vector(); } std::vector strs = Split(str.c_str(), ' '); CHECK_EQ(strs.size(), static_cast(n)); std::vector ret; ret.reserve(strs.size()); __StringToTHelper::value> helper; for (const auto& s : strs) { ret.push_back(helper(s)); } return ret; } template struct __StringToTHelperFast { const char* operator()(const char*p, T* out) const { return Atoi(p, out); } }; template struct __StringToTHelperFast { const char* operator()(const char*p, T* out) const { double tmp = 0.0f; auto ret = Atof(p, &tmp); *out = static_cast(tmp); return ret; } }; template inline static std::vector StringToArrayFast(const std::string& str, int n) { if (n == 0) { return std::vector(); } auto p_str = str.c_str(); __StringToTHelperFast::value> helper; std::vector ret(n); for (int i = 0; i < n; ++i) { p_str = helper(p_str, &ret[i]); } return ret; } template inline static std::string Join(const std::vector& strs, const char* delimiter, const bool force_C_locale = false) { if (strs.empty()) { return std::string(""); } std::stringstream str_buf; if (force_C_locale) { C_stringstream(str_buf); } str_buf << std::setprecision(std::numeric_limits::digits10 + 2); str_buf << strs[0]; for (size_t i = 1; i < strs.size(); ++i) { str_buf << delimiter; str_buf << strs[i]; } return str_buf.str(); } template<> inline std::string Join(const std::vector& strs, const char* delimiter, const bool force_C_locale) { if (strs.empty()) { return std::string(""); } std::stringstream str_buf; if (force_C_locale) { C_stringstream(str_buf); } str_buf << std::setprecision(std::numeric_limits::digits10 + 2); str_buf << static_cast(strs[0]); for (size_t i = 1; i < strs.size(); ++i) { str_buf << delimiter; str_buf << static_cast(strs[i]); } return str_buf.str(); } template inline static std::string Join(const std::vector& strs, size_t start, size_t end, const char* delimiter, const bool force_C_locale = false) { if (end - start <= 0) { return std::string(""); } start = std::min(start, static_cast(strs.size()) - 1); end = std::min(end, static_cast(strs.size())); std::stringstream str_buf; if (force_C_locale) { C_stringstream(str_buf); } str_buf << std::setprecision(std::numeric_limits::digits10 + 2); str_buf << strs[start]; for (size_t i = start + 1; i < end; ++i) { str_buf << delimiter; str_buf << strs[i]; } return str_buf.str(); } inline static int64_t Pow2RoundUp(int64_t x) { int64_t t = 1; for (int i = 0; i < 64; ++i) { if (t >= x) { return t; } t <<= 1; } return 0; } /*! * \brief Do inplace softmax transformation on p_rec * \param p_rec The input/output vector of the values. */ inline static void Softmax(std::vector* p_rec) { std::vector &rec = *p_rec; double wmax = rec[0]; for (size_t i = 1; i < rec.size(); ++i) { wmax = std::max(rec[i], wmax); } double wsum = 0.0f; for (size_t i = 0; i < rec.size(); ++i) { rec[i] = std::exp(rec[i] - wmax); wsum += rec[i]; } for (size_t i = 0; i < rec.size(); ++i) { rec[i] /= static_cast(wsum); } } inline static void Softmax(const double* input, double* output, int len) { double wmax = input[0]; for (int i = 1; i < len; ++i) { wmax = std::max(input[i], wmax); } double wsum = 0.0f; for (int i = 0; i < len; ++i) { output[i] = std::exp(input[i] - wmax); wsum += output[i]; } for (int i = 0; i < len; ++i) { output[i] /= static_cast(wsum); } } template std::vector ConstPtrInVectorWrapper(const std::vector>& input) { std::vector ret; for (auto t = input.begin(); t !=input.end(); ++t) { ret.push_back(t->get()); } return ret; } template inline static void SortForPair(std::vector* keys, std::vector* values, size_t start, bool is_reverse = false) { std::vector> arr; auto& ref_key = *keys; auto& ref_value = *values; for (size_t i = start; i < keys->size(); ++i) { arr.emplace_back(ref_key[i], ref_value[i]); } if (!is_reverse) { std::stable_sort(arr.begin(), arr.end(), [](const std::pair& a, const std::pair& b) { return a.first < b.first; }); } else { std::stable_sort(arr.begin(), arr.end(), [](const std::pair& a, const std::pair& b) { return a.first > b.first; }); } for (size_t i = start; i < arr.size(); ++i) { ref_key[i] = arr[i].first; ref_value[i] = arr[i].second; } } template inline static std::vector Vector2Ptr(std::vector>* data) { std::vector ptr(data->size()); auto& ref_data = *data; for (size_t i = 0; i < data->size(); ++i) { ptr[i] = ref_data[i].data(); } return ptr; } template inline static std::vector VectorSize(const std::vector>& data) { std::vector ret(data.size()); for (size_t i = 0; i < data.size(); ++i) { ret[i] = static_cast(data[i].size()); } return ret; } inline static double AvoidInf(double x) { if (std::isnan(x)) { return 0.0; } else if (x >= 1e300) { return 1e300; } else if (x <= -1e300) { return -1e300; } else { return x; } } inline static float AvoidInf(float x) { if (std::isnan(x)) { return 0.0f; } else if (x >= 1e38) { return 1e38f; } else if (x <= -1e38) { return -1e38f; } else { return x; } } template inline static typename std::iterator_traits<_Iter>::value_type* IteratorValType(_Iter) { return (0); } template inline static void ParallelSort(_RanIt _First, _RanIt _Last, _Pr _Pred, _VTRanIt*) { size_t len = _Last - _First; const size_t kMinInnerLen = 1024; int num_threads = OMP_NUM_THREADS(); if (len <= kMinInnerLen || num_threads <= 1) { std::sort(_First, _Last, _Pred); return; } size_t inner_size = (len + num_threads - 1) / num_threads; inner_size = std::max(inner_size, kMinInnerLen); num_threads = static_cast((len + inner_size - 1) / inner_size); #pragma omp parallel for num_threads(num_threads) schedule(static, 1) for (int i = 0; i < num_threads; ++i) { size_t left = inner_size*i; size_t right = left + inner_size; right = std::min(right, len); if (right > left) { std::sort(_First + left, _First + right, _Pred); } } // Buffer for merge. std::vector<_VTRanIt> temp_buf(len); _RanIt buf = temp_buf.begin(); size_t s = inner_size; // Recursive merge while (s < len) { int loop_size = static_cast((len + s * 2 - 1) / (s * 2)); #pragma omp parallel for num_threads(num_threads) schedule(static, 1) for (int i = 0; i < loop_size; ++i) { size_t left = i * 2 * s; size_t mid = left + s; size_t right = mid + s; right = std::min(len, right); if (mid >= right) { continue; } std::copy(_First + left, _First + mid, buf + left); std::merge(buf + left, buf + mid, _First + mid, _First + right, _First + left, _Pred); } s *= 2; } } template inline static void ParallelSort(_RanIt _First, _RanIt _Last, _Pr _Pred) { return ParallelSort(_First, _Last, _Pred, IteratorValType(_First)); } // Check that all y[] are in interval [ymin, ymax] (end points included); throws error if not template inline static void CheckElementsIntervalClosed(const T *y, T ymin, T ymax, int ny, const char *callername) { auto fatal_msg = [&y, &ymin, &ymax, &callername](int i) { std::ostringstream os; os << "[%s]: does not tolerate element [#%i = " << y[i] << "] outside [" << ymin << ", " << ymax << "]"; Log::Fatal(os.str().c_str(), callername, i); }; for (int i = 1; i < ny; i += 2) { if (y[i - 1] < y[i]) { if (y[i - 1] < ymin) { fatal_msg(i - 1); } else if (y[i] > ymax) { fatal_msg(i); } } else { if (y[i - 1] > ymax) { fatal_msg(i - 1); } else if (y[i] < ymin) { fatal_msg(i); } } } if (ny & 1) { // odd if (y[ny - 1] < ymin || y[ny - 1] > ymax) { fatal_msg(ny - 1); } } } // One-pass scan over array w with nw elements: find min, max and sum of elements; // this is useful for checking weight requirements. template inline static void ObtainMinMaxSum(const T1 *w, int nw, T1 *mi, T1 *ma, T2 *su) { T1 minw; T1 maxw; T1 sumw; int i; if (nw & 1) { // odd minw = w[0]; maxw = w[0]; sumw = w[0]; i = 2; } else { // even if (w[0] < w[1]) { minw = w[0]; maxw = w[1]; } else { minw = w[1]; maxw = w[0]; } sumw = w[0] + w[1]; i = 3; } for (; i < nw; i += 2) { if (w[i - 1] < w[i]) { minw = std::min(minw, w[i - 1]); maxw = std::max(maxw, w[i]); } else { minw = std::min(minw, w[i]); maxw = std::max(maxw, w[i - 1]); } sumw += w[i - 1] + w[i]; } if (mi != nullptr) { *mi = minw; } if (ma != nullptr) { *ma = maxw; } if (su != nullptr) { *su = static_cast(sumw); } } inline static std::vector EmptyBitset(int n) { int size = n / 32; if (n % 32 != 0) ++size; return std::vector(size); } template inline static void InsertBitset(std::vector* vec, const T val) { auto& ref_v = *vec; int i1 = val / 32; int i2 = val % 32; if (static_cast(vec->size()) < i1 + 1) { vec->resize(i1 + 1, 0); } ref_v[i1] |= (1 << i2); } template inline static std::vector ConstructBitset(const T* vals, int n) { std::vector ret; for (int i = 0; i < n; ++i) { int i1 = vals[i] / 32; int i2 = vals[i] % 32; if (static_cast(ret.size()) < i1 + 1) { ret.resize(i1 + 1, 0); } ret[i1] |= (1 << i2); } return ret; } template inline static bool FindInBitset(const uint32_t* bits, int n, T pos) { int i1 = pos / 32; if (i1 >= n) { return false; } int i2 = pos % 32; return (bits[i1] >> i2) & 1; } inline static bool CheckDoubleEqualOrdered(double a, double b) { double upper = std::nextafter(a, INFINITY); return b <= upper; } inline static double GetDoubleUpperBound(double a) { return std::nextafter(a, INFINITY); } inline static size_t GetLine(const char* str) { auto start = str; while (*str != '\0' && *str != '\n' && *str != '\r') { ++str; } return str - start; } inline static const char* SkipNewLine(const char* str) { if (*str == '\r') { ++str; } if (*str == '\n') { ++str; } return str; } template static int Sign(T x) { return (x > T(0)) - (x < T(0)); } template static T SafeLog(T x) { if (x > 0) { return std::log(x); } else { return -INFINITY; } } inline bool CheckAllowedJSON(const std::string& s) { unsigned char char_code; for (auto c : s) { char_code = static_cast(c); if (char_code == 34 // " || char_code == 44 // , || char_code == 58 // : || char_code == 91 // [ || char_code == 93 // ] || char_code == 123 // { || char_code == 125 // } ) { return false; } } return true; } inline int RoundInt(double x) { return static_cast(x + 0.5f); } template class AlignmentAllocator { public: typedef T value_type; typedef std::size_t size_type; typedef std::ptrdiff_t difference_type; typedef T* pointer; typedef const T* const_pointer; typedef T& reference; typedef const T& const_reference; inline AlignmentAllocator() throw() {} template inline AlignmentAllocator(const AlignmentAllocator&) throw() {} inline ~AlignmentAllocator() throw() {} inline pointer address(reference r) { return &r; } inline const_pointer address(const_reference r) const { return &r; } inline pointer allocate(size_type n) { return (pointer)_mm_malloc(n * sizeof(value_type), N); } inline void deallocate(pointer p, size_type) { _mm_free(p); } inline void construct(pointer p, const value_type& wert) { new (p) value_type(wert); } inline void destroy(pointer p) { p->~value_type(); } inline size_type max_size() const throw() { return size_type(-1) / sizeof(value_type); } template struct rebind { typedef AlignmentAllocator other; }; bool operator!=(const AlignmentAllocator& other) const { return !(*this == other); } // Returns true if and only if storage allocated from *this // can be deallocated from other, and vice versa. // Always returns true for stateless allocators. bool operator==(const AlignmentAllocator&) const { return true; } }; class Timer { public: Timer() { #ifdef TIMETAG int num_threads = OMP_NUM_THREADS(); start_time_.resize(num_threads); stats_.resize(num_threads); #endif // TIMETAG } ~Timer() { Print(); } #ifdef TIMETAG void Start(const std::string& name) { auto tid = omp_get_thread_num(); start_time_[tid][name] = std::chrono::steady_clock::now(); } void Stop(const std::string& name) { auto cur_time = std::chrono::steady_clock::now(); auto tid = omp_get_thread_num(); if (stats_[tid].find(name) == stats_[tid].end()) { stats_[tid][name] = std::chrono::duration(0); } stats_[tid][name] += cur_time - start_time_[tid][name]; } #else void Start(const std::string&) {} void Stop(const std::string&) {} #endif // TIMETAG void Print() const { #ifdef TIMETAG std::unordered_map> stats(stats_[0].begin(), stats_[0].end()); for (size_t i = 1; i < stats_.size(); ++i) { for (auto it = stats_[i].begin(); it != stats_[i].end(); ++it) { if (stats.find(it->first) == stats.end()) { stats[it->first] = it->second; } else { stats[it->first] += it->second; } } } std::map> ordered( stats.begin(), stats.end()); for (auto it = ordered.begin(); it != ordered.end(); ++it) { Log::Info("%s costs:\t %f", it->first.c_str(), it->second * 1e-3); } #endif // TIMETAG } #ifdef TIMETAG std::vector< std::unordered_map> start_time_; std::vector>> stats_; #endif // TIMETAG }; // Note: this class is not thread-safe, don't use it inside omp blocks class FunctionTimer { public: #ifdef TIMETAG FunctionTimer(const std::string& name, Timer& timer) : timer_(timer) { timer.Start(name); name_ = name; } ~FunctionTimer() { timer_.Stop(name_); } private: std::string name_; Timer& timer_; #else FunctionTimer(const std::string&, Timer&) {} #endif // TIMETAG }; } // namespace Common extern Common::Timer global_timer; /*! * Provides locale-independent alternatives to Common's methods. * Essential to make models robust to locale settings. */ namespace CommonC { template inline static std::string Join(const std::vector& strs, const char* delimiter) { return LightGBM::Common::Join(strs, delimiter, true); } template inline static std::string Join(const std::vector& strs, size_t start, size_t end, const char* delimiter) { return LightGBM::Common::Join(strs, start, end, delimiter, true); } inline static const char* Atof(const char* p, double* out) { return LightGBM::Common::Atof(p, out); } template struct __StringToTHelperFast { const char* operator()(const char*p, T* out) const { return LightGBM::Common::Atoi(p, out); } }; /*! * \warning Beware that ``Common::Atof`` in ``__StringToTHelperFast``, * has **less** floating point precision than ``__StringToTHelper``. * Both versions are kept to maintain bit-for-bit the "legacy" LightGBM behaviour in terms of precision. * Check ``StringToArrayFast`` and ``StringToArray`` for more details on this. */ template struct __StringToTHelperFast { const char* operator()(const char*p, T* out) const { double tmp = 0.0f; auto ret = Atof(p, &tmp); *out = static_cast(tmp); return ret; } }; template struct __StringToTHelper { T operator()(const std::string& str) const { T ret = 0; LightGBM::Common::Atoi(str.c_str(), &ret); return ret; } }; /*! * \warning Beware that ``Common::Atof`` in ``__StringToTHelperFast``, * has **less** floating point precision than ``__StringToTHelper``. * Both versions are kept to maintain bit-for-bit the "legacy" LightGBM behaviour in terms of precision. * Check ``StringToArrayFast`` and ``StringToArray`` for more details on this. * \note It is possible that ``fast_double_parser::parse_number`` is faster than ``Common::Atof``. */ template struct __StringToTHelper { T operator()(const std::string& str) const { double tmp; const char* end = Common::AtofPrecise(str.c_str(), &tmp); if (end == str.c_str()) { Log::Fatal("Failed to parse double: %s", str.c_str()); } return static_cast(tmp); } }; /*! * \warning Beware that due to internal use of ``Common::Atof`` in ``__StringToTHelperFast``, * this method has less precision for floating point numbers than ``StringToArray``, * which calls ``__StringToTHelper``. * As such, ``StringToArrayFast`` and ``StringToArray`` are not equivalent! * Both versions were kept to maintain bit-for-bit the "legacy" LightGBM behaviour in terms of precision. */ template inline static std::vector StringToArrayFast(const std::string& str, int n) { if (n == 0) { return std::vector(); } auto p_str = str.c_str(); __StringToTHelperFast::value> helper; std::vector ret(n); for (int i = 0; i < n; ++i) { p_str = helper(p_str, &ret[i]); } return ret; } /*! * \warning Do not replace calls to this method by ``StringToArrayFast``. * This method is more precise for floating point numbers. * Check ``StringToArrayFast`` for more details. */ template inline static std::vector StringToArray(const std::string& str, int n) { if (n == 0) { return std::vector(); } std::vector strs = LightGBM::Common::Split(str.c_str(), ' '); CHECK_EQ(strs.size(), static_cast(n)); std::vector ret; ret.reserve(strs.size()); __StringToTHelper::value> helper; for (const auto& s : strs) { ret.push_back(helper(s)); } return ret; } /*! * \warning Do not replace calls to this method by ``StringToArrayFast``. * This method is more precise for floating point numbers. * Check ``StringToArrayFast`` for more details. */ template inline static std::vector StringToArray(const std::string& str, char delimiter) { std::vector strs = LightGBM::Common::Split(str.c_str(), delimiter); std::vector ret; ret.reserve(strs.size()); __StringToTHelper::value> helper; for (const auto& s : strs) { ret.push_back(helper(s)); } return ret; } /*! * Safely formats a value onto a buffer according to a format string and null-terminates it. * * \note It checks that the full value was written or forcefully aborts. * This safety check serves to prevent incorrect internal API usage. * Correct usage will never incur in this problem: * - The received buffer size shall be sufficient at all times for the input format string and value. */ template inline static void format_to_buf(char* buffer, const size_t buf_len, const char* format, const T value) { auto result = fmt::format_to_n(buffer, buf_len, format, value); if (result.size >= buf_len) { Log::Fatal("Numerical conversion failed. Buffer is too small."); } buffer[result.size] = '\0'; } template struct __TToStringHelper { void operator()(T value, char* buffer, size_t buf_len) const { format_to_buf(buffer, buf_len, "{}", value); } }; template struct __TToStringHelper { void operator()(T value, char* buffer, size_t buf_len) const { format_to_buf(buffer, buf_len, "{:g}", value); } }; template struct __TToStringHelper { void operator()(T value, char* buffer, size_t buf_len) const { format_to_buf(buffer, buf_len, "{:.17g}", value); } }; /*! * Converts an array to a string with with values separated by the space character. * This method replaces Common's ``ArrayToString`` and ``ArrayToStringFast`` functionality * and is locale-independent. * * \note If ``high_precision_output`` is set to true, * floating point values are output with more digits of precision. */ template inline static std::string ArrayToString(const std::vector& arr, size_t n) { if (arr.empty() || n == 0) { return std::string(""); } __TToStringHelper::value, high_precision_output> helper; const size_t buf_len = high_precision_output ? 32 : 16; std::vector buffer(buf_len); std::stringstream str_buf; Common::C_stringstream(str_buf); helper(arr[0], buffer.data(), buf_len); str_buf << buffer.data(); for (size_t i = 1; i < std::min(n, arr.size()); ++i) { helper(arr[i], buffer.data(), buf_len); str_buf << ' ' << buffer.data(); } return str_buf.str(); } } // namespace CommonC } // namespace LightGBM #endif // LIGHTGBM_INCLUDE_LIGHTGBM_UTILS_COMMON_H_ ================================================ FILE: include/LightGBM/utils/file_io.h ================================================ /*! * Copyright (c) 2018-2026 Microsoft Corporation. All rights reserved. * Copyright (c) 2018-2026 The LightGBM developers. All rights reserved. * Licensed under the MIT License. See LICENSE file in the project root for license information. */ #ifndef LIGHTGBM_INCLUDE_LIGHTGBM_UTILS_FILE_IO_H_ #define LIGHTGBM_INCLUDE_LIGHTGBM_UTILS_FILE_IO_H_ #include #include #include #include #include #include #include #include namespace LightGBM { /*! * \brief An interface for writing files from buffers */ struct VirtualFileWriter : BinaryWriter { virtual ~VirtualFileWriter() {} /*! * \brief Initialize the writer * \return True when the file is available for writes */ virtual bool Init() = 0; /*! * \brief Create appropriate writer for filename * \param filename Filename of the data * \return File writer instance */ static std::unique_ptr Make(const std::string& filename); /*! * \brief Check filename existence * \param filename Filename of the data * \return True when the file exists */ static bool Exists(const std::string& filename); }; /** * \brief An interface for reading files into buffers */ struct VirtualFileReader { /*! * \brief Constructor * \param filename Filename of the data */ virtual ~VirtualFileReader() {} /*! * \brief Initialize the reader * \return True when the file is available for read */ virtual bool Init() = 0; /*! * \brief Read data into buffer * \param buffer Buffer to read data into * \param bytes Number of bytes to read * \return Number of bytes read */ virtual size_t Read(void* buffer, size_t bytes) const = 0; /*! * \brief Create appropriate reader for filename * \param filename Filename of the data * \return File reader instance */ static std::unique_ptr Make(const std::string& filename); }; } // namespace LightGBM #endif // LIGHTGBM_INCLUDE_LIGHTGBM_UTILS_FILE_IO_H_ ================================================ FILE: include/LightGBM/utils/json11.h ================================================ /* Copyright (c) 2013 Dropbox, Inc. * * Permission is hereby granted, free of charge, to any person obtaining a copy * of this software and associated documentation files (the "Software"), to deal * in the Software without restriction, including without limitation the rights * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell * copies of the Software, and to permit persons to whom the Software is * furnished to do so, subject to the following conditions: * * The above copyright notice and this permission notice shall be included in * all copies or substantial portions of the Software. * * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN * THE SOFTWARE. */ /* json11 * * json11 is a tiny JSON library for C++11, providing JSON parsing and * serialization. * * The core object provided by the library is json11::Json. A Json object * represents any JSON value: null, bool, number (int or double), string * (std::string), array (std::vector), or object (std::map). * * Json objects act like values: they can be assigned, copied, moved, compared * for equality or order, etc. There are also helper methods Json::dump, to * serialize a Json to a string, and Json::parse (static) to parse a std::string * as a Json object. * * Internally, the various types of Json object are represented by the JsonValue * class hierarchy. * * A note on numbers - JSON specifies the syntax of number formatting but not * its semantics, so some JSON implementations distinguish between integers and * floating-point numbers, while some don't. In json11, we choose the latter. * Because some JSON implementations (namely Javascript itself) treat all * numbers as the same type, distinguishing the two leads to JSON that will be * *silently* changed by a round-trip through those implementations. Dangerous! * To avoid that risk, json11 stores all numbers as double internally, but also * provides integer helpers. * * Fortunately, double-precision IEEE754 ('double') can precisely store any * integer in the range +/-2^53, which includes every 'int' on most systems. * (Timestamps often use int64 or long long to avoid the Y2038K problem; a * double storing microseconds since some epoch will be exact for +/- 275 * years.) */ #pragma once #include #include #include #include #include #include namespace json11_internal_lightgbm { enum JsonParse { STANDARD, COMMENTS }; class JsonValue; class Json final { public: // Types enum Type { NUL, NUMBER, BOOL, STRING, ARRAY, OBJECT }; // Array and object typedefs typedef std::vector array; typedef std::map object; // Constructors for the various types of JSON value. Json() noexcept; // NUL explicit Json(std::nullptr_t) noexcept; // NUL explicit Json(double value); // NUMBER explicit Json(int value); // NUMBER explicit Json(bool value); // BOOL explicit Json(const std::string &value); // STRING explicit Json(std::string &&value); // STRING explicit Json(const char *value); // STRING explicit Json(const array &values); // ARRAY explicit Json(array &&values); // ARRAY explicit Json(const object &values); // OBJECT explicit Json(object &&values); // OBJECT // Implicit constructor: anything with a to_json() function. template explicit Json(const T &t) : Json(t.to_json()) {} // Implicit constructor: map-like objects (std::map, std::unordered_map, etc) template < class M, typename std::enable_if< std::is_constructible< std::string, decltype(std::declval().begin()->first)>::value && std::is_constructible< Json, decltype(std::declval().begin()->second)>::value, int>::type = 0> explicit Json(const M &m) : Json(object(m.begin(), m.end())) {} // Implicit constructor: vector-like objects (std::list, std::vector, // std::set, etc) template ().begin())>::value, int>::type = 0> explicit Json(const V &v) : Json(array(v.begin(), v.end())) {} // This prevents Json(some_pointer) from accidentally producing a bool. Use // Json(bool(some_pointer)) if that behavior is desired. explicit Json(void *) = delete; // Accessors Type type() const; bool is_null() const { return type() == NUL; } bool is_number() const { return type() == NUMBER; } bool is_bool() const { return type() == BOOL; } bool is_string() const { return type() == STRING; } bool is_array() const { return type() == ARRAY; } bool is_object() const { return type() == OBJECT; } // Return the enclosed value if this is a number, 0 otherwise. Note that // json11 does not distinguish between integer and non-integer numbers - // number_value() and int_value() can both be applied to a NUMBER-typed // object. double number_value() const; int int_value() const; // Return the enclosed value if this is a boolean, false otherwise. bool bool_value() const; // Return the enclosed string if this is a string, "" otherwise. const std::string &string_value() const; // Return the enclosed std::vector if this is an array, or an empty vector // otherwise. const array &array_items() const; // Return the enclosed std::map if this is an object, or an empty map // otherwise. const object &object_items() const; // Return a reference to arr[i] if this is an array, Json() otherwise. const Json &operator[](size_t i) const; // Return a reference to obj[key] if this is an object, Json() otherwise. const Json &operator[](const std::string &key) const; // Serialize. void dump(std::string *out) const; std::string dump() const { std::string out; dump(&out); return out; } // Parse. If parse fails, return Json() and assign an error message to err. static Json parse(const std::string &in, std::string *err, JsonParse strategy = JsonParse::STANDARD); static Json parse(const char *in, std::string *err, JsonParse strategy = JsonParse::STANDARD) { if (in) { return parse(std::string(in), err, strategy); } else { *err = "null input"; return Json(nullptr); } } // Parse multiple objects, concatenated or separated by whitespace static std::vector parse_multi( const std::string &in, std::string::size_type *parser_stop_pos, std::string *err, JsonParse strategy = JsonParse::STANDARD); static inline std::vector parse_multi( const std::string &in, std::string *err, JsonParse strategy = JsonParse::STANDARD) { std::string::size_type parser_stop_pos; return parse_multi(in, &parser_stop_pos, err, strategy); } bool operator==(const Json &rhs) const; bool operator<(const Json &rhs) const; bool operator!=(const Json &rhs) const { return !(*this == rhs); } bool operator<=(const Json &rhs) const { return !(rhs < *this); } bool operator>(const Json &rhs) const { return (rhs < *this); } bool operator>=(const Json &rhs) const { return !(*this < rhs); } /* has_shape(types, err) * * Return true if this is a JSON object and, for each item in types, has a * field of the given type. If not, return false and set err to a descriptive * message. */ typedef std::initializer_list> shape; bool has_shape(const shape &types, std::string *err) const; private: std::shared_ptr m_ptr; }; // Internal class hierarchy - JsonValue objects are not exposed to users of this // API. class JsonValue { protected: friend class Json; friend class JsonInt; friend class JsonDouble; virtual Json::Type type() const = 0; virtual bool equals(const JsonValue *other) const = 0; virtual bool less(const JsonValue *other) const = 0; virtual void dump(std::string *out) const = 0; virtual double number_value() const; virtual int int_value() const; virtual bool bool_value() const; virtual const std::string &string_value() const; virtual const Json::array &array_items() const; virtual const Json &operator[](size_t i) const; virtual const Json::object &object_items() const; virtual const Json &operator[](const std::string &key) const; virtual ~JsonValue() {} }; } // namespace json11_internal_lightgbm ================================================ FILE: include/LightGBM/utils/log.h ================================================ /*! * Copyright (c) 2016-2026 Microsoft Corporation. All rights reserved. * Copyright (c) 2016-2026 The LightGBM developers. All rights reserved. * Licensed under the MIT License. See LICENSE file in the project root for * license information. */ #ifndef LIGHTGBM_INCLUDE_LIGHTGBM_UTILS_LOG_H_ #define LIGHTGBM_INCLUDE_LIGHTGBM_UTILS_LOG_H_ #include #include #include #include #include #include #include #include #ifdef LGB_R_BUILD #ifndef R_NO_REMAP #define R_NO_REMAP #endif #ifndef R_USE_C99_IN_CXX #define R_USE_C99_IN_CXX #endif #include extern "C" void R_FlushConsole(void); #endif namespace LightGBM { #if defined(_MSC_VER) #define THREAD_LOCAL __declspec(thread) #else #define THREAD_LOCAL thread_local #endif #ifndef CHECK #define CHECK(condition) \ if (!(condition)) \ Log::Fatal("Check failed: " #condition " at %s, line %d .\n", __FILE__, \ __LINE__); #endif #ifndef CHECK_EQ #define CHECK_EQ(a, b) CHECK((a) == (b)) #endif #ifndef CHECK_NE #define CHECK_NE(a, b) CHECK((a) != (b)) #endif #ifndef CHECK_GE #define CHECK_GE(a, b) CHECK((a) >= (b)) #endif #ifndef CHECK_LE #define CHECK_LE(a, b) CHECK((a) <= (b)) #endif #ifndef CHECK_GT #define CHECK_GT(a, b) CHECK((a) > (b)) #endif #ifndef CHECK_LT #define CHECK_LT(a, b) CHECK((a) < (b)) #endif #ifndef CHECK_NOTNULL #define CHECK_NOTNULL(pointer) \ if ((pointer) == nullptr) \ LightGBM::Log::Fatal(#pointer " Can't be NULL at %s, line %d .\n", \ __FILE__, __LINE__); #endif enum class LogLevel : int { Fatal = -1, Warning = 0, Info = 1, Debug = 2, }; /*! * \brief A static Log class */ class Log { public: using Callback = void (*)(const char *); /*! * \brief Resets the minimal log level. It is INFO by default. * \param level The new minimal log level. */ static void ResetLogLevel(LogLevel level) { GetLevel() = level; } static void ResetCallBack(Callback callback) { GetLogCallBack() = callback; } static void Debug(const char *format, ...) { va_list val; va_start(val, format); Write(LogLevel::Debug, "Debug", format, val); va_end(val); } static void Info(const char *format, ...) { va_list val; va_start(val, format); Write(LogLevel::Info, "Info", format, val); va_end(val); } static void Warning(const char *format, ...) { va_list val; va_start(val, format); Write(LogLevel::Warning, "Warning", format, val); va_end(val); } static void Fatal(const char *format, ...) { va_list val; const size_t kBufSize = 1024; char str_buf[kBufSize]; va_start(val, format); #ifdef _MSC_VER vsnprintf_s(str_buf, kBufSize, format, val); #else vsnprintf(str_buf, kBufSize, format, val); #endif va_end(val); // R code should write back to R's error stream, // otherwise to stderr #ifndef LGB_R_BUILD fprintf(stderr, "[LightGBM] [Fatal] %s\n", str_buf); fflush(stderr); #else REprintf("[LightGBM] [Fatal] %s\n", str_buf); R_FlushConsole(); #endif throw std::runtime_error(std::string(str_buf)); } private: static void Write(LogLevel level, const char *level_str, const char *format, va_list val) { if (level <= GetLevel()) { // omit the message with low level // R code should write back to R's output stream, // otherwise to stdout #ifndef LGB_R_BUILD if (GetLogCallBack() == nullptr) { printf("[LightGBM] [%s] ", level_str); vprintf(format, val); printf("\n"); fflush(stdout); } else { const size_t kBufSize = 512; char buf[kBufSize]; snprintf(buf, kBufSize, "[LightGBM] [%s] ", level_str); GetLogCallBack()(buf); vsnprintf(buf, kBufSize, format, val); GetLogCallBack()(buf); GetLogCallBack()("\n"); } #else Rprintf("[LightGBM] [%s] ", level_str); Rvprintf(format, val); Rprintf("\n"); R_FlushConsole(); #endif } } // a trick to use static variable in header file. // May be not good, but avoid to use an additional cpp file static LogLevel &GetLevel() { static THREAD_LOCAL LogLevel level = LogLevel::Info; return level; } static Callback &GetLogCallBack() { static THREAD_LOCAL Callback callback = nullptr; return callback; } }; } // namespace LightGBM #endif // LIGHTGBM_INCLUDE_LIGHTGBM_UTILS_LOG_H_ ================================================ FILE: include/LightGBM/utils/openmp_wrapper.h ================================================ /*! * Copyright (c) 2017-2026 Microsoft Corporation. All rights reserved. * Copyright (c) 2017-2026 The LightGBM developers. All rights reserved. * Licensed under the MIT License. See LICENSE file in the project root for license information. */ #ifndef LIGHTGBM_INCLUDE_LIGHTGBM_UTILS_OPENMP_WRAPPER_H_ #define LIGHTGBM_INCLUDE_LIGHTGBM_UTILS_OPENMP_WRAPPER_H_ #include // this can only be changed by LGBM_SetMaxThreads() LIGHTGBM_EXTERN_C int LGBM_MAX_NUM_THREADS; // this is modified by OMP_SET_NUM_THREADS(), for example // by passing num_thread through params LIGHTGBM_EXTERN_C int LGBM_DEFAULT_NUM_THREADS; #ifdef _OPENMP #include #include #include #include #include #include #include /* Get number of threads to use in OpenMP parallel regions. By default, this will return the result of omp_get_max_threads(), which is OpenMP-implementation dependent but generally can be controlled by environment variable OMP_NUM_THREADS. ref: - https://www.openmp.org/spec-html/5.0/openmpsu112.html - https://gcc.gnu.org/onlinedocs/libgomp/omp_005fget_005fmax_005fthreads.html */ LIGHTGBM_EXTERN_C int OMP_NUM_THREADS(); /* Update the default number of threads that'll be used in OpenMP parallel regions for LightGBM routines where the number of threads aren't directly supplied. */ LIGHTGBM_EXTERN_C void OMP_SET_NUM_THREADS(int num_threads); class ThreadExceptionHelper { public: ThreadExceptionHelper() { ex_ptr_ = nullptr; } ~ThreadExceptionHelper() { ReThrow(); } void ReThrow() { if (ex_ptr_ != nullptr) { std::rethrow_exception(ex_ptr_); } } void CaptureException() { // only catch first exception. if (ex_ptr_ != nullptr) { return; } std::unique_lock guard(lock_); if (ex_ptr_ != nullptr) { return; } ex_ptr_ = std::current_exception(); } private: std::exception_ptr ex_ptr_; std::mutex lock_; }; #define OMP_INIT_EX() ThreadExceptionHelper omp_except_helper #define OMP_LOOP_EX_BEGIN() try { #define OMP_LOOP_EX_END() \ } \ catch (std::exception & ex) { \ Log::Warning(ex.what()); \ omp_except_helper.CaptureException(); \ } \ catch (...) { \ omp_except_helper.CaptureException(); \ } #define OMP_THROW_EX() omp_except_helper.ReThrow() #else /* * To be compatible with OpenMP, define a nothrow macro which is used by gcc * openmp, but not by clang. * See also https://github.com/dmlc/dmlc-core/blob/3106c1cbdcc9fc9ef3a2c1d2196a7a6f6616c13d/include/dmlc/omp.h#L14 */ #if defined(__clang__) #undef __GOMP_NOTHROW #define __GOMP_NOTHROW #elif defined(__cplusplus) #undef __GOMP_NOTHROW #define __GOMP_NOTHROW throw() #else #undef __GOMP_NOTHROW #define __GOMP_NOTHROW __attribute__((__nothrow__)) #endif #ifdef _MSC_VER #pragma warning(disable : 4068) // disable unknown pragma warning #endif #ifdef __cplusplus extern "C" { #endif /** Fall here if no OPENMP support, so just simulate a single thread running. All #pragma omp should be ignored by the compiler **/ inline void OMP_SET_NUM_THREADS(int) __GOMP_NOTHROW {} inline int omp_get_thread_num() __GOMP_NOTHROW {return 0;} inline int OMP_NUM_THREADS() __GOMP_NOTHROW { return 1; } #ifdef __cplusplus } // extern "C" #endif #define OMP_INIT_EX() #define OMP_LOOP_EX_BEGIN() #define OMP_LOOP_EX_END() #define OMP_THROW_EX() #endif #endif // LIGHTGBM_INCLUDE_LIGHTGBM_UTILS_OPENMP_WRAPPER_H_ ================================================ FILE: include/LightGBM/utils/pipeline_reader.h ================================================ /*! * Copyright (c) 2016-2026 Microsoft Corporation. All rights reserved. * Copyright (c) 2016-2026 The LightGBM developers. All rights reserved. * Licensed under the MIT License. See LICENSE file in the project root for license information. */ #ifndef LIGHTGBM_INCLUDE_LIGHTGBM_UTILS_PIPELINE_READER_H_ #define LIGHTGBM_INCLUDE_LIGHTGBM_UTILS_PIPELINE_READER_H_ #include #include #include #include #include #include #include #include #include namespace LightGBM { /*! * \brief A pipeline file reader, use 2 threads, one read block from file, the other process the block */ class PipelineReader { public: /*! * \brief Read data from a file, use pipeline methods * \param filename Filename of data * \process_fun Process function */ static size_t Read(const char* filename, int skip_bytes, const std::function& process_fun) { auto reader = VirtualFileReader::Make(filename); if (!reader->Init()) { return 0; } size_t cnt = 0; const size_t buffer_size = 16 * 1024 * 1024; // buffer used for the process_fun auto buffer_process = std::vector(buffer_size); // buffer used for the file reading auto buffer_read = std::vector(buffer_size); size_t read_cnt = 0; if (skip_bytes > 0) { // skip first k bytes read_cnt = reader->Read(buffer_process.data(), skip_bytes); } // read first block read_cnt = reader->Read(buffer_process.data(), buffer_size); size_t last_read_cnt = 0; while (read_cnt > 0) { // start read thread std::thread read_worker = std::thread( [=, &last_read_cnt, &reader, &buffer_read] { last_read_cnt = reader->Read(buffer_read.data(), buffer_size); }); // start process cnt += process_fun(buffer_process.data(), read_cnt); // wait for read thread read_worker.join(); // exchange the buffer std::swap(buffer_process, buffer_read); read_cnt = last_read_cnt; } return cnt; } }; } // namespace LightGBM #endif // LIGHTGBM_INCLUDE_LIGHTGBM_UTILS_PIPELINE_READER_H_ ================================================ FILE: include/LightGBM/utils/random.h ================================================ /*! * Copyright (c) 2016-2026 Microsoft Corporation. All rights reserved. * Copyright (c) 2016-2026 The LightGBM developers. All rights reserved. * Licensed under the MIT License. See LICENSE file in the project root for license information. */ #ifndef LIGHTGBM_INCLUDE_LIGHTGBM_UTILS_RANDOM_H_ #define LIGHTGBM_INCLUDE_LIGHTGBM_UTILS_RANDOM_H_ #include #include #include #include namespace LightGBM { /*! * \brief A wrapper for random generator */ class Random { public: /*! * \brief Constructor, with random seed */ Random() { std::random_device rd; auto generator = std::mt19937(rd()); std::uniform_int_distribution distribution(0, x); x = distribution(generator); } /*! * \brief Constructor, with specific seed */ explicit Random(int seed) { x = seed; } /*! * \brief Generate random integer, int16 range. [0, 65536] * \param lower_bound lower bound * \param upper_bound upper bound * \return The random integer between [lower_bound, upper_bound) */ inline int NextShort(int lower_bound, int upper_bound) { return (RandInt16()) % (upper_bound - lower_bound) + lower_bound; } /*! * \brief Generate random integer, int32 range * \param lower_bound lower bound * \param upper_bound upper bound * \return The random integer between [lower_bound, upper_bound) */ inline int NextInt(int lower_bound, int upper_bound) { return (RandInt32()) % (upper_bound - lower_bound) + lower_bound; } /*! * \brief Generate random float data * \return The random float between [0.0, 1.0) */ inline float NextFloat() { // get random float in [0,1) return static_cast(RandInt16()) / (32768.0f); } /*! * \brief Sample K data from {0,1,...,N-1} * \param N * \param K * \return K Ordered sampled data from {0,1,...,N-1} */ inline std::vector Sample(int N, int K) { std::vector ret; ret.reserve(K); if (K > N || K <= 0) { return ret; } else if (K == N) { for (int i = 0; i < N; ++i) { ret.push_back(i); } } else if (K > 1 && K > (N / std::log2(K))) { for (int i = 0; i < N; ++i) { double prob = (K - ret.size()) / static_cast(N - i); if (NextFloat() < prob) { ret.push_back(i); } } } else { std::set sample_set; for (int r = N - K; r < N; ++r) { int v = NextInt(0, r + 1); if (!sample_set.insert(v).second) { sample_set.insert(r); } } for (auto iter = sample_set.begin(); iter != sample_set.end(); ++iter) { ret.push_back(*iter); } } return ret; } private: inline int RandInt16() { x = (214013 * x + 2531011); return static_cast((x >> 16) & 0x7FFF); } inline int RandInt32() { x = (214013 * x + 2531011); return static_cast(x & 0x7FFFFFFF); } unsigned int x = 123456789; }; } // namespace LightGBM #endif // LIGHTGBM_INCLUDE_LIGHTGBM_UTILS_RANDOM_H_ ================================================ FILE: include/LightGBM/utils/text_reader.h ================================================ /*! * Copyright (c) 2016-2026 Microsoft Corporation. All rights reserved. * Copyright (c) 2016-2026 The LightGBM developers. All rights reserved. * Licensed under the MIT License. See LICENSE file in the project root for license information. */ #ifndef LIGHTGBM_INCLUDE_LIGHTGBM_UTILS_TEXT_READER_H_ #define LIGHTGBM_INCLUDE_LIGHTGBM_UTILS_TEXT_READER_H_ #include #include #include #include #include #include #include #include namespace LightGBM { const size_t kGbs = size_t(1024) * 1024 * 1024; /*! * \brief Read text data from file */ template class TextReader { public: /*! * \brief Constructor * \param filename Filename of data * \param is_skip_first_line True if need to skip header */ TextReader(const char* filename, bool is_skip_first_line, size_t progress_interval_bytes = SIZE_MAX): filename_(filename), is_skip_first_line_(is_skip_first_line), read_progress_interval_bytes_(progress_interval_bytes) { if (is_skip_first_line_) { auto reader = VirtualFileReader::Make(filename); if (!reader->Init()) { Log::Fatal("Could not open %s", filename); } std::stringstream str_buf; char read_c; size_t nread = reader->Read(&read_c, 1); while (nread == 1) { if (read_c == '\n' || read_c == '\r') { break; } str_buf << read_c; ++skip_bytes_; nread = reader->Read(&read_c, 1); } if (read_c == '\r') { reader->Read(&read_c, 1); ++skip_bytes_; } if (read_c == '\n') { reader->Read(&read_c, 1); ++skip_bytes_; } first_line_ = str_buf.str(); Log::Debug("Skipped header \"%s\" in file %s", first_line_.c_str(), filename_); } } /*! * \brief Destructor */ ~TextReader() { Clear(); } /*! * \brief Clear cached data */ inline void Clear() { lines_.clear(); lines_.shrink_to_fit(); } /*! * \brief return first line of data */ inline std::string first_line() { return first_line_; } /*! * \brief Get text data that read from file * \return Text data, store in std::vector by line */ inline std::vector& Lines() { return lines_; } /*! * \brief Get joined text data that read from file * \return Text data, store in std::string, joined all lines by delimiter */ inline std::string JoinedLines(std::string delimiter = "\n") { std::stringstream ss; for (auto line : lines_) { ss << line << delimiter; } return ss.str(); } INDEX_T ReadAllAndProcess(const std::function& process_fun) { last_line_ = ""; INDEX_T total_cnt = 0; size_t bytes_read = 0; PipelineReader::Read(filename_, skip_bytes_, [&process_fun, &bytes_read, &total_cnt, this] (const char* buffer_process, size_t read_cnt) { size_t cnt = 0; size_t i = 0; size_t last_i = 0; // skip the break between \r and \n if (last_line_.size() == 0 && buffer_process[0] == '\n') { i = 1; last_i = i; } while (i < read_cnt) { if (buffer_process[i] == '\n' || buffer_process[i] == '\r') { if (last_line_.size() > 0) { last_line_.append(buffer_process + last_i, i - last_i); process_fun(total_cnt, last_line_.c_str(), last_line_.size()); last_line_ = ""; } else { process_fun(total_cnt, buffer_process + last_i, i - last_i); } ++cnt; ++i; ++total_cnt; // skip end of line while ((buffer_process[i] == '\n' || buffer_process[i] == '\r') && i < read_cnt) { ++i; } last_i = i; } else { ++i; } } if (last_i != read_cnt) { last_line_.append(buffer_process + last_i, read_cnt - last_i); } size_t prev_bytes_read = bytes_read; bytes_read += read_cnt; if (prev_bytes_read / read_progress_interval_bytes_ < bytes_read / read_progress_interval_bytes_) { Log::Debug("Read %.1f GBs from %s.", 1.0 * bytes_read / kGbs, filename_); } return cnt; }); // if last line of file doesn't contain end of line if (last_line_.size() > 0) { Log::Info("Warning: last line of %s has no end of line, still using this line", filename_); process_fun(total_cnt, last_line_.c_str(), last_line_.size()); ++total_cnt; last_line_ = ""; } return total_cnt; } /*! * \brief Read all text data from file in memory * \return number of lines of text data */ INDEX_T ReadAllLines() { return ReadAllAndProcess( [=](INDEX_T, const char* buffer, size_t size) { lines_.emplace_back(buffer, size); }); } std::vector ReadContent(size_t* out_len) { std::vector ret; *out_len = 0; auto reader = VirtualFileReader::Make(filename_); if (!reader->Init()) { return ret; } const size_t buffer_size = 16 * 1024 * 1024; auto buffer_read = std::vector(buffer_size); size_t read_cnt = 0; do { read_cnt = reader->Read(buffer_read.data(), buffer_size); ret.insert(ret.end(), buffer_read.begin(), buffer_read.begin() + read_cnt); *out_len += read_cnt; } while (read_cnt > 0); return ret; } INDEX_T SampleFromFile(Random* random, INDEX_T sample_cnt, std::vector* out_sampled_data) { INDEX_T cur_sample_cnt = 0; return ReadAllAndProcess([=, &random, &cur_sample_cnt, &out_sampled_data] (INDEX_T line_idx, const char* buffer, size_t size) { if (cur_sample_cnt < sample_cnt) { out_sampled_data->emplace_back(buffer, size); ++cur_sample_cnt; } else { const size_t idx = static_cast(random->NextInt(0, static_cast(line_idx + 1))); if (idx < static_cast(sample_cnt)) { out_sampled_data->operator[](idx) = std::string(buffer, size); } } }); } /*! * \brief Read part of text data from file in memory, use filter_fun to filter data * \param filter_fun Function that perform data filter * \param out_used_data_indices Store line indices that read text data * \return The number of total data */ INDEX_T ReadAndFilterLines(const std::function& filter_fun, std::vector* out_used_data_indices) { out_used_data_indices->clear(); INDEX_T total_cnt = ReadAllAndProcess( [&filter_fun, &out_used_data_indices, this] (INDEX_T line_idx , const char* buffer, size_t size) { bool is_used = filter_fun(line_idx); if (is_used) { out_used_data_indices->push_back(line_idx); lines_.emplace_back(buffer, size); } }); return total_cnt; } INDEX_T SampleAndFilterFromFile(const std::function& filter_fun, std::vector* out_used_data_indices, Random* random, INDEX_T sample_cnt, std::vector* out_sampled_data) { INDEX_T cur_sample_cnt = 0; out_used_data_indices->clear(); INDEX_T total_cnt = ReadAllAndProcess( [=, &filter_fun, &out_used_data_indices, &random, &cur_sample_cnt, &out_sampled_data] (INDEX_T line_idx, const char* buffer, size_t size) { bool is_used = filter_fun(line_idx); if (is_used) { out_used_data_indices->push_back(line_idx); if (cur_sample_cnt < sample_cnt) { out_sampled_data->emplace_back(buffer, size); ++cur_sample_cnt; } else { const size_t idx = static_cast(random->NextInt(0, static_cast(out_used_data_indices->size()))); if (idx < static_cast(sample_cnt)) { out_sampled_data->operator[](idx) = std::string(buffer, size); } } } }); return total_cnt; } INDEX_T CountLine() { return ReadAllAndProcess( [=](INDEX_T, const char*, size_t) { }); } INDEX_T ReadAllAndProcessParallelWithFilter(const std::function&)>& process_fun, const std::function& filter_fun) { last_line_ = ""; INDEX_T total_cnt = 0; size_t bytes_read = 0; INDEX_T used_cnt = 0; PipelineReader::Read(filename_, skip_bytes_, [&process_fun, &filter_fun, &total_cnt, &bytes_read, &used_cnt, this] (const char* buffer_process, size_t read_cnt) { size_t cnt = 0; size_t i = 0; size_t last_i = 0; INDEX_T start_idx = used_cnt; // skip the break between \r and \n if (last_line_.size() == 0 && buffer_process[0] == '\n') { i = 1; last_i = i; } while (i < read_cnt) { if (buffer_process[i] == '\n' || buffer_process[i] == '\r') { if (last_line_.size() > 0) { last_line_.append(buffer_process + last_i, i - last_i); if (filter_fun(used_cnt, total_cnt)) { lines_.push_back(last_line_); ++used_cnt; } last_line_ = ""; } else { if (filter_fun(used_cnt, total_cnt)) { lines_.emplace_back(buffer_process + last_i, i - last_i); ++used_cnt; } } ++cnt; ++i; ++total_cnt; // skip end of line while ((buffer_process[i] == '\n' || buffer_process[i] == '\r') && i < read_cnt) { ++i; } last_i = i; } else { ++i; } } process_fun(start_idx, lines_); lines_.clear(); if (last_i != read_cnt) { last_line_.append(buffer_process + last_i, read_cnt - last_i); } size_t prev_bytes_read = bytes_read; bytes_read += read_cnt; if (prev_bytes_read / read_progress_interval_bytes_ < bytes_read / read_progress_interval_bytes_) { Log::Debug("Read %.1f GBs from %s.", 1.0 * bytes_read / kGbs, filename_); } return cnt; }); // if last line of file doesn't contain end of line if (last_line_.size() > 0) { Log::Info("Warning: last line of %s has no end of line, still using this line", filename_); if (filter_fun(used_cnt, total_cnt)) { lines_.push_back(last_line_); process_fun(used_cnt, lines_); } lines_.clear(); ++total_cnt; ++used_cnt; last_line_ = ""; } return total_cnt; } INDEX_T ReadAllAndProcessParallel(const std::function&)>& process_fun) { return ReadAllAndProcessParallelWithFilter(process_fun, [](INDEX_T, INDEX_T) { return true; }); } INDEX_T ReadPartAndProcessParallel(const std::vector& used_data_indices, const std::function&)>& process_fun) { return ReadAllAndProcessParallelWithFilter(process_fun, [&used_data_indices](INDEX_T used_cnt, INDEX_T total_cnt) { if (static_cast(used_cnt) < used_data_indices.size() && total_cnt == used_data_indices[used_cnt]) { return true; } else { return false; } }); } private: /*! \brief Filename of text data */ const char* filename_; /*! \brief Cache the read text data */ std::vector lines_; /*! \brief Buffer for last line */ std::string last_line_; /*! \brief first line */ std::string first_line_ = ""; /*! \brief is skip first line */ bool is_skip_first_line_ = false; size_t read_progress_interval_bytes_; /*! \brief is skip first line */ int skip_bytes_ = 0; }; } // namespace LightGBM #endif // LIGHTGBM_INCLUDE_LIGHTGBM_UTILS_TEXT_READER_H_ ================================================ FILE: include/LightGBM/utils/threading.h ================================================ /*! * Copyright (c) 2016-2026 Microsoft Corporation. All rights reserved. * Copyright (c) 2016-2026 The LightGBM developers. All rights reserved. * Licensed under the MIT License. See LICENSE file in the project root for * license information. */ #ifndef LIGHTGBM_INCLUDE_LIGHTGBM_UTILS_THREADING_H_ #define LIGHTGBM_INCLUDE_LIGHTGBM_UTILS_THREADING_H_ #include #include #include #include #include #include namespace LightGBM { class Threading { public: template static inline void BlockInfo(INDEX_T cnt, INDEX_T min_cnt_per_block, int* out_nblock, INDEX_T* block_size) { int num_threads = OMP_NUM_THREADS(); BlockInfo(num_threads, cnt, min_cnt_per_block, out_nblock, block_size); } template static inline void BlockInfo(int num_threads, INDEX_T cnt, INDEX_T min_cnt_per_block, int* out_nblock, INDEX_T* block_size) { *out_nblock = std::min( num_threads, static_cast((cnt + min_cnt_per_block - 1) / min_cnt_per_block)); if (*out_nblock > 1) { *block_size = SIZE_ALIGNED((cnt + (*out_nblock) - 1) / (*out_nblock)); } else { *block_size = cnt; } } template static inline void BlockInfoForceSize(int num_threads, INDEX_T cnt, INDEX_T min_cnt_per_block, int* out_nblock, INDEX_T* block_size) { *out_nblock = std::min( num_threads, static_cast((cnt + min_cnt_per_block - 1) / min_cnt_per_block)); if (*out_nblock > 1) { *block_size = (cnt + (*out_nblock) - 1) / (*out_nblock); // force the block size to the times of min_cnt_per_block *block_size = (*block_size + min_cnt_per_block - 1) / min_cnt_per_block * min_cnt_per_block; } else { *block_size = cnt; } } template static inline void BlockInfoForceSize(INDEX_T cnt, INDEX_T min_cnt_per_block, int* out_nblock, INDEX_T* block_size) { int num_threads = OMP_NUM_THREADS(); BlockInfoForceSize(num_threads, cnt, min_cnt_per_block, out_nblock, block_size); } template static inline int For( INDEX_T start, INDEX_T end, INDEX_T min_block_size, const std::function& inner_fun) { int n_block = 1; INDEX_T num_inner = end - start; BlockInfo(num_inner, min_block_size, &n_block, &num_inner); OMP_INIT_EX(); #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static, 1) for (int i = 0; i < n_block; ++i) { OMP_LOOP_EX_BEGIN(); INDEX_T inner_start = start + num_inner * i; INDEX_T inner_end = std::min(end, inner_start + num_inner); if (inner_start < inner_end) { inner_fun(i, inner_start, inner_end); } OMP_LOOP_EX_END(); } OMP_THROW_EX(); return n_block; } }; template class ParallelPartitionRunner { public: ParallelPartitionRunner(INDEX_T num_data, INDEX_T min_block_size) : min_block_size_(min_block_size) { num_threads_ = OMP_NUM_THREADS(); left_.resize(num_data); if (TWO_BUFFER) { right_.resize(num_data); } offsets_.resize(num_threads_); left_cnts_.resize(num_threads_); right_cnts_.resize(num_threads_); left_write_pos_.resize(num_threads_); right_write_pos_.resize(num_threads_); } ~ParallelPartitionRunner() {} void ReSize(INDEX_T num_data) { left_.resize(num_data); if (TWO_BUFFER) { right_.resize(num_data); } } template INDEX_T Run( INDEX_T cnt, const std::function& func, INDEX_T* out) { int nblock = 1; INDEX_T inner_size = cnt; if (FORCE_SIZE) { Threading::BlockInfoForceSize(num_threads_, cnt, min_block_size_, &nblock, &inner_size); } else { Threading::BlockInfo(num_threads_, cnt, min_block_size_, &nblock, &inner_size); } OMP_INIT_EX(); #pragma omp parallel for schedule(static, 1) num_threads(num_threads_) for (int i = 0; i < nblock; ++i) { OMP_LOOP_EX_BEGIN(); INDEX_T cur_start = i * inner_size; INDEX_T cur_cnt = std::min(inner_size, cnt - cur_start); offsets_[i] = cur_start; if (cur_cnt <= 0) { left_cnts_[i] = 0; right_cnts_[i] = 0; continue; } auto left_ptr = left_.data() + cur_start; INDEX_T* right_ptr = nullptr; if (TWO_BUFFER) { right_ptr = right_.data() + cur_start; } // split data inner, reduce the times of function called INDEX_T cur_left_count = func(i, cur_start, cur_cnt, left_ptr, right_ptr); if (!TWO_BUFFER) { // reverse for one buffer std::reverse(left_ptr + cur_left_count, left_ptr + cur_cnt); } left_cnts_[i] = cur_left_count; right_cnts_[i] = cur_cnt - cur_left_count; OMP_LOOP_EX_END(); } OMP_THROW_EX(); left_write_pos_[0] = 0; right_write_pos_[0] = 0; for (int i = 1; i < nblock; ++i) { left_write_pos_[i] = left_write_pos_[i - 1] + left_cnts_[i - 1]; right_write_pos_[i] = right_write_pos_[i - 1] + right_cnts_[i - 1]; } data_size_t left_cnt = left_write_pos_[nblock - 1] + left_cnts_[nblock - 1]; auto right_start = out + left_cnt; #pragma omp parallel for schedule(static, 1) num_threads(num_threads_) for (int i = 0; i < nblock; ++i) { std::copy_n(left_.data() + offsets_[i], left_cnts_[i], out + left_write_pos_[i]); if (TWO_BUFFER) { std::copy_n(right_.data() + offsets_[i], right_cnts_[i], right_start + right_write_pos_[i]); } else { std::copy_n(left_.data() + offsets_[i] + left_cnts_[i], right_cnts_[i], right_start + right_write_pos_[i]); } } return left_cnt; } private: int num_threads_; INDEX_T min_block_size_; std::vector left_; std::vector right_; std::vector offsets_; std::vector left_cnts_; std::vector right_cnts_; std::vector left_write_pos_; std::vector right_write_pos_; }; } // namespace LightGBM #endif // LIGHTGBM_INCLUDE_LIGHTGBM_UTILS_THREADING_H_ ================================================ FILE: include/LightGBM/utils/yamc/alternate_shared_mutex.hpp ================================================ /* * alternate_shared_mutex.hpp * * MIT License * * Copyright (c) 2017 yohhoy * * Permission is hereby granted, free of charge, to any person obtaining a copy * of this software and associated documentation files (the "Software"), to deal * in the Software without restriction, including without limitation the rights * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell * copies of the Software, and to permit persons to whom the Software is * furnished to do so, subject to the following conditions: * * The above copyright notice and this permission notice shall be included in * all copies or substantial portions of the Software. * * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE * SOFTWARE. */ #ifndef LIGHTGBM_INCLUDE_LIGHTGBM_UTILS_YAMC_ALTERNATE_SHARED_MUTEX_HPP_ #define LIGHTGBM_INCLUDE_LIGHTGBM_UTILS_YAMC_ALTERNATE_SHARED_MUTEX_HPP_ #include #include #include #include #include "yamc_rwlock_sched.hpp" namespace yamc { /* * alternate implementation of shared mutex variants * * - yamc::alternate::shared_mutex * - yamc::alternate::shared_timed_mutex * - yamc::alternate::basic_shared_mutex * - yamc::alternate::basic_shared_timed_mutex */ namespace alternate { namespace detail { template class shared_mutex_base { protected: typename RwLockPolicy::state state_; std::condition_variable cv_; std::mutex mtx_; void lock() { std::unique_lock lk(mtx_); RwLockPolicy::before_wait_wlock(state_); while (RwLockPolicy::wait_wlock(state_)) { cv_.wait(lk); } RwLockPolicy::after_wait_wlock(state_); RwLockPolicy::acquire_wlock(&state_); } bool try_lock() { std::lock_guard lk(mtx_); if (RwLockPolicy::wait_wlock(state_)) return false; RwLockPolicy::acquire_wlock(state_); return true; } void unlock() { std::lock_guard lk(mtx_); RwLockPolicy::release_wlock(&state_); cv_.notify_all(); } void lock_shared() { std::unique_lock lk(mtx_); while (RwLockPolicy::wait_rlock(state_)) { cv_.wait(lk); } RwLockPolicy::acquire_rlock(&state_); } bool try_lock_shared() { std::lock_guard lk(mtx_); if (RwLockPolicy::wait_rlock(state_)) return false; RwLockPolicy::acquire_rlock(state_); return true; } void unlock_shared() { std::lock_guard lk(mtx_); if (RwLockPolicy::release_rlock(&state_)) { cv_.notify_all(); } } }; } // namespace detail template class basic_shared_mutex : private detail::shared_mutex_base { using base = detail::shared_mutex_base; public: basic_shared_mutex() = default; ~basic_shared_mutex() = default; basic_shared_mutex(const basic_shared_mutex&) = delete; basic_shared_mutex& operator=(const basic_shared_mutex&) = delete; using base::lock; using base::try_lock; using base::unlock; using base::lock_shared; using base::try_lock_shared; using base::unlock_shared; }; using shared_mutex = basic_shared_mutex; template class basic_shared_timed_mutex : private detail::shared_mutex_base { using base = detail::shared_mutex_base; using base::cv_; using base::mtx_; using base::state_; template bool do_try_lockwait(const std::chrono::time_point& tp) { std::unique_lock lk(mtx_); RwLockPolicy::before_wait_wlock(state_); while (RwLockPolicy::wait_wlock(state_)) { if (cv_.wait_until(lk, tp) == std::cv_status::timeout) { if (!RwLockPolicy::wait_wlock(state_)) // re-check predicate break; RwLockPolicy::after_wait_wlock(state_); return false; } } RwLockPolicy::after_wait_wlock(state_); RwLockPolicy::acquire_wlock(state_); return true; } template bool do_try_lock_sharedwait( const std::chrono::time_point& tp) { std::unique_lock lk(mtx_); while (RwLockPolicy::wait_rlock(state_)) { if (cv_.wait_until(lk, tp) == std::cv_status::timeout) { if (!RwLockPolicy::wait_rlock(state_)) // re-check predicate break; return false; } } RwLockPolicy::acquire_rlock(state_); return true; } public: basic_shared_timed_mutex() = default; ~basic_shared_timed_mutex() = default; basic_shared_timed_mutex(const basic_shared_timed_mutex&) = delete; basic_shared_timed_mutex& operator=(const basic_shared_timed_mutex&) = delete; using base::lock; using base::try_lock; using base::unlock; template bool try_lock_for(const std::chrono::duration& duration) { const auto tp = std::chrono::steady_clock::now() + duration; return do_try_lockwait(tp); } template bool try_lock_until(const std::chrono::time_point& tp) { return do_try_lockwait(tp); } using base::lock_shared; using base::try_lock_shared; using base::unlock_shared; template bool try_lock_shared_for(const std::chrono::duration& duration) { const auto tp = std::chrono::steady_clock::now() + duration; return do_try_lock_sharedwait(tp); } template bool try_lock_shared_until( const std::chrono::time_point& tp) { return do_try_lock_sharedwait(tp); } }; using shared_timed_mutex = basic_shared_timed_mutex; } // namespace alternate } // namespace yamc #endif // LIGHTGBM_INCLUDE_LIGHTGBM_UTILS_YAMC_ALTERNATE_SHARED_MUTEX_HPP_ ================================================ FILE: include/LightGBM/utils/yamc/yamc_rwlock_sched.hpp ================================================ /* * yamc_rwlock_sched.hpp * * MIT License * * Copyright (c) 2017 yohhoy * * Permission is hereby granted, free of charge, to any person obtaining a copy * of this software and associated documentation files (the "Software"), to deal * in the Software without restriction, including without limitation the rights * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell * copies of the Software, and to permit persons to whom the Software is * furnished to do so, subject to the following conditions: * * The above copyright notice and this permission notice shall be included in * all copies or substantial portions of the Software. * * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE * SOFTWARE. */ #ifndef LIGHTGBM_INCLUDE_LIGHTGBM_UTILS_YAMC_YAMC_RWLOCK_SCHED_HPP_ #define LIGHTGBM_INCLUDE_LIGHTGBM_UTILS_YAMC_YAMC_RWLOCK_SCHED_HPP_ #include #include /// default shared_mutex rwlock policy #ifndef YAMC_RWLOCK_SCHED_DEFAULT #define YAMC_RWLOCK_SCHED_DEFAULT yamc::rwlock::ReaderPrefer #endif namespace yamc { /* * readers-writer locking policy for basic_shared_(timed)_mutex * * - yamc::rwlock::ReaderPrefer * - yamc::rwlock::WriterPrefer */ namespace rwlock { /// Reader prefer scheduling /// /// NOTE: // This policy might introduce "Writer Starvation" if readers continuously // hold shared lock. PThreads rwlock implementation in Linux use this // scheduling policy as default. (see also PTHREAD_RWLOCK_PREFER_READER_NP) // struct ReaderPrefer { static const std::size_t writer_mask = ~(~std::size_t(0u) >> 1); // MSB 1bit static const std::size_t reader_mask = ~std::size_t(0u) >> 1; struct state { std::size_t rwcount = 0; }; static void before_wait_wlock(const state&) {} static void after_wait_wlock(const state&) {} static bool wait_wlock(const state& s) { return (s.rwcount != 0); } static void acquire_wlock(state* s) { assert(!(s->rwcount & writer_mask)); s->rwcount |= writer_mask; } static void release_wlock(state* s) { assert(s->rwcount & writer_mask); s->rwcount &= ~writer_mask; } static bool wait_rlock(const state& s) { return (s.rwcount & writer_mask) != 0; } static void acquire_rlock(state* s) { assert((s->rwcount & reader_mask) < reader_mask); ++(s->rwcount); } static bool release_rlock(state* s) { assert(0 < (s->rwcount & reader_mask)); return (--(s->rwcount) == 0); } }; /// Writer prefer scheduling /// /// NOTE: /// If there are waiting writer, new readers are blocked until all shared lock /// are released, // and the writer thread can get exclusive lock in preference to blocked // reader threads. This policy might introduce "Reader Starvation" if writers // continuously request exclusive lock. /// (see also PTHREAD_RWLOCK_PREFER_WRITER_NONRECURSIVE_NP) /// struct WriterPrefer { static const std::size_t locked = ~(~std::size_t(0u) >> 1); // MSB 1bit static const std::size_t wait_mask = ~std::size_t(0u) >> 1; struct state { std::size_t nwriter = 0; std::size_t nreader = 0; }; static void before_wait_wlock(state* s) { assert((s->nwriter & wait_mask) < wait_mask); ++(s->nwriter); } static bool wait_wlock(const state& s) { return ((s.nwriter & locked) || 0 < s.nreader); } static void after_wait_wlock(state* s) { assert(0 < (s->nwriter & wait_mask)); --(s->nwriter); } static void acquire_wlock(state* s) { assert(!(s->nwriter & locked)); s->nwriter |= locked; } static void release_wlock(state* s) { assert(s->nwriter & locked); s->nwriter &= ~locked; } static bool wait_rlock(const state& s) { return (s.nwriter != 0); } static void acquire_rlock(state* s) { assert(!(s->nwriter & locked)); ++(s->nreader); } static bool release_rlock(state* s) { assert(0 < s->nreader); return (--(s->nreader) == 0); } }; } // namespace rwlock } // namespace yamc #endif // LIGHTGBM_INCLUDE_LIGHTGBM_UTILS_YAMC_YAMC_RWLOCK_SCHED_HPP_ ================================================ FILE: include/LightGBM/utils/yamc/yamc_shared_lock.hpp ================================================ /* * yamc_shared_lock.hpp * * MIT License * * Copyright (c) 2017 yohhoy * Copyright (c) 2017-2026 Microsoft Corporation. All rights reserved. * Copyright (c) 2017-2026 The LightGBM developers. All rights reserved. * * Permission is hereby granted, free of charge, to any person obtaining a copy * of this software and associated documentation files (the "Software"), to deal * in the Software without restriction, including without limitation the rights * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell * copies of the Software, and to permit persons to whom the Software is * furnished to do so, subject to the following conditions: * * The above copyright notice and this permission notice shall be included in * all copies or substantial portions of the Software. * * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE * SOFTWARE. */ #ifndef LIGHTGBM_INCLUDE_LIGHTGBM_UTILS_YAMC_YAMC_SHARED_LOCK_HPP_ #define LIGHTGBM_INCLUDE_LIGHTGBM_UTILS_YAMC_YAMC_SHARED_LOCK_HPP_ #include #include #include #include #include // std::swap /* * std::shared_lock in C++14 Standard Library * * - yamc::shared_lock */ namespace yamc { template class shared_lock { void locking_precondition(const char* emsg) { if (pm_ == nullptr) { throw std::system_error( std::make_error_code(std::errc::operation_not_permitted), emsg); } if (owns_) { throw std::system_error( std::make_error_code(std::errc::resource_deadlock_would_occur), emsg); } } public: using mutex_type = Mutex; shared_lock() noexcept = default; explicit shared_lock(mutex_type* m) { m->lock_shared(); pm_ = m; owns_ = true; } shared_lock(const mutex_type& m, std::defer_lock_t) noexcept { pm_ = &m; owns_ = false; } shared_lock(const mutex_type& m, std::try_to_lock_t) { pm_ = &m; owns_ = m.try_lock_shared(); } shared_lock(const mutex_type& m, std::adopt_lock_t) { pm_ = &m; owns_ = true; } template shared_lock(const mutex_type& m, const std::chrono::time_point& abs_time) { pm_ = &m; owns_ = m.try_lock_shared_until(abs_time); } template shared_lock(const mutex_type& m, const std::chrono::duration& rel_time) { pm_ = &m; owns_ = m.try_lock_shared_for(rel_time); } ~shared_lock() { if (owns_) { assert(pm_ != nullptr); pm_->unlock_shared(); } } shared_lock(const shared_lock&) = delete; shared_lock& operator=(const shared_lock&) = delete; shared_lock(shared_lock&& rhs) noexcept { if (pm_ && owns_) { pm_->unlock_shared(); } pm_ = rhs.pm_; owns_ = rhs.owns_; rhs.pm_ = nullptr; rhs.owns_ = false; } shared_lock& operator=(shared_lock&& rhs) noexcept { if (pm_ && owns_) { pm_->unlock_shared(); } pm_ = rhs.pm_; owns_ = rhs.owns_; rhs.pm_ = nullptr; rhs.owns_ = false; return *this; } void lock() { locking_precondition("shared_lock::lock"); pm_->lock_shared(); owns_ = true; } bool try_lock() { locking_precondition("shared_lock::try_lock"); return (owns_ = pm_->try_lock_shared()); } template bool try_lock_for(const std::chrono::duration& rel_time) { locking_precondition("shared_lock::try_lock_for"); return (owns_ = pm_->try_lock_shared_for(rel_time)); } template bool try_lock_until( const std::chrono::time_point& abs_time) { locking_precondition("shared_lock::try_lock_until"); return (owns_ = pm_->try_lock_shared_until(abs_time)); } void unlock() { assert(pm_ != nullptr); if (!owns_) { throw std::system_error( std::make_error_code(std::errc::operation_not_permitted), "shared_lock::unlock"); } pm_->unlock_shared(); owns_ = false; } void swap(shared_lock& sl) noexcept { std::swap(pm_, sl.pm_); std::swap(owns_, sl.owns_); } mutex_type* release() noexcept { mutex_type* result = pm_; pm_ = nullptr; owns_ = false; return result; } bool owns_lock() const noexcept { return owns_; } explicit operator bool() const noexcept { return owns_; } mutex_type* mutex() const noexcept { return pm_; } private: mutex_type* pm_ = nullptr; bool owns_ = false; }; } // namespace yamc namespace std { /// std::swap() specialization for yamc::shared_lock type template void swap(yamc::shared_lock& lhs, yamc::shared_lock& rhs) noexcept { lhs.swap(rhs); } } // namespace std #endif // LIGHTGBM_INCLUDE_LIGHTGBM_UTILS_YAMC_YAMC_SHARED_LOCK_HPP_ ================================================ FILE: python-package/README.rst ================================================ LightGBM Python-package ======================= |License| |Python Versions| |PyPI Version| |PyPI Downloads| |conda Version| |conda Downloads| |API Docs| Installation ------------ Preparation ''''''''''' 32-bit Python is not supported. Please install 64-bit version. If 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>`__. | Install from `PyPI `_ '''''''''''''''''''''''''''''''''''''''''''''''''''''''' .. code:: sh pip install lightgbm Compiled library that is included in the wheel file supports both **GPU** (don't confuse with CUDA version) and **CPU** versions out of the box. This feature is available only for **Windows** and **Linux** currently. To use **GPU** version you only need to install OpenCL Runtime libraries. For NVIDIA and AMD GPU they are included in the ordinary drivers for your graphics card, so no action is required. If you would like your AMD or Intel CPU to act like a GPU (for testing and debugging), you can install `AMD APP SDK `_ on **Windows** and `PoCL `_ on **Linux**. Many modern Linux distributions provide packages for PoCL, look for ``pocl-opencl-icd`` on Debian-based distributions and ``pocl`` on RedHat-based distributions. For **Windows** users, `VC runtime `_ is needed if **Visual Studio** is not installed. For **macOS** users, the **OpenMP** library is needed. You can install it by the following command: ``brew install libomp``. | Use LightGBM with PyArrow ************************* To install all dependencies needed to use ``PyArrow`` in LightGBM, append ``[arrow]``. .. code:: sh pip install 'lightgbm[arrow]' | Use LightGBM with Dask ********************** Warning: Dask-package is only tested on macOS and Linux. To install all dependencies needed to use ``lightgbm.dask``, append ``[dask]``. .. code:: sh pip install 'lightgbm[dask]' | Use LightGBM with pandas ************************ To install all dependencies needed to use ``pandas`` in LightGBM, append ``[pandas]``. .. code:: sh pip install 'lightgbm[pandas]' | Use LightGBM Plotting Capabilities ********************************** To install all dependencies needed to use ``lightgbm.plotting``, append ``[plotting]``. .. code:: sh pip install 'lightgbm[plotting]' | Use LightGBM with scikit-learn ****************************** To install all dependencies needed to use ``lightgbm.sklearn``, append ``[scikit-learn]``. .. code:: sh pip install 'lightgbm[scikit-learn]' | Build from Sources ****************** .. code:: sh pip install lightgbm --no-binary lightgbm For **macOS** users, you 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 `__) first. - 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. For **Windows** users, **Visual Studio** (or `VS Build Tools `_) is needed. | Build Threadless Version ~~~~~~~~~~~~~~~~~~~~~~~~ .. code:: sh pip install lightgbm --no-binary lightgbm --config-settings=cmake.define.USE_OPENMP=OFF All requirements, except the **OpenMP** requirement, from `Build from Sources section <#build-from-sources>`__ apply for this installation option as well. It is **strongly not recommended** to use this version of LightGBM! | Build MPI Version ~~~~~~~~~~~~~~~~~ .. code:: sh pip install lightgbm --no-binary lightgbm --config-settings=cmake.define.USE_MPI=ON All requirements from `Build from Sources section <#build-from-sources>`__ apply for this installation option as well. For **Windows** users, compilation with **MinGW-w64** is not supported. **MPI** libraries are needed: details for installation can be found in `Installation Guide `__. | Build GPU Version ~~~~~~~~~~~~~~~~~ .. code:: sh pip install lightgbm --no-binary lightgbm --config-settings=cmake.define.USE_GPU=ON All requirements from `Build from Sources section <#build-from-sources>`__ apply for this installation option as well. For **macOS** users, the GPU version is not supported. **Boost** and **OpenCL** are needed: details for installation can be found in `Installation Guide `__. Almost 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 .. code:: sh 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" All available options that can be passed via ``cmake.define.{option}``. - BOOST_ROOT - Boost_DIR - Boost_INCLUDE_DIR - BOOST_LIBRARYDIR - OpenCL_INCLUDE_DIR - OpenCL_LIBRARY For more details see `FindBoost `__ and `FindOpenCL `__. Don't confuse with `CUDA version <#build-cuda-version>`__. To use the GPU version within Python, pass ``{"device": "gpu"}`` respectively in parameters. | Build CUDA Version ~~~~~~~~~~~~~~~~~~ .. code:: sh pip install lightgbm --no-binary lightgbm --config-settings=cmake.define.USE_CUDA=ON All requirements from `Build from Sources section <#build-from-sources>`__ apply for this installation option as well. For **macOS** and **Windows** users, the CUDA version is not supported. **CUDA** library is needed: details for installation can be found in `Installation Guide `__. Don't confuse with `GPU version <#build-gpu-version>`__. To use the CUDA version within Python, pass ``{"device": "cuda"}`` respectively in parameters. | Build with MinGW-w64 on Windows ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ .. code:: sh pip install lightgbm --no-binary lightgbm --config-settings=cmake.define.CMAKE_SH=CMAKE_SH-NOTFOUND --config-settings=cmake.args="-GMinGW Makefiles" `MinGW-w64 `_ should be installed first. It is recommended to use **Visual Studio** for its better multithreading efficiency in **Windows** for many-core systems (see `Question 4 `__ and `Question 8 `__). | Build 32-bit Version with 32-bit Python ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ .. code:: sh pip install lightgbm --no-binary lightgbm --config-settings=cmake.args="-AWin32" For **Windows** users, compilation with **MinGW-w64** is not supported. For **macOS** and **Linux** users, the 32-bit version is not supported. It is **strongly not recommended** to use this version of LightGBM! | Build with Time Costs Output ~~~~~~~~~~~~~~~~~~~~~~~~~~~~ .. code:: sh pip install lightgbm --no-binary lightgbm --config-settings=cmake.define.USE_TIMETAG=ON Use this option to make LightGBM output time costs for different internal routines, to investigate and benchmark its performance. | Install from `conda-forge channel `_ ''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''' ``lightgbm`` conda packages are available from the ``conda-forge`` channel. .. code:: sh conda install -c conda-forge lightgbm These packages support **CPU**, **GPU** and **CUDA** versions out of the box. **GPU**-enabled version is available only for **Windows** and **Linux** currently. **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. | Install from GitHub ''''''''''''''''''' All requirements from `Build from Sources section <#build-from-sources>`__ apply for this installation option as well. .. code:: sh git clone --recursive https://github.com/lightgbm-org/LightGBM.git cd LightGBM # export CXX=g++-14 CC=gcc-14 # macOS users, if you decided to compile with gcc, don't forget to specify compilers sh ./build-python.sh install Note: ``sudo`` (or administrator rights in **Windows**) may be needed to perform the command. Run ``sh ./build-python.sh install --user`` to install into user-specific instead of global site-packages directory. Run ``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. | Run ``sh ./build-python.sh install --nomp`` to disable **OpenMP** support. All requirements from `Build Threadless Version section <#build-threadless-version>`__ apply for this installation option as well. Run ``sh ./build-python.sh install --mpi`` to enable **MPI** support. All requirements from `Build MPI Version section <#build-mpi-version>`__ apply for this installation option as well. Run ``sh ./build-python.sh install --gpu`` to enable GPU support. All requirements from `Build GPU Version section <#build-gpu-version>`__ apply for this installation option as well. To pass additional options to **CMake** use the following syntax: ``sh ./build-python.sh install --gpu --opencl-include-dir="/usr/local/cuda/include/"``, see `Build GPU Version section <#build-gpu-version>`__ for the complete list of them. Run ``sh ./build-python.sh install --cuda`` to enable CUDA support. All requirements from `Build CUDA Version section <#build-cuda-version>`__ apply for this installation option as well. Run ``sh ./build-python.sh install --mingw``, if you want to use **MinGW-w64** on **Windows** instead of **Visual Studio**. All requirements from `Build with MinGW-w64 on Windows section <#build-with-mingw-w64-on-windows>`__ apply for this installation option as well. Run ``sh ./build-python.sh install --bit32``, if you want to use 32-bit version. All 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. Run ``sh ./build-python.sh install --time-costs``, if you want to output time costs for different internal routines. All requirements from `Build with Time Costs Output section <#build-with-time-costs-output>`__ apply for this installation option as well. | If you get any errors during installation or due to any other reasons, you may want to build dynamic library from sources by any method you prefer (see `Installation Guide `__). For example, you can use ``MSBuild`` tool and `solution file `__ from the repo. .. code:: sh MSBuild.exe windows/LightGBM.sln /p:Configuration=DLL /p:Platform=x64 /p:PlatformToolset=v143 After compiling dynamic library just run ``sh ./build-python.sh install --precompile`` to install the Python-package using that library. | Build Wheel File **************** You can run ``sh ./build-python.sh bdist_wheel`` to build a wheel file but not install it. That script requires some dependencies like ``build``, ``scikit-build-core``, and ``wheel``. In environments with restricted or no internet access, install those tools and then pass ``--no-isolation``. .. code:: sh sh ./build-python.sh bdist_wheel --no-isolation Troubleshooting --------------- Refer to `FAQ `_. Examples -------- Refer to the walk through examples in `Python guide folder `_. Supported Python Versions ------------------------- This project supports all Python versions until they reach end-of-life. For details on the support calendar for Python versions, see https://devguide.python.org/versions/. Development Guide ----------------- To check that a contribution to the package matches its style expectations, run the following from the root of the repo. .. code:: sh pre-commit run --all-files To run the tests locally and compute test coverage, install the Python package using one of the options mentioned above. Then run the following from the root of the repo. .. code:: sh pytest \ --cov=lightgbm \ --cov-report="term" \ --cov-report="html:htmlcov" \ tests/python_package_test/ Then open `htmlcov/index.html` to view a clickable coverage report. .. |License| image:: https://img.shields.io/github/license/lightgbm-org/lightgbm.svg :target: https://github.com/lightgbm-org/LightGBM/blob/master/LICENSE .. |Python Versions| image:: https://img.shields.io/pypi/pyversions/lightgbm.svg?logo=python&logoColor=white :target: https://pypi.org/project/lightgbm .. |PyPI Version| image:: https://img.shields.io/pypi/v/lightgbm.svg?logo=pypi&logoColor=white :target: https://pypi.org/project/lightgbm .. |PyPI Downloads| image:: https://img.shields.io/pepy/dt/lightgbm?logo=pypi&logoColor=white&label=pypi%20downloads :target: https://pepy.tech/project/lightgbm .. |conda Version| image:: https://img.shields.io/conda/vn/conda-forge/lightgbm?logo=conda-forge&logoColor=white&label=conda :target: https://anaconda.org/conda-forge/lightgbm .. |conda Downloads| image:: https://img.shields.io/conda/d/conda-forge/lightgbm?logo=conda-forge&logoColor=white&label=conda%20downloads :target: https://anaconda.org/conda-forge/lightgbm/files .. |API Docs| image:: https://readthedocs.org/projects/lightgbm/badge/?version=latest :target: https://lightgbm.readthedocs.io/en/latest/Python-API.html ================================================ FILE: python-package/lightgbm/__init__.py ================================================ # coding: utf-8 """LightGBM, Light Gradient Boosting Machine. Contributors: https://github.com/lightgbm-org/LightGBM/graphs/contributors. """ from pathlib import Path # .basic is intentionally loaded as early as possible, to dlopen() lib_lightgbm.{dll,dylib,so} # and its dependencies as early as possible from .basic import Booster, Dataset, Sequence, register_logger from .callback import EarlyStopException, early_stopping, log_evaluation, record_evaluation, reset_parameter from .engine import CVBooster, cv, train try: from .sklearn import LGBMClassifier, LGBMModel, LGBMRanker, LGBMRegressor except ImportError: pass try: from .plotting import create_tree_digraph, plot_importance, plot_metric, plot_split_value_histogram, plot_tree except ImportError: pass try: from .dask import DaskLGBMClassifier, DaskLGBMRanker, DaskLGBMRegressor except ImportError: pass _version_path = Path(__file__).resolve().parent / "VERSION.txt" if _version_path.is_file(): __version__ = _version_path.read_text(encoding="utf-8").strip() __all__ = [ "Dataset", "Booster", "CVBooster", "Sequence", "register_logger", "train", "cv", "LGBMModel", "LGBMRegressor", "LGBMClassifier", "LGBMRanker", "DaskLGBMRegressor", "DaskLGBMClassifier", "DaskLGBMRanker", "log_evaluation", "record_evaluation", "reset_parameter", "early_stopping", "EarlyStopException", "plot_importance", "plot_split_value_histogram", "plot_metric", "plot_tree", "create_tree_digraph", ] ================================================ FILE: python-package/lightgbm/basic.py ================================================ # coding: utf-8 """Wrapper for C API of LightGBM.""" # This import causes lib_lightgbm.{dll,dylib,so} to be loaded. # It's intentionally done here, as early as possible, to avoid issues like # "libgomp.so.1: cannot allocate memory in static TLS block" on aarch64 Linux. # # For details, see the "cannot allocate memory in static TLS block" entry in docs/FAQ.rst. from .libpath import _LIB # isort: skip import abc import ctypes import inspect import json import warnings from collections import OrderedDict from copy import deepcopy from enum import Enum from functools import wraps from os import SEEK_END, environ from os.path import getsize from pathlib import Path from tempfile import NamedTemporaryFile from typing import TYPE_CHECKING, Any, Callable, Dict, Iterable, Iterator, List, Optional, Set, Tuple, Union import numpy as np import scipy.sparse from .compat import ( CFFI_INSTALLED, PANDAS_INSTALLED, PYARROW_INSTALLED, arrow_cffi, arrow_is_boolean, arrow_is_floating, arrow_is_integer, concat, pa_Array, pa_chunked_array, pa_ChunkedArray, pa_compute, pa_Table, pd_CategoricalDtype, pd_DataFrame, pd_Series, ) if TYPE_CHECKING: from typing import Literal # typing.TypeGuard was only introduced in Python 3.10 try: from typing import TypeGuard except ImportError: from typing_extensions import TypeGuard __all__ = [ "Booster", "Dataset", "LGBMDeprecationWarning", "LightGBMError", "register_logger", "Sequence", ] _BoosterHandle = ctypes.c_void_p _DatasetHandle = ctypes.c_void_p _ctypes_int_ptr = Union[ "ctypes._Pointer[ctypes.c_int32]", "ctypes._Pointer[ctypes.c_int64]", ] _ctypes_int_array = Union[ "ctypes.Array[ctypes._Pointer[ctypes.c_int32]]", "ctypes.Array[ctypes._Pointer[ctypes.c_int64]]", ] _ctypes_float_ptr = Union[ "ctypes._Pointer[ctypes.c_float]", "ctypes._Pointer[ctypes.c_double]", ] _ctypes_float_array = Union[ "ctypes.Array[ctypes._Pointer[ctypes.c_float]]", "ctypes.Array[ctypes._Pointer[ctypes.c_double]]", ] _LGBM_EvalFunctionResultType = Tuple[str, float, bool] _LGBM_BoosterBestScoreType = Dict[str, Dict[str, float]] _LGBM_BoosterEvalMethodResultType = Tuple[str, str, float, bool] _LGBM_BoosterEvalMethodResultWithStandardDeviationType = Tuple[str, str, float, bool, float] _LGBM_CategoricalFeatureConfiguration = Union[List[str], List[int], "Literal['auto']"] _LGBM_FeatureNameConfiguration = Union[List[str], "Literal['auto']"] _LGBM_GroupType = Union[ List[float], List[int], np.ndarray, pd_Series, pa_Array, pa_ChunkedArray, ] _LGBM_PositionType = Union[ np.ndarray, pd_Series, ] _LGBM_InitScoreType = Union[ List[float], List[List[float]], np.ndarray, pd_Series, pd_DataFrame, pa_Table, pa_Array, pa_ChunkedArray, ] _LGBM_TrainDataType = Union[ str, Path, np.ndarray, pd_DataFrame, scipy.sparse.spmatrix, "Sequence", List["Sequence"], List[np.ndarray], pa_Table, ] _LGBM_LabelType = Union[ List[float], List[int], np.ndarray, pd_Series, pd_DataFrame, pa_Array, pa_ChunkedArray, ] _LGBM_PredictDataType = Union[ str, Path, np.ndarray, pd_DataFrame, scipy.sparse.spmatrix, pa_Table, ] _LGBM_PredictReturnType = Union[ np.ndarray, scipy.sparse.spmatrix, List[scipy.sparse.spmatrix], ] _LGBM_PredictSparseReturnType = Union[ scipy.sparse.spmatrix, List[scipy.sparse.spmatrix], ] _LGBM_WeightType = Union[ List[float], List[int], np.ndarray, pd_Series, pa_Array, pa_ChunkedArray, ] _LGBM_SetFieldType = Union[ List[List[float]], List[List[int]], List[float], List[int], np.ndarray, pd_Series, pd_DataFrame, pa_Table, pa_Array, pa_ChunkedArray, ] ZERO_THRESHOLD = 1e-35 _MULTICLASS_OBJECTIVES = {"multiclass", "multiclassova", "multiclass_ova", "ova", "ovr", "softmax"} class LightGBMError(Exception): """Error thrown by LightGBM.""" pass def _is_zero(x: float) -> bool: return -ZERO_THRESHOLD <= x <= ZERO_THRESHOLD def _get_sample_count(total_nrow: int, params: str) -> int: sample_cnt = ctypes.c_int(0) _safe_call( _LIB.LGBM_GetSampleCount( ctypes.c_int32(total_nrow), _c_str(params), ctypes.byref(sample_cnt), ) ) return sample_cnt.value def _np2d_to_np1d(mat: np.ndarray) -> Tuple[np.ndarray, int]: dtype: "np.typing.DTypeLike" if mat.dtype in (np.float32, np.float64): dtype = mat.dtype else: dtype = np.float32 order: "Literal['C', 'F']" if mat.flags["F_CONTIGUOUS"]: order = "F" layout = _C_API_IS_COL_MAJOR else: order = "C" layout = _C_API_IS_ROW_MAJOR # ensure dtype and order, copies if either do not match data = np.asarray(mat, dtype=dtype, order=order) # flatten array without copying return data.ravel(order=order), layout class _MissingType(Enum): NONE = "None" NAN = "NaN" ZERO = "Zero" class _DummyLogger: def info(self, msg: str) -> None: print(msg) # noqa: T201 def warning(self, msg: str) -> None: warnings.warn(msg, stacklevel=3) _LOGGER: Any = _DummyLogger() _INFO_METHOD_NAME = "info" _WARNING_METHOD_NAME = "warning" def _has_method(logger: Any, method_name: str) -> bool: return callable(getattr(logger, method_name, None)) def register_logger( logger: Any, info_method_name: str = "info", warning_method_name: str = "warning", ) -> None: """Register custom logger. Parameters ---------- logger : Any Custom logger. info_method_name : str, optional (default="info") Method used to log info messages. warning_method_name : str, optional (default="warning") Method used to log warning messages. """ if not _has_method(logger, info_method_name) or not _has_method(logger, warning_method_name): raise TypeError(f"Logger must provide '{info_method_name}' and '{warning_method_name}' method") global _LOGGER, _INFO_METHOD_NAME, _WARNING_METHOD_NAME _LOGGER = logger _INFO_METHOD_NAME = info_method_name _WARNING_METHOD_NAME = warning_method_name def _normalize_native_string(func: Callable[[str], None]) -> Callable[[str], None]: """Join log messages from native library which come by chunks.""" msg_normalized: List[str] = [] @wraps(func) def wrapper(msg: str) -> None: nonlocal msg_normalized if msg.strip() == "": msg = "".join(msg_normalized) msg_normalized = [] return func(msg) else: msg_normalized.append(msg) return wrapper def _log_info(msg: str) -> None: getattr(_LOGGER, _INFO_METHOD_NAME)(msg) def _log_warning(msg: str) -> None: getattr(_LOGGER, _WARNING_METHOD_NAME)(msg) @_normalize_native_string def _log_native(msg: str) -> None: getattr(_LOGGER, _INFO_METHOD_NAME)(msg) def _log_callback(msg: bytes) -> None: """Redirect logs from native library into Python.""" _log_native(str(msg.decode("utf-8"))) # connect the Python logger to logging in lib_lightgbm if environ.get("LIGHTGBM_BUILD_DOC", "False") != "True": _LIB.LGBM_GetLastError.restype = ctypes.c_char_p callback = ctypes.CFUNCTYPE(None, ctypes.c_char_p) _LIB.callback = callback(_log_callback) # type: ignore[attr-defined] if _LIB.LGBM_RegisterLogCallback(_LIB.callback) != 0: raise LightGBMError(_LIB.LGBM_GetLastError().decode("utf-8")) _NUMERIC_TYPES = (int, float, bool) def _safe_call(ret: int) -> None: """Check the return value from C API call. Parameters ---------- ret : int The return value from C API calls. """ if ret != 0: raise LightGBMError(_LIB.LGBM_GetLastError().decode("utf-8")) def _is_numeric(obj: Any) -> bool: """Check whether object is a number or not, include numpy number, etc.""" try: float(obj) return True except (TypeError, ValueError): # TypeError: obj is not a string or a number # ValueError: invalid literal return False def _is_numpy_1d_array(data: Any) -> bool: """Check whether data is a numpy 1-D array.""" return isinstance(data, np.ndarray) and len(data.shape) == 1 def _is_numpy_column_array(data: Any) -> bool: """Check whether data is a column numpy array.""" if not isinstance(data, np.ndarray): return False shape = data.shape return len(shape) == 2 and shape[1] == 1 def _cast_numpy_array_to_dtype(array: np.ndarray, dtype: "np.typing.DTypeLike") -> np.ndarray: """Cast numpy array to given dtype.""" if array.dtype == dtype: return array return array.astype(dtype=dtype, copy=False) def _is_1d_list(data: Any) -> bool: """Check whether data is a 1-D list.""" return isinstance(data, list) and (not data or _is_numeric(data[0])) def _is_list_of_numpy_arrays(data: Any) -> "TypeGuard[List[np.ndarray]]": return isinstance(data, list) and all(isinstance(x, np.ndarray) for x in data) def _is_list_of_sequences(data: Any) -> "TypeGuard[List[Sequence]]": return isinstance(data, list) and all(isinstance(x, Sequence) for x in data) def _is_1d_collection(data: Any) -> bool: """Check whether data is a 1-D collection.""" return _is_numpy_1d_array(data) or _is_numpy_column_array(data) or _is_1d_list(data) or isinstance(data, pd_Series) def _list_to_1d_numpy( *, data: Any, dtype: "np.typing.DTypeLike", name: str, ) -> np.ndarray: """Convert data to numpy 1-D array.""" if _is_numpy_1d_array(data): return _cast_numpy_array_to_dtype(data, dtype) elif _is_numpy_column_array(data): _log_warning("Converting column-vector to 1d array") array = data.ravel() return _cast_numpy_array_to_dtype(array, dtype) elif _is_1d_list(data): return np.asarray(data, dtype=dtype) elif isinstance(data, pd_Series): _check_for_bad_pandas_dtypes(data.to_frame().dtypes) return np.asarray(data, dtype=dtype) # SparseArray should be supported as well else: raise TypeError( f"Wrong type({type(data).__name__}) for {name}.\nIt should be list, numpy 1-D array or pandas Series" ) def _is_numpy_2d_array(data: Any) -> bool: """Check whether data is a numpy 2-D array.""" return isinstance(data, np.ndarray) and len(data.shape) == 2 and data.shape[1] > 1 def _is_2d_list(data: Any) -> bool: """Check whether data is a 2-D list.""" return isinstance(data, list) and len(data) > 0 and _is_1d_list(data[0]) def _is_2d_collection(data: Any) -> bool: """Check whether data is a 2-D collection.""" return _is_numpy_2d_array(data) or _is_2d_list(data) or isinstance(data, pd_DataFrame) def _is_pyarrow_array(data: Any) -> "TypeGuard[Union[pa_Array, pa_ChunkedArray]]": """Check whether data is a PyArrow array.""" return isinstance(data, (pa_Array, pa_ChunkedArray)) def _is_pyarrow_table(data: Any) -> "TypeGuard[pa_Table]": """Check whether data is a PyArrow table.""" return isinstance(data, pa_Table) class _ArrowCArray: """Simple wrapper around the C representation of an Arrow type.""" n_chunks: int chunks: arrow_cffi.CData schema: arrow_cffi.CData def __init__(self, n_chunks: int, chunks: arrow_cffi.CData, schema: arrow_cffi.CData): self.n_chunks = n_chunks self.chunks = chunks self.schema = schema @property def chunks_ptr(self) -> int: """Returns the address of the pointer to the list of chunks making up the array.""" return int(arrow_cffi.cast("uintptr_t", arrow_cffi.addressof(self.chunks[0]))) @property def schema_ptr(self) -> int: """Returns the address of the pointer to the schema of the array.""" return int(arrow_cffi.cast("uintptr_t", self.schema)) def _export_arrow_to_c(data: pa_Table) -> _ArrowCArray: """Export an Arrow type to its C representation.""" # Obtain objects to export if isinstance(data, pa_Array): export_objects = [data] elif isinstance(data, pa_ChunkedArray): export_objects = data.chunks elif isinstance(data, pa_Table): export_objects = data.to_batches() else: raise ValueError(f"data of type '{type(data)}' cannot be exported to Arrow") # Prepare export chunks = arrow_cffi.new("struct ArrowArray[]", len(export_objects)) schema = arrow_cffi.new("struct ArrowSchema*") # Export all objects for i, obj in enumerate(export_objects): chunk_ptr = int(arrow_cffi.cast("uintptr_t", arrow_cffi.addressof(chunks[i]))) if i == 0: schema_ptr = int(arrow_cffi.cast("uintptr_t", schema)) obj._export_to_c(chunk_ptr, schema_ptr) else: obj._export_to_c(chunk_ptr) return _ArrowCArray(len(chunks), chunks, schema) def _data_to_2d_numpy( data: Any, dtype: "np.typing.DTypeLike", name: str, ) -> np.ndarray: """Convert data to numpy 2-D array.""" if _is_numpy_2d_array(data): return _cast_numpy_array_to_dtype(data, dtype) if _is_2d_list(data): return np.array(data, dtype=dtype) if isinstance(data, pd_DataFrame): _check_for_bad_pandas_dtypes(data.dtypes) return _cast_numpy_array_to_dtype(data.values, dtype) raise TypeError( f"Wrong type({type(data).__name__}) for {name}.\n" "It should be list of lists, numpy 2-D array or pandas DataFrame" ) def _cfloat32_array_to_numpy(*, cptr: "ctypes._Pointer", length: int) -> np.ndarray: """Convert a ctypes float pointer array to a numpy array.""" if isinstance(cptr, ctypes.POINTER(ctypes.c_float)): return np.ctypeslib.as_array(cptr, shape=(length,)).copy() else: raise RuntimeError("Expected float pointer") def _cfloat64_array_to_numpy(*, cptr: "ctypes._Pointer", length: int) -> np.ndarray: """Convert a ctypes double pointer array to a numpy array.""" if isinstance(cptr, ctypes.POINTER(ctypes.c_double)): return np.ctypeslib.as_array(cptr, shape=(length,)).copy() else: raise RuntimeError("Expected double pointer") def _cint32_array_to_numpy(*, cptr: "ctypes._Pointer", length: int) -> np.ndarray: """Convert a ctypes int pointer array to a numpy array.""" if isinstance(cptr, ctypes.POINTER(ctypes.c_int32)): return np.ctypeslib.as_array(cptr, shape=(length,)).copy() else: raise RuntimeError("Expected int32 pointer") def _cint64_array_to_numpy(*, cptr: "ctypes._Pointer", length: int) -> np.ndarray: """Convert a ctypes int pointer array to a numpy array.""" if isinstance(cptr, ctypes.POINTER(ctypes.c_int64)): return np.ctypeslib.as_array(cptr, shape=(length,)).copy() else: raise RuntimeError("Expected int64 pointer") def _c_str(string: str) -> ctypes.c_char_p: """Convert a Python string to C string.""" return ctypes.c_char_p(string.encode("utf-8")) def _c_array(ctype: type, values: List[Any]) -> ctypes.Array: """Convert a Python array to C array.""" return (ctype * len(values))(*values) # type: ignore[operator] def _json_default_with_numpy(obj: Any) -> Any: """Convert numpy classes to JSON serializable objects.""" if isinstance(obj, (np.integer, np.floating, np.bool_)): return obj.item() elif isinstance(obj, np.ndarray): return obj.tolist() else: return obj def _to_string(x: Union[int, float, str, List]) -> str: if isinstance(x, list): val_list = ",".join(str(val) for val in x) return f"[{val_list}]" else: return str(x) def _param_dict_to_str(data: Optional[Dict[str, Any]]) -> str: """Convert Python dictionary to string, which is passed to C API.""" if data is None or not data: return "" pairs = [] for key, val in data.items(): if isinstance(val, (list, tuple, set)) or _is_numpy_1d_array(val): pairs.append(f"{key}={','.join(map(_to_string, val))}") elif isinstance(val, (str, Path, _NUMERIC_TYPES)) or _is_numeric(val): pairs.append(f"{key}={val}") elif val is not None: raise TypeError(f"Unknown type of parameter:{key}, got:{type(val).__name__}") return " ".join(pairs) class _TempFile: """Proxy class to workaround errors on Windows.""" def __enter__(self) -> "_TempFile": with NamedTemporaryFile(prefix="lightgbm_tmp_", delete=True) as f: self.name = f.name self.path = Path(self.name) return self def __exit__(self, exc_type: Any, exc_val: Any, exc_tb: Any) -> None: if self.path.is_file(): self.path.unlink() # DeprecationWarning is not shown by default, so let's create our own with higher level # ref: https://peps.python.org/pep-0565/#additional-use-case-for-futurewarning class LGBMDeprecationWarning(FutureWarning): """Custom deprecation warning.""" pass class _ConfigAliases: # lazy evaluation to allow import without dynamic library, e.g., for docs generation aliases = None @staticmethod def _get_all_param_aliases() -> Dict[str, List[str]]: buffer_len = 1 << 20 tmp_out_len = ctypes.c_int64(0) string_buffer = ctypes.create_string_buffer(buffer_len) ptr_string_buffer = ctypes.c_char_p(ctypes.addressof(string_buffer)) _safe_call( _LIB.LGBM_DumpParamAliases( ctypes.c_int64(buffer_len), ctypes.byref(tmp_out_len), ptr_string_buffer, ) ) actual_len = tmp_out_len.value # if buffer length is not long enough, re-allocate a buffer if actual_len > buffer_len: string_buffer = ctypes.create_string_buffer(actual_len) ptr_string_buffer = ctypes.c_char_p(ctypes.addressof(string_buffer)) _safe_call( _LIB.LGBM_DumpParamAliases( ctypes.c_int64(actual_len), ctypes.byref(tmp_out_len), ptr_string_buffer, ) ) return json.loads( string_buffer.value.decode("utf-8"), object_hook=lambda obj: {k: [k] + v for k, v in obj.items()} ) @classmethod def get(cls, *args: str) -> Set[str]: if cls.aliases is None: cls.aliases = cls._get_all_param_aliases() ret = set() for i in args: ret.update(cls.get_sorted(i)) return ret @classmethod def get_sorted(cls, name: str) -> List[str]: if cls.aliases is None: cls.aliases = cls._get_all_param_aliases() return cls.aliases.get(name, [name]) @classmethod def get_by_alias(cls, *args: str) -> Set[str]: if cls.aliases is None: cls.aliases = cls._get_all_param_aliases() ret = set(args) for arg in args: for aliases in cls.aliases.values(): if arg in aliases: ret.update(aliases) break return ret def _choose_param_value(main_param_name: str, params: Dict[str, Any], default_value: Any) -> Dict[str, Any]: """Get a single parameter value, accounting for aliases. Parameters ---------- main_param_name : str Name of the main parameter to get a value for. One of the keys of ``_ConfigAliases``. params : dict Dictionary of LightGBM parameters. default_value : Any Default value to use for the parameter, if none is found in ``params``. Returns ------- params : dict A ``params`` dict with exactly one value for ``main_param_name``, and all aliases ``main_param_name`` removed. If both ``main_param_name`` and one or more aliases for it are found, the value of ``main_param_name`` will be preferred. """ # avoid side effects on passed-in parameters params = deepcopy(params) aliases = _ConfigAliases.get_sorted(main_param_name) aliases = [a for a in aliases if a != main_param_name] # if main_param_name was provided, keep that value and remove all aliases if main_param_name in params.keys(): for param in aliases: params.pop(param, None) return params # if main param name was not found, search for an alias for param in aliases: if param in params.keys(): params[main_param_name] = params[param] break if main_param_name in params.keys(): for param in aliases: params.pop(param, None) return params # neither of main_param_name, aliases were found params[main_param_name] = default_value return params _MAX_INT32 = (1 << 31) - 1 """Macro definition of data type in C API of LightGBM""" _C_API_DTYPE_FLOAT32 = 0 _C_API_DTYPE_FLOAT64 = 1 _C_API_DTYPE_INT32 = 2 _C_API_DTYPE_INT64 = 3 """Macro definition of data order in matrix""" _C_API_IS_COL_MAJOR = 0 _C_API_IS_ROW_MAJOR = 1 """Macro definition of prediction type in C API of LightGBM""" _C_API_PREDICT_NORMAL = 0 _C_API_PREDICT_RAW_SCORE = 1 _C_API_PREDICT_LEAF_INDEX = 2 _C_API_PREDICT_CONTRIB = 3 """Macro definition of sparse matrix type""" _C_API_MATRIX_TYPE_CSR = 0 _C_API_MATRIX_TYPE_CSC = 1 """Macro definition of feature importance type""" _C_API_FEATURE_IMPORTANCE_SPLIT = 0 _C_API_FEATURE_IMPORTANCE_GAIN = 1 """Data type of data field""" _FIELD_TYPE_MAPPER = { "label": _C_API_DTYPE_FLOAT32, "weight": _C_API_DTYPE_FLOAT32, "init_score": _C_API_DTYPE_FLOAT64, "group": _C_API_DTYPE_INT32, "position": _C_API_DTYPE_INT32, } """String name to int feature importance type mapper""" _FEATURE_IMPORTANCE_TYPE_MAPPER = { "split": _C_API_FEATURE_IMPORTANCE_SPLIT, "gain": _C_API_FEATURE_IMPORTANCE_GAIN, } def _convert_from_sliced_object(data: np.ndarray) -> np.ndarray: """Fix the memory of multi-dimensional sliced object.""" if isinstance(data, np.ndarray) and isinstance(data.base, np.ndarray): if not data.flags.c_contiguous: _log_warning( "Usage of np.ndarray subset (sliced data) is not recommended " "due to it will double the peak memory cost in LightGBM." ) return np.copy(data) return data def _c_float_array(data: np.ndarray) -> Tuple[_ctypes_float_ptr, int, np.ndarray]: """Get pointer of float numpy array / list.""" if _is_1d_list(data): data = np.asarray(data) if _is_numpy_1d_array(data): data = _convert_from_sliced_object(data) assert data.flags.c_contiguous ptr_data: _ctypes_float_ptr if data.dtype == np.float32: ptr_data = data.ctypes.data_as(ctypes.POINTER(ctypes.c_float)) type_data = _C_API_DTYPE_FLOAT32 elif data.dtype == np.float64: ptr_data = data.ctypes.data_as(ctypes.POINTER(ctypes.c_double)) type_data = _C_API_DTYPE_FLOAT64 else: raise TypeError(f"Expected np.float32 or np.float64, met type({data.dtype})") else: raise TypeError(f"Unknown type({type(data).__name__})") return (ptr_data, type_data, data) # return `data` to avoid the temporary copy is freed def _c_int_array(data: np.ndarray) -> Tuple[_ctypes_int_ptr, int, np.ndarray]: """Get pointer of int numpy array / list.""" if _is_1d_list(data): data = np.asarray(data) if _is_numpy_1d_array(data): data = _convert_from_sliced_object(data) assert data.flags.c_contiguous ptr_data: _ctypes_int_ptr if data.dtype == np.int32: ptr_data = data.ctypes.data_as(ctypes.POINTER(ctypes.c_int32)) type_data = _C_API_DTYPE_INT32 elif data.dtype == np.int64: ptr_data = data.ctypes.data_as(ctypes.POINTER(ctypes.c_int64)) type_data = _C_API_DTYPE_INT64 else: raise TypeError(f"Expected np.int32 or np.int64, met type({data.dtype})") else: raise TypeError(f"Unknown type({type(data).__name__})") return (ptr_data, type_data, data) # return `data` to avoid the temporary copy is freed def _is_allowed_numpy_dtype(dtype: type) -> bool: float128 = getattr(np, "float128", type(None)) return issubclass(dtype, (np.integer, np.floating, np.bool_)) and not issubclass(dtype, (np.timedelta64, float128)) def _check_for_bad_pandas_dtypes(pandas_dtypes_series: pd_Series) -> None: bad_pandas_dtypes = [ f"{column_name}: {pandas_dtype}" for column_name, pandas_dtype in pandas_dtypes_series.items() if not _is_allowed_numpy_dtype(pandas_dtype.type) ] if bad_pandas_dtypes: raise ValueError( f"pandas dtypes must be int, float or bool.\nFields with bad pandas dtypes: {', '.join(bad_pandas_dtypes)}" ) def _pandas_to_numpy( data: pd_DataFrame, target_dtype: "np.typing.DTypeLike", ) -> np.ndarray: _check_for_bad_pandas_dtypes(data.dtypes) try: # most common case (no nullable dtypes) return data.to_numpy(dtype=target_dtype, copy=False) except TypeError: # 1.0 <= pd version < 1.1 and nullable dtypes, least common case # raises error because array is casted to type(pd.NA) and there's no na_value argument return data.astype(target_dtype, copy=False).values except ValueError: # data has nullable dtypes, but we can specify na_value argument and copy will be made return data.to_numpy(dtype=target_dtype, na_value=np.nan) def _data_from_pandas( data: pd_DataFrame, feature_name: _LGBM_FeatureNameConfiguration, categorical_feature: _LGBM_CategoricalFeatureConfiguration, pandas_categorical: Optional[List[List]], ) -> Tuple[np.ndarray, List[str], Union[List[str], List[int]], List[List]]: if len(data.shape) != 2 or data.shape[0] < 1: raise ValueError("Input data must be 2 dimensional and non empty.") # take shallow copy in case we modify categorical columns # whole column modifications don't change the original df data = data.copy(deep=False) # determine feature names if feature_name == "auto": feature_name = [str(col) for col in data.columns] # determine categorical features cat_cols = [col for col, dtype in zip(data.columns, data.dtypes) if isinstance(dtype, pd_CategoricalDtype)] cat_cols_not_ordered: List[str] = [col for col in cat_cols if not data[col].cat.ordered] if pandas_categorical is None: # train dataset pandas_categorical = [list(data[col].cat.categories) for col in cat_cols] else: if len(cat_cols) != len(pandas_categorical): raise ValueError("train and valid dataset categorical_feature do not match.") for col, category in zip(cat_cols, pandas_categorical): if list(data[col].cat.categories) != list(category): data[col] = data[col].cat.set_categories(category) if cat_cols: # cat_cols is list data[cat_cols] = data[cat_cols].apply(lambda x: x.cat.codes).replace({-1: np.nan}) # use cat cols from DataFrame if categorical_feature == "auto": categorical_feature = cat_cols_not_ordered df_dtypes = [dtype.type for dtype in data.dtypes] # so that the target dtype considers floats df_dtypes.append(np.float32) target_dtype = np.result_type(*df_dtypes) return ( _pandas_to_numpy(data, target_dtype=target_dtype), feature_name, categorical_feature, pandas_categorical, ) def _dump_pandas_categorical( pandas_categorical: Optional[List[List]], file_name: Optional[Union[str, Path]] = None, ) -> str: categorical_json = json.dumps(pandas_categorical, default=_json_default_with_numpy) pandas_str = f"\npandas_categorical:{categorical_json}\n" if file_name is not None: with open(file_name, "a") as f: f.write(pandas_str) return pandas_str def _load_pandas_categorical( file_name: Optional[Union[str, Path]] = None, model_str: Optional[str] = None, ) -> Optional[List[List]]: pandas_key = "pandas_categorical:" offset = -len(pandas_key) if file_name is not None: max_offset = -getsize(file_name) with open(file_name, "rb") as f: while True: offset = max(offset, max_offset) f.seek(offset, SEEK_END) lines = f.readlines() if len(lines) >= 2: break offset *= 2 last_line = lines[-1].decode("utf-8").strip() if not last_line.startswith(pandas_key): last_line = lines[-2].decode("utf-8").strip() elif model_str is not None: idx = model_str.rfind("\n", 0, offset) last_line = model_str[idx:].strip() if last_line.startswith(pandas_key): return json.loads(last_line[len(pandas_key) :]) else: return None class Sequence(abc.ABC): """ Generic data access interface. Object should support the following operations: .. code-block:: # Get total row number. >>> len(seq) # Random access by row index. Used for data sampling. >>> seq[10] # Range data access. Used to read data in batch when constructing Dataset. >>> seq[0:100] # Optionally specify batch_size to control range data read size. >>> seq.batch_size - With random access, **data sampling does not need to go through all data**. - With range data access, there's **no need to read all data into memory thus reduce memory usage**. .. versionadded:: 3.3.0 Attributes ---------- batch_size : int Default size of a batch. """ batch_size = 4096 # Defaults to read 4K rows in each batch. @abc.abstractmethod def __getitem__(self, idx: Union[int, slice, List[int]]) -> np.ndarray: """Return data for given row index. A basic implementation should look like this: .. code-block:: python if isinstance(idx, numbers.Integral): return self._get_one_line(idx) elif isinstance(idx, slice): return np.stack([self._get_one_line(i) for i in range(idx.start, idx.stop)]) elif isinstance(idx, list): # Only required if using ``Dataset.subset()``. return np.array([self._get_one_line(i) for i in idx]) else: raise TypeError(f"Sequence index must be integer, slice or list, got {type(idx).__name__}") Parameters ---------- idx : int, slice[int], list[int] Item index. Returns ------- result : numpy 1-D array or numpy 2-D array 1-D array if idx is int, 2-D array if idx is slice or list. """ raise NotImplementedError("Sub-classes of lightgbm.Sequence must implement __getitem__()") @abc.abstractmethod def __len__(self) -> int: """Return row count of this sequence.""" raise NotImplementedError("Sub-classes of lightgbm.Sequence must implement __len__()") class _InnerPredictor: """_InnerPredictor of LightGBM. Not exposed to user. Used only for prediction, usually used for continued training. .. note:: Can be converted from Booster, but cannot be converted to Booster. """ def __init__( self, booster_handle: _BoosterHandle, pandas_categorical: Optional[List[List]], pred_parameter: Dict[str, Any], manage_handle: bool, ): """Initialize the _InnerPredictor. Parameters ---------- booster_handle : object Handle of Booster. pandas_categorical : list of list, or None If provided, list of categories for ``pandas`` categorical columns. Where the ``i``th element of the list contains the categories for the ``i``th categorical feature. pred_parameter : dict Other parameters for the prediction. manage_handle : bool If ``True``, free the corresponding Booster on the C++ side when this Python object is deleted. """ self._handle = booster_handle self.__is_manage_handle = manage_handle self.pandas_categorical = pandas_categorical self.pred_parameter = _param_dict_to_str(pred_parameter) out_num_class = ctypes.c_int(0) _safe_call( _LIB.LGBM_BoosterGetNumClasses( self._handle, ctypes.byref(out_num_class), ) ) self.num_class = out_num_class.value @classmethod def from_booster( cls, booster: "Booster", pred_parameter: Dict[str, Any], ) -> "_InnerPredictor": """Initialize an ``_InnerPredictor`` from a ``Booster``. Parameters ---------- booster : Booster Booster. pred_parameter : dict Other parameters for the prediction. """ out_cur_iter = ctypes.c_int(0) _safe_call( _LIB.LGBM_BoosterGetCurrentIteration( booster._handle, ctypes.byref(out_cur_iter), ) ) return cls( booster_handle=booster._handle, pandas_categorical=booster.pandas_categorical, pred_parameter=pred_parameter, manage_handle=False, ) @classmethod def from_model_file( cls, model_file: Union[str, Path], pred_parameter: Dict[str, Any], ) -> "_InnerPredictor": """Initialize an ``_InnerPredictor`` from a text file containing a LightGBM model. Parameters ---------- model_file : str or pathlib.Path Path to the model file. pred_parameter : dict Other parameters for the prediction. """ booster_handle = ctypes.c_void_p() out_num_iterations = ctypes.c_int(0) _safe_call( _LIB.LGBM_BoosterCreateFromModelfile( _c_str(str(model_file)), ctypes.byref(out_num_iterations), ctypes.byref(booster_handle), ) ) return cls( booster_handle=booster_handle, pandas_categorical=_load_pandas_categorical(file_name=model_file), pred_parameter=pred_parameter, manage_handle=True, ) def __del__(self) -> None: try: if self.__is_manage_handle: _safe_call(_LIB.LGBM_BoosterFree(self._handle)) except AttributeError: pass def __getstate__(self) -> Dict[str, Any]: this = self.__dict__.copy() this.pop("handle", None) this.pop("_handle", None) return this def predict( self, data: _LGBM_PredictDataType, start_iteration: int = 0, num_iteration: int = -1, raw_score: bool = False, pred_leaf: bool = False, pred_contrib: bool = False, data_has_header: bool = False, validate_features: bool = False, ) -> _LGBM_PredictReturnType: """Predict logic. Parameters ---------- data : str, pathlib.Path, numpy array, pandas DataFrame, scipy.sparse or pyarrow Table Data source for prediction. If str or pathlib.Path, it represents the path to a text file (CSV, TSV, or LibSVM). start_iteration : int, optional (default=0) Start index of the iteration to predict. num_iteration : int, optional (default=-1) Iteration used for prediction. raw_score : bool, optional (default=False) Whether to predict raw scores. pred_leaf : bool, optional (default=False) Whether to predict leaf index. pred_contrib : bool, optional (default=False) Whether to predict feature contributions. data_has_header : bool, optional (default=False) Whether data has header. Used only for txt data. validate_features : bool, optional (default=False) If True, ensure that the features used to predict match the ones used to train. Used only if data is pandas DataFrame. .. versionadded:: 4.0.0 Returns ------- result : numpy array, scipy.sparse or list of scipy.sparse Prediction result. Can be sparse or a list of sparse objects (each element represents predictions for one class) for feature contributions (when ``pred_contrib=True``). """ if isinstance(data, Dataset): raise TypeError("Cannot use Dataset instance for prediction, please use raw data instead") if isinstance(data, pd_DataFrame) and validate_features: data_names = [str(x) for x in data.columns] ptr_names = (ctypes.c_char_p * len(data_names))() ptr_names[:] = [x.encode("utf-8") for x in data_names] _safe_call( _LIB.LGBM_BoosterValidateFeatureNames( self._handle, ptr_names, ctypes.c_int(len(data_names)), ) ) if isinstance(data, pd_DataFrame): data = _data_from_pandas( data=data, feature_name="auto", categorical_feature="auto", pandas_categorical=self.pandas_categorical, )[0] predict_type = _C_API_PREDICT_NORMAL if raw_score: predict_type = _C_API_PREDICT_RAW_SCORE if pred_leaf: predict_type = _C_API_PREDICT_LEAF_INDEX if pred_contrib: predict_type = _C_API_PREDICT_CONTRIB if isinstance(data, (str, Path)): with _TempFile() as f: _safe_call( _LIB.LGBM_BoosterPredictForFile( self._handle, _c_str(str(data)), ctypes.c_int(data_has_header), ctypes.c_int(predict_type), ctypes.c_int(start_iteration), ctypes.c_int(num_iteration), _c_str(self.pred_parameter), _c_str(f.name), ) ) preds = np.loadtxt(f.name, dtype=np.float64) nrow = preds.shape[0] elif isinstance(data, scipy.sparse.csr_matrix): # TODO: remove 'type: ignore[assignment]' when https://github.com/lightgbm-org/LightGBM/pull/6348 is resolved. preds, nrow = self.__pred_for_csr( # type: ignore[assignment] csr=data, start_iteration=start_iteration, num_iteration=num_iteration, predict_type=predict_type, ) elif isinstance(data, scipy.sparse.csc_matrix): # TODO: remove 'type: ignore[assignment]' when https://github.com/lightgbm-org/LightGBM/pull/6348 is resolved. preds, nrow = self.__pred_for_csc( # type: ignore[assignment] csc=data, start_iteration=start_iteration, num_iteration=num_iteration, predict_type=predict_type, ) elif isinstance(data, np.ndarray): preds, nrow = self.__pred_for_np2d( mat=data, start_iteration=start_iteration, num_iteration=num_iteration, predict_type=predict_type, ) elif _is_pyarrow_table(data): preds, nrow = self.__pred_for_pyarrow_table( table=data, start_iteration=start_iteration, num_iteration=num_iteration, predict_type=predict_type, ) elif isinstance(data, list): try: data = np.array(data) except BaseException as err: raise ValueError("Cannot convert data list to numpy array.") from err preds, nrow = self.__pred_for_np2d( mat=data, start_iteration=start_iteration, num_iteration=num_iteration, predict_type=predict_type, ) else: try: _log_warning("Converting data to scipy sparse matrix.") csr = scipy.sparse.csr_matrix(data) except BaseException as err: raise TypeError(f"Cannot predict data for type {type(data).__name__}") from err # TODO: remove 'type: ignore[assignment]' when https://github.com/lightgbm-org/LightGBM/pull/6348 is resolved. preds, nrow = self.__pred_for_csr( # type: ignore[assignment] csr=csr, start_iteration=start_iteration, num_iteration=num_iteration, predict_type=predict_type, ) if pred_leaf: preds = preds.astype(np.int32) is_sparse = isinstance(preds, (list, scipy.sparse.spmatrix)) if not is_sparse and (preds.size != nrow or pred_leaf or pred_contrib): if preds.size % nrow == 0: preds = preds.reshape(nrow, -1) else: raise ValueError(f"Length of predict result ({preds.size}) cannot be divide nrow ({nrow})") return preds def __get_num_preds( self, *, start_iteration: int, num_iteration: int, nrow: int, predict_type: int, ) -> int: """Get size of prediction result.""" if nrow > _MAX_INT32: raise LightGBMError( "LightGBM cannot perform prediction for data " f"with number of rows greater than MAX_INT32 ({_MAX_INT32}).\n" "You can split your data into chunks " "and then concatenate predictions for them" ) n_preds = ctypes.c_int64(0) _safe_call( _LIB.LGBM_BoosterCalcNumPredict( self._handle, ctypes.c_int(nrow), ctypes.c_int(predict_type), ctypes.c_int(start_iteration), ctypes.c_int(num_iteration), ctypes.byref(n_preds), ) ) return n_preds.value def __inner_predict_np2d( self, mat: np.ndarray, start_iteration: int, num_iteration: int, predict_type: int, preds: Optional[np.ndarray], ) -> Tuple[np.ndarray, int]: data, layout = _np2d_to_np1d(mat) ptr_data, type_ptr_data, _ = _c_float_array(data) n_preds = self.__get_num_preds( start_iteration=start_iteration, num_iteration=num_iteration, nrow=mat.shape[0], predict_type=predict_type, ) if preds is None: preds = np.empty(n_preds, dtype=np.float64) elif len(preds.shape) != 1 or len(preds) != n_preds: raise ValueError("Wrong length of pre-allocated predict array") out_num_preds = ctypes.c_int64(0) _safe_call( _LIB.LGBM_BoosterPredictForMat( self._handle, ptr_data, ctypes.c_int(type_ptr_data), ctypes.c_int32(mat.shape[0]), ctypes.c_int32(mat.shape[1]), ctypes.c_int(layout), ctypes.c_int(predict_type), ctypes.c_int(start_iteration), ctypes.c_int(num_iteration), _c_str(self.pred_parameter), ctypes.byref(out_num_preds), preds.ctypes.data_as(ctypes.POINTER(ctypes.c_double)), ) ) if n_preds != out_num_preds.value: raise ValueError("Wrong length for predict results") return preds, mat.shape[0] def __pred_for_np2d( self, mat: np.ndarray, start_iteration: int, num_iteration: int, predict_type: int, ) -> Tuple[np.ndarray, int]: """Predict for a 2-D numpy matrix.""" if len(mat.shape) != 2: raise ValueError("Input numpy.ndarray or list must be 2 dimensional") nrow = mat.shape[0] if nrow > _MAX_INT32: sections = np.arange(_MAX_INT32, nrow, _MAX_INT32) # __get_num_preds() cannot work with nrow > MAX_INT32, so calculate overall number of predictions piecemeal n_preds = [ self.__get_num_preds( start_iteration=start_iteration, num_iteration=num_iteration, nrow=int(i), predict_type=predict_type, ) for i in np.diff([0] + list(sections) + [nrow]) ] n_preds_sections = np.array([0] + n_preds, dtype=np.intp).cumsum() preds = np.empty(sum(n_preds), dtype=np.float64) for chunk, (start_idx_pred, end_idx_pred) in zip( np.array_split(mat, sections), zip(n_preds_sections, n_preds_sections[1:]) ): # avoid memory consumption by arrays concatenation operations self.__inner_predict_np2d( mat=chunk, start_iteration=start_iteration, num_iteration=num_iteration, predict_type=predict_type, preds=preds[start_idx_pred:end_idx_pred], ) return preds, nrow else: return self.__inner_predict_np2d( mat=mat, start_iteration=start_iteration, num_iteration=num_iteration, predict_type=predict_type, preds=None, ) def __create_sparse_native( self, cs: Union[scipy.sparse.csc_matrix, scipy.sparse.csr_matrix], out_shape: np.ndarray, out_ptr_indptr: "ctypes._Pointer", out_ptr_indices: "ctypes._Pointer", out_ptr_data: "ctypes._Pointer", indptr_type: int, data_type: int, is_csr: bool, ) -> _LGBM_PredictSparseReturnType: # create numpy array from output arrays data_indices_len = out_shape[0] indptr_len = out_shape[1] if indptr_type == _C_API_DTYPE_INT32: out_indptr = _cint32_array_to_numpy(cptr=out_ptr_indptr, length=indptr_len) elif indptr_type == _C_API_DTYPE_INT64: out_indptr = _cint64_array_to_numpy(cptr=out_ptr_indptr, length=indptr_len) else: raise TypeError("Expected int32 or int64 type for indptr") if data_type == _C_API_DTYPE_FLOAT32: out_data = _cfloat32_array_to_numpy(cptr=out_ptr_data, length=data_indices_len) elif data_type == _C_API_DTYPE_FLOAT64: out_data = _cfloat64_array_to_numpy(cptr=out_ptr_data, length=data_indices_len) else: raise TypeError("Expected float32 or float64 type for data") out_indices = _cint32_array_to_numpy(cptr=out_ptr_indices, length=data_indices_len) # break up indptr based on number of rows (note more than one matrix in multiclass case) per_class_indptr_shape = cs.indptr.shape[0] # for CSC there is extra column added if not is_csr: per_class_indptr_shape += 1 out_indptr_arrays = np.split(out_indptr, out_indptr.shape[0] / per_class_indptr_shape) # reformat output into a csr or csc matrix or list of csr or csc matrices cs_output_matrices = [] offset = 0 for cs_indptr in out_indptr_arrays: matrix_indptr_len = cs_indptr[cs_indptr.shape[0] - 1] cs_indices = out_indices[offset + cs_indptr[0] : offset + matrix_indptr_len] cs_data = out_data[offset + cs_indptr[0] : offset + matrix_indptr_len] offset += matrix_indptr_len # same shape as input csr or csc matrix except extra column for expected value cs_shape = [cs.shape[0], cs.shape[1] + 1] # note: make sure we copy data as it will be deallocated next if is_csr: cs_output_matrices.append(scipy.sparse.csr_matrix((cs_data, cs_indices, cs_indptr), cs_shape)) else: cs_output_matrices.append(scipy.sparse.csc_matrix((cs_data, cs_indices, cs_indptr), cs_shape)) # free the temporary native indptr, indices, and data _safe_call( _LIB.LGBM_BoosterFreePredictSparse( out_ptr_indptr, out_ptr_indices, out_ptr_data, ctypes.c_int(indptr_type), ctypes.c_int(data_type), ) ) if len(cs_output_matrices) == 1: return cs_output_matrices[0] return cs_output_matrices def __inner_predict_csr( self, csr: scipy.sparse.csr_matrix, start_iteration: int, num_iteration: int, predict_type: int, preds: Optional[np.ndarray], ) -> Tuple[np.ndarray, int]: nrow = len(csr.indptr) - 1 n_preds = self.__get_num_preds( start_iteration=start_iteration, num_iteration=num_iteration, nrow=nrow, predict_type=predict_type, ) if preds is None: preds = np.empty(n_preds, dtype=np.float64) elif len(preds.shape) != 1 or len(preds) != n_preds: raise ValueError("Wrong length of pre-allocated predict array") out_num_preds = ctypes.c_int64(0) ptr_indptr, type_ptr_indptr, _ = _c_int_array(csr.indptr) ptr_data, type_ptr_data, _ = _c_float_array(csr.data) assert csr.shape[1] <= _MAX_INT32 csr_indices = csr.indices.astype(np.int32, copy=False) _safe_call( _LIB.LGBM_BoosterPredictForCSR( self._handle, ptr_indptr, ctypes.c_int(type_ptr_indptr), csr_indices.ctypes.data_as(ctypes.POINTER(ctypes.c_int32)), ptr_data, ctypes.c_int(type_ptr_data), ctypes.c_int64(len(csr.indptr)), ctypes.c_int64(len(csr.data)), ctypes.c_int64(csr.shape[1]), ctypes.c_int(predict_type), ctypes.c_int(start_iteration), ctypes.c_int(num_iteration), _c_str(self.pred_parameter), ctypes.byref(out_num_preds), preds.ctypes.data_as(ctypes.POINTER(ctypes.c_double)), ) ) if n_preds != out_num_preds.value: raise ValueError("Wrong length for predict results") return preds, nrow def __inner_predict_csr_sparse( self, csr: scipy.sparse.csr_matrix, start_iteration: int, num_iteration: int, predict_type: int, ) -> Tuple[_LGBM_PredictSparseReturnType, int]: ptr_indptr, type_ptr_indptr, __ = _c_int_array(csr.indptr) ptr_data, type_ptr_data, _ = _c_float_array(csr.data) csr_indices = csr.indices.astype(np.int32, copy=False) matrix_type = _C_API_MATRIX_TYPE_CSR out_ptr_indptr: _ctypes_int_ptr if type_ptr_indptr == _C_API_DTYPE_INT32: out_ptr_indptr = ctypes.POINTER(ctypes.c_int32)() else: out_ptr_indptr = ctypes.POINTER(ctypes.c_int64)() out_ptr_indices = ctypes.POINTER(ctypes.c_int32)() out_ptr_data: _ctypes_float_ptr if type_ptr_data == _C_API_DTYPE_FLOAT32: out_ptr_data = ctypes.POINTER(ctypes.c_float)() else: out_ptr_data = ctypes.POINTER(ctypes.c_double)() out_shape = np.empty(2, dtype=np.int64) _safe_call( _LIB.LGBM_BoosterPredictSparseOutput( self._handle, ptr_indptr, ctypes.c_int(type_ptr_indptr), csr_indices.ctypes.data_as(ctypes.POINTER(ctypes.c_int32)), ptr_data, ctypes.c_int(type_ptr_data), ctypes.c_int64(len(csr.indptr)), ctypes.c_int64(len(csr.data)), ctypes.c_int64(csr.shape[1]), ctypes.c_int(predict_type), ctypes.c_int(start_iteration), ctypes.c_int(num_iteration), _c_str(self.pred_parameter), ctypes.c_int(matrix_type), out_shape.ctypes.data_as(ctypes.POINTER(ctypes.c_int64)), ctypes.byref(out_ptr_indptr), ctypes.byref(out_ptr_indices), ctypes.byref(out_ptr_data), ) ) matrices = self.__create_sparse_native( cs=csr, out_shape=out_shape, out_ptr_indptr=out_ptr_indptr, out_ptr_indices=out_ptr_indices, out_ptr_data=out_ptr_data, indptr_type=type_ptr_indptr, data_type=type_ptr_data, is_csr=True, ) nrow = len(csr.indptr) - 1 return matrices, nrow def __pred_for_csr( self, csr: scipy.sparse.csr_matrix, start_iteration: int, num_iteration: int, predict_type: int, ) -> Tuple[_LGBM_PredictSparseReturnType, int]: """Predict for a CSR data.""" if predict_type == _C_API_PREDICT_CONTRIB: return self.__inner_predict_csr_sparse( csr=csr, start_iteration=start_iteration, num_iteration=num_iteration, predict_type=predict_type, ) nrow = len(csr.indptr) - 1 if nrow > _MAX_INT32: sections = [0] + list(np.arange(_MAX_INT32, nrow, _MAX_INT32)) + [nrow] # __get_num_preds() cannot work with nrow > MAX_INT32, so calculate overall number of predictions piecemeal n_preds = [ self.__get_num_preds( start_iteration=start_iteration, num_iteration=num_iteration, nrow=int(i), predict_type=predict_type, ) for i in np.diff(sections) ] n_preds_sections = np.array([0] + n_preds, dtype=np.intp).cumsum() preds = np.empty(sum(n_preds), dtype=np.float64) for (start_idx, end_idx), (start_idx_pred, end_idx_pred) in zip( zip(sections, sections[1:]), zip(n_preds_sections, n_preds_sections[1:]) ): # avoid memory consumption by arrays concatenation operations self.__inner_predict_csr( csr=csr[start_idx:end_idx], start_iteration=start_iteration, num_iteration=num_iteration, predict_type=predict_type, preds=preds[start_idx_pred:end_idx_pred], ) return preds, nrow else: return self.__inner_predict_csr( csr=csr, start_iteration=start_iteration, num_iteration=num_iteration, predict_type=predict_type, preds=None, ) def __inner_predict_sparse_csc( self, csc: scipy.sparse.csc_matrix, start_iteration: int, num_iteration: int, predict_type: int, ) -> Tuple[_LGBM_PredictSparseReturnType, int]: ptr_indptr, type_ptr_indptr, __ = _c_int_array(csc.indptr) ptr_data, type_ptr_data, _ = _c_float_array(csc.data) csc_indices = csc.indices.astype(np.int32, copy=False) matrix_type = _C_API_MATRIX_TYPE_CSC out_ptr_indptr: _ctypes_int_ptr if type_ptr_indptr == _C_API_DTYPE_INT32: out_ptr_indptr = ctypes.POINTER(ctypes.c_int32)() else: out_ptr_indptr = ctypes.POINTER(ctypes.c_int64)() out_ptr_indices = ctypes.POINTER(ctypes.c_int32)() out_ptr_data: _ctypes_float_ptr if type_ptr_data == _C_API_DTYPE_FLOAT32: out_ptr_data = ctypes.POINTER(ctypes.c_float)() else: out_ptr_data = ctypes.POINTER(ctypes.c_double)() out_shape = np.empty(2, dtype=np.int64) _safe_call( _LIB.LGBM_BoosterPredictSparseOutput( self._handle, ptr_indptr, ctypes.c_int(type_ptr_indptr), csc_indices.ctypes.data_as(ctypes.POINTER(ctypes.c_int32)), ptr_data, ctypes.c_int(type_ptr_data), ctypes.c_int64(len(csc.indptr)), ctypes.c_int64(len(csc.data)), ctypes.c_int64(csc.shape[0]), ctypes.c_int(predict_type), ctypes.c_int(start_iteration), ctypes.c_int(num_iteration), _c_str(self.pred_parameter), ctypes.c_int(matrix_type), out_shape.ctypes.data_as(ctypes.POINTER(ctypes.c_int64)), ctypes.byref(out_ptr_indptr), ctypes.byref(out_ptr_indices), ctypes.byref(out_ptr_data), ) ) matrices = self.__create_sparse_native( cs=csc, out_shape=out_shape, out_ptr_indptr=out_ptr_indptr, out_ptr_indices=out_ptr_indices, out_ptr_data=out_ptr_data, indptr_type=type_ptr_indptr, data_type=type_ptr_data, is_csr=False, ) nrow = csc.shape[0] return matrices, nrow def __pred_for_csc( self, csc: scipy.sparse.csc_matrix, start_iteration: int, num_iteration: int, predict_type: int, ) -> Tuple[_LGBM_PredictSparseReturnType, int]: """Predict for a CSC data.""" nrow = csc.shape[0] if nrow > _MAX_INT32: return self.__pred_for_csr( csr=csc.tocsr(), start_iteration=start_iteration, num_iteration=num_iteration, predict_type=predict_type, ) if predict_type == _C_API_PREDICT_CONTRIB: return self.__inner_predict_sparse_csc( csc=csc, start_iteration=start_iteration, num_iteration=num_iteration, predict_type=predict_type, ) n_preds = self.__get_num_preds( start_iteration=start_iteration, num_iteration=num_iteration, nrow=nrow, predict_type=predict_type, ) preds = np.empty(n_preds, dtype=np.float64) out_num_preds = ctypes.c_int64(0) ptr_indptr, type_ptr_indptr, __ = _c_int_array(csc.indptr) ptr_data, type_ptr_data, _ = _c_float_array(csc.data) assert csc.shape[0] <= _MAX_INT32 csc_indices = csc.indices.astype(np.int32, copy=False) _safe_call( _LIB.LGBM_BoosterPredictForCSC( self._handle, ptr_indptr, ctypes.c_int(type_ptr_indptr), csc_indices.ctypes.data_as(ctypes.POINTER(ctypes.c_int32)), ptr_data, ctypes.c_int(type_ptr_data), ctypes.c_int64(len(csc.indptr)), ctypes.c_int64(len(csc.data)), ctypes.c_int64(csc.shape[0]), ctypes.c_int(predict_type), ctypes.c_int(start_iteration), ctypes.c_int(num_iteration), _c_str(self.pred_parameter), ctypes.byref(out_num_preds), preds.ctypes.data_as(ctypes.POINTER(ctypes.c_double)), ) ) if n_preds != out_num_preds.value: raise ValueError("Wrong length for predict results") return preds, nrow def __pred_for_pyarrow_table( self, table: pa_Table, start_iteration: int, num_iteration: int, predict_type: int, ) -> Tuple[np.ndarray, int]: """Predict for a PyArrow table.""" if not (PYARROW_INSTALLED and CFFI_INSTALLED): raise LightGBMError("Cannot predict from Arrow without 'pyarrow' and 'cffi' installed.") # Check that the input is valid: we only handle numbers (for now) if not all(arrow_is_integer(t) or arrow_is_floating(t) or arrow_is_boolean(t) for t in table.schema.types): raise ValueError("Arrow table may only have integer or floating point datatypes") # Prepare prediction output array n_preds = self.__get_num_preds( start_iteration=start_iteration, num_iteration=num_iteration, nrow=table.num_rows, predict_type=predict_type, ) preds = np.empty(n_preds, dtype=np.float64) out_num_preds = ctypes.c_int64(0) # Export Arrow table to C and run prediction c_array = _export_arrow_to_c(table) _safe_call( _LIB.LGBM_BoosterPredictForArrow( self._handle, ctypes.c_int64(c_array.n_chunks), ctypes.c_void_p(c_array.chunks_ptr), ctypes.c_void_p(c_array.schema_ptr), ctypes.c_int(predict_type), ctypes.c_int(start_iteration), ctypes.c_int(num_iteration), _c_str(self.pred_parameter), ctypes.byref(out_num_preds), preds.ctypes.data_as(ctypes.POINTER(ctypes.c_double)), ) ) if n_preds != out_num_preds.value: raise ValueError("Wrong length for predict results") return preds, table.num_rows def current_iteration(self) -> int: """Get the index of the current iteration. Returns ------- cur_iter : int The index of the current iteration. """ out_cur_iter = ctypes.c_int(0) _safe_call( _LIB.LGBM_BoosterGetCurrentIteration( self._handle, ctypes.byref(out_cur_iter), ) ) return out_cur_iter.value class Dataset: """ Dataset in LightGBM. LightGBM does not train on raw data. It discretizes continuous features into histogram bins, tries to combine categorical features, and automatically handles missing and infinite values. This class handles that preprocessing, and holds that alternative representation of the input data. """ def __init__( self, data: _LGBM_TrainDataType, label: Optional[_LGBM_LabelType] = None, reference: Optional["Dataset"] = None, weight: Optional[_LGBM_WeightType] = None, group: Optional[_LGBM_GroupType] = None, init_score: Optional[_LGBM_InitScoreType] = None, feature_name: _LGBM_FeatureNameConfiguration = "auto", categorical_feature: _LGBM_CategoricalFeatureConfiguration = "auto", params: Optional[Dict[str, Any]] = None, free_raw_data: bool = True, position: Optional[_LGBM_PositionType] = None, ): """Initialize Dataset. Parameters ---------- data : str, pathlib.Path, numpy array, pandas DataFrame, scipy.sparse, Sequence, list of Sequence, list of numpy array or pyarrow Table Data source of Dataset. If str or pathlib.Path, it represents the path to a text file (CSV, TSV, or LibSVM) or a LightGBM Dataset binary file. label : list, numpy 1-D array, pandas Series / one-column DataFrame, pyarrow Array, pyarrow ChunkedArray or None, optional (default=None) Label of the data. reference : Dataset or None, optional (default=None) If this is Dataset for validation, training data should be used as reference. weight : list, numpy 1-D array, pandas Series, pyarrow Array, pyarrow ChunkedArray or None, optional (default=None) Weight for each instance. Weights should be non-negative. group : list, numpy 1-D array, pandas Series, pyarrow Array, pyarrow ChunkedArray or None, optional (default=None) Group/query data. Only used in the learning-to-rank task. sum(group) = n_samples. For example, if you have a 100-document dataset with ``group = [10, 20, 40, 10, 10, 10]``, that means that you have 6 groups, 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. 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) Init score for Dataset. feature_name : list of str, or 'auto', optional (default="auto") Feature names. If 'auto' and data is pandas DataFrame or pyarrow Table, data columns names are used. categorical_feature : list of str or int, or 'auto', optional (default="auto") Categorical features. If list of int, interpreted as indices. If list of str, interpreted as feature names (need to specify ``feature_name`` as well). If 'auto' and data is pandas DataFrame, pandas unordered categorical columns are used. All values in categorical features will be cast to int32 and thus should be less than int32 max value (2147483647). Large values could be memory consuming. Consider using consecutive integers starting from zero. All negative values in categorical features will be treated as missing values. The output cannot be monotonically constrained with respect to a categorical feature. Floating point numbers in categorical features will be rounded towards 0. params : dict or None, optional (default=None) Other parameters for Dataset. free_raw_data : bool, optional (default=True) If True, raw data is freed after constructing inner Dataset. position : numpy 1-D array, pandas Series or None, optional (default=None) Position of items used in unbiased learning-to-rank task. """ self._handle: Optional[_DatasetHandle] = None self.data = data self.label = label self.reference = reference self.weight = weight self.group = group self.position = position self.init_score = init_score self.feature_name: _LGBM_FeatureNameConfiguration = feature_name self.categorical_feature: _LGBM_CategoricalFeatureConfiguration = categorical_feature self.params = deepcopy(params) self.free_raw_data = free_raw_data self.used_indices: Optional[List[int]] = None self._need_slice = True self._predictor: Optional[_InnerPredictor] = None self.pandas_categorical: Optional[List[List]] = None self._params_back_up: Optional[Dict[str, Any]] = None self.version = 0 self._start_row = 0 # Used when pushing rows one by one. def __del__(self) -> None: try: self._free_handle() except AttributeError: pass def _create_sample_indices(self, *, total_nrow: int) -> np.ndarray: """Get an array of randomly chosen indices from this ``Dataset``. Indices are sampled without replacement. Parameters ---------- total_nrow : int Total number of rows to sample from. If this value is greater than the value of parameter ``bin_construct_sample_cnt``, only ``bin_construct_sample_cnt`` indices will be used. If Dataset has multiple input data, this should be the sum of rows of every file. Returns ------- indices : numpy array Indices for sampled data. """ param_str = _param_dict_to_str(self.get_params()) sample_cnt = _get_sample_count(total_nrow, param_str) indices = np.empty(sample_cnt, dtype=np.int32) ptr_data, _, _ = _c_int_array(indices) actual_sample_cnt = ctypes.c_int32(0) _safe_call( _LIB.LGBM_SampleIndices( ctypes.c_int32(total_nrow), _c_str(param_str), ptr_data, ctypes.byref(actual_sample_cnt), ) ) assert sample_cnt == actual_sample_cnt.value return indices def _init_from_ref_dataset( self, total_nrow: int, ref_dataset: _DatasetHandle, ) -> "Dataset": """Create dataset from a reference dataset. Parameters ---------- total_nrow : int Number of rows expected to add to dataset. ref_dataset : object Handle of reference dataset to extract metadata from. Returns ------- self : Dataset Constructed Dataset object. """ self._handle = ctypes.c_void_p() _safe_call( _LIB.LGBM_DatasetCreateByReference( ref_dataset, ctypes.c_int64(total_nrow), ctypes.byref(self._handle), ) ) return self def _init_from_sample( self, sample_data: List[np.ndarray], sample_indices: List[np.ndarray], sample_cnt: int, total_nrow: int, ) -> "Dataset": """Create Dataset from sampled data structures. Parameters ---------- sample_data : list of numpy array Sample data for each column. sample_indices : list of numpy array Sample data row index for each column. sample_cnt : int Number of samples. total_nrow : int Total number of rows for all input files. Returns ------- self : Dataset Constructed Dataset object. """ ncol = len(sample_indices) assert len(sample_data) == ncol, "#sample data column != #column indices" for i in range(ncol): if sample_data[i].dtype != np.double: raise ValueError(f"sample_data[{i}] type {sample_data[i].dtype} is not double") if sample_indices[i].dtype != np.int32: raise ValueError(f"sample_indices[{i}] type {sample_indices[i].dtype} is not int32") # c type: double** # each double* element points to start of each column of sample data. sample_col_ptr: _ctypes_float_array = (ctypes.POINTER(ctypes.c_double) * ncol)() # c type int** # each int* points to start of indices for each column indices_col_ptr: _ctypes_int_array = (ctypes.POINTER(ctypes.c_int32) * ncol)() for i in range(ncol): sample_col_ptr[i] = _c_float_array(sample_data[i])[0] indices_col_ptr[i] = _c_int_array(sample_indices[i])[0] num_per_col = np.array([len(d) for d in sample_indices], dtype=np.int32) num_per_col_ptr, _, _ = _c_int_array(num_per_col) self._handle = ctypes.c_void_p() params_str = _param_dict_to_str(self.get_params()) _safe_call( _LIB.LGBM_DatasetCreateFromSampledColumn( ctypes.cast(sample_col_ptr, ctypes.POINTER(ctypes.POINTER(ctypes.c_double))), ctypes.cast(indices_col_ptr, ctypes.POINTER(ctypes.POINTER(ctypes.c_int32))), ctypes.c_int32(ncol), num_per_col_ptr, ctypes.c_int32(sample_cnt), ctypes.c_int32(total_nrow), ctypes.c_int64(total_nrow), _c_str(params_str), ctypes.byref(self._handle), ) ) return self def _push_rows(self, data: np.ndarray) -> "Dataset": """Add rows to Dataset. Parameters ---------- data : numpy 1-D array New data to add to the Dataset. Returns ------- self : Dataset Dataset object. """ nrow, ncol = data.shape data = data.reshape(data.size) data_ptr, data_type, _ = _c_float_array(data) _safe_call( _LIB.LGBM_DatasetPushRows( self._handle, data_ptr, data_type, ctypes.c_int32(nrow), ctypes.c_int32(ncol), ctypes.c_int32(self._start_row), ) ) self._start_row += nrow return self def get_params(self) -> Dict[str, Any]: """Get the used parameters in the Dataset. Returns ------- params : dict The used parameters in this Dataset object. """ if self.params is not None: # no min_data, nthreads and verbose in this function dataset_params = _ConfigAliases.get( "bin_construct_sample_cnt", "categorical_feature", "data_random_seed", "enable_bundle", "feature_pre_filter", "forcedbins_filename", "group_column", "header", "ignore_column", "is_enable_sparse", "label_column", "linear_tree", "max_bin", "max_bin_by_feature", "min_data_in_bin", "pre_partition", "precise_float_parser", "two_round", "use_missing", "weight_column", "zero_as_missing", ) return {k: v for k, v in self.params.items() if k in dataset_params} else: return {} def _free_handle(self) -> "Dataset": if self._handle is not None: _safe_call(_LIB.LGBM_DatasetFree(self._handle)) self._handle = None self._need_slice = True if self.used_indices is not None: self.data = None return self def _set_init_score_by_predictor( self, predictor: Optional[_InnerPredictor], data: _LGBM_TrainDataType, used_indices: Optional[Union[List[int], np.ndarray]], ) -> "Dataset": data_has_header = False if isinstance(data, (str, Path)) and self.params is not None: # check data has header or not data_has_header = any(self.params.get(alias, False) for alias in _ConfigAliases.get("header")) num_data = self.num_data() if predictor is not None: init_score: Union[np.ndarray, scipy.sparse.spmatrix] = predictor.predict( data=data, raw_score=True, data_has_header=data_has_header, ) init_score = init_score.ravel() if used_indices is not None: assert not self._need_slice if isinstance(data, (str, Path)): sub_init_score = np.empty(num_data * predictor.num_class, dtype=np.float64) assert num_data == len(used_indices) for i in range(len(used_indices)): for j in range(predictor.num_class): sub_init_score[i * predictor.num_class + j] = init_score[ used_indices[i] * predictor.num_class + j ] init_score = sub_init_score if predictor.num_class > 1: # need to regroup init_score new_init_score = np.empty(init_score.size, dtype=np.float64) for i in range(num_data): for j in range(predictor.num_class): new_init_score[j * num_data + i] = init_score[i * predictor.num_class + j] init_score = new_init_score elif self.init_score is not None: init_score = np.full_like(self.init_score, fill_value=0.0, dtype=np.float64) else: return self self.set_init_score(init_score) return self def _lazy_init( self, data: Optional[_LGBM_TrainDataType], label: Optional[_LGBM_LabelType], reference: Optional["Dataset"], weight: Optional[_LGBM_WeightType], group: Optional[_LGBM_GroupType], init_score: Optional[_LGBM_InitScoreType], predictor: Optional[_InnerPredictor], feature_name: _LGBM_FeatureNameConfiguration, categorical_feature: _LGBM_CategoricalFeatureConfiguration, params: Optional[Dict[str, Any]], position: Optional[_LGBM_PositionType], ) -> "Dataset": if data is None: self._handle = None return self if reference is not None: self.pandas_categorical = reference.pandas_categorical categorical_feature = reference.categorical_feature if isinstance(data, pd_DataFrame): data, feature_name, categorical_feature, self.pandas_categorical = _data_from_pandas( data=data, feature_name=feature_name, categorical_feature=categorical_feature, pandas_categorical=self.pandas_categorical, ) elif _is_pyarrow_table(data) and feature_name == "auto": feature_name = data.column_names # process for args params = {} if params is None else params args_names = inspect.signature(self.__class__._lazy_init).parameters.keys() for key in params.keys(): if key in args_names: _log_warning( f"{key} keyword has been found in `params` and will be ignored.\n" f"Please use {key} argument of the Dataset constructor to pass this parameter." ) # get categorical features if isinstance(categorical_feature, list): categorical_indices = set() feature_dict = {} if isinstance(feature_name, list): feature_dict = {name: i for i, name in enumerate(feature_name)} for name in categorical_feature: if isinstance(name, str) and name in feature_dict: categorical_indices.add(feature_dict[name]) elif isinstance(name, int): categorical_indices.add(name) else: raise TypeError(f"Wrong type({type(name).__name__}) or unknown name({name}) in categorical_feature") if categorical_indices: for cat_alias in _ConfigAliases.get("categorical_feature"): if cat_alias in params: # If the params[cat_alias] is equal to categorical_indices, do not report the warning. if not (isinstance(params[cat_alias], list) and set(params[cat_alias]) == categorical_indices): _log_warning(f"{cat_alias} in param dict is overridden.") params.pop(cat_alias, None) params["categorical_column"] = sorted(categorical_indices) params_str = _param_dict_to_str(params) self.params = params # process for reference dataset ref_dataset = None if isinstance(reference, Dataset): ref_dataset = reference.construct()._handle elif reference is not None: raise TypeError("Reference dataset should be None or dataset instance") # start construct data if isinstance(data, (str, Path)): self._handle = ctypes.c_void_p() _safe_call( _LIB.LGBM_DatasetCreateFromFile( _c_str(str(data)), _c_str(params_str), ref_dataset, ctypes.byref(self._handle), ) ) elif isinstance(data, scipy.sparse.csr_matrix): self.__init_from_csr(csr=data, params_str=params_str, ref_dataset=ref_dataset) elif isinstance(data, scipy.sparse.csc_matrix): self.__init_from_csc(csc=data, params_str=params_str, ref_dataset=ref_dataset) elif isinstance(data, np.ndarray): self.__init_from_np2d(mat=data, params_str=params_str, ref_dataset=ref_dataset) elif _is_pyarrow_table(data): self.__init_from_pyarrow_table(table=data, params_str=params_str, ref_dataset=ref_dataset) elif isinstance(data, list) and len(data) > 0: if _is_list_of_numpy_arrays(data): self.__init_from_list_np2d(mats=data, params_str=params_str, ref_dataset=ref_dataset) elif _is_list_of_sequences(data): self.__init_from_seqs(seqs=data, ref_dataset=ref_dataset) else: raise TypeError("Data list can only be of ndarray or Sequence") elif isinstance(data, Sequence): self.__init_from_seqs(seqs=[data], ref_dataset=ref_dataset) else: try: csr = scipy.sparse.csr_matrix(data) self.__init_from_csr(csr=csr, params_str=params_str, ref_dataset=ref_dataset) except BaseException as err: raise TypeError(f"Cannot initialize Dataset from {type(data).__name__}") from err if label is not None: self.set_label(label) if self.get_label() is None: raise ValueError("Label should not be None") if weight is not None: self.set_weight(weight) if group is not None: self.set_group(group) if position is not None: self.set_position(position) if isinstance(predictor, _InnerPredictor): if self._predictor is None and init_score is not None: _log_warning("The init_score will be overridden by the prediction of init_model.") self._set_init_score_by_predictor(predictor=predictor, data=data, used_indices=None) elif init_score is not None: self.set_init_score(init_score) elif predictor is not None: raise TypeError(f"Wrong predictor type {type(predictor).__name__}") # set feature names return self.set_feature_name(feature_name) @staticmethod def _yield_row_from_seqlist(seqs: List[Sequence], indices: Iterable[int]) -> Iterator[np.ndarray]: offset = 0 seq_id = 0 seq = seqs[seq_id] for row_id in indices: assert row_id >= offset, "sample indices are expected to be monotonic" while row_id >= offset + len(seq): offset += len(seq) seq_id += 1 seq = seqs[seq_id] id_in_seq = row_id - offset row = seq[id_in_seq] yield row if row.flags["OWNDATA"] else row.copy() def __sample(self, *, seqs: List[Sequence], total_nrow: int) -> Tuple[List[np.ndarray], List[np.ndarray]]: """Sample data from seqs. Mimics behavior in c_api.cpp:LGBM_DatasetCreateFromMats() Returns ------- sampled_rows, sampled_row_indices """ indices = self._create_sample_indices(total_nrow=total_nrow) # Select sampled rows, transpose to column order. sampled = np.array(list(self._yield_row_from_seqlist(seqs, indices))) sampled = sampled.T filtered = [] filtered_idx = [] sampled_row_range = np.arange(len(indices), dtype=np.int32) for col in sampled: col_predicate = (np.abs(col) > ZERO_THRESHOLD) | np.isnan(col) filtered_col = col[col_predicate] filtered_row_idx = sampled_row_range[col_predicate] filtered.append(filtered_col) filtered_idx.append(filtered_row_idx) return filtered, filtered_idx def __init_from_seqs( self, *, seqs: List[Sequence], ref_dataset: Optional[_DatasetHandle], ) -> "Dataset": """ Initialize data from list of Sequence objects. Sequence: Generic Data Access Object Supports random access and access by batch if properly defined by user Data scheme uniformity are trusted, not checked """ total_nrow = sum(len(seq) for seq in seqs) # create validation dataset from ref_dataset if ref_dataset is not None: self._init_from_ref_dataset(total_nrow, ref_dataset) else: param_str = _param_dict_to_str(self.get_params()) sample_cnt = _get_sample_count(total_nrow, param_str) sample_data, col_indices = self.__sample(seqs=seqs, total_nrow=total_nrow) self._init_from_sample(sample_data, col_indices, sample_cnt, total_nrow) for seq in seqs: nrow = len(seq) batch_size = getattr(seq, "batch_size", None) or Sequence.batch_size for start in range(0, nrow, batch_size): end = min(start + batch_size, nrow) self._push_rows(seq[start:end]) return self def __init_from_np2d( self, *, mat: np.ndarray, params_str: str, ref_dataset: Optional[_DatasetHandle], ) -> "Dataset": """Initialize data from a 2-D numpy matrix.""" if len(mat.shape) != 2: raise ValueError("Input numpy.ndarray must be 2 dimensional") self._handle = ctypes.c_void_p() data, layout = _np2d_to_np1d(mat) ptr_data, type_ptr_data, _ = _c_float_array(data) _safe_call( _LIB.LGBM_DatasetCreateFromMat( ptr_data, ctypes.c_int(type_ptr_data), ctypes.c_int32(mat.shape[0]), ctypes.c_int32(mat.shape[1]), ctypes.c_int(layout), _c_str(params_str), ref_dataset, ctypes.byref(self._handle), ) ) return self def __init_from_list_np2d( self, *, mats: List[np.ndarray], params_str: str, ref_dataset: Optional[_DatasetHandle], ) -> "Dataset": """Initialize data from a list of 2-D numpy matrices.""" ncol = mats[0].shape[1] nrow = np.empty((len(mats),), np.int32) ptr_data: _ctypes_float_array if mats[0].dtype == np.float64: ptr_data = (ctypes.POINTER(ctypes.c_double) * len(mats))() else: ptr_data = (ctypes.POINTER(ctypes.c_float) * len(mats))() layouts = (ctypes.c_int * len(mats))() holders = [] type_ptr_data = -1 for i, mat in enumerate(mats): if len(mat.shape) != 2: raise ValueError("Input numpy.ndarray must be 2 dimensional") if mat.shape[1] != ncol: raise ValueError("Input arrays must have same number of columns") nrow[i] = mat.shape[0] mat, layout = _np2d_to_np1d(mat) chunk_ptr_data, chunk_type_ptr_data, holder = _c_float_array(mat) if type_ptr_data != -1 and chunk_type_ptr_data != type_ptr_data: raise ValueError("Input chunks must have same type") ptr_data[i] = chunk_ptr_data layouts[i] = layout type_ptr_data = chunk_type_ptr_data holders.append(holder) self._handle = ctypes.c_void_p() _safe_call( _LIB.LGBM_DatasetCreateFromMats( ctypes.c_int32(len(mats)), ctypes.cast(ptr_data, ctypes.POINTER(ctypes.POINTER(ctypes.c_double))), ctypes.c_int(type_ptr_data), nrow.ctypes.data_as(ctypes.POINTER(ctypes.c_int32)), ctypes.c_int32(ncol), layouts, _c_str(params_str), ref_dataset, ctypes.byref(self._handle), ) ) return self def __init_from_csr( self, *, csr: scipy.sparse.csr_matrix, params_str: str, ref_dataset: Optional[_DatasetHandle], ) -> "Dataset": """Initialize data from a CSR matrix.""" if len(csr.indices) != len(csr.data): raise ValueError(f"Length mismatch: {len(csr.indices)} vs {len(csr.data)}") self._handle = ctypes.c_void_p() ptr_indptr, type_ptr_indptr, __ = _c_int_array(csr.indptr) ptr_data, type_ptr_data, _ = _c_float_array(csr.data) assert csr.shape[1] <= _MAX_INT32 csr_indices = csr.indices.astype(np.int32, copy=False) _safe_call( _LIB.LGBM_DatasetCreateFromCSR( ptr_indptr, ctypes.c_int(type_ptr_indptr), csr_indices.ctypes.data_as(ctypes.POINTER(ctypes.c_int32)), ptr_data, ctypes.c_int(type_ptr_data), ctypes.c_int64(len(csr.indptr)), ctypes.c_int64(len(csr.data)), ctypes.c_int64(csr.shape[1]), _c_str(params_str), ref_dataset, ctypes.byref(self._handle), ) ) return self def __init_from_csc( self, *, csc: scipy.sparse.csc_matrix, params_str: str, ref_dataset: Optional[_DatasetHandle], ) -> "Dataset": """Initialize data from a CSC matrix.""" if len(csc.indices) != len(csc.data): raise ValueError(f"Length mismatch: {len(csc.indices)} vs {len(csc.data)}") self._handle = ctypes.c_void_p() ptr_indptr, type_ptr_indptr, __ = _c_int_array(csc.indptr) ptr_data, type_ptr_data, _ = _c_float_array(csc.data) assert csc.shape[0] <= _MAX_INT32 csc_indices = csc.indices.astype(np.int32, copy=False) _safe_call( _LIB.LGBM_DatasetCreateFromCSC( ptr_indptr, ctypes.c_int(type_ptr_indptr), csc_indices.ctypes.data_as(ctypes.POINTER(ctypes.c_int32)), ptr_data, ctypes.c_int(type_ptr_data), ctypes.c_int64(len(csc.indptr)), ctypes.c_int64(len(csc.data)), ctypes.c_int64(csc.shape[0]), _c_str(params_str), ref_dataset, ctypes.byref(self._handle), ) ) return self def __init_from_pyarrow_table( self, *, table: pa_Table, params_str: str, ref_dataset: Optional[_DatasetHandle], ) -> "Dataset": """Initialize data from a PyArrow table.""" if not (PYARROW_INSTALLED and CFFI_INSTALLED): raise LightGBMError("Cannot init Dataset from Arrow without 'pyarrow' and 'cffi' installed.") # Check that the input is valid: we only handle numbers (for now) if not all(arrow_is_integer(t) or arrow_is_floating(t) or arrow_is_boolean(t) for t in table.schema.types): raise ValueError("Arrow table may only have integer or floating point datatypes") # Export Arrow table to C c_array = _export_arrow_to_c(table) self._handle = ctypes.c_void_p() _safe_call( _LIB.LGBM_DatasetCreateFromArrow( ctypes.c_int64(c_array.n_chunks), ctypes.c_void_p(c_array.chunks_ptr), ctypes.c_void_p(c_array.schema_ptr), _c_str(params_str), ref_dataset, ctypes.byref(self._handle), ) ) return self @staticmethod def _compare_params_for_warning( *, params: Dict[str, Any], other_params: Dict[str, Any], ignore_keys: Set[str], ) -> bool: """Compare two dictionaries with params ignoring some keys. It is only for the warning purpose. Parameters ---------- params : dict One dictionary with parameters to compare. other_params : dict Another dictionary with parameters to compare. ignore_keys : set Keys that should be ignored during comparing two dictionaries. Returns ------- compare_result : bool Returns whether two dictionaries with params are equal. """ for k, v in other_params.items(): if k not in ignore_keys: if k not in params or params[k] != v: return False for k, v in params.items(): if k not in ignore_keys: if k not in other_params or v != other_params[k]: return False return True def construct(self) -> "Dataset": """Lazy init. Returns ------- self : Dataset Constructed Dataset object. """ if self._handle is None: if self.reference is not None: reference_params = self.reference.get_params() params = self.get_params() if params != reference_params: if not self._compare_params_for_warning( params=params, other_params=reference_params, ignore_keys=_ConfigAliases.get("categorical_feature"), ): _log_warning("Overriding the parameters from Reference Dataset.") self._update_params(reference_params) if self.used_indices is None: # create valid self._lazy_init( data=self.data, label=self.label, reference=self.reference, weight=self.weight, group=self.group, position=self.position, init_score=self.init_score, predictor=self._predictor, feature_name=self.feature_name, categorical_feature="auto", params=self.params, ) else: # construct subset used_indices = _list_to_1d_numpy( data=self.used_indices, dtype=np.int32, name="used_indices", ) assert used_indices.flags.c_contiguous if self.reference.group is not None: group_info = np.array(self.reference.group).astype(np.int32, copy=False) _, self.group = np.unique( np.repeat(range(len(group_info)), repeats=group_info)[self.used_indices], return_counts=True ) self._handle = ctypes.c_void_p() params_str = _param_dict_to_str(self.params) _safe_call( _LIB.LGBM_DatasetGetSubset( self.reference.construct()._handle, used_indices.ctypes.data_as(ctypes.POINTER(ctypes.c_int32)), ctypes.c_int32(used_indices.shape[0]), _c_str(params_str), ctypes.byref(self._handle), ) ) if not self.free_raw_data: self.get_data() if self.group is not None: self.set_group(self.group) if self.position is not None: self.set_position(self.position) if self.get_label() is None: raise ValueError("Label should not be None.") if ( isinstance(self._predictor, _InnerPredictor) and self._predictor is not self.reference._predictor ): self.get_data() self._set_init_score_by_predictor( predictor=self._predictor, data=self.data, used_indices=used_indices ) else: # create train self._lazy_init( data=self.data, label=self.label, reference=None, weight=self.weight, group=self.group, init_score=self.init_score, predictor=self._predictor, feature_name=self.feature_name, categorical_feature=self.categorical_feature, params=self.params, position=self.position, ) if self.free_raw_data: self.data = None self.feature_name = self.get_feature_name() return self def create_valid( self, data: _LGBM_TrainDataType, label: Optional[_LGBM_LabelType] = None, weight: Optional[_LGBM_WeightType] = None, group: Optional[_LGBM_GroupType] = None, init_score: Optional[_LGBM_InitScoreType] = None, params: Optional[Dict[str, Any]] = None, position: Optional[_LGBM_PositionType] = None, ) -> "Dataset": """Create validation data align with current Dataset. Parameters ---------- data : str, pathlib.Path, numpy array, pandas DataFrame, scipy.sparse, Sequence, list of Sequence, list of numpy array or pyarrow Table Data source of Dataset. If str or pathlib.Path, it represents the path to a text file (CSV, TSV, or LibSVM) or a LightGBM Dataset binary file. label : list, numpy 1-D array, pandas Series / one-column DataFrame, pyarrow Array, pyarrow ChunkedArray or None, optional (default=None) Label of the data. weight : list, numpy 1-D array, pandas Series, pyarrow Array, pyarrow ChunkedArray or None, optional (default=None) Weight for each instance. Weights should be non-negative. group : list, numpy 1-D array, pandas Series, pyarrow Array, pyarrow ChunkedArray or None, optional (default=None) Group/query data. Only used in the learning-to-rank task. sum(group) = n_samples. For example, if you have a 100-document dataset with ``group = [10, 20, 40, 10, 10, 10]``, that means that you have 6 groups, 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. 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) Init score for Dataset. params : dict or None, optional (default=None) Other parameters for validation Dataset. position : numpy 1-D array, pandas Series or None, optional (default=None) Position of items used in unbiased learning-to-rank task. Returns ------- valid : Dataset Validation Dataset with reference to self. """ ret = Dataset( data, label=label, reference=self, weight=weight, group=group, position=position, init_score=init_score, params=params, free_raw_data=self.free_raw_data, ) ret._predictor = self._predictor ret.pandas_categorical = self.pandas_categorical return ret def subset( self, used_indices: List[int], params: Optional[Dict[str, Any]] = None, ) -> "Dataset": """Get subset of current Dataset. Parameters ---------- used_indices : list of int Indices used to create the subset. params : dict or None, optional (default=None) These parameters will be passed to Dataset constructor. Returns ------- subset : Dataset Subset of the current Dataset. """ if params is None: params = self.params ret = Dataset( None, reference=self, feature_name=self.feature_name, categorical_feature=self.categorical_feature, params=params, free_raw_data=self.free_raw_data, ) ret._predictor = self._predictor ret.pandas_categorical = self.pandas_categorical ret.used_indices = sorted(used_indices) return ret def save_binary(self, filename: Union[str, Path]) -> "Dataset": """Save Dataset to a binary file. .. note:: Please note that `init_score` is not saved in binary file. If you need it, please set it again after loading Dataset. Parameters ---------- filename : str or pathlib.Path Name of the output file. Returns ------- self : Dataset Returns self. """ _safe_call( _LIB.LGBM_DatasetSaveBinary( self.construct()._handle, _c_str(str(filename)), ) ) return self def _update_params(self, params: Optional[Dict[str, Any]]) -> "Dataset": if not params: return self params = deepcopy(params) def update() -> None: if not self.params: self.params = params else: self._params_back_up = deepcopy(self.params) self.params.update(params) if self._handle is None: update() elif params is not None: ret = _LIB.LGBM_DatasetUpdateParamChecking( _c_str(_param_dict_to_str(self.params)), _c_str(_param_dict_to_str(params)), ) if ret != 0: # could be updated if data is not freed if self.data is not None: update() self._free_handle() else: raise LightGBMError(_LIB.LGBM_GetLastError().decode("utf-8")) return self def _reverse_update_params(self) -> "Dataset": if self._handle is None: self.params = deepcopy(self._params_back_up) self._params_back_up = None return self def set_field( self, field_name: str, data: Optional[_LGBM_SetFieldType], ) -> "Dataset": """Set property into the Dataset. Parameters ---------- field_name : str The field name of the information. 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 The data to be set. Returns ------- self : Dataset Dataset with set property. """ if self._handle is None: raise Exception(f"Cannot set {field_name} before construct dataset") if data is None: # set to None _safe_call( _LIB.LGBM_DatasetSetField( self._handle, _c_str(field_name), None, ctypes.c_int(0), ctypes.c_int(_FIELD_TYPE_MAPPER[field_name]), ) ) return self # If the data is a arrow data, we can just pass it to C if _is_pyarrow_array(data) or _is_pyarrow_table(data): # If a table is being passed, we concatenate the columns. This is only valid for # 'init_score'. if _is_pyarrow_table(data): if field_name != "init_score": raise ValueError(f"pyarrow tables are not supported for field '{field_name}'") data = pa_chunked_array( [ chunk for array in data.columns # type: ignore for chunk in array.chunks ] ) c_array = _export_arrow_to_c(data) _safe_call( _LIB.LGBM_DatasetSetFieldFromArrow( self._handle, _c_str(field_name), ctypes.c_int64(c_array.n_chunks), ctypes.c_void_p(c_array.chunks_ptr), ctypes.c_void_p(c_array.schema_ptr), ) ) self.version += 1 return self dtype: "np.typing.DTypeLike" if field_name == "init_score": dtype = np.float64 if _is_1d_collection(data): data = _list_to_1d_numpy(data=data, dtype=dtype, name=field_name) elif _is_2d_collection(data): data = _data_to_2d_numpy(data=data, dtype=dtype, name=field_name) data = data.ravel(order="F") else: raise TypeError( "init_score must be list, numpy 1-D array or pandas Series.\n" "In multiclass classification init_score can also be a list of lists, numpy 2-D array or pandas DataFrame." ) else: if field_name in {"group", "position"}: dtype = np.int32 else: dtype = np.float32 data = _list_to_1d_numpy(data=data, dtype=dtype, name=field_name) ptr_data: Union[_ctypes_float_ptr, _ctypes_int_ptr] if data.dtype == np.float32 or data.dtype == np.float64: ptr_data, type_data, _ = _c_float_array(data) elif data.dtype == np.int32: ptr_data, type_data, _ = _c_int_array(data) else: raise TypeError(f"Expected np.float32/64 or np.int32, met type({data.dtype})") if type_data != _FIELD_TYPE_MAPPER[field_name]: raise TypeError("Input type error for set_field") _safe_call( _LIB.LGBM_DatasetSetField( self._handle, _c_str(field_name), ptr_data, ctypes.c_int(len(data)), ctypes.c_int(type_data), ) ) self.version += 1 return self def get_field(self, field_name: str) -> Optional[np.ndarray]: """Get property from the Dataset. Can only be run on a constructed Dataset. Unlike ``get_group()``, ``get_init_score()``, ``get_label()``, ``get_position()``, and ``get_weight()``, this method ignores any raw data passed into ``lgb.Dataset()`` on the Python side, and will only read data from the constructed C++ ``Dataset`` object. Parameters ---------- field_name : str The field name of the information. Returns ------- info : numpy array or None A numpy array with information from the Dataset. """ if self._handle is None: raise Exception(f"Cannot get {field_name} before construct Dataset") tmp_out_len = ctypes.c_int(0) out_type = ctypes.c_int(0) ret = ctypes.POINTER(ctypes.c_void_p)() _safe_call( _LIB.LGBM_DatasetGetField( self._handle, _c_str(field_name), ctypes.byref(tmp_out_len), ctypes.byref(ret), ctypes.byref(out_type), ) ) if out_type.value != _FIELD_TYPE_MAPPER[field_name]: raise TypeError("Return type error for get_field") if tmp_out_len.value == 0: return None if out_type.value == _C_API_DTYPE_INT32: arr = _cint32_array_to_numpy( cptr=ctypes.cast(ret, ctypes.POINTER(ctypes.c_int32)), length=tmp_out_len.value, ) elif out_type.value == _C_API_DTYPE_FLOAT32: arr = _cfloat32_array_to_numpy( cptr=ctypes.cast(ret, ctypes.POINTER(ctypes.c_float)), length=tmp_out_len.value, ) elif out_type.value == _C_API_DTYPE_FLOAT64: arr = _cfloat64_array_to_numpy( cptr=ctypes.cast(ret, ctypes.POINTER(ctypes.c_double)), length=tmp_out_len.value, ) else: raise TypeError("Unknown type") if field_name == "init_score": num_data = self.num_data() num_classes = arr.size // num_data if num_classes > 1: arr = arr.reshape((num_data, num_classes), order="F") return arr def set_categorical_feature( self, categorical_feature: _LGBM_CategoricalFeatureConfiguration, ) -> "Dataset": """Set categorical features. Parameters ---------- categorical_feature : list of str or int, or 'auto' Names or indices of categorical features. Returns ------- self : Dataset Dataset with set categorical features. """ if self.categorical_feature == categorical_feature: return self if self.data is not None: if self.categorical_feature is None: self.categorical_feature = categorical_feature return self._free_handle() elif categorical_feature == "auto": return self else: if self.categorical_feature != "auto": _log_warning( "categorical_feature in Dataset is overridden.\n" f"New categorical_feature is {list(categorical_feature)}" ) self.categorical_feature = categorical_feature return self._free_handle() else: raise LightGBMError( "Cannot set categorical feature after freed raw data, " "set free_raw_data=False when construct Dataset to avoid this." ) def _set_predictor( self, predictor: Optional[_InnerPredictor], ) -> "Dataset": """Set predictor for continued training. It is not recommended for user to call this function. Please use init_model argument in engine.train() or engine.cv() instead. """ if predictor is None and self._predictor is None: return self elif isinstance(predictor, _InnerPredictor) and isinstance(self._predictor, _InnerPredictor): if (predictor == self._predictor) and ( predictor.current_iteration() == self._predictor.current_iteration() ): return self if self._handle is None: self._predictor = predictor elif self.data is not None: self._predictor = predictor self._set_init_score_by_predictor( predictor=self._predictor, data=self.data, used_indices=None, ) elif self.used_indices is not None and self.reference is not None and self.reference.data is not None: self._predictor = predictor self._set_init_score_by_predictor( predictor=self._predictor, data=self.reference.data, used_indices=self.used_indices, ) else: raise LightGBMError( "Cannot set predictor after freed raw data, " "set free_raw_data=False when construct Dataset to avoid this." ) return self def set_reference(self, reference: "Dataset") -> "Dataset": """Set reference Dataset. Parameters ---------- reference : Dataset Reference that is used as a template to construct the current Dataset. Returns ------- self : Dataset Dataset with set reference. """ self.set_categorical_feature(reference.categorical_feature).set_feature_name( reference.feature_name )._set_predictor(reference._predictor) # we're done if self and reference share a common upstream reference if self.get_ref_chain().intersection(reference.get_ref_chain()): return self if self.data is not None: self.reference = reference return self._free_handle() else: raise LightGBMError( "Cannot set reference after freed raw data, " "set free_raw_data=False when construct Dataset to avoid this." ) def set_feature_name(self, feature_name: _LGBM_FeatureNameConfiguration) -> "Dataset": """Set feature name. Parameters ---------- feature_name : list of str Feature names. Returns ------- self : Dataset Dataset with set feature name. """ if feature_name != "auto": self.feature_name = feature_name if self._handle is not None and feature_name is not None and feature_name != "auto": if len(feature_name) != self.num_feature(): raise ValueError( f"Length of feature_name({len(feature_name)}) and num_feature({self.num_feature()}) don't match" ) c_feature_name = [_c_str(name) for name in feature_name] _safe_call( _LIB.LGBM_DatasetSetFeatureNames( self._handle, _c_array(ctypes.c_char_p, c_feature_name), ctypes.c_int(len(feature_name)), ) ) return self def set_label(self, label: Optional[_LGBM_LabelType]) -> "Dataset": """Set label of Dataset. Parameters ---------- label : list, numpy 1-D array, pandas Series / one-column DataFrame, pyarrow Array, pyarrow ChunkedArray or None The label information to be set into Dataset. Returns ------- self : Dataset Dataset with set label. """ self.label = label if self._handle is not None: if isinstance(label, pd_DataFrame): if len(label.columns) > 1: raise ValueError("DataFrame for label cannot have multiple columns") label_array = np.ravel(_pandas_to_numpy(label, target_dtype=np.float32)) elif _is_pyarrow_array(label): label_array = label else: label_array = _list_to_1d_numpy(data=label, dtype=np.float32, name="label") self.set_field("label", label_array) self.label = self.get_field("label") # original values can be modified at cpp side return self def set_weight( self, weight: Optional[_LGBM_WeightType], ) -> "Dataset": """Set weight of each instance. Parameters ---------- weight : list, numpy 1-D array, pandas Series, pyarrow Array, pyarrow ChunkedArray or None Weight to be set for each data point. Weights should be non-negative. Returns ------- self : Dataset Dataset with set weight. """ # Check if the weight contains values other than one if weight is not None: if _is_pyarrow_array(weight): if pa_compute.all(pa_compute.equal(weight, 1)).as_py(): weight = None elif np.all(weight == 1): weight = None self.weight = weight # Set field if self._handle is not None and weight is not None: if not _is_pyarrow_array(weight): weight = _list_to_1d_numpy(data=weight, dtype=np.float32, name="weight") self.set_field("weight", weight) self.weight = self.get_field("weight") # original values can be modified at cpp side return self def set_init_score( self, init_score: Optional[_LGBM_InitScoreType], ) -> "Dataset": """Set init score of Booster to start from. Parameters ---------- 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 Init score for Booster. Returns ------- self : Dataset Dataset with set init score. """ self.init_score = init_score if self._handle is not None and init_score is not None: self.set_field("init_score", init_score) self.init_score = self.get_field("init_score") # original values can be modified at cpp side return self def set_group( self, group: Optional[_LGBM_GroupType], ) -> "Dataset": """Set group size of Dataset (used for ranking). Parameters ---------- group : list, numpy 1-D array, pandas Series, pyarrow Array, pyarrow ChunkedArray or None Group/query data. Only used in the learning-to-rank task. sum(group) = n_samples. For example, if you have a 100-document dataset with ``group = [10, 20, 40, 10, 10, 10]``, that means that you have 6 groups, 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. Returns ------- self : Dataset Dataset with set group. """ self.group = group if self._handle is not None and group is not None: if not _is_pyarrow_array(group): group = _list_to_1d_numpy(data=group, dtype=np.int32, name="group") self.set_field("group", group) # original values can be modified at cpp side constructed_group = self.get_field("group") if constructed_group is not None: self.group = np.diff(constructed_group) return self def set_position( self, position: Optional[_LGBM_PositionType], ) -> "Dataset": """Set position of Dataset (used for ranking). Parameters ---------- position : numpy 1-D array, pandas Series or None, optional (default=None) Position of items used in unbiased learning-to-rank task. Returns ------- self : Dataset Dataset with set position. """ self.position = position if self._handle is not None and position is not None: position = _list_to_1d_numpy(data=position, dtype=np.int32, name="position") self.set_field("position", position) return self def get_feature_name(self) -> List[str]: """Get the names of columns (features) in the Dataset. Returns ------- feature_names : list of str The names of columns (features) in the Dataset. """ if self._handle is None: raise LightGBMError("Cannot get feature_name before construct dataset") num_feature = self.num_feature() tmp_out_len = ctypes.c_int(0) reserved_string_buffer_size = 255 required_string_buffer_size = ctypes.c_size_t(0) string_buffers = [ctypes.create_string_buffer(reserved_string_buffer_size) for _ in range(num_feature)] ptr_string_buffers = (ctypes.c_char_p * num_feature)(*map(ctypes.addressof, string_buffers)) # type: ignore[misc] _safe_call( _LIB.LGBM_DatasetGetFeatureNames( self._handle, ctypes.c_int(num_feature), ctypes.byref(tmp_out_len), ctypes.c_size_t(reserved_string_buffer_size), ctypes.byref(required_string_buffer_size), ptr_string_buffers, ) ) if num_feature != tmp_out_len.value: raise ValueError("Length of feature names doesn't equal with num_feature") actual_string_buffer_size = required_string_buffer_size.value # if buffer length is not long enough, reallocate buffers if reserved_string_buffer_size < actual_string_buffer_size: string_buffers = [ctypes.create_string_buffer(actual_string_buffer_size) for _ in range(num_feature)] ptr_string_buffers = (ctypes.c_char_p * num_feature)(*map(ctypes.addressof, string_buffers)) # type: ignore[misc] _safe_call( _LIB.LGBM_DatasetGetFeatureNames( self._handle, ctypes.c_int(num_feature), ctypes.byref(tmp_out_len), ctypes.c_size_t(actual_string_buffer_size), ctypes.byref(required_string_buffer_size), ptr_string_buffers, ) ) return [string_buffers[i].value.decode("utf-8") for i in range(num_feature)] def get_label(self) -> Optional[_LGBM_LabelType]: """Get the label of the Dataset. Returns ------- label : list, numpy 1-D array, pandas Series / one-column DataFrame, pyarrow Array, pyarrow ChunkedArray or None The label information from the Dataset. For a constructed ``Dataset``, this will only return a numpy array. """ if self.label is None: self.label = self.get_field("label") return self.label def get_weight(self) -> Optional[_LGBM_WeightType]: """Get the weight of the Dataset. Returns ------- weight : list, numpy 1-D array, pandas Series, pyarrow Array, pyarrow ChunkedArray or None Weight for each data point from the Dataset. Weights should be non-negative. For a constructed ``Dataset``, this will only return ``None`` or a numpy array. """ if self.weight is None: self.weight = self.get_field("weight") return self.weight def get_init_score(self) -> Optional[_LGBM_InitScoreType]: """Get the initial score of the Dataset. Returns ------- 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 Init score of Booster. For a constructed ``Dataset``, this will only return ``None`` or a numpy array. """ if self.init_score is None: self.init_score = self.get_field("init_score") return self.init_score def get_data(self) -> Optional[_LGBM_TrainDataType]: """Get the raw data of the Dataset. Returns ------- data : str, pathlib.Path, numpy array, pandas DataFrame, scipy.sparse, Sequence, list of Sequence, list of numpy array, pyarrow Table or None Raw data used in the Dataset construction. """ if self._handle is None: raise Exception("Cannot get data before construct Dataset") if self._need_slice and self.used_indices is not None and self.reference is not None: self.data = self.reference.data if self.data is not None: if isinstance(self.data, (np.ndarray, scipy.sparse.spmatrix)): self.data = self.data[self.used_indices, :] elif isinstance(self.data, pd_DataFrame): self.data = self.data.iloc[self.used_indices].copy() elif isinstance(self.data, Sequence): self.data = self.data[self.used_indices] elif isinstance(self.data, pa_Table): self.data = self.data.take(self.used_indices) elif _is_list_of_sequences(self.data) and len(self.data) > 0: self.data = np.array(list(self._yield_row_from_seqlist(self.data, self.used_indices))) else: _log_warning( f"Cannot subset {type(self.data).__name__} type of raw data.\nReturning original raw data" ) self._need_slice = False if self.data is None: raise LightGBMError( "Cannot call `get_data` after freed raw data, " "set free_raw_data=False when construct Dataset to avoid this." ) return self.data def get_group(self) -> Optional[_LGBM_GroupType]: """Get the group of the Dataset. Returns ------- group : list, numpy 1-D array, pandas Series, pyarrow Array, pyarrow ChunkedArray or None Group/query data. Only used in the learning-to-rank task. sum(group) = n_samples. For example, if you have a 100-document dataset with ``group = [10, 20, 40, 10, 10, 10]``, that means that you have 6 groups, 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. For a constructed ``Dataset``, this will only return ``None`` or a numpy array. """ if self.group is None: self.group = self.get_field("group") if self.group is not None: # group data from LightGBM is boundaries data, need to convert to group size self.group = np.diff(self.group) return self.group def get_position(self) -> Optional[_LGBM_PositionType]: """Get the position of the Dataset. Returns ------- position : numpy 1-D array, pandas Series or None Position of items used in unbiased learning-to-rank task. For a constructed ``Dataset``, this will only return ``None`` or a numpy array. """ if self.position is None: self.position = self.get_field("position") return self.position def num_data(self) -> int: """Get the number of rows in the Dataset. Returns ------- number_of_rows : int The number of rows in the Dataset. """ if self._handle is not None: ret = ctypes.c_int(0) _safe_call( _LIB.LGBM_DatasetGetNumData( self._handle, ctypes.byref(ret), ) ) return ret.value else: raise LightGBMError("Cannot get num_data before construct dataset") def num_feature(self) -> int: """Get the number of columns (features) in the Dataset. Returns ------- number_of_columns : int The number of columns (features) in the Dataset. """ if self._handle is not None: ret = ctypes.c_int(0) _safe_call( _LIB.LGBM_DatasetGetNumFeature( self._handle, ctypes.byref(ret), ) ) return ret.value else: raise LightGBMError("Cannot get num_feature before construct dataset") def feature_num_bin(self, feature: Union[int, str]) -> int: """Get the number of bins for a feature. .. versionadded:: 4.0.0 Parameters ---------- feature : int or str Index or name of the feature. Returns ------- number_of_bins : int The number of constructed bins for the feature in the Dataset. """ if self._handle is not None: if isinstance(feature, str): feature_index = self.feature_name.index(feature) else: feature_index = feature ret = ctypes.c_int(0) _safe_call( _LIB.LGBM_DatasetGetFeatureNumBin( self._handle, ctypes.c_int(feature_index), ctypes.byref(ret), ) ) return ret.value else: raise LightGBMError("Cannot get feature_num_bin before construct dataset") def get_ref_chain(self, ref_limit: int = 100) -> Set["Dataset"]: """Get a chain of Dataset objects. Starts with r, then goes to r.reference (if exists), then to r.reference.reference, etc. until we hit ``ref_limit`` or a reference loop. Parameters ---------- ref_limit : int, optional (default=100) The limit number of references. Returns ------- ref_chain : set of Dataset Chain of references of the Datasets. """ head = self ref_chain: Set[Dataset] = set() while len(ref_chain) < ref_limit: if isinstance(head, Dataset): ref_chain.add(head) if (head.reference is not None) and (head.reference not in ref_chain): head = head.reference else: break else: break return ref_chain def add_features_from(self, other: "Dataset") -> "Dataset": """Add features from other Dataset to the current Dataset. Both Datasets must be constructed before calling this method. Parameters ---------- other : Dataset The Dataset to take features from. Returns ------- self : Dataset Dataset with the new features added. """ if self._handle is None or other._handle is None: raise ValueError("Both source and target Datasets must be constructed before adding features") _safe_call( _LIB.LGBM_DatasetAddFeaturesFrom( self._handle, other._handle, ) ) was_none = self.data is None old_self_data_type = type(self.data).__name__ if other.data is None: self.data = None elif self.data is not None: if isinstance(self.data, np.ndarray): if isinstance(other.data, np.ndarray): self.data = np.hstack((self.data, other.data)) elif isinstance(other.data, scipy.sparse.spmatrix): self.data = np.hstack((self.data, other.data.toarray())) elif isinstance(other.data, pd_DataFrame): self.data = np.hstack((self.data, other.data.values)) else: self.data = None elif isinstance(self.data, scipy.sparse.spmatrix): sparse_format = self.data.getformat() if isinstance(other.data, (np.ndarray, scipy.sparse.spmatrix)): self.data = scipy.sparse.hstack((self.data, other.data), format=sparse_format) elif isinstance(other.data, pd_DataFrame): self.data = scipy.sparse.hstack((self.data, other.data.values), format=sparse_format) else: self.data = None elif isinstance(self.data, pd_DataFrame): if not PANDAS_INSTALLED: raise LightGBMError( "Cannot add features to DataFrame type of raw data " "without pandas installed. " "Install pandas and restart your session." ) if isinstance(other.data, np.ndarray): self.data = concat((self.data, pd_DataFrame(other.data)), axis=1, ignore_index=True) elif isinstance(other.data, scipy.sparse.spmatrix): self.data = concat((self.data, pd_DataFrame(other.data.toarray())), axis=1, ignore_index=True) elif isinstance(other.data, pd_DataFrame): self.data = concat((self.data, other.data), axis=1, ignore_index=True) else: self.data = None else: self.data = None if self.data is None: err_msg = ( f"Cannot add features from {type(other.data).__name__} type of raw data to " f"{old_self_data_type} type of raw data.\n" ) err_msg += ( "Set free_raw_data=False when construct Dataset to avoid this" if was_none else "Freeing raw data" ) _log_warning(err_msg) self.feature_name = self.get_feature_name() _log_warning( "Resetting categorical features.\n" "You can set new categorical features via ``set_categorical_feature`` method" ) self.categorical_feature = "auto" self.pandas_categorical = None return self def _dump_text(self, filename: Union[str, Path]) -> "Dataset": """Save Dataset to a text file. This format cannot be loaded back in by LightGBM, but is useful for debugging purposes. Parameters ---------- filename : str or pathlib.Path Name of the output file. Returns ------- self : Dataset Returns self. """ _safe_call( _LIB.LGBM_DatasetDumpText( self.construct()._handle, _c_str(str(filename)), ) ) return self _LGBM_CustomObjectiveFunction = Callable[ [np.ndarray, Dataset], Tuple[np.ndarray, np.ndarray], ] _LGBM_CustomEvalFunction = Union[ Callable[ [np.ndarray, Dataset], _LGBM_EvalFunctionResultType, ], Callable[ [np.ndarray, Dataset], List[_LGBM_EvalFunctionResultType], ], ] class Booster: """Booster in LightGBM.""" def __init__( self, params: Optional[Dict[str, Any]] = None, train_set: Optional[Dataset] = None, model_file: Optional[Union[str, Path]] = None, model_str: Optional[str] = None, ): """Initialize the Booster. Parameters ---------- params : dict or None, optional (default=None) Parameters for Booster. train_set : Dataset or None, optional (default=None) Training dataset. model_file : str, pathlib.Path or None, optional (default=None) Path to the model file. model_str : str or None, optional (default=None) Model will be loaded from this string. """ self._handle = ctypes.c_void_p() self._network = False self.__need_reload_eval_info = True self._train_data_name = "training" self.__set_objective_to_none = False self.best_iteration = -1 self.best_score: _LGBM_BoosterBestScoreType = {} params = {} if params is None else deepcopy(params) if train_set is not None: # Training task if not isinstance(train_set, Dataset): raise TypeError(f"Training data should be Dataset instance, met {type(train_set).__name__}") params = _choose_param_value( main_param_name="machines", params=params, default_value=None, ) # if "machines" is given, assume user wants to do distributed learning, and set up network if params["machines"] is None: params.pop("machines", None) else: machines = params["machines"] if isinstance(machines, str): num_machines_from_machine_list = len(machines.split(",")) elif isinstance(machines, (list, set)): num_machines_from_machine_list = len(machines) machines = ",".join(machines) else: raise ValueError("Invalid machines in params.") params = _choose_param_value( main_param_name="num_machines", params=params, default_value=num_machines_from_machine_list, ) params = _choose_param_value( main_param_name="local_listen_port", params=params, default_value=12400, ) self.set_network( machines=machines, local_listen_port=params["local_listen_port"], listen_time_out=params.get("time_out", 120), num_machines=params["num_machines"], ) # construct booster object train_set.construct() # copy the parameters from train_set params.update(train_set.get_params()) params_str = _param_dict_to_str(params) _safe_call( _LIB.LGBM_BoosterCreate( train_set._handle, _c_str(params_str), ctypes.byref(self._handle), ) ) # save reference to data self.train_set = train_set self.valid_sets: List[Dataset] = [] self.name_valid_sets: List[str] = [] self.__num_dataset = 1 self.__init_predictor = train_set._predictor if self.__init_predictor is not None: _safe_call( _LIB.LGBM_BoosterMerge( self._handle, self.__init_predictor._handle, ) ) out_num_class = ctypes.c_int(0) _safe_call( _LIB.LGBM_BoosterGetNumClasses( self._handle, ctypes.byref(out_num_class), ) ) self.__num_class = out_num_class.value # buffer for inner predict self.__inner_predict_buffer: List[Optional[np.ndarray]] = [None] self.__is_predicted_cur_iter = [False] self.__get_eval_info() self.pandas_categorical = train_set.pandas_categorical self.train_set_version = train_set.version elif model_file is not None: # Prediction task out_num_iterations = ctypes.c_int(0) _safe_call( _LIB.LGBM_BoosterCreateFromModelfile( _c_str(str(model_file)), ctypes.byref(out_num_iterations), ctypes.byref(self._handle), ) ) out_num_class = ctypes.c_int(0) _safe_call( _LIB.LGBM_BoosterGetNumClasses( self._handle, ctypes.byref(out_num_class), ) ) self.__num_class = out_num_class.value self.pandas_categorical = _load_pandas_categorical(file_name=model_file) if params: _log_warning("Ignoring params argument, using parameters from model file.") params = self._get_loaded_param() elif model_str is not None: self.model_from_string(model_str) if params: _log_warning("Ignoring params argument, using parameters from model string.") params = self._get_loaded_param() else: raise TypeError( "Need at least one training dataset or model file or model string to create Booster instance" ) self.params = params def __del__(self) -> None: try: if self._network: self.free_network() except AttributeError: pass try: if self._handle is not None: _safe_call(_LIB.LGBM_BoosterFree(self._handle)) except AttributeError: pass def __copy__(self) -> "Booster": return self.__deepcopy__(None) def __deepcopy__(self, *args: Any, **kwargs: Any) -> "Booster": model_str = self.model_to_string(num_iteration=-1) return Booster(model_str=model_str) def __getstate__(self) -> Dict[str, Any]: this = self.__dict__.copy() handle = this["_handle"] this.pop("train_set", None) this.pop("valid_sets", None) if handle is not None: this["_handle"] = self.model_to_string(num_iteration=-1) return this def __setstate__(self, state: Dict[str, Any]) -> None: model_str = state.get("_handle", state.get("handle", None)) if model_str is not None: handle = ctypes.c_void_p() out_num_iterations = ctypes.c_int(0) _safe_call( _LIB.LGBM_BoosterLoadModelFromString( _c_str(model_str), ctypes.byref(out_num_iterations), ctypes.byref(handle), ) ) state["_handle"] = handle self.__dict__.update(state) def _get_loaded_param(self) -> Dict[str, Any]: buffer_len = 1 << 20 tmp_out_len = ctypes.c_int64(0) string_buffer = ctypes.create_string_buffer(buffer_len) ptr_string_buffer = ctypes.c_char_p(ctypes.addressof(string_buffer)) _safe_call( _LIB.LGBM_BoosterGetLoadedParam( self._handle, ctypes.c_int64(buffer_len), ctypes.byref(tmp_out_len), ptr_string_buffer, ) ) actual_len = tmp_out_len.value # if buffer length is not long enough, re-allocate a buffer if actual_len > buffer_len: string_buffer = ctypes.create_string_buffer(actual_len) ptr_string_buffer = ctypes.c_char_p(ctypes.addressof(string_buffer)) _safe_call( _LIB.LGBM_BoosterGetLoadedParam( self._handle, ctypes.c_int64(actual_len), ctypes.byref(tmp_out_len), ptr_string_buffer, ) ) return json.loads(string_buffer.value.decode("utf-8")) def free_dataset(self) -> "Booster": """Free Booster's Datasets. Returns ------- self : Booster Booster without Datasets. """ self.__dict__.pop("train_set", None) self.__dict__.pop("valid_sets", None) self.__num_dataset = 0 return self def _free_buffer(self) -> "Booster": self.__inner_predict_buffer = [] self.__is_predicted_cur_iter = [] return self def set_network( self, machines: Union[List[str], Set[str], str], local_listen_port: int = 12400, listen_time_out: int = 120, num_machines: int = 1, ) -> "Booster": """Set the network configuration. Parameters ---------- machines : list, set or str Names of machines. local_listen_port : int, optional (default=12400) TCP listen port for local machines. listen_time_out : int, optional (default=120) Socket time-out in minutes. num_machines : int, optional (default=1) The number of machines for distributed learning application. Returns ------- self : Booster Booster with set network. """ if isinstance(machines, (list, set)): machines = ",".join(machines) _safe_call( _LIB.LGBM_NetworkInit( _c_str(machines), ctypes.c_int(local_listen_port), ctypes.c_int(listen_time_out), ctypes.c_int(num_machines), ) ) self._network = True return self def free_network(self) -> "Booster": """Free Booster's network. Returns ------- self : Booster Booster with freed network. """ _safe_call(_LIB.LGBM_NetworkFree()) self._network = False return self def trees_to_dataframe(self) -> pd_DataFrame: """Parse the fitted model and return in an easy-to-read pandas DataFrame. The returned DataFrame has the following columns. - ``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". - ``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. - ``node_index`` : str, unique identifier for a node. - ``left_child`` : str, ``node_index`` of the child node to the left of a split. ``None`` for leaf nodes. - ``right_child`` : str, ``node_index`` of the child node to the right of a split. ``None`` for leaf nodes. - ``parent_index`` : str, ``node_index`` of this node's parent. ``None`` for the root node. - ``split_feature`` : str, name of the feature used for splitting. ``None`` for leaf nodes. - ``split_gain`` : float64, gain from adding this split to the tree. ``NaN`` for leaf nodes. - ``threshold`` : float64, value of the feature used to decide which side of the split a record will go down. ``NaN`` for leaf nodes. - ``decision_type`` : str, logical operator describing how to compare a value to ``threshold``. For example, ``split_feature = "Column_10", threshold = 15, decision_type = "<="`` means that records where ``Column_10 <= 15`` follow the left side of the split, otherwise follows the right side of the split. ``None`` for leaf nodes. - ``missing_direction`` : str, split direction that missing values should go to. ``None`` for leaf nodes. - ``missing_type`` : str, describes what types of values are treated as missing. - ``value`` : float64, predicted value for this leaf node, multiplied by the learning rate. - ``weight`` : float64 or int64, sum of Hessian (second-order derivative of objective), summed over observations that fall in this node. - ``count`` : int64, number of records in the training data that fall into this node. Returns ------- result : pandas DataFrame Returns a pandas DataFrame of the parsed model. """ if not PANDAS_INSTALLED: raise LightGBMError( "This method cannot be run without pandas installed. " "You must install pandas and restart your session to use this method." ) if self.num_trees() == 0: raise LightGBMError("There are no trees in this Booster and thus nothing to parse") def _is_split_node(tree: Dict[str, Any]) -> bool: return "split_index" in tree.keys() def create_node_record( tree: Dict[str, Any], node_depth: int = 1, tree_index: Optional[int] = None, feature_names: Optional[List[str]] = None, parent_node: Optional[str] = None, ) -> Dict[str, Any]: def _get_node_index( tree: Dict[str, Any], tree_index: Optional[int], ) -> str: tree_num = f"{tree_index}-" if tree_index is not None else "" is_split = _is_split_node(tree) node_type = "S" if is_split else "L" # if a single node tree it won't have `leaf_index` so return 0 node_num = tree.get("split_index" if is_split else "leaf_index", 0) return f"{tree_num}{node_type}{node_num}" def _get_split_feature( *, tree: Dict[str, Any], feature_names: Optional[List[str]], ) -> Optional[str]: if _is_split_node(tree): if feature_names is not None: feature_name = feature_names[tree["split_feature"]] else: feature_name = tree["split_feature"] else: feature_name = None return feature_name def _is_single_node_tree(tree: Dict[str, Any]) -> bool: return set(tree.keys()) == {"leaf_value", "leaf_count"} # Create the node record, and populate universal data members node: Dict[str, Union[int, str, None]] = OrderedDict() node["tree_index"] = tree_index node["node_depth"] = node_depth node["node_index"] = _get_node_index(tree, tree_index) node["left_child"] = None node["right_child"] = None node["parent_index"] = parent_node node["split_feature"] = _get_split_feature(tree=tree, feature_names=feature_names) node["split_gain"] = None node["threshold"] = None node["decision_type"] = None node["missing_direction"] = None node["missing_type"] = None node["value"] = None node["weight"] = None node["count"] = None # Update values to reflect node type (leaf or split) if _is_split_node(tree): node["left_child"] = _get_node_index(tree["left_child"], tree_index) node["right_child"] = _get_node_index(tree["right_child"], tree_index) node["split_gain"] = tree["split_gain"] node["threshold"] = tree["threshold"] node["decision_type"] = tree["decision_type"] node["missing_direction"] = "left" if tree["default_left"] else "right" node["missing_type"] = tree["missing_type"] node["value"] = tree["internal_value"] node["weight"] = tree["internal_weight"] node["count"] = tree["internal_count"] else: node["value"] = tree["leaf_value"] if not _is_single_node_tree(tree): node["weight"] = tree["leaf_weight"] node["count"] = tree["leaf_count"] return node def tree_dict_to_node_list( tree: Dict[str, Any], node_depth: int = 1, tree_index: Optional[int] = None, feature_names: Optional[List[str]] = None, parent_node: Optional[str] = None, ) -> List[Dict[str, Any]]: node = create_node_record( tree=tree, node_depth=node_depth, tree_index=tree_index, feature_names=feature_names, parent_node=parent_node, ) res = [node] if _is_split_node(tree): # traverse the next level of the tree children = ["left_child", "right_child"] for child in children: subtree_list = tree_dict_to_node_list( tree=tree[child], node_depth=node_depth + 1, tree_index=tree_index, feature_names=feature_names, parent_node=node["node_index"], ) # In tree format, "subtree_list" is a list of node records (dicts), # and we add node to the list. res.extend(subtree_list) return res model_dict = self.dump_model() feature_names = model_dict["feature_names"] model_list = [] for tree in model_dict["tree_info"]: model_list.extend( tree_dict_to_node_list( tree=tree["tree_structure"], tree_index=tree["tree_index"], feature_names=feature_names ) ) return pd_DataFrame(model_list, columns=model_list[0].keys()) def set_train_data_name(self, name: str) -> "Booster": """Set the name to the training Dataset. Parameters ---------- name : str Name for the training Dataset. Returns ------- self : Booster Booster with set training Dataset name. """ self._train_data_name = name return self def add_valid(self, data: Dataset, name: str) -> "Booster": """Add validation data. Parameters ---------- data : Dataset Validation data. name : str Name of validation data. Returns ------- self : Booster Booster with set validation data. """ if not isinstance(data, Dataset): raise TypeError(f"Validation data should be Dataset instance, met {type(data).__name__}") if data._predictor is not self.__init_predictor: raise LightGBMError("Add validation data failed, you should use same predictor for these data") _safe_call( _LIB.LGBM_BoosterAddValidData( self._handle, data.construct()._handle, ) ) self.valid_sets.append(data) self.name_valid_sets.append(name) self.__num_dataset += 1 self.__inner_predict_buffer.append(None) self.__is_predicted_cur_iter.append(False) return self def reset_parameter(self, params: Dict[str, Any]) -> "Booster": """Reset parameters of Booster. Parameters ---------- params : dict New parameters for Booster. Returns ------- self : Booster Booster with new parameters. """ params_str = _param_dict_to_str(params) if params_str: _safe_call( _LIB.LGBM_BoosterResetParameter( self._handle, _c_str(params_str), ) ) self.params.update(params) return self def update( self, train_set: Optional[Dataset] = None, fobj: Optional[_LGBM_CustomObjectiveFunction] = None, ) -> bool: """Update Booster for one iteration. Parameters ---------- train_set : Dataset or None, optional (default=None) Training data. If None, last training data is used. fobj : callable or None, optional (default=None) Customized objective function. Should accept two parameters: preds, train_data, and return (grad, hess). preds : numpy 1-D array or numpy 2-D array (for multi-class task) The predicted values. Predicted values are returned before any transformation, e.g. they are raw margin instead of probability of positive class for binary task. train_data : Dataset The training dataset. grad : numpy 1-D array or numpy 2-D array (for multi-class task) The value of the first order derivative (gradient) of the loss with respect to the elements of preds for each sample point. hess : numpy 1-D array or numpy 2-D array (for multi-class task) The value of the second order derivative (Hessian) of the loss with respect to the elements of preds for each sample point. For multi-class task, preds are numpy 2-D array of shape = [n_samples, n_classes], and grad and hess should be returned in the same format. Returns ------- produced_empty_tree : bool ``True`` if the tree(s) produced by this iteration did not have any splits. This usually means that training is "finished" (calling ``update()`` again will not change the model's predictions). However, that is not always the case. For example, if you have added any randomness (like column sampling by setting ``feature_fraction_bynode < 1.0``), it is possible that another call to ``update()`` would produce a non-empty tree. """ # need reset training data if train_set is None and self.train_set_version != self.train_set.version: train_set = self.train_set is_the_same_train_set = False else: is_the_same_train_set = train_set is self.train_set and self.train_set_version == train_set.version if train_set is not None and not is_the_same_train_set: if not isinstance(train_set, Dataset): raise TypeError(f"Training data should be Dataset instance, met {type(train_set).__name__}") if train_set._predictor is not self.__init_predictor: raise LightGBMError("Replace training data failed, you should use same predictor for these data") self.train_set = train_set _safe_call( _LIB.LGBM_BoosterResetTrainingData( self._handle, self.train_set.construct()._handle, ) ) self.__inner_predict_buffer[0] = None self.train_set_version = self.train_set.version produced_empty_tree = ctypes.c_int(0) if fobj is None: if self.__set_objective_to_none: raise LightGBMError("Cannot update due to null objective function.") _safe_call( _LIB.LGBM_BoosterUpdateOneIter( self._handle, ctypes.byref(produced_empty_tree), ) ) self.__is_predicted_cur_iter = [False for _ in range(self.__num_dataset)] return produced_empty_tree.value == 1 else: if not self.__set_objective_to_none: self.reset_parameter({"objective": "none"}).__set_objective_to_none = True grad, hess = fobj(self.__inner_predict(data_idx=0), self.train_set) return self.__boost(grad=grad, hess=hess) def __boost( self, *, grad: np.ndarray, hess: np.ndarray, ) -> bool: """Boost Booster for one iteration with customized gradient statistics. .. note:: Score is returned before any transformation, e.g. it is raw margin instead of probability of positive class for binary task. For multi-class task, score are numpy 2-D array of shape = [n_samples, n_classes], and grad and hess should be returned in the same format. Parameters ---------- grad : numpy 1-D array or numpy 2-D array (for multi-class task) The value of the first order derivative (gradient) of the loss with respect to the elements of score for each sample point. hess : numpy 1-D array or numpy 2-D array (for multi-class task) The value of the second order derivative (Hessian) of the loss with respect to the elements of score for each sample point. Returns ------- produced_empty_tree : bool ``True`` if the tree(s) produced by this iteration did not have any splits. This usually means that training is "finished" (calling ``__boost()`` again will not change the model's predictions). However, that is not always the case. For example, if you have added any randomness (like column sampling by setting ``feature_fraction_bynode < 1.0``), it is possible that another call to ``__boost()`` would produce a non-empty tree. """ if self.__num_class > 1: grad = grad.ravel(order="F") hess = hess.ravel(order="F") grad = _list_to_1d_numpy(data=grad, dtype=np.float32, name="gradient") hess = _list_to_1d_numpy(data=hess, dtype=np.float32, name="hessian") assert grad.flags.c_contiguous assert hess.flags.c_contiguous if len(grad) != len(hess): raise ValueError(f"Lengths of gradient ({len(grad)}) and Hessian ({len(hess)}) don't match") num_train_data = self.train_set.num_data() if len(grad) != num_train_data * self.__num_class: raise ValueError( f"Lengths of gradient ({len(grad)}) and Hessian ({len(hess)}) " f"don't match training data length ({num_train_data}) * " f"number of models per one iteration ({self.__num_class})" ) produced_empty_tree = ctypes.c_int(0) _safe_call( _LIB.LGBM_BoosterUpdateOneIterCustom( self._handle, grad.ctypes.data_as(ctypes.POINTER(ctypes.c_float)), hess.ctypes.data_as(ctypes.POINTER(ctypes.c_float)), ctypes.byref(produced_empty_tree), ) ) self.__is_predicted_cur_iter = [False for _ in range(self.__num_dataset)] return produced_empty_tree.value == 1 def rollback_one_iter(self) -> "Booster": """Rollback one iteration. Returns ------- self : Booster Booster with rolled back one iteration. """ _safe_call(_LIB.LGBM_BoosterRollbackOneIter(self._handle)) self.__is_predicted_cur_iter = [False for _ in range(self.__num_dataset)] return self def current_iteration(self) -> int: """Get the index of the current iteration. Returns ------- cur_iter : int The index of the current iteration. """ out_cur_iter = ctypes.c_int(0) _safe_call( _LIB.LGBM_BoosterGetCurrentIteration( self._handle, ctypes.byref(out_cur_iter), ) ) return out_cur_iter.value def num_model_per_iteration(self) -> int: """Get number of models per iteration. Returns ------- model_per_iter : int The number of models per iteration. """ model_per_iter = ctypes.c_int(0) _safe_call( _LIB.LGBM_BoosterNumModelPerIteration( self._handle, ctypes.byref(model_per_iter), ) ) return model_per_iter.value def num_trees(self) -> int: """Get number of weak sub-models. Returns ------- num_trees : int The number of weak sub-models. """ num_trees = ctypes.c_int(0) _safe_call( _LIB.LGBM_BoosterNumberOfTotalModel( self._handle, ctypes.byref(num_trees), ) ) return num_trees.value def upper_bound(self) -> float: """Get upper bound value of a model. Returns ------- upper_bound : float Upper bound value of the model. """ ret = ctypes.c_double(0) _safe_call( _LIB.LGBM_BoosterGetUpperBoundValue( self._handle, ctypes.byref(ret), ) ) return ret.value def lower_bound(self) -> float: """Get lower bound value of a model. Returns ------- lower_bound : float Lower bound value of the model. """ ret = ctypes.c_double(0) _safe_call( _LIB.LGBM_BoosterGetLowerBoundValue( self._handle, ctypes.byref(ret), ) ) return ret.value def eval( self, data: Dataset, name: str, feval: Optional[Union[_LGBM_CustomEvalFunction, List[_LGBM_CustomEvalFunction]]] = None, ) -> List[_LGBM_BoosterEvalMethodResultType]: """Evaluate for data. Parameters ---------- data : Dataset Data for the evaluating. name : str Name of the data. feval : callable, list of callable, or None, optional (default=None) Customized evaluation function. Each evaluation function should accept two parameters: preds, eval_data, and return (eval_name, eval_result, is_higher_better) or list of such tuples. preds : numpy 1-D array or numpy 2-D array (for multi-class task) The predicted values. For multi-class task, preds are numpy 2-D array of shape = [n_samples, n_classes]. If custom objective function is used, predicted values are returned before any transformation, e.g. they are raw margin instead of probability of positive class for binary task in this case. eval_data : Dataset A ``Dataset`` to evaluate. eval_name : str The name of evaluation function (without whitespace). eval_result : float The eval result. is_higher_better : bool Is eval result higher better, e.g. AUC is ``is_higher_better``. Returns ------- result : list List with (dataset_name, eval_name, eval_result, is_higher_better) tuples. """ if not isinstance(data, Dataset): raise TypeError("Can only eval for Dataset instance") data_idx = -1 if data is self.train_set: data_idx = 0 else: for i in range(len(self.valid_sets)): if data is self.valid_sets[i]: data_idx = i + 1 break # need to push new valid data if data_idx == -1: self.add_valid(data, name) data_idx = self.__num_dataset - 1 return self.__inner_eval(data_name=name, data_idx=data_idx, feval=feval) def eval_train( self, feval: Optional[Union[_LGBM_CustomEvalFunction, List[_LGBM_CustomEvalFunction]]] = None, ) -> List[_LGBM_BoosterEvalMethodResultType]: """Evaluate for training data. Parameters ---------- feval : callable, list of callable, or None, optional (default=None) Customized evaluation function. Each evaluation function should accept two parameters: preds, eval_data, and return (eval_name, eval_result, is_higher_better) or list of such tuples. preds : numpy 1-D array or numpy 2-D array (for multi-class task) The predicted values. For multi-class task, preds are numpy 2-D array of shape = [n_samples, n_classes]. If custom objective function is used, predicted values are returned before any transformation, e.g. they are raw margin instead of probability of positive class for binary task in this case. eval_data : Dataset The training dataset. eval_name : str The name of evaluation function (without whitespace). eval_result : float The eval result. is_higher_better : bool Is eval result higher better, e.g. AUC is ``is_higher_better``. Returns ------- result : list List with (train_dataset_name, eval_name, eval_result, is_higher_better) tuples. """ return self.__inner_eval(data_name=self._train_data_name, data_idx=0, feval=feval) def eval_valid( self, feval: Optional[Union[_LGBM_CustomEvalFunction, List[_LGBM_CustomEvalFunction]]] = None, ) -> List[_LGBM_BoosterEvalMethodResultType]: """Evaluate for validation data. Parameters ---------- feval : callable, list of callable, or None, optional (default=None) Customized evaluation function. Each evaluation function should accept two parameters: preds, eval_data, and return (eval_name, eval_result, is_higher_better) or list of such tuples. preds : numpy 1-D array or numpy 2-D array (for multi-class task) The predicted values. For multi-class task, preds are numpy 2-D array of shape = [n_samples, n_classes]. If custom objective function is used, predicted values are returned before any transformation, e.g. they are raw margin instead of probability of positive class for binary task in this case. eval_data : Dataset The validation dataset. eval_name : str The name of evaluation function (without whitespace). eval_result : float The eval result. is_higher_better : bool Is eval result higher better, e.g. AUC is ``is_higher_better``. Returns ------- result : list List with (validation_dataset_name, eval_name, eval_result, is_higher_better) tuples. """ return [ item for i in range(1, self.__num_dataset) for item in self.__inner_eval(data_name=self.name_valid_sets[i - 1], data_idx=i, feval=feval) ] def save_model( self, filename: Union[str, Path], num_iteration: Optional[int] = None, start_iteration: int = 0, importance_type: str = "split", ) -> "Booster": """Save Booster to file. Parameters ---------- filename : str or pathlib.Path Filename to save Booster. num_iteration : int or None, optional (default=None) Index of the iteration that should be saved. If None, if the best iteration exists, it is saved; otherwise, all iterations are saved. If <= 0, all iterations are saved. start_iteration : int, optional (default=0) Start index of the iteration that should be saved. importance_type : str, optional (default="split") What type of feature importance should be saved. If "split", result contains numbers of times the feature is used in a model. If "gain", result contains total gains of splits which use the feature. Returns ------- self : Booster Returns self. """ if num_iteration is None: num_iteration = self.best_iteration importance_type_int = _FEATURE_IMPORTANCE_TYPE_MAPPER[importance_type] _safe_call( _LIB.LGBM_BoosterSaveModel( self._handle, ctypes.c_int(start_iteration), ctypes.c_int(num_iteration), ctypes.c_int(importance_type_int), _c_str(str(filename)), ) ) _dump_pandas_categorical(self.pandas_categorical, filename) return self def shuffle_models( self, start_iteration: int = 0, end_iteration: int = -1, ) -> "Booster": """Shuffle models. Parameters ---------- start_iteration : int, optional (default=0) The first iteration that will be shuffled. end_iteration : int, optional (default=-1) The last iteration that will be shuffled. If <= 0, means the last available iteration. Returns ------- self : Booster Booster with shuffled models. """ _safe_call( _LIB.LGBM_BoosterShuffleModels( self._handle, ctypes.c_int(start_iteration), ctypes.c_int(end_iteration), ) ) return self def model_from_string(self, model_str: str) -> "Booster": """Load Booster from a string. Parameters ---------- model_str : str Model will be loaded from this string. Returns ------- self : Booster Loaded Booster object. """ # ensure that existing Booster is freed before replacing it # with a new one createdfrom file _safe_call(_LIB.LGBM_BoosterFree(self._handle)) self._free_buffer() self._handle = ctypes.c_void_p() out_num_iterations = ctypes.c_int(0) _safe_call( _LIB.LGBM_BoosterLoadModelFromString( _c_str(model_str), ctypes.byref(out_num_iterations), ctypes.byref(self._handle), ) ) out_num_class = ctypes.c_int(0) _safe_call( _LIB.LGBM_BoosterGetNumClasses( self._handle, ctypes.byref(out_num_class), ) ) self.__num_class = out_num_class.value self.pandas_categorical = _load_pandas_categorical(model_str=model_str) return self def model_to_string( self, num_iteration: Optional[int] = None, start_iteration: int = 0, importance_type: str = "split", ) -> str: """Save Booster to string. Parameters ---------- num_iteration : int or None, optional (default=None) Index of the iteration that should be saved. If None, if the best iteration exists, it is saved; otherwise, all iterations are saved. If <= 0, all iterations are saved. start_iteration : int, optional (default=0) Start index of the iteration that should be saved. importance_type : str, optional (default="split") What type of feature importance should be saved. If "split", result contains numbers of times the feature is used in a model. If "gain", result contains total gains of splits which use the feature. Returns ------- str_repr : str String representation of Booster. """ if num_iteration is None: num_iteration = self.best_iteration importance_type_int = _FEATURE_IMPORTANCE_TYPE_MAPPER[importance_type] buffer_len = 1 << 20 tmp_out_len = ctypes.c_int64(0) string_buffer = ctypes.create_string_buffer(buffer_len) ptr_string_buffer = ctypes.c_char_p(ctypes.addressof(string_buffer)) _safe_call( _LIB.LGBM_BoosterSaveModelToString( self._handle, ctypes.c_int(start_iteration), ctypes.c_int(num_iteration), ctypes.c_int(importance_type_int), ctypes.c_int64(buffer_len), ctypes.byref(tmp_out_len), ptr_string_buffer, ) ) actual_len = tmp_out_len.value # if buffer length is not long enough, re-allocate a buffer if actual_len > buffer_len: string_buffer = ctypes.create_string_buffer(actual_len) ptr_string_buffer = ctypes.c_char_p(ctypes.addressof(string_buffer)) _safe_call( _LIB.LGBM_BoosterSaveModelToString( self._handle, ctypes.c_int(start_iteration), ctypes.c_int(num_iteration), ctypes.c_int(importance_type_int), ctypes.c_int64(actual_len), ctypes.byref(tmp_out_len), ptr_string_buffer, ) ) ret = string_buffer.value.decode("utf-8") ret += _dump_pandas_categorical(self.pandas_categorical) return ret def dump_model( self, num_iteration: Optional[int] = None, start_iteration: int = 0, importance_type: str = "split", object_hook: Optional[Callable[[Dict[str, Any]], Dict[str, Any]]] = None, ) -> Dict[str, Any]: """Dump Booster to JSON format. Parameters ---------- num_iteration : int or None, optional (default=None) Index of the iteration that should be dumped. If None, if the best iteration exists, it is dumped; otherwise, all iterations are dumped. If <= 0, all iterations are dumped. start_iteration : int, optional (default=0) Start index of the iteration that should be dumped. importance_type : str, optional (default="split") What type of feature importance should be dumped. If "split", result contains numbers of times the feature is used in a model. If "gain", result contains total gains of splits which use the feature. object_hook : callable or None, optional (default=None) If not None, ``object_hook`` is a function called while parsing the json string returned by the C API. It may be used to alter the json, to store specific values while building the json structure. It avoids walking through the structure again. It saves a significant amount of time if the number of trees is huge. Signature is ``def object_hook(node: dict) -> dict``. None is equivalent to ``lambda node: node``. See documentation of ``json.loads()`` for further details. Returns ------- json_repr : dict JSON format of Booster. """ if num_iteration is None: num_iteration = self.best_iteration importance_type_int = _FEATURE_IMPORTANCE_TYPE_MAPPER[importance_type] buffer_len = 1 << 20 tmp_out_len = ctypes.c_int64(0) string_buffer = ctypes.create_string_buffer(buffer_len) ptr_string_buffer = ctypes.c_char_p(ctypes.addressof(string_buffer)) _safe_call( _LIB.LGBM_BoosterDumpModel( self._handle, ctypes.c_int(start_iteration), ctypes.c_int(num_iteration), ctypes.c_int(importance_type_int), ctypes.c_int64(buffer_len), ctypes.byref(tmp_out_len), ptr_string_buffer, ) ) actual_len = tmp_out_len.value # if buffer length is not long enough, reallocate a buffer if actual_len > buffer_len: string_buffer = ctypes.create_string_buffer(actual_len) ptr_string_buffer = ctypes.c_char_p(ctypes.addressof(string_buffer)) _safe_call( _LIB.LGBM_BoosterDumpModel( self._handle, ctypes.c_int(start_iteration), ctypes.c_int(num_iteration), ctypes.c_int(importance_type_int), ctypes.c_int64(actual_len), ctypes.byref(tmp_out_len), ptr_string_buffer, ) ) ret = json.loads(string_buffer.value.decode("utf-8"), object_hook=object_hook) ret["pandas_categorical"] = json.loads( json.dumps( self.pandas_categorical, default=_json_default_with_numpy, ) ) return ret def predict( self, data: _LGBM_PredictDataType, start_iteration: int = 0, num_iteration: Optional[int] = None, raw_score: bool = False, pred_leaf: bool = False, pred_contrib: bool = False, data_has_header: bool = False, validate_features: bool = False, **kwargs: Any, ) -> _LGBM_PredictReturnType: """Make a prediction. Parameters ---------- data : str, pathlib.Path, numpy array, pandas DataFrame, scipy.sparse or pyarrow Table Data source for prediction. If str or pathlib.Path, it represents the path to a text file (CSV, TSV, or LibSVM). start_iteration : int, optional (default=0) Start index of the iteration to predict. If <= 0, starts from the first iteration. num_iteration : int or None, optional (default=None) Total number of iterations used in the prediction. If None, if the best iteration exists and start_iteration <= 0, the best iteration is used; otherwise, all iterations from ``start_iteration`` are used (no limits). If <= 0, all iterations from ``start_iteration`` are used (no limits). raw_score : bool, optional (default=False) Whether to predict raw scores. pred_leaf : bool, optional (default=False) Whether to predict leaf index. pred_contrib : bool, optional (default=False) Whether to predict feature contributions. .. note:: If you want to get more explanations for your model's predictions using SHAP values, like SHAP interaction values, you can install the shap package (https://github.com/slundberg/shap). Note that unlike the shap package, with ``pred_contrib`` we return a matrix with an extra column, where the last column is the expected value. data_has_header : bool, optional (default=False) Whether the data has header. Used only if data is str. validate_features : bool, optional (default=False) If True, ensure that the features used to predict match the ones used to train. Used only if data is pandas DataFrame. **kwargs Other parameters for the prediction. Returns ------- result : numpy array, scipy.sparse or list of scipy.sparse Prediction result. Can be sparse or a list of sparse objects (each element represents predictions for one class) for feature contributions (when ``pred_contrib=True``). """ predictor = _InnerPredictor.from_booster( booster=self, pred_parameter=deepcopy(kwargs), ) if num_iteration is None: if start_iteration <= 0: num_iteration = self.best_iteration else: num_iteration = -1 return predictor.predict( data=data, start_iteration=start_iteration, num_iteration=num_iteration, raw_score=raw_score, pred_leaf=pred_leaf, pred_contrib=pred_contrib, data_has_header=data_has_header, validate_features=validate_features, ) def refit( self, data: _LGBM_TrainDataType, label: _LGBM_LabelType, decay_rate: float = 0.9, reference: Optional[Dataset] = None, weight: Optional[_LGBM_WeightType] = None, group: Optional[_LGBM_GroupType] = None, init_score: Optional[_LGBM_InitScoreType] = None, feature_name: _LGBM_FeatureNameConfiguration = "auto", categorical_feature: _LGBM_CategoricalFeatureConfiguration = "auto", dataset_params: Optional[Dict[str, Any]] = None, free_raw_data: bool = True, validate_features: bool = False, **kwargs: Any, ) -> "Booster": """Refit the existing Booster by new data. Parameters ---------- data : str, pathlib.Path, numpy array, pandas DataFrame, scipy.sparse, Sequence, list of Sequence, list of numpy array or pyarrow Table Data source for refit. If str or pathlib.Path, it represents the path to a text file (CSV, TSV, or LibSVM). label : list, numpy 1-D array, pandas Series / one-column DataFrame, pyarrow Array or pyarrow ChunkedArray Label for refit. decay_rate : float, optional (default=0.9) Decay rate of refit, will use ``leaf_output = decay_rate * old_leaf_output + (1.0 - decay_rate) * new_leaf_output`` to refit trees. reference : Dataset or None, optional (default=None) Reference for ``data``. .. versionadded:: 4.0.0 weight : list, numpy 1-D array, pandas Series, pyarrow Array, pyarrow ChunkedArray or None, optional (default=None) Weight for each ``data`` instance. Weights should be non-negative. .. versionadded:: 4.0.0 group : list, numpy 1-D array, pandas Series, pyarrow Array, pyarrow ChunkedArray or None, optional (default=None) Group/query size for ``data``. Only used in the learning-to-rank task. sum(group) = n_samples. For example, if you have a 100-document dataset with ``group = [10, 20, 40, 10, 10, 10]``, that means that you have 6 groups, 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. .. versionadded:: 4.0.0 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) Init score for ``data``. .. versionadded:: 4.0.0 feature_name : list of str, or 'auto', optional (default="auto") Feature names for ``data``. If 'auto' and data is pandas DataFrame, data columns names are used. .. versionadded:: 4.0.0 categorical_feature : list of str or int, or 'auto', optional (default="auto") Categorical features for ``data``. If list of int, interpreted as indices. If list of str, interpreted as feature names (need to specify ``feature_name`` as well). If 'auto' and data is pandas DataFrame, pandas unordered categorical columns are used. All values in categorical features will be cast to int32 and thus should be less than int32 max value (2147483647). Large values could be memory consuming. Consider using consecutive integers starting from zero. All negative values in categorical features will be treated as missing values. The output cannot be monotonically constrained with respect to a categorical feature. Floating point numbers in categorical features will be rounded towards 0. .. versionadded:: 4.0.0 dataset_params : dict or None, optional (default=None) Other parameters for Dataset ``data``. .. versionadded:: 4.0.0 free_raw_data : bool, optional (default=True) If True, raw data is freed after constructing inner Dataset for ``data``. .. versionadded:: 4.0.0 validate_features : bool, optional (default=False) If True, ensure that the features used to refit the model match the original ones. Used only if data is pandas DataFrame. .. versionadded:: 4.0.0 **kwargs Other parameters for refit. These parameters will be passed to ``predict`` method. Returns ------- result : Booster Refitted Booster. """ if self.__set_objective_to_none: raise LightGBMError("Cannot refit due to null objective function.") if dataset_params is None: dataset_params = {} predictor = _InnerPredictor.from_booster(booster=self, pred_parameter=deepcopy(kwargs)) leaf_preds: np.ndarray = predictor.predict( # type: ignore[assignment] data=data, start_iteration=-1, pred_leaf=True, validate_features=validate_features, ) nrow, ncol = leaf_preds.shape out_is_linear = ctypes.c_int(0) _safe_call( _LIB.LGBM_BoosterGetLinear( self._handle, ctypes.byref(out_is_linear), ) ) new_params = _choose_param_value( main_param_name="linear_tree", params=self.params, default_value=None, ) new_params["linear_tree"] = bool(out_is_linear.value) new_params.update(dataset_params) train_set = Dataset( data=data, label=label, reference=reference, weight=weight, group=group, init_score=init_score, feature_name=feature_name, categorical_feature=categorical_feature, params=new_params, free_raw_data=free_raw_data, ) new_params["refit_decay_rate"] = decay_rate new_booster = Booster(new_params, train_set) # Copy models _safe_call( _LIB.LGBM_BoosterMerge( new_booster._handle, predictor._handle, ) ) leaf_preds = leaf_preds.reshape(-1) ptr_data, _, _ = _c_int_array(leaf_preds) _safe_call( _LIB.LGBM_BoosterRefit( new_booster._handle, ptr_data, ctypes.c_int32(nrow), ctypes.c_int32(ncol), ) ) new_booster._network = self._network return new_booster def get_leaf_output(self, tree_id: int, leaf_id: int) -> float: """Get the output of a leaf. Parameters ---------- tree_id : int The index of the tree. leaf_id : int The index of the leaf in the tree. Returns ------- result : float The output of the leaf. """ ret = ctypes.c_double(0) _safe_call( _LIB.LGBM_BoosterGetLeafValue( self._handle, ctypes.c_int(tree_id), ctypes.c_int(leaf_id), ctypes.byref(ret), ) ) return ret.value def set_leaf_output( self, tree_id: int, leaf_id: int, value: float, ) -> "Booster": """Set the output of a leaf. .. versionadded:: 4.0.0 Parameters ---------- tree_id : int The index of the tree. leaf_id : int The index of the leaf in the tree. value : float Value to set as the output of the leaf. Returns ------- self : Booster Booster with the leaf output set. """ _safe_call( _LIB.LGBM_BoosterSetLeafValue( self._handle, ctypes.c_int(tree_id), ctypes.c_int(leaf_id), ctypes.c_double(value), ) ) return self def num_feature(self) -> int: """Get number of features. Returns ------- num_feature : int The number of features. """ out_num_feature = ctypes.c_int(0) _safe_call( _LIB.LGBM_BoosterGetNumFeature( self._handle, ctypes.byref(out_num_feature), ) ) return out_num_feature.value def feature_name(self) -> List[str]: """Get names of features. Returns ------- result : list of str List with names of features. """ num_feature = self.num_feature() # Get name of features tmp_out_len = ctypes.c_int(0) reserved_string_buffer_size = 255 required_string_buffer_size = ctypes.c_size_t(0) string_buffers = [ctypes.create_string_buffer(reserved_string_buffer_size) for _ in range(num_feature)] ptr_string_buffers = (ctypes.c_char_p * num_feature)(*map(ctypes.addressof, string_buffers)) # type: ignore[misc] _safe_call( _LIB.LGBM_BoosterGetFeatureNames( self._handle, ctypes.c_int(num_feature), ctypes.byref(tmp_out_len), ctypes.c_size_t(reserved_string_buffer_size), ctypes.byref(required_string_buffer_size), ptr_string_buffers, ) ) if num_feature != tmp_out_len.value: raise ValueError("Length of feature names doesn't equal with num_feature") actual_string_buffer_size = required_string_buffer_size.value # if buffer length is not long enough, reallocate buffers if reserved_string_buffer_size < actual_string_buffer_size: string_buffers = [ctypes.create_string_buffer(actual_string_buffer_size) for _ in range(num_feature)] ptr_string_buffers = (ctypes.c_char_p * num_feature)(*map(ctypes.addressof, string_buffers)) # type: ignore[misc] _safe_call( _LIB.LGBM_BoosterGetFeatureNames( self._handle, ctypes.c_int(num_feature), ctypes.byref(tmp_out_len), ctypes.c_size_t(actual_string_buffer_size), ctypes.byref(required_string_buffer_size), ptr_string_buffers, ) ) return [string_buffers[i].value.decode("utf-8") for i in range(num_feature)] def feature_importance( self, importance_type: str = "split", iteration: Optional[int] = None, ) -> np.ndarray: """Get feature importances. Parameters ---------- importance_type : str, optional (default="split") How the importance is calculated. If "split", result contains numbers of times the feature is used in a model. If "gain", result contains total gains of splits which use the feature. iteration : int or None, optional (default=None) Limit number of iterations in the feature importance calculation. If None, if the best iteration exists, it is used; otherwise, all trees are used. If <= 0, all trees are used (no limits). Returns ------- result : numpy array Array with feature importances. """ if iteration is None: iteration = self.best_iteration importance_type_int = _FEATURE_IMPORTANCE_TYPE_MAPPER[importance_type] result = np.empty(self.num_feature(), dtype=np.float64) _safe_call( _LIB.LGBM_BoosterFeatureImportance( self._handle, ctypes.c_int(iteration), ctypes.c_int(importance_type_int), result.ctypes.data_as(ctypes.POINTER(ctypes.c_double)), ) ) if importance_type_int == _C_API_FEATURE_IMPORTANCE_SPLIT: return result.astype(np.int32) else: return result def get_split_value_histogram( self, feature: Union[int, str], bins: Optional[Union[int, str]] = None, xgboost_style: bool = False, ) -> Union[Tuple[np.ndarray, np.ndarray], np.ndarray, pd_DataFrame]: """Get split value histogram for the specified feature. Parameters ---------- feature : int or str The feature name or index the histogram is calculated for. If int, interpreted as index. If str, interpreted as name. .. warning:: Categorical features are not supported. bins : int, str or None, optional (default=None) The maximum number of bins. If None, or int and > number of unique split values and ``xgboost_style=True``, the number of bins equals number of unique split values. If str, it should be one from the list of the supported values by ``numpy.histogram()`` function. xgboost_style : bool, optional (default=False) Whether the returned result should be in the same form as it is in XGBoost. If False, the returned value is tuple of 2 numpy arrays as it is in ``numpy.histogram()`` function. If True, the returned value is matrix, in which the first column is the right edges of non-empty bins and the second one is the histogram values. Returns ------- result_tuple : tuple of 2 numpy arrays If ``xgboost_style=False``, the values of the histogram of used splitting values for the specified feature and the bin edges. result_array_like : numpy array or pandas DataFrame (if pandas is installed) If ``xgboost_style=True``, the histogram of used splitting values for the specified feature. """ def add(root: Dict[str, Any]) -> None: """Recursively add thresholds.""" if "split_index" in root: # non-leaf if feature_names is not None and isinstance(feature, str): split_feature = feature_names[root["split_feature"]] else: split_feature = root["split_feature"] if split_feature == feature: if isinstance(root["threshold"], str): raise LightGBMError("Cannot compute split value histogram for the categorical feature") values.append(root["threshold"]) add(root["left_child"]) add(root["right_child"]) model = self.dump_model() feature_names = model.get("feature_names") tree_infos = model["tree_info"] values: List[float] = [] for tree_info in tree_infos: add(tree_info["tree_structure"]) if bins is None or isinstance(bins, int) and xgboost_style: n_unique = len(np.unique(values)) bins = max(min(n_unique, bins) if bins is not None else n_unique, 1) hist, bin_edges = np.histogram(values, bins=bins) if xgboost_style: ret = np.column_stack((bin_edges[1:], hist)) ret = ret[ret[:, 1] > 0] if PANDAS_INSTALLED: return pd_DataFrame(ret, columns=["SplitValue", "Count"]) else: return ret else: return hist, bin_edges def __inner_eval( self, *, data_name: str, data_idx: int, feval: Optional[Union[_LGBM_CustomEvalFunction, List[_LGBM_CustomEvalFunction]]], ) -> List[_LGBM_BoosterEvalMethodResultType]: """Evaluate training or validation data.""" if data_idx >= self.__num_dataset: raise ValueError("Data_idx should be smaller than number of dataset") self.__get_eval_info() ret = [] if self.__num_inner_eval > 0: result = np.empty(self.__num_inner_eval, dtype=np.float64) tmp_out_len = ctypes.c_int(0) _safe_call( _LIB.LGBM_BoosterGetEval( self._handle, ctypes.c_int(data_idx), ctypes.byref(tmp_out_len), result.ctypes.data_as(ctypes.POINTER(ctypes.c_double)), ) ) if tmp_out_len.value != self.__num_inner_eval: raise ValueError("Wrong length of eval results") for i in range(self.__num_inner_eval): ret.append((data_name, self.__name_inner_eval[i], result[i], self.__higher_better_inner_eval[i])) if callable(feval): feval = [feval] if feval is not None: if data_idx == 0: cur_data = self.train_set else: cur_data = self.valid_sets[data_idx - 1] for eval_function in feval: if eval_function is None: continue feval_ret = eval_function(self.__inner_predict(data_idx=data_idx), cur_data) if isinstance(feval_ret, list): for eval_name, val, is_higher_better in feval_ret: ret.append((data_name, eval_name, val, is_higher_better)) else: eval_name, val, is_higher_better = feval_ret ret.append((data_name, eval_name, val, is_higher_better)) return ret def __inner_predict(self, *, data_idx: int) -> np.ndarray: """Predict for training and validation dataset.""" if data_idx >= self.__num_dataset: raise ValueError("Data_idx should be smaller than number of dataset") if self.__inner_predict_buffer[data_idx] is None: if data_idx == 0: n_preds = self.train_set.num_data() * self.__num_class else: n_preds = self.valid_sets[data_idx - 1].num_data() * self.__num_class self.__inner_predict_buffer[data_idx] = np.empty(n_preds, dtype=np.float64) # avoid to predict many time in one iteration if not self.__is_predicted_cur_iter[data_idx]: tmp_out_len = ctypes.c_int64(0) data_ptr = self.__inner_predict_buffer[data_idx].ctypes.data_as(ctypes.POINTER(ctypes.c_double)) # type: ignore[union-attr] _safe_call( _LIB.LGBM_BoosterGetPredict( self._handle, ctypes.c_int(data_idx), ctypes.byref(tmp_out_len), data_ptr, ) ) if tmp_out_len.value != len(self.__inner_predict_buffer[data_idx]): # type: ignore[arg-type] raise ValueError(f"Wrong length of predict results for data {data_idx}") self.__is_predicted_cur_iter[data_idx] = True result: np.ndarray = self.__inner_predict_buffer[data_idx] # type: ignore[assignment] if self.__num_class > 1: num_data = result.size // self.__num_class result = result.reshape(num_data, self.__num_class, order="F") return result def __get_eval_info(self) -> None: """Get inner evaluation count and names.""" if self.__need_reload_eval_info: self.__need_reload_eval_info = False out_num_eval = ctypes.c_int(0) # Get num of inner evals _safe_call( _LIB.LGBM_BoosterGetEvalCounts( self._handle, ctypes.byref(out_num_eval), ) ) self.__num_inner_eval = out_num_eval.value if self.__num_inner_eval > 0: # Get name of eval metrics tmp_out_len = ctypes.c_int(0) reserved_string_buffer_size = 255 required_string_buffer_size = ctypes.c_size_t(0) string_buffers = [ ctypes.create_string_buffer(reserved_string_buffer_size) for _ in range(self.__num_inner_eval) ] ptr_string_buffers = (ctypes.c_char_p * self.__num_inner_eval)(*map(ctypes.addressof, string_buffers)) # type: ignore[misc] _safe_call( _LIB.LGBM_BoosterGetEvalNames( self._handle, ctypes.c_int(self.__num_inner_eval), ctypes.byref(tmp_out_len), ctypes.c_size_t(reserved_string_buffer_size), ctypes.byref(required_string_buffer_size), ptr_string_buffers, ) ) if self.__num_inner_eval != tmp_out_len.value: raise ValueError("Length of eval names doesn't equal with num_evals") actual_string_buffer_size = required_string_buffer_size.value # if buffer length is not long enough, reallocate buffers if reserved_string_buffer_size < actual_string_buffer_size: string_buffers = [ ctypes.create_string_buffer(actual_string_buffer_size) for _ in range(self.__num_inner_eval) ] ptr_string_buffers = (ctypes.c_char_p * self.__num_inner_eval)( *map(ctypes.addressof, string_buffers) ) # type: ignore[misc] _safe_call( _LIB.LGBM_BoosterGetEvalNames( self._handle, ctypes.c_int(self.__num_inner_eval), ctypes.byref(tmp_out_len), ctypes.c_size_t(actual_string_buffer_size), ctypes.byref(required_string_buffer_size), ptr_string_buffers, ) ) self.__name_inner_eval = [string_buffers[i].value.decode("utf-8") for i in range(self.__num_inner_eval)] self.__higher_better_inner_eval = [ name.startswith(("auc", "ndcg@", "map@", "average_precision")) for name in self.__name_inner_eval ] ================================================ FILE: python-package/lightgbm/callback.py ================================================ # coding: utf-8 """Callbacks library.""" from collections import OrderedDict from dataclasses import dataclass from functools import partial from typing import TYPE_CHECKING, Any, Callable, Dict, List, Optional, Union from .basic import ( Booster, _ConfigAliases, _LGBM_BoosterEvalMethodResultType, _LGBM_BoosterEvalMethodResultWithStandardDeviationType, _log_info, _log_warning, ) if TYPE_CHECKING: from .engine import CVBooster __all__ = [ "EarlyStopException", "early_stopping", "log_evaluation", "record_evaluation", "reset_parameter", ] _EvalResultDict = Dict[str, Dict[str, List[Any]]] _EvalResultTuple = Union[ _LGBM_BoosterEvalMethodResultType, _LGBM_BoosterEvalMethodResultWithStandardDeviationType, ] _ListOfEvalResultTuples = Union[ List[_LGBM_BoosterEvalMethodResultType], List[_LGBM_BoosterEvalMethodResultWithStandardDeviationType], ] class EarlyStopException(Exception): """Exception of early stopping. Raise this from a callback passed in via keyword argument ``callbacks`` in ``cv()`` or ``train()`` to trigger early stopping. """ def __init__(self, best_iteration: int, best_score: _ListOfEvalResultTuples) -> None: """Create early stopping exception. Parameters ---------- best_iteration : int The best iteration stopped. 0-based... pass ``best_iteration=2`` to indicate that the third iteration was the best one. 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 Scores for each metric, on each validation set, as of the best iteration. """ super().__init__() self.best_iteration = best_iteration self.best_score = best_score # Callback environment used by callbacks @dataclass class CallbackEnv: model: Union[Booster, "CVBooster"] params: Dict[str, Any] iteration: int begin_iteration: int end_iteration: int evaluation_result_list: Optional[_ListOfEvalResultTuples] def _is_using_cv(env: CallbackEnv) -> bool: """Check if model in callback env is a CVBooster.""" # this import is here to avoid a circular import from .engine import CVBooster # noqa: PLC0415 return isinstance(env.model, CVBooster) def _format_eval_result(value: _EvalResultTuple, show_stdv: bool) -> str: """Format metric string.""" dataset_name, metric_name, metric_value, *_ = value out = f"{dataset_name}'s {metric_name}: {metric_value:g}" # tuples from cv() sometimes have a 5th item, with standard deviation of # the evaluation metric (taken over all cross-validation folds) if show_stdv and len(value) == 5: out += f" + {value[4]:g}" return out class _LogEvaluationCallback: """Internal log evaluation callable class.""" def __init__(self, period: int = 1, show_stdv: bool = True) -> None: self.order = 10 self.before_iteration = False self.period = period self.show_stdv = show_stdv def __call__(self, env: CallbackEnv) -> None: if self.period > 0 and env.evaluation_result_list and (env.iteration + 1) % self.period == 0: result = "\t".join([_format_eval_result(x, self.show_stdv) for x in env.evaluation_result_list]) _log_info(f"[{env.iteration + 1}]\t{result}") def log_evaluation(period: int = 1, show_stdv: bool = True) -> _LogEvaluationCallback: """Create a callback that logs the evaluation results. By default, standard output resource is used. Use ``register_logger()`` function to register a custom logger. Note ---- Requires at least one validation data. Parameters ---------- period : int, optional (default=1) The period to log the evaluation results. The last boosting stage or the boosting stage found by using ``early_stopping`` callback is also logged. show_stdv : bool, optional (default=True) Whether to log stdv (if provided). Returns ------- callback : _LogEvaluationCallback The callback that logs the evaluation results every ``period`` boosting iteration(s). """ return _LogEvaluationCallback(period=period, show_stdv=show_stdv) class _RecordEvaluationCallback: """Internal record evaluation callable class.""" def __init__(self, eval_result: _EvalResultDict) -> None: self.order = 20 self.before_iteration = False if not isinstance(eval_result, dict): raise TypeError("eval_result should be a dictionary") self.eval_result = eval_result def _init(self, env: CallbackEnv) -> None: if env.evaluation_result_list is None: raise RuntimeError( "record_evaluation() callback enabled but no evaluation results found. This is a probably bug in LightGBM. " "Please report it at https://github.com/lightgbm-org/LightGBM/issues" ) self.eval_result.clear() for item in env.evaluation_result_list: dataset_name, metric_name, *_ = item self.eval_result.setdefault(dataset_name, OrderedDict()) if len(item) == 4: self.eval_result[dataset_name].setdefault(metric_name, []) else: self.eval_result[dataset_name].setdefault(f"{metric_name}-mean", []) self.eval_result[dataset_name].setdefault(f"{metric_name}-stdv", []) def __call__(self, env: CallbackEnv) -> None: if env.iteration == env.begin_iteration: self._init(env) if env.evaluation_result_list is None: raise RuntimeError( "record_evaluation() callback enabled but no evaluation results found. This is a probably bug in LightGBM. " "Please report it at https://github.com/lightgbm-org/LightGBM/issues" ) for item in env.evaluation_result_list: # for cv(), 'metric_value' is actually a mean of metric values over all CV folds dataset_name, metric_name, metric_value, *_ = item if len(item) == 4: # train() self.eval_result[dataset_name][metric_name].append(metric_value) else: # cv() metric_std_dev = item[4] # type: ignore[misc] self.eval_result[dataset_name][f"{metric_name}-mean"].append(metric_value) self.eval_result[dataset_name][f"{metric_name}-stdv"].append(metric_std_dev) def record_evaluation(eval_result: Dict[str, Dict[str, List[Any]]]) -> Callable: """Create a callback that records the evaluation history into ``eval_result``. Parameters ---------- eval_result : dict Dictionary used to store all evaluation results of all validation sets. This should be initialized outside of your call to ``record_evaluation()`` and should be empty. Any initial contents of the dictionary will be deleted. .. rubric:: Example With two validation sets named 'eval' and 'train', and one evaluation metric named 'logloss' this dictionary after finishing a model training process will have the following structure: .. code-block:: { 'train': { 'logloss': [0.48253, 0.35953, ...] }, 'eval': { 'logloss': [0.480385, 0.357756, ...] } } Returns ------- callback : _RecordEvaluationCallback The callback that records the evaluation history into the passed dictionary. """ return _RecordEvaluationCallback(eval_result=eval_result) class _ResetParameterCallback: """Internal reset parameter callable class.""" def __init__(self, **kwargs: Union[list, Callable]) -> None: self.order = 10 self.before_iteration = True self.kwargs = kwargs def __call__(self, env: CallbackEnv) -> None: new_parameters = {} for key, value in self.kwargs.items(): if isinstance(value, list): if len(value) != env.end_iteration - env.begin_iteration: raise ValueError(f"Length of list {key!r} has to be equal to 'num_boost_round'.") new_param = value[env.iteration - env.begin_iteration] elif callable(value): new_param = value(env.iteration - env.begin_iteration) else: raise ValueError( "Only list and callable values are supported " "as a mapping from boosting round index to new parameter value." ) if new_param != env.params.get(key, None): new_parameters[key] = new_param if new_parameters: if isinstance(env.model, Booster): env.model.reset_parameter(new_parameters) else: # CVBooster holds a list of Booster objects, each needs to be updated for booster in env.model.boosters: booster.reset_parameter(new_parameters) env.params.update(new_parameters) def reset_parameter(**kwargs: Union[list, Callable]) -> Callable: """Create a callback that resets the parameter after the first iteration. .. note:: The initial parameter will still take in-effect on first iteration. Parameters ---------- **kwargs : value should be list or callable List of parameters for each boosting round or a callable that calculates the parameter in terms of current number of round (e.g. yields learning rate decay). If list lst, parameter = lst[current_round]. If callable func, parameter = func(current_round). Returns ------- callback : _ResetParameterCallback The callback that resets the parameter after the first iteration. """ return _ResetParameterCallback(**kwargs) class _EarlyStoppingCallback: """Internal early stopping callable class.""" def __init__( self, stopping_rounds: int, first_metric_only: bool = False, verbose: bool = True, min_delta: Union[float, List[float]] = 0.0, ) -> None: self.enabled = _should_enable_early_stopping(stopping_rounds) self.order = 30 self.before_iteration = False self.stopping_rounds = stopping_rounds self.first_metric_only = first_metric_only self.verbose = verbose self.min_delta = min_delta self._reset_storages() def _reset_storages(self) -> None: self.best_score: List[float] = [] self.best_iter: List[int] = [] self.best_score_list: List[_ListOfEvalResultTuples] = [] self.cmp_op: List[Callable[[float, float, float], bool]] = [] self.first_metric = "" def _gt_delta(self, *, curr_score: float, best_score: float, delta: float) -> bool: return curr_score > best_score + delta def _lt_delta(self, *, curr_score: float, best_score: float, delta: float) -> bool: return curr_score < best_score - delta def _is_train_set(self, *, dataset_name: str, env: CallbackEnv) -> bool: """Check, by name, if a given Dataset is the training data.""" # for lgb.cv() with eval_train_metric=True, evaluation is also done on the training set # and those metrics are considered for early stopping if _is_using_cv(env) and dataset_name == "train": return True # for lgb.train(), it's possible to pass the training data via valid_sets with any eval_name if isinstance(env.model, Booster) and dataset_name == env.model._train_data_name: return True return False def _init(self, env: CallbackEnv) -> None: if env.evaluation_result_list is None or env.evaluation_result_list == []: raise ValueError("For early stopping, at least one dataset and eval metric is required for evaluation") is_dart = any(env.params.get(alias, "") == "dart" for alias in _ConfigAliases.get("boosting")) if is_dart: self.enabled = False _log_warning("Early stopping is not available in dart mode") return # get details of the first dataset first_dataset_name, first_metric_name, *_ = env.evaluation_result_list[0] # validation sets are guaranteed to not be identical to the training data in cv() if isinstance(env.model, Booster): only_train_set = len(env.evaluation_result_list) == 1 and self._is_train_set( dataset_name=first_dataset_name, env=env, ) if only_train_set: self.enabled = False _log_warning("Only training set found, disabling early stopping.") return if self.verbose: _log_info(f"Training until validation scores don't improve for {self.stopping_rounds} rounds") self._reset_storages() n_metrics = len({m[1] for m in env.evaluation_result_list}) n_datasets = len(env.evaluation_result_list) // n_metrics if isinstance(self.min_delta, list): if not all(t >= 0 for t in self.min_delta): raise ValueError("Values for early stopping min_delta must be non-negative.") if len(self.min_delta) == 0: if self.verbose: _log_info("Disabling min_delta for early stopping.") deltas = [0.0] * n_datasets * n_metrics elif len(self.min_delta) == 1: if self.verbose: _log_info(f"Using {self.min_delta[0]} as min_delta for all metrics.") deltas = self.min_delta * n_datasets * n_metrics else: if len(self.min_delta) != n_metrics: raise ValueError("Must provide a single value for min_delta or as many as metrics.") if self.first_metric_only and self.verbose: _log_info(f"Using only {self.min_delta[0]} as early stopping min_delta.") deltas = self.min_delta * n_datasets else: if self.min_delta < 0: raise ValueError("Early stopping min_delta must be non-negative.") if self.min_delta > 0 and n_metrics > 1 and not self.first_metric_only and self.verbose: _log_info(f"Using {self.min_delta} as min_delta for all metrics.") deltas = [self.min_delta] * n_datasets * n_metrics self.first_metric = first_metric_name for eval_ret, delta in zip(env.evaluation_result_list, deltas): self.best_iter.append(0) if eval_ret[3]: # greater is better self.best_score.append(float("-inf")) self.cmp_op.append(partial(self._gt_delta, delta=delta)) else: self.best_score.append(float("inf")) self.cmp_op.append(partial(self._lt_delta, delta=delta)) def _final_iteration_check(self, *, env: CallbackEnv, metric_name: str, i: int) -> None: if env.iteration == env.end_iteration - 1: if self.verbose: best_score_str = "\t".join([_format_eval_result(x, show_stdv=True) for x in self.best_score_list[i]]) _log_info( f"Did not meet early stopping. Best iteration is:\n[{self.best_iter[i] + 1}]\t{best_score_str}" ) if self.first_metric_only: _log_info(f"Evaluated only: {metric_name}") raise EarlyStopException(self.best_iter[i], self.best_score_list[i]) def __call__(self, env: CallbackEnv) -> None: if env.iteration == env.begin_iteration: self._init(env) if not self.enabled: return if env.evaluation_result_list is None: raise RuntimeError( "early_stopping() callback enabled but no evaluation results found. This is a probably bug in LightGBM. " "Please report it at https://github.com/lightgbm-org/LightGBM/issues" ) # self.best_score_list is initialized to an empty list first_time_updating_best_score_list = self.best_score_list == [] for i in range(len(env.evaluation_result_list)): dataset_name, metric_name, metric_value, *_ = env.evaluation_result_list[i] if first_time_updating_best_score_list or self.cmp_op[i]( # type: ignore[call-arg] curr_score=metric_value, best_score=self.best_score[i] ): self.best_score[i] = metric_value self.best_iter[i] = env.iteration if first_time_updating_best_score_list: self.best_score_list.append(env.evaluation_result_list) else: self.best_score_list[i] = env.evaluation_result_list if self.first_metric_only and self.first_metric != metric_name: continue # use only the first metric for early stopping if self._is_train_set( dataset_name=dataset_name, env=env, ): continue # train data for lgb.cv or sklearn wrapper (underlying lgb.train) elif env.iteration - self.best_iter[i] >= self.stopping_rounds: if self.verbose: eval_result_str = "\t".join( [_format_eval_result(x, show_stdv=True) for x in self.best_score_list[i]] ) _log_info(f"Early stopping, best iteration is:\n[{self.best_iter[i] + 1}]\t{eval_result_str}") if self.first_metric_only: _log_info(f"Evaluated only: {metric_name}") raise EarlyStopException(self.best_iter[i], self.best_score_list[i]) self._final_iteration_check(env=env, metric_name=metric_name, i=i) def _should_enable_early_stopping(stopping_rounds: Any) -> bool: """Check if early stopping should be activated. This function will evaluate to True if the early stopping callback should be activated (i.e. stopping_rounds > 0). It also provides an informative error if the type is not int. """ if not isinstance(stopping_rounds, int): raise TypeError(f"early_stopping_round should be an integer. Got '{type(stopping_rounds).__name__}'") return stopping_rounds > 0 def early_stopping( stopping_rounds: int, first_metric_only: bool = False, verbose: bool = True, min_delta: Union[float, List[float]] = 0.0, ) -> _EarlyStoppingCallback: """Create a callback that activates early stopping. Activates early stopping. The model will train until the validation score doesn't improve by at least ``min_delta``. Validation score needs to improve at least every ``stopping_rounds`` round(s) to continue training. Requires at least one validation data and one metric. If there's more than one, will check all of them. But the training data is ignored anyway. To check only the first metric set ``first_metric_only`` to True. The index of iteration that has the best performance will be saved in the ``best_iteration`` attribute of a model. .. note:: If using ``boosting_type="dart"``, this callback has no effect and early stopping will not be performed. Parameters ---------- stopping_rounds : int The possible number of rounds without the trend occurrence. first_metric_only : bool, optional (default=False) Whether to use only the first metric for early stopping. verbose : bool, optional (default=True) Whether to log message with early stopping information. By default, standard output resource is used. Use ``register_logger()`` function to register a custom logger. min_delta : float or list of float, optional (default=0.0) Minimum improvement in score to keep training. If float, this single value is used for all metrics. If list, its length should match the total number of metrics. .. versionadded:: 4.0.0 Returns ------- callback : _EarlyStoppingCallback The callback that activates early stopping. """ return _EarlyStoppingCallback( stopping_rounds=stopping_rounds, first_metric_only=first_metric_only, verbose=verbose, min_delta=min_delta, ) ================================================ FILE: python-package/lightgbm/compat.py ================================================ # coding: utf-8 """Compatibility library.""" import inspect from typing import TYPE_CHECKING, Any, List # scikit-learn is intentionally imported first here, # see https://github.com/lightgbm-org/LightGBM/issues/6509 """sklearn""" try: from sklearn import __version__ as _sklearn_version from sklearn.base import BaseEstimator, ClassifierMixin, RegressorMixin from sklearn.preprocessing import LabelEncoder from sklearn.utils.class_weight import compute_sample_weight from sklearn.utils.multiclass import check_classification_targets from sklearn.utils.validation import assert_all_finite, check_array, check_X_y try: from sklearn.exceptions import NotFittedError from sklearn.model_selection import BaseCrossValidator, GroupKFold, StratifiedKFold except ImportError: from sklearn.cross_validation import BaseCrossValidator, GroupKFold, StratifiedKFold from sklearn.utils.validation import NotFittedError try: from sklearn.utils.validation import _check_sample_weight # As of https://github.com/scikit-learn/scikit-learn/pull/32212, scikit-learn started raising an error # when sample weights are all 0. This argument allow_all_zero_weights can be used switch back # to the old behavior of allowing them. # # This can be removed when the minimum scikit-learn version supported here is v1.9. SKLEARN_CHECK_SAMPLE_WEIGHT_HAS_ALLOW_ZERO_WEIGHTS_ARG = ( "allow_all_zero_weights" in inspect.signature(_check_sample_weight).parameters ) except ImportError: from sklearn.utils.validation import check_consistent_length SKLEARN_CHECK_SAMPLE_WEIGHT_HAS_ALLOW_ZERO_WEIGHTS_ARG = False # dummy function to support older version of scikit-learn def _check_sample_weight(sample_weight: Any, X: Any, dtype: Any = None) -> Any: check_consistent_length(sample_weight, X) return sample_weight try: from sklearn.utils.validation import validate_data except ImportError: # validate_data() was added in scikit-learn 1.6, this function roughly imitates it for older versions. # It can be removed when lightgbm's minimum scikit-learn version is at least 1.6. def validate_data( _estimator: Any, X: Any, y: Any = "no_validation", accept_sparse: bool = True, # 'force_all_finite' was renamed to 'ensure_all_finite' in scikit-learn 1.6 ensure_all_finite: bool = False, ensure_min_samples: int = 1, # trap other keyword arguments that only work on scikit-learn >=1.6, like 'reset' **ignored_kwargs: Any, ) -> Any: # it's safe to import _num_features unconditionally because: # # * it was first added in scikit-learn 0.24.2 # * lightgbm cannot be used with scikit-learn versions older than that # * this validate_data() re-implementation will not be called in scikit-learn>=1.6 # from sklearn.utils.validation import _num_features # noqa: PLC0415 # _num_features() raises a TypeError on 1-dimensional input. That's a problem # because scikit-learn's 'check_fit1d' estimator check sets that expectation that # estimators must raise a ValueError when a 1-dimensional input is passed to fit(). # # So here, lightgbm avoids calling _num_features() on 1-dimensional inputs. if hasattr(X, "shape") and len(X.shape) == 1: n_features_in_ = 1 else: n_features_in_ = _num_features(X) no_val_y = isinstance(y, str) and y == "no_validation" # NOTE: check_X_y() calls check_array() internally, so only need to call one or the other of them here if no_val_y: X = check_array( X, accept_sparse=accept_sparse, force_all_finite=ensure_all_finite, ensure_min_samples=ensure_min_samples, ) else: X, y = check_X_y( X, y, accept_sparse=accept_sparse, force_all_finite=ensure_all_finite, ensure_min_samples=ensure_min_samples, ) # this only needs to be updated at fit() time _estimator.n_features_in_ = n_features_in_ # raise the same error that scikit-learn's `validate_data()` does on scikit-learn>=1.6 if _estimator.__sklearn_is_fitted__() and _estimator._n_features != n_features_in_: raise ValueError( f"X has {n_features_in_} features, but {_estimator.__class__.__name__} " f"is expecting {_estimator._n_features} features as input." ) if no_val_y: return X else: return X, y SKLEARN_INSTALLED = True _LGBMBaseCrossValidator = BaseCrossValidator _LGBMModelBase = BaseEstimator _LGBMRegressorBase = RegressorMixin _LGBMClassifierBase = ClassifierMixin _LGBMLabelEncoder = LabelEncoder LGBMNotFittedError = NotFittedError _LGBMStratifiedKFold = StratifiedKFold _LGBMGroupKFold = GroupKFold _LGBMCheckSampleWeight = _check_sample_weight _LGBMAssertAllFinite = assert_all_finite _LGBMCheckClassificationTargets = check_classification_targets _LGBMComputeSampleWeight = compute_sample_weight _LGBMValidateData = validate_data except ImportError: SKLEARN_INSTALLED = False SKLEARN_CHECK_SAMPLE_WEIGHT_HAS_ALLOW_ZERO_WEIGHTS_ARG = False class _LGBMModelBase: # type: ignore """Dummy class for sklearn.base.BaseEstimator.""" pass class _LGBMClassifierBase: # type: ignore """Dummy class for sklearn.base.ClassifierMixin.""" pass class _LGBMRegressorBase: # type: ignore """Dummy class for sklearn.base.RegressorMixin.""" pass _LGBMBaseCrossValidator = None _LGBMLabelEncoder = None LGBMNotFittedError = ValueError _LGBMStratifiedKFold = None _LGBMGroupKFold = None _LGBMCheckSampleWeight = None _LGBMAssertAllFinite = None _LGBMCheckClassificationTargets = None _LGBMComputeSampleWeight = None _LGBMValidateData = None _sklearn_version = None # additional scikit-learn imports only for type hints if TYPE_CHECKING: # sklearn.utils.Tags can be imported unconditionally once # lightgbm's minimum scikit-learn version is 1.6 or higher try: from sklearn.utils import Tags as _sklearn_Tags except ImportError: _sklearn_Tags = None """pandas""" try: from pandas import DataFrame as pd_DataFrame from pandas import Series as pd_Series from pandas import concat try: from pandas import CategoricalDtype as pd_CategoricalDtype except ImportError: from pandas.api.types import CategoricalDtype as pd_CategoricalDtype PANDAS_INSTALLED = True except ImportError: PANDAS_INSTALLED = False class pd_Series: # type: ignore """Dummy class for pandas.Series.""" def __init__(self, *args: Any, **kwargs: Any): pass class pd_DataFrame: # type: ignore """Dummy class for pandas.DataFrame.""" def __init__(self, *args: Any, **kwargs: Any): pass class pd_CategoricalDtype: # type: ignore """Dummy class for pandas.CategoricalDtype.""" def __init__(self, *args: Any, **kwargs: Any): pass concat = None """matplotlib""" try: import matplotlib # noqa: F401 MATPLOTLIB_INSTALLED = True except ImportError: MATPLOTLIB_INSTALLED = False """graphviz""" try: import graphviz # noqa: F401 GRAPHVIZ_INSTALLED = True except ImportError: GRAPHVIZ_INSTALLED = False """dask""" try: from dask import delayed from dask.array import Array as dask_Array from dask.array import from_delayed as dask_array_from_delayed from dask.bag import from_delayed as dask_bag_from_delayed from dask.dataframe import DataFrame as dask_DataFrame from dask.dataframe import Series as dask_Series from dask.distributed import Client, Future, default_client, wait DASK_INSTALLED = True # catching 'ValueError' here because of this: # https://github.com/lightgbm-org/LightGBM/issues/6365#issuecomment-2002330003 # # That's potentially risky as dask does some significant import-time processing, # like loading configuration from environment variables and files, and catching # ValueError here might hide issues with that config-loading. # # But in exchange, it's less likely that 'import lightgbm' will fail for # dask-related reasons, which is beneficial for any workloads that are using # lightgbm but not its Dask functionality. except (ImportError, ValueError): DASK_INSTALLED = False dask_array_from_delayed = None # type: ignore[assignment] dask_bag_from_delayed = None # type: ignore[assignment] delayed = None default_client = None # type: ignore[assignment] wait = None # type: ignore[assignment] class Client: # type: ignore """Dummy class for dask.distributed.Client.""" def __init__(self, *args: Any, **kwargs: Any): pass class Future: # type: ignore """Dummy class for dask.distributed.Future.""" def __init__(self, *args: Any, **kwargs: Any): pass class dask_Array: # type: ignore """Dummy class for dask.array.Array.""" def __init__(self, *args: Any, **kwargs: Any): pass class dask_DataFrame: # type: ignore """Dummy class for dask.dataframe.DataFrame.""" def __init__(self, *args: Any, **kwargs: Any): pass class dask_Series: # type: ignore """Dummy class for dask.dataframe.Series.""" def __init__(self, *args: Any, **kwargs: Any): pass """pyarrow""" try: import pyarrow.compute as pa_compute from pyarrow import Array as pa_Array from pyarrow import ChunkedArray as pa_ChunkedArray from pyarrow import Table as pa_Table from pyarrow import array as pa_array from pyarrow import chunked_array as pa_chunked_array from pyarrow.types import is_boolean as arrow_is_boolean from pyarrow.types import is_floating as arrow_is_floating from pyarrow.types import is_integer as arrow_is_integer PYARROW_INSTALLED = True except ImportError: PYARROW_INSTALLED = False class pa_Array: # type: ignore """Dummy class for pa.Array.""" def __init__(self, *args: Any, **kwargs: Any): pass class pa_ChunkedArray: # type: ignore """Dummy class for pa.ChunkedArray.""" def __init__(self, *args: Any, **kwargs: Any): pass class pa_Table: # type: ignore """Dummy class for pa.Table.""" def __init__(self, *args: Any, **kwargs: Any): pass class pa_compute: # type: ignore """Dummy class for pyarrow.compute module.""" all = None equal = None pa_array = None pa_chunked_array = None arrow_is_boolean = None arrow_is_integer = None arrow_is_floating = None """cffi""" try: from pyarrow.cffi import ffi as arrow_cffi CFFI_INSTALLED = True except ImportError: CFFI_INSTALLED = False class arrow_cffi: # type: ignore """Dummy class for pyarrow.cffi.ffi.""" CData = None def __init__(self, *args: Any, **kwargs: Any): pass """cpu_count()""" try: from joblib import cpu_count def _LGBMCpuCount(only_physical_cores: bool = True) -> int: return cpu_count(only_physical_cores=only_physical_cores) except ImportError: try: from psutil import cpu_count def _LGBMCpuCount(only_physical_cores: bool = True) -> int: return cpu_count(logical=not only_physical_cores) or 1 except ImportError: from multiprocessing import cpu_count def _LGBMCpuCount(only_physical_cores: bool = True) -> int: return cpu_count() __all__: List[str] = [] ================================================ FILE: python-package/lightgbm/dask.py ================================================ # coding: utf-8 """Distributed training with LightGBM and dask.distributed. This module enables you to perform distributed training with LightGBM on dask.Array and dask.DataFrame collections. It is based on dask-lightgbm, which was based on dask-xgboost. """ import operator import socket from collections import defaultdict from copy import deepcopy from enum import Enum, auto from functools import partial from typing import Any, Dict, Iterable, List, Optional, Tuple, Type, Union from urllib.parse import urlparse import numpy as np import scipy.sparse as ss from .basic import LightGBMError, _choose_param_value, _ConfigAliases, _log_info, _log_warning from .compat import ( DASK_INSTALLED, PANDAS_INSTALLED, SKLEARN_INSTALLED, Client, Future, LGBMNotFittedError, concat, dask_Array, dask_array_from_delayed, dask_bag_from_delayed, dask_DataFrame, dask_Series, default_client, delayed, pd_DataFrame, pd_Series, wait, ) from .sklearn import ( LGBMClassifier, LGBMModel, LGBMRanker, LGBMRegressor, _LGBM_ScikitCustomObjectiveFunction, _LGBM_ScikitEvalMetricType, _lgbmmodel_doc_custom_eval_note, _lgbmmodel_doc_fit, _lgbmmodel_doc_predict, _validate_eval_set_Xy, ) __all__ = [ "DaskLGBMClassifier", "DaskLGBMRanker", "DaskLGBMRegressor", ] _DaskCollection = Union[dask_Array, dask_DataFrame, dask_Series] _DaskMatrixLike = Union[dask_Array, dask_DataFrame] _DaskVectorLike = Union[dask_Array, dask_Series] _DaskPart = Union[np.ndarray, pd_DataFrame, pd_Series, ss.spmatrix] class _RemoteSocket: def acquire(self) -> int: self.socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM) self.socket.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1) self.socket.bind(("", 0)) return self.socket.getsockname()[1] def release(self) -> None: self.socket.close() def _acquire_port() -> Tuple[_RemoteSocket, int]: s = _RemoteSocket() port = s.acquire() return s, port class _DatasetNames(Enum): """Placeholder names used by lightgbm.dask internals to say 'also evaluate the training data'. Avoid duplicating the training data when the validation set refers to elements of training data. """ TRAINSET = auto() SAMPLE_WEIGHT = auto() INIT_SCORE = auto() GROUP = auto() def _get_dask_client(client: Optional[Client]) -> Client: """Choose a Dask client to use. Parameters ---------- client : dask.distributed.Client or None Dask client. Returns ------- client : dask.distributed.Client A Dask client. """ if client is None: return default_client() else: return client def _assign_open_ports_to_workers( *, client: Client, workers: List[str], ) -> Tuple[Dict[str, Future], Dict[str, int]]: """Assign an open port to each worker. Returns ------- worker_to_socket_future: dict mapping from worker address to a future pointing to the remote socket. worker_to_port: dict mapping from worker address to an open port in the worker's host. """ # Acquire port in worker worker_to_future = {} for worker in workers: worker_to_future[worker] = client.submit( _acquire_port, workers=[worker], allow_other_workers=False, pure=False, ) # schedule futures to retrieve each element of the tuple worker_to_socket_future = {} worker_to_port_future = {} for worker, socket_future in worker_to_future.items(): worker_to_socket_future[worker] = client.submit(operator.itemgetter(0), socket_future) worker_to_port_future[worker] = client.submit(operator.itemgetter(1), socket_future) # retrieve ports worker_to_port = client.gather(worker_to_port_future) return worker_to_socket_future, worker_to_port def _concat(seq: List[_DaskPart]) -> _DaskPart: if isinstance(seq[0], np.ndarray): return np.concatenate(seq, axis=0) elif isinstance(seq[0], (pd_DataFrame, pd_Series)): return concat(seq, axis=0) elif isinstance(seq[0], ss.spmatrix): return ss.vstack(seq, format="csr") else: raise TypeError( f"Data must be one of: numpy arrays, pandas dataframes, sparse matrices (from scipy). Got {type(seq[0]).__name__}." ) def _remove_list_padding(*args: Any) -> List[List[Any]]: return [[z for z in arg if z is not None] for arg in args] def _pad_eval_names( *, lgbm_model: LGBMModel, required_names: List[str], ) -> LGBMModel: """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. Allows users to rely on expected eval_set names being present when fitting DaskLGBM estimators with ``eval_set``. """ for eval_name in required_names: if eval_name not in lgbm_model.evals_result_: lgbm_model.evals_result_[eval_name] = {} if eval_name not in lgbm_model.best_score_: lgbm_model.best_score_[eval_name] = {} return lgbm_model def _train_part( *, params: Dict[str, Any], model_factory: Type[LGBMModel], list_of_parts: List[Dict[str, _DaskPart]], machines: str, local_listen_port: int, num_machines: int, return_model: bool, time_out: int, remote_socket: _RemoteSocket, **kwargs: Any, ) -> Optional[LGBMModel]: network_params = { "machines": machines, "local_listen_port": local_listen_port, "time_out": time_out, "num_machines": num_machines, } params.update(network_params) is_ranker = issubclass(model_factory, LGBMRanker) # Concatenate many parts into one data = _concat([x["data"] for x in list_of_parts]) label = _concat([x["label"] for x in list_of_parts]) if "weight" in list_of_parts[0]: weight = _concat([x["weight"] for x in list_of_parts]) else: weight = None if "group" in list_of_parts[0]: group = _concat([x["group"] for x in list_of_parts]) else: group = None if "init_score" in list_of_parts[0]: init_score = _concat([x["init_score"] for x in list_of_parts]) else: init_score = None # construct local eval_set data. n_evals = max(len(x.get("eval_set", [])) for x in list_of_parts) eval_names = kwargs.pop("eval_names", None) eval_class_weight = kwargs.get("eval_class_weight") local_eval_set = None local_eval_names = None local_eval_sample_weight = None local_eval_init_score = None local_eval_group = None if n_evals: has_eval_sample_weight = any(x.get("eval_sample_weight") is not None for x in list_of_parts) has_eval_init_score = any(x.get("eval_init_score") is not None for x in list_of_parts) local_eval_set = [] evals_result_names = [] if has_eval_sample_weight: local_eval_sample_weight = [] if has_eval_init_score: local_eval_init_score = [] if is_ranker: local_eval_group = [] # store indices of eval_set components that were not contained within local parts. missing_eval_component_idx = [] # consolidate parts of each individual eval component. for i in range(n_evals): x_e = [] y_e = [] w_e = [] init_score_e = [] g_e = [] for part in list_of_parts: if not part.get("eval_set"): continue # require that eval_name exists in evaluated result data in case dropped due to padding. # in distributed training the 'training' eval_set is not detected, will have name 'valid_'. if eval_names: evals_result_name = eval_names[i] else: evals_result_name = f"valid_{i}" eval_set = part["eval_set"][i] if eval_set is _DatasetNames.TRAINSET: x_e.append(part["data"]) y_e.append(part["label"]) else: x_e.extend(eval_set[0]) y_e.extend(eval_set[1]) if evals_result_name not in evals_result_names: evals_result_names.append(evals_result_name) eval_weight = part.get("eval_sample_weight") if eval_weight: if eval_weight[i] is _DatasetNames.SAMPLE_WEIGHT: w_e.append(part["weight"]) else: w_e.extend(eval_weight[i]) eval_init_score = part.get("eval_init_score") if eval_init_score: if eval_init_score[i] is _DatasetNames.INIT_SCORE: init_score_e.append(part["init_score"]) else: init_score_e.extend(eval_init_score[i]) eval_group = part.get("eval_group") if eval_group: if eval_group[i] is _DatasetNames.GROUP: g_e.append(part["group"]) else: g_e.extend(eval_group[i]) # filter padding from eval parts then _concat each eval_set component. 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) if x_e: local_eval_set.append((_concat(x_e), _concat(y_e))) else: missing_eval_component_idx.append(i) continue if w_e: local_eval_sample_weight.append(_concat(w_e)) if init_score_e: local_eval_init_score.append(_concat(init_score_e)) if g_e: local_eval_group.append(_concat(g_e)) # reconstruct eval_set fit args/kwargs depending on which components of eval_set are on worker. eval_component_idx = [i for i in range(n_evals) if i not in missing_eval_component_idx] if eval_names: local_eval_names = [eval_names[i] for i in eval_component_idx] if eval_class_weight: kwargs["eval_class_weight"] = [eval_class_weight[i] for i in eval_component_idx] if local_eval_set is None: local_eval_X = None local_eval_y = None else: local_eval_X = tuple(X for X, _ in local_eval_set) local_eval_y = tuple(y for _, y in local_eval_set) model = model_factory(**params) if remote_socket is not None: remote_socket.release() try: if is_ranker: model.fit( data, label, sample_weight=weight, init_score=init_score, group=group, eval_X=local_eval_X, eval_y=local_eval_y, eval_sample_weight=local_eval_sample_weight, eval_init_score=local_eval_init_score, eval_group=local_eval_group, eval_names=local_eval_names, **kwargs, ) else: model.fit( data, label, sample_weight=weight, init_score=init_score, eval_X=local_eval_X, eval_y=local_eval_y, eval_sample_weight=local_eval_sample_weight, eval_init_score=local_eval_init_score, eval_names=local_eval_names, **kwargs, ) finally: if getattr(model, "fitted_", False): model.booster_.free_network() if n_evals: # ensure that expected keys for evals_result_ and best_score_ exist regardless of padding. model = _pad_eval_names(lgbm_model=model, required_names=evals_result_names) return model if return_model else None def _split_to_parts(*, data: _DaskCollection, is_matrix: bool) -> List[_DaskPart]: parts = data.to_delayed() if isinstance(parts, np.ndarray): if is_matrix: assert parts.shape[1] == 1 else: assert parts.ndim == 1 or parts.shape[1] == 1 parts = parts.flatten().tolist() return parts def _machines_to_worker_map( *, machines: str, worker_addresses: Iterable[str], ) -> Dict[str, int]: """Create a worker_map from machines list. Given ``machines`` and a list of Dask worker addresses, return a mapping where the keys are ``worker_addresses`` and the values are ports from ``machines``. Parameters ---------- machines : str A comma-delimited list of workers, of the form ``ip1:port,ip2:port``. worker_addresses : list of str 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. Returns ------- result : Dict[str, int] Dictionary where keys are work addresses in the form expected by Dask and values are a port for LightGBM to use. """ machine_addresses = machines.split(",") if len(set(machine_addresses)) != len(machine_addresses): raise ValueError( f"Found duplicates in 'machines' ({machines}). Each entry in 'machines' must be a unique IP-port combination." ) machine_to_port = defaultdict(set) for address in machine_addresses: host, port = address.split(":") machine_to_port[host].add(int(port)) out = {} for address in worker_addresses: worker_host = urlparse(address).hostname if not worker_host: raise ValueError(f"Could not parse host name from worker address '{address}'") out[address] = machine_to_port[worker_host].pop() return out def _train( *, client: Client, data: _DaskMatrixLike, label: _DaskCollection, params: Dict[str, Any], model_factory: Type[LGBMModel], sample_weight: Optional[_DaskVectorLike] = None, init_score: Optional[_DaskCollection] = None, group: Optional[_DaskVectorLike] = None, eval_set: Optional[List[Tuple[_DaskMatrixLike, _DaskCollection]]] = None, eval_names: Optional[List[str]] = None, eval_X: Optional[Union[_DaskMatrixLike, Tuple[_DaskMatrixLike]]] = None, eval_y: Optional[Union[_DaskCollection, Tuple[_DaskCollection]]] = None, eval_sample_weight: Optional[List[_DaskVectorLike]] = None, eval_class_weight: Optional[List[Union[dict, str]]] = None, eval_init_score: Optional[List[_DaskCollection]] = None, eval_group: Optional[List[_DaskVectorLike]] = None, eval_metric: Optional[_LGBM_ScikitEvalMetricType] = None, eval_at: Optional[Union[List[int], Tuple[int, ...]]] = None, **kwargs: Any, ) -> LGBMModel: """Inner train routine. Parameters ---------- client : dask.distributed.Client Dask client. data : Dask Array or Dask DataFrame of shape = [n_samples, n_features] Input feature matrix. label : Dask Array, Dask DataFrame or Dask Series of shape = [n_samples] The target values (class labels in classification, real numbers in regression). params : dict Parameters passed to constructor of the local underlying model. model_factory : lightgbm.LGBMClassifier, lightgbm.LGBMRegressor, or lightgbm.LGBMRanker class Class of the local underlying model. sample_weight : Dask Array or Dask Series of shape = [n_samples] or None, optional (default=None) Weights of training data. Weights should be non-negative. 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) Init score of training data. group : Dask Array or Dask Series or None, optional (default=None) Group/query data. Only used in the learning-to-rank task. sum(group) = n_samples. For example, if you have a 100-document dataset with ``group = [10, 20, 40, 10, 10, 10]``, that means that you have 6 groups, 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. eval_set : list of (X, y) tuples of Dask data collections, or None, optional (default=None) List of (X, y) tuple pairs to use as validation sets. Note, that not all workers may receive chunks of every eval set within ``eval_set``. When the returned lightgbm estimator is not trained using any chunks of a particular eval set, its corresponding component of ``evals_result_`` and ``best_score_`` will be empty dictionaries. eval_names : list of str, or None, optional (default=None) Names of eval_set. eval_X : Dask Array or Dask DataFrame, tuple thereof or None, optional (default=None) Feature matrix or tuple thereof, e.g. ``(X_val0, X_val1)``, to use as validation sets. eval_y : Dask Array or Dask DataFrame or Dask Series, tuple thereof or None, optional (default=None) Target values or tuple thereof, e.g. ``(y_val0, y_val1)``, to use as validation sets. eval_sample_weight : list of Dask Array or Dask Series, or None, optional (default=None) Weights for each validation set in eval_set. Weights should be non-negative. eval_class_weight : list of dict or str, or None, optional (default=None) Class weights, one dict or str for each validation set in eval_set. eval_init_score : list of Dask Array, Dask Series or Dask DataFrame (for multi-class task), or None, optional (default=None) Initial model score for each validation set in eval_set. eval_group : list of Dask Array or Dask Series, or None, optional (default=None) Group/query for each validation set in eval_set. eval_metric : str, callable, list or None, optional (default=None) If str, it should be a built-in evaluation metric to use. If callable, it should be a custom evaluation metric, see note below for more details. If list, it can be a list of built-in metrics, a list of custom evaluation metrics, or a mix of both. In either case, the ``metric`` from the Dask model parameters (or inferred from the objective) will be evaluated and used as well. Default: 'l2' for DaskLGBMRegressor, 'binary(multi)_logloss' for DaskLGBMClassifier, 'ndcg' for DaskLGBMRanker. eval_at : list or tuple of int, optional (default=None) The evaluation positions of the specified ranking metric. **kwargs Other parameters passed to ``fit`` method of the local underlying model. Returns ------- model : lightgbm.LGBMClassifier, lightgbm.LGBMRegressor, or lightgbm.LGBMRanker class Returns fitted underlying model. Note ---- This method handles setting up the following network parameters based on information about the Dask cluster referenced by ``client``. * ``local_listen_port``: port that each LightGBM worker opens a listening socket on, to accept connections from other workers. This can differ from LightGBM worker to LightGBM worker, but does not have to. * ``machines``: a comma-delimited list of all workers in the cluster, in the form ``ip:port,ip:port``. If running multiple Dask workers on the same host, use different ports for each worker. For example, for ``LocalCluster(n_workers=3)``, you might pass ``"127.0.0.1:12400,127.0.0.1:12401,127.0.0.1:12402"``. * ``num_machines``: number of LightGBM workers. * ``timeout``: time in minutes to wait before closing unused sockets. The default behavior of this function is to generate ``machines`` from the list of Dask workers which hold some piece of the training data, and to search for an open port on each worker to be used as ``local_listen_port``. If ``machines`` is provided explicitly in ``params``, this function uses the hosts and ports in that list directly, and does not do any searching. This means that if any of the Dask workers are missing from the list or any of those ports are not free when training starts, training will fail. If ``local_listen_port`` is provided in ``params`` and ``machines`` is not, this function constructs ``machines`` from the list of Dask workers which hold some piece of the training data, assuming that each one will use the same ``local_listen_port``. """ params = deepcopy(params) # capture whether local_listen_port or its aliases were provided listen_port_in_params = any(alias in params for alias in _ConfigAliases.get("local_listen_port")) # capture whether machines or its aliases were provided machines_in_params = any(alias in params for alias in _ConfigAliases.get("machines")) params = _choose_param_value( main_param_name="tree_learner", params=params, default_value="data", ) allowed_tree_learners = { "data", "data_parallel", "feature", "feature_parallel", "voting", "voting_parallel", } if params["tree_learner"] not in allowed_tree_learners: _log_warning( f'Parameter tree_learner set to {params["tree_learner"]}, which is not allowed. Using "data" as default' ) params["tree_learner"] = "data" # Some passed-in parameters can be removed: # * 'num_machines': set automatically from Dask worker list # * 'num_threads': overridden to match nthreads on each Dask process for param_alias in _ConfigAliases.get("num_machines", "num_threads"): if param_alias in params: _log_warning(f"Parameter {param_alias} will be ignored.") params.pop(param_alias) # Split arrays/dataframes into parts. Arrange parts into dicts to enforce co-locality data_parts = _split_to_parts(data=data, is_matrix=True) label_parts = _split_to_parts(data=label, is_matrix=False) parts = [{"data": x, "label": y} for (x, y) in zip(data_parts, label_parts)] n_parts = len(parts) if sample_weight is not None: weight_parts = _split_to_parts(data=sample_weight, is_matrix=False) for i in range(n_parts): parts[i]["weight"] = weight_parts[i] if group is not None: group_parts = _split_to_parts(data=group, is_matrix=False) for i in range(n_parts): parts[i]["group"] = group_parts[i] if init_score is not None: init_score_parts = _split_to_parts(data=init_score, is_matrix=False) for i in range(n_parts): parts[i]["init_score"] = init_score_parts[i] eval_set = _validate_eval_set_Xy(eval_set=eval_set, eval_X=eval_X, eval_y=eval_y) # evals_set will to be re-constructed into smaller lists of (X, y) tuples, where # X and y are each delayed sub-lists of original eval dask Collections. if eval_set: # find maximum number of parts in an individual eval set so that we can # pad eval sets when they come in different sizes. n_largest_eval_parts = max(x[0].npartitions for x in eval_set) eval_sets: Dict[ int, List[Union[_DatasetNames, Tuple[List[Optional[_DaskMatrixLike]], List[Optional[_DaskVectorLike]]]]] ] = defaultdict(list) if eval_sample_weight: eval_sample_weights: Dict[int, List[Union[_DatasetNames, List[Optional[_DaskVectorLike]]]]] = defaultdict( list ) if eval_group: eval_groups: Dict[int, List[Union[_DatasetNames, List[Optional[_DaskVectorLike]]]]] = defaultdict(list) if eval_init_score: eval_init_scores: Dict[int, List[Union[_DatasetNames, List[Optional[_DaskMatrixLike]]]]] = defaultdict(list) for i, (X_eval, y_eval) in enumerate(eval_set): n_this_eval_parts = X_eval.npartitions # when individual eval set is equivalent to training data, skip recomputing parts. if X_eval is data and y_eval is label: for parts_idx in range(n_parts): eval_sets[parts_idx].append(_DatasetNames.TRAINSET) else: eval_x_parts = _split_to_parts(data=X_eval, is_matrix=True) eval_y_parts = _split_to_parts(data=y_eval, is_matrix=False) for j in range(n_largest_eval_parts): parts_idx = j % n_parts # add None-padding for individual eval_set member if it is smaller than the largest member. if j < n_this_eval_parts: x_e = eval_x_parts[j] y_e = eval_y_parts[j] else: x_e = None y_e = None if j < n_parts: # first time a chunk of this eval set is added to this part. eval_sets[parts_idx].append(([x_e], [y_e])) else: # append additional chunks of this eval set to this part. eval_sets[parts_idx][-1][0].append(x_e) # type: ignore[index, union-attr] eval_sets[parts_idx][-1][1].append(y_e) # type: ignore[index, union-attr] if eval_sample_weight: if eval_sample_weight[i] is sample_weight: for parts_idx in range(n_parts): eval_sample_weights[parts_idx].append(_DatasetNames.SAMPLE_WEIGHT) else: eval_w_parts = _split_to_parts(data=eval_sample_weight[i], is_matrix=False) # ensure that all evaluation parts map uniquely to one part. for j in range(n_largest_eval_parts): if j < n_this_eval_parts: w_e = eval_w_parts[j] else: w_e = None parts_idx = j % n_parts if j < n_parts: eval_sample_weights[parts_idx].append([w_e]) else: eval_sample_weights[parts_idx][-1].append(w_e) # type: ignore[union-attr] if eval_init_score: if eval_init_score[i] is init_score: for parts_idx in range(n_parts): eval_init_scores[parts_idx].append(_DatasetNames.INIT_SCORE) else: eval_init_score_parts = _split_to_parts(data=eval_init_score[i], is_matrix=False) for j in range(n_largest_eval_parts): if j < n_this_eval_parts: init_score_e = eval_init_score_parts[j] else: init_score_e = None parts_idx = j % n_parts if j < n_parts: eval_init_scores[parts_idx].append([init_score_e]) else: eval_init_scores[parts_idx][-1].append(init_score_e) # type: ignore[union-attr] if eval_group: if eval_group[i] is group: for parts_idx in range(n_parts): eval_groups[parts_idx].append(_DatasetNames.GROUP) else: eval_g_parts = _split_to_parts(data=eval_group[i], is_matrix=False) for j in range(n_largest_eval_parts): if j < n_this_eval_parts: g_e = eval_g_parts[j] else: g_e = None parts_idx = j % n_parts if j < n_parts: eval_groups[parts_idx].append([g_e]) else: eval_groups[parts_idx][-1].append(g_e) # type: ignore[union-attr] # assign sub-eval_set components to worker parts. for parts_idx, e_set in eval_sets.items(): parts[parts_idx]["eval_set"] = e_set if eval_sample_weight: parts[parts_idx]["eval_sample_weight"] = eval_sample_weights[parts_idx] if eval_init_score: parts[parts_idx]["eval_init_score"] = eval_init_scores[parts_idx] if eval_group: parts[parts_idx]["eval_group"] = eval_groups[parts_idx] # Start computation in the background parts = list(map(delayed, parts)) parts = client.compute(parts) wait(parts) for part in parts: if part.status == "error": # type: ignore # trigger error locally return part # type: ignore[return-value] # Find locations of all parts and map them to particular Dask workers key_to_part_dict = {part.key: part for part in parts} # type: ignore who_has = client.who_has(parts) worker_map = defaultdict(list) for key, workers in who_has.items(): worker_map[next(iter(workers))].append(key_to_part_dict[key]) # Check that all workers were provided some of eval_set. Otherwise warn user that validation # data artifacts may not be populated depending on worker returning final estimator. if eval_set: for worker in worker_map: has_eval_set = False for part in worker_map[worker]: if "eval_set" in part.result(): # type: ignore[attr-defined] has_eval_set = True break if not has_eval_set: _log_warning( f"Worker {worker} was not allocated eval_set data. Therefore evals_result_ and best_score_ data may be unreliable. " "Try rebalancing data across workers." ) # assign general validation set settings to fit kwargs. if eval_names: kwargs["eval_names"] = eval_names if eval_class_weight: kwargs["eval_class_weight"] = eval_class_weight if eval_metric: kwargs["eval_metric"] = eval_metric if eval_at: kwargs["eval_at"] = eval_at master_worker = next(iter(worker_map)) worker_ncores = client.ncores() # resolve aliases for network parameters and pop the result off params. # these values are added back in calls to `_train_part()` params = _choose_param_value( main_param_name="local_listen_port", params=params, default_value=12400, ) local_listen_port = params.pop("local_listen_port") params = _choose_param_value( main_param_name="machines", params=params, default_value=None, ) machines = params.pop("machines") # figure out network params worker_to_socket_future: Dict[str, Future] = {} worker_addresses = worker_map.keys() if machines is not None: _log_info("Using passed-in 'machines' parameter") worker_address_to_port = _machines_to_worker_map( machines=machines, worker_addresses=worker_addresses, ) else: if listen_port_in_params: _log_info("Using passed-in 'local_listen_port' for all workers") unique_hosts = {urlparse(a).hostname for a in worker_addresses} if len(unique_hosts) < len(worker_addresses): msg = ( "'local_listen_port' was provided in Dask training parameters, but at least one " "machine in the cluster has multiple Dask worker processes running on it. Please omit " "'local_listen_port' or pass 'machines'." ) raise LightGBMError(msg) worker_address_to_port = dict.fromkeys(worker_addresses, local_listen_port) else: _log_info("Finding random open ports for workers") worker_to_socket_future, worker_address_to_port = _assign_open_ports_to_workers( client=client, workers=list(worker_map.keys()), ) machines = ",".join( [f"{urlparse(worker_address).hostname}:{port}" for worker_address, port in worker_address_to_port.items()] ) num_machines = len(worker_address_to_port) # Tell each worker to train on the parts that it has locally # # This code treats ``_train_part()`` calls as not "pure" because: # 1. there is randomness in the training process unless parameters ``seed`` # and ``deterministic`` are set # 2. even with those parameters set, the output of one ``_train_part()`` call # relies on global state (it and all the other LightGBM training processes # coordinate with each other) futures_classifiers = [ client.submit( _train_part, model_factory=model_factory, params={**params, "num_threads": worker_ncores[worker]}, list_of_parts=list_of_parts, machines=machines, local_listen_port=worker_address_to_port[worker], num_machines=num_machines, time_out=params.get("time_out", 120), remote_socket=worker_to_socket_future.get(worker, None), return_model=(worker == master_worker), workers=[worker], allow_other_workers=False, pure=False, **kwargs, ) for worker, list_of_parts in worker_map.items() ] results = client.gather(futures_classifiers) results = [v for v in results if v] model = results[0] # if network parameters were changed during training, remove them from the # returned model so that they're generated dynamically on every run based # on the Dask cluster you're connected to and which workers have pieces of # the training data if not listen_port_in_params: for param in _ConfigAliases.get("local_listen_port"): model._other_params.pop(param, None) if not machines_in_params: for param in _ConfigAliases.get("machines"): model._other_params.pop(param, None) for param in _ConfigAliases.get("num_machines", "timeout"): model._other_params.pop(param, None) return model def _predict_part( part: _DaskPart, *, model: LGBMModel, raw_score: bool, pred_proba: bool, pred_leaf: bool, pred_contrib: bool, **kwargs: Any, ) -> _DaskPart: result: _DaskPart if part.shape[0] == 0: result = np.array([]) elif pred_proba: result = model.predict_proba( part, raw_score=raw_score, pred_leaf=pred_leaf, pred_contrib=pred_contrib, **kwargs, ) else: result = model.predict( part, raw_score=raw_score, pred_leaf=pred_leaf, pred_contrib=pred_contrib, **kwargs, ) # dask.DataFrame.map_partitions() expects each call to return a pandas DataFrame or Series if isinstance(part, pd_DataFrame): # assert that 'result' is an array, only necessary because predict(..., pred_contrib=True) on # sparse matrices returns a list. # # This can be removed when https://github.com/lightgbm-org/LightGBM/pull/6348 is resolved. error_msg = ( f"predict(X) for lightgbm.dask estimators should always return an array, not '{type(result)}', when X is a pandas Dataframe. " "If you're seeing this message, it's a bug in lightgbm. Please report it at https://github.com/lightgbm-org/LightGBM/issues." ) assert hasattr(result, "shape"), error_msg if len(result.shape) == 2: result = pd_DataFrame(result, index=part.index) else: result = pd_Series(result, index=part.index, name="predictions") return result def _predict( *, model: LGBMModel, data: _DaskMatrixLike, client: Client, raw_score: bool = False, pred_proba: bool = False, pred_leaf: bool = False, pred_contrib: bool = False, **kwargs: Any, ) -> Union[dask_Array, List[dask_Array]]: """Inner predict routine. Parameters ---------- model : lightgbm.LGBMClassifier, lightgbm.LGBMRegressor, or lightgbm.LGBMRanker class Fitted underlying model. data : Dask Array or Dask DataFrame of shape = [n_samples, n_features] Input feature matrix. raw_score : bool, optional (default=False) Whether to predict raw scores. pred_proba : bool, optional (default=False) Should method return results of ``predict_proba`` (``pred_proba=True``) or ``predict`` (``pred_proba=False``). pred_leaf : bool, optional (default=False) Whether to predict leaf index. pred_contrib : bool, optional (default=False) Whether to predict feature contributions. **kwargs Other parameters passed to ``predict`` or ``predict_proba`` method. Returns ------- predicted_result : Dask Array of shape = [n_samples] or shape = [n_samples, n_classes] The predicted values. X_leaves : Dask Array of shape = [n_samples, n_trees] or shape = [n_samples, n_trees * n_classes] If ``pred_leaf=True``, the predicted leaf of every tree for each sample. 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] If ``pred_contrib=True``, the feature contributions for each sample. """ if not all((DASK_INSTALLED, PANDAS_INSTALLED, SKLEARN_INSTALLED)): raise LightGBMError("dask, pandas and scikit-learn are required for lightgbm.dask") if isinstance(data, dask_DataFrame): return data.map_partitions( _predict_part, model=model, raw_score=raw_score, pred_proba=pred_proba, pred_leaf=pred_leaf, pred_contrib=pred_contrib, **kwargs, ).values elif isinstance(data, dask_Array): # for multi-class classification with sparse matrices, pred_contrib predictions # are returned as a list of sparse matrices (one per class) num_classes = model._n_classes if num_classes > 2 and pred_contrib and isinstance(data._meta, ss.spmatrix): predict_function = partial( _predict_part, model=model, raw_score=False, pred_proba=pred_proba, pred_leaf=False, pred_contrib=True, **kwargs, ) delayed_chunks = data.to_delayed() bag = dask_bag_from_delayed(delayed_chunks[:, 0]) @delayed def _extract(items: List[Any], i: int) -> Any: return items[i] preds = bag.map_partitions(predict_function) # pred_contrib output will have one column per feature, # plus one more for the base value num_cols = model.n_features_ + 1 nrows_per_chunk = data.chunks[0] out: List[List[dask_Array]] = [[] for _ in range(num_classes)] # need to tell Dask the expected type and shape of individual preds pred_meta = data._meta for j, partition in enumerate(preds.to_delayed()): for i in range(num_classes): part = dask_array_from_delayed( value=_extract(partition, i), shape=(nrows_per_chunk[j], num_cols), meta=pred_meta, ) out[i].append(part) # by default, dask.array.concatenate() concatenates sparse arrays into a COO matrix # the code below is used instead to ensure that the sparse type is preserved during concatenation if isinstance(pred_meta, ss.csr_matrix): concat_fn = partial(ss.vstack, format="csr") elif isinstance(pred_meta, ss.csc_matrix): concat_fn = partial(ss.vstack, format="csc") else: concat_fn = ss.vstack # At this point, `out` is a list of lists of delayeds (each of which points to a matrix). # Concatenate them to return a list of Dask Arrays. out_arrays: List[dask_Array] = [] for i in range(num_classes): out_arrays.append( dask_array_from_delayed( value=delayed(concat_fn)(out[i]), shape=(data.shape[0], num_cols), meta=pred_meta, ) ) return out_arrays data_row = client.compute(data[[0]]).result() predict_fn = partial( _predict_part, model=model, raw_score=raw_score, pred_proba=pred_proba, pred_leaf=pred_leaf, pred_contrib=pred_contrib, **kwargs, ) pred_row = predict_fn(data_row) # type: ignore[misc] chunks: Tuple[int, ...] = (data.chunks[0],) map_blocks_kwargs = {} if len(pred_row.shape) > 1: chunks += (pred_row.shape[1],) else: map_blocks_kwargs["drop_axis"] = 1 return data.map_blocks( predict_fn, chunks=chunks, meta=pred_row, **map_blocks_kwargs, ) else: raise TypeError(f"Data must be either Dask Array or Dask DataFrame. Got {type(data).__name__}.") class _DaskLGBMModel: @property def client_(self) -> Client: """:obj:`dask.distributed.Client`: Dask client. This property can be passed in the constructor or updated with ``model.set_params(client=client)``. """ if not getattr(self, "fitted_", False): raise LGBMNotFittedError("Cannot access property client_ before calling fit().") return _get_dask_client(client=self.client) def _lgb_dask_getstate(self) -> Dict[Any, Any]: """Remove un-picklable attributes before serialization.""" client = self.__dict__.pop("client", None) self._other_params.pop("client", None) # type: ignore[attr-defined] out = deepcopy(self.__dict__) out.update({"client": None}) self.client = client return out def _lgb_dask_fit( self, *, model_factory: Type[LGBMModel], X: _DaskMatrixLike, y: _DaskCollection, sample_weight: Optional[_DaskVectorLike] = None, init_score: Optional[_DaskCollection] = None, group: Optional[_DaskVectorLike] = None, eval_set: Optional[List[Tuple[_DaskMatrixLike, _DaskCollection]]] = None, eval_names: Optional[List[str]] = None, eval_X: Optional[Union[_DaskMatrixLike, Tuple[_DaskMatrixLike]]] = None, eval_y: Optional[Union[_DaskCollection, Tuple[_DaskCollection]]] = None, eval_sample_weight: Optional[List[_DaskVectorLike]] = None, eval_class_weight: Optional[List[Union[dict, str]]] = None, eval_init_score: Optional[List[_DaskCollection]] = None, eval_group: Optional[List[_DaskVectorLike]] = None, eval_metric: Optional[_LGBM_ScikitEvalMetricType] = None, eval_at: Optional[Union[List[int], Tuple[int, ...]]] = None, **kwargs: Any, ) -> "_DaskLGBMModel": if not DASK_INSTALLED: raise LightGBMError("dask is required for lightgbm.dask") if not all((DASK_INSTALLED, PANDAS_INSTALLED, SKLEARN_INSTALLED)): raise LightGBMError("dask, pandas and scikit-learn are required for lightgbm.dask") params = self.get_params(True) # type: ignore[attr-defined] params.pop("client", None) model = _train( client=_get_dask_client(self.client), data=X, label=y, params=params, model_factory=model_factory, sample_weight=sample_weight, init_score=init_score, group=group, eval_set=eval_set, eval_names=eval_names, eval_X=eval_X, eval_y=eval_y, eval_sample_weight=eval_sample_weight, eval_class_weight=eval_class_weight, eval_init_score=eval_init_score, eval_group=eval_group, eval_metric=eval_metric, eval_at=eval_at, **kwargs, ) self.set_params(**model.get_params()) # type: ignore[attr-defined] self._lgb_dask_copy_extra_params(source=model, dest=self) # type: ignore[attr-defined] return self def _lgb_dask_to_local(self, model_factory: Type[LGBMModel]) -> LGBMModel: params = self.get_params() # type: ignore[attr-defined] params.pop("client", None) model = model_factory(**params) self._lgb_dask_copy_extra_params(source=self, dest=model) model._other_params.pop("client", None) return model @staticmethod def _lgb_dask_copy_extra_params( *, source: Union["_DaskLGBMModel", LGBMModel], dest: Union["_DaskLGBMModel", LGBMModel], ) -> None: params = source.get_params() # type: ignore[union-attr] attributes = source.__dict__ extra_param_names = set(attributes.keys()).difference(params.keys()) for name in extra_param_names: setattr(dest, name, attributes[name]) class DaskLGBMClassifier(LGBMClassifier, _DaskLGBMModel): """Distributed version of lightgbm.LGBMClassifier.""" def __init__( self, *, boosting_type: str = "gbdt", num_leaves: int = 31, max_depth: int = -1, learning_rate: float = 0.1, n_estimators: int = 100, subsample_for_bin: int = 200000, objective: Optional[Union[str, _LGBM_ScikitCustomObjectiveFunction]] = None, class_weight: Optional[Union[dict, str]] = None, min_split_gain: float = 0.0, min_child_weight: float = 1e-3, min_child_samples: int = 20, subsample: float = 1.0, subsample_freq: int = 0, colsample_bytree: float = 1.0, reg_alpha: float = 0.0, reg_lambda: float = 0.0, random_state: Optional[Union[int, np.random.RandomState, "np.random.Generator"]] = None, n_jobs: Optional[int] = None, importance_type: str = "split", client: Optional[Client] = None, **kwargs: Any, ): """Docstring is inherited from the lightgbm.LGBMClassifier.__init__.""" self.client = client super().__init__( boosting_type=boosting_type, num_leaves=num_leaves, max_depth=max_depth, learning_rate=learning_rate, n_estimators=n_estimators, subsample_for_bin=subsample_for_bin, objective=objective, class_weight=class_weight, min_split_gain=min_split_gain, min_child_weight=min_child_weight, min_child_samples=min_child_samples, subsample=subsample, subsample_freq=subsample_freq, colsample_bytree=colsample_bytree, reg_alpha=reg_alpha, reg_lambda=reg_lambda, random_state=random_state, n_jobs=n_jobs, importance_type=importance_type, **kwargs, ) _base_doc = LGBMClassifier.__init__.__doc__ _before_kwargs, _kwargs, _after_kwargs = _base_doc.partition("**kwargs") # type: ignore __init__.__doc__ = f""" {_before_kwargs}client : dask.distributed.Client or None, optional (default=None) {" ":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. {_kwargs}{_after_kwargs} """ def __getstate__(self) -> Dict[Any, Any]: return self._lgb_dask_getstate() def fit( # type: ignore[override] self, X: _DaskMatrixLike, y: _DaskCollection, sample_weight: Optional[_DaskVectorLike] = None, init_score: Optional[_DaskCollection] = None, eval_set: Optional[List[Tuple[_DaskMatrixLike, _DaskCollection]]] = None, eval_names: Optional[List[str]] = None, eval_sample_weight: Optional[List[_DaskVectorLike]] = None, eval_class_weight: Optional[List[Union[dict, str]]] = None, eval_init_score: Optional[List[_DaskCollection]] = None, eval_metric: Optional[_LGBM_ScikitEvalMetricType] = None, *, eval_X: Optional[Union[_DaskMatrixLike, Tuple[_DaskMatrixLike]]] = None, eval_y: Optional[Union[_DaskCollection, Tuple[_DaskCollection]]] = None, **kwargs: Any, ) -> "DaskLGBMClassifier": """Docstring is inherited from the lightgbm.LGBMClassifier.fit.""" self._lgb_dask_fit( model_factory=LGBMClassifier, X=X, y=y, sample_weight=sample_weight, init_score=init_score, eval_set=eval_set, eval_names=eval_names, eval_X=eval_X, eval_y=eval_y, eval_sample_weight=eval_sample_weight, eval_class_weight=eval_class_weight, eval_init_score=eval_init_score, eval_metric=eval_metric, **kwargs, ) return self _base_doc = _lgbmmodel_doc_fit.format( X_shape="Dask Array or Dask DataFrame of shape = [n_samples, n_features]", y_shape="Dask Array, Dask DataFrame or Dask Series of shape = [n_samples]", sample_weight_shape="Dask Array or Dask Series of shape = [n_samples] or None, optional (default=None)", 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)", group_shape="Dask Array or Dask Series or None, optional (default=None)", eval_sample_weight_shape="list of Dask Array or Dask Series, or None, optional (default=None)", eval_init_score_shape="list of Dask Array, Dask Series or Dask DataFrame (for multi-class task), or None, optional (default=None)", eval_group_shape="list of Dask Array or Dask Series, or None, optional (default=None)", ) # DaskLGBMClassifier does not support group, eval_group. _base_doc = _base_doc[: _base_doc.find("group :")] + _base_doc[_base_doc.find("eval_set :") :] _base_doc = _base_doc[: _base_doc.find("eval_group :")] + _base_doc[_base_doc.find("eval_metric :") :] # DaskLGBMClassifier support for callbacks and init_model is not tested fit.__doc__ = f"""{_base_doc[: _base_doc.find("callbacks :")]}**kwargs Other parameters passed through to ``LGBMClassifier.fit()``. Returns ------- self : lightgbm.DaskLGBMClassifier Returns self. {_lgbmmodel_doc_custom_eval_note} """ def predict( self, X: _DaskMatrixLike, # type: ignore[override] raw_score: bool = False, start_iteration: int = 0, num_iteration: Optional[int] = None, pred_leaf: bool = False, pred_contrib: bool = False, validate_features: bool = False, **kwargs: Any, ) -> dask_Array: """Docstring is inherited from the lightgbm.LGBMClassifier.predict.""" return _predict( model=self.to_local(), data=X, client=_get_dask_client(self.client), raw_score=raw_score, start_iteration=start_iteration, num_iteration=num_iteration, pred_leaf=pred_leaf, pred_contrib=pred_contrib, validate_features=validate_features, **kwargs, ) predict.__doc__ = _lgbmmodel_doc_predict.format( description="Return the predicted value for each sample.", X_shape="Dask Array or Dask DataFrame of shape = [n_samples, n_features]", output_name="predicted_result", predicted_result_shape="Dask Array of shape = [n_samples] or shape = [n_samples, n_classes]", X_leaves_shape="Dask Array of shape = [n_samples, n_trees] or shape = [n_samples, n_trees * n_classes]", 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]", ) def predict_proba( self, X: _DaskMatrixLike, # type: ignore[override] raw_score: bool = False, start_iteration: int = 0, num_iteration: Optional[int] = None, pred_leaf: bool = False, pred_contrib: bool = False, validate_features: bool = False, **kwargs: Any, ) -> dask_Array: """Docstring is inherited from the lightgbm.LGBMClassifier.predict_proba.""" return _predict( model=self.to_local(), data=X, pred_proba=True, client=_get_dask_client(self.client), raw_score=raw_score, start_iteration=start_iteration, num_iteration=num_iteration, pred_leaf=pred_leaf, pred_contrib=pred_contrib, validate_features=validate_features, **kwargs, ) predict_proba.__doc__ = _lgbmmodel_doc_predict.format( description="Return the predicted probability for each class for each sample.", X_shape="Dask Array or Dask DataFrame of shape = [n_samples, n_features]", output_name="predicted_probability", predicted_result_shape="Dask Array of shape = [n_samples] or shape = [n_samples, n_classes]", X_leaves_shape="Dask Array of shape = [n_samples, n_trees] or shape = [n_samples, n_trees * n_classes]", 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]", ) def to_local(self) -> LGBMClassifier: """Create regular version of lightgbm.LGBMClassifier from the distributed version. Returns ------- model : lightgbm.LGBMClassifier Local underlying model. """ return self._lgb_dask_to_local(LGBMClassifier) class DaskLGBMRegressor(LGBMRegressor, _DaskLGBMModel): """Distributed version of lightgbm.LGBMRegressor.""" def __init__( self, *, boosting_type: str = "gbdt", num_leaves: int = 31, max_depth: int = -1, learning_rate: float = 0.1, n_estimators: int = 100, subsample_for_bin: int = 200000, objective: Optional[Union[str, _LGBM_ScikitCustomObjectiveFunction]] = None, class_weight: Optional[Union[dict, str]] = None, min_split_gain: float = 0.0, min_child_weight: float = 1e-3, min_child_samples: int = 20, subsample: float = 1.0, subsample_freq: int = 0, colsample_bytree: float = 1.0, reg_alpha: float = 0.0, reg_lambda: float = 0.0, random_state: Optional[Union[int, np.random.RandomState, "np.random.Generator"]] = None, n_jobs: Optional[int] = None, importance_type: str = "split", client: Optional[Client] = None, **kwargs: Any, ): """Docstring is inherited from the lightgbm.LGBMRegressor.__init__.""" self.client = client super().__init__( boosting_type=boosting_type, num_leaves=num_leaves, max_depth=max_depth, learning_rate=learning_rate, n_estimators=n_estimators, subsample_for_bin=subsample_for_bin, objective=objective, class_weight=class_weight, min_split_gain=min_split_gain, min_child_weight=min_child_weight, min_child_samples=min_child_samples, subsample=subsample, subsample_freq=subsample_freq, colsample_bytree=colsample_bytree, reg_alpha=reg_alpha, reg_lambda=reg_lambda, random_state=random_state, n_jobs=n_jobs, importance_type=importance_type, **kwargs, ) _base_doc = LGBMRegressor.__init__.__doc__ _before_kwargs, _kwargs, _after_kwargs = _base_doc.partition("**kwargs") # type: ignore __init__.__doc__ = f""" {_before_kwargs}client : dask.distributed.Client or None, optional (default=None) {" ":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. {_kwargs}{_after_kwargs} """ def __getstate__(self) -> Dict[Any, Any]: return self._lgb_dask_getstate() def fit( # type: ignore[override] self, X: _DaskMatrixLike, y: _DaskCollection, sample_weight: Optional[_DaskVectorLike] = None, init_score: Optional[_DaskVectorLike] = None, eval_set: Optional[List[Tuple[_DaskMatrixLike, _DaskCollection]]] = None, eval_names: Optional[List[str]] = None, eval_sample_weight: Optional[List[_DaskVectorLike]] = None, eval_init_score: Optional[List[_DaskVectorLike]] = None, eval_metric: Optional[_LGBM_ScikitEvalMetricType] = None, *, eval_X: Optional[Union[_DaskMatrixLike, Tuple[_DaskMatrixLike]]] = None, eval_y: Optional[Union[_DaskCollection, Tuple[_DaskCollection]]] = None, **kwargs: Any, ) -> "DaskLGBMRegressor": """Docstring is inherited from the lightgbm.LGBMRegressor.fit.""" self._lgb_dask_fit( model_factory=LGBMRegressor, X=X, y=y, sample_weight=sample_weight, init_score=init_score, eval_set=eval_set, eval_names=eval_names, eval_X=eval_X, eval_y=eval_y, eval_sample_weight=eval_sample_weight, eval_init_score=eval_init_score, eval_metric=eval_metric, **kwargs, ) return self _base_doc = _lgbmmodel_doc_fit.format( X_shape="Dask Array or Dask DataFrame of shape = [n_samples, n_features]", y_shape="Dask Array, Dask DataFrame or Dask Series of shape = [n_samples]", sample_weight_shape="Dask Array or Dask Series of shape = [n_samples] or None, optional (default=None)", init_score_shape="Dask Array or Dask Series of shape = [n_samples] or None, optional (default=None)", group_shape="Dask Array or Dask Series or None, optional (default=None)", eval_sample_weight_shape="list of Dask Array or Dask Series, or None, optional (default=None)", eval_init_score_shape="list of Dask Array or Dask Series, or None, optional (default=None)", eval_group_shape="list of Dask Array or Dask Series, or None, optional (default=None)", ) # DaskLGBMRegressor does not support group, eval_class_weight, eval_group. _base_doc = _base_doc[: _base_doc.find("group :")] + _base_doc[_base_doc.find("eval_set :") :] _base_doc = _base_doc[: _base_doc.find("eval_class_weight :")] + _base_doc[_base_doc.find("eval_init_score :") :] _base_doc = _base_doc[: _base_doc.find("eval_group :")] + _base_doc[_base_doc.find("eval_metric :") :] # DaskLGBMRegressor support for callbacks and init_model is not tested fit.__doc__ = f"""{_base_doc[: _base_doc.find("callbacks :")]}**kwargs Other parameters passed through to ``LGBMRegressor.fit()``. Returns ------- self : lightgbm.DaskLGBMRegressor Returns self. {_lgbmmodel_doc_custom_eval_note} """ def predict( self, X: _DaskMatrixLike, # type: ignore[override] raw_score: bool = False, start_iteration: int = 0, num_iteration: Optional[int] = None, pred_leaf: bool = False, pred_contrib: bool = False, validate_features: bool = False, **kwargs: Any, ) -> dask_Array: """Docstring is inherited from the lightgbm.LGBMRegressor.predict.""" return _predict( model=self.to_local(), data=X, client=_get_dask_client(self.client), raw_score=raw_score, start_iteration=start_iteration, num_iteration=num_iteration, pred_leaf=pred_leaf, pred_contrib=pred_contrib, validate_features=validate_features, **kwargs, ) predict.__doc__ = _lgbmmodel_doc_predict.format( description="Return the predicted value for each sample.", X_shape="Dask Array or Dask DataFrame of shape = [n_samples, n_features]", output_name="predicted_result", predicted_result_shape="Dask Array of shape = [n_samples]", X_leaves_shape="Dask Array of shape = [n_samples, n_trees]", X_SHAP_values_shape="Dask Array of shape = [n_samples, n_features + 1]", ) def to_local(self) -> LGBMRegressor: """Create regular version of lightgbm.LGBMRegressor from the distributed version. Returns ------- model : lightgbm.LGBMRegressor Local underlying model. """ return self._lgb_dask_to_local(LGBMRegressor) class DaskLGBMRanker(LGBMRanker, _DaskLGBMModel): """Distributed version of lightgbm.LGBMRanker.""" def __init__( self, *, boosting_type: str = "gbdt", num_leaves: int = 31, max_depth: int = -1, learning_rate: float = 0.1, n_estimators: int = 100, subsample_for_bin: int = 200000, objective: Optional[Union[str, _LGBM_ScikitCustomObjectiveFunction]] = None, class_weight: Optional[Union[dict, str]] = None, min_split_gain: float = 0.0, min_child_weight: float = 1e-3, min_child_samples: int = 20, subsample: float = 1.0, subsample_freq: int = 0, colsample_bytree: float = 1.0, reg_alpha: float = 0.0, reg_lambda: float = 0.0, random_state: Optional[Union[int, np.random.RandomState, "np.random.Generator"]] = None, n_jobs: Optional[int] = None, importance_type: str = "split", client: Optional[Client] = None, **kwargs: Any, ): """Docstring is inherited from the lightgbm.LGBMRanker.__init__.""" self.client = client super().__init__( boosting_type=boosting_type, num_leaves=num_leaves, max_depth=max_depth, learning_rate=learning_rate, n_estimators=n_estimators, subsample_for_bin=subsample_for_bin, objective=objective, class_weight=class_weight, min_split_gain=min_split_gain, min_child_weight=min_child_weight, min_child_samples=min_child_samples, subsample=subsample, subsample_freq=subsample_freq, colsample_bytree=colsample_bytree, reg_alpha=reg_alpha, reg_lambda=reg_lambda, random_state=random_state, n_jobs=n_jobs, importance_type=importance_type, **kwargs, ) _base_doc = LGBMRanker.__init__.__doc__ _before_kwargs, _kwargs, _after_kwargs = _base_doc.partition("**kwargs") # type: ignore __init__.__doc__ = f""" {_before_kwargs}client : dask.distributed.Client or None, optional (default=None) {" ":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. {_kwargs}{_after_kwargs} """ def __getstate__(self) -> Dict[Any, Any]: return self._lgb_dask_getstate() def fit( # type: ignore[override] self, X: _DaskMatrixLike, y: _DaskCollection, sample_weight: Optional[_DaskVectorLike] = None, init_score: Optional[_DaskVectorLike] = None, group: Optional[_DaskVectorLike] = None, eval_set: Optional[List[Tuple[_DaskMatrixLike, _DaskCollection]]] = None, eval_names: Optional[List[str]] = None, eval_sample_weight: Optional[List[_DaskVectorLike]] = None, eval_init_score: Optional[List[_DaskVectorLike]] = None, eval_group: Optional[List[_DaskVectorLike]] = None, eval_metric: Optional[_LGBM_ScikitEvalMetricType] = None, eval_at: Union[List[int], Tuple[int, ...]] = (1, 2, 3, 4, 5), *, eval_X: Optional[Union[_DaskMatrixLike, Tuple[_DaskMatrixLike]]] = None, eval_y: Optional[Union[_DaskCollection, Tuple[_DaskCollection]]] = None, **kwargs: Any, ) -> "DaskLGBMRanker": """Docstring is inherited from the lightgbm.LGBMRanker.fit.""" self._lgb_dask_fit( model_factory=LGBMRanker, X=X, y=y, sample_weight=sample_weight, init_score=init_score, group=group, eval_set=eval_set, eval_names=eval_names, eval_X=eval_X, eval_y=eval_y, eval_sample_weight=eval_sample_weight, eval_init_score=eval_init_score, eval_group=eval_group, eval_metric=eval_metric, eval_at=eval_at, **kwargs, ) return self _base_doc = _lgbmmodel_doc_fit.format( X_shape="Dask Array or Dask DataFrame of shape = [n_samples, n_features]", y_shape="Dask Array, Dask DataFrame or Dask Series of shape = [n_samples]", sample_weight_shape="Dask Array or Dask Series of shape = [n_samples] or None, optional (default=None)", init_score_shape="Dask Array or Dask Series of shape = [n_samples] or None, optional (default=None)", group_shape="Dask Array or Dask Series or None, optional (default=None)", eval_sample_weight_shape="list of Dask Array or Dask Series, or None, optional (default=None)", eval_init_score_shape="list of Dask Array or Dask Series, or None, optional (default=None)", eval_group_shape="list of Dask Array or Dask Series, or None, optional (default=None)", ) # DaskLGBMRanker does not support eval_class_weight or early stopping _base_doc = _base_doc[: _base_doc.find("eval_class_weight :")] + _base_doc[_base_doc.find("eval_init_score :") :] _base_doc = ( _base_doc[: _base_doc.find("feature_name :")] + "eval_at : list or tuple of int, optional (default=(1, 2, 3, 4, 5))\n" + f"{' ':8}The evaluation positions of the specified metric.\n" + f"{' ':4}{_base_doc[_base_doc.find('feature_name :') :]}" ) # DaskLGBMRanker support for callbacks and init_model is not tested fit.__doc__ = f"""{_base_doc[: _base_doc.find("callbacks :")]}**kwargs Other parameters passed through to ``LGBMRanker.fit()``. Returns ------- self : lightgbm.DaskLGBMRanker Returns self. {_lgbmmodel_doc_custom_eval_note} """ def predict( self, X: _DaskMatrixLike, # type: ignore[override] raw_score: bool = False, start_iteration: int = 0, num_iteration: Optional[int] = None, pred_leaf: bool = False, pred_contrib: bool = False, validate_features: bool = False, **kwargs: Any, ) -> dask_Array: """Docstring is inherited from the lightgbm.LGBMRanker.predict.""" return _predict( model=self.to_local(), data=X, client=_get_dask_client(self.client), raw_score=raw_score, start_iteration=start_iteration, num_iteration=num_iteration, pred_leaf=pred_leaf, pred_contrib=pred_contrib, validate_features=validate_features, **kwargs, ) predict.__doc__ = _lgbmmodel_doc_predict.format( description="Return the predicted value for each sample.", X_shape="Dask Array or Dask DataFrame of shape = [n_samples, n_features]", output_name="predicted_result", predicted_result_shape="Dask Array of shape = [n_samples]", X_leaves_shape="Dask Array of shape = [n_samples, n_trees]", X_SHAP_values_shape="Dask Array of shape = [n_samples, n_features + 1]", ) def to_local(self) -> LGBMRanker: """Create regular version of lightgbm.LGBMRanker from the distributed version. Returns ------- model : lightgbm.LGBMRanker Local underlying model. """ return self._lgb_dask_to_local(LGBMRanker) ================================================ FILE: python-package/lightgbm/engine.py ================================================ # coding: utf-8 """Library with training routines of LightGBM.""" import copy import json from collections import OrderedDict, defaultdict from operator import attrgetter from pathlib import Path from typing import Any, Callable, Dict, Iterable, List, Optional, Tuple, Union import numpy as np from . import callback from .basic import ( Booster, Dataset, LightGBMError, _choose_param_value, _ConfigAliases, _InnerPredictor, _LGBM_BoosterEvalMethodResultType, _LGBM_BoosterEvalMethodResultWithStandardDeviationType, _LGBM_CustomObjectiveFunction, _LGBM_EvalFunctionResultType, _log_warning, ) from .compat import SKLEARN_INSTALLED, _LGBMBaseCrossValidator, _LGBMGroupKFold, _LGBMStratifiedKFold __all__ = [ "cv", "CVBooster", "train", ] _LGBM_CustomMetricFunction = Union[ Callable[ [np.ndarray, Dataset], _LGBM_EvalFunctionResultType, ], Callable[ [np.ndarray, Dataset], List[_LGBM_EvalFunctionResultType], ], ] _LGBM_PreprocFunction = Callable[ [Dataset, Dataset, Dict[str, Any]], Tuple[Dataset, Dataset, Dict[str, Any]], ] def _choose_num_iterations(*, num_boost_round_kwarg: int, params: Dict[str, Any]) -> Dict[str, Any]: """Choose number of boosting rounds. In ``train()`` and ``cv()``, there are multiple ways to provide configuration for the number of boosting rounds to perform: * the ``num_boost_round`` keyword argument * any of the ``num_iterations`` or its aliases via the ``params`` dictionary These should be preferred in the following order (first one found wins): 1. ``num_iterations`` provided via ``params`` (because it's the main parameter name) 2. any other aliases of ``num_iterations`` provided via ``params`` 3. the ``num_boost_round`` keyword argument This function handles that choice, and issuing helpful warnings in the cases where the result might be surprising. Returns ------- params : dict Parameters, with ``"num_iterations"`` set to the preferred value and all other aliases of ``num_iterations`` removed. """ num_iteration_configs_provided = { alias: params[alias] for alias in _ConfigAliases.get("num_iterations") if alias in params } # now that the relevant information has been pulled out of params, it's safe to overwrite it # with the content that should be used for training (i.e. with aliases resolved) params = _choose_param_value( main_param_name="num_iterations", params=params, default_value=num_boost_round_kwarg, ) # if there were not multiple boosting rounds configurations provided in params, # then by definition they cannot have conflicting values... no need to warn if len(num_iteration_configs_provided) <= 1: return params # if all the aliases have the same value, no need to warn if len(set(num_iteration_configs_provided.values())) <= 1: return params # if this line is reached, lightgbm should warn value_string = ", ".join(f"{alias}={val}" for alias, val in num_iteration_configs_provided.items()) _log_warning( f"Found conflicting values for num_iterations provided via 'params': {value_string}. " f"LightGBM will perform up to {params['num_iterations']} boosting rounds. " "To be confident in the maximum number of boosting rounds LightGBM will perform and to " "suppress this warning, modify 'params' so that only one of those is present." ) return params def train( params: Dict[str, Any], train_set: Dataset, num_boost_round: int = 100, valid_sets: Optional[List[Dataset]] = None, valid_names: Optional[List[str]] = None, feval: Optional[Union[_LGBM_CustomMetricFunction, List[_LGBM_CustomMetricFunction]]] = None, init_model: Optional[Union[str, Path, Booster]] = None, keep_training_booster: bool = False, callbacks: Optional[List[Callable]] = None, ) -> Booster: """Perform the training with given parameters. Parameters ---------- params : dict Parameters for training. Values passed through ``params`` take precedence over those supplied via arguments. train_set : Dataset Data to be trained on. num_boost_round : int, optional (default=100) Number of boosting iterations. valid_sets : list of Dataset, or None, optional (default=None) List of data to be evaluated on during training. valid_names : list of str, or None, optional (default=None) Names of ``valid_sets``. feval : callable, list of callable, or None, optional (default=None) Customized evaluation function. Each evaluation function should accept two parameters: preds, eval_data, and return (eval_name, eval_result, is_higher_better) or list of such tuples. preds : numpy 1-D array or numpy 2-D array (for multi-class task) The predicted values. For multi-class task, preds are numpy 2-D array of shape = [n_samples, n_classes]. If custom objective function is used, predicted values are returned before any transformation, e.g. they are raw margin instead of probability of positive class for binary task in this case. eval_data : Dataset A ``Dataset`` to evaluate. eval_name : str The name of evaluation function (without whitespaces). eval_result : float The eval result. is_higher_better : bool Is eval result higher better, e.g. AUC is ``is_higher_better``. To ignore the default metric corresponding to the used objective, set the ``metric`` parameter to the string ``"None"`` in ``params``. init_model : str, pathlib.Path, Booster or None, optional (default=None) Filename of LightGBM model or Booster instance used for continue training. keep_training_booster : bool, optional (default=False) Whether the returned Booster will be used to keep training. If False, the returned value will be converted into _InnerPredictor before returning. This means you won't be able to use ``eval``, ``eval_train`` or ``eval_valid`` methods of the returned Booster. When your model is very large and cause the memory error, you can try to set this param to ``True`` to avoid the model conversion performed during the internal call of ``model_to_string``. You can still use _InnerPredictor as ``init_model`` for future continue training. callbacks : list of callable, or None, optional (default=None) List of callback functions that are applied at each iteration. See Callbacks in Python API for more information. Note ---- A custom objective function can be provided for the ``objective`` parameter. It should accept two parameters: preds, train_data and return (grad, hess). preds : numpy 1-D array or numpy 2-D array (for multi-class task) The predicted values. Predicted values are returned before any transformation, e.g. they are raw margin instead of probability of positive class for binary task. train_data : Dataset The training dataset. grad : numpy 1-D array or numpy 2-D array (for multi-class task) The value of the first order derivative (gradient) of the loss with respect to the elements of preds for each sample point. hess : numpy 1-D array or numpy 2-D array (for multi-class task) The value of the second order derivative (Hessian) of the loss with respect to the elements of preds for each sample point. For multi-class task, preds are numpy 2-D array of shape = [n_samples, n_classes], and grad and hess should be returned in the same format. Returns ------- booster : Booster The trained Booster model. """ if not isinstance(train_set, Dataset): raise TypeError(f"train() only accepts Dataset object, train_set has type '{type(train_set).__name__}'.") if isinstance(valid_sets, list): for i, valid_item in enumerate(valid_sets): if not isinstance(valid_item, Dataset): raise TypeError( "Every item in valid_sets must be a Dataset object. " f"Item {i} has type '{type(valid_item).__name__}'." ) # create predictor first params = copy.deepcopy(params) params = _choose_param_value( main_param_name="objective", params=params, default_value=None, ) fobj: Optional[_LGBM_CustomObjectiveFunction] = None if callable(params["objective"]): fobj = params["objective"] params["objective"] = "none" params = _choose_num_iterations(num_boost_round_kwarg=num_boost_round, params=params) num_boost_round = params["num_iterations"] if num_boost_round <= 0: raise ValueError(f"Number of boosting rounds must be greater than 0. Got {num_boost_round}.") # setting early stopping via global params should be possible params = _choose_param_value( main_param_name="early_stopping_round", params=params, default_value=None, ) if params["early_stopping_round"] is None: params.pop("early_stopping_round") first_metric_only = params.get("first_metric_only", False) predictor: Optional[_InnerPredictor] = None if isinstance(init_model, (str, Path)): predictor = _InnerPredictor.from_model_file(model_file=init_model, pred_parameter=params) elif isinstance(init_model, Booster): predictor = _InnerPredictor.from_booster(booster=init_model, pred_parameter=dict(init_model.params, **params)) if predictor is not None: init_iteration = predictor.current_iteration() else: init_iteration = 0 train_set._update_params(params)._set_predictor(predictor) is_valid_contain_train = False train_data_name = "training" reduced_valid_sets = [] name_valid_sets = [] if valid_sets is not None: if isinstance(valid_sets, Dataset): valid_sets = [valid_sets] if isinstance(valid_names, str): valid_names = [valid_names] for i, valid_data in enumerate(valid_sets): # reduce cost for prediction training data if valid_data is train_set: is_valid_contain_train = True if valid_names is not None: train_data_name = valid_names[i] continue reduced_valid_sets.append(valid_data._update_params(params).set_reference(train_set)) if valid_names is not None and len(valid_names) > i: name_valid_sets.append(valid_names[i]) else: name_valid_sets.append(f"valid_{i}") # process callbacks if callbacks is None: callbacks_set = set() else: for i, cb in enumerate(callbacks): cb.__dict__.setdefault("order", i - len(callbacks)) callbacks_set = set(callbacks) if callback._should_enable_early_stopping(params.get("early_stopping_round", 0)): callbacks_set.add( callback.early_stopping( stopping_rounds=params["early_stopping_round"], # type: ignore[arg-type] first_metric_only=first_metric_only, min_delta=params.get("early_stopping_min_delta", 0.0), verbose=_choose_param_value( main_param_name="verbosity", params=params, default_value=1, ).pop("verbosity") > 0, ) ) callbacks_before_iter_set = {cb for cb in callbacks_set if getattr(cb, "before_iteration", False)} callbacks_after_iter_set = callbacks_set - callbacks_before_iter_set callbacks_before_iter = sorted(callbacks_before_iter_set, key=attrgetter("order")) callbacks_after_iter = sorted(callbacks_after_iter_set, key=attrgetter("order")) # construct booster try: booster = Booster(params=params, train_set=train_set) if is_valid_contain_train: booster.set_train_data_name(train_data_name) for valid_set, name_valid_set in zip(reduced_valid_sets, name_valid_sets): booster.add_valid(valid_set, name_valid_set) finally: train_set._reverse_update_params() for valid_set in reduced_valid_sets: valid_set._reverse_update_params() booster.best_iteration = 0 # start training for i in range(init_iteration, init_iteration + num_boost_round): for cb in callbacks_before_iter: cb( callback.CallbackEnv( model=booster, params=params, iteration=i, begin_iteration=init_iteration, end_iteration=init_iteration + num_boost_round, evaluation_result_list=None, ) ) booster.update(fobj=fobj) evaluation_result_list: List[_LGBM_BoosterEvalMethodResultType] = [] # check evaluation result. if valid_sets is not None: if is_valid_contain_train: evaluation_result_list.extend(booster.eval_train(feval)) evaluation_result_list.extend(booster.eval_valid(feval)) try: for cb in callbacks_after_iter: cb( callback.CallbackEnv( model=booster, params=params, iteration=i, begin_iteration=init_iteration, end_iteration=init_iteration + num_boost_round, evaluation_result_list=evaluation_result_list, ) ) except callback.EarlyStopException as earlyStopException: booster.best_iteration = earlyStopException.best_iteration + 1 # eval results from cv() have a 5th element with the standard deviation of metrics, # which is not needed for early stopping evaluation_result_list = [item[:4] for item in earlyStopException.best_score] break booster.best_score = defaultdict(OrderedDict) for dataset_name, eval_name, score, _ in evaluation_result_list: booster.best_score[dataset_name][eval_name] = score if not keep_training_booster: booster.model_from_string(booster.model_to_string()).free_dataset() return booster class CVBooster: """CVBooster in LightGBM. Auxiliary data structure to hold and redirect all boosters of ``cv()`` function. This class has the same methods as Booster class. All method calls, except for the following methods, are actually performed for underlying Boosters and then all returned results are returned in a list. - ``model_from_string()`` - ``model_to_string()`` - ``save_model()`` Attributes ---------- boosters : list of Booster The list of underlying fitted models. best_iteration : int The best iteration of fitted model. """ def __init__( self, model_file: Optional[Union[str, Path]] = None, ): """Initialize the CVBooster. Parameters ---------- model_file : str, pathlib.Path or None, optional (default=None) Path to the CVBooster model file. """ self.boosters: List[Booster] = [] self.best_iteration = -1 if model_file is not None: with open(model_file, "r") as file: self._from_dict(json.load(file)) def _from_dict(self, models: Dict[str, Any]) -> None: """Load CVBooster from dict.""" self.best_iteration = models["best_iteration"] self.boosters = [] for model_str in models["boosters"]: self.boosters.append(Booster(model_str=model_str)) def _to_dict( self, *, num_iteration: Optional[int], start_iteration: int, importance_type: str, ) -> Dict[str, Any]: """Serialize CVBooster to dict.""" models_str = [] for booster in self.boosters: models_str.append( booster.model_to_string( num_iteration=num_iteration, start_iteration=start_iteration, importance_type=importance_type ) ) return {"boosters": models_str, "best_iteration": self.best_iteration} def __getattr__(self, name: str) -> Callable[[Any, Any], List[Any]]: """Redirect methods call of CVBooster.""" def handler_function(*args: Any, **kwargs: Any) -> List[Any]: """Call methods with each booster, and concatenate their results.""" ret = [] for booster in self.boosters: ret.append(getattr(booster, name)(*args, **kwargs)) return ret return handler_function def __getstate__(self) -> Dict[str, Any]: return vars(self) def __setstate__(self, state: Dict[str, Any]) -> None: vars(self).update(state) def model_from_string(self, model_str: str) -> "CVBooster": """Load CVBooster from a string. Parameters ---------- model_str : str Model will be loaded from this string. Returns ------- self : CVBooster Loaded CVBooster object. """ self._from_dict(json.loads(model_str)) return self def model_to_string( self, num_iteration: Optional[int] = None, start_iteration: int = 0, importance_type: str = "split", ) -> str: """Save CVBooster to JSON string. Parameters ---------- num_iteration : int or None, optional (default=None) Index of the iteration that should be saved. If None, if the best iteration exists, it is saved; otherwise, all iterations are saved. If <= 0, all iterations are saved. start_iteration : int, optional (default=0) Start index of the iteration that should be saved. importance_type : str, optional (default="split") What type of feature importance should be saved. If "split", result contains numbers of times the feature is used in a model. If "gain", result contains total gains of splits which use the feature. Returns ------- str_repr : str JSON string representation of CVBooster. """ return json.dumps( self._to_dict(num_iteration=num_iteration, start_iteration=start_iteration, importance_type=importance_type) ) def save_model( self, filename: Union[str, Path], num_iteration: Optional[int] = None, start_iteration: int = 0, importance_type: str = "split", ) -> "CVBooster": """Save CVBooster to a file as JSON text. Parameters ---------- filename : str or pathlib.Path Filename to save CVBooster. num_iteration : int or None, optional (default=None) Index of the iteration that should be saved. If None, if the best iteration exists, it is saved; otherwise, all iterations are saved. If <= 0, all iterations are saved. start_iteration : int, optional (default=0) Start index of the iteration that should be saved. importance_type : str, optional (default="split") What type of feature importance should be saved. If "split", result contains numbers of times the feature is used in a model. If "gain", result contains total gains of splits which use the feature. Returns ------- self : CVBooster Returns self. """ with open(filename, "w") as file: json.dump( self._to_dict( num_iteration=num_iteration, start_iteration=start_iteration, importance_type=importance_type ), file, ) return self def _make_n_folds( *, full_data: Dataset, folds: Optional[Union[Iterable[Tuple[np.ndarray, np.ndarray]], _LGBMBaseCrossValidator]], nfold: int, params: Dict[str, Any], seed: int, fpreproc: Optional[_LGBM_PreprocFunction], stratified: bool, shuffle: bool, eval_train_metric: bool, ) -> CVBooster: """Make a n-fold list of Booster from random indices.""" full_data = full_data.construct() num_data = full_data.num_data() if folds is not None: if not hasattr(folds, "__iter__") and not hasattr(folds, "split"): raise AttributeError( "folds should be a generator or iterator of (train_idx, test_idx) tuples " "or scikit-learn splitter object with split method" ) if hasattr(folds, "split"): group_info = full_data.get_group() if group_info is not None: group_info = np.asarray(group_info, dtype=np.int32) flatted_group = np.repeat(range(len(group_info)), repeats=group_info) else: flatted_group = np.zeros(num_data, dtype=np.int32) folds = folds.split(X=np.empty(num_data), y=full_data.get_label(), groups=flatted_group) else: if any( params.get(obj_alias, "") in {"lambdarank", "rank_xendcg", "xendcg", "xe_ndcg", "xe_ndcg_mart", "xendcg_mart"} for obj_alias in _ConfigAliases.get("objective") ): if not SKLEARN_INSTALLED: raise LightGBMError("scikit-learn is required for ranking cv") # ranking task, split according to groups group_info = np.asarray(full_data.get_group(), dtype=np.int32) flatted_group = np.repeat(range(len(group_info)), repeats=group_info) group_kfold = _LGBMGroupKFold(n_splits=nfold) folds = group_kfold.split(X=np.empty(num_data), groups=flatted_group) elif stratified: if not SKLEARN_INSTALLED: raise LightGBMError("scikit-learn is required for stratified cv") skf = _LGBMStratifiedKFold(n_splits=nfold, shuffle=shuffle, random_state=seed) folds = skf.split(X=np.empty(num_data), y=full_data.get_label()) else: if shuffle: randidx = np.random.RandomState(seed).permutation(num_data) else: randidx = np.arange(num_data) kstep = int(num_data / nfold) test_id = [randidx[i : i + kstep] for i in range(0, num_data, kstep)] train_id = [np.concatenate([test_id[i] for i in range(nfold) if k != i]) for k in range(nfold)] folds = zip(train_id, test_id) ret = CVBooster() for train_idx, test_idx in folds: train_set = full_data.subset(sorted(train_idx)) valid_set = full_data.subset(sorted(test_idx)) # run preprocessing on the data set if needed if fpreproc is not None: train_set, valid_set, tparam = fpreproc(train_set, valid_set, params.copy()) else: tparam = params booster_for_fold = Booster(tparam, train_set) if eval_train_metric: booster_for_fold.add_valid(train_set, "train") booster_for_fold.add_valid(valid_set, "valid") ret.boosters.append(booster_for_fold) return ret def _agg_cv_result( raw_results: List[List[_LGBM_BoosterEvalMethodResultType]], ) -> List[_LGBM_BoosterEvalMethodResultWithStandardDeviationType]: """Aggregate cross-validation results.""" # build up 2 maps, of the form: # # OrderedDict{ # (, ): # } # # OrderedDict{ # (, ): list[] # } # metric_types: Dict[Tuple[str, str], bool] = OrderedDict() metric_values: Dict[Tuple[str, str], List[float]] = OrderedDict() for one_result in raw_results: for dataset_name, metric_name, metric_value, is_higher_better in one_result: key = (dataset_name, metric_name) metric_types[key] = is_higher_better metric_values.setdefault(key, []) metric_values[key].append(metric_value) # turn that into a list of tuples of the form: # # [ # (, , mean(), , std_dev()) # ] return [(k[0], k[1], float(np.mean(v)), metric_types[k], float(np.std(v))) for k, v in metric_values.items()] def cv( params: Dict[str, Any], train_set: Dataset, num_boost_round: int = 100, folds: Optional[Union[Iterable[Tuple[np.ndarray, np.ndarray]], _LGBMBaseCrossValidator]] = None, nfold: int = 5, stratified: bool = True, shuffle: bool = True, metrics: Optional[Union[str, List[str]]] = None, feval: Optional[Union[_LGBM_CustomMetricFunction, List[_LGBM_CustomMetricFunction]]] = None, init_model: Optional[Union[str, Path, Booster]] = None, fpreproc: Optional[_LGBM_PreprocFunction] = None, seed: int = 0, callbacks: Optional[List[Callable]] = None, eval_train_metric: bool = False, return_cvbooster: bool = False, ) -> Dict[str, Union[List[float], CVBooster]]: """Perform the cross-validation with given parameters. Parameters ---------- params : dict Parameters for training. Values passed through ``params`` take precedence over those supplied via arguments. train_set : Dataset Data to be trained on. num_boost_round : int, optional (default=100) Number of boosting iterations. folds : generator or iterator of (train_idx, test_idx) tuples, scikit-learn splitter object or None, optional (default=None) If generator or iterator, it should yield the train and test indices for each fold. If object, it should be one of the scikit-learn splitter classes (https://scikit-learn.org/stable/modules/classes.html#splitter-classes) and have ``split`` method. This argument has highest priority over other data split arguments. nfold : int, optional (default=5) Number of folds in CV. stratified : bool, optional (default=True) Whether to perform stratified sampling. shuffle : bool, optional (default=True) Whether to shuffle before splitting data. metrics : str, list of str, or None, optional (default=None) Evaluation metrics to be monitored while CV. If not None, the metric in ``params`` will be overridden. feval : callable, list of callable, or None, optional (default=None) Customized evaluation function. Each evaluation function should accept two parameters: preds, eval_data, and return (eval_name, eval_result, is_higher_better) or list of such tuples. preds : numpy 1-D array or numpy 2-D array (for multi-class task) The predicted values. For multi-class task, preds are numpy 2-D array of shape = [n_samples, n_classes]. If custom objective function is used, predicted values are returned before any transformation, e.g. they are raw margin instead of probability of positive class for binary task in this case. eval_data : Dataset A ``Dataset`` to evaluate. eval_name : str The name of evaluation function (without whitespace). eval_result : float The eval result. is_higher_better : bool Is eval result higher better, e.g. AUC is ``is_higher_better``. To ignore the default metric corresponding to the used objective, set ``metrics`` to the string ``"None"``. init_model : str, pathlib.Path, Booster or None, optional (default=None) Filename of LightGBM model or Booster instance used for continue training. fpreproc : callable or None, optional (default=None) Preprocessing function that takes (dtrain, dtest, params) and returns transformed versions of those. seed : int, optional (default=0) Seed used to generate the folds (passed to numpy.random.seed). callbacks : list of callable, or None, optional (default=None) List of callback functions that are applied at each iteration. See Callbacks in Python API for more information. eval_train_metric : bool, optional (default=False) Whether to display the train metric in progress. The score of the metric is calculated again after each training step, so there is some impact on performance. return_cvbooster : bool, optional (default=False) Whether to return Booster models trained on each fold through ``CVBooster``. Note ---- A custom objective function can be provided for the ``objective`` parameter. It should accept two parameters: preds, train_data and return (grad, hess). preds : numpy 1-D array or numpy 2-D array (for multi-class task) The predicted values. Predicted values are returned before any transformation, e.g. they are raw margin instead of probability of positive class for binary task. train_data : Dataset The training dataset. grad : numpy 1-D array or numpy 2-D array (for multi-class task) The value of the first order derivative (gradient) of the loss with respect to the elements of preds for each sample point. hess : numpy 1-D array or numpy 2-D array (for multi-class task) The value of the second order derivative (Hessian) of the loss with respect to the elements of preds for each sample point. For multi-class task, preds are numpy 2-D array of shape = [n_samples, n_classes], and grad and hess should be returned in the same format. Returns ------- eval_results : dict History of evaluation results of each metric. The dictionary has the following format: {'valid metric1-mean': [values], 'valid metric1-stdv': [values], 'valid metric2-mean': [values], 'valid metric2-stdv': [values], ...}. If ``return_cvbooster=True``, also returns trained boosters wrapped in a ``CVBooster`` object via ``cvbooster`` key. If ``eval_train_metric=True``, also returns the train metric history. In this case, the dictionary has the following format: {'train metric1-mean': [values], 'valid metric1-mean': [values], 'train metric2-mean': [values], 'valid metric2-mean': [values], ...}. """ if not isinstance(train_set, Dataset): raise TypeError(f"cv() only accepts Dataset object, train_set has type '{type(train_set).__name__}'.") params = copy.deepcopy(params) params = _choose_param_value( main_param_name="objective", params=params, default_value=None, ) fobj: Optional[_LGBM_CustomObjectiveFunction] = None if callable(params["objective"]): fobj = params["objective"] params["objective"] = "none" params = _choose_num_iterations(num_boost_round_kwarg=num_boost_round, params=params) num_boost_round = params["num_iterations"] if num_boost_round <= 0: raise ValueError(f"Number of boosting rounds must be greater than 0. Got {num_boost_round}.") # setting early stopping via global params should be possible params = _choose_param_value( main_param_name="early_stopping_round", params=params, default_value=None, ) if params["early_stopping_round"] is None: params.pop("early_stopping_round") first_metric_only = params.get("first_metric_only", False) if isinstance(init_model, (str, Path)): predictor = _InnerPredictor.from_model_file( model_file=init_model, pred_parameter=params, ) elif isinstance(init_model, Booster): predictor = _InnerPredictor.from_booster( booster=init_model, pred_parameter=dict(init_model.params, **params), ) else: predictor = None if metrics is not None: for metric_alias in _ConfigAliases.get("metric"): params.pop(metric_alias, None) params["metric"] = metrics train_set._update_params(params)._set_predictor(predictor) results = defaultdict(list) cvbooster = _make_n_folds( full_data=train_set, folds=folds, nfold=nfold, params=params, seed=seed, fpreproc=fpreproc, stratified=stratified, shuffle=shuffle, eval_train_metric=eval_train_metric, ) # setup callbacks if callbacks is None: callbacks_set = set() else: for i, cb in enumerate(callbacks): cb.__dict__.setdefault("order", i - len(callbacks)) callbacks_set = set(callbacks) if callback._should_enable_early_stopping(params.get("early_stopping_round", 0)): callbacks_set.add( callback.early_stopping( stopping_rounds=params["early_stopping_round"], # type: ignore[arg-type] first_metric_only=first_metric_only, min_delta=params.get("early_stopping_min_delta", 0.0), verbose=_choose_param_value( main_param_name="verbosity", params=params, default_value=1, ).pop("verbosity") > 0, ) ) callbacks_before_iter_set = {cb for cb in callbacks_set if getattr(cb, "before_iteration", False)} callbacks_after_iter_set = callbacks_set - callbacks_before_iter_set callbacks_before_iter = sorted(callbacks_before_iter_set, key=attrgetter("order")) callbacks_after_iter = sorted(callbacks_after_iter_set, key=attrgetter("order")) for i in range(num_boost_round): for cb in callbacks_before_iter: cb( callback.CallbackEnv( model=cvbooster, params=params, iteration=i, begin_iteration=0, end_iteration=num_boost_round, evaluation_result_list=None, ) ) cvbooster.update(fobj=fobj) # type: ignore[call-arg] res = _agg_cv_result(cvbooster.eval_valid(feval)) # type: ignore[call-arg] for dataset_name, metric_name, metric_mean, _, metric_std_dev in res: results[f"{dataset_name} {metric_name}-mean"].append(metric_mean) results[f"{dataset_name} {metric_name}-stdv"].append(metric_std_dev) try: for cb in callbacks_after_iter: cb( callback.CallbackEnv( model=cvbooster, params=params, iteration=i, begin_iteration=0, end_iteration=num_boost_round, evaluation_result_list=res, ) ) except callback.EarlyStopException as earlyStopException: cvbooster.best_iteration = earlyStopException.best_iteration + 1 for bst in cvbooster.boosters: bst.best_iteration = cvbooster.best_iteration for k in results: results[k] = results[k][: cvbooster.best_iteration] break if return_cvbooster: results["cvbooster"] = cvbooster # type: ignore[assignment] return dict(results) ================================================ FILE: python-package/lightgbm/libpath.py ================================================ # coding: utf-8 """Find the path to LightGBM dynamic library files.""" import ctypes from os import environ from pathlib import Path from platform import system from typing import List __all__: List[str] = [] def _find_lib_path() -> List[str]: """Find the path to LightGBM library files. Returns ------- lib_path: list of str List of all found library paths to LightGBM. """ curr_path = Path(__file__).resolve() dll_path = [ curr_path.parents[1], curr_path.parents[0] / "bin", curr_path.parents[0] / "lib", ] if system() in ("Windows", "Microsoft"): dll_path.append(curr_path.parents[1] / "Release") dll_path.append(curr_path.parents[1] / "windows" / "x64" / "DLL") dll_path = [p / "lib_lightgbm.dll" for p in dll_path] elif system() == "Darwin": dll_path = [p / "lib_lightgbm.dylib" for p in dll_path] else: dll_path = [p / "lib_lightgbm.so" for p in dll_path] lib_path = [str(p) for p in dll_path if p.is_file()] if not lib_path: dll_path_joined = "\n".join(map(str, dll_path)) raise Exception(f"Cannot find lightgbm library file in following paths:\n{dll_path_joined}") return lib_path # we don't need lib_lightgbm while building docs _LIB: ctypes.CDLL if environ.get("LIGHTGBM_BUILD_DOC", "False") == "True": from unittest.mock import Mock # isort: skip _LIB = Mock(ctypes.CDLL) # type: ignore else: _LIB = ctypes.cdll.LoadLibrary(_find_lib_path()[0]) ================================================ FILE: python-package/lightgbm/plotting.py ================================================ # coding: utf-8 """Plotting library.""" import math from copy import deepcopy from io import BytesIO from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union import numpy as np from .basic import Booster, _data_from_pandas, _is_zero, _log_warning, _MissingType from .compat import GRAPHVIZ_INSTALLED, MATPLOTLIB_INSTALLED, pd_DataFrame from .sklearn import LGBMModel __all__ = [ "create_tree_digraph", "plot_importance", "plot_metric", "plot_split_value_histogram", "plot_tree", ] if TYPE_CHECKING: import matplotlib def _check_not_tuple_of_2_elements(obj: Any, obj_name: str) -> None: """Check object is not tuple or does not have 2 elements.""" if not isinstance(obj, tuple) or len(obj) != 2: raise TypeError(f"{obj_name} must be a tuple of 2 elements.") def _float2str(value: float, precision: Optional[int]) -> str: return f"{value:.{precision}f}" if precision is not None and not isinstance(value, str) else str(value) def plot_importance( booster: Union[Booster, LGBMModel], ax: "Optional[matplotlib.axes.Axes]" = None, height: float = 0.2, xlim: Optional[Tuple[float, float]] = None, ylim: Optional[Tuple[float, float]] = None, title: Optional[str] = "Feature importance", xlabel: Optional[str] = "Feature importance", ylabel: Optional[str] = "Features", importance_type: str = "auto", max_num_features: Optional[int] = None, ignore_zero: bool = True, figsize: Optional[Tuple[float, float]] = None, dpi: Optional[int] = None, grid: bool = True, precision: Optional[int] = 3, **kwargs: Any, ) -> Any: """Plot model's feature importances. Parameters ---------- booster : Booster or LGBMModel Booster or LGBMModel instance which feature importance should be plotted. ax : matplotlib.axes.Axes or None, optional (default=None) Target axes instance. If None, new figure and axes will be created. height : float, optional (default=0.2) Bar height, passed to ``ax.barh()``. xlim : tuple of 2 elements or None, optional (default=None) Tuple passed to ``ax.xlim()``. ylim : tuple of 2 elements or None, optional (default=None) Tuple passed to ``ax.ylim()``. title : str or None, optional (default="Feature importance") Axes title. If None, title is disabled. xlabel : str or None, optional (default="Feature importance") X-axis title label. If None, title is disabled. @importance_type@ placeholder can be used, and it will be replaced with the value of ``importance_type`` parameter. ylabel : str or None, optional (default="Features") Y-axis title label. If None, title is disabled. importance_type : str, optional (default="auto") How the importance is calculated. If "auto", if ``booster`` parameter is LGBMModel, ``booster.importance_type`` attribute is used; "split" otherwise. If "split", result contains numbers of times the feature is used in a model. If "gain", result contains total gains of splits which use the feature. max_num_features : int or None, optional (default=None) Max number of top features displayed on plot. If None or <1, all features will be displayed. ignore_zero : bool, optional (default=True) Whether to ignore features with zero importance. figsize : tuple of 2 elements or None, optional (default=None) Figure size. dpi : int or None, optional (default=None) Resolution of the figure. grid : bool, optional (default=True) Whether to add a grid for axes. precision : int or None, optional (default=3) Used to restrict the display of floating point values to a certain precision. **kwargs Other parameters passed to ``ax.barh()``. Returns ------- ax : matplotlib.axes.Axes The plot with model's feature importances. """ if MATPLOTLIB_INSTALLED: import matplotlib.pyplot as plt # noqa: PLC0415 else: raise ImportError("You must install matplotlib and restart your session to plot importance.") if isinstance(booster, LGBMModel): if importance_type == "auto": importance_type = booster.importance_type booster = booster.booster_ elif isinstance(booster, Booster): if importance_type == "auto": importance_type = "split" else: raise TypeError("booster must be Booster or LGBMModel.") importance = booster.feature_importance(importance_type=importance_type) feature_name = booster.feature_name() if not len(importance): raise ValueError("Booster's feature_importance is empty.") tuples = sorted(zip(feature_name, importance), key=lambda x: x[1]) if ignore_zero: tuples = [x for x in tuples if x[1] > 0] if max_num_features is not None and max_num_features > 0: tuples = tuples[-max_num_features:] if not tuples: raise ValueError( "No non-zero feature importances found. The model may have no splits. " "Use ignore_zero=False to show all features." ) labels, values = zip(*tuples) if ax is None: if figsize is not None: _check_not_tuple_of_2_elements(figsize, "figsize") _, ax = plt.subplots(1, 1, figsize=figsize, dpi=dpi) ylocs = np.arange(len(values)) ax.barh(ylocs, values, align="center", height=height, **kwargs) for x, y in zip(values, ylocs): ax.text(x + 1, float(y), _float2str(x, precision) if importance_type == "gain" else x, va="center") ax.set_yticks(ylocs) ax.set_yticklabels(labels) if xlim is not None: _check_not_tuple_of_2_elements(xlim, "xlim") else: xlim = (0, max(values) * 1.1) ax.set_xlim(xlim) if ylim is not None: _check_not_tuple_of_2_elements(ylim, "ylim") else: ylim = (-1, len(values)) ax.set_ylim(ylim) if title is not None: ax.set_title(title) if xlabel is not None: xlabel = xlabel.replace("@importance_type@", importance_type) ax.set_xlabel(xlabel) if ylabel is not None: ax.set_ylabel(ylabel) ax.grid(grid) return ax def plot_split_value_histogram( booster: Union[Booster, LGBMModel], feature: Union[int, str], bins: Union[int, str, None] = None, ax: "Optional[matplotlib.axes.Axes]" = None, width_coef: float = 0.8, xlim: Optional[Tuple[float, float]] = None, ylim: Optional[Tuple[float, float]] = None, title: Optional[str] = "Split value histogram for feature with @index/name@ @feature@", xlabel: Optional[str] = "Feature split value", ylabel: Optional[str] = "Count", figsize: Optional[Tuple[float, float]] = None, dpi: Optional[int] = None, grid: bool = True, **kwargs: Any, ) -> Any: """Plot split value histogram for the specified feature of the model. Parameters ---------- booster : Booster or LGBMModel Booster or LGBMModel instance of which feature split value histogram should be plotted. feature : int or str The feature name or index the histogram is plotted for. If int, interpreted as index. If str, interpreted as name. bins : int, str or None, optional (default=None) The maximum number of bins. If None, the number of bins equals number of unique split values. If str, it should be one from the list of the supported values by ``numpy.histogram()`` function. ax : matplotlib.axes.Axes or None, optional (default=None) Target axes instance. If None, new figure and axes will be created. width_coef : float, optional (default=0.8) Coefficient for histogram bar width. xlim : tuple of 2 elements or None, optional (default=None) Tuple passed to ``ax.xlim()``. ylim : tuple of 2 elements or None, optional (default=None) Tuple passed to ``ax.ylim()``. title : str or None, optional (default="Split value histogram for feature with @index/name@ @feature@") Axes title. If None, title is disabled. @feature@ placeholder can be used, and it will be replaced with the value of ``feature`` parameter. @index/name@ placeholder can be used, and it will be replaced with ``index`` word in case of ``int`` type ``feature`` parameter or ``name`` word in case of ``str`` type ``feature`` parameter. xlabel : str or None, optional (default="Feature split value") X-axis title label. If None, title is disabled. ylabel : str or None, optional (default="Count") Y-axis title label. If None, title is disabled. figsize : tuple of 2 elements or None, optional (default=None) Figure size. dpi : int or None, optional (default=None) Resolution of the figure. grid : bool, optional (default=True) Whether to add a grid for axes. **kwargs Other parameters passed to ``ax.bar()``. Returns ------- ax : matplotlib.axes.Axes The plot with specified model's feature split value histogram. """ if MATPLOTLIB_INSTALLED: import matplotlib.pyplot as plt # noqa: PLC0415 from matplotlib.ticker import MaxNLocator # noqa: PLC0415 else: raise ImportError("You must install matplotlib and restart your session to plot split value histogram.") if isinstance(booster, LGBMModel): booster = booster.booster_ elif not isinstance(booster, Booster): raise TypeError("booster must be Booster or LGBMModel.") hist, split_bins = booster.get_split_value_histogram(feature=feature, bins=bins, xgboost_style=False) if np.count_nonzero(hist) == 0: raise ValueError(f"Cannot plot split value histogram, because feature {feature} was not used in splitting") width = width_coef * (split_bins[1] - split_bins[0]) centred = (split_bins[:-1] + split_bins[1:]) / 2 if ax is None: if figsize is not None: _check_not_tuple_of_2_elements(figsize, "figsize") _, ax = plt.subplots(1, 1, figsize=figsize, dpi=dpi) ax.bar(centred, hist, align="center", width=width, **kwargs) if xlim is not None: _check_not_tuple_of_2_elements(xlim, "xlim") else: range_result = split_bins[-1] - split_bins[0] xlim = (split_bins[0] - range_result * 0.2, split_bins[-1] + range_result * 0.2) ax.set_xlim(xlim) ax.yaxis.set_major_locator(MaxNLocator(integer=True)) if ylim is not None: _check_not_tuple_of_2_elements(ylim, "ylim") else: ylim = (0, max(hist) * 1.1) ax.set_ylim(ylim) if title is not None: title = title.replace("@feature@", str(feature)) title = title.replace("@index/name@", ("name" if isinstance(feature, str) else "index")) ax.set_title(title) if xlabel is not None: ax.set_xlabel(xlabel) if ylabel is not None: ax.set_ylabel(ylabel) ax.grid(grid) return ax def plot_metric( booster: Union[Dict, LGBMModel], metric: Optional[str] = None, dataset_names: Optional[List[str]] = None, ax: "Optional[matplotlib.axes.Axes]" = None, xlim: Optional[Tuple[float, float]] = None, ylim: Optional[Tuple[float, float]] = None, title: Optional[str] = "Metric during training", xlabel: Optional[str] = "Iterations", ylabel: Optional[str] = "@metric@", figsize: Optional[Tuple[float, float]] = None, dpi: Optional[int] = None, grid: bool = True, ) -> Any: """Plot one metric during training. Parameters ---------- booster : dict or LGBMModel Dictionary returned from ``lightgbm.train()`` or LGBMModel instance. metric : str or None, optional (default=None) The metric name to plot. Only one metric supported because different metrics have various scales. If None, first metric picked from dictionary (according to hashcode). dataset_names : list of str, or None, optional (default=None) List of the dataset names which are used to calculate metric to plot. If None, all datasets are used. ax : matplotlib.axes.Axes or None, optional (default=None) Target axes instance. If None, new figure and axes will be created. xlim : tuple of 2 elements or None, optional (default=None) Tuple passed to ``ax.xlim()``. ylim : tuple of 2 elements or None, optional (default=None) Tuple passed to ``ax.ylim()``. title : str or None, optional (default="Metric during training") Axes title. If None, title is disabled. xlabel : str or None, optional (default="Iterations") X-axis title label. If None, title is disabled. ylabel : str or None, optional (default="@metric@") Y-axis title label. If 'auto', metric name is used. If None, title is disabled. @metric@ placeholder can be used, and it will be replaced with metric name. figsize : tuple of 2 elements or None, optional (default=None) Figure size. dpi : int or None, optional (default=None) Resolution of the figure. grid : bool, optional (default=True) Whether to add a grid for axes. Returns ------- ax : matplotlib.axes.Axes The plot with metric's history over the training. """ if MATPLOTLIB_INSTALLED: import matplotlib.pyplot as plt # noqa: PLC0415 else: raise ImportError("You must install matplotlib and restart your session to plot metric.") if isinstance(booster, LGBMModel): eval_results = deepcopy(booster.evals_result_) elif isinstance(booster, dict): eval_results = deepcopy(booster) elif isinstance(booster, Booster): raise TypeError( "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`" ) else: raise TypeError("booster must be dict or LGBMModel.") num_data = len(eval_results) if not num_data: raise ValueError("eval results cannot be empty.") if ax is None: if figsize is not None: _check_not_tuple_of_2_elements(figsize, "figsize") _, ax = plt.subplots(1, 1, figsize=figsize, dpi=dpi) if dataset_names is None: dataset_names_iter = iter(eval_results.keys()) elif not isinstance(dataset_names, (list, tuple, set)) or not dataset_names: raise ValueError("dataset_names should be iterable and cannot be empty") else: dataset_names_iter = iter(dataset_names) name = next(dataset_names_iter) # take one as sample metrics_for_one = eval_results[name] num_metric = len(metrics_for_one) if metric is None: if num_metric > 1: _log_warning("More than one metric available, picking one to plot.") metric, results = metrics_for_one.popitem() else: if metric not in metrics_for_one: raise KeyError("No given metric in eval results.") results = metrics_for_one[metric] num_iteration = len(results) max_result = max(results) min_result = min(results) x_ = range(num_iteration) ax.plot(x_, results, label=name) for name in dataset_names_iter: metrics_for_one = eval_results[name] results = metrics_for_one[metric] max_result = max(*results, max_result) min_result = min(*results, min_result) ax.plot(x_, results, label=name) ax.legend(loc="best") if xlim is not None: _check_not_tuple_of_2_elements(xlim, "xlim") else: xlim = (0, num_iteration) ax.set_xlim(xlim) if ylim is not None: _check_not_tuple_of_2_elements(ylim, "ylim") else: range_result = max_result - min_result ylim = (min_result - range_result * 0.2, max_result + range_result * 0.2) ax.set_ylim(ylim) if title is not None: ax.set_title(title) if xlabel is not None: ax.set_xlabel(xlabel) if ylabel is not None: ylabel = ylabel.replace("@metric@", metric) ax.set_ylabel(ylabel) ax.grid(grid) return ax def _determine_direction_for_numeric_split( *, fval: float, threshold: float, missing_type_str: str, default_left: bool, ) -> str: missing_type = _MissingType(missing_type_str) if math.isnan(fval) and missing_type != _MissingType.NAN: fval = 0.0 if (missing_type == _MissingType.ZERO and _is_zero(fval)) or ( missing_type == _MissingType.NAN and math.isnan(fval) ): direction = "left" if default_left else "right" else: direction = "left" if fval <= threshold else "right" return direction def _determine_direction_for_categorical_split(fval: float, thresholds: str) -> str: if math.isnan(fval) or int(fval) < 0: return "right" int_thresholds = {int(t) for t in thresholds.split("||")} return "left" if int(fval) in int_thresholds else "right" def _to_graphviz( *, tree_info: Dict[str, Any], show_info: List[str], feature_names: Union[List[str], None], precision: Optional[int], orientation: str, constraints: Optional[List[int]], example_case: Optional[Union[np.ndarray, pd_DataFrame]], max_category_values: int, **kwargs: Any, ) -> Any: """Convert specified tree to graphviz instance. See: - https://graphviz.readthedocs.io/en/stable/api.html#digraph """ if GRAPHVIZ_INSTALLED: from graphviz import Digraph # noqa: PLC0415 else: raise ImportError("You must install graphviz and restart your session to plot tree.") def add( root: Dict[str, Any], total_count: int, parent: Optional[str], decision: Optional[str], highlight: bool ) -> None: """Recursively add node or edge.""" fillcolor = "white" style = "" tooltip = None if highlight: color = "blue" penwidth = "3" else: color = "black" penwidth = "1" if "split_index" in root: # non-leaf shape = "rectangle" l_dec = "yes" r_dec = "no" threshold = root["threshold"] if root["decision_type"] == "<=": operator = "≤" elif root["decision_type"] == "==": operator = "=" else: raise ValueError("Invalid decision type in tree model.") name = f"split{root['split_index']}" split_feature = root["split_feature"] if feature_names is not None: label = f"{feature_names[split_feature]} {operator}" else: label = f"feature {split_feature} {operator} " direction = None if example_case is not None: if root["decision_type"] == "==": direction = _determine_direction_for_categorical_split( fval=example_case[split_feature], thresholds=root["threshold"] ) else: direction = _determine_direction_for_numeric_split( fval=example_case[split_feature], threshold=root["threshold"], missing_type_str=root["missing_type"], default_left=root["default_left"], ) if root["decision_type"] == "==": category_values = root["threshold"].split("||") if len(category_values) > max_category_values: tooltip = root["threshold"] threshold = "||".join(category_values[:2]) + "||...||" + category_values[-1] label += f"{_float2str(threshold, precision)}" for info in ["split_gain", "internal_value", "internal_weight", "internal_count", "data_percentage"]: if info in show_info: output = info.split("_")[-1] if info in {"split_gain", "internal_value", "internal_weight"}: label += f"
{_float2str(root[info], precision)} {output}" elif info == "internal_count": label += f"
{output}: {root[info]}" elif info == "data_percentage": label += f"
{_float2str(root['internal_count'] / total_count * 100, 2)}% of data" if constraints: if constraints[root["split_feature"]] == 1: fillcolor = "#ddffdd" # light green if constraints[root["split_feature"]] == -1: fillcolor = "#ffdddd" # light red style = "filled" label = f"<{label}>" add( root=root["left_child"], total_count=total_count, parent=name, decision=l_dec, highlight=highlight and direction == "left", ) add( root=root["right_child"], total_count=total_count, parent=name, decision=r_dec, highlight=highlight and direction == "right", ) else: # leaf shape = "ellipse" name = f"leaf{root['leaf_index']}" label = f"leaf {root['leaf_index']}: " label += f"{_float2str(root['leaf_value'], precision)}" if "leaf_weight" in show_info: label += f"
{_float2str(root['leaf_weight'], precision)} weight" if "leaf_count" in show_info: label += f"
count: {root['leaf_count']}" if "data_percentage" in show_info: label += f"
{_float2str(root['leaf_count'] / total_count * 100, 2)}% of data" label = f"<{label}>" graph.node( name, label=label, shape=shape, style=style, fillcolor=fillcolor, color=color, penwidth=penwidth, tooltip=tooltip, ) if parent is not None: graph.edge(parent, name, decision, color=color, penwidth=penwidth) graph = Digraph(**kwargs) rankdir = "LR" if orientation == "horizontal" else "TB" graph.attr("graph", nodesep="0.05", ranksep="0.3", rankdir=rankdir) if "internal_count" in tree_info["tree_structure"]: add( root=tree_info["tree_structure"], total_count=tree_info["tree_structure"]["internal_count"], parent=None, decision=None, highlight=example_case is not None, ) else: raise Exception("Cannot plot trees with no split") if constraints: # "#ddffdd" is light green, "#ffdddd" is light red legend = """<
Monotone constraints
Increasing
Decreasing
>""" graph.node("legend", label=legend, shape="rectangle", color="white") return graph def create_tree_digraph( booster: Union[Booster, LGBMModel], tree_index: int = 0, show_info: Optional[List[str]] = None, precision: Optional[int] = 3, orientation: str = "horizontal", example_case: Optional[Union[np.ndarray, pd_DataFrame]] = None, max_category_values: int = 10, **kwargs: Any, ) -> Any: """Create a digraph representation of specified tree. Each node in the graph represents a node in the tree. Non-leaf nodes have labels like ``Column_10 <= 875.9``, which means "this node splits on the feature named "Column_10", with threshold 875.9". Leaf nodes have labels like ``leaf 2: 0.422``, which means "this node is a leaf node, and the predicted value for records that fall into this node is 0.422". The number (``2``) is an internal unique identifier and doesn't have any special meaning. .. note:: For more information please visit https://graphviz.readthedocs.io/en/stable/api.html#digraph. Parameters ---------- booster : Booster or LGBMModel Booster or LGBMModel instance to be converted. tree_index : int, optional (default=0) The index of a target tree to convert. show_info : list of str, or None, optional (default=None) What information should be shown in nodes. - ``'split_gain'`` : gain from adding this split to the model - ``'internal_value'`` : raw predicted value that would be produced by this node if it was a leaf node - ``'internal_count'`` : number of records from the training data that fall into this non-leaf node - ``'internal_weight'`` : total weight of all nodes that fall into this non-leaf node - ``'leaf_count'`` : number of records from the training data that fall into this leaf node - ``'leaf_weight'`` : total weight (sum of Hessian) of all observations that fall into this leaf node - ``'data_percentage'`` : percentage of training data that fall into this node precision : int or None, optional (default=3) Used to restrict the display of floating point values to a certain precision. orientation : str, optional (default='horizontal') Orientation of the tree. Can be 'horizontal' or 'vertical'. example_case : numpy 2-D array, pandas DataFrame or None, optional (default=None) Single row with the same structure as the training data. If not None, the plot will highlight the path that sample takes through the tree. .. versionadded:: 4.0.0 max_category_values : int, optional (default=10) 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. .. warning:: Consider wrapping the SVG string of the tree graph with ``IPython.display.HTML`` when running on JupyterLab to get the `tooltip `_ working right. Example: .. code-block:: python from IPython.display import HTML graph = lgb.create_tree_digraph(clf, max_category_values=5) HTML(graph._repr_image_svg_xml()) .. versionadded:: 4.0.0 **kwargs Other parameters passed to ``Digraph`` constructor. Check https://graphviz.readthedocs.io/en/stable/api.html#digraph for the full list of supported parameters. Returns ------- graph : graphviz.Digraph The digraph representation of specified tree. """ if isinstance(booster, LGBMModel): booster = booster.booster_ elif not isinstance(booster, Booster): raise TypeError("booster must be Booster or LGBMModel.") model = booster.dump_model() tree_infos = model["tree_info"] feature_names = model.get("feature_names", None) monotone_constraints = model.get("monotone_constraints", None) if tree_index < len(tree_infos): tree_info = tree_infos[tree_index] else: raise IndexError("tree_index is out of range.") if show_info is None: show_info = [] if example_case is not None: if not isinstance(example_case, (np.ndarray, pd_DataFrame)) or example_case.ndim != 2: raise ValueError("example_case must be a numpy 2-D array or a pandas DataFrame") if example_case.shape[0] != 1: raise ValueError("example_case must have a single row.") if isinstance(example_case, pd_DataFrame): example_case = _data_from_pandas( data=example_case, feature_name="auto", categorical_feature="auto", pandas_categorical=booster.pandas_categorical, )[0] example_case = example_case[0] return _to_graphviz( tree_info=tree_info, show_info=show_info, feature_names=feature_names, precision=precision, orientation=orientation, constraints=monotone_constraints, example_case=example_case, max_category_values=max_category_values, **kwargs, ) def plot_tree( booster: Union[Booster, LGBMModel], ax: "Optional[matplotlib.axes.Axes]" = None, tree_index: int = 0, figsize: Optional[Tuple[float, float]] = None, dpi: Optional[int] = None, show_info: Optional[List[str]] = None, precision: Optional[int] = 3, orientation: str = "horizontal", example_case: Optional[Union[np.ndarray, pd_DataFrame]] = None, **kwargs: Any, ) -> Any: """Plot specified tree. Each node in the graph represents a node in the tree. Non-leaf nodes have labels like ``Column_10 <= 875.9``, which means "this node splits on the feature named "Column_10", with threshold 875.9". Leaf nodes have labels like ``leaf 2: 0.422``, which means "this node is a leaf node, and the predicted value for records that fall into this node is 0.422". The number (``2``) is an internal unique identifier and doesn't have any special meaning. .. note:: It is preferable to use ``create_tree_digraph()`` because of its lossless quality and returned objects can be also rendered and displayed directly inside a Jupyter notebook. Parameters ---------- booster : Booster or LGBMModel Booster or LGBMModel instance to be plotted. ax : matplotlib.axes.Axes or None, optional (default=None) Target axes instance. If None, new figure and axes will be created. tree_index : int, optional (default=0) The index of a target tree to plot. figsize : tuple of 2 elements or None, optional (default=None) Figure size. dpi : int or None, optional (default=None) Resolution of the figure. show_info : list of str, or None, optional (default=None) What information should be shown in nodes. - ``'split_gain'`` : gain from adding this split to the model - ``'internal_value'`` : raw predicted value that would be produced by this node if it was a leaf node - ``'internal_count'`` : number of records from the training data that fall into this non-leaf node - ``'internal_weight'`` : total weight of all nodes that fall into this non-leaf node - ``'leaf_count'`` : number of records from the training data that fall into this leaf node - ``'leaf_weight'`` : total weight (sum of Hessian) of all observations that fall into this leaf node - ``'data_percentage'`` : percentage of training data that fall into this node precision : int or None, optional (default=3) Used to restrict the display of floating point values to a certain precision. orientation : str, optional (default='horizontal') Orientation of the tree. Can be 'horizontal' or 'vertical'. example_case : numpy 2-D array, pandas DataFrame or None, optional (default=None) Single row with the same structure as the training data. If not None, the plot will highlight the path that sample takes through the tree. .. versionadded:: 4.0.0 **kwargs Other parameters passed to ``Digraph`` constructor. Check https://graphviz.readthedocs.io/en/stable/api.html#digraph for the full list of supported parameters. Returns ------- ax : matplotlib.axes.Axes The plot with single tree. """ if MATPLOTLIB_INSTALLED: import matplotlib.image # noqa: PLC0415 import matplotlib.pyplot as plt # noqa: PLC0415 else: raise ImportError("You must install matplotlib and restart your session to plot tree.") if ax is None: if figsize is not None: _check_not_tuple_of_2_elements(figsize, "figsize") _, ax = plt.subplots(1, 1, figsize=figsize, dpi=dpi) graph = create_tree_digraph( booster=booster, tree_index=tree_index, show_info=show_info, precision=precision, orientation=orientation, example_case=example_case, **kwargs, ) s = BytesIO() s.write(graph.pipe(format="png")) s.seek(0) img = matplotlib.image.imread(s) ax.imshow(img) ax.axis("off") return ax ================================================ FILE: python-package/lightgbm/py.typed ================================================ ================================================ FILE: python-package/lightgbm/sklearn.py ================================================ # coding: utf-8 """Scikit-learn wrapper interface for LightGBM.""" import copy import warnings from inspect import signature from pathlib import Path from typing import TYPE_CHECKING, Any, Callable, Dict, List, Optional, Tuple, Union import numpy as np import scipy.sparse from .basic import ( _MULTICLASS_OBJECTIVES, Booster, Dataset, LGBMDeprecationWarning, LightGBMError, _choose_param_value, _ConfigAliases, _LGBM_BoosterBestScoreType, _LGBM_CategoricalFeatureConfiguration, _LGBM_EvalFunctionResultType, _LGBM_FeatureNameConfiguration, _LGBM_GroupType, _LGBM_InitScoreType, _LGBM_LabelType, _LGBM_PredictReturnType, _LGBM_WeightType, _log_warning, ) from .callback import _EvalResultDict, record_evaluation from .compat import ( SKLEARN_CHECK_SAMPLE_WEIGHT_HAS_ALLOW_ZERO_WEIGHTS_ARG, SKLEARN_INSTALLED, LGBMNotFittedError, _LGBMAssertAllFinite, _LGBMCheckClassificationTargets, _LGBMCheckSampleWeight, _LGBMClassifierBase, _LGBMComputeSampleWeight, _LGBMCpuCount, _LGBMLabelEncoder, _LGBMModelBase, _LGBMRegressorBase, _LGBMValidateData, _sklearn_version, pa_Table, pd_DataFrame, ) from .engine import train if TYPE_CHECKING: from .compat import _sklearn_Tags __all__ = [ "LGBMClassifier", "LGBMModel", "LGBMRanker", "LGBMRegressor", ] _LGBM_ScikitMatrixLike = Union[ List[Union[List[float], List[int]]], np.ndarray, pd_DataFrame, pa_Table, scipy.sparse.spmatrix, ] _LGBM_ScikitCustomObjectiveFunction = Union[ # f(labels, preds) Callable[ [Optional[np.ndarray], np.ndarray], Tuple[np.ndarray, np.ndarray], ], # f(labels, preds, weights) Callable[ [Optional[np.ndarray], np.ndarray, Optional[np.ndarray]], Tuple[np.ndarray, np.ndarray], ], # f(labels, preds, weights, group) Callable[ [Optional[np.ndarray], np.ndarray, Optional[np.ndarray], Optional[np.ndarray]], Tuple[np.ndarray, np.ndarray], ], ] _LGBM_ScikitCustomEvalFunction = Union[ # f(labels, preds) Callable[ [Optional[np.ndarray], np.ndarray], _LGBM_EvalFunctionResultType, ], Callable[ [Optional[np.ndarray], np.ndarray], List[_LGBM_EvalFunctionResultType], ], # f(labels, preds, weights) Callable[ [Optional[np.ndarray], np.ndarray, Optional[np.ndarray]], _LGBM_EvalFunctionResultType, ], Callable[ [Optional[np.ndarray], np.ndarray, Optional[np.ndarray]], List[_LGBM_EvalFunctionResultType], ], # f(labels, preds, weights, group) Callable[ [Optional[np.ndarray], np.ndarray, Optional[np.ndarray], Optional[np.ndarray]], _LGBM_EvalFunctionResultType, ], Callable[ [Optional[np.ndarray], np.ndarray, Optional[np.ndarray], Optional[np.ndarray]], List[_LGBM_EvalFunctionResultType], ], ] _LGBM_ScikitEvalMetricType = Union[ str, _LGBM_ScikitCustomEvalFunction, List[Union[str, _LGBM_ScikitCustomEvalFunction]], ] _LGBM_ScikitValidSet = Tuple[_LGBM_ScikitMatrixLike, _LGBM_LabelType] def _get_group_from_constructed_dataset(dataset: Dataset) -> Optional[np.ndarray]: group = dataset.get_group() error_msg = ( "Estimators in lightgbm.sklearn should only retrieve query groups from a constructed Dataset. " "If you're seeing this message, it's a bug in lightgbm. Please report it at https://github.com/lightgbm-org/LightGBM/issues." ) assert group is None or isinstance(group, np.ndarray), error_msg return group def _get_label_from_constructed_dataset(dataset: Dataset) -> np.ndarray: label = dataset.get_label() error_msg = ( "Estimators in lightgbm.sklearn should only retrieve labels from a constructed Dataset. " "If you're seeing this message, it's a bug in lightgbm. Please report it at https://github.com/lightgbm-org/LightGBM/issues." ) assert isinstance(label, np.ndarray), error_msg return label def _get_weight_from_constructed_dataset(dataset: Dataset) -> Optional[np.ndarray]: weight = dataset.get_weight() error_msg = ( "Estimators in lightgbm.sklearn should only retrieve weights from a constructed Dataset. " "If you're seeing this message, it's a bug in lightgbm. Please report it at https://github.com/lightgbm-org/LightGBM/issues." ) assert weight is None or isinstance(weight, np.ndarray), error_msg return weight class _ObjectiveFunctionWrapper: """Proxy class for objective function.""" def __init__(self, func: _LGBM_ScikitCustomObjectiveFunction): """Construct a proxy class. This class transforms objective function to match objective function with signature ``new_func(preds, dataset)`` as expected by ``lightgbm.engine.train``. Parameters ---------- func : callable Expects a callable with following signatures: ``func(y_true, y_pred)``, ``func(y_true, y_pred, weight)`` or ``func(y_true, y_pred, weight, group)`` and returns (grad, hess): y_true : numpy 1-D array of shape = [n_samples] The target values. 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) The predicted values. Predicted values are returned before any transformation, e.g. they are raw margin instead of probability of positive class for binary task. weight : numpy 1-D array of shape = [n_samples] The weight of samples. Weights should be non-negative. group : numpy 1-D array Group/query data. Only used in the learning-to-rank task. sum(group) = n_samples. For example, if you have a 100-document dataset with ``group = [10, 20, 40, 10, 10, 10]``, that means that you have 6 groups, 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. grad : numpy 1-D array of shape = [n_samples] or numpy 2-D array of shape [n_samples, n_classes] (for multi-class task) The value of the first order derivative (gradient) of the loss with respect to the elements of y_pred for each sample point. hess : numpy 1-D array of shape = [n_samples] or numpy 2-D array of shape = [n_samples, n_classes] (for multi-class task) The value of the second order derivative (Hessian) of the loss with respect to the elements of y_pred for each sample point. .. note:: For multi-class task, y_pred is a numpy 2-D array of shape = [n_samples, n_classes], and grad and hess should be returned in the same format. """ self.func = func def __call__( self, preds: np.ndarray, dataset: Dataset, ) -> Tuple[np.ndarray, np.ndarray]: """Call passed function with appropriate arguments. Parameters ---------- preds : numpy 1-D array of shape = [n_samples] or numpy 2-D array of shape = [n_samples, n_classes] (for multi-class task) The predicted values. dataset : Dataset The training dataset. Returns ------- grad : numpy 1-D array of shape = [n_samples] or numpy 2-D array of shape = [n_samples, n_classes] (for multi-class task) The value of the first order derivative (gradient) of the loss with respect to the elements of preds for each sample point. hess : numpy 1-D array of shape = [n_samples] or numpy 2-D array of shape = [n_samples, n_classes] (for multi-class task) The value of the second order derivative (Hessian) of the loss with respect to the elements of preds for each sample point. """ labels = _get_label_from_constructed_dataset(dataset) argc = len(signature(self.func).parameters) if argc == 2: grad, hess = self.func(labels, preds) # type: ignore[call-arg] return grad, hess weight = _get_weight_from_constructed_dataset(dataset) if argc == 3: grad, hess = self.func(labels, preds, weight) # type: ignore[call-arg] return grad, hess if argc == 4: group = _get_group_from_constructed_dataset(dataset) return self.func(labels, preds, weight, group) # type: ignore[call-arg] raise TypeError(f"Self-defined objective function should have 2, 3 or 4 arguments, got {argc}") class _EvalFunctionWrapper: """Proxy class for evaluation function.""" def __init__(self, func: _LGBM_ScikitCustomEvalFunction): """Construct a proxy class. This class transforms evaluation function to match evaluation function with signature ``new_func(preds, dataset)`` as expected by ``lightgbm.engine.train``. Parameters ---------- func : callable Expects a callable with following signatures: ``func(y_true, y_pred)``, ``func(y_true, y_pred, weight)`` or ``func(y_true, y_pred, weight, group)`` and returns (eval_name, eval_result, is_higher_better) or list of (eval_name, eval_result, is_higher_better): y_true : numpy 1-D array of shape = [n_samples] The target values. y_pred : numpy 1-D array of shape = [n_samples] or numpy 2-D array shape = [n_samples, n_classes] (for multi-class task) The predicted values. In case of custom ``objective``, predicted values are returned before any transformation, e.g. they are raw margin instead of probability of positive class for binary task in this case. weight : numpy 1-D array of shape = [n_samples] The weight of samples. Weights should be non-negative. group : numpy 1-D array Group/query data. Only used in the learning-to-rank task. sum(group) = n_samples. For example, if you have a 100-document dataset with ``group = [10, 20, 40, 10, 10, 10]``, that means that you have 6 groups, 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. eval_name : str The name of evaluation function (without whitespace). eval_result : float The eval result. is_higher_better : bool Is eval result higher better, e.g. AUC is ``is_higher_better``. """ self.func = func def __call__( self, preds: np.ndarray, dataset: Dataset, ) -> Union[_LGBM_EvalFunctionResultType, List[_LGBM_EvalFunctionResultType]]: """Call passed function with appropriate arguments. Parameters ---------- preds : numpy 1-D array of shape = [n_samples] or numpy 2-D array of shape = [n_samples, n_classes] (for multi-class task) The predicted values. dataset : Dataset The training dataset. Returns ------- eval_name : str The name of evaluation function (without whitespace). eval_result : float The eval result. is_higher_better : bool Is eval result higher better, e.g. AUC is ``is_higher_better``. """ labels = _get_label_from_constructed_dataset(dataset) argc = len(signature(self.func).parameters) if argc == 2: return self.func(labels, preds) # type: ignore[call-arg] weight = _get_weight_from_constructed_dataset(dataset) if argc == 3: return self.func(labels, preds, weight) # type: ignore[call-arg] if argc == 4: group = _get_group_from_constructed_dataset(dataset) return self.func(labels, preds, weight, group) # type: ignore[call-arg] raise TypeError(f"Self-defined eval function should have 2, 3 or 4 arguments, got {argc}") # documentation templates for LGBMModel methods are shared between the classes in # this module and those in the ``dask`` module _lgbmmodel_doc_fit = """ Build a gradient boosting model from the training set (X, y). Parameters ---------- X : {X_shape} Input feature matrix. y : {y_shape} The target values (class labels in classification, real numbers in regression). sample_weight : {sample_weight_shape} Weights of training data. Weights should be non-negative. init_score : {init_score_shape} Init score of training data. group : {group_shape} Group/query data. Only used in the learning-to-rank task. sum(group) = n_samples. For example, if you have a 100-document dataset with ``group = [10, 20, 40, 10, 10, 10]``, that means that you have 6 groups, 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. eval_set : list or None, optional (default=None) .. deprecated:: 4.7.0 A list of (X, y) tuple pairs to use as validation sets. Use ``eval_X`` and ``eval_y`` instead. eval_names : list of str, or None, optional (default=None) Names of eval_set. eval_sample_weight : {eval_sample_weight_shape} Weights of eval data. Weights should be non-negative. eval_class_weight : list or None, optional (default=None) Class weights of eval data. eval_init_score : {eval_init_score_shape} Init score of eval data. eval_group : {eval_group_shape} Group data of eval data. eval_metric : str, callable, list or None, optional (default=None) If str, it should be a built-in evaluation metric to use. If callable, it should be a custom evaluation metric, see note below for more details. If list, it can be a list of built-in metrics, a list of custom evaluation metrics, or a mix of both. In either case, the ``metric`` from the model parameters will be evaluated and used as well. Default: 'l2' for LGBMRegressor, 'logloss' for LGBMClassifier, 'ndcg' for LGBMRanker. feature_name : list of str, or 'auto', optional (default='auto') Feature names. If 'auto' and data is pandas DataFrame, data columns names are used. categorical_feature : list of str or int, or 'auto', optional (default='auto') Categorical features. If list of int, interpreted as indices. If list of str, interpreted as feature names (need to specify ``feature_name`` as well). If 'auto' and data is pandas DataFrame, pandas unordered categorical columns are used. All values in categorical features will be cast to int32 and thus should be less than int32 max value (2147483647). Large values could be memory consuming. Consider using consecutive integers starting from zero. All negative values in categorical features will be treated as missing values. The output cannot be monotonically constrained with respect to a categorical feature. Floating point numbers in categorical features will be rounded towards 0. callbacks : list of callable, or None, optional (default=None) List of callback functions that are applied at each iteration. See Callbacks in Python API for more information. init_model : str, pathlib.Path, Booster, LGBMModel or None, optional (default=None) Filename of LightGBM model, Booster instance or LGBMModel instance used for continue training. eval_X : {X_shape}, or tuple of such inputs, or None, optional (default=None) Feature matrix or tuple thereof, e.g. ``(X_val0, X_val1)``, to use as validation sets. eval_y : {y_shape}, or tuple of such inputs, or None, optional (default=None) Target values or tuple thereof, e.g. ``(y_val0, y_val1)``, to use as validation sets. Returns ------- self : LGBMModel Returns self. """ _lgbmmodel_doc_custom_eval_note = """ Note ---- Custom eval function expects a callable with following signatures: ``func(y_true, y_pred)``, ``func(y_true, y_pred, weight)`` or ``func(y_true, y_pred, weight, group)`` and returns (eval_name, eval_result, is_higher_better) or list of (eval_name, eval_result, is_higher_better): y_true : numpy 1-D array of shape = [n_samples] The target values. 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) The predicted values. In case of custom ``objective``, predicted values are returned before any transformation, e.g. they are raw margin instead of probability of positive class for binary task in this case. weight : numpy 1-D array of shape = [n_samples] The weight of samples. Weights should be non-negative. group : numpy 1-D array Group/query data. Only used in the learning-to-rank task. sum(group) = n_samples. For example, if you have a 100-document dataset with ``group = [10, 20, 40, 10, 10, 10]``, that means that you have 6 groups, 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. eval_name : str The name of evaluation function (without whitespace). eval_result : float The eval result. is_higher_better : bool Is eval result higher better, e.g. AUC is ``is_higher_better``. """ _lgbmmodel_doc_predict = """ {description} Parameters ---------- X : {X_shape} Input features matrix. raw_score : bool, optional (default=False) Whether to predict raw scores. start_iteration : int, optional (default=0) Start index of the iteration to predict. If <= 0, starts from the first iteration. num_iteration : int or None, optional (default=None) Total number of iterations used in the prediction. If None, if the best iteration exists and start_iteration <= 0, the best iteration is used; otherwise, all iterations from ``start_iteration`` are used (no limits). If <= 0, all iterations from ``start_iteration`` are used (no limits). pred_leaf : bool, optional (default=False) Whether to predict leaf index. pred_contrib : bool, optional (default=False) Whether to predict feature contributions. .. note:: If you want to get more explanations for your model's predictions using SHAP values, like SHAP interaction values, you can install the shap package (https://github.com/slundberg/shap). Note that unlike the shap package, with ``pred_contrib`` we return a matrix with an extra column, where the last column is the expected value. validate_features : bool, optional (default=False) If True, ensure that the features used to predict match the ones used to train. Used only if data is pandas DataFrame. **kwargs Other parameters for the prediction. Returns ------- {output_name} : {predicted_result_shape} The predicted values. X_leaves : {X_leaves_shape} If ``pred_leaf=True``, the predicted leaf of every tree for each sample. X_SHAP_values : {X_SHAP_values_shape} If ``pred_contrib=True``, the feature contributions for each sample. """ def _extract_evaluation_meta_data( *, collection: Optional[Union[Dict[Any, Any], List[Any]]], name: str, i: int, ) -> Optional[Any]: """Try to extract the ith element of one of the ``eval_*`` inputs.""" if collection is None: return None elif isinstance(collection, list): # It's possible, for example, to pass 3 eval sets through `eval_set`, # but only 1 init_score through `eval_init_score`. # # This if-else accounts for that possibility. if len(collection) > i: return collection[i] else: return None elif isinstance(collection, dict): return collection.get(i, None) else: raise TypeError(f"{name} should be dict or list") def _validate_eval_set_Xy( *, eval_set: Optional[List[_LGBM_ScikitValidSet]], eval_X: Optional[Union[_LGBM_ScikitMatrixLike, Tuple[_LGBM_ScikitMatrixLike]]], eval_y: Optional[Union[_LGBM_LabelType, Tuple[_LGBM_LabelType]]], ) -> Optional[List[_LGBM_ScikitValidSet]]: """Validate eval args. Returns ------- eval_set """ if eval_set is not None: msg = "The argument 'eval_set' is deprecated, use 'eval_X' and 'eval_y' instead." warnings.warn(msg, category=LGBMDeprecationWarning, stacklevel=2) if eval_X is not None or eval_y is not None: raise ValueError("Specify either 'eval_set' or 'eval_X' and 'eval_y', but not both.") if isinstance(eval_set, tuple): return [eval_set] else: return eval_set if (eval_X is None) != (eval_y is None): raise ValueError("You must specify eval_X and eval_y, not just one of them.") if eval_set is None and eval_X is not None: if isinstance(eval_X, tuple) != isinstance(eval_y, tuple): raise ValueError("If eval_X is a tuple, y_val must be a tuple of same length, and vice versa.") if isinstance(eval_X, tuple) and isinstance(eval_y, tuple): if len(eval_X) != len(eval_y): raise ValueError("If eval_X is a tuple, y_val must be a tuple of same length, and vice versa.") if isinstance(eval_X, tuple) and isinstance(eval_y, tuple): eval_set = list(zip(eval_X, eval_y)) else: eval_set = [(eval_X, eval_y)] return eval_set class LGBMModel(_LGBMModelBase): """Implementation of the scikit-learn API for LightGBM.""" def __init__( self, *, boosting_type: str = "gbdt", num_leaves: int = 31, max_depth: int = -1, learning_rate: float = 0.1, n_estimators: int = 100, subsample_for_bin: int = 200000, objective: Optional[Union[str, _LGBM_ScikitCustomObjectiveFunction]] = None, class_weight: Optional[Union[Dict, str]] = None, min_split_gain: float = 0.0, min_child_weight: float = 1e-3, min_child_samples: int = 20, subsample: float = 1.0, subsample_freq: int = 0, colsample_bytree: float = 1.0, reg_alpha: float = 0.0, reg_lambda: float = 0.0, random_state: Optional[Union[int, np.random.RandomState, np.random.Generator]] = None, n_jobs: Optional[int] = None, importance_type: str = "split", **kwargs: Any, ): r"""Construct a gradient boosting model. Parameters ---------- boosting_type : str, optional (default='gbdt') 'gbdt', traditional Gradient Boosting Decision Tree. 'dart', Dropouts meet Multiple Additive Regression Trees. 'rf', Random Forest. num_leaves : int, optional (default=31) Maximum tree leaves for base learners. max_depth : int, optional (default=-1) Maximum tree depth for base learners, <=0 means no limit. If setting this to a positive value, consider also changing ``num_leaves`` to ``<= 2^max_depth``. learning_rate : float, optional (default=0.1) Boosting learning rate. You can use ``callbacks`` parameter of ``fit`` method to shrink/adapt learning rate in training using ``reset_parameter`` callback. Note, that this will ignore the ``learning_rate`` argument in training. n_estimators : int, optional (default=100) Number of boosted trees to fit. subsample_for_bin : int, optional (default=200000) Number of samples for constructing bins. objective : str, callable or None, optional (default=None) Specify the learning task and the corresponding learning objective or a custom objective function to be used (see note below). Default: 'regression' for LGBMRegressor, 'binary' or 'multiclass' for LGBMClassifier, 'lambdarank' for LGBMRanker. class_weight : dict, 'balanced' or None, optional (default=None) Weights associated with classes in the form ``{class_label: weight}``. Use this parameter only for multi-class classification task; for binary classification task you may use ``is_unbalance`` or ``scale_pos_weight`` parameters. Note, that the usage of all these parameters will result in poor estimates of the individual class probabilities. You may want to consider performing probability calibration (https://scikit-learn.org/stable/modules/calibration.html) of your model. The 'balanced' mode uses the values of y to automatically adjust weights inversely proportional to class frequencies in the input data as ``n_samples / (n_classes * np.bincount(y))``. If None, all classes are supposed to have weight one. Note, that these weights will be multiplied with ``sample_weight`` (passed through the ``fit`` method) if ``sample_weight`` is specified. min_split_gain : float, optional (default=0.) Minimum loss reduction required to make a further partition on a leaf node of the tree. min_child_weight : float, optional (default=1e-3) Minimum sum of instance weight (Hessian) needed in a child (leaf). min_child_samples : int, optional (default=20) Minimum number of data needed in a child (leaf). subsample : float, optional (default=1.) Subsample ratio of the training instance. subsample_freq : int, optional (default=0) Frequency of subsample, <=0 means no enable. colsample_bytree : float, optional (default=1.) Subsample ratio of columns when constructing each tree. reg_alpha : float, optional (default=0.) L1 regularization term on weights. reg_lambda : float, optional (default=0.) L2 regularization term on weights. random_state : int, RandomState object or None, optional (default=None) Random number seed. If int, this number is used to seed the C++ code. If RandomState or Generator object (numpy), a random integer is picked based on its state to seed the C++ code. If None, default seeds in C++ code are used. n_jobs : int or None, optional (default=None) Number of parallel threads to use for training (can be changed at prediction time by passing it as an extra keyword argument). For better performance, it is recommended to set this to the number of physical cores in the CPU. Negative integers are interpreted as following joblib's formula (n_cpus + 1 + n_jobs), just like scikit-learn (so e.g. -1 means using all threads). A value of zero corresponds the default number of threads configured for OpenMP in the system. A value of ``None`` (the default) corresponds to using the number of physical cores in the system (its correct detection requires either the ``joblib`` or the ``psutil`` util libraries to be installed). .. versionchanged:: 4.0.0 importance_type : str, optional (default='split') The type of feature importance to be filled into ``feature_importances_``. If 'split', result contains numbers of times the feature is used in a model. If 'gain', result contains total gains of splits which use the feature. **kwargs Other parameters for the model. Check http://lightgbm.readthedocs.io/en/latest/Parameters.html for more parameters. .. warning:: \*\*kwargs is not supported in sklearn, it may cause unexpected issues. Note ---- A custom objective function can be provided for the ``objective`` parameter. In this case, it should have the signature ``objective(y_true, y_pred) -> grad, hess``, ``objective(y_true, y_pred, weight) -> grad, hess`` or ``objective(y_true, y_pred, weight, group) -> grad, hess``: y_true : numpy 1-D array of shape = [n_samples] The target values. 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) The predicted values. Predicted values are returned before any transformation, e.g. they are raw margin instead of probability of positive class for binary task. weight : numpy 1-D array of shape = [n_samples] The weight of samples. Weights should be non-negative. group : numpy 1-D array Group/query data. Only used in the learning-to-rank task. sum(group) = n_samples. For example, if you have a 100-document dataset with ``group = [10, 20, 40, 10, 10, 10]``, that means that you have 6 groups, 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. grad : numpy 1-D array of shape = [n_samples] or numpy 2-D array of shape = [n_samples, n_classes] (for multi-class task) The value of the first order derivative (gradient) of the loss with respect to the elements of y_pred for each sample point. hess : numpy 1-D array of shape = [n_samples] or numpy 2-D array of shape = [n_samples, n_classes] (for multi-class task) The value of the second order derivative (Hessian) of the loss with respect to the elements of y_pred for each sample point. For multi-class task, y_pred is a numpy 2-D array of shape = [n_samples, n_classes], and grad and hess should be returned in the same format. """ if not SKLEARN_INSTALLED: raise LightGBMError( "scikit-learn is required for lightgbm.sklearn. " "You must install scikit-learn and restart your session to use this module." ) self.boosting_type = boosting_type self.objective = objective self.num_leaves = num_leaves self.max_depth = max_depth self.learning_rate = learning_rate self.n_estimators = n_estimators self.subsample_for_bin = subsample_for_bin self.min_split_gain = min_split_gain self.min_child_weight = min_child_weight self.min_child_samples = min_child_samples self.subsample = subsample self.subsample_freq = subsample_freq self.colsample_bytree = colsample_bytree self.reg_alpha = reg_alpha self.reg_lambda = reg_lambda self.random_state = random_state self.n_jobs = n_jobs self.importance_type = importance_type self._Booster: Optional[Booster] = None self._evals_result: _EvalResultDict = {} self._best_score: _LGBM_BoosterBestScoreType = {} self._best_iteration: int = -1 self._other_params: Dict[str, Any] = {} self._objective = objective self.class_weight = class_weight self._class_weight: Optional[Union[Dict, str]] = None self._class_map: Optional[Dict[int, int]] = None self._n_features: int = -1 self._n_features_in: int = -1 self._classes: Optional[np.ndarray] = None self._n_classes: int = -1 self.set_params(**kwargs) # scikit-learn 1.6 introduced an __sklearn__tags() method intended to replace _more_tags(). # _more_tags() can be removed whenever lightgbm's minimum supported scikit-learn version # is >=1.6. # ref: https://github.com/lightgbm-org/LightGBM/pull/6651 def _more_tags(self) -> Dict[str, Any]: check_sample_weight_str = ( "In LightGBM, setting a sample's weight to 0 can produce a different result than omitting the sample. " "Such samples intentionally still affect count-based measures like 'min_data_in_leaf' " "(https://github.com/lightgbm-org/LightGBM/issues/5626#issuecomment-1712706678) and the estimated distribution " "of features for Dataset construction (see https://github.com/lightgbm-org/LightGBM/issues/5553)." ) # "check_sample_weight_equivalence" can be removed when lightgbm's # minimum supported scikit-learn version is at least 1.6 # ref: https://github.com/scikit-learn/scikit-learn/pull/30137 return { "allow_nan": True, "X_types": ["2darray", "sparse", "1dlabels"], "_xfail_checks": { "check_no_attributes_set_in_init": ( "scikit-learn incorrectly asserts that private attributes " "cannot be set in __init__: " "(see https://github.com/lightgbm-org/LightGBM/issues/2628)" ), "check_all_zero_sample_weights_error": ( "Beginning in scikit-learn 1.9, by default estimators are expected to reject " "sample weight arrays that are all-0. LightGBM intentionally accepts such arrays. " "LightGBM supports some operations where training on an all-0-weight input could make sense, " "like batch updates with training continuation or manual model creation with forced splits." ), "check_sample_weight_equivalence": check_sample_weight_str, "check_sample_weight_equivalence_on_dense_data": check_sample_weight_str, "check_sample_weight_equivalence_on_sparse_data": check_sample_weight_str, }, } @staticmethod def _update_sklearn_tags_from_dict( *, tags: "_sklearn_Tags", tags_dict: Dict[str, Any], ) -> "_sklearn_Tags": """Update ``sklearn.utils.Tags`` inherited from ``scikit-learn`` base classes. ``scikit-learn`` 1.6 introduced a dataclass-based interface for estimator tags. ref: https://github.com/scikit-learn/scikit-learn/pull/29677 This method handles updating that instance based on the value in ``self._more_tags()``. """ tags.input_tags.allow_nan = tags_dict["allow_nan"] tags.input_tags.sparse = "sparse" in tags_dict["X_types"] tags.target_tags.one_d_labels = "1dlabels" in tags_dict["X_types"] return tags def __sklearn_tags__(self) -> Optional["_sklearn_Tags"]: # _LGBMModelBase.__sklearn_tags__() cannot be called unconditionally, # because that method isn't defined for scikit-learn<1.6 if not hasattr(_LGBMModelBase, "__sklearn_tags__"): err_msg = ( "__sklearn_tags__() should not be called when using scikit-learn<1.6. " f"Detected version: {_sklearn_version}" ) raise AttributeError(err_msg) # take whatever tags are provided by BaseEstimator, then modify # them with LightGBM-specific values return self._update_sklearn_tags_from_dict( tags=super().__sklearn_tags__(), tags_dict=self._more_tags(), ) def __sklearn_is_fitted__(self) -> bool: return getattr(self, "fitted_", False) def get_params(self, deep: bool = True) -> Dict[str, Any]: """Get parameters for this estimator. Parameters ---------- deep : bool, optional (default=True) If True, will return the parameters for this estimator and contained subobjects that are estimators. Returns ------- params : dict Parameter names mapped to their values. """ # Based on: https://github.com/dmlc/xgboost/blob/bd92b1c9c0db3e75ec3dfa513e1435d518bb535d/python-package/xgboost/sklearn.py#L941 # which was based on: https://stackoverflow.com/questions/59248211 # # `get_params()` flows like this: # # 0. Get parameters in subclass (self.__class__) first, by using inspect. # 1. Get parameters in all parent classes (especially `LGBMModel`). # 2. Get whatever was passed via `**kwargs`. # 3. Merge them. # # This needs to accommodate being called recursively in the following # inheritance graphs (and similar for classification and ranking): # # DaskLGBMRegressor -> LGBMRegressor -> LGBMModel -> BaseEstimator # (custom subclass) -> LGBMRegressor -> LGBMModel -> BaseEstimator # LGBMRegressor -> LGBMModel -> BaseEstimator # (custom subclass) -> LGBMModel -> BaseEstimator # LGBMModel -> BaseEstimator # params = super().get_params(deep=deep) cp = copy.copy(self) # If the immediate parent defines get_params(), use that. if callable(getattr(cp.__class__.__bases__[0], "get_params", None)): cp.__class__ = cp.__class__.__bases__[0] # Otherwise, skip it and assume the next class will have it. # This is here primarily for cases where the first class in MRO is a scikit-learn mixin. else: cp.__class__ = cp.__class__.__bases__[1] params.update(cp.__class__.get_params(cp, deep)) params.update(self._other_params) return params def set_params(self, **params: Any) -> "LGBMModel": """Set the parameters of this estimator. Parameters ---------- **params Parameter names with their new values. Returns ------- self : object Returns self. """ for key, value in params.items(): setattr(self, key, value) if hasattr(self, f"_{key}"): setattr(self, f"_{key}", value) self._other_params[key] = value return self def _process_params(self, stage: str) -> Dict[str, Any]: """Process the parameters of this estimator based on its type, parameter aliases, etc. Parameters ---------- stage : str Name of the stage (can be ``fit`` or ``predict``) this method is called from. Returns ------- processed_params : dict Processed parameter names mapped to their values. """ assert stage in {"fit", "predict"} params = self.get_params() params.pop("objective", None) for alias in _ConfigAliases.get("objective"): if alias in params: obj = params.pop(alias) _log_warning(f"Found '{alias}' in params. Will use it instead of 'objective' argument") if stage == "fit": self._objective = obj if stage == "fit": if self._objective is None: if isinstance(self, LGBMRegressor): self._objective = "regression" elif isinstance(self, LGBMClassifier): if self._n_classes > 2: self._objective = "multiclass" else: self._objective = "binary" elif isinstance(self, LGBMRanker): self._objective = "lambdarank" else: raise ValueError("Unknown LGBMModel type.") if callable(self._objective): if stage == "fit": params["objective"] = _ObjectiveFunctionWrapper(self._objective) else: params["objective"] = "None" else: params["objective"] = self._objective params.pop("importance_type", None) params.pop("n_estimators", None) params.pop("class_weight", None) if isinstance(params["random_state"], np.random.RandomState): params["random_state"] = params["random_state"].randint(np.iinfo(np.int32).max) elif isinstance(params["random_state"], np.random.Generator): params["random_state"] = int(params["random_state"].integers(np.iinfo(np.int32).max)) if self._n_classes > 2: for alias in _ConfigAliases.get("num_class"): params.pop(alias, None) params["num_class"] = self._n_classes if hasattr(self, "_eval_at"): eval_at = self._eval_at for alias in _ConfigAliases.get("eval_at"): if alias in params: _log_warning(f"Found '{alias}' in params. Will use it instead of 'eval_at' argument") eval_at = params.pop(alias) params["eval_at"] = eval_at # register default metric for consistency with callable eval_metric case original_metric = self._objective if isinstance(self._objective, str) else None if original_metric is None: # try to deduce from class instance if isinstance(self, LGBMRegressor): original_metric = "l2" elif isinstance(self, LGBMClassifier): original_metric = "multi_logloss" if self._n_classes > 2 else "binary_logloss" elif isinstance(self, LGBMRanker): original_metric = "ndcg" # overwrite default metric by explicitly set metric params = _choose_param_value("metric", params, original_metric) # use joblib conventions for negative n_jobs, just like scikit-learn # at predict time, this is handled later due to the order of parameter updates if stage == "fit": params = _choose_param_value("num_threads", params, self.n_jobs) params["num_threads"] = self._process_n_jobs(params["num_threads"]) return params def _process_n_jobs(self, n_jobs: Optional[int]) -> int: """Convert special values of n_jobs to their actual values according to the formulas that apply. Parameters ---------- n_jobs : int or None The original value of n_jobs, potentially having special values such as 'None' or negative integers. Returns ------- n_jobs : int The value of n_jobs with special values converted to actual number of threads. """ if n_jobs is None: n_jobs = _LGBMCpuCount(only_physical_cores=True) elif n_jobs < 0: n_jobs = max(_LGBMCpuCount(only_physical_cores=False) + 1 + n_jobs, 1) return n_jobs def fit( self, X: _LGBM_ScikitMatrixLike, y: _LGBM_LabelType, sample_weight: Optional[_LGBM_WeightType] = None, init_score: Optional[_LGBM_InitScoreType] = None, group: Optional[_LGBM_GroupType] = None, eval_set: Optional[List[_LGBM_ScikitValidSet]] = None, eval_names: Optional[List[str]] = None, eval_sample_weight: Optional[List[_LGBM_WeightType]] = None, eval_class_weight: Optional[List[float]] = None, eval_init_score: Optional[List[_LGBM_InitScoreType]] = None, eval_group: Optional[List[_LGBM_GroupType]] = None, eval_metric: Optional[_LGBM_ScikitEvalMetricType] = None, feature_name: _LGBM_FeatureNameConfiguration = "auto", categorical_feature: _LGBM_CategoricalFeatureConfiguration = "auto", callbacks: Optional[List[Callable]] = None, init_model: Optional[Union[str, Path, Booster, "LGBMModel"]] = None, *, eval_X: Optional[Union[_LGBM_ScikitMatrixLike, Tuple[_LGBM_ScikitMatrixLike]]] = None, eval_y: Optional[Union[_LGBM_LabelType, Tuple[_LGBM_LabelType]]] = None, ) -> "LGBMModel": """Docstring is set after definition, using a template.""" params = self._process_params(stage="fit") # Do not modify original args in fit function # Refer to https://github.com/lightgbm-org/LightGBM/pull/2619 eval_metric_list: List[Union[str, _LGBM_ScikitCustomEvalFunction]] if eval_metric is None: eval_metric_list = [] elif isinstance(eval_metric, list): eval_metric_list = copy.deepcopy(eval_metric) else: eval_metric_list = [copy.deepcopy(eval_metric)] # Separate built-in from callable evaluation metrics eval_metrics_callable = [_EvalFunctionWrapper(f) for f in eval_metric_list if callable(f)] eval_metrics_builtin = [m for m in eval_metric_list if isinstance(m, str)] # concatenate metric from params (or default if not provided in params) and eval_metric params["metric"] = [params["metric"]] if isinstance(params["metric"], (str, type(None))) else params["metric"] params["metric"] = [e for e in eval_metrics_builtin if e not in params["metric"]] + params["metric"] params["metric"] = [metric for metric in params["metric"] if metric is not None] if not isinstance(X, (pd_DataFrame, pa_Table)): _X, _y = _LGBMValidateData( self, X, y, reset=True, # allow any input type (this validation is done further down, in lgb.Dataset()) accept_sparse=True, # do not raise an error if Inf of NaN values are found (LightGBM handles these internally) ensure_all_finite=False, # raise an error on 0-row and 1-row inputs ensure_min_samples=2, ) if sample_weight is not None: if SKLEARN_CHECK_SAMPLE_WEIGHT_HAS_ALLOW_ZERO_WEIGHTS_ARG: sample_weight = _LGBMCheckSampleWeight(sample_weight, _X, allow_all_zero_weights=True) else: sample_weight = _LGBMCheckSampleWeight(sample_weight, _X) else: _X, _y = X, y # for other data types, setting n_features_in_ is handled by _LGBMValidateData() in the branch above self.n_features_in_ = _X.shape[1] if self._class_weight is None: self._class_weight = self.class_weight if self._class_weight is not None: class_sample_weight = _LGBMComputeSampleWeight(self._class_weight, y) if sample_weight is None or len(sample_weight) == 0: sample_weight = class_sample_weight else: sample_weight = np.multiply(sample_weight, class_sample_weight) train_set = Dataset( data=_X, label=_y, weight=sample_weight, group=group, init_score=init_score, categorical_feature=categorical_feature, feature_name=feature_name, params=params, ) valid_sets: List[Dataset] = [] eval_set = _validate_eval_set_Xy(eval_set=eval_set, eval_X=eval_X, eval_y=eval_y) if eval_set is not None: # check eval_group (only relevant for ranking tasks) if eval_group is not None: if len(eval_group) != len(eval_set): raise ValueError( f"Length of eval_group ({len(eval_group)}) not equal to length of eval_set ({len(eval_set)})" ) for i, valid_data in enumerate(eval_set): # reduce cost for prediction training data if valid_data[0] is X and valid_data[1] is y: valid_set = train_set else: valid_weight = _extract_evaluation_meta_data( collection=eval_sample_weight, name="eval_sample_weight", i=i, ) valid_class_weight = _extract_evaluation_meta_data( collection=eval_class_weight, name="eval_class_weight", i=i, ) if valid_class_weight is not None: if isinstance(valid_class_weight, dict) and self._class_map is not None: valid_class_weight = {self._class_map[k]: v for k, v in valid_class_weight.items()} valid_class_sample_weight = _LGBMComputeSampleWeight(valid_class_weight, valid_data[1]) if valid_weight is None or len(valid_weight) == 0: valid_weight = valid_class_sample_weight else: valid_weight = np.multiply(valid_weight, valid_class_sample_weight) valid_init_score = _extract_evaluation_meta_data( collection=eval_init_score, name="eval_init_score", i=i, ) valid_group = _extract_evaluation_meta_data( collection=eval_group, name="eval_group", i=i, ) valid_set = Dataset( data=valid_data[0], label=valid_data[1], weight=valid_weight, group=valid_group, init_score=valid_init_score, categorical_feature="auto", params=params, ) valid_sets.append(valid_set) if isinstance(init_model, LGBMModel): init_model = init_model.booster_ if callbacks is None: callbacks = [] else: callbacks = copy.copy(callbacks) # don't use deepcopy here to allow non-serializable objects evals_result: _EvalResultDict = {} callbacks.append(record_evaluation(evals_result)) self._Booster = train( params=params, train_set=train_set, num_boost_round=self.n_estimators, valid_sets=valid_sets, valid_names=eval_names, feval=eval_metrics_callable, # type: ignore[arg-type] init_model=init_model, callbacks=callbacks, ) # This populates the property self.n_features_, the number of features in the fitted model, # and so should only be set after fitting. # # The related property self._n_features_in, which populates self.n_features_in_, # is set BEFORE fitting. self._n_features = self._Booster.num_feature() self._evals_result = evals_result self._best_iteration = self._Booster.best_iteration self._best_score = self._Booster.best_score self.fitted_ = True # free dataset self._Booster.free_dataset() del train_set, valid_sets return self fit.__doc__ = ( _lgbmmodel_doc_fit.format( X_shape="numpy array, pandas DataFrame, pyarrow Table, scipy.sparse, list of lists of int or float of shape = [n_samples, n_features]", y_shape="numpy array, pandas DataFrame, pandas Series, list of int or float, pyarrow Array, pyarrow ChunkedArray of shape = [n_samples]", 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)", 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)", group_shape="numpy array, pandas Series, pyarrow Array, pyarrow ChunkedArray, list of int or float, or None, optional (default=None)", eval_sample_weight_shape="list of array (same types as ``sample_weight`` supports), or None, optional (default=None)", eval_init_score_shape="list of array (same types as ``init_score`` supports), or None, optional (default=None)", eval_group_shape="list of array (same types as ``group`` supports), or None, optional (default=None)", ) + "\n\n" + _lgbmmodel_doc_custom_eval_note ) def predict( self, X: _LGBM_ScikitMatrixLike, raw_score: bool = False, start_iteration: int = 0, num_iteration: Optional[int] = None, pred_leaf: bool = False, pred_contrib: bool = False, validate_features: bool = False, **kwargs: Any, ) -> _LGBM_PredictReturnType: """Docstring is set after definition, using a template.""" if not self.__sklearn_is_fitted__(): raise LGBMNotFittedError("Estimator not fitted, call fit before exploiting the model.") if not isinstance(X, (pd_DataFrame, pa_Table)): X = _LGBMValidateData( self, X, # 'y' being omitted = run scikit-learn's check_array() instead of check_X_y() # # Prevent scikit-learn from deleting or modifying attributes like 'feature_names_in_' and 'n_features_in_'. # These shouldn't be changed at predict() time. reset=False, # allow any input type (this validation is done further down, in lgb.Dataset()) accept_sparse=True, # do not raise an error if Inf of NaN values are found (LightGBM handles these internally) ensure_all_finite=False, # raise an error on 0-row inputs ensure_min_samples=1, ) # retrieve original params that possibly can be used in both training and prediction # and then overwrite them (considering aliases) with params that were passed directly in prediction predict_params = self._process_params(stage="predict") for alias in _ConfigAliases.get_by_alias( "data", "X", "raw_score", "start_iteration", "num_iteration", "pred_leaf", "pred_contrib", *kwargs.keys(), ): predict_params.pop(alias, None) predict_params.update(kwargs) # number of threads can have values with special meaning which is only applied # in the scikit-learn interface, these should not reach the c++ side as-is predict_params = _choose_param_value("num_threads", predict_params, self.n_jobs) predict_params["num_threads"] = self._process_n_jobs(predict_params["num_threads"]) return self._Booster.predict( # type: ignore[union-attr] X, raw_score=raw_score, start_iteration=start_iteration, num_iteration=num_iteration, pred_leaf=pred_leaf, pred_contrib=pred_contrib, validate_features=validate_features, **predict_params, ) predict.__doc__ = _lgbmmodel_doc_predict.format( description="Return the predicted value for each sample.", X_shape="numpy array, pandas DataFrame, scipy.sparse, list of lists of int or float of shape = [n_samples, n_features]", output_name="predicted_result", predicted_result_shape="array-like of shape = [n_samples] or shape = [n_samples, n_classes]", X_leaves_shape="array-like of shape = [n_samples, n_trees] or shape = [n_samples, n_trees * n_classes]", 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", ) @property def n_features_(self) -> int: """:obj:`int`: The number of features of fitted model.""" if not self.__sklearn_is_fitted__(): raise LGBMNotFittedError("No n_features found. Need to call fit beforehand.") return self._n_features @property def n_features_in_(self) -> int: """:obj:`int`: The number of features of fitted model.""" if not self.__sklearn_is_fitted__(): raise LGBMNotFittedError("No n_features_in found. Need to call fit beforehand.") return self._n_features_in @n_features_in_.setter def n_features_in_(self, value: int) -> None: """Set number of features found in passed-in dataset. Starting with ``scikit-learn`` 1.6, ``scikit-learn`` expects to be able to directly set this property in functions like ``validate_data()``. .. note:: Do not call ``estimator.n_features_in_ = some_int`` or anything else that invokes this method. It is only here for compatibility with ``scikit-learn`` validation functions used internally in ``lightgbm``. """ self._n_features_in = value @property def best_score_(self) -> _LGBM_BoosterBestScoreType: """:obj:`dict`: The best score of fitted model.""" if not self.__sklearn_is_fitted__(): raise LGBMNotFittedError("No best_score found. Need to call fit beforehand.") return self._best_score @property def best_iteration_(self) -> int: """:obj:`int`: The best iteration of fitted model if ``early_stopping()`` callback has been specified.""" if not self.__sklearn_is_fitted__(): raise LGBMNotFittedError( "No best_iteration found. Need to call fit with early_stopping callback beforehand." ) return self._best_iteration @property def objective_(self) -> Union[str, _LGBM_ScikitCustomObjectiveFunction]: """:obj:`str` or :obj:`callable`: The concrete objective used while fitting this model.""" if not self.__sklearn_is_fitted__(): raise LGBMNotFittedError("No objective found. Need to call fit beforehand.") return self._objective # type: ignore[return-value] @property def n_estimators_(self) -> int: """:obj:`int`: True number of boosting iterations performed. This might be less than parameter ``n_estimators`` if early stopping was enabled or if boosting stopped early due to limits on complexity like ``min_gain_to_split``. .. versionadded:: 4.0.0 """ if not self.__sklearn_is_fitted__(): raise LGBMNotFittedError("No n_estimators found. Need to call fit beforehand.") return self._Booster.current_iteration() # type: ignore @property def n_iter_(self) -> int: """:obj:`int`: True number of boosting iterations performed. This might be less than parameter ``n_estimators`` if early stopping was enabled or if boosting stopped early due to limits on complexity like ``min_gain_to_split``. .. versionadded:: 4.0.0 """ if not self.__sklearn_is_fitted__(): raise LGBMNotFittedError("No n_iter found. Need to call fit beforehand.") return self._Booster.current_iteration() # type: ignore @property def booster_(self) -> Booster: """Booster: The underlying Booster of this model.""" if not self.__sklearn_is_fitted__(): raise LGBMNotFittedError("No booster found. Need to call fit beforehand.") return self._Booster # type: ignore[return-value] @property def evals_result_(self) -> _EvalResultDict: """:obj:`dict`: The evaluation results if validation sets have been specified.""" if not self.__sklearn_is_fitted__(): raise LGBMNotFittedError("No results found. Need to call fit with eval_set beforehand.") return self._evals_result @property def feature_importances_(self) -> np.ndarray: """:obj:`array` of shape = [n_features]: The feature importances (the higher, the more important). .. note:: ``importance_type`` attribute is passed to the function to configure the type of importance values to be extracted. """ if not self.__sklearn_is_fitted__(): raise LGBMNotFittedError("No feature_importances found. Need to call fit beforehand.") return self._Booster.feature_importance(importance_type=self.importance_type) # type: ignore[union-attr] @property def feature_name_(self) -> List[str]: """:obj:`list` of shape = [n_features]: The names of features. .. note:: If input does not contain feature names, they will be added during fitting in the format ``Column_0``, ``Column_1``, ..., ``Column_N``. """ if not self.__sklearn_is_fitted__(): raise LGBMNotFittedError("No feature_name found. Need to call fit beforehand.") return self._Booster.feature_name() # type: ignore[union-attr] @property def feature_names_in_(self) -> np.ndarray: """:obj:`array` of shape = [n_features]: scikit-learn compatible version of ``.feature_name_``. .. versionadded:: 4.5.0 """ if not self.__sklearn_is_fitted__(): raise LGBMNotFittedError("No feature_names_in_ found. Need to call fit beforehand.") return np.array(self.feature_name_) @feature_names_in_.deleter def feature_names_in_(self) -> None: """Intercept calls to delete ``feature_names_in_``. Some code paths in ``scikit-learn`` try to delete the ``feature_names_in_`` attribute on estimators when a new training dataset that doesn't have features is passed. LightGBM automatically assigns feature names to such datasets (like ``Column_0``, ``Column_1``, etc.) and so does not want that behavior. However, that behavior is coupled to ``scikit-learn`` automatically updating ``n_features_in_`` in those same code paths, which is necessary for compliance with its API (via argument ``reset`` to functions like ``validate_data()`` and ``check_array()``). .. note:: Do not call ``del estimator.feature_names_in_`` or anything else that invokes this method. It is only here for compatibility with ``scikit-learn`` validation functions used internally in ``lightgbm``. """ pass class LGBMRegressor(_LGBMRegressorBase, LGBMModel): """LightGBM regressor.""" # NOTE: all args from LGBMModel.__init__() are intentionally repeated here for # docs, help(), and tab completion. def __init__( self, *, boosting_type: str = "gbdt", num_leaves: int = 31, max_depth: int = -1, learning_rate: float = 0.1, n_estimators: int = 100, subsample_for_bin: int = 200000, objective: Optional[Union[str, _LGBM_ScikitCustomObjectiveFunction]] = None, class_weight: Optional[Union[Dict, str]] = None, min_split_gain: float = 0.0, min_child_weight: float = 1e-3, min_child_samples: int = 20, subsample: float = 1.0, subsample_freq: int = 0, colsample_bytree: float = 1.0, reg_alpha: float = 0.0, reg_lambda: float = 0.0, random_state: Optional[Union[int, np.random.RandomState, np.random.Generator]] = None, n_jobs: Optional[int] = None, importance_type: str = "split", **kwargs: Any, ) -> None: super().__init__( boosting_type=boosting_type, num_leaves=num_leaves, max_depth=max_depth, learning_rate=learning_rate, n_estimators=n_estimators, subsample_for_bin=subsample_for_bin, objective=objective, class_weight=class_weight, min_split_gain=min_split_gain, min_child_weight=min_child_weight, min_child_samples=min_child_samples, subsample=subsample, subsample_freq=subsample_freq, colsample_bytree=colsample_bytree, reg_alpha=reg_alpha, reg_lambda=reg_lambda, random_state=random_state, n_jobs=n_jobs, importance_type=importance_type, **kwargs, ) __init__.__doc__ = LGBMModel.__init__.__doc__ def _more_tags(self) -> Dict[str, Any]: # handle the case where RegressorMixin possibly provides _more_tags() if callable(getattr(_LGBMRegressorBase, "_more_tags", None)): tags = _LGBMRegressorBase._more_tags(self) else: tags = {} # override those with LightGBM-specific preferences tags.update(LGBMModel._more_tags(self)) return tags def __sklearn_tags__(self) -> "_sklearn_Tags": return super().__sklearn_tags__() def fit( # type: ignore[override] self, X: _LGBM_ScikitMatrixLike, y: _LGBM_LabelType, sample_weight: Optional[_LGBM_WeightType] = None, init_score: Optional[_LGBM_InitScoreType] = None, eval_set: Optional[List[_LGBM_ScikitValidSet]] = None, eval_names: Optional[List[str]] = None, eval_sample_weight: Optional[List[_LGBM_WeightType]] = None, eval_init_score: Optional[List[_LGBM_InitScoreType]] = None, eval_metric: Optional[_LGBM_ScikitEvalMetricType] = None, feature_name: _LGBM_FeatureNameConfiguration = "auto", categorical_feature: _LGBM_CategoricalFeatureConfiguration = "auto", callbacks: Optional[List[Callable]] = None, init_model: Optional[Union[str, Path, Booster, LGBMModel]] = None, *, eval_X: Optional[Union[_LGBM_ScikitMatrixLike, Tuple[_LGBM_ScikitMatrixLike]]] = None, eval_y: Optional[Union[_LGBM_LabelType, Tuple[_LGBM_LabelType]]] = None, ) -> "LGBMRegressor": """Docstring is inherited from the LGBMModel.""" super().fit( X, y, sample_weight=sample_weight, init_score=init_score, eval_set=eval_set, eval_X=eval_X, eval_y=eval_y, eval_names=eval_names, eval_sample_weight=eval_sample_weight, eval_init_score=eval_init_score, eval_metric=eval_metric, feature_name=feature_name, categorical_feature=categorical_feature, callbacks=callbacks, init_model=init_model, ) return self _base_doc = LGBMModel.fit.__doc__.replace("self : LGBMModel", "self : LGBMRegressor") # type: ignore _base_doc = ( _base_doc[: _base_doc.find("group :")] # type: ignore + _base_doc[_base_doc.find("eval_set :") :] ) # type: ignore _base_doc = _base_doc[: _base_doc.find("eval_class_weight :")] + _base_doc[_base_doc.find("eval_init_score :") :] fit.__doc__ = _base_doc[: _base_doc.find("eval_group :")] + _base_doc[_base_doc.find("eval_metric :") :] class LGBMClassifier(_LGBMClassifierBase, LGBMModel): """LightGBM classifier.""" # NOTE: all args from LGBMModel.__init__() are intentionally repeated here for # docs, help(), and tab completion. def __init__( self, *, boosting_type: str = "gbdt", num_leaves: int = 31, max_depth: int = -1, learning_rate: float = 0.1, n_estimators: int = 100, subsample_for_bin: int = 200000, objective: Optional[Union[str, _LGBM_ScikitCustomObjectiveFunction]] = None, class_weight: Optional[Union[Dict, str]] = None, min_split_gain: float = 0.0, min_child_weight: float = 1e-3, min_child_samples: int = 20, subsample: float = 1.0, subsample_freq: int = 0, colsample_bytree: float = 1.0, reg_alpha: float = 0.0, reg_lambda: float = 0.0, random_state: Optional[Union[int, np.random.RandomState, np.random.Generator]] = None, n_jobs: Optional[int] = None, importance_type: str = "split", **kwargs: Any, ) -> None: super().__init__( boosting_type=boosting_type, num_leaves=num_leaves, max_depth=max_depth, learning_rate=learning_rate, n_estimators=n_estimators, subsample_for_bin=subsample_for_bin, objective=objective, class_weight=class_weight, min_split_gain=min_split_gain, min_child_weight=min_child_weight, min_child_samples=min_child_samples, subsample=subsample, subsample_freq=subsample_freq, colsample_bytree=colsample_bytree, reg_alpha=reg_alpha, reg_lambda=reg_lambda, random_state=random_state, n_jobs=n_jobs, importance_type=importance_type, **kwargs, ) __init__.__doc__ = LGBMModel.__init__.__doc__ def _more_tags(self) -> Dict[str, Any]: # handle the case where ClassifierMixin possibly provides _more_tags() if callable(getattr(_LGBMClassifierBase, "_more_tags", None)): tags = _LGBMClassifierBase._more_tags(self) else: tags = {} # override those with LightGBM-specific preferences tags.update(LGBMModel._more_tags(self)) return tags def __sklearn_tags__(self) -> "_sklearn_Tags": tags = super().__sklearn_tags__() if tags is not None: tags.classifier_tags.multi_class = True tags.classifier_tags.multi_label = False return tags def fit( # type: ignore[override] self, X: _LGBM_ScikitMatrixLike, y: _LGBM_LabelType, sample_weight: Optional[_LGBM_WeightType] = None, init_score: Optional[_LGBM_InitScoreType] = None, eval_set: Optional[List[_LGBM_ScikitValidSet]] = None, eval_names: Optional[List[str]] = None, eval_sample_weight: Optional[List[_LGBM_WeightType]] = None, eval_class_weight: Optional[List[float]] = None, eval_init_score: Optional[List[_LGBM_InitScoreType]] = None, eval_metric: Optional[_LGBM_ScikitEvalMetricType] = None, feature_name: _LGBM_FeatureNameConfiguration = "auto", categorical_feature: _LGBM_CategoricalFeatureConfiguration = "auto", callbacks: Optional[List[Callable]] = None, init_model: Optional[Union[str, Path, Booster, LGBMModel]] = None, *, eval_X: Optional[Union[_LGBM_ScikitMatrixLike, Tuple[_LGBM_ScikitMatrixLike]]] = None, eval_y: Optional[Union[_LGBM_LabelType, Tuple[_LGBM_LabelType]]] = None, ) -> "LGBMClassifier": """Docstring is inherited from the LGBMModel.""" _LGBMAssertAllFinite(y) _LGBMCheckClassificationTargets(y) self._le = _LGBMLabelEncoder().fit(y) _y = self._le.transform(y) self._class_map = dict(zip(self._le.classes_, self._le.transform(self._le.classes_))) if isinstance(self.class_weight, dict): self._class_weight = {self._class_map[k]: v for k, v in self.class_weight.items()} self._classes = self._le.classes_ self._n_classes = len(self._classes) # type: ignore[arg-type] if self.objective is None: self._objective = None # adjust eval metrics to match whether binary or multiclass # classification is being performed if not callable(eval_metric): if isinstance(eval_metric, list): eval_metric_list = eval_metric elif isinstance(eval_metric, str): eval_metric_list = [eval_metric] else: eval_metric_list = [] if self.__is_multiclass: for index, metric in enumerate(eval_metric_list): if metric in {"logloss", "binary_logloss"}: eval_metric_list[index] = "multi_logloss" elif metric in {"error", "binary_error"}: eval_metric_list[index] = "multi_error" else: for index, metric in enumerate(eval_metric_list): if metric in {"logloss", "multi_logloss"}: eval_metric_list[index] = "binary_logloss" elif metric in {"error", "multi_error"}: eval_metric_list[index] = "binary_error" eval_metric = eval_metric_list # do not modify args, as it causes errors in model selection tools valid_sets: Optional[List[_LGBM_ScikitValidSet]] = None if eval_set is not None: if isinstance(eval_set, tuple): eval_set = [eval_set] valid_sets = [] for valid_x, valid_y in eval_set: if valid_x is X and valid_y is y: valid_sets.append((valid_x, _y)) else: valid_sets.append((valid_x, self._le.transform(valid_y))) super().fit( X, _y, sample_weight=sample_weight, init_score=init_score, eval_set=valid_sets, eval_names=eval_names, eval_X=eval_X, eval_y=eval_y, eval_sample_weight=eval_sample_weight, eval_class_weight=eval_class_weight, eval_init_score=eval_init_score, eval_metric=eval_metric, feature_name=feature_name, categorical_feature=categorical_feature, callbacks=callbacks, init_model=init_model, ) return self _base_doc = LGBMModel.fit.__doc__.replace("self : LGBMModel", "self : LGBMClassifier") # type: ignore _base_doc = ( _base_doc[: _base_doc.find("group :")] # type: ignore + _base_doc[_base_doc.find("eval_set :") :] ) # type: ignore fit.__doc__ = _base_doc[: _base_doc.find("eval_group :")] + _base_doc[_base_doc.find("eval_metric :") :] def predict( self, X: _LGBM_ScikitMatrixLike, raw_score: bool = False, start_iteration: int = 0, num_iteration: Optional[int] = None, pred_leaf: bool = False, pred_contrib: bool = False, validate_features: bool = False, **kwargs: Any, ) -> _LGBM_PredictReturnType: """Docstring is inherited from the LGBMModel.""" result = self.predict_proba( X=X, raw_score=raw_score, start_iteration=start_iteration, num_iteration=num_iteration, pred_leaf=pred_leaf, pred_contrib=pred_contrib, validate_features=validate_features, **kwargs, ) if callable(self._objective) or raw_score or pred_leaf or pred_contrib: return result else: class_index = np.argmax(result, axis=1) return self._le.inverse_transform(class_index) predict.__doc__ = LGBMModel.predict.__doc__ def predict_proba( self, X: _LGBM_ScikitMatrixLike, raw_score: bool = False, start_iteration: int = 0, num_iteration: Optional[int] = None, pred_leaf: bool = False, pred_contrib: bool = False, validate_features: bool = False, **kwargs: Any, ) -> _LGBM_PredictReturnType: """Docstring is set after definition, using a template.""" result = super().predict( X=X, raw_score=raw_score, start_iteration=start_iteration, num_iteration=num_iteration, pred_leaf=pred_leaf, pred_contrib=pred_contrib, validate_features=validate_features, **kwargs, ) if callable(self._objective) and not (raw_score or pred_leaf or pred_contrib): _log_warning( "Cannot compute class probabilities or labels " "due to the usage of customized objective function.\n" "Returning raw scores instead." ) return result elif self.__is_multiclass or raw_score or pred_leaf or pred_contrib: # type: ignore [operator] return result else: error_msg = ( "predict() should return np.ndarray when pred_contrib=False. " "If you're seeing this message, it's a bug in lightgbm. Please report it at https://github.com/lightgbm-org/LightGBM/issues." ) assert isinstance(result, np.ndarray), error_msg return np.vstack((1.0 - result, result)).transpose() predict_proba.__doc__ = _lgbmmodel_doc_predict.format( description="Return the predicted probability for each class for each sample.", X_shape="numpy array, pandas DataFrame, scipy.sparse, list of lists of int or float of shape = [n_samples, n_features]", output_name="predicted_probability", predicted_result_shape="array-like of shape = [n_samples] or shape = [n_samples, n_classes]", X_leaves_shape="array-like of shape = [n_samples, n_trees] or shape = [n_samples, n_trees * n_classes]", 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", ) @property def classes_(self) -> np.ndarray: """:obj:`array` of shape = [n_classes]: The class label array.""" if not self.__sklearn_is_fitted__(): raise LGBMNotFittedError("No classes found. Need to call fit beforehand.") return self._classes # type: ignore[return-value] @property def n_classes_(self) -> int: """:obj:`int`: The number of classes.""" if not self.__sklearn_is_fitted__(): raise LGBMNotFittedError("No classes found. Need to call fit beforehand.") return self._n_classes @property def __is_multiclass(self) -> bool: """:obj:`bool`: Indicator of whether the classifier is used for multiclass.""" return self._n_classes > 2 or (isinstance(self._objective, str) and self._objective in _MULTICLASS_OBJECTIVES) class LGBMRanker(LGBMModel): """LightGBM ranker. .. warning:: scikit-learn doesn't support ranking applications yet, therefore this class is not really compatible with the sklearn ecosystem. Please use this class mainly for training and applying ranking models in common sklearnish way. """ # NOTE: all args from LGBMModel.__init__() are intentionally repeated here for # docs, help(), and tab completion. def __init__( self, *, boosting_type: str = "gbdt", num_leaves: int = 31, max_depth: int = -1, learning_rate: float = 0.1, n_estimators: int = 100, subsample_for_bin: int = 200000, objective: Optional[Union[str, _LGBM_ScikitCustomObjectiveFunction]] = None, class_weight: Optional[Union[Dict, str]] = None, min_split_gain: float = 0.0, min_child_weight: float = 1e-3, min_child_samples: int = 20, subsample: float = 1.0, subsample_freq: int = 0, colsample_bytree: float = 1.0, reg_alpha: float = 0.0, reg_lambda: float = 0.0, random_state: Optional[Union[int, np.random.RandomState, np.random.Generator]] = None, n_jobs: Optional[int] = None, importance_type: str = "split", **kwargs: Any, ) -> None: super().__init__( boosting_type=boosting_type, num_leaves=num_leaves, max_depth=max_depth, learning_rate=learning_rate, n_estimators=n_estimators, subsample_for_bin=subsample_for_bin, objective=objective, class_weight=class_weight, min_split_gain=min_split_gain, min_child_weight=min_child_weight, min_child_samples=min_child_samples, subsample=subsample, subsample_freq=subsample_freq, colsample_bytree=colsample_bytree, reg_alpha=reg_alpha, reg_lambda=reg_lambda, random_state=random_state, n_jobs=n_jobs, importance_type=importance_type, **kwargs, ) __init__.__doc__ = LGBMModel.__init__.__doc__ def fit( # type: ignore[override] self, X: _LGBM_ScikitMatrixLike, y: _LGBM_LabelType, sample_weight: Optional[_LGBM_WeightType] = None, init_score: Optional[_LGBM_InitScoreType] = None, group: Optional[_LGBM_GroupType] = None, eval_set: Optional[List[_LGBM_ScikitValidSet]] = None, eval_names: Optional[List[str]] = None, eval_sample_weight: Optional[List[_LGBM_WeightType]] = None, eval_init_score: Optional[List[_LGBM_InitScoreType]] = None, eval_group: Optional[List[_LGBM_GroupType]] = None, eval_metric: Optional[_LGBM_ScikitEvalMetricType] = None, eval_at: Union[List[int], Tuple[int, ...]] = (1, 2, 3, 4, 5), feature_name: _LGBM_FeatureNameConfiguration = "auto", categorical_feature: _LGBM_CategoricalFeatureConfiguration = "auto", callbacks: Optional[List[Callable]] = None, init_model: Optional[Union[str, Path, Booster, LGBMModel]] = None, *, eval_X: Optional[Union[_LGBM_ScikitMatrixLike, Tuple[_LGBM_ScikitMatrixLike]]] = None, eval_y: Optional[Union[_LGBM_LabelType, Tuple[_LGBM_LabelType]]] = None, ) -> "LGBMRanker": """Docstring is inherited from the LGBMModel.""" # check group data if group is None: raise ValueError("Should set group for ranking task") if eval_group is None and (eval_set is not None or eval_X is not None or eval_y is not None): raise ValueError("eval_group cannot be None if any of eval_set, eval_X, or eval_y are provided") self._eval_at = eval_at super().fit( X, y, sample_weight=sample_weight, init_score=init_score, group=group, eval_set=eval_set, eval_names=eval_names, eval_X=eval_X, eval_y=eval_y, eval_sample_weight=eval_sample_weight, eval_init_score=eval_init_score, eval_group=eval_group, eval_metric=eval_metric, feature_name=feature_name, categorical_feature=categorical_feature, callbacks=callbacks, init_model=init_model, ) return self _base_doc = LGBMModel.fit.__doc__.replace("self : LGBMModel", "self : LGBMRanker") # type: ignore fit.__doc__ = ( _base_doc[: _base_doc.find("eval_class_weight :")] # type: ignore + _base_doc[_base_doc.find("eval_init_score :") :] ) # type: ignore _base_doc = fit.__doc__ _before_feature_name, _feature_name, _after_feature_name = _base_doc.partition("feature_name :") fit.__doc__ = f"""{_before_feature_name}eval_at : list or tuple of int, optional (default=(1, 2, 3, 4, 5)) The evaluation positions of the specified metric. {_feature_name}{_after_feature_name}""" ================================================ FILE: python-package/pyproject.toml ================================================ [project] classifiers = [ "Development Status :: 5 - Production/Stable", "Intended Audience :: Science/Research", "Natural Language :: English", "Operating System :: MacOS", "Operating System :: Microsoft :: Windows", "Operating System :: POSIX", "Operating System :: Unix", "Programming Language :: Python :: 3", "Programming Language :: Python :: 3.9", "Programming Language :: Python :: 3.10", "Programming Language :: Python :: 3.11", "Programming Language :: Python :: 3.12", "Programming Language :: Python :: 3.13", "Topic :: Scientific/Engineering :: Artificial Intelligence" ] dependencies = [ "numpy>=1.17.0", "scipy" ] description = "LightGBM Python-package" license = "MIT" license-files = [ "LICENSE" ] maintainers = [ {name = "Yu Shi", email = "yushi@microsoft.com"} ] name = "lightgbm" readme = "README.rst" requires-python = ">=3.9" version = "4.6.0.99" [project.optional-dependencies] arrow = [ "cffi>=1.15.1", "pyarrow>=6.0.1" ] dask = [ "dask[array,dataframe,distributed]>=2.0.0", "pandas>=0.24.0" ] pandas = [ "pandas>=0.24.0" ] plotting = [ "graphviz", "matplotlib" ] scikit-learn = [ "scikit-learn>=0.24.2" ] [project.urls] homepage = "https://github.com/lightgbm-org/LightGBM" documentation = "https://lightgbm.readthedocs.io/en/latest/" repository = "https://github.com/lightgbm-org/LightGBM.git" changelog = "https://github.com/lightgbm-org/LightGBM/releases" # start:build-system [build-system] requires = ["scikit-build-core>=0.11.0"] build-backend = "scikit_build_core.build" # based on https://github.com/scikit-build/scikit-build-core#configuration [tool.scikit-build] ninja.version = ">=1.11" ninja.make-fallback = true build.verbose = false build.targets = ["_lightgbm"] install.strip = true logging.level = "INFO" sdist.reproducible = true wheel.py-api = "py3" experimental = false strict-config = false minimum-version = "build-system.requires" [tool.scikit-build.cmake] version = "CMakeLists.txt" build-type = "Release" [tool.scikit-build.cmake.define] __BUILD_FOR_PYTHON = "ON" # end:build-system [tool.mypy] disallow_untyped_defs = true exclude = 'build/*|compile/*|docs/*|examples/*|external_libs/*|lightgbm-python/*|tests/*' ignore_missing_imports = true [tool.rstcheck] report_level = "WARNING" ignore_directives = [ "autoclass", "autofunction", "autosummary", "doxygenfile" ] [tool.ruff] exclude = [ "build", "compile", "external_libs", "lightgbm-python", ] line-length = 120 # this should be set to the oldest version of python LightGBM supports target-version = "py39" [tool.ruff.format] docstring-code-format = false exclude = [ "build/*.py", "compile/*.py", "external_libs/*.py", "lightgbm-python/*.py", ] indent-style = "space" quote-style = "double" skip-magic-trailing-comma = false [tool.ruff.lint] ignore = [ # (pydocstyle) Missing docstring in magic method "D105", # (pycodestyle) Line too long "E501", # (pylint) Too many branches "PLR0912", # (pylint) Too many arguments in function definition "PLR0913", # (pylint) Too many statements "PLR0915", # (pylint) Consider merging multiple comparisons "PLR1714", # (pylint) Magic value used in comparison "PLR2004", # (pylint) for loop variable overwritten by assignment target "PLW2901", # (pylint) use 'elif' instead of 'else' then 'if', to reduce indentation "PLR5501", # (flake8-pytest-style) `scope='function'` is implied in `@pytest.fixture()` "PT003" ] select = [ # flake8-bugbear "B", # flake8-comprehensions "C4", # pydocstyle "D", # pycodestyle (errors) "E", # pyflakes "F", # isort "I", # NumPy-specific rules "NPY", # pylint "PL", # flake8-pytest-style "PT", # flake8-return: unnecessary assignment before return "RET504", # flake8-return: superfluous-else-raise "RET506", # flake8-simplify: use dict.get() instead of an if-else block "SIM401", # flake8-print "T", # pycodestyle (warnings) "W", ] [tool.ruff.lint.flake8-pytest-style] raises-extend-require-match-for = ["*Exception", "*Error"] [tool.ruff.lint.per-file-ignores] ".ci/create-nuget.py" = [ # (flake8-print) flake8-print "T201" ] "docs/conf.py" = [ # (flake8-bugbear) raise exceptions with "raise ... from err" "B904", # (flake8-print) flake8-print "T" ] "examples/*" = [ # pydocstyle "D", # flake8-print "T" ] "python-package/lightgbm/basic.py" = [ # (pylint) Using the global statement is discouraged "PLW0603" ] "tests/*" = [ # (flake8-bugbear) Found useless expression "B018", # pydocstyle "D", # flake8-print "T" ] [tool.ruff.lint.pydocstyle] convention = "numpy" [tool.ruff.lint.isort] known-first-party = ["lightgbm"] ================================================ FILE: src/application/application.cpp ================================================ /*! * Copyright (c) 2016-2026 Microsoft Corporation. All rights reserved. * Copyright (c) 2016-2026 The LightGBM developers. All rights reserved. * Licensed under the MIT License. See LICENSE file in the project root for license information. */ #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include "predictor.hpp" namespace LightGBM { Application::Application(int argc, char** argv) { LoadParameters(argc, argv); // set number of threads for openmp OMP_SET_NUM_THREADS(config_.num_threads); if (config_.data.size() == 0 && config_.task != TaskType::kConvertModel) { Log::Fatal("No training/prediction data, application quit"); } if (config_.device_type == std::string("cuda")) { LGBM_config_::current_device = lgbm_device_cuda; } } Application::~Application() { if (config_.is_parallel) { Network::Dispose(); } } void Application::LoadParameters(int argc, char** argv) { std::unordered_map> all_params; std::unordered_map params; for (int i = 1; i < argc; ++i) { Config::KV2Map(&all_params, argv[i]); } // read parameters from config file bool config_file_ok = true; if (all_params.count("config") > 0) { TextReader config_reader(all_params["config"][0].c_str(), false); config_reader.ReadAllLines(); if (!config_reader.Lines().empty()) { for (auto& line : config_reader.Lines()) { // remove str after "#" if (line.size() > 0 && std::string::npos != line.find_first_of("#")) { line.erase(line.find_first_of("#")); } line = Common::Trim(line); if (line.size() == 0) { continue; } Config::KV2Map(&all_params, line.c_str()); } } else { config_file_ok = false; } } Config::SetVerbosity(all_params); // de-duplicate params Config::KeepFirstValues(all_params, ¶ms); if (!config_file_ok) { Log::Warning("Config file %s doesn't exist, will ignore", params["config"].c_str()); } ParameterAlias::KeyAliasTransform(¶ms); config_.Set(params); Log::Info("Finished loading parameters"); } void Application::LoadData() { auto start_time = std::chrono::high_resolution_clock::now(); std::unique_ptr predictor; // prediction is needed if using input initial model(continued train) PredictFunction predict_fun = nullptr; // need to continue training if (boosting_->NumberOfTotalModel() > 0 && config_.task != TaskType::KRefitTree) { predictor.reset(new Predictor(boosting_.get(), 0, -1, true, false, false, false, -1, -1)); predict_fun = predictor->GetPredictFunction(); } // sync up random seed for data partition if (config_.is_data_based_parallel) { config_.data_random_seed = Network::GlobalSyncUpByMin(config_.data_random_seed); } Log::Debug("Loading train file..."); DatasetLoader dataset_loader(config_, predict_fun, config_.num_class, config_.data.c_str()); // load Training data if (config_.is_data_based_parallel) { // load data for distributed training train_data_.reset(dataset_loader.LoadFromFile(config_.data.c_str(), Network::rank(), Network::num_machines())); } else { // load data for single machine train_data_.reset(dataset_loader.LoadFromFile(config_.data.c_str(), 0, 1)); } // need save binary file if (config_.save_binary) { train_data_->SaveBinaryFile(nullptr); } // create training metric if (config_.is_provide_training_metric) { for (auto metric_type : config_.metric) { auto metric = std::unique_ptr(Metric::CreateMetric(metric_type, config_)); if (metric == nullptr) { continue; } metric->Init(train_data_->metadata(), train_data_->num_data()); train_metric_.push_back(std::move(metric)); } } train_metric_.shrink_to_fit(); if (!config_.metric.empty()) { // only when have metrics then need to construct validation data // Add validation data, if it exists for (size_t i = 0; i < config_.valid.size(); ++i) { Log::Debug("Loading validation file #%zu...", (i + 1)); // add auto new_dataset = std::unique_ptr( dataset_loader.LoadFromFileAlignWithOtherDataset( config_.valid[i].c_str(), train_data_.get())); valid_datas_.push_back(std::move(new_dataset)); // need save binary file if (config_.save_binary) { valid_datas_.back()->SaveBinaryFile(nullptr); } // add metric for validation data valid_metrics_.emplace_back(); for (auto metric_type : config_.metric) { auto metric = std::unique_ptr(Metric::CreateMetric(metric_type, config_)); if (metric == nullptr) { continue; } metric->Init(valid_datas_.back()->metadata(), valid_datas_.back()->num_data()); valid_metrics_.back().push_back(std::move(metric)); } valid_metrics_.back().shrink_to_fit(); } valid_datas_.shrink_to_fit(); valid_metrics_.shrink_to_fit(); } auto end_time = std::chrono::high_resolution_clock::now(); // output used time on each iteration Log::Info("Finished loading data in %f seconds", std::chrono::duration(end_time - start_time) * 1e-3); } void Application::InitTrain() { if (config_.is_parallel) { // need init network Network::Init(config_); Log::Info("Finished initializing network"); config_.feature_fraction_seed = Network::GlobalSyncUpByMin(config_.feature_fraction_seed); config_.feature_fraction = Network::GlobalSyncUpByMin(config_.feature_fraction); config_.drop_seed = Network::GlobalSyncUpByMin(config_.drop_seed); } // create boosting boosting_.reset( Boosting::CreateBoosting(config_.boosting, config_.input_model.c_str(), config_.device_type, config_.num_gpu)); // create objective function objective_fun_.reset( ObjectiveFunction::CreateObjectiveFunction(config_.objective, config_)); // load training data LoadData(); if (config_.task == TaskType::kSaveBinary) { Log::Info("Save data as binary finished, exit"); exit(0); } // initialize the objective function objective_fun_->Init(train_data_->metadata(), train_data_->num_data()); // initialize the boosting boosting_->Init(&config_, train_data_.get(), objective_fun_.get(), Common::ConstPtrInVectorWrapper(train_metric_)); // add validation data into boosting for (size_t i = 0; i < valid_datas_.size(); ++i) { boosting_->AddValidDataset(valid_datas_[i].get(), Common::ConstPtrInVectorWrapper(valid_metrics_[i])); Log::Debug("Number of data points in validation set #%zu: %d", i + 1, valid_datas_[i]->num_data()); } Log::Info("Finished initializing training"); } void Application::Train() { Log::Info("Started training..."); boosting_->Train(config_.snapshot_freq, config_.output_model); boosting_->SaveModelToFile(0, -1, config_.saved_feature_importance_type, config_.output_model.c_str()); // convert model to if-else statement code if (config_.convert_model_language == std::string("cpp")) { boosting_->SaveModelToIfElse(-1, config_.convert_model.c_str()); } Log::Info("Finished training"); } void Application::Predict() { if (config_.task == TaskType::KRefitTree) { // create predictor Predictor predictor(boosting_.get(), 0, -1, false, true, false, false, 1, 1); predictor.Predict(config_.data.c_str(), config_.output_result.c_str(), config_.header, config_.predict_disable_shape_check, config_.precise_float_parser); TextReader result_reader(config_.output_result.c_str(), false); result_reader.ReadAllLines(); size_t nrow = result_reader.Lines().size(); size_t ncol = 0; if (nrow > 0) { ncol = Common::StringToArray(result_reader.Lines()[0], '\t').size(); } std::vector pred_leaf; pred_leaf.resize(nrow * ncol); #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) for (int irow = 0; irow < static_cast(nrow); ++irow) { auto line_vec = Common::StringToArray(result_reader.Lines()[irow], '\t'); CHECK_EQ(line_vec.size(), ncol); for (int i_row_item = 0; i_row_item < static_cast(ncol); ++i_row_item) { pred_leaf[irow * ncol + i_row_item] = line_vec[i_row_item]; } // Free memory result_reader.Lines()[irow].clear(); } DatasetLoader dataset_loader(config_, nullptr, config_.num_class, config_.data.c_str()); train_data_.reset(dataset_loader.LoadFromFile(config_.data.c_str(), 0, 1)); train_metric_.clear(); objective_fun_.reset(ObjectiveFunction::CreateObjectiveFunction(config_.objective, config_)); objective_fun_->Init(train_data_->metadata(), train_data_->num_data()); boosting_->Init(&config_, train_data_.get(), objective_fun_.get(), Common::ConstPtrInVectorWrapper(train_metric_)); boosting_->RefitTree(pred_leaf.data(), nrow, ncol); boosting_->SaveModelToFile(0, -1, config_.saved_feature_importance_type, config_.output_model.c_str()); Log::Info("Finished RefitTree"); } else { // create predictor Predictor predictor(boosting_.get(), config_.start_iteration_predict, config_.num_iteration_predict, config_.predict_raw_score, config_.predict_leaf_index, config_.predict_contrib, config_.pred_early_stop, config_.pred_early_stop_freq, config_.pred_early_stop_margin); predictor.Predict(config_.data.c_str(), config_.output_result.c_str(), config_.header, config_.predict_disable_shape_check, config_.precise_float_parser); Log::Info("Finished prediction"); } } void Application::InitPredict() { boosting_.reset( Boosting::CreateBoosting("gbdt", config_.input_model.c_str(), config_.device_type, config_.num_gpu)); Log::Info("Finished initializing prediction, total used %d iterations", boosting_->GetCurrentIteration()); } void Application::ConvertModel() { boosting_.reset( Boosting::CreateBoosting(config_.boosting, config_.input_model.c_str(), config_.device_type, config_.num_gpu)); boosting_->SaveModelToIfElse(-1, config_.convert_model.c_str()); } } // namespace LightGBM ================================================ FILE: src/application/predictor.hpp ================================================ /*! * Copyright (c) 2016-2026 Microsoft Corporation. All rights reserved. * Copyright (c) 2016-2026 The LightGBM developers. All rights reserved. * Licensed under the MIT License. See LICENSE file in the project root for license information. */ #ifndef LIGHTGBM_SRC_APPLICATION_PREDICTOR_HPP_ #define LIGHTGBM_SRC_APPLICATION_PREDICTOR_HPP_ #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include namespace LightGBM { /*! * \brief Used to predict data with input model */ class Predictor { public: /*! * \brief Constructor * \param boosting Input boosting model * \param start_iteration Start index of the iteration to predict * \param num_iteration Number of boosting round * \param is_raw_score True if need to predict result with raw score * \param predict_leaf_index True to output leaf index instead of prediction score * \param predict_contrib True to output feature contributions instead of prediction score */ Predictor(Boosting* boosting, int start_iteration, int num_iteration, bool is_raw_score, bool predict_leaf_index, bool predict_contrib, bool early_stop, int early_stop_freq, double early_stop_margin) { early_stop_ = CreatePredictionEarlyStopInstance( "none", LightGBM::PredictionEarlyStopConfig()); if (early_stop && !boosting->NeedAccuratePrediction()) { PredictionEarlyStopConfig pred_early_stop_config; CHECK_GT(early_stop_freq, 0); CHECK_GE(early_stop_margin, 0); pred_early_stop_config.margin_threshold = early_stop_margin; pred_early_stop_config.round_period = early_stop_freq; if (boosting->NumberOfClasses() == 1) { early_stop_ = CreatePredictionEarlyStopInstance("binary", pred_early_stop_config); } else { early_stop_ = CreatePredictionEarlyStopInstance("multiclass", pred_early_stop_config); } } boosting->InitPredict(start_iteration, num_iteration, predict_contrib); boosting_ = boosting; num_pred_one_row_ = boosting_->NumPredictOneRow(start_iteration, num_iteration, predict_leaf_index, predict_contrib); num_feature_ = boosting_->MaxFeatureIdx() + 1; predict_buf_.resize( OMP_NUM_THREADS(), std::vector>( num_feature_, 0.0f)); const int kFeatureThreshold = 100000; const size_t KSparseThreshold = static_cast(0.01 * num_feature_); if (predict_leaf_index) { predict_fun_ = [=](const std::vector>& features, double* output) { int tid = omp_get_thread_num(); if (num_feature_ > kFeatureThreshold && features.size() < KSparseThreshold) { auto buf = CopyToPredictMap(features); boosting_->PredictLeafIndexByMap(buf, output); } else { CopyToPredictBuffer(predict_buf_[tid].data(), features); // get result for leaf index boosting_->PredictLeafIndex(predict_buf_[tid].data(), output); ClearPredictBuffer(predict_buf_[tid].data(), predict_buf_[tid].size(), features); } }; } else if (predict_contrib) { if (boosting_->IsLinear()) { Log::Fatal("Predicting SHAP feature contributions is not implemented for linear trees."); } predict_fun_ = [=](const std::vector>& features, double* output) { int tid = omp_get_thread_num(); CopyToPredictBuffer(predict_buf_[tid].data(), features); // get feature importances boosting_->PredictContrib(predict_buf_[tid].data(), output); ClearPredictBuffer(predict_buf_[tid].data(), predict_buf_[tid].size(), features); }; predict_sparse_fun_ = [=](const std::vector>& features, std::vector>* output) { auto buf = CopyToPredictMap(features); // get sparse feature importances boosting_->PredictContribByMap(buf, output); }; } else { if (is_raw_score) { predict_fun_ = [=](const std::vector>& features, double* output) { int tid = omp_get_thread_num(); if (num_feature_ > kFeatureThreshold && features.size() < KSparseThreshold) { auto buf = CopyToPredictMap(features); boosting_->PredictRawByMap(buf, output, &early_stop_); } else { CopyToPredictBuffer(predict_buf_[tid].data(), features); boosting_->PredictRaw(predict_buf_[tid].data(), output, &early_stop_); ClearPredictBuffer(predict_buf_[tid].data(), predict_buf_[tid].size(), features); } }; } else { predict_fun_ = [=](const std::vector>& features, double* output) { int tid = omp_get_thread_num(); if (num_feature_ > kFeatureThreshold && features.size() < KSparseThreshold) { auto buf = CopyToPredictMap(features); boosting_->PredictByMap(buf, output, &early_stop_); } else { CopyToPredictBuffer(predict_buf_[tid].data(), features); boosting_->Predict(predict_buf_[tid].data(), output, &early_stop_); ClearPredictBuffer(predict_buf_[tid].data(), predict_buf_[tid].size(), features); } }; } } } /*! * \brief Destructor */ ~Predictor() { } inline const PredictFunction& GetPredictFunction() const { return predict_fun_; } inline const PredictSparseFunction& GetPredictSparseFunction() const { return predict_sparse_fun_; } /*! * \brief predicting on data, then saving result to disk * \param data_filename Filename of data * \param result_filename Filename of output result */ void Predict(const char* data_filename, const char* result_filename, bool header, bool disable_shape_check, bool precise_float_parser) { auto writer = VirtualFileWriter::Make(result_filename); if (!writer->Init()) { Log::Fatal("Prediction results file %s cannot be created", result_filename); } auto label_idx = header ? -1 : boosting_->LabelIdx(); auto parser = std::unique_ptr(Parser::CreateParser(data_filename, header, boosting_->MaxFeatureIdx() + 1, label_idx, precise_float_parser, boosting_->ParserConfigStr())); if (parser == nullptr) { Log::Fatal("Could not recognize the data format of data file %s", data_filename); } if (!header && !disable_shape_check && parser->NumFeatures() != boosting_->MaxFeatureIdx() + 1) { Log::Fatal("The number of features in data (%d) is not the same as it was in training data (%d).\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); } TextReader predict_data_reader(data_filename, header); std::vector feature_remapper(parser->NumFeatures(), -1); bool need_adjust = false; // skip raw feature remapping if trained model has parser config str which may contain actual feature names. if (header && boosting_->ParserConfigStr().empty()) { std::string first_line = predict_data_reader.first_line(); std::vector header_words = Common::Split(first_line.c_str(), "\t,"); std::unordered_map header_mapper; for (int i = 0; i < static_cast(header_words.size()); ++i) { if (header_mapper.count(header_words[i]) > 0) { Log::Fatal("Feature (%s) appears more than one time.", header_words[i].c_str()); } header_mapper[header_words[i]] = i; } const auto& fnames = boosting_->FeatureNames(); for (int i = 0; i < static_cast(fnames.size()); ++i) { if (header_mapper.count(fnames[i]) <= 0) { 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()); } else { feature_remapper[header_mapper.at(fnames[i])] = i; } } for (int i = 0; i < static_cast(feature_remapper.size()); ++i) { if (feature_remapper[i] >= 0 && i != feature_remapper[i]) { need_adjust = true; break; } } } // function for parse data std::function>*)> parser_fun; double tmp_label; parser_fun = [&parser, &feature_remapper, &tmp_label, need_adjust] (const char* buffer, std::vector>* feature) { parser->ParseOneLine(buffer, feature, &tmp_label); if (need_adjust) { int i = 0, j = static_cast(feature->size()); while (i < j) { if (feature_remapper[(*feature)[i].first] >= 0) { (*feature)[i].first = feature_remapper[(*feature)[i].first]; ++i; } else { // move the non-used features to the end of the feature vector std::swap((*feature)[i], (*feature)[--j]); } } feature->resize(i); } }; std::function&)> process_fun = [&parser_fun, &writer, this]( data_size_t, const std::vector& lines) { std::vector> oneline_features; std::vector result_to_write(lines.size()); OMP_INIT_EX(); #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) firstprivate(oneline_features) for (data_size_t i = 0; i < static_cast(lines.size()); ++i) { OMP_LOOP_EX_BEGIN(); oneline_features.clear(); // parser parser_fun(lines[i].c_str(), &oneline_features); // predict std::vector result(num_pred_one_row_); predict_fun_(oneline_features, result.data()); auto str_result = Common::Join(result, "\t"); result_to_write[i] = str_result; OMP_LOOP_EX_END(); } OMP_THROW_EX(); for (data_size_t i = 0; i < static_cast(result_to_write.size()); ++i) { writer->Write(result_to_write[i].c_str(), result_to_write[i].size()); writer->Write("\n", 1); } }; predict_data_reader.ReadAllAndProcessParallel(process_fun); } private: void CopyToPredictBuffer(double* pred_buf, const std::vector>& features) { for (const auto &feature : features) { if (feature.first < num_feature_) { pred_buf[feature.first] = feature.second; } } } void ClearPredictBuffer(double* pred_buf, size_t buf_size, const std::vector>& features) { if (features.size() > static_cast(buf_size / 2)) { std::memset(pred_buf, 0, sizeof(double)*(buf_size)); } else { for (const auto &feature : features) { if (feature.first < num_feature_) { pred_buf[feature.first] = 0.0f; } } } } std::unordered_map CopyToPredictMap(const std::vector>& features) { std::unordered_map buf; for (const auto &feature : features) { if (feature.first < num_feature_) { buf[feature.first] = feature.second; } } return buf; } /*! \brief Boosting model */ const Boosting* boosting_; /*! \brief function for prediction */ PredictFunction predict_fun_; PredictSparseFunction predict_sparse_fun_; PredictionEarlyStopInstance early_stop_; int num_feature_; int num_pred_one_row_; std::vector>> predict_buf_; }; } // namespace LightGBM #endif // LIGHTGBM_SRC_APPLICATION_PREDICTOR_HPP_ ================================================ FILE: src/boosting/bagging.hpp ================================================ /*! * Copyright (c) 2021-2026 Microsoft Corporation. All rights reserved. * Copyright (c) 2021-2026 The LightGBM developers. All rights reserved. * Licensed under the MIT License. See LICENSE file in the project root for license information. */ #ifndef LIGHTGBM_SRC_BOOSTING_BAGGING_HPP_ #define LIGHTGBM_SRC_BOOSTING_BAGGING_HPP_ #include #include namespace LightGBM { class BaggingSampleStrategy : public SampleStrategy { public: BaggingSampleStrategy(const Config* config, const Dataset* train_data, const ObjectiveFunction* objective_function, int num_tree_per_iteration) : need_re_bagging_(false) { config_ = config; train_data_ = train_data; num_data_ = train_data->num_data(); num_queries_ = train_data->metadata().num_queries(); query_boundaries_ = train_data->metadata().query_boundaries(); objective_function_ = objective_function; num_tree_per_iteration_ = num_tree_per_iteration; num_threads_ = OMP_NUM_THREADS(); } ~BaggingSampleStrategy() {} void Bagging(int iter, TreeLearner* tree_learner, score_t* /*gradients*/, score_t* /*hessians*/) override { Common::FunctionTimer fun_timer("GBDT::Bagging", global_timer); // if need bagging if ((bag_data_cnt_ < num_data_ && iter % config_->bagging_freq == 0) || need_re_bagging_) { need_re_bagging_ = false; if (!config_->bagging_by_query) { auto left_cnt = bagging_runner_.Run( num_data_, [=](int, data_size_t cur_start, data_size_t cur_cnt, data_size_t* left, data_size_t*) { data_size_t cur_left_count = 0; if (balanced_bagging_) { cur_left_count = BalancedBaggingHelper(cur_start, cur_cnt, left); } else { cur_left_count = BaggingHelper(cur_start, cur_cnt, left); } return cur_left_count; }, bag_data_indices_.data()); bag_data_cnt_ = left_cnt; } else { num_sampled_queries_ = bagging_runner_.Run( num_queries_, [=](int, data_size_t cur_start, data_size_t cur_cnt, data_size_t* left, data_size_t*) { data_size_t cur_left_count = 0; cur_left_count = BaggingHelper(cur_start, cur_cnt, left); return cur_left_count; }, bag_query_indices_.data()); sampled_query_boundaries_[0] = 0; OMP_INIT_EX(); #pragma omp parallel for schedule(static) num_threads(num_threads_) for (data_size_t i = 0; i < num_sampled_queries_; ++i) { OMP_LOOP_EX_BEGIN(); sampled_query_boundaries_[i + 1] = query_boundaries_[bag_query_indices_[i] + 1] - query_boundaries_[bag_query_indices_[i]]; OMP_LOOP_EX_END(); } OMP_THROW_EX(); 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) { for (data_size_t i = start_index + 1; i < end_index; ++i) { sampled_query_boundaries_[i] += sampled_query_boundaries_[i - 1]; } sampled_query_boundaries_thread_buffer_[thread_index] = sampled_query_boundaries_[end_index - 1]; }); for (int thread_index = 1; thread_index < num_blocks; ++thread_index) { sampled_query_boundaries_thread_buffer_[thread_index] += sampled_query_boundaries_thread_buffer_[thread_index - 1]; } Threading::For(0, num_sampled_queries_ + 1, 128, [this](int thread_index, data_size_t start_index, data_size_t end_index) { if (thread_index > 0) { for (data_size_t i = start_index; i < end_index; ++i) { sampled_query_boundaries_[i] += sampled_query_boundaries_thread_buffer_[thread_index - 1]; } } }); bag_data_cnt_ = sampled_query_boundaries_[num_sampled_queries_]; Threading::For(0, num_sampled_queries_, 1, [this](int /*thread_index*/, data_size_t start_index, data_size_t end_index) { for (data_size_t sampled_query_id = start_index; sampled_query_id < end_index; ++sampled_query_id) { const data_size_t query_index = bag_query_indices_[sampled_query_id]; const data_size_t data_index_start = query_boundaries_[query_index]; const data_size_t data_index_end = query_boundaries_[query_index + 1]; const data_size_t sampled_query_start = sampled_query_boundaries_[sampled_query_id]; for (data_size_t i = data_index_start; i < data_index_end; ++i) { bag_data_indices_[sampled_query_start + i - data_index_start] = i; } } }); } Log::Debug("Re-bagging, using %d data to train", bag_data_cnt_); // set bagging data to tree learner if (!is_use_subset_) { #ifdef USE_CUDA if (config_->device_type == std::string("cuda")) { CopyFromHostToCUDADevice(cuda_bag_data_indices_.RawData(), bag_data_indices_.data(), static_cast(num_data_), __FILE__, __LINE__); tree_learner->SetBaggingData(nullptr, cuda_bag_data_indices_.RawData(), bag_data_cnt_); } else { #endif // USE_CUDA tree_learner->SetBaggingData(nullptr, bag_data_indices_.data(), bag_data_cnt_); #ifdef USE_CUDA } #endif // USE_CUDA } else { // get subset tmp_subset_->ReSize(bag_data_cnt_); tmp_subset_->CopySubrow(train_data_, bag_data_indices_.data(), bag_data_cnt_, false); #ifdef USE_CUDA if (config_->device_type == std::string("cuda")) { CopyFromHostToCUDADevice(cuda_bag_data_indices_.RawData(), bag_data_indices_.data(), static_cast(num_data_), __FILE__, __LINE__); tree_learner->SetBaggingData(tmp_subset_.get(), cuda_bag_data_indices_.RawData(), bag_data_cnt_); } else { #endif // USE_CUDA tree_learner->SetBaggingData(tmp_subset_.get(), bag_data_indices_.data(), bag_data_cnt_); #ifdef USE_CUDA } #endif // USE_CUDA } } } void ResetSampleConfig(const Config* config, bool is_change_dataset) override { need_resize_gradients_ = false; // if need bagging, create buffer data_size_t num_pos_data = 0; if (objective_function_ != nullptr) { num_pos_data = objective_function_->NumPositiveData(); } bool balance_bagging_cond = (config->pos_bagging_fraction < 1.0 || config->neg_bagging_fraction < 1.0) && (num_pos_data > 0); if ((config->bagging_fraction < 1.0 || balance_bagging_cond) && config->bagging_freq > 0) { need_re_bagging_ = false; if (!is_change_dataset && config_ != nullptr && config_->bagging_fraction == config->bagging_fraction && config_->bagging_freq == config->bagging_freq && config_->pos_bagging_fraction == config->pos_bagging_fraction && config_->neg_bagging_fraction == config->neg_bagging_fraction) { config_ = config; return; } config_ = config; if (balance_bagging_cond) { balanced_bagging_ = true; bag_data_cnt_ = static_cast(num_pos_data * config_->pos_bagging_fraction) + static_cast((num_data_ - num_pos_data) * config_->neg_bagging_fraction); } else { bag_data_cnt_ = static_cast(config_->bagging_fraction * num_data_); } bag_data_indices_.resize(num_data_); #ifdef USE_CUDA if (config_->device_type == std::string("cuda")) { cuda_bag_data_indices_.Resize(num_data_); } #endif // USE_CUDA if (!config_->bagging_by_query) { bagging_runner_.ReSize(num_data_); } else { bagging_runner_.ReSize(num_queries_); sampled_query_boundaries_.resize(num_queries_ + 1, 0); sampled_query_boundaries_thread_buffer_.resize(num_threads_, 0); bag_query_indices_.resize(num_data_); } bagging_rands_.clear(); for (int i = 0; i < (num_data_ + bagging_rand_block_ - 1) / bagging_rand_block_; ++i) { bagging_rands_.emplace_back(config_->bagging_seed + i); } double average_bag_rate = (static_cast(bag_data_cnt_) / num_data_) / config_->bagging_freq; is_use_subset_ = false; if (config_->device_type != std::string("cuda")) { const int group_threshold_usesubset = 100; const double average_bag_rate_threshold = 0.5; if (average_bag_rate <= average_bag_rate_threshold && (train_data_->num_feature_groups() < group_threshold_usesubset)) { if (tmp_subset_ == nullptr || is_change_dataset) { tmp_subset_.reset(new Dataset(bag_data_cnt_)); tmp_subset_->CopyFeatureMapperFrom(train_data_); } is_use_subset_ = true; Log::Debug("Use subset for bagging"); } } need_re_bagging_ = true; if (is_use_subset_ && bag_data_cnt_ < num_data_) { // resize gradient vectors to copy the customized gradients for using subset data need_resize_gradients_ = true; } } else { bag_data_cnt_ = num_data_; bag_data_indices_.clear(); #ifdef USE_CUDA cuda_bag_data_indices_.Clear(); #endif // USE_CUDA bagging_runner_.ReSize(0); is_use_subset_ = false; } } bool IsHessianChange() const override { return false; } data_size_t num_sampled_queries() const override { return num_sampled_queries_; } const data_size_t* sampled_query_indices() const override { return bag_query_indices_.data(); } private: data_size_t BaggingHelper(data_size_t start, data_size_t cnt, data_size_t* buffer) { if (cnt <= 0) { return 0; } data_size_t cur_left_cnt = 0; data_size_t cur_right_pos = cnt; // random bagging, minimal unit is one record for (data_size_t i = 0; i < cnt; ++i) { auto cur_idx = start + i; if (bagging_rands_[cur_idx / bagging_rand_block_].NextFloat() < config_->bagging_fraction) { buffer[cur_left_cnt++] = cur_idx; } else { buffer[--cur_right_pos] = cur_idx; } } return cur_left_cnt; } data_size_t BalancedBaggingHelper(data_size_t start, data_size_t cnt, data_size_t* buffer) { if (cnt <= 0) { return 0; } auto label_ptr = train_data_->metadata().label(); data_size_t cur_left_cnt = 0; data_size_t cur_right_pos = cnt; // random bagging, minimal unit is one record for (data_size_t i = 0; i < cnt; ++i) { auto cur_idx = start + i; bool is_pos = label_ptr[start + i] > 0; bool is_in_bag = false; if (is_pos) { is_in_bag = bagging_rands_[cur_idx / bagging_rand_block_].NextFloat() < config_->pos_bagging_fraction; } else { is_in_bag = bagging_rands_[cur_idx / bagging_rand_block_].NextFloat() < config_->neg_bagging_fraction; } if (is_in_bag) { buffer[cur_left_cnt++] = cur_idx; } else { buffer[--cur_right_pos] = cur_idx; } } return cur_left_cnt; } /*! \brief whether need restart bagging in continued training */ bool need_re_bagging_; /*! \brief number of threads */ int num_threads_; /*! \brief query boundaries of the in-bag queries */ std::vector sampled_query_boundaries_; /*! \brief buffer for calculating sampled_query_boundaries_ */ std::vector sampled_query_boundaries_thread_buffer_; /*! \brief in-bag query indices */ std::vector> bag_query_indices_; /*! \brief number of queries in the training dataset */ data_size_t num_queries_; /*! \brief number of in-bag queries */ data_size_t num_sampled_queries_; /*! \brief query boundaries of the whole training dataset */ const data_size_t* query_boundaries_; }; } // namespace LightGBM #endif // LIGHTGBM_SRC_BOOSTING_BAGGING_HPP_ ================================================ FILE: src/boosting/boosting.cpp ================================================ /*! * Copyright (c) 2016-2026 Microsoft Corporation. All rights reserved. * Copyright (c) 2016-2026 The LightGBM developers. All rights reserved. * Licensed under the MIT License. See LICENSE file in the project root for license information. */ #include #include #include #include "dart.hpp" #include "gbdt.h" #include "rf.hpp" #ifdef USE_CUDA #include "cuda/nccl_gbdt.hpp" #endif // USE_CUDA namespace LightGBM { std::string GetBoostingTypeFromModelFile(const char* filename) { TextReader model_reader(filename, true); std::string type = model_reader.first_line(); return type; } bool Boosting::LoadFileToBoosting(Boosting* boosting, const char* filename) { auto start_time = std::chrono::steady_clock::now(); if (boosting != nullptr) { TextReader model_reader(filename, true); size_t buffer_len = 0; auto buffer = model_reader.ReadContent(&buffer_len); if (!boosting->LoadModelFromString(buffer.data(), buffer_len)) { return false; } } std::chrono::duration delta = (std::chrono::steady_clock::now() - start_time); Log::Debug("Time for loading model: %f seconds", 1e-3*delta); return true; } Boosting* Boosting::CreateBoosting(const std::string& type, const char* filename, const std::string& #ifdef USE_CUDA device_type #endif // USE_CUDA , const int #ifdef USE_CUDA num_gpu #endif // USE_CUDA ) { if (filename == nullptr || filename[0] == '\0') { if (type == std::string("gbdt")) { #ifdef USE_CUDA if (device_type == std::string("cuda") && num_gpu > 1) { return new NCCLGBDT(); } else { #endif // USE_CUDA return new GBDT(); #ifdef USE_CUDA } #endif // USE_CUDA } else if (type == std::string("dart")) { return new DART(); } else if (type == std::string("goss")) { return new GBDT(); } else if (type == std::string("rf")) { return new RF(); } else { return nullptr; } } else { std::unique_ptr ret; if (GetBoostingTypeFromModelFile(filename) == std::string("tree")) { if (type == std::string("gbdt")) { #ifdef USE_CUDA if (device_type == std::string("cuda") && num_gpu > 1) { ret.reset(new NCCLGBDT()); } else { #endif // USE_CUDA ret.reset(new GBDT()); #ifdef USE_CUDA } #endif // USE_CUDA } else if (type == std::string("dart")) { ret.reset(new DART()); } else if (type == std::string("goss")) { ret.reset(new GBDT()); } else if (type == std::string("rf")) { ret.reset(new RF()); } else { Log::Fatal("Unknown boosting type %s", type.c_str()); } LoadFileToBoosting(ret.get(), filename); } else { Log::Fatal("Unknown model format or submodel type in model file %s", filename); } return ret.release(); } } } // namespace LightGBM ================================================ FILE: src/boosting/cuda/cuda_score_updater.cpp ================================================ /*! * Copyright (c) 2021-2026 Microsoft Corporation. All rights reserved. * Copyright (c) 2021-2026 The LightGBM developers. All rights reserved. * Licensed under the MIT License. See LICENSE file in the project root for license information. */ #include "cuda_score_updater.hpp" #ifdef USE_CUDA namespace LightGBM { CUDAScoreUpdater::CUDAScoreUpdater(const Dataset* data, int num_tree_per_iteration, const bool boosting_on_cuda): ScoreUpdater(data, num_tree_per_iteration), num_threads_per_block_(1024), boosting_on_cuda_(boosting_on_cuda) { num_data_ = data->num_data(); int64_t total_size = static_cast(num_data_) * num_tree_per_iteration; InitCUDA(total_size); has_init_score_ = false; const double* init_score = data->metadata().init_score(); // if exists initial score, will start from it if (init_score != nullptr) { if ((data->metadata().num_init_score() % num_data_) != 0 || (data->metadata().num_init_score() / num_data_) != num_tree_per_iteration) { Log::Fatal("Number of class for initial score error"); } has_init_score_ = true; CopyFromHostToCUDADevice(cuda_score_.RawData(), init_score, total_size, __FILE__, __LINE__); } else { SetCUDAMemory(cuda_score_.RawData(), 0, static_cast(total_size), __FILE__, __LINE__); } SynchronizeCUDADevice(__FILE__, __LINE__); if (boosting_on_cuda_) { // clear host score buffer score_.clear(); score_.shrink_to_fit(); } } void CUDAScoreUpdater::InitCUDA(const size_t total_size) { cuda_score_.Resize(total_size); } CUDAScoreUpdater::~CUDAScoreUpdater() {} inline void CUDAScoreUpdater::AddScore(double val, int cur_tree_id) { Common::FunctionTimer fun_timer("CUDAScoreUpdater::AddScore", global_timer); const size_t offset = static_cast(num_data_) * cur_tree_id; LaunchAddScoreConstantKernel(val, offset); if (!boosting_on_cuda_) { CopyFromCUDADeviceToHost(score_.data() + offset, cuda_score_.RawData() + offset, static_cast(num_data_), __FILE__, __LINE__); } } inline void CUDAScoreUpdater::AddScore(const Tree* tree, int cur_tree_id) { Common::FunctionTimer fun_timer("ScoreUpdater::AddScore", global_timer); const size_t offset = static_cast(num_data_) * cur_tree_id; tree->AddPredictionToScore(data_, num_data_, cuda_score_.RawData() + offset); if (!boosting_on_cuda_) { CopyFromCUDADeviceToHost(score_.data() + offset, cuda_score_.RawData() + offset, static_cast(num_data_), __FILE__, __LINE__); } } inline void CUDAScoreUpdater::AddScore(const TreeLearner* tree_learner, const Tree* tree, int cur_tree_id) { Common::FunctionTimer fun_timer("ScoreUpdater::AddScore", global_timer); const size_t offset = static_cast(num_data_) * cur_tree_id; tree_learner->AddPredictionToScore(tree, cuda_score_.RawData() + offset); if (!boosting_on_cuda_) { CopyFromCUDADeviceToHost(score_.data() + offset, cuda_score_.RawData() + offset, static_cast(num_data_), __FILE__, __LINE__); } } inline void CUDAScoreUpdater::AddScore(const Tree* tree, const data_size_t* data_indices, data_size_t data_cnt, int cur_tree_id) { Common::FunctionTimer fun_timer("ScoreUpdater::AddScore", global_timer); const size_t offset = static_cast(num_data_) * cur_tree_id; tree->AddPredictionToScore(data_, data_indices, data_cnt, cuda_score_.RawData() + offset); if (!boosting_on_cuda_) { CopyFromCUDADeviceToHost(score_.data() + offset, cuda_score_.RawData() + offset, static_cast(num_data_), __FILE__, __LINE__); } } inline void CUDAScoreUpdater::MultiplyScore(double val, int cur_tree_id) { Common::FunctionTimer fun_timer("CUDAScoreUpdater::MultiplyScore", global_timer); const size_t offset = static_cast(num_data_) * cur_tree_id; LaunchMultiplyScoreConstantKernel(val, offset); if (!boosting_on_cuda_) { CopyFromCUDADeviceToHost(score_.data() + offset, cuda_score_.RawData() + offset, static_cast(num_data_), __FILE__, __LINE__); } } } // namespace LightGBM #endif // USE_CUDA ================================================ FILE: src/boosting/cuda/cuda_score_updater.cu ================================================ /*! * Copyright (c) 2021-2026 Microsoft Corporation. All rights reserved. * Copyright (c) 2021-2026 The LightGBM developers. All rights reserved. * Licensed under the MIT License. See LICENSE file in the project root for license information. */ #include "cuda_score_updater.hpp" #ifdef USE_CUDA namespace LightGBM { __global__ void AddScoreConstantKernel( const double val, const data_size_t num_data, double* score) { const data_size_t data_index = static_cast(threadIdx.x + blockIdx.x * blockDim.x); if (data_index < num_data) { score[data_index] += val; } } void CUDAScoreUpdater::LaunchAddScoreConstantKernel(const double val, const size_t offset) { const int num_blocks = (num_data_ + num_threads_per_block_) / num_threads_per_block_; Log::Debug("Adding init score = %lf", val); AddScoreConstantKernel<<>>(val, num_data_, cuda_score_.RawData() + offset); } __global__ void MultiplyScoreConstantKernel( const double val, const data_size_t num_data, double* score) { const data_size_t data_index = static_cast(threadIdx.x + blockIdx.x * blockDim.x); if (data_index < num_data) { score[data_index] *= val; } } void CUDAScoreUpdater::LaunchMultiplyScoreConstantKernel(const double val, const size_t offset) { const int num_blocks = (num_data_ + num_threads_per_block_) / num_threads_per_block_; MultiplyScoreConstantKernel<<>>(val, num_data_, cuda_score_.RawData() + offset); } } // namespace LightGBM #endif // USE_CUDA ================================================ FILE: src/boosting/cuda/cuda_score_updater.hpp ================================================ /*! * Copyright (c) 2021-2026 Microsoft Corporation. All rights reserved. * Copyright (c) 2021-2026 The LightGBM developers. All rights reserved. * Licensed under the MIT License. See LICENSE file in the project root for license information. */ #ifndef LIGHTGBM_SRC_BOOSTING_CUDA_CUDA_SCORE_UPDATER_HPP_ #define LIGHTGBM_SRC_BOOSTING_CUDA_CUDA_SCORE_UPDATER_HPP_ #ifdef USE_CUDA #include #include "../score_updater.hpp" namespace LightGBM { class CUDAScoreUpdater: public ScoreUpdater { public: CUDAScoreUpdater(const Dataset* data, int num_tree_per_iteration, const bool boosting_on_cuda); ~CUDAScoreUpdater(); void AddScore(double val, int cur_tree_id) override; inline void AddScore(const Tree* tree, int cur_tree_id) override; void AddScore(const TreeLearner* tree_learner, const Tree* tree, int cur_tree_id) override; inline void AddScore(const Tree* tree, const data_size_t* data_indices, data_size_t data_cnt, int cur_tree_id) override; inline void MultiplyScore(double val, int cur_tree_id) override; inline const double* score() const override { if (boosting_on_cuda_) { return cuda_score_.RawData(); } else { return score_.data(); } } /*! \brief Disable copy */ CUDAScoreUpdater& operator=(const CUDAScoreUpdater&) = delete; CUDAScoreUpdater(const CUDAScoreUpdater&) = delete; private: void InitCUDA(const size_t total_size); void LaunchAddScoreConstantKernel(const double val, const size_t offset); void LaunchMultiplyScoreConstantKernel(const double val, const size_t offset); CUDAVector cuda_score_; const int num_threads_per_block_; const bool boosting_on_cuda_; }; } // namespace LightGBM #endif // USE_CUDA #endif // LIGHTGBM_SRC_BOOSTING_CUDA_CUDA_SCORE_UPDATER_HPP_ ================================================ FILE: src/boosting/cuda/nccl_gbdt.cpp ================================================ /*! * Copyright (c) 2023-2026 Microsoft Corporation. All rights reserved. * Copyright (c) 2023-2026 The LightGBM developers. All rights reserved. * Licensed under the MIT License. See LICENSE file in the project root for license information. */ #include #include #include #include "nccl_gbdt.hpp" #include "nccl_gbdt_component.hpp" #ifdef USE_CUDA namespace LightGBM { template NCCLGBDT::NCCLGBDT(): GBDT_T() {} template NCCLGBDT::~NCCLGBDT() {} template void NCCLGBDT::Init( const Config* gbdt_config, const Dataset* train_data, const ObjectiveFunction* objective_function, const std::vector& training_metrics) { GBDT_T::Init(gbdt_config, train_data, objective_function, training_metrics); this->tree_learner_.reset(); nccl_topology_.reset(new NCCLTopology(this->config_->gpu_device_id, this->config_->num_gpu, this->config_->gpu_device_id_list, train_data->num_data())); nccl_topology_->InitNCCL(); nccl_topology_->InitPerDevice(&nccl_gbdt_components_); nccl_topology_->RunPerDevice(nccl_gbdt_components_, [this, gbdt_config, train_data] (NCCLGBDTComponent* nccl_gbdt_component) { nccl_gbdt_component->Init( gbdt_config, train_data, this->num_tree_per_iteration_, this->boosting_on_gpu_, this->is_constant_hessian_); }); } template void NCCLGBDT::BoostingThread(NCCLGBDTComponent* thread_data) { const ObjectiveFunction* objective_function = thread_data->objective_function(); score_t* gradients = thread_data->gradients(); score_t* hessians = thread_data->hessians(); const double* score = thread_data->train_score_updater()->score(); objective_function->GetGradients(score, gradients, hessians); } template void NCCLGBDT::Boosting() { Common::FunctionTimer fun_timer("NCCLGBDT::Boosting", global_timer); if (this->objective_function_ == nullptr) { Log::Fatal("No object function provided"); } nccl_topology_->DispatchPerDevice(&nccl_gbdt_components_, BoostingThread); } template double NCCLGBDT::BoostFromAverage(int class_id, bool update_scorer) { double init_score = GBDT_T::BoostFromAverage(class_id, update_scorer); if (init_score != 0.0) { nccl_topology_->RunPerDevice(nccl_gbdt_components_, [init_score, class_id] (NCCLGBDTComponent* thread_data) { thread_data->train_score_updater()->AddScore(init_score, class_id); }); } return init_score; } template void NCCLGBDT::TrainTreeLearnerThread(NCCLGBDTComponent* thread_data, const int class_id, const bool is_first_tree) { const data_size_t num_data_in_gpu = thread_data->num_data_in_gpu(); const score_t* gradients = thread_data->gradients() + class_id * num_data_in_gpu; const score_t* hessians = thread_data->hessians() + class_id * num_data_in_gpu; thread_data->SetTree(thread_data->tree_learner()->Train(gradients, hessians, is_first_tree)); } template bool NCCLGBDT::TrainOneIter(const score_t* gradients, const score_t* hessians) { Common::FunctionTimer fun_timer("NCCLGBDT::TrainOneIter", global_timer); std::vector init_scores(this->num_tree_per_iteration_, 0.0); // boosting first if (gradients == nullptr || hessians == nullptr) { for (int cur_tree_id = 0; cur_tree_id < this->num_tree_per_iteration_; ++cur_tree_id) { init_scores[cur_tree_id] = BoostFromAverage(cur_tree_id, true); } Boosting(); } else { nccl_topology_->RunPerDevice(nccl_gbdt_components_, [this, gradients, hessians] (NCCLGBDTComponent* thread_data) { const data_size_t data_start_index = thread_data->data_start_index(); const data_size_t num_data_in_gpu = thread_data->num_data_in_gpu(); for (int class_id = 0; class_id < this->num_class_; ++class_id) { CopyFromHostToCUDADevice( thread_data->gradients() + class_id * num_data_in_gpu, gradients + class_id * this->num_data_ + data_start_index, num_data_in_gpu, __FILE__, __LINE__); CopyFromHostToCUDADevice( thread_data->hessians() + class_id * num_data_in_gpu, hessians + class_id * this->num_data_ + data_start_index, num_data_in_gpu, __FILE__, __LINE__); } }); } bool should_continue = false; for (int cur_tree_id = 0; cur_tree_id < this->num_tree_per_iteration_; ++cur_tree_id) { if (this->class_need_train_[cur_tree_id] && this->train_data_->num_features() > 0) { if (this->data_sample_strategy_->is_use_subset() && this->data_sample_strategy_->bag_data_cnt() < this->num_data_) { Log::Fatal("Bagging is not supported for NCCLGBDT"); } bool is_first_tree = this->models_.size() < static_cast(this->num_tree_per_iteration_); nccl_topology_->DispatchPerDevice(&nccl_gbdt_components_, [is_first_tree, cur_tree_id] (NCCLGBDTComponent* thread_data) -> void { TrainTreeLearnerThread(thread_data, cur_tree_id, is_first_tree); }); } nccl_topology_->DispatchPerDevice(&nccl_gbdt_components_, [cur_tree_id, this, init_scores] (NCCLGBDTComponent* thread_data) -> void { this->UpdateScoreThread(thread_data, cur_tree_id, this->config_->learning_rate, init_scores[cur_tree_id]); }); nccl_topology_->RunOnMasterDevice(nccl_gbdt_components_, [&should_continue, this, cur_tree_id] (NCCLGBDTComponent* thread_data) -> void { if (thread_data->new_tree()->num_leaves() > 1) { should_continue = true; } for (auto& score_updater : this->valid_score_updater_) { score_updater->AddScore(thread_data->new_tree(), cur_tree_id); } }); if (!should_continue) { if (this->models_.size() < static_cast(this->num_tree_per_iteration_)) { Log::Warning("Training stopped with no splits."); } } // add model nccl_topology_->RunOnMasterDevice(nccl_gbdt_components_, [this] (NCCLGBDTComponent* thread_data) -> void { this->models_.emplace_back(thread_data->release_new_tree()); }); nccl_topology_->RunOnNonMasterDevice(nccl_gbdt_components_, [this] (NCCLGBDTComponent* thread_data) -> void { thread_data->clear_new_tree(); }); } if (!should_continue) { Log::Warning("Stopped training because there are no more leaves that meet the split requirements"); if (this->models_.size() > static_cast(this->num_tree_per_iteration_)) { for (int cur_tree_id = 0; cur_tree_id < this->num_tree_per_iteration_; ++cur_tree_id) { this->models_.pop_back(); } } return true; } ++this->iter_; return false; } template void NCCLGBDT::UpdateScoreThread(NCCLGBDTComponent* thread_data, const int cur_tree_id, const double shrinkage_rate, const double init_score) { if (thread_data->new_tree()->num_leaves() > 1) { // TODO(shiyu1994): implement bagging if (thread_data->objective_function() != nullptr && thread_data->objective_function()->IsRenewTreeOutput()) { // TODO(shiyu1994): implement renewing } thread_data->new_tree()->Shrinkage(shrinkage_rate); thread_data->train_score_updater()->AddScore( thread_data->tree_learner(), thread_data->new_tree(), cur_tree_id); if (std::fabs(init_score) > kEpsilon) { thread_data->new_tree()->AddBias(init_score); } } } template std::vector NCCLGBDT::EvalOneMetric(const Metric* metric, const double* score, const data_size_t num_data) const { if (score == this->train_score_updater_->score()) { // delegate to per gpu train score updater std::vector tmp_score(num_data * this->num_class_, 0.0f); nccl_topology_->RunPerDevice(nccl_gbdt_components_, [this, &tmp_score] (NCCLGBDTComponent* thread_data) { const data_size_t data_start = thread_data->data_start_index(); const data_size_t num_data_in_gpu = thread_data->num_data_in_gpu(); for (int class_id = 0; class_id < this->num_class_; ++class_id) { CopyFromCUDADeviceToHost(tmp_score.data() + class_id * this->num_data_ + data_start, thread_data->train_score_updater()->score() + class_id * num_data_in_gpu, static_cast(num_data_in_gpu), __FILE__, __LINE__); } }); return metric->Eval(tmp_score.data(), this->objective_function_); } else { return GBDT_T::EvalOneMetric(metric, score, num_data); } } template class NCCLGBDT; } // namespace LightGBM #endif // USE_CUDA ================================================ FILE: src/boosting/cuda/nccl_gbdt.hpp ================================================ /*! * Copyright (c) 2023-2026 Microsoft Corporation. All rights reserved. * Copyright (c) 2023-2026 The LightGBM developers. All rights reserved. * Licensed under the MIT License. See LICENSE file in the project root for license information. */ #ifndef LIGHTGBM_SRC_BOOSTING_CUDA_NCCL_GBDT_HPP_ #define LIGHTGBM_SRC_BOOSTING_CUDA_NCCL_GBDT_HPP_ #ifdef USE_CUDA #include #include #include #include #include #include #include "cuda_score_updater.hpp" #include "nccl_gbdt_component.hpp" #include "../gbdt.h" namespace LightGBM { template class NCCLGBDT: public GBDT_T { public: NCCLGBDT(); ~NCCLGBDT(); void Init(const Config* gbdt_config, const Dataset* train_data, const ObjectiveFunction* objective_function, const std::vector& training_metrics) override; void Boosting() override; void RefitTree(const int* /*tree_leaf_prediction*/, const size_t /*nrow*/, const size_t /*ncol*/) override { Log::Fatal("RefitTree is not supported for NCCLGBDT."); } bool TrainOneIter(const score_t* gradients, const score_t* hessians) override; const double* GetTrainingScore(int64_t* /*out_len*/) override { Log::Fatal("GetTrainingScore is not supported for NCCLGBDT."); } void ResetTrainingData(const Dataset* /*train_data*/, const ObjectiveFunction* /*objective_function*/, const std::vector& /*training_metrics*/) override { Log::Fatal("ResetTrainingData is not supported for NCCLGBDT."); } void ResetConfig(const Config* /*gbdt_config*/) override { Log::Fatal("ResetConfig is not supported for NCCLGBDT."); } private: struct BoostingThreadData { int gpu_index; ObjectiveFunction* gpu_objective_function; score_t* gradients; score_t* hessians; const double* score; BoostingThreadData() { gpu_index = 0; gpu_objective_function = nullptr; } }; struct TrainTreeLearnerThreadData { int gpu_index; TreeLearner* gpu_tree_learner; const score_t* gradients; const score_t* hessians; bool is_first_time; int class_id; data_size_t num_data_in_gpu; std::unique_ptr tree; TrainTreeLearnerThreadData() { gpu_index = 0; gpu_tree_learner = nullptr; gradients = nullptr; hessians = nullptr; is_first_time = false; class_id = 0; num_data_in_gpu = 0; tree.reset(nullptr); } }; struct UpdateScoreThreadData { int gpu_index; ScoreUpdater* gpu_score_updater; TreeLearner* gpu_tree_learner; Tree* tree; int cur_tree_id; UpdateScoreThreadData() { gpu_index = 0; gpu_score_updater = nullptr; gpu_tree_learner = nullptr; tree = nullptr; cur_tree_id = 0; } }; static void BoostingThread(NCCLGBDTComponent* thread_data); static void TrainTreeLearnerThread(NCCLGBDTComponent* thread_data, const int class_id, const bool is_first_tree); static void UpdateScoreThread(NCCLGBDTComponent* thread_data, const int cur_tree_id, const double shrinkage_rate, const double init_score); double BoostFromAverage(int class_id, bool update_scorer) override; void UpdateScore(const std::vector>& tree, const int cur_tree_id); void UpdateScore(const Tree* /*tree*/, const int /*cur_tree_id*/) { Log::Fatal("UpdateScore is not supported for NCCLGBDT."); } void RollbackOneIter() override { Log::Fatal("RollbackOneIter is not supported for NCCLGBDT."); } std::vector EvalOneMetric(const Metric* metric, const double* score, const data_size_t num_data) const override; int num_threads_; std::unique_ptr nccl_topology_; std::vector nccl_gpu_rank_; std::vector nccl_communicators_; std::vector> nccl_gbdt_components_; }; } // namespace LightGBM #endif // USE_CUDA #endif // LIGHTGBM_SRC_BOOSTING_CUDA_NCCL_GBDT_HPP_ ================================================ FILE: src/boosting/cuda/nccl_gbdt_component.hpp ================================================ /*! * Copyright (c) 2023-2026 Microsoft Corporation. All rights reserved. * Copyright (c) 2023-2026 The LightGBM developers. All rights reserved. * Licensed under the MIT License. See LICENSE file in the project root for license information. */ #ifndef LIGHTGBM_SRC_BOOSTING_CUDA_NCCL_GBDT_COMPONENT_HPP_ #define LIGHTGBM_SRC_BOOSTING_CUDA_NCCL_GBDT_COMPONENT_HPP_ #ifdef USE_CUDA #include #include #include #include #include #include #include "cuda_score_updater.hpp" #include "../../treelearner/cuda/cuda_single_gpu_tree_learner.hpp" namespace LightGBM { class NCCLGBDTComponent: public NCCLInfo { public: NCCLGBDTComponent() {} ~NCCLGBDTComponent() {} 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) { CUDASUCCESS_OR_FATAL(cudaGetDeviceCount(&num_gpu_in_node_)); const data_size_t num_data_per_gpu = (train_data->num_data() + num_gpu_in_node_ - 1) / num_gpu_in_node_; data_start_index_ = num_data_per_gpu * local_gpu_rank_; data_end_index_ = std::min(data_start_index_ + num_data_per_gpu, train_data->num_data()); num_data_in_gpu_ = data_end_index_ - data_start_index_; dataset_.reset(new Dataset(num_data_in_gpu_)); dataset_->ReSize(num_data_in_gpu_); dataset_->CopyFeatureMapperFrom(train_data); std::vector used_indices(num_data_in_gpu_); for (data_size_t data_index = data_start_index_; data_index < data_end_index_; ++data_index) { used_indices[data_index - data_start_index_] = data_index; } dataset_->CopySubrowToDevice(train_data, used_indices.data(), num_data_in_gpu_, true, gpu_device_id_); objective_function_.reset(ObjectiveFunction::CreateObjectiveFunctionCUDA(config->objective, *config)); objective_function_->SetNCCLInfo(nccl_communicator_, nccl_gpu_rank_, local_gpu_rank_, gpu_device_id_, train_data->num_data()); train_score_updater_.reset(new CUDAScoreUpdater(dataset_.get(), num_tree_per_iteration, boosting_on_gpu)); gradients_.reset(new CUDAVector(num_data_in_gpu_)); hessians_.reset(new CUDAVector(num_data_in_gpu_)); tree_learner_.reset(new CUDASingleGPUTreeLearner(config, boosting_on_gpu)); tree_learner_->SetNCCLInfo(nccl_communicator_, nccl_gpu_rank_, local_gpu_rank_, gpu_device_id_, train_data->num_data()); objective_function_->Init(dataset_->metadata(), dataset_->num_data()); tree_learner_->Init(dataset_.get(), is_constant_hessian); } ObjectiveFunction* objective_function() { return objective_function_.get(); } ScoreUpdater* train_score_updater() { return train_score_updater_.get(); } score_t* gradients() { return gradients_->RawData(); } score_t* hessians() { return hessians_->RawData(); } data_size_t num_data_in_gpu() const { return num_data_in_gpu_; } CUDASingleGPUTreeLearner* tree_learner() { return tree_learner_.get(); } void SetTree(Tree* tree) { new_tree_.reset(tree); } data_size_t data_start_index() const { return data_start_index_; } data_size_t data_end_index() const { return data_end_index_; } Tree* new_tree() { return new_tree_.get(); } Tree* release_new_tree() { return new_tree_.release(); } void clear_new_tree() { new_tree_.reset(nullptr); } private: std::unique_ptr objective_function_; std::unique_ptr train_score_updater_; std::unique_ptr> gradients_; std::unique_ptr> hessians_; std::unique_ptr dataset_; std::unique_ptr tree_learner_; std::unique_ptr new_tree_; data_size_t data_start_index_; data_size_t data_end_index_; data_size_t num_data_in_gpu_; }; } // namespace LightGBM #endif // USE_CUDA #endif // LIGHTGBM_SRC_BOOSTING_CUDA_NCCL_GBDT_COMPONENT_HPP_ ================================================ FILE: src/boosting/dart.hpp ================================================ /*! * Copyright (c) 2016-2026 Microsoft Corporation. All rights reserved. * Copyright (c) 2016-2026 The LightGBM developers. All rights reserved. * Licensed under the MIT License. See LICENSE file in the project root for license information. */ #ifndef LIGHTGBM_SRC_BOOSTING_DART_HPP_ #define LIGHTGBM_SRC_BOOSTING_DART_HPP_ #include #include #include #include #include #include #include "gbdt.h" #include "score_updater.hpp" namespace LightGBM { /*! * \brief DART algorithm implementation. including Training, prediction, bagging. */ class DART: public GBDT { public: /*! * \brief Constructor */ DART() : GBDT() { } /*! * \brief Destructor */ ~DART() { } /*! * \brief Initialization logic * \param config Config for boosting * \param train_data Training data * \param objective_function Training objective function * \param training_metrics Training metrics * \param output_model_filename Filename of output model */ void Init(const Config* config, const Dataset* train_data, const ObjectiveFunction* objective_function, const std::vector& training_metrics) override { GBDT::Init(config, train_data, objective_function, training_metrics); random_for_drop_ = Random(config_->drop_seed); sum_weight_ = 0.0f; } void ResetConfig(const Config* config) override { GBDT::ResetConfig(config); random_for_drop_ = Random(config_->drop_seed); sum_weight_ = 0.0f; } /*! * \brief one training iteration */ bool TrainOneIter(const score_t* gradient, const score_t* hessian) override { is_update_score_cur_iter_ = false; bool ret = GBDT::TrainOneIter(gradient, hessian); if (ret) { return ret; } // normalize Normalize(); if (!config_->uniform_drop) { tree_weight_.push_back(shrinkage_rate_); sum_weight_ += shrinkage_rate_; } return false; } /*! * \brief Get current training score * \param out_len length of returned score * \return training score */ const double* GetTrainingScore(int64_t* out_len) override { if (!is_update_score_cur_iter_) { // only drop one time in one iteration DroppingTrees(); is_update_score_cur_iter_ = true; } *out_len = static_cast(train_score_updater_->num_data()) * num_class_; return train_score_updater_->score(); } bool EvalAndCheckEarlyStopping() override { GBDT::OutputMetric(iter_); return false; } private: /*! * \brief drop trees based on drop_rate */ void DroppingTrees() { drop_index_.clear(); bool is_skip = random_for_drop_.NextFloat() < config_->skip_drop; // select dropping tree indices based on drop_rate and tree weights if (!is_skip) { double drop_rate = config_->drop_rate; if (!config_->uniform_drop) { double inv_average_weight = static_cast(tree_weight_.size()) / sum_weight_; if (config_->max_drop > 0) { drop_rate = std::min(drop_rate, config_->max_drop * inv_average_weight / sum_weight_); } for (int i = 0; i < iter_; ++i) { if (random_for_drop_.NextFloat() < drop_rate * tree_weight_[i] * inv_average_weight) { drop_index_.push_back(num_init_iteration_ + i); if (drop_index_.size() >= static_cast(config_->max_drop)) { break; } } } } else { if (config_->max_drop > 0) { drop_rate = std::min(drop_rate, config_->max_drop / static_cast(iter_)); } for (int i = 0; i < iter_; ++i) { if (random_for_drop_.NextFloat() < drop_rate) { drop_index_.push_back(num_init_iteration_ + i); if (drop_index_.size() >= static_cast(config_->max_drop)) { break; } } } } } // drop trees for (auto i : drop_index_) { for (int cur_tree_id = 0; cur_tree_id < num_tree_per_iteration_; ++cur_tree_id) { auto curr_tree = i * num_tree_per_iteration_ + cur_tree_id; models_[curr_tree]->Shrinkage(-1.0); train_score_updater_->AddScore(models_[curr_tree].get(), cur_tree_id); } } if (!config_->xgboost_dart_mode) { shrinkage_rate_ = config_->learning_rate / (1.0f + static_cast(drop_index_.size())); } else { if (drop_index_.empty()) { shrinkage_rate_ = config_->learning_rate; } else { shrinkage_rate_ = config_->learning_rate / (config_->learning_rate + static_cast(drop_index_.size())); } } } /*! * \brief normalize dropped trees * NOTE: num_drop_tree(k), learning_rate(lr), shrinkage_rate_ = lr / (k + 1) * step 1: shrink tree to -1 -> drop tree * step 2: shrink tree to k / (k + 1) - 1 from -1, by 1/(k+1) * -> normalize for valid data * step 3: shrink tree to k / (k + 1) from k / (k + 1) - 1, by -k * -> normalize for train data * end with tree weight = (k / (k + 1)) * old_weight */ void Normalize() { double k = static_cast(drop_index_.size()); if (!config_->xgboost_dart_mode) { for (auto i : drop_index_) { for (int cur_tree_id = 0; cur_tree_id < num_tree_per_iteration_; ++cur_tree_id) { auto curr_tree = i * num_tree_per_iteration_ + cur_tree_id; // update validation score models_[curr_tree]->Shrinkage(1.0f / (k + 1.0f)); for (auto& score_updater : valid_score_updater_) { score_updater->AddScore(models_[curr_tree].get(), cur_tree_id); } // update training score models_[curr_tree]->Shrinkage(-k); train_score_updater_->AddScore(models_[curr_tree].get(), cur_tree_id); } if (!config_->uniform_drop) { sum_weight_ -= tree_weight_[i - num_init_iteration_] * (1.0f / (k + 1.0f)); tree_weight_[i - num_init_iteration_] *= (k / (k + 1.0f)); } } } else { for (auto i : drop_index_) { for (int cur_tree_id = 0; cur_tree_id < num_tree_per_iteration_; ++cur_tree_id) { auto curr_tree = i * num_tree_per_iteration_ + cur_tree_id; // update validation score models_[curr_tree]->Shrinkage(shrinkage_rate_); for (auto& score_updater : valid_score_updater_) { score_updater->AddScore(models_[curr_tree].get(), cur_tree_id); } // update training score models_[curr_tree]->Shrinkage(-k / config_->learning_rate); train_score_updater_->AddScore(models_[curr_tree].get(), cur_tree_id); } if (!config_->uniform_drop) { sum_weight_ -= tree_weight_[i - num_init_iteration_] * (1.0f / (k + config_->learning_rate));; tree_weight_[i - num_init_iteration_] *= (k / (k + config_->learning_rate)); } } } } /*! \brief The weights of all trees, used to choose drop trees */ std::vector tree_weight_; /*! \brief sum weights of all trees */ double sum_weight_; /*! \brief The indices of dropping trees */ std::vector drop_index_; /*! \brief Random generator, used to select dropping trees */ Random random_for_drop_; /*! \brief Flag that the score is update on current iter or not*/ bool is_update_score_cur_iter_; }; } // namespace LightGBM #endif // LIGHTGBM_SRC_BOOSTING_DART_HPP_ ================================================ FILE: src/boosting/gbdt.cpp ================================================ /*! * Copyright (c) 2016-2026 Microsoft Corporation. All rights reserved. * Copyright (c) 2016-2026 The LightGBM developers. All rights reserved. * Licensed under the MIT License. See LICENSE file in the project root for license information. */ #include "gbdt.h" #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include namespace LightGBM { Common::Timer global_timer; int LGBM_config_::current_device = lgbm_device_cpu; int LGBM_config_::current_learner = use_cpu_learner; GBDT::GBDT() : iter_(0), train_data_(nullptr), config_(nullptr), objective_function_(nullptr), early_stopping_round_(0), early_stopping_min_delta_(0.0), es_first_metric_only_(false), max_feature_idx_(0), num_tree_per_iteration_(1), num_class_(1), num_iteration_for_pred_(0), shrinkage_rate_(0.1f), num_init_iteration_(0) { average_output_ = false; tree_learner_ = nullptr; linear_tree_ = false; data_sample_strategy_.reset(nullptr); gradients_pointer_ = nullptr; hessians_pointer_ = nullptr; boosting_on_gpu_ = false; } GBDT::~GBDT() { } void GBDT::Init(const Config* config, const Dataset* train_data, const ObjectiveFunction* objective_function, const std::vector& training_metrics) { CHECK_NOTNULL(train_data); train_data_ = train_data; if (!config->monotone_constraints.empty()) { CHECK_EQ(static_cast(train_data_->num_total_features()), config->monotone_constraints.size()); } if (!config->feature_contri.empty()) { CHECK_EQ(static_cast(train_data_->num_total_features()), config->feature_contri.size()); } iter_ = 0; num_iteration_for_pred_ = 0; max_feature_idx_ = 0; num_class_ = config->num_class; config_ = std::unique_ptr(new Config(*config)); early_stopping_round_ = config_->early_stopping_round; early_stopping_min_delta_ = config->early_stopping_min_delta; es_first_metric_only_ = config_->first_metric_only; shrinkage_rate_ = config_->learning_rate; if (config_->device_type == std::string("cuda")) { LGBM_config_::current_learner = use_cuda_learner; #ifdef USE_CUDA if (config_->device_type == std::string("cuda")) { const int gpu_device_id = config_->gpu_device_id >= 0 ? config_->gpu_device_id : 0; CUDASUCCESS_OR_FATAL(cudaSetDevice(gpu_device_id)); } #endif // USE_CUDA } // load forced_splits file if (!config->forcedsplits_filename.empty()) { std::ifstream forced_splits_file(config->forcedsplits_filename.c_str()); std::stringstream buffer; buffer << forced_splits_file.rdbuf(); std::string err; forced_splits_json_ = Json::parse(buffer.str(), &err); } objective_function_ = objective_function; num_tree_per_iteration_ = num_class_; if (objective_function_ != nullptr) { num_tree_per_iteration_ = objective_function_->NumModelPerIteration(); if (objective_function_->IsRenewTreeOutput() && !config->monotone_constraints.empty()) { Log::Fatal("Cannot use ``monotone_constraints`` in %s objective, please disable it.", objective_function_->GetName()); } } data_sample_strategy_.reset(SampleStrategy::CreateSampleStrategy(config_.get(), train_data_, objective_function_, num_tree_per_iteration_)); is_constant_hessian_ = GetIsConstHessian(objective_function); boosting_on_gpu_ = objective_function_ != nullptr && objective_function_->IsCUDAObjective() && !data_sample_strategy_->IsHessianChange(); // for sample strategy with Hessian change, fall back to boosting on CPU tree_learner_ = std::unique_ptr(TreeLearner::CreateTreeLearner(config_->tree_learner, config_->device_type, config_.get(), boosting_on_gpu_)); // init tree learner tree_learner_->Init(train_data_, is_constant_hessian_); tree_learner_->SetForcedSplit(&forced_splits_json_); // push training metrics training_metrics_.clear(); for (const auto& metric : training_metrics) { training_metrics_.push_back(metric); } training_metrics_.shrink_to_fit(); #ifdef USE_CUDA if (config_->device_type == std::string("cuda")) { train_score_updater_.reset(new CUDAScoreUpdater(train_data_, num_tree_per_iteration_, boosting_on_gpu_)); } else { #endif // USE_CUDA train_score_updater_.reset(new ScoreUpdater(train_data_, num_tree_per_iteration_)); #ifdef USE_CUDA } #endif // USE_CUDA num_data_ = train_data_->num_data(); // get max feature index max_feature_idx_ = train_data_->num_total_features() - 1; // get label index label_idx_ = train_data_->label_idx(); // get feature names feature_names_ = train_data_->feature_names(); feature_infos_ = train_data_->feature_infos(); monotone_constraints_ = config->monotone_constraints; // get parser config file content parser_config_str_ = train_data_->parser_config_str(); // check that forced splits does not use feature indices larger than dataset size CheckForcedSplitFeatures(); // if need bagging, create buffer data_sample_strategy_->ResetSampleConfig(config_.get(), true); ResetGradientBuffers(); class_need_train_ = std::vector(num_tree_per_iteration_, true); if (objective_function_ != nullptr && objective_function_->SkipEmptyClass()) { CHECK_EQ(num_tree_per_iteration_, num_class_); for (int i = 0; i < num_class_; ++i) { class_need_train_[i] = objective_function_->ClassNeedTrain(i); } } if (config_->linear_tree) { linear_tree_ = true; } } void GBDT::CheckForcedSplitFeatures() { std::queue forced_split_nodes; forced_split_nodes.push(forced_splits_json_); while (!forced_split_nodes.empty()) { Json node = forced_split_nodes.front(); forced_split_nodes.pop(); const int feature_index = node["feature"].int_value(); if (feature_index > max_feature_idx_) { Log::Fatal("Forced splits file includes feature index %d, but maximum feature index in dataset is %d", feature_index, max_feature_idx_); } if (node.object_items().count("left") > 0) { forced_split_nodes.push(node["left"]); } if (node.object_items().count("right") > 0) { forced_split_nodes.push(node["right"]); } } } void GBDT::AddValidDataset(const Dataset* valid_data, const std::vector& valid_metrics) { if (!train_data_->CheckAlign(*valid_data)) { Log::Fatal("Cannot add validation data, since it has different bin mappers with training data"); } // for a validation dataset, we need its score and metric auto new_score_updater = #ifdef USE_CUDA config_->device_type == std::string("cuda") ? std::unique_ptr(new CUDAScoreUpdater(valid_data, num_tree_per_iteration_, objective_function_ != nullptr && objective_function_->IsCUDAObjective())) : #endif // USE_CUDA std::unique_ptr(new ScoreUpdater(valid_data, num_tree_per_iteration_)); // update score for (int i = 0; i < iter_; ++i) { for (int cur_tree_id = 0; cur_tree_id < num_tree_per_iteration_; ++cur_tree_id) { auto curr_tree = (i + num_init_iteration_) * num_tree_per_iteration_ + cur_tree_id; new_score_updater->AddScore(models_[curr_tree].get(), cur_tree_id); } } valid_score_updater_.push_back(std::move(new_score_updater)); valid_metrics_.emplace_back(); for (const auto& metric : valid_metrics) { valid_metrics_.back().push_back(metric); } valid_metrics_.back().shrink_to_fit(); if (early_stopping_round_ > 0) { auto num_metrics = valid_metrics.size(); if (es_first_metric_only_) { num_metrics = 1; } best_iter_.emplace_back(num_metrics, 0); best_score_.emplace_back(num_metrics, kMinScore); best_msg_.emplace_back(num_metrics); } } void GBDT::Boosting() { Common::FunctionTimer fun_timer("GBDT::Boosting", global_timer); if (objective_function_ == nullptr) { Log::Fatal("No objective function provided"); } // objective function will calculate gradients and hessians int64_t num_score = 0; if (config_->bagging_by_query) { data_sample_strategy_->Bagging(iter_, tree_learner_.get(), gradients_.data(), hessians_.data()); objective_function_-> GetGradientsWithSampledQueries(GetTrainingScore(&num_score), data_sample_strategy_->num_sampled_queries(), data_sample_strategy_->sampled_query_indices(), gradients_pointer_, hessians_pointer_); } else { objective_function_-> GetGradients(GetTrainingScore(&num_score), gradients_pointer_, hessians_pointer_); } } void GBDT::Train(int snapshot_freq, const std::string& model_output_path) { Common::FunctionTimer fun_timer("GBDT::Train", global_timer); bool is_finished = false; auto start_time = std::chrono::steady_clock::now(); for (int iter = 0; iter < config_->num_iterations && !is_finished; ++iter) { is_finished = TrainOneIter(nullptr, nullptr); if (!is_finished) { is_finished = EvalAndCheckEarlyStopping(); } auto end_time = std::chrono::steady_clock::now(); // output used time per iteration Log::Info("%f seconds elapsed, finished iteration %d", std::chrono::duration(end_time - start_time) * 1e-3, iter + 1); if (snapshot_freq > 0 && (iter + 1) % snapshot_freq == 0) { std::string snapshot_out = model_output_path + ".snapshot_iter_" + std::to_string(iter + 1); SaveModelToFile(0, -1, config_->saved_feature_importance_type, snapshot_out.c_str()); } } } void GBDT::RefitTree(const int* tree_leaf_prediction, const size_t nrow, const size_t ncol) { CHECK_GT(nrow * ncol, 0); CHECK_EQ(static_cast(num_data_), nrow); CHECK_EQ(models_.size(), ncol); int num_iterations = static_cast(models_.size() / num_tree_per_iteration_); std::vector leaf_pred(num_data_); if (linear_tree_) { std::vector max_leaves_by_thread = std::vector(OMP_NUM_THREADS(), 0); #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) for (int i = 0; i < static_cast(nrow); ++i) { int tid = omp_get_thread_num(); for (size_t j = 0; j < ncol; ++j) { max_leaves_by_thread[tid] = std::max(max_leaves_by_thread[tid], tree_leaf_prediction[i * ncol + j]); } } int max_leaves = *std::max_element(max_leaves_by_thread.begin(), max_leaves_by_thread.end()); max_leaves += 1; tree_learner_->InitLinear(train_data_, max_leaves); } for (int iter = 0; iter < num_iterations; ++iter) { Boosting(); for (int tree_id = 0; tree_id < num_tree_per_iteration_; ++tree_id) { int model_index = iter * num_tree_per_iteration_ + tree_id; #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) for (int i = 0; i < num_data_; ++i) { leaf_pred[i] = tree_leaf_prediction[i * ncol + model_index]; CHECK_LT(leaf_pred[i], models_[model_index]->num_leaves()); } size_t offset = static_cast(tree_id) * num_data_; auto grad = gradients_pointer_ + offset; auto hess = hessians_pointer_ + offset; auto new_tree = tree_learner_->FitByExistingTree(models_[model_index].get(), leaf_pred, grad, hess); train_score_updater_->AddScore(tree_learner_.get(), new_tree, tree_id); models_[model_index].reset(new_tree); } } } /* If the custom "average" is implemented it will be used in place of the label average (if enabled) * * An improvement to this is to have options to explicitly choose * (i) standard average * (ii) custom average if available * (iii) any user defined scalar bias (e.g. using a new option "init_score" that overrides (i) and (ii) ) * * (i) and (ii) could be selected as say "auto_init_score" = 0 or 1 etc.. * */ double ObtainAutomaticInitialScore(const ObjectiveFunction* fobj, int class_id) { double init_score = 0.0; if (fobj != nullptr) { init_score = fobj->BoostFromScore(class_id); } if (Network::num_machines() > 1) { init_score = Network::GlobalSyncUpByMean(init_score); } return init_score; } double GBDT::BoostFromAverage(int class_id, bool update_scorer) { Common::FunctionTimer fun_timer("GBDT::BoostFromAverage", global_timer); // boosting from average label; or customized "average" if implemented for the current objective if (models_.empty() && !train_score_updater_->has_init_score() && objective_function_ != nullptr) { if (config_->boost_from_average || (train_data_ != nullptr && train_data_->num_features() == 0)) { double init_score = ObtainAutomaticInitialScore(objective_function_, class_id); if (std::fabs(init_score) > kEpsilon) { if (update_scorer) { train_score_updater_->AddScore(init_score, class_id); for (auto& score_updater : valid_score_updater_) { score_updater->AddScore(init_score, class_id); } } Log::Info("Start training from score %lf", init_score); return init_score; } } else if (std::string(objective_function_->GetName()) == std::string("regression_l1") || std::string(objective_function_->GetName()) == std::string("quantile") || std::string(objective_function_->GetName()) == std::string("mape")) { Log::Warning("Disabling boost_from_average in %s may cause the slow convergence", objective_function_->GetName()); } } return 0.0f; } bool GBDT::TrainOneIter(const score_t* gradients, const score_t* hessians) { Common::FunctionTimer fun_timer("GBDT::TrainOneIter", global_timer); std::vector init_scores(num_tree_per_iteration_, 0.0); // boosting first if (gradients == nullptr || hessians == nullptr) { for (int cur_tree_id = 0; cur_tree_id < num_tree_per_iteration_; ++cur_tree_id) { init_scores[cur_tree_id] = BoostFromAverage(cur_tree_id, true); } Boosting(); gradients = gradients_pointer_; hessians = hessians_pointer_; } else { // use customized objective function // the check below fails unless objective=custom is provided in the parameters on Booster creation CHECK(objective_function_ == nullptr); if (data_sample_strategy_->IsHessianChange()) { // need to copy customized gradients when using GOSS int64_t total_size = static_cast(num_data_) * num_tree_per_iteration_; #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) for (int64_t i = 0; i < total_size; ++i) { gradients_[i] = gradients[i]; hessians_[i] = hessians[i]; } CHECK_EQ(gradients_pointer_, gradients_.data()); CHECK_EQ(hessians_pointer_, hessians_.data()); gradients = gradients_pointer_; hessians = hessians_pointer_; } } // bagging logic if (!config_->bagging_by_query) { data_sample_strategy_->Bagging(iter_, tree_learner_.get(), gradients_.data(), hessians_.data()); } const bool is_use_subset = data_sample_strategy_->is_use_subset(); const data_size_t bag_data_cnt = data_sample_strategy_->bag_data_cnt(); const std::vector>& bag_data_indices = data_sample_strategy_->bag_data_indices(); if (objective_function_ == nullptr && is_use_subset && bag_data_cnt < num_data_ && !boosting_on_gpu_ && !data_sample_strategy_->IsHessianChange()) { ResetGradientBuffers(); } bool should_continue = false; for (int cur_tree_id = 0; cur_tree_id < num_tree_per_iteration_; ++cur_tree_id) { const size_t offset = static_cast(cur_tree_id) * num_data_; std::unique_ptr new_tree(new Tree(2, false, false)); if (class_need_train_[cur_tree_id] && train_data_->num_features() > 0) { auto grad = gradients + offset; auto hess = hessians + offset; // need to copy gradients for bagging subset. if (is_use_subset && bag_data_cnt < num_data_ && !boosting_on_gpu_) { for (int i = 0; i < bag_data_cnt; ++i) { gradients_pointer_[offset + i] = grad[bag_data_indices[i]]; hessians_pointer_[offset + i] = hess[bag_data_indices[i]]; } grad = gradients_pointer_ + offset; hess = hessians_pointer_ + offset; } bool is_first_tree = models_.size() < static_cast(num_tree_per_iteration_); new_tree.reset(tree_learner_->Train(grad, hess, is_first_tree)); } if (new_tree->num_leaves() > 1) { should_continue = true; auto score_ptr = train_score_updater_->score() + offset; auto residual_getter = [score_ptr](const label_t* label, int i) {return static_cast(label[i]) - score_ptr[i]; }; tree_learner_->RenewTreeOutput(new_tree.get(), objective_function_, residual_getter, num_data_, bag_data_indices.data(), bag_data_cnt, train_score_updater_->score()); // shrinkage by learning rate new_tree->Shrinkage(shrinkage_rate_); // update score UpdateScore(new_tree.get(), cur_tree_id); if (std::fabs(init_scores[cur_tree_id]) > kEpsilon) { new_tree->AddBias(init_scores[cur_tree_id]); } } else { // only add default score one-time if (models_.size() < static_cast(num_tree_per_iteration_)) { if (objective_function_ != nullptr && !config_->boost_from_average && !train_score_updater_->has_init_score()) { init_scores[cur_tree_id] = ObtainAutomaticInitialScore(objective_function_, cur_tree_id); // updates scores train_score_updater_->AddScore(init_scores[cur_tree_id], cur_tree_id); for (auto& score_updater : valid_score_updater_) { score_updater->AddScore(init_scores[cur_tree_id], cur_tree_id); } } new_tree->AsConstantTree(init_scores[cur_tree_id], num_data_); } else { // extend init_scores with zeros new_tree->AsConstantTree(0, num_data_); } } // add model models_.push_back(std::move(new_tree)); } if (!should_continue) { Log::Warning("Stopped training because there are no more leaves that meet the split requirements"); if (models_.size() > static_cast(num_tree_per_iteration_)) { for (int cur_tree_id = 0; cur_tree_id < num_tree_per_iteration_; ++cur_tree_id) { models_.pop_back(); } } return true; } ++iter_; return false; } void GBDT::RollbackOneIter() { if (iter_ <= 0) { return; } // reset score for (int cur_tree_id = 0; cur_tree_id < num_tree_per_iteration_; ++cur_tree_id) { auto curr_tree = models_.size() - num_tree_per_iteration_ + cur_tree_id; models_[curr_tree]->Shrinkage(-1.0); train_score_updater_->AddScore(models_[curr_tree].get(), cur_tree_id); for (auto& score_updater : valid_score_updater_) { score_updater->AddScore(models_[curr_tree].get(), cur_tree_id); } } // remove model for (int cur_tree_id = 0; cur_tree_id < num_tree_per_iteration_; ++cur_tree_id) { models_.pop_back(); } --iter_; } bool GBDT::EvalAndCheckEarlyStopping() { bool is_met_early_stopping = false; // print message for metric auto best_msg = OutputMetric(iter_); is_met_early_stopping = !best_msg.empty(); if (is_met_early_stopping) { Log::Info("Early stopping at iteration %d, the best iteration round is %d", iter_, iter_ - early_stopping_round_); Log::Info("Output of best iteration round:\n%s", best_msg.c_str()); // pop last early_stopping_round_ models for (int i = 0; i < early_stopping_round_ * num_tree_per_iteration_; ++i) { models_.pop_back(); } } return is_met_early_stopping; } void GBDT::UpdateScore(const Tree* tree, const int cur_tree_id) { Common::FunctionTimer fun_timer("GBDT::UpdateScore", global_timer); // update training score if (!data_sample_strategy_->is_use_subset()) { train_score_updater_->AddScore(tree_learner_.get(), tree, cur_tree_id); const data_size_t bag_data_cnt = data_sample_strategy_->bag_data_cnt(); // we need to predict out-of-bag scores of data for boosting if (num_data_ - bag_data_cnt > 0) { #ifdef USE_CUDA if (config_->device_type == std::string("cuda")) { train_score_updater_->AddScore(tree, data_sample_strategy_->cuda_bag_data_indices().RawData() + bag_data_cnt, num_data_ - bag_data_cnt, cur_tree_id); } else { #endif // USE_CUDA train_score_updater_->AddScore(tree, data_sample_strategy_->bag_data_indices().data() + bag_data_cnt, num_data_ - bag_data_cnt, cur_tree_id); #ifdef USE_CUDA } #endif // USE_CUDA } } else { train_score_updater_->AddScore(tree, cur_tree_id); } // update validation score for (auto& score_updater : valid_score_updater_) { score_updater->AddScore(tree, cur_tree_id); } } #ifdef USE_CUDA std::vector GBDT::EvalOneMetric(const Metric* metric, const double* score, const data_size_t num_data) const { #else std::vector GBDT::EvalOneMetric(const Metric* metric, const double* score, const data_size_t /*num_data*/) const { #endif // USE_CUDA #ifdef USE_CUDA const bool evaluation_on_cuda = metric->IsCUDAMetric(); if ((boosting_on_gpu_ && evaluation_on_cuda) || (!boosting_on_gpu_ && !evaluation_on_cuda)) { #endif // USE_CUDA return metric->Eval(score, objective_function_); #ifdef USE_CUDA } else if (boosting_on_gpu_ && !evaluation_on_cuda) { const size_t total_size = static_cast(num_data) * static_cast(num_tree_per_iteration_); if (total_size > host_score_.size()) { host_score_.resize(total_size, 0.0f); } CopyFromCUDADeviceToHost(host_score_.data(), score, total_size, __FILE__, __LINE__); return metric->Eval(host_score_.data(), objective_function_); } else { const size_t total_size = static_cast(num_data) * static_cast(num_tree_per_iteration_); if (total_size > cuda_score_.Size()) { cuda_score_.Resize(total_size); } CopyFromHostToCUDADevice(cuda_score_.RawData(), score, total_size, __FILE__, __LINE__); return metric->Eval(cuda_score_.RawData(), objective_function_); } #endif // USE_CUDA } std::string GBDT::OutputMetric(int iter) { bool need_output = (iter % config_->metric_freq) == 0; std::string ret = ""; std::stringstream msg_buf; std::vector> meet_early_stopping_pairs; // print training metric if (need_output) { for (auto& sub_metric : training_metrics_) { auto name = sub_metric->GetName(); auto scores = EvalOneMetric(sub_metric, train_score_updater_->score(), train_score_updater_->num_data()); for (size_t k = 0; k < name.size(); ++k) { std::stringstream tmp_buf; tmp_buf << "Iteration:" << iter << ", training " << name[k] << " : " << scores[k]; Log::Info(tmp_buf.str().c_str()); if (early_stopping_round_ > 0) { msg_buf << tmp_buf.str() << '\n'; } } } } // print validation metric if (need_output || early_stopping_round_ > 0) { for (size_t i = 0; i < valid_metrics_.size(); ++i) { for (size_t j = 0; j < valid_metrics_[i].size(); ++j) { auto test_scores = EvalOneMetric(valid_metrics_[i][j], valid_score_updater_[i]->score(), valid_score_updater_[i]->num_data()); auto name = valid_metrics_[i][j]->GetName(); for (size_t k = 0; k < name.size(); ++k) { std::stringstream tmp_buf; tmp_buf << "Iteration:" << iter << ", valid_" << i + 1 << " " << name[k] << " : " << test_scores[k]; if (need_output) { Log::Info(tmp_buf.str().c_str()); } if (early_stopping_round_ > 0) { msg_buf << tmp_buf.str() << '\n'; } } if (es_first_metric_only_ && j > 0) { continue; } if (ret.empty() && early_stopping_round_ > 0) { auto cur_score = valid_metrics_[i][j]->factor_to_bigger_better() * test_scores.back(); if (cur_score - best_score_[i][j] > early_stopping_min_delta_) { best_score_[i][j] = cur_score; best_iter_[i][j] = iter; meet_early_stopping_pairs.emplace_back(i, j); } else { if (iter - best_iter_[i][j] >= early_stopping_round_) { ret = best_msg_[i][j]; } } } } } } for (auto& pair : meet_early_stopping_pairs) { best_msg_[pair.first][pair.second] = msg_buf.str(); } return ret; } /*! \brief Get eval result */ std::vector GBDT::GetEvalAt(int data_idx) const { CHECK(data_idx >= 0 && data_idx <= static_cast(valid_score_updater_.size())); std::vector ret; if (data_idx == 0) { for (auto& sub_metric : training_metrics_) { auto scores = EvalOneMetric(sub_metric, train_score_updater_->score(), train_score_updater_->num_data()); for (auto score : scores) { ret.push_back(score); } } } else { auto used_idx = data_idx - 1; for (size_t j = 0; j < valid_metrics_[used_idx].size(); ++j) { auto test_scores = EvalOneMetric(valid_metrics_[used_idx][j], valid_score_updater_[used_idx]->score(), valid_score_updater_[used_idx]->num_data()); for (auto score : test_scores) { ret.push_back(score); } } } return ret; } /*! \brief Get training scores result */ const double* GBDT::GetTrainingScore(int64_t* out_len) { *out_len = static_cast(train_score_updater_->num_data()) * num_class_; return train_score_updater_->score(); } void GBDT::PredictContrib(const double* features, double* output) const { // set zero const int num_features = max_feature_idx_ + 1; std::memset(output, 0, sizeof(double) * num_tree_per_iteration_ * (num_features + 1)); const int end_iteration_for_pred = start_iteration_for_pred_ + num_iteration_for_pred_; for (int i = start_iteration_for_pred_; i < end_iteration_for_pred; ++i) { // predict all the trees for one iteration for (int k = 0; k < num_tree_per_iteration_; ++k) { models_[i * num_tree_per_iteration_ + k]->PredictContrib(features, num_features, output + k*(num_features + 1)); } } } void GBDT::PredictContribByMap(const std::unordered_map& features, std::vector>* output) const { const int num_features = max_feature_idx_ + 1; const int end_iteration_for_pred = start_iteration_for_pred_ + num_iteration_for_pred_; for (int i = start_iteration_for_pred_; i < end_iteration_for_pred; ++i) { // predict all the trees for one iteration for (int k = 0; k < num_tree_per_iteration_; ++k) { models_[i * num_tree_per_iteration_ + k]->PredictContribByMap(features, num_features, &((*output)[k])); } } } void GBDT::GetPredictAt(int data_idx, double* out_result, int64_t* out_len) { CHECK(data_idx >= 0 && data_idx <= static_cast(valid_score_updater_.size())); const double* raw_scores = nullptr; data_size_t num_data = 0; if (data_idx == 0) { raw_scores = GetTrainingScore(out_len); num_data = train_score_updater_->num_data(); } else { auto used_idx = data_idx - 1; raw_scores = valid_score_updater_[used_idx]->score(); num_data = valid_score_updater_[used_idx]->num_data(); *out_len = static_cast(num_data) * num_class_; } #ifdef USE_CUDA std::vector host_raw_scores; if (boosting_on_gpu_) { host_raw_scores.resize(static_cast(*out_len), 0.0); CopyFromCUDADeviceToHost(host_raw_scores.data(), raw_scores, static_cast(*out_len), __FILE__, __LINE__); raw_scores = host_raw_scores.data(); } #endif // USE_CUDA if (objective_function_ != nullptr) { #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) for (data_size_t i = 0; i < num_data; ++i) { std::vector tree_pred(num_tree_per_iteration_); for (int j = 0; j < num_tree_per_iteration_; ++j) { tree_pred[j] = raw_scores[j * num_data + i]; } std::vector tmp_result(num_class_); objective_function_->ConvertOutput(tree_pred.data(), tmp_result.data()); for (int j = 0; j < num_class_; ++j) { out_result[j * num_data + i] = static_cast(tmp_result[j]); } } } else { #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) for (data_size_t i = 0; i < num_data; ++i) { for (int j = 0; j < num_tree_per_iteration_; ++j) { out_result[j * num_data + i] = static_cast(raw_scores[j * num_data + i]); } } } } double GBDT::GetUpperBoundValue() const { double max_value = 0.0; for (const auto &tree : models_) { max_value += tree->GetUpperBoundValue(); } return max_value; } double GBDT::GetLowerBoundValue() const { double min_value = 0.0; for (const auto &tree : models_) { min_value += tree->GetLowerBoundValue(); } return min_value; } void GBDT::ResetTrainingData(const Dataset* train_data, const ObjectiveFunction* objective_function, const std::vector& training_metrics) { if (train_data != train_data_ && !train_data_->CheckAlign(*train_data)) { Log::Fatal("Cannot reset training data, since new training data has different bin mappers"); } objective_function_ = objective_function; data_sample_strategy_->UpdateObjectiveFunction(objective_function); if (objective_function_ != nullptr) { CHECK_EQ(num_tree_per_iteration_, objective_function_->NumModelPerIteration()); if (objective_function_->IsRenewTreeOutput() && !config_->monotone_constraints.empty()) { Log::Fatal("Cannot use ``monotone_constraints`` in %s objective, please disable it.", objective_function_->GetName()); } } is_constant_hessian_ = GetIsConstHessian(objective_function); // push training metrics training_metrics_.clear(); for (const auto& metric : training_metrics) { training_metrics_.push_back(metric); } training_metrics_.shrink_to_fit(); #ifdef USE_CUDA boosting_on_gpu_ = objective_function_ != nullptr && objective_function_->IsCUDAObjective() && !data_sample_strategy_->IsHessianChange(); // for sample strategy with Hessian change, fall back to boosting on CPU tree_learner_->ResetBoostingOnGPU(boosting_on_gpu_); #endif // USE_CUDA if (train_data != train_data_) { train_data_ = train_data; data_sample_strategy_->UpdateTrainingData(train_data); // not same training data, need reset score and others // create score tracker #ifdef USE_CUDA if (config_->device_type == std::string("cuda")) { train_score_updater_.reset(new CUDAScoreUpdater(train_data_, num_tree_per_iteration_, boosting_on_gpu_)); } else { #endif // USE_CUDA train_score_updater_.reset(new ScoreUpdater(train_data_, num_tree_per_iteration_)); #ifdef USE_CUDA } #endif // USE_CUDA // update score for (int i = 0; i < iter_; ++i) { for (int cur_tree_id = 0; cur_tree_id < num_tree_per_iteration_; ++cur_tree_id) { auto curr_tree = (i + num_init_iteration_) * num_tree_per_iteration_ + cur_tree_id; train_score_updater_->AddScore(models_[curr_tree].get(), cur_tree_id); } } num_data_ = train_data_->num_data(); ResetGradientBuffers(); max_feature_idx_ = train_data_->num_total_features() - 1; label_idx_ = train_data_->label_idx(); feature_names_ = train_data_->feature_names(); feature_infos_ = train_data_->feature_infos(); parser_config_str_ = train_data_->parser_config_str(); tree_learner_->ResetTrainingData(train_data, is_constant_hessian_); data_sample_strategy_->ResetSampleConfig(config_.get(), true); } else { tree_learner_->ResetIsConstantHessian(is_constant_hessian_); } } void GBDT::ResetConfig(const Config* config) { auto new_config = std::unique_ptr(new Config(*config)); if (!config->monotone_constraints.empty()) { CHECK_EQ(static_cast(train_data_->num_total_features()), config->monotone_constraints.size()); } if (!config->feature_contri.empty()) { CHECK_EQ(static_cast(train_data_->num_total_features()), config->feature_contri.size()); } if (objective_function_ != nullptr && objective_function_->IsRenewTreeOutput() && !config->monotone_constraints.empty()) { Log::Fatal("Cannot use ``monotone_constraints`` in %s objective, please disable it.", objective_function_->GetName()); } early_stopping_round_ = new_config->early_stopping_round; shrinkage_rate_ = new_config->learning_rate; if (tree_learner_ != nullptr) { tree_learner_->ResetConfig(new_config.get()); } boosting_on_gpu_ = objective_function_ != nullptr && objective_function_->IsCUDAObjective() && !data_sample_strategy_->IsHessianChange(); // for sample strategy with Hessian change, fall back to boosting on CPU tree_learner_->ResetBoostingOnGPU(boosting_on_gpu_); if (train_data_ != nullptr) { data_sample_strategy_->ResetSampleConfig(new_config.get(), false); if (data_sample_strategy_->NeedResizeGradients()) { // resize gradient vectors to copy the customized gradients for goss or bagging with subset ResetGradientBuffers(); } } if (config_.get() != nullptr && config_->forcedsplits_filename != new_config->forcedsplits_filename) { // load forced_splits file if (!new_config->forcedsplits_filename.empty()) { std::ifstream forced_splits_file( new_config->forcedsplits_filename.c_str()); std::stringstream buffer; buffer << forced_splits_file.rdbuf(); std::string err; forced_splits_json_ = Json::parse(buffer.str(), &err); tree_learner_->SetForcedSplit(&forced_splits_json_); } else { forced_splits_json_ = Json(); tree_learner_->SetForcedSplit(nullptr); } } config_.reset(new_config.release()); } void GBDT::ResetGradientBuffers() { const size_t total_size = static_cast(num_data_) * num_tree_per_iteration_; const bool is_use_subset = data_sample_strategy_->is_use_subset(); const data_size_t bag_data_cnt = data_sample_strategy_->bag_data_cnt(); if (objective_function_ != nullptr) { #ifdef USE_CUDA if (config_->device_type == std::string("cuda") && boosting_on_gpu_) { if (cuda_gradients_.Size() < total_size) { cuda_gradients_.Resize(total_size); cuda_hessians_.Resize(total_size); } gradients_pointer_ = cuda_gradients_.RawData(); hessians_pointer_ = cuda_hessians_.RawData(); } else { #endif // USE_CUDA if (gradients_.size() < total_size) { gradients_.resize(total_size); hessians_.resize(total_size); } gradients_pointer_ = gradients_.data(); hessians_pointer_ = hessians_.data(); #ifdef USE_CUDA } #endif // USE_CUDA } else if (data_sample_strategy_->IsHessianChange() || (is_use_subset && bag_data_cnt < num_data_ && !boosting_on_gpu_)) { if (gradients_.size() < total_size) { gradients_.resize(total_size); hessians_.resize(total_size); } gradients_pointer_ = gradients_.data(); hessians_pointer_ = hessians_.data(); } } } // namespace LightGBM ================================================ FILE: src/boosting/gbdt.h ================================================ /*! * Copyright (c) 2016-2026 Microsoft Corporation. All rights reserved. * Copyright (c) 2016-2026 The LightGBM developers. All rights reserved. * Licensed under the MIT License. See LICENSE file in the project root for license information. */ #ifndef LIGHTGBM_SRC_BOOSTING_GBDT_H_ #define LIGHTGBM_SRC_BOOSTING_GBDT_H_ #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include "cuda/cuda_score_updater.hpp" #include "score_updater.hpp" namespace LightGBM { using json11_internal_lightgbm::Json; /*! * \brief GBDT algorithm implementation. including Training, prediction, bagging. */ class GBDT : public GBDTBase { public: /*! * \brief Constructor */ GBDT(); /*! * \brief Destructor */ ~GBDT(); /*! * \brief Initialization logic * \param gbdt_config Config for boosting * \param train_data Training data * \param objective_function Training objective function * \param training_metrics Training metrics */ void Init(const Config* gbdt_config, const Dataset* train_data, const ObjectiveFunction* objective_function, const std::vector& training_metrics) override; /*! * \brief Traverse the tree of forced splits and check that all indices are less than the number of features. */ void CheckForcedSplitFeatures(); /*! * \brief Merge model from other boosting object. Will insert to the front of current boosting object * \param other */ void MergeFrom(const Boosting* other) override { auto other_gbdt = reinterpret_cast(other); // tmp move to other vector auto original_models = std::move(models_); models_ = std::vector>(); // push model from other first for (const auto& tree : other_gbdt->models_) { auto new_tree = std::unique_ptr(new Tree(*(tree.get()))); models_.push_back(std::move(new_tree)); } num_init_iteration_ = static_cast(models_.size()) / num_tree_per_iteration_; // push model in current object for (const auto& tree : original_models) { auto new_tree = std::unique_ptr(new Tree(*(tree.get()))); models_.push_back(std::move(new_tree)); } num_iteration_for_pred_ = static_cast(models_.size()) / num_tree_per_iteration_; } void ShuffleModels(int start_iter, int end_iter) override { int total_iter = static_cast(models_.size()) / num_tree_per_iteration_; start_iter = std::max(0, start_iter); if (end_iter <= 0) { end_iter = total_iter; } end_iter = std::min(total_iter, end_iter); auto original_models = std::move(models_); std::vector indices(total_iter); for (int i = 0; i < total_iter; ++i) { indices[i] = i; } Random tmp_rand(17); for (int i = start_iter; i < end_iter - 1; ++i) { int j = tmp_rand.NextShort(i + 1, end_iter); std::swap(indices[i], indices[j]); } models_ = std::vector>(); for (int i = 0; i < total_iter; ++i) { for (int j = 0; j < num_tree_per_iteration_; ++j) { int tree_idx = indices[i] * num_tree_per_iteration_ + j; auto new_tree = std::unique_ptr(new Tree(*(original_models[tree_idx].get()))); models_.push_back(std::move(new_tree)); } } } /*! * \brief Reset the training data * \param train_data New Training data * \param objective_function Training objective function * \param training_metrics Training metrics */ void ResetTrainingData(const Dataset* train_data, const ObjectiveFunction* objective_function, const std::vector& training_metrics) override; /*! * \brief Reset Boosting Config * \param gbdt_config Config for boosting */ void ResetConfig(const Config* gbdt_config) override; /*! * \brief Adding a validation dataset * \param valid_data Validation dataset * \param valid_metrics Metrics for validation dataset */ void AddValidDataset(const Dataset* valid_data, const std::vector& valid_metrics) override; /*! * \brief Perform a full training procedure * \param snapshot_freq frequency of snapshot * \param model_output_path path of model file */ void Train(int snapshot_freq, const std::string& model_output_path) override; void RefitTree(const int* tree_leaf_prediction, const size_t nrow, const size_t ncol) override; /*! * \brief Training logic * \param gradients nullptr for using default objective, otherwise use self-defined boosting * \param hessians nullptr for using default objective, otherwise use self-defined boosting * \return True if cannot train any more */ bool TrainOneIter(const score_t* gradients, const score_t* hessians) override; /*! * \brief Rollback one iteration */ void RollbackOneIter() override; /*! * \brief Get current iteration */ int GetCurrentIteration() const override { return static_cast(models_.size()) / num_tree_per_iteration_; } /*! * \brief Get parameters as a JSON string */ std::string GetLoadedParam() const override { if (loaded_parameter_.empty()) { return std::string("{}"); } const auto param_types = Config::ParameterTypes(); const auto lines = Common::Split(loaded_parameter_.c_str(), "\n"); bool first = true; std::stringstream str_buf; str_buf << "{"; for (const auto& line : lines) { const auto pair = Common::Split(line.c_str(), ":"); if (pair[1] == " ]") continue; const auto param = pair[0].substr(1); const auto value_str = pair[1].substr(1, pair[1].size() - 2); auto iter = param_types.find(param); if (iter == param_types.end()) { Log::Warning("Ignoring unrecognized parameter '%s' found in model string.", param.c_str()); continue; } std::string param_type = iter->second; if (first) { first = false; str_buf << "\""; } else { str_buf << ",\""; } str_buf << param << "\": "; if (param_type == "string") { str_buf << "\"" << value_str << "\""; } else if (param_type == "int") { int value; Common::Atoi(value_str.c_str(), &value); str_buf << value; } else if (param_type == "double") { double value; Common::Atof(value_str.c_str(), &value); str_buf << value; } else if (param_type == "bool") { bool value = value_str == "1"; str_buf << std::boolalpha << value; } else if (param_type.substr(0, 6) == "vector") { str_buf << "["; if (param_type.substr(7, 6) == "string") { const auto parts = Common::Split(value_str.c_str(), ","); str_buf << "\"" << Common::Join(parts, "\",\"") << "\""; } else { str_buf << value_str; } str_buf << "]"; } } str_buf << "}"; return str_buf.str(); } /*! * \brief Can use early stopping for prediction or not * \return True if cannot use early stopping for prediction */ bool NeedAccuratePrediction() const override { if (objective_function_ == nullptr) { return true; } else { return objective_function_->NeedAccuratePrediction(); } } /*! * \brief Get evaluation result at data_idx data * \param data_idx 0: training data, 1: 1st validation data * \return evaluation result */ std::vector GetEvalAt(int data_idx) const override; /*! * \brief Get current training score * \param out_len length of returned score * \return training score */ const double* GetTrainingScore(int64_t* out_len) override; /*! * \brief Get size of prediction at data_idx data * \param data_idx 0: training data, 1: 1st validation data * \return The size of prediction */ int64_t GetNumPredictAt(int data_idx) const override { CHECK(data_idx >= 0 && data_idx <= static_cast(valid_score_updater_.size())); data_size_t num_data = train_data_->num_data(); if (data_idx > 0) { num_data = valid_score_updater_[data_idx - 1]->num_data(); } return static_cast(num_data) * num_class_; } /*! * \brief Get prediction result at data_idx data * \param data_idx 0: training data, 1: 1st validation data * \param result used to store prediction result, should allocate memory before call this function * \param out_len length of returned score */ void GetPredictAt(int data_idx, double* out_result, int64_t* out_len) override; /*! * \brief Get number of prediction for one data * \param start_iteration Start index of the iteration to predict * \param num_iteration number of used iterations * \param is_pred_leaf True if predicting leaf index * \param is_pred_contrib True if predicting feature contribution * \return number of prediction */ inline int NumPredictOneRow(int start_iteration, int num_iteration, bool is_pred_leaf, bool is_pred_contrib) const override { int num_pred_in_one_row = num_class_; if (is_pred_leaf) { int max_iteration = GetCurrentIteration(); start_iteration = std::max(start_iteration, 0); start_iteration = std::min(start_iteration, max_iteration); if (num_iteration > 0) { num_pred_in_one_row *= static_cast(std::min(max_iteration - start_iteration, num_iteration)); } else { num_pred_in_one_row *= (max_iteration - start_iteration); } } else if (is_pred_contrib) { num_pred_in_one_row = num_tree_per_iteration_ * (max_feature_idx_ + 2); // +1 for 0-based indexing, +1 for baseline } return num_pred_in_one_row; } void PredictRaw(const double* features, double* output, const PredictionEarlyStopInstance* earlyStop) const override; void PredictRawByMap(const std::unordered_map& features, double* output, const PredictionEarlyStopInstance* early_stop) const override; void Predict(const double* features, double* output, const PredictionEarlyStopInstance* earlyStop) const override; void PredictByMap(const std::unordered_map& features, double* output, const PredictionEarlyStopInstance* early_stop) const override; void PredictLeafIndex(const double* features, double* output) const override; void PredictLeafIndexByMap(const std::unordered_map& features, double* output) const override; void PredictContrib(const double* features, double* output) const override; void PredictContribByMap(const std::unordered_map& features, std::vector>* output) const override; /*! * \brief Dump model to json format string * \param start_iteration The model will be saved start from * \param num_iteration Number of iterations that want to dump, -1 means dump all * \param feature_importance_type Type of feature importance, 0: split, 1: gain * \return Json format string of model */ std::string DumpModel(int start_iteration, int num_iteration, int feature_importance_type) const override; /*! * \brief Translate model to if-else statement * \param num_iteration Number of iterations that want to translate, -1 means translate all * \return if-else format codes of model */ std::string ModelToIfElse(int num_iteration) const override; /*! * \brief Translate model to if-else statement * \param num_iteration Number of iterations that want to translate, -1 means translate all * \param filename Filename that want to save to * \return is_finish Is training finished or not */ bool SaveModelToIfElse(int num_iteration, const char* filename) const override; /*! * \brief Save model to file * \param start_iteration The model will be saved start from * \param num_iterations Number of model that want to save, -1 means save all * \param feature_importance_type Type of feature importance, 0: split, 1: gain * \param filename Filename that want to save to * \return is_finish Is training finished or not */ bool SaveModelToFile(int start_iteration, int num_iterations, int feature_importance_type, const char* filename) const override; /*! * \brief Save model to string * \param start_iteration The model will be saved start from * \param num_iterations Number of model that want to save, -1 means save all * \param feature_importance_type Type of feature importance, 0: split, 1: gain * \return Non-empty string if succeeded */ std::string SaveModelToString(int start_iteration, int num_iterations, int feature_importance_type) const override; /*! * \brief Restore from a serialized buffer */ bool LoadModelFromString(const char* buffer, size_t len) override; /*! * \brief Calculate feature importances * \param num_iteration Number of model that want to use for feature importance, -1 means use all * \param importance_type: 0 for split, 1 for gain * \return vector of feature_importance */ std::vector FeatureImportance(int num_iteration, int importance_type) const override; /*! * \brief Calculate upper bound value * \return upper bound value */ double GetUpperBoundValue() const override; /*! * \brief Calculate lower bound value * \return lower bound value */ double GetLowerBoundValue() const override; /*! * \brief Get max feature index of this model * \return Max feature index of this model */ inline int MaxFeatureIdx() const override { return max_feature_idx_; } /*! * \brief Get feature names of this model * \return Feature names of this model */ inline std::vector FeatureNames() const override { return feature_names_; } /*! * \brief Get index of label column * \return index of label column */ inline int LabelIdx() const override { return label_idx_; } /*! * \brief Get number of weak sub-models * \return Number of weak sub-models */ inline int NumberOfTotalModel() const override { return static_cast(models_.size()); } /*! * \brief Get number of tree per iteration * \return number of tree per iteration */ inline int NumModelPerIteration() const override { return num_tree_per_iteration_; } /*! * \brief Get number of classes * \return Number of classes */ inline int NumberOfClasses() const override { return num_class_; } inline void InitPredict(int start_iteration, int num_iteration, bool is_pred_contrib) override { num_iteration_for_pred_ = static_cast(models_.size()) / num_tree_per_iteration_; start_iteration = std::max(start_iteration, 0); start_iteration = std::min(start_iteration, num_iteration_for_pred_); if (num_iteration > 0) { num_iteration_for_pred_ = std::min(num_iteration, num_iteration_for_pred_ - start_iteration); } else { num_iteration_for_pred_ = num_iteration_for_pred_ - start_iteration; } start_iteration_for_pred_ = start_iteration; if (is_pred_contrib && !models_initialized_) { std::lock_guard lock(instance_mutex_); if (models_initialized_) return; #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) for (int i = 0; i < static_cast(models_.size()); ++i) { models_[i]->RecomputeMaxDepth(); } models_initialized_ = true; } } inline double GetLeafValue(int tree_idx, int leaf_idx) const override { CHECK(tree_idx >= 0 && static_cast(tree_idx) < models_.size()); CHECK(leaf_idx >= 0 && leaf_idx < models_[tree_idx]->num_leaves()); return models_[tree_idx]->LeafOutput(leaf_idx); } inline void SetLeafValue(int tree_idx, int leaf_idx, double val) override { CHECK(tree_idx >= 0 && static_cast(tree_idx) < models_.size()); CHECK(leaf_idx >= 0 && leaf_idx < models_[tree_idx]->num_leaves()); models_[tree_idx]->SetLeafOutput(leaf_idx, val); } /*! * \brief Get Type name of this boosting object */ const char* SubModelName() const override { return "tree"; } bool IsLinear() const override { return linear_tree_; } inline std::string ParserConfigStr() const override {return parser_config_str_;} protected: virtual bool GetIsConstHessian(const ObjectiveFunction* objective_function) { if (objective_function != nullptr && !data_sample_strategy_->IsHessianChange()) { return objective_function->IsConstantHessian(); } else { return false; } } /*! * \brief Print eval result and check early stopping */ virtual bool EvalAndCheckEarlyStopping(); /*! * \brief reset config for bagging */ void ResetBaggingConfig(const Config* config, bool is_change_dataset); /*! * \brief calculate the objective function */ virtual void Boosting(); /*! * \brief updating score after tree was trained * \param tree Trained tree of this iteration * \param cur_tree_id Current tree for multiclass training */ virtual void UpdateScore(const Tree* tree, const int cur_tree_id); /*! * \brief eval results for one metric */ virtual std::vector EvalOneMetric(const Metric* metric, const double* score, const data_size_t num_data) const; /*! * \brief Print metric result of current iteration * \param iter Current iteration * \return best_msg if met early_stopping */ std::string OutputMetric(int iter); virtual double BoostFromAverage(int class_id, bool update_scorer); /*! * \brief Reset gradient buffers, must be called after sample strategy is reset */ void ResetGradientBuffers(); /*! \brief current iteration */ int iter_; /*! \brief Pointer to training data */ const Dataset* train_data_; /*! \brief Config of gbdt */ std::unique_ptr config_; /*! \brief Tree learner, will use this class to learn trees */ std::unique_ptr tree_learner_; /*! \brief Objective function */ const ObjectiveFunction* objective_function_; /*! \brief Store and update training data's score */ std::unique_ptr train_score_updater_; /*! \brief Metrics for training data */ std::vector training_metrics_; /*! \brief Store and update validation data's scores */ std::vector> valid_score_updater_; /*! \brief Metric for validation data */ std::vector> valid_metrics_; /*! \brief Number of rounds for early stopping */ int early_stopping_round_; /*! \brief Minimum improvement for early stopping */ double early_stopping_min_delta_; /*! \brief Only use first metric for early stopping */ bool es_first_metric_only_; /*! \brief Best iteration(s) for early stopping */ std::vector> best_iter_; /*! \brief Best score(s) for early stopping */ std::vector> best_score_; /*! \brief output message of best iteration */ std::vector> best_msg_; /*! \brief Trained models(trees) */ std::vector> models_; /*! \brief Max feature index of training data*/ int max_feature_idx_; /*! \brief Parser config file content */ std::string parser_config_str_ = ""; /*! \brief Are the models initialized (passed RecomputeMaxDepth phase) */ bool models_initialized_ = false; /*! \brief Mutex for exclusive models initialization */ std::mutex instance_mutex_; #ifdef USE_CUDA /*! \brief First order derivative of training data */ std::vector> gradients_; /*! \brief Second order derivative of training data */ std::vector> hessians_; #else /*! \brief First order derivative of training data */ std::vector> gradients_; /*! \brief Second order derivative of training data */ std::vector> hessians_; #endif /*! \brief Pointer to gradient vector, can be on CPU or GPU */ score_t* gradients_pointer_; /*! \brief Pointer to hessian vector, can be on CPU or GPU */ score_t* hessians_pointer_; /*! \brief Whether boosting is done on GPU, used for device_type=cuda */ bool boosting_on_gpu_; #ifdef USE_CUDA /*! \brief Gradient vector on GPU */ CUDAVector cuda_gradients_; /*! \brief Hessian vector on GPU */ CUDAVector cuda_hessians_; /*! \brief Buffer for scores when boosting is on GPU but evaluation is not, used only with device_type=cuda */ mutable std::vector host_score_; /*! \brief Buffer for scores when boosting is not on GPU but evaluation is, used only with device_type=cuda */ mutable CUDAVector cuda_score_; #endif // USE_CUDA /*! \brief Number of training data */ data_size_t num_data_; /*! \brief Number of trees per iterations */ int num_tree_per_iteration_; /*! \brief Number of class */ int num_class_; /*! \brief Index of label column */ data_size_t label_idx_; /*! \brief number of used model */ int num_iteration_for_pred_; /*! \brief Start iteration of used model */ int start_iteration_for_pred_; /*! \brief Shrinkage rate for one iteration */ double shrinkage_rate_; /*! \brief Number of loaded initial models */ int num_init_iteration_; /*! \brief Feature names */ std::vector feature_names_; std::vector feature_infos_; std::vector class_need_train_; bool is_constant_hessian_; std::unique_ptr loaded_objective_; bool average_output_; bool need_re_bagging_; bool balanced_bagging_; std::string loaded_parameter_; std::vector monotone_constraints_; Json forced_splits_json_; bool linear_tree_; std::unique_ptr data_sample_strategy_; }; } // namespace LightGBM #endif // LIGHTGBM_SRC_BOOSTING_GBDT_H_ ================================================ FILE: src/boosting/gbdt_model_text.cpp ================================================ /*! * Copyright (c) 2017-2026 Microsoft Corporation. All rights reserved. * Copyright (c) 2017-2026 The LightGBM developers. All rights reserved. * Licensed under the MIT License. See LICENSE file in the project root for license information. */ #include #include #include #include #include #include #include #include #include #include #include #include "gbdt.h" namespace LightGBM { const char* kModelVersion = "v4"; std::string GBDT::DumpModel(int start_iteration, int num_iteration, int feature_importance_type) const { std::stringstream str_buf; Common::C_stringstream(str_buf); str_buf << "{"; str_buf << "\"name\":\"" << SubModelName() << "\"," << '\n'; str_buf << "\"version\":\"" << kModelVersion << "\"," << '\n'; str_buf << "\"num_class\":" << num_class_ << "," << '\n'; str_buf << "\"num_tree_per_iteration\":" << num_tree_per_iteration_ << "," << '\n'; str_buf << "\"label_index\":" << label_idx_ << "," << '\n'; str_buf << "\"max_feature_idx\":" << max_feature_idx_ << "," << '\n'; if (objective_function_ != nullptr) { str_buf << "\"objective\":\"" << objective_function_->ToString() << "\",\n"; } str_buf << "\"average_output\":" << (average_output_ ? "true" : "false") << ",\n"; str_buf << "\"feature_names\":[\"" << CommonC::Join(feature_names_, "\",\"") << "\"]," << '\n'; str_buf << "\"monotone_constraints\":[" << CommonC::Join(monotone_constraints_, ",") << "]," << '\n'; str_buf << "\"feature_infos\":" << "{"; bool first_obj = true; for (size_t i = 0; i < feature_infos_.size(); ++i) { std::stringstream json_str_buf; Common::C_stringstream(json_str_buf); auto strs = Common::Split(feature_infos_[i].c_str(), ":"); if (strs[0][0] == '[') { strs[0].erase(0, 1); // remove '[' strs[1].erase(strs[1].size() - 1); // remove ']' double max_, min_; Common::Atof(strs[0].c_str(), &min_); Common::Atof(strs[1].c_str(), &max_); json_str_buf << std::setprecision(std::numeric_limits::digits10 + 2); json_str_buf << "{\"min_value\":" << Common::AvoidInf(min_) << ","; json_str_buf << "\"max_value\":" << Common::AvoidInf(max_) << ","; json_str_buf << "\"values\":[]}"; } else if (strs[0] != "none") { // categorical feature auto vals = CommonC::StringToArray(feature_infos_[i], ':'); auto max_idx = ArrayArgs::ArgMax(vals); auto min_idx = ArrayArgs::ArgMin(vals); json_str_buf << "{\"min_value\":" << vals[min_idx] << ","; json_str_buf << "\"max_value\":" << vals[max_idx] << ","; json_str_buf << "\"values\":[" << CommonC::Join(vals, ",") << "]}"; } else { // unused feature continue; } if (!first_obj) { str_buf << ","; } str_buf << "\"" << feature_names_[i] << "\":"; str_buf << json_str_buf.str(); first_obj = false; } str_buf << "}," << '\n'; str_buf << "\"tree_info\":["; int num_used_model = static_cast(models_.size()); int total_iteration = num_used_model / num_tree_per_iteration_; start_iteration = std::max(start_iteration, 0); start_iteration = std::min(start_iteration, total_iteration); if (num_iteration > 0) { int end_iteration = start_iteration + num_iteration; num_used_model = std::min(end_iteration * num_tree_per_iteration_ , num_used_model); } int start_model = start_iteration * num_tree_per_iteration_; for (int i = start_model; i < num_used_model; ++i) { if (i > start_model) { str_buf << ","; } str_buf << "{"; str_buf << "\"tree_index\":" << i << ","; str_buf << models_[i]->ToJSON(); str_buf << "}"; } str_buf << "]," << '\n'; std::vector feature_importances = FeatureImportance( num_iteration, feature_importance_type); // store the importance first std::vector> pairs; for (size_t i = 0; i < feature_importances.size(); ++i) { size_t feature_importances_int = static_cast(feature_importances[i]); if (feature_importances_int > 0) { pairs.emplace_back(feature_importances_int, feature_names_[i]); } } str_buf << '\n' << "\"feature_importances\":" << "{"; for (size_t i = 0; i < pairs.size(); ++i) { if (i > 0) { str_buf << ","; } str_buf << "\"" << pairs[i].second << "\":" << std::to_string(pairs[i].first); } str_buf << "}" << '\n'; str_buf << "}" << '\n'; return str_buf.str(); } std::string GBDT::ModelToIfElse(int num_iteration) const { std::stringstream str_buf; Common::C_stringstream(str_buf); str_buf << "#include \"gbdt.h\"" << '\n'; str_buf << "#include " << '\n'; str_buf << "#include " << '\n'; str_buf << "#include " << '\n'; str_buf << "#include " << '\n'; str_buf << "#include " << '\n'; str_buf << "#include " << '\n'; str_buf << "#include " << '\n'; str_buf << "#include " << '\n'; str_buf << "#include " << '\n'; str_buf << "#include " << '\n'; str_buf << "namespace LightGBM {" << '\n'; int num_used_model = static_cast(models_.size()); if (num_iteration > 0) { num_used_model = std::min(num_iteration * num_tree_per_iteration_, num_used_model); } // PredictRaw for (int i = 0; i < num_used_model; ++i) { str_buf << models_[i]->ToIfElse(i, false) << '\n'; } str_buf << "double (*PredictTreePtr[])(const double*) = { "; for (int i = 0; i < num_used_model; ++i) { if (i > 0) { str_buf << " , "; } str_buf << "PredictTree" << i; } str_buf << " };" << '\n' << '\n'; std::stringstream pred_str_buf; Common::C_stringstream(pred_str_buf); pred_str_buf << "\t" << "int early_stop_round_counter = 0;" << '\n'; pred_str_buf << "\t" << "std::memset(output, 0, sizeof(double) * num_tree_per_iteration_);" << '\n'; pred_str_buf << "\t" << "for (int i = 0; i < num_iteration_for_pred_; ++i) {" << '\n'; pred_str_buf << "\t\t" << "for (int k = 0; k < num_tree_per_iteration_; ++k) {" << '\n'; pred_str_buf << "\t\t\t" << "output[k] += (*PredictTreePtr[i * num_tree_per_iteration_ + k])(features);" << '\n'; pred_str_buf << "\t\t" << "}" << '\n'; pred_str_buf << "\t\t" << "++early_stop_round_counter;" << '\n'; pred_str_buf << "\t\t" << "if (early_stop->round_period == early_stop_round_counter) {" << '\n'; pred_str_buf << "\t\t\t" << "if (early_stop->callback_function(output, num_tree_per_iteration_))" << '\n'; pred_str_buf << "\t\t\t\t" << "return;" << '\n'; pred_str_buf << "\t\t\t" << "early_stop_round_counter = 0;" << '\n'; pred_str_buf << "\t\t" << "}" << '\n'; pred_str_buf << "\t" << "}" << '\n'; str_buf << "void GBDT::PredictRaw(const double* features, double *output, const PredictionEarlyStopInstance* early_stop) const {" << '\n'; str_buf << pred_str_buf.str(); str_buf << "}" << '\n'; str_buf << '\n'; // PredictRawByMap str_buf << "double (*PredictTreeByMapPtr[])(const std::unordered_map&) = { "; for (int i = 0; i < num_used_model; ++i) { if (i > 0) { str_buf << " , "; } str_buf << "PredictTree" << i << "ByMap"; } str_buf << " };" << '\n' << '\n'; std::stringstream pred_str_buf_map; Common::C_stringstream(pred_str_buf_map); pred_str_buf_map << "\t" << "int early_stop_round_counter = 0;" << '\n'; pred_str_buf_map << "\t" << "std::memset(output, 0, sizeof(double) * num_tree_per_iteration_);" << '\n'; pred_str_buf_map << "\t" << "for (int i = 0; i < num_iteration_for_pred_; ++i) {" << '\n'; pred_str_buf_map << "\t\t" << "for (int k = 0; k < num_tree_per_iteration_; ++k) {" << '\n'; pred_str_buf_map << "\t\t\t" << "output[k] += (*PredictTreeByMapPtr[i * num_tree_per_iteration_ + k])(features);" << '\n'; pred_str_buf_map << "\t\t" << "}" << '\n'; pred_str_buf_map << "\t\t" << "++early_stop_round_counter;" << '\n'; pred_str_buf_map << "\t\t" << "if (early_stop->round_period == early_stop_round_counter) {" << '\n'; pred_str_buf_map << "\t\t\t" << "if (early_stop->callback_function(output, num_tree_per_iteration_))" << '\n'; pred_str_buf_map << "\t\t\t\t" << "return;" << '\n'; pred_str_buf_map << "\t\t\t" << "early_stop_round_counter = 0;" << '\n'; pred_str_buf_map << "\t\t" << "}" << '\n'; pred_str_buf_map << "\t" << "}" << '\n'; str_buf << "void GBDT::PredictRawByMap(const std::unordered_map& features, double* output, const PredictionEarlyStopInstance* early_stop) const {" << '\n'; str_buf << pred_str_buf_map.str(); str_buf << "}" << '\n'; str_buf << '\n'; // Predict str_buf << "void GBDT::Predict(const double* features, double *output, const PredictionEarlyStopInstance* early_stop) const {" << '\n'; str_buf << "\t" << "PredictRaw(features, output, early_stop);" << '\n'; str_buf << "\t" << "if (average_output_) {" << '\n'; str_buf << "\t\t" << "for (int k = 0; k < num_tree_per_iteration_; ++k) {" << '\n'; str_buf << "\t\t\t" << "output[k] /= num_iteration_for_pred_;" << '\n'; str_buf << "\t\t" << "}" << '\n'; str_buf << "\t" << "}" << '\n'; str_buf << "\t" << "if (objective_function_ != nullptr) {" << '\n'; str_buf << "\t\t" << "objective_function_->ConvertOutput(output, output);" << '\n'; str_buf << "\t" << "}" << '\n'; str_buf << "}" << '\n'; str_buf << '\n'; // PredictByMap str_buf << "void GBDT::PredictByMap(const std::unordered_map& features, double* output, const PredictionEarlyStopInstance* early_stop) const {" << '\n'; str_buf << "\t" << "PredictRawByMap(features, output, early_stop);" << '\n'; str_buf << "\t" << "if (average_output_) {" << '\n'; str_buf << "\t\t" << "for (int k = 0; k < num_tree_per_iteration_; ++k) {" << '\n'; str_buf << "\t\t\t" << "output[k] /= num_iteration_for_pred_;" << '\n'; str_buf << "\t\t" << "}" << '\n'; str_buf << "\t" << "}" << '\n'; str_buf << "\t" << "if (objective_function_ != nullptr) {" << '\n'; str_buf << "\t\t" << "objective_function_->ConvertOutput(output, output);" << '\n'; str_buf << "\t" << "}" << '\n'; str_buf << "}" << '\n'; str_buf << '\n'; // PredictLeafIndex for (int i = 0; i < num_used_model; ++i) { str_buf << models_[i]->ToIfElse(i, true) << '\n'; } str_buf << "double (*PredictTreeLeafPtr[])(const double*) = { "; for (int i = 0; i < num_used_model; ++i) { if (i > 0) { str_buf << " , "; } str_buf << "PredictTree" << i << "Leaf"; } str_buf << " };" << '\n' << '\n'; str_buf << "void GBDT::PredictLeafIndex(const double* features, double *output) const {" << '\n'; str_buf << "\t" << "int total_tree = num_iteration_for_pred_ * num_tree_per_iteration_;" << '\n'; str_buf << "\t" << "for (int i = 0; i < total_tree; ++i) {" << '\n'; str_buf << "\t\t" << "output[i] = (*PredictTreeLeafPtr[i])(features);" << '\n'; str_buf << "\t" << "}" << '\n'; str_buf << "}" << '\n'; // PredictLeafIndexByMap str_buf << "double (*PredictTreeLeafByMapPtr[])(const std::unordered_map&) = { "; for (int i = 0; i < num_used_model; ++i) { if (i > 0) { str_buf << " , "; } str_buf << "PredictTree" << i << "LeafByMap"; } str_buf << " };" << '\n' << '\n'; str_buf << "void GBDT::PredictLeafIndexByMap(const std::unordered_map& features, double* output) const {" << '\n'; str_buf << "\t" << "int total_tree = num_iteration_for_pred_ * num_tree_per_iteration_;" << '\n'; str_buf << "\t" << "for (int i = 0; i < total_tree; ++i) {" << '\n'; str_buf << "\t\t" << "output[i] = (*PredictTreeLeafByMapPtr[i])(features);" << '\n'; str_buf << "\t" << "}" << '\n'; str_buf << "}" << '\n'; str_buf << "} // namespace LightGBM" << '\n'; return str_buf.str(); } bool GBDT::SaveModelToIfElse(int num_iteration, const char* filename) const { /*! \brief File to write models */ std::ofstream output_file; std::ifstream ifs(filename); if (ifs.good()) { std::string origin((std::istreambuf_iterator(ifs)), (std::istreambuf_iterator())); output_file.open(filename); output_file << "#define USE_HARD_CODE 0" << '\n'; output_file << "#ifndef USE_HARD_CODE" << '\n'; output_file << origin << '\n'; output_file << "#else" << '\n'; output_file << ModelToIfElse(num_iteration); output_file << "#endif" << '\n'; } else { output_file.open(filename); output_file << ModelToIfElse(num_iteration); } ifs.close(); output_file.close(); return static_cast(output_file); } std::string GBDT::SaveModelToString(int start_iteration, int num_iteration, int feature_importance_type) const { std::stringstream ss; Common::C_stringstream(ss); // output model type ss << SubModelName() << '\n'; ss << "version=" << kModelVersion << '\n'; // output number of class ss << "num_class=" << num_class_ << '\n'; ss << "num_tree_per_iteration=" << num_tree_per_iteration_ << '\n'; // output label index ss << "label_index=" << label_idx_ << '\n'; // output max_feature_idx ss << "max_feature_idx=" << max_feature_idx_ << '\n'; // output objective if (objective_function_ != nullptr) { ss << "objective=" << objective_function_->ToString() << '\n'; } if (average_output_) { ss << "average_output" << '\n'; } ss << "feature_names=" << CommonC::Join(feature_names_, " ") << '\n'; if (monotone_constraints_.size() != 0) { ss << "monotone_constraints=" << CommonC::Join(monotone_constraints_, " ") << '\n'; } ss << "feature_infos=" << CommonC::Join(feature_infos_, " ") << '\n'; int num_used_model = static_cast(models_.size()); int total_iteration = num_used_model / num_tree_per_iteration_; start_iteration = std::max(start_iteration, 0); start_iteration = std::min(start_iteration, total_iteration); if (num_iteration > 0) { int end_iteration = start_iteration + num_iteration; num_used_model = std::min(end_iteration * num_tree_per_iteration_, num_used_model); } int start_model = start_iteration * num_tree_per_iteration_; std::vector tree_strs(num_used_model - start_model); std::vector tree_sizes(num_used_model - start_model); // output tree models #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) for (int i = start_model; i < num_used_model; ++i) { const int idx = i - start_model; tree_strs[idx] = "Tree=" + std::to_string(idx) + '\n'; tree_strs[idx] += models_[i]->ToString() + '\n'; tree_sizes[idx] = tree_strs[idx].size(); } ss << "tree_sizes=" << CommonC::Join(tree_sizes, " ") << '\n'; ss << '\n'; for (int i = 0; i < num_used_model - start_model; ++i) { ss << tree_strs[i]; tree_strs[i].clear(); } ss << "end of trees" << "\n"; std::vector feature_importances = FeatureImportance( num_iteration, feature_importance_type); // store the importance first std::vector> pairs; for (size_t i = 0; i < feature_importances.size(); ++i) { size_t feature_importances_int = static_cast(feature_importances[i]); if (feature_importances_int > 0) { pairs.emplace_back(feature_importances_int, feature_names_[i]); } } // sort the importance std::stable_sort(pairs.begin(), pairs.end(), [](const std::pair& lhs, const std::pair& rhs) { return lhs.first > rhs.first; }); ss << '\n' << "feature_importances:" << '\n'; for (size_t i = 0; i < pairs.size(); ++i) { ss << pairs[i].second << "=" << std::to_string(pairs[i].first) << '\n'; } if (config_ != nullptr) { ss << "\nparameters:" << '\n'; ss << config_->ToString() << "\n"; ss << "end of parameters" << '\n'; } else if (!loaded_parameter_.empty()) { ss << "\nparameters:" << '\n'; ss << loaded_parameter_ << "\n"; ss << "end of parameters" << '\n'; } if (!parser_config_str_.empty()) { ss << "\nparser:" << '\n'; ss << parser_config_str_ << "\n"; ss << "end of parser" << '\n'; } return ss.str(); } bool GBDT::SaveModelToFile(int start_iteration, int num_iteration, int feature_importance_type, const char* filename) const { /*! \brief File to write models */ auto writer = VirtualFileWriter::Make(filename); if (!writer->Init()) { Log::Fatal("Model file %s is not available for writes", filename); } std::string str_to_write = SaveModelToString(start_iteration, num_iteration, feature_importance_type); auto size = writer->Write(str_to_write.c_str(), str_to_write.size()); return size > 0; } bool GBDT::LoadModelFromString(const char* buffer, size_t len) { // use serialized string to restore this object models_.clear(); auto c_str = buffer; auto p = c_str; auto end = p + len; std::unordered_map key_vals; while (p < end) { auto line_len = Common::GetLine(p); if (line_len > 0) { std::string cur_line(p, line_len); if (!Common::StartsWith(cur_line, "Tree=")) { auto strs = Common::Split(cur_line.c_str(), '='); if (strs.size() == 1) { key_vals[strs[0]] = ""; } else if (strs.size() == 2) { key_vals[strs[0]] = strs[1]; } else if (strs.size() > 2) { if (strs[0] == "feature_names") { key_vals[strs[0]] = cur_line.substr(std::strlen("feature_names=")); } else if (strs[0] == "monotone_constraints") { key_vals[strs[0]] = cur_line.substr(std::strlen("monotone_constraints=")); } else { // Use first 128 chars to avoid exceed the message buffer. Log::Fatal("Wrong line at model file: %s", cur_line.substr(0, std::min(128, cur_line.size())).c_str()); } } } else { break; } } p += line_len; p = Common::SkipNewLine(p); } // get number of classes if (key_vals.count("num_class")) { Common::Atoi(key_vals["num_class"].c_str(), &num_class_); } else { Log::Fatal("Model file doesn't specify the number of classes"); return false; } if (key_vals.count("num_tree_per_iteration")) { Common::Atoi(key_vals["num_tree_per_iteration"].c_str(), &num_tree_per_iteration_); } else { num_tree_per_iteration_ = num_class_; } // get index of label if (key_vals.count("label_index")) { Common::Atoi(key_vals["label_index"].c_str(), &label_idx_); } else { Log::Fatal("Model file doesn't specify the label index"); return false; } // get max_feature_idx first if (key_vals.count("max_feature_idx")) { Common::Atoi(key_vals["max_feature_idx"].c_str(), &max_feature_idx_); } else { Log::Fatal("Model file doesn't specify max_feature_idx"); return false; } // get average_output if (key_vals.count("average_output")) { average_output_ = true; } // get feature names if (key_vals.count("feature_names")) { feature_names_ = Common::Split(key_vals["feature_names"].c_str(), ' '); if (feature_names_.size() != static_cast(max_feature_idx_ + 1)) { Log::Fatal("Wrong size of feature_names"); return false; } } else { Log::Fatal("Model file doesn't contain feature_names"); return false; } // get monotone_constraints if (key_vals.count("monotone_constraints")) { monotone_constraints_ = CommonC::StringToArray(key_vals["monotone_constraints"].c_str(), ' '); if (monotone_constraints_.size() != static_cast(max_feature_idx_ + 1)) { Log::Fatal("Wrong size of monotone_constraints"); return false; } } if (key_vals.count("feature_infos")) { feature_infos_ = Common::Split(key_vals["feature_infos"].c_str(), ' '); if (feature_infos_.size() != static_cast(max_feature_idx_ + 1)) { Log::Fatal("Wrong size of feature_infos"); return false; } } else { Log::Fatal("Model file doesn't contain feature_infos"); return false; } if (key_vals.count("objective")) { auto str = key_vals["objective"]; loaded_objective_.reset(ObjectiveFunction::CreateObjectiveFunction(ParseObjectiveAlias(str))); objective_function_ = loaded_objective_.get(); } if (!key_vals.count("tree_sizes")) { while (p < end) { auto line_len = Common::GetLine(p); if (line_len > 0) { std::string cur_line(p, line_len); if (Common::StartsWith(cur_line, "Tree=")) { p += line_len; p = Common::SkipNewLine(p); size_t used_len = 0; models_.emplace_back(new Tree(p, &used_len)); p += used_len; } else { break; } } p = Common::SkipNewLine(p); } } else { std::vector tree_sizes = CommonC::StringToArray(key_vals["tree_sizes"].c_str(), ' '); std::vector tree_boundaries(tree_sizes.size() + 1, 0); int num_trees = static_cast(tree_sizes.size()); for (int i = 0; i < num_trees; ++i) { tree_boundaries[i + 1] = tree_boundaries[i] + tree_sizes[i]; models_.emplace_back(nullptr); } OMP_INIT_EX(); #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) for (int i = 0; i < num_trees; ++i) { OMP_LOOP_EX_BEGIN(); auto cur_p = p + tree_boundaries[i]; auto line_len = Common::GetLine(cur_p); std::string cur_line(cur_p, line_len); if (Common::StartsWith(cur_line, "Tree=")) { cur_p += line_len; cur_p = Common::SkipNewLine(cur_p); size_t used_len = 0; models_[i].reset(new Tree(cur_p, &used_len)); } else { Log::Fatal("Model format error, expect a tree here. met %s", cur_line.c_str()); } OMP_LOOP_EX_END(); } OMP_THROW_EX(); } num_iteration_for_pred_ = static_cast(models_.size()) / num_tree_per_iteration_; num_init_iteration_ = num_iteration_for_pred_; iter_ = 0; bool is_inparameter = false, is_inparser = false; std::stringstream ss; Common::C_stringstream(ss); while (p < end) { auto line_len = Common::GetLine(p); if (line_len > 0) { std::string cur_line(p, line_len); if (cur_line == std::string("parameters:")) { is_inparameter = true; } else if (cur_line == std::string("end of parameters")) { break; } else if (is_inparameter) { ss << cur_line << "\n"; if (Common::StartsWith(cur_line, "[linear_tree: ")) { int is_linear = 0; Common::Atoi(cur_line.substr(14, 1).c_str(), &is_linear); linear_tree_ = static_cast(is_linear); } } } p += line_len; p = Common::SkipNewLine(p); } if (!ss.str().empty()) { loaded_parameter_ = ss.str(); } ss.clear(); ss.str(""); while (p < end) { auto line_len = Common::GetLine(p); if (line_len > 0) { std::string cur_line(p, line_len); if (cur_line == std::string("parser:")) { is_inparser = true; } else if (cur_line == std::string("end of parser")) { p += line_len; p = Common::SkipNewLine(p); break; } else if (is_inparser) { ss << cur_line << "\n"; } } p += line_len; p = Common::SkipNewLine(p); } parser_config_str_ = ss.str(); ss.clear(); ss.str(""); return true; } std::vector GBDT::FeatureImportance(int num_iteration, int importance_type) const { int num_used_model = static_cast(models_.size()); if (num_iteration > 0) { num_iteration += 0; num_used_model = std::min(num_iteration * num_tree_per_iteration_, num_used_model); } std::vector feature_importances(max_feature_idx_ + 1, 0.0); if (importance_type == 0) { for (int iter = 0; iter < num_used_model; ++iter) { for (int split_idx = 0; split_idx < models_[iter]->num_leaves() - 1; ++split_idx) { if (models_[iter]->split_gain(split_idx) > 0) { #ifdef DEBUG CHECK_GE(models_[iter]->split_feature(split_idx), 0); #endif feature_importances[models_[iter]->split_feature(split_idx)] += 1.0; } } } } else if (importance_type == 1) { for (int iter = 0; iter < num_used_model; ++iter) { for (int split_idx = 0; split_idx < models_[iter]->num_leaves() - 1; ++split_idx) { if (models_[iter]->split_gain(split_idx) > 0) { #ifdef DEBUG CHECK_GE(models_[iter]->split_feature(split_idx), 0); #endif feature_importances[models_[iter]->split_feature(split_idx)] += models_[iter]->split_gain(split_idx); } } } } else { Log::Fatal("Unknown importance type: only support split=0 and gain=1"); } return feature_importances; } } // namespace LightGBM ================================================ FILE: src/boosting/gbdt_prediction.cpp ================================================ /*! * Copyright (c) 2017-2026 Microsoft Corporation. All rights reserved. * Copyright (c) 2017-2026 The LightGBM developers. All rights reserved. * Licensed under the MIT License. See LICENSE file in the project root for license information. */ #include #include #include #include #include "gbdt.h" namespace LightGBM { void GBDT::PredictRaw(const double* features, double* output, const PredictionEarlyStopInstance* early_stop) const { int early_stop_round_counter = 0; // set zero std::memset(output, 0, sizeof(double) * num_tree_per_iteration_); const int end_iteration_for_pred = start_iteration_for_pred_ + num_iteration_for_pred_; for (int i = start_iteration_for_pred_; i < end_iteration_for_pred; ++i) { // predict all the trees for one iteration for (int k = 0; k < num_tree_per_iteration_; ++k) { output[k] += models_[i * num_tree_per_iteration_ + k]->Predict(features); } // check early stopping ++early_stop_round_counter; if (early_stop->round_period == early_stop_round_counter) { if (early_stop->callback_function(output, num_tree_per_iteration_)) { return; } early_stop_round_counter = 0; } } } void GBDT::PredictRawByMap(const std::unordered_map& features, double* output, const PredictionEarlyStopInstance* early_stop) const { int early_stop_round_counter = 0; // set zero std::memset(output, 0, sizeof(double) * num_tree_per_iteration_); const int end_iteration_for_pred = start_iteration_for_pred_ + num_iteration_for_pred_; for (int i = start_iteration_for_pred_; i < end_iteration_for_pred; ++i) { // predict all the trees for one iteration for (int k = 0; k < num_tree_per_iteration_; ++k) { output[k] += models_[i * num_tree_per_iteration_ + k]->PredictByMap(features); } // check early stopping ++early_stop_round_counter; if (early_stop->round_period == early_stop_round_counter) { if (early_stop->callback_function(output, num_tree_per_iteration_)) { return; } early_stop_round_counter = 0; } } } void GBDT::Predict(const double* features, double* output, const PredictionEarlyStopInstance* early_stop) const { PredictRaw(features, output, early_stop); if (average_output_) { for (int k = 0; k < num_tree_per_iteration_; ++k) { output[k] /= num_iteration_for_pred_; } } if (objective_function_ != nullptr) { objective_function_->ConvertOutput(output, output); } } void GBDT::PredictByMap(const std::unordered_map& features, double* output, const PredictionEarlyStopInstance* early_stop) const { PredictRawByMap(features, output, early_stop); if (average_output_) { for (int k = 0; k < num_tree_per_iteration_; ++k) { output[k] /= num_iteration_for_pred_; } } if (objective_function_ != nullptr) { objective_function_->ConvertOutput(output, output); } } void GBDT::PredictLeafIndex(const double* features, double* output) const { int start_tree = start_iteration_for_pred_ * num_tree_per_iteration_; int num_trees = num_iteration_for_pred_ * num_tree_per_iteration_; const auto* models_ptr = models_.data() + start_tree; for (int i = 0; i < num_trees; ++i) { output[i] = models_ptr[i]->PredictLeafIndex(features); } } void GBDT::PredictLeafIndexByMap(const std::unordered_map& features, double* output) const { int start_tree = start_iteration_for_pred_ * num_tree_per_iteration_; int num_trees = num_iteration_for_pred_ * num_tree_per_iteration_; const auto* models_ptr = models_.data() + start_tree; for (int i = 0; i < num_trees; ++i) { output[i] = models_ptr[i]->PredictLeafIndexByMap(features); } } } // namespace LightGBM ================================================ FILE: src/boosting/goss.hpp ================================================ /*! * Copyright (c) 2021-2026 Microsoft Corporation. All rights reserved. * Copyright (c) 2021-2026 The LightGBM developers. All rights reserved. * Licensed under the MIT License. See LICENSE file in the project root for license information. */ #ifndef LIGHTGBM_SRC_BOOSTING_GOSS_HPP_ #define LIGHTGBM_SRC_BOOSTING_GOSS_HPP_ #include #include #include #include #include namespace LightGBM { class GOSSStrategy : public SampleStrategy { public: GOSSStrategy(const Config* config, const Dataset* train_data, int num_tree_per_iteration) { config_ = config; train_data_ = train_data; num_tree_per_iteration_ = num_tree_per_iteration; num_data_ = train_data->num_data(); } ~GOSSStrategy() { } void Bagging(int iter, TreeLearner* tree_learner, score_t* gradients, score_t* hessians) override { bag_data_cnt_ = num_data_; // not subsample for first iterations if (iter < static_cast(1.0f / config_->learning_rate)) { return; } auto left_cnt = bagging_runner_.Run( num_data_, [=](int, data_size_t cur_start, data_size_t cur_cnt, data_size_t* left, data_size_t*) { data_size_t cur_left_count = 0; cur_left_count = Helper(cur_start, cur_cnt, left, gradients, hessians); return cur_left_count; }, bag_data_indices_.data()); bag_data_cnt_ = left_cnt; // set bagging data to tree learner if (!is_use_subset_) { #ifdef USE_CUDA if (config_->device_type == std::string("cuda")) { CopyFromHostToCUDADevice(cuda_bag_data_indices_.RawData(), bag_data_indices_.data(), static_cast(num_data_), __FILE__, __LINE__); tree_learner->SetBaggingData(nullptr, cuda_bag_data_indices_.RawData(), bag_data_cnt_); } else { #endif // USE_CUDA tree_learner->SetBaggingData(nullptr, bag_data_indices_.data(), bag_data_cnt_); #ifdef USE_CUDA } #endif // USE_CUDA } else { // get subset tmp_subset_->ReSize(bag_data_cnt_); tmp_subset_->CopySubrow(train_data_, bag_data_indices_.data(), bag_data_cnt_, false); #ifdef USE_CUDA if (config_->device_type == std::string("cuda")) { CopyFromHostToCUDADevice(cuda_bag_data_indices_.RawData(), bag_data_indices_.data(), static_cast(num_data_), __FILE__, __LINE__); tree_learner->SetBaggingData(tmp_subset_.get(), cuda_bag_data_indices_.RawData(), bag_data_cnt_); } else { #endif // USE_CUDA tree_learner->SetBaggingData(tmp_subset_.get(), bag_data_indices_.data(), bag_data_cnt_); #ifdef USE_CUDA } #endif // USE_CUDA } } void ResetSampleConfig(const Config* config, bool /*is_change_dataset*/) override { // Cannot use bagging in GOSS config_ = config; need_resize_gradients_ = false; if (objective_function_ == nullptr) { // resize gradient vectors to copy the customized gradients for goss need_resize_gradients_ = true; } CHECK_LE(config_->top_rate + config_->other_rate, 1.0f); CHECK(config_->top_rate > 0.0f && config_->other_rate > 0.0f); if (config_->bagging_freq > 0 && config_->bagging_fraction != 1.0f) { Log::Fatal("Cannot use bagging in GOSS"); } Log::Info("Using GOSS"); balanced_bagging_ = false; bag_data_indices_.resize(num_data_); bagging_runner_.ReSize(num_data_); bagging_rands_.clear(); for (int i = 0; i < (num_data_ + bagging_rand_block_ - 1) / bagging_rand_block_; ++i) { bagging_rands_.emplace_back(config_->bagging_seed + i); } is_use_subset_ = false; if (config_->top_rate + config_->other_rate <= 0.5) { auto bag_data_cnt = static_cast((config_->top_rate + config_->other_rate) * num_data_); bag_data_cnt = std::max(1, bag_data_cnt); tmp_subset_.reset(new Dataset(bag_data_cnt)); tmp_subset_->CopyFeatureMapperFrom(train_data_); is_use_subset_ = true; } // flag to not bagging first bag_data_cnt_ = num_data_; } bool IsHessianChange() const override { return true; } private: data_size_t Helper(data_size_t start, data_size_t cnt, data_size_t* buffer, score_t* gradients, score_t* hessians) { if (cnt <= 0) { return 0; } std::vector tmp_gradients(cnt, 0.0f); for (data_size_t i = 0; i < cnt; ++i) { for (int cur_tree_id = 0; cur_tree_id < num_tree_per_iteration_; ++cur_tree_id) { size_t idx = static_cast(cur_tree_id) * num_data_ + start + i; tmp_gradients[i] += std::fabs(gradients[idx] * hessians[idx]); } } data_size_t top_k = static_cast(cnt * config_->top_rate); data_size_t other_k = static_cast(cnt * config_->other_rate); top_k = std::max(1, top_k); ArrayArgs::ArgMaxAtK(&tmp_gradients, 0, static_cast(tmp_gradients.size()), top_k - 1); score_t threshold = tmp_gradients[top_k - 1]; score_t multiply = static_cast(cnt - top_k) / other_k; data_size_t cur_left_cnt = 0; data_size_t cur_right_pos = cnt; data_size_t big_weight_cnt = 0; for (data_size_t i = 0; i < cnt; ++i) { auto cur_idx = start + i; score_t grad = 0.0f; for (int cur_tree_id = 0; cur_tree_id < num_tree_per_iteration_; ++cur_tree_id) { size_t idx = static_cast(cur_tree_id) * num_data_ + cur_idx; grad += std::fabs(gradients[idx] * hessians[idx]); } if (grad >= threshold) { buffer[cur_left_cnt++] = cur_idx; ++big_weight_cnt; } else { data_size_t sampled = cur_left_cnt - big_weight_cnt; data_size_t rest_need = other_k - sampled; data_size_t rest_all = (cnt - i) - (top_k - big_weight_cnt); double prob = (rest_need) / static_cast(rest_all); if (bagging_rands_[cur_idx / bagging_rand_block_].NextFloat() < prob) { buffer[cur_left_cnt++] = cur_idx; for (int cur_tree_id = 0; cur_tree_id < num_tree_per_iteration_; ++cur_tree_id) { size_t idx = static_cast(cur_tree_id) * num_data_ + cur_idx; gradients[idx] *= multiply; hessians[idx] *= multiply; } } else { buffer[--cur_right_pos] = cur_idx; } } } return cur_left_cnt; } }; } // namespace LightGBM #endif // LIGHTGBM_SRC_BOOSTING_GOSS_HPP_ ================================================ FILE: src/boosting/prediction_early_stop.cpp ================================================ /*! * Copyright (c) 2017-2026 Microsoft Corporation. All rights reserved. * Copyright (c) 2017-2026 The LightGBM developers. All rights reserved. * Licensed under the MIT License. See LICENSE file in the project root for license information. */ #include #include #include #include #include #include #include #include namespace LightGBM { PredictionEarlyStopInstance CreateNone(const PredictionEarlyStopConfig&) { return PredictionEarlyStopInstance{ [](const double*, int) { return false; }, std::numeric_limits::max() // make sure the lambda is almost never called }; } PredictionEarlyStopInstance CreateMulticlass(const PredictionEarlyStopConfig& config) { // margin_threshold will be captured by value const double margin_threshold = config.margin_threshold; return PredictionEarlyStopInstance{ [margin_threshold](const double* pred, int sz) { if (sz < 2) { Log::Fatal("Multiclass early stopping needs predictions to be of length two or larger"); } // copy and sort std::vector votes(static_cast(sz)); for (int i = 0; i < sz; ++i) { votes[i] = pred[i]; } std::partial_sort(votes.begin(), votes.begin() + 2, votes.end(), std::greater()); const auto margin = votes[0] - votes[1]; if (margin > margin_threshold) { return true; } return false; }, config.round_period }; } PredictionEarlyStopInstance CreateBinary(const PredictionEarlyStopConfig& config) { // margin_threshold will be captured by value const double margin_threshold = config.margin_threshold; return PredictionEarlyStopInstance{ [margin_threshold](const double* pred, int sz) { if (sz != 1) { Log::Fatal("Binary early stopping needs predictions to be of length one"); } const auto margin = 2.0 * fabs(pred[0]); if (margin > margin_threshold) { return true; } return false; }, config.round_period }; } PredictionEarlyStopInstance CreatePredictionEarlyStopInstance(const std::string& type, const PredictionEarlyStopConfig& config) { if (type == "none") { return CreateNone(config); } else if (type == "multiclass") { return CreateMulticlass(config); } else if (type == "binary") { return CreateBinary(config); } else { Log::Fatal("Unknown early stopping type: %s", type.c_str()); } // Fix for compiler warnings about reaching end of control return CreateNone(config); } } // namespace LightGBM ================================================ FILE: src/boosting/rf.hpp ================================================ /*! * Copyright (c) 2017-2026 Microsoft Corporation. All rights reserved. * Copyright (c) 2017-2026 The LightGBM developers. All rights reserved. * Licensed under the MIT License. See LICENSE file in the project root for license information. */ #ifndef LIGHTGBM_SRC_BOOSTING_RF_HPP_ #define LIGHTGBM_SRC_BOOSTING_RF_HPP_ #include #include #include #include #include #include #include #include #include "gbdt.h" #include "score_updater.hpp" namespace LightGBM { /*! * \brief Random Forest implementation */ class RF : public GBDT { public: RF() : GBDT() { average_output_ = true; } ~RF() {} void Init(const Config* config, const Dataset* train_data, const ObjectiveFunction* objective_function, const std::vector& training_metrics) override { if (config->data_sample_strategy == std::string("bagging")) { CHECK((config->bagging_freq > 0 && config->bagging_fraction < 1.0f && config->bagging_fraction > 0.0f) || (config->feature_fraction < 1.0f && config->feature_fraction > 0.0f)); } else { CHECK_EQ(config->data_sample_strategy, std::string("goss")); } GBDT::Init(config, train_data, objective_function, training_metrics); if (num_init_iteration_ > 0) { for (int cur_tree_id = 0; cur_tree_id < num_tree_per_iteration_; ++cur_tree_id) { MultiplyScore(cur_tree_id, 1.0f / num_init_iteration_); } } else { CHECK_EQ(train_data->metadata().init_score(), nullptr); } CHECK_EQ(num_tree_per_iteration_, num_class_); // not shrinkage rate for the RF shrinkage_rate_ = 1.0f; // only boosting one time Boosting(); if (data_sample_strategy_->is_use_subset() && data_sample_strategy_->bag_data_cnt() < num_data_) { tmp_grad_.resize(num_data_); tmp_hess_.resize(num_data_); } } void ResetConfig(const Config* config) override { if (config->data_sample_strategy == std::string("bagging")) { CHECK((config->bagging_freq > 0 && config->bagging_fraction < 1.0f && config->bagging_fraction > 0.0f) || (config->feature_fraction < 1.0f && config->feature_fraction > 0.0f)); } else { CHECK_EQ(config->data_sample_strategy, std::string("goss")); } GBDT::ResetConfig(config); // not shrinkage rate for the RF shrinkage_rate_ = 1.0f; } void ResetTrainingData(const Dataset* train_data, const ObjectiveFunction* objective_function, const std::vector& training_metrics) override { GBDT::ResetTrainingData(train_data, objective_function, training_metrics); if (iter_ + num_init_iteration_ > 0) { for (int cur_tree_id = 0; cur_tree_id < num_tree_per_iteration_; ++cur_tree_id) { train_score_updater_->MultiplyScore(1.0f / (iter_ + num_init_iteration_), cur_tree_id); } } CHECK_EQ(num_tree_per_iteration_, num_class_); // only boosting one time Boosting(); if (data_sample_strategy_->is_use_subset() && data_sample_strategy_->bag_data_cnt() < num_data_) { tmp_grad_.resize(num_data_); tmp_hess_.resize(num_data_); } } void Boosting() override { if (objective_function_ == nullptr) { Log::Fatal("RF mode do not support custom objective function, please use built-in objectives."); } init_scores_.resize(num_tree_per_iteration_, 0.0); for (int cur_tree_id = 0; cur_tree_id < num_tree_per_iteration_; ++cur_tree_id) { init_scores_[cur_tree_id] = BoostFromAverage(cur_tree_id, false); } size_t total_size = static_cast(num_data_) * num_tree_per_iteration_; std::vector tmp_scores(total_size, 0.0f); #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) for (int j = 0; j < num_tree_per_iteration_; ++j) { size_t offset = static_cast(j)* num_data_; for (data_size_t i = 0; i < num_data_; ++i) { tmp_scores[offset + i] = init_scores_[j]; } } objective_function_-> GetGradients(tmp_scores.data(), gradients_.data(), hessians_.data()); } bool TrainOneIter(const score_t* gradients, const score_t* hessians) override { // bagging logic data_sample_strategy_ ->Bagging(iter_, tree_learner_.get(), gradients_.data(), hessians_.data()); const bool is_use_subset = data_sample_strategy_->is_use_subset(); const data_size_t bag_data_cnt = data_sample_strategy_->bag_data_cnt(); const std::vector>& bag_data_indices = data_sample_strategy_->bag_data_indices(); // GOSSStrategy->Bagging may modify value of bag_data_cnt_ if (is_use_subset && bag_data_cnt < num_data_) { tmp_grad_.resize(num_data_); tmp_hess_.resize(num_data_); } CHECK_EQ(gradients, nullptr); CHECK_EQ(hessians, nullptr); gradients = gradients_.data(); hessians = hessians_.data(); for (int cur_tree_id = 0; cur_tree_id < num_tree_per_iteration_; ++cur_tree_id) { std::unique_ptr new_tree(new Tree(2, false, false)); size_t offset = static_cast(cur_tree_id)* num_data_; if (class_need_train_[cur_tree_id]) { auto grad = gradients + offset; auto hess = hessians + offset; if (is_use_subset && bag_data_cnt < num_data_ && !boosting_on_gpu_) { for (int i = 0; i < bag_data_cnt; ++i) { tmp_grad_[i] = grad[bag_data_indices[i]]; tmp_hess_[i] = hess[bag_data_indices[i]]; } grad = tmp_grad_.data(); hess = tmp_hess_.data(); } new_tree.reset(tree_learner_->Train(grad, hess, false)); } if (new_tree->num_leaves() > 1) { double pred = init_scores_[cur_tree_id]; auto residual_getter = [pred](const label_t* label, int i) {return static_cast(label[i]) - pred; }; tree_learner_->RenewTreeOutput(new_tree.get(), objective_function_, residual_getter, num_data_, bag_data_indices.data(), bag_data_cnt, train_score_updater_->score()); if (std::fabs(init_scores_[cur_tree_id]) > kEpsilon) { new_tree->AddBias(init_scores_[cur_tree_id]); } // update score MultiplyScore(cur_tree_id, (iter_ + num_init_iteration_)); UpdateScore(new_tree.get(), cur_tree_id); MultiplyScore(cur_tree_id, 1.0 / (iter_ + num_init_iteration_ + 1)); } else { // only add default score one-time if (models_.size() < static_cast(num_tree_per_iteration_)) { double output = 0.0; if (!class_need_train_[cur_tree_id]) { if (objective_function_ != nullptr) { output = objective_function_->BoostFromScore(cur_tree_id); } else { output = init_scores_[cur_tree_id]; } } new_tree->AsConstantTree(output, num_data_); MultiplyScore(cur_tree_id, (iter_ + num_init_iteration_)); UpdateScore(new_tree.get(), cur_tree_id); MultiplyScore(cur_tree_id, 1.0 / (iter_ + num_init_iteration_ + 1)); } } // add model models_.push_back(std::move(new_tree)); } ++iter_; return false; } void RollbackOneIter() override { if (iter_ <= 0) { return; } int cur_iter = iter_ + num_init_iteration_ - 1; // reset score for (int cur_tree_id = 0; cur_tree_id < num_tree_per_iteration_; ++cur_tree_id) { auto curr_tree = cur_iter * num_tree_per_iteration_ + cur_tree_id; models_[curr_tree]->Shrinkage(-1.0); MultiplyScore(cur_tree_id, (iter_ + num_init_iteration_)); train_score_updater_->AddScore(models_[curr_tree].get(), cur_tree_id); for (auto& score_updater : valid_score_updater_) { score_updater->AddScore(models_[curr_tree].get(), cur_tree_id); } MultiplyScore(cur_tree_id, 1.0f / (iter_ + num_init_iteration_ - 1)); } // remove model for (int cur_tree_id = 0; cur_tree_id < num_tree_per_iteration_; ++cur_tree_id) { models_.pop_back(); } --iter_; } void MultiplyScore(const int cur_tree_id, double val) { train_score_updater_->MultiplyScore(val, cur_tree_id); for (auto& score_updater : valid_score_updater_) { score_updater->MultiplyScore(val, cur_tree_id); } } void AddValidDataset(const Dataset* valid_data, const std::vector& valid_metrics) override { GBDT::AddValidDataset(valid_data, valid_metrics); if (iter_ + num_init_iteration_ > 0) { for (int cur_tree_id = 0; cur_tree_id < num_tree_per_iteration_; ++cur_tree_id) { valid_score_updater_.back()->MultiplyScore(1.0f / (iter_ + num_init_iteration_), cur_tree_id); } } } bool NeedAccuratePrediction() const override { // No early stopping for prediction return true; }; private: std::vector tmp_grad_; std::vector tmp_hess_; std::vector init_scores_; }; } // namespace LightGBM #endif // LIGHTGBM_SRC_BOOSTING_RF_HPP_ ================================================ FILE: src/boosting/sample_strategy.cpp ================================================ /*! * Copyright (c) 2021-2026 Microsoft Corporation. All rights reserved. * Copyright (c) 2021-2026 The LightGBM developers. All rights reserved. * Licensed under the MIT License. See LICENSE file in the project root for license information. */ #include #include #include "goss.hpp" #include "bagging.hpp" namespace LightGBM { SampleStrategy* SampleStrategy::CreateSampleStrategy( const Config* config, const Dataset* train_data, const ObjectiveFunction* objective_function, int num_tree_per_iteration) { if (config->data_sample_strategy == std::string("goss")) { return new GOSSStrategy(config, train_data, num_tree_per_iteration); } else { return new BaggingSampleStrategy(config, train_data, objective_function, num_tree_per_iteration); } } } // namespace LightGBM ================================================ FILE: src/boosting/score_updater.hpp ================================================ /*! * Copyright (c) 2017-2026 Microsoft Corporation. All rights reserved. * Copyright (c) 2017-2026 The LightGBM developers. All rights reserved. * Licensed under the MIT License. See LICENSE file in the project root for license information. */ #ifndef LIGHTGBM_SRC_BOOSTING_SCORE_UPDATER_HPP_ #define LIGHTGBM_SRC_BOOSTING_SCORE_UPDATER_HPP_ #include #include #include #include #include #include #include namespace LightGBM { /*! * \brief Used to store and update score for data */ class ScoreUpdater { public: /*! * \brief Constructor, will pass a const pointer of dataset * \param data This class will bind with this data set */ ScoreUpdater(const Dataset* data, int num_tree_per_iteration) : data_(data) { num_data_ = data->num_data(); int64_t total_size = static_cast(num_data_) * num_tree_per_iteration; score_.resize(total_size); // default start score is zero std::memset(score_.data(), 0, total_size * sizeof(double)); has_init_score_ = false; const double* init_score = data->metadata().init_score(); // if exists initial score, will start from it if (init_score != nullptr) { if ((data->metadata().num_init_score() % num_data_) != 0 || (data->metadata().num_init_score() / num_data_) != num_tree_per_iteration) { Log::Fatal("Number of class for initial score error"); } has_init_score_ = true; #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static, 512) if (total_size >= 1024) for (int64_t i = 0; i < total_size; ++i) { score_[i] = init_score[i]; } } } /*! \brief Destructor */ virtual ~ScoreUpdater() { } inline bool has_init_score() const { return has_init_score_; } virtual inline void AddScore(double val, int cur_tree_id) { Common::FunctionTimer fun_timer("ScoreUpdater::AddScore", global_timer); const size_t offset = static_cast(num_data_) * cur_tree_id; #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static, 512) if (num_data_ >= 1024) for (int i = 0; i < num_data_; ++i) { score_[offset + i] += val; } } virtual inline void MultiplyScore(double val, int cur_tree_id) { const size_t offset = static_cast(num_data_) * cur_tree_id; #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static, 512) if (num_data_ >= 1024) for (int i = 0; i < num_data_; ++i) { score_[offset + i] *= val; } } /*! * \brief Using tree model to get prediction number, then adding to scores for all data * Note: this function generally will be used on validation data too. * \param tree Trained tree model * \param cur_tree_id Current tree for multiclass training */ virtual inline void AddScore(const Tree* tree, int cur_tree_id) { Common::FunctionTimer fun_timer("ScoreUpdater::AddScore", global_timer); const size_t offset = static_cast(num_data_) * cur_tree_id; tree->AddPredictionToScore(data_, num_data_, score_.data() + offset); } /*! * \brief Adding prediction score, only used for training data. * The training data is partitioned into tree leaves after training * Based on which We can get prediction quickly. * \param tree_learner * \param cur_tree_id Current tree for multiclass training */ virtual inline void AddScore(const TreeLearner* tree_learner, const Tree* tree, int cur_tree_id) { Common::FunctionTimer fun_timer("ScoreUpdater::AddScore", global_timer); const size_t offset = static_cast(num_data_) * cur_tree_id; tree_learner->AddPredictionToScore(tree, score_.data() + offset); } /*! * \brief Using tree model to get prediction number, then adding to scores for parts of data * Used for prediction of training out-of-bag data * \param tree Trained tree model * \param data_indices Indices of data that will be processed * \param data_cnt Number of data that will be processed * \param cur_tree_id Current tree for multiclass training */ virtual inline void AddScore(const Tree* tree, const data_size_t* data_indices, data_size_t data_cnt, int cur_tree_id) { Common::FunctionTimer fun_timer("ScoreUpdater::AddScore", global_timer); const size_t offset = static_cast(num_data_) * cur_tree_id; tree->AddPredictionToScore(data_, data_indices, data_cnt, score_.data() + offset); } /*! \brief Pointer of score */ virtual inline const double* score() const { return score_.data(); } inline data_size_t num_data() const { return num_data_; } /*! \brief Disable copy */ ScoreUpdater& operator=(const ScoreUpdater&) = delete; /*! \brief Disable copy */ ScoreUpdater(const ScoreUpdater&) = delete; protected: /*! \brief Number of total data */ data_size_t num_data_; /*! \brief Pointer of data set */ const Dataset* data_; /*! \brief Scores for data set */ std::vector> score_; bool has_init_score_; }; } // namespace LightGBM #endif // LIGHTGBM_SRC_BOOSTING_SCORE_UPDATER_HPP_ ================================================ FILE: src/c_api.cpp ================================================ /*! * Copyright (c) 2016-2026 Microsoft Corporation. All rights reserved. * Copyright (c) 2016-2026 The LightGBM developers. All rights reserved. * Licensed under the MIT License. See LICENSE file in the project root for license information. */ #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include "application/predictor.hpp" #include #include namespace LightGBM { inline int LGBM_APIHandleException(const std::exception& ex) { LGBM_SetLastError(ex.what()); return -1; } inline int LGBM_APIHandleException(const std::string& ex) { LGBM_SetLastError(ex.c_str()); return -1; } #define API_BEGIN() try { #define API_END() } \ catch(std::exception& ex) { return LGBM_APIHandleException(ex); } \ catch(std::string& ex) { return LGBM_APIHandleException(ex); } \ catch(...) { return LGBM_APIHandleException("unknown exception"); } \ return 0; #define UNIQUE_LOCK(mtx) \ std::unique_lock lock(mtx); #define SHARED_LOCK(mtx) \ yamc::shared_lock lock(&mtx); const int PREDICTOR_TYPES = 4; // Single row predictor to abstract away caching logic class SingleRowPredictorInner { public: PredictFunction predict_function; int64_t num_pred_in_one_row; SingleRowPredictorInner(int predict_type, Boosting* boosting, const Config& config, int start_iter, int num_iter) { bool is_predict_leaf = false; bool is_raw_score = false; bool predict_contrib = false; if (predict_type == C_API_PREDICT_LEAF_INDEX) { is_predict_leaf = true; } else if (predict_type == C_API_PREDICT_RAW_SCORE) { is_raw_score = true; } else if (predict_type == C_API_PREDICT_CONTRIB) { predict_contrib = true; } early_stop_ = config.pred_early_stop; early_stop_freq_ = config.pred_early_stop_freq; early_stop_margin_ = config.pred_early_stop_margin; iter_ = num_iter; predictor_.reset(new Predictor(boosting, start_iter, iter_, is_raw_score, is_predict_leaf, predict_contrib, early_stop_, early_stop_freq_, early_stop_margin_)); num_pred_in_one_row = boosting->NumPredictOneRow(start_iter, iter_, is_predict_leaf, predict_contrib); predict_function = predictor_->GetPredictFunction(); num_total_model_ = boosting->NumberOfTotalModel(); } ~SingleRowPredictorInner() {} bool IsPredictorEqual(const Config& config, int iter, Boosting* boosting) { return early_stop_ == config.pred_early_stop && early_stop_freq_ == config.pred_early_stop_freq && early_stop_margin_ == config.pred_early_stop_margin && iter_ == iter && num_total_model_ == boosting->NumberOfTotalModel(); } private: std::unique_ptr predictor_; bool early_stop_; int early_stop_freq_; double early_stop_margin_; int iter_; int num_total_model_; }; /*! * \brief Object to store resources meant for single-row Fast Predict methods. * * For legacy reasons this is called `FastConfig` in the public C API. * * Meant to be used by the *Fast* predict methods only. * It stores the configuration and prediction resources for reuse across predictions. */ struct SingleRowPredictor { public: SingleRowPredictor(yamc::alternate::shared_mutex *booster_mutex, const char *parameters, const int data_type, const int32_t num_cols, int predict_type, Boosting *boosting, int start_iter, 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) { if (!config.predict_disable_shape_check && num_cols != boosting->MaxFeatureIdx() + 1) { Log::Fatal("The number of features in data (%d) is not the same as it was in training data (%d).\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); } } void Predict(std::function>(int row_idx)> get_row_fun, double* out_result, int64_t* out_len) const { UNIQUE_LOCK(single_row_predictor_mutex) yamc::shared_lock booster_shared_lock(booster_mutex); auto one_row = get_row_fun(0); single_row_predictor_inner.predict_function(one_row, out_result); *out_len = single_row_predictor_inner.num_pred_in_one_row; } public: Config config; const int data_type; const int32_t num_cols; private: SingleRowPredictorInner single_row_predictor_inner; // Prevent the booster from being modified while we have a predictor relying on it during prediction yamc::alternate::shared_mutex *booster_mutex; // If several threads try to predict at the same time using the same SingleRowPredictor // we want them to still provide correct values, so the mutex is necessary due to the shared // resources in the predictor. // However the recommended approach is to instantiate one SingleRowPredictor per thread, // to avoid contention here. mutable yamc::alternate::shared_mutex single_row_predictor_mutex; }; class Booster { public: explicit Booster(const char* filename) { boosting_.reset(Boosting::CreateBoosting("gbdt", filename, std::string("cpu"), 0)); } Booster(const Dataset* train_data, const char* parameters) { auto param = Config::Str2Map(parameters); config_.Set(param); OMP_SET_NUM_THREADS(config_.num_threads); // create boosting if (config_.input_model.size() > 0) { Log::Warning("Continued train from model is not supported for c_api,\n" "please use continued train with input score"); } boosting_.reset(Boosting::CreateBoosting(config_.boosting, nullptr, config_.device_type, config_.num_gpu)); train_data_ = train_data; CreateObjectiveAndMetrics(); // initialize the boosting if (config_.tree_learner == std::string("feature")) { Log::Fatal("Do not support feature parallel in c api"); } if (Network::num_machines() == 1 && config_.tree_learner != std::string("serial")) { Log::Warning("Only find one worker, will switch to serial tree learner"); config_.tree_learner = "serial"; } boosting_->Init(&config_, train_data_, objective_fun_.get(), Common::ConstPtrInVectorWrapper(train_metric_)); } void MergeFrom(const Booster* other) { UNIQUE_LOCK(mutex_) boosting_->MergeFrom(other->boosting_.get()); } ~Booster() { } void CreateObjectiveAndMetrics() { // create objective function objective_fun_.reset(ObjectiveFunction::CreateObjectiveFunction(config_.objective, config_)); if (objective_fun_ == nullptr) { Log::Info("Using self-defined objective function"); } // initialize the objective function if (objective_fun_ != nullptr) { objective_fun_->Init(train_data_->metadata(), train_data_->num_data()); } // create training metric train_metric_.clear(); for (auto metric_type : config_.metric) { auto metric = std::unique_ptr( Metric::CreateMetric(metric_type, config_)); if (metric == nullptr) { continue; } metric->Init(train_data_->metadata(), train_data_->num_data()); train_metric_.push_back(std::move(metric)); } train_metric_.shrink_to_fit(); } void ResetTrainingData(const Dataset* train_data) { if (train_data != train_data_) { UNIQUE_LOCK(mutex_) train_data_ = train_data; CreateObjectiveAndMetrics(); // reset the boosting boosting_->ResetTrainingData(train_data_, objective_fun_.get(), Common::ConstPtrInVectorWrapper(train_metric_)); } } static void CheckDatasetResetConfig( const Config& old_config, const std::unordered_map& new_param) { Config new_config; new_config.Set(new_param); if (new_param.count("data_random_seed") && new_config.data_random_seed != old_config.data_random_seed) { Log::Fatal("Cannot change data_random_seed after constructed Dataset handle."); } if (new_param.count("max_bin") && new_config.max_bin != old_config.max_bin) { Log::Fatal("Cannot change max_bin after constructed Dataset handle."); } if (new_param.count("max_bin_by_feature") && new_config.max_bin_by_feature != old_config.max_bin_by_feature) { Log::Fatal( "Cannot change max_bin_by_feature after constructed Dataset handle."); } if (new_param.count("bin_construct_sample_cnt") && new_config.bin_construct_sample_cnt != old_config.bin_construct_sample_cnt) { Log::Fatal( "Cannot change bin_construct_sample_cnt after constructed Dataset " "handle."); } if (new_param.count("min_data_in_bin") && new_config.min_data_in_bin != old_config.min_data_in_bin) { Log::Fatal( "Cannot change min_data_in_bin after constructed Dataset handle."); } if (new_param.count("use_missing") && new_config.use_missing != old_config.use_missing) { Log::Fatal("Cannot change use_missing after constructed Dataset handle."); } if (new_param.count("zero_as_missing") && new_config.zero_as_missing != old_config.zero_as_missing) { Log::Fatal( "Cannot change zero_as_missing after constructed Dataset handle."); } if (new_param.count("categorical_feature") && new_config.categorical_feature != old_config.categorical_feature) { Log::Fatal( "Cannot change categorical_feature after constructed Dataset " "handle."); } if (new_param.count("feature_pre_filter") && new_config.feature_pre_filter != old_config.feature_pre_filter) { Log::Fatal( "Cannot change feature_pre_filter after constructed Dataset handle."); } if (new_param.count("is_enable_sparse") && new_config.is_enable_sparse != old_config.is_enable_sparse) { Log::Fatal( "Cannot change is_enable_sparse after constructed Dataset handle."); } if (new_param.count("pre_partition") && new_config.pre_partition != old_config.pre_partition) { Log::Fatal( "Cannot change pre_partition after constructed Dataset handle."); } if (new_param.count("enable_bundle") && new_config.enable_bundle != old_config.enable_bundle) { Log::Fatal( "Cannot change enable_bundle after constructed Dataset handle."); } if (new_param.count("header") && new_config.header != old_config.header) { Log::Fatal("Cannot change header after constructed Dataset handle."); } if (new_param.count("two_round") && new_config.two_round != old_config.two_round) { Log::Fatal("Cannot change two_round after constructed Dataset handle."); } if (new_param.count("label_column") && new_config.label_column != old_config.label_column) { Log::Fatal( "Cannot change label_column after constructed Dataset handle."); } if (new_param.count("weight_column") && new_config.weight_column != old_config.weight_column) { Log::Fatal( "Cannot change weight_column after constructed Dataset handle."); } if (new_param.count("group_column") && new_config.group_column != old_config.group_column) { Log::Fatal( "Cannot change group_column after constructed Dataset handle."); } if (new_param.count("ignore_column") && new_config.ignore_column != old_config.ignore_column) { Log::Fatal( "Cannot change ignore_column after constructed Dataset handle."); } if (new_param.count("forcedbins_filename")) { Log::Fatal("Cannot change forced bins after constructed Dataset handle."); } if (new_param.count("min_data_in_leaf") && new_config.min_data_in_leaf < old_config.min_data_in_leaf && old_config.feature_pre_filter) { Log::Fatal( "Reducing `min_data_in_leaf` with `feature_pre_filter=true` may " "cause unexpected behaviour " "for features that were pre-filtered by the larger " "`min_data_in_leaf`.\n" "You need to set `feature_pre_filter=false` to dynamically change " "the `min_data_in_leaf`."); } if (new_param.count("linear_tree") && new_config.linear_tree != old_config.linear_tree) { Log::Fatal("Cannot change linear_tree after constructed Dataset handle."); } if (new_param.count("precise_float_parser") && new_config.precise_float_parser != old_config.precise_float_parser) { Log::Fatal("Cannot change precise_float_parser after constructed Dataset handle."); } } void ResetConfig(const char* parameters) { UNIQUE_LOCK(mutex_) auto param = Config::Str2Map(parameters); Config new_config; new_config.Set(param); if (param.count("num_class") && new_config.num_class != config_.num_class) { Log::Fatal("Cannot change num_class during training"); } if (param.count("boosting") && new_config.boosting != config_.boosting) { Log::Fatal("Cannot change boosting during training"); } if (param.count("metric") && new_config.metric != config_.metric) { Log::Fatal("Cannot change metric during training"); } CheckDatasetResetConfig(config_, param); config_.Set(param); OMP_SET_NUM_THREADS(config_.num_threads); if (param.count("objective")) { // create objective function objective_fun_.reset(ObjectiveFunction::CreateObjectiveFunction(config_.objective, config_)); if (objective_fun_ == nullptr) { Log::Info("Using self-defined objective function"); } // initialize the objective function if (objective_fun_ != nullptr) { objective_fun_->Init(train_data_->metadata(), train_data_->num_data()); } boosting_->ResetTrainingData(train_data_, objective_fun_.get(), Common::ConstPtrInVectorWrapper(train_metric_)); } boosting_->ResetConfig(&config_); } void AddValidData(const Dataset* valid_data) { UNIQUE_LOCK(mutex_) valid_metrics_.emplace_back(); for (auto metric_type : config_.metric) { auto metric = std::unique_ptr(Metric::CreateMetric(metric_type, config_)); if (metric == nullptr) { continue; } metric->Init(valid_data->metadata(), valid_data->num_data()); valid_metrics_.back().push_back(std::move(metric)); } valid_metrics_.back().shrink_to_fit(); boosting_->AddValidDataset(valid_data, Common::ConstPtrInVectorWrapper(valid_metrics_.back())); } bool TrainOneIter() { UNIQUE_LOCK(mutex_) return boosting_->TrainOneIter(nullptr, nullptr); } void Refit(const int32_t* leaf_preds, int32_t nrow, int32_t ncol) { UNIQUE_LOCK(mutex_) boosting_->RefitTree(leaf_preds, nrow, ncol); } bool TrainOneIter(const score_t* gradients, const score_t* hessians) { UNIQUE_LOCK(mutex_) return boosting_->TrainOneIter(gradients, hessians); } void RollbackOneIter() { UNIQUE_LOCK(mutex_) boosting_->RollbackOneIter(); } void SetSingleRowPredictorInner(int start_iteration, int num_iteration, int predict_type, const Config& config) { UNIQUE_LOCK(mutex_) if (single_row_predictor_[predict_type].get() == nullptr || !single_row_predictor_[predict_type]->IsPredictorEqual(config, num_iteration, boosting_.get())) { single_row_predictor_[predict_type].reset(new SingleRowPredictorInner(predict_type, boosting_.get(), config, start_iteration, num_iteration)); } } std::unique_ptr InitSingleRowPredictor(int predict_type, int start_iteration, int num_iteration, int data_type, int32_t num_cols, const char *parameters) { // Workaround https://github.com/lightgbm-org/LightGBM/issues/6142 by locking here // This is only a workaround because if predictors are initialized differently it may still behave incorrectly, // and because multiple racing Predictor initializations through LGBM_BoosterPredictForMat suffers from that same issue of Predictor init writing things in the booster. // Once #6142 is fixed (predictor doesn't write in the Booster as should have been the case since 1c35c3b9ede9adab8ccc5fd7b4b2b6af188a79f0), this line can be removed. UNIQUE_LOCK(mutex_) return std::unique_ptr(new SingleRowPredictor( &mutex_, parameters, data_type, num_cols, predict_type, boosting_.get(), start_iteration, num_iteration)); } void PredictSingleRow(int predict_type, int ncol, std::function>(int row_idx)> get_row_fun, const Config& config, double* out_result, int64_t* out_len) const { if (!config.predict_disable_shape_check && ncol != boosting_->MaxFeatureIdx() + 1) { Log::Fatal("The number of features in data (%d) is not the same as it was in training data (%d).\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); } UNIQUE_LOCK(mutex_) const auto& single_row_predictor = single_row_predictor_[predict_type]; auto one_row = get_row_fun(0); auto pred_wrt_ptr = out_result; single_row_predictor->predict_function(one_row, pred_wrt_ptr); *out_len = single_row_predictor->num_pred_in_one_row; } std::shared_ptr CreatePredictor(int start_iteration, int num_iteration, int predict_type, int ncol, const Config& config) const { if (!config.predict_disable_shape_check && ncol != boosting_->MaxFeatureIdx() + 1) { Log::Fatal("The number of features in data (%d) is not the same as it was in training data (%d).\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); } bool is_predict_leaf = false; bool is_raw_score = false; bool predict_contrib = false; if (predict_type == C_API_PREDICT_LEAF_INDEX) { is_predict_leaf = true; } else if (predict_type == C_API_PREDICT_RAW_SCORE) { is_raw_score = true; } else if (predict_type == C_API_PREDICT_CONTRIB) { predict_contrib = true; } else { is_raw_score = false; } return std::make_shared(boosting_.get(), start_iteration, num_iteration, is_raw_score, is_predict_leaf, predict_contrib, config.pred_early_stop, config.pred_early_stop_freq, config.pred_early_stop_margin); } void Predict(int start_iteration, int num_iteration, int predict_type, int nrow, int ncol, std::function>(int row_idx)> get_row_fun, const Config& config, double* out_result, int64_t* out_len) const { SHARED_LOCK(mutex_); auto predictor = CreatePredictor(start_iteration, num_iteration, predict_type, ncol, config); bool is_predict_leaf = false; bool predict_contrib = false; if (predict_type == C_API_PREDICT_LEAF_INDEX) { is_predict_leaf = true; } else if (predict_type == C_API_PREDICT_CONTRIB) { predict_contrib = true; } int64_t num_pred_in_one_row = boosting_->NumPredictOneRow(start_iteration, num_iteration, is_predict_leaf, predict_contrib); auto pred_fun = predictor->GetPredictFunction(); OMP_INIT_EX(); #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) for (int i = 0; i < nrow; ++i) { OMP_LOOP_EX_BEGIN(); auto one_row = get_row_fun(i); auto pred_wrt_ptr = out_result + static_cast(num_pred_in_one_row) * i; pred_fun(one_row, pred_wrt_ptr); OMP_LOOP_EX_END(); } OMP_THROW_EX(); *out_len = num_pred_in_one_row * nrow; } void PredictSparse(int start_iteration, int num_iteration, int predict_type, int64_t nrow, int ncol, std::function>(int64_t row_idx)> get_row_fun, const Config& config, int64_t* out_elements_size, std::vector>>* agg_ptr, int32_t** out_indices, void** out_data, int data_type, bool* is_data_float32_ptr, int num_matrices) const { auto predictor = CreatePredictor(start_iteration, num_iteration, predict_type, ncol, config); auto pred_sparse_fun = predictor->GetPredictSparseFunction(); std::vector>>& agg = *agg_ptr; OMP_INIT_EX(); #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) for (int64_t i = 0; i < nrow; ++i) { OMP_LOOP_EX_BEGIN(); auto one_row = get_row_fun(i); agg[i] = std::vector>(num_matrices); pred_sparse_fun(one_row, &agg[i]); OMP_LOOP_EX_END(); } OMP_THROW_EX(); // calculate the nonzero data and indices size int64_t elements_size = 0; for (int64_t i = 0; i < static_cast(agg.size()); ++i) { auto row_vector = agg[i]; for (int j = 0; j < static_cast(row_vector.size()); ++j) { elements_size += static_cast(row_vector[j].size()); } } *out_elements_size = elements_size; *is_data_float32_ptr = false; // allocate data and indices arrays if (data_type == C_API_DTYPE_FLOAT32) { *out_data = new float[elements_size]; *is_data_float32_ptr = true; } else if (data_type == C_API_DTYPE_FLOAT64) { *out_data = new double[elements_size]; } else { Log::Fatal("Unknown data type in PredictSparse"); return; } *out_indices = new int32_t[elements_size]; } void PredictSparseCSR(int start_iteration, int num_iteration, int predict_type, int64_t nrow, int ncol, std::function>(int64_t row_idx)> get_row_fun, const Config& config, int64_t* out_len, void** out_indptr, int indptr_type, int32_t** out_indices, void** out_data, int data_type) const { SHARED_LOCK(mutex_); // Get the number of trees per iteration (for multiclass scenario we output multiple sparse matrices) int num_matrices = boosting_->NumModelPerIteration(); bool is_indptr_int32 = false; bool is_data_float32 = false; int64_t indptr_size = (nrow + 1) * num_matrices; if (indptr_type == C_API_DTYPE_INT32) { *out_indptr = new int32_t[indptr_size]; is_indptr_int32 = true; } else if (indptr_type == C_API_DTYPE_INT64) { *out_indptr = new int64_t[indptr_size]; } else { Log::Fatal("Unknown indptr type in PredictSparseCSR"); return; } // aggregated per row feature contribution results std::vector>> agg(nrow); int64_t elements_size = 0; PredictSparse(start_iteration, num_iteration, predict_type, nrow, ncol, get_row_fun, config, &elements_size, &agg, out_indices, out_data, data_type, &is_data_float32, num_matrices); std::vector row_sizes(num_matrices * nrow); std::vector row_matrix_offsets(num_matrices * nrow); std::vector matrix_offsets(num_matrices); int64_t row_vector_cnt = 0; for (int m = 0; m < num_matrices; ++m) { for (int64_t i = 0; i < static_cast(agg.size()); ++i) { auto row_vector = agg[i]; auto row_vector_size = row_vector[m].size(); // keep track of the row_vector sizes for parallelization row_sizes[row_vector_cnt] = static_cast(row_vector_size); if (i == 0) { row_matrix_offsets[row_vector_cnt] = 0; } else { row_matrix_offsets[row_vector_cnt] = static_cast(row_sizes[row_vector_cnt - 1] + row_matrix_offsets[row_vector_cnt - 1]); } row_vector_cnt++; } if (m == 0) { matrix_offsets[m] = 0; } if (m + 1 < num_matrices) { matrix_offsets[m + 1] = static_cast(matrix_offsets[m] + row_matrix_offsets[row_vector_cnt - 1] + row_sizes[row_vector_cnt - 1]); } } // copy vector results to output for each row int64_t indptr_index = 0; for (int m = 0; m < num_matrices; ++m) { if (is_indptr_int32) { (reinterpret_cast(*out_indptr))[indptr_index] = 0; } else { (reinterpret_cast(*out_indptr))[indptr_index] = 0; } indptr_index++; int64_t matrix_start_index = m * static_cast(agg.size()); OMP_INIT_EX(); #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) for (int64_t i = 0; i < static_cast(agg.size()); ++i) { OMP_LOOP_EX_BEGIN(); auto row_vector = agg[i]; int64_t row_start_index = matrix_start_index + i; int64_t element_index = row_matrix_offsets[row_start_index] + matrix_offsets[m]; int64_t indptr_loop_index = indptr_index + i; for (auto it = row_vector[m].begin(); it != row_vector[m].end(); ++it) { (*out_indices)[element_index] = it->first; if (is_data_float32) { (reinterpret_cast(*out_data))[element_index] = static_cast(it->second); } else { (reinterpret_cast(*out_data))[element_index] = it->second; } element_index++; } int64_t indptr_value = row_matrix_offsets[row_start_index] + row_sizes[row_start_index]; if (is_indptr_int32) { (reinterpret_cast(*out_indptr))[indptr_loop_index] = static_cast(indptr_value); } else { (reinterpret_cast(*out_indptr))[indptr_loop_index] = indptr_value; } OMP_LOOP_EX_END(); } OMP_THROW_EX(); indptr_index += static_cast(agg.size()); } out_len[0] = elements_size; out_len[1] = indptr_size; } void PredictSparseCSC(int start_iteration, int num_iteration, int predict_type, int64_t nrow, int ncol, std::function>(int64_t row_idx)> get_row_fun, const Config& config, int64_t* out_len, void** out_col_ptr, int col_ptr_type, int32_t** out_indices, void** out_data, int data_type) const { SHARED_LOCK(mutex_); // Get the number of trees per iteration (for multiclass scenario we output multiple sparse matrices) int num_matrices = boosting_->NumModelPerIteration(); auto predictor = CreatePredictor(start_iteration, num_iteration, predict_type, ncol, config); auto pred_sparse_fun = predictor->GetPredictSparseFunction(); bool is_col_ptr_int32 = false; bool is_data_float32 = false; int num_output_cols = ncol + 1; int col_ptr_size = (num_output_cols + 1) * num_matrices; if (col_ptr_type == C_API_DTYPE_INT32) { *out_col_ptr = new int32_t[col_ptr_size]; is_col_ptr_int32 = true; } else if (col_ptr_type == C_API_DTYPE_INT64) { *out_col_ptr = new int64_t[col_ptr_size]; } else { Log::Fatal("Unknown col_ptr type in PredictSparseCSC"); return; } // aggregated per row feature contribution results std::vector>> agg(nrow); int64_t elements_size = 0; PredictSparse(start_iteration, num_iteration, predict_type, nrow, ncol, get_row_fun, config, &elements_size, &agg, out_indices, out_data, data_type, &is_data_float32, num_matrices); // calculate number of elements per column to construct // the CSC matrix with random access std::vector> column_sizes(num_matrices); for (int m = 0; m < num_matrices; ++m) { column_sizes[m] = std::vector(num_output_cols, 0); for (int64_t i = 0; i < static_cast(agg.size()); ++i) { auto row_vector = agg[i]; for (auto it = row_vector[m].begin(); it != row_vector[m].end(); ++it) { column_sizes[m][it->first] += 1; } } } // keep track of column counts std::vector> column_counts(num_matrices); // keep track of beginning index for each column std::vector> column_start_indices(num_matrices); // keep track of beginning index for each matrix std::vector matrix_start_indices(num_matrices, 0); int col_ptr_index = 0; for (int m = 0; m < num_matrices; ++m) { int64_t col_ptr_value = 0; column_start_indices[m] = std::vector(num_output_cols, 0); column_counts[m] = std::vector(num_output_cols, 0); if (is_col_ptr_int32) { (reinterpret_cast(*out_col_ptr))[col_ptr_index] = static_cast(col_ptr_value); } else { (reinterpret_cast(*out_col_ptr))[col_ptr_index] = col_ptr_value; } col_ptr_index++; for (int64_t i = 1; i < static_cast(column_sizes[m].size()); ++i) { column_start_indices[m][i] = column_sizes[m][i - 1] + column_start_indices[m][i - 1]; if (is_col_ptr_int32) { (reinterpret_cast(*out_col_ptr))[col_ptr_index] = static_cast(column_start_indices[m][i]); } else { (reinterpret_cast(*out_col_ptr))[col_ptr_index] = column_start_indices[m][i]; } col_ptr_index++; } int64_t last_elem_index = static_cast(column_sizes[m].size()) - 1; int64_t last_column_start_index = column_start_indices[m][last_elem_index]; int64_t last_column_size = column_sizes[m][last_elem_index]; if (is_col_ptr_int32) { (reinterpret_cast(*out_col_ptr))[col_ptr_index] = static_cast(last_column_start_index + last_column_size); } else { (reinterpret_cast(*out_col_ptr))[col_ptr_index] = last_column_start_index + last_column_size; } if (m + 1 < num_matrices) { matrix_start_indices[m + 1] = matrix_start_indices[m] + last_column_start_index + last_column_size; } col_ptr_index++; } // Note: we parallelize across matrices instead of rows because of the column_counts[m][col_idx] increment inside the loop OMP_INIT_EX(); #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) for (int m = 0; m < num_matrices; ++m) { OMP_LOOP_EX_BEGIN(); for (int64_t i = 0; i < static_cast(agg.size()); ++i) { auto row_vector = agg[i]; for (auto it = row_vector[m].begin(); it != row_vector[m].end(); ++it) { int64_t col_idx = it->first; int64_t element_index = column_start_indices[m][col_idx] + matrix_start_indices[m] + column_counts[m][col_idx]; // store the row index (*out_indices)[element_index] = static_cast(i); // update column count column_counts[m][col_idx]++; if (is_data_float32) { (reinterpret_cast(*out_data))[element_index] = static_cast(it->second); } else { (reinterpret_cast(*out_data))[element_index] = it->second; } } } OMP_LOOP_EX_END(); } OMP_THROW_EX(); out_len[0] = elements_size; out_len[1] = col_ptr_size; } void Predict(int start_iteration, int num_iteration, int predict_type, const char* data_filename, int data_has_header, const Config& config, const char* result_filename) const { SHARED_LOCK(mutex_) bool is_predict_leaf = false; bool is_raw_score = false; bool predict_contrib = false; if (predict_type == C_API_PREDICT_LEAF_INDEX) { is_predict_leaf = true; } else if (predict_type == C_API_PREDICT_RAW_SCORE) { is_raw_score = true; } else if (predict_type == C_API_PREDICT_CONTRIB) { predict_contrib = true; } else { is_raw_score = false; } Predictor predictor(boosting_.get(), start_iteration, num_iteration, is_raw_score, is_predict_leaf, predict_contrib, config.pred_early_stop, config.pred_early_stop_freq, config.pred_early_stop_margin); bool bool_data_has_header = data_has_header > 0 ? true : false; predictor.Predict(data_filename, result_filename, bool_data_has_header, config.predict_disable_shape_check, config.precise_float_parser); } void GetPredictAt(int data_idx, double* out_result, int64_t* out_len) const { boosting_->GetPredictAt(data_idx, out_result, out_len); } void SaveModelToFile(int start_iteration, int num_iteration, int feature_importance_type, const char* filename) const { boosting_->SaveModelToFile(start_iteration, num_iteration, feature_importance_type, filename); } void LoadModelFromString(const char* model_str) { size_t len = std::strlen(model_str); boosting_->LoadModelFromString(model_str, len); } std::string SaveModelToString(int start_iteration, int num_iteration, int feature_importance_type) const { return boosting_->SaveModelToString(start_iteration, num_iteration, feature_importance_type); } std::string DumpModel(int start_iteration, int num_iteration, int feature_importance_type) const { return boosting_->DumpModel(start_iteration, num_iteration, feature_importance_type); } std::vector FeatureImportance(int num_iteration, int importance_type) const { return boosting_->FeatureImportance(num_iteration, importance_type); } double UpperBoundValue() const { SHARED_LOCK(mutex_) return boosting_->GetUpperBoundValue(); } double LowerBoundValue() const { SHARED_LOCK(mutex_) return boosting_->GetLowerBoundValue(); } double GetLeafValue(int tree_idx, int leaf_idx) const { SHARED_LOCK(mutex_) return dynamic_cast(boosting_.get())->GetLeafValue(tree_idx, leaf_idx); } void SetLeafValue(int tree_idx, int leaf_idx, double val) { UNIQUE_LOCK(mutex_) dynamic_cast(boosting_.get())->SetLeafValue(tree_idx, leaf_idx, val); } void ShuffleModels(int start_iter, int end_iter) { UNIQUE_LOCK(mutex_) boosting_->ShuffleModels(start_iter, end_iter); } int GetEvalCounts() const { SHARED_LOCK(mutex_) int ret = 0; for (const auto& metric : train_metric_) { ret += static_cast(metric->GetName().size()); } return ret; } int GetEvalNames(char** out_strs, const int len, const size_t buffer_len, size_t *out_buffer_len) const { SHARED_LOCK(mutex_) *out_buffer_len = 0; int idx = 0; for (const auto& metric : train_metric_) { for (const auto& name : metric->GetName()) { if (idx < len) { std::memcpy(out_strs[idx], name.c_str(), std::min(name.size() + 1, buffer_len)); out_strs[idx][buffer_len - 1] = '\0'; } *out_buffer_len = std::max(name.size() + 1, *out_buffer_len); ++idx; } } return idx; } int GetFeatureNames(char** out_strs, const int len, const size_t buffer_len, size_t *out_buffer_len) const { SHARED_LOCK(mutex_) *out_buffer_len = 0; int idx = 0; for (const auto& name : boosting_->FeatureNames()) { if (idx < len) { std::memcpy(out_strs[idx], name.c_str(), std::min(name.size() + 1, buffer_len)); out_strs[idx][buffer_len - 1] = '\0'; } *out_buffer_len = std::max(name.size() + 1, *out_buffer_len); ++idx; } return idx; } const Boosting* GetBoosting() const { return boosting_.get(); } private: const Dataset* train_data_; std::unique_ptr boosting_; std::unique_ptr single_row_predictor_[PREDICTOR_TYPES]; /*! \brief All configs */ Config config_; /*! \brief Metric for training data */ std::vector> train_metric_; /*! \brief Metrics for validation data */ std::vector>> valid_metrics_; /*! \brief Training objective function */ std::unique_ptr objective_fun_; /*! \brief mutex for threading safe call */ mutable yamc::alternate::shared_mutex mutex_; }; } // namespace LightGBM // explicitly declare symbols from LightGBM namespace using LightGBM::AllgatherFunction; using LightGBM::ArrowChunkedArray; using LightGBM::ArrowTable; using LightGBM::Booster; using LightGBM::Common::CheckElementsIntervalClosed; using LightGBM::Common::RemoveQuotationSymbol; using LightGBM::Common::Vector2Ptr; using LightGBM::Common::VectorSize; using LightGBM::Config; using LightGBM::data_size_t; using LightGBM::Dataset; using LightGBM::DatasetLoader; using LightGBM::kZeroThreshold; using LightGBM::LGBM_APIHandleException; using LightGBM::Log; using LightGBM::Network; using LightGBM::Random; using LightGBM::ReduceScatterFunction; using LightGBM::SingleRowPredictor; // some help functions used to convert data std::function(int row_idx)> RowFunctionFromDenseMatrix(const void* data, int num_row, int num_col, int data_type, int is_row_major); std::function>(int row_idx)> RowPairFunctionFromDenseMatrix(const void* data, int num_row, int num_col, int data_type, int is_row_major); std::function>(int row_idx)> RowPairFunctionFromDenseRows(const void** data, int num_col, int data_type); template std::function>(T idx)> RowFunctionFromCSR(const void* indptr, int indptr_type, const int32_t* indices, const void* data, int data_type, int64_t nindptr, int64_t nelem); // Row iterator of on column for CSC matrix class CSC_RowIterator { public: CSC_RowIterator(const void* col_ptr, int col_ptr_type, const int32_t* indices, const void* data, int data_type, int64_t ncol_ptr, int64_t nelem, int col_idx); ~CSC_RowIterator() {} // return value at idx, only can access by ascent order double Get(int idx); // return next non-zero pair, if index < 0, means no more data std::pair NextNonZero(); private: int nonzero_idx_ = 0; int cur_idx_ = -1; double cur_val_ = 0.0f; bool is_end_ = false; std::function(int idx)> iter_fun_; }; // start of c_api functions const char* LGBM_GetLastError() { return LastErrorMsg(); } int LGBM_DumpParamAliases(int64_t buffer_len, int64_t* out_len, char* out_str) { API_BEGIN(); std::string aliases = Config::DumpAliases(); *out_len = static_cast(aliases.size()) + 1; if (*out_len <= buffer_len) { std::memcpy(out_str, aliases.c_str(), *out_len); } API_END(); } int LGBM_RegisterLogCallback(void (*callback)(const char*)) { API_BEGIN(); Log::ResetCallBack(callback); API_END(); } static inline int SampleCount(int32_t total_nrow, const Config& config) { return static_cast(total_nrow < config.bin_construct_sample_cnt ? total_nrow : config.bin_construct_sample_cnt); } static inline std::vector CreateSampleIndices(int32_t total_nrow, const Config& config) { Random rand(config.data_random_seed); int sample_cnt = SampleCount(total_nrow, config); return rand.Sample(total_nrow, sample_cnt); } int LGBM_GetSampleCount(int32_t num_total_row, const char* parameters, int* out) { API_BEGIN(); if (out == nullptr) { Log::Fatal("LGBM_GetSampleCount output is nullptr"); } auto param = Config::Str2Map(parameters); Config config; config.Set(param); *out = SampleCount(num_total_row, config); API_END(); } int LGBM_SampleIndices(int32_t num_total_row, const char* parameters, void* out, int32_t* out_len) { // This API is to keep python binding's behavior the same with C++ implementation. // Sample count, random seed etc. should be provided in parameters. API_BEGIN(); if (out == nullptr) { Log::Fatal("LGBM_SampleIndices output is nullptr"); } auto param = Config::Str2Map(parameters); Config config; config.Set(param); auto sample_indices = CreateSampleIndices(num_total_row, config); memcpy(out, sample_indices.data(), sizeof(int32_t) * sample_indices.size()); *out_len = static_cast(sample_indices.size()); API_END(); } int LGBM_ByteBufferGetAt(ByteBufferHandle handle, int32_t index, uint8_t* out_val) { API_BEGIN(); LightGBM::ByteBuffer* byteBuffer = reinterpret_cast(handle); *out_val = byteBuffer->GetAt(index); API_END(); } int LGBM_ByteBufferFree(ByteBufferHandle handle) { API_BEGIN(); delete reinterpret_cast(handle); API_END(); } int LGBM_DatasetCreateFromFile(const char* filename, const char* parameters, const DatasetHandle reference, DatasetHandle* out) { API_BEGIN(); auto param = Config::Str2Map(parameters); Config config; config.Set(param); OMP_SET_NUM_THREADS(config.num_threads); DatasetLoader loader(config, nullptr, 1, filename); if (reference == nullptr) { if (Network::num_machines() == 1) { *out = loader.LoadFromFile(filename); } else { *out = loader.LoadFromFile(filename, Network::rank(), Network::num_machines()); } } else { *out = loader.LoadFromFileAlignWithOtherDataset(filename, reinterpret_cast(reference)); } API_END(); } int LGBM_DatasetCreateFromSampledColumn(double** sample_data, int** sample_indices, int32_t ncol, const int* num_per_col, int32_t num_sample_row, int32_t num_local_row, int64_t num_dist_row, const char* parameters, DatasetHandle* out) { API_BEGIN(); auto param = Config::Str2Map(parameters); Config config; config.Set(param); OMP_SET_NUM_THREADS(config.num_threads); DatasetLoader loader(config, nullptr, 1, nullptr); *out = loader.ConstructFromSampleData(sample_data, sample_indices, ncol, num_per_col, num_sample_row, static_cast(num_local_row), num_dist_row); API_END(); } int LGBM_DatasetCreateByReference(const DatasetHandle reference, int64_t num_total_row, DatasetHandle* out) { API_BEGIN(); std::unique_ptr ret; data_size_t nrows = static_cast(num_total_row); ret.reset(new Dataset(nrows)); const Dataset* reference_dataset = reinterpret_cast(reference); ret->CreateValid(reference_dataset); ret->InitByReference(nrows, reference_dataset); *out = ret.release(); API_END(); } int LGBM_DatasetCreateFromSerializedReference(const void* ref_buffer, int32_t ref_buffer_size, int64_t num_row, int32_t num_classes, const char* parameters, DatasetHandle* out) { API_BEGIN(); auto param = Config::Str2Map(parameters); Config config; config.Set(param); OMP_SET_NUM_THREADS(config.num_threads); DatasetLoader loader(config, nullptr, 1, nullptr); *out = loader.LoadFromSerializedReference(static_cast(ref_buffer), static_cast(ref_buffer_size), static_cast(num_row), num_classes); API_END(); } int LGBM_DatasetInitStreaming(DatasetHandle dataset, int32_t has_weights, int32_t has_init_scores, int32_t has_queries, int32_t nclasses, int32_t nthreads, int32_t omp_max_threads) { API_BEGIN(); auto p_dataset = reinterpret_cast(dataset); auto num_data = p_dataset->num_data(); p_dataset->InitStreaming(num_data, has_weights, has_init_scores, has_queries, nclasses, nthreads, omp_max_threads); p_dataset->set_wait_for_manual_finish(true); API_END(); } int LGBM_DatasetPushRows(DatasetHandle dataset, const void* data, int data_type, int32_t nrow, int32_t ncol, int32_t start_row) { API_BEGIN(); auto p_dataset = reinterpret_cast(dataset); auto get_row_fun = RowFunctionFromDenseMatrix(data, nrow, ncol, data_type, 1); if (p_dataset->has_raw()) { p_dataset->ResizeRaw(p_dataset->num_numeric_features() + nrow); } OMP_INIT_EX(); #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) for (int i = 0; i < nrow; ++i) { OMP_LOOP_EX_BEGIN(); const int tid = omp_get_thread_num(); auto one_row = get_row_fun(i); p_dataset->PushOneRow(tid, start_row + i, one_row); OMP_LOOP_EX_END(); } OMP_THROW_EX(); if (!p_dataset->wait_for_manual_finish() && (start_row + nrow == p_dataset->num_data())) { p_dataset->FinishLoad(); } API_END(); } int LGBM_DatasetPushRowsWithMetadata(DatasetHandle dataset, const void* data, int data_type, int32_t nrow, int32_t ncol, int32_t start_row, const float* labels, const float* weights, const double* init_scores, const int32_t* queries, int32_t tid) { API_BEGIN(); #ifdef LABEL_T_USE_DOUBLE Log::Fatal("Don't support LABEL_T_USE_DOUBLE"); #endif if (!data) { Log::Fatal("data cannot be null."); } auto p_dataset = reinterpret_cast(dataset); auto get_row_fun = RowFunctionFromDenseMatrix(data, nrow, ncol, data_type, 1); if (p_dataset->has_raw()) { p_dataset->ResizeRaw(p_dataset->num_numeric_features() + nrow); } const int max_omp_threads = p_dataset->omp_max_threads() > 0 ? p_dataset->omp_max_threads() : OMP_NUM_THREADS(); OMP_INIT_EX(); #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) for (int i = 0; i < nrow; ++i) { OMP_LOOP_EX_BEGIN(); // convert internal thread id to be unique based on external thread id const int internal_tid = omp_get_thread_num() + (max_omp_threads * tid); auto one_row = get_row_fun(i); p_dataset->PushOneRow(internal_tid, start_row + i, one_row); OMP_LOOP_EX_END(); } OMP_THROW_EX(); p_dataset->InsertMetadataAt(start_row, nrow, labels, weights, init_scores, queries); if (!p_dataset->wait_for_manual_finish() && (start_row + nrow == p_dataset->num_data())) { p_dataset->FinishLoad(); } API_END(); } int LGBM_DatasetPushRowsByCSR(DatasetHandle dataset, const void* indptr, int indptr_type, const int32_t* indices, const void* data, int data_type, int64_t nindptr, int64_t nelem, int64_t, int64_t start_row) { API_BEGIN(); auto p_dataset = reinterpret_cast(dataset); auto get_row_fun = RowFunctionFromCSR(indptr, indptr_type, indices, data, data_type, nindptr, nelem); int32_t nrow = static_cast(nindptr - 1); if (p_dataset->has_raw()) { p_dataset->ResizeRaw(p_dataset->num_numeric_features() + nrow); } OMP_INIT_EX(); #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) for (int i = 0; i < nrow; ++i) { OMP_LOOP_EX_BEGIN(); const int tid = omp_get_thread_num(); auto one_row = get_row_fun(i); p_dataset->PushOneRow(tid, static_cast(start_row + i), one_row); OMP_LOOP_EX_END(); } OMP_THROW_EX(); if (!p_dataset->wait_for_manual_finish() && (start_row + nrow == static_cast(p_dataset->num_data()))) { p_dataset->FinishLoad(); } API_END(); } int LGBM_DatasetPushRowsByCSRWithMetadata(DatasetHandle dataset, const void* indptr, int indptr_type, const int32_t* indices, const void* data, int data_type, int64_t nindptr, int64_t nelem, int64_t start_row, const float* labels, const float* weights, const double* init_scores, const int32_t* queries, int32_t tid) { API_BEGIN(); #ifdef LABEL_T_USE_DOUBLE Log::Fatal("Don't support LABEL_T_USE_DOUBLE"); #endif if (!data) { Log::Fatal("data cannot be null."); } auto p_dataset = reinterpret_cast(dataset); auto get_row_fun = RowFunctionFromCSR(indptr, indptr_type, indices, data, data_type, nindptr, nelem); int32_t nrow = static_cast(nindptr - 1); if (p_dataset->has_raw()) { p_dataset->ResizeRaw(p_dataset->num_numeric_features() + nrow); } const int max_omp_threads = p_dataset->omp_max_threads() > 0 ? p_dataset->omp_max_threads() : OMP_NUM_THREADS(); OMP_INIT_EX(); #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) for (int i = 0; i < nrow; ++i) { OMP_LOOP_EX_BEGIN(); // convert internal thread id to be unique based on external thread id const int internal_tid = omp_get_thread_num() + (max_omp_threads * tid); auto one_row = get_row_fun(i); p_dataset->PushOneRow(internal_tid, static_cast(start_row + i), one_row); OMP_LOOP_EX_END(); } OMP_THROW_EX(); p_dataset->InsertMetadataAt(static_cast(start_row), nrow, labels, weights, init_scores, queries); if (!p_dataset->wait_for_manual_finish() && (start_row + nrow == static_cast(p_dataset->num_data()))) { p_dataset->FinishLoad(); } API_END(); } int LGBM_DatasetSetWaitForManualFinish(DatasetHandle dataset, int wait) { API_BEGIN(); auto p_dataset = reinterpret_cast(dataset); p_dataset->set_wait_for_manual_finish(wait); API_END(); } int LGBM_DatasetMarkFinished(DatasetHandle dataset) { API_BEGIN(); auto p_dataset = reinterpret_cast(dataset); p_dataset->FinishLoad(); API_END(); } int LGBM_DatasetCreateFromMat(const void* data, int data_type, int32_t nrow, int32_t ncol, int is_row_major, const char* parameters, const DatasetHandle reference, DatasetHandle* out) { return LGBM_DatasetCreateFromMats(1, &data, data_type, &nrow, ncol, &is_row_major, parameters, reference, out); } int LGBM_DatasetCreateFromMats(int32_t nmat, const void** data, int data_type, int32_t* nrow, int32_t ncol, int* is_row_major, const char* parameters, const DatasetHandle reference, DatasetHandle* out) { API_BEGIN(); auto param = Config::Str2Map(parameters); Config config; config.Set(param); OMP_SET_NUM_THREADS(config.num_threads); std::unique_ptr ret; int32_t total_nrow = 0; for (int j = 0; j < nmat; ++j) { total_nrow += nrow[j]; } std::vector(int row_idx)>> get_row_fun; for (int j = 0; j < nmat; ++j) { get_row_fun.push_back(RowFunctionFromDenseMatrix(data[j], nrow[j], ncol, data_type, is_row_major[j])); } if (reference == nullptr) { // sample data first auto sample_indices = CreateSampleIndices(total_nrow, config); int sample_cnt = static_cast(sample_indices.size()); std::vector> sample_values(ncol); std::vector> sample_idx(ncol); int offset = 0; int j = 0; for (size_t i = 0; i < sample_indices.size(); ++i) { auto idx = sample_indices[i]; while ((idx - offset) >= nrow[j]) { offset += nrow[j]; ++j; } auto row = get_row_fun[j](static_cast(idx - offset)); for (size_t k = 0; k < row.size(); ++k) { if (std::fabs(row[k]) > kZeroThreshold || std::isnan(row[k])) { sample_values[k].emplace_back(row[k]); sample_idx[k].emplace_back(static_cast(i)); } } } DatasetLoader loader(config, nullptr, 1, nullptr); ret.reset(loader.ConstructFromSampleData(Vector2Ptr(&sample_values).data(), Vector2Ptr(&sample_idx).data(), ncol, VectorSize(sample_values).data(), sample_cnt, total_nrow, total_nrow)); } else { ret.reset(new Dataset(total_nrow)); ret->CreateValid( reinterpret_cast(reference)); if (ret->has_raw()) { ret->ResizeRaw(total_nrow); } } int32_t start_row = 0; for (int j = 0; j < nmat; ++j) { OMP_INIT_EX(); #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) for (int i = 0; i < nrow[j]; ++i) { OMP_LOOP_EX_BEGIN(); const int tid = omp_get_thread_num(); auto one_row = get_row_fun[j](i); ret->PushOneRow(tid, start_row + i, one_row); OMP_LOOP_EX_END(); } OMP_THROW_EX(); start_row += nrow[j]; } ret->FinishLoad(); *out = ret.release(); API_END(); } int LGBM_DatasetCreateFromCSR(const void* indptr, int indptr_type, const int32_t* indices, const void* data, int data_type, int64_t nindptr, int64_t nelem, int64_t num_col, const char* parameters, const DatasetHandle reference, DatasetHandle* out) { API_BEGIN(); if (num_col <= 0) { Log::Fatal("The number of columns should be greater than zero."); } else if (num_col >= INT32_MAX) { Log::Fatal("The number of columns should be smaller than INT32_MAX."); } auto param = Config::Str2Map(parameters); Config config; config.Set(param); OMP_SET_NUM_THREADS(config.num_threads); std::unique_ptr ret; auto get_row_fun = RowFunctionFromCSR(indptr, indptr_type, indices, data, data_type, nindptr, nelem); int32_t nrow = static_cast(nindptr - 1); if (reference == nullptr) { // sample data first auto sample_indices = CreateSampleIndices(nrow, config); int sample_cnt = static_cast(sample_indices.size()); std::vector> sample_values(num_col); std::vector> sample_idx(num_col); for (size_t i = 0; i < sample_indices.size(); ++i) { auto idx = sample_indices[i]; auto row = get_row_fun(static_cast(idx)); for (std::pair& inner_data : row) { CHECK_LT(inner_data.first, num_col); if (std::fabs(inner_data.second) > kZeroThreshold || std::isnan(inner_data.second)) { sample_values[inner_data.first].emplace_back(inner_data.second); sample_idx[inner_data.first].emplace_back(static_cast(i)); } } } DatasetLoader loader(config, nullptr, 1, nullptr); ret.reset(loader.ConstructFromSampleData(Vector2Ptr(&sample_values).data(), Vector2Ptr(&sample_idx).data(), static_cast(num_col), VectorSize(sample_values).data(), sample_cnt, nrow, nrow)); } else { ret.reset(new Dataset(nrow)); ret->CreateValid( reinterpret_cast(reference)); if (ret->has_raw()) { ret->ResizeRaw(nrow); } } OMP_INIT_EX(); #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) for (int i = 0; i < static_cast(nindptr - 1); ++i) { OMP_LOOP_EX_BEGIN(); const int tid = omp_get_thread_num(); auto one_row = get_row_fun(i); ret->PushOneRow(tid, i, one_row); OMP_LOOP_EX_END(); } OMP_THROW_EX(); ret->FinishLoad(); *out = ret.release(); API_END(); } int LGBM_DatasetCreateFromCSRFunc(void* get_row_funptr, int num_rows, int64_t num_col, const char* parameters, const DatasetHandle reference, DatasetHandle* out) { API_BEGIN(); if (num_col <= 0) { Log::Fatal("The number of columns should be greater than zero."); } else if (num_col >= INT32_MAX) { Log::Fatal("The number of columns should be smaller than INT32_MAX."); } auto get_row_fun = *static_cast>&)>*>(get_row_funptr); auto param = Config::Str2Map(parameters); Config config; config.Set(param); OMP_SET_NUM_THREADS(config.num_threads); std::unique_ptr ret; int32_t nrow = num_rows; if (reference == nullptr) { // sample data first auto sample_indices = CreateSampleIndices(nrow, config); int sample_cnt = static_cast(sample_indices.size()); std::vector> sample_values(num_col); std::vector> sample_idx(num_col); // local buffer to re-use memory std::vector> buffer; for (size_t i = 0; i < sample_indices.size(); ++i) { auto idx = sample_indices[i]; get_row_fun(static_cast(idx), buffer); for (std::pair& inner_data : buffer) { CHECK_LT(inner_data.first, num_col); if (std::fabs(inner_data.second) > kZeroThreshold || std::isnan(inner_data.second)) { sample_values[inner_data.first].emplace_back(inner_data.second); sample_idx[inner_data.first].emplace_back(static_cast(i)); } } } DatasetLoader loader(config, nullptr, 1, nullptr); ret.reset(loader.ConstructFromSampleData(Vector2Ptr(&sample_values).data(), Vector2Ptr(&sample_idx).data(), static_cast(num_col), VectorSize(sample_values).data(), sample_cnt, nrow, nrow)); } else { ret.reset(new Dataset(nrow)); ret->CreateValid( reinterpret_cast(reference)); if (ret->has_raw()) { ret->ResizeRaw(nrow); } } OMP_INIT_EX(); std::vector> thread_buffer; #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) private(thread_buffer) for (int i = 0; i < num_rows; ++i) { OMP_LOOP_EX_BEGIN(); { const int tid = omp_get_thread_num(); get_row_fun(i, thread_buffer); ret->PushOneRow(tid, i, thread_buffer); } OMP_LOOP_EX_END(); } OMP_THROW_EX(); ret->FinishLoad(); *out = ret.release(); API_END(); } int LGBM_DatasetCreateFromCSC(const void* col_ptr, int col_ptr_type, const int32_t* indices, const void* data, int data_type, int64_t ncol_ptr, int64_t nelem, int64_t num_row, const char* parameters, const DatasetHandle reference, DatasetHandle* out) { API_BEGIN(); auto param = Config::Str2Map(parameters); Config config; config.Set(param); OMP_SET_NUM_THREADS(config.num_threads); std::unique_ptr ret; int32_t nrow = static_cast(num_row); if (reference == nullptr) { // sample data first auto sample_indices = CreateSampleIndices(nrow, config); int sample_cnt = static_cast(sample_indices.size()); std::vector> sample_values(ncol_ptr - 1); std::vector> sample_idx(ncol_ptr - 1); OMP_INIT_EX(); #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) for (int i = 0; i < static_cast(sample_values.size()); ++i) { OMP_LOOP_EX_BEGIN(); CSC_RowIterator col_it(col_ptr, col_ptr_type, indices, data, data_type, ncol_ptr, nelem, i); for (int j = 0; j < sample_cnt; j++) { auto val = col_it.Get(sample_indices[j]); if (std::fabs(val) > kZeroThreshold || std::isnan(val)) { sample_values[i].emplace_back(val); sample_idx[i].emplace_back(j); } } OMP_LOOP_EX_END(); } OMP_THROW_EX(); DatasetLoader loader(config, nullptr, 1, nullptr); ret.reset(loader.ConstructFromSampleData(Vector2Ptr(&sample_values).data(), Vector2Ptr(&sample_idx).data(), static_cast(sample_values.size()), VectorSize(sample_values).data(), sample_cnt, nrow, nrow)); } else { ret.reset(new Dataset(nrow)); ret->CreateValid( reinterpret_cast(reference)); } OMP_INIT_EX(); #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) for (int i = 0; i < static_cast(ncol_ptr - 1); ++i) { OMP_LOOP_EX_BEGIN(); const int tid = omp_get_thread_num(); int feature_idx = ret->InnerFeatureIndex(i); if (feature_idx < 0) { continue; } int group = ret->Feature2Group(feature_idx); int sub_feature = ret->Feature2SubFeature(feature_idx); CSC_RowIterator col_it(col_ptr, col_ptr_type, indices, data, data_type, ncol_ptr, nelem, i); auto bin_mapper = ret->FeatureBinMapper(feature_idx); if (bin_mapper->GetDefaultBin() == bin_mapper->GetMostFreqBin()) { int row_idx = 0; while (row_idx < nrow) { auto pair = col_it.NextNonZero(); row_idx = pair.first; // no more data if (row_idx < 0) { break; } ret->PushOneData(tid, row_idx, group, feature_idx, sub_feature, pair.second); } } else { for (int row_idx = 0; row_idx < nrow; ++row_idx) { auto val = col_it.Get(row_idx); ret->PushOneData(tid, row_idx, group, feature_idx, sub_feature, val); } } OMP_LOOP_EX_END(); } OMP_THROW_EX(); ret->FinishLoad(); *out = ret.release(); API_END(); } int LGBM_DatasetCreateFromArrow(int64_t n_chunks, const struct ArrowArray* chunks, const struct ArrowSchema* schema, const char* parameters, const DatasetHandle reference, DatasetHandle *out) { API_BEGIN(); auto param = Config::Str2Map(parameters); Config config; config.Set(param); OMP_SET_NUM_THREADS(config.num_threads); std::unique_ptr ret; // Prepare the Arrow data ArrowTable table(n_chunks, chunks, schema); // Initialize the dataset if (reference == nullptr) { // If there is no reference dataset, we first sample indices auto sample_indices = CreateSampleIndices(static_cast(table.get_num_rows()), config); auto sample_count = static_cast(sample_indices.size()); std::vector> sample_values(table.get_num_columns()); std::vector> sample_idx(table.get_num_columns()); // Then, we obtain sample values by parallelizing across columns OMP_INIT_EX(); #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) for (int64_t j = 0; j < table.get_num_columns(); ++j) { OMP_LOOP_EX_BEGIN(); // Values need to be copied from the record batches. sample_values[j].reserve(sample_indices.size()); sample_idx[j].reserve(sample_indices.size()); // The chunks are iterated over in the inner loop as columns can be treated independently. int last_idx = 0; int i = 0; auto it = table.get_column(j).begin(); for (auto idx : sample_indices) { std::advance(it, idx - last_idx); auto v = *it; if (std::fabs(v) > kZeroThreshold || std::isnan(v)) { sample_values[j].emplace_back(v); sample_idx[j].emplace_back(i); } last_idx = idx; i++; } OMP_LOOP_EX_END(); } OMP_THROW_EX(); // Finally, we initialize a loader from the sampled values DatasetLoader loader(config, nullptr, 1, nullptr); ret.reset(loader.ConstructFromSampleData(Vector2Ptr(&sample_values).data(), Vector2Ptr(&sample_idx).data(), table.get_num_columns(), VectorSize(sample_values).data(), sample_count, table.get_num_rows(), table.get_num_rows())); } else { ret.reset(new Dataset(static_cast(table.get_num_rows()))); ret->CreateValid(reinterpret_cast(reference)); if (ret->has_raw()) { ret->ResizeRaw(static_cast(table.get_num_rows())); } } // After sampling and properly initializing all bins, we can add our data to the dataset. Here, // we parallelize across rows. OMP_INIT_EX(); #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) for (int64_t j = 0; j < table.get_num_columns(); ++j) { OMP_LOOP_EX_BEGIN(); const int tid = omp_get_thread_num(); data_size_t idx = 0; auto column = table.get_column(j); for (auto it = column.begin(), end = column.end(); it != end; ++it) { ret->PushOneValue(tid, idx++, j, *it); } OMP_LOOP_EX_END(); } OMP_THROW_EX(); ret->FinishLoad(); *out = ret.release(); API_END(); } int LGBM_DatasetGetSubset( const DatasetHandle handle, const int32_t* used_row_indices, int32_t num_used_row_indices, const char* parameters, DatasetHandle* out) { API_BEGIN(); auto param = Config::Str2Map(parameters); Config config; config.Set(param); OMP_SET_NUM_THREADS(config.num_threads); auto full_dataset = reinterpret_cast(handle); CHECK_GT(num_used_row_indices, 0); const int32_t lower = 0; const int32_t upper = full_dataset->num_data() - 1; CheckElementsIntervalClosed(used_row_indices, lower, upper, num_used_row_indices, "Used indices of subset"); if (!std::is_sorted(used_row_indices, used_row_indices + num_used_row_indices)) { Log::Fatal("used_row_indices should be sorted in Subset"); } auto ret = std::unique_ptr(new Dataset(num_used_row_indices)); ret->CopyFeatureMapperFrom(full_dataset); ret->CopySubrow(full_dataset, used_row_indices, num_used_row_indices, true); *out = ret.release(); API_END(); } int LGBM_DatasetSetFeatureNames( DatasetHandle handle, const char** feature_names, int num_feature_names) { API_BEGIN(); auto dataset = reinterpret_cast(handle); std::vector feature_names_str; for (int i = 0; i < num_feature_names; ++i) { feature_names_str.emplace_back(feature_names[i]); } dataset->set_feature_names(feature_names_str); API_END(); } int LGBM_DatasetGetFeatureNames( DatasetHandle handle, const int len, int* num_feature_names, const size_t buffer_len, size_t* out_buffer_len, char** feature_names) { API_BEGIN(); *out_buffer_len = 0; auto dataset = reinterpret_cast(handle); auto inside_feature_name = dataset->feature_names(); *num_feature_names = static_cast(inside_feature_name.size()); for (int i = 0; i < *num_feature_names; ++i) { if (i < len) { std::memcpy(feature_names[i], inside_feature_name[i].c_str(), std::min(inside_feature_name[i].size() + 1, buffer_len)); feature_names[i][buffer_len - 1] = '\0'; } *out_buffer_len = std::max(inside_feature_name[i].size() + 1, *out_buffer_len); } API_END(); } #ifdef _MSC_VER #pragma warning(disable : 4702) #endif int LGBM_DatasetFree(DatasetHandle handle) { API_BEGIN(); delete reinterpret_cast(handle); API_END(); } int LGBM_DatasetSaveBinary(DatasetHandle handle, const char* filename) { API_BEGIN(); auto dataset = reinterpret_cast(handle); dataset->SaveBinaryFile(filename); API_END(); } int LGBM_DatasetSerializeReferenceToBinary(DatasetHandle handle, ByteBufferHandle* out, int32_t* out_len) { API_BEGIN(); auto dataset = reinterpret_cast(handle); std::unique_ptr ret; ret.reset(new LightGBM::ByteBuffer()); dataset->SerializeReference(ret.get()); *out_len = static_cast(ret->GetSize()); *out = ret.release(); API_END(); } int LGBM_DatasetDumpText(DatasetHandle handle, const char* filename) { API_BEGIN(); auto dataset = reinterpret_cast(handle); dataset->DumpTextFile(filename); API_END(); } int LGBM_DatasetSetField(DatasetHandle handle, const char* field_name, const void* field_data, int num_element, int type) { API_BEGIN(); auto dataset = reinterpret_cast(handle); bool is_success = false; if (type == C_API_DTYPE_FLOAT32) { is_success = dataset->SetFloatField(field_name, reinterpret_cast(field_data), static_cast(num_element)); } else if (type == C_API_DTYPE_INT32) { is_success = dataset->SetIntField(field_name, reinterpret_cast(field_data), static_cast(num_element)); } else if (type == C_API_DTYPE_FLOAT64) { is_success = dataset->SetDoubleField(field_name, reinterpret_cast(field_data), static_cast(num_element)); } if (!is_success) { Log::Fatal("Input data type error or field not found"); } API_END(); } int LGBM_DatasetSetFieldFromArrow(DatasetHandle handle, const char* field_name, int64_t n_chunks, const struct ArrowArray* chunks, const struct ArrowSchema* schema) { API_BEGIN(); auto dataset = reinterpret_cast(handle); ArrowChunkedArray ca(n_chunks, chunks, schema); auto is_success = dataset->SetFieldFromArrow(field_name, ca); if (!is_success) { Log::Fatal("Input field is not supported"); } API_END(); } int LGBM_DatasetGetField(DatasetHandle handle, const char* field_name, int* out_len, const void** out_ptr, int* out_type) { API_BEGIN(); auto dataset = reinterpret_cast(handle); bool is_success = false; if (dataset->GetFloatField(field_name, out_len, reinterpret_cast(out_ptr))) { *out_type = C_API_DTYPE_FLOAT32; is_success = true; } else if (dataset->GetIntField(field_name, out_len, reinterpret_cast(out_ptr))) { *out_type = C_API_DTYPE_INT32; is_success = true; } else if (dataset->GetDoubleField(field_name, out_len, reinterpret_cast(out_ptr))) { *out_type = C_API_DTYPE_FLOAT64; is_success = true; } if (!is_success) { Log::Fatal("Field not found"); } if (*out_ptr == nullptr) { *out_len = 0; } API_END(); } int LGBM_DatasetUpdateParamChecking(const char* old_parameters, const char* new_parameters) { API_BEGIN(); auto old_param = Config::Str2Map(old_parameters); Config old_config; old_config.Set(old_param); auto new_param = Config::Str2Map(new_parameters); Booster::CheckDatasetResetConfig(old_config, new_param); API_END(); } int LGBM_DatasetGetNumData(DatasetHandle handle, int* out) { API_BEGIN(); auto dataset = reinterpret_cast(handle); *out = dataset->num_data(); API_END(); } int LGBM_DatasetGetNumFeature(DatasetHandle handle, int* out) { API_BEGIN(); auto dataset = reinterpret_cast(handle); *out = dataset->num_total_features(); API_END(); } int LGBM_DatasetGetFeatureNumBin(DatasetHandle handle, int feature, int* out) { API_BEGIN(); auto dataset = reinterpret_cast(handle); int num_features = dataset->num_total_features(); if (feature < 0 || feature >= num_features) { Log::Fatal("Tried to retrieve number of bins for feature index %d, " "but the valid feature indices are [0, %d].", feature, num_features - 1); } int inner_idx = dataset->InnerFeatureIndex(feature); if (inner_idx >= 0) { *out = dataset->FeatureNumBin(inner_idx); } else { *out = 0; } API_END(); } int LGBM_DatasetAddFeaturesFrom(DatasetHandle target, DatasetHandle source) { API_BEGIN(); auto target_d = reinterpret_cast(target); auto source_d = reinterpret_cast(source); target_d->AddFeaturesFrom(source_d); API_END(); } // ---- start of booster int LGBM_BoosterCreate(const DatasetHandle train_data, const char* parameters, BoosterHandle* out) { API_BEGIN(); const Dataset* p_train_data = reinterpret_cast(train_data); auto ret = std::unique_ptr(new Booster(p_train_data, parameters)); *out = ret.release(); API_END(); } int LGBM_BoosterCreateFromModelfile( const char* filename, int* out_num_iterations, BoosterHandle* out) { API_BEGIN(); auto ret = std::unique_ptr(new Booster(filename)); *out_num_iterations = ret->GetBoosting()->GetCurrentIteration(); *out = ret.release(); API_END(); } int LGBM_BoosterLoadModelFromString( const char* model_str, int* out_num_iterations, BoosterHandle* out) { API_BEGIN(); auto ret = std::unique_ptr(new Booster(nullptr)); ret->LoadModelFromString(model_str); *out_num_iterations = ret->GetBoosting()->GetCurrentIteration(); *out = ret.release(); API_END(); } int LGBM_BoosterGetLoadedParam( BoosterHandle handle, int64_t buffer_len, int64_t* out_len, char* out_str) { API_BEGIN(); Booster* ref_booster = reinterpret_cast(handle); std::string params = ref_booster->GetBoosting()->GetLoadedParam(); *out_len = static_cast(params.size()) + 1; if (*out_len <= buffer_len) { std::memcpy(out_str, params.c_str(), *out_len); } API_END(); } #ifdef _MSC_VER #pragma warning(disable : 4702) #endif int LGBM_BoosterFree(BoosterHandle handle) { API_BEGIN(); delete reinterpret_cast(handle); API_END(); } int LGBM_BoosterShuffleModels(BoosterHandle handle, int start_iter, int end_iter) { API_BEGIN(); Booster* ref_booster = reinterpret_cast(handle); ref_booster->ShuffleModels(start_iter, end_iter); API_END(); } int LGBM_BoosterMerge(BoosterHandle handle, BoosterHandle other_handle) { API_BEGIN(); Booster* ref_booster = reinterpret_cast(handle); Booster* ref_other_booster = reinterpret_cast(other_handle); ref_booster->MergeFrom(ref_other_booster); API_END(); } int LGBM_BoosterAddValidData(BoosterHandle handle, const DatasetHandle valid_data) { API_BEGIN(); Booster* ref_booster = reinterpret_cast(handle); const Dataset* p_dataset = reinterpret_cast(valid_data); ref_booster->AddValidData(p_dataset); API_END(); } int LGBM_BoosterResetTrainingData(BoosterHandle handle, const DatasetHandle train_data) { API_BEGIN(); Booster* ref_booster = reinterpret_cast(handle); const Dataset* p_dataset = reinterpret_cast(train_data); ref_booster->ResetTrainingData(p_dataset); API_END(); } int LGBM_BoosterResetParameter(BoosterHandle handle, const char* parameters) { API_BEGIN(); Booster* ref_booster = reinterpret_cast(handle); ref_booster->ResetConfig(parameters); API_END(); } int LGBM_BoosterGetNumClasses(BoosterHandle handle, int* out_len) { API_BEGIN(); Booster* ref_booster = reinterpret_cast(handle); *out_len = ref_booster->GetBoosting()->NumberOfClasses(); API_END(); } int LGBM_BoosterGetLinear(BoosterHandle handle, int* out) { API_BEGIN(); Booster* ref_booster = reinterpret_cast(handle); if (ref_booster->GetBoosting()->IsLinear()) { *out = 1; } else { *out = 0; } API_END(); } int LGBM_BoosterRefit(BoosterHandle handle, const int32_t* leaf_preds, int32_t nrow, int32_t ncol) { API_BEGIN(); Booster* ref_booster = reinterpret_cast(handle); ref_booster->Refit(leaf_preds, nrow, ncol); API_END(); } int LGBM_BoosterUpdateOneIter(BoosterHandle handle, int* produced_empty_tree) { API_BEGIN(); Booster* ref_booster = reinterpret_cast(handle); if (ref_booster->TrainOneIter()) { *produced_empty_tree = 1; } else { *produced_empty_tree = 0; } API_END(); } int LGBM_BoosterUpdateOneIterCustom(BoosterHandle handle, const float* grad, const float* hess, int* produced_empty_tree) { API_BEGIN(); #ifdef SCORE_T_USE_DOUBLE (void) handle; // UNUSED VARIABLE (void) grad; // UNUSED VARIABLE (void) hess; // UNUSED VARIABLE (void) produced_empty_tree; // UNUSED VARIABLE Log::Fatal("Don't support custom loss function when SCORE_T_USE_DOUBLE is enabled"); #else Booster* ref_booster = reinterpret_cast(handle); if (ref_booster->TrainOneIter(grad, hess)) { *produced_empty_tree = 1; } else { *produced_empty_tree = 0; } #endif API_END(); } int LGBM_BoosterRollbackOneIter(BoosterHandle handle) { API_BEGIN(); Booster* ref_booster = reinterpret_cast(handle); ref_booster->RollbackOneIter(); API_END(); } int LGBM_BoosterGetCurrentIteration(BoosterHandle handle, int* out_iteration) { API_BEGIN(); Booster* ref_booster = reinterpret_cast(handle); *out_iteration = ref_booster->GetBoosting()->GetCurrentIteration(); API_END(); } int LGBM_BoosterNumModelPerIteration(BoosterHandle handle, int* out_tree_per_iteration) { API_BEGIN(); Booster* ref_booster = reinterpret_cast(handle); *out_tree_per_iteration = ref_booster->GetBoosting()->NumModelPerIteration(); API_END(); } int LGBM_BoosterNumberOfTotalModel(BoosterHandle handle, int* out_models) { API_BEGIN(); Booster* ref_booster = reinterpret_cast(handle); *out_models = ref_booster->GetBoosting()->NumberOfTotalModel(); API_END(); } int LGBM_BoosterGetEvalCounts(BoosterHandle handle, int* out_len) { API_BEGIN(); Booster* ref_booster = reinterpret_cast(handle); *out_len = ref_booster->GetEvalCounts(); API_END(); } int LGBM_BoosterGetEvalNames(BoosterHandle handle, const int len, int* out_len, const size_t buffer_len, size_t* out_buffer_len, char** out_strs) { API_BEGIN(); Booster* ref_booster = reinterpret_cast(handle); *out_len = ref_booster->GetEvalNames(out_strs, len, buffer_len, out_buffer_len); API_END(); } int LGBM_BoosterGetFeatureNames(BoosterHandle handle, const int len, int* out_len, const size_t buffer_len, size_t* out_buffer_len, char** out_strs) { API_BEGIN(); Booster* ref_booster = reinterpret_cast(handle); *out_len = ref_booster->GetFeatureNames(out_strs, len, buffer_len, out_buffer_len); API_END(); } int LGBM_BoosterGetNumFeature(BoosterHandle handle, int* out_len) { API_BEGIN(); Booster* ref_booster = reinterpret_cast(handle); *out_len = ref_booster->GetBoosting()->MaxFeatureIdx() + 1; API_END(); } int LGBM_BoosterGetEval(BoosterHandle handle, int data_idx, int* out_len, double* out_results) { API_BEGIN(); Booster* ref_booster = reinterpret_cast(handle); auto boosting = ref_booster->GetBoosting(); auto result_buf = boosting->GetEvalAt(data_idx); *out_len = static_cast(result_buf.size()); for (size_t i = 0; i < result_buf.size(); ++i) { (out_results)[i] = static_cast(result_buf[i]); } API_END(); } int LGBM_BoosterGetNumPredict(BoosterHandle handle, int data_idx, int64_t* out_len) { API_BEGIN(); auto boosting = reinterpret_cast(handle)->GetBoosting(); *out_len = boosting->GetNumPredictAt(data_idx); API_END(); } int LGBM_BoosterGetPredict(BoosterHandle handle, int data_idx, int64_t* out_len, double* out_result) { API_BEGIN(); Booster* ref_booster = reinterpret_cast(handle); ref_booster->GetPredictAt(data_idx, out_result, out_len); API_END(); } int LGBM_BoosterPredictForFile(BoosterHandle handle, const char* data_filename, int data_has_header, int predict_type, int start_iteration, int num_iteration, const char* parameter, const char* result_filename) { API_BEGIN(); auto param = Config::Str2Map(parameter); Config config; config.Set(param); OMP_SET_NUM_THREADS(config.num_threads); Booster* ref_booster = reinterpret_cast(handle); ref_booster->Predict(start_iteration, num_iteration, predict_type, data_filename, data_has_header, config, result_filename); API_END(); } int LGBM_BoosterCalcNumPredict(BoosterHandle handle, int num_row, int predict_type, int start_iteration, int num_iteration, int64_t* out_len) { API_BEGIN(); Booster* ref_booster = reinterpret_cast(handle); *out_len = static_cast(num_row) * ref_booster->GetBoosting()->NumPredictOneRow(start_iteration, num_iteration, predict_type == C_API_PREDICT_LEAF_INDEX, predict_type == C_API_PREDICT_CONTRIB); API_END(); } // Naming: In future versions of LightGBM, public API named around `FastConfig` should be made named around // `SingleRowPredictor`, because it is specific to single row prediction, and doesn't actually hold only config. // For now this is kept as `FastConfig` for backwards compatibility. // At the same time, one should consider removing the old non-fast single row public API that stores its Predictor // in the Booster, because that will enable removing these Predictors from the Booster, and associated initialization // code. int LGBM_FastConfigFree(FastConfigHandle fastConfig) { API_BEGIN(); delete reinterpret_cast(fastConfig); API_END(); } int LGBM_BoosterPredictForCSR(BoosterHandle handle, const void* indptr, int indptr_type, const int32_t* indices, const void* data, int data_type, int64_t nindptr, int64_t nelem, int64_t num_col, int predict_type, int start_iteration, int num_iteration, const char* parameter, int64_t* out_len, double* out_result) { API_BEGIN(); if (num_col <= 0) { Log::Fatal("The number of columns should be greater than zero."); } else if (num_col >= INT32_MAX) { Log::Fatal("The number of columns should be smaller than INT32_MAX."); } auto param = Config::Str2Map(parameter); Config config; config.Set(param); OMP_SET_NUM_THREADS(config.num_threads); Booster* ref_booster = reinterpret_cast(handle); auto get_row_fun = RowFunctionFromCSR(indptr, indptr_type, indices, data, data_type, nindptr, nelem); int nrow = static_cast(nindptr - 1); ref_booster->Predict(start_iteration, num_iteration, predict_type, nrow, static_cast(num_col), get_row_fun, config, out_result, out_len); API_END(); } int LGBM_BoosterPredictSparseOutput(BoosterHandle handle, const void* indptr, int indptr_type, const int32_t* indices, const void* data, int data_type, int64_t nindptr, int64_t nelem, int64_t num_col_or_row, int predict_type, int start_iteration, int num_iteration, const char* parameter, int matrix_type, int64_t* out_len, void** out_indptr, int32_t** out_indices, void** out_data) { API_BEGIN(); Booster* ref_booster = reinterpret_cast(handle); auto param = Config::Str2Map(parameter); Config config; config.Set(param); OMP_SET_NUM_THREADS(config.num_threads); if (matrix_type == C_API_MATRIX_TYPE_CSR) { if (num_col_or_row <= 0) { Log::Fatal("The number of columns should be greater than zero."); } else if (num_col_or_row >= INT32_MAX) { Log::Fatal("The number of columns should be smaller than INT32_MAX."); } auto get_row_fun = RowFunctionFromCSR(indptr, indptr_type, indices, data, data_type, nindptr, nelem); int64_t nrow = nindptr - 1; ref_booster->PredictSparseCSR(start_iteration, num_iteration, predict_type, nrow, static_cast(num_col_or_row), get_row_fun, config, out_len, out_indptr, indptr_type, out_indices, out_data, data_type); } else if (matrix_type == C_API_MATRIX_TYPE_CSC) { int num_threads = OMP_NUM_THREADS(); int ncol = static_cast(nindptr - 1); std::vector> iterators(num_threads, std::vector()); for (int i = 0; i < num_threads; ++i) { for (int j = 0; j < ncol; ++j) { iterators[i].emplace_back(indptr, indptr_type, indices, data, data_type, nindptr, nelem, j); } } std::function>(int64_t row_idx)> get_row_fun = [&iterators, ncol](int64_t i) { std::vector> one_row; one_row.reserve(ncol); const int tid = omp_get_thread_num(); for (int j = 0; j < ncol; ++j) { auto val = iterators[tid][j].Get(static_cast(i)); if (std::fabs(val) > kZeroThreshold || std::isnan(val)) { one_row.emplace_back(j, val); } } return one_row; }; ref_booster->PredictSparseCSC(start_iteration, num_iteration, predict_type, num_col_or_row, ncol, get_row_fun, config, out_len, out_indptr, indptr_type, out_indices, out_data, data_type); } else { Log::Fatal("Unknown matrix type in LGBM_BoosterPredictSparseOutput"); } API_END(); } int LGBM_BoosterFreePredictSparse(void* indptr, int32_t* indices, void* data, int indptr_type, int data_type) { API_BEGIN(); if (indptr_type == C_API_DTYPE_INT32) { delete[] reinterpret_cast(indptr); } else if (indptr_type == C_API_DTYPE_INT64) { delete[] reinterpret_cast(indptr); } else { Log::Fatal("Unknown indptr type in LGBM_BoosterFreePredictSparse"); } delete[] indices; if (data_type == C_API_DTYPE_FLOAT32) { delete[] reinterpret_cast(data); } else if (data_type == C_API_DTYPE_FLOAT64) { delete[] reinterpret_cast(data); } else { Log::Fatal("Unknown data type in LGBM_BoosterFreePredictSparse"); } API_END(); } int LGBM_BoosterPredictForCSRSingleRow(BoosterHandle handle, const void* indptr, int indptr_type, const int32_t* indices, const void* data, int data_type, int64_t nindptr, int64_t nelem, int64_t num_col, int predict_type, int start_iteration, int num_iteration, const char* parameter, int64_t* out_len, double* out_result) { API_BEGIN(); if (num_col <= 0) { Log::Fatal("The number of columns should be greater than zero."); } else if (num_col >= INT32_MAX) { Log::Fatal("The number of columns should be smaller than INT32_MAX."); } auto param = Config::Str2Map(parameter); Config config; config.Set(param); OMP_SET_NUM_THREADS(config.num_threads); Booster* ref_booster = reinterpret_cast(handle); auto get_row_fun = RowFunctionFromCSR(indptr, indptr_type, indices, data, data_type, nindptr, nelem); ref_booster->SetSingleRowPredictorInner(start_iteration, num_iteration, predict_type, config); ref_booster->PredictSingleRow(predict_type, static_cast(num_col), get_row_fun, config, out_result, out_len); API_END(); } int LGBM_BoosterPredictForCSRSingleRowFastInit(BoosterHandle handle, const int predict_type, const int start_iteration, const int num_iteration, const int data_type, const int64_t num_col, const char* parameter, FastConfigHandle *out_fastConfig) { API_BEGIN(); if (num_col <= 0) { Log::Fatal("The number of columns should be greater than zero."); } else if (num_col >= INT32_MAX) { Log::Fatal("The number of columns should be smaller than INT32_MAX."); } Booster* ref_booster = reinterpret_cast(handle); std::unique_ptr single_row_predictor = ref_booster->InitSingleRowPredictor(start_iteration, num_iteration, predict_type, data_type, static_cast(num_col), parameter); OMP_SET_NUM_THREADS(single_row_predictor->config.num_threads); *out_fastConfig = single_row_predictor.release(); API_END(); } int LGBM_BoosterPredictForCSRSingleRowFast(FastConfigHandle fastConfig_handle, const void* indptr, const int indptr_type, const int32_t* indices, const void* data, const int64_t nindptr, const int64_t nelem, int64_t* out_len, double* out_result) { API_BEGIN(); SingleRowPredictor *single_row_predictor = reinterpret_cast(fastConfig_handle); auto get_row_fun = RowFunctionFromCSR(indptr, indptr_type, indices, data, single_row_predictor->data_type, nindptr, nelem); single_row_predictor->Predict(get_row_fun, out_result, out_len); API_END(); } int LGBM_BoosterPredictForCSC(BoosterHandle handle, const void* col_ptr, int col_ptr_type, const int32_t* indices, const void* data, int data_type, int64_t ncol_ptr, int64_t nelem, int64_t num_row, int predict_type, int start_iteration, int num_iteration, const char* parameter, int64_t* out_len, double* out_result) { API_BEGIN(); Booster* ref_booster = reinterpret_cast(handle); auto param = Config::Str2Map(parameter); Config config; config.Set(param); OMP_SET_NUM_THREADS(config.num_threads); int num_threads = OMP_NUM_THREADS(); int ncol = static_cast(ncol_ptr - 1); std::vector> iterators(num_threads, std::vector()); for (int i = 0; i < num_threads; ++i) { for (int j = 0; j < ncol; ++j) { iterators[i].emplace_back(col_ptr, col_ptr_type, indices, data, data_type, ncol_ptr, nelem, j); } } std::function>(int row_idx)> get_row_fun = [&iterators, ncol](int i) { std::vector> one_row; one_row.reserve(ncol); const int tid = omp_get_thread_num(); for (int j = 0; j < ncol; ++j) { auto val = iterators[tid][j].Get(i); if (std::fabs(val) > kZeroThreshold || std::isnan(val)) { one_row.emplace_back(j, val); } } return one_row; }; ref_booster->Predict(start_iteration, num_iteration, predict_type, static_cast(num_row), ncol, get_row_fun, config, out_result, out_len); API_END(); } int LGBM_BoosterValidateFeatureNames(BoosterHandle handle, const char** data_names, int data_num_features) { API_BEGIN(); int booster_num_features; size_t out_buffer_len; LGBM_BoosterGetFeatureNames(handle, 0, &booster_num_features, 0, &out_buffer_len, nullptr); if (booster_num_features != data_num_features) { Log::Fatal("Model was trained on %d features, but got %d input features to predict.", booster_num_features, data_num_features); } std::vector> tmp_names(booster_num_features, std::vector(out_buffer_len)); std::vector booster_names = Vector2Ptr(&tmp_names); LGBM_BoosterGetFeatureNames(handle, data_num_features, &booster_num_features, out_buffer_len, &out_buffer_len, booster_names.data()); for (int i = 0; i < booster_num_features; ++i) { if (strcmp(data_names[i], booster_names[i]) != 0) { Log::Fatal("Expected '%s' at position %d but found '%s'", booster_names[i], i, data_names[i]); } } API_END(); } int LGBM_BoosterPredictForMat(BoosterHandle handle, const void* data, int data_type, int32_t nrow, int32_t ncol, int is_row_major, int predict_type, int start_iteration, int num_iteration, const char* parameter, int64_t* out_len, double* out_result) { API_BEGIN(); auto param = Config::Str2Map(parameter); Config config; config.Set(param); OMP_SET_NUM_THREADS(config.num_threads); Booster* ref_booster = reinterpret_cast(handle); auto get_row_fun = RowPairFunctionFromDenseMatrix(data, nrow, ncol, data_type, is_row_major); ref_booster->Predict(start_iteration, num_iteration, predict_type, nrow, ncol, get_row_fun, config, out_result, out_len); API_END(); } int LGBM_BoosterPredictForMatSingleRow(BoosterHandle handle, const void* data, int data_type, int32_t ncol, int is_row_major, int predict_type, int start_iteration, int num_iteration, const char* parameter, int64_t* out_len, double* out_result) { API_BEGIN(); auto param = Config::Str2Map(parameter); Config config; config.Set(param); OMP_SET_NUM_THREADS(config.num_threads); Booster* ref_booster = reinterpret_cast(handle); auto get_row_fun = RowPairFunctionFromDenseMatrix(data, 1, ncol, data_type, is_row_major); ref_booster->SetSingleRowPredictorInner(start_iteration, num_iteration, predict_type, config); ref_booster->PredictSingleRow(predict_type, ncol, get_row_fun, config, out_result, out_len); API_END(); } int LGBM_BoosterPredictForMatSingleRowFastInit(BoosterHandle handle, const int predict_type, const int start_iteration, const int num_iteration, const int data_type, const int32_t ncol, const char* parameter, FastConfigHandle *out_fastConfig) { API_BEGIN(); Booster* ref_booster = reinterpret_cast(handle); std::unique_ptr single_row_predictor = ref_booster->InitSingleRowPredictor(predict_type, start_iteration, num_iteration, data_type, ncol, parameter); OMP_SET_NUM_THREADS(single_row_predictor->config.num_threads); *out_fastConfig = single_row_predictor.release(); API_END(); } int LGBM_BoosterPredictForMatSingleRowFast(FastConfigHandle fastConfig_handle, const void* data, int64_t* out_len, double* out_result) { API_BEGIN(); SingleRowPredictor *single_row_predictor = reinterpret_cast(fastConfig_handle); // Single row in row-major format: auto get_row_fun = RowPairFunctionFromDenseMatrix(data, 1, single_row_predictor->num_cols, single_row_predictor->data_type, 1); single_row_predictor->Predict(get_row_fun, out_result, out_len); API_END(); } int LGBM_BoosterPredictForMats(BoosterHandle handle, const void** data, int data_type, int32_t nrow, int32_t ncol, int predict_type, int start_iteration, int num_iteration, const char* parameter, int64_t* out_len, double* out_result) { API_BEGIN(); auto param = Config::Str2Map(parameter); Config config; config.Set(param); OMP_SET_NUM_THREADS(config.num_threads); Booster* ref_booster = reinterpret_cast(handle); auto get_row_fun = RowPairFunctionFromDenseRows(data, ncol, data_type); ref_booster->Predict(start_iteration, num_iteration, predict_type, nrow, ncol, get_row_fun, config, out_result, out_len); API_END(); } int LGBM_BoosterPredictForArrow(BoosterHandle handle, int64_t n_chunks, const struct ArrowArray* chunks, const struct ArrowSchema* schema, int predict_type, int start_iteration, int num_iteration, const char* parameter, int64_t* out_len, double* out_result) { API_BEGIN(); // Apply the configuration auto param = Config::Str2Map(parameter); Config config; config.Set(param); OMP_SET_NUM_THREADS(config.num_threads); // Set up chunked array and iterators for all columns ArrowTable table(n_chunks, chunks, schema); std::vector> its; its.reserve(table.get_num_columns()); for (int64_t j = 0; j < table.get_num_columns(); ++j) { its.emplace_back(table.get_column(j).begin()); } // Build row function auto num_columns = table.get_num_columns(); auto row_fn = [num_columns, &its] (int row_idx) { std::vector> result; result.reserve(num_columns); for (int64_t j = 0; j < num_columns; ++j) { result.emplace_back(static_cast(j), its[j][row_idx]); } return result; }; // Run prediction Booster* ref_booster = reinterpret_cast(handle); ref_booster->Predict(start_iteration, num_iteration, predict_type, static_cast(table.get_num_rows()), static_cast(table.get_num_columns()), row_fn, config, out_result, out_len); API_END(); } int LGBM_BoosterSaveModel(BoosterHandle handle, int start_iteration, int num_iteration, int feature_importance_type, const char* filename) { API_BEGIN(); Booster* ref_booster = reinterpret_cast(handle); ref_booster->SaveModelToFile(start_iteration, num_iteration, feature_importance_type, filename); API_END(); } int LGBM_BoosterSaveModelToString(BoosterHandle handle, int start_iteration, int num_iteration, int feature_importance_type, int64_t buffer_len, int64_t* out_len, char* out_str) { API_BEGIN(); Booster* ref_booster = reinterpret_cast(handle); std::string model = ref_booster->SaveModelToString( start_iteration, num_iteration, feature_importance_type); *out_len = static_cast(model.size()) + 1; if (*out_len <= buffer_len) { std::memcpy(out_str, model.c_str(), *out_len); } API_END(); } int LGBM_BoosterDumpModel(BoosterHandle handle, int start_iteration, int num_iteration, int feature_importance_type, int64_t buffer_len, int64_t* out_len, char* out_str) { API_BEGIN(); Booster* ref_booster = reinterpret_cast(handle); std::string model = ref_booster->DumpModel(start_iteration, num_iteration, feature_importance_type); *out_len = static_cast(model.size()) + 1; if (*out_len <= buffer_len) { std::memcpy(out_str, model.c_str(), *out_len); } API_END(); } int LGBM_BoosterGetLeafValue(BoosterHandle handle, int tree_idx, int leaf_idx, double* out_val) { API_BEGIN(); Booster* ref_booster = reinterpret_cast(handle); *out_val = static_cast(ref_booster->GetLeafValue(tree_idx, leaf_idx)); API_END(); } int LGBM_BoosterSetLeafValue(BoosterHandle handle, int tree_idx, int leaf_idx, double val) { API_BEGIN(); Booster* ref_booster = reinterpret_cast(handle); ref_booster->SetLeafValue(tree_idx, leaf_idx, val); API_END(); } int LGBM_BoosterFeatureImportance(BoosterHandle handle, int num_iteration, int importance_type, double* out_results) { API_BEGIN(); Booster* ref_booster = reinterpret_cast(handle); std::vector feature_importances = ref_booster->FeatureImportance(num_iteration, importance_type); for (size_t i = 0; i < feature_importances.size(); ++i) { (out_results)[i] = feature_importances[i]; } API_END(); } int LGBM_BoosterGetUpperBoundValue(BoosterHandle handle, double* out_results) { API_BEGIN(); Booster* ref_booster = reinterpret_cast(handle); double max_value = ref_booster->UpperBoundValue(); *out_results = max_value; API_END(); } int LGBM_BoosterGetLowerBoundValue(BoosterHandle handle, double* out_results) { API_BEGIN(); Booster* ref_booster = reinterpret_cast(handle); double min_value = ref_booster->LowerBoundValue(); *out_results = min_value; API_END(); } int LGBM_NetworkInit(const char* machines, int local_listen_port, int listen_time_out, int num_machines) { API_BEGIN(); Config config; config.machines = RemoveQuotationSymbol(std::string(machines)); config.local_listen_port = local_listen_port; config.num_machines = num_machines; config.time_out = listen_time_out; if (num_machines > 1) { Network::Init(config); } API_END(); } int LGBM_NetworkFree() { API_BEGIN(); Network::Dispose(); API_END(); } int LGBM_NetworkInitWithFunctions(int num_machines, int rank, void* reduce_scatter_ext_fun, void* allgather_ext_fun) { API_BEGIN(); if (num_machines > 1) { Network::Init(num_machines, rank, (ReduceScatterFunction)reduce_scatter_ext_fun, (AllgatherFunction)allgather_ext_fun); } API_END(); } int LGBM_SetMaxThreads(int num_threads) { API_BEGIN(); if (num_threads <= 0) { LGBM_MAX_NUM_THREADS = -1; } else { LGBM_MAX_NUM_THREADS = num_threads; } API_END(); } int LGBM_GetMaxThreads(int* out) { API_BEGIN(); *out = LGBM_MAX_NUM_THREADS; API_END(); } // ---- start of some help functions template std::function(int row_idx)> RowFunctionFromDenseMatrix_helper(const void* data, int num_row, int num_col, int is_row_major) { const T* data_ptr = reinterpret_cast(data); if (is_row_major) { return [=] (int row_idx) { std::vector ret(num_col); auto tmp_ptr = data_ptr + static_cast(num_col) * row_idx; for (int i = 0; i < num_col; ++i) { ret[i] = static_cast(*(tmp_ptr + i)); } return ret; }; } else { return [=] (int row_idx) { std::vector ret(num_col); for (int i = 0; i < num_col; ++i) { ret[i] = static_cast(*(data_ptr + static_cast(num_row) * i + row_idx)); } return ret; }; } } std::function(int row_idx)> RowFunctionFromDenseMatrix(const void* data, int num_row, int num_col, int data_type, int is_row_major) { if (data_type == C_API_DTYPE_FLOAT32) { return RowFunctionFromDenseMatrix_helper(data, num_row, num_col, is_row_major); } else if (data_type == C_API_DTYPE_FLOAT64) { return RowFunctionFromDenseMatrix_helper(data, num_row, num_col, is_row_major); } Log::Fatal("Unknown data type in RowFunctionFromDenseMatrix"); return nullptr; } std::function>(int row_idx)> RowPairFunctionFromDenseMatrix(const void* data, int num_row, int num_col, int data_type, int is_row_major) { auto inner_function = RowFunctionFromDenseMatrix(data, num_row, num_col, data_type, is_row_major); if (inner_function != nullptr) { return [inner_function] (int row_idx) { auto raw_values = inner_function(row_idx); std::vector> ret; ret.reserve(raw_values.size()); for (int i = 0; i < static_cast(raw_values.size()); ++i) { if (std::fabs(raw_values[i]) > kZeroThreshold || std::isnan(raw_values[i])) { ret.emplace_back(i, raw_values[i]); } } return ret; }; } return nullptr; } // data is array of pointers to individual rows std::function>(int row_idx)> RowPairFunctionFromDenseRows(const void** data, int num_col, int data_type) { return [=](int row_idx) { auto inner_function = RowFunctionFromDenseMatrix(data[row_idx], 1, num_col, data_type, /* is_row_major */ true); auto raw_values = inner_function(0); std::vector> ret; ret.reserve(raw_values.size()); for (int i = 0; i < static_cast(raw_values.size()); ++i) { if (std::fabs(raw_values[i]) > kZeroThreshold || std::isnan(raw_values[i])) { ret.emplace_back(i, raw_values[i]); } } return ret; }; } template std::function>(T idx)> RowFunctionFromCSR_helper(const void* indptr, const int32_t* indices, const void* data) { const T1* data_ptr = reinterpret_cast(data); const T2* ptr_indptr = reinterpret_cast(indptr); return [=] (T idx) { std::vector> ret; int64_t start = ptr_indptr[idx]; int64_t end = ptr_indptr[idx + 1]; if (end - start > 0) { ret.reserve(end - start); } for (int64_t i = start; i < end; ++i) { ret.emplace_back(indices[i], data_ptr[i]); } return ret; }; } template std::function>(T idx)> RowFunctionFromCSR(const void* indptr, int indptr_type, const int32_t* indices, const void* data, int data_type, int64_t , int64_t ) { if (data_type == C_API_DTYPE_FLOAT32) { if (indptr_type == C_API_DTYPE_INT32) { return RowFunctionFromCSR_helper(indptr, indices, data); } else if (indptr_type == C_API_DTYPE_INT64) { return RowFunctionFromCSR_helper(indptr, indices, data); } } else if (data_type == C_API_DTYPE_FLOAT64) { if (indptr_type == C_API_DTYPE_INT32) { return RowFunctionFromCSR_helper(indptr, indices, data); } else if (indptr_type == C_API_DTYPE_INT64) { return RowFunctionFromCSR_helper(indptr, indices, data); } } Log::Fatal("Unknown data type in RowFunctionFromCSR"); return nullptr; } template std::function(int idx)> IterateFunctionFromCSC_helper(const void* col_ptr, const int32_t* indices, const void* data, int col_idx) { const T1* data_ptr = reinterpret_cast(data); const T2* ptr_col_ptr = reinterpret_cast(col_ptr); int64_t start = ptr_col_ptr[col_idx]; int64_t end = ptr_col_ptr[col_idx + 1]; return [=] (int offset) { int64_t i = static_cast(start + offset); if (i >= end) { return std::make_pair(-1, 0.0); } int idx = static_cast(indices[i]); double val = static_cast(data_ptr[i]); return std::make_pair(idx, val); }; } std::function(int idx)> IterateFunctionFromCSC(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) { CHECK(col_idx < ncol_ptr && col_idx >= 0); if (data_type == C_API_DTYPE_FLOAT32) { if (col_ptr_type == C_API_DTYPE_INT32) { return IterateFunctionFromCSC_helper(col_ptr, indices, data, col_idx); } else if (col_ptr_type == C_API_DTYPE_INT64) { return IterateFunctionFromCSC_helper(col_ptr, indices, data, col_idx); } } else if (data_type == C_API_DTYPE_FLOAT64) { if (col_ptr_type == C_API_DTYPE_INT32) { return IterateFunctionFromCSC_helper(col_ptr, indices, data, col_idx); } else if (col_ptr_type == C_API_DTYPE_INT64) { return IterateFunctionFromCSC_helper(col_ptr, indices, data, col_idx); } } Log::Fatal("Unknown data type in CSC matrix"); return nullptr; } CSC_RowIterator::CSC_RowIterator(const void* col_ptr, int col_ptr_type, const int32_t* indices, const void* data, int data_type, int64_t ncol_ptr, int64_t nelem, int col_idx) { iter_fun_ = IterateFunctionFromCSC(col_ptr, col_ptr_type, indices, data, data_type, ncol_ptr, nelem, col_idx); } double CSC_RowIterator::Get(int idx) { while (idx > cur_idx_ && !is_end_) { auto ret = iter_fun_(nonzero_idx_); if (ret.first < 0) { is_end_ = true; break; } cur_idx_ = ret.first; cur_val_ = ret.second; ++nonzero_idx_; } if (idx == cur_idx_) { return cur_val_; } else { return 0.0f; } } std::pair CSC_RowIterator::NextNonZero() { if (!is_end_) { auto ret = iter_fun_(nonzero_idx_); ++nonzero_idx_; if (ret.first < 0) { is_end_ = true; } return ret; } else { return std::make_pair(-1, 0.0); } } ================================================ FILE: src/cuda/cuda_algorithms.cu ================================================ /*! * Copyright (c) 2021-2026 Microsoft Corporation. All rights reserved. * Copyright (c) 2021-2026 The LightGBM developers. All rights reserved. * Licensed under the MIT License. See LICENSE file in the project root for license information. * Modifications Copyright(C) 2023 Advanced Micro Devices, Inc. All rights reserved. */ #ifdef USE_CUDA #include #include #include namespace LightGBM { template __global__ void ShufflePrefixSumGlobalKernel(T* values, size_t len, T* block_prefix_sum_buffer) { __shared__ T shared_mem_buffer[WARPSIZE]; const size_t index = static_cast(threadIdx.x + blockIdx.x * blockDim.x); T value = 0; if (index < len) { value = values[index]; } const T prefix_sum_value = ShufflePrefixSum(value, shared_mem_buffer); values[index] = prefix_sum_value; if (threadIdx.x == blockDim.x - 1) { block_prefix_sum_buffer[blockIdx.x] = prefix_sum_value; } } template __global__ void ShufflePrefixSumGlobalReduceBlockKernel(T* block_prefix_sum_buffer, int num_blocks) { __shared__ T shared_mem_buffer[WARPSIZE]; const int num_blocks_per_thread = (num_blocks + GLOBAL_PREFIX_SUM_BLOCK_SIZE - 2) / (GLOBAL_PREFIX_SUM_BLOCK_SIZE - 1); int thread_block_start = threadIdx.x == 0 ? 0 : (threadIdx.x - 1) * num_blocks_per_thread; int thread_block_end = threadIdx.x == 0 ? 0 : min(thread_block_start + num_blocks_per_thread, num_blocks); T base = 0; for (int block_index = thread_block_start; block_index < thread_block_end; ++block_index) { base += block_prefix_sum_buffer[block_index]; } base = ShufflePrefixSum(base, shared_mem_buffer); thread_block_start = threadIdx.x == blockDim.x - 1 ? 0 : threadIdx.x * num_blocks_per_thread; thread_block_end = threadIdx.x == blockDim.x - 1 ? 0 : min(thread_block_start + num_blocks_per_thread, num_blocks); for (int block_index = thread_block_start + 1; block_index < thread_block_end; ++block_index) { block_prefix_sum_buffer[block_index] += block_prefix_sum_buffer[block_index - 1]; } for (int block_index = thread_block_start; block_index < thread_block_end; ++block_index) { block_prefix_sum_buffer[block_index] += base; } } template __global__ void ShufflePrefixSumGlobalAddBase(size_t len, const T* block_prefix_sum_buffer, T* values) { const T base = blockIdx.x == 0 ? 0 : block_prefix_sum_buffer[blockIdx.x - 1]; const size_t index = static_cast(threadIdx.x + blockIdx.x * blockDim.x); if (index < len) { values[index] += base; } } template void ShufflePrefixSumGlobal(T* values, size_t len, T* block_prefix_sum_buffer) { const int num_blocks = (static_cast(len) + GLOBAL_PREFIX_SUM_BLOCK_SIZE - 1) / GLOBAL_PREFIX_SUM_BLOCK_SIZE; ShufflePrefixSumGlobalKernel<<>>(values, len, block_prefix_sum_buffer); ShufflePrefixSumGlobalReduceBlockKernel<<<1, GLOBAL_PREFIX_SUM_BLOCK_SIZE>>>(block_prefix_sum_buffer, num_blocks); ShufflePrefixSumGlobalAddBase<<>>(len, block_prefix_sum_buffer, values); } template void ShufflePrefixSumGlobal(uint16_t* values, size_t len, uint16_t* block_prefix_sum_buffer); template void ShufflePrefixSumGlobal(uint32_t* values, size_t len, uint32_t* block_prefix_sum_buffer); template void ShufflePrefixSumGlobal(uint64_t* values, size_t len, uint64_t* block_prefix_sum_buffer); __global__ void BitonicArgSortItemsGlobalKernel(const double* scores, const int num_queries, const data_size_t* cuda_query_boundaries, data_size_t* out_indices) { const int query_index_start = static_cast(blockIdx.x) * BITONIC_SORT_QUERY_ITEM_BLOCK_SIZE; const int query_index_end = min(query_index_start + BITONIC_SORT_QUERY_ITEM_BLOCK_SIZE, num_queries); for (int query_index = query_index_start; query_index < query_index_end; ++query_index) { const data_size_t query_item_start = cuda_query_boundaries[query_index]; const data_size_t query_item_end = cuda_query_boundaries[query_index + 1]; const data_size_t num_items_in_query = query_item_end - query_item_start; BitonicArgSortDevice(scores + query_item_start, out_indices + query_item_start, num_items_in_query); __syncthreads(); } } void BitonicArgSortItemsGlobal( const double* scores, const int num_queries, const data_size_t* cuda_query_boundaries, data_size_t* out_indices) { const int num_blocks = (num_queries + BITONIC_SORT_QUERY_ITEM_BLOCK_SIZE - 1) / BITONIC_SORT_QUERY_ITEM_BLOCK_SIZE; BitonicArgSortItemsGlobalKernel<<>>( scores, num_queries, cuda_query_boundaries, out_indices); SynchronizeCUDADevice(__FILE__, __LINE__); } template __global__ void BlockReduceSum(T* block_buffer, const data_size_t num_blocks) { __shared__ T shared_buffer[WARPSIZE]; T thread_sum = 0; for (data_size_t block_index = static_cast(threadIdx.x); block_index < num_blocks; block_index += static_cast(blockDim.x)) { thread_sum += block_buffer[block_index]; } thread_sum = ShuffleReduceSum(thread_sum, shared_buffer, blockDim.x); if (threadIdx.x == 0) { block_buffer[0] = thread_sum; } } template __global__ void ShuffleReduceSumGlobalKernel(const VAL_T* values, const data_size_t num_value, REDUCE_T* block_buffer) { __shared__ REDUCE_T shared_buffer[WARPSIZE]; const data_size_t data_index = static_cast(blockIdx.x * blockDim.x + threadIdx.x); const REDUCE_T value = (data_index < num_value ? static_cast(values[data_index]) : 0.0f); const REDUCE_T reduce_value = ShuffleReduceSum(value, shared_buffer, blockDim.x); if (threadIdx.x == 0) { block_buffer[blockIdx.x] = reduce_value; } } template void ShuffleReduceSumGlobal(const VAL_T* values, size_t n, REDUCE_T* block_buffer) { const data_size_t num_value = static_cast(n); const data_size_t num_blocks = (num_value + GLOBAL_PREFIX_SUM_BLOCK_SIZE - 1) / GLOBAL_PREFIX_SUM_BLOCK_SIZE; ShuffleReduceSumGlobalKernel<<>>(values, num_value, block_buffer); BlockReduceSum<<<1, GLOBAL_PREFIX_SUM_BLOCK_SIZE>>>(block_buffer, num_blocks); } template void ShuffleReduceSumGlobal(const label_t* values, size_t n, double* block_buffer); template void ShuffleReduceSumGlobal(const double* values, size_t n, double* block_buffer); template __global__ void ShuffleReduceMinGlobalKernel(const VAL_T* values, const data_size_t num_value, REDUCE_T* block_buffer) { __shared__ REDUCE_T shared_buffer[WARPSIZE]; const data_size_t data_index = static_cast(blockIdx.x * blockDim.x + threadIdx.x); const REDUCE_T value = (data_index < num_value ? static_cast(values[data_index]) : 0.0f); const REDUCE_T reduce_value = ShuffleReduceMin(value, shared_buffer, blockDim.x); if (threadIdx.x == 0) { block_buffer[blockIdx.x] = reduce_value; } } template __global__ void ShuffleBlockReduceMin(T* block_buffer, const data_size_t num_blocks) { __shared__ T shared_buffer[WARPSIZE]; T thread_min = 0; for (data_size_t block_index = static_cast(threadIdx.x); block_index < num_blocks; block_index += static_cast(blockDim.x)) { const T value = block_buffer[block_index]; if (value < thread_min) { thread_min = value; } } thread_min = ShuffleReduceMin(thread_min, shared_buffer, blockDim.x); if (threadIdx.x == 0) { block_buffer[0] = thread_min; } } template void ShuffleReduceMinGlobal(const VAL_T* values, size_t n, REDUCE_T* block_buffer) { const data_size_t num_value = static_cast(n); const data_size_t num_blocks = (num_value + GLOBAL_PREFIX_SUM_BLOCK_SIZE - 1) / GLOBAL_PREFIX_SUM_BLOCK_SIZE; ShuffleReduceMinGlobalKernel<<>>(values, num_value, block_buffer); ShuffleBlockReduceMin<<<1, GLOBAL_PREFIX_SUM_BLOCK_SIZE>>>(block_buffer, num_blocks); } template void ShuffleReduceMinGlobal(const label_t* values, size_t n, double* block_buffer); template __global__ void ShuffleReduceDotProdGlobalKernel(const VAL_T* values1, const VAL_T* values2, const data_size_t num_value, REDUCE_T* block_buffer) { __shared__ REDUCE_T shared_buffer[WARPSIZE]; const data_size_t data_index = static_cast(blockIdx.x * blockDim.x + threadIdx.x); const REDUCE_T value1 = (data_index < num_value ? static_cast(values1[data_index]) : 0.0f); const REDUCE_T value2 = (data_index < num_value ? static_cast(values2[data_index]) : 0.0f); const REDUCE_T reduce_value = ShuffleReduceSum(value1 * value2, shared_buffer, blockDim.x); if (threadIdx.x == 0) { block_buffer[blockIdx.x] = reduce_value; } } template void ShuffleReduceDotProdGlobal(const VAL_T* values1, const VAL_T* values2, size_t n, REDUCE_T* block_buffer) { const data_size_t num_value = static_cast(n); const data_size_t num_blocks = (num_value + GLOBAL_PREFIX_SUM_BLOCK_SIZE - 1) / GLOBAL_PREFIX_SUM_BLOCK_SIZE; ShuffleReduceDotProdGlobalKernel<<>>(values1, values2, num_value, block_buffer); BlockReduceSum<<<1, GLOBAL_PREFIX_SUM_BLOCK_SIZE>>>(block_buffer, num_blocks); } template void ShuffleReduceDotProdGlobal(const label_t* values1, const label_t* values2, size_t n, double* block_buffer); template __global__ void GlobalInclusiveArgPrefixSumKernel( const INDEX_T* sorted_indices, const VAL_T* in_values, REDUCE_T* out_values, REDUCE_T* block_buffer, data_size_t num_data) { __shared__ REDUCE_T shared_buffer[WARPSIZE]; const data_size_t data_index = static_cast(threadIdx.x + blockIdx.x * blockDim.x); REDUCE_T value = static_cast(data_index < num_data ? in_values[sorted_indices[data_index]] : 0); __syncthreads(); value = ShufflePrefixSum(value, shared_buffer); if (data_index < num_data) { out_values[data_index] = value; } if (threadIdx.x == blockDim.x - 1) { block_buffer[blockIdx.x + 1] = value; } } template __global__ void GlobalInclusivePrefixSumReduceBlockKernel(T* block_buffer, data_size_t num_blocks) { __shared__ T shared_buffer[WARPSIZE]; T thread_sum = 0; const data_size_t num_blocks_per_thread = (num_blocks + static_cast(blockDim.x)) / static_cast(blockDim.x); const data_size_t thread_start_block_index = static_cast(threadIdx.x) * num_blocks_per_thread; const data_size_t thread_end_block_index = min(thread_start_block_index + num_blocks_per_thread, num_blocks + 1); for (data_size_t block_index = thread_start_block_index; block_index < thread_end_block_index; ++block_index) { thread_sum += block_buffer[block_index]; } ShufflePrefixSumExclusive(thread_sum, shared_buffer); for (data_size_t block_index = thread_start_block_index; block_index < thread_end_block_index; ++block_index) { block_buffer[block_index] += thread_sum; } } template __global__ void GlobalInclusivePrefixSumAddBlockBaseKernel(const T* block_buffer, T* values, data_size_t num_data) { const T block_sum_base = block_buffer[blockIdx.x]; const data_size_t data_index = static_cast(threadIdx.x + blockIdx.x * blockDim.x); if (data_index < num_data) { values[data_index] += block_sum_base; } } template void GlobalInclusiveArgPrefixSum(const INDEX_T* sorted_indices, const VAL_T* in_values, REDUCE_T* out_values, REDUCE_T* block_buffer, size_t n) { const data_size_t num_data = static_cast(n); const data_size_t num_blocks = (num_data + GLOBAL_PREFIX_SUM_BLOCK_SIZE - 1) / GLOBAL_PREFIX_SUM_BLOCK_SIZE; GlobalInclusiveArgPrefixSumKernel<<>>( sorted_indices, in_values, out_values, block_buffer, num_data); SynchronizeCUDADevice(__FILE__, __LINE__); GlobalInclusivePrefixSumReduceBlockKernel<<<1, GLOBAL_PREFIX_SUM_BLOCK_SIZE>>>( block_buffer, num_blocks); SynchronizeCUDADevice(__FILE__, __LINE__); GlobalInclusivePrefixSumAddBlockBaseKernel<<>>( block_buffer, out_values, num_data); SynchronizeCUDADevice(__FILE__, __LINE__); } template void GlobalInclusiveArgPrefixSum(const data_size_t* sorted_indices, const label_t* in_values, double* out_values, double* block_buffer, size_t n); template __global__ void BitonicArgSortGlobalKernel(const VAL_T* values, INDEX_T* indices, const int num_total_data) { const int thread_index = static_cast(threadIdx.x); const int low = static_cast(blockIdx.x * BITONIC_SORT_NUM_ELEMENTS); const bool outer_ascending = ASCENDING ? (blockIdx.x % 2 == 0) : (blockIdx.x % 2 == 1); const VAL_T* values_pointer = values + low; INDEX_T* indices_pointer = indices + low; const int num_data = min(BITONIC_SORT_NUM_ELEMENTS, num_total_data - low); __shared__ VAL_T shared_values[BITONIC_SORT_NUM_ELEMENTS]; __shared__ INDEX_T shared_indices[BITONIC_SORT_NUM_ELEMENTS]; if (thread_index < num_data) { shared_values[thread_index] = values_pointer[thread_index]; shared_indices[thread_index] = static_cast(thread_index + blockIdx.x * blockDim.x); } __syncthreads(); for (int depth = BITONIC_SORT_DEPTH - 1; depth >= 1; --depth) { const int segment_length = 1 << (BITONIC_SORT_DEPTH - depth); const int segment_index = thread_index / segment_length; const bool ascending = outer_ascending ? (segment_index % 2 == 0) : (segment_index % 2 == 1); const int num_total_segment = (num_data + segment_length - 1) / segment_length; { const int inner_depth = depth; const int inner_segment_length_half = 1 << (BITONIC_SORT_DEPTH - 1 - inner_depth); const int inner_segment_index_half = thread_index / inner_segment_length_half; const int offset = ((inner_segment_index_half >> 1) == num_total_segment - 1 && ascending == outer_ascending) ? (num_total_segment * segment_length - num_data) : 0; const int segment_start = segment_index * segment_length; if (inner_segment_index_half % 2 == 0) { if (thread_index >= offset + segment_start) { const int index_to_compare = thread_index + inner_segment_length_half - offset; const INDEX_T this_index = shared_indices[thread_index]; const INDEX_T other_index = shared_indices[index_to_compare]; const VAL_T this_value = shared_values[thread_index]; const VAL_T other_value = shared_values[index_to_compare]; if (index_to_compare < num_data && (this_value > other_value) == ascending) { shared_indices[thread_index] = other_index; shared_indices[index_to_compare] = this_index; shared_values[thread_index] = other_value; shared_values[index_to_compare] = this_value; } } } __syncthreads(); } for (int inner_depth = depth + 1; inner_depth < BITONIC_SORT_DEPTH; ++inner_depth) { const int inner_segment_length_half = 1 << (BITONIC_SORT_DEPTH - 1 - inner_depth); const int inner_segment_index_half = thread_index / inner_segment_length_half; if (inner_segment_index_half % 2 == 0) { const int index_to_compare = thread_index + inner_segment_length_half; const INDEX_T this_index = shared_indices[thread_index]; const INDEX_T other_index = shared_indices[index_to_compare]; const VAL_T this_value = shared_values[thread_index]; const VAL_T other_value = shared_values[index_to_compare]; if (index_to_compare < num_data && (this_value > other_value) == ascending) { shared_indices[thread_index] = other_index; shared_indices[index_to_compare] = this_index; shared_values[thread_index] = other_value; shared_values[index_to_compare] = this_value; } } __syncthreads(); } } if (thread_index < num_data) { indices_pointer[thread_index] = shared_indices[thread_index]; } } template __global__ void BitonicArgSortMergeKernel(const VAL_T* values, INDEX_T* indices, const int segment_length, const int len) { const int thread_index = static_cast(threadIdx.x + blockIdx.x * blockDim.x); const int segment_index = thread_index / segment_length; const bool ascending = ASCENDING ? (segment_index % 2 == 0) : (segment_index % 2 == 1); __shared__ VAL_T shared_values[BITONIC_SORT_NUM_ELEMENTS]; __shared__ INDEX_T shared_indices[BITONIC_SORT_NUM_ELEMENTS]; const int offset = static_cast(blockIdx.x * blockDim.x); const int local_len = min(BITONIC_SORT_NUM_ELEMENTS, len - offset); if (thread_index < len) { const INDEX_T index = indices[thread_index]; shared_values[threadIdx.x] = values[index]; shared_indices[threadIdx.x] = index; } __syncthreads(); int half_segment_length = BITONIC_SORT_NUM_ELEMENTS / 2; while (half_segment_length >= 1) { const int half_segment_index = static_cast(threadIdx.x) / half_segment_length; if (half_segment_index % 2 == 0) { const int index_to_compare = static_cast(threadIdx.x) + half_segment_length; const INDEX_T this_index = shared_indices[threadIdx.x]; const INDEX_T other_index = shared_indices[index_to_compare]; const VAL_T this_value = shared_values[threadIdx.x]; const VAL_T other_value = shared_values[index_to_compare]; if (index_to_compare < local_len && ((this_value > other_value) == ascending)) { shared_indices[threadIdx.x] = other_index; shared_indices[index_to_compare] = this_index; shared_values[threadIdx.x] = other_value; shared_values[index_to_compare] = this_value; } } __syncthreads(); half_segment_length >>= 1; } if (thread_index < len) { indices[thread_index] = shared_indices[threadIdx.x]; } } template __global__ void BitonicArgCompareKernel(const VAL_T* values, INDEX_T* indices, const int half_segment_length, const int outer_segment_length, const int len) { const int thread_index = static_cast(threadIdx.x + blockIdx.x * blockDim.x); const int segment_index = thread_index / outer_segment_length; const int half_segment_index = thread_index / half_segment_length; const bool ascending = ASCENDING ? (segment_index % 2 == 0) : (segment_index % 2 == 1); if (half_segment_index % 2 == 0) { const int num_total_segment = (len + outer_segment_length - 1) / outer_segment_length; if (BEGIN && (half_segment_index >> 1) == num_total_segment - 1 && ascending == ASCENDING) { const int offset = num_total_segment * outer_segment_length - len; const int segment_start = segment_index * outer_segment_length; if (thread_index >= offset + segment_start) { const int index_to_compare = thread_index + half_segment_length - offset; if (index_to_compare < len) { const INDEX_T this_index = indices[thread_index]; const INDEX_T other_index = indices[index_to_compare]; if ((values[this_index] > values[other_index]) == ascending) { indices[thread_index] = other_index; indices[index_to_compare] = this_index; } } } } else { const int index_to_compare = thread_index + half_segment_length; if (index_to_compare < len) { const INDEX_T this_index = indices[thread_index]; const INDEX_T other_index = indices[index_to_compare]; if ((values[this_index] > values[other_index]) == ascending) { indices[thread_index] = other_index; indices[index_to_compare] = this_index; } } } } } template void BitonicArgSortGlobalHelper(const VAL_T* values, INDEX_T* indices, const size_t len) { int max_depth = 1; int len_to_shift = static_cast(len) - 1; while (len_to_shift > 0) { ++max_depth; len_to_shift >>= 1; } const int num_blocks = (static_cast(len) + BITONIC_SORT_NUM_ELEMENTS - 1) / BITONIC_SORT_NUM_ELEMENTS; BitonicArgSortGlobalKernel<<>>(values, indices, static_cast(len)); SynchronizeCUDADevice(__FILE__, __LINE__); for (int depth = max_depth - 11; depth >= 1; --depth) { const int segment_length = (1 << (max_depth - depth)); int half_segment_length = (segment_length >> 1); { BitonicArgCompareKernel<<>>( values, indices, half_segment_length, segment_length, static_cast(len)); SynchronizeCUDADevice(__FILE__, __LINE__); half_segment_length >>= 1; } for (int inner_depth = depth + 1; inner_depth <= max_depth - 11; ++inner_depth) { BitonicArgCompareKernel<<>>( values, indices, half_segment_length, segment_length, static_cast(len)); SynchronizeCUDADevice(__FILE__, __LINE__); half_segment_length >>= 1; } BitonicArgSortMergeKernel<<>>( values, indices, segment_length, static_cast(len)); SynchronizeCUDADevice(__FILE__, __LINE__); } } template <> void BitonicArgSortGlobal(const double* values, data_size_t* indices, const size_t len) { BitonicArgSortGlobalHelper(values, indices, len); } template <> void BitonicArgSortGlobal(const double* values, data_size_t* indices, const size_t len) { BitonicArgSortGlobalHelper(values, indices, len); } template <> void BitonicArgSortGlobal(const label_t* values, data_size_t* indices, const size_t len) { BitonicArgSortGlobalHelper(values, indices, len); } template <> void BitonicArgSortGlobal(const data_size_t* values, int* indices, const size_t len) { BitonicArgSortGlobalHelper(values, indices, len); } } // namespace LightGBM #endif // USE_CUDA ================================================ FILE: src/cuda/cuda_utils.cpp ================================================ /*! * Copyright (c) 2021-2026 Microsoft Corporation. All rights reserved. * Copyright (c) 2021-2026 The LightGBM developers. All rights reserved. * Licensed under the MIT License. See LICENSE file in the project root for license information. */ #ifdef USE_CUDA #include #include namespace LightGBM { void SynchronizeCUDADevice(const char* file, const int line) { gpuAssert(cudaDeviceSynchronize(), file, line); } void SynchronizeCUDAStream(cudaStream_t cuda_stream, const char* file, const int line) { gpuAssert(cudaStreamSynchronize(cuda_stream), file, line); } void PrintLastCUDAError() { const char* error_name = cudaGetErrorName(cudaGetLastError()); Log::Fatal(error_name); } void SetCUDADevice(int gpu_device_id, const char* file, int line) { int cur_gpu_device_id = 0; CUDASUCCESS_OR_FATAL_OUTER(cudaGetDevice(&cur_gpu_device_id)); if (cur_gpu_device_id != gpu_device_id) { CUDASUCCESS_OR_FATAL_OUTER(cudaSetDevice(gpu_device_id)); } } int GetCUDADevice(const char* file, int line) { int cur_gpu_device_id = 0; CUDASUCCESS_OR_FATAL_OUTER(cudaGetDevice(&cur_gpu_device_id)); return cur_gpu_device_id; } cudaStream_t CUDAStreamCreate() { cudaStream_t cuda_stream; CUDASUCCESS_OR_FATAL(cudaStreamCreate(&cuda_stream)); return cuda_stream; } void CUDAStreamDestroy(cudaStream_t cuda_stream) { CUDASUCCESS_OR_FATAL(cudaStreamDestroy(cuda_stream)); } void NCCLGroupStart() { NCCLCHECK(ncclGroupStart()); } void NCCLGroupEnd() { NCCLCHECK(ncclGroupEnd()); } } // namespace LightGBM #endif // USE_CUDA ================================================ FILE: src/io/bin.cpp ================================================ /*! * Copyright (c) 2016-2026 Microsoft Corporation. All rights reserved. * Copyright (c) 2016-2026 The LightGBM developers. All rights reserved. * Licensed under the MIT License. See LICENSE file in the project root for license information. */ #include #include #include #include #include #include #include #include #include #include #include "dense_bin.hpp" #include "multi_val_dense_bin.hpp" #include "multi_val_sparse_bin.hpp" #include "sparse_bin.hpp" namespace LightGBM { BinMapper::BinMapper(): num_bin_(1), is_trivial_(true), bin_type_(BinType::NumericalBin) { bin_upper_bound_.clear(); bin_upper_bound_.push_back(std::numeric_limits::infinity()); } // deep copy function for BinMapper BinMapper::BinMapper(const BinMapper& other) { num_bin_ = other.num_bin_; missing_type_ = other.missing_type_; is_trivial_ = other.is_trivial_; sparse_rate_ = other.sparse_rate_; bin_type_ = other.bin_type_; if (bin_type_ == BinType::NumericalBin) { bin_upper_bound_ = other.bin_upper_bound_; } else { bin_2_categorical_ = other.bin_2_categorical_; categorical_2_bin_ = other.categorical_2_bin_; } min_val_ = other.min_val_; max_val_ = other.max_val_; default_bin_ = other.default_bin_; most_freq_bin_ = other.most_freq_bin_; } BinMapper::BinMapper(const void* memory) { CopyFrom(reinterpret_cast(memory)); } BinMapper::~BinMapper() { } bool NeedFilter(const std::vector& cnt_in_bin, int total_cnt, int filter_cnt, BinType bin_type) { if (bin_type == BinType::NumericalBin) { int sum_left = 0; for (size_t i = 0; i < cnt_in_bin.size() - 1; ++i) { sum_left += cnt_in_bin[i]; if (sum_left >= filter_cnt && total_cnt - sum_left >= filter_cnt) { return false; } } } else { if (cnt_in_bin.size() <= 2) { for (size_t i = 0; i < cnt_in_bin.size() - 1; ++i) { int sum_left = cnt_in_bin[i]; if (sum_left >= filter_cnt && total_cnt - sum_left >= filter_cnt) { return false; } } } else { return false; } } return true; } std::vector GreedyFindBin(const double* distinct_values, const int* counts, int num_distinct_values, int max_bin, size_t total_cnt, int min_data_in_bin) { std::vector bin_upper_bound; CHECK_GT(max_bin, 0); if (num_distinct_values <= max_bin) { bin_upper_bound.clear(); int cur_cnt_inbin = 0; for (int i = 0; i < num_distinct_values - 1; ++i) { cur_cnt_inbin += counts[i]; if (cur_cnt_inbin >= min_data_in_bin) { auto val = Common::GetDoubleUpperBound((distinct_values[i] + distinct_values[i + 1]) / 2.0); if (bin_upper_bound.empty() || !Common::CheckDoubleEqualOrdered(bin_upper_bound.back(), val)) { bin_upper_bound.push_back(val); cur_cnt_inbin = 0; } } } cur_cnt_inbin += counts[num_distinct_values - 1]; bin_upper_bound.push_back(std::numeric_limits::infinity()); } else { if (min_data_in_bin > 0) { max_bin = std::min(max_bin, static_cast(total_cnt / min_data_in_bin)); max_bin = std::max(max_bin, 1); } double mean_bin_size = static_cast(total_cnt) / max_bin; // mean size for one bin int rest_bin_cnt = max_bin; int rest_sample_cnt = static_cast(total_cnt); std::vector is_big_count_value(num_distinct_values, false); for (int i = 0; i < num_distinct_values; ++i) { if (counts[i] >= mean_bin_size) { is_big_count_value[i] = true; --rest_bin_cnt; rest_sample_cnt -= counts[i]; } } mean_bin_size = static_cast(rest_sample_cnt) / rest_bin_cnt; std::vector upper_bounds(max_bin, std::numeric_limits::infinity()); std::vector lower_bounds(max_bin, std::numeric_limits::infinity()); int bin_cnt = 0; lower_bounds[bin_cnt] = distinct_values[0]; int cur_cnt_inbin = 0; for (int i = 0; i < num_distinct_values - 1; ++i) { if (!is_big_count_value[i]) { rest_sample_cnt -= counts[i]; } cur_cnt_inbin += counts[i]; // need a new bin if (is_big_count_value[i] || cur_cnt_inbin >= mean_bin_size || (is_big_count_value[i + 1] && cur_cnt_inbin >= std::max(1.0, mean_bin_size * 0.5f))) { upper_bounds[bin_cnt] = distinct_values[i]; ++bin_cnt; lower_bounds[bin_cnt] = distinct_values[i + 1]; if (bin_cnt >= max_bin - 1) { break; } cur_cnt_inbin = 0; if (!is_big_count_value[i]) { --rest_bin_cnt; mean_bin_size = rest_sample_cnt / static_cast(rest_bin_cnt); } } } ++bin_cnt; // update bin upper bound bin_upper_bound.clear(); for (int i = 0; i < bin_cnt - 1; ++i) { auto val = Common::GetDoubleUpperBound((upper_bounds[i] + lower_bounds[i + 1]) / 2.0); if (bin_upper_bound.empty() || !Common::CheckDoubleEqualOrdered(bin_upper_bound.back(), val)) { bin_upper_bound.push_back(val); } } // last bin upper bound bin_upper_bound.push_back(std::numeric_limits::infinity()); } return bin_upper_bound; } std::vector FindBinWithPredefinedBin(const double* distinct_values, const int* counts, int num_distinct_values, int max_bin, size_t total_sample_cnt, int min_data_in_bin, const std::vector& forced_upper_bounds) { std::vector bin_upper_bound; // get number of positive and negative distinct values int left_cnt = -1; for (int i = 0; i < num_distinct_values; ++i) { if (distinct_values[i] > -kZeroThreshold) { left_cnt = i; break; } } if (left_cnt < 0) { left_cnt = num_distinct_values; } int right_start = -1; for (int i = left_cnt; i < num_distinct_values; ++i) { if (distinct_values[i] > kZeroThreshold) { right_start = i; break; } } // include zero bounds and infinity bound if (max_bin == 2) { if (left_cnt == 0) { bin_upper_bound.push_back(kZeroThreshold); } else { bin_upper_bound.push_back(-kZeroThreshold); } } else if (max_bin >= 3) { if (left_cnt > 0) { bin_upper_bound.push_back(-kZeroThreshold); } if (right_start >= 0) { bin_upper_bound.push_back(kZeroThreshold); } } bin_upper_bound.push_back(std::numeric_limits::infinity()); // add forced bounds, excluding zeros since we have already added zero bounds int max_to_insert = max_bin - static_cast(bin_upper_bound.size()); int num_inserted = 0; for (size_t i = 0; i < forced_upper_bounds.size(); ++i) { if (num_inserted >= max_to_insert) { break; } if (std::fabs(forced_upper_bounds[i]) > kZeroThreshold) { bin_upper_bound.push_back(forced_upper_bounds[i]); ++num_inserted; } } std::stable_sort(bin_upper_bound.begin(), bin_upper_bound.end()); // find remaining bounds int free_bins = max_bin - static_cast(bin_upper_bound.size()); std::vector bounds_to_add; int value_ind = 0; for (size_t i = 0; i < bin_upper_bound.size(); ++i) { int cnt_in_bin = 0; int distinct_cnt_in_bin = 0; int bin_start = value_ind; while ((value_ind < num_distinct_values) && (distinct_values[value_ind] < bin_upper_bound[i])) { cnt_in_bin += counts[value_ind]; ++distinct_cnt_in_bin; ++value_ind; } int bins_remaining = max_bin - static_cast(bin_upper_bound.size()) - static_cast(bounds_to_add.size()); int num_sub_bins = static_cast(std::lround((static_cast(cnt_in_bin) * free_bins / total_sample_cnt))); num_sub_bins = std::min(num_sub_bins, bins_remaining) + 1; if (i == bin_upper_bound.size() - 1) { num_sub_bins = bins_remaining + 1; } std::vector new_upper_bounds = GreedyFindBin(distinct_values + bin_start, counts + bin_start, distinct_cnt_in_bin, num_sub_bins, cnt_in_bin, min_data_in_bin); bounds_to_add.insert(bounds_to_add.end(), new_upper_bounds.begin(), new_upper_bounds.end() - 1); // last bound is infinity } bin_upper_bound.insert(bin_upper_bound.end(), bounds_to_add.begin(), bounds_to_add.end()); std::stable_sort(bin_upper_bound.begin(), bin_upper_bound.end()); CHECK_LE(bin_upper_bound.size(), static_cast(max_bin)); return bin_upper_bound; } std::vector FindBinWithZeroAsOneBin(const double* distinct_values, const int* counts, int num_distinct_values, int max_bin, size_t total_sample_cnt, int min_data_in_bin) { std::vector bin_upper_bound; int left_cnt_data = 0; int cnt_zero = 0; int right_cnt_data = 0; for (int i = 0; i < num_distinct_values; ++i) { if (distinct_values[i] <= -kZeroThreshold) { left_cnt_data += counts[i]; } else if (distinct_values[i] > kZeroThreshold) { right_cnt_data += counts[i]; } else { cnt_zero += counts[i]; } } int left_cnt = -1; for (int i = 0; i < num_distinct_values; ++i) { if (distinct_values[i] > -kZeroThreshold) { left_cnt = i; break; } } if (left_cnt < 0) { left_cnt = num_distinct_values; } if ((left_cnt > 0) && (max_bin > 1)) { int left_max_bin = static_cast(static_cast(left_cnt_data) / (total_sample_cnt - cnt_zero) * (max_bin - 1)); left_max_bin = std::max(1, left_max_bin); bin_upper_bound = GreedyFindBin(distinct_values, counts, left_cnt, left_max_bin, left_cnt_data, min_data_in_bin); if (bin_upper_bound.size() > 0) { bin_upper_bound.back() = -kZeroThreshold; } } int right_start = -1; for (int i = left_cnt; i < num_distinct_values; ++i) { if (distinct_values[i] > kZeroThreshold) { right_start = i; break; } } int right_max_bin = max_bin - 1 - static_cast(bin_upper_bound.size()); if (right_start >= 0 && right_max_bin > 0) { auto right_bounds = GreedyFindBin(distinct_values + right_start, counts + right_start, num_distinct_values - right_start, right_max_bin, right_cnt_data, min_data_in_bin); bin_upper_bound.push_back(kZeroThreshold); bin_upper_bound.insert(bin_upper_bound.end(), right_bounds.begin(), right_bounds.end()); } else { bin_upper_bound.push_back(std::numeric_limits::infinity()); } CHECK_LE(bin_upper_bound.size(), static_cast(max_bin)); return bin_upper_bound; } std::vector FindBinWithZeroAsOneBin(const double* distinct_values, const int* counts, int num_distinct_values, int max_bin, size_t total_sample_cnt, int min_data_in_bin, const std::vector& forced_upper_bounds) { if (forced_upper_bounds.empty()) { return FindBinWithZeroAsOneBin(distinct_values, counts, num_distinct_values, max_bin, total_sample_cnt, min_data_in_bin); } else { return FindBinWithPredefinedBin(distinct_values, counts, num_distinct_values, max_bin, total_sample_cnt, min_data_in_bin, forced_upper_bounds); } } void BinMapper::FindBin(double* values, int num_sample_values, size_t total_sample_cnt, int max_bin, int min_data_in_bin, int min_split_data, bool pre_filter, BinType bin_type, bool use_missing, bool zero_as_missing, const std::vector& forced_upper_bounds) { int na_cnt = 0; int non_na_cnt = 0; for (int i = 0; i < num_sample_values; ++i) { if (!std::isnan(values[i])) { values[non_na_cnt++] = values[i]; } } if (!use_missing) { missing_type_ = MissingType::None; } else if (zero_as_missing) { missing_type_ = MissingType::Zero; } else { if (non_na_cnt == num_sample_values) { missing_type_ = MissingType::None; } else { missing_type_ = MissingType::NaN; na_cnt = num_sample_values - non_na_cnt; } } num_sample_values = non_na_cnt; bin_type_ = bin_type; default_bin_ = 0; int zero_cnt = static_cast(total_sample_cnt - num_sample_values - na_cnt); // find distinct_values first std::vector distinct_values; std::vector counts; // count of data points for each distinct feature value. std::stable_sort(values, values + num_sample_values); // push zero in the front if (num_sample_values == 0 || (values[0] > 0.0f && zero_cnt > 0)) { distinct_values.push_back(0.0f); counts.push_back(zero_cnt); } if (num_sample_values > 0) { distinct_values.push_back(values[0]); counts.push_back(1); } for (int i = 1; i < num_sample_values; ++i) { if (!Common::CheckDoubleEqualOrdered(values[i - 1], values[i])) { if (values[i - 1] < 0.0f && values[i] > 0.0f) { distinct_values.push_back(0.0f); counts.push_back(zero_cnt); } distinct_values.push_back(values[i]); counts.push_back(1); } else { // use the large value distinct_values.back() = values[i]; ++counts.back(); } } // push zero in the back if (num_sample_values > 0 && values[num_sample_values - 1] < 0.0f && zero_cnt > 0) { distinct_values.push_back(0.0f); counts.push_back(zero_cnt); } min_val_ = distinct_values.front(); max_val_ = distinct_values.back(); std::vector cnt_in_bin; // count of data points in each bin. int num_distinct_values = static_cast(distinct_values.size()); if (bin_type_ == BinType::NumericalBin) { if (missing_type_ == MissingType::Zero) { bin_upper_bound_ = FindBinWithZeroAsOneBin(distinct_values.data(), counts.data(), num_distinct_values, max_bin, total_sample_cnt, min_data_in_bin, forced_upper_bounds); if (bin_upper_bound_.size() == 2) { missing_type_ = MissingType::None; } } else if (missing_type_ == MissingType::None) { bin_upper_bound_ = FindBinWithZeroAsOneBin(distinct_values.data(), counts.data(), num_distinct_values, max_bin, total_sample_cnt, min_data_in_bin, forced_upper_bounds); } else { bin_upper_bound_ = FindBinWithZeroAsOneBin(distinct_values.data(), counts.data(), num_distinct_values, max_bin - 1, total_sample_cnt - na_cnt, min_data_in_bin, forced_upper_bounds); bin_upper_bound_.push_back(NaN); } num_bin_ = static_cast(bin_upper_bound_.size()); { cnt_in_bin.resize(num_bin_, 0); int i_bin = 0; for (int i = 0; i < num_distinct_values; ++i) { while (distinct_values[i] > bin_upper_bound_[i_bin] && i_bin < num_bin_ - 1) { ++i_bin; } cnt_in_bin[i_bin] += counts[i]; } if (missing_type_ == MissingType::NaN) { cnt_in_bin[num_bin_ - 1] = na_cnt; } } CHECK_LE(num_bin_, max_bin); } else { // convert to int type first std::vector distinct_values_int; std::vector counts_int; for (size_t i = 0; i < distinct_values.size(); ++i) { int val = static_cast(distinct_values[i]); if (val < 0) { na_cnt += counts[i]; Log::Warning("Met negative value in categorical features, will convert it to NaN"); } else { if (distinct_values_int.empty() || val != distinct_values_int.back()) { distinct_values_int.push_back(val); counts_int.push_back(counts[i]); } else { counts_int.back() += counts[i]; } } } int rest_cnt = static_cast(total_sample_cnt - na_cnt); if (rest_cnt > 0) { const int SPARSE_RATIO = 100; if (distinct_values_int.back() / SPARSE_RATIO > static_cast(distinct_values_int.size())) { Log::Warning("Met categorical feature which contains sparse values. " "Consider renumbering to consecutive integers started from zero"); } // sort by counts in descending order Common::SortForPair(&counts_int, &distinct_values_int, 0, true); // will ignore the categorical of small counts int cut_cnt = static_cast( Common::RoundInt((total_sample_cnt - na_cnt) * 0.99f)); size_t cur_cat_idx = 0; // index of current category. categorical_2_bin_.clear(); bin_2_categorical_.clear(); int used_cnt = 0; int distinct_cnt = static_cast(distinct_values_int.size()); if (na_cnt > 0) { ++distinct_cnt; } max_bin = std::min(distinct_cnt, max_bin); cnt_in_bin.clear(); // Push the dummy bin for NaN bin_2_categorical_.push_back(-1); categorical_2_bin_[-1] = 0; cnt_in_bin.push_back(0); num_bin_ = 1; while (cur_cat_idx < distinct_values_int.size() && (used_cnt < cut_cnt || num_bin_ < max_bin)) { if (counts_int[cur_cat_idx] < min_data_in_bin && cur_cat_idx > 1) { break; } bin_2_categorical_.push_back(distinct_values_int[cur_cat_idx]); categorical_2_bin_[distinct_values_int[cur_cat_idx]] = static_cast(num_bin_); used_cnt += counts_int[cur_cat_idx]; cnt_in_bin.push_back(counts_int[cur_cat_idx]); ++num_bin_; ++cur_cat_idx; } // Use MissingType::None to represent this bin contains all categoricals if (cur_cat_idx == distinct_values_int.size() && na_cnt == 0) { missing_type_ = MissingType::None; } else { missing_type_ = MissingType::NaN; } // fix count of NaN bin cnt_in_bin[0] = static_cast(total_sample_cnt - used_cnt); } } // check trivial(num_bin_ == 1) feature if (num_bin_ <= 1) { is_trivial_ = true; } else { is_trivial_ = false; } // check useless bin if (!is_trivial_ && pre_filter && NeedFilter(cnt_in_bin, static_cast(total_sample_cnt), min_split_data, bin_type_)) { is_trivial_ = true; } if (!is_trivial_) { default_bin_ = ValueToBin(0); most_freq_bin_ = static_cast(ArrayArgs::ArgMax(cnt_in_bin)); const double max_sparse_rate = static_cast(cnt_in_bin[most_freq_bin_]) / total_sample_cnt; // When most_freq_bin_ != default_bin_, there are some additional data loading costs. // so use most_freq_bin_ = default_bin_ when there is not so sparse if (most_freq_bin_ != default_bin_ && max_sparse_rate < kSparseThreshold) { most_freq_bin_ = default_bin_; } sparse_rate_ = static_cast(cnt_in_bin[most_freq_bin_]) / total_sample_cnt; } else { sparse_rate_ = 1.0f; } } void BinMapper::CopyTo(char * buffer) const { std::memcpy(buffer, &num_bin_, sizeof(num_bin_)); buffer += VirtualFileWriter::AlignedSize(sizeof(num_bin_)); std::memcpy(buffer, &missing_type_, sizeof(missing_type_)); buffer += VirtualFileWriter::AlignedSize(sizeof(missing_type_)); std::memcpy(buffer, &is_trivial_, sizeof(is_trivial_)); buffer += VirtualFileWriter::AlignedSize(sizeof(is_trivial_)); std::memcpy(buffer, &sparse_rate_, sizeof(sparse_rate_)); buffer += sizeof(sparse_rate_); std::memcpy(buffer, &bin_type_, sizeof(bin_type_)); buffer += VirtualFileWriter::AlignedSize(sizeof(bin_type_)); std::memcpy(buffer, &min_val_, sizeof(min_val_)); buffer += sizeof(min_val_); std::memcpy(buffer, &max_val_, sizeof(max_val_)); buffer += sizeof(max_val_); std::memcpy(buffer, &default_bin_, sizeof(default_bin_)); buffer += VirtualFileWriter::AlignedSize(sizeof(default_bin_)); std::memcpy(buffer, &most_freq_bin_, sizeof(most_freq_bin_)); buffer += VirtualFileWriter::AlignedSize(sizeof(most_freq_bin_)); if (bin_type_ == BinType::NumericalBin) { std::memcpy(buffer, bin_upper_bound_.data(), num_bin_ * sizeof(double)); } else { std::memcpy(buffer, bin_2_categorical_.data(), num_bin_ * sizeof(int)); } } void BinMapper::CopyFrom(const char * buffer) { std::memcpy(&num_bin_, buffer, sizeof(num_bin_)); buffer += VirtualFileWriter::AlignedSize(sizeof(num_bin_)); std::memcpy(&missing_type_, buffer, sizeof(missing_type_)); buffer += VirtualFileWriter::AlignedSize(sizeof(missing_type_)); std::memcpy(&is_trivial_, buffer, sizeof(is_trivial_)); buffer += VirtualFileWriter::AlignedSize(sizeof(is_trivial_)); std::memcpy(&sparse_rate_, buffer, sizeof(sparse_rate_)); buffer += sizeof(sparse_rate_); std::memcpy(&bin_type_, buffer, sizeof(bin_type_)); buffer += VirtualFileWriter::AlignedSize(sizeof(bin_type_)); std::memcpy(&min_val_, buffer, sizeof(min_val_)); buffer += sizeof(min_val_); std::memcpy(&max_val_, buffer, sizeof(max_val_)); buffer += sizeof(max_val_); std::memcpy(&default_bin_, buffer, sizeof(default_bin_)); buffer += VirtualFileWriter::AlignedSize(sizeof(default_bin_)); std::memcpy(&most_freq_bin_, buffer, sizeof(most_freq_bin_)); buffer += VirtualFileWriter::AlignedSize(sizeof(most_freq_bin_)); if (bin_type_ == BinType::NumericalBin) { bin_upper_bound_ = std::vector(num_bin_); std::memcpy(bin_upper_bound_.data(), buffer, num_bin_ * sizeof(double)); } else { bin_2_categorical_ = std::vector(num_bin_); std::memcpy(bin_2_categorical_.data(), buffer, num_bin_ * sizeof(int)); categorical_2_bin_.clear(); for (int i = 0; i < num_bin_; ++i) { categorical_2_bin_[bin_2_categorical_[i]] = static_cast(i); } } } void BinMapper::SaveBinaryToFile(BinaryWriter* writer) const { writer->AlignedWrite(&num_bin_, sizeof(num_bin_)); writer->AlignedWrite(&missing_type_, sizeof(missing_type_)); writer->AlignedWrite(&is_trivial_, sizeof(is_trivial_)); writer->Write(&sparse_rate_, sizeof(sparse_rate_)); writer->AlignedWrite(&bin_type_, sizeof(bin_type_)); writer->Write(&min_val_, sizeof(min_val_)); writer->Write(&max_val_, sizeof(max_val_)); writer->AlignedWrite(&default_bin_, sizeof(default_bin_)); writer->AlignedWrite(&most_freq_bin_, sizeof(most_freq_bin_)); if (bin_type_ == BinType::NumericalBin) { writer->Write(bin_upper_bound_.data(), sizeof(double) * num_bin_); } else { writer->Write(bin_2_categorical_.data(), sizeof(int) * num_bin_); } } size_t BinMapper::SizesInByte() const { size_t ret = VirtualFileWriter::AlignedSize(sizeof(num_bin_)) + VirtualFileWriter::AlignedSize(sizeof(missing_type_)) + VirtualFileWriter::AlignedSize(sizeof(is_trivial_)) + sizeof(sparse_rate_) + VirtualFileWriter::AlignedSize(sizeof(bin_type_)) + sizeof(min_val_) + sizeof(max_val_) + VirtualFileWriter::AlignedSize(sizeof(default_bin_)) + VirtualFileWriter::AlignedSize(sizeof(most_freq_bin_)); if (bin_type_ == BinType::NumericalBin) { ret += sizeof(double) * num_bin_; } else { ret += sizeof(int) * num_bin_; } return ret; } template class DenseBin; template class DenseBin; template class DenseBin; template class DenseBin; template class SparseBin; template class SparseBin; template class SparseBin; template class MultiValDenseBin; template class MultiValDenseBin; template class MultiValDenseBin; Bin* Bin::CreateDenseBin(data_size_t num_data, int num_bin) { if (num_bin <= 16) { return new DenseBin(num_data); } else if (num_bin <= 256) { return new DenseBin(num_data); } else if (num_bin <= 65536) { return new DenseBin(num_data); } else { return new DenseBin(num_data); } } Bin* Bin::CreateSparseBin(data_size_t num_data, int num_bin) { if (num_bin <= 256) { return new SparseBin(num_data); } else if (num_bin <= 65536) { return new SparseBin(num_data); } else { return new SparseBin(num_data); } } MultiValBin* MultiValBin::CreateMultiValBin(data_size_t num_data, int num_bin, int num_feature, double sparse_rate, const std::vector& offsets) { if (sparse_rate >= multi_val_bin_sparse_threshold) { const double average_element_per_row = (1.0 - sparse_rate) * num_feature; return CreateMultiValSparseBin(num_data, num_bin, average_element_per_row); } else { return CreateMultiValDenseBin(num_data, num_bin, num_feature, offsets); } } MultiValBin* MultiValBin::CreateMultiValDenseBin(data_size_t num_data, int num_bin, int num_feature, const std::vector& offsets) { // calculate max bin of all features to select the int type in MultiValDenseBin int max_bin = 0; for (int i = 0; i < static_cast(offsets.size()) - 1; ++i) { int feature_bin = offsets[i + 1] - offsets[i]; if (feature_bin > max_bin) { max_bin = feature_bin; } } if (max_bin <= 256) { return new MultiValDenseBin(num_data, num_bin, num_feature, offsets); } else if (max_bin <= 65536) { return new MultiValDenseBin(num_data, num_bin, num_feature, offsets); } else { return new MultiValDenseBin(num_data, num_bin, num_feature, offsets); } } MultiValBin* MultiValBin::CreateMultiValSparseBin(data_size_t num_data, int num_bin, double estimate_element_per_row) { size_t estimate_total_entries = static_cast(estimate_element_per_row * 1.1 * num_data); if (estimate_total_entries <= std::numeric_limits::max()) { if (num_bin <= 256) { return new MultiValSparseBin( num_data, num_bin, estimate_element_per_row); } else if (num_bin <= 65536) { return new MultiValSparseBin( num_data, num_bin, estimate_element_per_row); } else { return new MultiValSparseBin( num_data, num_bin, estimate_element_per_row); } } else if (estimate_total_entries <= std::numeric_limits::max()) { if (num_bin <= 256) { return new MultiValSparseBin( num_data, num_bin, estimate_element_per_row); } else if (num_bin <= 65536) { return new MultiValSparseBin( num_data, num_bin, estimate_element_per_row); } else { return new MultiValSparseBin( num_data, num_bin, estimate_element_per_row); } } else { if (num_bin <= 256) { return new MultiValSparseBin( num_data, num_bin, estimate_element_per_row); } else if (num_bin <= 65536) { return new MultiValSparseBin( num_data, num_bin, estimate_element_per_row); } else { return new MultiValSparseBin( num_data, num_bin, estimate_element_per_row); } } } template <> const void* DenseBin::GetColWiseData( uint8_t* bit_type, bool* is_sparse, std::vector* bin_iterator, const int /*num_threads*/) const { *is_sparse = false; *bit_type = 8; bin_iterator->clear(); return reinterpret_cast(data_.data()); } template <> const void* DenseBin::GetColWiseData( uint8_t* bit_type, bool* is_sparse, std::vector* bin_iterator, const int /*num_threads*/) const { *is_sparse = false; *bit_type = 16; bin_iterator->clear(); return reinterpret_cast(data_.data()); } template <> const void* DenseBin::GetColWiseData( uint8_t* bit_type, bool* is_sparse, std::vector* bin_iterator, const int /*num_threads*/) const { *is_sparse = false; *bit_type = 32; bin_iterator->clear(); return reinterpret_cast(data_.data()); } template <> const void* DenseBin::GetColWiseData( uint8_t* bit_type, bool* is_sparse, std::vector* bin_iterator, const int /*num_threads*/) const { *is_sparse = false; *bit_type = 4; bin_iterator->clear(); return reinterpret_cast(data_.data()); } template <> const void* DenseBin::GetColWiseData( uint8_t* bit_type, bool* is_sparse, BinIterator** bin_iterator) const { *is_sparse = false; *bit_type = 8; *bin_iterator = nullptr; return reinterpret_cast(data_.data()); } template <> const void* DenseBin::GetColWiseData( uint8_t* bit_type, bool* is_sparse, BinIterator** bin_iterator) const { *is_sparse = false; *bit_type = 16; *bin_iterator = nullptr; return reinterpret_cast(data_.data()); } template <> const void* DenseBin::GetColWiseData( uint8_t* bit_type, bool* is_sparse, BinIterator** bin_iterator) const { *is_sparse = false; *bit_type = 32; *bin_iterator = nullptr; return reinterpret_cast(data_.data()); } template <> const void* DenseBin::GetColWiseData( uint8_t* bit_type, bool* is_sparse, BinIterator** bin_iterator) const { *is_sparse = false; *bit_type = 4; *bin_iterator = nullptr; return reinterpret_cast(data_.data()); } template <> const void* SparseBin::GetColWiseData( uint8_t* bit_type, bool* is_sparse, std::vector* bin_iterator, const int num_threads) const { *is_sparse = true; *bit_type = 8; for (int thread_index = 0; thread_index < num_threads; ++thread_index) { bin_iterator->emplace_back(new SparseBinIterator(this, 0)); } return nullptr; } template <> const void* SparseBin::GetColWiseData( uint8_t* bit_type, bool* is_sparse, std::vector* bin_iterator, const int num_threads) const { *is_sparse = true; *bit_type = 16; for (int thread_index = 0; thread_index < num_threads; ++thread_index) { bin_iterator->emplace_back(new SparseBinIterator(this, 0)); } return nullptr; } template <> const void* SparseBin::GetColWiseData( uint8_t* bit_type, bool* is_sparse, std::vector* bin_iterator, const int num_threads) const { *is_sparse = true; *bit_type = 32; for (int thread_index = 0; thread_index < num_threads; ++thread_index) { bin_iterator->emplace_back(new SparseBinIterator(this, 0)); } return nullptr; } template <> const void* SparseBin::GetColWiseData( uint8_t* bit_type, bool* is_sparse, BinIterator** bin_iterator) const { *is_sparse = true; *bit_type = 8; *bin_iterator = new SparseBinIterator(this, 0); return nullptr; } template <> const void* SparseBin::GetColWiseData( uint8_t* bit_type, bool* is_sparse, BinIterator** bin_iterator) const { *is_sparse = true; *bit_type = 16; *bin_iterator = new SparseBinIterator(this, 0); return nullptr; } template <> const void* SparseBin::GetColWiseData( uint8_t* bit_type, bool* is_sparse, BinIterator** bin_iterator) const { *is_sparse = true; *bit_type = 32; *bin_iterator = new SparseBinIterator(this, 0); return nullptr; } #ifdef USE_CUDA template <> const void* MultiValDenseBin::GetRowWiseData(uint8_t* bit_type, size_t* total_size, bool* is_sparse, const void** out_data_ptr, uint8_t* data_ptr_bit_type) const { const uint8_t* to_return = data_.data(); *bit_type = 8; *total_size = static_cast(num_data_) * static_cast(num_feature_); CHECK_EQ(*total_size, data_.size()); *is_sparse = false; *out_data_ptr = nullptr; *data_ptr_bit_type = 0; return to_return; } template <> const void* MultiValDenseBin::GetRowWiseData(uint8_t* bit_type, size_t* total_size, bool* is_sparse, const void** out_data_ptr, uint8_t* data_ptr_bit_type) const { const uint16_t* data_ptr = data_.data(); const uint8_t* to_return = reinterpret_cast(data_ptr); *bit_type = 16; *total_size = static_cast(num_data_) * static_cast(num_feature_); CHECK_EQ(*total_size, data_.size()); *is_sparse = false; *out_data_ptr = nullptr; *data_ptr_bit_type = 0; return to_return; } template <> const void* MultiValDenseBin::GetRowWiseData(uint8_t* bit_type, size_t* total_size, bool* is_sparse, const void** out_data_ptr, uint8_t* data_ptr_bit_type) const { const uint32_t* data_ptr = data_.data(); const uint8_t* to_return = reinterpret_cast(data_ptr); *bit_type = 32; *total_size = static_cast(num_data_) * static_cast(num_feature_); CHECK_EQ(*total_size, data_.size()); *is_sparse = false; *out_data_ptr = nullptr; *data_ptr_bit_type = 0; return to_return; } template <> const void* MultiValSparseBin::GetRowWiseData( uint8_t* bit_type, size_t* total_size, bool* is_sparse, const void** out_data_ptr, uint8_t* data_ptr_bit_type) const { const uint8_t* to_return = data_.data(); *bit_type = 8; *total_size = data_.size(); *is_sparse = true; *out_data_ptr = reinterpret_cast(row_ptr_.data()); *data_ptr_bit_type = 16; return to_return; } template <> const void* MultiValSparseBin::GetRowWiseData( uint8_t* bit_type, size_t* total_size, bool* is_sparse, const void** out_data_ptr, uint8_t* data_ptr_bit_type) const { const uint8_t* to_return = reinterpret_cast(data_.data()); *bit_type = 16; *total_size = data_.size(); *is_sparse = true; *out_data_ptr = reinterpret_cast(row_ptr_.data()); *data_ptr_bit_type = 16; return to_return; } template <> const void* MultiValSparseBin::GetRowWiseData( uint8_t* bit_type, size_t* total_size, bool* is_sparse, const void** out_data_ptr, uint8_t* data_ptr_bit_type) const { const uint8_t* to_return = reinterpret_cast(data_.data()); *bit_type = 32; *total_size = data_.size(); *is_sparse = true; *out_data_ptr = reinterpret_cast(row_ptr_.data()); *data_ptr_bit_type = 16; return to_return; } template <> const void* MultiValSparseBin::GetRowWiseData( uint8_t* bit_type, size_t* total_size, bool* is_sparse, const void** out_data_ptr, uint8_t* data_ptr_bit_type) const { const uint8_t* to_return = data_.data(); *bit_type = 8; *total_size = data_.size(); *is_sparse = true; *out_data_ptr = reinterpret_cast(row_ptr_.data()); *data_ptr_bit_type = 32; return to_return; } template <> const void* MultiValSparseBin::GetRowWiseData( uint8_t* bit_type, size_t* total_size, bool* is_sparse, const void** out_data_ptr, uint8_t* data_ptr_bit_type) const { const uint8_t* to_return = reinterpret_cast(data_.data()); *bit_type = 16; *total_size = data_.size(); *is_sparse = true; *out_data_ptr = reinterpret_cast(row_ptr_.data()); *data_ptr_bit_type = 32; return to_return; } template <> const void* MultiValSparseBin::GetRowWiseData( uint8_t* bit_type, size_t* total_size, bool* is_sparse, const void** out_data_ptr, uint8_t* data_ptr_bit_type) const { const uint8_t* to_return = reinterpret_cast(data_.data()); *bit_type = 32; *total_size = data_.size(); *is_sparse = true; *out_data_ptr = reinterpret_cast(row_ptr_.data()); *data_ptr_bit_type = 32; return to_return; } template <> const void* MultiValSparseBin::GetRowWiseData( uint8_t* bit_type, size_t* total_size, bool* is_sparse, const void** out_data_ptr, uint8_t* data_ptr_bit_type) const { const uint8_t* to_return = data_.data(); *bit_type = 8; *total_size = data_.size(); *is_sparse = true; *out_data_ptr = reinterpret_cast(row_ptr_.data()); *data_ptr_bit_type = 64; return to_return; } template <> const void* MultiValSparseBin::GetRowWiseData( uint8_t* bit_type, size_t* total_size, bool* is_sparse, const void** out_data_ptr, uint8_t* data_ptr_bit_type) const { const uint8_t* to_return = reinterpret_cast(data_.data()); *bit_type = 16; *total_size = data_.size(); *is_sparse = true; *out_data_ptr = reinterpret_cast(row_ptr_.data()); *data_ptr_bit_type = 64; return to_return; } template <> const void* MultiValSparseBin::GetRowWiseData( uint8_t* bit_type, size_t* total_size, bool* is_sparse, const void** out_data_ptr, uint8_t* data_ptr_bit_type) const { const uint8_t* to_return = reinterpret_cast(data_.data()); *bit_type = 32; *total_size = data_.size(); *is_sparse = true; *out_data_ptr = reinterpret_cast(row_ptr_.data()); *data_ptr_bit_type = 64; return to_return; } #endif // USE_CUDA } // namespace LightGBM ================================================ FILE: src/io/config.cpp ================================================ /*! * Copyright (c) 2016-2026 Microsoft Corporation. All rights reserved. * Copyright (c) 2016-2026 The LightGBM developers. All rights reserved. * Licensed under the MIT License. See LICENSE file in the project root for license information. */ #include #include #include #include #include #include #include #include #include #include #include #include namespace LightGBM { void Config::KV2Map(std::unordered_map>* params, const char* kv) { std::vector tmp_strs = Common::Split(kv, '='); if (tmp_strs.size() == 2 || tmp_strs.size() == 1) { std::string key = Common::RemoveQuotationSymbol(Common::Trim(tmp_strs[0])); std::string value = ""; if (tmp_strs.size() == 2) { value = Common::RemoveQuotationSymbol(Common::Trim(tmp_strs[1])); } if (key.size() > 0) { params->operator[](key).emplace_back(value); } } else { Log::Warning("Unknown parameter %s", kv); } } void GetFirstValueAsInt(const std::unordered_map>& params, std::string key, int* out) { const auto pair = params.find(key); if (pair != params.end()) { auto candidate = pair->second[0].c_str(); if (!Common::AtoiAndCheck(candidate, out)) { Log::Fatal("Parameter %s should be of type int, got \"%s\"", key.c_str(), candidate); } } } void Config::SetVerbosity(const std::unordered_map>& params) { int verbosity = 1; // if "verbosity" was found in params, prefer that to any other aliases const auto verbosity_iter = params.find("verbosity"); if (verbosity_iter != params.end()) { GetFirstValueAsInt(params, "verbosity", &verbosity); } else { // if "verbose" was found in params and "verbosity" was not, use that value const auto verbose_iter = params.find("verbose"); if (verbose_iter != params.end()) { GetFirstValueAsInt(params, "verbose", &verbosity); } else { // if "verbosity" and "verbose" were both missing from params, don't modify LightGBM's log level return; } } // otherwise, update LightGBM's log level based on the passed-in value if (verbosity < 0) { LightGBM::Log::ResetLogLevel(LightGBM::LogLevel::Fatal); } else if (verbosity == 0) { LightGBM::Log::ResetLogLevel(LightGBM::LogLevel::Warning); } else if (verbosity == 1) { LightGBM::Log::ResetLogLevel(LightGBM::LogLevel::Info); } else { LightGBM::Log::ResetLogLevel(LightGBM::LogLevel::Debug); } } void Config::KeepFirstValues(const std::unordered_map>& params, std::unordered_map* out) { for (auto pair = params.begin(); pair != params.end(); ++pair) { auto name = pair->first.c_str(); auto values = pair->second; out->emplace(name, values[0]); for (size_t i = 1; i < pair->second.size(); ++i) { Log::Warning("%s is set=%s, %s=%s will be ignored. Current value: %s=%s", name, values[0].c_str(), name, values[i].c_str(), name, values[0].c_str()); } } } std::unordered_map Config::Str2Map(const char* parameters) { std::unordered_map> all_params; std::unordered_map params; auto args = Common::Split(parameters, " \t\n\r"); for (auto arg : args) { KV2Map(&all_params, Common::Trim(arg).c_str()); } SetVerbosity(all_params); KeepFirstValues(all_params, ¶ms); ParameterAlias::KeyAliasTransform(¶ms); return params; } void GetBoostingType(const std::unordered_map& params, std::string* boosting) { std::string value; if (Config::GetString(params, "boosting", &value)) { std::transform(value.begin(), value.end(), value.begin(), [](unsigned char c){ return std::tolower(c); }); if (value == std::string("gbdt") || value == std::string("gbrt")) { *boosting = "gbdt"; } else if (value == std::string("dart")) { *boosting = "dart"; } else if (value == std::string("goss")) { *boosting = "goss"; } else if (value == std::string("rf") || value == std::string("random_forest")) { *boosting = "rf"; } else { Log::Fatal("Unknown boosting type %s", value.c_str()); } } } void GetDataSampleStrategy(const std::unordered_map& params, std::string* strategy) { std::string value; if (Config::GetString(params, "data_sample_strategy", &value)) { std::transform(value.begin(), value.end(), value.begin(), [](unsigned char c){ return std::tolower(c); }); if (value == std::string("goss")) { *strategy = "goss"; } else if (value == std::string("bagging")) { *strategy = "bagging"; } else { Log::Fatal("Unknown sample strategy %s", value.c_str()); } } } void ParseMetrics(const std::string& value, std::vector* out_metric) { std::unordered_set metric_sets; out_metric->clear(); std::vector metrics = Common::Split(value.c_str(), ','); for (auto& met : metrics) { auto type = ParseMetricAlias(met); if (metric_sets.count(type) <= 0) { out_metric->push_back(type); metric_sets.insert(type); } } } void GetObjectiveType(const std::unordered_map& params, std::string* objective) { std::string value; if (Config::GetString(params, "objective", &value)) { std::transform(value.begin(), value.end(), value.begin(), [](unsigned char c){ return std::tolower(c); }); *objective = ParseObjectiveAlias(value); } } void GetMetricType(const std::unordered_map& params, const std::string& objective, std::vector* metric) { std::string value; if (Config::GetString(params, "metric", &value)) { std::transform(value.begin(), value.end(), value.begin(), [](unsigned char c){ return std::tolower(c); }); ParseMetrics(value, metric); } // add names of objective function if not providing metric if (metric->empty() && value.size() == 0) { ParseMetrics(objective, metric); } } void GetTaskType(const std::unordered_map& params, TaskType* task) { std::string value; if (Config::GetString(params, "task", &value)) { std::transform(value.begin(), value.end(), value.begin(), [](unsigned char c){ return std::tolower(c); }); if (value == std::string("train") || value == std::string("training")) { *task = TaskType::kTrain; } else if (value == std::string("predict") || value == std::string("prediction") || value == std::string("test")) { *task = TaskType::kPredict; } else if (value == std::string("convert_model")) { *task = TaskType::kConvertModel; } else if (value == std::string("refit") || value == std::string("refit_tree")) { *task = TaskType::KRefitTree; } else if (value == std::string("save_binary")) { *task = TaskType::kSaveBinary; } else { Log::Fatal("Unknown task type %s", value.c_str()); } } } void GetDeviceType(const std::unordered_map& params, std::string* device_type) { std::string value; if (Config::GetString(params, "device_type", &value)) { std::transform(value.begin(), value.end(), value.begin(), [](unsigned char c){ return std::tolower(c); }); if (value == std::string("cpu")) { *device_type = "cpu"; } else if (value == std::string("gpu")) { *device_type = "gpu"; } else if (value == std::string("cuda")) { *device_type = "cuda"; } else { Log::Fatal("Unknown device type %s", value.c_str()); } } } void GetTreeLearnerType(const std::unordered_map& params, std::string* tree_learner) { std::string value; if (Config::GetString(params, "tree_learner", &value)) { std::transform(value.begin(), value.end(), value.begin(), [](unsigned char c){ return std::tolower(c); }); if (value == std::string("serial")) { *tree_learner = "serial"; } else if (value == std::string("feature") || value == std::string("feature_parallel")) { *tree_learner = "feature"; } else if (value == std::string("data") || value == std::string("data_parallel")) { *tree_learner = "data"; } else if (value == std::string("voting") || value == std::string("voting_parallel")) { *tree_learner = "voting"; } else { Log::Fatal("Unknown tree learner type %s", value.c_str()); } } } void Config::GetAucMuWeights() { if (auc_mu_weights.empty()) { // equal weights for all classes auc_mu_weights_matrix = std::vector> (num_class, std::vector(num_class, 1)); for (size_t i = 0; i < static_cast(num_class); ++i) { auc_mu_weights_matrix[i][i] = 0; } } else { auc_mu_weights_matrix = std::vector> (num_class, std::vector(num_class, 0)); if (auc_mu_weights.size() != static_cast(num_class * num_class)) { Log::Fatal("auc_mu_weights must have %d elements, but found %zu", num_class * num_class, auc_mu_weights.size()); } for (size_t i = 0; i < static_cast(num_class); ++i) { for (size_t j = 0; j < static_cast(num_class); ++j) { if (i == j) { auc_mu_weights_matrix[i][j] = 0; if (std::fabs(auc_mu_weights[i * num_class + j]) > kZeroThreshold) { 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); } } else { if (std::fabs(auc_mu_weights[i * num_class + j]) < kZeroThreshold) { 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); } auc_mu_weights_matrix[i][j] = auc_mu_weights[i * num_class + j]; } } } } } void Config::GetInteractionConstraints() { if (interaction_constraints == "") { interaction_constraints_vector = std::vector>(); } else { interaction_constraints_vector = Common::StringToArrayofArrays(interaction_constraints, '[', ']', ','); } } void Config::Set(const std::unordered_map& params) { // generate seeds by seed. if (GetInt(params, "seed", &seed)) { Random rand(seed); int int_max = std::numeric_limits::max(); data_random_seed = static_cast(rand.NextShort(0, int_max)); bagging_seed = static_cast(rand.NextShort(0, int_max)); drop_seed = static_cast(rand.NextShort(0, int_max)); feature_fraction_seed = static_cast(rand.NextShort(0, int_max)); objective_seed = static_cast(rand.NextShort(0, int_max)); extra_seed = static_cast(rand.NextShort(0, int_max)); } GetTaskType(params, &task); GetBoostingType(params, &boosting); GetDataSampleStrategy(params, &data_sample_strategy); GetObjectiveType(params, &objective); GetMetricType(params, objective, &metric); GetDeviceType(params, &device_type); if (device_type == std::string("cuda")) { LGBM_config_::current_device = lgbm_device_cuda; } GetTreeLearnerType(params, &tree_learner); GetMembersFromString(params); GetAucMuWeights(); GetInteractionConstraints(); // sort eval_at std::sort(eval_at.begin(), eval_at.end()); std::vector new_valid; for (size_t i = 0; i < valid.size(); ++i) { if (valid[i] != data) { // Only push the non-training data new_valid.push_back(valid[i]); } else { is_provide_training_metric = true; } } valid = new_valid; if ((task == TaskType::kSaveBinary) && !save_binary) { Log::Info("save_binary parameter set to true because task is save_binary"); save_binary = true; } // check for conflicts CheckParamConflict(params); } bool CheckMultiClassObjective(const std::string& objective) { return (objective == std::string("multiclass") || objective == std::string("multiclassova")); } void Config::CheckParamConflict(const std::unordered_map& params) { // check if objective, metric, and num_class match int num_class_check = num_class; bool objective_type_multiclass = CheckMultiClassObjective(objective) || (objective == std::string("custom") && num_class_check > 1); if (objective_type_multiclass) { if (num_class_check <= 1) { Log::Fatal("Number of classes should be specified and greater than 1 for multiclass training"); } } else { if (task == TaskType::kTrain && num_class_check != 1) { Log::Fatal("Number of classes must be 1 for non-multiclass training"); } } for (std::string metric_type : metric) { bool metric_type_multiclass = (CheckMultiClassObjective(metric_type) || metric_type == std::string("multi_logloss") || metric_type == std::string("multi_error") || metric_type == std::string("auc_mu") || (metric_type == std::string("custom") && num_class_check > 1)); if ((objective_type_multiclass && !metric_type_multiclass) || (!objective_type_multiclass && metric_type_multiclass)) { Log::Fatal("Multiclass objective and metrics don't match"); } } if (num_machines > 1) { is_parallel = true; } else { is_parallel = false; tree_learner = "serial"; } bool is_single_tree_learner = tree_learner == std::string("serial"); if (is_single_tree_learner) { is_parallel = false; num_machines = 1; } if (is_single_tree_learner || tree_learner == std::string("feature")) { is_data_based_parallel = false; } else if (tree_learner == std::string("data") || tree_learner == std::string("voting")) { is_data_based_parallel = true; if (histogram_pool_size >= 0 && tree_learner == std::string("data")) { Log::Warning("Histogram LRU queue was enabled (histogram_pool_size=%f).\n" "Will disable this to reduce communication costs", histogram_pool_size); // Change pool size to -1 (no limit) when using data parallel to reduce communication costs histogram_pool_size = -1; } } if (is_data_based_parallel) { if (!forcedsplits_filename.empty()) { Log::Fatal("Don't support forcedsplits in %s tree learner", tree_learner.c_str()); } } // max_depth defaults to -1, so max_depth>0 implies "you explicitly overrode the default" // // Changing max_depth while leaving num_leaves at its default (31) can lead to 2 undesirable situations: // // * (0 <= max_depth <= 4) it's not possible to produce a tree with 31 leaves // - this block reduces num_leaves to 2^max_depth // * (max_depth > 4) 31 leaves is less than a full depth-wise tree, which might lead to underfitting // - this block warns about that // ref: https://github.com/lightgbm-org/LightGBM/issues/2898#issuecomment-1002860601 if (max_depth > 0 && (params.count("num_leaves") == 0 || params.at("num_leaves").empty())) { double full_num_leaves = std::pow(2, max_depth); if (full_num_leaves > num_leaves) { Log::Warning("Provided parameters constrain tree depth (max_depth=%d) without explicitly setting 'num_leaves'. " "This can lead to underfitting. To resolve this warning, pass 'num_leaves' (<=%.0f) in params. " "Alternatively, pass (max_depth=-1) and just use 'num_leaves' to constrain model complexity.", max_depth, full_num_leaves); } if (full_num_leaves < num_leaves) { // Fits in an int, and is more restrictive than the current num_leaves num_leaves = static_cast(full_num_leaves); } } if (device_type == std::string("gpu")) { // force col-wise for gpu version force_col_wise = true; force_row_wise = false; if (deterministic) { Log::Warning("Although \"deterministic\" is set, the results ran by GPU may be non-deterministic."); } if (use_quantized_grad) { Log::Warning("Quantized training is not supported by GPU tree learner. Switch to full precision training."); use_quantized_grad = false; } } else if (device_type == std::string("cuda")) { // force row-wise for cuda version force_col_wise = false; force_row_wise = true; if (deterministic) { Log::Warning("Although \"deterministic\" is set, the results ran by GPU may be non-deterministic."); } } // linear tree learner must be serial type and run on CPU device if (linear_tree) { if (device_type != std::string("cpu") && device_type != std::string("gpu")) { device_type = "cpu"; Log::Warning("Linear tree learner only works with CPU and GPU. Falling back to CPU now."); } if (tree_learner != std::string("serial")) { tree_learner = "serial"; Log::Warning("Linear tree learner must be serial."); } if (zero_as_missing) { Log::Fatal("zero_as_missing must be false when fitting linear trees."); } if (objective == std::string("regression_l1")) { Log::Fatal("Cannot use regression_l1 objective when fitting linear trees."); } } // min_data_in_leaf must be at least 2 if path smoothing is active. This is because when the split is calculated // the count is calculated using the proportion of hessian in the leaf which is rounded up to nearest int, so it can // be 1 when there is actually no data in the leaf. In rare cases this can cause a bug because with path smoothing the // calculated split gain can be positive even with zero gradient and hessian. if (path_smooth > kEpsilon && min_data_in_leaf < 2) { min_data_in_leaf = 2; Log::Warning("min_data_in_leaf has been increased to 2 because this is required when path smoothing is active."); } if (is_parallel && (monotone_constraints_method == std::string("intermediate") || monotone_constraints_method == std::string("advanced"))) { // In distributed mode, local node doesn't have histograms on all features, cannot perform "intermediate" monotone constraints. Log::Warning("Cannot use \"intermediate\" or \"advanced\" monotone constraints in distributed learning, auto set to \"basic\" method."); monotone_constraints_method = "basic"; } if (feature_fraction_bynode != 1.0 && (monotone_constraints_method == std::string("intermediate") || monotone_constraints_method == std::string("advanced"))) { // "intermediate" monotone constraints need to recompute splits. If the features are sampled when computing the // split initially, then the sampling needs to be recorded or done once again, which is currently not supported Log::Warning("Cannot use \"intermediate\" or \"advanced\" monotone constraints with feature fraction different from 1, auto set monotone constraints to \"basic\" method."); monotone_constraints_method = "basic"; } if (max_depth > 0 && monotone_penalty >= max_depth) { Log::Warning("Monotone penalty greater than tree depth. Monotone features won't be used."); } if (min_data_in_leaf <= 0 && min_sum_hessian_in_leaf <= kEpsilon) { Log::Warning( "Cannot set both min_data_in_leaf and min_sum_hessian_in_leaf to 0. " "Will set min_data_in_leaf to 1."); min_data_in_leaf = 1; } if (boosting == std::string("goss")) { boosting = std::string("gbdt"); data_sample_strategy = std::string("goss"); Log::Warning("Found boosting=goss. For backwards compatibility reasons, LightGBM interprets this as boosting=gbdt, data_sample_strategy=goss." "To suppress this warning, set data_sample_strategy=goss instead."); } if (bagging_by_query && data_sample_strategy != std::string("bagging")) { Log::Warning("bagging_by_query=true is only compatible with data_sample_strategy=bagging. Setting bagging_by_query=false."); bagging_by_query = false; } } std::string Config::ToString() const { std::stringstream str_buf; str_buf << "[boosting: " << boosting << "]\n"; str_buf << "[objective: " << objective << "]\n"; str_buf << "[metric: " << Common::Join(metric, ",") << "]\n"; str_buf << "[tree_learner: " << tree_learner << "]\n"; str_buf << "[device_type: " << device_type << "]\n"; str_buf << SaveMembersToString(); return str_buf.str(); } const std::string Config::DumpAliases() { auto map = Config::parameter2aliases(); for (auto& pair : map) { std::sort(pair.second.begin(), pair.second.end(), SortAlias); } std::stringstream str_buf; str_buf << "{\n"; bool first = true; for (const auto& pair : map) { if (first) { str_buf << " \""; first = false; } else { str_buf << " , \""; } str_buf << pair.first << "\": ["; if (pair.second.size() > 0) { str_buf << "\"" << CommonC::Join(pair.second, "\", \"") << "\""; } str_buf << "]\n"; } str_buf << "}\n"; return str_buf.str(); } } // namespace LightGBM ================================================ FILE: src/io/config_auto.cpp ================================================ /*! * Copyright (c) 2018-2026 Microsoft Corporation. All rights reserved. * Copyright (c) 2018-2026 The LightGBM developers. All rights reserved. * Licensed under the MIT License. See LICENSE file in the project root for license information. * * \note * This file is auto generated by LightGBM\.ci\parameter-generator.py from LightGBM\include\LightGBM\config.h file. */ #include #include #include #include #include namespace LightGBM { const std::unordered_map& Config::alias_table() { static std::unordered_map aliases({ {"config_file", "config"}, {"task_type", "task"}, {"objective_type", "objective"}, {"app", "objective"}, {"application", "objective"}, {"loss", "objective"}, {"boosting_type", "boosting"}, {"boost", "boosting"}, {"train", "data"}, {"train_data", "data"}, {"train_data_file", "data"}, {"data_filename", "data"}, {"test", "valid"}, {"valid_data", "valid"}, {"valid_data_file", "valid"}, {"test_data", "valid"}, {"test_data_file", "valid"}, {"valid_filenames", "valid"}, {"num_iteration", "num_iterations"}, {"n_iter", "num_iterations"}, {"num_tree", "num_iterations"}, {"num_trees", "num_iterations"}, {"num_round", "num_iterations"}, {"num_rounds", "num_iterations"}, {"nrounds", "num_iterations"}, {"num_boost_round", "num_iterations"}, {"n_estimators", "num_iterations"}, {"max_iter", "num_iterations"}, {"shrinkage_rate", "learning_rate"}, {"eta", "learning_rate"}, {"num_leaf", "num_leaves"}, {"max_leaves", "num_leaves"}, {"max_leaf", "num_leaves"}, {"max_leaf_nodes", "num_leaves"}, {"tree", "tree_learner"}, {"tree_type", "tree_learner"}, {"tree_learner_type", "tree_learner"}, {"num_thread", "num_threads"}, {"nthread", "num_threads"}, {"nthreads", "num_threads"}, {"n_jobs", "num_threads"}, {"device", "device_type"}, {"random_seed", "seed"}, {"random_state", "seed"}, {"hist_pool_size", "histogram_pool_size"}, {"min_data_per_leaf", "min_data_in_leaf"}, {"min_data", "min_data_in_leaf"}, {"min_child_samples", "min_data_in_leaf"}, {"min_samples_leaf", "min_data_in_leaf"}, {"min_sum_hessian_per_leaf", "min_sum_hessian_in_leaf"}, {"min_sum_hessian", "min_sum_hessian_in_leaf"}, {"min_hessian", "min_sum_hessian_in_leaf"}, {"min_child_weight", "min_sum_hessian_in_leaf"}, {"sub_row", "bagging_fraction"}, {"subsample", "bagging_fraction"}, {"bagging", "bagging_fraction"}, {"pos_sub_row", "pos_bagging_fraction"}, {"pos_subsample", "pos_bagging_fraction"}, {"pos_bagging", "pos_bagging_fraction"}, {"neg_sub_row", "neg_bagging_fraction"}, {"neg_subsample", "neg_bagging_fraction"}, {"neg_bagging", "neg_bagging_fraction"}, {"subsample_freq", "bagging_freq"}, {"bagging_fraction_seed", "bagging_seed"}, {"sub_feature", "feature_fraction"}, {"colsample_bytree", "feature_fraction"}, {"sub_feature_bynode", "feature_fraction_bynode"}, {"colsample_bynode", "feature_fraction_bynode"}, {"extra_tree", "extra_trees"}, {"early_stopping_rounds", "early_stopping_round"}, {"early_stopping", "early_stopping_round"}, {"n_iter_no_change", "early_stopping_round"}, {"max_tree_output", "max_delta_step"}, {"max_leaf_output", "max_delta_step"}, {"reg_alpha", "lambda_l1"}, {"l1_regularization", "lambda_l1"}, {"reg_lambda", "lambda_l2"}, {"lambda", "lambda_l2"}, {"l2_regularization", "lambda_l2"}, {"min_split_gain", "min_gain_to_split"}, {"rate_drop", "drop_rate"}, {"topk", "top_k"}, {"mc", "monotone_constraints"}, {"monotone_constraint", "monotone_constraints"}, {"monotonic_cst", "monotone_constraints"}, {"monotone_constraining_method", "monotone_constraints_method"}, {"mc_method", "monotone_constraints_method"}, {"monotone_splits_penalty", "monotone_penalty"}, {"ms_penalty", "monotone_penalty"}, {"mc_penalty", "monotone_penalty"}, {"feature_contrib", "feature_contri"}, {"fc", "feature_contri"}, {"fp", "feature_contri"}, {"feature_penalty", "feature_contri"}, {"fs", "forcedsplits_filename"}, {"forced_splits_filename", "forcedsplits_filename"}, {"forced_splits_file", "forcedsplits_filename"}, {"forced_splits", "forcedsplits_filename"}, {"verbose", "verbosity"}, {"model_input", "input_model"}, {"model_in", "input_model"}, {"model_output", "output_model"}, {"model_out", "output_model"}, {"save_period", "snapshot_freq"}, {"linear_trees", "linear_tree"}, {"max_bins", "max_bin"}, {"subsample_for_bin", "bin_construct_sample_cnt"}, {"data_seed", "data_random_seed"}, {"is_sparse", "is_enable_sparse"}, {"enable_sparse", "is_enable_sparse"}, {"sparse", "is_enable_sparse"}, {"is_enable_bundle", "enable_bundle"}, {"bundle", "enable_bundle"}, {"is_pre_partition", "pre_partition"}, {"two_round_loading", "two_round"}, {"use_two_round_loading", "two_round"}, {"has_header", "header"}, {"label", "label_column"}, {"weight", "weight_column"}, {"group", "group_column"}, {"group_id", "group_column"}, {"query_column", "group_column"}, {"query", "group_column"}, {"query_id", "group_column"}, {"ignore_feature", "ignore_column"}, {"blacklist", "ignore_column"}, {"cat_feature", "categorical_feature"}, {"categorical_column", "categorical_feature"}, {"cat_column", "categorical_feature"}, {"categorical_features", "categorical_feature"}, {"is_save_binary", "save_binary"}, {"is_save_binary_file", "save_binary"}, {"is_predict_raw_score", "predict_raw_score"}, {"predict_rawscore", "predict_raw_score"}, {"raw_score", "predict_raw_score"}, {"is_predict_leaf_index", "predict_leaf_index"}, {"leaf_index", "predict_leaf_index"}, {"is_predict_contrib", "predict_contrib"}, {"contrib", "predict_contrib"}, {"predict_result", "output_result"}, {"prediction_result", "output_result"}, {"predict_name", "output_result"}, {"prediction_name", "output_result"}, {"pred_name", "output_result"}, {"name_pred", "output_result"}, {"convert_model_file", "convert_model"}, {"num_classes", "num_class"}, {"unbalance", "is_unbalance"}, {"unbalanced_sets", "is_unbalance"}, {"metrics", "metric"}, {"metric_types", "metric"}, {"output_freq", "metric_freq"}, {"training_metric", "is_provide_training_metric"}, {"is_training_metric", "is_provide_training_metric"}, {"train_metric", "is_provide_training_metric"}, {"ndcg_eval_at", "eval_at"}, {"ndcg_at", "eval_at"}, {"map_eval_at", "eval_at"}, {"map_at", "eval_at"}, {"num_machine", "num_machines"}, {"local_port", "local_listen_port"}, {"port", "local_listen_port"}, {"machine_list_file", "machine_list_filename"}, {"machine_list", "machine_list_filename"}, {"mlist", "machine_list_filename"}, {"workers", "machines"}, {"nodes", "machines"}, }); return aliases; } const std::unordered_set& Config::parameter_set() { static std::unordered_set params({ "config", "task", "objective", "boosting", "data_sample_strategy", "data", "valid", "num_iterations", "learning_rate", "num_leaves", "tree_learner", "num_threads", "device_type", "seed", "deterministic", "force_col_wise", "force_row_wise", "histogram_pool_size", "max_depth", "min_data_in_leaf", "min_sum_hessian_in_leaf", "bagging_fraction", "pos_bagging_fraction", "neg_bagging_fraction", "bagging_freq", "bagging_seed", "bagging_by_query", "feature_fraction", "feature_fraction_bynode", "feature_fraction_seed", "extra_trees", "extra_seed", "early_stopping_round", "early_stopping_min_delta", "first_metric_only", "max_delta_step", "lambda_l1", "lambda_l2", "linear_lambda", "min_gain_to_split", "drop_rate", "max_drop", "skip_drop", "xgboost_dart_mode", "uniform_drop", "drop_seed", "top_rate", "other_rate", "min_data_per_group", "max_cat_threshold", "cat_l2", "cat_smooth", "max_cat_to_onehot", "top_k", "monotone_constraints", "monotone_constraints_method", "monotone_penalty", "feature_contri", "forcedsplits_filename", "refit_decay_rate", "cegb_tradeoff", "cegb_penalty_split", "cegb_penalty_feature_lazy", "cegb_penalty_feature_coupled", "path_smooth", "interaction_constraints", "verbosity", "input_model", "output_model", "saved_feature_importance_type", "snapshot_freq", "use_quantized_grad", "num_grad_quant_bins", "quant_train_renew_leaf", "stochastic_rounding", "linear_tree", "max_bin", "max_bin_by_feature", "min_data_in_bin", "bin_construct_sample_cnt", "data_random_seed", "is_enable_sparse", "enable_bundle", "use_missing", "zero_as_missing", "feature_pre_filter", "pre_partition", "two_round", "header", "label_column", "weight_column", "group_column", "ignore_column", "categorical_feature", "forcedbins_filename", "save_binary", "precise_float_parser", "parser_config_file", "start_iteration_predict", "num_iteration_predict", "predict_raw_score", "predict_leaf_index", "predict_contrib", "predict_disable_shape_check", "pred_early_stop", "pred_early_stop_freq", "pred_early_stop_margin", "output_result", "convert_model_language", "convert_model", "objective_seed", "num_class", "is_unbalance", "scale_pos_weight", "sigmoid", "boost_from_average", "reg_sqrt", "alpha", "fair_c", "poisson_max_delta_step", "tweedie_variance_power", "lambdarank_truncation_level", "lambdarank_norm", "label_gain", "lambdarank_position_bias_regularization", "metric", "metric_freq", "is_provide_training_metric", "eval_at", "multi_error_top_k", "auc_mu_weights", "num_machines", "local_listen_port", "time_out", "machine_list_filename", "machines", "gpu_platform_id", "gpu_device_id", "gpu_device_id_list", "gpu_use_dp", "num_gpu", }); return params; } void Config::GetMembersFromString(const std::unordered_map& params) { std::string tmp_str = ""; GetString(params, "data", &data); if (GetString(params, "valid", &tmp_str)) { valid = Common::Split(tmp_str.c_str(), ','); } GetInt(params, "num_iterations", &num_iterations); CHECK_GE(num_iterations, 0); GetDouble(params, "learning_rate", &learning_rate); CHECK_GT(learning_rate, 0.0); GetInt(params, "num_leaves", &num_leaves); CHECK_GT(num_leaves, 1); CHECK_LE(num_leaves, 131072); GetInt(params, "num_threads", &num_threads); GetBool(params, "deterministic", &deterministic); GetBool(params, "force_col_wise", &force_col_wise); GetBool(params, "force_row_wise", &force_row_wise); GetDouble(params, "histogram_pool_size", &histogram_pool_size); GetInt(params, "max_depth", &max_depth); GetInt(params, "min_data_in_leaf", &min_data_in_leaf); CHECK_GE(min_data_in_leaf, 0); GetDouble(params, "min_sum_hessian_in_leaf", &min_sum_hessian_in_leaf); CHECK_GE(min_sum_hessian_in_leaf, 0.0); GetDouble(params, "bagging_fraction", &bagging_fraction); CHECK_GT(bagging_fraction, 0.0); CHECK_LE(bagging_fraction, 1.0); GetDouble(params, "pos_bagging_fraction", &pos_bagging_fraction); CHECK_GT(pos_bagging_fraction, 0.0); CHECK_LE(pos_bagging_fraction, 1.0); GetDouble(params, "neg_bagging_fraction", &neg_bagging_fraction); CHECK_GT(neg_bagging_fraction, 0.0); CHECK_LE(neg_bagging_fraction, 1.0); GetInt(params, "bagging_freq", &bagging_freq); GetInt(params, "bagging_seed", &bagging_seed); GetBool(params, "bagging_by_query", &bagging_by_query); GetDouble(params, "feature_fraction", &feature_fraction); CHECK_GT(feature_fraction, 0.0); CHECK_LE(feature_fraction, 1.0); GetDouble(params, "feature_fraction_bynode", &feature_fraction_bynode); CHECK_GT(feature_fraction_bynode, 0.0); CHECK_LE(feature_fraction_bynode, 1.0); GetInt(params, "feature_fraction_seed", &feature_fraction_seed); GetBool(params, "extra_trees", &extra_trees); GetInt(params, "extra_seed", &extra_seed); GetInt(params, "early_stopping_round", &early_stopping_round); GetDouble(params, "early_stopping_min_delta", &early_stopping_min_delta); CHECK_GE(early_stopping_min_delta, 0.0); GetBool(params, "first_metric_only", &first_metric_only); GetDouble(params, "max_delta_step", &max_delta_step); GetDouble(params, "lambda_l1", &lambda_l1); CHECK_GE(lambda_l1, 0.0); GetDouble(params, "lambda_l2", &lambda_l2); CHECK_GE(lambda_l2, 0.0); GetDouble(params, "linear_lambda", &linear_lambda); CHECK_GE(linear_lambda, 0.0); GetDouble(params, "min_gain_to_split", &min_gain_to_split); CHECK_GE(min_gain_to_split, 0.0); GetDouble(params, "drop_rate", &drop_rate); CHECK_GE(drop_rate, 0.0); CHECK_LE(drop_rate, 1.0); GetInt(params, "max_drop", &max_drop); GetDouble(params, "skip_drop", &skip_drop); CHECK_GE(skip_drop, 0.0); CHECK_LE(skip_drop, 1.0); GetBool(params, "xgboost_dart_mode", &xgboost_dart_mode); GetBool(params, "uniform_drop", &uniform_drop); GetInt(params, "drop_seed", &drop_seed); GetDouble(params, "top_rate", &top_rate); CHECK_GE(top_rate, 0.0); CHECK_LE(top_rate, 1.0); GetDouble(params, "other_rate", &other_rate); CHECK_GE(other_rate, 0.0); CHECK_LE(other_rate, 1.0); GetInt(params, "min_data_per_group", &min_data_per_group); CHECK_GT(min_data_per_group, 0); GetInt(params, "max_cat_threshold", &max_cat_threshold); CHECK_GT(max_cat_threshold, 0); GetDouble(params, "cat_l2", &cat_l2); CHECK_GE(cat_l2, 0.0); GetDouble(params, "cat_smooth", &cat_smooth); CHECK_GE(cat_smooth, 0.0); GetInt(params, "max_cat_to_onehot", &max_cat_to_onehot); CHECK_GT(max_cat_to_onehot, 0); GetInt(params, "top_k", &top_k); CHECK_GT(top_k, 0); if (GetString(params, "monotone_constraints", &tmp_str)) { monotone_constraints = Common::StringToArray(tmp_str, ','); } GetString(params, "monotone_constraints_method", &monotone_constraints_method); GetDouble(params, "monotone_penalty", &monotone_penalty); CHECK_GE(monotone_penalty, 0.0); if (GetString(params, "feature_contri", &tmp_str)) { feature_contri = Common::StringToArray(tmp_str, ','); } GetString(params, "forcedsplits_filename", &forcedsplits_filename); GetDouble(params, "refit_decay_rate", &refit_decay_rate); CHECK_GE(refit_decay_rate, 0.0); CHECK_LE(refit_decay_rate, 1.0); GetDouble(params, "cegb_tradeoff", &cegb_tradeoff); CHECK_GE(cegb_tradeoff, 0.0); GetDouble(params, "cegb_penalty_split", &cegb_penalty_split); CHECK_GE(cegb_penalty_split, 0.0); if (GetString(params, "cegb_penalty_feature_lazy", &tmp_str)) { cegb_penalty_feature_lazy = Common::StringToArray(tmp_str, ','); } if (GetString(params, "cegb_penalty_feature_coupled", &tmp_str)) { cegb_penalty_feature_coupled = Common::StringToArray(tmp_str, ','); } GetDouble(params, "path_smooth", &path_smooth); CHECK_GE(path_smooth, 0.0); GetString(params, "interaction_constraints", &interaction_constraints); GetInt(params, "verbosity", &verbosity); GetString(params, "input_model", &input_model); GetString(params, "output_model", &output_model); GetInt(params, "saved_feature_importance_type", &saved_feature_importance_type); GetInt(params, "snapshot_freq", &snapshot_freq); GetBool(params, "use_quantized_grad", &use_quantized_grad); GetInt(params, "num_grad_quant_bins", &num_grad_quant_bins); GetBool(params, "quant_train_renew_leaf", &quant_train_renew_leaf); GetBool(params, "stochastic_rounding", &stochastic_rounding); GetBool(params, "linear_tree", &linear_tree); GetInt(params, "max_bin", &max_bin); CHECK_GT(max_bin, 1); if (GetString(params, "max_bin_by_feature", &tmp_str)) { max_bin_by_feature = Common::StringToArray(tmp_str, ','); } GetInt(params, "min_data_in_bin", &min_data_in_bin); CHECK_GT(min_data_in_bin, 0); GetInt(params, "bin_construct_sample_cnt", &bin_construct_sample_cnt); CHECK_GT(bin_construct_sample_cnt, 0); GetInt(params, "data_random_seed", &data_random_seed); GetBool(params, "is_enable_sparse", &is_enable_sparse); GetBool(params, "enable_bundle", &enable_bundle); GetBool(params, "use_missing", &use_missing); GetBool(params, "zero_as_missing", &zero_as_missing); GetBool(params, "feature_pre_filter", &feature_pre_filter); GetBool(params, "pre_partition", &pre_partition); GetBool(params, "two_round", &two_round); GetBool(params, "header", &header); GetString(params, "label_column", &label_column); GetString(params, "weight_column", &weight_column); GetString(params, "group_column", &group_column); GetString(params, "ignore_column", &ignore_column); GetString(params, "categorical_feature", &categorical_feature); GetString(params, "forcedbins_filename", &forcedbins_filename); GetBool(params, "save_binary", &save_binary); GetBool(params, "precise_float_parser", &precise_float_parser); GetString(params, "parser_config_file", &parser_config_file); GetInt(params, "start_iteration_predict", &start_iteration_predict); GetInt(params, "num_iteration_predict", &num_iteration_predict); GetBool(params, "predict_raw_score", &predict_raw_score); GetBool(params, "predict_leaf_index", &predict_leaf_index); GetBool(params, "predict_contrib", &predict_contrib); GetBool(params, "predict_disable_shape_check", &predict_disable_shape_check); GetBool(params, "pred_early_stop", &pred_early_stop); GetInt(params, "pred_early_stop_freq", &pred_early_stop_freq); GetDouble(params, "pred_early_stop_margin", &pred_early_stop_margin); GetString(params, "output_result", &output_result); GetString(params, "convert_model_language", &convert_model_language); GetString(params, "convert_model", &convert_model); GetInt(params, "objective_seed", &objective_seed); GetInt(params, "num_class", &num_class); CHECK_GT(num_class, 0); GetBool(params, "is_unbalance", &is_unbalance); GetDouble(params, "scale_pos_weight", &scale_pos_weight); CHECK_GT(scale_pos_weight, 0.0); GetDouble(params, "sigmoid", &sigmoid); CHECK_GT(sigmoid, 0.0); GetBool(params, "boost_from_average", &boost_from_average); GetBool(params, "reg_sqrt", ®_sqrt); GetDouble(params, "alpha", &alpha); CHECK_GT(alpha, 0.0); GetDouble(params, "fair_c", &fair_c); CHECK_GT(fair_c, 0.0); GetDouble(params, "poisson_max_delta_step", &poisson_max_delta_step); CHECK_GT(poisson_max_delta_step, 0.0); GetDouble(params, "tweedie_variance_power", &tweedie_variance_power); CHECK_GE(tweedie_variance_power, 1.0); CHECK_LT(tweedie_variance_power, 2.0); GetInt(params, "lambdarank_truncation_level", &lambdarank_truncation_level); CHECK_GT(lambdarank_truncation_level, 0); GetBool(params, "lambdarank_norm", &lambdarank_norm); if (GetString(params, "label_gain", &tmp_str)) { label_gain = Common::StringToArray(tmp_str, ','); } GetDouble(params, "lambdarank_position_bias_regularization", &lambdarank_position_bias_regularization); CHECK_GE(lambdarank_position_bias_regularization, 0.0); GetInt(params, "metric_freq", &metric_freq); CHECK_GT(metric_freq, 0); GetBool(params, "is_provide_training_metric", &is_provide_training_metric); if (GetString(params, "eval_at", &tmp_str)) { eval_at = Common::StringToArray(tmp_str, ','); } GetInt(params, "multi_error_top_k", &multi_error_top_k); CHECK_GT(multi_error_top_k, 0); if (GetString(params, "auc_mu_weights", &tmp_str)) { auc_mu_weights = Common::StringToArray(tmp_str, ','); } GetInt(params, "num_machines", &num_machines); CHECK_GT(num_machines, 0); GetInt(params, "local_listen_port", &local_listen_port); CHECK_GT(local_listen_port, 0); GetInt(params, "time_out", &time_out); CHECK_GT(time_out, 0); GetString(params, "machine_list_filename", &machine_list_filename); GetString(params, "machines", &machines); GetInt(params, "gpu_platform_id", &gpu_platform_id); GetInt(params, "gpu_device_id", &gpu_device_id); GetString(params, "gpu_device_id_list", &gpu_device_id_list); GetBool(params, "gpu_use_dp", &gpu_use_dp); GetInt(params, "num_gpu", &num_gpu); CHECK_GT(num_gpu, 0); } std::string Config::SaveMembersToString() const { std::stringstream str_buf; str_buf << "[data_sample_strategy: " << data_sample_strategy << "]\n"; str_buf << "[data: " << data << "]\n"; str_buf << "[valid: " << Common::Join(valid, ",") << "]\n"; str_buf << "[num_iterations: " << num_iterations << "]\n"; str_buf << "[learning_rate: " << learning_rate << "]\n"; str_buf << "[num_leaves: " << num_leaves << "]\n"; str_buf << "[num_threads: " << num_threads << "]\n"; str_buf << "[seed: " << seed << "]\n"; str_buf << "[deterministic: " << deterministic << "]\n"; str_buf << "[force_col_wise: " << force_col_wise << "]\n"; str_buf << "[force_row_wise: " << force_row_wise << "]\n"; str_buf << "[histogram_pool_size: " << histogram_pool_size << "]\n"; str_buf << "[max_depth: " << max_depth << "]\n"; str_buf << "[min_data_in_leaf: " << min_data_in_leaf << "]\n"; str_buf << "[min_sum_hessian_in_leaf: " << min_sum_hessian_in_leaf << "]\n"; str_buf << "[bagging_fraction: " << bagging_fraction << "]\n"; str_buf << "[pos_bagging_fraction: " << pos_bagging_fraction << "]\n"; str_buf << "[neg_bagging_fraction: " << neg_bagging_fraction << "]\n"; str_buf << "[bagging_freq: " << bagging_freq << "]\n"; str_buf << "[bagging_seed: " << bagging_seed << "]\n"; str_buf << "[bagging_by_query: " << bagging_by_query << "]\n"; str_buf << "[feature_fraction: " << feature_fraction << "]\n"; str_buf << "[feature_fraction_bynode: " << feature_fraction_bynode << "]\n"; str_buf << "[feature_fraction_seed: " << feature_fraction_seed << "]\n"; str_buf << "[extra_trees: " << extra_trees << "]\n"; str_buf << "[extra_seed: " << extra_seed << "]\n"; str_buf << "[early_stopping_round: " << early_stopping_round << "]\n"; str_buf << "[early_stopping_min_delta: " << early_stopping_min_delta << "]\n"; str_buf << "[first_metric_only: " << first_metric_only << "]\n"; str_buf << "[max_delta_step: " << max_delta_step << "]\n"; str_buf << "[lambda_l1: " << lambda_l1 << "]\n"; str_buf << "[lambda_l2: " << lambda_l2 << "]\n"; str_buf << "[linear_lambda: " << linear_lambda << "]\n"; str_buf << "[min_gain_to_split: " << min_gain_to_split << "]\n"; str_buf << "[drop_rate: " << drop_rate << "]\n"; str_buf << "[max_drop: " << max_drop << "]\n"; str_buf << "[skip_drop: " << skip_drop << "]\n"; str_buf << "[xgboost_dart_mode: " << xgboost_dart_mode << "]\n"; str_buf << "[uniform_drop: " << uniform_drop << "]\n"; str_buf << "[drop_seed: " << drop_seed << "]\n"; str_buf << "[top_rate: " << top_rate << "]\n"; str_buf << "[other_rate: " << other_rate << "]\n"; str_buf << "[min_data_per_group: " << min_data_per_group << "]\n"; str_buf << "[max_cat_threshold: " << max_cat_threshold << "]\n"; str_buf << "[cat_l2: " << cat_l2 << "]\n"; str_buf << "[cat_smooth: " << cat_smooth << "]\n"; str_buf << "[max_cat_to_onehot: " << max_cat_to_onehot << "]\n"; str_buf << "[top_k: " << top_k << "]\n"; str_buf << "[monotone_constraints: " << Common::Join(Common::ArrayCast(monotone_constraints), ",") << "]\n"; str_buf << "[monotone_constraints_method: " << monotone_constraints_method << "]\n"; str_buf << "[monotone_penalty: " << monotone_penalty << "]\n"; str_buf << "[feature_contri: " << Common::Join(feature_contri, ",") << "]\n"; str_buf << "[forcedsplits_filename: " << forcedsplits_filename << "]\n"; str_buf << "[refit_decay_rate: " << refit_decay_rate << "]\n"; str_buf << "[cegb_tradeoff: " << cegb_tradeoff << "]\n"; str_buf << "[cegb_penalty_split: " << cegb_penalty_split << "]\n"; str_buf << "[cegb_penalty_feature_lazy: " << Common::Join(cegb_penalty_feature_lazy, ",") << "]\n"; str_buf << "[cegb_penalty_feature_coupled: " << Common::Join(cegb_penalty_feature_coupled, ",") << "]\n"; str_buf << "[path_smooth: " << path_smooth << "]\n"; str_buf << "[interaction_constraints: " << interaction_constraints << "]\n"; str_buf << "[verbosity: " << verbosity << "]\n"; str_buf << "[saved_feature_importance_type: " << saved_feature_importance_type << "]\n"; str_buf << "[use_quantized_grad: " << use_quantized_grad << "]\n"; str_buf << "[num_grad_quant_bins: " << num_grad_quant_bins << "]\n"; str_buf << "[quant_train_renew_leaf: " << quant_train_renew_leaf << "]\n"; str_buf << "[stochastic_rounding: " << stochastic_rounding << "]\n"; str_buf << "[linear_tree: " << linear_tree << "]\n"; str_buf << "[max_bin: " << max_bin << "]\n"; str_buf << "[max_bin_by_feature: " << Common::Join(max_bin_by_feature, ",") << "]\n"; str_buf << "[min_data_in_bin: " << min_data_in_bin << "]\n"; str_buf << "[bin_construct_sample_cnt: " << bin_construct_sample_cnt << "]\n"; str_buf << "[data_random_seed: " << data_random_seed << "]\n"; str_buf << "[is_enable_sparse: " << is_enable_sparse << "]\n"; str_buf << "[enable_bundle: " << enable_bundle << "]\n"; str_buf << "[use_missing: " << use_missing << "]\n"; str_buf << "[zero_as_missing: " << zero_as_missing << "]\n"; str_buf << "[feature_pre_filter: " << feature_pre_filter << "]\n"; str_buf << "[pre_partition: " << pre_partition << "]\n"; str_buf << "[two_round: " << two_round << "]\n"; str_buf << "[header: " << header << "]\n"; str_buf << "[label_column: " << label_column << "]\n"; str_buf << "[weight_column: " << weight_column << "]\n"; str_buf << "[group_column: " << group_column << "]\n"; str_buf << "[ignore_column: " << ignore_column << "]\n"; str_buf << "[categorical_feature: " << categorical_feature << "]\n"; str_buf << "[forcedbins_filename: " << forcedbins_filename << "]\n"; str_buf << "[precise_float_parser: " << precise_float_parser << "]\n"; str_buf << "[parser_config_file: " << parser_config_file << "]\n"; str_buf << "[objective_seed: " << objective_seed << "]\n"; str_buf << "[num_class: " << num_class << "]\n"; str_buf << "[is_unbalance: " << is_unbalance << "]\n"; str_buf << "[scale_pos_weight: " << scale_pos_weight << "]\n"; str_buf << "[sigmoid: " << sigmoid << "]\n"; str_buf << "[boost_from_average: " << boost_from_average << "]\n"; str_buf << "[reg_sqrt: " << reg_sqrt << "]\n"; str_buf << "[alpha: " << alpha << "]\n"; str_buf << "[fair_c: " << fair_c << "]\n"; str_buf << "[poisson_max_delta_step: " << poisson_max_delta_step << "]\n"; str_buf << "[tweedie_variance_power: " << tweedie_variance_power << "]\n"; str_buf << "[lambdarank_truncation_level: " << lambdarank_truncation_level << "]\n"; str_buf << "[lambdarank_norm: " << lambdarank_norm << "]\n"; str_buf << "[label_gain: " << Common::Join(label_gain, ",") << "]\n"; str_buf << "[lambdarank_position_bias_regularization: " << lambdarank_position_bias_regularization << "]\n"; str_buf << "[eval_at: " << Common::Join(eval_at, ",") << "]\n"; str_buf << "[multi_error_top_k: " << multi_error_top_k << "]\n"; str_buf << "[auc_mu_weights: " << Common::Join(auc_mu_weights, ",") << "]\n"; str_buf << "[num_machines: " << num_machines << "]\n"; str_buf << "[local_listen_port: " << local_listen_port << "]\n"; str_buf << "[time_out: " << time_out << "]\n"; str_buf << "[machine_list_filename: " << machine_list_filename << "]\n"; str_buf << "[machines: " << machines << "]\n"; str_buf << "[gpu_platform_id: " << gpu_platform_id << "]\n"; str_buf << "[gpu_device_id: " << gpu_device_id << "]\n"; str_buf << "[gpu_device_id_list: " << gpu_device_id_list << "]\n"; str_buf << "[gpu_use_dp: " << gpu_use_dp << "]\n"; str_buf << "[num_gpu: " << num_gpu << "]\n"; return str_buf.str(); } const std::unordered_map>& Config::parameter2aliases() { static std::unordered_map> map({ {"config", {"config_file"}}, {"task", {"task_type"}}, {"objective", {"objective_type", "app", "application", "loss"}}, {"boosting", {"boosting_type", "boost"}}, {"data_sample_strategy", {}}, {"data", {"train", "train_data", "train_data_file", "data_filename"}}, {"valid", {"test", "valid_data", "valid_data_file", "test_data", "test_data_file", "valid_filenames"}}, {"num_iterations", {"num_iteration", "n_iter", "num_tree", "num_trees", "num_round", "num_rounds", "nrounds", "num_boost_round", "n_estimators", "max_iter"}}, {"learning_rate", {"shrinkage_rate", "eta"}}, {"num_leaves", {"num_leaf", "max_leaves", "max_leaf", "max_leaf_nodes"}}, {"tree_learner", {"tree", "tree_type", "tree_learner_type"}}, {"num_threads", {"num_thread", "nthread", "nthreads", "n_jobs"}}, {"device_type", {"device"}}, {"seed", {"random_seed", "random_state"}}, {"deterministic", {}}, {"force_col_wise", {}}, {"force_row_wise", {}}, {"histogram_pool_size", {"hist_pool_size"}}, {"max_depth", {}}, {"min_data_in_leaf", {"min_data_per_leaf", "min_data", "min_child_samples", "min_samples_leaf"}}, {"min_sum_hessian_in_leaf", {"min_sum_hessian_per_leaf", "min_sum_hessian", "min_hessian", "min_child_weight"}}, {"bagging_fraction", {"sub_row", "subsample", "bagging"}}, {"pos_bagging_fraction", {"pos_sub_row", "pos_subsample", "pos_bagging"}}, {"neg_bagging_fraction", {"neg_sub_row", "neg_subsample", "neg_bagging"}}, {"bagging_freq", {"subsample_freq"}}, {"bagging_seed", {"bagging_fraction_seed"}}, {"bagging_by_query", {}}, {"feature_fraction", {"sub_feature", "colsample_bytree"}}, {"feature_fraction_bynode", {"sub_feature_bynode", "colsample_bynode"}}, {"feature_fraction_seed", {}}, {"extra_trees", {"extra_tree"}}, {"extra_seed", {}}, {"early_stopping_round", {"early_stopping_rounds", "early_stopping", "n_iter_no_change"}}, {"early_stopping_min_delta", {}}, {"first_metric_only", {}}, {"max_delta_step", {"max_tree_output", "max_leaf_output"}}, {"lambda_l1", {"reg_alpha", "l1_regularization"}}, {"lambda_l2", {"reg_lambda", "lambda", "l2_regularization"}}, {"linear_lambda", {}}, {"min_gain_to_split", {"min_split_gain"}}, {"drop_rate", {"rate_drop"}}, {"max_drop", {}}, {"skip_drop", {}}, {"xgboost_dart_mode", {}}, {"uniform_drop", {}}, {"drop_seed", {}}, {"top_rate", {}}, {"other_rate", {}}, {"min_data_per_group", {}}, {"max_cat_threshold", {}}, {"cat_l2", {}}, {"cat_smooth", {}}, {"max_cat_to_onehot", {}}, {"top_k", {"topk"}}, {"monotone_constraints", {"mc", "monotone_constraint", "monotonic_cst"}}, {"monotone_constraints_method", {"monotone_constraining_method", "mc_method"}}, {"monotone_penalty", {"monotone_splits_penalty", "ms_penalty", "mc_penalty"}}, {"feature_contri", {"feature_contrib", "fc", "fp", "feature_penalty"}}, {"forcedsplits_filename", {"fs", "forced_splits_filename", "forced_splits_file", "forced_splits"}}, {"refit_decay_rate", {}}, {"cegb_tradeoff", {}}, {"cegb_penalty_split", {}}, {"cegb_penalty_feature_lazy", {}}, {"cegb_penalty_feature_coupled", {}}, {"path_smooth", {}}, {"interaction_constraints", {}}, {"verbosity", {"verbose"}}, {"input_model", {"model_input", "model_in"}}, {"output_model", {"model_output", "model_out"}}, {"saved_feature_importance_type", {}}, {"snapshot_freq", {"save_period"}}, {"use_quantized_grad", {}}, {"num_grad_quant_bins", {}}, {"quant_train_renew_leaf", {}}, {"stochastic_rounding", {}}, {"linear_tree", {"linear_trees"}}, {"max_bin", {"max_bins"}}, {"max_bin_by_feature", {}}, {"min_data_in_bin", {}}, {"bin_construct_sample_cnt", {"subsample_for_bin"}}, {"data_random_seed", {"data_seed"}}, {"is_enable_sparse", {"is_sparse", "enable_sparse", "sparse"}}, {"enable_bundle", {"is_enable_bundle", "bundle"}}, {"use_missing", {}}, {"zero_as_missing", {}}, {"feature_pre_filter", {}}, {"pre_partition", {"is_pre_partition"}}, {"two_round", {"two_round_loading", "use_two_round_loading"}}, {"header", {"has_header"}}, {"label_column", {"label"}}, {"weight_column", {"weight"}}, {"group_column", {"group", "group_id", "query_column", "query", "query_id"}}, {"ignore_column", {"ignore_feature", "blacklist"}}, {"categorical_feature", {"cat_feature", "categorical_column", "cat_column", "categorical_features"}}, {"forcedbins_filename", {}}, {"save_binary", {"is_save_binary", "is_save_binary_file"}}, {"precise_float_parser", {}}, {"parser_config_file", {}}, {"start_iteration_predict", {}}, {"num_iteration_predict", {}}, {"predict_raw_score", {"is_predict_raw_score", "predict_rawscore", "raw_score"}}, {"predict_leaf_index", {"is_predict_leaf_index", "leaf_index"}}, {"predict_contrib", {"is_predict_contrib", "contrib"}}, {"predict_disable_shape_check", {}}, {"pred_early_stop", {}}, {"pred_early_stop_freq", {}}, {"pred_early_stop_margin", {}}, {"output_result", {"predict_result", "prediction_result", "predict_name", "prediction_name", "pred_name", "name_pred"}}, {"convert_model_language", {}}, {"convert_model", {"convert_model_file"}}, {"objective_seed", {}}, {"num_class", {"num_classes"}}, {"is_unbalance", {"unbalance", "unbalanced_sets"}}, {"scale_pos_weight", {}}, {"sigmoid", {}}, {"boost_from_average", {}}, {"reg_sqrt", {}}, {"alpha", {}}, {"fair_c", {}}, {"poisson_max_delta_step", {}}, {"tweedie_variance_power", {}}, {"lambdarank_truncation_level", {}}, {"lambdarank_norm", {}}, {"label_gain", {}}, {"lambdarank_position_bias_regularization", {}}, {"metric", {"metrics", "metric_types"}}, {"metric_freq", {"output_freq"}}, {"is_provide_training_metric", {"training_metric", "is_training_metric", "train_metric"}}, {"eval_at", {"ndcg_eval_at", "ndcg_at", "map_eval_at", "map_at"}}, {"multi_error_top_k", {}}, {"auc_mu_weights", {}}, {"num_machines", {"num_machine"}}, {"local_listen_port", {"local_port", "port"}}, {"time_out", {}}, {"machine_list_filename", {"machine_list_file", "machine_list", "mlist"}}, {"machines", {"workers", "nodes"}}, {"gpu_platform_id", {}}, {"gpu_device_id", {}}, {"gpu_device_id_list", {}}, {"gpu_use_dp", {}}, {"num_gpu", {}}, }); return map; } const std::unordered_map& Config::ParameterTypes() { static std::unordered_map map({ {"config", "string"}, {"objective", "string"}, {"boosting", "string"}, {"data_sample_strategy", "string"}, {"data", "string"}, {"valid", "vector"}, {"num_iterations", "int"}, {"learning_rate", "double"}, {"num_leaves", "int"}, {"tree_learner", "string"}, {"num_threads", "int"}, {"device_type", "string"}, {"seed", "int"}, {"deterministic", "bool"}, {"force_col_wise", "bool"}, {"force_row_wise", "bool"}, {"histogram_pool_size", "double"}, {"max_depth", "int"}, {"min_data_in_leaf", "int"}, {"min_sum_hessian_in_leaf", "double"}, {"bagging_fraction", "double"}, {"pos_bagging_fraction", "double"}, {"neg_bagging_fraction", "double"}, {"bagging_freq", "int"}, {"bagging_seed", "int"}, {"bagging_by_query", "bool"}, {"feature_fraction", "double"}, {"feature_fraction_bynode", "double"}, {"feature_fraction_seed", "int"}, {"extra_trees", "bool"}, {"extra_seed", "int"}, {"early_stopping_round", "int"}, {"early_stopping_min_delta", "double"}, {"first_metric_only", "bool"}, {"max_delta_step", "double"}, {"lambda_l1", "double"}, {"lambda_l2", "double"}, {"linear_lambda", "double"}, {"min_gain_to_split", "double"}, {"drop_rate", "double"}, {"max_drop", "int"}, {"skip_drop", "double"}, {"xgboost_dart_mode", "bool"}, {"uniform_drop", "bool"}, {"drop_seed", "int"}, {"top_rate", "double"}, {"other_rate", "double"}, {"min_data_per_group", "int"}, {"max_cat_threshold", "int"}, {"cat_l2", "double"}, {"cat_smooth", "double"}, {"max_cat_to_onehot", "int"}, {"top_k", "int"}, {"monotone_constraints", "vector"}, {"monotone_constraints_method", "string"}, {"monotone_penalty", "double"}, {"feature_contri", "vector"}, {"forcedsplits_filename", "string"}, {"refit_decay_rate", "double"}, {"cegb_tradeoff", "double"}, {"cegb_penalty_split", "double"}, {"cegb_penalty_feature_lazy", "vector"}, {"cegb_penalty_feature_coupled", "vector"}, {"path_smooth", "double"}, {"interaction_constraints", "vector>"}, {"verbosity", "int"}, {"input_model", "string"}, {"output_model", "string"}, {"saved_feature_importance_type", "int"}, {"snapshot_freq", "int"}, {"use_quantized_grad", "bool"}, {"num_grad_quant_bins", "int"}, {"quant_train_renew_leaf", "bool"}, {"stochastic_rounding", "bool"}, {"linear_tree", "bool"}, {"max_bin", "int"}, {"max_bin_by_feature", "vector"}, {"min_data_in_bin", "int"}, {"bin_construct_sample_cnt", "int"}, {"data_random_seed", "int"}, {"is_enable_sparse", "bool"}, {"enable_bundle", "bool"}, {"use_missing", "bool"}, {"zero_as_missing", "bool"}, {"feature_pre_filter", "bool"}, {"pre_partition", "bool"}, {"two_round", "bool"}, {"header", "bool"}, {"label_column", "string"}, {"weight_column", "string"}, {"group_column", "string"}, {"ignore_column", "vector"}, {"categorical_feature", "vector"}, {"forcedbins_filename", "string"}, {"save_binary", "bool"}, {"precise_float_parser", "bool"}, {"parser_config_file", "string"}, {"start_iteration_predict", "int"}, {"num_iteration_predict", "int"}, {"predict_raw_score", "bool"}, {"predict_leaf_index", "bool"}, {"predict_contrib", "bool"}, {"predict_disable_shape_check", "bool"}, {"pred_early_stop", "bool"}, {"pred_early_stop_freq", "int"}, {"pred_early_stop_margin", "double"}, {"output_result", "string"}, {"convert_model_language", "string"}, {"convert_model", "string"}, {"objective_seed", "int"}, {"num_class", "int"}, {"is_unbalance", "bool"}, {"scale_pos_weight", "double"}, {"sigmoid", "double"}, {"boost_from_average", "bool"}, {"reg_sqrt", "bool"}, {"alpha", "double"}, {"fair_c", "double"}, {"poisson_max_delta_step", "double"}, {"tweedie_variance_power", "double"}, {"lambdarank_truncation_level", "int"}, {"lambdarank_norm", "bool"}, {"label_gain", "vector"}, {"lambdarank_position_bias_regularization", "double"}, {"metric", "vector"}, {"metric_freq", "int"}, {"is_provide_training_metric", "bool"}, {"eval_at", "vector"}, {"multi_error_top_k", "int"}, {"auc_mu_weights", "vector"}, {"num_machines", "int"}, {"local_listen_port", "int"}, {"time_out", "int"}, {"machine_list_filename", "string"}, {"machines", "string"}, {"gpu_platform_id", "int"}, {"gpu_device_id", "int"}, {"gpu_device_id_list", "string"}, {"gpu_use_dp", "bool"}, {"num_gpu", "int"}, }); return map; } } // namespace LightGBM ================================================ FILE: src/io/cuda/cuda_column_data.cpp ================================================ /*! * Copyright (c) 2021-2026 Microsoft Corporation. All rights reserved. * Copyright (c) 2021-2026 The LightGBM developers. All rights reserved. * Licensed under the MIT License. See LICENSE file in the project root for license information. */ #ifdef USE_CUDA #include #include #include namespace LightGBM { CUDAColumnData::CUDAColumnData(const data_size_t num_data, const int gpu_device_id) { num_threads_ = OMP_NUM_THREADS(); num_data_ = num_data; gpu_device_id_ = gpu_device_id >= 0 ? gpu_device_id : 0; SetCUDADevice(gpu_device_id_, __FILE__, __LINE__); data_by_column_.clear(); } CUDAColumnData::~CUDAColumnData() {} template void CUDAColumnData::InitOneColumnData(const void* in_column_data, BinIterator* bin_iterator, CUDAVector* out_column_data_pointer) { CUDAVector cuda_column_data; if (!IS_SPARSE) { if (IS_4BIT) { std::vector expanded_column_data(num_data_, 0); const BIN_TYPE* in_column_data_reintrepreted = reinterpret_cast(in_column_data); for (data_size_t i = 0; i < num_data_; ++i) { expanded_column_data[i] = static_cast((in_column_data_reintrepreted[i >> 1] >> ((i & 1) << 2)) & 0xf); } cuda_column_data.InitFromHostVector(expanded_column_data); } else { cuda_column_data.InitFromHostMemory(reinterpret_cast(in_column_data), static_cast(num_data_)); } } else { // need to iterate bin iterator std::vector expanded_column_data(num_data_, 0); for (data_size_t i = 0; i < num_data_; ++i) { expanded_column_data[i] = static_cast(bin_iterator->RawGet(i)); } cuda_column_data.InitFromHostVector(expanded_column_data); } out_column_data_pointer->MoveFrom(cuda_column_data, sizeof(BIN_TYPE) * cuda_column_data.Size()); } void CUDAColumnData::Init(const int num_columns, const std::vector& column_data, const std::vector& column_bin_iterator, const std::vector& column_bit_type, const std::vector& feature_max_bin, const std::vector& feature_min_bin, const std::vector& feature_offset, const std::vector& feature_most_freq_bin, const std::vector& feature_default_bin, const std::vector& feature_missing_is_zero, const std::vector& feature_missing_is_na, const std::vector& feature_mfb_is_zero, const std::vector& feature_mfb_is_na, const std::vector& feature_to_column) { num_columns_ = num_columns; column_bit_type_ = column_bit_type; feature_max_bin_ = feature_max_bin; feature_min_bin_ = feature_min_bin; feature_offset_ = feature_offset; feature_most_freq_bin_ = feature_most_freq_bin; feature_default_bin_ = feature_default_bin; feature_missing_is_zero_ = feature_missing_is_zero; feature_missing_is_na_ = feature_missing_is_na; feature_mfb_is_zero_ = feature_mfb_is_zero; feature_mfb_is_na_ = feature_mfb_is_na; for (int column_index = 0; column_index < num_columns_; ++column_index) { data_by_column_.emplace_back(new CUDAVector()); } OMP_INIT_EX(); #pragma omp parallel num_threads(num_threads_) { SetCUDADevice(gpu_device_id_, __FILE__, __LINE__); #pragma omp for schedule(static) for (int column_index = 0; column_index < num_columns_; ++column_index) { OMP_LOOP_EX_BEGIN(); const int8_t bit_type = column_bit_type[column_index]; if (column_data[column_index] != nullptr) { // is dense column if (bit_type == 4) { column_bit_type_[column_index] = 8; InitOneColumnData(column_data[column_index], nullptr, data_by_column_[column_index].get()); } else if (bit_type == 8) { InitOneColumnData(column_data[column_index], nullptr, data_by_column_[column_index].get()); } else if (bit_type == 16) { InitOneColumnData(column_data[column_index], nullptr, data_by_column_[column_index].get()); } else if (bit_type == 32) { InitOneColumnData(column_data[column_index], nullptr, data_by_column_[column_index].get()); } else { Log::Fatal("Unknown column bit type %d", bit_type); } } else { // is sparse column if (bit_type == 8) { InitOneColumnData(nullptr, column_bin_iterator[column_index], data_by_column_[column_index].get()); } else if (bit_type == 16) { InitOneColumnData(nullptr, column_bin_iterator[column_index], data_by_column_[column_index].get()); } else if (bit_type == 32) { InitOneColumnData(nullptr, column_bin_iterator[column_index], data_by_column_[column_index].get()); } else { Log::Fatal("Unknown column bit type %d", bit_type); } } OMP_LOOP_EX_END(); } } OMP_THROW_EX(); feature_to_column_ = feature_to_column; cuda_data_by_column_.InitFromHostVector(GetDataByColumnPointers(data_by_column_)); InitColumnMetaInfo(); } void CUDAColumnData::CopySubrow( const CUDAColumnData* full_set, const data_size_t* used_indices, const data_size_t num_used_indices) { num_threads_ = full_set->num_threads_; num_columns_ = full_set->num_columns_; column_bit_type_ = full_set->column_bit_type_; feature_min_bin_ = full_set->feature_min_bin_; feature_max_bin_ = full_set->feature_max_bin_; feature_offset_ = full_set->feature_offset_; feature_most_freq_bin_ = full_set->feature_most_freq_bin_; feature_default_bin_ = full_set->feature_default_bin_; feature_missing_is_zero_ = full_set->feature_missing_is_zero_; feature_missing_is_na_ = full_set->feature_missing_is_na_; feature_mfb_is_zero_ = full_set->feature_mfb_is_zero_; feature_mfb_is_na_ = full_set->feature_mfb_is_na_; feature_to_column_ = full_set->feature_to_column_; if (cuda_used_indices_.Size() == 0) { // initialize the subset cuda column data const size_t num_used_indices_size = static_cast(num_used_indices); cuda_used_indices_.Resize(num_used_indices_size); for (int column_index = 0; column_index < num_columns_; ++column_index) { data_by_column_.emplace_back(new CUDAVector()); } OMP_INIT_EX(); #pragma omp parallel num_threads(num_threads_) { SetCUDADevice(gpu_device_id_, __FILE__, __LINE__); #pragma omp for schedule(static) for (int column_index = 0; column_index < num_columns_; ++column_index) { OMP_LOOP_EX_BEGIN(); const uint8_t bit_type = column_bit_type_[column_index]; if (bit_type == 8) { CUDAVector column_data; column_data.Resize(num_used_indices_size); data_by_column_[column_index]->MoveFrom(column_data, sizeof(uint8_t) * column_data.Size()); } else if (bit_type == 16) { CUDAVector column_data; column_data.Resize(num_used_indices_size); data_by_column_[column_index]->MoveFrom(column_data, sizeof(uint16_t) * column_data.Size()); } else if (bit_type == 32) { CUDAVector column_data; column_data.Resize(num_used_indices_size); data_by_column_[column_index]->MoveFrom(column_data, sizeof(uint32_t) * column_data.Size()); } OMP_LOOP_EX_END(); } } OMP_THROW_EX(); cuda_data_by_column_.InitFromHostVector(GetDataByColumnPointers(data_by_column_)); InitColumnMetaInfo(); cur_subset_buffer_size_ = num_used_indices; } else { if (num_used_indices > cur_subset_buffer_size_) { ResizeWhenCopySubrow(num_used_indices); cur_subset_buffer_size_ = num_used_indices; } } cuda_used_indices_.InitFromHostMemory(used_indices, static_cast(num_used_indices)); num_used_indices_ = num_used_indices; LaunchCopySubrowKernel(full_set->cuda_data_by_column()); SynchronizeCUDADevice(__FILE__, __LINE__); } void CUDAColumnData::ResizeWhenCopySubrow(const data_size_t num_used_indices) { const size_t num_used_indices_size = static_cast(num_used_indices); cuda_used_indices_.Resize(num_used_indices_size); OMP_INIT_EX(); #pragma omp parallel num_threads(num_threads_) { SetCUDADevice(gpu_device_id_, __FILE__, __LINE__); #pragma omp for schedule(static) for (int column_index = 0; column_index < num_columns_; ++column_index) { OMP_LOOP_EX_BEGIN(); const uint8_t bit_type = column_bit_type_[column_index]; if (bit_type == 8) { data_by_column_[column_index]->Resize(sizeof(uint8_t) * num_used_indices_size); } else if (bit_type == 16) { data_by_column_[column_index]->Resize(sizeof(uint16_t) * num_used_indices_size); } else if (bit_type == 32) { data_by_column_[column_index]->Resize(sizeof(uint32_t) * num_used_indices_size); } OMP_LOOP_EX_END(); } } OMP_THROW_EX(); cuda_data_by_column_.InitFromHostVector(GetDataByColumnPointers(data_by_column_)); } void CUDAColumnData::InitColumnMetaInfo() { cuda_column_bit_type_.InitFromHostVector(column_bit_type_); cuda_feature_max_bin_.InitFromHostVector(feature_max_bin_); cuda_feature_min_bin_.InitFromHostVector(feature_min_bin_); cuda_feature_offset_.InitFromHostVector(feature_offset_); cuda_feature_most_freq_bin_.InitFromHostVector(feature_most_freq_bin_); cuda_feature_default_bin_.InitFromHostVector(feature_default_bin_); cuda_feature_missing_is_zero_.InitFromHostVector(feature_missing_is_zero_); cuda_feature_missing_is_na_.InitFromHostVector(feature_missing_is_na_); cuda_feature_mfb_is_zero_.InitFromHostVector(feature_mfb_is_zero_); cuda_feature_mfb_is_na_.InitFromHostVector(feature_mfb_is_na_); cuda_feature_to_column_.InitFromHostVector(feature_to_column_); } } // namespace LightGBM #endif // USE_CUDA ================================================ FILE: src/io/cuda/cuda_column_data.cu ================================================ /*! * Copyright (c) 2021-2026 Microsoft Corporation. All rights reserved. * Copyright (c) 2021-2026 The LightGBM developers. All rights reserved. * Licensed under the MIT License. See LICENSE file in the project root for license information. */ #ifdef USE_CUDA #include #define COPY_SUBROW_BLOCK_SIZE_COLUMN_DATA (1024) namespace LightGBM { __global__ void CopySubrowKernel_ColumnData( uint8_t* const* in_cuda_data_by_column, const uint8_t* cuda_column_bit_type, const data_size_t* cuda_used_indices, const data_size_t num_used_indices, const int num_column, uint8_t** out_cuda_data_by_column) { const data_size_t local_data_index = static_cast(threadIdx.x + blockIdx.x * blockDim.x); if (local_data_index < num_used_indices) { for (int column_index = 0; column_index < num_column; ++column_index) { const uint8_t* in_column_data = in_cuda_data_by_column[column_index]; uint8_t* out_column_data = out_cuda_data_by_column[column_index]; const uint8_t bit_type = cuda_column_bit_type[column_index]; if (bit_type == 8) { const uint8_t* true_in_column_data = reinterpret_cast(in_column_data); uint8_t* true_out_column_data = reinterpret_cast(out_column_data); const data_size_t global_data_index = cuda_used_indices[local_data_index]; true_out_column_data[local_data_index] = true_in_column_data[global_data_index]; } else if (bit_type == 16) { const uint16_t* true_in_column_data = reinterpret_cast(in_column_data); uint16_t* true_out_column_data = reinterpret_cast(out_column_data); const data_size_t global_data_index = cuda_used_indices[local_data_index]; true_out_column_data[local_data_index] = true_in_column_data[global_data_index]; } else if (bit_type == 32) { const uint32_t* true_in_column_data = reinterpret_cast(in_column_data); uint32_t* true_out_column_data = reinterpret_cast(out_column_data); const data_size_t global_data_index = cuda_used_indices[local_data_index]; true_out_column_data[local_data_index] = true_in_column_data[global_data_index]; } } } } void CUDAColumnData::LaunchCopySubrowKernel(uint8_t* const* in_cuda_data_by_column) { const int num_blocks = (num_used_indices_ + COPY_SUBROW_BLOCK_SIZE_COLUMN_DATA - 1) / COPY_SUBROW_BLOCK_SIZE_COLUMN_DATA; CopySubrowKernel_ColumnData<<>>( in_cuda_data_by_column, cuda_column_bit_type_.RawData(), cuda_used_indices_.RawData(), num_used_indices_, num_columns_, cuda_data_by_column_.RawData()); } } // namespace LightGBM #endif // USE_CUDA ================================================ FILE: src/io/cuda/cuda_metadata.cpp ================================================ /*! * Copyright (c) 2021-2026 Microsoft Corporation. All rights reserved. * Copyright (c) 2021-2026 The LightGBM developers. All rights reserved. * Licensed under the MIT License. See LICENSE file in the project root for license information. */ #ifdef USE_CUDA #include #include namespace LightGBM { CUDAMetadata::CUDAMetadata(const int gpu_device_id) { if (gpu_device_id >= 0) { SetCUDADevice(gpu_device_id, __FILE__, __LINE__); } else { SetCUDADevice(0, __FILE__, __LINE__); } } CUDAMetadata::~CUDAMetadata() {} void CUDAMetadata::Init(const std::vector& label, const std::vector& weight, const std::vector& query_boundaries, const std::vector& query_weights, const std::vector& init_score) { if (label.size() == 0) { cuda_label_.Clear(); } else { cuda_label_.InitFromHostVector(label); } if (weight.size() == 0) { cuda_weights_.Clear(); } else { cuda_weights_.InitFromHostVector(weight); } if (query_boundaries.size() == 0) { cuda_query_boundaries_.Clear(); } else { cuda_query_boundaries_.InitFromHostVector(query_boundaries); } if (query_weights.size() == 0) { cuda_query_weights_.Clear(); } else { cuda_query_weights_.InitFromHostVector(query_weights); } if (init_score.size() == 0) { cuda_init_score_.Clear(); } else { cuda_init_score_.InitFromHostVector(init_score); } SynchronizeCUDADevice(__FILE__, __LINE__); } void CUDAMetadata::SetLabel(const label_t* label, data_size_t len) { cuda_label_.InitFromHostMemory(label, static_cast(len)); } void CUDAMetadata::SetWeights(const label_t* weights, data_size_t len) { cuda_weights_.InitFromHostMemory(weights, static_cast(len)); } void CUDAMetadata::SetQuery(const data_size_t* query_boundaries, const label_t* query_weights, data_size_t num_queries) { cuda_query_boundaries_.InitFromHostMemory(query_boundaries, static_cast(num_queries) + 1); if (query_weights != nullptr) { cuda_query_weights_.InitFromHostMemory(query_weights, static_cast(num_queries)); } } void CUDAMetadata::SetInitScore(const double* init_score, data_size_t len) { cuda_init_score_.InitFromHostMemory(init_score, len); } } // namespace LightGBM #endif // USE_CUDA ================================================ FILE: src/io/cuda/cuda_row_data.cpp ================================================ /*! * Copyright (c) 2021-2026 Microsoft Corporation. All rights reserved. * Copyright (c) 2021-2026 The LightGBM developers. All rights reserved. * Licensed under the MIT License. See LICENSE file in the project root for license information. */ #ifdef USE_CUDA #include #include namespace LightGBM { CUDARowData::CUDARowData(const Dataset* train_data, const TrainingShareStates* train_share_state, const int gpu_device_id, const bool gpu_use_dp): gpu_device_id_(gpu_device_id), gpu_use_dp_(gpu_use_dp) { num_threads_ = OMP_NUM_THREADS(); num_data_ = train_data->num_data(); const auto& feature_hist_offsets = train_share_state->feature_hist_offsets(); if (gpu_use_dp_) { shared_hist_size_ = DP_SHARED_HIST_SIZE; } else { shared_hist_size_ = SP_SHARED_HIST_SIZE; } if (feature_hist_offsets.empty()) { num_total_bin_ = 0; } else { num_total_bin_ = static_cast(feature_hist_offsets.back()); } num_feature_group_ = train_data->num_feature_groups(); num_feature_ = train_data->num_features(); if (gpu_device_id >= 0) { SetCUDADevice(gpu_device_id, __FILE__, __LINE__); } else { SetCUDADevice(0, __FILE__, __LINE__); } } CUDARowData::~CUDARowData() {} void CUDARowData::Init(const Dataset* train_data, TrainingShareStates* train_share_state) { if (num_feature_ == 0) { return; } DivideCUDAFeatureGroups(train_data, train_share_state); bit_type_ = 0; size_t total_size = 0; const void* host_row_ptr = nullptr; row_ptr_bit_type_ = 0; const void* host_data = train_share_state->GetRowWiseData(&bit_type_, &total_size, &is_sparse_, &host_row_ptr, &row_ptr_bit_type_); if (bit_type_ == 8) { if (!is_sparse_) { std::vector partitioned_data; GetDenseDataPartitioned(reinterpret_cast(host_data), &partitioned_data); cuda_data_uint8_t_.InitFromHostVector(partitioned_data); } else { if (row_ptr_bit_type_ == 16) { InitSparseData( reinterpret_cast(host_data), reinterpret_cast(host_row_ptr), &cuda_data_uint8_t_, &cuda_row_ptr_uint16_t_, &cuda_partition_ptr_uint16_t_); } else if (row_ptr_bit_type_ == 32) { InitSparseData( reinterpret_cast(host_data), reinterpret_cast(host_row_ptr), &cuda_data_uint8_t_, &cuda_row_ptr_uint32_t_, &cuda_partition_ptr_uint32_t_); } else if (row_ptr_bit_type_ == 64) { InitSparseData( reinterpret_cast(host_data), reinterpret_cast(host_row_ptr), &cuda_data_uint8_t_, &cuda_row_ptr_uint64_t_, &cuda_partition_ptr_uint64_t_); } else { Log::Fatal("Unknown data ptr bit type %d", row_ptr_bit_type_); } } } else if (bit_type_ == 16) { if (!is_sparse_) { std::vector partitioned_data; GetDenseDataPartitioned(reinterpret_cast(host_data), &partitioned_data); cuda_data_uint16_t_.InitFromHostVector(partitioned_data); } else { if (row_ptr_bit_type_ == 16) { InitSparseData( reinterpret_cast(host_data), reinterpret_cast(host_row_ptr), &cuda_data_uint16_t_, &cuda_row_ptr_uint16_t_, &cuda_partition_ptr_uint16_t_); } else if (row_ptr_bit_type_ == 32) { InitSparseData( reinterpret_cast(host_data), reinterpret_cast(host_row_ptr), &cuda_data_uint16_t_, &cuda_row_ptr_uint32_t_, &cuda_partition_ptr_uint32_t_); } else if (row_ptr_bit_type_ == 64) { InitSparseData( reinterpret_cast(host_data), reinterpret_cast(host_row_ptr), &cuda_data_uint16_t_, &cuda_row_ptr_uint64_t_, &cuda_partition_ptr_uint64_t_); } else { Log::Fatal("Unknown data ptr bit type %d", row_ptr_bit_type_); } } } else if (bit_type_ == 32) { if (!is_sparse_) { std::vector partitioned_data; GetDenseDataPartitioned(reinterpret_cast(host_data), &partitioned_data); cuda_data_uint32_t_.InitFromHostVector(partitioned_data); } else { if (row_ptr_bit_type_ == 16) { InitSparseData( reinterpret_cast(host_data), reinterpret_cast(host_row_ptr), &cuda_data_uint32_t_, &cuda_row_ptr_uint16_t_, &cuda_partition_ptr_uint16_t_); } else if (row_ptr_bit_type_ == 32) { InitSparseData( reinterpret_cast(host_data), reinterpret_cast(host_row_ptr), &cuda_data_uint32_t_, &cuda_row_ptr_uint32_t_, &cuda_partition_ptr_uint32_t_); } else if (row_ptr_bit_type_ == 64) { InitSparseData( reinterpret_cast(host_data), reinterpret_cast(host_row_ptr), &cuda_data_uint32_t_, &cuda_row_ptr_uint64_t_, &cuda_partition_ptr_uint64_t_); } else { Log::Fatal("Unknown data ptr bit type %d", row_ptr_bit_type_); } } } else { Log::Fatal("Unknown bit type = %d", bit_type_); } SynchronizeCUDADevice(__FILE__, __LINE__); } void CUDARowData::DivideCUDAFeatureGroups(const Dataset* train_data, TrainingShareStates* share_state) { const uint32_t max_num_bin_per_partition = shared_hist_size_ / 2; const std::vector& column_hist_offsets = share_state->column_hist_offsets(); std::vector feature_group_num_feature_offsets; int offsets = 0; int prev_group_index = -1; for (int feature_index = 0; feature_index < num_feature_; ++feature_index) { const int feature_group_index = train_data->Feature2Group(feature_index); if (prev_group_index == -1 || feature_group_index != prev_group_index) { feature_group_num_feature_offsets.emplace_back(offsets); prev_group_index = feature_group_index; } ++offsets; } CHECK_EQ(offsets, num_feature_); feature_group_num_feature_offsets.emplace_back(offsets); uint32_t start_hist_offset = 0; feature_partition_column_index_offsets_.clear(); column_hist_offsets_.clear(); partition_hist_offsets_.clear(); feature_partition_column_index_offsets_.emplace_back(0); partition_hist_offsets_.emplace_back(0); const int num_feature_groups = train_data->num_feature_groups(); int column_index = 0; num_feature_partitions_ = 0; large_bin_partitions_.clear(); small_bin_partitions_.clear(); for (int feature_group_index = 0; feature_group_index < num_feature_groups; ++feature_group_index) { if (!train_data->IsMultiGroup(feature_group_index)) { const uint32_t column_feature_hist_start = column_hist_offsets[column_index]; const uint32_t column_feature_hist_end = column_hist_offsets[column_index + 1]; const uint32_t num_bin_in_dense_group = column_feature_hist_end - column_feature_hist_start; // if one column has too many bins, use a separate partition for that column if (num_bin_in_dense_group > max_num_bin_per_partition) { feature_partition_column_index_offsets_.emplace_back(column_index + 1); start_hist_offset = column_feature_hist_end; partition_hist_offsets_.emplace_back(start_hist_offset); large_bin_partitions_.emplace_back(num_feature_partitions_); ++num_feature_partitions_; column_hist_offsets_.emplace_back(0); ++column_index; continue; } // try if adding this column exceed the maximum number per partition const uint32_t cur_hist_num_bin = column_feature_hist_end - start_hist_offset; if (cur_hist_num_bin > max_num_bin_per_partition) { feature_partition_column_index_offsets_.emplace_back(column_index); start_hist_offset = column_feature_hist_start; partition_hist_offsets_.emplace_back(start_hist_offset); small_bin_partitions_.emplace_back(num_feature_partitions_); ++num_feature_partitions_; } column_hist_offsets_.emplace_back(column_hist_offsets[column_index] - start_hist_offset); if (feature_group_index == num_feature_groups - 1) { feature_partition_column_index_offsets_.emplace_back(column_index + 1); partition_hist_offsets_.emplace_back(column_hist_offsets.back()); small_bin_partitions_.emplace_back(num_feature_partitions_); ++num_feature_partitions_; } ++column_index; } else { const int group_feature_index_start = feature_group_num_feature_offsets[feature_group_index]; const int num_feature_in_group = feature_group_num_feature_offsets[feature_group_index + 1] - group_feature_index_start; for (int sub_feature_index = 0; sub_feature_index < num_feature_in_group; ++sub_feature_index) { const int feature_index = group_feature_index_start + sub_feature_index; const uint32_t column_feature_hist_start = column_hist_offsets[column_index]; const uint32_t column_feature_hist_end = column_hist_offsets[column_index + 1]; const uint32_t num_bin_in_dense_group = column_feature_hist_end - column_feature_hist_start; // if one column has too many bins, use a separate partition for that column if (num_bin_in_dense_group > max_num_bin_per_partition) { feature_partition_column_index_offsets_.emplace_back(column_index + 1); start_hist_offset = column_feature_hist_end; partition_hist_offsets_.emplace_back(start_hist_offset); large_bin_partitions_.emplace_back(num_feature_partitions_); ++num_feature_partitions_; column_hist_offsets_.emplace_back(0); ++column_index; continue; } // try if adding this column exceed the maximum number per partition const uint32_t cur_hist_num_bin = column_feature_hist_end - start_hist_offset; if (cur_hist_num_bin > max_num_bin_per_partition) { feature_partition_column_index_offsets_.emplace_back(column_index); start_hist_offset = column_feature_hist_start; partition_hist_offsets_.emplace_back(start_hist_offset); small_bin_partitions_.emplace_back(num_feature_partitions_); ++num_feature_partitions_; } column_hist_offsets_.emplace_back(column_hist_offsets[column_index] - start_hist_offset); if (feature_group_index == num_feature_groups - 1 && sub_feature_index == num_feature_in_group - 1) { CHECK_EQ(feature_index, num_feature_ - 1); feature_partition_column_index_offsets_.emplace_back(column_index + 1); partition_hist_offsets_.emplace_back(column_hist_offsets.back()); small_bin_partitions_.emplace_back(num_feature_partitions_); ++num_feature_partitions_; } ++column_index; } } } column_hist_offsets_.emplace_back(column_hist_offsets.back() - start_hist_offset); max_num_column_per_partition_ = 0; for (size_t i = 0; i < feature_partition_column_index_offsets_.size() - 1; ++i) { const int num_column = feature_partition_column_index_offsets_[i + 1] - feature_partition_column_index_offsets_[i]; if (num_column > max_num_column_per_partition_) { max_num_column_per_partition_ = num_column; } } cuda_feature_partition_column_index_offsets_.InitFromHostVector(feature_partition_column_index_offsets_); cuda_column_hist_offsets_.InitFromHostVector(column_hist_offsets_); cuda_partition_hist_offsets_.InitFromHostVector(partition_hist_offsets_); } template void CUDARowData::GetDenseDataPartitioned(const BIN_TYPE* row_wise_data, std::vector* partitioned_data) { const int num_total_columns = feature_partition_column_index_offsets_.back(); partitioned_data->resize(static_cast(num_total_columns) * static_cast(num_data_), 0); BIN_TYPE* out_data = partitioned_data->data(); Threading::For(0, num_data_, 512, [this, num_total_columns, row_wise_data, out_data] (int /*thread_index*/, data_size_t start, data_size_t end) { for (size_t i = 0; i < feature_partition_column_index_offsets_.size() - 1; ++i) { const int num_prev_columns = static_cast(feature_partition_column_index_offsets_[i]); const size_t offset = static_cast(num_data_) * static_cast(num_prev_columns); const int partition_column_start = feature_partition_column_index_offsets_[i]; const int partition_column_end = feature_partition_column_index_offsets_[i + 1]; const int num_columns_in_cur_partition = partition_column_end - partition_column_start; for (data_size_t data_index = start; data_index < end; ++data_index) { const size_t data_offset = offset + static_cast(data_index) * num_columns_in_cur_partition; const size_t read_data_offset = static_cast(data_index) * num_total_columns; for (int column_index = 0; column_index < num_columns_in_cur_partition; ++column_index) { const size_t true_column_index = read_data_offset + column_index + partition_column_start; const BIN_TYPE bin = row_wise_data[true_column_index]; out_data[data_offset + column_index] = bin; } } } }); } template void CUDARowData::GetSparseDataPartitioned( const BIN_TYPE* row_wise_data, const DATA_PTR_TYPE* row_ptr, std::vector>* partitioned_data, std::vector>* partitioned_row_ptr, std::vector* partition_ptr) { const int num_partitions = static_cast(feature_partition_column_index_offsets_.size()) - 1; partitioned_data->resize(num_partitions); partitioned_row_ptr->resize(num_partitions); std::vector thread_max_elements_per_row(num_threads_, 0); Threading::For(0, num_partitions, 1, [partitioned_data, partitioned_row_ptr, row_ptr, row_wise_data, &thread_max_elements_per_row, this] (int thread_index, int start, int end) { for (int partition_index = start; partition_index < end; ++partition_index) { std::vector& data_for_this_partition = partitioned_data->at(partition_index); std::vector& row_ptr_for_this_partition = partitioned_row_ptr->at(partition_index); const int partition_hist_start = partition_hist_offsets_[partition_index]; const int partition_hist_end = partition_hist_offsets_[partition_index + 1]; DATA_PTR_TYPE offset = 0; row_ptr_for_this_partition.clear(); data_for_this_partition.clear(); row_ptr_for_this_partition.emplace_back(offset); for (data_size_t data_index = 0; data_index < num_data_; ++data_index) { const DATA_PTR_TYPE row_start = row_ptr[data_index]; const DATA_PTR_TYPE row_end = row_ptr[data_index + 1]; const BIN_TYPE* row_data_start = row_wise_data + row_start; const BIN_TYPE* row_data_end = row_wise_data + row_end; const size_t partition_start_in_row = std::lower_bound(row_data_start, row_data_end, partition_hist_start) - row_data_start; const size_t partition_end_in_row = std::lower_bound(row_data_start, row_data_end, partition_hist_end) - row_data_start; for (size_t pos = partition_start_in_row; pos < partition_end_in_row; ++pos) { const BIN_TYPE bin = row_data_start[pos]; CHECK_GE(bin, static_cast(partition_hist_start)); data_for_this_partition.emplace_back(bin - partition_hist_start); } CHECK_GE(partition_end_in_row, partition_start_in_row); const data_size_t num_elements_in_row = partition_end_in_row - partition_start_in_row; offset += static_cast(num_elements_in_row); row_ptr_for_this_partition.emplace_back(offset); if (num_elements_in_row > thread_max_elements_per_row[thread_index]) { thread_max_elements_per_row[thread_index] = num_elements_in_row; } } } }); partition_ptr->clear(); DATA_PTR_TYPE offset = 0; partition_ptr->emplace_back(offset); for (size_t i = 0; i < partitioned_row_ptr->size(); ++i) { offset += partitioned_row_ptr->at(i).back(); partition_ptr->emplace_back(offset); } max_num_column_per_partition_ = 0; for (int thread_index = 0; thread_index < num_threads_; ++thread_index) { if (thread_max_elements_per_row[thread_index] > max_num_column_per_partition_) { max_num_column_per_partition_ = thread_max_elements_per_row[thread_index]; } } } template void CUDARowData::InitSparseData(const BIN_TYPE* host_data, const ROW_PTR_TYPE* host_row_ptr, CUDAVector* cuda_data, CUDAVector* cuda_row_ptr, CUDAVector* cuda_partition_ptr) { std::vector> partitioned_data; std::vector> partitioned_data_ptr; std::vector partition_ptr; GetSparseDataPartitioned(host_data, host_row_ptr, &partitioned_data, &partitioned_data_ptr, &partition_ptr); cuda_partition_ptr->InitFromHostVector(partition_ptr); cuda_data->Resize(partition_ptr.back()); cuda_row_ptr->Resize((num_data_ + 1) * partitioned_data_ptr.size()); for (size_t i = 0; i < partitioned_data.size(); ++i) { const std::vector& data_ptr_for_this_partition = partitioned_data_ptr[i]; const std::vector& data_for_this_partition = partitioned_data[i]; CopyFromHostToCUDADevice(cuda_data->RawData() + partition_ptr[i], data_for_this_partition.data(), data_for_this_partition.size(), __FILE__, __LINE__); CopyFromHostToCUDADevice(cuda_row_ptr->RawData() + i * (num_data_ + 1), data_ptr_for_this_partition.data(), data_ptr_for_this_partition.size(), __FILE__, __LINE__); } } template const BIN_TYPE* CUDARowData::GetBin() const { if (bit_type_ == 8) { return reinterpret_cast(cuda_data_uint8_t_.RawData()); } else if (bit_type_ == 16) { return reinterpret_cast(cuda_data_uint16_t_.RawData()); } else if (bit_type_ == 32) { return reinterpret_cast(cuda_data_uint32_t_.RawData()); } else { Log::Fatal("Unknown bit_type %d for GetBin.", bit_type_); } } template const uint8_t* CUDARowData::GetBin() const; template const uint16_t* CUDARowData::GetBin() const; template const uint32_t* CUDARowData::GetBin() const; template const PTR_TYPE* CUDARowData::GetRowPtr() const { if (row_ptr_bit_type_ == 16) { return reinterpret_cast(cuda_row_ptr_uint16_t_.RawData()); } else if (row_ptr_bit_type_ == 32) { return reinterpret_cast(cuda_row_ptr_uint32_t_.RawData()); } else if (row_ptr_bit_type_ == 64) { return reinterpret_cast(cuda_row_ptr_uint64_t_.RawData()); } else { Log::Fatal("Unknown row_ptr_bit_type = %d for GetRowPtr.", row_ptr_bit_type_); } } template const uint16_t* CUDARowData::GetRowPtr() const; template const uint32_t* CUDARowData::GetRowPtr() const; template const uint64_t* CUDARowData::GetRowPtr() const; template const PTR_TYPE* CUDARowData::GetPartitionPtr() const { if (row_ptr_bit_type_ == 16) { return reinterpret_cast(cuda_partition_ptr_uint16_t_.RawData()); } else if (row_ptr_bit_type_ == 32) { return reinterpret_cast(cuda_partition_ptr_uint32_t_.RawData()); } else if (row_ptr_bit_type_ == 64) { return reinterpret_cast(cuda_partition_ptr_uint64_t_.RawData()); } else { Log::Fatal("Unknown row_ptr_bit_type = %d for GetPartitionPtr.", row_ptr_bit_type_); } } template const uint16_t* CUDARowData::GetPartitionPtr() const; template const uint32_t* CUDARowData::GetPartitionPtr() const; template const uint64_t* CUDARowData::GetPartitionPtr() const; } // namespace LightGBM #endif // USE_CUDA ================================================ FILE: src/io/cuda/cuda_tree.cpp ================================================ /*! * Copyright (c) 2021-2026 Microsoft Corporation. All rights reserved. * Copyright (c) 2021-2026 The LightGBM developers. All rights reserved. * Licensed under the MIT License. See LICENSE file in the project root for license information. */ #ifdef USE_CUDA #include namespace LightGBM { CUDATree::CUDATree(int max_leaves, bool track_branch_features, bool is_linear, const int gpu_device_id, const bool has_categorical_feature): Tree(max_leaves, track_branch_features, is_linear), num_threads_per_block_add_prediction_to_score_(1024) { is_cuda_tree_ = true; if (gpu_device_id >= 0) { SetCUDADevice(gpu_device_id, __FILE__, __LINE__); } else { SetCUDADevice(0, __FILE__, __LINE__); } if (has_categorical_feature) { cuda_cat_boundaries_.Resize(max_leaves); cuda_cat_boundaries_inner_.Resize(max_leaves); } InitCUDAMemory(); } CUDATree::CUDATree(const Tree* host_tree): Tree(*host_tree), num_threads_per_block_add_prediction_to_score_(1024) { is_cuda_tree_ = true; InitCUDA(); } CUDATree::~CUDATree() { gpuAssert(cudaStreamDestroy(cuda_stream_), __FILE__, __LINE__); } void CUDATree::InitCUDAMemory() { cuda_left_child_.Resize(static_cast(max_leaves_)); cuda_right_child_.Resize(static_cast(max_leaves_)); cuda_split_feature_inner_.Resize(static_cast(max_leaves_)); cuda_split_feature_.Resize(static_cast(max_leaves_)); cuda_leaf_depth_.Resize(static_cast(max_leaves_)); cuda_leaf_parent_.Resize(static_cast(max_leaves_)); cuda_threshold_in_bin_.Resize(static_cast(max_leaves_)); cuda_threshold_.Resize(static_cast(max_leaves_)); cuda_decision_type_.Resize(static_cast(max_leaves_)); cuda_leaf_value_.Resize(static_cast(max_leaves_)); cuda_internal_weight_.Resize(static_cast(max_leaves_)); cuda_internal_value_.Resize(static_cast(max_leaves_)); cuda_leaf_weight_.Resize(static_cast(max_leaves_)); cuda_leaf_count_.Resize(static_cast(max_leaves_)); cuda_internal_count_.Resize(static_cast(max_leaves_)); cuda_split_gain_.Resize(static_cast(max_leaves_)); SetCUDAMemory(cuda_leaf_value_.RawData(), 0.0f, 1, __FILE__, __LINE__); SetCUDAMemory(cuda_leaf_weight_.RawData(), 0.0f, 1, __FILE__, __LINE__); SetCUDAMemory(cuda_leaf_parent_.RawData(), -1, 1, __FILE__, __LINE__); CUDASUCCESS_OR_FATAL(cudaStreamCreate(&cuda_stream_)); SynchronizeCUDADevice(__FILE__, __LINE__); } void CUDATree::InitCUDA() { cuda_left_child_.InitFromHostVector(left_child_); cuda_right_child_.InitFromHostVector(right_child_); cuda_split_feature_inner_.InitFromHostVector(split_feature_inner_); cuda_split_feature_.InitFromHostVector(split_feature_); cuda_threshold_in_bin_.InitFromHostVector(threshold_in_bin_); cuda_threshold_.InitFromHostVector(threshold_); cuda_leaf_depth_.InitFromHostVector(leaf_depth_); cuda_decision_type_.InitFromHostVector(decision_type_); cuda_internal_weight_.InitFromHostVector(internal_weight_); cuda_internal_value_.InitFromHostVector(internal_value_); cuda_internal_count_.InitFromHostVector(internal_count_); cuda_leaf_count_.InitFromHostVector(leaf_count_); cuda_split_gain_.InitFromHostVector(split_gain_); cuda_leaf_value_.InitFromHostVector(leaf_value_); cuda_leaf_weight_.InitFromHostVector(leaf_weight_); cuda_leaf_parent_.InitFromHostVector(leaf_parent_); CUDASUCCESS_OR_FATAL(cudaStreamCreate(&cuda_stream_)); SynchronizeCUDADevice(__FILE__, __LINE__); } int CUDATree::Split(const int leaf_index, const int real_feature_index, const double real_threshold, const MissingType missing_type, const CUDASplitInfo* cuda_split_info) { LaunchSplitKernel(leaf_index, real_feature_index, real_threshold, missing_type, cuda_split_info); RecordBranchFeatures(leaf_index, num_leaves_, real_feature_index); ++num_leaves_; return num_leaves_ - 1; } int CUDATree::SplitCategorical(const int leaf_index, const int real_feature_index, const MissingType missing_type, const CUDASplitInfo* cuda_split_info, uint32_t* cuda_bitset, size_t cuda_bitset_len, uint32_t* cuda_bitset_inner, size_t cuda_bitset_inner_len) { LaunchSplitCategoricalKernel(leaf_index, real_feature_index, missing_type, cuda_split_info, cuda_bitset_len, cuda_bitset_inner_len); cuda_bitset_.PushBack(cuda_bitset, cuda_bitset_len); cuda_bitset_inner_.PushBack(cuda_bitset_inner, cuda_bitset_inner_len); ++num_leaves_; ++num_cat_; RecordBranchFeatures(leaf_index, num_leaves_, real_feature_index); return num_leaves_ - 1; } void CUDATree::RecordBranchFeatures(const int left_leaf_index, const int right_leaf_index, const int real_feature_index) { if (track_branch_features_) { branch_features_[right_leaf_index] = branch_features_[left_leaf_index]; branch_features_[right_leaf_index].push_back(real_feature_index); branch_features_[left_leaf_index].push_back(real_feature_index); } } void CUDATree::AddPredictionToScore(const Dataset* data, data_size_t num_data, double* score) const { LaunchAddPredictionToScoreKernel(data, nullptr, num_data, score); SynchronizeCUDADevice(__FILE__, __LINE__); } void CUDATree::AddPredictionToScore(const Dataset* data, const data_size_t* used_data_indices, data_size_t num_data, double* score) const { LaunchAddPredictionToScoreKernel(data, used_data_indices, num_data, score); SynchronizeCUDADevice(__FILE__, __LINE__); } inline void CUDATree::Shrinkage(double rate) { Tree::Shrinkage(rate); LaunchShrinkageKernel(rate); } inline void CUDATree::AddBias(double val) { Tree::AddBias(val); LaunchAddBiasKernel(val); } void CUDATree::ToHost() { left_child_.resize(max_leaves_ - 1); right_child_.resize(max_leaves_ - 1); split_feature_inner_.resize(max_leaves_ - 1); split_feature_.resize(max_leaves_ - 1); threshold_in_bin_.resize(max_leaves_ - 1); threshold_.resize(max_leaves_ - 1); decision_type_.resize(max_leaves_ - 1, 0); split_gain_.resize(max_leaves_ - 1); leaf_parent_.resize(max_leaves_); leaf_value_.resize(max_leaves_); leaf_weight_.resize(max_leaves_); leaf_count_.resize(max_leaves_); internal_value_.resize(max_leaves_ - 1); internal_weight_.resize(max_leaves_ - 1); internal_count_.resize(max_leaves_ - 1); leaf_depth_.resize(max_leaves_); const size_t num_leaves_size = static_cast(num_leaves_); CopyFromCUDADeviceToHost(left_child_.data(), cuda_left_child_.RawData(), num_leaves_size - 1, __FILE__, __LINE__); CopyFromCUDADeviceToHost(right_child_.data(), cuda_right_child_.RawData(), num_leaves_size - 1, __FILE__, __LINE__); CopyFromCUDADeviceToHost(split_feature_inner_.data(), cuda_split_feature_inner_.RawData(), num_leaves_size - 1, __FILE__, __LINE__); CopyFromCUDADeviceToHost(split_feature_.data(), cuda_split_feature_.RawData(), num_leaves_size - 1, __FILE__, __LINE__); CopyFromCUDADeviceToHost(threshold_in_bin_.data(), cuda_threshold_in_bin_.RawData(), num_leaves_size - 1, __FILE__, __LINE__); CopyFromCUDADeviceToHost(threshold_.data(), cuda_threshold_.RawData(), num_leaves_size - 1, __FILE__, __LINE__); CopyFromCUDADeviceToHost(decision_type_.data(), cuda_decision_type_.RawData(), num_leaves_size - 1, __FILE__, __LINE__); CopyFromCUDADeviceToHost(split_gain_.data(), cuda_split_gain_.RawData(), num_leaves_size - 1, __FILE__, __LINE__); CopyFromCUDADeviceToHost(leaf_parent_.data(), cuda_leaf_parent_.RawData(), num_leaves_size - 1, __FILE__, __LINE__); CopyFromCUDADeviceToHost(leaf_value_.data(), cuda_leaf_value_.RawData(), num_leaves_size, __FILE__, __LINE__); CopyFromCUDADeviceToHost(leaf_weight_.data(), cuda_leaf_weight_.RawData(), num_leaves_size, __FILE__, __LINE__); CopyFromCUDADeviceToHost(leaf_count_.data(), cuda_leaf_count_.RawData(), num_leaves_size, __FILE__, __LINE__); CopyFromCUDADeviceToHost(internal_value_.data(), cuda_internal_value_.RawData(), num_leaves_size - 1, __FILE__, __LINE__); CopyFromCUDADeviceToHost(internal_weight_.data(), cuda_internal_weight_.RawData(), num_leaves_size - 1, __FILE__, __LINE__); CopyFromCUDADeviceToHost(internal_count_.data(), cuda_internal_count_.RawData(), num_leaves_size - 1, __FILE__, __LINE__); CopyFromCUDADeviceToHost(leaf_depth_.data(), cuda_leaf_depth_.RawData(), num_leaves_size, __FILE__, __LINE__); if (num_cat_ > 0) { cuda_cat_boundaries_inner_.Resize(num_cat_ + 1); cuda_cat_boundaries_.Resize(num_cat_ + 1); cat_boundaries_ = cuda_cat_boundaries_.ToHost(); cat_boundaries_inner_ = cuda_cat_boundaries_inner_.ToHost(); cat_threshold_ = cuda_bitset_.ToHost(); cat_threshold_inner_ = cuda_bitset_inner_.ToHost(); } SynchronizeCUDADevice(__FILE__, __LINE__); } void CUDATree::SyncLeafOutputFromHostToCUDA() { CopyFromHostToCUDADevice(cuda_leaf_value_.RawData(), leaf_value_.data(), leaf_value_.size(), __FILE__, __LINE__); } void CUDATree::SyncLeafOutputFromCUDAToHost() { CopyFromCUDADeviceToHost(leaf_value_.data(), cuda_leaf_value_.RawData(), leaf_value_.size(), __FILE__, __LINE__); } void CUDATree::AsConstantTree(double val, int count) { Tree::AsConstantTree(val, count); CopyFromHostToCUDADevice(cuda_leaf_value_.RawData(), &val, 1, __FILE__, __LINE__); CopyFromHostToCUDADevice(cuda_leaf_count_.RawData(), &count, 1, __FILE__, __LINE__); } } // namespace LightGBM #endif // USE_CUDA ================================================ FILE: src/io/cuda/cuda_tree.cu ================================================ /*! * Copyright (c) 2021-2026 Microsoft Corporation. All rights reserved. * Copyright (c) 2021-2026 The LightGBM developers. All rights reserved. * Licensed under the MIT License. See LICENSE file in the project root for license information. */ #ifdef USE_CUDA #include namespace LightGBM { __device__ void SetDecisionTypeCUDA(int8_t* decision_type, bool input, int8_t mask) { if (input) { (*decision_type) |= mask; } else { (*decision_type) &= (127 - mask); } } __device__ void SetMissingTypeCUDA(int8_t* decision_type, int8_t input) { (*decision_type) &= 3; (*decision_type) |= (input << 2); } __device__ bool GetDecisionTypeCUDA(int8_t decision_type, int8_t mask) { return (decision_type & mask) > 0; } __device__ int8_t GetMissingTypeCUDA(int8_t decision_type) { return (decision_type >> 2) & 3; } __device__ bool IsZeroCUDA(double fval) { return (fval >= -kZeroThreshold && fval <= kZeroThreshold); } template __device__ bool FindInBitsetCUDA(const uint32_t* bits, int n, T pos) { int i1 = pos / 32; if (i1 >= n) { return false; } int i2 = pos % 32; return (bits[i1] >> i2) & 1; } __global__ void SplitKernel( // split information const int leaf_index, const int real_feature_index, const double real_threshold, const MissingType missing_type, const CUDASplitInfo* cuda_split_info, // tree structure const int num_leaves, int* leaf_parent, int* leaf_depth, int* left_child, int* right_child, int* split_feature_inner, int* split_feature, float* split_gain, double* internal_weight, double* internal_value, data_size_t* internal_count, double* leaf_weight, double* leaf_value, data_size_t* leaf_count, int8_t* decision_type, uint32_t* threshold_in_bin, double* threshold) { const int new_node_index = num_leaves - 1; const int thread_index = static_cast(threadIdx.x + blockIdx.x * blockDim.x); const int parent_index = leaf_parent[leaf_index]; if (thread_index == 0) { if (parent_index >= 0) { // if cur node is left child if (left_child[parent_index] == ~leaf_index) { left_child[parent_index] = new_node_index; } else { right_child[parent_index] = new_node_index; } } left_child[new_node_index] = ~leaf_index; right_child[new_node_index] = ~num_leaves; leaf_parent[leaf_index] = new_node_index; leaf_parent[num_leaves] = new_node_index; } else if (thread_index == 1) { // add new node split_feature_inner[new_node_index] = cuda_split_info->inner_feature_index; } else if (thread_index == 2) { split_feature[new_node_index] = real_feature_index; } else if (thread_index == 3) { split_gain[new_node_index] = static_cast(cuda_split_info->gain); } else if (thread_index == 4) { // save current leaf value to internal node before change internal_weight[new_node_index] = cuda_split_info->left_sum_hessians + cuda_split_info->right_sum_hessians; leaf_weight[leaf_index] = cuda_split_info->left_sum_hessians; } else if (thread_index == 5) { internal_value[new_node_index] = leaf_value[leaf_index]; leaf_value[leaf_index] = isnan(cuda_split_info->left_value) ? 0.0f : cuda_split_info->left_value; } else if (thread_index == 6) { internal_count[new_node_index] = cuda_split_info->left_count + cuda_split_info->right_count; } else if (thread_index == 7) { leaf_count[leaf_index] = cuda_split_info->left_count; } else if (thread_index == 8) { leaf_value[num_leaves] = isnan(cuda_split_info->right_value) ? 0.0f : cuda_split_info->right_value; } else if (thread_index == 9) { leaf_weight[num_leaves] = cuda_split_info->right_sum_hessians; } else if (thread_index == 10) { leaf_count[num_leaves] = cuda_split_info->right_count; } else if (thread_index == 11) { // update leaf depth leaf_depth[num_leaves] = leaf_depth[leaf_index] + 1; leaf_depth[leaf_index]++; } else if (thread_index == 12) { decision_type[new_node_index] = 0; SetDecisionTypeCUDA(&decision_type[new_node_index], false, kCategoricalMask); SetDecisionTypeCUDA(&decision_type[new_node_index], cuda_split_info->default_left, kDefaultLeftMask); SetMissingTypeCUDA(&decision_type[new_node_index], static_cast(missing_type)); } else if (thread_index == 13) { threshold_in_bin[new_node_index] = cuda_split_info->threshold; } else if (thread_index == 14) { threshold[new_node_index] = real_threshold; } } void CUDATree::LaunchSplitKernel(const int leaf_index, const int real_feature_index, const double real_threshold, const MissingType missing_type, const CUDASplitInfo* cuda_split_info) { SplitKernel<<<3, 5, 0, cuda_stream_>>>( // split information leaf_index, real_feature_index, real_threshold, missing_type, cuda_split_info, // tree structure num_leaves_, cuda_leaf_parent_.RawData(), cuda_leaf_depth_.RawData(), cuda_left_child_.RawData(), cuda_right_child_.RawData(), cuda_split_feature_inner_.RawData(), cuda_split_feature_.RawData(), cuda_split_gain_.RawData(), cuda_internal_weight_.RawData(), cuda_internal_value_.RawData(), cuda_internal_count_.RawData(), cuda_leaf_weight_.RawData(), cuda_leaf_value_.RawData(), cuda_leaf_count_.RawData(), cuda_decision_type_.RawData(), cuda_threshold_in_bin_.RawData(), cuda_threshold_.RawData()); } __global__ void SplitCategoricalKernel( // split information const int leaf_index, const int real_feature_index, const MissingType missing_type, const CUDASplitInfo* cuda_split_info, // tree structure const int num_leaves, int* leaf_parent, int* leaf_depth, int* left_child, int* right_child, int* split_feature_inner, int* split_feature, float* split_gain, double* internal_weight, double* internal_value, data_size_t* internal_count, double* leaf_weight, double* leaf_value, data_size_t* leaf_count, int8_t* decision_type, uint32_t* threshold_in_bin, double* threshold, size_t cuda_bitset_len, size_t cuda_bitset_inner_len, int num_cat, int* cuda_cat_boundaries, int* cuda_cat_boundaries_inner) { const int new_node_index = num_leaves - 1; const int thread_index = static_cast(threadIdx.x + blockIdx.x * blockDim.x); const int parent_index = leaf_parent[leaf_index]; if (thread_index == 0) { if (parent_index >= 0) { // if cur node is left child if (left_child[parent_index] == ~leaf_index) { left_child[parent_index] = new_node_index; } else { right_child[parent_index] = new_node_index; } } left_child[new_node_index] = ~leaf_index; right_child[new_node_index] = ~num_leaves; leaf_parent[leaf_index] = new_node_index; leaf_parent[num_leaves] = new_node_index; } else if (thread_index == 1) { // add new node split_feature_inner[new_node_index] = cuda_split_info->inner_feature_index; } else if (thread_index == 2) { split_feature[new_node_index] = real_feature_index; } else if (thread_index == 3) { split_gain[new_node_index] = static_cast(cuda_split_info->gain); } else if (thread_index == 4) { // save current leaf value to internal node before change internal_weight[new_node_index] = cuda_split_info->left_sum_hessians + cuda_split_info->right_sum_hessians; leaf_weight[leaf_index] = cuda_split_info->left_sum_hessians; } else if (thread_index == 5) { internal_value[new_node_index] = leaf_value[leaf_index]; leaf_value[leaf_index] = isnan(cuda_split_info->left_value) ? 0.0f : cuda_split_info->left_value; } else if (thread_index == 6) { internal_count[new_node_index] = cuda_split_info->left_count + cuda_split_info->right_count; } else if (thread_index == 7) { leaf_count[leaf_index] = cuda_split_info->left_count; } else if (thread_index == 8) { leaf_value[num_leaves] = isnan(cuda_split_info->right_value) ? 0.0f : cuda_split_info->right_value; } else if (thread_index == 9) { leaf_weight[num_leaves] = cuda_split_info->right_sum_hessians; } else if (thread_index == 10) { leaf_count[num_leaves] = cuda_split_info->right_count; } else if (thread_index == 11) { // update leaf depth leaf_depth[num_leaves] = leaf_depth[leaf_index] + 1; leaf_depth[leaf_index]++; } else if (thread_index == 12) { decision_type[new_node_index] = 0; SetDecisionTypeCUDA(&decision_type[new_node_index], true, kCategoricalMask); SetMissingTypeCUDA(&decision_type[new_node_index], static_cast(missing_type)); } else if (thread_index == 13) { threshold_in_bin[new_node_index] = num_cat; } else if (thread_index == 14) { threshold[new_node_index] = num_cat; } else if (thread_index == 15) { if (num_cat == 0) { cuda_cat_boundaries[num_cat] = 0; } cuda_cat_boundaries[num_cat + 1] = cuda_cat_boundaries[num_cat] + cuda_bitset_len; } else if (thread_index == 16) { if (num_cat == 0) { cuda_cat_boundaries_inner[num_cat] = 0; } cuda_cat_boundaries_inner[num_cat + 1] = cuda_cat_boundaries_inner[num_cat] + cuda_bitset_inner_len; } } void CUDATree::LaunchSplitCategoricalKernel(const int leaf_index, const int real_feature_index, const MissingType missing_type, const CUDASplitInfo* cuda_split_info, size_t cuda_bitset_len, size_t cuda_bitset_inner_len) { SplitCategoricalKernel<<<3, 6, 0, cuda_stream_>>>( // split information leaf_index, real_feature_index, missing_type, cuda_split_info, // tree structure num_leaves_, cuda_leaf_parent_.RawData(), cuda_leaf_depth_.RawData(), cuda_left_child_.RawData(), cuda_right_child_.RawData(), cuda_split_feature_inner_.RawData(), cuda_split_feature_.RawData(), cuda_split_gain_.RawData(), cuda_internal_weight_.RawData(), cuda_internal_value_.RawData(), cuda_internal_count_.RawData(), cuda_leaf_weight_.RawData(), cuda_leaf_value_.RawData(), cuda_leaf_count_.RawData(), cuda_decision_type_.RawData(), cuda_threshold_in_bin_.RawData(), cuda_threshold_.RawData(), cuda_bitset_len, cuda_bitset_inner_len, num_cat_, cuda_cat_boundaries_.RawData(), cuda_cat_boundaries_inner_.RawData()); } __global__ void ShrinkageKernel(const double rate, double* cuda_leaf_value, const int num_leaves) { const int leaf_index = static_cast(blockIdx.x * blockDim.x + threadIdx.x); if (leaf_index < num_leaves) { cuda_leaf_value[leaf_index] *= rate; } } void CUDATree::LaunchShrinkageKernel(const double rate) { const int num_threads_per_block = 1024; const int num_blocks = (num_leaves_ + num_threads_per_block - 1) / num_threads_per_block; ShrinkageKernel<<>>(rate, cuda_leaf_value_.RawData(), num_leaves_); } __global__ void AddBiasKernel(const double val, double* cuda_leaf_value, const int num_leaves) { const int leaf_index = static_cast(blockIdx.x * blockDim.x + threadIdx.x); if (leaf_index < num_leaves) { cuda_leaf_value[leaf_index] += val; } } void CUDATree::LaunchAddBiasKernel(const double val) { const int num_threads_per_block = 1024; const int num_blocks = (num_leaves_ + num_threads_per_block - 1) / num_threads_per_block; AddBiasKernel<<>>(val, cuda_leaf_value_.RawData(), num_leaves_); } template __global__ void AddPredictionToScoreKernel( // dataset information const data_size_t num_data, uint8_t* const* cuda_data_by_column, const uint8_t* cuda_column_bit_type, const uint32_t* cuda_feature_min_bin, const uint32_t* cuda_feature_max_bin, const uint32_t* cuda_feature_offset, const uint32_t* cuda_feature_default_bin, const uint32_t* cuda_feature_most_freq_bin, const int* cuda_feature_to_column, const data_size_t* cuda_used_indices, // tree information const uint32_t* cuda_threshold_in_bin, const int8_t* cuda_decision_type, const int* cuda_split_feature_inner, const int* cuda_left_child, const int* cuda_right_child, const double* cuda_leaf_value, const uint32_t* cuda_bitset_inner, const int* cuda_cat_boundaries_inner, // output double* score) { const data_size_t inner_data_index = static_cast(threadIdx.x + blockIdx.x * blockDim.x); if (inner_data_index < num_data) { const data_size_t data_index = USE_INDICES ? cuda_used_indices[inner_data_index] : inner_data_index; int node = 0; while (node >= 0) { const int split_feature_inner = cuda_split_feature_inner[node]; const int column = cuda_feature_to_column[split_feature_inner]; const uint32_t default_bin = cuda_feature_default_bin[split_feature_inner]; const uint32_t most_freq_bin = cuda_feature_most_freq_bin[split_feature_inner]; const uint32_t max_bin = cuda_feature_max_bin[split_feature_inner]; const uint32_t min_bin = cuda_feature_min_bin[split_feature_inner]; const uint32_t offset = cuda_feature_offset[split_feature_inner]; const uint8_t column_bit_type = cuda_column_bit_type[column]; uint32_t bin = 0; if (column_bit_type == 8) { bin = static_cast((reinterpret_cast(cuda_data_by_column[column]))[data_index]); } else if (column_bit_type == 16) { bin = static_cast((reinterpret_cast(cuda_data_by_column[column]))[data_index]); } else if (column_bit_type == 32) { bin = static_cast((reinterpret_cast(cuda_data_by_column[column]))[data_index]); } if (bin >= min_bin && bin <= max_bin) { bin = bin - min_bin + offset; } else { bin = most_freq_bin; } const int8_t decision_type = cuda_decision_type[node]; if (GetDecisionTypeCUDA(decision_type, kCategoricalMask)) { int cat_idx = static_cast(cuda_threshold_in_bin[node]); if (FindInBitsetCUDA(cuda_bitset_inner + cuda_cat_boundaries_inner[cat_idx], cuda_cat_boundaries_inner[cat_idx + 1] - cuda_cat_boundaries_inner[cat_idx], bin)) { node = cuda_left_child[node]; } else { node = cuda_right_child[node]; } } else { const uint32_t threshold_in_bin = cuda_threshold_in_bin[node]; const int8_t missing_type = GetMissingTypeCUDA(decision_type); const bool default_left = ((decision_type & kDefaultLeftMask) > 0); if ((missing_type == 1 && bin == default_bin) || (missing_type == 2 && bin == max_bin)) { if (default_left) { node = cuda_left_child[node]; } else { node = cuda_right_child[node]; } } else { if (bin <= threshold_in_bin) { node = cuda_left_child[node]; } else { node = cuda_right_child[node]; } } } } score[data_index] += cuda_leaf_value[~node]; } } void CUDATree::LaunchAddPredictionToScoreKernel( const Dataset* data, const data_size_t* used_data_indices, data_size_t num_data, double* score) const { const CUDAColumnData* cuda_column_data = data->cuda_column_data(); const int num_blocks = (num_data + num_threads_per_block_add_prediction_to_score_ - 1) / num_threads_per_block_add_prediction_to_score_; if (used_data_indices == nullptr) { AddPredictionToScoreKernel<<>>( // dataset information num_data, cuda_column_data->cuda_data_by_column(), cuda_column_data->cuda_column_bit_type(), cuda_column_data->cuda_feature_min_bin(), cuda_column_data->cuda_feature_max_bin(), cuda_column_data->cuda_feature_offset(), cuda_column_data->cuda_feature_default_bin(), cuda_column_data->cuda_feature_most_freq_bin(), cuda_column_data->cuda_feature_to_column(), nullptr, // tree information cuda_threshold_in_bin_.RawData(), cuda_decision_type_.RawData(), cuda_split_feature_inner_.RawData(), cuda_left_child_.RawData(), cuda_right_child_.RawData(), cuda_leaf_value_.RawData(), cuda_bitset_inner_.RawDataReadOnly(), cuda_cat_boundaries_inner_.RawDataReadOnly(), // output score); } else { AddPredictionToScoreKernel<<>>( // dataset information num_data, cuda_column_data->cuda_data_by_column(), cuda_column_data->cuda_column_bit_type(), cuda_column_data->cuda_feature_min_bin(), cuda_column_data->cuda_feature_max_bin(), cuda_column_data->cuda_feature_offset(), cuda_column_data->cuda_feature_default_bin(), cuda_column_data->cuda_feature_most_freq_bin(), cuda_column_data->cuda_feature_to_column(), used_data_indices, // tree information cuda_threshold_in_bin_.RawData(), cuda_decision_type_.RawData(), cuda_split_feature_inner_.RawData(), cuda_left_child_.RawData(), cuda_right_child_.RawData(), cuda_leaf_value_.RawData(), cuda_bitset_inner_.RawDataReadOnly(), cuda_cat_boundaries_inner_.RawDataReadOnly(), // output score); } SynchronizeCUDADevice(__FILE__, __LINE__); } } // namespace LightGBM #endif // USE_CUDA ================================================ FILE: src/io/dataset.cpp ================================================ /*! * Copyright (c) 2016-2026 Microsoft Corporation. All rights reserved. * Copyright (c) 2016-2026 The LightGBM developers. All rights reserved. * Licensed under the MIT License. See LICENSE file in the project root for * license information. */ #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include namespace LightGBM { const int Dataset::kSerializedReferenceVersionLength = 2; const char* Dataset::serialized_reference_version = "v1"; const char* Dataset::binary_file_token = "______LightGBM_Binary_File_Token______\n"; const char* Dataset::binary_serialized_reference_token = "______LightGBM_Binary_Serialized_Token______\n"; Dataset::Dataset() { data_filename_ = "noname"; num_data_ = 0; is_finish_load_ = false; wait_for_manual_finish_ = false; has_raw_ = false; } Dataset::Dataset(data_size_t num_data) { CHECK_GT(num_data, 0); data_filename_ = "noname"; num_data_ = num_data; metadata_.Init(num_data_, NO_SPECIFIC, NO_SPECIFIC); is_finish_load_ = false; wait_for_manual_finish_ = false; group_bin_boundaries_.push_back(0); has_raw_ = false; } Dataset::~Dataset() {} std::vector> OneFeaturePerGroup(const std::vector& used_features) { std::vector> features_in_group; features_in_group.resize(used_features.size()); for (size_t i = 0; i < used_features.size(); ++i) { features_in_group[i].emplace_back(used_features[i]); } return features_in_group; } int GetConflictCount(const std::vector& mark, const int* indices, int num_indices, data_size_t max_cnt) { int ret = 0; for (int i = 0; i < num_indices; ++i) { if (mark[indices[i]]) { ++ret; } if (ret > max_cnt) { return -1; } } return ret; } void MarkUsed(std::vector* mark, const int* indices, data_size_t num_indices) { auto& ref_mark = *mark; for (int i = 0; i < num_indices; ++i) { ref_mark[indices[i]] = true; } } std::vector FixSampleIndices(const BinMapper* bin_mapper, int num_total_samples, int num_indices, const int* sample_indices, const double* sample_values) { std::vector ret; if (bin_mapper->GetDefaultBin() == bin_mapper->GetMostFreqBin()) { return ret; } int i = 0, j = 0; while (i < num_total_samples) { if (j < num_indices && sample_indices[j] < i) { ++j; } else if (j < num_indices && sample_indices[j] == i) { if (bin_mapper->ValueToBin(sample_values[j]) != bin_mapper->GetMostFreqBin()) { ret.push_back(i); } ++i; } else { ret.push_back(i++); } } return ret; } std::vector> FindGroups( const std::vector>& bin_mappers, const std::vector& find_order, int** sample_indices, const int* num_per_col, int num_sample_col, data_size_t total_sample_cnt, data_size_t num_data, bool is_use_gpu, bool is_sparse, std::vector* multi_val_group) { const int max_search_group = 100; const int max_bin_per_group = 256; const data_size_t single_val_max_conflict_cnt = static_cast(total_sample_cnt / 10000); multi_val_group->clear(); Random rand(num_data); std::vector> features_in_group; std::vector> conflict_marks; std::vector group_used_row_cnt; std::vector group_total_data_cnt; std::vector group_num_bin; // first round: fill the single val group for (auto fidx : find_order) { bool is_filtered_feature = fidx >= num_sample_col; const data_size_t cur_non_zero_cnt = is_filtered_feature ? 0 : num_per_col[fidx]; std::vector available_groups; for (int gid = 0; gid < static_cast(features_in_group.size()); ++gid) { auto cur_num_bin = group_num_bin[gid] + bin_mappers[fidx]->num_bin() + (bin_mappers[fidx]->GetMostFreqBin() == 0 ? -1 : 0); if (group_total_data_cnt[gid] + cur_non_zero_cnt <= total_sample_cnt + single_val_max_conflict_cnt) { if (!is_use_gpu || cur_num_bin <= max_bin_per_group) { available_groups.push_back(gid); } } } std::vector search_groups; if (!available_groups.empty()) { int last = static_cast(available_groups.size()) - 1; auto indices = rand.Sample(last, std::min(last, max_search_group - 1)); // always push the last group search_groups.push_back(available_groups.back()); for (auto idx : indices) { search_groups.push_back(available_groups[idx]); } } int best_gid = -1; int best_conflict_cnt = -1; for (auto gid : search_groups) { const data_size_t rest_max_cnt = single_val_max_conflict_cnt - group_total_data_cnt[gid] + group_used_row_cnt[gid]; const data_size_t cnt = is_filtered_feature ? 0 : GetConflictCount(conflict_marks[gid], sample_indices[fidx], num_per_col[fidx], rest_max_cnt); if (cnt >= 0 && cnt <= rest_max_cnt && cnt <= cur_non_zero_cnt / 2) { best_gid = gid; best_conflict_cnt = cnt; break; } } if (best_gid >= 0) { features_in_group[best_gid].push_back(fidx); group_total_data_cnt[best_gid] += cur_non_zero_cnt; group_used_row_cnt[best_gid] += cur_non_zero_cnt - best_conflict_cnt; if (!is_filtered_feature) { MarkUsed(&conflict_marks[best_gid], sample_indices[fidx], num_per_col[fidx]); } group_num_bin[best_gid] += bin_mappers[fidx]->num_bin() + (bin_mappers[fidx]->GetDefaultBin() == 0 ? -1 : 0); } else { features_in_group.emplace_back(); features_in_group.back().push_back(fidx); conflict_marks.emplace_back(total_sample_cnt, false); if (!is_filtered_feature) { MarkUsed(&(conflict_marks.back()), sample_indices[fidx], num_per_col[fidx]); } group_total_data_cnt.emplace_back(cur_non_zero_cnt); group_used_row_cnt.emplace_back(cur_non_zero_cnt); group_num_bin.push_back( 1 + bin_mappers[fidx]->num_bin() + (bin_mappers[fidx]->GetMostFreqBin() == 0 ? -1 : 0)); } } if (!is_sparse) { multi_val_group->resize(features_in_group.size(), false); return features_in_group; } std::vector second_round_features; std::vector> features_in_group2; std::vector> conflict_marks2; const double dense_threshold = 0.4; for (int gid = 0; gid < static_cast(features_in_group.size()); ++gid) { const double dense_rate = static_cast(group_used_row_cnt[gid]) / total_sample_cnt; if (dense_rate >= dense_threshold) { features_in_group2.push_back(std::move(features_in_group[gid])); conflict_marks2.push_back(std::move(conflict_marks[gid])); } else { for (auto fidx : features_in_group[gid]) { second_round_features.push_back(fidx); } } } features_in_group = features_in_group2; conflict_marks = conflict_marks2; multi_val_group->resize(features_in_group.size(), false); if (!second_round_features.empty()) { features_in_group.emplace_back(); conflict_marks.emplace_back(total_sample_cnt, false); bool is_multi_val = is_use_gpu ? true : false; int conflict_cnt = 0; for (auto fidx : second_round_features) { features_in_group.back().push_back(fidx); if (!is_multi_val) { const int rest_max_cnt = single_val_max_conflict_cnt - conflict_cnt; const auto cnt = GetConflictCount(conflict_marks.back(), sample_indices[fidx], num_per_col[fidx], rest_max_cnt); conflict_cnt += cnt; if (cnt < 0 || conflict_cnt > single_val_max_conflict_cnt) { is_multi_val = true; continue; } MarkUsed(&(conflict_marks.back()), sample_indices[fidx], num_per_col[fidx]); } } multi_val_group->push_back(is_multi_val); } return features_in_group; } std::vector> FastFeatureBundling( const std::vector>& bin_mappers, int** sample_indices, double** sample_values, const int* num_per_col, int num_sample_col, data_size_t total_sample_cnt, const std::vector& used_features, data_size_t num_data, bool is_use_gpu, bool is_sparse, std::vector* multi_val_group) { Common::FunctionTimer fun_timer("Dataset::FastFeatureBundling", global_timer); std::vector feature_non_zero_cnt; feature_non_zero_cnt.reserve(used_features.size()); // put dense feature first for (auto fidx : used_features) { if (fidx < num_sample_col) { feature_non_zero_cnt.emplace_back(num_per_col[fidx]); } else { feature_non_zero_cnt.emplace_back(0); } } // sort by non zero cnt std::vector sorted_idx; sorted_idx.reserve(used_features.size()); for (int i = 0; i < static_cast(used_features.size()); ++i) { sorted_idx.emplace_back(i); } // sort by non zero cnt, bigger first std::stable_sort(sorted_idx.begin(), sorted_idx.end(), [&feature_non_zero_cnt](int a, int b) { return feature_non_zero_cnt[a] > feature_non_zero_cnt[b]; }); std::vector feature_order_by_cnt; feature_order_by_cnt.reserve(sorted_idx.size()); for (auto sidx : sorted_idx) { feature_order_by_cnt.push_back(used_features[sidx]); } std::vector> tmp_indices; std::vector tmp_num_per_col(num_sample_col, 0); for (auto fidx : used_features) { if (fidx >= num_sample_col) { continue; } auto ret = FixSampleIndices( bin_mappers[fidx].get(), static_cast(total_sample_cnt), num_per_col[fidx], sample_indices[fidx], sample_values[fidx]); if (!ret.empty()) { tmp_indices.push_back(ret); tmp_num_per_col[fidx] = static_cast(ret.size()); sample_indices[fidx] = tmp_indices.back().data(); } else { tmp_num_per_col[fidx] = num_per_col[fidx]; } } std::vector group_is_multi_val, group_is_multi_val2; auto features_in_group = FindGroups(bin_mappers, used_features, sample_indices, tmp_num_per_col.data(), num_sample_col, total_sample_cnt, num_data, is_use_gpu, is_sparse, &group_is_multi_val); auto group2 = FindGroups(bin_mappers, feature_order_by_cnt, sample_indices, tmp_num_per_col.data(), num_sample_col, total_sample_cnt, num_data, is_use_gpu, is_sparse, &group_is_multi_val2); if (features_in_group.size() > group2.size()) { features_in_group = group2; group_is_multi_val = group_is_multi_val2; } // shuffle groups int num_group = static_cast(features_in_group.size()); Random tmp_rand(num_data); for (int i = 0; i < num_group - 1; ++i) { int j = tmp_rand.NextShort(i + 1, num_group); std::swap(features_in_group[i], features_in_group[j]); // Using std::swap for vector will cause the wrong result. std::swap(group_is_multi_val[i], group_is_multi_val[j]); } *multi_val_group = group_is_multi_val; return features_in_group; } void Dataset::Construct(std::vector>* bin_mappers, int num_total_features, const std::vector>& forced_bins, int** sample_non_zero_indices, double** sample_values, const int* num_per_col, int num_sample_col, size_t total_sample_cnt, const Config& io_config) { num_total_features_ = num_total_features; CHECK_EQ(num_total_features_, static_cast(bin_mappers->size())); // get num_features std::vector used_features; auto& ref_bin_mappers = *bin_mappers; for (int i = 0; i < static_cast(bin_mappers->size()); ++i) { if (ref_bin_mappers[i] != nullptr && !ref_bin_mappers[i]->is_trivial()) { used_features.emplace_back(i); } } if (used_features.empty()) { Log::Warning( "There are no meaningful features which satisfy the provided configuration. " "Decreasing Dataset parameters min_data_in_bin or min_data_in_leaf and re-constructing " "Dataset might resolve this warning."); } auto features_in_group = OneFeaturePerGroup(used_features); auto is_sparse = io_config.is_enable_sparse; if (io_config.device_type == std::string("cuda")) { LGBM_config_::current_device = lgbm_device_cuda; if ((io_config.device_type == std::string("cuda")) && is_sparse) { Log::Warning("Using sparse features with CUDA is currently not supported."); is_sparse = false; } } std::vector group_is_multi_val(used_features.size(), 0); if (io_config.enable_bundle && !used_features.empty()) { bool lgbm_is_gpu_used = io_config.device_type == std::string("gpu") || io_config.device_type == std::string("cuda"); features_in_group = FastFeatureBundling( *bin_mappers, sample_non_zero_indices, sample_values, num_per_col, num_sample_col, static_cast(total_sample_cnt), used_features, num_data_, lgbm_is_gpu_used, is_sparse, &group_is_multi_val); } num_features_ = 0; for (const auto& fs : features_in_group) { num_features_ += static_cast(fs.size()); } int cur_fidx = 0; used_feature_map_ = std::vector(num_total_features_, -1); num_groups_ = static_cast(features_in_group.size()); real_feature_idx_.resize(num_features_); feature2group_.resize(num_features_); feature2subfeature_.resize(num_features_); feature_need_push_zeros_.clear(); group_bin_boundaries_.clear(); uint64_t num_total_bin = 0; group_bin_boundaries_.push_back(num_total_bin); group_feature_start_.resize(num_groups_); group_feature_cnt_.resize(num_groups_); for (int i = 0; i < num_groups_; ++i) { auto cur_features = features_in_group[i]; int cur_cnt_features = static_cast(cur_features.size()); group_feature_start_[i] = cur_fidx; group_feature_cnt_[i] = cur_cnt_features; // get bin_mappers std::vector> cur_bin_mappers; for (int j = 0; j < cur_cnt_features; ++j) { int real_fidx = cur_features[j]; used_feature_map_[real_fidx] = cur_fidx; real_feature_idx_[cur_fidx] = real_fidx; feature2group_[cur_fidx] = i; feature2subfeature_[cur_fidx] = j; cur_bin_mappers.emplace_back(ref_bin_mappers[real_fidx].release()); if (cur_bin_mappers.back()->GetDefaultBin() != cur_bin_mappers.back()->GetMostFreqBin()) { feature_need_push_zeros_.push_back(cur_fidx); } ++cur_fidx; } feature_groups_.emplace_back(std::unique_ptr( new FeatureGroup(cur_cnt_features, group_is_multi_val[i], &cur_bin_mappers, num_data_, i))); num_total_bin += feature_groups_[i]->num_total_bin_; group_bin_boundaries_.push_back(num_total_bin); } if (!io_config.max_bin_by_feature.empty()) { CHECK_EQ(static_cast(num_total_features_), io_config.max_bin_by_feature.size()); CHECK_GT(*(std::min_element(io_config.max_bin_by_feature.begin(), io_config.max_bin_by_feature.end())), 1); max_bin_by_feature_.resize(num_total_features_); max_bin_by_feature_.assign(io_config.max_bin_by_feature.begin(), io_config.max_bin_by_feature.end()); } forced_bin_bounds_ = forced_bins; max_bin_ = io_config.max_bin; min_data_in_bin_ = io_config.min_data_in_bin; bin_construct_sample_cnt_ = io_config.bin_construct_sample_cnt; use_missing_ = io_config.use_missing; zero_as_missing_ = io_config.zero_as_missing; has_raw_ = false; if (io_config.linear_tree) { has_raw_ = true; } numeric_feature_map_ = std::vector(num_features_, -1); num_numeric_features_ = 0; for (int i = 0; i < num_features_; ++i) { if (FeatureBinMapper(i)->bin_type() == BinType::NumericalBin) { numeric_feature_map_[i] = num_numeric_features_; ++num_numeric_features_; } } device_type_ = io_config.device_type; gpu_device_id_ = io_config.gpu_device_id; } void Dataset::FinishLoad() { if (is_finish_load_) { return; } if (num_groups_ > 0) { for (int i = 0; i < num_groups_; ++i) { feature_groups_[i]->FinishLoad(); } } metadata_.FinishLoad(); #ifdef USE_CUDA if (device_type_ == std::string("cuda")) { CreateCUDAColumnData(); metadata_.CreateCUDAMetadata(gpu_device_id_); } else { cuda_column_data_.reset(nullptr); } #endif // USE_CUDA is_finish_load_ = true; } void PushDataToMultiValBin( data_size_t num_data, const std::vector most_freq_bins, const std::vector offsets, std::vector>>* iters, MultiValBin* ret) { Common::FunctionTimer fun_time("Dataset::PushDataToMultiValBin", global_timer); if (ret->IsSparse()) { Threading::For( 0, num_data, 1024, [&](int tid, data_size_t start, data_size_t end) { std::vector cur_data; cur_data.reserve(most_freq_bins.size()); for (size_t j = 0; j < most_freq_bins.size(); ++j) { (*iters)[tid][j]->Reset(start); } for (data_size_t i = start; i < end; ++i) { cur_data.clear(); for (size_t j = 0; j < most_freq_bins.size(); ++j) { // for sparse multi value bin, we store the feature bin values with offset added auto cur_bin = (*iters)[tid][j]->Get(i); if (cur_bin == most_freq_bins[j]) { continue; } cur_bin += offsets[j]; if (most_freq_bins[j] == 0) { cur_bin -= 1; } cur_data.push_back(cur_bin); } ret->PushOneRow(tid, i, cur_data); } }); } else { Threading::For( 0, num_data, 1024, [&](int tid, data_size_t start, data_size_t end) { std::vector cur_data(most_freq_bins.size(), 0); for (size_t j = 0; j < most_freq_bins.size(); ++j) { (*iters)[tid][j]->Reset(start); } for (data_size_t i = start; i < end; ++i) { for (size_t j = 0; j < most_freq_bins.size(); ++j) { // for dense multi value bin, the feature bin values without offsets are used auto cur_bin = (*iters)[tid][j]->Get(i); cur_data[j] = cur_bin; } ret->PushOneRow(tid, i, cur_data); } }); } } MultiValBin* Dataset::GetMultiBinFromSparseFeatures(const std::vector& offsets) const { Common::FunctionTimer fun_time("Dataset::GetMultiBinFromSparseFeatures", global_timer); int multi_group_id = -1; for (int i = 0; i < num_groups_; ++i) { if (feature_groups_[i]->is_multi_val_) { if (multi_group_id < 0) { multi_group_id = i; } else { Log::Fatal("Bug. There should be only one multi-val group."); } } } if (multi_group_id < 0) { return nullptr; } const int num_feature = feature_groups_[multi_group_id]->num_feature_; int num_threads = OMP_NUM_THREADS(); std::vector>> iters(num_threads); std::vector most_freq_bins; double sum_sparse_rate = 0; for (int i = 0; i < num_feature; ++i) { #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static, 1) for (int tid = 0; tid < num_threads; ++tid) { iters[tid].emplace_back( feature_groups_[multi_group_id]->SubFeatureIterator(i)); } most_freq_bins.push_back( feature_groups_[multi_group_id]->bin_mappers_[i]->GetMostFreqBin()); sum_sparse_rate += feature_groups_[multi_group_id]->bin_mappers_[i]->sparse_rate(); } sum_sparse_rate /= num_feature; Log::Debug("Dataset::GetMultiBinFromSparseFeatures: sparse rate %f", sum_sparse_rate); std::unique_ptr ret; ret.reset(MultiValBin::CreateMultiValBin(num_data_, offsets.back(), num_feature, sum_sparse_rate, offsets)); PushDataToMultiValBin(num_data_, most_freq_bins, offsets, &iters, ret.get()); ret->FinishLoad(); return ret.release(); } MultiValBin* Dataset::GetMultiBinFromAllFeatures(const std::vector& offsets) const { Common::FunctionTimer fun_time("Dataset::GetMultiBinFromAllFeatures", global_timer); int num_threads = OMP_NUM_THREADS(); double sum_dense_ratio = 0; std::unique_ptr ret; std::vector>> iters(num_threads); std::vector most_freq_bins; int ncol = 0; for (int gid = 0; gid < num_groups_; ++gid) { if (feature_groups_[gid]->is_multi_val_) { ncol += feature_groups_[gid]->num_feature_; } else { ++ncol; } for (int fid = 0; fid < feature_groups_[gid]->num_feature_; ++fid) { const auto& bin_mapper = feature_groups_[gid]->bin_mappers_[fid]; sum_dense_ratio += 1.0f - bin_mapper->sparse_rate(); } } sum_dense_ratio /= ncol; for (int gid = 0; gid < num_groups_; ++gid) { if (feature_groups_[gid]->is_multi_val_) { for (int fid = 0; fid < feature_groups_[gid]->num_feature_; ++fid) { const auto& bin_mapper = feature_groups_[gid]->bin_mappers_[fid]; most_freq_bins.push_back(bin_mapper->GetMostFreqBin()); #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static, 1) for (int tid = 0; tid < num_threads; ++tid) { iters[tid].emplace_back( feature_groups_[gid]->SubFeatureIterator(fid)); } } } else { most_freq_bins.push_back(0); for (int tid = 0; tid < num_threads; ++tid) { iters[tid].emplace_back(feature_groups_[gid]->FeatureGroupIterator()); } } } CHECK(static_cast(most_freq_bins.size()) == ncol); Log::Debug("Dataset::GetMultiBinFromAllFeatures: sparse rate %f", 1.0 - sum_dense_ratio); ret.reset(MultiValBin::CreateMultiValBin( num_data_, offsets.back(), static_cast(most_freq_bins.size()), 1.0 - sum_dense_ratio, offsets)); PushDataToMultiValBin(num_data_, most_freq_bins, offsets, &iters, ret.get()); ret->FinishLoad(); return ret.release(); } template TrainingShareStates* Dataset::GetShareStates( score_t* gradients, score_t* hessians, const std::vector& is_feature_used, bool is_constant_hessian, bool force_col_wise, bool force_row_wise, const int num_grad_quant_bins) const { Common::FunctionTimer fun_timer("Dataset::TestMultiThreadingMethod", global_timer); if (force_col_wise && force_row_wise) { Log::Fatal( "Cannot set both of `force_col_wise` and `force_row_wise` to `true` at " "the same time"); } if (num_groups_ <= 0) { TrainingShareStates* share_state = new TrainingShareStates(); share_state->is_col_wise = true; share_state->is_constant_hessian = is_constant_hessian; return share_state; } if (force_col_wise) { TrainingShareStates* share_state = new TrainingShareStates(); std::vector offsets; share_state->CalcBinOffsets( feature_groups_, &offsets, true); share_state->SetMultiValBin(GetMultiBinFromSparseFeatures(offsets), num_data_, feature_groups_, false, true, num_grad_quant_bins); share_state->is_col_wise = true; share_state->is_constant_hessian = is_constant_hessian; return share_state; } else if (force_row_wise) { TrainingShareStates* share_state = new TrainingShareStates(); std::vector offsets; share_state->CalcBinOffsets( feature_groups_, &offsets, false); share_state->SetMultiValBin(GetMultiBinFromAllFeatures(offsets), num_data_, feature_groups_, false, false, num_grad_quant_bins); share_state->is_col_wise = false; share_state->is_constant_hessian = is_constant_hessian; return share_state; } else { std::unique_ptr sparse_bin; std::unique_ptr all_bin; std::unique_ptr col_wise_state; std::unique_ptr row_wise_state; col_wise_state.reset(new TrainingShareStates()); row_wise_state.reset(new TrainingShareStates()); std::chrono::duration col_wise_init_time, row_wise_init_time; auto start_time = std::chrono::steady_clock::now(); std::vector col_wise_offsets; col_wise_state->CalcBinOffsets(feature_groups_, &col_wise_offsets, true); col_wise_state->SetMultiValBin(GetMultiBinFromSparseFeatures(col_wise_offsets), num_data_, feature_groups_, false, true, num_grad_quant_bins); col_wise_init_time = std::chrono::steady_clock::now() - start_time; start_time = std::chrono::steady_clock::now(); std::vector row_wise_offsets; row_wise_state->CalcBinOffsets(feature_groups_, &row_wise_offsets, false); row_wise_state->SetMultiValBin(GetMultiBinFromAllFeatures(row_wise_offsets), num_data_, feature_groups_, false, false, num_grad_quant_bins); row_wise_init_time = std::chrono::steady_clock::now() - start_time; uint64_t max_total_bin = std::max(row_wise_state->num_hist_total_bin(), col_wise_state->num_hist_total_bin()); std::vector> hist_data(max_total_bin * 2); Log::Debug( "init for col-wise cost %f seconds, init for row-wise cost %f seconds", col_wise_init_time * 1e-3, row_wise_init_time * 1e-3); col_wise_state->is_col_wise = true; col_wise_state->is_constant_hessian = is_constant_hessian; InitTrain(is_feature_used, col_wise_state.get()); row_wise_state->is_col_wise = false; row_wise_state->is_constant_hessian = is_constant_hessian; InitTrain(is_feature_used, row_wise_state.get()); std::chrono::duration col_wise_time, row_wise_time; start_time = std::chrono::steady_clock::now(); ConstructHistograms(is_feature_used, nullptr, num_data_, gradients, hessians, gradients, hessians, col_wise_state.get(), hist_data.data()); col_wise_time = std::chrono::steady_clock::now() - start_time; start_time = std::chrono::steady_clock::now(); ConstructHistograms(is_feature_used, nullptr, num_data_, gradients, hessians, gradients, hessians, row_wise_state.get(), hist_data.data()); row_wise_time = std::chrono::steady_clock::now() - start_time; if (col_wise_time < row_wise_time) { auto overhead_cost = row_wise_init_time + row_wise_time + col_wise_time; Log::Info( "Auto-choosing col-wise multi-threading, the overhead of testing was " "%f seconds.\n" "You can set `force_col_wise=true` to remove the overhead.", overhead_cost * 1e-3); return col_wise_state.release(); } else { auto overhead_cost = col_wise_init_time + row_wise_time + col_wise_time; Log::Info( "Auto-choosing row-wise multi-threading, the overhead of testing was " "%f seconds.\n" "You can set `force_row_wise=true` to remove the overhead.\n" "And if memory is not enough, you can set `force_col_wise=true`.", overhead_cost * 1e-3); if (row_wise_state->IsSparseRowwise()) { Log::Debug("Using Sparse Multi-Val Bin"); } else { Log::Debug("Using Dense Multi-Val Bin"); } return row_wise_state.release(); } } } template TrainingShareStates* Dataset::GetShareStates( score_t* gradients, score_t* hessians, const std::vector& is_feature_used, bool is_constant_hessian, bool force_col_wise, bool force_row_wise, const int num_grad_quant_bins) const; template TrainingShareStates* Dataset::GetShareStates( score_t* gradients, score_t* hessians, const std::vector& is_feature_used, bool is_constant_hessian, bool force_col_wise, bool force_row_wise, const int num_grad_quant_bins) const; template TrainingShareStates* Dataset::GetShareStates( score_t* gradients, score_t* hessians, const std::vector& is_feature_used, bool is_constant_hessian, bool force_col_wise, bool force_row_wise, const int num_grad_quant_bins) const; void Dataset::CopyFeatureMapperFrom(const Dataset* dataset) { feature_groups_.clear(); num_features_ = dataset->num_features_; num_groups_ = dataset->num_groups_; has_raw_ = dataset->has_raw(); // copy feature bin mapper data for (int i = 0; i < num_groups_; ++i) { feature_groups_.emplace_back( new FeatureGroup(*dataset->feature_groups_[i], num_data_)); } feature_groups_.shrink_to_fit(); used_feature_map_ = dataset->used_feature_map_; num_total_features_ = dataset->num_total_features_; feature_names_ = dataset->feature_names_; label_idx_ = dataset->label_idx_; real_feature_idx_ = dataset->real_feature_idx_; feature2group_ = dataset->feature2group_; feature2subfeature_ = dataset->feature2subfeature_; group_bin_boundaries_ = dataset->group_bin_boundaries_; group_feature_start_ = dataset->group_feature_start_; group_feature_cnt_ = dataset->group_feature_cnt_; forced_bin_bounds_ = dataset->forced_bin_bounds_; feature_need_push_zeros_ = dataset->feature_need_push_zeros_; max_bin_ = dataset->max_bin_; min_data_in_bin_ = dataset->min_data_in_bin_; bin_construct_sample_cnt_ = dataset->bin_construct_sample_cnt_; use_missing_ = dataset->use_missing_; zero_as_missing_ = dataset->zero_as_missing_; } void Dataset::CreateValid(const Dataset* dataset) { feature_groups_.clear(); num_features_ = dataset->num_features_; num_groups_ = num_features_; max_bin_ = dataset->max_bin_; min_data_in_bin_ = dataset->min_data_in_bin_; bin_construct_sample_cnt_ = dataset->bin_construct_sample_cnt_; use_missing_ = dataset->use_missing_; zero_as_missing_ = dataset->zero_as_missing_; feature2group_.clear(); feature2subfeature_.clear(); has_raw_ = dataset->has_raw(); numeric_feature_map_ = dataset->numeric_feature_map_; num_numeric_features_ = dataset->num_numeric_features_; // copy feature bin mapper data feature_need_push_zeros_.clear(); group_bin_boundaries_.clear(); uint64_t num_total_bin = 0; group_bin_boundaries_.push_back(num_total_bin); group_feature_start_.resize(num_groups_); group_feature_cnt_.resize(num_groups_); for (int i = 0; i < num_features_; ++i) { std::vector> bin_mappers; bin_mappers.emplace_back(new BinMapper(*(dataset->FeatureBinMapper(i)))); if (bin_mappers.back()->GetDefaultBin() != bin_mappers.back()->GetMostFreqBin()) { feature_need_push_zeros_.push_back(i); } feature_groups_.emplace_back(new FeatureGroup(&bin_mappers, num_data_)); feature2group_.push_back(i); feature2subfeature_.push_back(0); num_total_bin += feature_groups_[i]->num_total_bin_; group_bin_boundaries_.push_back(num_total_bin); group_feature_start_[i] = i; group_feature_cnt_[i] = 1; } feature_groups_.shrink_to_fit(); used_feature_map_ = dataset->used_feature_map_; num_total_features_ = dataset->num_total_features_; feature_names_ = dataset->feature_names_; label_idx_ = dataset->label_idx_; real_feature_idx_ = dataset->real_feature_idx_; forced_bin_bounds_ = dataset->forced_bin_bounds_; device_type_ = dataset->device_type_; gpu_device_id_ = dataset->gpu_device_id_; } void Dataset::ReSize(data_size_t num_data) { if (num_data_ != num_data) { num_data_ = num_data; OMP_INIT_EX(); #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) for (int group = 0; group < num_groups_; ++group) { OMP_LOOP_EX_BEGIN(); feature_groups_[group]->ReSize(num_data_); OMP_LOOP_EX_END(); } OMP_THROW_EX(); } } void Dataset::CopySubrowHostPart(const Dataset* fullset, const data_size_t* used_indices, data_size_t num_used_indices, bool need_meta_data) { CHECK_EQ(num_used_indices, num_data_); std::vector group_ids, subfeature_ids; group_ids.reserve(num_features_); subfeature_ids.reserve(num_features_); for (int group = 0; group < num_groups_; ++group) { if (fullset->IsMultiGroup(group)) { for (int sub_feature = 0; sub_feature < fullset->feature_groups_[group]->num_feature_; ++sub_feature) { group_ids.emplace_back(group); subfeature_ids.emplace_back(sub_feature); } } else { group_ids.emplace_back(group); subfeature_ids.emplace_back(-1); } } int num_copy_tasks = static_cast(group_ids.size()); OMP_INIT_EX(); #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(dynamic) for (int task_id = 0; task_id < num_copy_tasks; ++task_id) { OMP_LOOP_EX_BEGIN(); int group = group_ids[task_id]; int subfeature = subfeature_ids[task_id]; feature_groups_[group]->CopySubrowByCol(fullset->feature_groups_[group].get(), used_indices, num_used_indices, subfeature); OMP_LOOP_EX_END(); } OMP_THROW_EX(); if (need_meta_data) { metadata_.Init(fullset->metadata_, used_indices, num_used_indices); } is_finish_load_ = true; numeric_feature_map_ = fullset->numeric_feature_map_; num_numeric_features_ = fullset->num_numeric_features_; if (has_raw_) { ResizeRaw(num_used_indices); #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) for (int i = 0; i < num_used_indices; ++i) { for (int j = 0; j < num_numeric_features_; ++j) { raw_data_[j][i] = fullset->raw_data_[j][used_indices[i]]; } } } } void Dataset::CopySubrow(const Dataset* fullset, const data_size_t* used_indices, data_size_t num_used_indices, bool need_meta_data) { CopySubrowHostPart(fullset, used_indices, num_used_indices, need_meta_data); // update CUDA storage for column data and metadata device_type_ = fullset->device_type_; gpu_device_id_ = fullset->gpu_device_id_; #ifdef USE_CUDA if (device_type_ == std::string("cuda")) { if (cuda_column_data_ == nullptr) { cuda_column_data_.reset(new CUDAColumnData(num_used_indices, gpu_device_id_)); metadata_.CreateCUDAMetadata(gpu_device_id_); } cuda_column_data_->CopySubrow(fullset->cuda_column_data(), used_indices, num_used_indices); } #endif // USE_CUDA } void Dataset::CopySubrowToDevice(const Dataset* fullset, const data_size_t* used_indices, data_size_t num_used_indices, bool need_meta_data, int gpu_device_id) { CopySubrowHostPart(fullset, used_indices, num_used_indices, need_meta_data); // update CUDA storage for column data and metadata device_type_ = fullset->device_type_; gpu_device_id_ = gpu_device_id; #ifdef USE_CUDA if (device_type_ == std::string("cuda")) { if (cuda_column_data_ == nullptr) { cuda_column_data_.reset(new CUDAColumnData(num_used_indices, gpu_device_id_)); metadata_.CreateCUDAMetadata(gpu_device_id_); } cuda_column_data_->CopySubrow(fullset->cuda_column_data(), used_indices, num_used_indices); } #endif // USE_CUDA } bool Dataset::SetFieldFromArrow(const char* field_name, const ArrowChunkedArray &ca) { std::string name(field_name); name = Common::Trim(name); if (name == std::string("label") || name == std::string("target")) { metadata_.SetLabel(ca); } else if (name == std::string("weight") || name == std::string("weights")) { metadata_.SetWeights(ca); } else if (name == std::string("init_score")) { metadata_.SetInitScore(ca); } else if (name == std::string("query") || name == std::string("group")) { metadata_.SetQuery(ca); } else { return false; } return true; } bool Dataset::SetFloatField(const char* field_name, const float* field_data, data_size_t num_element) { std::string name(field_name); name = Common::Trim(name); if (name == std::string("label") || name == std::string("target")) { #ifdef LABEL_T_USE_DOUBLE Log::Fatal("Don't support LABEL_T_USE_DOUBLE"); #else metadata_.SetLabel(field_data, num_element); #endif } else if (name == std::string("weight") || name == std::string("weights")) { #ifdef LABEL_T_USE_DOUBLE Log::Fatal("Don't support LABEL_T_USE_DOUBLE"); #else metadata_.SetWeights(field_data, num_element); #endif } else { return false; } return true; } bool Dataset::SetDoubleField(const char* field_name, const double* field_data, data_size_t num_element) { std::string name(field_name); name = Common::Trim(name); if (name == std::string("init_score")) { metadata_.SetInitScore(field_data, num_element); } else { return false; } return true; } bool Dataset::SetIntField(const char* field_name, const int* field_data, data_size_t num_element) { std::string name(field_name); name = Common::Trim(name); if (name == std::string("query") || name == std::string("group")) { metadata_.SetQuery(field_data, num_element); } else if (name == std::string("position")) { metadata_.SetPosition(field_data, num_element); } else { return false; } return true; } bool Dataset::GetFloatField(const char* field_name, data_size_t* out_len, const float** out_ptr) { std::string name(field_name); name = Common::Trim(name); if (name == std::string("label") || name == std::string("target")) { #ifdef LABEL_T_USE_DOUBLE Log::Fatal("Don't support LABEL_T_USE_DOUBLE"); #else *out_ptr = metadata_.label(); *out_len = num_data_; #endif } else if (name == std::string("weight") || name == std::string("weights")) { #ifdef LABEL_T_USE_DOUBLE Log::Fatal("Don't support LABEL_T_USE_DOUBLE"); #else *out_ptr = metadata_.weights(); *out_len = num_data_; #endif } else { return false; } return true; } bool Dataset::GetDoubleField(const char* field_name, data_size_t* out_len, const double** out_ptr) { std::string name(field_name); name = Common::Trim(name); if (name == std::string("init_score")) { *out_ptr = metadata_.init_score(); *out_len = static_cast(metadata_.num_init_score()); } else { return false; } return true; } bool Dataset::GetIntField(const char* field_name, data_size_t* out_len, const int** out_ptr) { std::string name(field_name); name = Common::Trim(name); if (name == std::string("query") || name == std::string("group")) { *out_ptr = metadata_.query_boundaries(); *out_len = metadata_.num_queries() + 1; } else if (name == std::string("position")) { *out_ptr = metadata_.positions(); *out_len = num_data_; } else { return false; } return true; } void Dataset::SaveBinaryFile(const char* bin_filename) { if (bin_filename != nullptr && std::string(bin_filename) == data_filename_) { Log::Warning("Binary file %s already exists", bin_filename); return; } // if not pass a filename, just append ".bin" of original file std::string bin_filename_str(data_filename_); if (bin_filename == nullptr || bin_filename[0] == '\0') { bin_filename_str.append(".bin"); bin_filename = bin_filename_str.c_str(); } bool is_file_existed = false; if (VirtualFileWriter::Exists(bin_filename)) { is_file_existed = true; Log::Warning("File %s exists, cannot save binary to it", bin_filename); } if (!is_file_existed) { auto writer = VirtualFileWriter::Make(bin_filename); if (!writer->Init()) { Log::Fatal("Cannot write binary data to %s ", bin_filename); } Log::Info("Saving data to binary file %s", bin_filename); size_t size_of_token = std::strlen(binary_file_token); writer->AlignedWrite(binary_file_token, size_of_token); // Write the basic header information for the dataset SerializeHeader(writer.get()); // get size of meta data size_t size_of_metadata = metadata_.SizesInByte(); writer->Write(&size_of_metadata, sizeof(size_of_metadata)); // write meta data metadata_.SaveBinaryToFile(writer.get()); // write feature data for (int i = 0; i < num_groups_; ++i) { // get size of feature size_t size_of_feature = feature_groups_[i]->SizesInByte(); writer->Write(&size_of_feature, sizeof(size_of_feature)); // write feature feature_groups_[i]->SerializeToBinary(writer.get()); } // write raw data; use row-major order so we can read row-by-row if (has_raw_) { for (int i = 0; i < num_data_; ++i) { for (int j = 0; j < num_features_; ++j) { int feat_ind = numeric_feature_map_[j]; if (feat_ind > -1) { writer->Write(&raw_data_[feat_ind][i], sizeof(float)); } } } } } } void Dataset::SerializeReference(ByteBuffer* buffer) { Log::Info("Saving data reference to binary buffer"); // Calculate approximate size of output and reserve space size_t size_of_token = std::strlen(binary_serialized_reference_token); size_t initial_capacity = size_of_token + GetSerializedHeaderSize(); // write feature group definitions for (int i = 0; i < num_groups_; ++i) { initial_capacity += feature_groups_[i]->SizesInByte(/* include_data */ false); } // Give a little extra just in case, to avoid unnecessary resizes buffer->Reserve(static_cast(1.1 * static_cast(initial_capacity))); // Write token that marks the data as binary reference, and the version buffer->AlignedWrite(binary_serialized_reference_token, size_of_token); buffer->AlignedWrite(serialized_reference_version, kSerializedReferenceVersionLength); // Write the basic definition of the overall dataset SerializeHeader(buffer); // write feature group definitions for (int i = 0; i < num_groups_; ++i) { // get size of feature size_t size_of_feature = feature_groups_[i]->SizesInByte(false); buffer->Write(&size_of_feature, sizeof(size_of_feature)); // write feature feature_groups_[i]->SerializeToBinary(buffer, /* include_data */ false); } } size_t Dataset::GetSerializedHeaderSize() { size_t size_of_header = VirtualFileWriter::AlignedSize(sizeof(num_data_)) + VirtualFileWriter::AlignedSize(sizeof(num_features_)) + VirtualFileWriter::AlignedSize(sizeof(num_total_features_)) + VirtualFileWriter::AlignedSize(sizeof(int) * num_total_features_) + VirtualFileWriter::AlignedSize(sizeof(label_idx_)) + VirtualFileWriter::AlignedSize(sizeof(num_groups_)) + 3 * VirtualFileWriter::AlignedSize(sizeof(int) * num_features_) + sizeof(uint64_t) * (num_groups_ + 1) + 2 * VirtualFileWriter::AlignedSize(sizeof(int) * num_groups_) + VirtualFileWriter::AlignedSize(sizeof(int32_t) * num_total_features_) + VirtualFileWriter::AlignedSize(sizeof(int)) * 3 + VirtualFileWriter::AlignedSize(sizeof(bool)) * 3; // size of feature names and forced bins for (int i = 0; i < num_total_features_; ++i) { size_of_header += VirtualFileWriter::AlignedSize(feature_names_[i].size()) + VirtualFileWriter::AlignedSize(sizeof(int)) + forced_bin_bounds_[i].size() * sizeof(double) + VirtualFileWriter::AlignedSize(sizeof(int)); } return size_of_header; } void Dataset::SerializeHeader(BinaryWriter* writer) { size_t size_of_header = GetSerializedHeaderSize(); writer->Write(&size_of_header, sizeof(size_of_header)); // write header writer->AlignedWrite(&num_data_, sizeof(num_data_)); writer->AlignedWrite(&num_features_, sizeof(num_features_)); writer->AlignedWrite(&num_total_features_, sizeof(num_total_features_)); writer->AlignedWrite(&label_idx_, sizeof(label_idx_)); writer->AlignedWrite(&max_bin_, sizeof(max_bin_)); writer->AlignedWrite(&bin_construct_sample_cnt_, sizeof(bin_construct_sample_cnt_)); writer->AlignedWrite(&min_data_in_bin_, sizeof(min_data_in_bin_)); writer->AlignedWrite(&use_missing_, sizeof(use_missing_)); writer->AlignedWrite(&zero_as_missing_, sizeof(zero_as_missing_)); writer->AlignedWrite(&has_raw_, sizeof(has_raw_)); writer->AlignedWrite(used_feature_map_.data(), sizeof(int) * num_total_features_); writer->AlignedWrite(&num_groups_, sizeof(num_groups_)); writer->AlignedWrite(real_feature_idx_.data(), sizeof(int) * num_features_); writer->AlignedWrite(feature2group_.data(), sizeof(int) * num_features_); writer->AlignedWrite(feature2subfeature_.data(), sizeof(int) * num_features_); writer->Write(group_bin_boundaries_.data(), sizeof(uint64_t) * (num_groups_ + 1)); writer->AlignedWrite(group_feature_start_.data(), sizeof(int) * num_groups_); writer->AlignedWrite(group_feature_cnt_.data(), sizeof(int) * num_groups_); if (max_bin_by_feature_.empty()) { ArrayArgs::Assign(&max_bin_by_feature_, -1, num_total_features_); } writer->AlignedWrite(max_bin_by_feature_.data(), sizeof(int32_t) * num_total_features_); if (ArrayArgs::CheckAll(max_bin_by_feature_, -1)) { max_bin_by_feature_.clear(); } // write feature names for (int i = 0; i < num_total_features_; ++i) { int str_len = static_cast(feature_names_[i].size()); writer->AlignedWrite(&str_len, sizeof(int)); const char* c_str = feature_names_[i].c_str(); writer->AlignedWrite(c_str, sizeof(char) * str_len); } // write forced bins for (int i = 0; i < num_total_features_; ++i) { int num_bounds = static_cast(forced_bin_bounds_[i].size()); writer->AlignedWrite(&num_bounds, sizeof(int)); for (size_t j = 0; j < forced_bin_bounds_[i].size(); ++j) { writer->Write(&forced_bin_bounds_[i][j], sizeof(double)); } } } void Dataset::DumpTextFile(const char* text_filename) { FILE* file = NULL; #if _MSC_VER fopen_s(&file, text_filename, "wt"); #else file = fopen(text_filename, "wt"); #endif fprintf(file, "num_features: %d\n", num_features_); fprintf(file, "num_total_features: %d\n", num_total_features_); fprintf(file, "num_groups: %d\n", num_groups_); fprintf(file, "num_data: %d\n", num_data_); fprintf(file, "feature_names: "); for (auto n : feature_names_) { fprintf(file, "%s, ", n.c_str()); } fprintf(file, "\nmax_bin_by_feature: "); for (auto i : max_bin_by_feature_) { fprintf(file, "%d, ", i); } fprintf(file, "\n"); for (auto n : feature_names_) { fprintf(file, "%s, ", n.c_str()); } fprintf(file, "\nforced_bins: "); for (int i = 0; i < num_total_features_; ++i) { fprintf(file, "\nfeature %d: ", i); for (size_t j = 0; j < forced_bin_bounds_[i].size(); ++j) { fprintf(file, "%lf, ", forced_bin_bounds_[i][j]); } } std::vector> iterators; iterators.reserve(num_features_); for (int j = 0; j < num_features_; ++j) { auto group_idx = feature2group_[j]; auto sub_idx = feature2subfeature_[j]; iterators.emplace_back( feature_groups_[group_idx]->SubFeatureIterator(sub_idx)); } for (data_size_t i = 0; i < num_data_; ++i) { fprintf(file, "\n"); for (int j = 0; j < num_total_features_; ++j) { auto inner_feature_idx = used_feature_map_[j]; if (inner_feature_idx < 0) { fprintf(file, "NA, "); } else { fprintf(file, "%d, ", iterators[inner_feature_idx]->Get(i)); } } } fclose(file); } void Dataset::InitTrain(const std::vector& is_feature_used, TrainingShareStates* share_state) const { Common::FunctionTimer fun_time("Dataset::InitTrain", global_timer); share_state->InitTrain(group_feature_start_, feature_groups_, is_feature_used); } template void Dataset::ConstructHistogramsMultiVal(const data_size_t* data_indices, data_size_t num_data, const score_t* gradients, const score_t* hessians, TrainingShareStates* share_state, hist_t* hist_data) const { Common::FunctionTimer fun_time("Dataset::ConstructHistogramsMultiVal", global_timer); share_state->ConstructHistograms( data_indices, num_data, gradients, hessians, hist_data); } template void Dataset::ConstructHistogramsInner( const std::vector& is_feature_used, const data_size_t* data_indices, data_size_t num_data, const score_t* gradients, const score_t* hessians, score_t* ordered_gradients, score_t* ordered_hessians, TrainingShareStates* share_state, hist_t* hist_data) const { if (!share_state->is_col_wise) { return ConstructHistogramsMultiVal( data_indices, num_data, gradients, hessians, share_state, hist_data); } std::vector used_dense_group; int multi_val_groud_id = -1; used_dense_group.reserve(num_groups_); for (int group = 0; group < num_groups_; ++group) { const int f_start = group_feature_start_[group]; const int f_cnt = group_feature_cnt_[group]; bool is_group_used = false; for (int j = 0; j < f_cnt; ++j) { const int fidx = f_start + j; if (is_feature_used[fidx]) { is_group_used = true; break; } } if (is_group_used) { if (feature_groups_[group]->is_multi_val_) { multi_val_groud_id = group; } else { used_dense_group.push_back(group); } } } int num_used_dense_group = static_cast(used_dense_group.size()); global_timer.Start("Dataset::dense_bin_histogram"); auto ptr_ordered_grad = gradients; auto ptr_ordered_hess = hessians; if (num_used_dense_group > 0) { if (USE_QUANT_GRAD) { int16_t* ordered_gradients_and_hessians = reinterpret_cast(ordered_gradients); const int16_t* gradients_and_hessians = reinterpret_cast(gradients); if (USE_INDICES) { #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static, 512) if (num_data >= 1024) for (data_size_t i = 0; i < num_data; ++i) { ordered_gradients_and_hessians[i] = gradients_and_hessians[data_indices[i]]; } ptr_ordered_grad = reinterpret_cast(ordered_gradients); ptr_ordered_hess = nullptr; } } else { if (USE_INDICES) { if (USE_HESSIAN) { #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static, 512) if (num_data >= 1024) for (data_size_t i = 0; i < num_data; ++i) { ordered_gradients[i] = gradients[data_indices[i]]; ordered_hessians[i] = hessians[data_indices[i]]; } ptr_ordered_grad = ordered_gradients; ptr_ordered_hess = ordered_hessians; } else { #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static, 512) if (num_data >= 1024) for (data_size_t i = 0; i < num_data; ++i) { ordered_gradients[i] = gradients[data_indices[i]]; } ptr_ordered_grad = ordered_gradients; } } } OMP_INIT_EX(); #pragma omp parallel for schedule(static) num_threads(share_state->num_threads) for (int gi = 0; gi < num_used_dense_group; ++gi) { OMP_LOOP_EX_BEGIN(); int group = used_dense_group[gi]; const int num_bin = feature_groups_[group]->num_total_bin_; if (USE_QUANT_GRAD) { if (HIST_BITS == 16) { auto data_ptr = reinterpret_cast(reinterpret_cast(hist_data) + group_bin_boundaries_[group]); std::memset(reinterpret_cast(data_ptr), 0, num_bin * kInt16HistEntrySize); if (USE_HESSIAN) { if (USE_INDICES) { feature_groups_[group]->bin_data_->ConstructHistogramInt16( data_indices, 0, num_data, ptr_ordered_grad, ptr_ordered_hess, data_ptr); } else { feature_groups_[group]->bin_data_->ConstructHistogramInt16( 0, num_data, ptr_ordered_grad, ptr_ordered_hess, data_ptr); } } else { if (USE_INDICES) { feature_groups_[group]->bin_data_->ConstructHistogramInt16( data_indices, 0, num_data, ptr_ordered_grad, data_ptr); } else { feature_groups_[group]->bin_data_->ConstructHistogramInt16( 0, num_data, ptr_ordered_grad, data_ptr); } } } else { auto data_ptr = hist_data + group_bin_boundaries_[group]; std::memset(reinterpret_cast(data_ptr), 0, num_bin * kInt32HistEntrySize); if (USE_HESSIAN) { if (USE_INDICES) { feature_groups_[group]->bin_data_->ConstructHistogramInt32( data_indices, 0, num_data, ptr_ordered_grad, ptr_ordered_hess, data_ptr); } else { feature_groups_[group]->bin_data_->ConstructHistogramInt32( 0, num_data, ptr_ordered_grad, ptr_ordered_hess, data_ptr); } } else { if (USE_INDICES) { feature_groups_[group]->bin_data_->ConstructHistogramInt32( data_indices, 0, num_data, ptr_ordered_grad, data_ptr); } else { feature_groups_[group]->bin_data_->ConstructHistogramInt32( 0, num_data, ptr_ordered_grad, data_ptr); } } } } else { auto data_ptr = hist_data + group_bin_boundaries_[group] * 2; std::memset(reinterpret_cast(data_ptr), 0, num_bin * kHistEntrySize); if (USE_HESSIAN) { if (USE_INDICES) { feature_groups_[group]->bin_data_->ConstructHistogram( data_indices, 0, num_data, ptr_ordered_grad, ptr_ordered_hess, data_ptr); } else { feature_groups_[group]->bin_data_->ConstructHistogram( 0, num_data, ptr_ordered_grad, ptr_ordered_hess, data_ptr); } } else { if (USE_INDICES) { feature_groups_[group]->bin_data_->ConstructHistogram( data_indices, 0, num_data, ptr_ordered_grad, data_ptr); } else { feature_groups_[group]->bin_data_->ConstructHistogram( 0, num_data, ptr_ordered_grad, data_ptr); } auto cnt_dst = reinterpret_cast(data_ptr + 1); for (int i = 0; i < num_bin * 2; i += 2) { data_ptr[i + 1] = static_cast(cnt_dst[i]) * hessians[0]; } } } OMP_LOOP_EX_END(); } OMP_THROW_EX(); } global_timer.Stop("Dataset::dense_bin_histogram"); if (multi_val_groud_id >= 0) { if (USE_QUANT_GRAD) { if (HIST_BITS == 32) { int32_t* hist_data_ptr = reinterpret_cast(hist_data); if (num_used_dense_group > 0) { ConstructHistogramsMultiVal( data_indices, num_data, ptr_ordered_grad, ptr_ordered_hess, share_state, reinterpret_cast(hist_data_ptr + group_bin_boundaries_[multi_val_groud_id] * 2)); } else { ConstructHistogramsMultiVal( data_indices, num_data, gradients, hessians, share_state, reinterpret_cast(hist_data_ptr + group_bin_boundaries_[multi_val_groud_id] * 2)); } } else if (HIST_BITS == 16) { int16_t* hist_data_ptr = reinterpret_cast(hist_data); if (num_used_dense_group > 0) { ConstructHistogramsMultiVal( data_indices, num_data, ptr_ordered_grad, ptr_ordered_hess, share_state, reinterpret_cast(hist_data_ptr + group_bin_boundaries_[multi_val_groud_id] * 2)); } else { ConstructHistogramsMultiVal( data_indices, num_data, gradients, hessians, share_state, reinterpret_cast(hist_data_ptr + group_bin_boundaries_[multi_val_groud_id] * 2)); } } } else { if (num_used_dense_group > 0) { ConstructHistogramsMultiVal( data_indices, num_data, ptr_ordered_grad, ptr_ordered_hess, share_state, hist_data + group_bin_boundaries_[multi_val_groud_id] * 2); } else { ConstructHistogramsMultiVal( data_indices, num_data, gradients, hessians, share_state, hist_data + group_bin_boundaries_[multi_val_groud_id] * 2); } } } } // explicitly initialize template methods, for cross module call #define CONSTRUCT_HISTOGRAMS_INNER_PARMA \ const std::vector& is_feature_used, const data_size_t* data_indices, \ data_size_t num_data, const score_t* gradients, const score_t* hessians, \ score_t* ordered_gradients, score_t* ordered_hessians, \ TrainingShareStates* share_state, hist_t* hist_data // explicitly initialize template methods, for cross module call template void Dataset::ConstructHistogramsInner(CONSTRUCT_HISTOGRAMS_INNER_PARMA) const; template void Dataset::ConstructHistogramsInner(CONSTRUCT_HISTOGRAMS_INNER_PARMA) const; template void Dataset::ConstructHistogramsInner(CONSTRUCT_HISTOGRAMS_INNER_PARMA) const; template void Dataset::ConstructHistogramsInner(CONSTRUCT_HISTOGRAMS_INNER_PARMA) const; template void Dataset::ConstructHistogramsInner(CONSTRUCT_HISTOGRAMS_INNER_PARMA) const; template void Dataset::ConstructHistogramsInner(CONSTRUCT_HISTOGRAMS_INNER_PARMA) const; template void Dataset::ConstructHistogramsInner(CONSTRUCT_HISTOGRAMS_INNER_PARMA) const; template void Dataset::ConstructHistogramsInner(CONSTRUCT_HISTOGRAMS_INNER_PARMA) const; template void Dataset::ConstructHistogramsInner(CONSTRUCT_HISTOGRAMS_INNER_PARMA) const; template void Dataset::ConstructHistogramsInner(CONSTRUCT_HISTOGRAMS_INNER_PARMA) const; template void Dataset::ConstructHistogramsInner(CONSTRUCT_HISTOGRAMS_INNER_PARMA) const; template void Dataset::ConstructHistogramsInner(CONSTRUCT_HISTOGRAMS_INNER_PARMA) const; void Dataset::FixHistogram(int feature_idx, double sum_gradient, double sum_hessian, hist_t* data) const { const int group = feature2group_[feature_idx]; const int sub_feature = feature2subfeature_[feature_idx]; const BinMapper* bin_mapper = feature_groups_[group]->bin_mappers_[sub_feature].get(); const int most_freq_bin = bin_mapper->GetMostFreqBin(); if (most_freq_bin > 0) { const int num_bin = bin_mapper->num_bin(); GET_GRAD(data, most_freq_bin) = sum_gradient; GET_HESS(data, most_freq_bin) = sum_hessian; for (int i = 0; i < num_bin; ++i) { if (i != most_freq_bin) { GET_GRAD(data, most_freq_bin) -= GET_GRAD(data, i); GET_HESS(data, most_freq_bin) -= GET_HESS(data, i); } } } } template void Dataset::FixHistogramInt(int feature_idx, int64_t int_sum_gradient_and_hessian, hist_t* data) const { const int group = feature2group_[feature_idx]; const int sub_feature = feature2subfeature_[feature_idx]; const BinMapper* bin_mapper = feature_groups_[group]->bin_mappers_[sub_feature].get(); const int most_freq_bin = bin_mapper->GetMostFreqBin(); PACKED_HIST_BIN_T* data_ptr = reinterpret_cast(data); PACKED_HIST_ACC_T int_sum_gradient_and_hessian_local = HIST_BITS_ACC == 16 ? ((static_cast(int_sum_gradient_and_hessian >> 32) << 16) | static_cast(int_sum_gradient_and_hessian & 0x0000ffff)) : int_sum_gradient_and_hessian; if (most_freq_bin > 0) { const int num_bin = bin_mapper->num_bin(); if (HIST_BITS_BIN == HIST_BITS_ACC) { for (int i = 0; i < num_bin; ++i) { if (i != most_freq_bin) { int_sum_gradient_and_hessian_local -= data_ptr[i]; } } data_ptr[most_freq_bin] = int_sum_gradient_and_hessian_local; } else { CHECK_EQ(HIST_BITS_ACC, 32); CHECK_EQ(HIST_BITS_BIN, 16); for (int i = 0; i < num_bin; ++i) { if (i != most_freq_bin) { const PACKED_HIST_BIN_T packed_hist = data_ptr[i]; const PACKED_HIST_ACC_T packed_hist_acc = (static_cast(static_cast(packed_hist >> 16)) << 32) | static_cast(packed_hist & 0x0000ffff); int_sum_gradient_and_hessian_local -= packed_hist_acc; } } PACKED_HIST_BIN_T int_sum_gradient_and_hessian_local_bin = (static_cast(int_sum_gradient_and_hessian_local >> 32) << 16) | static_cast(int_sum_gradient_and_hessian_local & 0x0000ffff); data_ptr[most_freq_bin] = int_sum_gradient_and_hessian_local_bin; } } } template void Dataset::FixHistogramInt(int feature_idx, int64_t int_sum_gradient_and_hessian, hist_t* data) const; template void Dataset::FixHistogramInt(int feature_idx, int64_t int_sum_gradient_and_hessian, hist_t* data) const; template void PushVector(std::vector* dest, const std::vector& src) { dest->reserve(dest->size() + src.size()); for (auto i : src) { dest->push_back(i); } } template void PushOffset(std::vector* dest, const std::vector& src, const T& offset) { dest->reserve(dest->size() + src.size()); for (auto i : src) { dest->push_back(i + offset); } } template void PushClearIfEmpty(std::vector* dest, const size_t dest_len, const std::vector& src, const size_t src_len, const T& deflt) { if (!dest->empty() && !src.empty()) { PushVector(dest, src); } else if (!dest->empty() && src.empty()) { for (size_t i = 0; i < src_len; ++i) { dest->push_back(deflt); } } else if (dest->empty() && !src.empty()) { for (size_t i = 0; i < dest_len; ++i) { dest->push_back(deflt); } PushVector(dest, src); } } void Dataset::AddFeaturesFrom(Dataset* other) { if (other->num_data_ != num_data_) { Log::Fatal( "Cannot add features from other Dataset with a different number of " "rows"); } if (other->has_raw_ != has_raw_) { Log::Fatal("Can only add features from other Dataset if both or neither have raw data."); } int mv_gid = -1; int other_mv_gid = -1; for (int i = 0; i < num_groups_; ++i) { if (IsMultiGroup(i)) { mv_gid = i; } } for (int i = 0; i < other->num_groups_; ++i) { if (other->IsMultiGroup(i)) { other_mv_gid = i; } } // Only one multi-val group, just simply merge if (mv_gid < 0 || other_mv_gid < 0) { PushVector(&feature2subfeature_, other->feature2subfeature_); PushVector(&group_feature_cnt_, other->group_feature_cnt_); feature_groups_.reserve(other->feature_groups_.size()); for (auto& fg : other->feature_groups_) { const int cur_group_id = static_cast(feature_groups_.size()); feature_groups_.emplace_back(new FeatureGroup(*fg, true, cur_group_id)); } for (auto feature_idx : other->used_feature_map_) { if (feature_idx >= 0) { used_feature_map_.push_back(feature_idx + num_features_); } else { used_feature_map_.push_back(-1); // Unused feature. } } PushOffset(&real_feature_idx_, other->real_feature_idx_, num_total_features_); PushOffset(&feature2group_, other->feature2group_, num_groups_); auto bin_offset = group_bin_boundaries_.back(); // Skip the leading 0 when copying group_bin_boundaries. for (auto i = other->group_bin_boundaries_.begin() + 1; i < other->group_bin_boundaries_.end(); ++i) { group_bin_boundaries_.push_back(*i + bin_offset); } PushOffset(&group_feature_start_, other->group_feature_start_, num_features_); num_groups_ += other->num_groups_; num_features_ += other->num_features_; } else { std::vector> features_in_group; for (int i = 0; i < num_groups_; ++i) { int f_start = group_feature_start_[i]; int f_cnt = group_feature_cnt_[i]; features_in_group.emplace_back(); for (int j = 0; j < f_cnt; ++j) { const int real_fidx = real_feature_idx_[f_start + j]; features_in_group.back().push_back(real_fidx); } } feature_groups_[mv_gid]->AddFeaturesFrom( other->feature_groups_[other_mv_gid].get(), mv_gid); for (int i = 0; i < other->num_groups_; ++i) { int f_start = other->group_feature_start_[i]; int f_cnt = other->group_feature_cnt_[i]; if (i == other_mv_gid) { for (int j = 0; j < f_cnt; ++j) { const int real_fidx = other->real_feature_idx_[f_start + j] + num_total_features_; features_in_group[mv_gid].push_back(real_fidx); } } else { features_in_group.emplace_back(); for (int j = 0; j < f_cnt; ++j) { const int real_fidx = other->real_feature_idx_[f_start + j] + num_total_features_; features_in_group.back().push_back(real_fidx); } feature_groups_.emplace_back( new FeatureGroup(*other->feature_groups_[i], false, -1)); } } // regenerate other fields num_groups_ += other->num_groups_ - 1; CHECK(num_groups_ == static_cast(features_in_group.size())); num_features_ += other->num_features_; int cur_fidx = 0; used_feature_map_ = std::vector(num_total_features_ + other->num_total_features_, -1); real_feature_idx_.resize(num_features_); feature2group_.resize(num_features_); feature2subfeature_.resize(num_features_); group_feature_start_.resize(num_groups_); group_feature_cnt_.resize(num_groups_); group_bin_boundaries_.clear(); uint64_t num_total_bin = 0; group_bin_boundaries_.push_back(num_total_bin); for (int i = 0; i < num_groups_; ++i) { auto cur_features = features_in_group[i]; int cur_cnt_features = static_cast(cur_features.size()); group_feature_start_[i] = cur_fidx; group_feature_cnt_[i] = cur_cnt_features; for (int j = 0; j < cur_cnt_features; ++j) { int real_fidx = cur_features[j]; used_feature_map_[real_fidx] = cur_fidx; real_feature_idx_[cur_fidx] = real_fidx; feature2group_[cur_fidx] = i; feature2subfeature_[cur_fidx] = j; ++cur_fidx; } num_total_bin += feature_groups_[i]->num_total_bin_; group_bin_boundaries_.push_back(num_total_bin); } } std::unordered_set feature_names_set; for (const auto& val : feature_names_) { feature_names_set.emplace(val); } for (const auto& val : other->feature_names_) { std::string new_name = val; int cnt = 2; while (feature_names_set.count(new_name)) { new_name = "D" + std::to_string(cnt) + "_" + val; ++cnt; } if (new_name != val) { Log::Warning( "Find the same feature name (%s) in Dataset::AddFeaturesFrom, change " "its name to (%s)", val.c_str(), new_name.c_str()); } feature_names_set.emplace(new_name); feature_names_.push_back(new_name); } PushVector(&forced_bin_bounds_, other->forced_bin_bounds_); PushClearIfEmpty(&max_bin_by_feature_, num_total_features_, other->max_bin_by_feature_, other->num_total_features_, -1); num_total_features_ += other->num_total_features_; for (size_t i = 0; i < (other->numeric_feature_map_).size(); ++i) { int feat_ind = other->numeric_feature_map_[i]; if (feat_ind > -1) { numeric_feature_map_.push_back(feat_ind + num_numeric_features_); } else { numeric_feature_map_.push_back(-1); } } num_numeric_features_ += other->num_numeric_features_; if (has_raw_) { for (int i = 0; i < other->num_numeric_features_; ++i) { raw_data_.push_back(other->raw_data_[i]); } } #ifdef USE_CUDA if (device_type_ == std::string("cuda")) { CreateCUDAColumnData(); } else { cuda_column_data_ = nullptr; } #endif // USE_CUDA } const void* Dataset::GetColWiseData( const int feature_group_index, const int sub_feature_index, uint8_t* bit_type, bool* is_sparse, std::vector* bin_iterator, const int num_threads) const { return feature_groups_[feature_group_index]->GetColWiseData(sub_feature_index, bit_type, is_sparse, bin_iterator, num_threads); } const void* Dataset::GetColWiseData( const int feature_group_index, const int sub_feature_index, uint8_t* bit_type, bool* is_sparse, BinIterator** bin_iterator) const { return feature_groups_[feature_group_index]->GetColWiseData(sub_feature_index, bit_type, is_sparse, bin_iterator); } #ifdef USE_CUDA void Dataset::CreateCUDAColumnData() { cuda_column_data_.reset(new CUDAColumnData(num_data_, gpu_device_id_)); int num_columns = 0; std::vector column_data; std::vector column_bin_iterator; std::vector column_bit_type; int feature_index = 0; std::vector feature_to_column(num_features_, -1); std::vector feature_max_bins(num_features_, 0); std::vector feature_min_bins(num_features_, 0); std::vector feature_offsets(num_features_, 0); std::vector feature_most_freq_bins(num_features_, 0); std::vector feature_default_bin(num_features_, 0); std::vector feature_missing_is_zero(num_features_, 0); std::vector feature_missing_is_na(num_features_, 0); std::vector feature_mfb_is_zero(num_features_, 0); std::vector feature_mfb_is_na(num_features_, 0); for (int feature_group_index = 0; feature_group_index < num_groups_; ++feature_group_index) { if (feature_groups_[feature_group_index]->is_multi_val_) { for (int sub_feature_index = 0; sub_feature_index < feature_groups_[feature_group_index]->num_feature_; ++sub_feature_index) { uint8_t bit_type = 0; bool is_sparse = false; BinIterator* bin_iterator = nullptr; const void* one_column_data = GetColWiseData(feature_group_index, sub_feature_index, &bit_type, &is_sparse, &bin_iterator); column_data.emplace_back(one_column_data); column_bin_iterator.emplace_back(bin_iterator); column_bit_type.emplace_back(bit_type); feature_to_column[feature_index] = num_columns; ++num_columns; const BinMapper* feature_bin_mapper = FeatureBinMapper(feature_index); feature_max_bins[feature_index] = feature_max_bin(feature_index); feature_min_bins[feature_index] = feature_min_bin(feature_index); const uint32_t most_freq_bin = feature_bin_mapper->GetMostFreqBin(); feature_offsets[feature_index] = static_cast(most_freq_bin == 0); feature_most_freq_bins[feature_index] = most_freq_bin; feature_default_bin[feature_index] = feature_bin_mapper->GetDefaultBin(); if (feature_bin_mapper->missing_type() == MissingType::Zero) { feature_missing_is_zero[feature_index] = 1; feature_missing_is_na[feature_index] = 0; if (feature_default_bin[feature_index] == feature_most_freq_bins[feature_index]) { feature_mfb_is_zero[feature_index] = 1; } else { feature_mfb_is_zero[feature_index] = 0; } feature_mfb_is_na[feature_index] = 0; } else if (feature_bin_mapper->missing_type() == MissingType::NaN) { feature_missing_is_zero[feature_index] = 0; feature_missing_is_na[feature_index] = 1; feature_mfb_is_zero[feature_index] = 0; if (feature_most_freq_bins[feature_index] + feature_min_bins[feature_index] == feature_max_bins[feature_index] && feature_most_freq_bins[feature_index] > 0) { feature_mfb_is_na[feature_index] = 1; } else { feature_mfb_is_na[feature_index] = 0; } } else { feature_missing_is_zero[feature_index] = 0; feature_missing_is_na[feature_index] = 0; feature_mfb_is_zero[feature_index] = 0; feature_mfb_is_na[feature_index] = 0; } ++feature_index; } } else { uint8_t bit_type = 0; bool is_sparse = false; BinIterator* bin_iterator = nullptr; const void* one_column_data = GetColWiseData(feature_group_index, -1, &bit_type, &is_sparse, &bin_iterator); column_data.emplace_back(one_column_data); column_bin_iterator.emplace_back(bin_iterator); column_bit_type.emplace_back(bit_type); for (int sub_feature_index = 0; sub_feature_index < feature_groups_[feature_group_index]->num_feature_; ++sub_feature_index) { feature_to_column[feature_index] = num_columns; const BinMapper* feature_bin_mapper = FeatureBinMapper(feature_index); feature_max_bins[feature_index] = feature_max_bin(feature_index); feature_min_bins[feature_index] = feature_min_bin(feature_index); const uint32_t most_freq_bin = feature_bin_mapper->GetMostFreqBin(); feature_offsets[feature_index] = static_cast(most_freq_bin == 0); feature_most_freq_bins[feature_index] = most_freq_bin; feature_default_bin[feature_index] = feature_bin_mapper->GetDefaultBin(); if (feature_bin_mapper->missing_type() == MissingType::Zero) { feature_missing_is_zero[feature_index] = 1; feature_missing_is_na[feature_index] = 0; if (feature_default_bin[feature_index] == feature_most_freq_bins[feature_index]) { feature_mfb_is_zero[feature_index] = 1; } else { feature_mfb_is_zero[feature_index] = 0; } feature_mfb_is_na[feature_index] = 0; } else if (feature_bin_mapper->missing_type() == MissingType::NaN) { feature_missing_is_zero[feature_index] = 0; feature_missing_is_na[feature_index] = 1; feature_mfb_is_zero[feature_index] = 0; if (feature_most_freq_bins[feature_index] + feature_min_bins[feature_index] == feature_max_bins[feature_index] && feature_most_freq_bins[feature_index] > 0) { feature_mfb_is_na[feature_index] = 1; } else { feature_mfb_is_na[feature_index] = 0; } } else { feature_missing_is_zero[feature_index] = 0; feature_missing_is_na[feature_index] = 0; feature_mfb_is_zero[feature_index] = 0; feature_mfb_is_na[feature_index] = 0; } ++feature_index; } ++num_columns; } } cuda_column_data_->Init(num_columns, column_data, column_bin_iterator, column_bit_type, feature_max_bins, feature_min_bins, feature_offsets, feature_most_freq_bins, feature_default_bin, feature_missing_is_zero, feature_missing_is_na, feature_mfb_is_zero, feature_mfb_is_na, feature_to_column); } #endif // USE_CUDA } // namespace LightGBM ================================================ FILE: src/io/dataset_loader.cpp ================================================ /*! * Copyright (c) 2016-2026 Microsoft Corporation. All rights reserved. * Copyright (c) 2016-2026 The LightGBM developers. All rights reserved. * Licensed under the MIT License. See LICENSE file in the project root for license information. */ #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include namespace LightGBM { using json11_internal_lightgbm::Json; DatasetLoader::DatasetLoader(const Config& io_config, const PredictFunction& predict_fun, int num_class, const char* filename) :config_(io_config), random_(config_.data_random_seed), predict_fun_(predict_fun), num_class_(num_class) { label_idx_ = 0; weight_idx_ = NO_SPECIFIC; group_idx_ = NO_SPECIFIC; SetHeader(filename); store_raw_ = false; if (io_config.linear_tree) { store_raw_ = true; } } DatasetLoader::~DatasetLoader() { } void DatasetLoader::SetHeader(const char* filename) { std::unordered_map name2idx; std::string name_prefix("name:"); if (filename != nullptr && CheckCanLoadFromBin(filename) == "") { TextReader text_reader(filename, config_.header); // get column names if (config_.header) { std::string first_line = text_reader.first_line(); feature_names_ = Common::Split(first_line.c_str(), "\t,"); } else if (!config_.parser_config_file.empty()) { // support to get header from parser config, so could utilize following label name to id mapping logic. TextReader parser_config_reader(config_.parser_config_file.c_str(), false); parser_config_reader.ReadAllLines(); std::string parser_config_str = parser_config_reader.JoinedLines(); if (!parser_config_str.empty()) { std::string header_in_parser_config = Common::GetFromParserConfig(parser_config_str, "header"); if (!header_in_parser_config.empty()) { Log::Info("Get raw column names from parser config."); feature_names_ = Common::Split(header_in_parser_config.c_str(), "\t,"); } } } // load label idx first if (config_.label_column.size() > 0) { if (Common::StartsWith(config_.label_column, name_prefix)) { std::string name = config_.label_column.substr(name_prefix.size()); label_idx_ = -1; for (int i = 0; i < static_cast(feature_names_.size()); ++i) { if (name == feature_names_[i]) { label_idx_ = i; break; } } if (label_idx_ >= 0) { Log::Info("Using column %s as label", name.c_str()); } else { Log::Fatal("Could not find label column %s in data file \n" "or data file doesn't contain header", name.c_str()); } } else { if (!Common::AtoiAndCheck(config_.label_column.c_str(), &label_idx_)) { Log::Fatal("label_column is not a number,\n" "if you want to use a column name,\n" "please add the prefix \"name:\" to the column name"); } Log::Info("Using column number %d as label", label_idx_); } } if (!config_.parser_config_file.empty()) { // if parser config file exists, feature names may be changed after customized parser applied. // clear here so could use default filled feature names during dataset construction. // may improve by saving real feature names defined in parser in the future. if (!feature_names_.empty()) { feature_names_.clear(); } } if (!feature_names_.empty()) { // erase label column name feature_names_.erase(feature_names_.begin() + label_idx_); for (size_t i = 0; i < feature_names_.size(); ++i) { name2idx[feature_names_[i]] = static_cast(i); } } // load ignore columns if (config_.ignore_column.size() > 0) { if (Common::StartsWith(config_.ignore_column, name_prefix)) { std::string names = config_.ignore_column.substr(name_prefix.size()); for (auto name : Common::Split(names.c_str(), ',')) { if (name2idx.count(name) > 0) { int tmp = name2idx[name]; ignore_features_.emplace(tmp); } else { Log::Fatal("Could not find ignore column %s in data file", name.c_str()); } } } else { for (auto token : Common::Split(config_.ignore_column.c_str(), ',')) { int tmp = 0; if (!Common::AtoiAndCheck(token.c_str(), &tmp)) { Log::Fatal("ignore_column is not a number,\n" "if you want to use a column name,\n" "please add the prefix \"name:\" to the column name"); } ignore_features_.emplace(tmp); } } } // load weight idx if (config_.weight_column.size() > 0) { if (Common::StartsWith(config_.weight_column, name_prefix)) { std::string name = config_.weight_column.substr(name_prefix.size()); if (name2idx.count(name) > 0) { weight_idx_ = name2idx[name]; Log::Info("Using column %s as weight", name.c_str()); } else { Log::Fatal("Could not find weight column %s in data file", name.c_str()); } } else { if (!Common::AtoiAndCheck(config_.weight_column.c_str(), &weight_idx_)) { Log::Fatal("weight_column is not a number,\n" "if you want to use a column name,\n" "please add the prefix \"name:\" to the column name"); } Log::Info("Using column number %d as weight", weight_idx_); } ignore_features_.emplace(weight_idx_); } // load group idx if (config_.group_column.size() > 0) { if (Common::StartsWith(config_.group_column, name_prefix)) { std::string name = config_.group_column.substr(name_prefix.size()); if (name2idx.count(name) > 0) { group_idx_ = name2idx[name]; Log::Info("Using column %s as group/query id", name.c_str()); } else { Log::Fatal("Could not find group/query column %s in data file", name.c_str()); } } else { if (!Common::AtoiAndCheck(config_.group_column.c_str(), &group_idx_)) { Log::Fatal("group_column is not a number,\n" "if you want to use a column name,\n" "please add the prefix \"name:\" to the column name"); } Log::Info("Using column number %d as group/query id", group_idx_); } ignore_features_.emplace(group_idx_); } } if (config_.categorical_feature.size() > 0) { if (Common::StartsWith(config_.categorical_feature, name_prefix)) { std::string names = config_.categorical_feature.substr(name_prefix.size()); for (auto name : Common::Split(names.c_str(), ',')) { if (name2idx.count(name) > 0) { int tmp = name2idx[name]; categorical_features_.emplace(tmp); } else { Log::Fatal("Could not find categorical_feature %s in data file", name.c_str()); } } } else { for (auto token : Common::Split(config_.categorical_feature.c_str(), ',')) { int tmp = 0; if (!Common::AtoiAndCheck(token.c_str(), &tmp)) { Log::Fatal("categorical_feature is not a number,\n" "if you want to use a column name,\n" "please add the prefix \"name:\" to the column name"); } categorical_features_.emplace(tmp); } } } } void CheckSampleSize(size_t sample_cnt, size_t num_data) { if (static_cast(sample_cnt) / num_data < 0.2f && sample_cnt < 100000) { Log::Warning( "Using too small ``bin_construct_sample_cnt`` may encounter " "unexpected " "errors and poor accuracy."); } } Dataset* DatasetLoader::LoadFromFile(const char* filename, int rank, int num_machines) { // don't support query id in data file when using distributed training if (num_machines > 1 && !config_.pre_partition) { if (group_idx_ > 0) { Log::Fatal("Using a query id without pre-partitioning the data file is not supported for distributed training.\n" "Please use an additional query file or pre-partition the data"); } } auto dataset = std::unique_ptr(new Dataset()); if (store_raw_) { dataset->SetHasRaw(true); } data_size_t num_global_data = 0; std::vector used_data_indices; auto bin_filename = CheckCanLoadFromBin(filename); bool is_load_from_binary = false; if (bin_filename.size() == 0) { dataset->parser_config_str_ = Parser::GenerateParserConfigStr(filename, config_.parser_config_file.c_str(), config_.header, label_idx_); auto parser = std::unique_ptr(Parser::CreateParser(filename, config_.header, 0, label_idx_, config_.precise_float_parser, dataset->parser_config_str_)); if (parser == nullptr) { Log::Fatal("Could not recognize data format of %s", filename); } dataset->data_filename_ = filename; dataset->label_idx_ = label_idx_; dataset->metadata_.Init(filename); if (!config_.two_round) { // read data to memory auto text_data = LoadTextDataToMemory(filename, dataset->metadata_, rank, num_machines, &num_global_data, &used_data_indices); dataset->num_data_ = static_cast(text_data.size()); // sample data auto sample_data = SampleTextDataFromMemory(text_data); CheckSampleSize(sample_data.size(), static_cast(dataset->num_data_)); // construct feature bin mappers & clear sample data ConstructBinMappersFromTextData(rank, num_machines, sample_data, parser.get(), dataset.get()); std::vector().swap(sample_data); if (dataset->has_raw()) { dataset->ResizeRaw(dataset->num_data_); } // initialize label dataset->metadata_.Init(dataset->num_data_, weight_idx_, group_idx_); // extract features ExtractFeaturesFromMemory(&text_data, parser.get(), dataset.get()); text_data.clear(); } else { // sample data from file auto sample_data = SampleTextDataFromFile(filename, dataset->metadata_, rank, num_machines, &num_global_data, &used_data_indices); if (used_data_indices.size() > 0) { dataset->num_data_ = static_cast(used_data_indices.size()); } else { dataset->num_data_ = num_global_data; } CheckSampleSize(sample_data.size(), static_cast(dataset->num_data_)); // construct feature bin mappers & clear sample data ConstructBinMappersFromTextData(rank, num_machines, sample_data, parser.get(), dataset.get()); std::vector().swap(sample_data); if (dataset->has_raw()) { dataset->ResizeRaw(dataset->num_data_); } // initialize label dataset->metadata_.Init(dataset->num_data_, weight_idx_, group_idx_); Log::Info("Making second pass..."); // extract features ExtractFeaturesFromFile(filename, parser.get(), used_data_indices, dataset.get()); } } else { // load data from binary file is_load_from_binary = true; Log::Info("Load from binary file %s", bin_filename.c_str()); dataset.reset(LoadFromBinFile(filename, bin_filename.c_str(), rank, num_machines, &num_global_data, &used_data_indices)); // checks whether there's a initial score file when loaded from binary data files // the initial score file should with suffix ".bin.init" dataset->metadata_.LoadInitialScore(bin_filename); dataset->device_type_ = config_.device_type; dataset->gpu_device_id_ = config_.gpu_device_id; #ifdef USE_CUDA if (config_.device_type == std::string("cuda")) { dataset->CreateCUDAColumnData(); dataset->metadata_.CreateCUDAMetadata(dataset->gpu_device_id_); } else { dataset->cuda_column_data_ = nullptr; } #endif // USE_CUDA } // check meta data dataset->metadata_.CheckOrPartition(num_global_data, used_data_indices); // need to check training data CheckDataset(dataset.get(), is_load_from_binary); return dataset.release(); } Dataset* DatasetLoader::LoadFromFileAlignWithOtherDataset(const char* filename, const Dataset* train_data) { data_size_t num_global_data = 0; std::vector used_data_indices; auto dataset = std::unique_ptr(new Dataset()); if (store_raw_) { dataset->SetHasRaw(true); } auto bin_filename = CheckCanLoadFromBin(filename); if (bin_filename.size() == 0) { auto parser = std::unique_ptr(Parser::CreateParser(filename, config_.header, 0, label_idx_, config_.precise_float_parser, train_data->parser_config_str_)); if (parser == nullptr) { Log::Fatal("Could not recognize data format of %s", filename); } dataset->data_filename_ = filename; dataset->label_idx_ = label_idx_; dataset->metadata_.Init(filename); if (!config_.two_round) { // read data in memory auto text_data = LoadTextDataToMemory(filename, dataset->metadata_, 0, 1, &num_global_data, &used_data_indices); dataset->num_data_ = static_cast(text_data.size()); // initialize label dataset->metadata_.Init(dataset->num_data_, weight_idx_, group_idx_); dataset->CreateValid(train_data); if (dataset->has_raw()) { dataset->ResizeRaw(dataset->num_data_); } // extract features ExtractFeaturesFromMemory(&text_data, parser.get(), dataset.get()); text_data.clear(); } else { TextReader text_reader(filename, config_.header); // Get number of lines of data file dataset->num_data_ = static_cast(text_reader.CountLine()); num_global_data = dataset->num_data_; // initialize label dataset->metadata_.Init(dataset->num_data_, weight_idx_, group_idx_); dataset->CreateValid(train_data); if (dataset->has_raw()) { dataset->ResizeRaw(dataset->num_data_); } // extract features ExtractFeaturesFromFile(filename, parser.get(), used_data_indices, dataset.get()); } } else { // load data from binary file dataset.reset(LoadFromBinFile(filename, bin_filename.c_str(), 0, 1, &num_global_data, &used_data_indices)); // checks whether there's a initial score file when loaded from binary data files // the initial score file should with suffix ".bin.init" dataset->metadata_.LoadInitialScore(bin_filename); } // not need to check validation data // check meta data dataset->metadata_.CheckOrPartition(num_global_data, used_data_indices); return dataset.release(); } Dataset* DatasetLoader::LoadFromSerializedReference(const char* binary_data, size_t buffer_size, data_size_t num_data, int32_t num_classes) { auto dataset = std::unique_ptr(new Dataset(num_data)); auto mem_ptr = binary_data; // check token const size_t size_of_token = std::strlen(Dataset::binary_serialized_reference_token); size_t size_of_token_in_input = VirtualFileWriter::AlignedSize(sizeof(char) * size_of_token); if (buffer_size < size_of_token_in_input) { Log::Fatal("Binary definition file error: token has the wrong size"); } if (std::string(mem_ptr, size_of_token) != std::string(Dataset::binary_serialized_reference_token)) { Log::Fatal("Input file is not LightGBM binary reference file"); } mem_ptr += size_of_token_in_input; size_t size_of_version = VirtualFileWriter::AlignedSize(Dataset::kSerializedReferenceVersionLength); std::string version(mem_ptr, Dataset::kSerializedReferenceVersionLength); if (version != std::string(Dataset::serialized_reference_version)) { Log::Fatal("Unexpected version of serialized binary data: %s", version.c_str()); } mem_ptr += size_of_version; size_t size_of_header = *(reinterpret_cast(mem_ptr)); mem_ptr += sizeof(size_t); LoadHeaderFromMemory(dataset.get(), mem_ptr); dataset->num_data_ = num_data; // update to the given num_data mem_ptr += size_of_header; // read feature group definitions for (int i = 0; i < dataset->num_groups_; ++i) { // read feature size const size_t size_of_feature = *(reinterpret_cast(mem_ptr)); mem_ptr += sizeof(size_t); dataset->feature_groups_.emplace_back(std::unique_ptr(new FeatureGroup(mem_ptr, num_data, i))); mem_ptr += size_of_feature; } dataset->feature_groups_.shrink_to_fit(); dataset->numeric_feature_map_ = std::vector(dataset->num_features_, false); dataset->num_numeric_features_ = 0; for (int i = 0; i < dataset->num_features_; ++i) { if (dataset->FeatureBinMapper(i)->bin_type() == BinType::CategoricalBin) { dataset->numeric_feature_map_[i] = -1; } else { dataset->numeric_feature_map_[i] = dataset->num_numeric_features_; ++dataset->num_numeric_features_; } } int has_weights = config_.weight_column.size() > 0; int has_init_scores = num_classes > 0; int has_queries = config_.group_column.size() > 0; dataset->metadata_.Init(num_data, has_weights, has_init_scores, has_queries, num_classes); Log::Info("Loaded reference dataset: %d features, %d num_data", dataset->num_features_, num_data); return dataset.release(); } Dataset* DatasetLoader::LoadFromBinFile(const char* data_filename, const char* bin_filename, int rank, int num_machines, int* num_global_data, std::vector* used_data_indices) { auto dataset = std::unique_ptr(new Dataset()); auto reader = VirtualFileReader::Make(bin_filename); dataset->data_filename_ = data_filename; if (!reader->Init()) { Log::Fatal("Could not read binary data from %s", bin_filename); } // buffer to read binary file size_t buffer_size = 16 * 1024 * 1024; auto buffer = std::vector(buffer_size); // check token size_t size_of_token = std::strlen(Dataset::binary_file_token); size_t read_cnt = reader->Read( buffer.data(), VirtualFileWriter::AlignedSize(sizeof(char) * size_of_token)); if (read_cnt < sizeof(char) * size_of_token) { Log::Fatal("Binary file error: token has the wrong size"); } if (std::string(buffer.data()) != std::string(Dataset::binary_file_token)) { Log::Fatal("Input file is not LightGBM binary file"); } // read size of header read_cnt = reader->Read(buffer.data(), sizeof(size_t)); if (read_cnt != sizeof(size_t)) { Log::Fatal("Binary file error: header has the wrong size"); } size_t size_of_head = *(reinterpret_cast(buffer.data())); // re-allocate space if not enough if (size_of_head > buffer_size) { buffer_size = size_of_head; buffer.resize(buffer_size); } // read header read_cnt = reader->Read(buffer.data(), size_of_head); if (read_cnt != size_of_head) { Log::Fatal("Binary file error: header is incorrect"); } // get header const char* mem_ptr = buffer.data(); LoadHeaderFromMemory(dataset.get(), mem_ptr); // read size of meta data read_cnt = reader->Read(buffer.data(), sizeof(size_t)); if (read_cnt != sizeof(size_t)) { Log::Fatal("Binary file error: meta data has the wrong size"); } size_t size_of_metadata = *(reinterpret_cast(buffer.data())); // re-allocate space if not enough if (size_of_metadata > buffer_size) { buffer_size = size_of_metadata; buffer.resize(buffer_size); } // read meta data read_cnt = reader->Read(buffer.data(), size_of_metadata); if (read_cnt != size_of_metadata) { Log::Fatal("Binary file error: meta data is incorrect"); } // load meta data dataset->metadata_.LoadFromMemory(buffer.data()); *num_global_data = dataset->num_data_; used_data_indices->clear(); // sample local used data if need to partition if (num_machines > 1 && !config_.pre_partition) { const data_size_t* query_boundaries = dataset->metadata_.query_boundaries(); if (query_boundaries == nullptr) { // if not contain query file, minimal sample unit is one record for (data_size_t i = 0; i < dataset->num_data_; ++i) { if (random_.NextShort(0, num_machines) == rank) { used_data_indices->push_back(i); } } } else { // if contain query file, minimal sample unit is one query data_size_t num_queries = dataset->metadata_.num_queries(); data_size_t qid = -1; bool is_query_used = false; for (data_size_t i = 0; i < dataset->num_data_; ++i) { if (qid >= num_queries) { Log::Fatal("Current query exceeds the range of the query file,\n" "please ensure the query file is correct"); } if (i >= query_boundaries[qid + 1]) { // if is new query is_query_used = false; if (random_.NextShort(0, num_machines) == rank) { is_query_used = true; } ++qid; } if (is_query_used) { used_data_indices->push_back(i); } } } dataset->num_data_ = static_cast((*used_data_indices).size()); } dataset->metadata_.PartitionLabel(*used_data_indices); // read feature data for (int i = 0; i < dataset->num_groups_; ++i) { // read feature size read_cnt = reader->Read(buffer.data(), sizeof(size_t)); if (read_cnt != sizeof(size_t)) { Log::Fatal("Binary file error: feature %d has the wrong size", i); } size_t size_of_feature = *(reinterpret_cast(buffer.data())); // re-allocate space if not enough if (size_of_feature > buffer_size) { buffer_size = size_of_feature; buffer.resize(buffer_size); } read_cnt = reader->Read(buffer.data(), size_of_feature); if (read_cnt != size_of_feature) { Log::Fatal("Binary file error: feature %d is incorrect, read count: %zu", i, read_cnt); } dataset->feature_groups_.emplace_back(std::unique_ptr( new FeatureGroup(buffer.data(), *num_global_data, *used_data_indices, i))); } dataset->feature_groups_.shrink_to_fit(); // raw data dataset->numeric_feature_map_ = std::vector(dataset->num_features_, false); dataset->num_numeric_features_ = 0; for (int i = 0; i < dataset->num_features_; ++i) { if (dataset->FeatureBinMapper(i)->bin_type() == BinType::CategoricalBin) { dataset->numeric_feature_map_[i] = -1; } else { dataset->numeric_feature_map_[i] = dataset->num_numeric_features_; ++dataset->num_numeric_features_; } } if (dataset->has_raw()) { dataset->ResizeRaw(dataset->num_data()); size_t row_size = dataset->num_numeric_features_ * sizeof(float); if (row_size > buffer_size) { buffer_size = row_size; buffer.resize(buffer_size); } for (int i = 0; i < dataset->num_data(); ++i) { read_cnt = reader->Read(buffer.data(), row_size); if (read_cnt != row_size) { Log::Fatal("Binary file error: row %d of raw data is incorrect, read count: %zu", i, read_cnt); } mem_ptr = buffer.data(); const float* tmp_ptr_raw_row = reinterpret_cast(mem_ptr); for (int j = 0; j < dataset->num_features(); ++j) { int feat_ind = dataset->numeric_feature_map_[j]; if (feat_ind >= 0) { dataset->raw_data_[feat_ind][i] = tmp_ptr_raw_row[feat_ind]; } } mem_ptr += row_size; } } dataset->is_finish_load_ = true; return dataset.release(); } Dataset* DatasetLoader::ConstructFromSampleData(double** sample_values, int** sample_indices, int num_col, const int* num_per_col, size_t total_sample_size, data_size_t num_local_data, int64_t num_dist_data) { CheckSampleSize(total_sample_size, static_cast(num_dist_data)); int num_total_features = num_col; if (Network::num_machines() > 1) { num_total_features = Network::GlobalSyncUpByMax(num_total_features); } std::vector> bin_mappers(num_total_features); // fill feature_names_ if not header if (feature_names_.empty()) { for (int i = 0; i < num_col; ++i) { std::stringstream str_buf; str_buf << "Column_" << i; feature_names_.push_back(str_buf.str()); } } if (!config_.max_bin_by_feature.empty()) { CHECK_EQ(static_cast(num_col), config_.max_bin_by_feature.size()); CHECK_GT(*(std::min_element(config_.max_bin_by_feature.begin(), config_.max_bin_by_feature.end())), 1); } // get forced split std::string forced_bins_path = config_.forcedbins_filename; std::vector> forced_bin_bounds = DatasetLoader::GetForcedBins(forced_bins_path, num_col, categorical_features_); const data_size_t filter_cnt = static_cast( static_cast(config_.min_data_in_leaf * total_sample_size) / num_dist_data); if (Network::num_machines() == 1) { // if only one machine, find bin locally OMP_INIT_EX(); #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(guided) for (int i = 0; i < num_col; ++i) { OMP_LOOP_EX_BEGIN(); if (ignore_features_.count(i) > 0) { bin_mappers[i] = nullptr; continue; } BinType bin_type = BinType::NumericalBin; if (categorical_features_.count(i)) { bin_type = BinType::CategoricalBin; bool feat_is_unconstrained = ((config_.monotone_constraints.size() == 0) || (config_.monotone_constraints[i] == 0)); if (!feat_is_unconstrained) { Log::Fatal("The output cannot be monotone with respect to categorical features"); } } bin_mappers[i].reset(new BinMapper()); if (config_.max_bin_by_feature.empty()) { bin_mappers[i]->FindBin(sample_values[i], num_per_col[i], total_sample_size, config_.max_bin, config_.min_data_in_bin, filter_cnt, config_.feature_pre_filter, bin_type, config_.use_missing, config_.zero_as_missing, forced_bin_bounds[i]); } else { bin_mappers[i]->FindBin(sample_values[i], num_per_col[i], total_sample_size, config_.max_bin_by_feature[i], config_.min_data_in_bin, filter_cnt, config_.feature_pre_filter, bin_type, config_.use_missing, config_.zero_as_missing, forced_bin_bounds[i]); } OMP_LOOP_EX_END(); } OMP_THROW_EX(); } else { // if have multi-machines, need to find bin distributed // different machines will find bin for different features int num_machines = Network::num_machines(); int rank = Network::rank(); // start and len will store the process feature indices for different machines // machine i will find bins for features in [ start[i], start[i] + len[i] ) std::vector start(num_machines); std::vector len(num_machines); int step = (num_total_features + num_machines - 1) / num_machines; if (step < 1) { step = 1; } start[0] = 0; for (int i = 0; i < num_machines - 1; ++i) { len[i] = std::min(step, num_total_features - start[i]); start[i + 1] = start[i] + len[i]; } len[num_machines - 1] = num_total_features - start[num_machines - 1]; OMP_INIT_EX(); #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(guided) for (int i = 0; i < len[rank]; ++i) { OMP_LOOP_EX_BEGIN(); if (ignore_features_.count(start[rank] + i) > 0) { continue; } BinType bin_type = BinType::NumericalBin; if (categorical_features_.count(start[rank] + i)) { bin_type = BinType::CategoricalBin; } bin_mappers[i].reset(new BinMapper()); if (num_col <= start[rank] + i) { continue; } if (config_.max_bin_by_feature.empty()) { bin_mappers[i]->FindBin(sample_values[start[rank] + i], num_per_col[start[rank] + i], total_sample_size, config_.max_bin, config_.min_data_in_bin, filter_cnt, config_.feature_pre_filter, bin_type, config_.use_missing, config_.zero_as_missing, forced_bin_bounds[i]); } else { bin_mappers[i]->FindBin(sample_values[start[rank] + i], num_per_col[start[rank] + i], total_sample_size, config_.max_bin_by_feature[start[rank] + i], config_.min_data_in_bin, filter_cnt, config_.feature_pre_filter, bin_type, config_.use_missing, config_.zero_as_missing, forced_bin_bounds[i]); } OMP_LOOP_EX_END(); } OMP_THROW_EX(); comm_size_t self_buf_size = 0; for (int i = 0; i < len[rank]; ++i) { if (ignore_features_.count(start[rank] + i) > 0) { continue; } self_buf_size += static_cast(bin_mappers[i]->SizesInByte()); } std::vector input_buffer(self_buf_size); auto cp_ptr = input_buffer.data(); for (int i = 0; i < len[rank]; ++i) { if (ignore_features_.count(start[rank] + i) > 0) { continue; } bin_mappers[i]->CopyTo(cp_ptr); cp_ptr += bin_mappers[i]->SizesInByte(); // free bin_mappers[i].reset(nullptr); } std::vector size_len = Network::GlobalArray(self_buf_size); std::vector size_start(num_machines, 0); for (int i = 1; i < num_machines; ++i) { size_start[i] = size_start[i - 1] + size_len[i - 1]; } comm_size_t total_buffer_size = size_start[num_machines - 1] + size_len[num_machines - 1]; std::vector output_buffer(total_buffer_size); // gather global feature bin mappers Network::Allgather(input_buffer.data(), size_start.data(), size_len.data(), output_buffer.data(), total_buffer_size); cp_ptr = output_buffer.data(); // restore features bins from buffer for (int i = 0; i < num_total_features; ++i) { if (ignore_features_.count(i) > 0) { bin_mappers[i] = nullptr; continue; } bin_mappers[i].reset(new BinMapper()); bin_mappers[i]->CopyFrom(cp_ptr); cp_ptr += bin_mappers[i]->SizesInByte(); } } CheckCategoricalFeatureNumBin(bin_mappers, config_.max_bin, config_.max_bin_by_feature); auto dataset = std::unique_ptr(new Dataset(num_local_data)); dataset->Construct(&bin_mappers, num_total_features, forced_bin_bounds, sample_indices, sample_values, num_per_col, num_col, total_sample_size, config_); if (dataset->has_raw()) { dataset->ResizeRaw(num_local_data); } dataset->set_feature_names(feature_names_); return dataset.release(); } // ---- private functions ---- void DatasetLoader::LoadHeaderFromMemory(Dataset* dataset, const char* buffer) { // get header const char* mem_ptr = buffer; dataset->num_data_ = *(reinterpret_cast(mem_ptr)); mem_ptr += VirtualFileWriter::AlignedSize(sizeof(dataset->num_data_)); dataset->num_features_ = *(reinterpret_cast(mem_ptr)); mem_ptr += VirtualFileWriter::AlignedSize(sizeof(dataset->num_features_)); dataset->num_total_features_ = *(reinterpret_cast(mem_ptr)); mem_ptr += VirtualFileWriter::AlignedSize(sizeof(dataset->num_total_features_)); dataset->label_idx_ = *(reinterpret_cast(mem_ptr)); mem_ptr += VirtualFileWriter::AlignedSize(sizeof(dataset->label_idx_)); dataset->max_bin_ = *(reinterpret_cast(mem_ptr)); mem_ptr += VirtualFileWriter::AlignedSize(sizeof(dataset->max_bin_)); dataset->bin_construct_sample_cnt_ = *(reinterpret_cast(mem_ptr)); mem_ptr += VirtualFileWriter::AlignedSize(sizeof(dataset->bin_construct_sample_cnt_)); dataset->min_data_in_bin_ = *(reinterpret_cast(mem_ptr)); mem_ptr += VirtualFileWriter::AlignedSize(sizeof(dataset->min_data_in_bin_)); dataset->use_missing_ = *(reinterpret_cast(mem_ptr)); mem_ptr += VirtualFileWriter::AlignedSize(sizeof(dataset->use_missing_)); dataset->zero_as_missing_ = *(reinterpret_cast(mem_ptr)); mem_ptr += VirtualFileWriter::AlignedSize(sizeof(dataset->zero_as_missing_)); dataset->has_raw_ = *(reinterpret_cast(mem_ptr)); mem_ptr += VirtualFileWriter::AlignedSize(sizeof(dataset->has_raw_)); const int* tmp_feature_map = reinterpret_cast(mem_ptr); dataset->used_feature_map_.clear(); for (int i = 0; i < dataset->num_total_features_; ++i) { dataset->used_feature_map_.push_back(tmp_feature_map[i]); } mem_ptr += VirtualFileWriter::AlignedSize(sizeof(int) * dataset->num_total_features_); // num_groups dataset->num_groups_ = *(reinterpret_cast(mem_ptr)); mem_ptr += VirtualFileWriter::AlignedSize(sizeof(dataset->num_groups_)); // real_feature_idx_ const int* tmp_ptr_real_feature_idx_ = reinterpret_cast(mem_ptr); dataset->real_feature_idx_.clear(); for (int i = 0; i < dataset->num_features_; ++i) { dataset->real_feature_idx_.push_back(tmp_ptr_real_feature_idx_[i]); } mem_ptr += VirtualFileWriter::AlignedSize(sizeof(int) * dataset->num_features_); // feature2group const int* tmp_ptr_feature2group = reinterpret_cast(mem_ptr); dataset->feature2group_.clear(); for (int i = 0; i < dataset->num_features_; ++i) { dataset->feature2group_.push_back(tmp_ptr_feature2group[i]); } mem_ptr += VirtualFileWriter::AlignedSize(sizeof(int) * dataset->num_features_); // feature2subfeature const int* tmp_ptr_feature2subfeature = reinterpret_cast(mem_ptr); dataset->feature2subfeature_.clear(); for (int i = 0; i < dataset->num_features_; ++i) { dataset->feature2subfeature_.push_back(tmp_ptr_feature2subfeature[i]); } mem_ptr += VirtualFileWriter::AlignedSize(sizeof(int) * dataset->num_features_); // group_bin_boundaries const uint64_t* tmp_ptr_group_bin_boundaries = reinterpret_cast(mem_ptr); dataset->group_bin_boundaries_.clear(); for (int i = 0; i < dataset->num_groups_ + 1; ++i) { dataset->group_bin_boundaries_.push_back(tmp_ptr_group_bin_boundaries[i]); } mem_ptr += sizeof(uint64_t) * (dataset->num_groups_ + 1); // group_feature_start_ const int* tmp_ptr_group_feature_start = reinterpret_cast(mem_ptr); dataset->group_feature_start_.clear(); for (int i = 0; i < dataset->num_groups_; ++i) { dataset->group_feature_start_.push_back(tmp_ptr_group_feature_start[i]); } mem_ptr += VirtualFileWriter::AlignedSize(sizeof(int) * (dataset->num_groups_)); // group_feature_cnt_ const int* tmp_ptr_group_feature_cnt = reinterpret_cast(mem_ptr); dataset->group_feature_cnt_.clear(); for (int i = 0; i < dataset->num_groups_; ++i) { dataset->group_feature_cnt_.push_back(tmp_ptr_group_feature_cnt[i]); } mem_ptr += VirtualFileWriter::AlignedSize(sizeof(int) * (dataset->num_groups_)); if (!config_.max_bin_by_feature.empty()) { CHECK_EQ(static_cast(dataset->num_total_features_), config_.max_bin_by_feature.size()); CHECK_GT(*(std::min_element(config_.max_bin_by_feature.begin(), config_.max_bin_by_feature.end())), 1); dataset->max_bin_by_feature_.resize(dataset->num_total_features_); dataset->max_bin_by_feature_.assign(config_.max_bin_by_feature.begin(), config_.max_bin_by_feature.end()); } else { const int32_t* tmp_ptr_max_bin_by_feature = reinterpret_cast(mem_ptr); dataset->max_bin_by_feature_.clear(); for (int i = 0; i < dataset->num_total_features_; ++i) { dataset->max_bin_by_feature_.push_back(tmp_ptr_max_bin_by_feature[i]); } } mem_ptr += VirtualFileWriter::AlignedSize(sizeof(int32_t) * (dataset->num_total_features_)); if (ArrayArgs::CheckAll(dataset->max_bin_by_feature_, -1)) { dataset->max_bin_by_feature_.clear(); } // get feature names dataset->feature_names_.clear(); for (int i = 0; i < dataset->num_total_features_; ++i) { int str_len = *(reinterpret_cast(mem_ptr)); mem_ptr += VirtualFileWriter::AlignedSize(sizeof(int)); std::stringstream str_buf; auto tmp_arr = reinterpret_cast(mem_ptr); for (int j = 0; j < str_len; ++j) { char tmp_char = tmp_arr[j]; str_buf << tmp_char; } mem_ptr += VirtualFileWriter::AlignedSize(sizeof(char) * str_len); dataset->feature_names_.emplace_back(str_buf.str()); } // get forced_bin_bounds_ dataset->forced_bin_bounds_ = std::vector>(dataset->num_total_features_, std::vector()); for (int i = 0; i < dataset->num_total_features_; ++i) { int num_bounds = *(reinterpret_cast(mem_ptr)); mem_ptr += VirtualFileWriter::AlignedSize(sizeof(int)); dataset->forced_bin_bounds_[i] = std::vector(); const double* tmp_ptr_forced_bounds = reinterpret_cast(mem_ptr); for (int j = 0; j < num_bounds; ++j) { double bound = tmp_ptr_forced_bounds[j]; dataset->forced_bin_bounds_[i].push_back(bound); } mem_ptr += num_bounds * sizeof(double); } } void DatasetLoader::CheckDataset(const Dataset* dataset, bool is_load_from_binary) { if (dataset->num_data_ <= 0) { Log::Fatal("Data file %s is empty", dataset->data_filename_.c_str()); } if (dataset->feature_names_.size() != static_cast(dataset->num_total_features_)) { Log::Fatal("Size of feature name error, should be %d, got %d", dataset->num_total_features_, static_cast(dataset->feature_names_.size())); } bool is_feature_order_by_group = true; int last_group = -1; int last_sub_feature = -1; // if features are ordered, not need to use hist_buf for (int i = 0; i < dataset->num_features_; ++i) { int group = dataset->feature2group_[i]; int sub_feature = dataset->feature2subfeature_[i]; if (group < last_group) { is_feature_order_by_group = false; } else if (group == last_group) { if (sub_feature <= last_sub_feature) { is_feature_order_by_group = false; break; } } last_group = group; last_sub_feature = sub_feature; } if (!is_feature_order_by_group) { Log::Fatal("Features in dataset should be ordered by group"); } if (is_load_from_binary) { if (dataset->max_bin_ != config_.max_bin) { Log::Fatal("Dataset was constructed with parameter max_bin=%d. It cannot be changed to %d when loading from binary file.", dataset->max_bin_, config_.max_bin); } if (dataset->min_data_in_bin_ != config_.min_data_in_bin) { Log::Fatal("Dataset was constructed with parameter min_data_in_bin=%d. It cannot be changed to %d when loading from binary file.", dataset->min_data_in_bin_, config_.min_data_in_bin); } if (dataset->use_missing_ != config_.use_missing) { Log::Fatal("Dataset was constructed with parameter use_missing=%d. It cannot be changed to %d when loading from binary file.", dataset->use_missing_, config_.use_missing); } if (dataset->zero_as_missing_ != config_.zero_as_missing) { Log::Fatal("Dataset was constructed with parameter zero_as_missing=%d. It cannot be changed to %d when loading from binary file.", dataset->zero_as_missing_, config_.zero_as_missing); } if (dataset->bin_construct_sample_cnt_ != config_.bin_construct_sample_cnt) { Log::Fatal("Dataset was constructed with parameter bin_construct_sample_cnt=%d. It cannot be changed to %d when loading from binary file.", dataset->bin_construct_sample_cnt_, config_.bin_construct_sample_cnt); } if ((dataset->max_bin_by_feature_.size() != config_.max_bin_by_feature.size()) || !std::equal(dataset->max_bin_by_feature_.begin(), dataset->max_bin_by_feature_.end(), config_.max_bin_by_feature.begin())) { Log::Fatal("Parameter max_bin_by_feature cannot be changed when loading from binary file."); } if (config_.label_column != "") { 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."); } if (config_.weight_column != "") { 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."); } if (config_.group_column != "") { 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."); } if (config_.ignore_column != "") { 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."); } if (config_.two_round) { 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."); } if (config_.header) { Log::Warning("Parameter header works only in case of loading data directly from text file. It will be ignored when loading from binary file."); } } } std::vector DatasetLoader::LoadTextDataToMemory(const char* filename, const Metadata& metadata, int rank, int num_machines, int* num_global_data, std::vector* used_data_indices) { TextReader text_reader(filename, config_.header, config_.file_load_progress_interval_bytes); used_data_indices->clear(); if (num_machines == 1 || config_.pre_partition) { // read all lines *num_global_data = text_reader.ReadAllLines(); } else { // need partition data // get query data const data_size_t* query_boundaries = metadata.query_boundaries(); if (query_boundaries == nullptr) { // if not contain query data, minimal sample unit is one record *num_global_data = text_reader.ReadAndFilterLines([this, rank, num_machines](data_size_t) { if (random_.NextShort(0, num_machines) == rank) { return true; } else { return false; } }, used_data_indices); } else { // if contain query data, minimal sample unit is one query data_size_t num_queries = metadata.num_queries(); data_size_t qid = -1; bool is_query_used = false; *num_global_data = text_reader.ReadAndFilterLines( [this, rank, num_machines, &qid, &query_boundaries, &is_query_used, num_queries] (data_size_t line_idx) { if (qid >= num_queries) { Log::Fatal("Current query exceeds the range of the query file,\n" "please ensure the query file is correct"); } if (line_idx >= query_boundaries[qid + 1]) { // if is new query is_query_used = false; if (random_.NextShort(0, num_machines) == rank) { is_query_used = true; } ++qid; } return is_query_used; }, used_data_indices); } } return std::move(text_reader.Lines()); } std::vector DatasetLoader::SampleTextDataFromMemory(const std::vector& data) { int sample_cnt = config_.bin_construct_sample_cnt; if (static_cast(sample_cnt) > data.size()) { sample_cnt = static_cast(data.size()); } auto sample_indices = random_.Sample(static_cast(data.size()), sample_cnt); std::vector out(sample_indices.size()); for (size_t i = 0; i < sample_indices.size(); ++i) { const size_t idx = sample_indices[i]; out[i] = data[idx]; } return out; } std::vector DatasetLoader::SampleTextDataFromFile(const char* filename, const Metadata& metadata, int rank, int num_machines, int* num_global_data, std::vector* used_data_indices) { const data_size_t sample_cnt = static_cast(config_.bin_construct_sample_cnt); TextReader text_reader(filename, config_.header, config_.file_load_progress_interval_bytes); std::vector out_data; if (num_machines == 1 || config_.pre_partition) { *num_global_data = static_cast(text_reader.SampleFromFile(&random_, sample_cnt, &out_data)); } else { // need partition data // get query data const data_size_t* query_boundaries = metadata.query_boundaries(); if (query_boundaries == nullptr) { // if not contain query file, minimal sample unit is one record *num_global_data = text_reader.SampleAndFilterFromFile([this, rank, num_machines] (data_size_t) { if (random_.NextShort(0, num_machines) == rank) { return true; } else { return false; } }, used_data_indices, &random_, sample_cnt, &out_data); } else { // if contain query file, minimal sample unit is one query data_size_t num_queries = metadata.num_queries(); data_size_t qid = -1; bool is_query_used = false; *num_global_data = text_reader.SampleAndFilterFromFile( [this, rank, num_machines, &qid, &query_boundaries, &is_query_used, num_queries] (data_size_t line_idx) { if (qid >= num_queries) { Log::Fatal("Query id exceeds the range of the query file, " "please ensure the query file is correct"); } if (line_idx >= query_boundaries[qid + 1]) { // if is new query is_query_used = false; if (random_.NextShort(0, num_machines) == rank) { is_query_used = true; } ++qid; } return is_query_used; }, used_data_indices, &random_, sample_cnt, &out_data); } } return out_data; } void DatasetLoader::ConstructBinMappersFromTextData(int rank, int num_machines, const std::vector& sample_data, const Parser* parser, Dataset* dataset) { auto t1 = std::chrono::high_resolution_clock::now(); std::vector> sample_values; std::vector> sample_indices; std::vector> oneline_features; double label; for (int i = 0; i < static_cast(sample_data.size()); ++i) { oneline_features.clear(); // parse features parser->ParseOneLine(sample_data[i].c_str(), &oneline_features, &label); for (std::pair& inner_data : oneline_features) { if (static_cast(inner_data.first) >= sample_values.size()) { sample_values.resize(inner_data.first + 1); sample_indices.resize(inner_data.first + 1); } if (std::fabs(inner_data.second) > kZeroThreshold || std::isnan(inner_data.second)) { sample_values[inner_data.first].emplace_back(inner_data.second); sample_indices[inner_data.first].emplace_back(i); } } } dataset->feature_groups_.clear(); dataset->num_total_features_ = std::max(static_cast(sample_values.size()), parser->NumFeatures()); if (num_machines > 1) { dataset->num_total_features_ = Network::GlobalSyncUpByMax(dataset->num_total_features_); } if (!feature_names_.empty()) { CHECK_EQ(dataset->num_total_features_, static_cast(feature_names_.size())); } if (!config_.max_bin_by_feature.empty()) { CHECK_EQ(static_cast(dataset->num_total_features_), config_.max_bin_by_feature.size()); CHECK_GT(*(std::min_element(config_.max_bin_by_feature.begin(), config_.max_bin_by_feature.end())), 1); } // get forced split std::string forced_bins_path = config_.forcedbins_filename; std::vector> forced_bin_bounds = DatasetLoader::GetForcedBins(forced_bins_path, dataset->num_total_features_, categorical_features_); // check the range of label_idx, weight_idx and group_idx // skip label check if user input parser config file, // because label id is got from raw features while dataset features are consistent with customized parser. if (dataset->parser_config_str_.empty()) { CHECK(label_idx_ >= 0 && label_idx_ <= dataset->num_total_features_); } CHECK(weight_idx_ < 0 || weight_idx_ < dataset->num_total_features_); CHECK(group_idx_ < 0 || group_idx_ < dataset->num_total_features_); // fill feature_names_ if not header if (feature_names_.empty()) { for (int i = 0; i < dataset->num_total_features_; ++i) { std::stringstream str_buf; str_buf << "Column_" << i; feature_names_.push_back(str_buf.str()); } } dataset->set_feature_names(feature_names_); std::vector> bin_mappers(dataset->num_total_features_); const data_size_t filter_cnt = static_cast( static_cast(config_.min_data_in_leaf* sample_data.size()) / dataset->num_data_); // start find bins if (num_machines == 1) { // if only one machine, find bin locally OMP_INIT_EX(); #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(guided) for (int i = 0; i < static_cast(sample_values.size()); ++i) { OMP_LOOP_EX_BEGIN(); if (ignore_features_.count(i) > 0) { bin_mappers[i] = nullptr; continue; } BinType bin_type = BinType::NumericalBin; if (categorical_features_.count(i)) { bin_type = BinType::CategoricalBin; } bin_mappers[i].reset(new BinMapper()); if (config_.max_bin_by_feature.empty()) { bin_mappers[i]->FindBin(sample_values[i].data(), static_cast(sample_values[i].size()), sample_data.size(), config_.max_bin, config_.min_data_in_bin, filter_cnt, config_.feature_pre_filter, bin_type, config_.use_missing, config_.zero_as_missing, forced_bin_bounds[i]); } else { bin_mappers[i]->FindBin(sample_values[i].data(), static_cast(sample_values[i].size()), sample_data.size(), config_.max_bin_by_feature[i], config_.min_data_in_bin, filter_cnt, config_.feature_pre_filter, bin_type, config_.use_missing, config_.zero_as_missing, forced_bin_bounds[i]); } OMP_LOOP_EX_END(); } OMP_THROW_EX(); } else { // start and len will store the process feature indices for different machines // machine i will find bins for features in [ start[i], start[i] + len[i] ) std::vector start(num_machines); std::vector len(num_machines); int step = (dataset->num_total_features_ + num_machines - 1) / num_machines; if (step < 1) { step = 1; } start[0] = 0; for (int i = 0; i < num_machines - 1; ++i) { len[i] = std::min(step, dataset->num_total_features_ - start[i]); start[i + 1] = start[i] + len[i]; } len[num_machines - 1] = dataset->num_total_features_ - start[num_machines - 1]; OMP_INIT_EX(); #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(guided) for (int i = 0; i < len[rank]; ++i) { OMP_LOOP_EX_BEGIN(); if (ignore_features_.count(start[rank] + i) > 0) { continue; } BinType bin_type = BinType::NumericalBin; if (categorical_features_.count(start[rank] + i)) { bin_type = BinType::CategoricalBin; } bin_mappers[i].reset(new BinMapper()); if (static_cast(sample_values.size()) <= start[rank] + i) { continue; } if (config_.max_bin_by_feature.empty()) { bin_mappers[i]->FindBin(sample_values[start[rank] + i].data(), static_cast(sample_values[start[rank] + i].size()), sample_data.size(), config_.max_bin, config_.min_data_in_bin, filter_cnt, config_.feature_pre_filter, bin_type, config_.use_missing, config_.zero_as_missing, forced_bin_bounds[i]); } else { bin_mappers[i]->FindBin(sample_values[start[rank] + i].data(), static_cast(sample_values[start[rank] + i].size()), sample_data.size(), config_.max_bin_by_feature[i], config_.min_data_in_bin, filter_cnt, config_.feature_pre_filter, bin_type, config_.use_missing, config_.zero_as_missing, forced_bin_bounds[i]); } OMP_LOOP_EX_END(); } OMP_THROW_EX(); comm_size_t self_buf_size = 0; for (int i = 0; i < len[rank]; ++i) { if (ignore_features_.count(start[rank] + i) > 0) { continue; } self_buf_size += static_cast(bin_mappers[i]->SizesInByte()); } std::vector input_buffer(self_buf_size); auto cp_ptr = input_buffer.data(); for (int i = 0; i < len[rank]; ++i) { if (ignore_features_.count(start[rank] + i) > 0) { continue; } bin_mappers[i]->CopyTo(cp_ptr); cp_ptr += bin_mappers[i]->SizesInByte(); // free bin_mappers[i].reset(nullptr); } std::vector size_len = Network::GlobalArray(self_buf_size); std::vector size_start(num_machines, 0); for (int i = 1; i < num_machines; ++i) { size_start[i] = size_start[i - 1] + size_len[i - 1]; } comm_size_t total_buffer_size = size_start[num_machines - 1] + size_len[num_machines - 1]; std::vector output_buffer(total_buffer_size); // gather global feature bin mappers Network::Allgather(input_buffer.data(), size_start.data(), size_len.data(), output_buffer.data(), total_buffer_size); cp_ptr = output_buffer.data(); // restore features bins from buffer for (int i = 0; i < dataset->num_total_features_; ++i) { if (ignore_features_.count(i) > 0) { bin_mappers[i] = nullptr; continue; } bin_mappers[i].reset(new BinMapper()); bin_mappers[i]->CopyFrom(cp_ptr); cp_ptr += bin_mappers[i]->SizesInByte(); } } CheckCategoricalFeatureNumBin(bin_mappers, config_.max_bin, config_.max_bin_by_feature); dataset->Construct(&bin_mappers, dataset->num_total_features_, forced_bin_bounds, Common::Vector2Ptr(&sample_indices).data(), Common::Vector2Ptr(&sample_values).data(), Common::VectorSize(sample_indices).data(), static_cast(sample_indices.size()), sample_data.size(), config_); if (dataset->has_raw()) { dataset->ResizeRaw(static_cast(sample_data.size())); } auto t2 = std::chrono::high_resolution_clock::now(); Log::Info("Construct bin mappers from text data time %.2f seconds", std::chrono::duration(t2 - t1) * 1e-3); } /*! \brief Extract local features from memory */ void DatasetLoader::ExtractFeaturesFromMemory(std::vector* text_data, const Parser* parser, Dataset* dataset) { std::vector> oneline_features; double tmp_label = 0.0f; auto& ref_text_data = *text_data; std::vector feature_row(dataset->num_features_); if (!predict_fun_) { OMP_INIT_EX(); // if doesn't need to prediction with initial model #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) private(oneline_features) firstprivate(tmp_label, feature_row) for (data_size_t i = 0; i < dataset->num_data_; ++i) { OMP_LOOP_EX_BEGIN(); const int tid = omp_get_thread_num(); oneline_features.clear(); // parser parser->ParseOneLine(ref_text_data[i].c_str(), &oneline_features, &tmp_label); // set label dataset->metadata_.SetLabelAt(i, static_cast(tmp_label)); // free processed line: ref_text_data[i].clear(); // shrink_to_fit will be very slow in linux, and seems not free memory, disable for now // text_reader_->Lines()[i].shrink_to_fit(); std::vector is_feature_added(dataset->num_features_, false); // push data for (auto& inner_data : oneline_features) { if (inner_data.first >= dataset->num_total_features_) { continue; } int feature_idx = dataset->used_feature_map_[inner_data.first]; if (feature_idx >= 0) { is_feature_added[feature_idx] = true; // if is used feature int group = dataset->feature2group_[feature_idx]; int sub_feature = dataset->feature2subfeature_[feature_idx]; dataset->feature_groups_[group]->PushData(tid, sub_feature, i, inner_data.second); if (dataset->has_raw()) { feature_row[feature_idx] = static_cast(inner_data.second); } } else { if (inner_data.first == weight_idx_) { dataset->metadata_.SetWeightAt(i, static_cast(inner_data.second)); } else if (inner_data.first == group_idx_) { dataset->metadata_.SetQueryAt(i, static_cast(inner_data.second)); } } } if (dataset->has_raw()) { for (size_t j = 0; j < feature_row.size(); ++j) { int feat_ind = dataset->numeric_feature_map_[j]; if (feat_ind >= 0) { dataset->raw_data_[feat_ind][i] = feature_row[j]; } } } dataset->FinishOneRow(tid, i, is_feature_added); OMP_LOOP_EX_END(); } OMP_THROW_EX(); } else { OMP_INIT_EX(); // if need to prediction with initial model std::vector init_score(static_cast(dataset->num_data_) * num_class_); #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) private(oneline_features) firstprivate(tmp_label, feature_row) for (data_size_t i = 0; i < dataset->num_data_; ++i) { OMP_LOOP_EX_BEGIN(); const int tid = omp_get_thread_num(); oneline_features.clear(); // parser parser->ParseOneLine(ref_text_data[i].c_str(), &oneline_features, &tmp_label); // set initial score std::vector oneline_init_score(num_class_); predict_fun_(oneline_features, oneline_init_score.data()); for (int k = 0; k < num_class_; ++k) { init_score[k * dataset->num_data_ + i] = static_cast(oneline_init_score[k]); } // set label dataset->metadata_.SetLabelAt(i, static_cast(tmp_label)); // free processed line: ref_text_data[i].clear(); // shrink_to_fit will be very slow in Linux, and seems not free memory, disable for now // text_reader_->Lines()[i].shrink_to_fit(); // push data std::vector is_feature_added(dataset->num_features_, false); for (auto& inner_data : oneline_features) { if (inner_data.first >= dataset->num_total_features_) { continue; } int feature_idx = dataset->used_feature_map_[inner_data.first]; if (feature_idx >= 0) { is_feature_added[feature_idx] = true; // if is used feature int group = dataset->feature2group_[feature_idx]; int sub_feature = dataset->feature2subfeature_[feature_idx]; dataset->feature_groups_[group]->PushData(tid, sub_feature, i, inner_data.second); if (dataset->has_raw()) { feature_row[feature_idx] = static_cast(inner_data.second); } } else { if (inner_data.first == weight_idx_) { dataset->metadata_.SetWeightAt(i, static_cast(inner_data.second)); } else if (inner_data.first == group_idx_) { dataset->metadata_.SetQueryAt(i, static_cast(inner_data.second)); } } } dataset->FinishOneRow(tid, i, is_feature_added); if (dataset->has_raw()) { for (size_t j = 0; j < feature_row.size(); ++j) { int feat_ind = dataset->numeric_feature_map_[j]; if (feat_ind >= 0) { dataset->raw_data_[feat_ind][i] = feature_row[j]; } } } OMP_LOOP_EX_END(); } OMP_THROW_EX(); // metadata_ will manage space of init_score dataset->metadata_.SetInitScore(init_score.data(), dataset->num_data_ * num_class_); } dataset->FinishLoad(); // text data can be free after loaded feature values text_data->clear(); } /*! \brief Extract local features from file */ void DatasetLoader::ExtractFeaturesFromFile(const char* filename, const Parser* parser, const std::vector& used_data_indices, Dataset* dataset) { std::vector init_score; if (predict_fun_) { init_score = std::vector(static_cast(dataset->num_data_) * num_class_); } std::function&)> process_fun = [this, &init_score, &parser, &dataset] (data_size_t start_idx, const std::vector& lines) { std::vector> oneline_features; double tmp_label = 0.0f; std::vector feature_row(dataset->num_features_); OMP_INIT_EX(); #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) private(oneline_features) firstprivate(tmp_label, feature_row) for (data_size_t i = 0; i < static_cast(lines.size()); ++i) { OMP_LOOP_EX_BEGIN(); const int tid = omp_get_thread_num(); oneline_features.clear(); // parser parser->ParseOneLine(lines[i].c_str(), &oneline_features, &tmp_label); // set initial score if (!init_score.empty()) { std::vector oneline_init_score(num_class_); predict_fun_(oneline_features, oneline_init_score.data()); for (int k = 0; k < num_class_; ++k) { init_score[k * dataset->num_data_ + start_idx + i] = static_cast(oneline_init_score[k]); } } // set label dataset->metadata_.SetLabelAt(start_idx + i, static_cast(tmp_label)); std::vector is_feature_added(dataset->num_features_, false); // push data for (auto& inner_data : oneline_features) { if (inner_data.first >= dataset->num_total_features_) { continue; } int feature_idx = dataset->used_feature_map_[inner_data.first]; if (feature_idx >= 0) { is_feature_added[feature_idx] = true; // if is used feature int group = dataset->feature2group_[feature_idx]; int sub_feature = dataset->feature2subfeature_[feature_idx]; dataset->feature_groups_[group]->PushData(tid, sub_feature, start_idx + i, inner_data.second); if (dataset->has_raw()) { feature_row[feature_idx] = static_cast(inner_data.second); } } else { if (inner_data.first == weight_idx_) { dataset->metadata_.SetWeightAt(start_idx + i, static_cast(inner_data.second)); } else if (inner_data.first == group_idx_) { dataset->metadata_.SetQueryAt(start_idx + i, static_cast(inner_data.second)); } } } if (dataset->has_raw()) { for (size_t j = 0; j < feature_row.size(); ++j) { int feat_ind = dataset->numeric_feature_map_[j]; if (feat_ind >= 0) { dataset->raw_data_[feat_ind][i] = feature_row[j]; } } } dataset->FinishOneRow(tid, i, is_feature_added); OMP_LOOP_EX_END(); } OMP_THROW_EX(); }; TextReader text_reader(filename, config_.header, config_.file_load_progress_interval_bytes); if (!used_data_indices.empty()) { // only need part of data text_reader.ReadPartAndProcessParallel(used_data_indices, process_fun); } else { // need full data text_reader.ReadAllAndProcessParallel(process_fun); } // metadata_ will manage space of init_score if (!init_score.empty()) { dataset->metadata_.SetInitScore(init_score.data(), dataset->num_data_ * num_class_); } dataset->FinishLoad(); } /*! \brief Check can load from binary file */ std::string DatasetLoader::CheckCanLoadFromBin(const char* filename) { std::string bin_filename(filename); bin_filename.append(".bin"); auto reader = VirtualFileReader::Make(bin_filename.c_str()); if (!reader->Init()) { bin_filename = std::string(filename); reader = VirtualFileReader::Make(bin_filename.c_str()); if (!reader->Init()) { Log::Fatal("Cannot open data file %s", bin_filename.c_str()); } } size_t buffer_size = 256; auto buffer = std::vector(buffer_size); // read size of token size_t size_of_token = std::strlen(Dataset::binary_file_token); size_t read_cnt = reader->Read(buffer.data(), size_of_token); if (read_cnt == size_of_token && std::string(buffer.data()) == std::string(Dataset::binary_file_token)) { return bin_filename; } else { return std::string(); } } std::vector> DatasetLoader::GetForcedBins(std::string forced_bins_path, int num_total_features, const std::unordered_set& categorical_features) { std::vector> forced_bins(num_total_features, std::vector()); if (forced_bins_path != "") { std::ifstream forced_bins_stream(forced_bins_path.c_str()); if (forced_bins_stream.fail()) { Log::Warning("Could not open %s. Will ignore.", forced_bins_path.c_str()); } else { std::stringstream buffer; buffer << forced_bins_stream.rdbuf(); std::string err; Json forced_bins_json = Json::parse(buffer.str(), &err); CHECK(forced_bins_json.is_array()); std::vector forced_bins_arr = forced_bins_json.array_items(); for (size_t i = 0; i < forced_bins_arr.size(); ++i) { int feature_num = forced_bins_arr[i]["feature"].int_value(); CHECK_LT(feature_num, num_total_features); if (categorical_features.count(feature_num)) { Log::Warning("Feature %d is categorical. Will ignore forced bins for this feature.", feature_num); } else { std::vector bounds_arr = forced_bins_arr[i]["bin_upper_bound"].array_items(); for (size_t j = 0; j < bounds_arr.size(); ++j) { forced_bins[feature_num].push_back(bounds_arr[j].number_value()); } } } // remove duplicates for (int i = 0; i < num_total_features; ++i) { auto new_end = std::unique(forced_bins[i].begin(), forced_bins[i].end()); forced_bins[i].erase(new_end, forced_bins[i].end()); } } } return forced_bins; } void DatasetLoader::CheckCategoricalFeatureNumBin( const std::vector>& bin_mappers, const int max_bin, const std::vector& max_bin_by_feature) const { bool need_warning = false; if (bin_mappers.size() < 1024) { for (size_t i = 0; i < bin_mappers.size(); ++i) { const int max_bin_for_this_feature = max_bin_by_feature.empty() ? max_bin : max_bin_by_feature[i]; if (bin_mappers[i] != nullptr && bin_mappers[i]->bin_type() == BinType::CategoricalBin && bin_mappers[i]->num_bin() > max_bin_for_this_feature) { need_warning = true; break; } } } else { const int num_threads = OMP_NUM_THREADS(); std::vector thread_need_warning(num_threads, false); Threading::For(0, bin_mappers.size(), 1, [&bin_mappers, &thread_need_warning, &max_bin_by_feature, max_bin] (int thread_index, size_t start, size_t end) { for (size_t i = start; i < end; ++i) { thread_need_warning[thread_index] = false; const int max_bin_for_this_feature = max_bin_by_feature.empty() ? max_bin : max_bin_by_feature[i]; if (bin_mappers[i] != nullptr && bin_mappers[i]->bin_type() == BinType::CategoricalBin && bin_mappers[i]->num_bin() > max_bin_for_this_feature) { thread_need_warning[thread_index] = true; break; } } }); for (int thread_index = 0; thread_index < num_threads; ++thread_index) { if (thread_need_warning[thread_index]) { need_warning = true; break; } } } if (need_warning) { Log::Warning("Categorical features with more bins than the configured maximum bin number found."); Log::Warning("For categorical features, max_bin and max_bin_by_feature may be ignored with a large number of categories."); } } } // namespace LightGBM ================================================ FILE: src/io/dense_bin.hpp ================================================ /*! * Copyright (c) 2016-2026 Microsoft Corporation. All rights reserved. * Copyright (c) 2016-2026 The LightGBM developers. All rights reserved. * Licensed under the MIT License. See LICENSE file in the project root for * license information. */ #ifndef LIGHTGBM_SRC_IO_DENSE_BIN_HPP_ #define LIGHTGBM_SRC_IO_DENSE_BIN_HPP_ #include #include #include #include #include namespace LightGBM { template class DenseBin; template class DenseBinIterator : public BinIterator { public: explicit DenseBinIterator(const DenseBin* bin_data, uint32_t min_bin, uint32_t max_bin, uint32_t most_freq_bin) : bin_data_(bin_data), min_bin_(static_cast(min_bin)), max_bin_(static_cast(max_bin)), most_freq_bin_(static_cast(most_freq_bin)) { if (most_freq_bin_ == 0) { offset_ = 1; } else { offset_ = 0; } } inline uint32_t RawGet(data_size_t idx) override; inline uint32_t Get(data_size_t idx) override; inline void Reset(data_size_t) override {} private: const DenseBin* bin_data_; VAL_T min_bin_; VAL_T max_bin_; VAL_T most_freq_bin_; uint8_t offset_; }; /*! * \brief Used to store bins for dense feature * Use template to reduce memory cost */ template class DenseBin : public Bin { public: friend DenseBinIterator; explicit DenseBin(data_size_t num_data) : num_data_(num_data) { if (IS_4BIT) { CHECK_EQ(sizeof(VAL_T), 1); data_.resize((num_data_ + 1) / 2, static_cast(0)); buf_.resize((num_data_ + 1) / 2, static_cast(0)); } else { data_.resize(num_data_, static_cast(0)); } } ~DenseBin() {} void Push(int, data_size_t idx, uint32_t value) override { if (IS_4BIT) { const int i1 = idx >> 1; const int i2 = (idx & 1) << 2; const uint8_t val = static_cast(value) << i2; if (i2 == 0) { data_[i1] = val; } else { buf_[i1] = val; } } else { data_[idx] = static_cast(value); } } void ReSize(data_size_t num_data) override { if (num_data_ != num_data) { num_data_ = num_data; if (IS_4BIT) { data_.resize((num_data_ + 1) / 2, static_cast(0)); } else { data_.resize(num_data_); } } } BinIterator* GetIterator(uint32_t min_bin, uint32_t max_bin, uint32_t most_freq_bin) const override; template void ConstructHistogramInner(const data_size_t* data_indices, data_size_t start, data_size_t end, const score_t* ordered_gradients, const score_t* ordered_hessians, hist_t* out) const { data_size_t i = start; hist_t* grad = out; hist_t* hess = out + 1; hist_cnt_t* cnt = reinterpret_cast(hess); if (USE_PREFETCH) { const data_size_t pf_offset = 64 / sizeof(VAL_T); const data_size_t pf_end = end - pf_offset; for (; i < pf_end; ++i) { const auto idx = USE_INDICES ? data_indices[i] : i; const auto pf_idx = USE_INDICES ? data_indices[i + pf_offset] : i + pf_offset; if (IS_4BIT) { PREFETCH_T0(data_.data() + (pf_idx >> 1)); } else { PREFETCH_T0(data_.data() + pf_idx); } const auto ti = static_cast(data(idx)) << 1; if (USE_HESSIAN) { grad[ti] += ordered_gradients[i]; hess[ti] += ordered_hessians[i]; } else { grad[ti] += ordered_gradients[i]; ++cnt[ti]; } } } for (; i < end; ++i) { const auto idx = USE_INDICES ? data_indices[i] : i; const auto ti = static_cast(data(idx)) << 1; if (USE_HESSIAN) { grad[ti] += ordered_gradients[i]; hess[ti] += ordered_hessians[i]; } else { grad[ti] += ordered_gradients[i]; ++cnt[ti]; } } } void ConstructHistogram(const data_size_t* data_indices, data_size_t start, data_size_t end, const score_t* ordered_gradients, const score_t* ordered_hessians, hist_t* out) const override { ConstructHistogramInner( data_indices, start, end, ordered_gradients, ordered_hessians, out); } void ConstructHistogram(data_size_t start, data_size_t end, const score_t* ordered_gradients, const score_t* ordered_hessians, hist_t* out) const override { ConstructHistogramInner( nullptr, start, end, ordered_gradients, ordered_hessians, out); } void ConstructHistogram(const data_size_t* data_indices, data_size_t start, data_size_t end, const score_t* ordered_gradients, hist_t* out) const override { ConstructHistogramInner(data_indices, start, end, ordered_gradients, nullptr, out); } void ConstructHistogram(data_size_t start, data_size_t end, const score_t* ordered_gradients, hist_t* out) const override { ConstructHistogramInner( nullptr, start, end, ordered_gradients, nullptr, out); } template void ConstructHistogramIntInner(const data_size_t* data_indices, data_size_t start, data_size_t end, const score_t* ordered_gradients, hist_t* out) const { data_size_t i = start; PACKED_HIST_T* out_ptr = reinterpret_cast(out); const int16_t* gradients_ptr = reinterpret_cast(ordered_gradients); const VAL_T* data_ptr_base = data_.data(); if (USE_PREFETCH) { const data_size_t pf_offset = 64 / sizeof(VAL_T); const data_size_t pf_end = end - pf_offset; for (; i < pf_end; ++i) { const auto idx = USE_INDICES ? data_indices[i] : i; const auto pf_idx = USE_INDICES ? data_indices[i + pf_offset] : i + pf_offset; if (IS_4BIT) { PREFETCH_T0(data_ptr_base + (pf_idx >> 1)); } else { PREFETCH_T0(data_ptr_base + pf_idx); } const auto ti = static_cast(data(idx)); const int16_t gradient_16 = gradients_ptr[i]; if (USE_HESSIAN) { const PACKED_HIST_T gradient_packed = HIST_BITS == 8 ? gradient_16 : (static_cast(static_cast(gradient_16 >> 8)) << HIST_BITS) | (gradient_16 & 0xff); out_ptr[ti] += gradient_packed; } else { const PACKED_HIST_T gradient_packed = HIST_BITS == 8 ? gradient_16 : (static_cast(static_cast(gradient_16 >> 8)) << HIST_BITS) | (1); out_ptr[ti] += gradient_packed; } } } for (; i < end; ++i) { const auto idx = USE_INDICES ? data_indices[i] : i; const auto ti = static_cast(data(idx)); const int16_t gradient_16 = gradients_ptr[i]; if (USE_HESSIAN) { const PACKED_HIST_T gradient_packed = HIST_BITS == 8 ? gradient_16 : (static_cast(static_cast(gradient_16 >> 8)) << HIST_BITS) | (gradient_16 & 0xff); out_ptr[ti] += gradient_packed; } else { const PACKED_HIST_T gradient_packed = HIST_BITS == 8 ? gradient_16 : (static_cast(static_cast(gradient_16 >> 8)) << HIST_BITS) | (1); out_ptr[ti] += gradient_packed; } } } void ConstructHistogramInt8(const data_size_t* data_indices, data_size_t start, data_size_t end, const score_t* ordered_gradients, const score_t* /*ordered_hessians*/, hist_t* out) const override { ConstructHistogramIntInner( data_indices, start, end, ordered_gradients, out); } void ConstructHistogramInt8(data_size_t start, data_size_t end, const score_t* ordered_gradients, const score_t* /*ordered_hessians*/, hist_t* out) const override { ConstructHistogramIntInner( nullptr, start, end, ordered_gradients, out); } void ConstructHistogramInt8(const data_size_t* data_indices, data_size_t start, data_size_t end, const score_t* ordered_gradients, hist_t* out) const override { ConstructHistogramIntInner( data_indices, start, end, ordered_gradients, out); } void ConstructHistogramInt8(data_size_t start, data_size_t end, const score_t* ordered_gradients, hist_t* out) const override { ConstructHistogramIntInner( nullptr, start, end, ordered_gradients, out); } void ConstructHistogramInt16(const data_size_t* data_indices, data_size_t start, data_size_t end, const score_t* ordered_gradients, const score_t* /*ordered_hessians*/, hist_t* out) const override { ConstructHistogramIntInner( data_indices, start, end, ordered_gradients, out); } void ConstructHistogramInt16(data_size_t start, data_size_t end, const score_t* ordered_gradients, const score_t* /*ordered_hessians*/, hist_t* out) const override { ConstructHistogramIntInner( nullptr, start, end, ordered_gradients, out); } void ConstructHistogramInt16(const data_size_t* data_indices, data_size_t start, data_size_t end, const score_t* ordered_gradients, hist_t* out) const override { ConstructHistogramIntInner( data_indices, start, end, ordered_gradients, out); } void ConstructHistogramInt16(data_size_t start, data_size_t end, const score_t* ordered_gradients, hist_t* out) const override { ConstructHistogramIntInner( nullptr, start, end, ordered_gradients, out); } void ConstructHistogramInt32(const data_size_t* data_indices, data_size_t start, data_size_t end, const score_t* ordered_gradients, const score_t* /*ordered_hessians*/, hist_t* out) const override { ConstructHistogramIntInner( data_indices, start, end, ordered_gradients, out); } void ConstructHistogramInt32(data_size_t start, data_size_t end, const score_t* ordered_gradients, const score_t* /*ordered_hessians*/, hist_t* out) const override { ConstructHistogramIntInner( nullptr, start, end, ordered_gradients, out); } void ConstructHistogramInt32(const data_size_t* data_indices, data_size_t start, data_size_t end, const score_t* ordered_gradients, hist_t* out) const override { ConstructHistogramIntInner( data_indices, start, end, ordered_gradients, out); } void ConstructHistogramInt32(data_size_t start, data_size_t end, const score_t* ordered_gradients, hist_t* out) const override { ConstructHistogramIntInner( nullptr, start, end, ordered_gradients, out); } template data_size_t SplitInner(uint32_t min_bin, uint32_t max_bin, uint32_t default_bin, uint32_t most_freq_bin, bool default_left, uint32_t threshold, const data_size_t* data_indices, data_size_t cnt, data_size_t* lte_indices, data_size_t* gt_indices) const { auto th = static_cast(threshold + min_bin); auto t_zero_bin = static_cast(min_bin + default_bin); if (most_freq_bin == 0) { --th; --t_zero_bin; } const auto minb = static_cast(min_bin); const auto maxb = static_cast(max_bin); data_size_t lte_count = 0; data_size_t gt_count = 0; data_size_t* default_indices = gt_indices; data_size_t* default_count = >_count; data_size_t* missing_default_indices = gt_indices; data_size_t* missing_default_count = >_count; if (most_freq_bin <= threshold) { default_indices = lte_indices; default_count = <e_count; } if (MISS_IS_ZERO || MISS_IS_NA) { if (default_left) { missing_default_indices = lte_indices; missing_default_count = <e_count; } } if (min_bin < max_bin) { for (data_size_t i = 0; i < cnt; ++i) { const data_size_t idx = data_indices[i]; const auto bin = data(idx); if ((MISS_IS_ZERO && !MFB_IS_ZERO && bin == t_zero_bin) || (MISS_IS_NA && !MFB_IS_NA && bin == maxb)) { missing_default_indices[(*missing_default_count)++] = idx; } else if ((USE_MIN_BIN && (bin < minb || bin > maxb)) || (!USE_MIN_BIN && bin == 0)) { if ((MISS_IS_NA && MFB_IS_NA) || (MISS_IS_ZERO && MFB_IS_ZERO)) { missing_default_indices[(*missing_default_count)++] = idx; } else { default_indices[(*default_count)++] = idx; } } else if (bin > th) { gt_indices[gt_count++] = idx; } else { lte_indices[lte_count++] = idx; } } } else { data_size_t* max_bin_indices = gt_indices; data_size_t* max_bin_count = >_count; if (maxb <= th) { max_bin_indices = lte_indices; max_bin_count = <e_count; } for (data_size_t i = 0; i < cnt; ++i) { const data_size_t idx = data_indices[i]; const auto bin = data(idx); if (MISS_IS_ZERO && !MFB_IS_ZERO && bin == t_zero_bin) { missing_default_indices[(*missing_default_count)++] = idx; } else if (bin != maxb) { if ((MISS_IS_NA && MFB_IS_NA) || (MISS_IS_ZERO && MFB_IS_ZERO)) { missing_default_indices[(*missing_default_count)++] = idx; } else { default_indices[(*default_count)++] = idx; } } else { if (MISS_IS_NA && !MFB_IS_NA) { missing_default_indices[(*missing_default_count)++] = idx; } else { max_bin_indices[(*max_bin_count)++] = idx; } } } } return lte_count; } data_size_t Split(uint32_t min_bin, uint32_t max_bin, uint32_t default_bin, uint32_t most_freq_bin, MissingType missing_type, bool default_left, uint32_t threshold, const data_size_t* data_indices, data_size_t cnt, data_size_t* lte_indices, data_size_t* gt_indices) const override { #define ARGUMENTS \ min_bin, max_bin, default_bin, most_freq_bin, default_left, threshold, \ data_indices, cnt, lte_indices, gt_indices if (missing_type == MissingType::None) { return SplitInner(ARGUMENTS); } else if (missing_type == MissingType::Zero) { if (default_bin == most_freq_bin) { return SplitInner(ARGUMENTS); } else { return SplitInner(ARGUMENTS); } } else { if (max_bin == most_freq_bin + min_bin && most_freq_bin > 0) { return SplitInner(ARGUMENTS); } else { return SplitInner(ARGUMENTS); } } #undef ARGUMENTS } data_size_t Split(uint32_t max_bin, uint32_t default_bin, uint32_t most_freq_bin, MissingType missing_type, bool default_left, uint32_t threshold, const data_size_t* data_indices, data_size_t cnt, data_size_t* lte_indices, data_size_t* gt_indices) const override { #define ARGUMENTS \ 1, max_bin, default_bin, most_freq_bin, default_left, threshold, \ data_indices, cnt, lte_indices, gt_indices if (missing_type == MissingType::None) { return SplitInner(ARGUMENTS); } else if (missing_type == MissingType::Zero) { if (default_bin == most_freq_bin) { return SplitInner(ARGUMENTS); } else { return SplitInner(ARGUMENTS); } } else { if (max_bin == most_freq_bin + 1 && most_freq_bin > 0) { return SplitInner(ARGUMENTS); } else { return SplitInner(ARGUMENTS); } } #undef ARGUMENTS } template data_size_t SplitCategoricalInner(uint32_t min_bin, uint32_t max_bin, uint32_t most_freq_bin, const uint32_t* threshold, int num_threshold, const data_size_t* data_indices, data_size_t cnt, data_size_t* lte_indices, data_size_t* gt_indices) const { data_size_t lte_count = 0; data_size_t gt_count = 0; data_size_t* default_indices = gt_indices; data_size_t* default_count = >_count; int8_t offset = most_freq_bin == 0 ? 1 : 0; if (most_freq_bin > 0 && Common::FindInBitset(threshold, num_threshold, most_freq_bin)) { default_indices = lte_indices; default_count = <e_count; } for (data_size_t i = 0; i < cnt; ++i) { const data_size_t idx = data_indices[i]; const uint32_t bin = data(idx); if (USE_MIN_BIN && (bin < min_bin || bin > max_bin)) { default_indices[(*default_count)++] = idx; } else if (!USE_MIN_BIN && bin == 0) { default_indices[(*default_count)++] = idx; } else if (Common::FindInBitset(threshold, num_threshold, bin - min_bin + offset)) { lte_indices[lte_count++] = idx; } else { gt_indices[gt_count++] = idx; } } return lte_count; } data_size_t SplitCategorical(uint32_t min_bin, uint32_t max_bin, uint32_t most_freq_bin, const uint32_t* threshold, int num_threshold, const data_size_t* data_indices, data_size_t cnt, data_size_t* lte_indices, data_size_t* gt_indices) const override { return SplitCategoricalInner(min_bin, max_bin, most_freq_bin, threshold, num_threshold, data_indices, cnt, lte_indices, gt_indices); } data_size_t SplitCategorical(uint32_t max_bin, uint32_t most_freq_bin, const uint32_t* threshold, int num_threshold, const data_size_t* data_indices, data_size_t cnt, data_size_t* lte_indices, data_size_t* gt_indices) const override { return SplitCategoricalInner(1, max_bin, most_freq_bin, threshold, num_threshold, data_indices, cnt, lte_indices, gt_indices); } data_size_t num_data() const override { return num_data_; } void* get_data() override { return data_.data(); } void FinishLoad() override { if (IS_4BIT) { if (buf_.empty()) { return; } int len = (num_data_ + 1) / 2; for (int i = 0; i < len; ++i) { data_[i] |= buf_[i]; } buf_.clear(); } } void LoadFromMemory( const void* memory, const std::vector& local_used_indices) override { const VAL_T* mem_data = reinterpret_cast(memory); if (!local_used_indices.empty()) { if (IS_4BIT) { const data_size_t rest = num_data_ & 1; for (int i = 0; i < num_data_ - rest; i += 2) { // get old bins data_size_t idx = local_used_indices[i]; const auto bin1 = static_cast( (mem_data[idx >> 1] >> ((idx & 1) << 2)) & 0xf); idx = local_used_indices[i + 1]; const auto bin2 = static_cast( (mem_data[idx >> 1] >> ((idx & 1) << 2)) & 0xf); // add const int i1 = i >> 1; data_[i1] = (bin1 | (bin2 << 4)); } if (rest) { data_size_t idx = local_used_indices[num_data_ - 1]; data_[num_data_ >> 1] = (mem_data[idx >> 1] >> ((idx & 1) << 2)) & 0xf; } } else { for (int i = 0; i < num_data_; ++i) { data_[i] = mem_data[local_used_indices[i]]; } } } else { for (size_t i = 0; i < data_.size(); ++i) { data_[i] = mem_data[i]; } } } inline VAL_T data(data_size_t idx) const { if (IS_4BIT) { return (data_[idx >> 1] >> ((idx & 1) << 2)) & 0xf; } else { return data_[idx]; } } void CopySubrow(const Bin* full_bin, const data_size_t* used_indices, data_size_t num_used_indices) override { auto other_bin = dynamic_cast*>(full_bin); if (IS_4BIT) { const data_size_t rest = num_used_indices & 1; for (int i = 0; i < num_used_indices - rest; i += 2) { data_size_t idx = used_indices[i]; const auto bin1 = static_cast( (other_bin->data_[idx >> 1] >> ((idx & 1) << 2)) & 0xf); idx = used_indices[i + 1]; const auto bin2 = static_cast( (other_bin->data_[idx >> 1] >> ((idx & 1) << 2)) & 0xf); const int i1 = i >> 1; data_[i1] = (bin1 | (bin2 << 4)); } if (rest) { data_size_t idx = used_indices[num_used_indices - 1]; data_[num_used_indices >> 1] = (other_bin->data_[idx >> 1] >> ((idx & 1) << 2)) & 0xf; } } else { for (int i = 0; i < num_used_indices; ++i) { data_[i] = other_bin->data_[used_indices[i]]; } } } void SaveBinaryToFile(BinaryWriter* writer) const override { writer->AlignedWrite(data_.data(), sizeof(VAL_T) * data_.size()); } size_t SizesInByte() const override { return VirtualFileWriter::AlignedSize(sizeof(VAL_T) * data_.size()); } DenseBin* Clone() override; const void* GetColWiseData(uint8_t* bit_type, bool* is_sparse, std::vector* bin_iterator, const int num_threads) const override; const void* GetColWiseData(uint8_t* bit_type, bool* is_sparse, BinIterator** bin_iterator) const override; private: data_size_t num_data_; #ifdef USE_CUDA std::vector> data_; #else std::vector> data_; #endif std::vector buf_; DenseBin(const DenseBin& other) : num_data_(other.num_data_), data_(other.data_) {} }; template DenseBin* DenseBin::Clone() { return new DenseBin(*this); } template uint32_t DenseBinIterator::Get(data_size_t idx) { auto ret = bin_data_->data(idx); if (ret >= min_bin_ && ret <= max_bin_) { return ret - min_bin_ + offset_; } else { return most_freq_bin_; } } template inline uint32_t DenseBinIterator::RawGet(data_size_t idx) { return bin_data_->data(idx); } template BinIterator* DenseBin::GetIterator( uint32_t min_bin, uint32_t max_bin, uint32_t most_freq_bin) const { return new DenseBinIterator(this, min_bin, max_bin, most_freq_bin); } } // namespace LightGBM #endif // LIGHTGBM_SRC_IO_DENSE_BIN_HPP_ ================================================ FILE: src/io/file_io.cpp ================================================ /*! * Copyright (c) 2018-2026 Microsoft Corporation. All rights reserved. * Copyright (c) 2018-2026 The LightGBM developers. All rights reserved. * Licensed under the MIT License. See LICENSE file in the project root for * license information. */ #include #include #include #include #include #include #include #include namespace LightGBM { struct LocalFile : VirtualFileReader, VirtualFileWriter { LocalFile(const std::string& filename, const std::string& mode) : filename_(filename), mode_(mode) {} virtual ~LocalFile() { if (file_ != NULL) { fclose(file_); } } bool Init() { if (file_ == NULL) { #if _MSC_VER fopen_s(&file_, filename_.c_str(), mode_.c_str()); #else file_ = fopen(filename_.c_str(), mode_.c_str()); #endif } return file_ != NULL; } bool Exists() const { LocalFile file(filename_, "rb"); return file.Init(); } size_t Read(void* buffer, size_t bytes) const { return fread(buffer, 1, bytes, file_); } size_t Write(const void* buffer, size_t bytes) { return fwrite(buffer, bytes, 1, file_) == 1 ? bytes : 0; } private: FILE* file_ = NULL; const std::string filename_; const std::string mode_; }; std::unique_ptr VirtualFileReader::Make( const std::string& filename) { return std::unique_ptr(new LocalFile(filename, "rb")); } std::unique_ptr VirtualFileWriter::Make( const std::string& filename) { return std::unique_ptr(new LocalFile(filename, "wb")); } bool VirtualFileWriter::Exists(const std::string& filename) { LocalFile file(filename, "rb"); return file.Exists(); } } // namespace LightGBM ================================================ FILE: src/io/json11.cpp ================================================ /* Copyright (c) 2013 Dropbox, Inc. * * Permission is hereby granted, free of charge, to any person obtaining a copy * of this software and associated documentation files (the "Software"), to deal * in the Software without restriction, including without limitation the rights * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell * copies of the Software, and to permit persons to whom the Software is * furnished to do so, subject to the following conditions: * * The above copyright notice and this permission notice shall be included in * all copies or substantial portions of the Software. * * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN * THE SOFTWARE. */ #include #include #include #include #include #include #include #include #include #include #include namespace json11_internal_lightgbm { static const int max_depth = 200; using std::initializer_list; using std::make_shared; using std::map; using std::string; using std::vector; using LightGBM::Log; /* Helper for representing null - just a do-nothing struct, plus comparison * operators so the helpers in JsonValue work. We can't use nullptr_t because * it may not be orderable. */ struct NullStruct { bool operator==(NullStruct) const { return true; } bool operator<(NullStruct) const { return false; } }; /* * * * * * * * * * * * * * * * * * * * * Serialization */ static void dump(NullStruct, string *out) { *out += "null"; } static void dump(double value, string *out) { if (std::isfinite(value)) { char buf[32]; snprintf(buf, sizeof buf, "%.17g", value); *out += buf; } else { *out += "null"; } } static void dump(int value, string *out) { char buf[32]; snprintf(buf, sizeof buf, "%d", value); *out += buf; } static void dump(bool value, string *out) { *out += value ? "true" : "false"; } static void dump(const string &value, string *out) { *out += '"'; for (size_t i = 0; i < value.length(); i++) { const char ch = value[i]; if (ch == '\\') { *out += "\\\\"; } else if (ch == '"') { *out += "\\\""; } else if (ch == '\b') { *out += "\\b"; } else if (ch == '\f') { *out += "\\f"; } else if (ch == '\n') { *out += "\\n"; } else if (ch == '\r') { *out += "\\r"; } else if (ch == '\t') { *out += "\\t"; } else if (static_cast(ch) <= 0x1f) { char buf[8]; snprintf(buf, sizeof buf, "\\u%04x", ch); *out += buf; } else if (static_cast(ch) == 0xe2 && static_cast(value[i + 1]) == 0x80 && static_cast(value[i + 2]) == 0xa8) { *out += "\\u2028"; i += 2; } else if (static_cast(ch) == 0xe2 && static_cast(value[i + 1]) == 0x80 && static_cast(value[i + 2]) == 0xa9) { *out += "\\u2029"; i += 2; } else { *out += ch; } } *out += '"'; } static void dump(const Json::array &values, string *out) { bool first = true; *out += "["; for (const auto &value : values) { if (!first) *out += ", "; value.dump(out); first = false; } *out += "]"; } static void dump(const Json::object &values, string *out) { bool first = true; *out += "{"; for (const auto &kv : values) { if (!first) *out += ", "; dump(kv.first, out); *out += ": "; kv.second.dump(out); first = false; } *out += "}"; } void Json::dump(string *out) const { m_ptr->dump(out); } /* * * * * * * * * * * * * * * * * * * * * Value wrappers */ template class Value : public JsonValue { protected: // Constructors explicit Value(const T &value) : m_value(value) {} explicit Value(T &&value) : m_value(std::move(value)) {} // Get type tag Json::Type type() const override { return tag; } // Comparisons bool equals(const JsonValue *other) const override { return m_value == static_cast *>(other)->m_value; } bool less(const JsonValue *other) const override { return m_value < (static_cast *>(other)->m_value); } const T m_value; void dump(string *out) const override { json11_internal_lightgbm::dump(m_value, out); } }; class JsonDouble final : public Value { double number_value() const override { return m_value; } int int_value() const override { return static_cast(m_value); } bool equals(const JsonValue *other) const override { return m_value == other->number_value(); } bool less(const JsonValue *other) const override { return m_value < other->number_value(); } public: explicit JsonDouble(double value) : Value(value) {} }; class JsonInt final : public Value { double number_value() const override { return m_value; } int int_value() const override { return m_value; } bool equals(const JsonValue *other) const override { return m_value == other->number_value(); } bool less(const JsonValue *other) const override { return m_value < other->number_value(); } public: explicit JsonInt(int value) : Value(value) {} }; class JsonBoolean final : public Value { bool bool_value() const override { return m_value; } public: explicit JsonBoolean(bool value) : Value(value) {} }; class JsonString final : public Value { const string &string_value() const override { return m_value; } public: explicit JsonString(const string &value) : Value(value) {} explicit JsonString(string &&value) : Value(std::move(value)) {} }; class JsonArray final : public Value { const Json::array &array_items() const override { return m_value; } const Json &operator[](size_t i) const override; public: explicit JsonArray(const Json::array &value) : Value(value) {} explicit JsonArray(Json::array &&value) : Value(std::move(value)) {} }; class JsonObject final : public Value { const Json::object &object_items() const override { return m_value; } const Json &operator[](const string &key) const override; public: explicit JsonObject(const Json::object &value) : Value(value) {} explicit JsonObject(Json::object &&value) : Value(std::move(value)) {} }; class JsonNull final : public Value { public: JsonNull() : Value({}) {} }; /* * * * * * * * * * * * * * * * * * * * * Static globals - static-init-safe */ struct Statics { const std::shared_ptr null = make_shared(); const std::shared_ptr t = make_shared(true); const std::shared_ptr f = make_shared(false); const string empty_string; const vector empty_vector; const map empty_map; Statics() {} }; static const Statics &statics() { static const Statics s{}; return s; } static const Json &static_null() { // This has to be separate, not in Statics, because Json() accesses // statics().null. static const Json json_null; return json_null; } /* * * * * * * * * * * * * * * * * * * * * Constructors */ Json::Json() noexcept : m_ptr(statics().null) {} Json::Json(std::nullptr_t) noexcept : m_ptr(statics().null) {} Json::Json(double value) : m_ptr(make_shared(value)) {} Json::Json(int value) : m_ptr(make_shared(value)) {} Json::Json(bool value) : m_ptr(value ? statics().t : statics().f) {} Json::Json(const string &value) : m_ptr(make_shared(value)) {} Json::Json(string &&value) : m_ptr(make_shared(std::move(value))) {} Json::Json(const char *value) : m_ptr(make_shared(value)) {} Json::Json(const Json::array &values) : m_ptr(make_shared(values)) {} Json::Json(Json::array &&values) : m_ptr(make_shared(std::move(values))) {} Json::Json(const Json::object &values) : m_ptr(make_shared(values)) {} Json::Json(Json::object &&values) : m_ptr(make_shared(std::move(values))) {} /* * * * * * * * * * * * * * * * * * * * * Accessors */ Json::Type Json::type() const { return m_ptr->type(); } double Json::number_value() const { return m_ptr->number_value(); } int Json::int_value() const { return m_ptr->int_value(); } bool Json::bool_value() const { return m_ptr->bool_value(); } const string &Json::string_value() const { return m_ptr->string_value(); } const vector &Json::array_items() const { return m_ptr->array_items(); } const map &Json::object_items() const { return m_ptr->object_items(); } const Json &Json::operator[](size_t i) const { return (*m_ptr)[i]; } const Json &Json::operator[](const string &key) const { return (*m_ptr)[key]; } double JsonValue::number_value() const { return 0; } int JsonValue::int_value() const { return 0; } bool JsonValue::bool_value() const { return false; } const string &JsonValue::string_value() const { return statics().empty_string; } const vector &JsonValue::array_items() const { return statics().empty_vector; } const map &JsonValue::object_items() const { return statics().empty_map; } const Json &JsonValue::operator[](size_t) const { return static_null(); } const Json &JsonValue::operator[](const string &) const { return static_null(); } const Json &JsonObject::operator[](const string &key) const { auto iter = m_value.find(key); return (iter == m_value.end()) ? static_null() : iter->second; } const Json &JsonArray::operator[](size_t i) const { if (i >= m_value.size()) return static_null(); else return m_value[i]; } /* * * * * * * * * * * * * * * * * * * * * Comparison */ bool Json::operator==(const Json &other) const { if (m_ptr == other.m_ptr) return true; if (m_ptr->type() != other.m_ptr->type()) return false; return m_ptr->equals(other.m_ptr.get()); } bool Json::operator<(const Json &other) const { if (m_ptr == other.m_ptr) return false; if (m_ptr->type() != other.m_ptr->type()) return m_ptr->type() < other.m_ptr->type(); return m_ptr->less(other.m_ptr.get()); } /* * * * * * * * * * * * * * * * * * * * * Parsing */ /* esc(c) * * Format char c suitable for printing in an error message. */ static inline string esc(char c) { char buf[12]; if (static_cast(c) >= 0x20 && static_cast(c) <= 0x7f) { snprintf(buf, sizeof buf, "'%c' (%d)", c, c); } else { snprintf(buf, sizeof buf, "(%d)", c); } return string(buf); } template static inline bool in_range(T x, T lower, T upper) { return (x >= lower && x <= upper); } namespace { /* JsonParser * * Object that tracks all state of an in-progress parse. */ struct JsonParser final { /* State */ const char *str; const size_t str_len; size_t i; string *err; bool failed; const JsonParse strategy; /* fail(msg, err_ret = Json()) * * Mark this parse as failed. */ Json fail(string &&msg) { return fail(std::move(msg), Json()); } template T fail(string &&msg, const T err_ret) { if (!failed) *err = std::move(msg); failed = true; return err_ret; } /* consume_whitespace() * * Advance until the current character is non-whitespace. */ void consume_whitespace() { while (str[i] == ' ' || str[i] == '\r' || str[i] == '\n' || str[i] == '\t') i++; } /* consume_comment() * * Advance comments (c-style inline and multiline). */ bool consume_comment() { bool comment_found = false; if (str[i] == '/') { i++; if (i == str_len) return fail("Unexpected end of input after start of comment", false); if (str[i] == '/') { // inline comment i++; // advance until next line, or end of input while (i < str_len && str[i] != '\n') { i++; } comment_found = true; } else if (str[i] == '*') { // multiline comment i++; if (i > str_len - 2) return fail("Unexpected end of input inside multi-line comment", false); // advance until closing tokens while (!(str[i] == '*' && str[i + 1] == '/')) { i++; if (i > str_len - 2) return fail("Unexpected end of input inside multi-line comment", false); } i += 2; comment_found = true; } else { return fail("Malformed comment", false); } } return comment_found; } /* consume_garbage() * * Advance until the current character is non-whitespace and non-comment. */ void consume_garbage() { consume_whitespace(); if (strategy == JsonParse::COMMENTS) { bool comment_found = false; do { comment_found = consume_comment(); if (failed) return; consume_whitespace(); } while (comment_found); } } /* get_next_token() * * Return the next non-whitespace character. If the end of the input is * reached, flag an error and return 0. */ char get_next_token() { consume_garbage(); if (failed) return char{0}; if (i == str_len) return fail("Unexpected end of input", char{0}); return str[i++]; } /* encode_utf8(pt, out) * * Encode pt as UTF-8 and add it to out. */ void encode_utf8(int64_t pt, string* out) { if (pt < 0) return; if (pt < 0x80) { *out += static_cast(pt); } else if (pt < 0x800) { *out += static_cast((pt >> 6) | 0xC0); *out += static_cast((pt & 0x3F) | 0x80); } else if (pt < 0x10000) { *out += static_cast((pt >> 12) | 0xE0); *out += static_cast(((pt >> 6) & 0x3F) | 0x80); *out += static_cast((pt & 0x3F) | 0x80); } else { *out += static_cast((pt >> 18) | 0xF0); *out += static_cast(((pt >> 12) & 0x3F) | 0x80); *out += static_cast(((pt >> 6) & 0x3F) | 0x80); *out += static_cast((pt & 0x3F) | 0x80); } } /* parse_string() * * Parse a string, starting at the current position. */ string parse_string() { string out; int64_t last_escaped_codepoint = -1; while (true) { if (i == str_len) return fail("Unexpected end of input in string", ""); char ch = str[i++]; if (ch == '"') { encode_utf8(last_escaped_codepoint, &out); return out; } if (in_range(ch, 0, 0x1f)) return fail("Unescaped " + esc(ch) + " in string", ""); // The usual case: non-escaped characters if (ch != '\\') { encode_utf8(last_escaped_codepoint, &out); last_escaped_codepoint = -1; out += ch; continue; } // Handle escapes if (i == str_len) return fail("Unexpected end of input in string", ""); ch = str[i++]; if (ch == 'u') { // Extract 4-byte escape sequence string esc = string(str + i, 4); // Explicitly check length of the substring. The following loop // relies on std::string returning the terminating NUL when // accessing str[length]. Checking here reduces brittleness. if (esc.length() < 4) { return fail("Bad \\u escape: " + esc, ""); } for (size_t j = 0; j < 4; j++) { if (!in_range(esc[j], 'a', 'f') && !in_range(esc[j], 'A', 'F') && !in_range(esc[j], '0', '9')) return fail("Bad \\u escape: " + esc, ""); } int64_t codepoint = static_cast(strtol(esc.data(), nullptr, 16)); // JSON specifies that characters outside the BMP shall be encoded as a // pair of 4-hex-digit \u escapes encoding their surrogate pair // components. Check whether we're in the middle of such a beast: the // previous codepoint was an escaped lead (high) surrogate, and this is // a trail (low) surrogate. if (in_range(last_escaped_codepoint, 0xD800, 0xDBFF) && in_range(codepoint, 0xDC00, 0xDFFF)) { // Reassemble the two surrogate pairs into one astral-plane character, // per the UTF-16 algorithm. encode_utf8((((last_escaped_codepoint - 0xD800) << 10) | (codepoint - 0xDC00)) + 0x10000, &out); last_escaped_codepoint = -1; } else { encode_utf8(last_escaped_codepoint, &out); last_escaped_codepoint = codepoint; } i += 4; continue; } encode_utf8(last_escaped_codepoint, &out); last_escaped_codepoint = -1; if (ch == 'b') { out += '\b'; } else if (ch == 'f') { out += '\f'; } else if (ch == 'n') { out += '\n'; } else if (ch == 'r') { out += '\r'; } else if (ch == 't') { out += '\t'; } else if (ch == '"' || ch == '\\' || ch == '/') { out += ch; } else { return fail("Invalid escape character " + esc(ch), ""); } } } /* parse_number() * * Parse a double. */ Json parse_number() { size_t start_pos = i; if (str[i] == '-') i++; // Integer part if (str[i] == '0') { i++; if (in_range(str[i], '0', '9')) return fail("Leading 0s not permitted in numbers"); } else if (in_range(str[i], '1', '9')) { i++; while (in_range(str[i], '0', '9')) i++; } else { return fail("Invalid " + esc(str[i]) + " in number"); } if (str[i] != '.' && str[i] != 'e' && str[i] != 'E' && (i - start_pos) <= static_cast(std::numeric_limits::digits10)) { return Json(std::atoi(str + start_pos)); } // Decimal part if (str[i] == '.') { i++; if (!in_range(str[i], '0', '9')) return fail("At least one digit required in fractional part"); while (in_range(str[i], '0', '9')) i++; } // Exponent part if (str[i] == 'e' || str[i] == 'E') { i++; if (str[i] == '+' || str[i] == '-') i++; if (!in_range(str[i], '0', '9')) return fail("At least one digit required in exponent"); while (in_range(str[i], '0', '9')) i++; } return Json(std::strtod(str + start_pos, nullptr)); } /* expect(str, res) * * Expect that 'str' starts at the character that was just read. If it does, * advance the input and return res. If not, flag an error. */ Json expect(const string &expected, Json res) { CHECK_NE(i, 0) i--; auto substr = string(str + i, expected.length()); if (substr == expected) { i += expected.length(); return res; } else { return fail("Parse error: expected " + expected + ", got " + substr); } } /* parse_json() * * Parse a JSON object. */ Json parse_json(int depth) { if (depth > max_depth) { return fail("Exceeded maximum nesting depth"); } char ch = get_next_token(); if (failed) return Json(); if (ch == '-' || (ch >= '0' && ch <= '9')) { i--; return parse_number(); } if (ch == 't') return expect("true", Json(true)); if (ch == 'f') return expect("false", Json(false)); if (ch == 'n') return expect("null", Json()); if (ch == '"') return Json(parse_string()); if (ch == '{') { map data; ch = get_next_token(); if (ch == '}') return Json(data); while (1) { if (ch != '"') return fail("Expected '\"' in object, got " + esc(ch)); string key = parse_string(); if (failed) return Json(); ch = get_next_token(); if (ch != ':') return fail("Expected ':' in object, got " + esc(ch)); data[std::move(key)] = parse_json(depth + 1); if (failed) return Json(); ch = get_next_token(); if (ch == '}') break; if (ch != ',') return fail("Expected ',' in object, got " + esc(ch)); ch = get_next_token(); } return Json(data); } if (ch == '[') { vector data; ch = get_next_token(); if (ch == ']') return Json(data); while (1) { i--; data.push_back(parse_json(depth + 1)); if (failed) return Json(); ch = get_next_token(); if (ch == ']') break; if (ch != ',') return fail("Expected ',' in list, got " + esc(ch)); ch = get_next_token(); (void)ch; } return Json(data); } return fail("Expected value, got " + esc(ch)); } }; } // namespace Json Json::parse(const string &in, string *err, JsonParse strategy) { JsonParser parser{in.c_str(), in.size(), 0, err, false, strategy}; Json result = parser.parse_json(0); // Check for any trailing garbage parser.consume_garbage(); if (parser.failed) return Json(); if (parser.i != in.size()) return parser.fail("Unexpected trailing " + esc(in[parser.i])); return result; } // Documented in json11.hpp vector Json::parse_multi(const string &in, std::string::size_type *parser_stop_pos, string *err, JsonParse strategy) { JsonParser parser{in.c_str(), in.size(), 0, err, false, strategy}; *parser_stop_pos = 0; vector json_vec; while (parser.i != in.size() && !parser.failed) { json_vec.push_back(parser.parse_json(0)); if (parser.failed) break; // Check for another object parser.consume_garbage(); if (parser.failed) break; *parser_stop_pos = parser.i; } return json_vec; } /* * * * * * * * * * * * * * * * * * * * * Shape-checking */ bool Json::has_shape(const shape &types, string *err) const { if (!is_object()) { *err = "Expected JSON object, got " + dump(); return false; } for (auto &item : types) { if ((*this)[item.first].type() != item.second) { *err = "Bad type for " + item.first + " in " + dump(); return false; } } return true; } } // namespace json11_internal_lightgbm ================================================ FILE: src/io/metadata.cpp ================================================ /*! * Copyright (c) 2016-2026 Microsoft Corporation. All rights reserved. * Copyright (c) 2016-2026 The LightGBM developers. All rights reserved. * Licensed under the MIT License. See LICENSE file in the project root for license information. */ #include #include #include #include #include #include namespace LightGBM { Metadata::Metadata() { num_weights_ = 0; num_init_score_ = 0; num_data_ = 0; num_queries_ = 0; num_positions_ = 0; weight_load_from_file_ = false; position_load_from_file_ = false; query_load_from_file_ = false; init_score_load_from_file_ = false; #ifdef USE_CUDA cuda_metadata_ = nullptr; #endif // USE_CUDA } void Metadata::Init(const char* data_filename) { data_filename_ = data_filename; // for lambdarank, it needs query data for partition data in distributed learning LoadQueryBoundaries(); LoadWeights(); LoadPositions(); CalculateQueryWeights(); LoadInitialScore(data_filename_); } Metadata::~Metadata() { } void Metadata::Init(data_size_t num_data, int weight_idx, int query_idx) { num_data_ = num_data; label_ = std::vector(num_data_); if (weight_idx >= 0) { if (!weights_.empty()) { Log::Info("Using weights in data file, ignoring the additional weights file"); weights_.clear(); } weights_ = std::vector(num_data_, 0.0f); num_weights_ = num_data_; weight_load_from_file_ = false; } if (query_idx >= 0) { if (!query_boundaries_.empty()) { Log::Info("Using query id in data file, ignoring the additional query file"); query_boundaries_.clear(); } if (!query_weights_.empty()) { query_weights_.clear(); } queries_ = std::vector(num_data_, 0); query_load_from_file_ = false; } } void Metadata::InitByReference(data_size_t num_data, const Metadata* reference) { int has_weights = reference->num_weights_ > 0; int has_init_scores = reference->num_init_score_ > 0; int has_queries = reference->num_queries_ > 0; int nclasses = reference->num_init_score_classes(); Init(num_data, has_weights, has_init_scores, has_queries, nclasses); } void Metadata::Init(data_size_t num_data, int32_t has_weights, int32_t has_init_scores, int32_t has_queries, int32_t nclasses) { num_data_ = num_data; label_ = std::vector(num_data_); if (has_weights) { if (!weights_.empty()) { Log::Fatal("Calling Init() on Metadata weights that have already been initialized"); } weights_.resize(num_data_, 0.0f); num_weights_ = num_data_; weight_load_from_file_ = false; } if (has_init_scores) { if (!init_score_.empty()) { Log::Fatal("Calling Init() on Metadata initial scores that have already been initialized"); } num_init_score_ = static_cast(num_data) * nclasses; init_score_.resize(num_init_score_, 0); } if (has_queries) { if (!query_weights_.empty()) { Log::Fatal("Calling Init() on Metadata queries that have already been initialized"); } queries_.resize(num_data_, 0); query_load_from_file_ = false; } } void Metadata::Init(const Metadata& fullset, const data_size_t* used_indices, data_size_t num_used_indices) { num_data_ = num_used_indices; label_.resize(num_used_indices); #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static, 512) if (num_used_indices >= 1024) for (data_size_t i = 0; i < num_used_indices; ++i) { label_[i] = fullset.label_[used_indices[i]]; } if (!fullset.weights_.empty()) { weights_ = std::vector(num_used_indices); num_weights_ = num_used_indices; #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static, 512) if (num_used_indices >= 1024) for (data_size_t i = 0; i < num_used_indices; ++i) { weights_[i] = fullset.weights_[used_indices[i]]; } } else { num_weights_ = 0; } if (!fullset.init_score_.empty()) { int num_class = static_cast(fullset.num_init_score_ / fullset.num_data_); init_score_ = std::vector(static_cast(num_used_indices) * num_class); num_init_score_ = static_cast(num_used_indices) * num_class; #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) for (int k = 0; k < num_class; ++k) { const size_t offset_dest = static_cast(k) * num_data_; const size_t offset_src = static_cast(k) * fullset.num_data_; for (data_size_t i = 0; i < num_used_indices; ++i) { init_score_[offset_dest + i] = fullset.init_score_[offset_src + used_indices[i]]; } } } else { num_init_score_ = 0; } if (!fullset.query_boundaries_.empty()) { std::vector used_query; data_size_t data_idx = 0; for (data_size_t qid = 0; qid < num_queries_ && data_idx < num_used_indices; ++qid) { data_size_t start = fullset.query_boundaries_[qid]; data_size_t end = fullset.query_boundaries_[qid + 1]; data_size_t len = end - start; if (used_indices[data_idx] > start) { continue; } else if (used_indices[data_idx] == start) { if (num_used_indices >= data_idx + len && used_indices[data_idx + len - 1] == end - 1) { used_query.push_back(qid); data_idx += len; } else { Log::Fatal("Data partition error, data didn't match queries"); } } else { Log::Fatal("Data partition error, data didn't match queries"); } } query_boundaries_ = std::vector(used_query.size() + 1); num_queries_ = static_cast(used_query.size()); query_boundaries_[0] = 0; for (data_size_t i = 0; i < num_queries_; ++i) { data_size_t qid = used_query[i]; data_size_t len = fullset.query_boundaries_[qid + 1] - fullset.query_boundaries_[qid]; query_boundaries_[i + 1] = query_boundaries_[i] + len; } } else { num_queries_ = 0; } } void Metadata::PartitionLabel(const std::vector& used_indices) { if (used_indices.empty()) { return; } auto old_label = label_; num_data_ = static_cast(used_indices.size()); label_ = std::vector(num_data_); #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static, 512) if (num_data_ >= 1024) for (data_size_t i = 0; i < num_data_; ++i) { label_[i] = old_label[used_indices[i]]; } old_label.clear(); } void Metadata::CalculateQueryBoundaries() { if (!queries_.empty()) { // need convert query_id to boundaries std::vector tmp_buffer; data_size_t last_qid = -1; data_size_t cur_cnt = 0; for (data_size_t i = 0; i < num_data_; ++i) { if (last_qid != queries_[i]) { if (cur_cnt > 0) { tmp_buffer.push_back(cur_cnt); } cur_cnt = 0; last_qid = queries_[i]; } ++cur_cnt; } tmp_buffer.push_back(cur_cnt); query_boundaries_ = std::vector(tmp_buffer.size() + 1); num_queries_ = static_cast(tmp_buffer.size()); query_boundaries_[0] = 0; for (size_t i = 0; i < tmp_buffer.size(); ++i) { query_boundaries_[i + 1] = query_boundaries_[i] + tmp_buffer[i]; } CalculateQueryWeights(); queries_.clear(); } } void Metadata::CheckOrPartition(data_size_t num_all_data, const std::vector& used_data_indices) { if (used_data_indices.empty()) { CalculateQueryBoundaries(); // check weights if (!weights_.empty() && num_weights_ != num_data_) { weights_.clear(); num_weights_ = 0; Log::Fatal("Weights size doesn't match data size"); } // check positions if (!positions_.empty() && num_positions_ != num_data_) { Log::Fatal("Positions size (%i) doesn't match data size (%i)", num_positions_, num_data_); positions_.clear(); num_positions_ = 0; } // check query boundaries if (!query_boundaries_.empty() && query_boundaries_[num_queries_] != num_data_) { query_boundaries_.clear(); num_queries_ = 0; Log::Fatal("Query size doesn't match data size"); } // contain initial score file if (!init_score_.empty() && (num_init_score_ % num_data_) != 0) { init_score_.clear(); num_init_score_ = 0; Log::Fatal("Initial score size doesn't match data size"); } } else { if (!queries_.empty()) { Log::Fatal("Cannot used query_id for distributed training"); } data_size_t num_used_data = static_cast(used_data_indices.size()); // check weights if (weight_load_from_file_) { if (weights_.size() > 0 && num_weights_ != num_all_data) { weights_.clear(); num_weights_ = 0; Log::Fatal("Weights size doesn't match data size"); } // get local weights if (!weights_.empty()) { auto old_weights = weights_; num_weights_ = num_data_; weights_ = std::vector(num_data_); #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static, 512) for (int i = 0; i < static_cast(used_data_indices.size()); ++i) { weights_[i] = old_weights[used_data_indices[i]]; } old_weights.clear(); } } // check positions if (position_load_from_file_) { if (positions_.size() > 0 && num_positions_ != num_all_data) { positions_.clear(); num_positions_ = 0; Log::Fatal("Positions size (%i) doesn't match data size (%i)", num_positions_, num_data_); } // get local positions if (!positions_.empty()) { auto old_positions = positions_; num_positions_ = num_data_; positions_ = std::vector(num_data_); #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static, 512) for (int i = 0; i < static_cast(used_data_indices.size()); ++i) { positions_[i] = old_positions[used_data_indices[i]]; } old_positions.clear(); } } if (query_load_from_file_) { // check query boundaries if (!query_boundaries_.empty() && query_boundaries_[num_queries_] != num_all_data) { query_boundaries_.clear(); num_queries_ = 0; Log::Fatal("Query size doesn't match data size"); } // get local query boundaries if (!query_boundaries_.empty()) { std::vector used_query; data_size_t data_idx = 0; for (data_size_t qid = 0; qid < num_queries_ && data_idx < num_used_data; ++qid) { data_size_t start = query_boundaries_[qid]; data_size_t end = query_boundaries_[qid + 1]; data_size_t len = end - start; if (used_data_indices[data_idx] > start) { continue; } else if (used_data_indices[data_idx] == start) { if (num_used_data >= data_idx + len && used_data_indices[data_idx + len - 1] == end - 1) { used_query.push_back(qid); data_idx += len; } else { Log::Fatal("Data partition error, data didn't match queries"); } } else { Log::Fatal("Data partition error, data didn't match queries"); } } auto old_query_boundaries = query_boundaries_; query_boundaries_ = std::vector(used_query.size() + 1); num_queries_ = static_cast(used_query.size()); query_boundaries_[0] = 0; for (data_size_t i = 0; i < num_queries_; ++i) { data_size_t qid = used_query[i]; data_size_t len = old_query_boundaries[qid + 1] - old_query_boundaries[qid]; query_boundaries_[i + 1] = query_boundaries_[i] + len; } old_query_boundaries.clear(); } } if (init_score_load_from_file_) { // contain initial score file if (!init_score_.empty() && (num_init_score_ % num_all_data) != 0) { init_score_.clear(); num_init_score_ = 0; Log::Fatal("Initial score size doesn't match data size"); } // get local initial scores if (!init_score_.empty()) { auto old_scores = init_score_; int num_class = static_cast(num_init_score_ / num_all_data); num_init_score_ = static_cast(num_data_) * num_class; init_score_ = std::vector(num_init_score_); #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) for (int k = 0; k < num_class; ++k) { const size_t offset_dest = static_cast(k) * num_data_; const size_t offset_src = static_cast(k) * num_all_data; for (size_t i = 0; i < used_data_indices.size(); ++i) { init_score_[offset_dest + i] = old_scores[offset_src + used_data_indices[i]]; } } old_scores.clear(); } } // re-calculate query weight CalculateQueryWeights(); } if (num_queries_ > 0) { Log::Debug("Number of queries in %s: %i. Average number of rows per query: %f.", data_filename_.c_str(), static_cast(num_queries_), static_cast(num_data_) / num_queries_); } } template void Metadata::SetInitScoresFromIterator(It first, It last) { std::lock_guard lock(mutex_); // Clear init scores on empty input if (last - first == 0) { init_score_.clear(); num_init_score_ = 0; return; } if (((last - first) % num_data_) != 0) { Log::Fatal("Initial score size doesn't match data size"); } if (init_score_.empty()) { init_score_.resize(last - first); } num_init_score_ = last - first; #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static, 512) if (num_init_score_ >= 1024) for (int64_t i = 0; i < num_init_score_; ++i) { init_score_[i] = Common::AvoidInf(first[i]); } init_score_load_from_file_ = false; #ifdef USE_CUDA if (cuda_metadata_ != nullptr) { cuda_metadata_->SetInitScore(init_score_.data(), init_score_.size()); } #endif // USE_CUDA } void Metadata::SetInitScore(const double* init_score, data_size_t len) { SetInitScoresFromIterator(init_score, init_score + len); } void Metadata::SetInitScore(const ArrowChunkedArray& array) { SetInitScoresFromIterator(array.begin(), array.end()); } void Metadata::InsertInitScores(const double* init_scores, data_size_t start_index, data_size_t len, data_size_t source_size) { if (num_init_score_ <= 0) { Log::Fatal("Inserting initial score data into dataset with no initial scores"); } if (start_index + len > num_data_) { // Note that len here is row count, not num_init_score, so we compare against num_data Log::Fatal("Inserted initial score data is too large for dataset"); } if (init_score_.empty()) { init_score_.resize(num_init_score_); } int nclasses = num_init_score_classes(); for (int32_t col = 0; col < nclasses; ++col) { int32_t dest_offset = num_data_ * col + start_index; // We need to use source_size here, because len might not equal size (due to a partially loaded dataset) int32_t source_offset = source_size * col; memcpy(init_score_.data() + dest_offset, init_scores + source_offset, sizeof(double) * len); } init_score_load_from_file_ = false; // CUDA is handled after all insertions are complete } template void Metadata::SetLabelsFromIterator(It first, It last) { std::lock_guard lock(mutex_); if (num_data_ != last - first) { Log::Fatal("Length of labels differs from the length of #data"); } if (label_.empty()) { label_.resize(num_data_); } #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static, 512) if (num_data_ >= 1024) for (data_size_t i = 0; i < num_data_; ++i) { label_[i] = Common::AvoidInf(first[i]); } #ifdef USE_CUDA if (cuda_metadata_ != nullptr) { cuda_metadata_->SetLabel(label_.data(), label_.size()); } #endif // USE_CUDA } void Metadata::SetLabel(const label_t* label, data_size_t len) { if (label == nullptr) { Log::Fatal("label cannot be nullptr"); } SetLabelsFromIterator(label, label + len); } void Metadata::SetLabel(const ArrowChunkedArray& array) { SetLabelsFromIterator(array.begin(), array.end()); } void Metadata::InsertLabels(const label_t* labels, data_size_t start_index, data_size_t len) { if (labels == nullptr) { Log::Fatal("label cannot be nullptr"); } if (start_index + len > num_data_) { Log::Fatal("Inserted label data is too large for dataset"); } if (label_.empty()) { label_.resize(num_data_); } memcpy(label_.data() + start_index, labels, sizeof(label_t) * len); // CUDA is handled after all insertions are complete } template void Metadata::SetWeightsFromIterator(It first, It last) { std::lock_guard lock(mutex_); // Clear weights on empty input if (last - first == 0) { weights_.clear(); num_weights_ = 0; return; } if (num_data_ != last - first) { Log::Fatal("Length of weights differs from the length of #data"); } if (weights_.empty()) { weights_.resize(num_data_); } num_weights_ = num_data_; #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static, 512) if (num_weights_ >= 1024) for (data_size_t i = 0; i < num_weights_; ++i) { weights_[i] = Common::AvoidInf(first[i]); } CalculateQueryWeights(); weight_load_from_file_ = false; #ifdef USE_CUDA if (cuda_metadata_ != nullptr) { cuda_metadata_->SetWeights(weights_.data(), weights_.size()); } #endif // USE_CUDA } void Metadata::SetWeights(const label_t* weights, data_size_t len) { SetWeightsFromIterator(weights, weights + len); } void Metadata::SetWeights(const ArrowChunkedArray& array) { SetWeightsFromIterator(array.begin(), array.end()); } void Metadata::InsertWeights(const label_t* weights, data_size_t start_index, data_size_t len) { if (!weights) { Log::Fatal("Passed null weights"); } if (num_weights_ <= 0) { Log::Fatal("Inserting weight data into dataset with no weights"); } if (start_index + len > num_weights_) { Log::Fatal("Inserted weight data is too large for dataset"); } if (weights_.empty()) { weights_.resize(num_weights_); } memcpy(weights_.data() + start_index, weights, sizeof(label_t) * len); weight_load_from_file_ = false; // CUDA is handled after all insertions are complete } template void Metadata::SetQueriesFromIterator(It first, It last) { std::lock_guard lock(mutex_); // Clear query boundaries on empty input if (last - first == 0) { query_boundaries_.clear(); num_queries_ = 0; return; } data_size_t sum = 0; #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) reduction(+:sum) for (data_size_t i = 0; i < static_cast(last - first); ++i) { sum += first[i]; } if (num_data_ != sum) { Log::Fatal("Sum of query counts (%i) differs from the length of #data (%i)", num_data_, sum); } num_queries_ = last - first; query_boundaries_.resize(num_queries_ + 1); query_boundaries_[0] = 0; for (data_size_t i = 0; i < num_queries_; ++i) { query_boundaries_[i + 1] = query_boundaries_[i] + first[i]; } CalculateQueryWeights(); query_load_from_file_ = false; #ifdef USE_CUDA if (cuda_metadata_ != nullptr) { if (query_weights_.size() > 0) { CHECK_EQ(query_weights_.size(), static_cast(num_queries_)); cuda_metadata_->SetQuery(query_boundaries_.data(), query_weights_.data(), num_queries_); } else { cuda_metadata_->SetQuery(query_boundaries_.data(), nullptr, num_queries_); } } #endif // USE_CUDA } void Metadata::SetQuery(const data_size_t* query, data_size_t len) { SetQueriesFromIterator(query, query + len); } void Metadata::SetQuery(const ArrowChunkedArray& array) { SetQueriesFromIterator(array.begin(), array.end()); } void Metadata::SetPosition(const data_size_t* positions, data_size_t len) { std::lock_guard lock(mutex_); // save to nullptr if (positions == nullptr || len == 0) { positions_.clear(); num_positions_ = 0; return; } #ifdef USE_CUDA Log::Fatal("Positions in learning to rank is not supported in CUDA version yet."); #endif // USE_CUDA if (num_data_ != len) { Log::Fatal("Positions size (%i) doesn't match data size (%i)", len, num_data_); } if (positions_.empty()) { positions_.resize(num_data_); } else { Log::Warning("Overwriting positions in dataset."); } num_positions_ = num_data_; position_load_from_file_ = false; position_ids_.clear(); std::unordered_map map_id2pos; for (data_size_t i = 0; i < num_positions_; ++i) { if (map_id2pos.count(positions[i]) == 0) { int pos = static_cast(map_id2pos.size()); map_id2pos[positions[i]] = pos; position_ids_.push_back(std::to_string(positions[i])); } } Log::Debug("number of unique positions found = %ld", position_ids_.size()); #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static, 512) if (num_positions_ >= 1024) for (data_size_t i = 0; i < num_positions_; ++i) { positions_[i] = map_id2pos.at(positions[i]); } } void Metadata::InsertQueries(const data_size_t* queries, data_size_t start_index, data_size_t len) { if (!queries) { Log::Fatal("Passed null queries"); } if (queries_.size() <= 0) { Log::Fatal("Inserting query data into dataset with no queries"); } if (static_cast(start_index + len) > queries_.size()) { Log::Fatal("Inserted query data is too large for dataset"); } memcpy(queries_.data() + start_index, queries, sizeof(data_size_t) * len); query_load_from_file_ = false; // CUDA is handled after all insertions are complete } void Metadata::LoadWeights() { num_weights_ = 0; std::string weight_filename(data_filename_); // default weight file name weight_filename.append(".weight"); TextReader reader(weight_filename.c_str(), false); reader.ReadAllLines(); if (reader.Lines().empty()) { return; } Log::Info("Loading weights..."); num_weights_ = static_cast(reader.Lines().size()); weights_ = std::vector(num_weights_); #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) for (data_size_t i = 0; i < num_weights_; ++i) { double tmp_weight = 0.0f; Common::Atof(reader.Lines()[i].c_str(), &tmp_weight); weights_[i] = Common::AvoidInf(static_cast(tmp_weight)); } weight_load_from_file_ = true; } void Metadata::LoadPositions() { num_positions_ = 0; std::string position_filename(data_filename_); // default position file name position_filename.append(".position"); TextReader reader(position_filename.c_str(), false); reader.ReadAllLines(); if (reader.Lines().empty()) { return; } Log::Info("Loading positions from %s ...", position_filename.c_str()); num_positions_ = static_cast(reader.Lines().size()); positions_ = std::vector(num_positions_); position_ids_ = std::vector(); std::unordered_map map_id2pos; for (data_size_t i = 0; i < num_positions_; ++i) { std::string& line = reader.Lines()[i]; if (map_id2pos.count(line) == 0) { map_id2pos[line] = static_cast(position_ids_.size()); position_ids_.push_back(line); } positions_[i] = map_id2pos.at(line); } position_load_from_file_ = true; } void Metadata::LoadInitialScore(const std::string& data_filename) { num_init_score_ = 0; std::string init_score_filename(data_filename); init_score_filename = std::string(data_filename); // default init_score file name init_score_filename.append(".init"); TextReader reader(init_score_filename.c_str(), false); reader.ReadAllLines(); if (reader.Lines().empty()) { return; } Log::Info("Loading initial scores..."); // use first line to count number class int num_class = static_cast(Common::Split(reader.Lines()[0].c_str(), '\t').size()); data_size_t num_line = static_cast(reader.Lines().size()); num_init_score_ = static_cast(num_line) * num_class; init_score_ = std::vector(num_init_score_); if (num_class == 1) { #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) for (data_size_t i = 0; i < num_line; ++i) { double tmp = 0.0f; Common::Atof(reader.Lines()[i].c_str(), &tmp); init_score_[i] = Common::AvoidInf(static_cast(tmp)); } } else { std::vector oneline_init_score; #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) for (data_size_t i = 0; i < num_line; ++i) { double tmp = 0.0f; oneline_init_score = Common::Split(reader.Lines()[i].c_str(), '\t'); if (static_cast(oneline_init_score.size()) != num_class) { Log::Fatal("Invalid initial score file. Redundant or insufficient columns"); } for (int k = 0; k < num_class; ++k) { Common::Atof(oneline_init_score[k].c_str(), &tmp); init_score_[static_cast(k) * num_line + i] = Common::AvoidInf(static_cast(tmp)); } } } init_score_load_from_file_ = true; } void Metadata::LoadQueryBoundaries() { num_queries_ = 0; std::string query_filename(data_filename_); // default query file name query_filename.append(".query"); TextReader reader(query_filename.c_str(), false); reader.ReadAllLines(); if (reader.Lines().empty()) { return; } Log::Info("Calculating query boundaries..."); query_boundaries_ = std::vector(reader.Lines().size() + 1); num_queries_ = static_cast(reader.Lines().size()); query_boundaries_[0] = 0; for (size_t i = 0; i < reader.Lines().size(); ++i) { int tmp_cnt; Common::Atoi(reader.Lines()[i].c_str(), &tmp_cnt); query_boundaries_[i + 1] = query_boundaries_[i] + static_cast(tmp_cnt); } query_load_from_file_ = true; } void Metadata::CalculateQueryWeights() { if (weights_.size() == 0 || query_boundaries_.size() == 0) { return; } query_weights_.clear(); Log::Info("Calculating query weights..."); query_weights_ = std::vector(num_queries_); for (data_size_t i = 0; i < num_queries_; ++i) { query_weights_[i] = 0.0f; for (data_size_t j = query_boundaries_[i]; j < query_boundaries_[i + 1]; ++j) { query_weights_[i] += weights_[j]; } query_weights_[i] /= (query_boundaries_[i + 1] - query_boundaries_[i]); } } void Metadata::InsertAt(data_size_t start_index, data_size_t count, const float* labels, const float* weights, const double* init_scores, const int32_t* queries) { if (num_data_ < count + start_index) { Log::Fatal("Length of metadata is too long to append #data"); } InsertLabels(labels, start_index, count); if (weights) { InsertWeights(weights, start_index, count); } if (init_scores) { InsertInitScores(init_scores, start_index, count, count); } if (queries) { InsertQueries(queries, start_index, count); } } void Metadata::FinishLoad() { CalculateQueryBoundaries(); } #ifdef USE_CUDA void Metadata::CreateCUDAMetadata(const int gpu_device_id) { cuda_metadata_.reset(new CUDAMetadata(gpu_device_id)); cuda_metadata_->Init(label_, weights_, query_boundaries_, query_weights_, init_score_); } #endif // USE_CUDA void Metadata::LoadFromMemory(const void* memory) { const char* mem_ptr = reinterpret_cast(memory); num_data_ = *(reinterpret_cast(mem_ptr)); mem_ptr += VirtualFileWriter::AlignedSize(sizeof(num_data_)); num_weights_ = *(reinterpret_cast(mem_ptr)); mem_ptr += VirtualFileWriter::AlignedSize(sizeof(num_weights_)); num_queries_ = *(reinterpret_cast(mem_ptr)); mem_ptr += VirtualFileWriter::AlignedSize(sizeof(num_queries_)); if (!label_.empty()) { label_.clear(); } label_ = std::vector(num_data_); std::memcpy(label_.data(), mem_ptr, sizeof(label_t) * num_data_); mem_ptr += VirtualFileWriter::AlignedSize(sizeof(label_t) * num_data_); if (num_weights_ > 0) { if (!weights_.empty()) { weights_.clear(); } weights_ = std::vector(num_weights_); std::memcpy(weights_.data(), mem_ptr, sizeof(label_t) * num_weights_); mem_ptr += VirtualFileWriter::AlignedSize(sizeof(label_t) * num_weights_); weight_load_from_file_ = true; } if (num_queries_ > 0) { if (!query_boundaries_.empty()) { query_boundaries_.clear(); } query_boundaries_ = std::vector(num_queries_ + 1); std::memcpy(query_boundaries_.data(), mem_ptr, sizeof(data_size_t) * (num_queries_ + 1)); mem_ptr += VirtualFileWriter::AlignedSize(sizeof(data_size_t) * (num_queries_ + 1)); query_load_from_file_ = true; } CalculateQueryWeights(); } void Metadata::SaveBinaryToFile(BinaryWriter* writer) const { writer->AlignedWrite(&num_data_, sizeof(num_data_)); writer->AlignedWrite(&num_weights_, sizeof(num_weights_)); writer->AlignedWrite(&num_queries_, sizeof(num_queries_)); writer->AlignedWrite(label_.data(), sizeof(label_t) * num_data_); if (!weights_.empty()) { writer->AlignedWrite(weights_.data(), sizeof(label_t) * num_weights_); } if (!query_boundaries_.empty()) { writer->AlignedWrite(query_boundaries_.data(), sizeof(data_size_t) * (num_queries_ + 1)); } if (num_init_score_ > 0) { Log::Warning("Please note that `init_score` is not saved in binary file.\n" "If you need it, please set it again after loading Dataset."); } } size_t Metadata::SizesInByte() const { size_t size = VirtualFileWriter::AlignedSize(sizeof(num_data_)) + VirtualFileWriter::AlignedSize(sizeof(num_weights_)) + VirtualFileWriter::AlignedSize(sizeof(num_queries_)); size += VirtualFileWriter::AlignedSize(sizeof(label_t) * num_data_); if (!weights_.empty()) { size += VirtualFileWriter::AlignedSize(sizeof(label_t) * num_weights_); } if (!query_boundaries_.empty()) { size += VirtualFileWriter::AlignedSize(sizeof(data_size_t) * (num_queries_ + 1)); } return size; } } // namespace LightGBM ================================================ FILE: src/io/multi_val_dense_bin.hpp ================================================ /*! * Copyright (c) 2020-2026 Microsoft Corporation. All rights reserved. * Copyright (c) 2020-2026 The LightGBM developers. All rights reserved. * Licensed under the MIT License. See LICENSE file in the project root for license information. */ #ifndef LIGHTGBM_SRC_IO_MULTI_VAL_DENSE_BIN_HPP_ #define LIGHTGBM_SRC_IO_MULTI_VAL_DENSE_BIN_HPP_ #include #include #include #include #include #include #include namespace LightGBM { template class MultiValDenseBin : public MultiValBin { public: explicit MultiValDenseBin(data_size_t num_data, int num_bin, int num_feature, const std::vector& offsets) : num_data_(num_data), num_bin_(num_bin), num_feature_(num_feature), offsets_(offsets) { data_.resize(static_cast(num_data_) * num_feature_, static_cast(0)); } ~MultiValDenseBin() { } data_size_t num_data() const override { return num_data_; } int num_bin() const override { return num_bin_; } double num_element_per_row() const override { return num_feature_; } const std::vector& offsets() const override { return offsets_; } void PushOneRow(int , data_size_t idx, const std::vector& values) override { auto start = RowPtr(idx); for (auto i = 0; i < num_feature_; ++i) { data_[start + i] = static_cast(values[i]); } } void FinishLoad() override { } bool IsSparse() override { return false; } template void ConstructHistogramInner(const data_size_t* data_indices, data_size_t start, data_size_t end, const score_t* gradients, const score_t* hessians, hist_t* out) const { data_size_t i = start; hist_t* grad = out; hist_t* hess = out + 1; if (USE_PREFETCH) { const data_size_t pf_offset = 32 / sizeof(VAL_T); const data_size_t pf_end = end - pf_offset; for (; i < pf_end; ++i) { const auto idx = USE_INDICES ? data_indices[i] : i; const auto pf_idx = USE_INDICES ? data_indices[i + pf_offset] : i + pf_offset; if (!ORDERED) { PREFETCH_T0(gradients + pf_idx); PREFETCH_T0(hessians + pf_idx); } PREFETCH_T0(data_.data() + RowPtr(pf_idx)); const auto j_start = RowPtr(idx); const VAL_T* data_ptr = data_.data() + j_start; const score_t gradient = ORDERED ? gradients[i] : gradients[idx]; const score_t hessian = ORDERED ? hessians[i] : hessians[idx]; for (int j = 0; j < num_feature_; ++j) { const uint32_t bin = static_cast(data_ptr[j]); const auto ti = (bin + offsets_[j]) << 1; grad[ti] += gradient; hess[ti] += hessian; } } } for (; i < end; ++i) { const auto idx = USE_INDICES ? data_indices[i] : i; const auto j_start = RowPtr(idx); const VAL_T* data_ptr = data_.data() + j_start; const score_t gradient = ORDERED ? gradients[i] : gradients[idx]; const score_t hessian = ORDERED ? hessians[i] : hessians[idx]; for (int j = 0; j < num_feature_; ++j) { const uint32_t bin = static_cast(data_ptr[j]); const auto ti = (bin + offsets_[j]) << 1; grad[ti] += gradient; hess[ti] += hessian; } } } void ConstructHistogram(const data_size_t* data_indices, data_size_t start, data_size_t end, const score_t* gradients, const score_t* hessians, hist_t* out) const override { ConstructHistogramInner(data_indices, start, end, gradients, hessians, out); } void ConstructHistogram(data_size_t start, data_size_t end, const score_t* gradients, const score_t* hessians, hist_t* out) const override { ConstructHistogramInner( nullptr, start, end, gradients, hessians, out); } void ConstructHistogramOrdered(const data_size_t* data_indices, data_size_t start, data_size_t end, const score_t* gradients, const score_t* hessians, hist_t* out) const override { ConstructHistogramInner(data_indices, start, end, gradients, hessians, out); } template void ConstructHistogramIntInner(const data_size_t* data_indices, data_size_t start, data_size_t end, const score_t* gradients_and_hessians, hist_t* out) const { data_size_t i = start; const VAL_T* data_ptr_base = data_.data(); const int16_t* gradients_and_hessians_ptr = reinterpret_cast(gradients_and_hessians); PACKED_HIST_T* out_ptr = reinterpret_cast(out); if (USE_PREFETCH) { const data_size_t pf_offset = 32 / sizeof(VAL_T); const data_size_t pf_end = end - pf_offset; for (; i < pf_end; ++i) { const auto idx = USE_INDICES ? data_indices[i] : i; const auto pf_idx = USE_INDICES ? data_indices[i + pf_offset] : i + pf_offset; if (!ORDERED) { PREFETCH_T0(gradients_and_hessians_ptr + pf_idx); } PREFETCH_T0(data_ptr_base + RowPtr(pf_idx)); const auto j_start = RowPtr(idx); const VAL_T* data_ptr = data_ptr_base + j_start; const int16_t gradient_16 = gradients_and_hessians_ptr[idx]; const PACKED_HIST_T gradient_packed = (HIST_BITS == 8) ? gradient_16 : ((static_cast(static_cast(gradient_16 >> 8)) << HIST_BITS) | static_cast(gradient_16 & 0xff)); for (int j = 0; j < num_feature_; ++j) { const uint32_t bin = static_cast(data_ptr[j]); const auto ti = (bin + offsets_[j]); out_ptr[ti] += gradient_packed; } } } for (; i < end; ++i) { const auto idx = USE_INDICES ? data_indices[i] : i; const auto j_start = RowPtr(idx); const VAL_T* data_ptr = data_ptr_base + j_start; const int16_t gradient_16 = gradients_and_hessians_ptr[idx]; const PACKED_HIST_T gradient_packed = (HIST_BITS == 8) ? gradient_16 : ((static_cast(static_cast(gradient_16 >> 8)) << HIST_BITS) | static_cast(gradient_16 & 0xff)); for (int j = 0; j < num_feature_; ++j) { const uint32_t bin = static_cast(data_ptr[j]); const auto ti = (bin + offsets_[j]); out_ptr[ti] += gradient_packed; } } } void ConstructHistogramInt32(const data_size_t* data_indices, data_size_t start, data_size_t end, const score_t* gradients, const score_t* /*hessians*/, hist_t* out) const override { ConstructHistogramIntInner(data_indices, start, end, gradients, out); } void ConstructHistogramInt32(data_size_t start, data_size_t end, const score_t* gradients, const score_t* /*hessians*/, hist_t* out) const override { ConstructHistogramIntInner( nullptr, start, end, gradients, out); } void ConstructHistogramOrderedInt32(const data_size_t* data_indices, data_size_t start, data_size_t end, const score_t* gradients, const score_t* /*hessians*/, hist_t* out) const override { ConstructHistogramIntInner(data_indices, start, end, gradients, out); } void ConstructHistogramInt16(const data_size_t* data_indices, data_size_t start, data_size_t end, const score_t* gradients, const score_t* /*hessians*/, hist_t* out) const override { ConstructHistogramIntInner(data_indices, start, end, gradients, out); } void ConstructHistogramInt16(data_size_t start, data_size_t end, const score_t* gradients, const score_t* /*hessians*/, hist_t* out) const override { ConstructHistogramIntInner( nullptr, start, end, gradients, out); } void ConstructHistogramOrderedInt16(const data_size_t* data_indices, data_size_t start, data_size_t end, const score_t* gradients, const score_t* /*hessians*/, hist_t* out) const override { ConstructHistogramIntInner(data_indices, start, end, gradients, out); } void ConstructHistogramInt8(const data_size_t* data_indices, data_size_t start, data_size_t end, const score_t* gradients, const score_t* /*hessians*/, hist_t* out) const override { ConstructHistogramIntInner(data_indices, start, end, gradients, out); } void ConstructHistogramInt8(data_size_t start, data_size_t end, const score_t* gradients, const score_t* /*hessians*/, hist_t* out) const override { ConstructHistogramIntInner( nullptr, start, end, gradients, out); } void ConstructHistogramOrderedInt8(const data_size_t* data_indices, data_size_t start, data_size_t end, const score_t* gradients, const score_t* /*hessians*/, hist_t* out) const override { ConstructHistogramIntInner(data_indices, start, end, gradients, out); } MultiValBin* CreateLike(data_size_t num_data, int num_bin, int num_feature, double, const std::vector& offsets) const override { return new MultiValDenseBin(num_data, num_bin, num_feature, offsets); } void ReSize(data_size_t num_data, int num_bin, int num_feature, double, const std::vector& offsets) override { num_data_ = num_data; num_bin_ = num_bin; num_feature_ = num_feature; offsets_ = offsets; size_t new_size = static_cast(num_feature_) * num_data_; if (data_.size() < new_size) { data_.resize(new_size, 0); } } template void CopyInner(const MultiValBin* full_bin, const data_size_t* used_indices, data_size_t num_used_indices, const std::vector& used_feature_index) { const auto other_bin = reinterpret_cast*>(full_bin); if (SUBROW) { CHECK_EQ(num_data_, num_used_indices); } int n_block = 1; data_size_t block_size = num_data_; Threading::BlockInfo(num_data_, 1024, &n_block, &block_size); #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static, 1) for (int tid = 0; tid < n_block; ++tid) { data_size_t start = tid * block_size; data_size_t end = std::min(num_data_, start + block_size); for (data_size_t i = start; i < end; ++i) { const auto j_start = RowPtr(i); const auto other_j_start = SUBROW ? other_bin->RowPtr(used_indices[i]) : other_bin->RowPtr(i); for (int j = 0; j < num_feature_; ++j) { if (SUBCOL) { if (other_bin->data_[other_j_start + used_feature_index[j]] > 0) { data_[j_start + j] = static_cast( other_bin->data_[other_j_start + used_feature_index[j]]); } else { data_[j_start + j] = 0; } } else { data_[j_start + j] = static_cast(other_bin->data_[other_j_start + j]); } } } } } void CopySubrow(const MultiValBin* full_bin, const data_size_t* used_indices, data_size_t num_used_indices) override { CopyInner(full_bin, used_indices, num_used_indices, std::vector()); } void CopySubcol(const MultiValBin* full_bin, const std::vector& used_feature_index, const std::vector&, const std::vector&, const std::vector&) override { CopyInner(full_bin, nullptr, num_data_, used_feature_index); } void CopySubrowAndSubcol(const MultiValBin* full_bin, const data_size_t* used_indices, data_size_t num_used_indices, const std::vector& used_feature_index, const std::vector&, const std::vector&, const std::vector&) override { CopyInner(full_bin, used_indices, num_used_indices, used_feature_index); } inline size_t RowPtr(data_size_t idx) const { return static_cast(idx) * num_feature_; } MultiValDenseBin* Clone() override; #ifdef USE_CUDA const void* GetRowWiseData(uint8_t* bit_type, size_t* total_size, bool* is_sparse, const void** out_data_ptr, uint8_t* data_ptr_bit_type) const override; #endif // USE_CUDA private: data_size_t num_data_; int num_bin_; int num_feature_; std::vector offsets_; std::vector> data_; MultiValDenseBin(const MultiValDenseBin& other) : num_data_(other.num_data_), num_bin_(other.num_bin_), num_feature_(other.num_feature_), offsets_(other.offsets_), data_(other.data_) { } }; template MultiValDenseBin* MultiValDenseBin::Clone() { return new MultiValDenseBin(*this); } } // namespace LightGBM #endif // LIGHTGBM_SRC_IO_MULTI_VAL_DENSE_BIN_HPP_ ================================================ FILE: src/io/multi_val_sparse_bin.hpp ================================================ /*! * Copyright (c) 2020-2026 Microsoft Corporation. All rights reserved. * Copyright (c) 2020-2026 The LightGBM developers. All rights reserved. * Licensed under the MIT License. See LICENSE file in the project root for license information. */ #ifndef LIGHTGBM_SRC_IO_MULTI_VAL_SPARSE_BIN_HPP_ #define LIGHTGBM_SRC_IO_MULTI_VAL_SPARSE_BIN_HPP_ #include #include #include #include #include #include #include namespace LightGBM { template class MultiValSparseBin : public MultiValBin { public: explicit MultiValSparseBin(data_size_t num_data, int num_bin, double estimate_element_per_row) : num_data_(num_data), num_bin_(num_bin), estimate_element_per_row_(estimate_element_per_row) { row_ptr_.resize(num_data_ + 1, 0); INDEX_T estimate_num_data = static_cast(estimate_element_per_row_ * 1.1 * num_data_); int num_threads = OMP_NUM_THREADS(); if (num_threads > 1) { t_data_.resize(num_threads - 1); for (size_t i = 0; i < t_data_.size(); ++i) { t_data_[i].resize(estimate_num_data / num_threads); } } t_size_.resize(num_threads, 0); data_.resize(estimate_num_data / num_threads); } ~MultiValSparseBin() {} data_size_t num_data() const override { return num_data_; } int num_bin() const override { return num_bin_; } double num_element_per_row() const override { return estimate_element_per_row_; } const std::vector& offsets() const override { return offsets_; } void PushOneRow(int tid, data_size_t idx, const std::vector& values) override { const int pre_alloc_size = 50; row_ptr_[idx + 1] = static_cast(values.size()); if (tid == 0) { if (t_size_[tid] + row_ptr_[idx + 1] > static_cast(data_.size())) { data_.resize(t_size_[tid] + row_ptr_[idx + 1] * pre_alloc_size); } for (auto val : values) { data_[t_size_[tid]++] = static_cast(val); } } else { if (t_size_[tid] + row_ptr_[idx + 1] > static_cast(t_data_[tid - 1].size())) { t_data_[tid - 1].resize(t_size_[tid] + row_ptr_[idx + 1] * pre_alloc_size); } for (auto val : values) { t_data_[tid - 1][t_size_[tid]++] = static_cast(val); } } } void MergeData(const INDEX_T* sizes) { Common::FunctionTimer fun_time("MultiValSparseBin::MergeData", global_timer); for (data_size_t i = 0; i < num_data_; ++i) { row_ptr_[i + 1] += row_ptr_[i]; } if (t_data_.size() > 0) { std::vector offsets(1 + t_data_.size()); offsets[0] = sizes[0]; for (size_t tid = 0; tid < t_data_.size() - 1; ++tid) { offsets[tid + 1] = offsets[tid] + sizes[tid + 1]; } data_.resize(row_ptr_[num_data_]); #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static, 1) for (int tid = 0; tid < static_cast(t_data_.size()); ++tid) { std::copy_n(t_data_[tid].data(), sizes[tid + 1], data_.data() + offsets[tid]); } } else { data_.resize(row_ptr_[num_data_]); } } void FinishLoad() override { MergeData(t_size_.data()); t_size_.clear(); row_ptr_.shrink_to_fit(); data_.shrink_to_fit(); t_data_.clear(); t_data_.shrink_to_fit(); // update estimate_element_per_row_ by all data estimate_element_per_row_ = static_cast(row_ptr_[num_data_]) / num_data_; } bool IsSparse() override { return true; } template void ConstructHistogramInner(const data_size_t* data_indices, data_size_t start, data_size_t end, const score_t* gradients, const score_t* hessians, hist_t* out) const { data_size_t i = start; hist_t* grad = out; hist_t* hess = out + 1; const VAL_T* data_ptr = data_.data(); if (USE_PREFETCH) { const data_size_t pf_offset = 32 / sizeof(VAL_T); const data_size_t pf_end = end - pf_offset; for (; i < pf_end; ++i) { const auto idx = USE_INDICES ? data_indices[i] : i; const auto pf_idx = USE_INDICES ? data_indices[i + pf_offset] : i + pf_offset; if (!ORDERED) { PREFETCH_T0(gradients + pf_idx); PREFETCH_T0(hessians + pf_idx); } PREFETCH_T0(row_ptr_.data() + pf_idx); PREFETCH_T0(data_ptr + row_ptr_[pf_idx]); const auto j_start = RowPtr(idx); const auto j_end = RowPtr(idx + 1); const score_t gradient = ORDERED ? gradients[i] : gradients[idx]; const score_t hessian = ORDERED ? hessians[i] : hessians[idx]; for (auto j = j_start; j < j_end; ++j) { const auto ti = static_cast(data_ptr[j]) << 1; grad[ti] += gradient; hess[ti] += hessian; } } } for (; i < end; ++i) { const auto idx = USE_INDICES ? data_indices[i] : i; const auto j_start = RowPtr(idx); const auto j_end = RowPtr(idx + 1); const score_t gradient = ORDERED ? gradients[i] : gradients[idx]; const score_t hessian = ORDERED ? hessians[i] : hessians[idx]; for (auto j = j_start; j < j_end; ++j) { const auto ti = static_cast(data_ptr[j]) << 1; grad[ti] += gradient; hess[ti] += hessian; } } } void ConstructHistogram(const data_size_t* data_indices, data_size_t start, data_size_t end, const score_t* gradients, const score_t* hessians, hist_t* out) const override { ConstructHistogramInner(data_indices, start, end, gradients, hessians, out); } void ConstructHistogram(data_size_t start, data_size_t end, const score_t* gradients, const score_t* hessians, hist_t* out) const override { ConstructHistogramInner( nullptr, start, end, gradients, hessians, out); } void ConstructHistogramOrdered(const data_size_t* data_indices, data_size_t start, data_size_t end, const score_t* gradients, const score_t* hessians, hist_t* out) const override { ConstructHistogramInner(data_indices, start, end, gradients, hessians, out); } template void ConstructHistogramIntInner(const data_size_t* data_indices, data_size_t start, data_size_t end, const score_t* gradients_and_hessians, hist_t* out) const { data_size_t i = start; PACKED_HIST_T* out_ptr = reinterpret_cast(out); const int16_t* gradients_and_hessians_ptr = reinterpret_cast(gradients_and_hessians); const VAL_T* data_ptr = data_.data(); const INDEX_T* row_ptr_base = row_ptr_.data(); if (USE_PREFETCH) { const data_size_t pf_offset = 32 / sizeof(VAL_T); const data_size_t pf_end = end - pf_offset; for (; i < pf_end; ++i) { const auto idx = USE_INDICES ? data_indices[i] : i; const auto pf_idx = USE_INDICES ? data_indices[i + pf_offset] : i + pf_offset; if (!ORDERED) { PREFETCH_T0(gradients_and_hessians_ptr + pf_idx); } PREFETCH_T0(row_ptr_base + pf_idx); PREFETCH_T0(data_ptr + row_ptr_[pf_idx]); const auto j_start = RowPtr(idx); const auto j_end = RowPtr(idx + 1); const int16_t gradient_16 = ORDERED ? gradients_and_hessians_ptr[i] : gradients_and_hessians_ptr[idx]; const PACKED_HIST_T gradient_packed = (HIST_BITS == 8) ? gradient_16 : ((static_cast(static_cast(gradient_16 >> 8)) << HIST_BITS) | static_cast(gradient_16 & 0xff)); for (auto j = j_start; j < j_end; ++j) { const auto ti = static_cast(data_ptr[j]); out_ptr[ti] += gradient_packed; } } } for (; i < end; ++i) { const auto idx = USE_INDICES ? data_indices[i] : i; const auto j_start = RowPtr(idx); const auto j_end = RowPtr(idx + 1); const int16_t gradient_16 = ORDERED ? gradients_and_hessians_ptr[i] : gradients_and_hessians_ptr[idx]; const PACKED_HIST_T gradient_packed = (HIST_BITS == 8) ? gradient_16 : ((static_cast(static_cast(gradient_16 >> 8)) << HIST_BITS) | static_cast(gradient_16 & 0xff)); for (auto j = j_start; j < j_end; ++j) { const auto ti = static_cast(data_ptr[j]); out_ptr[ti] += gradient_packed; } } } void ConstructHistogramInt32(const data_size_t* data_indices, data_size_t start, data_size_t end, const score_t* gradients, const score_t* /*hessians*/, hist_t* out) const override { ConstructHistogramIntInner(data_indices, start, end, gradients, out); } void ConstructHistogramInt32(data_size_t start, data_size_t end, const score_t* gradients, const score_t* /*hessians*/, hist_t* out) const override { ConstructHistogramIntInner( nullptr, start, end, gradients, out); } void ConstructHistogramOrderedInt32(const data_size_t* data_indices, data_size_t start, data_size_t end, const score_t* gradients, const score_t* /*hessians*/, hist_t* out) const override { ConstructHistogramIntInner(data_indices, start, end, gradients, out); } void ConstructHistogramInt16(const data_size_t* data_indices, data_size_t start, data_size_t end, const score_t* gradients, const score_t* /*hessians*/, hist_t* out) const override { ConstructHistogramIntInner(data_indices, start, end, gradients, out); } void ConstructHistogramInt16(data_size_t start, data_size_t end, const score_t* gradients, const score_t* /*hessians*/, hist_t* out) const override { ConstructHistogramIntInner( nullptr, start, end, gradients, out); } void ConstructHistogramOrderedInt16(const data_size_t* data_indices, data_size_t start, data_size_t end, const score_t* gradients, const score_t* /*hessians*/, hist_t* out) const override { ConstructHistogramIntInner(data_indices, start, end, gradients, out); } void ConstructHistogramInt8(const data_size_t* data_indices, data_size_t start, data_size_t end, const score_t* gradients, const score_t* /*hessians*/, hist_t* out) const override { ConstructHistogramIntInner(data_indices, start, end, gradients, out); } void ConstructHistogramInt8(data_size_t start, data_size_t end, const score_t* gradients, const score_t* /*hessians*/, hist_t* out) const override { ConstructHistogramIntInner( nullptr, start, end, gradients, out); } void ConstructHistogramOrderedInt8(const data_size_t* data_indices, data_size_t start, data_size_t end, const score_t* gradients, const score_t* /*hessians*/, hist_t* out) const override { ConstructHistogramIntInner(data_indices, start, end, gradients, out); } MultiValBin* CreateLike(data_size_t num_data, int num_bin, int, double estimate_element_per_row, const std::vector& /*offsets*/) const override { return new MultiValSparseBin(num_data, num_bin, estimate_element_per_row); } void ReSize(data_size_t num_data, int num_bin, int, double estimate_element_per_row, const std::vector& /*offsets*/) override { num_data_ = num_data; num_bin_ = num_bin; estimate_element_per_row_ = estimate_element_per_row; INDEX_T estimate_num_data = static_cast(estimate_element_per_row_ * 1.1 * num_data_); size_t npart = 1 + t_data_.size(); INDEX_T avg_num_data = static_cast(estimate_num_data / npart); if (static_cast(data_.size()) < avg_num_data) { data_.resize(avg_num_data, 0); } for (size_t i = 0; i < t_data_.size(); ++i) { if (static_cast(t_data_[i].size()) < avg_num_data) { t_data_[i].resize(avg_num_data, 0); } } if (num_data_ + 1 > static_cast(row_ptr_.size())) { row_ptr_.resize(num_data_ + 1); } } template void CopyInner(const MultiValBin* full_bin, const data_size_t* used_indices, data_size_t num_used_indices, const std::vector& lower, const std::vector& upper, const std::vector& delta) { const auto other = reinterpret_cast*>(full_bin); if (SUBROW) { CHECK_EQ(num_data_, num_used_indices); } int n_block = 1; data_size_t block_size = num_data_; Threading::BlockInfo(static_cast(t_data_.size() + 1), num_data_, 1024, &n_block, &block_size); std::vector sizes(t_data_.size() + 1, 0); const int pre_alloc_size = 50; #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static, 1) for (int tid = 0; tid < n_block; ++tid) { data_size_t start = tid * block_size; data_size_t end = std::min(num_data_, start + block_size); auto& buf = (tid == 0) ? data_ : t_data_[tid - 1]; INDEX_T size = 0; for (data_size_t i = start; i < end; ++i) { const auto j_start = SUBROW ? other->RowPtr(used_indices[i]) : other->RowPtr(i); const auto j_end = SUBROW ? other->RowPtr(used_indices[i] + 1) : other->RowPtr(i + 1); if (size + (j_end - j_start) > static_cast(buf.size())) { buf.resize(size + (j_end - j_start) * pre_alloc_size); } int k = 0; const auto pre_size = size; for (auto j = j_start; j < j_end; ++j) { const auto val = other->data_[j]; if (SUBCOL) { while (val >= upper[k]) { ++k; } if (val >= lower[k]) { buf[size++] = static_cast(val - delta[k]); } } else { buf[size++] = val; } } row_ptr_[i + 1] = size - pre_size; } sizes[tid] = size; } MergeData(sizes.data()); } void CopySubrow(const MultiValBin* full_bin, const data_size_t* used_indices, data_size_t num_used_indices) override { CopyInner(full_bin, used_indices, num_used_indices, std::vector(), std::vector(), std::vector()); } void CopySubcol(const MultiValBin* full_bin, const std::vector&, const std::vector& lower, const std::vector& upper, const std::vector& delta) override { CopyInner(full_bin, nullptr, num_data_, lower, upper, delta); } void CopySubrowAndSubcol(const MultiValBin* full_bin, const data_size_t* used_indices, data_size_t num_used_indices, const std::vector&, const std::vector& lower, const std::vector& upper, const std::vector& delta) override { CopyInner(full_bin, used_indices, num_used_indices, lower, upper, delta); } inline INDEX_T RowPtr(data_size_t idx) const { return row_ptr_[idx]; } MultiValSparseBin* Clone() override; #ifdef USE_CUDA const void* GetRowWiseData(uint8_t* bit_type, size_t* total_size, bool* is_sparse, const void** out_data_ptr, uint8_t* data_ptr_bit_type) const override; #endif // USE_CUDA private: data_size_t num_data_; int num_bin_; double estimate_element_per_row_; std::vector> data_; std::vector> row_ptr_; std::vector>> t_data_; std::vector t_size_; std::vector offsets_; MultiValSparseBin(const MultiValSparseBin& other) : num_data_(other.num_data_), num_bin_(other.num_bin_), estimate_element_per_row_(other.estimate_element_per_row_), data_(other.data_), row_ptr_(other.row_ptr_) {} }; template MultiValSparseBin* MultiValSparseBin::Clone() { return new MultiValSparseBin(*this); } } // namespace LightGBM #endif // LIGHTGBM_SRC_IO_MULTI_VAL_SPARSE_BIN_HPP_ ================================================ FILE: src/io/parser.cpp ================================================ /*! * Copyright (c) 2016-2026 Microsoft Corporation. All rights reserved. * Copyright (c) 2016-2026 The LightGBM developers. All rights reserved. * Licensed under the MIT License. See LICENSE file in the project root for license information. */ #include "parser.hpp" #include #include #include #include #include #include namespace LightGBM { void GetStatistic(const char* str, int* comma_cnt, int* tab_cnt, int* colon_cnt) { *comma_cnt = 0; *tab_cnt = 0; *colon_cnt = 0; for (int i = 0; str[i] != '\0'; ++i) { if (str[i] == ',') { ++(*comma_cnt); } else if (str[i] == '\t') { ++(*tab_cnt); } else if (str[i] == ':') { ++(*colon_cnt); } } } int GetLabelIdxForLibsvm(const std::string& str, int num_features, int label_idx) { if (num_features <= 0) { return label_idx; } auto str2 = Common::Trim(str); auto pos_space = str2.find_first_of(" \f\n\r\t\v"); auto pos_colon = str2.find_first_of(":"); if (pos_space == std::string::npos || pos_space < pos_colon) { return label_idx; } else { return -1; } } int GetLabelIdxForTSV(const std::string& str, int num_features, int label_idx) { if (num_features <= 0) { return label_idx; } auto str2 = Common::Trim(str); auto tokens = Common::Split(str2.c_str(), '\t'); if (static_cast(tokens.size()) == num_features) { return -1; } else { return label_idx; } } int GetLabelIdxForCSV(const std::string& str, int num_features, int label_idx) { if (num_features <= 0) { return label_idx; } auto str2 = Common::Trim(str); auto tokens = Common::Split(str2.c_str(), ','); if (static_cast(tokens.size()) == num_features) { return -1; } else { return label_idx; } } enum DataType { INVALID, CSV, TSV, LIBSVM }; void GetLine(std::stringstream* ss, std::string* line, const VirtualFileReader* reader, std::vector* buffer, size_t buffer_size) { std::getline(*ss, *line); while (ss->eof()) { size_t read_len = reader->Read(buffer->data(), buffer_size); if (read_len <= 0) { break; } ss->clear(); ss->str(std::string(buffer->data(), read_len)); std::string tmp; std::getline(*ss, tmp); *line += tmp; } } std::vector ReadKLineFromFile(const char* filename, bool header, int k) { auto reader = VirtualFileReader::Make(filename); if (!reader->Init()) { Log::Fatal("Data file %s doesn't exist.", filename); } std::vector ret; std::string cur_line; const size_t buffer_size = 1024 * 1024; auto buffer = std::vector(buffer_size); size_t read_len = reader->Read(buffer.data(), buffer_size); if (read_len <= 0) { Log::Fatal("Data file %s couldn't be read.", filename); } std::string read_str = std::string(buffer.data(), read_len); std::stringstream tmp_file(read_str); if (header) { if (!tmp_file.eof()) { GetLine(&tmp_file, &cur_line, reader.get(), &buffer, buffer_size); } } for (int i = 0; i < k; ++i) { if (!tmp_file.eof()) { GetLine(&tmp_file, &cur_line, reader.get(), &buffer, buffer_size); cur_line = Common::Trim(cur_line); if (!cur_line.empty()) { ret.push_back(cur_line); } } else { break; } } if (ret.empty()) { Log::Fatal("Data file %s should have at least one line.", filename); } else if (ret.size() == 1) { Log::Warning("Data file %s only has one line.", filename); } return ret; } int GetNumColFromLIBSVMFile(const char* filename, bool header) { auto reader = VirtualFileReader::Make(filename); if (!reader->Init()) { Log::Fatal("Data file %s doesn't exist.", filename); } std::vector ret; std::string cur_line; const size_t buffer_size = 1024 * 1024; auto buffer = std::vector(buffer_size); size_t read_len = reader->Read(buffer.data(), buffer_size); if (read_len <= 0) { Log::Fatal("Data file %s couldn't be read.", filename); } std::string read_str = std::string(buffer.data(), read_len); std::stringstream tmp_file(read_str); if (header) { if (!tmp_file.eof()) { GetLine(&tmp_file, &cur_line, reader.get(), &buffer, buffer_size); } } int max_col_idx = 0; int max_line_idx = 0; const int stop_round = 1 << 7; const int max_line = 1 << 13; for (int i = 0; i < max_line; ++i) { if (!tmp_file.eof()) { GetLine(&tmp_file, &cur_line, reader.get(), &buffer, buffer_size); cur_line = Common::Trim(cur_line); auto colon_pos = cur_line.find_last_of(":"); auto space_pos = cur_line.find_last_of(" \f\t\v"); auto sub_str = cur_line.substr(space_pos + 1, space_pos - colon_pos - 1); int cur_idx = 0; Common::Atoi(sub_str.c_str(), &cur_idx); if (cur_idx > max_col_idx) { max_col_idx = cur_idx; max_line_idx = i; } if (i - max_line_idx >= stop_round) { break; } } else { break; } } CHECK_GT(max_col_idx, 0); return max_col_idx; } DataType GetDataType(const char* filename, bool header, const std::vector& lines, int* num_col) { DataType type = DataType::INVALID; if (lines.empty()) { return type; } int comma_cnt = 0; int tab_cnt = 0; int colon_cnt = 0; GetStatistic(lines[0].c_str(), &comma_cnt, &tab_cnt, &colon_cnt); size_t num_lines = lines.size(); if (num_lines == 1) { if (colon_cnt > 0) { type = DataType::LIBSVM; } else if (tab_cnt > 0) { type = DataType::TSV; } else if (comma_cnt > 0) { type = DataType::CSV; } } else { int comma_cnt2 = 0; int tab_cnt2 = 0; int colon_cnt2 = 0; GetStatistic(lines[1].c_str(), &comma_cnt2, &tab_cnt2, &colon_cnt2); if (colon_cnt > 0 || colon_cnt2 > 0) { type = DataType::LIBSVM; } else if (tab_cnt == tab_cnt2 && tab_cnt > 0) { type = DataType::TSV; } else if (comma_cnt == comma_cnt2 && comma_cnt > 0) { type = DataType::CSV; } if (type == DataType::TSV || type == DataType::CSV) { // valid the type for (size_t i = 2; i < num_lines; ++i) { GetStatistic(lines[i].c_str(), &comma_cnt2, &tab_cnt2, &colon_cnt2); if (type == DataType::TSV && tab_cnt2 != tab_cnt) { type = DataType::INVALID; break; } else if (type == DataType::CSV && comma_cnt != comma_cnt2) { type = DataType::INVALID; break; } } } } if (type == DataType::LIBSVM) { int max_col_idx = GetNumColFromLIBSVMFile(filename, header); *num_col = max_col_idx + 1; } else if (type == DataType::CSV) { *num_col = comma_cnt + 1; } else if (type == DataType::TSV) { *num_col = tab_cnt + 1; } return type; } // parser factory implementation. ParserFactory& ParserFactory::getInstance() { static ParserFactory factory; return factory; } void ParserFactory::Register(std::string class_name, std::function m_objc) { if (m_objc) { object_map_.insert( std::map>::value_type(class_name, m_objc)); } } Parser* ParserFactory::getObject(std::string class_name, std::string config_str) { std::map>::const_iterator iter = object_map_.find(class_name); if (iter != object_map_.end()) { return iter->second(config_str); } else { Log::Fatal("Cannot find parser class '%s', please register first or check config format.", class_name.c_str()); return nullptr; } } Parser* Parser::CreateParser(const char* filename, bool header, int num_features, int label_idx, bool precise_float_parser) { const int n_read_line = 32; auto lines = ReadKLineFromFile(filename, header, n_read_line); int num_col = 0; DataType type = GetDataType(filename, header, lines, &num_col); if (type == DataType::INVALID) { Log::Fatal("Unknown format of training data. Only CSV, TSV, and LibSVM (zero-based) formatted text files are supported."); } std::unique_ptr ret; int output_label_index = -1; AtofFunc atof = precise_float_parser ? Common::AtofPrecise : Common::Atof; if (type == DataType::LIBSVM) { output_label_index = GetLabelIdxForLibsvm(lines[0], num_features, label_idx); ret.reset(new LibSVMParser(output_label_index, num_col, atof)); } else if (type == DataType::TSV) { output_label_index = GetLabelIdxForTSV(lines[0], num_features, label_idx); ret.reset(new TSVParser(output_label_index, num_col, atof)); } else if (type == DataType::CSV) { output_label_index = GetLabelIdxForCSV(lines[0], num_features, label_idx); ret.reset(new CSVParser(output_label_index, num_col, atof)); } if (output_label_index < 0 && label_idx >= 0) { Log::Info("Data file %s doesn't contain a label column.", filename); } return ret.release(); } Parser* Parser::CreateParser(const char* filename, bool header, int num_features, int label_idx, bool precise_float_parser, std::string parser_config_str) { // customized parser add-on. if (!parser_config_str.empty()) { std::unique_ptr ret; std::string class_name = Common::GetFromParserConfig(parser_config_str, "className"); Log::Info("Custom parser class name: %s", class_name.c_str()); Parser* p = ParserFactory::getInstance().getObject(class_name, parser_config_str); ret.reset(p); return ret.release(); } return CreateParser(filename, header, num_features, label_idx, precise_float_parser); } std::string Parser::GenerateParserConfigStr(const char* filename, const char* parser_config_filename, bool header, int label_idx) { TextReader parser_config_reader(parser_config_filename, false); parser_config_reader.ReadAllLines(); std::string parser_config_str = parser_config_reader.JoinedLines(); if (!parser_config_str.empty()) { // save header to parser config in case needed. if (header && Common::GetFromParserConfig(parser_config_str, "header").empty()) { TextReader text_reader(filename, header); parser_config_str = Common::SaveToParserConfig(parser_config_str, "header", text_reader.first_line()); } // save label id to parser config in case needed. if (Common::GetFromParserConfig(parser_config_str, "labelId").empty()) { parser_config_str = Common::SaveToParserConfig(parser_config_str, "labelId", std::to_string(label_idx)); } } return parser_config_str; } } // namespace LightGBM ================================================ FILE: src/io/parser.hpp ================================================ /*! * Copyright (c) 2016-2026 Microsoft Corporation. All rights reserved. * Copyright (c) 2016-2026 The LightGBM developers. All rights reserved. * Licensed under the MIT License. See LICENSE file in the project root for license information. */ #ifndef LIGHTGBM_SRC_IO_PARSER_HPP_ #define LIGHTGBM_SRC_IO_PARSER_HPP_ #include #include #include #include #include #include namespace LightGBM { class CSVParser: public Parser { public: explicit CSVParser(int label_idx, int total_columns, AtofFunc atof) :label_idx_(label_idx), total_columns_(total_columns), atof_(atof) { } inline void ParseOneLine(const char* str, std::vector>* out_features, double* out_label) const override { int idx = 0; double val = 0.0f; int offset = 0; *out_label = 0.0f; while (*str != '\0') { str = atof_(str, &val); if (idx == label_idx_) { *out_label = val; offset = -1; } else if (std::fabs(val) > kZeroThreshold || std::isnan(val)) { out_features->emplace_back(idx + offset, val); } ++idx; if (*str == ',') { ++str; } else if (*str != '\0') { Log::Fatal("Input format error when parsing as CSV"); } } } inline int NumFeatures() const override { return total_columns_ - (label_idx_ >= 0); } private: int label_idx_ = 0; int total_columns_ = -1; AtofFunc atof_; }; class TSVParser: public Parser { public: explicit TSVParser(int label_idx, int total_columns, AtofFunc atof) :label_idx_(label_idx), total_columns_(total_columns), atof_(atof) { } inline void ParseOneLine(const char* str, std::vector>* out_features, double* out_label) const override { int idx = 0; double val = 0.0f; int offset = 0; while (*str != '\0') { str = atof_(str, &val); if (idx == label_idx_) { *out_label = val; offset = -1; } else if (std::fabs(val) > kZeroThreshold || std::isnan(val)) { out_features->emplace_back(idx + offset, val); } ++idx; if (*str == '\t') { ++str; } else if (*str != '\0') { Log::Fatal("Input format error when parsing as TSV"); } } } inline int NumFeatures() const override { return total_columns_ - (label_idx_ >= 0); } private: int label_idx_ = 0; int total_columns_ = -1; AtofFunc atof_; }; class LibSVMParser: public Parser { public: explicit LibSVMParser(int label_idx, int total_columns, AtofFunc atof) :label_idx_(label_idx), total_columns_(total_columns), atof_(atof) { if (label_idx > 0) { Log::Fatal("Label should be the first column in a LibSVM file"); } } inline void ParseOneLine(const char* str, std::vector>* out_features, double* out_label) const override { int idx = 0; double val = 0.0f; if (label_idx_ == 0) { str = atof_(str, &val); *out_label = val; str = Common::SkipSpaceAndTab(str); } while (*str != '\0') { str = Common::Atoi(str, &idx); str = Common::SkipSpaceAndTab(str); if (*str == ':') { ++str; str = Common::Atof(str, &val); out_features->emplace_back(idx, val); } else { Log::Fatal("Input format error when parsing as LibSVM"); } str = Common::SkipSpaceAndTab(str); } } inline int NumFeatures() const override { return total_columns_; } private: int label_idx_ = 0; int total_columns_ = -1; AtofFunc atof_; }; } // namespace LightGBM #endif // LIGHTGBM_SRC_IO_PARSER_HPP_ ================================================ FILE: src/io/sparse_bin.hpp ================================================ /*! * Copyright (c) 2016-2026 Microsoft Corporation. All rights reserved. * Copyright (c) 2016-2026 The LightGBM developers. All rights reserved. * Licensed under the MIT License. See LICENSE file in the project root for * license information. */ #ifndef LIGHTGBM_SRC_IO_SPARSE_BIN_HPP_ #define LIGHTGBM_SRC_IO_SPARSE_BIN_HPP_ #include #include #include #include #include #include #include #include #include namespace LightGBM { template class SparseBin; const size_t kNumFastIndex = 64; template class SparseBinIterator : public BinIterator { public: SparseBinIterator(const SparseBin* bin_data, uint32_t min_bin, uint32_t max_bin, uint32_t most_freq_bin) : bin_data_(bin_data), min_bin_(static_cast(min_bin)), max_bin_(static_cast(max_bin)), most_freq_bin_(static_cast(most_freq_bin)) { if (most_freq_bin_ == 0) { offset_ = 1; } else { offset_ = 0; } Reset(0); } SparseBinIterator(const SparseBin* bin_data, data_size_t start_idx) : bin_data_(bin_data) { Reset(start_idx); } inline uint32_t RawGet(data_size_t idx) override; inline VAL_T InnerRawGet(data_size_t idx); inline uint32_t Get(data_size_t idx) override { VAL_T ret = InnerRawGet(idx); if (ret >= min_bin_ && ret <= max_bin_) { return ret - min_bin_ + offset_; } else { return most_freq_bin_; } } inline void Reset(data_size_t idx) override; private: const SparseBin* bin_data_; data_size_t cur_pos_; data_size_t i_delta_; VAL_T min_bin_; VAL_T max_bin_; VAL_T most_freq_bin_; uint8_t offset_; }; template class SparseBin : public Bin { public: friend class SparseBinIterator; explicit SparseBin(data_size_t num_data) : num_data_(num_data) { int num_threads = OMP_NUM_THREADS(); push_buffers_.resize(num_threads); } ~SparseBin() {} void InitStreaming(uint32_t num_thread, int32_t omp_max_threads) override { // Each external thread needs its own set of OpenMP push buffers, // so allocate num_thread times the maximum number of OMP threads per external thread push_buffers_.resize(omp_max_threads * num_thread); }; void ReSize(data_size_t num_data) override { num_data_ = num_data; } void Push(int tid, data_size_t idx, uint32_t value) override { auto cur_bin = static_cast(value); if (cur_bin != 0) { push_buffers_[tid].emplace_back(idx, cur_bin); } } BinIterator* GetIterator(uint32_t min_bin, uint32_t max_bin, uint32_t most_freq_bin) const override; #define ACC_GH(hist, i, g, h) \ const auto ti = static_cast(i) << 1; \ hist[ti] += g; \ hist[ti + 1] += h; void ConstructHistogram(const data_size_t* data_indices, data_size_t start, data_size_t end, const score_t* ordered_gradients, const score_t* ordered_hessians, hist_t* out) const override { data_size_t i_delta, cur_pos; InitIndex(data_indices[start], &i_delta, &cur_pos); data_size_t i = start; for (;;) { if (cur_pos < data_indices[i]) { cur_pos += deltas_[++i_delta]; if (i_delta >= num_vals_) { break; } } else if (cur_pos > data_indices[i]) { if (++i >= end) { break; } } else { const VAL_T bin = vals_[i_delta]; ACC_GH(out, bin, ordered_gradients[i], ordered_hessians[i]); if (++i >= end) { break; } cur_pos += deltas_[++i_delta]; if (i_delta >= num_vals_) { break; } } } } void ConstructHistogram(data_size_t start, data_size_t end, const score_t* ordered_gradients, const score_t* ordered_hessians, hist_t* out) const override { data_size_t i_delta, cur_pos; InitIndex(start, &i_delta, &cur_pos); while (cur_pos < start && i_delta < num_vals_) { cur_pos += deltas_[++i_delta]; } while (cur_pos < end && i_delta < num_vals_) { const VAL_T bin = vals_[i_delta]; ACC_GH(out, bin, ordered_gradients[cur_pos], ordered_hessians[cur_pos]); cur_pos += deltas_[++i_delta]; } } void ConstructHistogram(const data_size_t* data_indices, data_size_t start, data_size_t end, const score_t* ordered_gradients, hist_t* out) const override { data_size_t i_delta, cur_pos; InitIndex(data_indices[start], &i_delta, &cur_pos); data_size_t i = start; hist_t* grad = out; hist_cnt_t* cnt = reinterpret_cast(out + 1); for (;;) { if (cur_pos < data_indices[i]) { cur_pos += deltas_[++i_delta]; if (i_delta >= num_vals_) { break; } } else if (cur_pos > data_indices[i]) { if (++i >= end) { break; } } else { const uint32_t ti = static_cast(vals_[i_delta]) << 1; grad[ti] += ordered_gradients[i]; ++cnt[ti]; if (++i >= end) { break; } cur_pos += deltas_[++i_delta]; if (i_delta >= num_vals_) { break; } } } } void ConstructHistogram(data_size_t start, data_size_t end, const score_t* ordered_gradients, hist_t* out) const override { data_size_t i_delta, cur_pos; InitIndex(start, &i_delta, &cur_pos); hist_t* grad = out; hist_cnt_t* cnt = reinterpret_cast(out + 1); while (cur_pos < start && i_delta < num_vals_) { cur_pos += deltas_[++i_delta]; } while (cur_pos < end && i_delta < num_vals_) { const uint32_t ti = static_cast(vals_[i_delta]) << 1; grad[ti] += ordered_gradients[cur_pos]; ++cnt[ti]; cur_pos += deltas_[++i_delta]; } } #undef ACC_GH template void ConstructIntHistogramInner(data_size_t start, data_size_t end, const score_t* ordered_gradients_and_hessians, hist_t* out) const { data_size_t i_delta, cur_pos; InitIndex(start, &i_delta, &cur_pos); if (USE_HESSIAN) { PACKED_HIST_T* out_ptr = reinterpret_cast(out); const int16_t* gradients_and_hessians_ptr = reinterpret_cast(ordered_gradients_and_hessians); while (cur_pos < start && i_delta < num_vals_) { cur_pos += deltas_[++i_delta]; } while (cur_pos < end && i_delta < num_vals_) { const VAL_T bin = vals_[i_delta]; const int16_t gradient_16 = gradients_and_hessians_ptr[cur_pos]; const PACKED_HIST_T gradient_64 = (static_cast(static_cast(gradient_16 >> 8)) << HIST_BITS) | (gradient_16 & 0xff); out_ptr[bin] += gradient_64; cur_pos += deltas_[++i_delta]; } } else { GRAD_HIST_T* grad = reinterpret_cast(out); HESS_HIST_T* cnt = reinterpret_cast(out) + 1; const int8_t* gradients_and_hessians_ptr = reinterpret_cast(ordered_gradients_and_hessians); while (cur_pos < start && i_delta < num_vals_) { cur_pos += deltas_[++i_delta]; } while (cur_pos < end && i_delta < num_vals_) { const uint32_t ti = static_cast(vals_[i_delta]) << 1; grad[ti] += gradients_and_hessians_ptr[cur_pos]; ++cnt[ti]; cur_pos += deltas_[++i_delta]; } } } template void ConstructIntHistogramInner(const data_size_t* data_indices, data_size_t start, data_size_t end, const score_t* ordered_gradients_and_hessians, hist_t* out) const { data_size_t i_delta, cur_pos; InitIndex(data_indices[start], &i_delta, &cur_pos); data_size_t i = start; if (USE_HESSIAN) { PACKED_HIST_T* out_ptr = reinterpret_cast(out); const int16_t* gradients_and_hessians_ptr = reinterpret_cast(ordered_gradients_and_hessians); for (;;) { if (cur_pos < data_indices[i]) { cur_pos += deltas_[++i_delta]; if (i_delta >= num_vals_) { break; } } else if (cur_pos > data_indices[i]) { if (++i >= end) { break; } } else { const VAL_T bin = vals_[i_delta]; const int16_t gradient_16 = gradients_and_hessians_ptr[i]; const PACKED_HIST_T gradient_packed = (HIST_BITS == 8) ? gradient_16 : (static_cast(static_cast(gradient_16 >> 8)) << HIST_BITS) | (gradient_16 & 0xff); out_ptr[bin] += gradient_packed; if (++i >= end) { break; } cur_pos += deltas_[++i_delta]; if (i_delta >= num_vals_) { break; } } } } else { GRAD_HIST_T* grad = reinterpret_cast(out); HESS_HIST_T* cnt = reinterpret_cast(out) + 1; const int8_t* gradients_and_hessians_ptr = reinterpret_cast(ordered_gradients_and_hessians); for (;;) { if (cur_pos < data_indices[i]) { cur_pos += deltas_[++i_delta]; if (i_delta >= num_vals_) { break; } } else if (cur_pos > data_indices[i]) { if (++i >= end) { break; } } else { const uint32_t ti = static_cast(vals_[i_delta]) << 1; grad[ti] += gradients_and_hessians_ptr[i << 1]; ++cnt[ti]; if (++i >= end) { break; } cur_pos += deltas_[++i_delta]; if (i_delta >= num_vals_) { break; } } } } } void ConstructHistogramInt32(const data_size_t* data_indices, data_size_t start, data_size_t end, const score_t* ordered_gradients, const score_t* /*ordered_hessians*/, hist_t* out) const override { ConstructIntHistogramInner(data_indices, start, end, ordered_gradients, out); } void ConstructHistogramInt32(data_size_t start, data_size_t end, const score_t* ordered_gradients, const score_t* /*ordered_hessians*/, hist_t* out) const override { ConstructIntHistogramInner(start, end, ordered_gradients, out); } void ConstructHistogramInt32(const data_size_t* data_indices, data_size_t start, data_size_t end, const score_t* ordered_gradients, hist_t* out) const override { ConstructIntHistogramInner(data_indices, start, end, ordered_gradients, out); } void ConstructHistogramInt32(data_size_t start, data_size_t end, const score_t* ordered_gradients, hist_t* out) const override { ConstructIntHistogramInner(start, end, ordered_gradients, out); } void ConstructHistogramInt16(const data_size_t* data_indices, data_size_t start, data_size_t end, const score_t* ordered_gradients, const score_t* /*ordered_hessians*/, hist_t* out) const override { ConstructIntHistogramInner(data_indices, start, end, ordered_gradients, out); } void ConstructHistogramInt16(data_size_t start, data_size_t end, const score_t* ordered_gradients, const score_t* /*ordered_hessians*/, hist_t* out) const override { ConstructIntHistogramInner(start, end, ordered_gradients, out); } void ConstructHistogramInt16(const data_size_t* data_indices, data_size_t start, data_size_t end, const score_t* ordered_gradients, hist_t* out) const override { ConstructIntHistogramInner(data_indices, start, end, ordered_gradients, out); } void ConstructHistogramInt16(data_size_t start, data_size_t end, const score_t* ordered_gradients, hist_t* out) const override { ConstructIntHistogramInner(start, end, ordered_gradients, out); } void ConstructHistogramInt8(const data_size_t* data_indices, data_size_t start, data_size_t end, const score_t* ordered_gradients, const score_t* /*ordered_hessians*/, hist_t* out) const override { ConstructIntHistogramInner(data_indices, start, end, ordered_gradients, out); } void ConstructHistogramInt8(data_size_t start, data_size_t end, const score_t* ordered_gradients, const score_t* /*ordered_hessians*/, hist_t* out) const override { ConstructIntHistogramInner(start, end, ordered_gradients, out); } void ConstructHistogramInt8(const data_size_t* data_indices, data_size_t start, data_size_t end, const score_t* ordered_gradients, hist_t* out) const override { ConstructIntHistogramInner(data_indices, start, end, ordered_gradients, out); } void ConstructHistogramInt8(data_size_t start, data_size_t end, const score_t* ordered_gradients, hist_t* out) const override { ConstructIntHistogramInner(start, end, ordered_gradients, out); } inline void NextNonzeroFast(data_size_t* i_delta, data_size_t* cur_pos) const { *cur_pos += deltas_[++(*i_delta)]; if (*i_delta >= num_vals_) { *cur_pos = num_data_; } } inline bool NextNonzero(data_size_t* i_delta, data_size_t* cur_pos) const { *cur_pos += deltas_[++(*i_delta)]; if (*i_delta < num_vals_) { return true; } else { *cur_pos = num_data_; return false; } } template data_size_t SplitInner(uint32_t min_bin, uint32_t max_bin, uint32_t default_bin, uint32_t most_freq_bin, bool default_left, uint32_t threshold, const data_size_t* data_indices, data_size_t cnt, data_size_t* lte_indices, data_size_t* gt_indices) const { auto th = static_cast(threshold + min_bin); auto t_zero_bin = static_cast(min_bin + default_bin); if (most_freq_bin == 0) { --th; --t_zero_bin; } const auto minb = static_cast(min_bin); const auto maxb = static_cast(max_bin); data_size_t lte_count = 0; data_size_t gt_count = 0; data_size_t* default_indices = gt_indices; data_size_t* default_count = >_count; data_size_t* missing_default_indices = gt_indices; data_size_t* missing_default_count = >_count; if (most_freq_bin <= threshold) { default_indices = lte_indices; default_count = <e_count; } if (MISS_IS_ZERO || MISS_IS_NA) { if (default_left) { missing_default_indices = lte_indices; missing_default_count = <e_count; } } SparseBinIterator iterator(this, data_indices[0]); if (min_bin < max_bin) { for (data_size_t i = 0; i < cnt; ++i) { const data_size_t idx = data_indices[i]; const auto bin = iterator.InnerRawGet(idx); if ((MISS_IS_ZERO && !MFB_IS_ZERO && bin == t_zero_bin) || (MISS_IS_NA && !MFB_IS_NA && bin == maxb)) { missing_default_indices[(*missing_default_count)++] = idx; } else if ((USE_MIN_BIN && (bin < minb || bin > maxb)) || (!USE_MIN_BIN && bin == 0)) { if ((MISS_IS_NA && MFB_IS_NA) || (MISS_IS_ZERO && MFB_IS_ZERO)) { missing_default_indices[(*missing_default_count)++] = idx; } else { default_indices[(*default_count)++] = idx; } } else if (bin > th) { gt_indices[gt_count++] = idx; } else { lte_indices[lte_count++] = idx; } } } else { data_size_t* max_bin_indices = gt_indices; data_size_t* max_bin_count = >_count; if (maxb <= th) { max_bin_indices = lte_indices; max_bin_count = <e_count; } for (data_size_t i = 0; i < cnt; ++i) { const data_size_t idx = data_indices[i]; const auto bin = iterator.InnerRawGet(idx); if (MISS_IS_ZERO && !MFB_IS_ZERO && bin == t_zero_bin) { missing_default_indices[(*missing_default_count)++] = idx; } else if (bin != maxb) { if ((MISS_IS_NA && MFB_IS_NA) || (MISS_IS_ZERO && MFB_IS_ZERO)) { missing_default_indices[(*missing_default_count)++] = idx; } else { default_indices[(*default_count)++] = idx; } } else { if (MISS_IS_NA && !MFB_IS_NA) { missing_default_indices[(*missing_default_count)++] = idx; } else { max_bin_indices[(*max_bin_count)++] = idx; } } } } return lte_count; } data_size_t Split(uint32_t min_bin, uint32_t max_bin, uint32_t default_bin, uint32_t most_freq_bin, MissingType missing_type, bool default_left, uint32_t threshold, const data_size_t* data_indices, data_size_t cnt, data_size_t* lte_indices, data_size_t* gt_indices) const override { #define ARGUMENTS \ min_bin, max_bin, default_bin, most_freq_bin, default_left, threshold, \ data_indices, cnt, lte_indices, gt_indices if (missing_type == MissingType::None) { return SplitInner(ARGUMENTS); } else if (missing_type == MissingType::Zero) { if (default_bin == most_freq_bin) { return SplitInner(ARGUMENTS); } else { return SplitInner(ARGUMENTS); } } else { if (max_bin == most_freq_bin + min_bin && most_freq_bin > 0) { return SplitInner(ARGUMENTS); } else { return SplitInner(ARGUMENTS); } } #undef ARGUMENTS } data_size_t Split(uint32_t max_bin, uint32_t default_bin, uint32_t most_freq_bin, MissingType missing_type, bool default_left, uint32_t threshold, const data_size_t* data_indices, data_size_t cnt, data_size_t* lte_indices, data_size_t* gt_indices) const override { #define ARGUMENTS \ 1, max_bin, default_bin, most_freq_bin, default_left, threshold, \ data_indices, cnt, lte_indices, gt_indices if (missing_type == MissingType::None) { return SplitInner(ARGUMENTS); } else if (missing_type == MissingType::Zero) { if (default_bin == most_freq_bin) { return SplitInner(ARGUMENTS); } else { return SplitInner(ARGUMENTS); } } else { if (max_bin == most_freq_bin + 1 && most_freq_bin > 0) { return SplitInner(ARGUMENTS); } else { return SplitInner(ARGUMENTS); } } #undef ARGUMENTS } template data_size_t SplitCategoricalInner(uint32_t min_bin, uint32_t max_bin, uint32_t most_freq_bin, const uint32_t* threshold, int num_threshold, const data_size_t* data_indices, data_size_t cnt, data_size_t* lte_indices, data_size_t* gt_indices) const { data_size_t lte_count = 0; data_size_t gt_count = 0; data_size_t* default_indices = gt_indices; data_size_t* default_count = >_count; SparseBinIterator iterator(this, data_indices[0]); int8_t offset = most_freq_bin == 0 ? 1 : 0; if (most_freq_bin > 0 && Common::FindInBitset(threshold, num_threshold, most_freq_bin)) { default_indices = lte_indices; default_count = <e_count; } for (data_size_t i = 0; i < cnt; ++i) { const data_size_t idx = data_indices[i]; const uint32_t bin = iterator.RawGet(idx); if (USE_MIN_BIN && (bin < min_bin || bin > max_bin)) { default_indices[(*default_count)++] = idx; } else if (!USE_MIN_BIN && bin == 0) { default_indices[(*default_count)++] = idx; } else if (Common::FindInBitset(threshold, num_threshold, bin - min_bin + offset)) { lte_indices[lte_count++] = idx; } else { gt_indices[gt_count++] = idx; } } return lte_count; } data_size_t SplitCategorical(uint32_t min_bin, uint32_t max_bin, uint32_t most_freq_bin, const uint32_t* threshold, int num_threshold, const data_size_t* data_indices, data_size_t cnt, data_size_t* lte_indices, data_size_t* gt_indices) const override { return SplitCategoricalInner(min_bin, max_bin, most_freq_bin, threshold, num_threshold, data_indices, cnt, lte_indices, gt_indices); } data_size_t SplitCategorical(uint32_t max_bin, uint32_t most_freq_bin, const uint32_t* threshold, int num_threshold, const data_size_t* data_indices, data_size_t cnt, data_size_t* lte_indices, data_size_t* gt_indices) const override { return SplitCategoricalInner(1, max_bin, most_freq_bin, threshold, num_threshold, data_indices, cnt, lte_indices, gt_indices); } data_size_t num_data() const override { return num_data_; } void* get_data() override { return nullptr; } void FinishLoad() override { // get total non zero size size_t pair_cnt = 0; for (size_t i = 0; i < push_buffers_.size(); ++i) { pair_cnt += push_buffers_[i].size(); } std::vector>& idx_val_pairs = push_buffers_[0]; idx_val_pairs.reserve(pair_cnt); for (size_t i = 1; i < push_buffers_.size(); ++i) { idx_val_pairs.insert(idx_val_pairs.end(), push_buffers_[i].begin(), push_buffers_[i].end()); push_buffers_[i].clear(); push_buffers_[i].shrink_to_fit(); } // sort by data index std::sort(idx_val_pairs.begin(), idx_val_pairs.end(), [](const std::pair& a, const std::pair& b) { return a.first < b.first; }); // load delta array LoadFromPair(idx_val_pairs); } void LoadFromPair( const std::vector>& idx_val_pairs) { deltas_.clear(); vals_.clear(); deltas_.reserve(idx_val_pairs.size()); vals_.reserve(idx_val_pairs.size()); // transform to delta array data_size_t last_idx = 0; for (size_t i = 0; i < idx_val_pairs.size(); ++i) { const data_size_t cur_idx = idx_val_pairs[i].first; const VAL_T bin = idx_val_pairs[i].second; data_size_t cur_delta = cur_idx - last_idx; // disallow the multi-val in one row if (i > 0 && cur_delta == 0) { continue; } while (cur_delta >= 256) { deltas_.push_back(255); vals_.push_back(0); cur_delta -= 255; } deltas_.push_back(static_cast(cur_delta)); vals_.push_back(bin); last_idx = cur_idx; } // avoid out of range deltas_.push_back(0); num_vals_ = static_cast(vals_.size()); // reduce memory cost deltas_.shrink_to_fit(); vals_.shrink_to_fit(); // generate fast index GetFastIndex(); } void GetFastIndex() { fast_index_.clear(); // get shift cnt data_size_t mod_size = (num_data_ + kNumFastIndex - 1) / kNumFastIndex; data_size_t pow2_mod_size = 1; fast_index_shift_ = 0; while (pow2_mod_size < mod_size) { pow2_mod_size <<= 1; ++fast_index_shift_; } // build fast index data_size_t i_delta = -1; data_size_t cur_pos = 0; data_size_t next_threshold = 0; while (NextNonzero(&i_delta, &cur_pos)) { while (next_threshold <= cur_pos) { fast_index_.emplace_back(i_delta, cur_pos); next_threshold += pow2_mod_size; } } // avoid out of range while (next_threshold < num_data_) { fast_index_.emplace_back(num_vals_ - 1, cur_pos); next_threshold += pow2_mod_size; } fast_index_.shrink_to_fit(); } void SaveBinaryToFile(BinaryWriter* writer) const override { writer->AlignedWrite(&num_vals_, sizeof(num_vals_)); writer->AlignedWrite(deltas_.data(), sizeof(uint8_t) * (num_vals_ + 1)); writer->AlignedWrite(vals_.data(), sizeof(VAL_T) * num_vals_); } size_t SizesInByte() const override { return VirtualFileWriter::AlignedSize(sizeof(num_vals_)) + VirtualFileWriter::AlignedSize(sizeof(uint8_t) * (num_vals_ + 1)) + VirtualFileWriter::AlignedSize(sizeof(VAL_T) * num_vals_); } void LoadFromMemory( const void* memory, const std::vector& local_used_indices) override { const char* mem_ptr = reinterpret_cast(memory); data_size_t tmp_num_vals = *(reinterpret_cast(mem_ptr)); mem_ptr += VirtualFileWriter::AlignedSize(sizeof(tmp_num_vals)); const uint8_t* tmp_delta = reinterpret_cast(mem_ptr); mem_ptr += VirtualFileWriter::AlignedSize(sizeof(uint8_t) * (tmp_num_vals + 1)); const VAL_T* tmp_vals = reinterpret_cast(mem_ptr); deltas_.clear(); vals_.clear(); num_vals_ = tmp_num_vals; for (data_size_t i = 0; i < num_vals_; ++i) { deltas_.push_back(tmp_delta[i]); vals_.push_back(tmp_vals[i]); } deltas_.push_back(0); // reduce memory cost deltas_.shrink_to_fit(); vals_.shrink_to_fit(); if (local_used_indices.empty()) { // generate fast index GetFastIndex(); } else { std::vector> tmp_pair; data_size_t cur_pos = 0; data_size_t j = -1; for (data_size_t i = 0; i < static_cast(local_used_indices.size()); ++i) { const data_size_t idx = local_used_indices[i]; while (cur_pos < idx && j < num_vals_) { NextNonzero(&j, &cur_pos); } if (cur_pos == idx && j < num_vals_ && vals_[j] > 0) { // new row index is i tmp_pair.emplace_back(i, vals_[j]); } } LoadFromPair(tmp_pair); } } void CopySubrow(const Bin* full_bin, const data_size_t* used_indices, data_size_t num_used_indices) override { auto other_bin = dynamic_cast*>(full_bin); deltas_.clear(); vals_.clear(); data_size_t start = 0; if (num_used_indices > 0) { start = used_indices[0]; } SparseBinIterator iterator(other_bin, start); // transform to delta array data_size_t last_idx = 0; for (data_size_t i = 0; i < num_used_indices; ++i) { auto bin = iterator.InnerRawGet(used_indices[i]); if (bin > 0) { data_size_t cur_delta = i - last_idx; while (cur_delta >= 256) { deltas_.push_back(255); vals_.push_back(0); cur_delta -= 255; } deltas_.push_back(static_cast(cur_delta)); vals_.push_back(bin); last_idx = i; } } // avoid out of range deltas_.push_back(0); num_vals_ = static_cast(vals_.size()); // reduce memory cost deltas_.shrink_to_fit(); vals_.shrink_to_fit(); // generate fast index GetFastIndex(); } SparseBin* Clone() override; SparseBin(const SparseBin& other) : num_data_(other.num_data_), deltas_(other.deltas_), vals_(other.vals_), num_vals_(other.num_vals_), push_buffers_(other.push_buffers_), fast_index_(other.fast_index_), fast_index_shift_(other.fast_index_shift_) {} void InitIndex(data_size_t start_idx, data_size_t* i_delta, data_size_t* cur_pos) const { auto idx = start_idx >> fast_index_shift_; if (static_cast(idx) < fast_index_.size()) { const auto fast_pair = fast_index_[start_idx >> fast_index_shift_]; *i_delta = fast_pair.first; *cur_pos = fast_pair.second; } else { *i_delta = -1; *cur_pos = 0; } } const void* GetColWiseData(uint8_t* bit_type, bool* is_sparse, std::vector* bin_iterator, const int num_threads) const override; const void* GetColWiseData(uint8_t* bit_type, bool* is_sparse, BinIterator** bin_iterator) const override; private: data_size_t num_data_; std::vector> deltas_; std::vector> vals_; data_size_t num_vals_; std::vector>> push_buffers_; std::vector> fast_index_; data_size_t fast_index_shift_; }; template SparseBin* SparseBin::Clone() { return new SparseBin(*this); } template inline uint32_t SparseBinIterator::RawGet(data_size_t idx) { return InnerRawGet(idx); } template inline VAL_T SparseBinIterator::InnerRawGet(data_size_t idx) { while (cur_pos_ < idx) { bin_data_->NextNonzeroFast(&i_delta_, &cur_pos_); } if (cur_pos_ == idx) { return bin_data_->vals_[i_delta_]; } else { return 0; } } template inline void SparseBinIterator::Reset(data_size_t start_idx) { bin_data_->InitIndex(start_idx, &i_delta_, &cur_pos_); } template BinIterator* SparseBin::GetIterator(uint32_t min_bin, uint32_t max_bin, uint32_t most_freq_bin) const { return new SparseBinIterator(this, min_bin, max_bin, most_freq_bin); } } // namespace LightGBM #endif // LIGHTGBM_SRC_IO_SPARSE_BIN_HPP_ ================================================ FILE: src/io/train_share_states.cpp ================================================ /*! * Copyright (c) 2016-2026 Microsoft Corporation. All rights reserved. * Copyright (c) 2016-2026 The LightGBM developers. All rights reserved. * Licensed under the MIT License. See LICENSE file in the project root for * license information. */ #include #include #include #include namespace LightGBM { MultiValBinWrapper::MultiValBinWrapper(MultiValBin* bin, data_size_t num_data, const std::vector& feature_groups_contained, const int num_grad_quant_bins): feature_groups_contained_(feature_groups_contained) { num_threads_ = OMP_NUM_THREADS(); num_data_ = num_data; multi_val_bin_.reset(bin); if (bin == nullptr) { return; } num_bin_ = bin->num_bin(); num_bin_aligned_ = (num_bin_ + kAlignedSize - 1) / kAlignedSize * kAlignedSize; num_grad_quant_bins_ = num_grad_quant_bins; } void MultiValBinWrapper::InitTrain(const std::vector& group_feature_start, const std::vector>& feature_groups, const std::vector& is_feature_used, const data_size_t* bagging_use_indices, data_size_t bagging_indices_cnt) { is_use_subcol_ = false; if (multi_val_bin_ == nullptr) { return; } CopyMultiValBinSubset(group_feature_start, feature_groups, is_feature_used, bagging_use_indices, bagging_indices_cnt); const auto cur_multi_val_bin = (is_use_subcol_ || is_use_subrow_) ? multi_val_bin_subset_.get() : multi_val_bin_.get(); if (cur_multi_val_bin != nullptr) { num_bin_ = cur_multi_val_bin->num_bin(); num_bin_aligned_ = (num_bin_ + kAlignedSize - 1) / kAlignedSize * kAlignedSize; auto num_element_per_row = cur_multi_val_bin->num_element_per_row(); min_block_size_ = std::min(static_cast(0.3f * num_bin_ / (num_element_per_row + kZeroThreshold)) + 1, 1024); min_block_size_ = std::max(min_block_size_, 32); } } template void MultiValBinWrapper::HistMove(const std::vector>& hist_buf) { if (!is_use_subcol_ && INNER_HIST_BITS != 8) { return; } if (USE_QUANT_GRAD) { if (HIST_BITS == 32) { const int64_t* src = reinterpret_cast(hist_buf.data()) + hist_buf.size() / 2 - static_cast(num_bin_aligned_); #pragma omp parallel for schedule(static) num_threads(num_threads_) for (int i = 0; i < static_cast(hist_move_src_.size()); ++i) { std::copy_n(src + hist_move_src_[i] / 2, hist_move_size_[i] / 2, reinterpret_cast(origin_hist_data_) + hist_move_dest_[i] / 2); } } else if (HIST_BITS == 16) { if (is_use_subcol_) { const int32_t* src = reinterpret_cast(hist_buf.data()) + hist_buf.size() / 2 - static_cast(num_bin_aligned_); #pragma omp parallel for schedule(static) num_threads(num_threads_) for (int i = 0; i < static_cast(hist_move_src_.size()); ++i) { std::copy_n(src + hist_move_src_[i] / 2, hist_move_size_[i] / 2, reinterpret_cast(origin_hist_data_) + hist_move_dest_[i] / 2); } } else { CHECK_EQ(INNER_HIST_BITS, 8); const int32_t* src = reinterpret_cast(hist_buf.data()) + hist_buf.size() / 2; int32_t* orig_ptr = reinterpret_cast(origin_hist_data_); #pragma omp parallel for schedule(static) num_threads(num_threads_) for (int i = 0; i < num_bin_; ++i) { orig_ptr[i] = src[i]; } } } } else { const hist_t* src = hist_buf.data() + hist_buf.size() - 2 * static_cast(num_bin_aligned_); #pragma omp parallel for schedule(static) num_threads(num_threads_) for (int i = 0; i < static_cast(hist_move_src_.size()); ++i) { std::copy_n(src + hist_move_src_[i], hist_move_size_[i], origin_hist_data_ + hist_move_dest_[i]); } } } template void MultiValBinWrapper::HistMove(const std::vector>& hist_buf); template void MultiValBinWrapper::HistMove(const std::vector>& hist_buf); template void MultiValBinWrapper::HistMove(const std::vector>& hist_buf); template void MultiValBinWrapper::HistMove(const std::vector>& hist_buf); template void MultiValBinWrapper::HistMove(const std::vector>& hist_buf); template void MultiValBinWrapper::HistMove(const std::vector>& hist_buf); template void MultiValBinWrapper::HistMerge(std::vector>* hist_buf) { int n_bin_block = 1; int bin_block_size = num_bin_; Threading::BlockInfo(num_threads_, num_bin_, 512, &n_bin_block, &bin_block_size); if (USE_QUANT_GRAD) { if (HIST_BITS == 32) { int64_t* dst = reinterpret_cast(origin_hist_data_); if (is_use_subcol_) { dst = reinterpret_cast(hist_buf->data()) + hist_buf->size() / 2 - static_cast(num_bin_aligned_); } #pragma omp parallel for schedule(static, 1) num_threads(num_threads_) for (int t = 0; t < n_bin_block; ++t) { const int start = t * bin_block_size; const int end = std::min(start + bin_block_size, num_bin_); for (int tid = 1; tid < n_data_block_; ++tid) { auto src_ptr = reinterpret_cast(hist_buf->data()) + static_cast(num_bin_aligned_) * (tid - 1); for (int i = start; i < end; ++i) { dst[i] += src_ptr[i]; } } } } else if (HIST_BITS == 16 && INNER_HIST_BITS == 16) { int32_t* dst = reinterpret_cast(origin_hist_data_); if (is_use_subcol_) { dst = reinterpret_cast(hist_buf->data()) + hist_buf->size() / 2 - static_cast(num_bin_aligned_); } #pragma omp parallel for schedule(static, 1) num_threads(num_threads_) for (int t = 0; t < n_bin_block; ++t) { const int start = t * bin_block_size; const int end = std::min(start + bin_block_size, num_bin_); for (int tid = 1; tid < n_data_block_; ++tid) { auto src_ptr = reinterpret_cast(hist_buf->data()) + static_cast(num_bin_aligned_) * (tid - 1); for (int i = start; i < end; ++i) { dst[i] += src_ptr[i]; } } } } else if (HIST_BITS == 16 && INNER_HIST_BITS == 8) { int32_t* dst = reinterpret_cast(hist_buf->data()) + hist_buf->size() / 2; std::memset(reinterpret_cast(dst), 0, num_bin_ * kInt16HistBufferEntrySize); #pragma omp parallel for schedule(static, 1) num_threads(num_threads_) for (int t = 0; t < n_bin_block; ++t) { const int start = t * bin_block_size; const int end = std::min(start + bin_block_size, num_bin_); for (int tid = 0; tid < n_data_block_; ++tid) { auto src_ptr = reinterpret_cast(hist_buf->data()) + static_cast(num_bin_aligned_) * tid; for (int i = start; i < end; ++i) { const int16_t packed_hist = src_ptr[i]; const int32_t packed_hist_int32 = (static_cast(static_cast(packed_hist >> 8)) << 16) | static_cast(packed_hist & 0x00ff); dst[i] += packed_hist_int32; } } } } } else { hist_t* dst = origin_hist_data_; if (is_use_subcol_) { dst = hist_buf->data() + hist_buf->size() - 2 * static_cast(num_bin_aligned_); } #pragma omp parallel for schedule(static, 1) num_threads(num_threads_) for (int t = 0; t < n_bin_block; ++t) { const int start = t * bin_block_size; const int end = std::min(start + bin_block_size, num_bin_); for (int tid = 1; tid < n_data_block_; ++tid) { auto src_ptr = hist_buf->data() + static_cast(num_bin_aligned_) * 2 * (tid - 1); for (int i = start * 2; i < end * 2; ++i) { dst[i] += src_ptr[i]; } } } } } template void MultiValBinWrapper::HistMerge(std::vector>* hist_buf); template void MultiValBinWrapper::HistMerge(std::vector>* hist_buf); template void MultiValBinWrapper::HistMerge(std::vector>* hist_buf); template void MultiValBinWrapper::HistMerge(std::vector>* hist_buf); template void MultiValBinWrapper::HistMerge(std::vector>* hist_buf); template void MultiValBinWrapper::HistMerge(std::vector>* hist_buf); void MultiValBinWrapper::ResizeHistBuf(std::vector>* hist_buf, MultiValBin* sub_multi_val_bin, hist_t* origin_hist_data) { num_bin_ = sub_multi_val_bin->num_bin(); num_bin_aligned_ = (num_bin_ + kAlignedSize - 1) / kAlignedSize * kAlignedSize; origin_hist_data_ = origin_hist_data; size_t new_buf_size = static_cast(n_data_block_) * static_cast(num_bin_aligned_) * 2; if (hist_buf->size() < new_buf_size) { hist_buf->resize(new_buf_size); } } void MultiValBinWrapper::CopyMultiValBinSubset( const std::vector& group_feature_start, const std::vector>& feature_groups, const std::vector& is_feature_used, const data_size_t* bagging_use_indices, data_size_t bagging_indices_cnt) { double sum_used_dense_ratio = 0.0; double sum_dense_ratio = 0.0; int num_used = 0; int total = 0; std::vector used_feature_index; for (int i : feature_groups_contained_) { int f_start = group_feature_start[i]; if (feature_groups[i]->is_multi_val_) { for (int j = 0; j < feature_groups[i]->num_feature_; ++j) { const auto dense_rate = 1.0 - feature_groups[i]->bin_mappers_[j]->sparse_rate(); if (is_feature_used[f_start + j]) { ++num_used; used_feature_index.push_back(total); sum_used_dense_ratio += dense_rate; } sum_dense_ratio += dense_rate; ++total; } } else { bool is_group_used = false; double dense_rate = 0; for (int j = 0; j < feature_groups[i]->num_feature_; ++j) { if (is_feature_used[f_start + j]) { is_group_used = true; } dense_rate += 1.0 - feature_groups[i]->bin_mappers_[j]->sparse_rate(); } if (is_group_used) { ++num_used; used_feature_index.push_back(total); sum_used_dense_ratio += dense_rate; } sum_dense_ratio += dense_rate; ++total; } } const double k_subfeature_threshold = 0.6; if (sum_used_dense_ratio >= sum_dense_ratio * k_subfeature_threshold) { // only need to copy subset if (is_use_subrow_ && !is_subrow_copied_) { if (multi_val_bin_subset_ == nullptr) { multi_val_bin_subset_.reset(multi_val_bin_->CreateLike( bagging_indices_cnt, multi_val_bin_->num_bin(), total, multi_val_bin_->num_element_per_row(), multi_val_bin_->offsets())); } else { multi_val_bin_subset_->ReSize( bagging_indices_cnt, multi_val_bin_->num_bin(), total, multi_val_bin_->num_element_per_row(), multi_val_bin_->offsets()); } multi_val_bin_subset_->CopySubrow( multi_val_bin_.get(), bagging_use_indices, bagging_indices_cnt); // avoid to copy subset many times is_subrow_copied_ = true; } } else { is_use_subcol_ = true; std::vector upper_bound; std::vector lower_bound; std::vector delta; std::vector offsets; hist_move_src_.clear(); hist_move_dest_.clear(); hist_move_size_.clear(); const int offset = multi_val_bin_->IsSparse() ? 1 : 0; int num_total_bin = offset; int new_num_total_bin = offset; offsets.push_back(static_cast(new_num_total_bin)); for (int i : feature_groups_contained_) { int f_start = group_feature_start[i]; if (feature_groups[i]->is_multi_val_) { for (int j = 0; j < feature_groups[i]->num_feature_; ++j) { const auto& bin_mapper = feature_groups[i]->bin_mappers_[j]; if (i == 0 && j == 0 && bin_mapper->GetMostFreqBin() > 0) { num_total_bin = 1; } int cur_num_bin = bin_mapper->num_bin(); if (bin_mapper->GetMostFreqBin() == 0) { cur_num_bin -= offset; } num_total_bin += cur_num_bin; if (is_feature_used[f_start + j]) { new_num_total_bin += cur_num_bin; offsets.push_back(static_cast(new_num_total_bin)); lower_bound.push_back(num_total_bin - cur_num_bin); upper_bound.push_back(num_total_bin); hist_move_src_.push_back( (new_num_total_bin - cur_num_bin) * 2); hist_move_dest_.push_back((num_total_bin - cur_num_bin) * 2); hist_move_size_.push_back(cur_num_bin * 2); delta.push_back(num_total_bin - new_num_total_bin); } } } else { bool is_group_used = false; for (int j = 0; j < feature_groups[i]->num_feature_; ++j) { if (is_feature_used[f_start + j]) { is_group_used = true; break; } } int cur_num_bin = feature_groups[i]->bin_offsets_.back() - offset; num_total_bin += cur_num_bin; if (is_group_used) { new_num_total_bin += cur_num_bin; offsets.push_back(static_cast(new_num_total_bin)); lower_bound.push_back(num_total_bin - cur_num_bin); upper_bound.push_back(num_total_bin); hist_move_src_.push_back( (new_num_total_bin - cur_num_bin) * 2); hist_move_dest_.push_back((num_total_bin - cur_num_bin) * 2); hist_move_size_.push_back(cur_num_bin * 2); delta.push_back(num_total_bin - new_num_total_bin); } } } // avoid out of range lower_bound.push_back(num_total_bin); upper_bound.push_back(num_total_bin); data_size_t num_data = is_use_subrow_ ? bagging_indices_cnt : num_data_; if (multi_val_bin_subset_ == nullptr) { multi_val_bin_subset_.reset(multi_val_bin_->CreateLike( num_data, new_num_total_bin, num_used, sum_used_dense_ratio, offsets)); } else { multi_val_bin_subset_->ReSize(num_data, new_num_total_bin, num_used, sum_used_dense_ratio, offsets); } if (is_use_subrow_) { multi_val_bin_subset_->CopySubrowAndSubcol( multi_val_bin_.get(), bagging_use_indices, bagging_indices_cnt, used_feature_index, lower_bound, upper_bound, delta); // may need to recopy subset is_subrow_copied_ = false; } else { multi_val_bin_subset_->CopySubcol( multi_val_bin_.get(), used_feature_index, lower_bound, upper_bound, delta); } } } void TrainingShareStates::CalcBinOffsets(const std::vector>& feature_groups, std::vector* offsets, bool in_is_col_wise) { offsets->clear(); feature_hist_offsets_.clear(); if (in_is_col_wise) { uint32_t cur_num_bin = 0; uint32_t hist_cur_num_bin = 0; for (int group = 0; group < static_cast(feature_groups.size()); ++group) { const std::unique_ptr& feature_group = feature_groups[group]; if (feature_group->is_multi_val_) { if (feature_group->is_dense_multi_val_) { for (int i = 0; i < feature_group->num_feature_; ++i) { const std::unique_ptr& bin_mapper = feature_group->bin_mappers_[i]; if (group == 0 && i == 0 && bin_mapper->GetMostFreqBin() > 0) { cur_num_bin += 1; hist_cur_num_bin += 1; } offsets->push_back(cur_num_bin); feature_hist_offsets_.push_back(hist_cur_num_bin); int num_bin = bin_mapper->num_bin(); hist_cur_num_bin += num_bin; if (bin_mapper->GetMostFreqBin() == 0) { feature_hist_offsets_.back() += 1; } cur_num_bin += num_bin; } offsets->push_back(cur_num_bin); CHECK(cur_num_bin == feature_group->bin_offsets_.back()); } else { cur_num_bin += 1; hist_cur_num_bin += 1; for (int i = 0; i < feature_group->num_feature_; ++i) { offsets->push_back(cur_num_bin); feature_hist_offsets_.push_back(hist_cur_num_bin); const std::unique_ptr& bin_mapper = feature_group->bin_mappers_[i]; int num_bin = bin_mapper->num_bin(); if (bin_mapper->GetMostFreqBin() == 0) { num_bin -= 1; } hist_cur_num_bin += num_bin; cur_num_bin += num_bin; } offsets->push_back(cur_num_bin); CHECK(cur_num_bin == feature_group->bin_offsets_.back()); } } else { for (int i = 0; i < feature_group->num_feature_; ++i) { feature_hist_offsets_.push_back(hist_cur_num_bin + feature_group->bin_offsets_[i]); } hist_cur_num_bin += feature_group->bin_offsets_.back(); } } feature_hist_offsets_.push_back(hist_cur_num_bin); num_hist_total_bin_ = static_cast(feature_hist_offsets_.back()); } else { double sum_dense_ratio = 0.0f; int ncol = 0; for (int gid = 0; gid < static_cast(feature_groups.size()); ++gid) { if (feature_groups[gid]->is_multi_val_) { ncol += feature_groups[gid]->num_feature_; } else { ++ncol; } for (int fid = 0; fid < feature_groups[gid]->num_feature_; ++fid) { const auto& bin_mapper = feature_groups[gid]->bin_mappers_[fid]; sum_dense_ratio += 1.0f - bin_mapper->sparse_rate(); } } sum_dense_ratio /= ncol; const bool is_sparse_row_wise = (1.0f - sum_dense_ratio) >= MultiValBin::multi_val_bin_sparse_threshold ? 1 : 0; if (is_sparse_row_wise) { int cur_num_bin = 1; uint32_t hist_cur_num_bin = 1; for (int group = 0; group < static_cast(feature_groups.size()); ++group) { const std::unique_ptr& feature_group = feature_groups[group]; if (feature_group->is_multi_val_) { for (int i = 0; i < feature_group->num_feature_; ++i) { offsets->push_back(cur_num_bin); feature_hist_offsets_.push_back(hist_cur_num_bin); const std::unique_ptr& bin_mapper = feature_group->bin_mappers_[i]; int num_bin = bin_mapper->num_bin(); if (bin_mapper->GetMostFreqBin() == 0) { num_bin -= 1; } cur_num_bin += num_bin; hist_cur_num_bin += num_bin; } } else { offsets->push_back(cur_num_bin); cur_num_bin += feature_group->bin_offsets_.back() - 1; for (int i = 0; i < feature_group->num_feature_; ++i) { feature_hist_offsets_.push_back(hist_cur_num_bin + feature_group->bin_offsets_[i] - 1); } hist_cur_num_bin += feature_group->bin_offsets_.back() - 1; } } offsets->push_back(cur_num_bin); feature_hist_offsets_.push_back(hist_cur_num_bin); } else { int cur_num_bin = 0; uint32_t hist_cur_num_bin = 0; for (int group = 0; group < static_cast(feature_groups.size()); ++group) { const std::unique_ptr& feature_group = feature_groups[group]; if (feature_group->is_multi_val_) { for (int i = 0; i < feature_group->num_feature_; ++i) { const std::unique_ptr& bin_mapper = feature_group->bin_mappers_[i]; if (group == 0 && i == 0 && bin_mapper->GetMostFreqBin() > 0) { cur_num_bin += 1; hist_cur_num_bin += 1; } offsets->push_back(cur_num_bin); feature_hist_offsets_.push_back(hist_cur_num_bin); int num_bin = bin_mapper->num_bin(); cur_num_bin += num_bin; hist_cur_num_bin += num_bin; if (bin_mapper->GetMostFreqBin() == 0) { feature_hist_offsets_.back() += 1; } } } else { offsets->push_back(cur_num_bin); cur_num_bin += feature_group->bin_offsets_.back(); for (int i = 0; i < feature_group->num_feature_; ++i) { feature_hist_offsets_.push_back(hist_cur_num_bin + feature_group->bin_offsets_[i]); } hist_cur_num_bin += feature_group->bin_offsets_.back(); } } offsets->push_back(cur_num_bin); feature_hist_offsets_.push_back(hist_cur_num_bin); } num_hist_total_bin_ = static_cast(feature_hist_offsets_.back()); } #ifdef USE_CUDA column_hist_offsets_ = *offsets; #endif // USE_CUDA } void TrainingShareStates::SetMultiValBin(MultiValBin* bin, data_size_t num_data, const std::vector>& feature_groups, bool dense_only, bool sparse_only, const int num_grad_quant_bins) { num_threads = OMP_NUM_THREADS(); if (bin == nullptr) { return; } std::vector feature_groups_contained; for (int group = 0; group < static_cast(feature_groups.size()); ++group) { const auto& feature_group = feature_groups[group]; if (feature_group->is_multi_val_) { if (!dense_only) { feature_groups_contained.push_back(group); } } else if (!sparse_only) { feature_groups_contained.push_back(group); } } num_total_bin_ += bin->num_bin(); num_elements_per_row_ += bin->num_element_per_row(); multi_val_bin_wrapper_.reset(new MultiValBinWrapper( bin, num_data, feature_groups_contained, num_grad_quant_bins)); } } // namespace LightGBM ================================================ FILE: src/io/tree.cpp ================================================ /*! * Copyright (c) 2016-2026 Microsoft Corporation. All rights reserved. * Copyright (c) 2016-2026 The LightGBM developers. All rights reserved. * Licensed under the MIT License. See LICENSE file in the project root for license information. */ #include #include #include #include #include #include #include #include #include #include #include namespace LightGBM { Tree::Tree(int max_leaves, bool track_branch_features, bool is_linear) :max_leaves_(max_leaves), track_branch_features_(track_branch_features) { left_child_.resize(max_leaves_ - 1); right_child_.resize(max_leaves_ - 1); split_feature_inner_.resize(max_leaves_ - 1); split_feature_.resize(max_leaves_ - 1); threshold_in_bin_.resize(max_leaves_ - 1); threshold_.resize(max_leaves_ - 1); decision_type_.resize(max_leaves_ - 1, 0); split_gain_.resize(max_leaves_ - 1); leaf_parent_.resize(max_leaves_); leaf_value_.resize(max_leaves_); leaf_weight_.resize(max_leaves_); leaf_count_.resize(max_leaves_); internal_value_.resize(max_leaves_ - 1); internal_weight_.resize(max_leaves_ - 1); internal_count_.resize(max_leaves_ - 1); leaf_depth_.resize(max_leaves_); if (track_branch_features_) { branch_features_ = std::vector>(max_leaves_); } // root is in the depth 0 leaf_depth_[0] = 0; num_leaves_ = 1; leaf_value_[0] = 0.0f; leaf_weight_[0] = 0.0f; leaf_parent_[0] = -1; shrinkage_ = 1.0f; num_cat_ = 0; cat_boundaries_.push_back(0); cat_boundaries_inner_.push_back(0); max_depth_ = -1; is_linear_ = is_linear; if (is_linear_) { leaf_coeff_.resize(max_leaves_); leaf_const_ = std::vector(max_leaves_, 0); leaf_features_.resize(max_leaves_); leaf_features_inner_.resize(max_leaves_); } #ifdef USE_CUDA is_cuda_tree_ = false; #endif // USE_CUDA } int Tree::Split(int leaf, int feature, int real_feature, uint32_t threshold_bin, double threshold_double, double left_value, double right_value, int left_cnt, int right_cnt, double left_weight, double right_weight, float gain, MissingType missing_type, bool default_left) { Split(leaf, feature, real_feature, left_value, right_value, left_cnt, right_cnt, left_weight, right_weight, gain); int new_node_idx = num_leaves_ - 1; decision_type_[new_node_idx] = 0; SetDecisionType(&decision_type_[new_node_idx], false, kCategoricalMask); SetDecisionType(&decision_type_[new_node_idx], default_left, kDefaultLeftMask); SetMissingType(&decision_type_[new_node_idx], static_cast(missing_type)); threshold_in_bin_[new_node_idx] = threshold_bin; threshold_[new_node_idx] = threshold_double; ++num_leaves_; return num_leaves_ - 1; } int Tree::SplitCategorical(int leaf, int feature, int real_feature, const uint32_t* threshold_bin, int num_threshold_bin, const uint32_t* threshold, int num_threshold, double left_value, double right_value, data_size_t left_cnt, data_size_t right_cnt, double left_weight, double right_weight, float gain, MissingType missing_type) { Split(leaf, feature, real_feature, left_value, right_value, left_cnt, right_cnt, left_weight, right_weight, gain); int new_node_idx = num_leaves_ - 1; decision_type_[new_node_idx] = 0; SetDecisionType(&decision_type_[new_node_idx], true, kCategoricalMask); SetMissingType(&decision_type_[new_node_idx], static_cast(missing_type)); threshold_in_bin_[new_node_idx] = num_cat_; threshold_[new_node_idx] = num_cat_; ++num_cat_; cat_boundaries_.push_back(cat_boundaries_.back() + num_threshold); for (int i = 0; i < num_threshold; ++i) { cat_threshold_.push_back(threshold[i]); } cat_boundaries_inner_.push_back(cat_boundaries_inner_.back() + num_threshold_bin); for (int i = 0; i < num_threshold_bin; ++i) { cat_threshold_inner_.push_back(threshold_bin[i]); } ++num_leaves_; return num_leaves_ - 1; } #define PredictionFun(niter, fidx_in_iter, start_pos, decision_fun, iter_idx, \ data_idx) \ std::vector> iter((niter)); \ for (int i = 0; i < (niter); ++i) { \ iter[i].reset(data->FeatureIterator((fidx_in_iter))); \ iter[i]->Reset((start_pos)); \ } \ for (data_size_t i = start; i < end; ++i) { \ int node = 0; \ while (node >= 0) { \ node = decision_fun(iter[(iter_idx)]->Get((data_idx)), node, \ default_bins[node], max_bins[node]); \ } \ score[(data_idx)] += static_cast(leaf_value_[~node]); \ }\ #define PredictionFunLinear(niter, fidx_in_iter, start_pos, decision_fun, \ iter_idx, data_idx) \ std::vector> iter((niter)); \ for (int i = 0; i < (niter); ++i) { \ iter[i].reset(data->FeatureIterator((fidx_in_iter))); \ iter[i]->Reset((start_pos)); \ } \ for (data_size_t i = start; i < end; ++i) { \ int node = 0; \ if (num_leaves_ > 1) { \ while (node >= 0) { \ node = decision_fun(iter[(iter_idx)]->Get((data_idx)), node, \ default_bins[node], max_bins[node]); \ } \ node = ~node; \ } \ double add_score = leaf_const_[node]; \ bool nan_found = false; \ const double* coeff_ptr = leaf_coeff_[node].data(); \ const float** data_ptr = feat_ptr[node].data(); \ for (size_t j = 0; j < leaf_features_inner_[node].size(); ++j) { \ float feat_val = data_ptr[j][(data_idx)]; \ if (std::isnan(feat_val)) { \ nan_found = true; \ break; \ } \ add_score += coeff_ptr[j] * feat_val; \ } \ if (nan_found) { \ score[(data_idx)] += leaf_value_[node]; \ } else { \ score[(data_idx)] += add_score; \ } \ }\ void Tree::AddPredictionToScore(const Dataset* data, data_size_t num_data, double* score) const { if (!is_linear_ && num_leaves_ <= 1) { if (leaf_value_[0] != 0.0f) { #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static, 512) if (num_data >= 1024) for (data_size_t i = 0; i < num_data; ++i) { score[i] += leaf_value_[0]; } } return; } std::vector default_bins(num_leaves_ - 1); std::vector max_bins(num_leaves_ - 1); for (int i = 0; i < num_leaves_ - 1; ++i) { const int fidx = split_feature_inner_[i]; auto bin_mapper = data->FeatureBinMapper(fidx); default_bins[i] = bin_mapper->GetDefaultBin(); max_bins[i] = bin_mapper->num_bin() - 1; } if (is_linear_) { std::vector> feat_ptr(num_leaves_); for (int leaf_num = 0; leaf_num < num_leaves_; ++leaf_num) { for (int feat : leaf_features_inner_[leaf_num]) { feat_ptr[leaf_num].push_back(data->raw_index(feat)); } } if (num_cat_ > 0) { if (data->num_features() > num_leaves_ - 1) { Threading::For(0, num_data, 512, [this, &data, score, &default_bins, &max_bins, &feat_ptr] (int, data_size_t start, data_size_t end) { PredictionFunLinear(num_leaves_ - 1, split_feature_inner_[i], start, DecisionInner, node, i); }); } else { Threading::For(0, num_data, 512, [this, &data, score, &default_bins, &max_bins, &feat_ptr] (int, data_size_t start, data_size_t end) { PredictionFunLinear(data->num_features(), i, start, DecisionInner, split_feature_inner_[node], i); }); } } else { if (data->num_features() > num_leaves_ - 1) { Threading::For(0, num_data, 512, [this, &data, score, &default_bins, &max_bins, &feat_ptr] (int, data_size_t start, data_size_t end) { PredictionFunLinear(num_leaves_ - 1, split_feature_inner_[i], start, NumericalDecisionInner, node, i); }); } else { Threading::For(0, num_data, 512, [this, &data, score, &default_bins, &max_bins, &feat_ptr] (int, data_size_t start, data_size_t end) { PredictionFunLinear(data->num_features(), i, start, NumericalDecisionInner, split_feature_inner_[node], i); }); } } } else { if (num_cat_ > 0) { if (data->num_features() > num_leaves_ - 1) { Threading::For(0, num_data, 512, [this, &data, score, &default_bins, &max_bins] (int, data_size_t start, data_size_t end) { PredictionFun(num_leaves_ - 1, split_feature_inner_[i], start, DecisionInner, node, i); }); } else { Threading::For(0, num_data, 512, [this, &data, score, &default_bins, &max_bins] (int, data_size_t start, data_size_t end) { PredictionFun(data->num_features(), i, start, DecisionInner, split_feature_inner_[node], i); }); } } else { if (data->num_features() > num_leaves_ - 1) { Threading::For(0, num_data, 512, [this, &data, score, &default_bins, &max_bins] (int, data_size_t start, data_size_t end) { PredictionFun(num_leaves_ - 1, split_feature_inner_[i], start, NumericalDecisionInner, node, i); }); } else { Threading::For(0, num_data, 512, [this, &data, score, &default_bins, &max_bins] (int, data_size_t start, data_size_t end) { PredictionFun(data->num_features(), i, start, NumericalDecisionInner, split_feature_inner_[node], i); }); } } } } void Tree::AddPredictionToScore(const Dataset* data, const data_size_t* used_data_indices, data_size_t num_data, double* score) const { if (!is_linear_ && num_leaves_ <= 1) { if (leaf_value_[0] != 0.0f) { #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static, 512) if (num_data >= 1024) for (data_size_t i = 0; i < num_data; ++i) { score[used_data_indices[i]] += leaf_value_[0]; } } return; } std::vector default_bins(num_leaves_ - 1); std::vector max_bins(num_leaves_ - 1); for (int i = 0; i < num_leaves_ - 1; ++i) { const int fidx = split_feature_inner_[i]; auto bin_mapper = data->FeatureBinMapper(fidx); default_bins[i] = bin_mapper->GetDefaultBin(); max_bins[i] = bin_mapper->num_bin() - 1; } if (is_linear_) { std::vector> feat_ptr(num_leaves_); for (int leaf_num = 0; leaf_num < num_leaves_; ++leaf_num) { for (int feat : leaf_features_inner_[leaf_num]) { feat_ptr[leaf_num].push_back(data->raw_index(feat)); } } if (num_cat_ > 0) { if (data->num_features() > num_leaves_ - 1) { Threading::For(0, num_data, 512, [this, &data, score, used_data_indices, &default_bins, &max_bins, &feat_ptr] (int, data_size_t start, data_size_t end) { PredictionFunLinear(num_leaves_ - 1, split_feature_inner_[i], used_data_indices[start], DecisionInner, node, used_data_indices[i]); }); } else { Threading::For(0, num_data, 512, [this, &data, score, used_data_indices, &default_bins, &max_bins, &feat_ptr] (int, data_size_t start, data_size_t end) { PredictionFunLinear(data->num_features(), i, used_data_indices[start], DecisionInner, split_feature_inner_[node], used_data_indices[i]); }); } } else { if (data->num_features() > num_leaves_ - 1) { Threading::For(0, num_data, 512, [this, &data, score, used_data_indices, &default_bins, &max_bins, &feat_ptr] (int, data_size_t start, data_size_t end) { PredictionFunLinear(num_leaves_ - 1, split_feature_inner_[i], used_data_indices[start], NumericalDecisionInner, node, used_data_indices[i]); }); } else { Threading::For(0, num_data, 512, [this, &data, score, used_data_indices, &default_bins, &max_bins, &feat_ptr] (int, data_size_t start, data_size_t end) { PredictionFunLinear(data->num_features(), i, used_data_indices[start], NumericalDecisionInner, split_feature_inner_[node], used_data_indices[i]); }); } } } else { if (num_cat_ > 0) { if (data->num_features() > num_leaves_ - 1) { Threading::For(0, num_data, 512, [this, &data, score, used_data_indices, &default_bins, &max_bins] (int, data_size_t start, data_size_t end) { PredictionFun(num_leaves_ - 1, split_feature_inner_[i], used_data_indices[start], DecisionInner, node, used_data_indices[i]); }); } else { Threading::For(0, num_data, 512, [this, &data, score, used_data_indices, &default_bins, &max_bins] (int, data_size_t start, data_size_t end) { PredictionFun(data->num_features(), i, used_data_indices[start], DecisionInner, split_feature_inner_[node], used_data_indices[i]); }); } } else { if (data->num_features() > num_leaves_ - 1) { Threading::For(0, num_data, 512, [this, &data, score, used_data_indices, &default_bins, &max_bins] (int, data_size_t start, data_size_t end) { PredictionFun(num_leaves_ - 1, split_feature_inner_[i], used_data_indices[start], NumericalDecisionInner, node, used_data_indices[i]); }); } else { Threading::For(0, num_data, 512, [this, &data, score, used_data_indices, &default_bins, &max_bins] (int, data_size_t start, data_size_t end) { PredictionFun(data->num_features(), i, used_data_indices[start], NumericalDecisionInner, split_feature_inner_[node], used_data_indices[i]); }); } } } } #undef PredictionFun #undef PredictionFunLinear double Tree::GetUpperBoundValue() const { double upper_bound = leaf_value_[0]; for (int i = 1; i < num_leaves_; ++i) { if (leaf_value_[i] > upper_bound) { upper_bound = leaf_value_[i]; } } return upper_bound; } double Tree::GetLowerBoundValue() const { double lower_bound = leaf_value_[0]; for (int i = 1; i < num_leaves_; ++i) { if (leaf_value_[i] < lower_bound) { lower_bound = leaf_value_[i]; } } return lower_bound; } std::string Tree::ToString() const { std::stringstream str_buf; Common::C_stringstream(str_buf); using CommonC::ArrayToString; str_buf << "num_leaves=" << num_leaves_ << '\n'; str_buf << "num_cat=" << num_cat_ << '\n'; str_buf << "split_feature=" << ArrayToString(split_feature_, num_leaves_ - 1) << '\n'; str_buf << "split_gain=" << ArrayToString(split_gain_, num_leaves_ - 1) << '\n'; str_buf << "threshold=" << ArrayToString(threshold_, num_leaves_ - 1) << '\n'; str_buf << "decision_type=" << ArrayToString(Common::ArrayCast(decision_type_), num_leaves_ - 1) << '\n'; str_buf << "left_child=" << ArrayToString(left_child_, num_leaves_ - 1) << '\n'; str_buf << "right_child=" << ArrayToString(right_child_, num_leaves_ - 1) << '\n'; str_buf << "leaf_value=" << ArrayToString(leaf_value_, num_leaves_) << '\n'; str_buf << "leaf_weight=" << ArrayToString(leaf_weight_, num_leaves_) << '\n'; str_buf << "leaf_count=" << ArrayToString(leaf_count_, num_leaves_) << '\n'; str_buf << "internal_value=" << ArrayToString(internal_value_, num_leaves_ - 1) << '\n'; str_buf << "internal_weight=" << ArrayToString(internal_weight_, num_leaves_ - 1) << '\n'; str_buf << "internal_count=" << ArrayToString(internal_count_, num_leaves_ - 1) << '\n'; if (num_cat_ > 0) { str_buf << "cat_boundaries=" << ArrayToString(cat_boundaries_, num_cat_ + 1) << '\n'; str_buf << "cat_threshold=" << ArrayToString(cat_threshold_, cat_threshold_.size()) << '\n'; } str_buf << "is_linear=" << is_linear_ << '\n'; if (is_linear_) { str_buf << "leaf_const=" << ArrayToString(leaf_const_, num_leaves_) << '\n'; std::vector num_feat(num_leaves_); for (int i = 0; i < num_leaves_; ++i) { num_feat[i] = static_cast(leaf_coeff_[i].size()); } str_buf << "num_features=" << ArrayToString(num_feat, num_leaves_) << '\n'; str_buf << "leaf_features="; for (int i = 0; i < num_leaves_; ++i) { if (num_feat[i] > 0) { str_buf << ArrayToString(leaf_features_[i], leaf_features_[i].size()) << ' '; } str_buf << ' '; } str_buf << '\n'; str_buf << "leaf_coeff="; for (int i = 0; i < num_leaves_; ++i) { if (num_feat[i] > 0) { str_buf << ArrayToString(leaf_coeff_[i], leaf_coeff_[i].size()) << ' '; } str_buf << ' '; } str_buf << '\n'; } str_buf << "shrinkage=" << shrinkage_ << '\n'; str_buf << '\n'; return str_buf.str(); } std::string Tree::ToJSON() const { std::stringstream str_buf; Common::C_stringstream(str_buf); str_buf << std::setprecision(std::numeric_limits::digits10 + 2); str_buf << "\"num_leaves\":" << num_leaves_ << "," << '\n'; str_buf << "\"num_cat\":" << num_cat_ << "," << '\n'; str_buf << "\"shrinkage\":" << shrinkage_ << "," << '\n'; if (num_leaves_ == 1) { str_buf << "\"tree_structure\":{"; str_buf << "\"leaf_value\":" << leaf_value_[0] << ", " << '\n'; if (is_linear_) { str_buf << "\"leaf_count\":" << leaf_count_[0] << ", " << '\n'; str_buf << LinearModelToJSON(0); } else { str_buf << "\"leaf_count\":" << leaf_count_[0]; } str_buf << "}" << '\n'; } else { str_buf << "\"tree_structure\":" << NodeToJSON(0) << '\n'; } return str_buf.str(); } std::string Tree::LinearModelToJSON(int index) const { std::stringstream str_buf; Common::C_stringstream(str_buf); str_buf << std::setprecision(std::numeric_limits::digits10 + 2); str_buf << "\"leaf_const\":" << leaf_const_[index] << "," << "\n"; int num_features = static_cast(leaf_features_[index].size()); if (num_features > 0) { str_buf << "\"leaf_features\":["; for (int i = 0; i < num_features - 1; ++i) { str_buf << leaf_features_[index][i] << ", "; } str_buf << leaf_features_[index][num_features - 1] << "]" << ", " << "\n"; str_buf << "\"leaf_coeff\":["; for (int i = 0; i < num_features - 1; ++i) { str_buf << leaf_coeff_[index][i] << ", "; } str_buf << leaf_coeff_[index][num_features - 1] << "]" << "\n"; } else { str_buf << "\"leaf_features\":[],\n"; str_buf << "\"leaf_coeff\":[]\n"; } return str_buf.str(); } std::string Tree::NodeToJSON(int index) const { std::stringstream str_buf; Common::C_stringstream(str_buf); str_buf << std::setprecision(std::numeric_limits::digits10 + 2); if (index >= 0) { // non-leaf str_buf << "{" << '\n'; str_buf << "\"split_index\":" << index << "," << '\n'; str_buf << "\"split_feature\":" << split_feature_[index] << "," << '\n'; str_buf << "\"split_gain\":" << Common::AvoidInf(split_gain_[index]) << "," << '\n'; if (GetDecisionType(decision_type_[index], kCategoricalMask)) { int cat_idx = static_cast(threshold_[index]); std::vector cats; for (int i = cat_boundaries_[cat_idx]; i < cat_boundaries_[cat_idx + 1]; ++i) { for (int j = 0; j < 32; ++j) { int cat = (i - cat_boundaries_[cat_idx]) * 32 + j; if (Common::FindInBitset(cat_threshold_.data() + cat_boundaries_[cat_idx], cat_boundaries_[cat_idx + 1] - cat_boundaries_[cat_idx], cat)) { cats.push_back(cat); } } } str_buf << "\"threshold\":\"" << CommonC::Join(cats, "||") << "\"," << '\n'; str_buf << "\"decision_type\":\"==\"," << '\n'; } else { str_buf << "\"threshold\":" << Common::AvoidInf(threshold_[index]) << "," << '\n'; str_buf << "\"decision_type\":\"<=\"," << '\n'; } if (GetDecisionType(decision_type_[index], kDefaultLeftMask)) { str_buf << "\"default_left\":true," << '\n'; } else { str_buf << "\"default_left\":false," << '\n'; } uint8_t missing_type = GetMissingType(decision_type_[index]); if (missing_type == MissingType::None) { str_buf << "\"missing_type\":\"None\"," << '\n'; } else if (missing_type == MissingType::Zero) { str_buf << "\"missing_type\":\"Zero\"," << '\n'; } else { str_buf << "\"missing_type\":\"NaN\"," << '\n'; } str_buf << "\"internal_value\":" << internal_value_[index] << "," << '\n'; str_buf << "\"internal_weight\":" << internal_weight_[index] << "," << '\n'; str_buf << "\"internal_count\":" << internal_count_[index] << "," << '\n'; str_buf << "\"left_child\":" << NodeToJSON(left_child_[index]) << "," << '\n'; str_buf << "\"right_child\":" << NodeToJSON(right_child_[index]) << '\n'; str_buf << "}"; } else { // leaf index = ~index; str_buf << "{" << '\n'; str_buf << "\"leaf_index\":" << index << "," << '\n'; str_buf << "\"leaf_value\":" << leaf_value_[index] << "," << '\n'; str_buf << "\"leaf_weight\":" << leaf_weight_[index] << "," << '\n'; if (is_linear_) { str_buf << "\"leaf_count\":" << leaf_count_[index] << "," << '\n'; str_buf << LinearModelToJSON(index); } else { str_buf << "\"leaf_count\":" << leaf_count_[index] << '\n'; } str_buf << "}"; } return str_buf.str(); } std::string Tree::NumericalDecisionIfElse(int node) const { std::stringstream str_buf; Common::C_stringstream(str_buf); str_buf << std::setprecision(std::numeric_limits::digits10 + 2); uint8_t missing_type = GetMissingType(decision_type_[node]); bool default_left = GetDecisionType(decision_type_[node], kDefaultLeftMask); if (missing_type != MissingType::NaN) { str_buf << "if (std::isnan(fval)) fval = 0.0;"; } if (missing_type == MissingType::Zero) { if (default_left) { str_buf << "if (Tree::IsZero(fval)) {"; } else { str_buf << "if (!Tree::IsZero(fval)) {"; } } else if (missing_type == MissingType::NaN) { if (default_left) { str_buf << "if (std::isnan(fval)) {"; } else { str_buf << "if (!std::isnan(fval)) {"; } } else { str_buf << "if (fval <= " << threshold_[node] << ") {"; } return str_buf.str(); } std::string Tree::CategoricalDecisionIfElse(int node) const { std::stringstream str_buf; Common::C_stringstream(str_buf); int cat_idx = static_cast(threshold_[node]); str_buf << "if (std::isnan(fval)) { int_fval = -1; } else { int_fval = static_cast(fval); }"; str_buf << "if (int_fval >= 0 && int_fval < 32 * ("; str_buf << cat_boundaries_[cat_idx + 1] - cat_boundaries_[cat_idx]; str_buf << ") && (((cat_threshold[" << cat_boundaries_[cat_idx]; str_buf << " + int_fval / 32] >> (int_fval & 31)) & 1))) {"; return str_buf.str(); } std::string Tree::ToIfElse(int index, bool predict_leaf_index) const { std::stringstream str_buf; Common::C_stringstream(str_buf); str_buf << "double PredictTree" << index; if (predict_leaf_index) { str_buf << "Leaf"; } str_buf << "(const double* arr) { "; if (num_leaves_ <= 1) { str_buf << "return " << leaf_value_[0] << ";"; } else { str_buf << "const std::vector cat_threshold = {"; for (size_t i = 0; i < cat_threshold_.size(); ++i) { if (i != 0) { str_buf << ","; } str_buf << cat_threshold_[i]; } str_buf << "};"; // use this for the missing value conversion str_buf << "double fval = 0.0f; "; if (num_cat_ > 0) { str_buf << "int int_fval = 0; "; } str_buf << NodeToIfElse(0, predict_leaf_index); } str_buf << " }" << '\n'; // Predict func by Map to ifelse str_buf << "double PredictTree" << index; if (predict_leaf_index) { str_buf << "LeafByMap"; } else { str_buf << "ByMap"; } str_buf << "(const std::unordered_map& arr) { "; if (num_leaves_ <= 1) { str_buf << "return " << leaf_value_[0] << ";"; } else { str_buf << "const std::vector cat_threshold = {"; for (size_t i = 0; i < cat_threshold_.size(); ++i) { if (i != 0) { str_buf << ","; } str_buf << cat_threshold_[i]; } str_buf << "};"; // use this for the missing value conversion str_buf << "double fval = 0.0f; "; if (num_cat_ > 0) { str_buf << "int int_fval = 0; "; } str_buf << NodeToIfElseByMap(0, predict_leaf_index); } str_buf << " }" << '\n'; return str_buf.str(); } std::string Tree::NodeToIfElse(int index, bool predict_leaf_index) const { std::stringstream str_buf; Common::C_stringstream(str_buf); str_buf << std::setprecision(std::numeric_limits::digits10 + 2); if (index >= 0) { // non-leaf str_buf << "fval = arr[" << split_feature_[index] << "];"; if (GetDecisionType(decision_type_[index], kCategoricalMask) == 0) { str_buf << NumericalDecisionIfElse(index); } else { str_buf << CategoricalDecisionIfElse(index); } // left subtree str_buf << NodeToIfElse(left_child_[index], predict_leaf_index); str_buf << " } else { "; // right subtree str_buf << NodeToIfElse(right_child_[index], predict_leaf_index); str_buf << " }"; } else { // leaf str_buf << "return "; if (predict_leaf_index) { str_buf << ~index; } else { str_buf << leaf_value_[~index]; } str_buf << ";"; } return str_buf.str(); } std::string Tree::NodeToIfElseByMap(int index, bool predict_leaf_index) const { std::stringstream str_buf; Common::C_stringstream(str_buf); str_buf << std::setprecision(std::numeric_limits::digits10 + 2); if (index >= 0) { // non-leaf str_buf << "fval = arr.count(" << split_feature_[index] << ") > 0 ? arr.at(" << split_feature_[index] << ") : 0.0f;"; if (GetDecisionType(decision_type_[index], kCategoricalMask) == 0) { str_buf << NumericalDecisionIfElse(index); } else { str_buf << CategoricalDecisionIfElse(index); } // left subtree str_buf << NodeToIfElseByMap(left_child_[index], predict_leaf_index); str_buf << " } else { "; // right subtree str_buf << NodeToIfElseByMap(right_child_[index], predict_leaf_index); str_buf << " }"; } else { // leaf str_buf << "return "; if (predict_leaf_index) { str_buf << ~index; } else { str_buf << leaf_value_[~index]; } str_buf << ";"; } return str_buf.str(); } Tree::Tree(const char* str, size_t* used_len) { auto p = str; std::unordered_map key_vals; const int max_num_line = 22; int read_line = 0; while (read_line < max_num_line) { if (*p == '\r' || *p == '\n') break; auto start = p; while (*p != '=') ++p; std::string key(start, p - start); ++p; start = p; while (*p != '\r' && *p != '\n') ++p; key_vals[key] = std::string(start, p - start); ++read_line; if (*p == '\r') ++p; if (*p == '\n') ++p; } *used_len = p - str; if (key_vals.count("num_leaves") <= 0) { Log::Fatal("Tree model should contain num_leaves field"); } Common::Atoi(key_vals["num_leaves"].c_str(), &num_leaves_); if (key_vals.count("num_cat") <= 0) { Log::Fatal("Tree model should contain num_cat field"); } Common::Atoi(key_vals["num_cat"].c_str(), &num_cat_); if (key_vals.count("leaf_value")) { leaf_value_ = CommonC::StringToArray(key_vals["leaf_value"], num_leaves_); } else { Log::Fatal("Tree model string format error, should contain leaf_value field"); } if (key_vals.count("shrinkage")) { CommonC::Atof(key_vals["shrinkage"].c_str(), &shrinkage_); } else { shrinkage_ = 1.0f; } if (key_vals.count("is_linear")) { int is_linear_int; Common::Atoi(key_vals["is_linear"].c_str(), &is_linear_int); is_linear_ = static_cast(is_linear_int); } else { is_linear_ = false; } if (key_vals.count("leaf_count")) { leaf_count_ = CommonC::StringToArrayFast(key_vals["leaf_count"], num_leaves_); } else { leaf_count_.resize(num_leaves_); } #ifdef USE_CUDA is_cuda_tree_ = false; #endif // USE_CUDA if ((num_leaves_ <= 1) && !is_linear_) { return; } if (key_vals.count("left_child")) { left_child_ = CommonC::StringToArrayFast(key_vals["left_child"], num_leaves_ - 1); } else { Log::Fatal("Tree model string format error, should contain left_child field"); } if (key_vals.count("right_child")) { right_child_ = CommonC::StringToArrayFast(key_vals["right_child"], num_leaves_ - 1); } else { Log::Fatal("Tree model string format error, should contain right_child field"); } if (key_vals.count("split_feature")) { split_feature_ = CommonC::StringToArrayFast(key_vals["split_feature"], num_leaves_ - 1); } else { Log::Fatal("Tree model string format error, should contain split_feature field"); } if (key_vals.count("threshold")) { threshold_ = CommonC::StringToArray(key_vals["threshold"], num_leaves_ - 1); } else { Log::Fatal("Tree model string format error, should contain threshold field"); } if (key_vals.count("split_gain")) { split_gain_ = CommonC::StringToArrayFast(key_vals["split_gain"], num_leaves_ - 1); } else { split_gain_.resize(num_leaves_ - 1); } if (key_vals.count("internal_count")) { internal_count_ = CommonC::StringToArrayFast(key_vals["internal_count"], num_leaves_ - 1); } else { internal_count_.resize(num_leaves_ - 1); } if (key_vals.count("internal_value")) { internal_value_ = CommonC::StringToArrayFast(key_vals["internal_value"], num_leaves_ - 1); } else { internal_value_.resize(num_leaves_ - 1); } if (key_vals.count("internal_weight")) { internal_weight_ = CommonC::StringToArrayFast(key_vals["internal_weight"], num_leaves_ - 1); } else { internal_weight_.resize(num_leaves_ - 1); } if (key_vals.count("leaf_weight")) { leaf_weight_ = CommonC::StringToArray(key_vals["leaf_weight"], num_leaves_); } else { leaf_weight_.resize(num_leaves_); } if (key_vals.count("decision_type")) { decision_type_ = CommonC::StringToArrayFast(key_vals["decision_type"], num_leaves_ - 1); } else { decision_type_ = std::vector(num_leaves_ - 1, 0); } if (is_linear_) { if (key_vals.count("leaf_const")) { leaf_const_ = Common::StringToArray(key_vals["leaf_const"], num_leaves_); } else { leaf_const_.resize(num_leaves_); } std::vector num_feat; if (key_vals.count("num_features")) { num_feat = Common::StringToArrayFast(key_vals["num_features"], num_leaves_); } leaf_coeff_.resize(num_leaves_); leaf_features_.resize(num_leaves_); leaf_features_inner_.resize(num_leaves_); if (num_feat.size() > 0) { int total_num_feat = 0; for (size_t i = 0; i < num_feat.size(); ++i) { total_num_feat += num_feat[i]; } std::vector all_leaf_features; if (key_vals.count("leaf_features")) { all_leaf_features = Common::StringToArrayFast(key_vals["leaf_features"], total_num_feat); } std::vector all_leaf_coeff; if (key_vals.count("leaf_coeff")) { all_leaf_coeff = Common::StringToArray(key_vals["leaf_coeff"], total_num_feat); } int sum_num_feat = 0; for (int i = 0; i < num_leaves_; ++i) { if (num_feat[i] > 0) { if (key_vals.count("leaf_features")) { leaf_features_[i].assign(all_leaf_features.begin() + sum_num_feat, all_leaf_features.begin() + sum_num_feat + num_feat[i]); } if (key_vals.count("leaf_coeff")) { leaf_coeff_[i].assign(all_leaf_coeff.begin() + sum_num_feat, all_leaf_coeff.begin() + sum_num_feat + num_feat[i]); } } sum_num_feat += num_feat[i]; } } } if (num_cat_ > 0) { if (key_vals.count("cat_boundaries")) { cat_boundaries_ = CommonC::StringToArrayFast(key_vals["cat_boundaries"], num_cat_ + 1); } else { Log::Fatal("Tree model should contain cat_boundaries field."); } if (key_vals.count("cat_threshold")) { cat_threshold_ = CommonC::StringToArrayFast(key_vals["cat_threshold"], cat_boundaries_.back()); } else { Log::Fatal("Tree model should contain cat_threshold field"); } } max_depth_ = -1; } void Tree::ExtendPath(PathElement *unique_path, int unique_depth, double zero_fraction, double one_fraction, int feature_index) { unique_path[unique_depth].feature_index = feature_index; unique_path[unique_depth].zero_fraction = zero_fraction; unique_path[unique_depth].one_fraction = one_fraction; unique_path[unique_depth].pweight = (unique_depth == 0 ? 1 : 0); for (int i = unique_depth - 1; i >= 0; i--) { unique_path[i + 1].pweight += one_fraction*unique_path[i].pweight*(i + 1) / static_cast(unique_depth + 1); unique_path[i].pweight = zero_fraction*unique_path[i].pweight*(unique_depth - i) / static_cast(unique_depth + 1); } } void Tree::UnwindPath(PathElement *unique_path, int unique_depth, int path_index) { const double one_fraction = unique_path[path_index].one_fraction; const double zero_fraction = unique_path[path_index].zero_fraction; double next_one_portion = unique_path[unique_depth].pweight; for (int i = unique_depth - 1; i >= 0; --i) { if (one_fraction != 0) { const double tmp = unique_path[i].pweight; unique_path[i].pweight = next_one_portion*(unique_depth + 1) / static_cast((i + 1)*one_fraction); next_one_portion = tmp - unique_path[i].pweight*zero_fraction*(unique_depth - i) / static_cast(unique_depth + 1); } else { unique_path[i].pweight = (unique_path[i].pweight*(unique_depth + 1)) / static_cast(zero_fraction*(unique_depth - i)); } } for (int i = path_index; i < unique_depth; ++i) { unique_path[i].feature_index = unique_path[i + 1].feature_index; unique_path[i].zero_fraction = unique_path[i + 1].zero_fraction; unique_path[i].one_fraction = unique_path[i + 1].one_fraction; } } double Tree::UnwoundPathSum(const PathElement *unique_path, int unique_depth, int path_index) { const double one_fraction = unique_path[path_index].one_fraction; const double zero_fraction = unique_path[path_index].zero_fraction; double next_one_portion = unique_path[unique_depth].pweight; double total = 0; for (int i = unique_depth - 1; i >= 0; --i) { if (one_fraction != 0) { const double tmp = next_one_portion*(unique_depth + 1) / static_cast((i + 1)*one_fraction); total += tmp; next_one_portion = unique_path[i].pweight - tmp*zero_fraction*((unique_depth - i) / static_cast(unique_depth + 1)); } else { total += (unique_path[i].pweight / zero_fraction) / ((unique_depth - i) / static_cast(unique_depth + 1)); } } return total; } // recursive computation of SHAP values for a decision tree void Tree::TreeSHAP(const double *feature_values, double *phi, int node, int unique_depth, PathElement *parent_unique_path, double parent_zero_fraction, double parent_one_fraction, int parent_feature_index) const { // extend the unique path PathElement* unique_path = parent_unique_path + unique_depth; if (unique_depth > 0) { std::copy(parent_unique_path, parent_unique_path + unique_depth, unique_path); } ExtendPath(unique_path, unique_depth, parent_zero_fraction, parent_one_fraction, parent_feature_index); // leaf node if (node < 0) { for (int i = 1; i <= unique_depth; ++i) { const double w = UnwoundPathSum(unique_path, unique_depth, i); const PathElement &el = unique_path[i]; phi[el.feature_index] += w*(el.one_fraction - el.zero_fraction)*leaf_value_[~node]; } // internal node } else { const int hot_index = Decision(feature_values[split_feature_[node]], node); const int cold_index = (hot_index == left_child_[node] ? right_child_[node] : left_child_[node]); const double w = data_count(node); const double hot_zero_fraction = data_count(hot_index) / w; const double cold_zero_fraction = data_count(cold_index) / w; double incoming_zero_fraction = 1; double incoming_one_fraction = 1; // see if we have already split on this feature, // if so we undo that split so we can redo it for this node int path_index = 0; for (; path_index <= unique_depth; ++path_index) { if (unique_path[path_index].feature_index == split_feature_[node]) break; } if (path_index != unique_depth + 1) { incoming_zero_fraction = unique_path[path_index].zero_fraction; incoming_one_fraction = unique_path[path_index].one_fraction; UnwindPath(unique_path, unique_depth, path_index); unique_depth -= 1; } TreeSHAP(feature_values, phi, hot_index, unique_depth + 1, unique_path, hot_zero_fraction*incoming_zero_fraction, incoming_one_fraction, split_feature_[node]); TreeSHAP(feature_values, phi, cold_index, unique_depth + 1, unique_path, cold_zero_fraction*incoming_zero_fraction, 0, split_feature_[node]); } } // recursive sparse computation of SHAP values for a decision tree void Tree::TreeSHAPByMap(const std::unordered_map& feature_values, std::unordered_map* phi, int node, int unique_depth, PathElement *parent_unique_path, double parent_zero_fraction, double parent_one_fraction, int parent_feature_index) const { // extend the unique path PathElement* unique_path = parent_unique_path + unique_depth; if (unique_depth > 0) { std::copy(parent_unique_path, parent_unique_path + unique_depth, unique_path); } ExtendPath(unique_path, unique_depth, parent_zero_fraction, parent_one_fraction, parent_feature_index); // leaf node if (node < 0) { for (int i = 1; i <= unique_depth; ++i) { const double w = UnwoundPathSum(unique_path, unique_depth, i); const PathElement &el = unique_path[i]; (*phi)[el.feature_index] += w*(el.one_fraction - el.zero_fraction)*leaf_value_[~node]; } // internal node } else { const int hot_index = Decision(feature_values.count(split_feature_[node]) > 0 ? feature_values.at(split_feature_[node]) : 0.0f, node); const int cold_index = (hot_index == left_child_[node] ? right_child_[node] : left_child_[node]); const double w = data_count(node); const double hot_zero_fraction = data_count(hot_index) / w; const double cold_zero_fraction = data_count(cold_index) / w; double incoming_zero_fraction = 1; double incoming_one_fraction = 1; // see if we have already split on this feature, // if so we undo that split so we can redo it for this node int path_index = 0; for (; path_index <= unique_depth; ++path_index) { if (unique_path[path_index].feature_index == split_feature_[node]) break; } if (path_index != unique_depth + 1) { incoming_zero_fraction = unique_path[path_index].zero_fraction; incoming_one_fraction = unique_path[path_index].one_fraction; UnwindPath(unique_path, unique_depth, path_index); unique_depth -= 1; } TreeSHAPByMap(feature_values, phi, hot_index, unique_depth + 1, unique_path, hot_zero_fraction*incoming_zero_fraction, incoming_one_fraction, split_feature_[node]); TreeSHAPByMap(feature_values, phi, cold_index, unique_depth + 1, unique_path, cold_zero_fraction*incoming_zero_fraction, 0, split_feature_[node]); } } double Tree::ExpectedValue() const { if (num_leaves_ == 1) return LeafOutput(0); const double total_count = internal_count_[0]; double exp_value = 0.0; for (int i = 0; i < num_leaves(); ++i) { exp_value += (leaf_count_[i] / total_count)*LeafOutput(i); } return exp_value; } void Tree::RecomputeMaxDepth() { if (num_leaves_ == 1) { max_depth_ = 0; } else { if (leaf_depth_.size() == 0) { RecomputeLeafDepths(0, 0); } max_depth_ = leaf_depth_[0]; for (int i = 1; i < num_leaves(); ++i) { if (max_depth_ < leaf_depth_[i]) max_depth_ = leaf_depth_[i]; } } } } // namespace LightGBM ================================================ FILE: src/main.cpp ================================================ /*! * Copyright (c) 2016-2026 Microsoft Corporation. All rights reserved. * Copyright (c) 2016-2026 The LightGBM developers. All rights reserved. * Licensed under the MIT License. See LICENSE file in the project root for license information. */ #include #include #include #ifdef USE_MPI #include "network/linkers.h" #endif int main(int argc, char** argv) { bool success = false; try { LightGBM::Application app(argc, argv); app.Run(); #ifdef USE_MPI LightGBM::Linkers::MpiFinalizeIfIsParallel(); #endif success = true; } catch (const std::exception& ex) { std::cerr << "Met Exceptions:" << std::endl; std::cerr << ex.what() << std::endl; } catch (const std::string& ex) { std::cerr << "Met Exceptions:" << std::endl; std::cerr << ex << std::endl; } catch (...) { std::cerr << "Unknown Exceptions" << std::endl; } if (!success) { #ifdef USE_MPI LightGBM::Linkers::MpiAbortIfIsParallel(); #endif exit(-1); } } ================================================ FILE: src/metric/binary_metric.hpp ================================================ /*! * Copyright (c) 2016-2026 Microsoft Corporation. All rights reserved. * Copyright (c) 2016-2026 The LightGBM developers. All rights reserved. * Licensed under the MIT License. See LICENSE file in the project root for license information. */ #ifndef LIGHTGBM_SRC_METRIC_BINARY_METRIC_HPP_ #define LIGHTGBM_SRC_METRIC_BINARY_METRIC_HPP_ #include #include #include #include #include #include #include namespace LightGBM { /*! * \brief Metric for binary classification task. * Use static class "PointWiseLossCalculator" to calculate loss point-wise */ template class BinaryMetric: public Metric { public: explicit BinaryMetric(const Config&) { } virtual ~BinaryMetric() { } void Init(const Metadata& metadata, data_size_t num_data) override { name_.emplace_back(PointWiseLossCalculator::Name()); num_data_ = num_data; // get label label_ = metadata.label(); // get weights weights_ = metadata.weights(); if (weights_ == nullptr) { sum_weights_ = static_cast(num_data_); } else { sum_weights_ = 0.0f; for (data_size_t i = 0; i < num_data; ++i) { sum_weights_ += weights_[i]; } } } const std::vector& GetName() const override { return name_; } double factor_to_bigger_better() const override { return -1.0f; } std::vector Eval(const double* score, const ObjectiveFunction* objective) const override { double sum_loss = 0.0f; if (objective == nullptr) { if (weights_ == nullptr) { #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) reduction(+:sum_loss) for (data_size_t i = 0; i < num_data_; ++i) { // add loss sum_loss += PointWiseLossCalculator::LossOnPoint(label_[i], score[i]); } } else { #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) reduction(+:sum_loss) for (data_size_t i = 0; i < num_data_; ++i) { // add loss sum_loss += PointWiseLossCalculator::LossOnPoint(label_[i], score[i]) * weights_[i]; } } } else { if (weights_ == nullptr) { #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) reduction(+:sum_loss) for (data_size_t i = 0; i < num_data_; ++i) { double prob = 0; objective->ConvertOutput(&score[i], &prob); // add loss sum_loss += PointWiseLossCalculator::LossOnPoint(label_[i], prob); } } else { #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) reduction(+:sum_loss) for (data_size_t i = 0; i < num_data_; ++i) { double prob = 0; objective->ConvertOutput(&score[i], &prob); // add loss sum_loss += PointWiseLossCalculator::LossOnPoint(label_[i], prob) * weights_[i]; } } } double loss = sum_loss / sum_weights_; return std::vector(1, loss); } protected: /*! \brief Number of data */ data_size_t num_data_; /*! \brief Pointer of label */ const label_t* label_; /*! \brief Pointer of weighs */ const label_t* weights_; /*! \brief Sum weights */ double sum_weights_; /*! \brief Name of test set */ std::vector name_; }; /*! * \brief Log loss metric for binary classification task. */ class BinaryLoglossMetric: public BinaryMetric { public: explicit BinaryLoglossMetric(const Config& config) :BinaryMetric(config) {} inline static double LossOnPoint(label_t label, double prob) { if (label <= 0) { if (1.0f - prob > kEpsilon) { return -std::log(1.0f - prob); } } else { if (prob > kEpsilon) { return -std::log(prob); } } return -std::log(kEpsilon); } inline static const char* Name() { return "binary_logloss"; } }; /*! * \brief Error rate metric for binary classification task. */ class BinaryErrorMetric: public BinaryMetric { public: explicit BinaryErrorMetric(const Config& config) :BinaryMetric(config) {} inline static double LossOnPoint(label_t label, double prob) { if (prob <= 0.5f) { return label > 0; } else { return label <= 0; } } inline static const char* Name() { return "binary_error"; } }; /*! * \brief Auc Metric for binary classification task. */ class AUCMetric: public Metric { public: explicit AUCMetric(const Config&) { } virtual ~AUCMetric() { } const std::vector& GetName() const override { return name_; } double factor_to_bigger_better() const override { return 1.0f; } void Init(const Metadata& metadata, data_size_t num_data) override { name_.emplace_back("auc"); num_data_ = num_data; // get label label_ = metadata.label(); // get weights weights_ = metadata.weights(); if (weights_ == nullptr) { sum_weights_ = static_cast(num_data_); } else { sum_weights_ = 0.0f; for (data_size_t i = 0; i < num_data; ++i) { sum_weights_ += weights_[i]; } } } std::vector Eval(const double* score, const ObjectiveFunction*) const override { // get indices sorted by score, descent order std::vector sorted_idx; for (data_size_t i = 0; i < num_data_; ++i) { sorted_idx.emplace_back(i); } Common::ParallelSort(sorted_idx.begin(), sorted_idx.end(), [score](data_size_t a, data_size_t b) {return score[a] > score[b]; }); // temp sum of positive label double cur_pos = 0.0f; // total sum of positive label double sum_pos = 0.0f; // accumulate of AUC double accum = 0.0f; // temp sum of negative label double cur_neg = 0.0f; double threshold = score[sorted_idx[0]]; if (weights_ == nullptr) { // no weights for (data_size_t i = 0; i < num_data_; ++i) { const label_t cur_label = label_[sorted_idx[i]]; const double cur_score = score[sorted_idx[i]]; // new threshold if (cur_score != threshold) { threshold = cur_score; // accumulate accum += cur_neg*(cur_pos * 0.5f + sum_pos); sum_pos += cur_pos; // reset cur_neg = cur_pos = 0.0f; } cur_neg += (cur_label <= 0); cur_pos += (cur_label > 0); } } else { // has weights for (data_size_t i = 0; i < num_data_; ++i) { const label_t cur_label = label_[sorted_idx[i]]; const double cur_score = score[sorted_idx[i]]; const label_t cur_weight = weights_[sorted_idx[i]]; // new threshold if (cur_score != threshold) { threshold = cur_score; // accumulate accum += cur_neg*(cur_pos * 0.5f + sum_pos); sum_pos += cur_pos; // reset cur_neg = cur_pos = 0.0f; } cur_neg += (cur_label <= 0)*cur_weight; cur_pos += (cur_label > 0)*cur_weight; } } accum += cur_neg*(cur_pos * 0.5f + sum_pos); sum_pos += cur_pos; double auc = 1.0f; if (sum_pos > 0.0f && sum_pos != sum_weights_) { auc = accum / (sum_pos *(sum_weights_ - sum_pos)); } return std::vector(1, auc); } private: /*! \brief Number of data */ data_size_t num_data_; /*! \brief Pointer of label */ const label_t* label_; /*! \brief Pointer of weighs */ const label_t* weights_; /*! \brief Sum weights */ double sum_weights_; /*! \brief Name of test set */ std::vector name_; }; /*! * \brief Average Precision Metric for binary classification task. */ class AveragePrecisionMetric: public Metric { public: explicit AveragePrecisionMetric(const Config&) { } virtual ~AveragePrecisionMetric() { } const std::vector& GetName() const override { return name_; } double factor_to_bigger_better() const override { return 1.0f; } void Init(const Metadata& metadata, data_size_t num_data) override { name_.emplace_back("average_precision"); num_data_ = num_data; // get label label_ = metadata.label(); // get weights weights_ = metadata.weights(); if (weights_ == nullptr) { sum_weights_ = static_cast(num_data_); } else { sum_weights_ = 0.0f; for (data_size_t i = 0; i < num_data; ++i) { sum_weights_ += weights_[i]; } } } std::vector Eval(const double* score, const ObjectiveFunction*) const override { // get indices sorted by score, descending order std::vector sorted_idx; for (data_size_t i = 0; i < num_data_; ++i) { sorted_idx.emplace_back(i); } Common::ParallelSort(sorted_idx.begin(), sorted_idx.end(), [score](data_size_t a, data_size_t b) {return score[a] > score[b]; }); // temp sum of positive label double cur_actual_pos = 0.0f; // total sum of positive label double sum_actual_pos = 0.0f; // total sum of predicted positive double sum_pred_pos = 0.0f; // accumulated precision double accum_prec = 1.0f; // accumulated pr-auc double accum = 0.0f; // temp sum of negative label double cur_neg = 0.0f; double threshold = score[sorted_idx[0]]; if (weights_ == nullptr) { // no weights for (data_size_t i = 0; i < num_data_; ++i) { const label_t cur_label = label_[sorted_idx[i]]; const double cur_score = score[sorted_idx[i]]; // new threshold if (cur_score != threshold) { threshold = cur_score; // accumulate sum_actual_pos += cur_actual_pos; sum_pred_pos += cur_actual_pos + cur_neg; accum_prec = sum_actual_pos / sum_pred_pos; accum += cur_actual_pos * accum_prec; // reset cur_neg = cur_actual_pos = 0.0f; } cur_neg += (cur_label <= 0); cur_actual_pos += (cur_label > 0); } } else { // has weights for (data_size_t i = 0; i < num_data_; ++i) { const label_t cur_label = label_[sorted_idx[i]]; const double cur_score = score[sorted_idx[i]]; const label_t cur_weight = weights_[sorted_idx[i]]; // new threshold if (cur_score != threshold) { threshold = cur_score; // accumulate sum_actual_pos += cur_actual_pos; sum_pred_pos += cur_actual_pos + cur_neg; accum_prec = sum_actual_pos / sum_pred_pos; accum += cur_actual_pos * accum_prec; // reset cur_neg = cur_actual_pos = 0.0f; } cur_neg += (cur_label <= 0) * cur_weight; cur_actual_pos += (cur_label > 0) * cur_weight; } } sum_actual_pos += cur_actual_pos; sum_pred_pos += cur_actual_pos + cur_neg; accum_prec = sum_actual_pos / sum_pred_pos; accum += cur_actual_pos * accum_prec; double ap = 1.0f; if (sum_actual_pos > 0.0f && sum_actual_pos != sum_weights_) { ap = accum / sum_actual_pos; } return std::vector(1, ap); } private: /*! \brief Number of data */ data_size_t num_data_; /*! \brief Pointer of label */ const label_t* label_; /*! \brief Pointer of weighs */ const label_t* weights_; /*! \brief Sum weights */ double sum_weights_; /*! \brief Name of test set */ std::vector name_; }; } // namespace LightGBM #endif // LIGHTGBM_SRC_METRIC_BINARY_METRIC_HPP_ ================================================ FILE: src/metric/cuda/cuda_binary_metric.cpp ================================================ /*! * Copyright (c) 2022-2026 Microsoft Corporation. All rights reserved. * Copyright (c) 2022-2026 The LightGBM developers. All rights reserved. * Licensed under the MIT License. See LICENSE file in the project root for * license information. */ #ifdef USE_CUDA #include "cuda_binary_metric.hpp" #include namespace LightGBM { template std::vector CUDABinaryMetricInterface::Eval(const double* score, const ObjectiveFunction* objective) const { const double* score_convert = score; if (objective != nullptr && objective->NeedConvertOutputCUDA()) { this->score_convert_buffer_.Resize(static_cast(this->num_data_) * static_cast(this->num_class_)); score_convert = objective->ConvertOutputCUDA(this->num_data_, score, this->score_convert_buffer_.RawData()); } double sum_loss = 0.0, sum_weight = 0.0; this->LaunchEvalKernel(score_convert, &sum_loss, &sum_weight); const double eval_score = sum_loss / sum_weight; return std::vector{eval_score}; } CUDABinaryLoglossMetric::CUDABinaryLoglossMetric(const Config& config):CUDABinaryMetricInterface(config) {} CUDABinaryErrorMetric::CUDABinaryErrorMetric(const Config& config):CUDABinaryMetricInterface(config) {} } // namespace LightGBM #endif // USE_CUDA ================================================ FILE: src/metric/cuda/cuda_binary_metric.hpp ================================================ /*! * Copyright (c) 2022-2026 Microsoft Corporation. All rights reserved. * Copyright (c) 2022-2026 The LightGBM developers. All rights reserved. * Licensed under the MIT License. See LICENSE file in the project root for * license information. */ #ifndef LIGHTGBM_SRC_METRIC_CUDA_CUDA_BINARY_METRIC_HPP_ #define LIGHTGBM_SRC_METRIC_CUDA_CUDA_BINARY_METRIC_HPP_ #ifdef USE_CUDA #include #include #include #include "cuda_regression_metric.hpp" #include "../binary_metric.hpp" namespace LightGBM { template class CUDABinaryMetricInterface: public CUDAPointwiseMetricInterface { public: explicit CUDABinaryMetricInterface(const Config& config): CUDAPointwiseMetricInterface(config) {} virtual ~CUDABinaryMetricInterface() {} std::vector Eval(const double* score, const ObjectiveFunction* objective) const override; }; class CUDABinaryLoglossMetric: public CUDABinaryMetricInterface { public: explicit CUDABinaryLoglossMetric(const Config& config); virtual ~CUDABinaryLoglossMetric() {} __device__ static double MetricOnPointCUDA(label_t label, double score, const double /*param*/) { // score should have been converted to probability if (label <= 0) { if (1.0f - score > kEpsilon) { return -log(1.0f - score); } } else { if (score > kEpsilon) { return -log(score); } } return -log(kEpsilon); } }; class CUDABinaryErrorMetric: public CUDABinaryMetricInterface { public: explicit CUDABinaryErrorMetric(const Config& config); virtual ~CUDABinaryErrorMetric() {} __device__ inline static double MetricOnPointCUDA(label_t label, double score, const double /*param*/) { if (score <= 0.5f) { return label > 0; } else { return label <= 0; } } }; } // namespace LightGBM #endif // USE_CUDA #endif // LIGHTGBM_SRC_METRIC_CUDA_CUDA_BINARY_METRIC_HPP_ ================================================ FILE: src/metric/cuda/cuda_pointwise_metric.cpp ================================================ /*! * Copyright (c) 2022-2026 Microsoft Corporation. All rights reserved. * Copyright (c) 2022-2026 The LightGBM developers. All rights reserved. * Licensed under the MIT License. See LICENSE file in the project root for * license information. */ #ifdef USE_CUDA #include "cuda_binary_metric.hpp" #include "cuda_pointwise_metric.hpp" #include "cuda_regression_metric.hpp" namespace LightGBM { template void CUDAPointwiseMetricInterface::Init(const Metadata& metadata, data_size_t num_data) { CUDAMetricInterface::Init(metadata, num_data); const int max_num_reduce_blocks = (this->num_data_ + NUM_DATA_PER_EVAL_THREAD - 1) / NUM_DATA_PER_EVAL_THREAD; if (this->cuda_weights_ == nullptr) { reduce_block_buffer_.Resize(max_num_reduce_blocks); } else { reduce_block_buffer_.Resize(max_num_reduce_blocks * 2); } const int max_num_reduce_blocks_inner = (max_num_reduce_blocks + NUM_DATA_PER_EVAL_THREAD - 1) / NUM_DATA_PER_EVAL_THREAD; if (this->cuda_weights_ == nullptr) { reduce_block_buffer_inner_.Resize(max_num_reduce_blocks_inner); } else { reduce_block_buffer_inner_.Resize(max_num_reduce_blocks_inner * 2); } } // Regression metrics template void CUDAPointwiseMetricInterface::Init(const Metadata& metadata, data_size_t num_data); template void CUDAPointwiseMetricInterface::Init(const Metadata& metadata, data_size_t num_data); template void CUDAPointwiseMetricInterface::Init(const Metadata& metadata, data_size_t num_data); template void CUDAPointwiseMetricInterface::Init(const Metadata& metadata, data_size_t num_data); template void CUDAPointwiseMetricInterface::Init(const Metadata& metadata, data_size_t num_data); template void CUDAPointwiseMetricInterface::Init(const Metadata& metadata, data_size_t num_data); template void CUDAPointwiseMetricInterface::Init(const Metadata& metadata, data_size_t num_data); template void CUDAPointwiseMetricInterface::Init(const Metadata& metadata, data_size_t num_data); template void CUDAPointwiseMetricInterface::Init(const Metadata& metadata, data_size_t num_data); template void CUDAPointwiseMetricInterface::Init(const Metadata& metadata, data_size_t num_data); template void CUDAPointwiseMetricInterface::Init(const Metadata& metadata, data_size_t num_data); // Binary metrics template void CUDAPointwiseMetricInterface::Init(const Metadata& metadata, data_size_t num_data); template void CUDAPointwiseMetricInterface::Init(const Metadata& metadata, data_size_t num_data); } // namespace LightGBM #endif // USE_CUDA ================================================ FILE: src/metric/cuda/cuda_pointwise_metric.cu ================================================ /*! * Copyright (c) 2022-2026 Microsoft Corporation. All rights reserved. * Copyright (c) 2022-2026 The LightGBM developers. All rights reserved. * Licensed under the MIT License. See LICENSE file in the project root for * license information. * Modifications Copyright(C) 2023 Advanced Micro Devices, Inc. All rights reserved. */ #ifdef USE_CUDA #include #include #include "cuda_binary_metric.hpp" #include "cuda_pointwise_metric.hpp" #include "cuda_regression_metric.hpp" namespace LightGBM { template __global__ void EvalKernel(const data_size_t num_data, const label_t* labels, const label_t* weights, const double* scores, double* reduce_block_buffer, const double param) { __shared__ double shared_mem_buffer[WARPSIZE]; const data_size_t index = static_cast(threadIdx.x + blockIdx.x * blockDim.x); double point_metric = 0.0; if (index < num_data) { point_metric = USE_WEIGHTS ? CUDA_METRIC::MetricOnPointCUDA(labels[index], scores[index], param) * weights[index] : CUDA_METRIC::MetricOnPointCUDA(labels[index], scores[index], param); } const double block_sum_point_metric = ShuffleReduceSum(point_metric, shared_mem_buffer, NUM_DATA_PER_EVAL_THREAD); if (threadIdx.x == 0) { reduce_block_buffer[blockIdx.x] = block_sum_point_metric; } if (USE_WEIGHTS) { double weight = 0.0; if (index < num_data) { weight = static_cast(weights[index]); const double block_sum_weight = ShuffleReduceSum(weight, shared_mem_buffer, NUM_DATA_PER_EVAL_THREAD); if (threadIdx.x == 0) { reduce_block_buffer[blockIdx.x + gridDim.x] = block_sum_weight; } } } } template void CUDAPointwiseMetricInterface::LaunchEvalKernel(const double* score, double* sum_loss, double* sum_weight) const { const int num_blocks = (this->num_data_ + NUM_DATA_PER_EVAL_THREAD - 1) / NUM_DATA_PER_EVAL_THREAD; if (this->cuda_weights_ != nullptr) { EvalKernel<<>>( this->num_data_, this->cuda_labels_, this->cuda_weights_, score, reduce_block_buffer_.RawData(), GetParamFromConfig()); } else { EvalKernel<<>>( this->num_data_, this->cuda_labels_, this->cuda_weights_, score, reduce_block_buffer_.RawData(), GetParamFromConfig()); } ShuffleReduceSumGlobal(reduce_block_buffer_.RawData(), num_blocks, reduce_block_buffer_inner_.RawData()); CopyFromCUDADeviceToHost(sum_loss, reduce_block_buffer_inner_.RawData(), 1, __FILE__, __LINE__); *sum_weight = static_cast(this->num_data_); if (this->cuda_weights_ != nullptr) { ShuffleReduceSumGlobal(reduce_block_buffer_.RawData() + num_blocks, num_blocks, reduce_block_buffer_inner_.RawData()); CopyFromCUDADeviceToHost(sum_weight, reduce_block_buffer_inner_.RawData(), 1, __FILE__, __LINE__); } } // Regression metrics template void CUDAPointwiseMetricInterface::LaunchEvalKernel(const double* score, double* sum_loss, double* sum_weight) const; template void CUDAPointwiseMetricInterface::LaunchEvalKernel(const double* score, double* sum_loss, double* sum_weight) const; template void CUDAPointwiseMetricInterface::LaunchEvalKernel(const double* score, double* sum_loss, double* sum_weight) const; template void CUDAPointwiseMetricInterface::LaunchEvalKernel(const double* score, double* sum_loss, double* sum_weight) const; template void CUDAPointwiseMetricInterface::LaunchEvalKernel(const double* score, double* sum_loss, double* sum_weight) const; template void CUDAPointwiseMetricInterface::LaunchEvalKernel(const double* score, double* sum_loss, double* sum_weight) const; template void CUDAPointwiseMetricInterface::LaunchEvalKernel(const double* score, double* sum_loss, double* sum_weight) const; template void CUDAPointwiseMetricInterface::LaunchEvalKernel(const double* score, double* sum_loss, double* sum_weight) const; template void CUDAPointwiseMetricInterface::LaunchEvalKernel(const double* score, double* sum_loss, double* sum_weight) const; template void CUDAPointwiseMetricInterface::LaunchEvalKernel(const double* score, double* sum_loss, double* sum_weight) const; template void CUDAPointwiseMetricInterface::LaunchEvalKernel(const double* score, double* sum_loss, double* sum_weight) const; // Binary metrics template void CUDAPointwiseMetricInterface::LaunchEvalKernel(const double* score, double* sum_loss, double* sum_weight) const; template void CUDAPointwiseMetricInterface::LaunchEvalKernel(const double* score, double* sum_loss, double* sum_weight) const; } // namespace LightGBM #endif // USE_CUDA ================================================ FILE: src/metric/cuda/cuda_pointwise_metric.hpp ================================================ /*! * Copyright (c) 2022-2026 Microsoft Corporation. All rights reserved. * Copyright (c) 2022-2026 The LightGBM developers. All rights reserved. * Licensed under the MIT License. See LICENSE file in the project root for * license information. */ #ifndef LIGHTGBM_SRC_METRIC_CUDA_CUDA_POINTWISE_METRIC_HPP_ #define LIGHTGBM_SRC_METRIC_CUDA_CUDA_POINTWISE_METRIC_HPP_ #ifdef USE_CUDA #include #include #include #define NUM_DATA_PER_EVAL_THREAD (1024) namespace LightGBM { template class CUDAPointwiseMetricInterface: public CUDAMetricInterface { public: explicit CUDAPointwiseMetricInterface(const Config& config): CUDAMetricInterface(config), num_class_(config.num_class) {} virtual ~CUDAPointwiseMetricInterface() {} void Init(const Metadata& metadata, data_size_t num_data) override; protected: void LaunchEvalKernel(const double* score_convert, double* sum_loss, double* sum_weight) const; virtual double GetParamFromConfig() const { return 0.0; } mutable CUDAVector score_convert_buffer_; CUDAVector reduce_block_buffer_; CUDAVector reduce_block_buffer_inner_; const int num_class_; }; } // namespace LightGBM #endif // USE_CUDA #endif // LIGHTGBM_SRC_METRIC_CUDA_CUDA_POINTWISE_METRIC_HPP_ ================================================ FILE: src/metric/cuda/cuda_regression_metric.cpp ================================================ /*! * Copyright (c) 2022-2026 Microsoft Corporation. All rights reserved. * Copyright (c) 2022-2026 The LightGBM developers. All rights reserved. * Licensed under the MIT License. See LICENSE file in the project root for * license information. */ #ifdef USE_CUDA #include #include "cuda_regression_metric.hpp" namespace LightGBM { template std::vector CUDARegressionMetricInterface::Eval(const double* score, const ObjectiveFunction* objective) const { const double* score_convert = score; if (objective != nullptr && objective->NeedConvertOutputCUDA()) { this->score_convert_buffer_.Resize(static_cast(this->num_data_) * static_cast(this->num_class_)); score_convert = objective->ConvertOutputCUDA(this->num_data_, score, this->score_convert_buffer_.RawData()); } double sum_loss = 0.0, sum_weight = 0.0; this->LaunchEvalKernel(score_convert, &sum_loss, &sum_weight); const double eval_score = this->AverageLoss(sum_loss, sum_weight); return std::vector{eval_score}; } CUDARMSEMetric::CUDARMSEMetric(const Config& config): CUDARegressionMetricInterface(config) {} CUDAL2Metric::CUDAL2Metric(const Config& config): CUDARegressionMetricInterface(config) {} CUDAQuantileMetric::CUDAQuantileMetric(const Config& config): CUDARegressionMetricInterface(config), alpha_(config.alpha) {} CUDAL1Metric::CUDAL1Metric(const Config& config): CUDARegressionMetricInterface(config) {} CUDAHuberLossMetric::CUDAHuberLossMetric(const Config& config): CUDARegressionMetricInterface(config), alpha_(config.alpha) {} CUDAFairLossMetric::CUDAFairLossMetric(const Config& config): CUDARegressionMetricInterface(config) , fair_c_(config.fair_c) {} CUDAPoissonMetric::CUDAPoissonMetric(const Config& config): CUDARegressionMetricInterface(config) {} CUDAMAPEMetric::CUDAMAPEMetric(const Config& config): CUDARegressionMetricInterface(config) {} CUDAGammaMetric::CUDAGammaMetric(const Config& config): CUDARegressionMetricInterface(config) {} CUDAGammaDevianceMetric::CUDAGammaDevianceMetric(const Config& config): CUDARegressionMetricInterface(config) {} CUDATweedieMetric::CUDATweedieMetric(const Config& config): CUDARegressionMetricInterface(config) , tweedie_variance_power_(config.tweedie_variance_power) {} } // namespace LightGBM #endif // USE_CUDA ================================================ FILE: src/metric/cuda/cuda_regression_metric.hpp ================================================ /*! * Copyright (c) 2022-2026 Microsoft Corporation. All rights reserved. * Copyright (c) 2022-2026 The LightGBM developers. All rights reserved. * Licensed under the MIT License. See LICENSE file in the project root for * license information. */ #ifndef LIGHTGBM_SRC_METRIC_CUDA_CUDA_REGRESSION_METRIC_HPP_ #define LIGHTGBM_SRC_METRIC_CUDA_CUDA_REGRESSION_METRIC_HPP_ #ifdef USE_CUDA #include #include #include #include "cuda_pointwise_metric.hpp" #include "../regression_metric.hpp" namespace LightGBM { template class CUDARegressionMetricInterface: public CUDAPointwiseMetricInterface { public: explicit CUDARegressionMetricInterface(const Config& config): CUDAPointwiseMetricInterface(config) {} virtual ~CUDARegressionMetricInterface() {} std::vector Eval(const double* score, const ObjectiveFunction* objective) const override; }; class CUDARMSEMetric: public CUDARegressionMetricInterface { public: explicit CUDARMSEMetric(const Config& config); virtual ~CUDARMSEMetric() {} __device__ inline static double MetricOnPointCUDA(label_t label, double score, double /*alpha*/) { return (score - label) * (score - label); } }; class CUDAL2Metric : public CUDARegressionMetricInterface { public: explicit CUDAL2Metric(const Config& config); virtual ~CUDAL2Metric() {} __device__ inline static double MetricOnPointCUDA(label_t label, double score, double /*alpha*/) { return (score - label) * (score - label); } }; class CUDAQuantileMetric : public CUDARegressionMetricInterface { public: explicit CUDAQuantileMetric(const Config& config); virtual ~CUDAQuantileMetric() {} __device__ inline static double MetricOnPointCUDA(label_t label, double score, double alpha) { double delta = label - score; if (delta < 0) { return (alpha - 1.0f) * delta; } else { return alpha * delta; } } double GetParamFromConfig() const override { return alpha_; } private: const double alpha_; }; class CUDAL1Metric : public CUDARegressionMetricInterface { public: explicit CUDAL1Metric(const Config& config); virtual ~CUDAL1Metric() {} __device__ inline static double MetricOnPointCUDA(label_t label, double score, double /*alpha*/) { return std::fabs(score - label); } }; class CUDAHuberLossMetric : public CUDARegressionMetricInterface { public: explicit CUDAHuberLossMetric(const Config& config); virtual ~CUDAHuberLossMetric() {} __device__ inline static double MetricOnPointCUDA(label_t label, double score, double alpha) { const double diff = score - label; if (std::abs(diff) <= alpha) { return 0.5f * diff * diff; } else { return alpha * (std::abs(diff) - 0.5f * alpha); } } double GetParamFromConfig() const override { return alpha_; } private: const double alpha_; }; class CUDAFairLossMetric : public CUDARegressionMetricInterface { public: explicit CUDAFairLossMetric(const Config& config); virtual ~CUDAFairLossMetric() {} __device__ inline static double MetricOnPointCUDA(label_t label, double score, double fair_c) { const double x = std::fabs(score - label); const double c = fair_c; return c * x - c * c * std::log1p(x / c); } double GetParamFromConfig() const override { return fair_c_; } private: const double fair_c_; }; class CUDAPoissonMetric : public CUDARegressionMetricInterface { public: explicit CUDAPoissonMetric(const Config& config); virtual ~CUDAPoissonMetric() {} __device__ inline static double MetricOnPointCUDA(label_t label, double score, double /*alpha*/) { const double eps = 1e-10f; if (score < eps) { score = eps; } return score - label * std::log(score); } }; class CUDAMAPEMetric : public CUDARegressionMetricInterface { public: explicit CUDAMAPEMetric(const Config& config); virtual ~CUDAMAPEMetric() {} __device__ inline static double MetricOnPointCUDA(label_t label, double score, double /*alpha*/) { return std::fabs((label - score)) / fmax(1.0f, std::fabs(label)); } }; class CUDAGammaMetric : public CUDARegressionMetricInterface { public: explicit CUDAGammaMetric(const Config& config); virtual ~CUDAGammaMetric() {} __device__ inline static double MetricOnPointCUDA(label_t label, double score, double /*alpha*/) { const double psi = 1.0; const double theta = -1.0 / score; const double a = psi; const double b = -SafeLog(-theta); const double c = 1. / psi * SafeLog(label / psi) - SafeLog(label) - 0; // 0 = std::lgamma(1.0 / psi) = std::lgamma(1.0); return -((label * theta - b) / a + c); } }; class CUDAGammaDevianceMetric : public CUDARegressionMetricInterface { public: explicit CUDAGammaDevianceMetric(const Config& config); virtual ~CUDAGammaDevianceMetric() {} __device__ inline static double MetricOnPointCUDA(label_t label, double score, double /*alpha*/) { const double epsilon = 1.0e-9; const double tmp = label / (score + epsilon); return tmp - SafeLog(tmp) - 1; } }; class CUDATweedieMetric : public CUDARegressionMetricInterface { public: explicit CUDATweedieMetric(const Config& config); virtual ~CUDATweedieMetric() {} __device__ inline static double MetricOnPointCUDA(label_t label, double score, double tweedie_variance_power) { const double rho = tweedie_variance_power; const double eps = 1e-10f; if (score < eps) { score = eps; } const double a = label * std::exp((1 - rho) * std::log(score)) / (1 - rho); const double b = std::exp((2 - rho) * std::log(score)) / (2 - rho); return -a + b; } double GetParamFromConfig() const override { return tweedie_variance_power_; } private: const double tweedie_variance_power_; }; } // namespace LightGBM #endif // USE_CUDA #endif // LIGHTGBM_SRC_METRIC_CUDA_CUDA_REGRESSION_METRIC_HPP_ ================================================ FILE: src/metric/dcg_calculator.cpp ================================================ /*! * Copyright (c) 2016-2026 Microsoft Corporation. All rights reserved. * Copyright (c) 2016-2026 The LightGBM developers. All rights reserved. * Licensed under the MIT License. See LICENSE file in the project root for license information. */ #include #include #include #include #include namespace LightGBM { /*! \brief Declaration for some static members */ std::vector DCGCalculator::label_gain_; std::vector DCGCalculator::discount_; const data_size_t DCGCalculator::kMaxPosition = 10000; void DCGCalculator::DefaultEvalAt(std::vector* eval_at) { auto& ref_eval_at = *eval_at; if (ref_eval_at.empty()) { for (int i = 1; i <= 5; ++i) { ref_eval_at.push_back(i); } } else { for (size_t i = 0; i < eval_at->size(); ++i) { CHECK_GT(ref_eval_at[i], 0); } } } void DCGCalculator::DefaultLabelGain(std::vector* label_gain) { if (!label_gain->empty()) { return; } // label_gain = 2^i - 1, may overflow, so we use 31 here const int max_label = 31; label_gain->push_back(0.0f); for (int i = 1; i < max_label; ++i) { label_gain->push_back(static_cast((1 << i) - 1)); } } void DCGCalculator::Init(const std::vector& input_label_gain) { label_gain_.resize(input_label_gain.size()); for (size_t i = 0; i < input_label_gain.size(); ++i) { label_gain_[i] = static_cast(input_label_gain[i]); } discount_.resize(kMaxPosition); for (data_size_t i = 0; i < kMaxPosition; ++i) { discount_[i] = 1.0 / std::log2(2.0 + i); } } double DCGCalculator::CalMaxDCGAtK(data_size_t k, const label_t* label, data_size_t num_data) { double ret = 0.0f; // counts for all labels std::vector label_cnt(label_gain_.size(), 0); for (data_size_t i = 0; i < num_data; ++i) { ++label_cnt[static_cast(label[i])]; } int top_label = static_cast(label_gain_.size()) - 1; if (k > num_data) { k = num_data; } // start from top label, and accumulate DCG for (data_size_t j = 0; j < k; ++j) { while (top_label > 0 && label_cnt[top_label] <= 0) { top_label -= 1; } if (top_label < 0) { break; } ret += discount_[j] * label_gain_[top_label]; label_cnt[top_label] -= 1; } return ret; } void DCGCalculator::CalMaxDCG(const std::vector& ks, const label_t* label, data_size_t num_data, std::vector* out) { std::vector label_cnt(label_gain_.size(), 0); // counts for all labels for (data_size_t i = 0; i < num_data; ++i) { ++label_cnt[static_cast(label[i])]; } double cur_result = 0.0f; data_size_t cur_left = 0; int top_label = static_cast(label_gain_.size()) - 1; // calculate k Max DCG by one pass for (size_t i = 0; i < ks.size(); ++i) { data_size_t cur_k = ks[i]; if (cur_k > num_data) { cur_k = num_data; } for (data_size_t j = cur_left; j < cur_k; ++j) { while (top_label > 0 && label_cnt[top_label] <= 0) { top_label -= 1; } if (top_label < 0) { break; } cur_result += discount_[j] * label_gain_[top_label]; label_cnt[top_label] -= 1; } (*out)[i] = cur_result; cur_left = cur_k; } } void DCGCalculator::CalDCG(const std::vector& ks, const label_t* label, const double * score, data_size_t num_data, std::vector* out) { // get sorted indices by score std::vector sorted_idx(num_data); for (data_size_t i = 0; i < num_data; ++i) { sorted_idx[i] = i; } std::stable_sort(sorted_idx.begin(), sorted_idx.end(), [score](data_size_t a, data_size_t b) {return score[a] > score[b]; }); double cur_result = 0.0f; data_size_t cur_left = 0; // calculate multi dcg by one pass for (size_t i = 0; i < ks.size(); ++i) { data_size_t cur_k = ks[i]; if (cur_k > num_data) { cur_k = num_data; } for (data_size_t j = cur_left; j < cur_k; ++j) { data_size_t idx = sorted_idx[j]; cur_result += label_gain_[static_cast(label[idx])] * discount_[j]; } (*out)[i] = cur_result; cur_left = cur_k; } } void DCGCalculator::CheckMetadata(const Metadata& metadata, data_size_t num_queries) { const data_size_t* query_boundaries = metadata.query_boundaries(); if (num_queries > 0 && query_boundaries != nullptr) { for (data_size_t i = 0; i < num_queries; i++) { data_size_t num_rows = query_boundaries[i + 1] - query_boundaries[i]; if (num_rows > kMaxPosition) { Log::Fatal("Number of rows %i exceeds upper limit of %i for a query", static_cast(num_rows), static_cast(kMaxPosition)); } } } } void DCGCalculator::CheckLabel(const label_t* label, data_size_t num_data) { for (data_size_t i = 0; i < num_data; ++i) { label_t delta = std::fabs(label[i] - static_cast(label[i])); if (delta > kEpsilon) { Log::Fatal("label should be int type (met %f) for ranking task,\n" "for the gain of label, please set the label_gain parameter", label[i]); } if (label[i] < 0) { Log::Fatal("Label should be non-negative (met %f) for ranking task", label[i]); } if (static_cast(label[i]) >= label_gain_.size()) { Log::Fatal("Label %zu is not less than the number of label mappings (%zu)", static_cast(label[i]), label_gain_.size()); } } } } // namespace LightGBM ================================================ FILE: src/metric/map_metric.hpp ================================================ /*! * Copyright (c) 2017-2026 Microsoft Corporation. All rights reserved. * Copyright (c) 2017-2026 The LightGBM developers. All rights reserved. * Licensed under the MIT License. See LICENSE file in the project root for license information. */ #ifndef LIGHTGBM_SRC_METRIC_MAP_METRIC_HPP_ #define LIGHTGBM_SRC_METRIC_MAP_METRIC_HPP_ #include #include #include #include #include #include #include #include namespace LightGBM { class MapMetric:public Metric { public: explicit MapMetric(const Config& config) { // get eval position eval_at_ = config.eval_at; DCGCalculator::DefaultEvalAt(&eval_at_); } ~MapMetric() { } void Init(const Metadata& metadata, data_size_t num_data) override { for (auto k : eval_at_) { name_.emplace_back(std::string("map@") + std::to_string(k)); } num_data_ = num_data; // get label label_ = metadata.label(); // get query boundaries query_boundaries_ = metadata.query_boundaries(); if (query_boundaries_ == nullptr) { Log::Fatal("For MAP metric, there should be query information"); } num_queries_ = metadata.num_queries(); Log::Info("Total groups: %d, total data: %d", num_queries_, num_data_); // get query weights query_weights_ = metadata.query_weights(); if (query_weights_ == nullptr) { sum_query_weights_ = static_cast(num_queries_); } else { sum_query_weights_ = 0.0f; for (data_size_t i = 0; i < num_queries_; ++i) { sum_query_weights_ += query_weights_[i]; } } npos_per_query_.resize(num_queries_, 0); for (data_size_t i = 0; i < num_queries_; ++i) { for (data_size_t j = query_boundaries_[i]; j < query_boundaries_[i + 1]; ++j) { if (label_[j] > 0.5f) { ++npos_per_query_[i]; } } } } const std::vector& GetName() const override { return name_; } double factor_to_bigger_better() const override { return 1.0f; } void CalMapAtK(std::vector ks, data_size_t npos, const label_t* label, const double* score, data_size_t num_data, std::vector* out) const { // get sorted indices by score std::vector sorted_idx; for (data_size_t i = 0; i < num_data; ++i) { sorted_idx.emplace_back(i); } std::stable_sort(sorted_idx.begin(), sorted_idx.end(), [score](data_size_t a, data_size_t b) {return score[a] > score[b]; }); int num_hit = 0; double sum_ap = 0.0f; data_size_t cur_left = 0; for (size_t i = 0; i < ks.size(); ++i) { data_size_t cur_k = static_cast(ks[i]); if (cur_k > num_data) { cur_k = num_data; } for (data_size_t j = cur_left; j < cur_k; ++j) { data_size_t idx = sorted_idx[j]; if (label[idx] > 0.5f) { ++num_hit; sum_ap += static_cast(num_hit) / (j + 1.0f); } } if (npos > 0) { (*out)[i] = sum_ap / std::min(npos, cur_k); } else { (*out)[i] = 1.0f; } cur_left = cur_k; } } std::vector Eval(const double* score, const ObjectiveFunction*) const override { // some buffers for multi-threading sum up int num_threads = OMP_NUM_THREADS(); std::vector> result_buffer_; for (int i = 0; i < num_threads; ++i) { result_buffer_.emplace_back(eval_at_.size(), 0.0f); } std::vector tmp_map(eval_at_.size(), 0.0f); if (query_weights_ == nullptr) { #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(guided) firstprivate(tmp_map) for (data_size_t i = 0; i < num_queries_; ++i) { const int tid = omp_get_thread_num(); CalMapAtK(eval_at_, npos_per_query_[i], label_ + query_boundaries_[i], score + query_boundaries_[i], query_boundaries_[i + 1] - query_boundaries_[i], &tmp_map); for (size_t j = 0; j < eval_at_.size(); ++j) { result_buffer_[tid][j] += tmp_map[j]; } } } else { #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(guided) firstprivate(tmp_map) for (data_size_t i = 0; i < num_queries_; ++i) { const int tid = omp_get_thread_num(); CalMapAtK(eval_at_, npos_per_query_[i], label_ + query_boundaries_[i], score + query_boundaries_[i], query_boundaries_[i + 1] - query_boundaries_[i], &tmp_map); for (size_t j = 0; j < eval_at_.size(); ++j) { result_buffer_[tid][j] += tmp_map[j] * query_weights_[i]; } } } // Get final average MAP std::vector result(eval_at_.size(), 0.0f); for (size_t j = 0; j < result.size(); ++j) { for (int i = 0; i < num_threads; ++i) { result[j] += result_buffer_[i][j]; } result[j] /= sum_query_weights_; } return result; } private: /*! \brief Number of data */ data_size_t num_data_; /*! \brief Pointer of label */ const label_t* label_; /*! \brief Query boundaries information */ const data_size_t* query_boundaries_; /*! \brief Number of queries */ data_size_t num_queries_; /*! \brief Weights of queries */ const label_t* query_weights_; /*! \brief Sum weights of queries */ double sum_query_weights_; /*! \brief Evaluate position of Nmap */ std::vector eval_at_; std::vector name_; std::vector npos_per_query_; }; } // namespace LightGBM #endif // LIGHTGBM_SRC_METRIC_MAP_METRIC_HPP_ ================================================ FILE: src/metric/metric.cpp ================================================ /*! * Copyright (c) 2016-2026 Microsoft Corporation. All rights reserved. * Copyright (c) 2016-2026 The LightGBM developers. All rights reserved. * Licensed under the MIT License. See LICENSE file in the project root for license information. */ #include #include #include "binary_metric.hpp" #include "map_metric.hpp" #include "multiclass_metric.hpp" #include "rank_metric.hpp" #include "regression_metric.hpp" #include "xentropy_metric.hpp" #include "cuda/cuda_binary_metric.hpp" #include "cuda/cuda_regression_metric.hpp" namespace LightGBM { Metric* Metric::CreateMetric(const std::string& type, const Config& config) { #ifdef USE_CUDA if (config.device_type == std::string("cuda") && config.boosting == std::string("gbdt")) { if (type == std::string("l2")) { return new CUDAL2Metric(config); } else if (type == std::string("rmse")) { return new CUDARMSEMetric(config); } else if (type == std::string("l1")) { return new CUDAL1Metric(config); } else if (type == std::string("quantile")) { return new CUDAQuantileMetric(config); } else if (type == std::string("huber")) { return new CUDAHuberLossMetric(config); } else if (type == std::string("fair")) { return new CUDAFairLossMetric(config); } else if (type == std::string("poisson")) { return new CUDAPoissonMetric(config); } else if (type == std::string("binary_logloss")) { return new CUDABinaryLoglossMetric(config); } else if (type == std::string("binary_error")) { return new CUDABinaryErrorMetric(config); } else if (type == std::string("auc")) { Log::Warning("Metric auc is not implemented in cuda version. Fall back to evaluation on CPU."); return new AUCMetric(config); } else if (type == std::string("average_precision")) { Log::Warning("Metric average_precision is not implemented in cuda version. Fall back to evaluation on CPU."); return new AveragePrecisionMetric(config); } else if (type == std::string("auc_mu")) { Log::Warning("Metric auc_mu is not implemented in cuda version. Fall back to evaluation on CPU."); return new AucMuMetric(config); } else if (type == std::string("ndcg")) { Log::Warning("Metric ndcg is not implemented in cuda version. Fall back to evaluation on CPU."); return new NDCGMetric(config); } else if (type == std::string("map")) { Log::Warning("Metric map is not implemented in cuda version. Fall back to evaluation on CPU."); return new MapMetric(config); } else if (type == std::string("multi_logloss")) { Log::Warning("Metric multi_logloss is not implemented in cuda version. Fall back to evaluation on CPU."); return new MultiSoftmaxLoglossMetric(config); } else if (type == std::string("multi_error")) { Log::Warning("Metric multi_error is not implemented in cuda version. Fall back to evaluation on CPU."); return new MultiErrorMetric(config); } else if (type == std::string("cross_entropy")) { Log::Warning("Metric cross_entropy is not implemented in cuda version. Fall back to evaluation on CPU."); return new CrossEntropyMetric(config); } else if (type == std::string("cross_entropy_lambda")) { Log::Warning("Metric cross_entropy_lambda is not implemented in cuda version. Fall back to evaluation on CPU."); return new CrossEntropyLambdaMetric(config); } else if (type == std::string("kullback_leibler")) { Log::Warning("Metric kullback_leibler is not implemented in cuda version. Fall back to evaluation on CPU."); return new KullbackLeiblerDivergence(config); } else if (type == std::string("mape")) { return new CUDAMAPEMetric(config); } else if (type == std::string("gamma")) { return new CUDAGammaMetric(config); } else if (type == std::string("gamma_deviance")) { return new CUDAGammaDevianceMetric(config); } else if (type == std::string("tweedie")) { return new CUDATweedieMetric(config); } else if (type == std::string("r2")) { Log::Warning("Metric r2 is not implemented in cuda version. Fall back to evaluation on CPU."); return new R2Metric(config); } } else { #endif // USE_CUDA if (type == std::string("l2")) { return new L2Metric(config); } else if (type == std::string("rmse")) { return new RMSEMetric(config); } else if (type == std::string("l1")) { return new L1Metric(config); } else if (type == std::string("quantile")) { return new QuantileMetric(config); } else if (type == std::string("huber")) { return new HuberLossMetric(config); } else if (type == std::string("fair")) { return new FairLossMetric(config); } else if (type == std::string("poisson")) { return new PoissonMetric(config); } else if (type == std::string("binary_logloss")) { return new BinaryLoglossMetric(config); } else if (type == std::string("binary_error")) { return new BinaryErrorMetric(config); } else if (type == std::string("auc")) { return new AUCMetric(config); } else if (type == std::string("average_precision")) { return new AveragePrecisionMetric(config); } else if (type == std::string("auc_mu")) { return new AucMuMetric(config); } else if (type == std::string("ndcg")) { return new NDCGMetric(config); } else if (type == std::string("map")) { return new MapMetric(config); } else if (type == std::string("multi_logloss")) { return new MultiSoftmaxLoglossMetric(config); } else if (type == std::string("multi_error")) { return new MultiErrorMetric(config); } else if (type == std::string("cross_entropy")) { return new CrossEntropyMetric(config); } else if (type == std::string("cross_entropy_lambda")) { return new CrossEntropyLambdaMetric(config); } else if (type == std::string("kullback_leibler")) { return new KullbackLeiblerDivergence(config); } else if (type == std::string("mape")) { return new MAPEMetric(config); } else if (type == std::string("gamma")) { return new GammaMetric(config); } else if (type == std::string("gamma_deviance")) { return new GammaDevianceMetric(config); } else if (type == std::string("tweedie")) { return new TweedieMetric(config); } else if (type == std::string("r2")) { return new R2Metric(config); } #ifdef USE_CUDA } #endif // USE_CUDA return nullptr; } } // namespace LightGBM ================================================ FILE: src/metric/multiclass_metric.hpp ================================================ /*! * Copyright (c) 2016-2026 Microsoft Corporation. All rights reserved. * Copyright (c) 2016-2026 The LightGBM developers. All rights reserved. * Licensed under the MIT License. See LICENSE file in the project root for license information. */ #ifndef LIGHTGBM_SRC_METRIC_MULTICLASS_METRIC_HPP_ #define LIGHTGBM_SRC_METRIC_MULTICLASS_METRIC_HPP_ #include #include #include #include #include #include namespace LightGBM { /*! * \brief Metric for multiclass task. * Use static class "PointWiseLossCalculator" to calculate loss point-wise */ template class MulticlassMetric: public Metric { public: explicit MulticlassMetric(const Config& config) :config_(config) { num_class_ = config.num_class; } virtual ~MulticlassMetric() { } void Init(const Metadata& metadata, data_size_t num_data) override { name_.emplace_back(PointWiseLossCalculator::Name(config_)); num_data_ = num_data; // get label label_ = metadata.label(); // get weights weights_ = metadata.weights(); if (weights_ == nullptr) { sum_weights_ = static_cast(num_data_); } else { sum_weights_ = 0.0f; for (data_size_t i = 0; i < num_data_; ++i) { sum_weights_ += weights_[i]; } } } const std::vector& GetName() const override { return name_; } double factor_to_bigger_better() const override { return -1.0f; } std::vector Eval(const double* score, const ObjectiveFunction* objective) const override { double sum_loss = 0.0; int num_tree_per_iteration = num_class_; int num_pred_per_row = num_class_; if (objective != nullptr) { num_tree_per_iteration = objective->NumModelPerIteration(); num_pred_per_row = objective->NumPredictOneRow(); } if (objective != nullptr) { if (weights_ == nullptr) { #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) reduction(+:sum_loss) for (data_size_t i = 0; i < num_data_; ++i) { std::vector raw_score(num_tree_per_iteration); for (int k = 0; k < num_tree_per_iteration; ++k) { size_t idx = static_cast(num_data_) * k + i; raw_score[k] = static_cast(score[idx]); } std::vector rec(num_pred_per_row); objective->ConvertOutput(raw_score.data(), rec.data()); // add loss sum_loss += PointWiseLossCalculator::LossOnPoint(label_[i], &rec, config_); } } else { #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) reduction(+:sum_loss) for (data_size_t i = 0; i < num_data_; ++i) { std::vector raw_score(num_tree_per_iteration); for (int k = 0; k < num_tree_per_iteration; ++k) { size_t idx = static_cast(num_data_) * k + i; raw_score[k] = static_cast(score[idx]); } std::vector rec(num_pred_per_row); objective->ConvertOutput(raw_score.data(), rec.data()); // add loss sum_loss += PointWiseLossCalculator::LossOnPoint(label_[i], &rec, config_) * weights_[i]; } } } else { if (weights_ == nullptr) { #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) reduction(+:sum_loss) for (data_size_t i = 0; i < num_data_; ++i) { std::vector rec(num_tree_per_iteration); for (int k = 0; k < num_tree_per_iteration; ++k) { size_t idx = static_cast(num_data_) * k + i; rec[k] = static_cast(score[idx]); } // add loss sum_loss += PointWiseLossCalculator::LossOnPoint(label_[i], &rec, config_); } } else { #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) reduction(+:sum_loss) for (data_size_t i = 0; i < num_data_; ++i) { std::vector rec(num_tree_per_iteration); for (int k = 0; k < num_tree_per_iteration; ++k) { size_t idx = static_cast(num_data_) * k + i; rec[k] = static_cast(score[idx]); } // add loss sum_loss += PointWiseLossCalculator::LossOnPoint(label_[i], &rec, config_) * weights_[i]; } } } double loss = sum_loss / sum_weights_; return std::vector(1, loss); } private: /*! \brief Number of data */ data_size_t num_data_; /*! \brief Pointer of label */ const label_t* label_; /*! \brief Pointer of weighs */ const label_t* weights_; /*! \brief Sum weights */ double sum_weights_; /*! \brief Name of this test set */ std::vector name_; int num_class_; /*! \brief config parameters*/ Config config_; }; /*! \brief top-k error for multiclass task; if k=1 (default) this is the usual multi-error */ class MultiErrorMetric: public MulticlassMetric { public: explicit MultiErrorMetric(const Config& config) :MulticlassMetric(config) {} inline static double LossOnPoint(label_t label, std::vector* score, const Config& config) { size_t k = static_cast(label); auto& ref_score = *score; int num_larger = 0; for (size_t i = 0; i < score->size(); ++i) { if (ref_score[i] >= ref_score[k]) ++num_larger; if (num_larger > config.multi_error_top_k) return 1.0f; } return 0.0f; } inline static const std::string Name(const Config& config) { if (config.multi_error_top_k == 1) { return "multi_error"; } else { return "multi_error@" + std::to_string(config.multi_error_top_k); } } }; /*! \brief Logloss for multiclass task */ class MultiSoftmaxLoglossMetric: public MulticlassMetric { public: explicit MultiSoftmaxLoglossMetric(const Config& config) :MulticlassMetric(config) {} inline static double LossOnPoint(label_t label, std::vector* score, const Config&) { size_t k = static_cast(label); auto& ref_score = *score; if (ref_score[k] > kEpsilon) { return static_cast(-std::log(ref_score[k])); } else { return -std::log(kEpsilon); } } inline static const std::string Name(const Config&) { return "multi_logloss"; } }; /*! \brief AUC mu for multiclass task*/ class AucMuMetric : public Metric { public: explicit AucMuMetric(const Config& config) : config_(config) { num_class_ = config.num_class; class_weights_ = config.auc_mu_weights_matrix; } virtual ~AucMuMetric() {} const std::vector& GetName() const override { return name_; } double factor_to_bigger_better() const override { return 1.0f; } void Init(const Metadata& metadata, data_size_t num_data) override { name_.emplace_back("auc_mu"); num_data_ = num_data; label_ = metadata.label(); // get weights weights_ = metadata.weights(); if (weights_ == nullptr) { sum_weights_ = static_cast(num_data_); } else { sum_weights_ = 0.0f; for (data_size_t i = 0; i < num_data_; ++i) { sum_weights_ += weights_[i]; } } // sort the data indices by true class sorted_data_idx_ = std::vector(num_data_, 0); for (data_size_t i = 0; i < num_data_; ++i) { sorted_data_idx_[i] = i; } Common::ParallelSort(sorted_data_idx_.begin(), sorted_data_idx_.end(), [this](data_size_t a, data_size_t b) { return label_[a] < label_[b]; }); // get size of each class class_sizes_ = std::vector(num_class_, 0); for (data_size_t i = 0; i < num_data_; ++i) { data_size_t curr_label = static_cast(label_[i]); ++class_sizes_[curr_label]; } // get total weight of data in each class class_data_weights_ = std::vector(num_class_, 0); if (weights_ != nullptr) { for (data_size_t i = 0; i < num_data_; ++i) { data_size_t curr_label = static_cast(label_[i]); class_data_weights_[curr_label] += weights_[i]; } } } std::vector Eval(const double* score, const ObjectiveFunction*) const override { // the notation follows that used in the paper introducing the auc-mu metric: // https://proceedings.mlr.press/v97/kleiman19a.html auto S = std::vector>(num_class_, std::vector(num_class_, 0)); int i_start = 0; for (int i = 0; i < num_class_; ++i) { int j_start = i_start + class_sizes_[i]; for (int j = i + 1; j < num_class_; ++j) { std::vector curr_v; for (int k = 0; k < num_class_; ++k) { curr_v.emplace_back(class_weights_[i][k] - class_weights_[j][k]); } double t1 = curr_v[i] - curr_v[j]; // extract the data indices belonging to class i or j std::vector class_i_j_indices; class_i_j_indices.assign(sorted_data_idx_.begin() + i_start, sorted_data_idx_.begin() + i_start + class_sizes_[i]); class_i_j_indices.insert(class_i_j_indices.end(), sorted_data_idx_.begin() + j_start, sorted_data_idx_.begin() + j_start + class_sizes_[j]); // sort according to distance from separating hyperplane std::vector> dist; for (data_size_t k = 0; static_cast(k) < class_i_j_indices.size(); ++k) { data_size_t a = class_i_j_indices[k]; double v_a = 0; for (int m = 0; m < num_class_; ++m) { v_a += curr_v[m] * score[num_data_ * m + a]; } dist.push_back(std::pair(a, t1 * v_a)); } Common::ParallelSort(dist.begin(), dist.end(), [this](std::pair a, std::pair b) { // if scores are equal, put j class first if (std::fabs(a.second - b.second) < kEpsilon) { return label_[a.first] > label_[b.first]; } else if (a.second < b.second) { return true; } else { return false; } }); // calculate AUC double num_j = 0; double last_j_dist = 0; double num_current_j = 0; if (weights_ == nullptr) { for (size_t k = 0; k < dist.size(); ++k) { data_size_t a = dist[k].first; double curr_dist = dist[k].second; if (label_[a] == i) { if (std::fabs(curr_dist - last_j_dist) < kEpsilon) { S[i][j] += num_j - 0.5 * num_current_j; // members of class j with same distance as a contribute 0.5 } else { S[i][j] += num_j; } } else { ++num_j; if (std::fabs(curr_dist - last_j_dist) < kEpsilon) { ++num_current_j; } else { last_j_dist = dist[k].second; num_current_j = 1; } } } } else { for (size_t k = 0; k < dist.size(); ++k) { data_size_t a = dist[k].first; double curr_dist = dist[k].second; double curr_weight = weights_[a]; if (label_[a] == i) { if (std::fabs(curr_dist - last_j_dist) < kEpsilon) { S[i][j] += curr_weight * (num_j - 0.5 * num_current_j); // members of class j with same distance as a contribute 0.5 } else { S[i][j] += curr_weight * num_j; } } else { num_j += curr_weight; if (std::fabs(curr_dist - last_j_dist) < kEpsilon) { num_current_j += curr_weight; } else { last_j_dist = dist[k].second; num_current_j = curr_weight; } } } } j_start += class_sizes_[j]; } i_start += class_sizes_[i]; } double ans = 0; for (int i = 0; i < num_class_; ++i) { for (int j = i + 1; j < num_class_; ++j) { if (weights_ == nullptr) { ans += (S[i][j] / class_sizes_[i]) / class_sizes_[j]; } else { ans += (S[i][j] / class_data_weights_[i]) / class_data_weights_[j]; } } } ans = (2.0 * ans / num_class_) / (num_class_ - 1); return std::vector(1, ans); } private: /*! \brief Number of data*/ data_size_t num_data_; /*! \brief Pointer to label*/ const label_t* label_; /*! \brief Name of this metric*/ std::vector name_; /*! \brief Number of classes*/ int num_class_; /*! \brief Class auc-mu weights*/ std::vector> class_weights_; /*! \brief Data weights */ const label_t* weights_; /*! \brief Sum of data weights */ double sum_weights_; /*! \brief Sum of data weights in each class*/ std::vector class_data_weights_; /*! \brief Number of data in each class*/ std::vector class_sizes_; /*! \brief config parameters*/ Config config_; /*! \brief index to data, sorted by true class*/ std::vector sorted_data_idx_; }; } // namespace LightGBM #endif // LIGHTGBM_SRC_METRIC_MULTICLASS_METRIC_HPP_ ================================================ FILE: src/metric/rank_metric.hpp ================================================ /*! * Copyright (c) 2016-2026 Microsoft Corporation. All rights reserved. * Copyright (c) 2016-2026 The LightGBM developers. All rights reserved. * Licensed under the MIT License. See LICENSE file in the project root for license information. */ #ifndef LIGHTGBM_SRC_METRIC_RANK_METRIC_HPP_ #define LIGHTGBM_SRC_METRIC_RANK_METRIC_HPP_ #include #include #include #include #include #include #include namespace LightGBM { class NDCGMetric:public Metric { public: explicit NDCGMetric(const Config& config) { // get eval position eval_at_ = config.eval_at; auto label_gain = config.label_gain; DCGCalculator::DefaultEvalAt(&eval_at_); DCGCalculator::DefaultLabelGain(&label_gain); // initialize DCG calculator DCGCalculator::Init(label_gain); } ~NDCGMetric() { } void Init(const Metadata& metadata, data_size_t num_data) override { for (auto k : eval_at_) { name_.emplace_back(std::string("ndcg@") + std::to_string(k)); } num_data_ = num_data; // get label label_ = metadata.label(); num_queries_ = metadata.num_queries(); DCGCalculator::CheckMetadata(metadata, num_queries_); DCGCalculator::CheckLabel(label_, num_data_); // get query boundaries query_boundaries_ = metadata.query_boundaries(); if (query_boundaries_ == nullptr) { Log::Fatal("The NDCG metric requires query information"); } // get query weights query_weights_ = metadata.query_weights(); if (query_weights_ == nullptr) { sum_query_weights_ = static_cast(num_queries_); } else { sum_query_weights_ = 0.0f; for (data_size_t i = 0; i < num_queries_; ++i) { sum_query_weights_ += query_weights_[i]; } } inverse_max_dcgs_.resize(num_queries_); // cache the inverse max DCG for all queries, used to calculate NDCG #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) for (data_size_t i = 0; i < num_queries_; ++i) { inverse_max_dcgs_[i].resize(eval_at_.size(), 0.0f); DCGCalculator::CalMaxDCG(eval_at_, label_ + query_boundaries_[i], query_boundaries_[i + 1] - query_boundaries_[i], &inverse_max_dcgs_[i]); for (size_t j = 0; j < inverse_max_dcgs_[i].size(); ++j) { if (inverse_max_dcgs_[i][j] > 0.0f) { inverse_max_dcgs_[i][j] = 1.0f / inverse_max_dcgs_[i][j]; } else { // marking negative for all negative queries. // if one meet this query, it's ndcg will be set as -1. inverse_max_dcgs_[i][j] = -1.0f; } } } } const std::vector& GetName() const override { return name_; } double factor_to_bigger_better() const override { return 1.0f; } std::vector Eval(const double* score, const ObjectiveFunction*) const override { int num_threads = OMP_NUM_THREADS(); // some buffers for multi-threading sum up std::vector> result_buffer_; for (int i = 0; i < num_threads; ++i) { result_buffer_.emplace_back(eval_at_.size(), 0.0f); } std::vector tmp_dcg(eval_at_.size(), 0.0f); if (query_weights_ == nullptr) { #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) firstprivate(tmp_dcg) for (data_size_t i = 0; i < num_queries_; ++i) { const int tid = omp_get_thread_num(); // if all doc in this query are all negative, let its NDCG=1 if (inverse_max_dcgs_[i][0] <= 0.0f) { for (size_t j = 0; j < eval_at_.size(); ++j) { result_buffer_[tid][j] += 1.0f; } } else { // calculate DCG DCGCalculator::CalDCG(eval_at_, label_ + query_boundaries_[i], score + query_boundaries_[i], query_boundaries_[i + 1] - query_boundaries_[i], &tmp_dcg); // calculate NDCG for (size_t j = 0; j < eval_at_.size(); ++j) { result_buffer_[tid][j] += tmp_dcg[j] * inverse_max_dcgs_[i][j]; } } } } else { #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) firstprivate(tmp_dcg) for (data_size_t i = 0; i < num_queries_; ++i) { const int tid = omp_get_thread_num(); // if all doc in this query are all negative, let its NDCG=1 if (inverse_max_dcgs_[i][0] <= 0.0f) { for (size_t j = 0; j < eval_at_.size(); ++j) { result_buffer_[tid][j] += 1.0f; } } else { // calculate DCG DCGCalculator::CalDCG(eval_at_, label_ + query_boundaries_[i], score + query_boundaries_[i], query_boundaries_[i + 1] - query_boundaries_[i], &tmp_dcg); // calculate NDCG for (size_t j = 0; j < eval_at_.size(); ++j) { result_buffer_[tid][j] += tmp_dcg[j] * inverse_max_dcgs_[i][j] * query_weights_[i]; } } } } // Get final average NDCG std::vector result(eval_at_.size(), 0.0f); for (size_t j = 0; j < result.size(); ++j) { for (int i = 0; i < num_threads; ++i) { result[j] += result_buffer_[i][j]; } result[j] /= sum_query_weights_; } return result; } private: /*! \brief Number of data */ data_size_t num_data_; /*! \brief Pointer of label */ const label_t* label_; /*! \brief Name of test set */ std::vector name_; /*! \brief Query boundaries information */ const data_size_t* query_boundaries_; /*! \brief Number of queries */ data_size_t num_queries_; /*! \brief Weights of queries */ const label_t* query_weights_; /*! \brief Sum weights of queries */ double sum_query_weights_; /*! \brief Evaluate position of NDCG */ std::vector eval_at_; /*! \brief Cache the inverse max dcg for all queries */ std::vector> inverse_max_dcgs_; }; } // namespace LightGBM #endif // LIGHTGBM_SRC_METRIC_RANK_METRIC_HPP_ ================================================ FILE: src/metric/regression_metric.hpp ================================================ /*! * Copyright (c) 2016-2026 Microsoft Corporation. All rights reserved. * Copyright (c) 2016-2026 The LightGBM developers. All rights reserved. * Licensed under the MIT License. See LICENSE file in the project root for license information. */ #ifndef LIGHTGBM_SRC_METRIC_REGRESSION_METRIC_HPP_ #define LIGHTGBM_SRC_METRIC_REGRESSION_METRIC_HPP_ #include #include #include #include #include #include namespace LightGBM { /*! * \brief Metric for regression task. * Use static class "PointWiseLossCalculator" to calculate loss point-wise */ template class RegressionMetric: public Metric { public: explicit RegressionMetric(const Config& config) :config_(config) { } virtual ~RegressionMetric() { } const std::vector& GetName() const override { return name_; } double factor_to_bigger_better() const override { return -1.0f; } void Init(const Metadata& metadata, data_size_t num_data) override { name_.emplace_back(PointWiseLossCalculator::Name()); num_data_ = num_data; // get label label_ = metadata.label(); // get weights weights_ = metadata.weights(); if (weights_ == nullptr) { sum_weights_ = static_cast(num_data_); } else { sum_weights_ = 0.0f; for (data_size_t i = 0; i < num_data_; ++i) { sum_weights_ += weights_[i]; } } for (data_size_t i = 0; i < num_data_; ++i) { PointWiseLossCalculator::CheckLabel(label_[i]); } } std::vector Eval(const double* score, const ObjectiveFunction* objective) const override { double sum_loss = 0.0f; if (objective == nullptr) { if (weights_ == nullptr) { #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) reduction(+:sum_loss) for (data_size_t i = 0; i < num_data_; ++i) { // add loss sum_loss += PointWiseLossCalculator::LossOnPoint(label_[i], score[i], config_); } } else { #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) reduction(+:sum_loss) for (data_size_t i = 0; i < num_data_; ++i) { // add loss sum_loss += PointWiseLossCalculator::LossOnPoint(label_[i], score[i], config_) * weights_[i]; } } } else { if (weights_ == nullptr) { #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) reduction(+:sum_loss) for (data_size_t i = 0; i < num_data_; ++i) { // add loss double t = 0; objective->ConvertOutput(&score[i], &t); sum_loss += PointWiseLossCalculator::LossOnPoint(label_[i], t, config_); } } else { #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) reduction(+:sum_loss) for (data_size_t i = 0; i < num_data_; ++i) { // add loss double t = 0; objective->ConvertOutput(&score[i], &t); sum_loss += PointWiseLossCalculator::LossOnPoint(label_[i], t, config_) * weights_[i]; } } } double loss = PointWiseLossCalculator::AverageLoss(sum_loss, sum_weights_); return std::vector(1, loss); } inline static double AverageLoss(double sum_loss, double sum_weights) { return sum_loss / sum_weights; } inline static void CheckLabel(label_t) { } protected: /*! \brief Number of data */ data_size_t num_data_; /*! \brief Pointer of label */ const label_t* label_; /*! \brief Pointer of weighs */ const label_t* weights_; /*! \brief Sum weights */ double sum_weights_; /*! \brief Name of this test set */ Config config_; std::vector name_; }; /*! \brief RMSE loss for regression task */ class RMSEMetric: public RegressionMetric { public: explicit RMSEMetric(const Config& config) :RegressionMetric(config) {} inline static double LossOnPoint(label_t label, double score, const Config&) { return (score - label)*(score - label); } inline static double AverageLoss(double sum_loss, double sum_weights) { // need sqrt the result for RMSE loss return std::sqrt(sum_loss / sum_weights); } inline static const char* Name() { return "rmse"; } }; /*! \brief L2 loss for regression task */ class L2Metric: public RegressionMetric { public: explicit L2Metric(const Config& config) :RegressionMetric(config) {} inline static double LossOnPoint(label_t label, double score, const Config&) { return (score - label)*(score - label); } inline static const char* Name() { return "l2"; } }; /*! \brief Quantile loss for regression task */ class QuantileMetric : public RegressionMetric { public: explicit QuantileMetric(const Config& config) :RegressionMetric(config) { } inline static double LossOnPoint(label_t label, double score, const Config& config) { double delta = label - score; if (delta < 0) { return (config.alpha - 1.0f) * delta; } else { return config.alpha * delta; } } inline static const char* Name() { return "quantile"; } }; /*! \brief L1 loss for regression task */ class L1Metric: public RegressionMetric { public: explicit L1Metric(const Config& config) :RegressionMetric(config) {} inline static double LossOnPoint(label_t label, double score, const Config&) { return std::fabs(score - label); } inline static const char* Name() { return "l1"; } }; /*! \brief Huber loss for regression task */ class HuberLossMetric: public RegressionMetric { public: explicit HuberLossMetric(const Config& config) :RegressionMetric(config) { } inline static double LossOnPoint(label_t label, double score, const Config& config) { const double diff = score - label; if (std::abs(diff) <= config.alpha) { return 0.5f * diff * diff; } else { return config.alpha * (std::abs(diff) - 0.5f * config.alpha); } } inline static const char* Name() { return "huber"; } }; /*! \brief Fair loss for regression task */ // http://research.microsoft.com/en-us/um/people/zhang/INRIA/Publis/Tutorial-Estim/node24.html class FairLossMetric: public RegressionMetric { public: explicit FairLossMetric(const Config& config) :RegressionMetric(config) { } inline static double LossOnPoint(label_t label, double score, const Config& config) { const double x = std::fabs(score - label); const double c = config.fair_c; return c * x - c * c * std::log1p(x / c); } inline static const char* Name() { return "fair"; } }; /*! \brief Poisson regression loss for regression task */ class PoissonMetric: public RegressionMetric { public: explicit PoissonMetric(const Config& config) :RegressionMetric(config) { } inline static double LossOnPoint(label_t label, double score, const Config&) { const double eps = 1e-10f; if (score < eps) { score = eps; } return score - label * std::log(score); } inline static const char* Name() { return "poisson"; } }; /*! \brief MAPE regression loss for regression task */ class MAPEMetric : public RegressionMetric { public: explicit MAPEMetric(const Config& config) :RegressionMetric(config) { } inline static double LossOnPoint(label_t label, double score, const Config&) { return std::fabs((label - score)) / std::max(1.0f, std::fabs(label)); } inline static const char* Name() { return "mape"; } }; class GammaMetric : public RegressionMetric { public: explicit GammaMetric(const Config& config) :RegressionMetric(config) { } inline static double LossOnPoint(label_t label, double score, const Config&) { const double psi = 1.0; const double theta = -1.0 / score; const double a = psi; const double b = -Common::SafeLog(-theta); const double c = 1. / psi * Common::SafeLog(label / psi) - Common::SafeLog(label) - 0; // 0 = std::lgamma(1.0 / psi) = std::lgamma(1.0); return -((label * theta - b) / a + c); } inline static const char* Name() { return "gamma"; } inline static void CheckLabel(label_t label) { CHECK_GT(label, 0); } }; class GammaDevianceMetric : public RegressionMetric { public: explicit GammaDevianceMetric(const Config& config) :RegressionMetric(config) { } inline static double LossOnPoint(label_t label, double score, const Config&) { const double epsilon = 1.0e-9; const double tmp = label / (score + epsilon); return tmp - Common::SafeLog(tmp) - 1; } inline static const char* Name() { return "gamma_deviance"; } inline static double AverageLoss(double sum_loss, double) { return sum_loss * 2; } inline static void CheckLabel(label_t label) { CHECK_GT(label, 0); } }; class TweedieMetric : public RegressionMetric { public: explicit TweedieMetric(const Config& config) :RegressionMetric(config) { } inline static double LossOnPoint(label_t label, double score, const Config& config) { const double rho = config.tweedie_variance_power; const double eps = 1e-10f; if (score < eps) { score = eps; } const double a = label * std::exp((1 - rho) * std::log(score)) / (1 - rho); const double b = std::exp((2 - rho) * std::log(score)) / (2 - rho); return -a + b; } inline static const char* Name() { return "tweedie"; } }; class R2Metric: public Metric { public: explicit R2Metric(const Config& config) :config_(config) {} const std::vector& GetName() const override { return name_; } double factor_to_bigger_better() const override { return 1.0f; } void Init(const Metadata& metadata, data_size_t num_data) override { name_.emplace_back("r2"); num_data_ = num_data; label_ = metadata.label(); weights_ = metadata.weights(); double sum_label = 0.0f; if (weights_ == nullptr) { sum_weights_ = static_cast(num_data_); #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) reduction(+:sum_label) for (data_size_t i = 0; i < num_data_; ++i) { sum_label += label_[i]; } } else { double local_sum_weights = 0.0f; #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) reduction(+:local_sum_weights, sum_label) for (data_size_t i = 0; i < num_data_; ++i) { local_sum_weights += weights_[i]; sum_label += label_[i] * weights_[i]; } sum_weights_ = local_sum_weights; } label_mean_ = sum_label / sum_weights_; total_sum_squares_ = 0.0f; double local_total_sum_squares = 0.0f; if (weights_ == nullptr) { #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) reduction(+:local_total_sum_squares) for (data_size_t i = 0; i < num_data_; ++i) { double diff = label_[i] - label_mean_; local_total_sum_squares += diff * diff; } } else { #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) reduction(+:local_total_sum_squares) for (data_size_t i = 0; i < num_data_; ++i) { double diff = label_[i] - label_mean_; local_total_sum_squares += diff * diff * weights_[i]; } } total_sum_squares_ = local_total_sum_squares; } std::vector Eval(const double* score, const ObjectiveFunction* objective) const override { double residual_sum_squares = 0.0f; if (objective == nullptr) { if (weights_ == nullptr) { #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) reduction(+:residual_sum_squares) for (data_size_t i = 0; i < num_data_; ++i) { double diff = label_[i] - score[i]; residual_sum_squares += diff * diff; } } else { #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) reduction(+:residual_sum_squares) for (data_size_t i = 0; i < num_data_; ++i) { double diff = label_[i] - score[i]; residual_sum_squares += diff * diff * weights_[i]; } } } else { if (weights_ == nullptr) { #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) reduction(+:residual_sum_squares) for (data_size_t i = 0; i < num_data_; ++i) { double t = 0; objective->ConvertOutput(&score[i], &t); double diff = label_[i] - t; residual_sum_squares += diff * diff; } } else { #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) reduction(+:residual_sum_squares) for (data_size_t i = 0; i < num_data_; ++i) { double t = 0; objective->ConvertOutput(&score[i], &t); double diff = label_[i] - t; residual_sum_squares += diff * diff * weights_[i]; } } } double r2 = 1.0 - (residual_sum_squares / total_sum_squares_); if (std::fabs(total_sum_squares_) < kZeroThreshold) { return std::vector(1, std::fabs(residual_sum_squares) < kZeroThreshold ? 1.0 : 0.0); } return std::vector(1, r2); } protected: data_size_t num_data_; const label_t* label_; const label_t* weights_; double sum_weights_; Config config_; std::vector name_; // Custom members for R2 calculation double label_mean_; double total_sum_squares_; }; } // namespace LightGBM #endif // LIGHTGBM_SRC_METRIC_REGRESSION_METRIC_HPP_ ================================================ FILE: src/metric/xentropy_metric.hpp ================================================ /*! * Copyright (c) 2017-2026 Microsoft Corporation. All rights reserved. * Copyright (c) 2017-2026 The LightGBM developers. All rights reserved. * Licensed under the MIT License. See LICENSE file in the project root for license information. */ #ifndef LIGHTGBM_SRC_METRIC_XENTROPY_METRIC_HPP_ #define LIGHTGBM_SRC_METRIC_XENTROPY_METRIC_HPP_ #include #include #include #include #include #include #include #include /* * Implements three related metrics: * * (1) standard cross-entropy that can be used for continuous labels in [0, 1] * (2) "intensity-weighted" cross-entropy, also for continuous labels in [0, 1] * (3) Kullback-Leibler divergence, also for continuous labels in [0, 1] * * (3) adds an offset term to (1); the entropy of the label * * See xentropy_objective.hpp for further details. * */ namespace LightGBM { // label should be in interval [0, 1]; // prob should be in interval (0, 1); prob is clipped if needed inline static double XentLoss(label_t label, double prob) { const double log_arg_epsilon = 1.0e-12; double a = label; if (prob > log_arg_epsilon) { a *= std::log(prob); } else { a *= std::log(log_arg_epsilon); } double b = 1.0f - label; if (1.0f - prob > log_arg_epsilon) { b *= std::log(1.0f - prob); } else { b *= std::log(log_arg_epsilon); } return - (a + b); } // hhat >(=) 0 assumed; and weight > 0 required; but not checked here inline static double XentLambdaLoss(label_t label, label_t weight, double hhat) { return XentLoss(label, 1.0f - std::exp(-weight * hhat)); } // Computes the (negative) entropy for label p; p should be in interval [0, 1]; // This is used to presum the KL-divergence offset term (to be _added_ to the cross-entropy loss). // NOTE: x*log(x) = 0 for x=0,1; so only add when in (0, 1); avoid log(0)*0 inline static double YentLoss(double p) { double hp = 0.0; if (p > 0) hp += p * std::log(p); double q = 1.0f - p; if (q > 0) hp += q * std::log(q); return hp; } // // CrossEntropyMetric : "xentropy" : (optional) weights are used linearly // class CrossEntropyMetric : public Metric { public: explicit CrossEntropyMetric(const Config&) {} virtual ~CrossEntropyMetric() {} void Init(const Metadata& metadata, data_size_t num_data) override { name_.emplace_back("cross_entropy"); num_data_ = num_data; label_ = metadata.label(); weights_ = metadata.weights(); CHECK_NOTNULL(label_); // ensure that labels are in interval [0, 1], interval ends included Common::CheckElementsIntervalClosed(label_, 0.0f, 1.0f, num_data_, GetName()[0].c_str()); Log::Info("[%s:%s]: (metric) labels passed interval [0, 1] check", GetName()[0].c_str(), __func__); // check that weights are non-negative and sum is positive if (weights_ == nullptr) { sum_weights_ = static_cast(num_data_); } else { label_t minw; Common::ObtainMinMaxSum(weights_, num_data_, &minw, static_cast(nullptr), &sum_weights_); if (minw < 0.0f) { Log::Fatal("[%s:%s]: (metric) weights not allowed to be negative", GetName()[0].c_str(), __func__); } } // check weight sum (may fail to be zero) if (sum_weights_ <= 0.0f) { Log::Fatal("[%s:%s]: sum-of-weights = %f is non-positive", __func__, GetName()[0].c_str(), sum_weights_); } Log::Info("[%s:%s]: sum-of-weights = %f", GetName()[0].c_str(), __func__, sum_weights_); } std::vector Eval(const double* score, const ObjectiveFunction* objective) const override { double sum_loss = 0.0f; if (objective == nullptr) { if (weights_ == nullptr) { #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) reduction(+:sum_loss) for (data_size_t i = 0; i < num_data_; ++i) { sum_loss += XentLoss(label_[i], score[i]); // NOTE: does not work unless score is a probability } } else { #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) reduction(+:sum_loss) for (data_size_t i = 0; i < num_data_; ++i) { sum_loss += XentLoss(label_[i], score[i]) * weights_[i]; // NOTE: does not work unless score is a probability } } } else { if (weights_ == nullptr) { #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) reduction(+:sum_loss) for (data_size_t i = 0; i < num_data_; ++i) { double p = 0; objective->ConvertOutput(&score[i], &p); sum_loss += XentLoss(label_[i], p); } } else { #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) reduction(+:sum_loss) for (data_size_t i = 0; i < num_data_; ++i) { double p = 0; objective->ConvertOutput(&score[i], &p); sum_loss += XentLoss(label_[i], p) * weights_[i]; } } } double loss = sum_loss / sum_weights_; return std::vector(1, loss); } const std::vector& GetName() const override { return name_; } double factor_to_bigger_better() const override { return -1.0f; // negative means smaller loss is better, positive means larger loss is better } private: /*! \brief Number of data points */ data_size_t num_data_; /*! \brief Pointer to label */ const label_t* label_; /*! \brief Pointer to weights */ const label_t* weights_; /*! \brief Sum of weights */ double sum_weights_; /*! \brief Name of this metric */ std::vector name_; }; // // CrossEntropyLambdaMetric : "xentlambda" : (optional) weights have a different meaning than for "xentropy" // ATTENTION: Supposed to be used when the objective also is "xentlambda" // class CrossEntropyLambdaMetric : public Metric { public: explicit CrossEntropyLambdaMetric(const Config&) {} virtual ~CrossEntropyLambdaMetric() {} void Init(const Metadata& metadata, data_size_t num_data) override { name_.emplace_back("cross_entropy_lambda"); num_data_ = num_data; label_ = metadata.label(); weights_ = metadata.weights(); CHECK_NOTNULL(label_); Common::CheckElementsIntervalClosed(label_, 0.0f, 1.0f, num_data_, GetName()[0].c_str()); Log::Info("[%s:%s]: (metric) labels passed interval [0, 1] check", GetName()[0].c_str(), __func__); // check all weights are strictly positive; throw error if not if (weights_ != nullptr) { label_t minw; Common::ObtainMinMaxSum(weights_, num_data_, &minw, static_cast(nullptr), static_cast(nullptr)); if (minw <= 0.0f) { Log::Fatal("[%s:%s]: (metric) all weights must be positive", GetName()[0].c_str(), __func__); } } } std::vector Eval(const double* score, const ObjectiveFunction* objective) const override { double sum_loss = 0.0f; if (objective == nullptr) { if (weights_ == nullptr) { #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) reduction(+:sum_loss) for (data_size_t i = 0; i < num_data_; ++i) { double hhat = std::log1p(std::exp(score[i])); // auto-convert sum_loss += XentLambdaLoss(label_[i], 1.0f, hhat); } } else { #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) reduction(+:sum_loss) for (data_size_t i = 0; i < num_data_; ++i) { double hhat = std::log1p(std::exp(score[i])); // auto-convert sum_loss += XentLambdaLoss(label_[i], weights_[i], hhat); } } } else { if (weights_ == nullptr) { #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) reduction(+:sum_loss) for (data_size_t i = 0; i < num_data_; ++i) { double hhat = 0; objective->ConvertOutput(&score[i], &hhat); // NOTE: this only works if objective = "xentlambda" sum_loss += XentLambdaLoss(label_[i], 1.0f, hhat); } } else { #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) reduction(+:sum_loss) for (data_size_t i = 0; i < num_data_; ++i) { double hhat = 0; objective->ConvertOutput(&score[i], &hhat); // NOTE: this only works if objective = "xentlambda" sum_loss += XentLambdaLoss(label_[i], weights_[i], hhat); } } } return std::vector(1, sum_loss / static_cast(num_data_)); } const std::vector& GetName() const override { return name_; } double factor_to_bigger_better() const override { return -1.0f; } private: /*! \brief Number of data points */ data_size_t num_data_; /*! \brief Pointer to label */ const label_t* label_; /*! \brief Pointer to weights */ const label_t* weights_; /*! \brief Name of this metric */ std::vector name_; }; // // KullbackLeiblerDivergence : "kldiv" : (optional) weights are used linearly // class KullbackLeiblerDivergence : public Metric { public: explicit KullbackLeiblerDivergence(const Config&) {} virtual ~KullbackLeiblerDivergence() {} void Init(const Metadata& metadata, data_size_t num_data) override { name_.emplace_back("kullback_leibler"); num_data_ = num_data; label_ = metadata.label(); weights_ = metadata.weights(); CHECK_NOTNULL(label_); Common::CheckElementsIntervalClosed(label_, 0.0f, 1.0f, num_data_, GetName()[0].c_str()); Log::Info("[%s:%s]: (metric) labels passed interval [0, 1] check", GetName()[0].c_str(), __func__); if (weights_ == nullptr) { sum_weights_ = static_cast(num_data_); } else { label_t minw; Common::ObtainMinMaxSum(weights_, num_data_, &minw, static_cast(nullptr), &sum_weights_); if (minw < 0.0f) { Log::Fatal("[%s:%s]: (metric) at least one weight is negative", GetName()[0].c_str(), __func__); } } // check weight sum if (sum_weights_ <= 0.0f) { Log::Fatal("[%s:%s]: sum-of-weights = %f is non-positive", GetName()[0].c_str(), __func__, sum_weights_); } Log::Info("[%s:%s]: sum-of-weights = %f", GetName()[0].c_str(), __func__, sum_weights_); // evaluate offset term presum_label_entropy_ = 0.0f; if (weights_ == nullptr) { for (data_size_t i = 0; i < num_data; ++i) { presum_label_entropy_ += YentLoss(label_[i]); } } else { for (data_size_t i = 0; i < num_data; ++i) { presum_label_entropy_ += YentLoss(label_[i]) * weights_[i]; } } presum_label_entropy_ /= sum_weights_; // communicate the value of the offset term to be added Log::Info("%s offset term = %f", GetName()[0].c_str(), presum_label_entropy_); } std::vector Eval(const double* score, const ObjectiveFunction* objective) const override { double sum_loss = 0.0f; if (objective == nullptr) { if (weights_ == nullptr) { #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) reduction(+:sum_loss) for (data_size_t i = 0; i < num_data_; ++i) { sum_loss += XentLoss(label_[i], score[i]); // NOTE: does not work unless score is a probability } } else { #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) reduction(+:sum_loss) for (data_size_t i = 0; i < num_data_; ++i) { sum_loss += XentLoss(label_[i], score[i]) * weights_[i]; // NOTE: does not work unless score is a probability } } } else { if (weights_ == nullptr) { #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) reduction(+:sum_loss) for (data_size_t i = 0; i < num_data_; ++i) { double p = 0; objective->ConvertOutput(&score[i], &p); sum_loss += XentLoss(label_[i], p); } } else { #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) reduction(+:sum_loss) for (data_size_t i = 0; i < num_data_; ++i) { double p = 0; objective->ConvertOutput(&score[i], &p); sum_loss += XentLoss(label_[i], p) * weights_[i]; } } } double loss = presum_label_entropy_ + sum_loss / sum_weights_; return std::vector(1, loss); } const std::vector& GetName() const override { return name_; } double factor_to_bigger_better() const override { return -1.0f; } private: /*! \brief Number of data points */ data_size_t num_data_; /*! \brief Pointer to label */ const label_t* label_; /*! \brief Pointer to weights */ const label_t* weights_; /*! \brief Sum of weights */ double sum_weights_; /*! \brief Offset term to cross-entropy; precomputed during init */ double presum_label_entropy_; /*! \brief Name of this metric */ std::vector name_; }; } // end namespace LightGBM #endif // LIGHTGBM_SRC_METRIC_XENTROPY_METRIC_HPP_ ================================================ FILE: src/network/linker_topo.cpp ================================================ /*! * Copyright (c) 2016-2026 Microsoft Corporation. All rights reserved. * Copyright (c) 2016-2026 The LightGBM developers. All rights reserved. * Licensed under the MIT License. See LICENSE file in the project root for license information. */ #include #include #include #include #include #include namespace LightGBM { BruckMap::BruckMap() { k = 0; } BruckMap::BruckMap(int n) { k = n; // default set to -1 for (int i = 0; i < n; ++i) { in_ranks.push_back(-1); out_ranks.push_back(-1); } } BruckMap BruckMap::Construct(int rank, int num_machines) { // distance at k-th communication, distance[k] = 2^k std::vector distance; int k = 0; for (k = 0; (1 << k) < num_machines; ++k) { distance.push_back(1 << k); } BruckMap bruckMap(k); for (int j = 0; j < k; ++j) { // set incoming rank at k-th communication const int in_rank = (rank + distance[j]) % num_machines; bruckMap.in_ranks[j] = in_rank; // set outgoing rank at k-th communication const int out_rank = (rank - distance[j] + num_machines) % num_machines; bruckMap.out_ranks[j] = out_rank; } return bruckMap; } RecursiveHalvingMap::RecursiveHalvingMap() { k = 0; } RecursiveHalvingMap::RecursiveHalvingMap(int in_k, RecursiveHalvingNodeType _type, bool _is_power_of_2) { type = _type; k = in_k; is_power_of_2 = _is_power_of_2; if (type != RecursiveHalvingNodeType::Other) { for (int i = 0; i < k; ++i) { // default set as -1 ranks.push_back(-1); send_block_start.push_back(-1); send_block_len.push_back(-1); recv_block_start.push_back(-1); recv_block_len.push_back(-1); } } } RecursiveHalvingMap RecursiveHalvingMap::Construct(int rank, int num_machines) { // construct all recursive halving map for all machines int k = 0; while ((1 << k) <= num_machines) { ++k; } // let 1 << k <= num_machines --k; // distance of each communication std::vector distance; for (int i = 0; i < k; ++i) { distance.push_back(1 << (k - 1 - i)); } if ((1 << k) == num_machines) { RecursiveHalvingMap rec_map(k, RecursiveHalvingNodeType::Normal, true); // if num_machines = 2^k, don't need to group machines for (int i = 0; i < k; ++i) { // communication direction, %2 == 0 is positive const int dir = ((rank / distance[i]) % 2 == 0) ? 1 : -1; // neighbor at k-th communication const int next_node_idx = rank + dir * distance[i]; rec_map.ranks[i] = next_node_idx; // receive data block at k-th communication const int recv_block_start = rank / distance[i]; rec_map.recv_block_start[i] = recv_block_start * distance[i]; rec_map.recv_block_len[i] = distance[i]; // send data block at k-th communication const int send_block_start = next_node_idx / distance[i]; rec_map.send_block_start[i] = send_block_start * distance[i]; rec_map.send_block_len[i] = distance[i]; } return rec_map; } else { // if num_machines != 2^k, need to group machines int lower_power_of_2 = 1 << k; int rest = num_machines - lower_power_of_2; std::vector node_type(num_machines); for (int i = 0; i < num_machines; ++i) { node_type[i] = RecursiveHalvingNodeType::Normal; } // group, two machine in one group, total "rest" groups will have 2 machines. for (int i = 0; i < rest; ++i) { int right = num_machines - i * 2 - 1; int left = num_machines - i * 2 - 2; // let left machine as group leader node_type[left] = RecursiveHalvingNodeType::GroupLeader; node_type[right] = RecursiveHalvingNodeType::Other; } int group_cnt = 0; // cache block information for groups, group with 2 machines will have double block size std::vector group_block_start(lower_power_of_2); std::vector group_block_len(lower_power_of_2, 0); // convert from group to node leader std::vector group_to_node(lower_power_of_2); // convert from node to group std::vector node_to_group(num_machines); for (int i = 0; i < num_machines; ++i) { // meet new group if (node_type[i] == RecursiveHalvingNodeType::Normal || node_type[i] == RecursiveHalvingNodeType::GroupLeader) { group_to_node[group_cnt++] = i; } node_to_group[i] = group_cnt - 1; // add block len for this group group_block_len[group_cnt - 1]++; } // calculate the group block start group_block_start[0] = 0; for (int i = 1; i < lower_power_of_2; ++i) { group_block_start[i] = group_block_start[i - 1] + group_block_len[i - 1]; } RecursiveHalvingMap rec_map(k, node_type[rank], false); if (node_type[rank] == RecursiveHalvingNodeType::Other) { rec_map.neighbor = rank - 1; // not need to construct return rec_map; } if (node_type[rank] == RecursiveHalvingNodeType::GroupLeader) { rec_map.neighbor = rank + 1; } const int cur_group_idx = node_to_group[rank]; for (int i = 0; i < k; ++i) { const int dir = ((cur_group_idx / distance[i]) % 2 == 0) ? 1 : -1; const int next_node_idx = group_to_node[(cur_group_idx + dir * distance[i])]; rec_map.ranks[i] = next_node_idx; // get receive block information const int recv_block_start = cur_group_idx / distance[i]; rec_map.recv_block_start[i] = group_block_start[static_cast(recv_block_start) * distance[i]]; int recv_block_len = 0; // accumulate block len for (int j = 0; j < distance[i]; ++j) { recv_block_len += group_block_len[recv_block_start * distance[i] + j]; } rec_map.recv_block_len[i] = recv_block_len; // get send block information const int send_block_start = (cur_group_idx + dir * distance[i]) / distance[i]; rec_map.send_block_start[i] = group_block_start[static_cast(send_block_start) * distance[i]]; int send_block_len = 0; // accumulate block len for (int j = 0; j < distance[i]; ++j) { send_block_len += group_block_len[send_block_start * distance[i] + j]; } rec_map.send_block_len[i] = send_block_len; } return rec_map; } } } // namespace LightGBM ================================================ FILE: src/network/linkers.h ================================================ /*! * Copyright (c) 2016-2026 Microsoft Corporation. All rights reserved. * Copyright (c) 2016-2026 The LightGBM developers. All rights reserved. * Licensed under the MIT License. See LICENSE file in the project root for license information. */ #ifndef LIGHTGBM_SRC_NETWORK_LINKERS_H_ #define LIGHTGBM_SRC_NETWORK_LINKERS_H_ #include #include #include #include #include #include #include #include #include #include #include #ifdef USE_SOCKET #include "socket_wrapper.hpp" #endif #ifdef USE_MPI #include #define MPI_SAFE_CALL(mpi_return) CHECK((mpi_return) == MPI_SUCCESS) #endif namespace LightGBM { /*! * \brief A network basic communication wrapper. * Will wrap low level communication methods, e.g. mpi, socket and so on. * This class will wrap all linkers to other machines if needs */ class Linkers { public: Linkers() { is_init_ = false; } /*! * \brief Constructor * \param config Config of network settings */ explicit Linkers(Config config); /*! * \brief Destructor */ ~Linkers(); /*! * \brief Recv data, blocking * \param rank Which rank will send data to local machine * \param data Pointer of receive data * \param len Recv size, will block until receive len size of data */ inline void Recv(int rank, char* data, int len) const; inline void Recv(int rank, char* data, int64_t len) const; /*! * \brief Send data, blocking * \param rank Which rank local machine will send to * \param data Pointer of send data * \param len Send size */ inline void Send(int rank, char* data, int len) const; inline void Send(int rank, char* data, int64_t len) const; /*! * \brief Send and Recv at same time, blocking * \param send_rank * \param send_data * \param send_len * \param recv_rank * \param recv_data * \param recv_len */ inline void SendRecv(int send_rank, char* send_data, int send_len, int recv_rank, char* recv_data, int recv_len); inline void SendRecv(int send_rank, char* send_data, int64_t send_len, int recv_rank, char* recv_data, int64_t recv_len); /*! * \brief Get rank of local machine */ inline int rank(); /*! * \brief Get total number of machines */ inline int num_machines(); /*! * \brief Get Bruck map of this network */ inline const BruckMap& bruck_map(); /*! * \brief Get Recursive Halving map of this network */ inline const RecursiveHalvingMap& recursive_halving_map(); #ifdef USE_SOCKET /*! * \brief Bind local listen to port * \param port Local listen port */ void TryBind(int port); /*! * \brief Set socket to rank * \param rank * \param socket */ void SetLinker(int rank, const TcpSocket& socket); /*! * \brief Thread for listening * \param incoming_cnt Number of incoming machines */ void ListenThread(int incoming_cnt); /*! * \brief Construct network topo */ void Construct(); /*! * \brief Parser machines information from file * \param machines * \param filename */ void ParseMachineList(const std::string& machines, const std::string& filename); /*! * \brief Check one linker is connected or not * \param rank * \return True if linker is connected */ bool CheckLinker(int rank); /*! * \brief Print connected linkers */ void PrintLinkers(); #endif // USE_SOCKET #ifdef USE_MPI /*! * \brief Check if MPI has been initialized */ static bool IsMpiInitialized(); /*! * \brief Finalize the MPI session if it was initialized */ static void MpiFinalizeIfIsParallel(); /*! * \brief Abort the MPI session if it was initialized (called in case there was a error that needs abrupt ending) */ static void MpiAbortIfIsParallel(); #endif private: /*! \brief Rank of local machine */ int rank_; /*! \brief Total number machines */ int num_machines_; /*! \brief Bruck map */ BruckMap bruck_map_; /*! \brief Recursive Halving map */ RecursiveHalvingMap recursive_halving_map_; std::chrono::duration network_time_; bool is_init_; #ifdef USE_SOCKET /*! \brief use to store client ips */ std::vector client_ips_; /*! \brief use to store client ports */ std::vector client_ports_; /*! \brief time out for sockets, in minutes */ int socket_timeout_; /*! \brief Local listen ports */ int local_listen_port_; /*! \brief Linkers */ std::vector> linkers_; /*! \brief Local socket listener */ std::unique_ptr listener_; #endif // USE_SOCKET }; inline int Linkers::rank() { return rank_; } inline int Linkers::num_machines() { return num_machines_; } inline const BruckMap& Linkers::bruck_map() { return bruck_map_; } inline const RecursiveHalvingMap& Linkers::recursive_halving_map() { return recursive_halving_map_; } inline void Linkers::Recv(int rank, char* data, int64_t len) const { int64_t used = 0; do { int cur_size = static_cast(std::min(len - used, INT32_MAX)); Recv(rank, data + used, cur_size); used += cur_size; } while (used < len); } inline void Linkers::Send(int rank, char* data, int64_t len) const { int64_t used = 0; do { int cur_size = static_cast(std::min(len - used, INT32_MAX)); Send(rank, data + used, cur_size); used += cur_size; } while (used < len); } inline void Linkers::SendRecv(int send_rank, char* send_data, int64_t send_len, int recv_rank, char* recv_data, int64_t recv_len) { auto start_time = std::chrono::high_resolution_clock::now(); std::thread send_worker( [this, send_rank, send_data, send_len]() { Send(send_rank, send_data, send_len); }); Recv(recv_rank, recv_data, recv_len); send_worker.join(); // wait for send complete auto end_time = std::chrono::high_resolution_clock::now(); // output used time on each iteration network_time_ += std::chrono::duration(end_time - start_time); } #ifdef USE_SOCKET inline void Linkers::Recv(int rank, char* data, int len) const { int recv_cnt = 0; while (recv_cnt < len) { recv_cnt += linkers_[rank]->Recv(data + recv_cnt, // len - recv_cnt std::min(len - recv_cnt, SocketConfig::kMaxReceiveSize)); } } inline void Linkers::Send(int rank, char* data, int len) const { if (len <= 0) { return; } int send_cnt = 0; while (send_cnt < len) { send_cnt += linkers_[rank]->Send(data + send_cnt, len - send_cnt); } } inline void Linkers::SendRecv(int send_rank, char* send_data, int send_len, int recv_rank, char* recv_data, int recv_len) { auto start_time = std::chrono::high_resolution_clock::now(); if (send_len < SocketConfig::kSocketBufferSize) { // if buffer is enough, send will non-blocking Send(send_rank, send_data, send_len); Recv(recv_rank, recv_data, recv_len); } else { // if buffer is not enough, use another thread to send, since send will be blocking std::thread send_worker( [this, send_rank, send_data, send_len]() { Send(send_rank, send_data, send_len); }); Recv(recv_rank, recv_data, recv_len); send_worker.join(); } // wait for send complete auto end_time = std::chrono::high_resolution_clock::now(); // output used time on each iteration network_time_ += std::chrono::duration(end_time - start_time); } #endif // USE_SOCKET #ifdef USE_MPI inline void Linkers::Recv(int rank, char* data, int len) const { MPI_Status status; int read_cnt = 0; while (read_cnt < len) { MPI_SAFE_CALL(MPI_Recv(data + read_cnt, len - read_cnt, MPI_BYTE, rank, MPI_ANY_TAG, MPI_COMM_WORLD, &status)); int cur_cnt; MPI_SAFE_CALL(MPI_Get_count(&status, MPI_BYTE, &cur_cnt)); read_cnt += cur_cnt; } } inline void Linkers::Send(int rank, char* data, int len) const { if (len <= 0) { return; } MPI_Status status; MPI_Request send_request; MPI_SAFE_CALL(MPI_Isend(data, len, MPI_BYTE, rank, 0, MPI_COMM_WORLD, &send_request)); MPI_SAFE_CALL(MPI_Wait(&send_request, &status)); } inline void Linkers::SendRecv(int send_rank, char* send_data, int send_len, int recv_rank, char* recv_data, int recv_len) { MPI_Request send_request; // send first, non-blocking MPI_SAFE_CALL(MPI_Isend(send_data, send_len, MPI_BYTE, send_rank, 0, MPI_COMM_WORLD, &send_request)); // then receive, blocking MPI_Status status; int read_cnt = 0; while (read_cnt < recv_len) { MPI_SAFE_CALL(MPI_Recv(recv_data + read_cnt, recv_len - read_cnt, MPI_BYTE, recv_rank, 0, MPI_COMM_WORLD, &status)); int cur_cnt; MPI_SAFE_CALL(MPI_Get_count(&status, MPI_BYTE, &cur_cnt)); read_cnt += cur_cnt; } // wait for send complete MPI_SAFE_CALL(MPI_Wait(&send_request, &status)); } #endif // USE_MPI } // namespace LightGBM #endif // LIGHTGBM_SRC_NETWORK_LINKERS_H_ ================================================ FILE: src/network/linkers_mpi.cpp ================================================ /*! * Copyright (c) 2016-2026 Microsoft Corporation. All rights reserved. * Copyright (c) 2016-2026 The LightGBM developers. All rights reserved. * Licensed under the MIT License. See LICENSE file in the project root for license information. */ #ifdef USE_MPI #include "linkers.h" #include namespace LightGBM { Linkers::Linkers(Config) { is_init_ = false; int argc = 0; char**argv = nullptr; int flag = 0; MPI_SAFE_CALL(MPI_Initialized(&flag)); // test if MPI has been initialized if (!flag) { // if MPI not started, start it MPI_SAFE_CALL(MPI_Init_thread(&argc, &argv, MPI_THREAD_SERIALIZED, &flag)); } MPI_SAFE_CALL(MPI_Comm_size(MPI_COMM_WORLD, &num_machines_)); MPI_SAFE_CALL(MPI_Comm_rank(MPI_COMM_WORLD, &rank_)); // wait for all client start up MPI_SAFE_CALL(MPI_Barrier(MPI_COMM_WORLD)); bruck_map_ = BruckMap::Construct(rank_, num_machines_); recursive_halving_map_ = RecursiveHalvingMap::Construct(rank_, num_machines_); is_init_ = true; } Linkers::~Linkers() { // 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. // Instead we will handle finalize/abort for MPI in main(). } bool Linkers::IsMpiInitialized() { int is_mpi_init; MPI_SAFE_CALL(MPI_Initialized(&is_mpi_init)); return is_mpi_init; } void Linkers::MpiFinalizeIfIsParallel() { if (IsMpiInitialized()) { Log::Debug("Finalizing MPI session."); MPI_SAFE_CALL(MPI_Finalize()); } } void Linkers::MpiAbortIfIsParallel() { try { if (IsMpiInitialized()) { std::cerr << "Aborting MPI communication." << std::endl << std::flush; MPI_SAFE_CALL(MPI_Abort(MPI_COMM_WORLD, -1));; } } catch (...) { std::cerr << "Exception was raised before aborting MPI. Aborting process..." << std::endl << std::flush; abort(); } } } // namespace LightGBM #endif // USE_MPI ================================================ FILE: src/network/linkers_socket.cpp ================================================ /*! * Copyright (c) 2016-2026 Microsoft Corporation. All rights reserved. * Copyright (c) 2016-2026 The LightGBM developers. All rights reserved. * Licensed under the MIT License. See LICENSE file in the project root for license information. */ #ifdef USE_SOCKET #include #include #include #include #include #include #include #include #include #include #include #include #include "linkers.h" namespace LightGBM { Linkers::Linkers(Config config) { is_init_ = false; // start up socket TcpSocket::Startup(); network_time_ = std::chrono::duration(0); num_machines_ = config.num_machines; local_listen_port_ = config.local_listen_port; socket_timeout_ = config.time_out; rank_ = -1; // parse clients from file ParseMachineList(config.machines, config.machine_list_filename); if (rank_ == -1) { // get ip list of local machine std::unordered_set local_ip_list = TcpSocket::GetLocalIpList(); // get local rank for (size_t i = 0; i < client_ips_.size(); ++i) { if (local_ip_list.count(client_ips_[i]) > 0 && client_ports_[i] == local_listen_port_) { rank_ = static_cast(i); break; } } } if (rank_ == -1) { Log::Fatal("Machine list file doesn't contain the local machine"); } // construct listener listener_ = std::unique_ptr(new TcpSocket()); TryBind(local_listen_port_); for (int i = 0; i < num_machines_; ++i) { linkers_.push_back(nullptr); } // construct communication topo bruck_map_ = BruckMap::Construct(rank_, num_machines_); recursive_halving_map_ = RecursiveHalvingMap::Construct(rank_, num_machines_); // construct linkers Construct(); // free listener listener_->Close(); is_init_ = true; } Linkers::~Linkers() { if (is_init_) { for (size_t i = 0; i < linkers_.size(); ++i) { if (linkers_[i] != nullptr) { linkers_[i]->Close(); } } TcpSocket::Finalize(); Log::Info("Finished linking network in %f seconds", network_time_ * 1e-3); } } void Linkers::ParseMachineList(const std::string& machines, const std::string& filename) { std::vector lines; if (machines.empty()) { TextReader machine_list_reader(filename.c_str(), false); machine_list_reader.ReadAllLines(); if (machine_list_reader.Lines().empty()) { Log::Fatal("Machine list file %s doesn't exist", filename.c_str()); } lines = machine_list_reader.Lines(); } else { lines = Common::Split(machines.c_str(), ','); } for (auto& line : lines) { line = Common::Trim(line); if (line.find("rank=") != std::string::npos) { std::vector str_after_split = Common::Split(line.c_str(), '='); Common::Atoi(str_after_split[1].c_str(), &rank_); continue; } std::vector str_after_split = Common::Split(line.c_str(), ' '); if (str_after_split.size() != 2) { str_after_split = Common::Split(line.c_str(), ':'); if (str_after_split.size() != 2) { continue; } } if (client_ips_.size() >= static_cast(num_machines_)) { Log::Warning("machine_list size is larger than the parameter num_machines, ignoring redundant entries"); break; } str_after_split[0] = Common::Trim(str_after_split[0]); str_after_split[1] = Common::Trim(str_after_split[1]); client_ips_.push_back(str_after_split[0]); client_ports_.push_back(atoi(str_after_split[1].c_str())); } if (client_ips_.empty()) { Log::Fatal("Cannot find any ip and port.\n" "Please check machine_list_filename or machines parameter"); } if (client_ips_.size() != static_cast(num_machines_)) { Log::Warning("World size is larger than the machine_list size, change world size to %zu", client_ips_.size()); num_machines_ = static_cast(client_ips_.size()); } } void Linkers::TryBind(int port) { Log::Info("Trying to bind port %d...", port); if (listener_->Bind(port)) { Log::Info("Binding port %d succeeded", port); } else { Log::Fatal("Binding port %d failed", port); } } void Linkers::SetLinker(int rank, const TcpSocket& socket) { linkers_[rank].reset(new TcpSocket(socket)); // set timeout linkers_[rank]->SetTimeout(socket_timeout_ * 1000 * 60); } void Linkers::ListenThread(int incoming_cnt) { Log::Info("Listening..."); char buffer[100]; int connected_cnt = 0; while (connected_cnt < incoming_cnt) { // accept incoming socket TcpSocket handler = listener_->Accept(); if (handler.IsClosed()) { continue; } // receive rank int read_cnt = 0; int size_of_int = static_cast(sizeof(int)); while (read_cnt < size_of_int) { int cur_read_cnt = handler.Recv(buffer + read_cnt, size_of_int - read_cnt); read_cnt += cur_read_cnt; } int* ptr_in_rank = reinterpret_cast(buffer); int in_rank = *ptr_in_rank; if (in_rank < 0 || in_rank >= num_machines_) { Log::Fatal("Invalid rank %d found during initialization of linkers. The world size is %d.", in_rank, num_machines_); } // add new socket SetLinker(in_rank, handler); ++connected_cnt; } } void Linkers::Construct() { // save ranks that need to connect with std::unordered_map need_connect; for (int i = 0; i < num_machines_; ++i) { if (i != rank_) { need_connect[i] = 1; } } int incoming_cnt = 0; for (auto it = need_connect.begin(); it != need_connect.end(); ++it) { int machine_rank = it->first; if (machine_rank < rank_) { ++incoming_cnt; } } // start listener listener_->SetTimeout(socket_timeout_ * 1000 * 60); listener_->Listen(incoming_cnt); std::thread listen_thread(&Linkers::ListenThread, this, incoming_cnt); const int connect_fail_constant_factor = 20; const int connect_fail_retries_scale_factor = static_cast(num_machines_ / connect_fail_constant_factor); const int connect_fail_retry_cnt = std::max(connect_fail_constant_factor, connect_fail_retries_scale_factor); const int connect_fail_retry_first_delay_interval = 200; // 0.2 s const float connect_fail_retry_delay_factor = 1.3f; // start connect for (auto it = need_connect.begin(); it != need_connect.end(); ++it) { int out_rank = it->first; // let smaller rank connect to larger rank if (out_rank > rank_) { int connect_fail_delay_time = connect_fail_retry_first_delay_interval; for (int i = 0; i < connect_fail_retry_cnt; ++i) { TcpSocket cur_socket; if (cur_socket.Connect(client_ips_[out_rank].c_str(), client_ports_[out_rank])) { // send local rank cur_socket.Send(reinterpret_cast(&rank_), sizeof(rank_)); SetLinker(out_rank, cur_socket); break; } else { Log::Warning("Connecting to rank %d failed, waiting for %d milliseconds", out_rank, connect_fail_delay_time); cur_socket.Close(); std::this_thread::sleep_for(std::chrono::milliseconds(connect_fail_delay_time)); connect_fail_delay_time = static_cast(connect_fail_delay_time * connect_fail_retry_delay_factor); } } } } // wait for listener listen_thread.join(); // print connected linkers PrintLinkers(); } bool Linkers::CheckLinker(int rank) { if (linkers_[rank] == nullptr || linkers_[rank]->IsClosed()) { return false; } return true; } void Linkers::PrintLinkers() { for (int i = 0; i < num_machines_; ++i) { if (CheckLinker(i)) { Log::Info("Connected to rank %d", i); } } } } // namespace LightGBM #endif // USE_SOCKET ================================================ FILE: src/network/network.cpp ================================================ /*! * Copyright (c) 2016-2026 Microsoft Corporation. All rights reserved. * Copyright (c) 2016-2026 The LightGBM developers. All rights reserved. * Licensed under the MIT License. See LICENSE file in the project root for license information. */ #include #include #include #include #include #include #include #include "linkers.h" namespace LightGBM { // static member definition THREAD_LOCAL int Network::num_machines_ = 1; THREAD_LOCAL int Network::rank_ = 0; THREAD_LOCAL std::unique_ptr Network::linkers_; THREAD_LOCAL BruckMap Network::bruck_map_; THREAD_LOCAL RecursiveHalvingMap Network::recursive_halving_map_; THREAD_LOCAL std::vector Network::block_start_; THREAD_LOCAL std::vector Network::block_len_; THREAD_LOCAL comm_size_t Network::buffer_size_ = 0; THREAD_LOCAL std::vector Network::buffer_; THREAD_LOCAL ReduceScatterFunction Network::reduce_scatter_ext_fun_ = nullptr; THREAD_LOCAL AllgatherFunction Network::allgather_ext_fun_ = nullptr; void Network::Init(Config config) { if (config.num_machines > 1) { linkers_.reset(new Linkers(config)); rank_ = linkers_->rank(); num_machines_ = linkers_->num_machines(); bruck_map_ = linkers_->bruck_map(); recursive_halving_map_ = linkers_->recursive_halving_map(); block_start_ = std::vector(num_machines_); block_len_ = std::vector(num_machines_); buffer_size_ = 1024 * 1024; buffer_.resize(buffer_size_); Log::Info("Local rank: %d, total number of machines: %d", rank_, num_machines_); } } void Network::Init(int num_machines, int rank, ReduceScatterFunction reduce_scatter_ext_fun, AllgatherFunction allgather_ext_fun) { if (num_machines > 1) { rank_ = rank; num_machines_ = num_machines; block_start_ = std::vector(num_machines_); block_len_ = std::vector(num_machines_); buffer_size_ = 1024 * 1024; buffer_.resize(buffer_size_); reduce_scatter_ext_fun_ = reduce_scatter_ext_fun; allgather_ext_fun_ = allgather_ext_fun; Log::Info("Local rank: %d, total number of machines: %d", rank_, num_machines_); } } void Network::Dispose() { num_machines_ = 1; rank_ = 0; linkers_.reset(new Linkers()); reduce_scatter_ext_fun_ = nullptr; allgather_ext_fun_ = nullptr; } void Network::Allreduce(char* input, comm_size_t input_size, int type_size, char* output, const ReduceFunction& reducer) { if (num_machines_ <= 1) { Log::Fatal("Please initialize the network interface first"); } comm_size_t count = input_size / type_size; // if small package or small count , do it by all gather.(reduce the communication times.) if (count < num_machines_ || input_size < 4096) { AllreduceByAllGather(input, input_size, type_size, output, reducer); return; } // assign the blocks to every rank. comm_size_t step = (count + num_machines_ - 1) / num_machines_; if (step < 1) { step = 1; } block_start_[0] = 0; for (int i = 0; i < num_machines_ - 1; ++i) { block_len_[i] = std::min(step * type_size, input_size - block_start_[i]); block_start_[i + 1] = block_start_[i] + block_len_[i]; } block_len_[num_machines_ - 1] = input_size - block_start_[num_machines_ - 1]; // do reduce scatter ReduceScatter(input, input_size, type_size, block_start_.data(), block_len_.data(), output, input_size, reducer); // do all gather Allgather(output, block_start_.data(), block_len_.data(), output, input_size); } void Network::AllreduceByAllGather(char* input, comm_size_t input_size, int type_size, char* output, const ReduceFunction& reducer) { if (num_machines_ <= 1) { Log::Fatal("Please initialize the network interface first"); } // assign blocks comm_size_t all_size = input_size * num_machines_; block_start_[0] = 0; block_len_[0] = input_size; for (int i = 1; i < num_machines_; ++i) { block_start_[i] = block_start_[i - 1] + block_len_[i - 1]; block_len_[i] = input_size; } // need use buffer here, since size of "output" is smaller than size after all gather if (input_size*num_machines_ > buffer_size_) { buffer_size_ = input_size*num_machines_; buffer_.resize(buffer_size_); } Allgather(input, block_start_.data(), block_len_.data(), buffer_.data(), all_size); for (int i = 1; i < num_machines_; ++i) { reducer(buffer_.data() + block_start_[i], buffer_.data() + block_start_[0], type_size, input_size); } // copy back std::memcpy(output, buffer_.data(), input_size); } void Network::Allgather(char* input, comm_size_t send_size, char* output) { if (num_machines_ <= 1) { Log::Fatal("Please initialize the network interface first"); return; } // assign blocks block_start_[0] = 0; block_len_[0] = send_size; for (int i = 1; i < num_machines_; ++i) { block_start_[i] = block_start_[i - 1] + block_len_[i - 1]; block_len_[i] = send_size; } // start all gather Allgather(input, block_start_.data(), block_len_.data(), output, send_size * num_machines_); } void Network::Allgather(char* input, const comm_size_t* block_start, const comm_size_t* block_len, char* output, comm_size_t all_size) { if (num_machines_ <= 1) { Log::Fatal("Please initialize the network interface first"); } if (allgather_ext_fun_ != nullptr) { return allgather_ext_fun_(input, block_len[rank_], block_start, block_len, num_machines_, output, all_size); } const comm_size_t kRingThreshold = 10 * 1024 * 1024; // 10MB const int kRingNodeThreshold = 64; if (all_size > kRingThreshold && num_machines_ < kRingNodeThreshold) { // when num_machines is small and data is large AllgatherRing(input, block_start, block_len, output, all_size); } else if (recursive_halving_map_.is_power_of_2) { AllgatherRecursiveDoubling(input, block_start, block_len, output, all_size); } else { AllgatherBruck(input, block_start, block_len, output, all_size); } } void Network::AllgatherBruck(char* input, const comm_size_t* block_start, const comm_size_t* block_len, char* output, comm_size_t all_size) { comm_size_t write_pos = 0; // use output as receive buffer std::memcpy(output, input, block_len[rank_]); write_pos += block_len[rank_]; int accumulated_block = 1; for (int i = 0; i < bruck_map_.k; ++i) { // get current local block size int cur_block_size = std::min(1 << i, num_machines_ - accumulated_block); // get out rank int out_rank = bruck_map_.out_ranks[i]; // get in rank int in_rank = bruck_map_.in_ranks[i]; // get send information comm_size_t need_send_len = 0; // get recv information comm_size_t need_recv_len = 0; for (int j = 0; j < cur_block_size; ++j) { need_send_len += block_len[(rank_ + j) % num_machines_]; need_recv_len += block_len[(rank_ + accumulated_block + j) % num_machines_]; } // send and recv at same time linkers_->SendRecv(out_rank, output, need_send_len, in_rank, output + write_pos, need_recv_len); write_pos += need_recv_len; accumulated_block += cur_block_size; } // rotate in-place std::reverse(output, output + all_size); std::reverse(output, output + block_start[rank_]); std::reverse(output + block_start[rank_], output + all_size); } void Network::AllgatherRecursiveDoubling(char* input, const comm_size_t* block_start, const comm_size_t* block_len, char* output, comm_size_t) { // use output as receive buffer std::memcpy(output + block_start[rank_], input, block_len[rank_]); for (int i = 0; i < bruck_map_.k; ++i) { // get current local block size int cur_step = 1 << i; const int vgroup = rank_ / cur_step; const int vrank = vgroup * cur_step; int target = rank_ + cur_step; int target_vrank = (vgroup + 1) * cur_step; if (vgroup & 1) { target = rank_ - cur_step; target_vrank = (vgroup - 1) * cur_step; } // get send information comm_size_t need_send_len = 0; // get recv information comm_size_t need_recv_len = 0; for (int j = 0; j < cur_step; ++j) { need_send_len += block_len[(vrank + j)]; need_recv_len += block_len[(target_vrank + j)]; } // send and recv at same time linkers_->SendRecv(target, output + block_start[vrank], need_send_len, target, output + block_start[target_vrank], need_recv_len); } } void Network::AllgatherRing(char* input, const comm_size_t* block_start, const comm_size_t* block_len, char* output, comm_size_t) { // use output as receive buffer std::memcpy(output + block_start[rank_], input, block_len[rank_]); int out_rank = (rank_ + 1) % num_machines_; int in_rank = (rank_ - 1 + num_machines_) % num_machines_; int out_block = rank_; int in_block = in_rank; for (int i = 1; i < num_machines_; ++i) { // send and recv at same time linkers_->SendRecv(out_rank, output + block_start[out_block], block_len[out_block], in_rank, output + block_start[in_block], block_len[in_block]); out_block = (out_block - 1 + num_machines_) % num_machines_; in_block = (in_block - 1 + num_machines_) % num_machines_; } } void Network::ReduceScatter(char* input, comm_size_t input_size, int type_size, const comm_size_t* block_start, const comm_size_t* block_len, char* output, comm_size_t output_size, const ReduceFunction& reducer) { if (num_machines_ <= 1) { Log::Fatal("Please initialize the network interface first"); } if (reduce_scatter_ext_fun_ != nullptr) { return reduce_scatter_ext_fun_(input, input_size, type_size, block_start, block_len, num_machines_, output, output_size, reducer); } const comm_size_t kRingThreshold = 10 * 1024 * 1024; // 10MB if (recursive_halving_map_.is_power_of_2 || input_size < kRingThreshold) { ReduceScatterRecursiveHalving(input, input_size, type_size, block_start, block_len, output, output_size, reducer); } else { ReduceScatterRing(input, input_size, type_size, block_start, block_len, output, output_size, reducer); } } void Network::ReduceScatterRecursiveHalving(char* input, comm_size_t input_size, int type_size, const comm_size_t* block_start, const comm_size_t* block_len, char* output, comm_size_t, const ReduceFunction& reducer) { if (!recursive_halving_map_.is_power_of_2) { if (recursive_halving_map_.type == RecursiveHalvingNodeType::Other) { // send local data to neighbor first linkers_->Send(recursive_halving_map_.neighbor, input, input_size); } else if (recursive_halving_map_.type == RecursiveHalvingNodeType::GroupLeader) { // receive neighbor data first int need_recv_cnt = input_size; linkers_->Recv(recursive_halving_map_.neighbor, output, need_recv_cnt); // reduce reducer(output, input, type_size, input_size); } } if (recursive_halving_map_.type != RecursiveHalvingNodeType::Other) { for (int i = 0; i < recursive_halving_map_.k; ++i) { // get target int target = recursive_halving_map_.ranks[i]; comm_size_t send_block_start = recursive_halving_map_.send_block_start[i]; comm_size_t recv_block_start = recursive_halving_map_.recv_block_start[i]; // get send information comm_size_t send_size = 0; for (int j = 0; j < recursive_halving_map_.send_block_len[i]; ++j) { send_size += block_len[send_block_start + j]; } // get recv information comm_size_t need_recv_cnt = 0; for (int j = 0; j < recursive_halving_map_.recv_block_len[i]; ++j) { need_recv_cnt += block_len[recv_block_start + j]; } // send and recv at same time linkers_->SendRecv(target, input + block_start[send_block_start], send_size, target, output, need_recv_cnt); // reduce reducer(output, input + block_start[recv_block_start], type_size, need_recv_cnt); } } if (!recursive_halving_map_.is_power_of_2) { if (recursive_halving_map_.type == RecursiveHalvingNodeType::GroupLeader) { // send result to neighbor linkers_->Send(recursive_halving_map_.neighbor, input + block_start[recursive_halving_map_.neighbor], block_len[recursive_halving_map_.neighbor]); } else if (recursive_halving_map_.type == RecursiveHalvingNodeType::Other) { // receive result from neighbor int need_recv_cnt = block_len[rank_]; linkers_->Recv(recursive_halving_map_.neighbor, output, need_recv_cnt); return; } } // copy result std::memcpy(output, input + block_start[rank_], block_len[rank_]); } void Network::ReduceScatterRing(char* input, comm_size_t, int type_size, const comm_size_t* block_start, const comm_size_t* block_len, char* output, comm_size_t, const ReduceFunction& reducer) { const int out_rank = (rank_ + 1) % num_machines_; const int in_rank = (rank_ - 1 + num_machines_) % num_machines_; int out_block = in_rank; int in_block = (in_rank - 1 + num_machines_) % num_machines_; for (int i = 1; i < num_machines_; ++i) { linkers_->SendRecv(out_rank, input + block_start[out_block], block_len[out_block], in_rank, output, block_len[in_block]); reducer(output, input + block_start[in_block], type_size, block_len[in_block]); out_block = (out_block - 1 + num_machines_) % num_machines_; in_block = (in_block - 1 + num_machines_) % num_machines_; } std::memcpy(output, input + block_start[rank_], block_len[rank_]); } int Network::rank() { return rank_; } int Network::num_machines() { return num_machines_; } } // namespace LightGBM ================================================ FILE: src/network/socket_wrapper.hpp ================================================ /*! * Copyright (c) 2016-2026 Microsoft Corporation. All rights reserved. * Copyright (c) 2016-2026 The LightGBM developers. All rights reserved. * Licensed under the MIT License. See LICENSE file in the project root for license information. */ #ifndef LIGHTGBM_SRC_NETWORK_SOCKET_WRAPPER_HPP_ #define LIGHTGBM_SRC_NETWORK_SOCKET_WRAPPER_HPP_ #ifdef USE_SOCKET #include #include #include #include #include #if defined(_WIN32) #ifdef _MSC_VER #define NOMINMAX #endif #include #include #include #else #include #include #include #include #include #include #include #include #include #include #include #endif // defined(_WIN32) #ifdef _MSC_VER #pragma comment(lib, "Ws2_32.lib") #pragma comment(lib, "IPHLPAPI.lib") #endif namespace LightGBM { #ifndef _WIN32 typedef int SOCKET; const int INVALID_SOCKET = -1; #define SOCKET_ERROR -1 #endif #ifdef _WIN32 // existence of inet_pton is checked in CMakeLists.txt and configure.win, then stored in WIN_HAS_INET_PTON #ifndef WIN_HAS_INET_PTON inline int inet_pton(int af, const char *src, void *dst) { struct sockaddr_storage ss; int size = sizeof(ss); char src_copy[INET6_ADDRSTRLEN + 1]; ZeroMemory(&ss, sizeof(ss)); /* stupid non-const API */ strncpy(src_copy, src, INET6_ADDRSTRLEN + 1); src_copy[INET6_ADDRSTRLEN] = 0; if (WSAStringToAddress(src_copy, af, NULL, (struct sockaddr *)&ss, &size) == 0) { switch (af) { case AF_INET: *(struct in_addr *)dst = ((struct sockaddr_in *)&ss)->sin_addr; return 1; case AF_INET6: *(struct in6_addr *)dst = ((struct sockaddr_in6 *)&ss)->sin6_addr; return 1; } } return 0; } #endif #endif #define MALLOC(x) HeapAlloc(GetProcessHeap(), 0, (x)) #define FREE(x) HeapFree(GetProcessHeap(), 0, (x)) namespace SocketConfig { const int kSocketBufferSize = 100 * 1000; const int kMaxReceiveSize = 100 * 1000; const int kNoDelay = 1; } class TcpSocket { public: TcpSocket() { sockfd_ = socket(AF_INET, SOCK_STREAM, IPPROTO_TCP); if (sockfd_ == INVALID_SOCKET) { Log::Fatal("Socket construction error"); return; } ConfigSocket(); } explicit TcpSocket(SOCKET socket) { sockfd_ = socket; if (sockfd_ == INVALID_SOCKET) { Log::Fatal("Passed socket error"); return; } ConfigSocket(); } TcpSocket(const TcpSocket &object) { sockfd_ = object.sockfd_; ConfigSocket(); } ~TcpSocket() { } inline void SetTimeout(int timeout_ms) { #if defined(_WIN32) DWORD timeout = static_cast(timeout_ms); setsockopt(sockfd_, SOL_SOCKET, SO_RCVTIMEO, reinterpret_cast(&timeout), sizeof(timeout)); #else struct timeval tv; tv.tv_sec = timeout_ms / 1000; tv.tv_usec = (timeout_ms % 1000) * 1000; setsockopt(sockfd_, SOL_SOCKET, SO_RCVTIMEO, &tv, sizeof(tv)); #endif } inline void ConfigSocket() { if (sockfd_ == INVALID_SOCKET) { return; } if (setsockopt(sockfd_, SOL_SOCKET, SO_RCVBUF, reinterpret_cast(&SocketConfig::kSocketBufferSize), sizeof(SocketConfig::kSocketBufferSize)) != 0) { Log::Warning("Set SO_RCVBUF failed, please increase your net.core.rmem_max to 100k at least"); } if (setsockopt(sockfd_, SOL_SOCKET, SO_SNDBUF, reinterpret_cast(&SocketConfig::kSocketBufferSize), sizeof(SocketConfig::kSocketBufferSize)) != 0) { Log::Warning("Set SO_SNDBUF failed, please increase your net.core.wmem_max to 100k at least"); } if (setsockopt(sockfd_, IPPROTO_TCP, TCP_NODELAY, reinterpret_cast(&SocketConfig::kNoDelay), sizeof(SocketConfig::kNoDelay)) != 0) { Log::Warning("Set TCP_NODELAY failed"); } } inline static void Startup() { #if defined(_WIN32) WSADATA wsa_data; if (WSAStartup(MAKEWORD(2, 2), &wsa_data) == -1) { Log::Fatal("Socket error: WSAStartup error"); } if (LOBYTE(wsa_data.wVersion) != 2 || HIBYTE(wsa_data.wVersion) != 2) { WSACleanup(); Log::Fatal("Socket error: Winsock.dll version error"); } #else #endif } inline static void Finalize() { #if defined(_WIN32) WSACleanup(); #endif } inline static int GetLastError() { #if defined(_WIN32) return WSAGetLastError(); #else return errno; #endif } #if defined(_WIN32) inline static std::unordered_set GetLocalIpList() { std::unordered_set ip_list; char buffer[512]; // get hostName if (gethostname(buffer, sizeof(buffer)) == SOCKET_ERROR) { Log::Fatal("Error code %d, when getting local host name", WSAGetLastError()); } // push local ip PIP_ADAPTER_INFO pAdapterInfo; PIP_ADAPTER_INFO pAdapter = NULL; DWORD dwRetVal = 0; ULONG ulOutBufLen = sizeof(IP_ADAPTER_INFO); pAdapterInfo = reinterpret_cast(MALLOC(sizeof(IP_ADAPTER_INFO))); if (pAdapterInfo == NULL) { Log::Fatal("GetAdaptersinfo error: allocating memory"); } // Make an initial call to GetAdaptersInfo to get // the necessary size into the ulOutBufLen variable if (GetAdaptersInfo(pAdapterInfo, &ulOutBufLen) == ERROR_BUFFER_OVERFLOW) { FREE(pAdapterInfo); pAdapterInfo = reinterpret_cast(MALLOC(ulOutBufLen)); if (pAdapterInfo == NULL) { Log::Fatal("GetAdaptersinfo error: allocating memory"); } } if ((dwRetVal = GetAdaptersInfo(pAdapterInfo, &ulOutBufLen)) == NO_ERROR) { pAdapter = pAdapterInfo; while (pAdapter) { ip_list.insert(pAdapter->IpAddressList.IpAddress.String); pAdapter = pAdapter->Next; } } else { Log::Fatal("GetAdaptersinfo error: code %d", dwRetVal); } if (pAdapterInfo) FREE(pAdapterInfo); return ip_list; } #else inline static std::unordered_set GetLocalIpList() { std::unordered_set ip_list; struct ifaddrs * ifAddrStruct = NULL; struct ifaddrs * ifa = NULL; void * tmpAddrPtr = NULL; getifaddrs(&ifAddrStruct); for (ifa = ifAddrStruct; ifa != NULL; ifa = ifa->ifa_next) { if (!ifa->ifa_addr) { continue; } if (ifa->ifa_addr->sa_family == AF_INET) { // NOLINTNEXTLINE tmpAddrPtr = &((struct sockaddr_in *)ifa->ifa_addr)->sin_addr; char addressBuffer[INET_ADDRSTRLEN]; inet_ntop(AF_INET, tmpAddrPtr, addressBuffer, INET_ADDRSTRLEN); ip_list.insert(std::string(addressBuffer)); } } if (ifAddrStruct != NULL) freeifaddrs(ifAddrStruct); return ip_list; } #endif inline static sockaddr_in GetAddress(const char* url, int port) { sockaddr_in addr = sockaddr_in(); std::memset(&addr, 0, sizeof(sockaddr_in)); inet_pton(AF_INET, url, &addr.sin_addr); addr.sin_family = AF_INET; addr.sin_port = htons(static_cast(port)); return addr; } inline bool Bind(int port) { sockaddr_in local_addr = GetAddress("0.0.0.0", port); if (bind(sockfd_, reinterpret_cast(&local_addr), sizeof(sockaddr_in)) == 0) { return true; } return false; } inline bool Connect(const char *url, int port) { sockaddr_in server_addr = GetAddress(url, port); if (connect(sockfd_, reinterpret_cast(&server_addr), sizeof(sockaddr_in)) == 0) { return true; } return false; } inline void Listen(int backlog = 128) { listen(sockfd_, backlog); } inline TcpSocket Accept() { SOCKET newfd = accept(sockfd_, NULL, NULL); if (newfd == INVALID_SOCKET) { int err_code = GetLastError(); #if defined(_WIN32) Log::Fatal("Socket accept error (code: %d)", err_code); #else Log::Fatal("Socket accept error, %s (code: %d)", std::strerror(err_code), err_code); #endif } return TcpSocket(newfd); } inline int Send(const char *buf_, int len, int flag = 0) { int cur_cnt = send(sockfd_, buf_, len, flag); if (cur_cnt == SOCKET_ERROR) { int err_code = GetLastError(); #if defined(_WIN32) Log::Fatal("Socket send error (code: %d)", err_code); #else Log::Fatal("Socket send error, %s (code: %d)", std::strerror(err_code), err_code); #endif } return cur_cnt; } inline int Recv(char *buf_, int len, int flags = 0) { int cur_cnt = recv(sockfd_, buf_ , len , flags); if (cur_cnt == SOCKET_ERROR) { int err_code = GetLastError(); #if defined(_WIN32) Log::Fatal("Socket recv error (code: %d)", err_code); #else Log::Fatal("Socket recv error, %s (code: %d)", std::strerror(err_code), err_code); #endif } return cur_cnt; } inline bool IsClosed() { return sockfd_ == INVALID_SOCKET; } inline void Close() { if (!IsClosed()) { #if defined(_WIN32) closesocket(sockfd_); #else close(sockfd_); #endif sockfd_ = INVALID_SOCKET; } } private: SOCKET sockfd_; }; } // namespace LightGBM #endif // USE_SOCKET #endif // LIGHTGBM_SRC_NETWORK_SOCKET_WRAPPER_HPP_ ================================================ FILE: src/objective/binary_objective.hpp ================================================ /*! * Copyright (c) 2016-2026 Microsoft Corporation. All rights reserved. * Copyright (c) 2016-2026 The LightGBM developers. All rights reserved. * Licensed under the MIT License. See LICENSE file in the project root for license information. */ #ifndef LIGHTGBM_SRC_OBJECTIVE_BINARY_OBJECTIVE_HPP_ #define LIGHTGBM_SRC_OBJECTIVE_BINARY_OBJECTIVE_HPP_ #include #include #include #include #include #include #include namespace LightGBM { /*! * \brief Objective function for binary classification */ class BinaryLogloss: public ObjectiveFunction { public: explicit BinaryLogloss(const Config& config, std::function is_pos = nullptr) : deterministic_(config.deterministic) { sigmoid_ = static_cast(config.sigmoid); if (sigmoid_ <= 0.0) { Log::Fatal("Sigmoid parameter %f should be greater than zero", sigmoid_); } is_unbalance_ = config.is_unbalance; scale_pos_weight_ = static_cast(config.scale_pos_weight); if (is_unbalance_ && std::fabs(scale_pos_weight_ - 1.0f) > 1e-6) { Log::Fatal("Cannot set is_unbalance and scale_pos_weight at the same time"); } is_pos_ = is_pos; if (is_pos_ == nullptr) { is_pos_ = [](label_t label) { return label > 0; }; } } explicit BinaryLogloss(const std::vector& strs) : deterministic_(false) { sigmoid_ = -1; for (auto str : strs) { auto tokens = Common::Split(str.c_str(), ':'); if (tokens.size() == 2) { if (tokens[0] == std::string("sigmoid")) { Common::Atof(tokens[1].c_str(), &sigmoid_); } } } if (sigmoid_ <= 0.0) { Log::Fatal("Sigmoid parameter %f should be greater than zero", sigmoid_); } } ~BinaryLogloss() {} void Init(const Metadata& metadata, data_size_t num_data) override { num_data_ = num_data; label_ = metadata.label(); weights_ = metadata.weights(); data_size_t cnt_positive = 0; data_size_t cnt_negative = 0; // count for positive and negative samples #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) reduction(+:cnt_positive, cnt_negative) for (data_size_t i = 0; i < num_data_; ++i) { if (is_pos_(label_[i])) { ++cnt_positive; } else { ++cnt_negative; } } num_pos_data_ = cnt_positive; if (Network::num_machines() > 1) { cnt_positive = Network::GlobalSyncUpBySum(cnt_positive); cnt_negative = Network::GlobalSyncUpBySum(cnt_negative); } need_train_ = true; if (cnt_negative == 0 || cnt_positive == 0) { Log::Warning("Contains only one class"); // not need to boost. need_train_ = false; } Log::Info("Number of positive: %d, number of negative: %d", cnt_positive, cnt_negative); // use -1 for negative class, and 1 for positive class label_val_[0] = -1; label_val_[1] = 1; // weight for label label_weights_[0] = 1.0f; label_weights_[1] = 1.0f; // if using unbalance, change the labels weight if (is_unbalance_ && cnt_positive > 0 && cnt_negative > 0) { if (cnt_positive > cnt_negative) { label_weights_[1] = 1.0f; label_weights_[0] = static_cast(cnt_positive) / cnt_negative; } else { label_weights_[1] = static_cast(cnt_negative) / cnt_positive; label_weights_[0] = 1.0f; } } label_weights_[1] *= scale_pos_weight_; } void GetGradients(const double* score, score_t* gradients, score_t* hessians) const override { if (!need_train_) { return; } if (weights_ == nullptr) { #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) for (data_size_t i = 0; i < num_data_; ++i) { // get label and label weights const int is_pos = is_pos_(label_[i]); const int label = label_val_[is_pos]; const double label_weight = label_weights_[is_pos]; // calculate gradients and hessians const double response = -label * sigmoid_ / (1.0f + std::exp(label * sigmoid_ * score[i])); const double abs_response = fabs(response); gradients[i] = static_cast(response * label_weight); hessians[i] = static_cast(abs_response * (sigmoid_ - abs_response) * label_weight); } } else { #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) for (data_size_t i = 0; i < num_data_; ++i) { // get label and label weights const int is_pos = is_pos_(label_[i]); const int label = label_val_[is_pos]; const double label_weight = label_weights_[is_pos]; // calculate gradients and hessians const double response = -label * sigmoid_ / (1.0f + std::exp(label * sigmoid_ * score[i])); const double abs_response = fabs(response); gradients[i] = static_cast(response * label_weight * weights_[i]); hessians[i] = static_cast(abs_response * (sigmoid_ - abs_response) * label_weight * weights_[i]); } } } // implement custom average to boost from (if enabled among options) double BoostFromScore(int) const override { double suml = 0.0f; double sumw = 0.0f; if (weights_ != nullptr) { #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) reduction(+:suml, sumw) if (!deterministic_) for (data_size_t i = 0; i < num_data_; ++i) { suml += is_pos_(label_[i]) * weights_[i]; sumw += weights_[i]; } } else { sumw = static_cast(num_data_); #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) reduction(+:suml) if (!deterministic_) for (data_size_t i = 0; i < num_data_; ++i) { suml += is_pos_(label_[i]); } } if (Network::num_machines() > 1) { suml = Network::GlobalSyncUpBySum(suml); sumw = Network::GlobalSyncUpBySum(sumw); } double pavg = suml / sumw; pavg = std::min(pavg, 1.0 - kEpsilon); pavg = std::max(pavg, kEpsilon); double initscore = std::log(pavg / (1.0f - pavg)) / sigmoid_; Log::Info("[%s:%s]: pavg=%f -> initscore=%f", GetName(), __func__, pavg, initscore); return initscore; } bool ClassNeedTrain(int /*class_id*/) const override { return need_train_; } const char* GetName() const override { return "binary"; } void ConvertOutput(const double* input, double* output) const override { output[0] = 1.0f / (1.0f + std::exp(-sigmoid_ * input[0])); } std::string ToString() const override { std::stringstream str_buf; str_buf << GetName() << " "; str_buf << "sigmoid:" << sigmoid_; return str_buf.str(); } bool SkipEmptyClass() const override { return true; } bool NeedAccuratePrediction() const override { return false; } data_size_t NumPositiveData() const override { return num_pos_data_; } protected: /*! \brief Number of data */ data_size_t num_data_; /*! \brief Number of positive samples */ data_size_t num_pos_data_; /*! \brief Pointer of label */ const label_t* label_; /*! \brief True if using unbalance training */ bool is_unbalance_; /*! \brief Sigmoid parameter */ double sigmoid_; /*! \brief Values for positive and negative labels */ int label_val_[2]; /*! \brief Weights for positive and negative labels */ double label_weights_[2]; /*! \brief Weights for data */ const label_t* weights_; double scale_pos_weight_; std::function is_pos_; bool need_train_; const bool deterministic_; }; } // namespace LightGBM #endif // LIGHTGBM_SRC_OBJECTIVE_BINARY_OBJECTIVE_HPP_ ================================================ FILE: src/objective/cuda/cuda_binary_objective.cpp ================================================ /*! * Copyright (c) 2021-2026 Microsoft Corporation. All rights reserved. * Copyright (c) 2021-2026 The LightGBM developers. All rights reserved. * Licensed under the MIT License. See LICENSE file in the project root for * license information. */ #ifdef USE_CUDA #include "cuda_binary_objective.hpp" #include #include namespace LightGBM { CUDABinaryLogloss::CUDABinaryLogloss(const Config& config): CUDAObjectiveInterface(config), ova_class_id_(-1) { cuda_label_ = nullptr; cuda_weights_ = nullptr; } CUDABinaryLogloss::CUDABinaryLogloss(const Config& config, const int ova_class_id): CUDAObjectiveInterface(config), ova_class_id_(ova_class_id) { is_pos_ = [ova_class_id](label_t label) { return static_cast(label) == ova_class_id; }; } CUDABinaryLogloss::CUDABinaryLogloss(const std::vector& strs): CUDAObjectiveInterface(strs) {} CUDABinaryLogloss::~CUDABinaryLogloss() {} void CUDABinaryLogloss::Init(const Metadata& metadata, data_size_t num_data) { CUDAObjectiveInterface::Init(metadata, num_data); if (ova_class_id_ == -1) { cuda_label_ = metadata.cuda_metadata()->cuda_label(); cuda_ova_label_.Clear(); } else { cuda_ova_label_.Resize(static_cast(num_data)); CopyFromHostToCUDADevice(cuda_ova_label_.RawData(), metadata.cuda_metadata()->cuda_label(), static_cast(num_data), __FILE__, __LINE__); LaunchResetOVACUDALabelKernel(); cuda_label_ = cuda_ova_label_.RawData(); } cuda_weights_ = metadata.cuda_metadata()->cuda_weights(); cuda_boost_from_score_.Resize(1); SetCUDAMemory(cuda_boost_from_score_.RawData(), 0, 1, __FILE__, __LINE__); cuda_sum_weights_.Resize(1); SetCUDAMemory(cuda_sum_weights_.RawData(), 0, 1, __FILE__, __LINE__); if (label_weights_[0] != 1.0f || label_weights_[1] != 1.0f) { cuda_label_weights_.Resize(2); CopyFromHostToCUDADevice(cuda_label_weights_.RawData(), label_weights_, 2, __FILE__, __LINE__); } else { cuda_label_weights_.Clear(); } } } // namespace LightGBM #endif // USE_CUDA ================================================ FILE: src/objective/cuda/cuda_binary_objective.cu ================================================ /*! * Copyright (c) 2021-2026 Microsoft Corporation. All rights reserved. * Copyright (c) 2021-2026 The LightGBM developers. All rights reserved. * Licensed under the MIT License. See LICENSE file in the project root for * license information. * Modifications Copyright(C) 2023 Advanced Micro Devices, Inc. All rights reserved. */ #ifdef USE_CUDA #include "cuda_binary_objective.hpp" #include #include namespace LightGBM { template __global__ void BoostFromScoreKernel_1_BinaryLogloss(const label_t* cuda_labels, const data_size_t num_data, double* out_cuda_sum_labels, double* out_cuda_sum_weights, const label_t* cuda_weights) { __shared__ double shared_buffer[WARPSIZE]; const uint32_t mask = 0xffffffff; const uint32_t warpLane = threadIdx.x % warpSize; const uint32_t warpID = threadIdx.x / warpSize; const uint32_t num_warp = blockDim.x / warpSize; const data_size_t index = static_cast(threadIdx.x + blockIdx.x * blockDim.x); double label_value = 0.0; double weight_value = 0.0; if (index < num_data) { if (USE_WEIGHT) { const label_t cuda_label = cuda_labels[index]; const double sample_weight = cuda_weights[index]; const label_t label = cuda_label > 0 ? 1 : 0; label_value = label * sample_weight; weight_value = sample_weight; } else { const label_t cuda_label = cuda_labels[index]; label_value = cuda_label > 0 ? 1 : 0; } } for (uint32_t offset = warpSize / 2; offset >= 1; offset >>= 1) { label_value += __shfl_down_sync(mask, label_value, offset); } if (warpLane == 0) { shared_buffer[warpID] = label_value; } __syncthreads(); if (warpID == 0) { label_value = (warpLane < num_warp ? shared_buffer[warpLane] : 0); for (uint32_t offset = warpSize / 2; offset >= 1; offset >>= 1) { label_value += __shfl_down_sync(mask, label_value, offset); } } __syncthreads(); if (USE_WEIGHT) { for (uint32_t offset = warpSize / 2; offset >= 1; offset >>= 1) { weight_value += __shfl_down_sync(mask, weight_value, offset); } if (warpLane == 0) { shared_buffer[warpID] = weight_value; } __syncthreads(); if (warpID == 0) { weight_value = (warpLane < num_warp ? shared_buffer[warpLane] : 0); for (uint32_t offset = warpSize / 2; offset >= 1; offset >>= 1) { weight_value += __shfl_down_sync(mask, weight_value, offset); } } __syncthreads(); } if (threadIdx.x == 0) { atomicAdd_system(out_cuda_sum_labels, label_value); if (USE_WEIGHT) { atomicAdd_system(out_cuda_sum_weights, weight_value); } } } template __global__ void BoostFromScoreKernel_2_BinaryLogloss(double* out_cuda_sum_labels, double* out_cuda_sum_weights, const data_size_t num_data, const double sigmoid) { const double suml = *out_cuda_sum_labels; const double sumw = USE_WEIGHT ? *out_cuda_sum_weights : static_cast(num_data); double pavg = suml / sumw; pavg = min(pavg, 1.0 - kEpsilon); pavg = max(pavg, kEpsilon); const double init_score = log(pavg / (1.0f - pavg)) / sigmoid; *out_cuda_sum_weights = pavg; *out_cuda_sum_labels = init_score; } double CUDABinaryLogloss::LaunchCalcInitScoreKernel(const int /*class_id*/) const { const int num_blocks = (num_data_ + CALC_INIT_SCORE_BLOCK_SIZE_BINARY - 1) / CALC_INIT_SCORE_BLOCK_SIZE_BINARY; SetCUDAMemory(cuda_boost_from_score_.RawData(), 0, 1, __FILE__, __LINE__); if (cuda_weights_ == nullptr) { BoostFromScoreKernel_1_BinaryLogloss<<>> (cuda_label_, num_data_, cuda_boost_from_score_.RawData(), cuda_sum_weights_.RawData(), cuda_weights_); } else { BoostFromScoreKernel_1_BinaryLogloss<<>> (cuda_label_, num_data_, cuda_boost_from_score_.RawData(), cuda_sum_weights_.RawData(), cuda_weights_); } SynchronizeCUDADevice(__FILE__, __LINE__); if (cuda_weights_ == nullptr) { if (nccl_communicator_ == nullptr) { BoostFromScoreKernel_2_BinaryLogloss<<<1, 1>>>(cuda_boost_from_score_.RawData(), cuda_sum_weights_.RawData(), num_data_, sigmoid_); } else { NCCLAllReduce(cuda_boost_from_score_.RawData(), cuda_boost_from_score_.RawData(), 1, ncclFloat64, ncclSum, nccl_communicator_); const data_size_t global_num_data = NCCLAllReduce(num_data_, ncclInt32, ncclSum, nccl_communicator_); BoostFromScoreKernel_2_BinaryLogloss<<<1, 1>>>(cuda_boost_from_score_.RawData(), cuda_sum_weights_.RawData(), global_num_data, sigmoid_); } } else { if (nccl_communicator_ == nullptr) { BoostFromScoreKernel_2_BinaryLogloss<<<1, 1>>>(cuda_boost_from_score_.RawData(), cuda_sum_weights_.RawData(), num_data_, sigmoid_); } else { NCCLAllReduce(cuda_boost_from_score_.RawData(), cuda_boost_from_score_.RawData(), 1, ncclFloat64, ncclSum, nccl_communicator_); NCCLAllReduce(cuda_sum_weights_.RawData(), cuda_sum_weights_.RawData(), 1, ncclFloat64, ncclSum, nccl_communicator_); BoostFromScoreKernel_2_BinaryLogloss<<<1, 1>>>(cuda_boost_from_score_.RawData(), cuda_sum_weights_.RawData(), num_data_, sigmoid_); } } SynchronizeCUDADevice(__FILE__, __LINE__); double boost_from_score = 0.0f; CopyFromCUDADeviceToHost(&boost_from_score, cuda_boost_from_score_.RawData(), 1, __FILE__, __LINE__); double pavg = 0.0f; CopyFromCUDADeviceToHost(&pavg, cuda_sum_weights_.RawData(), 1, __FILE__, __LINE__); // for some test cases in test_utilities.py which check the log output Log::Info("[%s:%s]: pavg=%f -> initscore=%f", GetName(), "BoostFromScore", pavg, boost_from_score); return boost_from_score; } template __global__ void GetGradientsKernel_BinaryLogloss(const double* cuda_scores, const label_t* cuda_labels, const double* cuda_label_weights, const label_t* cuda_weights, const double sigmoid, const data_size_t num_data, score_t* cuda_out_gradients, score_t* cuda_out_hessians) { const data_size_t data_index = static_cast(blockDim.x * blockIdx.x + threadIdx.x); if (data_index < num_data) { const label_t cuda_label = static_cast(cuda_labels[data_index]); const int label = cuda_label > 0 ? 1 : -1; const double response = -label * sigmoid / (1.0f + exp(label * sigmoid * cuda_scores[data_index])); const double abs_response = fabs(response); if (!USE_WEIGHT) { if (USE_LABEL_WEIGHT) { const double label_weight = cuda_label_weights[label]; cuda_out_gradients[data_index] = static_cast(response * label_weight); cuda_out_hessians[data_index] = static_cast(abs_response * (sigmoid - abs_response) * label_weight); } else { cuda_out_gradients[data_index] = static_cast(response); cuda_out_hessians[data_index] = static_cast(abs_response * (sigmoid - abs_response)); } } else { const double sample_weight = cuda_weights[data_index]; if (USE_LABEL_WEIGHT) { const double label_weight = cuda_label_weights[label]; cuda_out_gradients[data_index] = static_cast(response * label_weight * sample_weight); cuda_out_hessians[data_index] = static_cast(abs_response * (sigmoid - abs_response) * label_weight * sample_weight); } else { cuda_out_gradients[data_index] = static_cast(response * sample_weight); cuda_out_hessians[data_index] = static_cast(abs_response * (sigmoid - abs_response) * sample_weight); } } } } #define GetGradientsKernel_BinaryLogloss_ARGS \ scores, \ cuda_label_, \ cuda_label_weights_.RawData(), \ cuda_weights_, \ sigmoid_, \ num_data_, \ gradients, \ hessians void CUDABinaryLogloss::LaunchGetGradientsKernel(const double* scores, score_t* gradients, score_t* hessians) const { const int num_blocks = (num_data_ + GET_GRADIENTS_BLOCK_SIZE_BINARY - 1) / GET_GRADIENTS_BLOCK_SIZE_BINARY; if (cuda_label_weights_.Size() == 0) { if (cuda_weights_ == nullptr) { GetGradientsKernel_BinaryLogloss<<>>(GetGradientsKernel_BinaryLogloss_ARGS); } else { GetGradientsKernel_BinaryLogloss<<>>(GetGradientsKernel_BinaryLogloss_ARGS); } } else { if (cuda_weights_ == nullptr) { GetGradientsKernel_BinaryLogloss<<>>(GetGradientsKernel_BinaryLogloss_ARGS); } else { GetGradientsKernel_BinaryLogloss<<>>(GetGradientsKernel_BinaryLogloss_ARGS); } } } #undef GetGradientsKernel_BinaryLogloss_ARGS __global__ void ConvertOutputCUDAKernel_BinaryLogloss(const double sigmoid, const data_size_t num_data, const double* input, double* output) { const data_size_t data_index = static_cast(blockIdx.x * blockDim.x + threadIdx.x); if (data_index < num_data) { output[data_index] = 1.0f / (1.0f + exp(-sigmoid * input[data_index])); } } const double* CUDABinaryLogloss::LaunchConvertOutputCUDAKernel(const data_size_t num_data, const double* input, double* output) const { const int num_blocks = (num_data + GET_GRADIENTS_BLOCK_SIZE_BINARY - 1) / GET_GRADIENTS_BLOCK_SIZE_BINARY; ConvertOutputCUDAKernel_BinaryLogloss<<>>(sigmoid_, num_data, input, output); return output; } __global__ void ResetOVACUDALabelKernel( const int ova_class_id, const data_size_t num_data, label_t* cuda_label) { const data_size_t data_index = static_cast(threadIdx.x + blockIdx.x * blockDim.x); if (data_index < num_data) { const int int_label = static_cast(cuda_label[data_index]); cuda_label[data_index] = (int_label == ova_class_id ? 1.0f : 0.0f); } } void CUDABinaryLogloss::LaunchResetOVACUDALabelKernel() const { const int num_blocks = (num_data_ + GET_GRADIENTS_BLOCK_SIZE_BINARY - 1) / GET_GRADIENTS_BLOCK_SIZE_BINARY; ResetOVACUDALabelKernel<<>>(ova_class_id_, num_data_, cuda_ova_label_.RawData()); } } // namespace LightGBM #endif // USE_CUDA ================================================ FILE: src/objective/cuda/cuda_binary_objective.hpp ================================================ /*! * Copyright (c) 2021-2026 Microsoft Corporation. All rights reserved. * Copyright (c) 2021-2026 The LightGBM developers. All rights reserved. * Licensed under the MIT License. See LICENSE file in the project root for * license information. */ #ifndef LIGHTGBM_SRC_OBJECTIVE_CUDA_CUDA_BINARY_OBJECTIVE_HPP_ #define LIGHTGBM_SRC_OBJECTIVE_CUDA_CUDA_BINARY_OBJECTIVE_HPP_ #ifdef USE_CUDA #define GET_GRADIENTS_BLOCK_SIZE_BINARY (1024) #define CALC_INIT_SCORE_BLOCK_SIZE_BINARY (1024) #include #include #include #include "../binary_objective.hpp" namespace LightGBM { class CUDABinaryLogloss : public CUDAObjectiveInterface { public: explicit CUDABinaryLogloss(const Config& config); explicit CUDABinaryLogloss(const Config& config, const int ova_class_id); explicit CUDABinaryLogloss(const std::vector& strs); ~CUDABinaryLogloss(); void Init(const Metadata& metadata, data_size_t num_data) override; bool NeedConvertOutputCUDA() const override { return true; } private: void LaunchGetGradientsKernel(const double* scores, score_t* gradients, score_t* hessians) const override; double LaunchCalcInitScoreKernel(const int class_id) const override; const double* LaunchConvertOutputCUDAKernel(const data_size_t num_data, const double* input, double* output) const override; void LaunchResetOVACUDALabelKernel() const; // CUDA memory, held by other objects const label_t* cuda_label_; CUDAVector cuda_ova_label_; const label_t* cuda_weights_; // CUDA memory, held by this object CUDAVector cuda_boost_from_score_; CUDAVector cuda_sum_weights_; CUDAVector cuda_label_weights_; const int ova_class_id_ = -1; }; } // namespace LightGBM #endif // USE_CUDA #endif // LIGHTGBM_SRC_OBJECTIVE_CUDA_CUDA_BINARY_OBJECTIVE_HPP_ ================================================ FILE: src/objective/cuda/cuda_multiclass_objective.cpp ================================================ /*! * Copyright (c) 2021-2026 Microsoft Corporation. All rights reserved. * Copyright (c) 2021-2026 The LightGBM developers. All rights reserved. * Licensed under the MIT License. See LICENSE file in the project root for license information. */ #ifdef USE_CUDA #include "cuda_multiclass_objective.hpp" #include #include namespace LightGBM { CUDAMulticlassSoftmax::CUDAMulticlassSoftmax(const Config& config): CUDAObjectiveInterface(config) {} CUDAMulticlassSoftmax::CUDAMulticlassSoftmax(const std::vector& strs): CUDAObjectiveInterface(strs) {} CUDAMulticlassSoftmax::~CUDAMulticlassSoftmax() {} void CUDAMulticlassSoftmax::Init(const Metadata& metadata, data_size_t num_data) { CUDAObjectiveInterface::Init(metadata, num_data); cuda_softmax_buffer_.Resize(static_cast(num_data) * static_cast(num_class_)); SynchronizeCUDADevice(__FILE__, __LINE__); } CUDAMulticlassOVA::CUDAMulticlassOVA(const Config& config): CUDAObjectiveInterface(config) { for (int i = 0; i < num_class_; ++i) { cuda_binary_loss_.emplace_back(new CUDABinaryLogloss(config, i)); } } CUDAMulticlassOVA::CUDAMulticlassOVA(const std::vector& strs): CUDAObjectiveInterface(strs) {} CUDAMulticlassOVA::~CUDAMulticlassOVA() {} void CUDAMulticlassOVA::Init(const Metadata& metadata, data_size_t num_data) { MulticlassOVA::Init(metadata, num_data); for (int i = 0; i < num_class_; ++i) { cuda_binary_loss_[i]->Init(metadata, num_data); } } void CUDAMulticlassOVA::GetGradients(const double* score, score_t* gradients, score_t* hessians) const { for (int i = 0; i < num_class_; ++i) { int64_t offset = static_cast(num_data_) * i; cuda_binary_loss_[i]->GetGradients(score + offset, gradients + offset, hessians + offset); } } const double* CUDAMulticlassOVA::ConvertOutputCUDA(const data_size_t num_data, const double* input, double* output) const { for (int i = 0; i < num_class_; ++i) { cuda_binary_loss_[i]->ConvertOutputCUDA(num_data, input + i * num_data, output + i * num_data); } return output; } } // namespace LightGBM #endif // USE_CUDA ================================================ FILE: src/objective/cuda/cuda_multiclass_objective.cu ================================================ [File too large to display: 4.5 KB] ================================================ FILE: src/objective/cuda/cuda_multiclass_objective.hpp ================================================ [File too large to display: 2.4 KB] ================================================ FILE: src/objective/cuda/cuda_rank_objective.cpp ================================================ [File too large to display: 2.4 KB] ================================================ FILE: src/objective/cuda/cuda_rank_objective.cu ================================================ /*! * Copyright (c) 2021-2026 Microsoft Corporation. All rights reserved. * Copyright (c) 2021-2026 The LightGBM developers. All rights reserved. * Licensed under the MIT License. See LICENSE file in the project root for * license information. * Modifications Copyright(C) 2023 Advanced Micro Devices, Inc. All rights reserved. */ #ifdef USE_CUDA #include "cuda_rank_objective.hpp" #include #include #include namespace LightGBM { template __global__ void GetGradientsKernel_LambdarankNDCG(const double* cuda_scores, const label_t* cuda_labels, const data_size_t num_data, const data_size_t num_queries, const data_size_t* cuda_query_boundaries, const double* cuda_inverse_max_dcgs, const bool norm, const double sigmoid, const int truncation_level, const double* cuda_label_gain, const data_size_t num_rank_label, score_t* cuda_out_gradients, score_t* cuda_out_hessians) { __shared__ score_t shared_scores[MAX_ITEM_GREATER_THAN_1024 ? 2048 : 1024]; __shared__ uint16_t shared_indices[MAX_ITEM_GREATER_THAN_1024 ? 2048 : 1024]; __shared__ score_t shared_lambdas[MAX_ITEM_GREATER_THAN_1024 ? 2048 : 1024]; __shared__ score_t shared_hessians[MAX_ITEM_GREATER_THAN_1024 ? 2048 : 1024]; __shared__ double shared_label_gain[NUM_RANK_LABEL > 1024 ? 1 : NUM_RANK_LABEL]; const double* label_gain_ptr = nullptr; if (NUM_RANK_LABEL <= 1024) { for (uint32_t i = threadIdx.x; i < num_rank_label; i += blockDim.x) { shared_label_gain[i] = cuda_label_gain[i]; } __syncthreads(); label_gain_ptr = shared_label_gain; } else { label_gain_ptr = cuda_label_gain; } const data_size_t query_index_start = static_cast(blockIdx.x) * NUM_QUERY_PER_BLOCK; const data_size_t query_index_end = min(query_index_start + NUM_QUERY_PER_BLOCK, num_queries); for (data_size_t query_index = query_index_start; query_index < query_index_end; ++query_index) { const double inverse_max_dcg = cuda_inverse_max_dcgs[query_index]; const data_size_t query_start = cuda_query_boundaries[query_index]; const data_size_t query_end = cuda_query_boundaries[query_index + 1]; const data_size_t query_item_count = query_end - query_start; const double* cuda_scores_pointer = cuda_scores + query_start; score_t* cuda_out_gradients_pointer = cuda_out_gradients + query_start; score_t* cuda_out_hessians_pointer = cuda_out_hessians + query_start; const label_t* cuda_label_pointer = cuda_labels + query_start; if (threadIdx.x < query_item_count) { shared_scores[threadIdx.x] = cuda_scores_pointer[threadIdx.x]; shared_indices[threadIdx.x] = static_cast(threadIdx.x); shared_lambdas[threadIdx.x] = 0.0f; shared_hessians[threadIdx.x] = 0.0f; } else { shared_scores[threadIdx.x] = kMinScore; shared_indices[threadIdx.x] = static_cast(threadIdx.x); } if (MAX_ITEM_GREATER_THAN_1024) { if (query_item_count > 1024) { const unsigned int threadIdx_x_plus_1024 = threadIdx.x + 1024; if (threadIdx_x_plus_1024 < query_item_count) { shared_scores[threadIdx_x_plus_1024] = cuda_scores_pointer[threadIdx_x_plus_1024]; shared_indices[threadIdx_x_plus_1024] = static_cast(threadIdx_x_plus_1024); shared_lambdas[threadIdx_x_plus_1024] = 0.0f; shared_hessians[threadIdx_x_plus_1024] = 0.0f; } else { shared_scores[threadIdx_x_plus_1024] = kMinScore; shared_indices[threadIdx_x_plus_1024] = static_cast(threadIdx_x_plus_1024); } } } __syncthreads(); if (MAX_ITEM_GREATER_THAN_1024) { if (query_item_count > 1024) { BitonicArgSort_2048(shared_scores, shared_indices); } else { BitonicArgSort_1024(shared_scores, shared_indices, static_cast(query_item_count)); } } else { BitonicArgSort_1024(shared_scores, shared_indices, static_cast(query_item_count)); } __syncthreads(); // get best and worst score const double best_score = shared_scores[shared_indices[0]]; data_size_t worst_idx = query_item_count - 1; if (worst_idx > 0 && shared_scores[shared_indices[worst_idx]] == kMinScore) { worst_idx -= 1; } const double worst_score = shared_scores[shared_indices[worst_idx]]; __shared__ double sum_lambdas; if (threadIdx.x == 0) { sum_lambdas = 0.0f; } __syncthreads(); // start accumulate lambdas by pairs that contain at least one document above truncation level const data_size_t num_items_i = min(query_item_count - 1, truncation_level); const data_size_t num_j_per_i = query_item_count - 1; const data_size_t s = num_j_per_i - num_items_i + 1; const data_size_t num_pairs = (num_j_per_i + s) * num_items_i / 2; double thread_sum_lambdas = 0.0f; for (data_size_t pair_index = static_cast(threadIdx.x); pair_index < num_pairs; pair_index += static_cast(blockDim.x)) { const double square = 2 * static_cast(pair_index) + s * s - s; const double sqrt_result = floor(sqrt(square)); const data_size_t row_index = static_cast(floor(sqrt(square - sqrt_result)) + 1 - s); const data_size_t i = num_items_i - 1 - row_index; const data_size_t j = num_j_per_i - (pair_index - (2 * s + row_index - 1) * row_index / 2); if (cuda_label_pointer[shared_indices[i]] != cuda_label_pointer[shared_indices[j]] && shared_scores[shared_indices[j]] != kMinScore) { data_size_t high_rank, low_rank; if (cuda_label_pointer[shared_indices[i]] > cuda_label_pointer[shared_indices[j]]) { high_rank = i; low_rank = j; } else { high_rank = j; low_rank = i; } const data_size_t high = shared_indices[high_rank]; const int high_label = static_cast(cuda_label_pointer[high]); const double high_score = shared_scores[high]; const double high_label_gain = label_gain_ptr[high_label]; const double high_discount = log2(2.0f + high_rank); const data_size_t low = shared_indices[low_rank]; const int low_label = static_cast(cuda_label_pointer[low]); const double low_score = shared_scores[low]; const double low_label_gain = label_gain_ptr[low_label]; const double low_discount = log2(2.0f + low_rank); const double delta_score = high_score - low_score; // get dcg gap const double dcg_gap = high_label_gain - low_label_gain; // get discount of this pair const double paired_discount = fabs(high_discount - low_discount); // get delta NDCG double delta_pair_NDCG = dcg_gap * paired_discount * inverse_max_dcg; // regular the delta_pair_NDCG by score distance if (norm && best_score != worst_score) { delta_pair_NDCG /= (0.01f + fabs(delta_score)); } // calculate lambda for this pair double p_lambda = 1.0f / (1.0f + exp(sigmoid * delta_score)); double p_hessian = p_lambda * (1.0f - p_lambda); // update p_lambda *= -sigmoid * delta_pair_NDCG; p_hessian *= sigmoid * sigmoid * delta_pair_NDCG; atomicAdd_block(shared_lambdas + low, -static_cast(p_lambda)); atomicAdd_block(shared_hessians + low, static_cast(p_hessian)); atomicAdd_block(shared_lambdas + high, static_cast(p_lambda)); atomicAdd_block(shared_hessians + high, static_cast(p_hessian)); // lambda is negative, so use minus to accumulate thread_sum_lambdas -= 2 * p_lambda; } } atomicAdd_block(&sum_lambdas, thread_sum_lambdas); __syncthreads(); if (norm && sum_lambdas > 0) { const double norm_factor = log2(1 + sum_lambdas) / sum_lambdas; if (threadIdx.x < static_cast(query_item_count)) { cuda_out_gradients_pointer[threadIdx.x] = static_cast(shared_lambdas[threadIdx.x] * norm_factor); cuda_out_hessians_pointer[threadIdx.x] = static_cast(shared_hessians[threadIdx.x] * norm_factor); } if (MAX_ITEM_GREATER_THAN_1024) { if (query_item_count > 1024) { const unsigned int threadIdx_x_plus_1024 = threadIdx.x + 1024; if (threadIdx_x_plus_1024 < static_cast(query_item_count)) { cuda_out_gradients_pointer[threadIdx_x_plus_1024] = static_cast(shared_lambdas[threadIdx_x_plus_1024] * norm_factor); cuda_out_hessians_pointer[threadIdx_x_plus_1024] = static_cast(shared_hessians[threadIdx_x_plus_1024] * norm_factor); } } } } else { if (threadIdx.x < static_cast(query_item_count)) { cuda_out_gradients_pointer[threadIdx.x] = static_cast(shared_lambdas[threadIdx.x]); cuda_out_hessians_pointer[threadIdx.x] = static_cast(shared_hessians[threadIdx.x]); } if (MAX_ITEM_GREATER_THAN_1024) { if (query_item_count > 1024) { const unsigned int threadIdx_x_plus_1024 = threadIdx.x + 1024; if (threadIdx_x_plus_1024 < static_cast(query_item_count)) { cuda_out_gradients_pointer[threadIdx_x_plus_1024] = static_cast(shared_lambdas[threadIdx_x_plus_1024]); cuda_out_hessians_pointer[threadIdx_x_plus_1024] = static_cast(shared_hessians[threadIdx_x_plus_1024]); } } } } __syncthreads(); } } template __global__ void GetGradientsKernel_LambdarankNDCG_Sorted( const double* cuda_scores, const int* cuda_item_indices_buffer, const label_t* cuda_labels, const data_size_t num_data, const data_size_t num_queries, const data_size_t* cuda_query_boundaries, const double* cuda_inverse_max_dcgs, const bool norm, const double sigmoid, const int truncation_level, const double* cuda_label_gain, const data_size_t num_rank_label, score_t* cuda_out_gradients, score_t* cuda_out_hessians) { __shared__ double shared_label_gain[NUM_RANK_LABEL > 1024 ? 1 : NUM_RANK_LABEL]; const double* label_gain_ptr = nullptr; if (NUM_RANK_LABEL <= 1024) { for (uint32_t i = threadIdx.x; i < static_cast(num_rank_label); i += blockDim.x) { shared_label_gain[i] = cuda_label_gain[i]; } __syncthreads(); label_gain_ptr = shared_label_gain; } else { label_gain_ptr = cuda_label_gain; } const data_size_t query_index_start = static_cast(blockIdx.x) * NUM_QUERY_PER_BLOCK; const data_size_t query_index_end = min(query_index_start + NUM_QUERY_PER_BLOCK, num_queries); for (data_size_t query_index = query_index_start; query_index < query_index_end; ++query_index) { const double inverse_max_dcg = cuda_inverse_max_dcgs[query_index]; const data_size_t query_start = cuda_query_boundaries[query_index]; const data_size_t query_end = cuda_query_boundaries[query_index + 1]; const data_size_t query_item_count = query_end - query_start; const double* cuda_scores_pointer = cuda_scores + query_start; const int* cuda_item_indices_buffer_pointer = cuda_item_indices_buffer + query_start; score_t* cuda_out_gradients_pointer = cuda_out_gradients + query_start; score_t* cuda_out_hessians_pointer = cuda_out_hessians + query_start; const label_t* cuda_label_pointer = cuda_labels + query_start; // get best and worst score const double best_score = cuda_scores_pointer[cuda_item_indices_buffer_pointer[0]]; data_size_t worst_idx = query_item_count - 1; if (worst_idx > 0 && cuda_scores_pointer[cuda_item_indices_buffer_pointer[worst_idx]] == kMinScore) { worst_idx -= 1; } const double worst_score = cuda_scores_pointer[cuda_item_indices_buffer_pointer[worst_idx]]; __shared__ double sum_lambdas; if (threadIdx.x == 0) { sum_lambdas = 0.0f; } for (int item_index = static_cast(threadIdx.x); item_index < query_item_count; item_index += static_cast(blockDim.x)) { cuda_out_gradients_pointer[item_index] = 0.0f; cuda_out_hessians_pointer[item_index] = 0.0f; } __syncthreads(); // start accumulate lambdas by pairs that contain at least one document above truncation level const data_size_t num_items_i = min(query_item_count - 1, truncation_level); const data_size_t num_j_per_i = query_item_count - 1; const data_size_t s = num_j_per_i - num_items_i + 1; const data_size_t num_pairs = (num_j_per_i + s) * num_items_i / 2; double thread_sum_lambdas = 0.0f; for (data_size_t pair_index = static_cast(threadIdx.x); pair_index < num_pairs; pair_index += static_cast(blockDim.x)) { const double square = 2 * static_cast(pair_index) + s * s - s; const double sqrt_result = floor(sqrt(square)); const data_size_t row_index = static_cast(floor(sqrt(square - sqrt_result)) + 1 - s); const data_size_t i = num_items_i - 1 - row_index; const data_size_t j = num_j_per_i - (pair_index - (2 * s + row_index - 1) * row_index / 2); if (j > i) { // skip pairs with the same labels 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) { data_size_t high_rank, low_rank; if (cuda_label_pointer[cuda_item_indices_buffer_pointer[i]] > cuda_label_pointer[cuda_item_indices_buffer_pointer[j]]) { high_rank = i; low_rank = j; } else { high_rank = j; low_rank = i; } const data_size_t high = cuda_item_indices_buffer_pointer[high_rank]; const int high_label = static_cast(cuda_label_pointer[high]); const double high_score = cuda_scores_pointer[high]; const double high_label_gain = label_gain_ptr[high_label]; const double high_discount = log2(2.0f + high_rank); const data_size_t low = cuda_item_indices_buffer_pointer[low_rank]; const int low_label = static_cast(cuda_label_pointer[low]); const double low_score = cuda_scores_pointer[low]; const double low_label_gain = label_gain_ptr[low_label]; const double low_discount = log2(2.0f + low_rank); const double delta_score = high_score - low_score; // get dcg gap const double dcg_gap = high_label_gain - low_label_gain; // get discount of this pair const double paired_discount = fabs(high_discount - low_discount); // get delta NDCG double delta_pair_NDCG = dcg_gap * paired_discount * inverse_max_dcg; // regular the delta_pair_NDCG by score distance if (norm && best_score != worst_score) { delta_pair_NDCG /= (0.01f + fabs(delta_score)); } // calculate lambda for this pair double p_lambda = 1.0f / (1.0f + exp(sigmoid * delta_score)); double p_hessian = p_lambda * (1.0f - p_lambda); // update p_lambda *= -sigmoid * delta_pair_NDCG; p_hessian *= sigmoid * sigmoid * delta_pair_NDCG; atomicAdd_block(cuda_out_gradients_pointer + low, -static_cast(p_lambda)); atomicAdd_block(cuda_out_hessians_pointer + low, static_cast(p_hessian)); atomicAdd_block(cuda_out_gradients_pointer + high, static_cast(p_lambda)); atomicAdd_block(cuda_out_hessians_pointer + high, static_cast(p_hessian)); // lambda is negative, so use minus to accumulate thread_sum_lambdas -= 2 * p_lambda; } } } atomicAdd_block(&sum_lambdas, thread_sum_lambdas); __syncthreads(); if (norm && sum_lambdas > 0) { const double norm_factor = log2(1 + sum_lambdas) / sum_lambdas; for (int item_index = static_cast(threadIdx.x); item_index < query_item_count; item_index += static_cast(blockDim.x)) { cuda_out_gradients_pointer[item_index] *= norm_factor; cuda_out_hessians_pointer[item_index] *= norm_factor; } } __syncthreads(); } } void CUDALambdarankNDCG::LaunchGetGradientsKernel(const double* score, score_t* gradients, score_t* hessians) const { const int num_blocks = (num_queries_ + NUM_QUERY_PER_BLOCK - 1) / NUM_QUERY_PER_BLOCK; const data_size_t num_rank_label = static_cast(label_gain_.size()); const int device_index = GetCUDADevice(__FILE__, __LINE__); cudaDeviceProp device_prop; CUDASUCCESS_OR_FATAL(cudaGetDeviceProperties(&device_prop, device_index)); #define GetGradientsKernel_LambdarankNDCG_ARGS \ score, cuda_labels_, num_data_, \ num_queries_, cuda_query_boundaries_, cuda_inverse_max_dcgs_.RawData(), \ norm_, sigmoid_, truncation_level_, cuda_label_gain_.RawData(), num_rank_label, \ gradients, hessians #define GetGradientsKernel_LambdarankNDCG_Sorted_ARGS \ score, cuda_item_indices_buffer_.RawData(), cuda_labels_, num_data_, \ num_queries_, cuda_query_boundaries_, cuda_inverse_max_dcgs_.RawData(), \ norm_, sigmoid_, truncation_level_, cuda_label_gain_.RawData(), num_rank_label, \ gradients, hessians if (max_items_in_query_aligned_ <= 1024) { if (num_rank_label <= 32 && device_prop.warpSize == 32) { GetGradientsKernel_LambdarankNDCG<<>>(GetGradientsKernel_LambdarankNDCG_ARGS); } else if (num_rank_label <= 64) { GetGradientsKernel_LambdarankNDCG<<>>(GetGradientsKernel_LambdarankNDCG_ARGS); } else if (num_rank_label <= 128) { GetGradientsKernel_LambdarankNDCG<<>>(GetGradientsKernel_LambdarankNDCG_ARGS); } else if (num_rank_label <= 256) { GetGradientsKernel_LambdarankNDCG<<>>(GetGradientsKernel_LambdarankNDCG_ARGS); } else if (num_rank_label <= 512) { GetGradientsKernel_LambdarankNDCG<<>>(GetGradientsKernel_LambdarankNDCG_ARGS); } else if (num_rank_label <= 1024) { GetGradientsKernel_LambdarankNDCG<<>>(GetGradientsKernel_LambdarankNDCG_ARGS); } else { GetGradientsKernel_LambdarankNDCG<<>>(GetGradientsKernel_LambdarankNDCG_ARGS); } } else if (max_items_in_query_aligned_ <= 2048) { if (num_rank_label <= 32 && device_prop.warpSize == 32) { GetGradientsKernel_LambdarankNDCG<<>>(GetGradientsKernel_LambdarankNDCG_ARGS); } else if (num_rank_label <= 64) { GetGradientsKernel_LambdarankNDCG<<>>(GetGradientsKernel_LambdarankNDCG_ARGS); } else if (num_rank_label <= 128) { GetGradientsKernel_LambdarankNDCG<<>>(GetGradientsKernel_LambdarankNDCG_ARGS); } else if (num_rank_label <= 256) { GetGradientsKernel_LambdarankNDCG<<>>(GetGradientsKernel_LambdarankNDCG_ARGS); } else if (num_rank_label <= 512) { GetGradientsKernel_LambdarankNDCG<<>>(GetGradientsKernel_LambdarankNDCG_ARGS); } else if (num_rank_label <= 1024) { GetGradientsKernel_LambdarankNDCG<<>>(GetGradientsKernel_LambdarankNDCG_ARGS); } else { GetGradientsKernel_LambdarankNDCG<<>>(GetGradientsKernel_LambdarankNDCG_ARGS); } } else { BitonicArgSortItemsGlobal(score, num_queries_, cuda_query_boundaries_, cuda_item_indices_buffer_.RawData()); if (num_rank_label <= 32 && device_prop.warpSize == 32) { GetGradientsKernel_LambdarankNDCG_Sorted<32><<>>(GetGradientsKernel_LambdarankNDCG_Sorted_ARGS); } else if (num_rank_label <= 64) { GetGradientsKernel_LambdarankNDCG_Sorted<64><<>>(GetGradientsKernel_LambdarankNDCG_Sorted_ARGS); } else if (num_rank_label <= 128) { GetGradientsKernel_LambdarankNDCG_Sorted<128><<>>(GetGradientsKernel_LambdarankNDCG_Sorted_ARGS); } else if (num_rank_label <= 256) { GetGradientsKernel_LambdarankNDCG_Sorted<256><<>>(GetGradientsKernel_LambdarankNDCG_Sorted_ARGS); } else if (num_rank_label <= 512) { GetGradientsKernel_LambdarankNDCG_Sorted<512><<>>(GetGradientsKernel_LambdarankNDCG_Sorted_ARGS); } else if (num_rank_label <= 1024) { GetGradientsKernel_LambdarankNDCG_Sorted<1024><<>>(GetGradientsKernel_LambdarankNDCG_Sorted_ARGS); } else { GetGradientsKernel_LambdarankNDCG_Sorted<2048><<>>(GetGradientsKernel_LambdarankNDCG_Sorted_ARGS); } } SynchronizeCUDADevice(__FILE__, __LINE__); #undef GetGradientsKernel_LambdarankNDCG_ARGS #undef GetGradientsKernel_LambdarankNDCG_Sorted_ARGS } __device__ __forceinline__ double CUDAPhi(const label_t l, double g) { return pow(2.0f, static_cast(l)) - g; } template __global__ void GetGradientsKernel_RankXENDCG_SharedMemory( const double* cuda_scores, const label_t* cuda_labels, const double* cuda_item_rands, const data_size_t num_data, const data_size_t num_queries, const data_size_t* cuda_query_boundaries, score_t* cuda_out_gradients, score_t* cuda_out_hessians) { const data_size_t query_index_start = static_cast(blockIdx.x) * NUM_QUERY_PER_BLOCK; const data_size_t query_index_end = min(query_index_start + NUM_QUERY_PER_BLOCK, num_queries); for (data_size_t query_index = query_index_start; query_index < query_index_end; ++query_index) { const data_size_t item_index_start = cuda_query_boundaries[query_index]; const data_size_t item_index_end = cuda_query_boundaries[query_index + 1]; const data_size_t query_item_count = item_index_end - item_index_start; score_t* cuda_out_gradients_pointer = cuda_out_gradients + item_index_start; score_t* cuda_out_hessians_pointer = cuda_out_hessians + item_index_start; const label_t* cuda_labels_pointer = cuda_labels + item_index_start; const double* cuda_scores_pointer = cuda_scores + item_index_start; const double* cuda_item_rands_pointer = cuda_item_rands + item_index_start; const data_size_t block_reduce_size = query_item_count >= 1024 ? 1024 : query_item_count; __shared__ double shared_rho[SHARED_MEMORY_SIZE]; // assert that warpSize == 32 __shared__ double shared_buffer[1024 / WARPSIZE]; __shared__ double shared_params[SHARED_MEMORY_SIZE]; __shared__ score_t shared_lambdas[SHARED_MEMORY_SIZE]; __shared__ double reduce_result; if (query_item_count <= 1) { for (data_size_t i = 0; i <= query_item_count; ++i) { cuda_out_gradients_pointer[i] = 0.0f; cuda_out_hessians_pointer[i] = 0.0f; } __syncthreads(); } else { // compute softmax double thread_reduce_result = kMinScore; for (data_size_t i = static_cast(threadIdx.x); i < query_item_count; i += static_cast(blockDim.x)) { const double rho = cuda_scores_pointer[i]; shared_rho[i] = rho; if (rho > thread_reduce_result) { thread_reduce_result = rho; } } __syncthreads(); thread_reduce_result = ShuffleReduceMax(thread_reduce_result, shared_buffer, block_reduce_size); if (threadIdx.x == 0) { reduce_result = thread_reduce_result; } __syncthreads(); thread_reduce_result = 0.0f; for (data_size_t i = static_cast(threadIdx.x); i < query_item_count; i += static_cast(blockDim.x)) { const double exp_value = exp(shared_rho[i] - reduce_result); shared_rho[i] = exp_value; thread_reduce_result += exp_value; } thread_reduce_result = ShuffleReduceSum(thread_reduce_result, shared_buffer, block_reduce_size); if (threadIdx.x == 0) { reduce_result = thread_reduce_result; } __syncthreads(); for (data_size_t i = static_cast(threadIdx.x); i < query_item_count; i += static_cast(blockDim.x)) { shared_rho[i] /= reduce_result; } __syncthreads(); // compute params thread_reduce_result = 0.0f; for (data_size_t i = static_cast(threadIdx.x); i < query_item_count; i += static_cast(blockDim.x)) { const double param_value = CUDAPhi(cuda_labels_pointer[i], cuda_item_rands_pointer[i]); shared_params[i] = param_value; thread_reduce_result += param_value; } thread_reduce_result = ShuffleReduceSum(thread_reduce_result, shared_buffer, block_reduce_size); if (threadIdx.x == 0) { reduce_result = thread_reduce_result; reduce_result = 1.0f / max(kEpsilon, reduce_result); } __syncthreads(); const double inv_denominator = reduce_result; thread_reduce_result = 0.0f; for (data_size_t i = static_cast(threadIdx.x); i < query_item_count; i += static_cast(blockDim.x)) { const double term = -shared_params[i] * inv_denominator + shared_rho[i]; shared_lambdas[i] = static_cast(term); shared_params[i] = term / (1.0f - shared_rho[i]); thread_reduce_result += shared_params[i]; } thread_reduce_result = ShuffleReduceSum(thread_reduce_result, shared_buffer, block_reduce_size); if (threadIdx.x == 0) { reduce_result = thread_reduce_result; } __syncthreads(); const double sum_l1 = reduce_result; thread_reduce_result = 0.0f; for (data_size_t i = static_cast(threadIdx.x); i < query_item_count; i += static_cast(blockDim.x)) { const double term = shared_rho[i] * (sum_l1 - shared_params[i]); shared_lambdas[i] += static_cast(term); shared_params[i] = term / (1.0f - shared_rho[i]); thread_reduce_result += shared_params[i]; } thread_reduce_result = ShuffleReduceSum(thread_reduce_result, shared_buffer, block_reduce_size); if (threadIdx.x == 0) { reduce_result = thread_reduce_result; } __syncthreads(); const double sum_l2 = reduce_result; for (data_size_t i = static_cast(threadIdx.x); i < query_item_count; i += static_cast(blockDim.x)) { shared_lambdas[i] += static_cast(shared_rho[i] * (sum_l2 - shared_params[i])); cuda_out_hessians_pointer[i] = static_cast(shared_rho[i] * (1.0f - shared_rho[i])); } for (data_size_t i = static_cast(threadIdx.x); i < query_item_count; i += static_cast(blockDim.x)) { cuda_out_gradients_pointer[i] = shared_lambdas[i]; } __syncthreads(); } } } __global__ void GetGradientsKernel_RankXENDCG_GlobalMemory( const double* cuda_scores, const label_t* cuda_labels, const double* cuda_item_rands, const data_size_t num_data, const data_size_t num_queries, const data_size_t* cuda_query_boundaries, double* cuda_params_buffer, score_t* cuda_out_gradients, score_t* cuda_out_hessians) { const data_size_t query_index_start = static_cast(blockIdx.x) * NUM_QUERY_PER_BLOCK; const data_size_t query_index_end = min(query_index_start + NUM_QUERY_PER_BLOCK, num_queries); for (data_size_t query_index = query_index_start; query_index < query_index_end; ++query_index) { const data_size_t item_index_start = cuda_query_boundaries[query_index]; const data_size_t item_index_end = cuda_query_boundaries[query_index + 1]; const data_size_t query_item_count = item_index_end - item_index_start; score_t* cuda_out_gradients_pointer = cuda_out_gradients + item_index_start; score_t* cuda_out_hessians_pointer = cuda_out_hessians + item_index_start; const label_t* cuda_labels_pointer = cuda_labels + item_index_start; const double* cuda_scores_pointer = cuda_scores + item_index_start; const double* cuda_item_rands_pointer = cuda_item_rands + item_index_start; double* cuda_params_buffer_pointer = cuda_params_buffer + item_index_start; const data_size_t block_reduce_size = query_item_count > 1024 ? 1024 : query_item_count; // assert that warpSize == 32, so we use buffer size 1024 / 32 = 32 __shared__ double shared_buffer[1024 / WARPSIZE]; __shared__ double reduce_result; if (query_item_count <= 1) { for (data_size_t i = 0; i <= query_item_count; ++i) { cuda_out_gradients_pointer[i] = 0.0f; cuda_out_hessians_pointer[i] = 0.0f; } __syncthreads(); } else { // compute softmax double thread_reduce_result = kMinScore; for (data_size_t i = static_cast(threadIdx.x); i < query_item_count; i += static_cast(blockDim.x)) { const double rho = cuda_scores_pointer[i]; if (rho > thread_reduce_result) { thread_reduce_result = rho; } } __syncthreads(); thread_reduce_result = ShuffleReduceMax(thread_reduce_result, shared_buffer, block_reduce_size); if (threadIdx.x == 0) { reduce_result = thread_reduce_result; } __syncthreads(); thread_reduce_result = 0.0f; for (data_size_t i = static_cast(threadIdx.x); i < query_item_count; i += static_cast(blockDim.x)) { const double exp_value = exp(cuda_scores_pointer[i] - reduce_result); cuda_out_hessians_pointer[i] = exp_value; thread_reduce_result += exp_value; } thread_reduce_result = ShuffleReduceSum(thread_reduce_result, shared_buffer, block_reduce_size); if (threadIdx.x == 0) { reduce_result = thread_reduce_result; } __syncthreads(); // store probability into hessians for (data_size_t i = static_cast(threadIdx.x); i < query_item_count; i += static_cast(blockDim.x)) { cuda_out_hessians_pointer[i] /= reduce_result; } __syncthreads(); // compute params thread_reduce_result = 0.0f; for (data_size_t i = static_cast(threadIdx.x); i < query_item_count; i += static_cast(blockDim.x)) { const double param_value = CUDAPhi(cuda_labels_pointer[i], cuda_item_rands_pointer[i]); cuda_params_buffer_pointer[i] = param_value; thread_reduce_result += param_value; } thread_reduce_result = ShuffleReduceSum(thread_reduce_result, shared_buffer, block_reduce_size); if (threadIdx.x == 0) { reduce_result = thread_reduce_result; reduce_result = 1.0f / max(kEpsilon, reduce_result); } __syncthreads(); const double inv_denominator = reduce_result; thread_reduce_result = 0.0f; for (data_size_t i = static_cast(threadIdx.x); i < query_item_count; i += static_cast(blockDim.x)) { const double term = -cuda_params_buffer_pointer[i] * inv_denominator + cuda_out_hessians_pointer[i]; cuda_out_gradients_pointer[i] = static_cast(term); const double param = term / (1.0f - cuda_out_hessians_pointer[i]); cuda_params_buffer_pointer[i] = param; thread_reduce_result += param; } thread_reduce_result = ShuffleReduceSum(thread_reduce_result, shared_buffer, block_reduce_size); if (threadIdx.x == 0) { reduce_result = thread_reduce_result; } __syncthreads(); const double sum_l1 = reduce_result; thread_reduce_result = 0.0f; for (data_size_t i = static_cast(threadIdx.x); i < query_item_count; i += static_cast(blockDim.x)) { const double term = cuda_out_hessians_pointer[i] * (sum_l1 - cuda_params_buffer_pointer[i]); cuda_out_gradients_pointer[i] += static_cast(term); const double param = term / (1.0f - cuda_out_hessians_pointer[i]); cuda_params_buffer_pointer[i] = param; thread_reduce_result += param; } thread_reduce_result = ShuffleReduceSum(thread_reduce_result, shared_buffer, block_reduce_size); if (threadIdx.x == 0) { reduce_result = thread_reduce_result; } __syncthreads(); const double sum_l2 = reduce_result; for (data_size_t i = static_cast(threadIdx.x); i < query_item_count; i += static_cast(blockDim.x)) { const double prob = cuda_out_hessians_pointer[i]; cuda_out_gradients_pointer[i] += static_cast(prob * (sum_l2 - cuda_params_buffer_pointer[i])); cuda_out_hessians_pointer[i] = static_cast(prob * (1.0f - prob)); } __syncthreads(); } } } void CUDARankXENDCG::LaunchGetGradientsKernel(const double* score, score_t* gradients, score_t* hessians) const { GenerateItemRands(); CopyFromHostToCUDADevice(cuda_item_rands_.RawData(), item_rands_.data(), item_rands_.size(), __FILE__, __LINE__); const int num_blocks = (num_queries_ + NUM_QUERY_PER_BLOCK - 1) / NUM_QUERY_PER_BLOCK; if (max_items_in_query_aligned_ <= 1024) { GetGradientsKernel_RankXENDCG_SharedMemory<1024><<>>( score, cuda_labels_, cuda_item_rands_.RawData(), num_data_, num_queries_, cuda_query_boundaries_, gradients, hessians); } else if (max_items_in_query_aligned_ <= 2 * 1024) { GetGradientsKernel_RankXENDCG_SharedMemory<2 * 1024><<>>( score, cuda_labels_, cuda_item_rands_.RawData(), num_data_, num_queries_, cuda_query_boundaries_, gradients, hessians); } else { GetGradientsKernel_RankXENDCG_GlobalMemory<<>>( score, cuda_labels_, cuda_item_rands_.RawData(), num_data_, num_queries_, cuda_query_boundaries_, cuda_params_buffer_.RawData(), gradients, hessians); } SynchronizeCUDADevice(__FILE__, __LINE__); } } // namespace LightGBM #endif // USE_CUDA ================================================ FILE: src/objective/cuda/cuda_rank_objective.hpp ================================================ /*! * Copyright (c) 2021-2026 Microsoft Corporation. All rights reserved. * Copyright (c) 2021-2026 The LightGBM developers. All rights reserved. * Licensed under the MIT License. See LICENSE file in the project root for * license information. */ #ifndef LIGHTGBM_SRC_OBJECTIVE_CUDA_CUDA_RANK_OBJECTIVE_HPP_ #define LIGHTGBM_SRC_OBJECTIVE_CUDA_CUDA_RANK_OBJECTIVE_HPP_ #ifdef USE_CUDA #define NUM_QUERY_PER_BLOCK (10) #include #include #include #include #include #include "../rank_objective.hpp" namespace LightGBM { template class CUDALambdaRankObjectiveInterface : public CUDAObjectiveInterface { public: explicit CUDALambdaRankObjectiveInterface(const Config& config): CUDAObjectiveInterface(config) {} explicit CUDALambdaRankObjectiveInterface(const std::vector& strs): CUDAObjectiveInterface(strs) {} ~CUDALambdaRankObjectiveInterface() {} void Init(const Metadata& metadata, data_size_t num_data) override { CUDAObjectiveInterface::Init(metadata, num_data); const int num_threads = OMP_NUM_THREADS(); std::vector thread_max_num_items_in_query(num_threads); Threading::For(0, this->num_queries_, 1, [this, &thread_max_num_items_in_query] (int thread_index, data_size_t start, data_size_t end) { for (data_size_t query_index = start; query_index < end; ++query_index) { const data_size_t query_item_count = this->query_boundaries_[query_index + 1] - this->query_boundaries_[query_index]; if (query_item_count > thread_max_num_items_in_query[thread_index]) { thread_max_num_items_in_query[thread_index] = query_item_count; } } }); data_size_t max_items_in_query = 0; for (int thread_index = 0; thread_index < num_threads; ++thread_index) { if (thread_max_num_items_in_query[thread_index] > max_items_in_query) { max_items_in_query = thread_max_num_items_in_query[thread_index]; } } max_items_in_query_aligned_ = 1; --max_items_in_query; while (max_items_in_query > 0) { max_items_in_query >>= 1; max_items_in_query_aligned_ <<= 1; } if (max_items_in_query_aligned_ > 2048) { cuda_item_indices_buffer_.Resize(static_cast(metadata.query_boundaries()[metadata.num_queries()])); } this->cuda_labels_ = metadata.cuda_metadata()->cuda_label(); cuda_query_boundaries_ = metadata.cuda_metadata()->cuda_query_boundaries(); } protected: // CUDA memory, held by this object CUDAVector cuda_item_indices_buffer_; // CUDA memory, held by other objects const data_size_t* cuda_query_boundaries_; // Host memory int max_items_in_query_aligned_; }; class CUDALambdarankNDCG: public CUDALambdaRankObjectiveInterface { public: explicit CUDALambdarankNDCG(const Config& config); explicit CUDALambdarankNDCG(const std::vector& strs); void Init(const Metadata& mdtadata, data_size_t num_data) override; ~CUDALambdarankNDCG(); private: void LaunchGetGradientsKernel(const double* score, score_t* gradients, score_t* hessians) const override; // CUDA memory, held by this object CUDAVector cuda_inverse_max_dcgs_; CUDAVector cuda_label_gain_; }; class CUDARankXENDCG : public CUDALambdaRankObjectiveInterface { public: explicit CUDARankXENDCG(const Config& config); explicit CUDARankXENDCG(const std::vector& strs); ~CUDARankXENDCG(); void Init(const Metadata& metadata, data_size_t num_data) override; protected: void LaunchGetGradientsKernel(const double* score, score_t* gradients, score_t* hessians) const; void GenerateItemRands() const; mutable std::vector item_rands_; CUDAVector cuda_item_rands_; CUDAVector cuda_params_buffer_; }; } // namespace LightGBM #endif // USE_CUDA #endif // LIGHTGBM_SRC_OBJECTIVE_CUDA_CUDA_RANK_OBJECTIVE_HPP_ ================================================ FILE: src/objective/cuda/cuda_regression_objective.cpp ================================================ /*! * Copyright (c) 2021-2026 Microsoft Corporation. All rights reserved. * Copyright (c) 2021-2026 The LightGBM developers. All rights reserved. * Licensed under the MIT License. See LICENSE file in the project root for * license information. */ #ifdef USE_CUDA #include "cuda_regression_objective.hpp" #include #include namespace LightGBM { CUDARegressionL2loss::CUDARegressionL2loss(const Config& config): CUDARegressionObjectiveInterface(config) {} CUDARegressionL2loss::CUDARegressionL2loss(const std::vector& strs): CUDARegressionObjectiveInterface(strs) {} CUDARegressionL2loss::~CUDARegressionL2loss() {} void CUDARegressionL2loss::Init(const Metadata& metadata, data_size_t num_data) { CUDARegressionObjectiveInterface::Init(metadata, num_data); } CUDARegressionL1loss::CUDARegressionL1loss(const Config& config): CUDARegressionObjectiveInterface(config) {} CUDARegressionL1loss::CUDARegressionL1loss(const std::vector& strs): CUDARegressionObjectiveInterface(strs) {} CUDARegressionL1loss::~CUDARegressionL1loss() {} void CUDARegressionL1loss::Init(const Metadata& metadata, data_size_t num_data) { CUDARegressionObjectiveInterface::Init(metadata, num_data); cuda_data_indices_buffer_.Resize(static_cast(num_data)); cuda_percentile_result_.Resize(1); if (cuda_weights_ != nullptr) { const int num_blocks = (num_data + GET_GRADIENTS_BLOCK_SIZE_REGRESSION - 1) / GET_GRADIENTS_BLOCK_SIZE_REGRESSION + 1; cuda_weights_prefix_sum_.Resize(static_cast(num_data)); cuda_weights_prefix_sum_buffer_.Resize(static_cast(num_blocks)); cuda_weight_by_leaf_buffer_.Resize(static_cast(num_data)); } cuda_residual_buffer_.Resize(static_cast(num_data)); } CUDARegressionHuberLoss::CUDARegressionHuberLoss(const Config& config): CUDARegressionObjectiveInterface(config) {} CUDARegressionHuberLoss::CUDARegressionHuberLoss(const std::vector& strs): CUDARegressionObjectiveInterface(strs) {} CUDARegressionHuberLoss::~CUDARegressionHuberLoss() {} CUDARegressionFairLoss::CUDARegressionFairLoss(const Config& config): CUDARegressionObjectiveInterface(config) {} CUDARegressionFairLoss::CUDARegressionFairLoss(const std::vector& strs): CUDARegressionObjectiveInterface(strs) {} CUDARegressionFairLoss::~CUDARegressionFairLoss() {} CUDARegressionPoissonLoss::CUDARegressionPoissonLoss(const Config& config): CUDARegressionObjectiveInterface(config) {} CUDARegressionPoissonLoss::CUDARegressionPoissonLoss(const std::vector& strs): CUDARegressionObjectiveInterface(strs) {} CUDARegressionPoissonLoss::~CUDARegressionPoissonLoss() {} void CUDARegressionPoissonLoss::Init(const Metadata& metadata, data_size_t num_data) { CUDARegressionObjectiveInterface::Init(metadata, num_data); LaunchCheckLabelKernel(); } double CUDARegressionPoissonLoss::LaunchCalcInitScoreKernel(const int class_id) const { return Common::SafeLog(CUDARegressionObjectiveInterface::LaunchCalcInitScoreKernel(class_id)); } CUDARegressionQuantileloss::CUDARegressionQuantileloss(const Config& config): CUDARegressionObjectiveInterface(config) {} CUDARegressionQuantileloss::CUDARegressionQuantileloss(const std::vector& strs): CUDARegressionObjectiveInterface(strs) {} CUDARegressionQuantileloss::~CUDARegressionQuantileloss() {} void CUDARegressionQuantileloss::Init(const Metadata& metadata, data_size_t num_data) { CUDARegressionObjectiveInterface::Init(metadata, num_data); cuda_data_indices_buffer_.Resize(static_cast(num_data)); cuda_percentile_result_.Resize(1); if (cuda_weights_ != nullptr) { const int num_blocks = (num_data + GET_GRADIENTS_BLOCK_SIZE_REGRESSION - 1) / GET_GRADIENTS_BLOCK_SIZE_REGRESSION + 1; cuda_weights_prefix_sum_.Resize(static_cast(num_data)); cuda_weights_prefix_sum_buffer_.Resize(static_cast(num_blocks)); cuda_weight_by_leaf_buffer_.Resize(static_cast(num_data)); } cuda_residual_buffer_.Resize(static_cast(num_data)); } } // namespace LightGBM #endif // USE_CUDA ================================================ FILE: src/objective/cuda/cuda_regression_objective.cu ================================================ /*! * Copyright (c) 2021-2026 Microsoft Corporation. All rights reserved. * Copyright (c) 2021-2026 The LightGBM developers. All rights reserved. * Licensed under the MIT License. See LICENSE file in the project root for * license information. */ #ifdef USE_CUDA #include "cuda_regression_objective.hpp" #include namespace LightGBM { template void CUDARegressionObjectiveInterface::Init(const Metadata& metadata, data_size_t num_data) { CUDAObjectiveInterface::Init(metadata, num_data); const data_size_t num_get_gradients_blocks = (this->num_data_ + GET_GRADIENTS_BLOCK_SIZE_REGRESSION - 1) / GET_GRADIENTS_BLOCK_SIZE_REGRESSION; cuda_block_buffer_.Resize(static_cast(num_get_gradients_blocks)); if (this->sqrt_) { cuda_trans_label_.Resize(this->trans_label_.size()); CopyFromHostToCUDADevice(cuda_trans_label_.RawData(), this->trans_label_.data(), this->trans_label_.size(), __FILE__, __LINE__); this->cuda_labels_ = cuda_trans_label_.RawData(); } } template void CUDARegressionObjectiveInterface::Init(const Metadata& metadata, data_size_t num_data); template void CUDARegressionObjectiveInterface::Init(const Metadata& metadata, data_size_t num_data); template void CUDARegressionObjectiveInterface::Init(const Metadata& metadata, data_size_t num_data); template void CUDARegressionObjectiveInterface::Init(const Metadata& metadata, data_size_t num_data); template void CUDARegressionObjectiveInterface::Init(const Metadata& metadata, data_size_t num_data); template void CUDARegressionObjectiveInterface::Init(const Metadata& metadata, data_size_t num_data); template double CUDARegressionObjectiveInterface::LaunchCalcInitScoreKernel(const int /*class_id*/) const { double label_sum = 0.0f, weight_sum = 0.0f; if (this->cuda_weights_ == nullptr) { ShuffleReduceSumGlobal(this->cuda_labels_, static_cast(this->num_data_), cuda_block_buffer_.RawData()); CopyFromCUDADeviceToHost(&label_sum, cuda_block_buffer_.RawData(), 1, __FILE__, __LINE__); weight_sum = static_cast(this->num_data_); } else { ShuffleReduceDotProdGlobal(this->cuda_labels_, this->cuda_weights_, static_cast(this->num_data_), cuda_block_buffer_.RawData()); CopyFromCUDADeviceToHost(&label_sum, cuda_block_buffer_.RawData(), 1, __FILE__, __LINE__); ShuffleReduceSumGlobal(this->cuda_weights_, static_cast(this->num_data_), cuda_block_buffer_.RawData()); CopyFromCUDADeviceToHost(&weight_sum, cuda_block_buffer_.RawData(), 1, __FILE__, __LINE__); } return label_sum / weight_sum; } template double CUDARegressionObjectiveInterface::LaunchCalcInitScoreKernel(const int class_id) const; template double CUDARegressionObjectiveInterface::LaunchCalcInitScoreKernel(const int class_id) const; template double CUDARegressionObjectiveInterface::LaunchCalcInitScoreKernel(const int class_id) const; template double CUDARegressionObjectiveInterface::LaunchCalcInitScoreKernel(const int class_id) const; template double CUDARegressionObjectiveInterface::LaunchCalcInitScoreKernel(const int class_id) const; template double CUDARegressionObjectiveInterface::LaunchCalcInitScoreKernel(const int class_id) const; __global__ void ConvertOutputCUDAKernel_Regression(const bool sqrt, const data_size_t num_data, const double* input, double* output) { const int data_index = static_cast(blockIdx.x * blockDim.x + threadIdx.x); if (data_index < num_data) { if (sqrt) { const double sign = input[data_index] >= 0.0f ? 1 : -1; output[data_index] = sign * input[data_index] * input[data_index]; } else { output[data_index] = input[data_index]; } } } const double* CUDARegressionL2loss::LaunchConvertOutputCUDAKernel(const data_size_t num_data, const double* input, double* output) const { const int num_blocks = (num_data + GET_GRADIENTS_BLOCK_SIZE_REGRESSION - 1) / GET_GRADIENTS_BLOCK_SIZE_REGRESSION; if (sqrt_) { ConvertOutputCUDAKernel_Regression<<>>(sqrt_, num_data, input, output); return output; } else { return input; } } template __global__ void GetGradientsKernel_RegressionL2(const double* cuda_scores, const label_t* cuda_labels, const label_t* cuda_weights, const data_size_t num_data, score_t* cuda_out_gradients, score_t* cuda_out_hessians) { const data_size_t data_index = static_cast(blockDim.x * blockIdx.x + threadIdx.x); if (data_index < num_data) { if (!USE_WEIGHT) { cuda_out_gradients[data_index] = static_cast(cuda_scores[data_index] - cuda_labels[data_index]); cuda_out_hessians[data_index] = 1.0f; } else { const score_t weight = static_cast(cuda_weights[data_index]); cuda_out_gradients[data_index] = static_cast(cuda_scores[data_index] - cuda_labels[data_index]) * weight; cuda_out_hessians[data_index] = weight; } } } void CUDARegressionL2loss::LaunchGetGradientsKernel(const double* score, score_t* gradients, score_t* hessians) const { const int num_blocks = (num_data_ + GET_GRADIENTS_BLOCK_SIZE_REGRESSION - 1) / GET_GRADIENTS_BLOCK_SIZE_REGRESSION; if (cuda_weights_ == nullptr) { GetGradientsKernel_RegressionL2<<>>(score, cuda_labels_, nullptr, num_data_, gradients, hessians); } else { GetGradientsKernel_RegressionL2<<>>(score, cuda_labels_, cuda_weights_, num_data_, gradients, hessians); } } double CUDARegressionL1loss::LaunchCalcInitScoreKernel(const int /*class_id*/) const { const double alpha = 0.5f; if (cuda_weights_ == nullptr) { PercentileGlobal( cuda_labels_, nullptr, cuda_data_indices_buffer_.RawData(), nullptr, nullptr, alpha, num_data_, cuda_percentile_result_.RawData()); } else { PercentileGlobal( cuda_labels_, cuda_weights_, cuda_data_indices_buffer_.RawData(), cuda_weights_prefix_sum_.RawData(), cuda_weights_prefix_sum_buffer_.RawData(), alpha, num_data_, cuda_percentile_result_.RawData()); } label_t percentile_result = 0.0f; CopyFromCUDADeviceToHost(&percentile_result, cuda_percentile_result_.RawData(), 1, __FILE__, __LINE__); SynchronizeCUDADevice(__FILE__, __LINE__); return static_cast(percentile_result); } template __global__ void GetGradientsKernel_RegressionL1(const double* cuda_scores, const label_t* cuda_labels, const label_t* cuda_weights, const data_size_t num_data, score_t* cuda_out_gradients, score_t* cuda_out_hessians) { const data_size_t data_index = static_cast(blockDim.x * blockIdx.x + threadIdx.x); if (data_index < num_data) { if (!USE_WEIGHT) { const double diff = cuda_scores[data_index] - static_cast(cuda_labels[data_index]); cuda_out_gradients[data_index] = static_cast((diff > 0.0f) - (diff < 0.0f)); cuda_out_hessians[data_index] = 1.0f; } else { const double diff = cuda_scores[data_index] - static_cast(cuda_labels[data_index]); const score_t weight = static_cast(cuda_weights[data_index]); cuda_out_gradients[data_index] = static_cast((diff > 0.0f) - (diff < 0.0f)) * weight; cuda_out_hessians[data_index] = weight; } } } void CUDARegressionL1loss::LaunchGetGradientsKernel(const double* score, score_t* gradients, score_t* hessians) const { const int num_blocks = (num_data_ + GET_GRADIENTS_BLOCK_SIZE_REGRESSION - 1) / GET_GRADIENTS_BLOCK_SIZE_REGRESSION; if (cuda_weights_ == nullptr) { GetGradientsKernel_RegressionL1<<>>(score, cuda_labels_, nullptr, num_data_, gradients, hessians); } else { GetGradientsKernel_RegressionL1<<>>(score, cuda_labels_, cuda_weights_, num_data_, gradients, hessians); } } template __global__ void RenewTreeOutputCUDAKernel_RegressionL1( const double* score, const label_t* label, const label_t* weight, double* residual_buffer, label_t* weight_by_leaf, double* weight_prefix_sum_buffer, const data_size_t* data_indices_in_leaf, const data_size_t* num_data_in_leaf, const data_size_t* data_start_in_leaf, data_size_t* data_indices_buffer, double* leaf_value) { const int leaf_index = static_cast(blockIdx.x); const data_size_t data_start = data_start_in_leaf[leaf_index]; const data_size_t num_data = num_data_in_leaf[leaf_index]; data_size_t* data_indices_buffer_pointer = data_indices_buffer + data_start; const label_t* weight_by_leaf_pointer = weight_by_leaf + data_start; double* weight_prefix_sum_buffer_pointer = weight_prefix_sum_buffer + data_start; const double* residual_buffer_pointer = residual_buffer + data_start; const double alpha = 0.5f; 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)) { const data_size_t data_index = data_indices_in_leaf[inner_data_index]; const label_t data_label = label[data_index]; const double data_score = score[data_index]; residual_buffer[inner_data_index] = static_cast(data_label) - data_score; if (USE_WEIGHT) { weight_by_leaf[inner_data_index] = weight[data_index]; } } __syncthreads(); const double renew_leaf_value = PercentileDevice( residual_buffer_pointer, weight_by_leaf_pointer, data_indices_buffer_pointer, weight_prefix_sum_buffer_pointer, alpha, num_data); if (threadIdx.x == 0) { leaf_value[leaf_index] = renew_leaf_value; } } void CUDARegressionL1loss::LaunchRenewTreeOutputCUDAKernel( const double* score, const data_size_t* data_indices_in_leaf, const data_size_t* num_data_in_leaf, const data_size_t* data_start_in_leaf, const int num_leaves, double* leaf_value) const { if (cuda_weights_ == nullptr) { RenewTreeOutputCUDAKernel_RegressionL1<<>>( score, cuda_labels_, cuda_weights_, cuda_residual_buffer_.RawData(), cuda_weight_by_leaf_buffer_.RawData(), cuda_weights_prefix_sum_.RawData(), data_indices_in_leaf, num_data_in_leaf, data_start_in_leaf, cuda_data_indices_buffer_.RawData(), leaf_value); } else { RenewTreeOutputCUDAKernel_RegressionL1<<>>( score, cuda_labels_, cuda_weights_, cuda_residual_buffer_.RawData(), cuda_weight_by_leaf_buffer_.RawData(), cuda_weights_prefix_sum_.RawData(), data_indices_in_leaf, num_data_in_leaf, data_start_in_leaf, cuda_data_indices_buffer_.RawData(), leaf_value); } SynchronizeCUDADevice(__FILE__, __LINE__); } template __global__ void GetGradientsKernel_Huber(const double* cuda_scores, const label_t* cuda_labels, const label_t* cuda_weights, const data_size_t num_data, const double alpha, score_t* cuda_out_gradients, score_t* cuda_out_hessians) { const data_size_t data_index = static_cast(blockDim.x * blockIdx.x + threadIdx.x); if (data_index < num_data) { if (!USE_WEIGHT) { const double diff = cuda_scores[data_index] - static_cast(cuda_labels[data_index]); if (fabs(diff) <= alpha) { cuda_out_gradients[data_index] = static_cast(diff); } else { const score_t sign = static_cast((diff > 0.0f) - (diff < 0.0f)); cuda_out_gradients[data_index] = static_cast(sign * alpha); } cuda_out_hessians[data_index] = 1.0f; } else { const double diff = cuda_scores[data_index] - static_cast(cuda_labels[data_index]); const score_t weight = static_cast(cuda_weights[data_index]); if (fabs(diff) <= alpha) { cuda_out_gradients[data_index] = static_cast(diff) * weight; } else { const score_t sign = static_cast((diff > 0.0f) - (diff < 0.0f)); cuda_out_gradients[data_index] = static_cast(sign * alpha) * weight; } cuda_out_hessians[data_index] = weight; } } } void CUDARegressionHuberLoss::LaunchGetGradientsKernel(const double* score, score_t* gradients, score_t* hessians) const { const int num_blocks = (num_data_ + GET_GRADIENTS_BLOCK_SIZE_REGRESSION - 1) / GET_GRADIENTS_BLOCK_SIZE_REGRESSION; if (cuda_weights_ == nullptr) { GetGradientsKernel_Huber<<>>(score, cuda_labels_, nullptr, num_data_, alpha_, gradients, hessians); } else { GetGradientsKernel_Huber<<>>(score, cuda_labels_, cuda_weights_, num_data_, alpha_, gradients, hessians); } } template __global__ void GetGradientsKernel_Fair(const double* cuda_scores, const label_t* cuda_labels, const label_t* cuda_weights, const data_size_t num_data, const double c, score_t* cuda_out_gradients, score_t* cuda_out_hessians) { const data_size_t data_index = static_cast(blockDim.x * blockIdx.x + threadIdx.x); if (data_index < num_data) { if (!USE_WEIGHT) { const double diff = cuda_scores[data_index] - static_cast(cuda_labels[data_index]); cuda_out_gradients[data_index] = static_cast(c * diff / (fabs(diff) + c)); cuda_out_hessians[data_index] = static_cast(c * c / ((fabs(diff) + c) * (fabs(diff) + c))); } else { const double diff = cuda_scores[data_index] - static_cast(cuda_labels[data_index]); const score_t weight = static_cast(cuda_weights[data_index]); cuda_out_gradients[data_index] = static_cast(c * diff / (fabs(diff) + c) * weight); cuda_out_hessians[data_index] = static_cast(c * c / ((fabs(diff) + c) * (fabs(diff) + c)) * weight); } } } void CUDARegressionFairLoss::LaunchGetGradientsKernel(const double* score, score_t* gradients, score_t* hessians) const { const int num_blocks = (num_data_ + GET_GRADIENTS_BLOCK_SIZE_REGRESSION - 1) / GET_GRADIENTS_BLOCK_SIZE_REGRESSION; if (cuda_weights_ == nullptr) { GetGradientsKernel_Fair<<>>(score, cuda_labels_, nullptr, num_data_, c_, gradients, hessians); } else { GetGradientsKernel_Fair<<>>(score, cuda_labels_, cuda_weights_, num_data_, c_, gradients, hessians); } } void CUDARegressionPoissonLoss::LaunchCheckLabelKernel() const { ShuffleReduceSumGlobal(cuda_labels_, static_cast(num_data_), cuda_block_buffer_.RawData()); double label_sum = 0.0f; CopyFromCUDADeviceToHost(&label_sum, cuda_block_buffer_.RawData(), 1, __FILE__, __LINE__); ShuffleReduceMinGlobal(cuda_labels_, static_cast(num_data_), cuda_block_buffer_.RawData()); double label_min = 0.0f; CopyFromCUDADeviceToHost(&label_min, cuda_block_buffer_.RawData(), 1, __FILE__, __LINE__); if (label_min < 0.0f) { Log::Fatal("[%s]: at least one target label is negative", GetName()); } if (label_sum == 0.0f) { Log::Fatal("[%s]: sum of labels is zero", GetName()); } } template __global__ void GetGradientsKernel_Poisson(const double* cuda_scores, const label_t* cuda_labels, const label_t* cuda_weights, const data_size_t num_data, const double max_delta_step, score_t* cuda_out_gradients, score_t* cuda_out_hessians) { const data_size_t data_index = static_cast(blockDim.x * blockIdx.x + threadIdx.x); const double exp_max_delta_step = std::exp(max_delta_step); if (data_index < num_data) { if (!USE_WEIGHT) { const double exp_score = exp(cuda_scores[data_index]); cuda_out_gradients[data_index] = static_cast(exp_score - cuda_labels[data_index]); cuda_out_hessians[data_index] = static_cast(exp_score * exp_max_delta_step); } else { const double exp_score = exp(cuda_scores[data_index]); const score_t weight = static_cast(cuda_weights[data_index]); cuda_out_gradients[data_index] = static_cast((exp_score - cuda_labels[data_index]) * weight); cuda_out_hessians[data_index] = static_cast(exp_score * exp_max_delta_step * weight); } } } void CUDARegressionPoissonLoss::LaunchGetGradientsKernel(const double* score, score_t* gradients, score_t* hessians) const { const int num_blocks = (num_data_ + GET_GRADIENTS_BLOCK_SIZE_REGRESSION - 1) / GET_GRADIENTS_BLOCK_SIZE_REGRESSION; if (cuda_weights_ == nullptr) { GetGradientsKernel_Poisson<<>>( score, cuda_labels_, nullptr, num_data_, max_delta_step_, gradients, hessians); } else { GetGradientsKernel_Poisson<<>>( score, cuda_labels_, cuda_weights_, num_data_, max_delta_step_, gradients, hessians); } } __global__ void ConvertOutputCUDAKernel_Regression_Poisson(const data_size_t num_data, const double* input, double* output) { const int data_index = static_cast(blockIdx.x * blockDim.x + threadIdx.x); if (data_index < num_data) { output[data_index] = exp(input[data_index]); } } const double* CUDARegressionPoissonLoss::LaunchConvertOutputCUDAKernel(const data_size_t num_data, const double* input, double* output) const { const int num_blocks = (num_data + GET_GRADIENTS_BLOCK_SIZE_REGRESSION - 1) / GET_GRADIENTS_BLOCK_SIZE_REGRESSION; ConvertOutputCUDAKernel_Regression_Poisson<<>>(num_data, input, output); return output; } double CUDARegressionQuantileloss::LaunchCalcInitScoreKernel(const int /*class_id*/) const { if (cuda_weights_ == nullptr) { PercentileGlobal( cuda_labels_, nullptr, cuda_data_indices_buffer_.RawData(), nullptr, nullptr, alpha_, num_data_, cuda_percentile_result_.RawData()); } else { PercentileGlobal( cuda_labels_, cuda_weights_, cuda_data_indices_buffer_.RawData(), cuda_weights_prefix_sum_.RawData(), cuda_weights_prefix_sum_buffer_.RawData(), alpha_, num_data_, cuda_percentile_result_.RawData()); } label_t percentile_result = 0.0f; CopyFromCUDADeviceToHost(&percentile_result, cuda_percentile_result_.RawData(), 1, __FILE__, __LINE__); SynchronizeCUDADevice(__FILE__, __LINE__); return static_cast(percentile_result); } template __global__ void RenewTreeOutputCUDAKernel_RegressionQuantile( const double* score, const label_t* label, const label_t* weight, double* residual_buffer, label_t* weight_by_leaf, double* weight_prefix_sum_buffer, const data_size_t* data_indices_in_leaf, const data_size_t* num_data_in_leaf, const data_size_t* data_start_in_leaf, data_size_t* data_indices_buffer, double* leaf_value, const double alpha) { const int leaf_index = static_cast(blockIdx.x); const data_size_t data_start = data_start_in_leaf[leaf_index]; const data_size_t num_data = num_data_in_leaf[leaf_index]; data_size_t* data_indices_buffer_pointer = data_indices_buffer + data_start; const label_t* weight_by_leaf_pointer = weight_by_leaf + data_start; double* weight_prefix_sum_buffer_pointer = weight_prefix_sum_buffer + data_start; const double* residual_buffer_pointer = residual_buffer + data_start; 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)) { const data_size_t data_index = data_indices_in_leaf[inner_data_index]; const label_t data_label = label[data_index]; const double data_score = score[data_index]; residual_buffer[inner_data_index] = static_cast(data_label) - data_score; if (USE_WEIGHT) { weight_by_leaf[inner_data_index] = weight[data_index]; } } __syncthreads(); const double renew_leaf_value = PercentileDevice( residual_buffer_pointer, weight_by_leaf_pointer, data_indices_buffer_pointer, weight_prefix_sum_buffer_pointer, alpha, num_data); if (threadIdx.x == 0) { leaf_value[leaf_index] = renew_leaf_value; } } void CUDARegressionQuantileloss::LaunchRenewTreeOutputCUDAKernel( const double* score, const data_size_t* data_indices_in_leaf, const data_size_t* num_data_in_leaf, const data_size_t* data_start_in_leaf, const int num_leaves, double* leaf_value) const { if (cuda_weights_ == nullptr) { RenewTreeOutputCUDAKernel_RegressionQuantile<<>>( score, cuda_labels_, cuda_weights_, cuda_residual_buffer_.RawData(), cuda_weight_by_leaf_buffer_.RawData(), cuda_weights_prefix_sum_.RawData(), data_indices_in_leaf, num_data_in_leaf, data_start_in_leaf, cuda_data_indices_buffer_.RawData(), leaf_value, alpha_); } else { RenewTreeOutputCUDAKernel_RegressionQuantile<<>>( score, cuda_labels_, cuda_weights_, cuda_residual_buffer_.RawData(), cuda_weight_by_leaf_buffer_.RawData(), cuda_weights_prefix_sum_.RawData(), data_indices_in_leaf, num_data_in_leaf, data_start_in_leaf, cuda_data_indices_buffer_.RawData(), leaf_value, alpha_); } SynchronizeCUDADevice(__FILE__, __LINE__); } template __global__ void GetGradientsKernel_RegressionQuantile(const double* cuda_scores, const label_t* cuda_labels, const label_t* cuda_weights, const data_size_t num_data, const double alpha, score_t* cuda_out_gradients, score_t* cuda_out_hessians) { const data_size_t data_index = static_cast(blockDim.x * blockIdx.x + threadIdx.x); if (data_index < num_data) { if (!USE_WEIGHT) { const double diff = cuda_scores[data_index] - static_cast(cuda_labels[data_index]); if (diff >= 0.0f) { cuda_out_gradients[data_index] = (1.0f - alpha); } else { cuda_out_gradients[data_index] = -alpha; } cuda_out_hessians[data_index] = 1.0f; } else { const double diff = cuda_scores[data_index] - static_cast(cuda_labels[data_index]); const score_t weight = static_cast(cuda_weights[data_index]); if (diff >= 0.0f) { cuda_out_gradients[data_index] = (1.0f - alpha) * weight; } else { cuda_out_gradients[data_index] = -alpha * weight; } cuda_out_hessians[data_index] = weight; } } } void CUDARegressionQuantileloss::LaunchGetGradientsKernel(const double* score, score_t* gradients, score_t* hessians) const { const int num_blocks = (num_data_ + GET_GRADIENTS_BLOCK_SIZE_REGRESSION - 1) / GET_GRADIENTS_BLOCK_SIZE_REGRESSION; if (cuda_weights_ == nullptr) { GetGradientsKernel_RegressionQuantile<<>>(score, cuda_labels_, nullptr, num_data_, alpha_, gradients, hessians); } else { GetGradientsKernel_RegressionQuantile<<>>(score, cuda_labels_, cuda_weights_, num_data_, alpha_, gradients, hessians); } } } // namespace LightGBM #endif // USE_CUDA ================================================ FILE: src/objective/cuda/cuda_regression_objective.hpp ================================================ /*! * Copyright (c) 2021-2026 Microsoft Corporation. All rights reserved. * Copyright (c) 2021-2026 The LightGBM developers. All rights reserved. * Licensed under the MIT License. See LICENSE file in the project root for * license information. */ #ifndef LIGHTGBM_SRC_OBJECTIVE_CUDA_CUDA_REGRESSION_OBJECTIVE_HPP_ #define LIGHTGBM_SRC_OBJECTIVE_CUDA_CUDA_REGRESSION_OBJECTIVE_HPP_ #ifdef USE_CUDA #define GET_GRADIENTS_BLOCK_SIZE_REGRESSION (1024) #include #include #include #include "../regression_objective.hpp" namespace LightGBM { template class CUDARegressionObjectiveInterface: public CUDAObjectiveInterface { public: explicit CUDARegressionObjectiveInterface(const Config& config): CUDAObjectiveInterface(config) {} explicit CUDARegressionObjectiveInterface(const std::vector& strs): CUDAObjectiveInterface(strs) {} void Init(const Metadata& metadata, data_size_t num_data) override; protected: double LaunchCalcInitScoreKernel(const int class_id) const override; CUDAVector cuda_block_buffer_; CUDAVector cuda_trans_label_; }; class CUDARegressionL2loss : public CUDARegressionObjectiveInterface { public: explicit CUDARegressionL2loss(const Config& config); explicit CUDARegressionL2loss(const std::vector& strs); ~CUDARegressionL2loss(); void Init(const Metadata& metadata, data_size_t num_data) override; protected: void LaunchGetGradientsKernel(const double* score, score_t* gradients, score_t* hessians) const override; const double* LaunchConvertOutputCUDAKernel(const data_size_t num_data, const double* input, double* output) const override; bool NeedConvertOutputCUDA() const override { return sqrt_; } }; class CUDARegressionL1loss : public CUDARegressionObjectiveInterface { public: explicit CUDARegressionL1loss(const Config& config); explicit CUDARegressionL1loss(const std::vector& strs); ~CUDARegressionL1loss(); void Init(const Metadata& metadata, data_size_t num_data) override; protected: CUDAVector cuda_data_indices_buffer_; CUDAVector cuda_weights_prefix_sum_; CUDAVector cuda_weights_prefix_sum_buffer_; CUDAVector cuda_residual_buffer_; CUDAVector cuda_weight_by_leaf_buffer_; CUDAVector cuda_percentile_result_; double LaunchCalcInitScoreKernel(const int class_id) const override; void LaunchGetGradientsKernel(const double* score, score_t* gradients, score_t* hessians) const override; void LaunchRenewTreeOutputCUDAKernel( const double* score, const data_size_t* data_indices_in_leaf, const data_size_t* num_data_in_leaf, const data_size_t* data_start_in_leaf, const int num_leaves, double* leaf_value) const override; }; class CUDARegressionHuberLoss : public CUDARegressionObjectiveInterface { public: explicit CUDARegressionHuberLoss(const Config& config); explicit CUDARegressionHuberLoss(const std::vector& strs); ~CUDARegressionHuberLoss(); private: void LaunchGetGradientsKernel(const double* score, score_t* gradients, score_t* hessians) const override; }; // http://research.microsoft.com/en-us/um/people/zhang/INRIA/Publis/Tutorial-Estim/node24.html class CUDARegressionFairLoss : public CUDARegressionObjectiveInterface { public: explicit CUDARegressionFairLoss(const Config& config); explicit CUDARegressionFairLoss(const std::vector& strs); ~CUDARegressionFairLoss(); private: void LaunchGetGradientsKernel(const double* score, score_t* gradients, score_t* hessians) const override; }; class CUDARegressionPoissonLoss : public CUDARegressionObjectiveInterface { public: explicit CUDARegressionPoissonLoss(const Config& config); explicit CUDARegressionPoissonLoss(const std::vector& strs); ~CUDARegressionPoissonLoss(); void Init(const Metadata& metadata, data_size_t num_data) override; private: void LaunchGetGradientsKernel(const double* score, score_t* gradients, score_t* hessians) const override; const double* LaunchConvertOutputCUDAKernel(const data_size_t num_data, const double* input, double* output) const override; bool NeedConvertOutputCUDA() const override { return true; } double LaunchCalcInitScoreKernel(const int class_id) const override; void LaunchCheckLabelKernel() const; }; class CUDARegressionQuantileloss : public CUDARegressionObjectiveInterface { public: explicit CUDARegressionQuantileloss(const Config& config); explicit CUDARegressionQuantileloss(const std::vector& strs); ~CUDARegressionQuantileloss(); void Init(const Metadata& metadata, data_size_t num_data) override; protected: void LaunchGetGradientsKernel(const double* score, score_t* gradients, score_t* hessians) const override; double LaunchCalcInitScoreKernel(const int class_id) const override; void LaunchRenewTreeOutputCUDAKernel( const double* score, const data_size_t* data_indices_in_leaf, const data_size_t* num_data_in_leaf, const data_size_t* data_start_in_leaf, const int num_leaves, double* leaf_value) const override; CUDAVector cuda_data_indices_buffer_; CUDAVector cuda_weights_prefix_sum_; CUDAVector cuda_weights_prefix_sum_buffer_; CUDAVector cuda_residual_buffer_; CUDAVector cuda_weight_by_leaf_buffer_; CUDAVector cuda_percentile_result_; }; } // namespace LightGBM #endif // USE_CUDA #endif // LIGHTGBM_SRC_OBJECTIVE_CUDA_CUDA_REGRESSION_OBJECTIVE_HPP_ ================================================ FILE: src/objective/multiclass_objective.hpp ================================================ /*! * Copyright (c) 2016-2026 Microsoft Corporation. All rights reserved. * Copyright (c) 2016-2026 The LightGBM developers. All rights reserved. * Licensed under the MIT License. See LICENSE file in the project root for license information. */ #ifndef LIGHTGBM_SRC_OBJECTIVE_MULTICLASS_OBJECTIVE_HPP_ #define LIGHTGBM_SRC_OBJECTIVE_MULTICLASS_OBJECTIVE_HPP_ #include #include #include #include #include #include #include #include #include "binary_objective.hpp" namespace LightGBM { /*! * \brief Objective function for multiclass classification, use softmax as objective functions */ class MulticlassSoftmax: public ObjectiveFunction { public: explicit MulticlassSoftmax(const Config& config) { num_class_ = config.num_class; // This factor is to rescale the redundant form of K-classification, to the non-redundant form. // 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). // This is from the Friedman GBDT paper. factor_ = static_cast(num_class_) / (num_class_ - 1.0f); } explicit MulticlassSoftmax(const std::vector& strs) { num_class_ = -1; for (auto str : strs) { auto tokens = Common::Split(str.c_str(), ':'); if (tokens.size() == 2) { if (tokens[0] == std::string("num_class")) { Common::Atoi(tokens[1].c_str(), &num_class_); } } } if (num_class_ < 0) { Log::Fatal("Objective should contain num_class field"); } factor_ = static_cast(num_class_) / (num_class_ - 1.0f); } ~MulticlassSoftmax() { } void Init(const Metadata& metadata, data_size_t num_data) override { num_data_ = num_data; label_ = metadata.label(); weights_ = metadata.weights(); label_int_.resize(num_data_); class_init_probs_.resize(num_class_, 0.0); double sum_weight = 0.0; for (int i = 0; i < num_data_; ++i) { label_int_[i] = static_cast(label_[i]); if (label_int_[i] < 0 || label_int_[i] >= num_class_) { Log::Fatal("Label must be in [0, %d), but found %d in label", num_class_, label_int_[i]); } if (weights_ == nullptr) { class_init_probs_[label_int_[i]] += 1.0; } else { class_init_probs_[label_int_[i]] += weights_[i]; sum_weight += weights_[i]; } } if (weights_ == nullptr) { sum_weight = num_data_; } if (Network::num_machines() > 1) { sum_weight = Network::GlobalSyncUpBySum(sum_weight); for (int i = 0; i < num_class_; ++i) { class_init_probs_[i] = Network::GlobalSyncUpBySum(class_init_probs_[i]); } } for (int i = 0; i < num_class_; ++i) { class_init_probs_[i] /= sum_weight; } } void GetGradients(const double* score, score_t* gradients, score_t* hessians) const override { if (weights_ == nullptr) { std::vector rec; #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) private(rec) for (data_size_t i = 0; i < num_data_; ++i) { rec.resize(num_class_); for (int k = 0; k < num_class_; ++k) { size_t idx = static_cast(num_data_) * k + i; rec[k] = static_cast(score[idx]); } Common::Softmax(&rec); for (int k = 0; k < num_class_; ++k) { auto p = rec[k]; size_t idx = static_cast(num_data_) * k + i; if (label_int_[i] == k) { gradients[idx] = static_cast(p - 1.0f); } else { gradients[idx] = static_cast(p); } hessians[idx] = static_cast(factor_ * p * (1.0f - p)); } } } else { std::vector rec; #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) private(rec) for (data_size_t i = 0; i < num_data_; ++i) { rec.resize(num_class_); for (int k = 0; k < num_class_; ++k) { size_t idx = static_cast(num_data_) * k + i; rec[k] = static_cast(score[idx]); } Common::Softmax(&rec); for (int k = 0; k < num_class_; ++k) { auto p = rec[k]; size_t idx = static_cast(num_data_) * k + i; if (label_int_[i] == k) { gradients[idx] = static_cast((p - 1.0f) * weights_[i]); } else { gradients[idx] = static_cast(p * weights_[i]); } hessians[idx] = static_cast((factor_ * p * (1.0f - p))* weights_[i]); } } } } void ConvertOutput(const double* input, double* output) const override { Common::Softmax(input, output, num_class_); } const char* GetName() const override { return "multiclass"; } std::string ToString() const override { std::stringstream str_buf; str_buf << GetName() << " "; str_buf << "num_class:" << num_class_; return str_buf.str(); } bool SkipEmptyClass() const override { return true; } int NumModelPerIteration() const override { return num_class_; } int NumPredictOneRow() const override { return num_class_; } bool NeedAccuratePrediction() const override { return false; } double BoostFromScore(int class_id) const override { return std::log(std::max(kEpsilon, class_init_probs_[class_id])); } bool ClassNeedTrain(int class_id) const override { if (std::fabs(class_init_probs_[class_id]) <= kEpsilon || std::fabs(class_init_probs_[class_id]) >= 1.0 - kEpsilon) { return false; } else { return true; } } protected: double factor_; /*! \brief Number of data */ data_size_t num_data_; /*! \brief Number of classes */ int num_class_; /*! \brief Pointer of label */ const label_t* label_; /*! \brief Corresponding integers of label_ */ std::vector label_int_; /*! \brief Weights for data */ const label_t* weights_; std::vector class_init_probs_; }; /*! * \brief Objective function for multiclass classification, use one-vs-all binary objective function */ class MulticlassOVA: public ObjectiveFunction { public: explicit MulticlassOVA(const Config& config) { num_class_ = config.num_class; for (int i = 0; i < num_class_; ++i) { binary_loss_.emplace_back( new BinaryLogloss(config, [i](label_t label) { return static_cast(label) == i; })); } sigmoid_ = config.sigmoid; } explicit MulticlassOVA(const std::vector& strs) { num_class_ = -1; sigmoid_ = -1; for (auto str : strs) { auto tokens = Common::Split(str.c_str(), ':'); if (tokens.size() == 2) { if (tokens[0] == std::string("num_class")) { Common::Atoi(tokens[1].c_str(), &num_class_); } else if (tokens[0] == std::string("sigmoid")) { Common::Atof(tokens[1].c_str(), &sigmoid_); } } } if (num_class_ < 0) { Log::Fatal("Objective should contain num_class field"); } if (sigmoid_ <= 0.0) { Log::Fatal("Sigmoid parameter %f should be greater than zero", sigmoid_); } } ~MulticlassOVA() { } void Init(const Metadata& metadata, data_size_t num_data) override { num_data_ = num_data; for (int i = 0; i < num_class_; ++i) { binary_loss_[i]->Init(metadata, num_data); } } void GetGradients(const double* score, score_t* gradients, score_t* hessians) const override { for (int i = 0; i < num_class_; ++i) { int64_t offset = static_cast(num_data_) * i; binary_loss_[i]->GetGradients(score + offset, gradients + offset, hessians + offset); } } const char* GetName() const override { return "multiclassova"; } void ConvertOutput(const double* input, double* output) const override { for (int i = 0; i < num_class_; ++i) { output[i] = 1.0f / (1.0f + std::exp(-sigmoid_ * input[i])); } } std::string ToString() const override { std::stringstream str_buf; str_buf << GetName() << " "; str_buf << "num_class:" << num_class_ << " "; str_buf << "sigmoid:" << sigmoid_; return str_buf.str(); } bool SkipEmptyClass() const override { return true; } int NumModelPerIteration() const override { return num_class_; } int NumPredictOneRow() const override { return num_class_; } bool NeedAccuratePrediction() const override { return false; } double BoostFromScore(int class_id) const override { return binary_loss_[class_id]->BoostFromScore(0); } bool ClassNeedTrain(int class_id) const override { return binary_loss_[class_id]->ClassNeedTrain(0); } protected: /*! \brief Number of data */ data_size_t num_data_; /*! \brief Number of classes */ int num_class_; std::vector> binary_loss_; double sigmoid_; }; } // namespace LightGBM #endif // LIGHTGBM_SRC_OBJECTIVE_MULTICLASS_OBJECTIVE_HPP_ ================================================ FILE: src/objective/objective_function.cpp ================================================ /*! * Copyright (c) 2016-2026 Microsoft Corporation. All rights reserved. * Copyright (c) 2016-2026 The LightGBM developers. All rights reserved. * Licensed under the MIT License. See LICENSE file in the project root for license information. */ #include #include #include "binary_objective.hpp" #include "multiclass_objective.hpp" #include "rank_objective.hpp" #include "regression_objective.hpp" #include "xentropy_objective.hpp" #include #include "cuda/cuda_binary_objective.hpp" #include "cuda/cuda_multiclass_objective.hpp" #include "cuda/cuda_rank_objective.hpp" #include "cuda/cuda_regression_objective.hpp" namespace LightGBM { #ifdef USE_CUDA ObjectiveFunction* ObjectiveFunction::CreateObjectiveFunctionCUDA(const std::string& type, const Config& config) { if (type == std::string("regression")) { return new CUDARegressionL2loss(config); } else if (type == std::string("regression_l1")) { return new CUDARegressionL1loss(config); } else if (type == std::string("quantile")) { return new CUDARegressionQuantileloss(config); } else if (type == std::string("huber")) { return new CUDARegressionHuberLoss(config); } else if (type == std::string("fair")) { return new CUDARegressionFairLoss(config); } else if (type == std::string("poisson")) { return new CUDARegressionPoissonLoss(config); } else if (type == std::string("binary")) { return new CUDABinaryLogloss(config); } else if (type == std::string("lambdarank")) { return new CUDALambdarankNDCG(config); } else if (type == std::string("rank_xendcg")) { return new CUDARankXENDCG(config); } else if (type == std::string("multiclass")) { return new CUDAMulticlassSoftmax(config); } else if (type == std::string("multiclassova")) { return new CUDAMulticlassOVA(config); } else if (type == std::string("cross_entropy")) { Log::Warning("Objective cross_entropy is not implemented in cuda version. Fall back to boosting on CPU."); return new CrossEntropy(config); } else if (type == std::string("cross_entropy_lambda")) { Log::Warning("Objective cross_entropy_lambda is not implemented in cuda version. Fall back to boosting on CPU."); return new CrossEntropyLambda(config); } else if (type == std::string("mape")) { Log::Warning("Objective mape is not implemented in cuda version. Fall back to boosting on CPU."); return new RegressionMAPELOSS(config); } else if (type == std::string("gamma")) { Log::Warning("Objective gamma is not implemented in cuda version. Fall back to boosting on CPU."); return new RegressionGammaLoss(config); } else if (type == std::string("tweedie")) { Log::Warning("Objective tweedie is not implemented in cuda version. Fall back to boosting on CPU."); return new RegressionTweedieLoss(config); } else if (type == std::string("custom")) { Log::Warning("Using customized objective with cuda. This requires copying gradients from CPU to GPU, which can be slow."); return nullptr; } } #endif // USE_CUDA ObjectiveFunction* ObjectiveFunction::CreateObjectiveFunction(const std::string& type, const Config& config) { #ifdef USE_CUDA if (config.device_type == std::string("cuda") && config.data_sample_strategy != std::string("goss") && config.boosting != std::string("rf")) { return CreateObjectiveFunctionCUDA(type, config); } else { #endif // USE_CUDA if (type == std::string("regression")) { return new RegressionL2loss(config); } else if (type == std::string("regression_l1")) { return new RegressionL1loss(config); } else if (type == std::string("quantile")) { return new RegressionQuantileloss(config); } else if (type == std::string("huber")) { return new RegressionHuberLoss(config); } else if (type == std::string("fair")) { return new RegressionFairLoss(config); } else if (type == std::string("poisson")) { return new RegressionPoissonLoss(config); } else if (type == std::string("binary")) { return new BinaryLogloss(config); } else if (type == std::string("lambdarank")) { return new LambdarankNDCG(config); } else if (type == std::string("rank_xendcg")) { return new RankXENDCG(config); } else if (type == std::string("multiclass")) { return new MulticlassSoftmax(config); } else if (type == std::string("multiclassova")) { return new MulticlassOVA(config); } else if (type == std::string("cross_entropy")) { return new CrossEntropy(config); } else if (type == std::string("cross_entropy_lambda")) { return new CrossEntropyLambda(config); } else if (type == std::string("mape")) { return new RegressionMAPELOSS(config); } else if (type == std::string("gamma")) { return new RegressionGammaLoss(config); } else if (type == std::string("tweedie")) { return new RegressionTweedieLoss(config); } else if (type == std::string("custom")) { return nullptr; } #ifdef USE_CUDA } #endif // USE_CUDA Log::Fatal("Unknown objective type name: %s", type.c_str()); return nullptr; } ObjectiveFunction* ObjectiveFunction::CreateObjectiveFunction(const std::string& str) { auto strs = Common::Split(str.c_str(), ' '); auto type = strs[0]; if (type == std::string("regression")) { return new RegressionL2loss(strs); } else if (type == std::string("regression_l1")) { return new RegressionL1loss(strs); } else if (type == std::string("quantile")) { return new RegressionQuantileloss(strs); } else if (type == std::string("huber")) { return new RegressionHuberLoss(strs); } else if (type == std::string("fair")) { return new RegressionFairLoss(strs); } else if (type == std::string("poisson")) { return new RegressionPoissonLoss(strs); } else if (type == std::string("binary")) { return new BinaryLogloss(strs); } else if (type == std::string("lambdarank")) { return new LambdarankNDCG(strs); } else if (type == std::string("rank_xendcg")) { return new RankXENDCG(strs); } else if (type == std::string("multiclass")) { return new MulticlassSoftmax(strs); } else if (type == std::string("multiclassova")) { return new MulticlassOVA(strs); } else if (type == std::string("cross_entropy")) { return new CrossEntropy(strs); } else if (type == std::string("cross_entropy_lambda")) { return new CrossEntropyLambda(strs); } else if (type == std::string("mape")) { return new RegressionMAPELOSS(strs); } else if (type == std::string("gamma")) { return new RegressionGammaLoss(strs); } else if (type == std::string("tweedie")) { return new RegressionTweedieLoss(strs); } else if (type == std::string("custom")) { return nullptr; } Log::Fatal("Unknown objective type name: %s", type.c_str()); return nullptr; } } // namespace LightGBM ================================================ FILE: src/objective/rank_objective.hpp ================================================ /*! * Copyright (c) 2020-2026 Microsoft Corporation. All rights reserved. * Copyright (c) 2020-2026 The LightGBM developers. All rights reserved. * Licensed under the MIT License. See LICENSE file in the project root for * license information. */ #ifndef LIGHTGBM_SRC_OBJECTIVE_RANK_OBJECTIVE_HPP_ #define LIGHTGBM_SRC_OBJECTIVE_RANK_OBJECTIVE_HPP_ #include #include #include #include #include #include #include #include #include namespace LightGBM { /*! * \brief Objective function for Ranking */ class RankingObjective : public ObjectiveFunction { public: explicit RankingObjective(const Config& config) : seed_(config.objective_seed) { learning_rate_ = config.learning_rate; position_bias_regularization_ = config.lambdarank_position_bias_regularization; } explicit RankingObjective(const std::vector&) : seed_(0) {} ~RankingObjective() {} void Init(const Metadata& metadata, data_size_t num_data) override { num_data_ = num_data; // get label label_ = metadata.label(); // get weights weights_ = metadata.weights(); // get positions positions_ = metadata.positions(); // get position ids position_ids_ = metadata.position_ids(); // get number of different position ids num_position_ids_ = static_cast(metadata.num_position_ids()); // get boundaries query_boundaries_ = metadata.query_boundaries(); if (query_boundaries_ == nullptr) { Log::Fatal("Ranking tasks require query information"); } num_queries_ = metadata.num_queries(); // initialize position bias vectors pos_biases_.resize(num_position_ids_, 0.0); } void GetGradientsWithSampledQueries(const double* score, const data_size_t num_sampled_queries, const data_size_t* sampled_query_indices, score_t* gradients, score_t* hessians) const override { const data_size_t num_queries = (sampled_query_indices == nullptr ? num_queries_ : num_sampled_queries); #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(guided) for (data_size_t i = 0; i < num_queries; ++i) { const data_size_t query_index = (sampled_query_indices == nullptr ? i : sampled_query_indices[i]); const data_size_t start = query_boundaries_[query_index]; const data_size_t cnt = query_boundaries_[query_index + 1] - query_boundaries_[query_index]; std::vector score_adjusted; if (num_position_ids_ > 0) { for (data_size_t j = 0; j < cnt; ++j) { score_adjusted.push_back(score[start + j] + pos_biases_[positions_[start + j]]); } } GetGradientsForOneQuery(query_index, cnt, label_ + start, num_position_ids_ > 0 ? score_adjusted.data() : score + start, gradients + start, hessians + start); if (weights_ != nullptr) { for (data_size_t j = 0; j < cnt; ++j) { gradients[start + j] = static_cast(gradients[start + j] * weights_[start + j]); hessians[start + j] = static_cast(hessians[start + j] * weights_[start + j]); } } } if (num_position_ids_ > 0) { UpdatePositionBiasFactors(gradients, hessians); } } void GetGradients(const double* score, score_t* gradients, score_t* hessians) const override { GetGradientsWithSampledQueries(score, num_queries_, nullptr, gradients, hessians); } virtual void GetGradientsForOneQuery(data_size_t query_id, data_size_t cnt, const label_t* label, const double* score, score_t* lambdas, score_t* hessians) const = 0; virtual void UpdatePositionBiasFactors(const score_t* /*lambdas*/, const score_t* /*hessians*/) const {} const char* GetName() const override = 0; std::string ToString() const override { std::stringstream str_buf; str_buf << GetName(); return str_buf.str(); } bool NeedAccuratePrediction() const override { return false; } protected: int seed_; data_size_t num_queries_; /*! \brief Number of data */ data_size_t num_data_; /*! \brief Pointer of label */ const label_t* label_; /*! \brief Pointer of weights */ const label_t* weights_; /*! \brief Pointer of positions */ const data_size_t* positions_; /*! \brief Pointer of position IDs */ const std::string* position_ids_; /*! \brief Pointer of label */ data_size_t num_position_ids_; /*! \brief Query boundaries */ const data_size_t* query_boundaries_; /*! \brief Position bias factors */ mutable std::vector pos_biases_; /*! \brief Learning rate to update position bias factors */ double learning_rate_; /*! \brief Position bias regularization */ double position_bias_regularization_; }; /*! * \brief Objective function for LambdaRank with NDCG */ class LambdarankNDCG : public RankingObjective { public: explicit LambdarankNDCG(const Config& config) : RankingObjective(config), sigmoid_(config.sigmoid), norm_(config.lambdarank_norm), truncation_level_(config.lambdarank_truncation_level) { label_gain_ = config.label_gain; // initialize DCG calculator DCGCalculator::DefaultLabelGain(&label_gain_); DCGCalculator::Init(label_gain_); sigmoid_table_.clear(); inverse_max_dcgs_.clear(); if (sigmoid_ <= 0.0) { Log::Fatal("Sigmoid param %f should be greater than zero", sigmoid_); } } explicit LambdarankNDCG(const std::vector& strs) : RankingObjective(strs) {} ~LambdarankNDCG() {} void Init(const Metadata& metadata, data_size_t num_data) override { RankingObjective::Init(metadata, num_data); DCGCalculator::CheckMetadata(metadata, num_queries_); DCGCalculator::CheckLabel(label_, num_data_); inverse_max_dcgs_.resize(num_queries_); #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) for (data_size_t i = 0; i < num_queries_; ++i) { inverse_max_dcgs_[i] = DCGCalculator::CalMaxDCGAtK( truncation_level_, label_ + query_boundaries_[i], query_boundaries_[i + 1] - query_boundaries_[i]); if (inverse_max_dcgs_[i] > 0.0) { inverse_max_dcgs_[i] = 1.0f / inverse_max_dcgs_[i]; } } // construct Sigmoid table to speed up Sigmoid transform ConstructSigmoidTable(); } inline void GetGradientsForOneQuery(data_size_t query_id, data_size_t cnt, const label_t* label, const double* score, score_t* lambdas, score_t* hessians) const override { // get max DCG on current query const double inverse_max_dcg = inverse_max_dcgs_[query_id]; // initialize with zero for (data_size_t i = 0; i < cnt; ++i) { lambdas[i] = 0.0f; hessians[i] = 0.0f; } // get sorted indices for scores std::vector sorted_idx(cnt); for (data_size_t i = 0; i < cnt; ++i) { sorted_idx[i] = i; } std::stable_sort( sorted_idx.begin(), sorted_idx.end(), [score](data_size_t a, data_size_t b) { return score[a] > score[b]; }); // get best and worst score const double best_score = score[sorted_idx[0]]; data_size_t worst_idx = cnt - 1; if (worst_idx > 0 && score[sorted_idx[worst_idx]] == kMinScore) { worst_idx -= 1; } const double worst_score = score[sorted_idx[worst_idx]]; double sum_lambdas = 0.0; // start accumulate lambdas by pairs that contain at least one document above truncation level for (data_size_t i = 0; i < cnt - 1 && i < truncation_level_; ++i) { if (score[sorted_idx[i]] == kMinScore) { continue; } for (data_size_t j = i + 1; j < cnt; ++j) { if (score[sorted_idx[j]] == kMinScore) { continue; } // skip pairs with the same labels if (label[sorted_idx[i]] == label[sorted_idx[j]]) { continue; } data_size_t high_rank, low_rank; if (label[sorted_idx[i]] > label[sorted_idx[j]]) { high_rank = i; low_rank = j; } else { high_rank = j; low_rank = i; } const data_size_t high = sorted_idx[high_rank]; const int high_label = static_cast(label[high]); const double high_score = score[high]; const double high_label_gain = label_gain_[high_label]; const double high_discount = DCGCalculator::GetDiscount(high_rank); const data_size_t low = sorted_idx[low_rank]; const int low_label = static_cast(label[low]); const double low_score = score[low]; const double low_label_gain = label_gain_[low_label]; const double low_discount = DCGCalculator::GetDiscount(low_rank); const double delta_score = high_score - low_score; // get dcg gap const double dcg_gap = high_label_gain - low_label_gain; // get discount of this pair const double paired_discount = fabs(high_discount - low_discount); // get delta NDCG double delta_pair_NDCG = dcg_gap * paired_discount * inverse_max_dcg; // regular the delta_pair_NDCG by score distance if (norm_ && best_score != worst_score) { delta_pair_NDCG /= (0.01f + fabs(delta_score)); } // calculate lambda for this pair double p_lambda = GetSigmoid(delta_score); double p_hessian = p_lambda * (1.0f - p_lambda); // update p_lambda *= -sigmoid_ * delta_pair_NDCG; p_hessian *= sigmoid_ * sigmoid_ * delta_pair_NDCG; lambdas[low] -= static_cast(p_lambda); hessians[low] += static_cast(p_hessian); lambdas[high] += static_cast(p_lambda); hessians[high] += static_cast(p_hessian); // lambda is negative, so use minus to accumulate sum_lambdas -= 2 * p_lambda; } } if (norm_ && sum_lambdas > 0) { double norm_factor = std::log2(1 + sum_lambdas) / sum_lambdas; for (data_size_t i = 0; i < cnt; ++i) { lambdas[i] = static_cast(lambdas[i] * norm_factor); hessians[i] = static_cast(hessians[i] * norm_factor); } } } inline double GetSigmoid(double score) const { if (score <= min_sigmoid_input_) { // too small, use lower bound return sigmoid_table_[0]; } else if (score >= max_sigmoid_input_) { // too large, use upper bound return sigmoid_table_[_sigmoid_bins - 1]; } else { return sigmoid_table_[static_cast((score - min_sigmoid_input_) * sigmoid_table_idx_factor_)]; } } void ConstructSigmoidTable() { // get boundary min_sigmoid_input_ = min_sigmoid_input_ / sigmoid_ / 2; max_sigmoid_input_ = -min_sigmoid_input_; sigmoid_table_.resize(_sigmoid_bins); // get score to bin factor sigmoid_table_idx_factor_ = _sigmoid_bins / (max_sigmoid_input_ - min_sigmoid_input_); // cache for (size_t i = 0; i < _sigmoid_bins; ++i) { const double score = i / sigmoid_table_idx_factor_ + min_sigmoid_input_; sigmoid_table_[i] = 1.0f / (1.0f + std::exp(score * sigmoid_)); } } void UpdatePositionBiasFactors(const score_t* lambdas, const score_t* hessians) const override { /// get number of threads int num_threads = OMP_NUM_THREADS(); // create per-thread buffers for first and second derivatives of utility w.r.t. position bias factors std::vector bias_first_derivatives(num_position_ids_ * num_threads, 0.0); std::vector bias_second_derivatives(num_position_ids_ * num_threads, 0.0); std::vector instance_counts(num_position_ids_ * num_threads, 0); #pragma omp parallel for schedule(guided) num_threads(num_threads) for (data_size_t i = 0; i < num_data_; i++) { // get thread ID const int tid = omp_get_thread_num(); size_t offset = static_cast(positions_[i] + tid * num_position_ids_); // accumulate first derivatives of utility w.r.t. position bias factors, for each position bias_first_derivatives[offset] -= lambdas[i]; // accumulate second derivatives of utility w.r.t. position bias factors, for each position bias_second_derivatives[offset] -= hessians[i]; instance_counts[offset]++; } #pragma omp parallel for schedule(guided) num_threads(num_threads) for (data_size_t i = 0; i < num_position_ids_; i++) { double bias_first_derivative = 0.0; double bias_second_derivative = 0.0; int instance_count = 0; // aggregate derivatives from per-thread buffers for (int tid = 0; tid < num_threads; tid++) { size_t offset = static_cast(i + tid * num_position_ids_); bias_first_derivative += bias_first_derivatives[offset]; bias_second_derivative += bias_second_derivatives[offset]; instance_count += instance_counts[offset]; } // L2 regularization on position bias factors bias_first_derivative -= pos_biases_[i] * position_bias_regularization_ * instance_count; bias_second_derivative -= position_bias_regularization_ * instance_count; // do Newton-Raphson step to update position bias factors pos_biases_[i] += learning_rate_ * bias_first_derivative / (std::abs(bias_second_derivative) + 0.001); } LogDebugPositionBiasFactors(); } const char* GetName() const override { return "lambdarank"; } protected: void LogDebugPositionBiasFactors() const { std::stringstream message_stream; message_stream << std::setw(15) << "position" << std::setw(15) << "bias_factor" << std::endl; Log::Debug(message_stream.str().c_str()); message_stream.str(""); for (int i = 0; i < num_position_ids_; ++i) { message_stream << std::setw(15) << position_ids_[i] << std::setw(15) << pos_biases_[i]; Log::Debug(message_stream.str().c_str()); message_stream.str(""); } } /*! \brief Sigmoid param */ double sigmoid_; /*! \brief Normalize the lambdas or not */ bool norm_; /*! \brief Truncation position for max DCG */ int truncation_level_; /*! \brief Cache inverse max DCG, speed up calculation */ std::vector inverse_max_dcgs_; /*! \brief Cache result for sigmoid transform to speed up */ std::vector sigmoid_table_; /*! \brief Gains for labels */ std::vector label_gain_; /*! \brief Number of bins in simoid table */ size_t _sigmoid_bins = 1024 * 1024; /*! \brief Minimal input of sigmoid table */ double min_sigmoid_input_ = -50; /*! \brief Maximal input of Sigmoid table */ double max_sigmoid_input_ = 50; /*! \brief Factor that covert score to bin in Sigmoid table */ double sigmoid_table_idx_factor_; }; /*! * \brief Implementation of the learning-to-rank objective function, XE_NDCG * [arxiv.org/abs/1911.09798]. */ class RankXENDCG : public RankingObjective { public: explicit RankXENDCG(const Config& config) : RankingObjective(config) {} explicit RankXENDCG(const std::vector& strs) : RankingObjective(strs) {} ~RankXENDCG() {} void Init(const Metadata& metadata, data_size_t num_data) override { RankingObjective::Init(metadata, num_data); for (data_size_t i = 0; i < num_queries_; ++i) { rands_.emplace_back(seed_ + i); } } inline void GetGradientsForOneQuery(data_size_t query_id, data_size_t cnt, const label_t* label, const double* score, score_t* lambdas, score_t* hessians) const override { // Skip groups with too few items. if (cnt <= 1) { for (data_size_t i = 0; i < cnt; ++i) { lambdas[i] = 0.0f; hessians[i] = 0.0f; } return; } // Turn scores into a probability distribution using Softmax. std::vector rho(cnt, 0.0); Common::Softmax(score, rho.data(), cnt); // An auxiliary buffer of parameters used to form the ground-truth // distribution and compute the loss. std::vector params(cnt); double inv_denominator = 0; for (data_size_t i = 0; i < cnt; ++i) { params[i] = Phi(label[i], rands_[query_id].NextFloat()); inv_denominator += params[i]; } // sum_labels will always be positive number inv_denominator = 1. / std::max(kEpsilon, inv_denominator); // Approximate gradients and inverse Hessian. // First order terms. double sum_l1 = 0.0; for (data_size_t i = 0; i < cnt; ++i) { double term = -params[i] * inv_denominator + rho[i]; lambdas[i] = static_cast(term); // Params will now store terms needed to compute second-order terms. params[i] = term / (1. - rho[i]); sum_l1 += params[i]; } // Second order terms. double sum_l2 = 0.0; for (data_size_t i = 0; i < cnt; ++i) { double term = rho[i] * (sum_l1 - params[i]); lambdas[i] += static_cast(term); // Params will now store terms needed to compute third-order terms. params[i] = term / (1. - rho[i]); sum_l2 += params[i]; } for (data_size_t i = 0; i < cnt; ++i) { lambdas[i] += static_cast(rho[i] * (sum_l2 - params[i])); hessians[i] = static_cast(rho[i] * (1.0 - rho[i])); } } double Phi(const label_t l, double g) const { return Common::Pow(2, static_cast(l)) - g; } const char* GetName() const override { return "rank_xendcg"; } protected: mutable std::vector rands_; }; } // namespace LightGBM #endif // LIGHTGBM_SRC_OBJECTIVE_RANK_OBJECTIVE_HPP_ ================================================ FILE: src/objective/regression_objective.hpp ================================================ [File too large to display: 27.9 KB] ================================================ FILE: src/objective/xentropy_objective.hpp ================================================ /*! * Copyright (c) 2017-2026 Microsoft Corporation. All rights reserved. * Copyright (c) 2017-2026 The LightGBM developers. All rights reserved. * Licensed under the MIT License. See LICENSE file in the project root for license information. */ #ifndef LIGHTGBM_SRC_OBJECTIVE_XENTROPY_OBJECTIVE_HPP_ #define LIGHTGBM_SRC_OBJECTIVE_XENTROPY_OBJECTIVE_HPP_ #include #include #include #include #include #include #include #include /* * Implements gradients and Hessians for the following point losses. * Target y is anything in interval [0, 1]. * * (1) CrossEntropy; "xentropy"; * * loss(y, p, w) = { -(1-y)*log(1-p)-y*log(p) }*w, * with probability p = 1/(1+exp(-f)), where f is being boosted * * ConvertToOutput: f -> p * * (2) CrossEntropyLambda; "xentlambda" * * loss(y, p, w) = -(1-y)*log(1-p)-y*log(p), * with p = 1-exp(-lambda*w), lambda = log(1+exp(f)), f being boosted, and w > 0 * * ConvertToOutput: f -> lambda * * (1) and (2) are the same if w=1; but outputs still differ. * */ namespace LightGBM { /*! * \brief Objective function for cross-entropy (with optional linear weights) */ class CrossEntropy: public ObjectiveFunction { public: explicit CrossEntropy(const Config& config) : deterministic_(config.deterministic) {} explicit CrossEntropy(const std::vector&) : deterministic_(false) { } ~CrossEntropy() {} void Init(const Metadata& metadata, data_size_t num_data) override { num_data_ = num_data; label_ = metadata.label(); weights_ = metadata.weights(); CHECK_NOTNULL(label_); Common::CheckElementsIntervalClosed(label_, 0.0f, 1.0f, num_data_, GetName()); Log::Info("[%s:%s]: (objective) labels passed interval [0, 1] check", GetName(), __func__); if (weights_ != nullptr) { label_t minw; double sumw; Common::ObtainMinMaxSum(weights_, num_data_, &minw, static_cast(nullptr), &sumw); if (minw < 0.0f) { Log::Fatal("[%s]: at least one weight is negative", GetName()); } if (sumw == 0.0f) { Log::Fatal("[%s]: sum of weights is zero", GetName()); } } } void GetGradients(const double* score, score_t* gradients, score_t* hessians) const override { // z = expit(score) = 1 / (1 + exp(-score)) // gradient = z - label = expit(score) - label // Numerically more stable, see http://fa.bianp.net/blog/2019/evaluate_logistic/ // if score < 0: // exp_tmp = exp(score) // return ((1 - label) * exp_tmp - label) / (1 + exp_tmp) // else: // exp_tmp = exp(-score) // return ((1 - label) - label * exp_tmp) / (1 + exp_tmp) // Note that optimal speed would be achieved, at the cost of precision, by // return expit(score) - y_true // i.e. no "if else" and an own inline implementation of expit. // The case distinction score < 0 in the stable implementation does not // provide significant better precision apart from protecting overflow of exp(..). // The branch (if else), however, can incur runtime costs of up to 30%. // Instead, we help branch prediction by almost always ending in the first if clause // and making the second branch (else) a bit simpler. This has the exact same // precision but is faster than the stable implementation. // As branching criteria, we use the same cutoff as in log1pexp, see link above. // Note that the maximal value to get gradient = -1 with label = 1 is -37.439198610162731 // (based on mpmath), and scipy.special.logit(np.finfo(float).eps) ~ -36.04365. if (weights_ == nullptr) { // compute pointwise gradients and Hessians with implied unit weights #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) for (data_size_t i = 0; i < num_data_; ++i) { if (score[i] > -37.0) { const double exp_tmp = std::exp(-score[i]); gradients[i] = static_cast(((1.0f - label_[i]) - label_[i] * exp_tmp) / (1.0f + exp_tmp)); hessians[i] = static_cast(exp_tmp / ((1 + exp_tmp) * (1 + exp_tmp))); } else { const double exp_tmp = std::exp(score[i]); gradients[i] = static_cast(exp_tmp - label_[i]); hessians[i] = static_cast(exp_tmp); } } } else { // compute pointwise gradients and Hessians with given weights #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) for (data_size_t i = 0; i < num_data_; ++i) { if (score[i] > -37.0) { const double exp_tmp = std::exp(-score[i]); gradients[i] = static_cast(((1.0f - label_[i]) - label_[i] * exp_tmp) / (1.0f + exp_tmp) * weights_[i]); hessians[i] = static_cast(exp_tmp / ((1 + exp_tmp) * (1 + exp_tmp)) * weights_[i]); } else { const double exp_tmp = std::exp(score[i]); gradients[i] = static_cast((exp_tmp - label_[i]) * weights_[i]); hessians[i] = static_cast(exp_tmp * weights_[i]); } } } } const char* GetName() const override { return "cross_entropy"; } // convert score to a probability void ConvertOutput(const double* input, double* output) const override { output[0] = 1.0f / (1.0f + std::exp(-input[0])); } std::string ToString() const override { std::stringstream str_buf; str_buf << GetName(); return str_buf.str(); } // implement custom average to boost from (if enabled among options) double BoostFromScore(int) const override { double suml = 0.0f; double sumw = 0.0f; if (weights_ != nullptr) { #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) reduction(+:suml, sumw) if (!deterministic_) for (data_size_t i = 0; i < num_data_; ++i) { suml += static_cast(label_[i]) * weights_[i]; sumw += weights_[i]; } } else { sumw = static_cast(num_data_); #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) reduction(+:suml) if (!deterministic_) for (data_size_t i = 0; i < num_data_; ++i) { suml += label_[i]; } } double pavg = suml / sumw; pavg = std::min(pavg, 1.0 - kEpsilon); pavg = std::max(pavg, kEpsilon); double initscore = std::log(pavg / (1.0f - pavg)); Log::Info("[%s:%s]: pavg = %f -> initscore = %f", GetName(), __func__, pavg, initscore); return initscore; } private: /*! \brief Number of data points */ data_size_t num_data_; /*! \brief Pointer for label */ const label_t* label_; /*! \brief Weights for data */ const label_t* weights_; const bool deterministic_; }; /*! * \brief Objective function for alternative parameterization of cross-entropy (see top of file for explanation) */ class CrossEntropyLambda: public ObjectiveFunction { public: explicit CrossEntropyLambda(const Config& config) : deterministic_(config.deterministic) { min_weight_ = max_weight_ = 0.0f; } explicit CrossEntropyLambda(const std::vector&) : deterministic_(false) {} ~CrossEntropyLambda() {} void Init(const Metadata& metadata, data_size_t num_data) override { num_data_ = num_data; label_ = metadata.label(); weights_ = metadata.weights(); CHECK_NOTNULL(label_); Common::CheckElementsIntervalClosed(label_, 0.0f, 1.0f, num_data_, GetName()); Log::Info("[%s:%s]: (objective) labels passed interval [0, 1] check", GetName(), __func__); if (weights_ != nullptr) { Common::ObtainMinMaxSum(weights_, num_data_, &min_weight_, &max_weight_, static_cast(nullptr)); if (min_weight_ <= 0.0f) { Log::Fatal("[%s]: at least one weight is non-positive", GetName()); } // Issue an info statement about this ratio double weight_ratio = max_weight_ / min_weight_; Log::Info("[%s:%s]: min, max weights = %f, %f; ratio = %f", GetName(), __func__, min_weight_, max_weight_, weight_ratio); } else { // all weights are implied to be unity; no need to do anything } } void GetGradients(const double* score, score_t* gradients, score_t* hessians) const override { if (weights_ == nullptr) { // compute pointwise gradients and Hessians with implied unit weights; exactly equivalent to CrossEntropy with unit weights #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) for (data_size_t i = 0; i < num_data_; ++i) { const double z = 1.0f / (1.0f + std::exp(-score[i])); gradients[i] = static_cast(z - label_[i]); hessians[i] = static_cast(z * (1.0f - z)); } } else { // compute pointwise gradients and Hessians with given weights #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) for (data_size_t i = 0; i < num_data_; ++i) { const double w = weights_[i]; const double y = label_[i]; const double epf = std::exp(score[i]); const double hhat = std::log1p(epf); const double z = 1.0f - std::exp(-w*hhat); const double enf = 1.0f / epf; // = std::exp(-score[i]); gradients[i] = static_cast((1.0f - y / z) * w / (1.0f + enf)); const double c = 1.0f / (1.0f - z); double d = 1.0f + epf; const double a = w * epf / (d * d); d = c - 1.0f; const double b = (c / (d * d) ) * (1.0f + w * epf - c); hessians[i] = static_cast(a * (1.0f + y * b)); } } } const char* GetName() const override { return "cross_entropy_lambda"; } // // ATTENTION: the function output is the "normalized exponential parameter" lambda > 0, not the probability // // If this code would read: output[0] = 1.0f / (1.0f + std::exp(-input[0])); // The output would still not be the probability unless the weights are unity. // // Let z = 1 / (1 + exp(-f)), then prob(z) = 1-(1-z)^w, where w is the weight for the specific point. // void ConvertOutput(const double* input, double* output) const override { output[0] = std::log1p(std::exp(input[0])); } std::string ToString() const override { std::stringstream str_buf; str_buf << GetName(); return str_buf.str(); } double BoostFromScore(int) const override { double suml = 0.0f; double sumw = 0.0f; if (weights_ != nullptr) { #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) reduction(+:suml, sumw) if (!deterministic_) for (data_size_t i = 0; i < num_data_; ++i) { suml += static_cast(label_[i]) * weights_[i]; sumw += weights_[i]; } } else { sumw = static_cast(num_data_); #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) reduction(+:suml) if (!deterministic_) for (data_size_t i = 0; i < num_data_; ++i) { suml += label_[i]; } } double havg = suml / sumw; double initscore = std::log(std::expm1(havg)); Log::Info("[%s:%s]: havg = %f -> initscore = %f", GetName(), __func__, havg, initscore); return initscore; } private: /*! \brief Number of data points */ data_size_t num_data_; /*! \brief Pointer for label */ const label_t* label_; /*! \brief Weights for data */ const label_t* weights_; /*! \brief Minimum weight found during init */ label_t min_weight_; /*! \brief Maximum weight found during init */ label_t max_weight_; const bool deterministic_; }; } // end namespace LightGBM #endif // LIGHTGBM_SRC_OBJECTIVE_XENTROPY_OBJECTIVE_HPP_ ================================================ FILE: src/treelearner/col_sampler.hpp ================================================ [File too large to display: 7.8 KB] ================================================ FILE: src/treelearner/cost_effective_gradient_boosting.hpp ================================================ /*! * Copyright (c) 2019-2026 Microsoft Corporation. All rights reserved. * Copyright (c) 2019-2026 The LightGBM developers. All rights reserved. * Licensed under the MIT License. See LICENSE file in the project root for * license information. */ #ifndef LIGHTGBM_SRC_TREELEARNER_COST_EFFECTIVE_GRADIENT_BOOSTING_HPP_ #define LIGHTGBM_SRC_TREELEARNER_COST_EFFECTIVE_GRADIENT_BOOSTING_HPP_ #include #include #include #include #include #include #include "data_partition.hpp" #include "serial_tree_learner.h" #include "split_info.hpp" namespace LightGBM { class CostEfficientGradientBoosting { public: explicit CostEfficientGradientBoosting(const SerialTreeLearner* tree_learner) : init_(false), tree_learner_(tree_learner) {} static bool IsEnable(const Config* config) { if (config->cegb_tradeoff >= 1.0f && config->cegb_penalty_split <= 0.0f && config->cegb_penalty_feature_coupled.empty() && config->cegb_penalty_feature_lazy.empty()) { return false; } else { return true; } } void Init() { auto train_data = tree_learner_->train_data_; if (!init_) { splits_per_leaf_.resize( static_cast(tree_learner_->config_->num_leaves) * train_data->num_features()); is_feature_used_in_split_.clear(); is_feature_used_in_split_.resize(train_data->num_features()); } if (!tree_learner_->config_->cegb_penalty_feature_coupled.empty() && tree_learner_->config_->cegb_penalty_feature_coupled.size() != static_cast(train_data->num_total_features())) { Log::Fatal( "cegb_penalty_feature_coupled should be the same size as feature " "number."); } if (!tree_learner_->config_->cegb_penalty_feature_lazy.empty()) { if (tree_learner_->config_->cegb_penalty_feature_lazy.size() != static_cast(train_data->num_total_features())) { Log::Fatal( "cegb_penalty_feature_lazy should be the same size as feature " "number."); } if (!init_) { feature_used_in_data_ = Common::EmptyBitset(train_data->num_features() * tree_learner_->num_data_); } } init_ = true; } void BeforeTrain() { // clear the splits in splits_per_leaf_ Threading::For(0, splits_per_leaf_.size(), 1024, [this] (int /*thread_index*/, size_t start, size_t end) { for (size_t i = start; i < end; ++i) { splits_per_leaf_[i].Reset(); } }); } double DeltaGain(int feature_index, int real_fidx, int leaf_index, int num_data_in_leaf, SplitInfo split_info) { auto config = tree_learner_->config_; double delta = config->cegb_tradeoff * config->cegb_penalty_split * num_data_in_leaf; if (!config->cegb_penalty_feature_coupled.empty() && !is_feature_used_in_split_[feature_index]) { delta += config->cegb_tradeoff * config->cegb_penalty_feature_coupled[real_fidx]; } if (!config->cegb_penalty_feature_lazy.empty()) { delta += config->cegb_tradeoff * CalculateOndemandCosts(feature_index, real_fidx, leaf_index); } splits_per_leaf_[static_cast(leaf_index) * tree_learner_->train_data_->num_features() + feature_index] = split_info; return delta; } void UpdateLeafBestSplits(Tree* tree, int best_leaf, const SplitInfo* best_split_info, std::vector* best_split_per_leaf) { auto config = tree_learner_->config_; auto train_data = tree_learner_->train_data_; const int inner_feature_index = train_data->InnerFeatureIndex(best_split_info->feature); auto& ref_best_split_per_leaf = *best_split_per_leaf; if (!config->cegb_penalty_feature_coupled.empty() && !is_feature_used_in_split_[inner_feature_index]) { is_feature_used_in_split_[inner_feature_index] = true; for (int i = 0; i < tree->num_leaves(); ++i) { if (i == best_leaf) continue; auto split = &splits_per_leaf_[static_cast(i) * train_data->num_features() + inner_feature_index]; split->gain += config->cegb_tradeoff * config->cegb_penalty_feature_coupled[best_split_info->feature]; // Avoid to update the leaf that cannot split if (ref_best_split_per_leaf[i].gain > kMinScore && *split > ref_best_split_per_leaf[i]) { ref_best_split_per_leaf[i] = *split; } } } if (!config->cegb_penalty_feature_lazy.empty()) { data_size_t cnt_leaf_data = 0; auto tmp_idx = tree_learner_->data_partition_->GetIndexOnLeaf( best_leaf, &cnt_leaf_data); for (data_size_t i_input = 0; i_input < cnt_leaf_data; ++i_input) { int real_idx = tmp_idx[i_input]; Common::InsertBitset( &feature_used_in_data_, train_data->num_data() * inner_feature_index + real_idx); } } } private: double CalculateOndemandCosts(int feature_index, int real_fidx, int leaf_index) const { if (tree_learner_->config_->cegb_penalty_feature_lazy.empty()) { return 0.0f; } auto train_data = tree_learner_->train_data_; double penalty = tree_learner_->config_->cegb_penalty_feature_lazy[real_fidx]; double total = 0.0f; data_size_t cnt_leaf_data = 0; auto tmp_idx = tree_learner_->data_partition_->GetIndexOnLeaf( leaf_index, &cnt_leaf_data); for (data_size_t i_input = 0; i_input < cnt_leaf_data; ++i_input) { int real_idx = tmp_idx[i_input]; if (Common::FindInBitset( feature_used_in_data_.data(), train_data->num_data() * train_data->num_features(), train_data->num_data() * feature_index + real_idx)) { continue; } total += penalty; } return total; } bool init_; const SerialTreeLearner* tree_learner_; std::vector splits_per_leaf_; std::vector is_feature_used_in_split_; std::vector feature_used_in_data_; }; } // namespace LightGBM #endif // LIGHTGBM_SRC_TREELEARNER_COST_EFFECTIVE_GRADIENT_BOOSTING_HPP_ ================================================ FILE: src/treelearner/cuda/cuda_best_split_finder.cpp ================================================ /*! * Copyright (c) 2021 Microsoft Corporation. All rights reserved. * Licensed under the MIT License. See LICENSE file in the project root for * license information. */ #ifdef USE_CUDA #include #include #include "cuda_best_split_finder.hpp" #include "cuda_leaf_splits.hpp" namespace LightGBM { CUDABestSplitFinder::CUDABestSplitFinder( const hist_t* cuda_hist, const Dataset* train_data, const std::vector& feature_hist_offsets, const bool select_features_by_node, const Config* config): num_features_(train_data->num_features()), num_leaves_(config->num_leaves), feature_hist_offsets_(feature_hist_offsets), lambda_l1_(config->lambda_l1), lambda_l2_(config->lambda_l2), min_data_in_leaf_(config->min_data_in_leaf), min_sum_hessian_in_leaf_(config->min_sum_hessian_in_leaf), min_gain_to_split_(config->min_gain_to_split), cat_smooth_(config->cat_smooth), cat_l2_(config->cat_l2), max_cat_threshold_(config->max_cat_threshold), min_data_per_group_(config->min_data_per_group), max_cat_to_onehot_(config->max_cat_to_onehot), extra_trees_(config->extra_trees), extra_seed_(config->extra_seed), use_smoothing_(config->path_smooth > 0), path_smooth_(config->path_smooth), num_total_bin_(feature_hist_offsets.empty() ? 0 : static_cast(feature_hist_offsets.back())), select_features_by_node_(select_features_by_node), cuda_hist_(cuda_hist) { InitFeatureMetaInfo(train_data); if (has_categorical_feature_ && config->use_quantized_grad) { Log::Fatal("Quantized training on GPU with categorical features is not supported yet."); } } CUDABestSplitFinder::~CUDABestSplitFinder() { gpuAssert(cudaStreamDestroy(cuda_streams_[0]), __FILE__, __LINE__); gpuAssert(cudaStreamDestroy(cuda_streams_[1]), __FILE__, __LINE__); cuda_streams_.clear(); cuda_streams_.shrink_to_fit(); } void CUDABestSplitFinder::InitFeatureMetaInfo(const Dataset* train_data) { feature_missing_type_.resize(num_features_); feature_mfb_offsets_.resize(num_features_); feature_default_bins_.resize(num_features_); feature_num_bins_.resize(num_features_); max_num_bin_in_feature_ = 0; has_categorical_feature_ = false; max_num_categorical_bin_ = 0; is_categorical_.resize(train_data->num_features(), 0); for (int inner_feature_index = 0; inner_feature_index < num_features_; ++inner_feature_index) { const BinMapper* bin_mapper = train_data->FeatureBinMapper(inner_feature_index); if (bin_mapper->bin_type() == BinType::CategoricalBin) { has_categorical_feature_ = true; is_categorical_[inner_feature_index] = 1; if (bin_mapper->num_bin() > max_num_categorical_bin_) { max_num_categorical_bin_ = bin_mapper->num_bin(); } } const MissingType missing_type = bin_mapper->missing_type(); feature_missing_type_[inner_feature_index] = missing_type; feature_mfb_offsets_[inner_feature_index] = static_cast(bin_mapper->GetMostFreqBin() == 0); feature_default_bins_[inner_feature_index] = bin_mapper->GetDefaultBin(); feature_num_bins_[inner_feature_index] = static_cast(bin_mapper->num_bin()); const int num_bin_hist = bin_mapper->num_bin() - feature_mfb_offsets_[inner_feature_index]; if (num_bin_hist > max_num_bin_in_feature_) { max_num_bin_in_feature_ = num_bin_hist; } } if (max_num_bin_in_feature_ > NUM_THREADS_PER_BLOCK_BEST_SPLIT_FINDER) { use_global_memory_ = true; } else { use_global_memory_ = false; } } void CUDABestSplitFinder::Init() { InitCUDAFeatureMetaInfo(); cuda_streams_.resize(2); CUDASUCCESS_OR_FATAL(cudaStreamCreate(&cuda_streams_[0])); CUDASUCCESS_OR_FATAL(cudaStreamCreate(&cuda_streams_[1])); cuda_best_split_info_buffer_.Resize(8); if (use_global_memory_) { cuda_feature_hist_grad_buffer_.Resize(static_cast(num_total_bin_)); cuda_feature_hist_hess_buffer_.Resize(static_cast(num_total_bin_)); if (has_categorical_feature_) { cuda_feature_hist_stat_buffer_.Resize(static_cast(num_total_bin_)); cuda_feature_hist_index_buffer_.Resize(static_cast(num_total_bin_)); } } if (select_features_by_node_) { is_feature_used_by_smaller_node_.Resize(num_features_); is_feature_used_by_larger_node_.Resize(num_features_); } } void CUDABestSplitFinder::InitCUDAFeatureMetaInfo() { cuda_is_feature_used_bytree_.Resize(static_cast(num_features_)); // initialize split find task information (a split find task is one pass through the histogram of a feature) num_tasks_ = 0; for (int inner_feature_index = 0; inner_feature_index < num_features_; ++inner_feature_index) { const uint32_t num_bin = feature_num_bins_[inner_feature_index]; const MissingType missing_type = feature_missing_type_[inner_feature_index]; if (num_bin > 2 && missing_type != MissingType::None && !is_categorical_[inner_feature_index]) { num_tasks_ += 2; } else { ++num_tasks_; } } split_find_tasks_.resize(num_tasks_); split_find_tasks_.shrink_to_fit(); int cur_task_index = 0; for (int inner_feature_index = 0; inner_feature_index < num_features_; ++inner_feature_index) { const uint32_t num_bin = feature_num_bins_[inner_feature_index]; const MissingType missing_type = feature_missing_type_[inner_feature_index]; if (num_bin > 2 && missing_type != MissingType::None && !is_categorical_[inner_feature_index]) { if (missing_type == MissingType::Zero) { SplitFindTask* new_task = &split_find_tasks_[cur_task_index]; new_task->reverse = false; new_task->skip_default_bin = true; new_task->na_as_missing = false; new_task->inner_feature_index = inner_feature_index; new_task->assume_out_default_left = false; new_task->is_categorical = false; uint32_t num_bin = feature_num_bins_[inner_feature_index]; new_task->is_one_hot = false; new_task->hist_offset = feature_hist_offsets_[inner_feature_index]; new_task->mfb_offset = feature_mfb_offsets_[inner_feature_index]; new_task->default_bin = feature_default_bins_[inner_feature_index]; new_task->num_bin = num_bin; ++cur_task_index; new_task = &split_find_tasks_[cur_task_index]; new_task->reverse = true; new_task->skip_default_bin = true; new_task->na_as_missing = false; new_task->inner_feature_index = inner_feature_index; new_task->assume_out_default_left = true; new_task->is_categorical = false; num_bin = feature_num_bins_[inner_feature_index]; new_task->is_one_hot = false; new_task->hist_offset = feature_hist_offsets_[inner_feature_index]; new_task->default_bin = feature_default_bins_[inner_feature_index]; new_task->mfb_offset = feature_mfb_offsets_[inner_feature_index]; new_task->num_bin = num_bin; ++cur_task_index; } else { SplitFindTask* new_task = &split_find_tasks_[cur_task_index]; new_task->reverse = false; new_task->skip_default_bin = false; new_task->na_as_missing = true; new_task->inner_feature_index = inner_feature_index; new_task->assume_out_default_left = false; new_task->is_categorical = false; uint32_t num_bin = feature_num_bins_[inner_feature_index]; new_task->is_one_hot = false; new_task->hist_offset = feature_hist_offsets_[inner_feature_index]; new_task->mfb_offset = feature_mfb_offsets_[inner_feature_index]; new_task->default_bin = feature_default_bins_[inner_feature_index]; new_task->num_bin = num_bin; ++cur_task_index; new_task = &split_find_tasks_[cur_task_index]; new_task->reverse = true; new_task->skip_default_bin = false; new_task->na_as_missing = true; new_task->inner_feature_index = inner_feature_index; new_task->assume_out_default_left = true; new_task->is_categorical = false; num_bin = feature_num_bins_[inner_feature_index]; new_task->is_one_hot = false; new_task->hist_offset = feature_hist_offsets_[inner_feature_index]; new_task->mfb_offset = feature_mfb_offsets_[inner_feature_index]; new_task->default_bin = feature_default_bins_[inner_feature_index]; new_task->num_bin = num_bin; ++cur_task_index; } } else { SplitFindTask& new_task = split_find_tasks_[cur_task_index]; const uint32_t num_bin = feature_num_bins_[inner_feature_index]; if (is_categorical_[inner_feature_index]) { new_task.reverse = false; new_task.is_categorical = true; new_task.is_one_hot = (static_cast(num_bin) <= max_cat_to_onehot_); } else { new_task.reverse = true; new_task.is_categorical = false; new_task.is_one_hot = false; } new_task.skip_default_bin = false; new_task.na_as_missing = false; new_task.inner_feature_index = inner_feature_index; if (missing_type != MissingType::NaN && !is_categorical_[inner_feature_index]) { new_task.assume_out_default_left = true; } else { new_task.assume_out_default_left = false; } new_task.hist_offset = feature_hist_offsets_[inner_feature_index]; new_task.mfb_offset = feature_mfb_offsets_[inner_feature_index]; new_task.default_bin = feature_default_bins_[inner_feature_index]; new_task.num_bin = num_bin; ++cur_task_index; } } CHECK_EQ(cur_task_index, static_cast(split_find_tasks_.size())); if (extra_trees_) { cuda_randoms_.Resize(num_tasks_ * 2); LaunchInitCUDARandomKernel(); } const int num_task_blocks = (num_tasks_ + NUM_TASKS_PER_SYNC_BLOCK - 1) / NUM_TASKS_PER_SYNC_BLOCK; const size_t cuda_best_leaf_split_info_buffer_size = static_cast(num_task_blocks) * static_cast(num_leaves_); cuda_leaf_best_split_info_.Resize(cuda_best_leaf_split_info_buffer_size); cuda_split_find_tasks_.Resize(num_tasks_); CopyFromHostToCUDADevice(cuda_split_find_tasks_.RawData(), split_find_tasks_.data(), split_find_tasks_.size(), __FILE__, __LINE__); const size_t output_buffer_size = 2 * static_cast(num_tasks_); cuda_best_split_info_.Resize(output_buffer_size); max_num_categories_in_split_ = std::min(max_cat_threshold_, max_num_categorical_bin_ / 2); cuda_cat_threshold_feature_.Resize(max_num_categories_in_split_ * output_buffer_size); cuda_cat_threshold_real_feature_.Resize(max_num_categories_in_split_ * output_buffer_size); cuda_cat_threshold_leaf_.Resize(max_num_categories_in_split_ * cuda_best_leaf_split_info_buffer_size); cuda_cat_threshold_real_leaf_.Resize(max_num_categories_in_split_ * cuda_best_leaf_split_info_buffer_size); 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); AllocateCatVectors(cuda_best_split_info_.RawData(), cuda_cat_threshold_feature_.RawData(), cuda_cat_threshold_real_feature_.RawData(), output_buffer_size); } void CUDABestSplitFinder::ResetTrainingData( const hist_t* cuda_hist, const Dataset* train_data, const std::vector& feature_hist_offsets) { cuda_hist_ = cuda_hist; num_features_ = train_data->num_features(); feature_hist_offsets_ = feature_hist_offsets; InitFeatureMetaInfo(train_data); cuda_is_feature_used_bytree_.Clear(); cuda_best_split_info_.Clear(); InitCUDAFeatureMetaInfo(); } void CUDABestSplitFinder::ResetConfig(const Config* config, const hist_t* cuda_hist) { num_leaves_ = config->num_leaves; lambda_l1_ = config->lambda_l1; lambda_l2_ = config->lambda_l2; min_data_in_leaf_ = config->min_data_in_leaf; min_sum_hessian_in_leaf_ = config->min_sum_hessian_in_leaf; min_gain_to_split_ = config->min_gain_to_split; cat_smooth_ = config->cat_smooth; cat_l2_ = config->cat_l2; max_cat_threshold_ = config->max_cat_threshold; min_data_per_group_ = config->min_data_per_group; max_cat_to_onehot_ = config->max_cat_to_onehot; extra_trees_ = config->extra_trees; extra_seed_ = config->extra_seed; use_smoothing_ = (config->path_smooth > 0.0f); path_smooth_ = config->path_smooth; cuda_hist_ = cuda_hist; const int num_task_blocks = (num_tasks_ + NUM_TASKS_PER_SYNC_BLOCK - 1) / NUM_TASKS_PER_SYNC_BLOCK; size_t cuda_best_leaf_split_info_buffer_size = static_cast(num_task_blocks) * static_cast(num_leaves_); cuda_leaf_best_split_info_.Resize(cuda_best_leaf_split_info_buffer_size); max_num_categories_in_split_ = std::min(max_cat_threshold_, max_num_categorical_bin_ / 2); size_t total_cat_threshold_size = max_num_categories_in_split_ * cuda_best_leaf_split_info_buffer_size; cuda_cat_threshold_leaf_.Resize(total_cat_threshold_size); cuda_cat_threshold_real_leaf_.Resize(total_cat_threshold_size); 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); cuda_best_leaf_split_info_buffer_size = 2 * static_cast(num_tasks_); total_cat_threshold_size = max_num_categories_in_split_ * cuda_best_leaf_split_info_buffer_size; cuda_cat_threshold_feature_.Resize(total_cat_threshold_size); cuda_cat_threshold_real_feature_.Resize(total_cat_threshold_size); AllocateCatVectors(cuda_best_split_info_.RawData(), cuda_cat_threshold_feature_.RawData(), cuda_cat_threshold_real_feature_.RawData(), cuda_best_leaf_split_info_buffer_size); } void CUDABestSplitFinder::BeforeTrain(const std::vector& is_feature_used_bytree) { CopyFromHostToCUDADevice(cuda_is_feature_used_bytree_.RawData(), is_feature_used_bytree.data(), is_feature_used_bytree.size(), __FILE__, __LINE__); } void CUDABestSplitFinder::FindBestSplitsForLeaf( const CUDALeafSplitsStruct* smaller_leaf_splits, const CUDALeafSplitsStruct* larger_leaf_splits, const int smaller_leaf_index, const int larger_leaf_index, const data_size_t num_data_in_smaller_leaf, const data_size_t num_data_in_larger_leaf, const double sum_hessians_in_smaller_leaf, const double sum_hessians_in_larger_leaf, const score_t* grad_scale, const score_t* hess_scale, const uint8_t smaller_num_bits_in_histogram_bins, const uint8_t larger_num_bits_in_histogram_bins) { const bool is_smaller_leaf_valid = (num_data_in_smaller_leaf > min_data_in_leaf_ && sum_hessians_in_smaller_leaf > min_sum_hessian_in_leaf_); const bool is_larger_leaf_valid = (num_data_in_larger_leaf > min_data_in_leaf_ && sum_hessians_in_larger_leaf > min_sum_hessian_in_leaf_ && larger_leaf_index >= 0); if (grad_scale != nullptr && hess_scale != nullptr) { LaunchFindBestSplitsDiscretizedForLeafKernel(smaller_leaf_splits, larger_leaf_splits, smaller_leaf_index, larger_leaf_index, is_smaller_leaf_valid, is_larger_leaf_valid, 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); } else { LaunchFindBestSplitsForLeafKernel(smaller_leaf_splits, larger_leaf_splits, smaller_leaf_index, larger_leaf_index, is_smaller_leaf_valid, is_larger_leaf_valid, num_data_in_smaller_leaf, num_data_in_larger_leaf); } global_timer.Start("CUDABestSplitFinder::LaunchSyncBestSplitForLeafKernel"); LaunchSyncBestSplitForLeafKernel(smaller_leaf_index, larger_leaf_index, is_smaller_leaf_valid, is_larger_leaf_valid); SynchronizeCUDADevice(__FILE__, __LINE__); global_timer.Stop("CUDABestSplitFinder::LaunchSyncBestSplitForLeafKernel"); } const CUDASplitInfo* CUDABestSplitFinder::FindBestFromAllSplits( const int cur_num_leaves, const int smaller_leaf_index, const int larger_leaf_index, int* smaller_leaf_best_split_feature, uint32_t* smaller_leaf_best_split_threshold, uint8_t* smaller_leaf_best_split_default_left, int* larger_leaf_best_split_feature, uint32_t* larger_leaf_best_split_threshold, uint8_t* larger_leaf_best_split_default_left, int* best_leaf_index, int* num_cat_threshold) { LaunchFindBestFromAllSplitsKernel( cur_num_leaves, smaller_leaf_index, larger_leaf_index, smaller_leaf_best_split_feature, smaller_leaf_best_split_threshold, smaller_leaf_best_split_default_left, larger_leaf_best_split_feature, larger_leaf_best_split_threshold, larger_leaf_best_split_default_left, best_leaf_index, num_cat_threshold); SynchronizeCUDADevice(__FILE__, __LINE__); return cuda_leaf_best_split_info_.RawData() + (*best_leaf_index); } void CUDABestSplitFinder::AllocateCatVectors(CUDASplitInfo* cuda_split_infos, uint32_t* cat_threshold_vec, int* cat_threshold_real_vec, size_t len) { LaunchAllocateCatVectorsKernel(cuda_split_infos, cat_threshold_vec, cat_threshold_real_vec, len); } void CUDABestSplitFinder::SetUsedFeatureByNode(const std::vector& is_feature_used_by_smaller_node, const std::vector& is_feature_used_by_larger_node) { if (select_features_by_node_) { CopyFromHostToCUDADevice(is_feature_used_by_smaller_node_.RawData(), is_feature_used_by_smaller_node.data(), is_feature_used_by_smaller_node.size(), __FILE__, __LINE__); CopyFromHostToCUDADevice(is_feature_used_by_larger_node_.RawData(), is_feature_used_by_larger_node.data(), is_feature_used_by_larger_node.size(), __FILE__, __LINE__); } } } // namespace LightGBM #endif // USE_CUDA ================================================ FILE: src/treelearner/cuda/cuda_best_split_finder.cu ================================================ /*! * Copyright (c) 2021 Microsoft Corporation. All rights reserved. * Licensed under the MIT License. See LICENSE file in the project root for * license information. * Modifications Copyright(C) 2023 Advanced Micro Devices, Inc. All rights reserved. */ #ifdef USE_CUDA #include "cuda_best_split_finder.hpp" #include #include #include #include namespace LightGBM { __device__ void ReduceBestGainWarp(double gain, bool found, uint32_t thread_index, double* out_gain, bool* out_found, uint32_t* out_thread_index) { const uint32_t mask = 0xffffffff; const uint32_t warpLane = threadIdx.x % warpSize; for (uint32_t offset = warpSize / 2; offset > 0; offset >>= 1) { const bool other_found = __shfl_down_sync(mask, found, offset); const double other_gain = __shfl_down_sync(mask, gain, offset); const uint32_t other_thread_index = __shfl_down_sync(mask, thread_index, offset); if ((other_found && found && other_gain > gain) || (!found && other_found)) { found = other_found; gain = other_gain; thread_index = other_thread_index; } } if (warpLane == 0) { *out_gain = gain; *out_found = found; *out_thread_index = thread_index; } } __device__ uint32_t ReduceBestGainBlock(double gain, bool found, uint32_t thread_index) { const uint32_t mask = 0xffffffff; for (uint32_t offset = warpSize / 2; offset > 0; offset >>= 1) { const bool other_found = __shfl_down_sync(mask, found, offset); const double other_gain = __shfl_down_sync(mask, gain, offset); const uint32_t other_thread_index = __shfl_down_sync(mask, thread_index, offset); if ((other_found && found && other_gain > gain) || (!found && other_found)) { found = other_found; gain = other_gain; thread_index = other_thread_index; } } return thread_index; } __device__ uint32_t ReduceBestGain(double gain, bool found, uint32_t thread_index, double* shared_gain_buffer, bool* shared_found_buffer, uint32_t* shared_thread_index_buffer) { const uint32_t warpID = threadIdx.x / warpSize; const uint32_t warpLane = threadIdx.x % warpSize; const uint32_t num_warp = blockDim.x / warpSize; ReduceBestGainWarp(gain, found, thread_index, shared_gain_buffer + warpID, shared_found_buffer + warpID, shared_thread_index_buffer + warpID); __syncthreads(); if (warpID == 0) { gain = warpLane < num_warp ? shared_gain_buffer[warpLane] : kMinScore; found = warpLane < num_warp ? shared_found_buffer[warpLane] : false; thread_index = warpLane < num_warp ? shared_thread_index_buffer[warpLane] : 0; thread_index = ReduceBestGainBlock(gain, found, thread_index); } return thread_index; } __device__ void ReduceBestGainForLeaves(double* gain, int* leaves, int cuda_cur_num_leaves) { const unsigned int tid = threadIdx.x; for (unsigned int s = 1; s < cuda_cur_num_leaves; s *= 2) { if (tid % (2 * s) == 0 && (tid + s) < cuda_cur_num_leaves) { const uint32_t tid_s = tid + s; if ((leaves[tid] == -1 && leaves[tid_s] != -1) || (leaves[tid] != -1 && leaves[tid_s] != -1 && gain[tid_s] > gain[tid])) { gain[tid] = gain[tid_s]; leaves[tid] = leaves[tid_s]; } } __syncthreads(); } } __device__ void ReduceBestGainForLeavesWarp(double gain, int leaf_index, double* out_gain, int* out_leaf_index) { const uint32_t mask = 0xffffffff; const uint32_t warpLane = threadIdx.x % warpSize; for (uint32_t offset = warpSize / 2; offset > 0; offset >>= 1) { const int other_leaf_index = __shfl_down_sync(mask, leaf_index, offset); const double other_gain = __shfl_down_sync(mask, gain, offset); if ((leaf_index != -1 && other_leaf_index != -1 && other_gain > gain) || (leaf_index == -1 && other_leaf_index != -1)) { gain = other_gain; leaf_index = other_leaf_index; } } if (warpLane == 0) { *out_gain = gain; *out_leaf_index = leaf_index; } } __device__ int ReduceBestGainForLeavesBlock(double gain, int leaf_index) { const uint32_t mask = 0xffffffff; for (uint32_t offset = warpSize / 2; offset > 0; offset >>= 1) { const int other_leaf_index = __shfl_down_sync(mask, leaf_index, offset); const double other_gain = __shfl_down_sync(mask, gain, offset); if ((leaf_index != -1 && other_leaf_index != -1 && other_gain > gain) || (leaf_index == -1 && other_leaf_index != -1)) { gain = other_gain; leaf_index = other_leaf_index; } } return leaf_index; } __device__ int ReduceBestGainForLeaves(double gain, int leaf_index, double* shared_gain_buffer, int* shared_leaf_index_buffer) { const uint32_t warpID = threadIdx.x / warpSize; const uint32_t warpLane = threadIdx.x % warpSize; const uint32_t num_warp = blockDim.x / warpSize; ReduceBestGainForLeavesWarp(gain, leaf_index, shared_gain_buffer + warpID, shared_leaf_index_buffer + warpID); __syncthreads(); if (warpID == 0) { gain = warpLane < num_warp ? shared_gain_buffer[warpLane] : kMinScore; leaf_index = warpLane < num_warp ? shared_leaf_index_buffer[warpLane] : -1; leaf_index = ReduceBestGainForLeavesBlock(gain, leaf_index); } return leaf_index; } template __device__ void FindBestSplitsForLeafKernelInner( // input feature information const hist_t* feature_hist_ptr, // input task information const SplitFindTask* task, CUDARandom* cuda_random, // input config parameter values const double lambda_l1, const double lambda_l2, const double path_smooth, const data_size_t min_data_in_leaf, const double min_sum_hessian_in_leaf, const double min_gain_to_split, // input parent node information const double parent_gain, const double sum_gradients, const double sum_hessians, const data_size_t num_data, const double parent_output, // output parameters CUDASplitInfo* cuda_best_split_info) { const double cnt_factor = num_data / sum_hessians; const double min_gain_shift = parent_gain + min_gain_to_split; cuda_best_split_info->is_valid = false; hist_t local_grad_hist = 0.0f; hist_t local_hess_hist = 0.0f; double local_gain = 0.0f; bool threshold_found = false; uint32_t threshold_value = 0; __shared__ int rand_threshold; if (USE_RAND && threadIdx.x == 0) { if (task->num_bin - 2 > 0) { rand_threshold = cuda_random->NextInt(0, task->num_bin - 2); } } __shared__ uint32_t best_thread_index; __shared__ double shared_double_buffer[WARPSIZE]; __shared__ bool shared_bool_buffer[WARPSIZE]; __shared__ uint32_t shared_int_buffer[WARPSIZE]; const unsigned int threadIdx_x = threadIdx.x; const bool skip_sum = REVERSE ? (task->skip_default_bin && (task->num_bin - 1 - threadIdx_x) == static_cast(task->default_bin)) : (task->skip_default_bin && (threadIdx_x + task->mfb_offset) == static_cast(task->default_bin)); const uint32_t feature_num_bin_minus_offset = task->num_bin - task->mfb_offset; if (!REVERSE) { if (task->na_as_missing && task->mfb_offset == 1) { if (threadIdx_x < static_cast(task->num_bin) && threadIdx_x > 0) { const unsigned int bin_offset = (threadIdx_x - 1) << 1; local_grad_hist = feature_hist_ptr[bin_offset]; local_hess_hist = feature_hist_ptr[bin_offset + 1]; } } else { if (threadIdx_x < feature_num_bin_minus_offset && !skip_sum) { const unsigned int bin_offset = threadIdx_x << 1; local_grad_hist = feature_hist_ptr[bin_offset]; local_hess_hist = feature_hist_ptr[bin_offset + 1]; } } } else { if (threadIdx_x >= static_cast(task->na_as_missing) && threadIdx_x < feature_num_bin_minus_offset && !skip_sum) { const unsigned int read_index = feature_num_bin_minus_offset - 1 - threadIdx_x; const unsigned int bin_offset = read_index << 1; local_grad_hist = feature_hist_ptr[bin_offset]; local_hess_hist = feature_hist_ptr[bin_offset + 1]; } } __syncthreads(); if (!REVERSE && task->na_as_missing && task->mfb_offset == 1) { const hist_t sum_gradients_non_default = ShuffleReduceSum(local_grad_hist, shared_double_buffer, blockDim.x); __syncthreads(); const hist_t sum_hessians_non_default = ShuffleReduceSum(local_hess_hist, shared_double_buffer, blockDim.x); if (threadIdx_x == 0) { local_grad_hist += (sum_gradients - sum_gradients_non_default); local_hess_hist += (sum_hessians - sum_hessians_non_default); } } if (threadIdx_x == 0) { local_hess_hist += kEpsilon; } local_gain = kMinScore; local_grad_hist = ShufflePrefixSum(local_grad_hist, shared_double_buffer); __syncthreads(); local_hess_hist = ShufflePrefixSum(local_hess_hist, shared_double_buffer); if (REVERSE) { if (threadIdx_x >= static_cast(task->na_as_missing) && threadIdx_x <= task->num_bin - 2 && !skip_sum) { const double sum_right_gradient = local_grad_hist; const double sum_right_hessian = local_hess_hist; const data_size_t right_count = static_cast(__double2int_rn(sum_right_hessian * cnt_factor)); const double sum_left_gradient = sum_gradients - sum_right_gradient; const double sum_left_hessian = sum_hessians - sum_right_hessian; const data_size_t left_count = num_data - right_count; if (sum_left_hessian >= min_sum_hessian_in_leaf && left_count >= min_data_in_leaf && sum_right_hessian >= min_sum_hessian_in_leaf && right_count >= min_data_in_leaf && (!USE_RAND || static_cast(task->num_bin - 2 - threadIdx_x) == rand_threshold)) { double current_gain = CUDALeafSplits::GetSplitGains( sum_left_gradient, sum_left_hessian, sum_right_gradient, sum_right_hessian, lambda_l1, lambda_l2, path_smooth, left_count, right_count, parent_output); // gain with split is worse than without split if (current_gain > min_gain_shift) { local_gain = current_gain - min_gain_shift; threshold_value = static_cast(task->num_bin - 2 - threadIdx_x); threshold_found = true; } } } } else { const uint32_t end = (task->na_as_missing && task->mfb_offset == 1) ? static_cast(task->num_bin - 2) : feature_num_bin_minus_offset - 2; if (threadIdx_x <= end && !skip_sum) { const double sum_left_gradient = local_grad_hist; const double sum_left_hessian = local_hess_hist; const data_size_t left_count = static_cast(__double2int_rn(sum_left_hessian * cnt_factor)); const double sum_right_gradient = sum_gradients - sum_left_gradient; const double sum_right_hessian = sum_hessians - sum_left_hessian; const data_size_t right_count = num_data - left_count; if (sum_left_hessian >= min_sum_hessian_in_leaf && left_count >= min_data_in_leaf && sum_right_hessian >= min_sum_hessian_in_leaf && right_count >= min_data_in_leaf && (!USE_RAND || static_cast(threadIdx_x + task->mfb_offset) == rand_threshold)) { double current_gain = CUDALeafSplits::GetSplitGains( sum_left_gradient, sum_left_hessian, sum_right_gradient, sum_right_hessian, lambda_l1, lambda_l2, path_smooth, left_count, right_count, parent_output); // gain with split is worse than without split if (current_gain > min_gain_shift) { local_gain = current_gain - min_gain_shift; threshold_value = (task->na_as_missing && task->mfb_offset == 1) ? static_cast(threadIdx_x) : static_cast(threadIdx_x + task->mfb_offset); threshold_found = true; } } } } __syncthreads(); const uint32_t result = ReduceBestGain(local_gain, threshold_found, threadIdx_x, shared_double_buffer, shared_bool_buffer, shared_int_buffer); if (threadIdx_x == 0) { best_thread_index = result; } __syncthreads(); if (threshold_found && threadIdx_x == best_thread_index) { cuda_best_split_info->is_valid = true; cuda_best_split_info->threshold = threshold_value; cuda_best_split_info->gain = local_gain; cuda_best_split_info->default_left = task->assume_out_default_left; if (REVERSE) { const double sum_right_gradient = local_grad_hist; const double sum_right_hessian = local_hess_hist - kEpsilon; const data_size_t right_count = static_cast(__double2int_rn(sum_right_hessian * cnt_factor)); const double sum_left_gradient = sum_gradients - sum_right_gradient; const double sum_left_hessian = sum_hessians - sum_right_hessian - kEpsilon; const data_size_t left_count = num_data - right_count; const double left_output = CUDALeafSplits::CalculateSplittedLeafOutput(sum_left_gradient, sum_left_hessian, lambda_l1, lambda_l2, path_smooth, left_count, parent_output); const double right_output = CUDALeafSplits::CalculateSplittedLeafOutput(sum_right_gradient, sum_right_hessian, lambda_l1, lambda_l2, path_smooth, right_count, parent_output); cuda_best_split_info->left_sum_gradients = sum_left_gradient; cuda_best_split_info->left_sum_hessians = sum_left_hessian; cuda_best_split_info->left_count = left_count; cuda_best_split_info->right_sum_gradients = sum_right_gradient; cuda_best_split_info->right_sum_hessians = sum_right_hessian; cuda_best_split_info->right_count = right_count; cuda_best_split_info->left_value = left_output; cuda_best_split_info->left_gain = CUDALeafSplits::GetLeafGainGivenOutput(sum_left_gradient, sum_left_hessian, lambda_l1, lambda_l2, left_output); cuda_best_split_info->right_value = right_output; cuda_best_split_info->right_gain = CUDALeafSplits::GetLeafGainGivenOutput(sum_right_gradient, sum_right_hessian, lambda_l1, lambda_l2, right_output); } else { const double sum_left_gradient = local_grad_hist; const double sum_left_hessian = local_hess_hist - kEpsilon; const data_size_t left_count = static_cast(__double2int_rn(sum_left_hessian * cnt_factor)); const double sum_right_gradient = sum_gradients - sum_left_gradient; const double sum_right_hessian = sum_hessians - sum_left_hessian - kEpsilon; const data_size_t right_count = num_data - left_count; const double left_output = CUDALeafSplits::CalculateSplittedLeafOutput(sum_left_gradient, sum_left_hessian, lambda_l1, lambda_l2, path_smooth, left_count, parent_output); const double right_output = CUDALeafSplits::CalculateSplittedLeafOutput(sum_right_gradient, sum_right_hessian, lambda_l1, lambda_l2, path_smooth, right_count, parent_output); cuda_best_split_info->left_sum_gradients = sum_left_gradient; cuda_best_split_info->left_sum_hessians = sum_left_hessian; cuda_best_split_info->left_count = left_count; cuda_best_split_info->right_sum_gradients = sum_right_gradient; cuda_best_split_info->right_sum_hessians = sum_right_hessian; cuda_best_split_info->right_count = right_count; cuda_best_split_info->left_value = left_output; cuda_best_split_info->left_gain = CUDALeafSplits::GetLeafGainGivenOutput(sum_left_gradient, sum_left_hessian, lambda_l1, lambda_l2, left_output); cuda_best_split_info->right_value = right_output; cuda_best_split_info->right_gain = CUDALeafSplits::GetLeafGainGivenOutput(sum_right_gradient, sum_right_hessian, lambda_l1, lambda_l2, right_output); } } } template __device__ void FindBestSplitsDiscretizedForLeafKernelInner( // input feature information const BIN_HIST_TYPE* feature_hist_ptr, // input task information const SplitFindTask* task, CUDARandom* cuda_random, // input config parameter values const double lambda_l1, const double lambda_l2, const double path_smooth, const data_size_t min_data_in_leaf, const double min_sum_hessian_in_leaf, const double min_gain_to_split, // input parent node information const double parent_gain, const int64_t sum_gradients_hessians, const data_size_t num_data, const double parent_output, // gradient scale const double grad_scale, const double hess_scale, // output parameters CUDASplitInfo* cuda_best_split_info) { const double sum_hessians = static_cast(sum_gradients_hessians & 0x00000000ffffffff) * hess_scale; const double cnt_factor = num_data / sum_hessians; const double min_gain_shift = parent_gain + min_gain_to_split; cuda_best_split_info->is_valid = false; ACC_HIST_TYPE local_grad_hess_hist = 0; double local_gain = 0.0f; bool threshold_found = false; uint32_t threshold_value = 0; __shared__ int rand_threshold; if (USE_RAND && threadIdx.x == 0) { if (task->num_bin - 2 > 0) { rand_threshold = cuda_random->NextInt(0, task->num_bin - 2); } } __shared__ uint32_t best_thread_index; __shared__ double shared_double_buffer[WARPSIZE]; __shared__ bool shared_bool_buffer[WARPSIZE]; __shared__ uint32_t shared_int_buffer[2 * WARPSIZE]; // need 2 * WARPSIZE since the actual ACC_HIST_TYPE could be long int const unsigned int threadIdx_x = threadIdx.x; const bool skip_sum = REVERSE ? (task->skip_default_bin && (task->num_bin - 1 - threadIdx_x) == static_cast(task->default_bin)) : (task->skip_default_bin && (threadIdx_x + task->mfb_offset) == static_cast(task->default_bin)); const uint32_t feature_num_bin_minus_offset = task->num_bin - task->mfb_offset; if (!REVERSE) { if (threadIdx_x < feature_num_bin_minus_offset && !skip_sum) { const unsigned int bin_offset = threadIdx_x; if (USE_16BIT_BIN_HIST && !USE_16BIT_ACC_HIST) { const int32_t local_grad_hess_hist_int32 = feature_hist_ptr[bin_offset]; local_grad_hess_hist = (static_cast(static_cast(local_grad_hess_hist_int32 >> 16)) << 32) | (static_cast(local_grad_hess_hist_int32 & 0x0000ffff)); } else { local_grad_hess_hist = feature_hist_ptr[bin_offset]; } } } else { if (threadIdx_x >= static_cast(task->na_as_missing) && threadIdx_x < feature_num_bin_minus_offset && !skip_sum) { const unsigned int read_index = feature_num_bin_minus_offset - 1 - threadIdx_x; if (USE_16BIT_BIN_HIST && !USE_16BIT_ACC_HIST) { const int32_t local_grad_hess_hist_int32 = feature_hist_ptr[read_index]; local_grad_hess_hist = (static_cast(static_cast(local_grad_hess_hist_int32 >> 16)) << 32) | (static_cast(local_grad_hess_hist_int32 & 0x0000ffff)); } else { local_grad_hess_hist = feature_hist_ptr[read_index]; } } } __syncthreads(); local_gain = kMinScore; local_grad_hess_hist = ShufflePrefixSum(local_grad_hess_hist, reinterpret_cast(shared_int_buffer)); double sum_left_gradient = 0.0f; double sum_left_hessian = 0.0f; double sum_right_gradient = 0.0f; double sum_right_hessian = 0.0f; data_size_t left_count = 0; data_size_t right_count = 0; int64_t sum_left_gradient_hessian = 0; int64_t sum_right_gradient_hessian = 0; if (REVERSE) { if (threadIdx_x >= static_cast(task->na_as_missing) && threadIdx_x <= task->num_bin - 2 && !skip_sum) { sum_right_gradient_hessian = USE_16BIT_ACC_HIST ? (static_cast(static_cast(local_grad_hess_hist >> 16)) << 32) | static_cast(local_grad_hess_hist & 0x0000ffff) : local_grad_hess_hist; sum_right_gradient = static_cast(static_cast((sum_right_gradient_hessian & 0xffffffff00000000) >> 32)) * grad_scale; sum_right_hessian = static_cast(static_cast(sum_right_gradient_hessian & 0x00000000ffffffff)) * hess_scale; right_count = static_cast(__double2int_rn(sum_right_hessian * cnt_factor)); sum_left_gradient_hessian = sum_gradients_hessians - sum_right_gradient_hessian; sum_left_gradient = static_cast(static_cast((sum_left_gradient_hessian & 0xffffffff00000000)>> 32)) * grad_scale; sum_left_hessian = static_cast(static_cast(sum_left_gradient_hessian & 0x00000000ffffffff)) * hess_scale; left_count = num_data - right_count; if (sum_left_hessian >= min_sum_hessian_in_leaf && left_count >= min_data_in_leaf && sum_right_hessian >= min_sum_hessian_in_leaf && right_count >= min_data_in_leaf && (!USE_RAND || static_cast(task->num_bin - 2 - threadIdx_x) == rand_threshold)) { double current_gain = CUDALeafSplits::GetSplitGains( sum_left_gradient, sum_left_hessian + kEpsilon, sum_right_gradient, sum_right_hessian + kEpsilon, lambda_l1, lambda_l2, path_smooth, left_count, right_count, parent_output); // gain with split is worse than without split if (current_gain > min_gain_shift) { local_gain = current_gain - min_gain_shift; threshold_value = static_cast(task->num_bin - 2 - threadIdx_x); threshold_found = true; } } } } else { if (threadIdx_x <= feature_num_bin_minus_offset - 2 && !skip_sum) { sum_left_gradient_hessian = USE_16BIT_ACC_HIST ? (static_cast(static_cast(local_grad_hess_hist >> 16)) << 32) | static_cast(local_grad_hess_hist & 0x0000ffff) : local_grad_hess_hist; sum_left_gradient = static_cast(static_cast((sum_left_gradient_hessian & 0xffffffff00000000) >> 32)) * grad_scale; sum_left_hessian = static_cast(static_cast(sum_left_gradient_hessian & 0x00000000ffffffff)) * hess_scale; left_count = static_cast(__double2int_rn(sum_left_hessian * cnt_factor)); sum_right_gradient_hessian = sum_gradients_hessians - sum_left_gradient_hessian; sum_right_gradient = static_cast(static_cast((sum_right_gradient_hessian & 0xffffffff00000000) >> 32)) * grad_scale; sum_right_hessian = static_cast(static_cast(sum_right_gradient_hessian & 0x00000000ffffffff)) * hess_scale; right_count = num_data - left_count; if (sum_left_hessian >= min_sum_hessian_in_leaf && left_count >= min_data_in_leaf && sum_right_hessian >= min_sum_hessian_in_leaf && right_count >= min_data_in_leaf && (!USE_RAND || static_cast(threadIdx_x + task->mfb_offset) == rand_threshold)) { double current_gain = CUDALeafSplits::GetSplitGains( sum_left_gradient, sum_left_hessian + kEpsilon, sum_right_gradient, sum_right_hessian + kEpsilon, lambda_l1, lambda_l2, path_smooth, left_count, right_count, parent_output); // gain with split is worse than without split if (current_gain > min_gain_shift) { local_gain = current_gain - min_gain_shift; threshold_value = static_cast(threadIdx_x + task->mfb_offset); threshold_found = true; } } } } __syncthreads(); const uint32_t result = ReduceBestGain(local_gain, threshold_found, threadIdx_x, shared_double_buffer, shared_bool_buffer, shared_int_buffer); if (threadIdx_x == 0) { best_thread_index = result; } __syncthreads(); if (threshold_found && threadIdx_x == best_thread_index) { cuda_best_split_info->is_valid = true; cuda_best_split_info->threshold = threshold_value; cuda_best_split_info->gain = local_gain; cuda_best_split_info->default_left = task->assume_out_default_left; const double left_output = CUDALeafSplits::CalculateSplittedLeafOutput(sum_left_gradient, sum_left_hessian, lambda_l1, lambda_l2, path_smooth, left_count, parent_output); const double right_output = CUDALeafSplits::CalculateSplittedLeafOutput(sum_right_gradient, sum_right_hessian, lambda_l1, lambda_l2, path_smooth, right_count, parent_output); cuda_best_split_info->left_sum_gradients = sum_left_gradient; cuda_best_split_info->left_sum_hessians = sum_left_hessian; cuda_best_split_info->left_sum_of_gradients_hessians = sum_left_gradient_hessian; cuda_best_split_info->left_count = left_count; cuda_best_split_info->right_sum_gradients = sum_right_gradient; cuda_best_split_info->right_sum_hessians = sum_right_hessian; cuda_best_split_info->right_sum_of_gradients_hessians = sum_right_gradient_hessian; cuda_best_split_info->right_count = right_count; cuda_best_split_info->left_value = left_output; cuda_best_split_info->left_gain = CUDALeafSplits::GetLeafGainGivenOutput(sum_left_gradient, sum_left_hessian, lambda_l1, lambda_l2, left_output); cuda_best_split_info->right_value = right_output; cuda_best_split_info->right_gain = CUDALeafSplits::GetLeafGainGivenOutput(sum_right_gradient, sum_right_hessian, lambda_l1, lambda_l2, right_output); } } template __device__ void FindBestSplitsForLeafKernelCategoricalInner( // input feature information const hist_t* feature_hist_ptr, // input task information const SplitFindTask* task, CUDARandom* cuda_random, // input config parameter values const double lambda_l1, const double lambda_l2, const double path_smooth, const data_size_t min_data_in_leaf, const double min_sum_hessian_in_leaf, const double min_gain_to_split, const double cat_smooth, const double cat_l2, const int max_cat_threshold, const int min_data_per_group, // input parent node information const double parent_gain, const double sum_gradients, const double sum_hessians, const data_size_t num_data, const double parent_output, // output parameters CUDASplitInfo* cuda_best_split_info) { __shared__ double shared_gain_buffer[WARPSIZE]; __shared__ bool shared_found_buffer[WARPSIZE]; __shared__ uint32_t shared_thread_index_buffer[WARPSIZE]; __shared__ uint32_t best_thread_index; const double cnt_factor = num_data / sum_hessians; const double min_gain_shift = parent_gain + min_gain_to_split; double l2 = lambda_l2; double local_gain = min_gain_shift; bool threshold_found = false; cuda_best_split_info->is_valid = false; const int bin_start = 1 - task->mfb_offset; const int bin_end = task->num_bin - task->mfb_offset; const int threadIdx_x = static_cast(threadIdx.x); __shared__ int rand_threshold; if (task->is_one_hot) { if (USE_RAND && threadIdx.x == 0) { rand_threshold = 0; if (bin_end > bin_start) { rand_threshold = cuda_random->NextInt(bin_start, bin_end); } } __syncthreads(); if (threadIdx_x >= bin_start && threadIdx_x < bin_end) { const int bin_offset = (threadIdx_x << 1); const hist_t grad = feature_hist_ptr[bin_offset]; const hist_t hess = feature_hist_ptr[bin_offset + 1]; data_size_t cnt = static_cast(__double2int_rn(hess * cnt_factor)); if (cnt >= min_data_in_leaf && hess >= min_sum_hessian_in_leaf) { const data_size_t other_count = num_data - cnt; if (other_count >= min_data_in_leaf) { const double sum_other_hessian = sum_hessians - hess - kEpsilon; if (sum_other_hessian >= min_sum_hessian_in_leaf && (!USE_RAND || static_cast(threadIdx_x) == rand_threshold)) { const double sum_other_gradient = sum_gradients - grad; double current_gain = CUDALeafSplits::GetSplitGains( sum_other_gradient, sum_other_hessian, grad, hess + kEpsilon, lambda_l1, l2, path_smooth, other_count, cnt, parent_output); if (current_gain > min_gain_shift) { local_gain = current_gain; threshold_found = true; } } } } } __syncthreads(); const uint32_t result = ReduceBestGain(local_gain, threshold_found, threadIdx_x, shared_gain_buffer, shared_found_buffer, shared_thread_index_buffer); if (threadIdx_x == 0) { best_thread_index = result; } __syncthreads(); if (threshold_found && threadIdx_x == best_thread_index) { cuda_best_split_info->is_valid = true; cuda_best_split_info->num_cat_threshold = 1; cuda_best_split_info->gain = local_gain - min_gain_shift; *(cuda_best_split_info->cat_threshold) = static_cast(threadIdx_x + task->mfb_offset); cuda_best_split_info->default_left = false; const int bin_offset = (threadIdx_x << 1); const hist_t sum_left_gradient = feature_hist_ptr[bin_offset]; const hist_t sum_left_hessian = feature_hist_ptr[bin_offset + 1]; const data_size_t left_count = static_cast(__double2int_rn(sum_left_hessian * cnt_factor)); const double sum_right_gradient = sum_gradients - sum_left_gradient; const double sum_right_hessian = sum_hessians - sum_left_hessian; const data_size_t right_count = static_cast(__double2int_rn(sum_right_hessian * cnt_factor)); const double left_output = CUDALeafSplits::CalculateSplittedLeafOutput(sum_left_gradient, sum_left_hessian, lambda_l1, l2, path_smooth, left_count, parent_output); const double right_output = CUDALeafSplits::CalculateSplittedLeafOutput(sum_right_gradient, sum_right_hessian, lambda_l1, l2, path_smooth, right_count, parent_output); cuda_best_split_info->left_sum_gradients = sum_left_gradient; cuda_best_split_info->left_sum_hessians = sum_left_hessian; cuda_best_split_info->left_count = left_count; cuda_best_split_info->right_sum_gradients = sum_right_gradient; cuda_best_split_info->right_sum_hessians = sum_right_hessian; cuda_best_split_info->right_count = right_count; cuda_best_split_info->left_value = left_output; cuda_best_split_info->left_gain = CUDALeafSplits::GetLeafGainGivenOutput(sum_left_gradient, sum_left_hessian, lambda_l1, l2, left_output); cuda_best_split_info->right_value = right_output; cuda_best_split_info->right_gain = CUDALeafSplits::GetLeafGainGivenOutput(sum_right_gradient, sum_right_hessian, lambda_l1, l2, right_output); } } else { __shared__ double shared_value_buffer[NUM_THREADS_PER_BLOCK_BEST_SPLIT_FINDER]; __shared__ int16_t shared_index_buffer[NUM_THREADS_PER_BLOCK_BEST_SPLIT_FINDER]; __shared__ uint16_t shared_mem_buffer_uint16[WARPSIZE]; __shared__ double shared_mem_buffer_double[WARPSIZE]; __shared__ int used_bin; l2 += cat_l2; uint16_t is_valid_bin = 0; int best_dir = 0; double best_sum_left_gradient = 0.0f; double best_sum_left_hessian = 0.0f; if (threadIdx_x >= bin_start && threadIdx_x < bin_end) { const int bin_offset = (threadIdx_x << 1); const double hess = feature_hist_ptr[bin_offset + 1]; if (__double2int_rn(hess * cnt_factor) >= cat_smooth) { const double grad = feature_hist_ptr[bin_offset]; shared_value_buffer[threadIdx_x] = grad / (hess + cat_smooth); is_valid_bin = 1; } else { shared_value_buffer[threadIdx_x] = kMaxScore; } } else { shared_value_buffer[threadIdx_x] = kMaxScore; } shared_index_buffer[threadIdx_x] = threadIdx_x; __syncthreads(); const int local_used_bin = ShuffleReduceSum(is_valid_bin, shared_mem_buffer_uint16, blockDim.x); if (threadIdx_x == 0) { used_bin = local_used_bin; } __syncthreads(); BitonicArgSort_1024(shared_value_buffer, shared_index_buffer, bin_end); __syncthreads(); const int max_num_cat = min(max_cat_threshold, (used_bin + 1) / 2); if (USE_RAND) { rand_threshold = 0; const int max_threshold = max(min(max_num_cat, used_bin) - 1, 0); if (max_threshold > 0) { rand_threshold = cuda_random->NextInt(0, max_threshold); } } // left to right double grad = 0.0f; double hess = 0.0f; if (threadIdx_x < used_bin && threadIdx_x < max_num_cat) { const int bin_offset = (shared_index_buffer[threadIdx_x] << 1); grad = feature_hist_ptr[bin_offset]; hess = feature_hist_ptr[bin_offset + 1]; } if (threadIdx_x == 0) { hess += kEpsilon; } __syncthreads(); double sum_left_gradient = ShufflePrefixSum(grad, shared_mem_buffer_double); __syncthreads(); double sum_left_hessian = ShufflePrefixSum(hess, shared_mem_buffer_double); if (threadIdx_x < used_bin && threadIdx_x < max_num_cat) { const data_size_t left_count = static_cast(__double2int_rn(sum_left_hessian * cnt_factor)); const double sum_right_gradient = sum_gradients - sum_left_gradient; const double sum_right_hessian = sum_hessians - sum_left_hessian; const data_size_t right_count = num_data - left_count; if (sum_left_hessian >= min_sum_hessian_in_leaf && left_count >= min_data_in_leaf && sum_right_hessian >= min_sum_hessian_in_leaf && right_count >= min_data_in_leaf && (!USE_RAND || threadIdx_x == static_cast(rand_threshold))) { double current_gain = CUDALeafSplits::GetSplitGains( sum_left_gradient, sum_left_hessian, sum_right_gradient, sum_right_hessian, lambda_l1, l2, path_smooth, left_count, right_count, parent_output); // gain with split is worse than without split if (current_gain > local_gain) { local_gain = current_gain; threshold_found = true; best_dir = 1; best_sum_left_gradient = sum_left_gradient; best_sum_left_hessian = sum_left_hessian; } } } __syncthreads(); // right to left grad = 0.0f; hess = 0.0f; if (threadIdx_x < used_bin && threadIdx_x < max_num_cat) { const int bin_offset = (shared_index_buffer[used_bin - 1 - threadIdx_x] << 1); grad = feature_hist_ptr[bin_offset]; hess = feature_hist_ptr[bin_offset + 1]; } if (threadIdx_x == 0) { hess += kEpsilon; } __syncthreads(); sum_left_gradient = ShufflePrefixSum(grad, shared_mem_buffer_double); __syncthreads(); sum_left_hessian = ShufflePrefixSum(hess, shared_mem_buffer_double); if (threadIdx_x < used_bin && threadIdx_x < max_num_cat) { const data_size_t left_count = static_cast(__double2int_rn(sum_left_hessian * cnt_factor)); const double sum_right_gradient = sum_gradients - sum_left_gradient; const double sum_right_hessian = sum_hessians - sum_left_hessian; const data_size_t right_count = num_data - left_count; if (sum_left_hessian >= min_sum_hessian_in_leaf && left_count >= min_data_in_leaf && sum_right_hessian >= min_sum_hessian_in_leaf && right_count >= min_data_in_leaf && (!USE_RAND || threadIdx_x == static_cast(rand_threshold))) { double current_gain = CUDALeafSplits::GetSplitGains( sum_left_gradient, sum_left_hessian, sum_right_gradient, sum_right_hessian, lambda_l1, l2, path_smooth, left_count, right_count, parent_output); // gain with split is worse than without split if (current_gain > local_gain) { local_gain = current_gain; threshold_found = true; best_dir = -1; best_sum_left_gradient = sum_left_gradient; best_sum_left_hessian = sum_left_hessian; } } } __syncthreads(); const uint32_t result = ReduceBestGain(local_gain, threshold_found, threadIdx_x, shared_gain_buffer, shared_found_buffer, shared_thread_index_buffer); if (threadIdx_x == 0) { best_thread_index = result; } __syncthreads(); if (threshold_found && threadIdx_x == best_thread_index) { cuda_best_split_info->is_valid = true; cuda_best_split_info->num_cat_threshold = threadIdx_x + 1; cuda_best_split_info->gain = local_gain - min_gain_shift; if (best_dir == 1) { for (int i = 0; i < threadIdx_x + 1; ++i) { (cuda_best_split_info->cat_threshold)[i] = shared_index_buffer[i] + task->mfb_offset; } } else { for (int i = 0; i < threadIdx_x + 1; ++i) { (cuda_best_split_info->cat_threshold)[i] = shared_index_buffer[used_bin - 1 - i] + task->mfb_offset; } } cuda_best_split_info->default_left = false; const hist_t sum_left_gradient = best_sum_left_gradient; const hist_t sum_left_hessian = best_sum_left_hessian; const data_size_t left_count = static_cast(__double2int_rn(sum_left_hessian * cnt_factor)); const double sum_right_gradient = sum_gradients - sum_left_gradient; const double sum_right_hessian = sum_hessians - sum_left_hessian; const data_size_t right_count = static_cast(__double2int_rn(sum_right_hessian * cnt_factor)); const double left_output = CUDALeafSplits::CalculateSplittedLeafOutput(sum_left_gradient, sum_left_hessian, lambda_l1, l2, path_smooth, left_count, parent_output); const double right_output = CUDALeafSplits::CalculateSplittedLeafOutput(sum_right_gradient, sum_right_hessian, lambda_l1, l2, path_smooth, right_count, parent_output); cuda_best_split_info->left_sum_gradients = sum_left_gradient; cuda_best_split_info->left_sum_hessians = sum_left_hessian; cuda_best_split_info->left_count = left_count; cuda_best_split_info->right_sum_gradients = sum_right_gradient; cuda_best_split_info->right_sum_hessians = sum_right_hessian; cuda_best_split_info->right_count = right_count; cuda_best_split_info->left_value = left_output; cuda_best_split_info->left_gain = CUDALeafSplits::GetLeafGainGivenOutput(sum_left_gradient, sum_left_hessian, lambda_l1, l2, left_output); cuda_best_split_info->right_value = right_output; cuda_best_split_info->right_gain = CUDALeafSplits::GetLeafGainGivenOutput(sum_right_gradient, sum_right_hessian, lambda_l1, l2, right_output); } } } template __global__ void FindBestSplitsForLeafKernel( // input feature information const int8_t* is_feature_used_bytree, // input task information const int num_tasks, const SplitFindTask* tasks, CUDARandom* cuda_randoms, // input leaf information const CUDALeafSplitsStruct* smaller_leaf_splits, const CUDALeafSplitsStruct* larger_leaf_splits, // input config parameter values const data_size_t min_data_in_leaf, const double min_sum_hessian_in_leaf, const double min_gain_to_split, const double lambda_l1, const double lambda_l2, const double path_smooth, const double cat_smooth, const double cat_l2, const int max_cat_threshold, const int min_data_per_group, // output CUDASplitInfo* cuda_best_split_info, // global num data in leaf const data_size_t global_num_data_in_smaller_leaf, const data_size_t global_num_data_in_larger_leaf) { const unsigned int task_index = blockIdx.x; const SplitFindTask* task = tasks + task_index; const int inner_feature_index = task->inner_feature_index; const double parent_gain = IS_LARGER ? larger_leaf_splits->gain : smaller_leaf_splits->gain; const double sum_gradients = IS_LARGER ? larger_leaf_splits->sum_of_gradients : smaller_leaf_splits->sum_of_gradients; const double sum_hessians = (IS_LARGER ? larger_leaf_splits->sum_of_hessians : smaller_leaf_splits->sum_of_hessians) + 2 * kEpsilon; const data_size_t num_data = IS_LARGER ? global_num_data_in_larger_leaf : global_num_data_in_smaller_leaf; const double parent_output = IS_LARGER ? larger_leaf_splits->leaf_value : smaller_leaf_splits->leaf_value; const unsigned int output_offset = IS_LARGER ? (task_index + num_tasks) : task_index; CUDASplitInfo* out = cuda_best_split_info + output_offset; CUDARandom* cuda_random = USE_RAND ? (IS_LARGER ? cuda_randoms + task_index * 2 + 1 : cuda_randoms + task_index * 2) : nullptr; if (is_feature_used_bytree[inner_feature_index]) { const hist_t* hist_ptr = (IS_LARGER ? larger_leaf_splits->hist_in_leaf : smaller_leaf_splits->hist_in_leaf) + task->hist_offset * 2; if (task->is_categorical) { FindBestSplitsForLeafKernelCategoricalInner( // input feature information hist_ptr, // input task information task, cuda_random, // input config parameter values lambda_l1, lambda_l2, path_smooth, min_data_in_leaf, min_sum_hessian_in_leaf, min_gain_to_split, cat_smooth, cat_l2, max_cat_threshold, min_data_per_group, // input parent node information parent_gain, sum_gradients, sum_hessians, num_data, parent_output, // output parameters out); } else { if (!task->reverse) { FindBestSplitsForLeafKernelInner( // input feature information hist_ptr, // input task information task, cuda_random, // input config parameter values lambda_l1, lambda_l2, path_smooth, min_data_in_leaf, min_sum_hessian_in_leaf, min_gain_to_split, // input parent node information parent_gain, sum_gradients, sum_hessians, num_data, parent_output, // output parameters out); } else { FindBestSplitsForLeafKernelInner( // input feature information hist_ptr, // input task information task, cuda_random, // input config parameter values lambda_l1, lambda_l2, path_smooth, min_data_in_leaf, min_sum_hessian_in_leaf, min_gain_to_split, // input parent node information parent_gain, sum_gradients, sum_hessians, num_data, parent_output, // output parameters out); } } } else { out->is_valid = false; } } template __global__ void FindBestSplitsDiscretizedForLeafKernel( // input feature information const int8_t* is_feature_used_bytree, // input task information const int num_tasks, const SplitFindTask* tasks, CUDARandom* cuda_randoms, // input leaf information const CUDALeafSplitsStruct* smaller_leaf_splits, const CUDALeafSplitsStruct* larger_leaf_splits, const uint8_t smaller_leaf_num_bits_in_histogram_bin, const uint8_t larger_leaf_num_bits_in_histogram_bin, // input config parameter values const data_size_t min_data_in_leaf, const double min_sum_hessian_in_leaf, const double min_gain_to_split, const double lambda_l1, const double lambda_l2, const double path_smooth, const double cat_smooth, const double cat_l2, const int max_cat_threshold, const int min_data_per_group, const int max_cat_to_onehot, // gradient scale const score_t* grad_scale, const score_t* hess_scale, // output CUDASplitInfo* cuda_best_split_info, // global num data in leaf const data_size_t global_num_data_in_smaller_leaf, const data_size_t global_num_data_in_larger_leaf) { const unsigned int task_index = blockIdx.x; const SplitFindTask* task = tasks + task_index; const int inner_feature_index = task->inner_feature_index; const double parent_gain = IS_LARGER ? larger_leaf_splits->gain : smaller_leaf_splits->gain; const int64_t sum_gradients_hessians = IS_LARGER ? larger_leaf_splits->sum_of_gradients_hessians : smaller_leaf_splits->sum_of_gradients_hessians; const data_size_t num_data = IS_LARGER ? global_num_data_in_larger_leaf : global_num_data_in_smaller_leaf; const double parent_output = IS_LARGER ? larger_leaf_splits->leaf_value : smaller_leaf_splits->leaf_value; const unsigned int output_offset = IS_LARGER ? (task_index + num_tasks) : task_index; CUDASplitInfo* out = cuda_best_split_info + output_offset; CUDARandom* cuda_random = USE_RAND ? (IS_LARGER ? cuda_randoms + task_index * 2 + 1 : cuda_randoms + task_index * 2) : nullptr; const bool use_16bit_bin = IS_LARGER ? (larger_leaf_num_bits_in_histogram_bin <= 16) : (smaller_leaf_num_bits_in_histogram_bin <= 16); if (is_feature_used_bytree[inner_feature_index]) { if (task->is_categorical) { __threadfence(); // ensure store issued before trap #if defined(USE_ROCM) __builtin_trap(); #else asm("trap;"); #endif } else { if (!task->reverse) { if (use_16bit_bin) { const int32_t* hist_ptr = reinterpret_cast(IS_LARGER ? larger_leaf_splits->hist_in_leaf : smaller_leaf_splits->hist_in_leaf) + task->hist_offset; FindBestSplitsDiscretizedForLeafKernelInner( // input feature information hist_ptr, // input task information task, cuda_random, // input config parameter values lambda_l1, lambda_l2, path_smooth, min_data_in_leaf, min_sum_hessian_in_leaf, min_gain_to_split, // input parent node information parent_gain, sum_gradients_hessians, num_data, parent_output, // gradient scale *grad_scale, *hess_scale, // output parameters out); } else { const int32_t* hist_ptr = reinterpret_cast(IS_LARGER ? larger_leaf_splits->hist_in_leaf : smaller_leaf_splits->hist_in_leaf) + task->hist_offset; FindBestSplitsDiscretizedForLeafKernelInner( // input feature information hist_ptr, // input task information task, cuda_random, // input config parameter values lambda_l1, lambda_l2, path_smooth, min_data_in_leaf, min_sum_hessian_in_leaf, min_gain_to_split, // input parent node information parent_gain, sum_gradients_hessians, num_data, parent_output, // gradient scale *grad_scale, *hess_scale, // output parameters out); } } else { if (use_16bit_bin) { const int32_t* hist_ptr = reinterpret_cast(IS_LARGER ? larger_leaf_splits->hist_in_leaf : smaller_leaf_splits->hist_in_leaf) + task->hist_offset; FindBestSplitsDiscretizedForLeafKernelInner( // input feature information hist_ptr, // input task information task, cuda_random, // input config parameter values lambda_l1, lambda_l2, path_smooth, min_data_in_leaf, min_sum_hessian_in_leaf, min_gain_to_split, // input parent node information parent_gain, sum_gradients_hessians, num_data, parent_output, // gradient scale *grad_scale, *hess_scale, // output parameters out); } else { const int32_t* hist_ptr = reinterpret_cast(IS_LARGER ? larger_leaf_splits->hist_in_leaf : smaller_leaf_splits->hist_in_leaf) + task->hist_offset; FindBestSplitsDiscretizedForLeafKernelInner( // input feature information hist_ptr, // input task information task, cuda_random, // input config parameter values lambda_l1, lambda_l2, path_smooth, min_data_in_leaf, min_sum_hessian_in_leaf, min_gain_to_split, // input parent node information parent_gain, sum_gradients_hessians, num_data, parent_output, // gradient scale *grad_scale, *hess_scale, // output parameters out); } } } } else { out->is_valid = false; } } template __device__ void FindBestSplitsForLeafKernelInner_GlobalMemory( // input feature information const hist_t* feature_hist_ptr, // input task information const SplitFindTask* task, CUDARandom* cuda_random, // input config parameter values const double lambda_l1, const double lambda_l2, const double path_smooth, const data_size_t min_data_in_leaf, const double min_sum_hessian_in_leaf, const double min_gain_to_split, // input parent node information const double parent_gain, const double sum_gradients, const double sum_hessians, const data_size_t num_data, const double parent_output, // output parameters CUDASplitInfo* cuda_best_split_info, // buffer hist_t* hist_grad_buffer_ptr, hist_t* hist_hess_buffer_ptr) { const double cnt_factor = num_data / sum_hessians; const double min_gain_shift = parent_gain + min_gain_to_split; cuda_best_split_info->is_valid = false; double local_gain = 0.0f; bool threshold_found = false; uint32_t threshold_value = 0; __shared__ int rand_threshold; if (USE_RAND && threadIdx.x == 0) { if (task->num_bin - 2 > 0) { rand_threshold = cuda_random->NextInt(0, task->num_bin - 2); } } __shared__ uint32_t best_thread_index; __shared__ double shared_double_buffer[WARPSIZE]; __shared__ bool shared_found_buffer[WARPSIZE]; __shared__ uint32_t shared_thread_index_buffer[WARPSIZE]; const unsigned int threadIdx_x = threadIdx.x; const uint32_t feature_num_bin_minus_offset = task->num_bin - task->mfb_offset; if (!REVERSE) { if (task->na_as_missing && task->mfb_offset == 1) { uint32_t bin_start = threadIdx_x > 0 ? threadIdx_x : blockDim.x; hist_t thread_sum_gradients = 0.0f; hist_t thread_sum_hessians = 0.0f; for (unsigned int bin = bin_start; bin < static_cast(task->num_bin); bin += blockDim.x) { const unsigned int bin_offset = (bin - 1) << 1; const hist_t grad = feature_hist_ptr[bin_offset]; const hist_t hess = feature_hist_ptr[bin_offset + 1]; hist_grad_buffer_ptr[bin] = grad; hist_hess_buffer_ptr[bin] = hess; thread_sum_gradients += grad; thread_sum_hessians += hess; } const hist_t sum_gradients_non_default = ShuffleReduceSum(thread_sum_gradients, shared_double_buffer, blockDim.x); __syncthreads(); const hist_t sum_hessians_non_default = ShuffleReduceSum(thread_sum_hessians, shared_double_buffer, blockDim.x); if (threadIdx_x == 0) { hist_grad_buffer_ptr[0] = sum_gradients - sum_gradients_non_default; hist_hess_buffer_ptr[0] = sum_hessians - sum_hessians_non_default; } } else { for (unsigned int bin = threadIdx_x; bin < feature_num_bin_minus_offset; bin += blockDim.x) { const bool skip_sum = (task->skip_default_bin && (bin + task->mfb_offset) == static_cast(task->default_bin)); if (!skip_sum) { const unsigned int bin_offset = bin << 1; hist_grad_buffer_ptr[bin] = feature_hist_ptr[bin_offset]; hist_hess_buffer_ptr[bin] = feature_hist_ptr[bin_offset + 1]; } else { hist_grad_buffer_ptr[bin] = 0.0f; hist_hess_buffer_ptr[bin] = 0.0f; } } } } else { for (unsigned int bin = threadIdx_x; bin < feature_num_bin_minus_offset; bin += blockDim.x) { const bool skip_sum = bin >= static_cast(task->na_as_missing) && (task->skip_default_bin && (task->num_bin - 1 - bin) == static_cast(task->default_bin)); if (!skip_sum) { const unsigned int read_index = feature_num_bin_minus_offset - 1 - bin; const unsigned int bin_offset = read_index << 1; hist_grad_buffer_ptr[bin] = feature_hist_ptr[bin_offset]; hist_hess_buffer_ptr[bin] = feature_hist_ptr[bin_offset + 1]; } else { hist_grad_buffer_ptr[bin] = 0.0f; hist_hess_buffer_ptr[bin] = 0.0f; } } } __syncthreads(); if (threadIdx_x == 0) { hist_hess_buffer_ptr[0] += kEpsilon; } local_gain = kMinScore; GlobalMemoryPrefixSum(hist_grad_buffer_ptr, static_cast(feature_num_bin_minus_offset)); __syncthreads(); GlobalMemoryPrefixSum(hist_hess_buffer_ptr, static_cast(feature_num_bin_minus_offset)); if (REVERSE) { for (unsigned int bin = threadIdx_x; bin < feature_num_bin_minus_offset; bin += blockDim.x) { const bool skip_sum = (bin >= static_cast(task->na_as_missing) && (task->skip_default_bin && (task->num_bin - 1 - bin) == static_cast(task->default_bin))); if (!skip_sum) { const double sum_right_gradient = hist_grad_buffer_ptr[bin]; const double sum_right_hessian = hist_hess_buffer_ptr[bin]; const data_size_t right_count = static_cast(__double2int_rn(sum_right_hessian * cnt_factor)); const double sum_left_gradient = sum_gradients - sum_right_gradient; const double sum_left_hessian = sum_hessians - sum_right_hessian; const data_size_t left_count = num_data - right_count; if (sum_left_hessian >= min_sum_hessian_in_leaf && left_count >= min_data_in_leaf && sum_right_hessian >= min_sum_hessian_in_leaf && right_count >= min_data_in_leaf && (!USE_RAND || static_cast(task->num_bin - 2 - bin) == rand_threshold)) { double current_gain = CUDALeafSplits::GetSplitGains( sum_left_gradient, sum_left_hessian, sum_right_gradient, sum_right_hessian, lambda_l1, lambda_l2, path_smooth, left_count, right_count, parent_output); // gain with split is worse than without split if (current_gain > min_gain_shift) { local_gain = current_gain - min_gain_shift; threshold_value = static_cast(task->num_bin - 2 - bin); threshold_found = true; } } } } } else { const uint32_t end = (task->na_as_missing && task->mfb_offset == 1) ? static_cast(task->num_bin - 2) : feature_num_bin_minus_offset - 2; for (unsigned int bin = threadIdx_x; bin <= end; bin += blockDim.x) { const bool skip_sum = (task->skip_default_bin && (bin + task->mfb_offset) == static_cast(task->default_bin)); if (!skip_sum) { const double sum_left_gradient = hist_grad_buffer_ptr[bin]; const double sum_left_hessian = hist_hess_buffer_ptr[bin]; const data_size_t left_count = static_cast(__double2int_rn(sum_left_hessian * cnt_factor)); const double sum_right_gradient = sum_gradients - sum_left_gradient; const double sum_right_hessian = sum_hessians - sum_left_hessian; const data_size_t right_count = num_data - left_count; if (sum_left_hessian >= min_sum_hessian_in_leaf && left_count >= min_data_in_leaf && sum_right_hessian >= min_sum_hessian_in_leaf && right_count >= min_data_in_leaf && (!USE_RAND || static_cast(bin + task->mfb_offset) == rand_threshold)) { double current_gain = CUDALeafSplits::GetSplitGains( sum_left_gradient, sum_left_hessian, sum_right_gradient, sum_right_hessian, lambda_l1, lambda_l2, path_smooth, left_count, right_count, parent_output); // gain with split is worse than without split if (current_gain > min_gain_shift) { local_gain = current_gain - min_gain_shift; threshold_value = (task->na_as_missing && task->mfb_offset == 1) ? bin : static_cast(bin + task->mfb_offset); threshold_found = true; } } } } } __syncthreads(); const uint32_t result = ReduceBestGain(local_gain, threshold_found, threadIdx_x, shared_double_buffer, shared_found_buffer, shared_thread_index_buffer); if (threadIdx_x == 0) { best_thread_index = result; } __syncthreads(); if (threshold_found && threadIdx_x == best_thread_index) { cuda_best_split_info->is_valid = true; cuda_best_split_info->threshold = threshold_value; cuda_best_split_info->gain = local_gain; cuda_best_split_info->default_left = task->assume_out_default_left; if (REVERSE) { const unsigned int best_bin = static_cast(task->num_bin - 2 - threshold_value); const double sum_right_gradient = hist_grad_buffer_ptr[best_bin]; const double sum_right_hessian = hist_hess_buffer_ptr[best_bin] - kEpsilon; const data_size_t right_count = static_cast(__double2int_rn(sum_right_hessian * cnt_factor)); const double sum_left_gradient = sum_gradients - sum_right_gradient; const double sum_left_hessian = sum_hessians - sum_right_hessian - kEpsilon; const data_size_t left_count = num_data - right_count; const double left_output = CUDALeafSplits::CalculateSplittedLeafOutput(sum_left_gradient, sum_left_hessian, lambda_l1, lambda_l2, path_smooth, left_count, parent_output); const double right_output = CUDALeafSplits::CalculateSplittedLeafOutput(sum_right_gradient, sum_right_hessian, lambda_l1, lambda_l2, path_smooth, right_count, parent_output); cuda_best_split_info->left_sum_gradients = sum_left_gradient; cuda_best_split_info->left_sum_hessians = sum_left_hessian; cuda_best_split_info->left_count = left_count; cuda_best_split_info->right_sum_gradients = sum_right_gradient; cuda_best_split_info->right_sum_hessians = sum_right_hessian; cuda_best_split_info->right_count = right_count; cuda_best_split_info->left_value = left_output; cuda_best_split_info->left_gain = CUDALeafSplits::GetLeafGainGivenOutput(sum_left_gradient, sum_left_hessian, lambda_l1, lambda_l2, left_output); cuda_best_split_info->right_value = right_output; cuda_best_split_info->right_gain = CUDALeafSplits::GetLeafGainGivenOutput(sum_right_gradient, sum_right_hessian, lambda_l1, lambda_l2, right_output); } else { const unsigned int best_bin = (task->na_as_missing && task->mfb_offset == 1) ? threshold_value : static_cast(threshold_value - task->mfb_offset); const double sum_left_gradient = hist_grad_buffer_ptr[best_bin]; const double sum_left_hessian = hist_hess_buffer_ptr[best_bin] - kEpsilon; const data_size_t left_count = static_cast(__double2int_rn(sum_left_hessian * cnt_factor)); const double sum_right_gradient = sum_gradients - sum_left_gradient; const double sum_right_hessian = sum_hessians - sum_left_hessian - kEpsilon; const data_size_t right_count = num_data - left_count; const double left_output = CUDALeafSplits::CalculateSplittedLeafOutput(sum_left_gradient, sum_left_hessian, lambda_l1, lambda_l2, path_smooth, left_count, parent_output); const double right_output = CUDALeafSplits::CalculateSplittedLeafOutput(sum_right_gradient, sum_right_hessian, lambda_l1, lambda_l2, path_smooth, right_count, parent_output); cuda_best_split_info->left_sum_gradients = sum_left_gradient; cuda_best_split_info->left_sum_hessians = sum_left_hessian; cuda_best_split_info->left_count = left_count; cuda_best_split_info->right_sum_gradients = sum_right_gradient; cuda_best_split_info->right_sum_hessians = sum_right_hessian; cuda_best_split_info->right_count = right_count; cuda_best_split_info->left_value = left_output; cuda_best_split_info->left_gain = CUDALeafSplits::GetLeafGainGivenOutput(sum_left_gradient, sum_left_hessian, lambda_l1, lambda_l2, left_output); cuda_best_split_info->right_value = right_output; cuda_best_split_info->right_gain = CUDALeafSplits::GetLeafGainGivenOutput(sum_right_gradient, sum_right_hessian, lambda_l1, lambda_l2, right_output); } } } template __device__ void FindBestSplitsForLeafKernelCategoricalInner_GlobalMemory( // input feature information const hist_t* feature_hist_ptr, // input task information const SplitFindTask* task, CUDARandom* cuda_random, // input config parameter values const double lambda_l1, const double lambda_l2, const double path_smooth, const data_size_t min_data_in_leaf, const double min_sum_hessian_in_leaf, const double min_gain_to_split, const double cat_smooth, const double cat_l2, const int max_cat_threshold, const int min_data_per_group, // input parent node information const double parent_gain, const double sum_gradients, const double sum_hessians, const data_size_t num_data, const double parent_output, // buffer hist_t* hist_grad_buffer_ptr, hist_t* hist_hess_buffer_ptr, hist_t* hist_stat_buffer_ptr, data_size_t* hist_index_buffer_ptr, // output parameters CUDASplitInfo* cuda_best_split_info) { __shared__ double shared_gain_buffer[WARPSIZE]; __shared__ bool shared_found_buffer[WARPSIZE]; __shared__ uint32_t shared_thread_index_buffer[WARPSIZE]; __shared__ uint32_t best_thread_index; const double cnt_factor = num_data / sum_hessians; const double min_gain_shift = parent_gain + min_gain_to_split; double l2 = lambda_l2; double local_gain = kMinScore; bool threshold_found = false; cuda_best_split_info->is_valid = false; __shared__ int rand_threshold; const int bin_start = 1 - task->mfb_offset; const int bin_end = task->num_bin - task->mfb_offset; int best_threshold = -1; const int threadIdx_x = static_cast(threadIdx.x); if (task->is_one_hot) { if (USE_RAND && threadIdx.x == 0) { rand_threshold = 0; if (bin_end > bin_start) { rand_threshold = cuda_random->NextInt(bin_start, bin_end); } } __syncthreads(); for (int bin = bin_start + threadIdx_x; bin < bin_end; bin += static_cast(blockDim.x)) { const int bin_offset = (bin << 1); const hist_t grad = feature_hist_ptr[bin_offset]; const hist_t hess = feature_hist_ptr[bin_offset + 1]; data_size_t cnt = static_cast(__double2int_rn(hess * cnt_factor)); if (cnt >= min_data_in_leaf && hess >= min_sum_hessian_in_leaf) { const data_size_t other_count = num_data - cnt; if (other_count >= min_data_in_leaf) { const double sum_other_hessian = sum_hessians - hess - kEpsilon; if (sum_other_hessian >= min_sum_hessian_in_leaf && (!USE_RAND || bin == rand_threshold)) { const double sum_other_gradient = sum_gradients - grad; double current_gain = CUDALeafSplits::GetSplitGains( sum_other_gradient, sum_other_hessian, grad, hess + kEpsilon, lambda_l1, l2, path_smooth, other_count, cnt, parent_output); if (current_gain > min_gain_shift) { best_threshold = bin; local_gain = current_gain - min_gain_shift; threshold_found = true; } } } } } __syncthreads(); const uint32_t result = ReduceBestGain(local_gain, threshold_found, threadIdx_x, shared_gain_buffer, shared_found_buffer, shared_thread_index_buffer); if (threadIdx_x == 0) { best_thread_index = result; } __syncthreads(); if (threshold_found && threadIdx_x == best_thread_index) { cuda_best_split_info->is_valid = true; cuda_best_split_info->num_cat_threshold = 1; cuda_best_split_info->cat_threshold = new uint32_t[1]; *(cuda_best_split_info->cat_threshold) = static_cast(best_threshold); cuda_best_split_info->default_left = false; const int bin_offset = (best_threshold << 1); const hist_t sum_left_gradient = feature_hist_ptr[bin_offset]; const hist_t sum_left_hessian = feature_hist_ptr[bin_offset + 1]; const data_size_t left_count = static_cast(__double2int_rn(sum_left_hessian * cnt_factor)); const double sum_right_gradient = sum_gradients - sum_left_gradient; const double sum_right_hessian = sum_hessians - sum_left_hessian; const data_size_t right_count = static_cast(__double2int_rn(sum_right_hessian * cnt_factor)); const double left_output = CUDALeafSplits::CalculateSplittedLeafOutput(sum_left_gradient, sum_left_hessian, lambda_l1, l2, path_smooth, left_count, parent_output); const double right_output = CUDALeafSplits::CalculateSplittedLeafOutput(sum_right_gradient, sum_right_hessian, lambda_l1, l2, path_smooth, right_count, parent_output); cuda_best_split_info->left_sum_gradients = sum_left_gradient; cuda_best_split_info->left_sum_hessians = sum_left_hessian; cuda_best_split_info->left_count = left_count; cuda_best_split_info->right_sum_gradients = sum_right_gradient; cuda_best_split_info->right_sum_hessians = sum_right_hessian; cuda_best_split_info->right_count = right_count; cuda_best_split_info->left_value = left_output; cuda_best_split_info->left_gain = CUDALeafSplits::GetLeafGainGivenOutput(sum_left_gradient, sum_left_hessian, lambda_l1, l2, left_output); cuda_best_split_info->right_value = right_output; cuda_best_split_info->right_gain = CUDALeafSplits::GetLeafGainGivenOutput(sum_right_gradient, sum_right_hessian, lambda_l1, l2, right_output); } } else { __shared__ uint16_t shared_mem_buffer_uint16[WARPSIZE]; __shared__ int used_bin; l2 += cat_l2; uint16_t is_valid_bin = 0; int best_dir = 0; double best_sum_left_gradient = 0.0f; double best_sum_left_hessian = 0.0f; for (int bin = 0; bin < bin_end; bin += static_cast(blockDim.x)) { if (bin >= bin_start) { const int bin_offset = (bin << 1); const double hess = feature_hist_ptr[bin_offset + 1]; if (__double2int_rn(hess * cnt_factor) >= cat_smooth) { const double grad = feature_hist_ptr[bin_offset]; hist_stat_buffer_ptr[bin] = grad / (hess + cat_smooth); hist_index_buffer_ptr[bin] = threadIdx_x; is_valid_bin = 1; } else { hist_stat_buffer_ptr[bin] = kMaxScore; hist_index_buffer_ptr[bin] = -1; } } } __syncthreads(); const int local_used_bin = ShuffleReduceSum(is_valid_bin, shared_mem_buffer_uint16, blockDim.x); if (threadIdx_x == 0) { used_bin = local_used_bin; } __syncthreads(); BitonicArgSortDevice( hist_stat_buffer_ptr, hist_index_buffer_ptr, task->num_bin - task->mfb_offset); const int max_num_cat = min(max_cat_threshold, (used_bin + 1) / 2); if (USE_RAND) { rand_threshold = 0; const int max_threshold = max(min(max_num_cat, used_bin) - 1, 0); if (max_threshold > 0) { rand_threshold = cuda_random->NextInt(0, max_threshold); } } __syncthreads(); // left to right for (int bin = static_cast(threadIdx_x); bin < used_bin && bin < max_num_cat; bin += static_cast(blockDim.x)) { const int bin_offset = (hist_index_buffer_ptr[bin] << 1); hist_grad_buffer_ptr[bin] = feature_hist_ptr[bin_offset]; hist_hess_buffer_ptr[bin] = feature_hist_ptr[bin_offset + 1]; } if (threadIdx_x == 0) { hist_hess_buffer_ptr[0] += kEpsilon; } __syncthreads(); GlobalMemoryPrefixSum(hist_grad_buffer_ptr, static_cast(bin_end)); __syncthreads(); GlobalMemoryPrefixSum(hist_hess_buffer_ptr, static_cast(bin_end)); for (int bin = static_cast(threadIdx_x); bin < used_bin && bin < max_num_cat; bin += static_cast(blockDim.x)) { const double sum_left_gradient = hist_grad_buffer_ptr[bin]; const double sum_left_hessian = hist_hess_buffer_ptr[bin]; const data_size_t left_count = static_cast(__double2int_rn(sum_left_hessian * cnt_factor)); const double sum_right_gradient = sum_gradients - sum_left_gradient; const double sum_right_hessian = sum_hessians - sum_left_hessian; const data_size_t right_count = num_data - left_count; if (sum_left_hessian >= min_sum_hessian_in_leaf && left_count >= min_data_in_leaf && sum_right_hessian >= min_sum_hessian_in_leaf && right_count >= min_data_in_leaf) { double current_gain = CUDALeafSplits::GetSplitGains( sum_left_gradient, sum_left_hessian, sum_right_gradient, sum_right_hessian, lambda_l1, l2, path_smooth, left_count, right_count, parent_output); // gain with split is worse than without split if (current_gain > min_gain_shift) { local_gain = current_gain - min_gain_shift; threshold_found = true; best_dir = 1; best_sum_left_gradient = sum_left_gradient; best_sum_left_hessian = sum_left_hessian; best_threshold = bin; } } } __syncthreads(); // right to left for (int bin = static_cast(threadIdx_x); bin < used_bin && bin < max_num_cat; bin += static_cast(blockDim.x)) { const int bin_offset = (hist_index_buffer_ptr[used_bin - 1 - bin] << 1); hist_grad_buffer_ptr[bin] = feature_hist_ptr[bin_offset]; hist_hess_buffer_ptr[bin] = feature_hist_ptr[bin_offset + 1]; } if (threadIdx_x == 0) { hist_hess_buffer_ptr[0] += kEpsilon; } __syncthreads(); GlobalMemoryPrefixSum(hist_grad_buffer_ptr, static_cast(bin_end)); __syncthreads(); GlobalMemoryPrefixSum(hist_hess_buffer_ptr, static_cast(bin_end)); for (int bin = static_cast(threadIdx_x); bin < used_bin && bin < max_num_cat; bin += static_cast(blockDim.x)) { const double sum_left_gradient = hist_grad_buffer_ptr[bin]; const double sum_left_hessian = hist_hess_buffer_ptr[bin]; const data_size_t left_count = static_cast(__double2int_rn(sum_left_hessian * cnt_factor)); const double sum_right_gradient = sum_gradients - sum_left_gradient; const double sum_right_hessian = sum_hessians - sum_left_hessian; const data_size_t right_count = num_data - left_count; if (sum_left_hessian >= min_sum_hessian_in_leaf && left_count >= min_data_in_leaf && sum_right_hessian >= min_sum_hessian_in_leaf && right_count >= min_data_in_leaf) { double current_gain = CUDALeafSplits::GetSplitGains( sum_left_gradient, sum_left_hessian, sum_right_gradient, sum_right_hessian, lambda_l1, l2, path_smooth, left_count, right_count, parent_output); // gain with split is worse than without split if (current_gain > min_gain_shift) { local_gain = current_gain - min_gain_shift; threshold_found = true; best_dir = -1; best_sum_left_gradient = sum_left_gradient; best_sum_left_hessian = sum_left_hessian; best_threshold = bin; } } } __syncthreads(); const uint32_t result = ReduceBestGain(local_gain, threshold_found, threadIdx_x, shared_gain_buffer, shared_found_buffer, shared_thread_index_buffer); if (threadIdx_x == 0) { best_thread_index = result; } __syncthreads(); if (threshold_found && threadIdx_x == best_thread_index) { cuda_best_split_info->is_valid = true; cuda_best_split_info->num_cat_threshold = best_threshold + 1; cuda_best_split_info->cat_threshold = new uint32_t[best_threshold + 1]; cuda_best_split_info->gain = local_gain; if (best_dir == 1) { for (int i = 0; i < best_threshold + 1; ++i) { (cuda_best_split_info->cat_threshold)[i] = hist_index_buffer_ptr[i] + task->mfb_offset; } } else { for (int i = 0; i < best_threshold + 1; ++i) { (cuda_best_split_info->cat_threshold)[i] = hist_index_buffer_ptr[used_bin - 1 - i] + task->mfb_offset; } } cuda_best_split_info->default_left = false; const hist_t sum_left_gradient = best_sum_left_gradient; const hist_t sum_left_hessian = best_sum_left_hessian; const data_size_t left_count = static_cast(__double2int_rn(sum_left_hessian * cnt_factor)); const double sum_right_gradient = sum_gradients - sum_left_gradient; const double sum_right_hessian = sum_hessians - sum_left_hessian; const data_size_t right_count = static_cast(__double2int_rn(sum_right_hessian * cnt_factor)); const double left_output = CUDALeafSplits::CalculateSplittedLeafOutput(sum_left_gradient, sum_left_hessian, lambda_l1, l2, path_smooth, left_count, parent_output); const double right_output = CUDALeafSplits::CalculateSplittedLeafOutput(sum_right_gradient, sum_right_hessian, lambda_l1, l2, path_smooth, right_count, parent_output); cuda_best_split_info->left_sum_gradients = sum_left_gradient; cuda_best_split_info->left_sum_hessians = sum_left_hessian; cuda_best_split_info->left_count = left_count; cuda_best_split_info->right_sum_gradients = sum_right_gradient; cuda_best_split_info->right_sum_hessians = sum_right_hessian; cuda_best_split_info->right_count = right_count; cuda_best_split_info->left_value = left_output; cuda_best_split_info->left_gain = CUDALeafSplits::GetLeafGainGivenOutput(sum_left_gradient, sum_left_hessian, lambda_l1, l2, left_output); cuda_best_split_info->right_value = right_output; cuda_best_split_info->right_gain = CUDALeafSplits::GetLeafGainGivenOutput(sum_right_gradient, sum_right_hessian, lambda_l1, l2, right_output); } } } template __global__ void FindBestSplitsForLeafKernel_GlobalMemory( // input feature information const int8_t* is_feature_used_bytree, // input task information const int num_tasks, const SplitFindTask* tasks, CUDARandom* cuda_randoms, // input leaf information const CUDALeafSplitsStruct* smaller_leaf_splits, const CUDALeafSplitsStruct* larger_leaf_splits, // input config parameter values const data_size_t min_data_in_leaf, const double min_sum_hessian_in_leaf, const double min_gain_to_split, const double lambda_l1, const double lambda_l2, const double path_smooth, const double cat_smooth, const double cat_l2, const int max_cat_threshold, const int min_data_per_group, // output CUDASplitInfo* cuda_best_split_info, // global num data in leaf const data_size_t global_num_data_in_smaller_leaf, const data_size_t global_num_data_in_larger_leaf, // buffer hist_t* feature_hist_grad_buffer, hist_t* feature_hist_hess_buffer, hist_t* feature_hist_stat_buffer, data_size_t* feature_hist_index_buffer) { const unsigned int task_index = blockIdx.x; const SplitFindTask* task = tasks + task_index; const double parent_gain = IS_LARGER ? larger_leaf_splits->gain : smaller_leaf_splits->gain; const double sum_gradients = IS_LARGER ? larger_leaf_splits->sum_of_gradients : smaller_leaf_splits->sum_of_gradients; const double sum_hessians = (IS_LARGER ? larger_leaf_splits->sum_of_hessians : smaller_leaf_splits->sum_of_hessians) + 2 * kEpsilon; const data_size_t num_data = IS_LARGER ? global_num_data_in_larger_leaf : global_num_data_in_smaller_leaf; const double parent_output = IS_LARGER ? larger_leaf_splits->leaf_value : smaller_leaf_splits->leaf_value; const unsigned int output_offset = IS_LARGER ? (task_index + num_tasks) : task_index; CUDASplitInfo* out = cuda_best_split_info + output_offset; CUDARandom* cuda_random = USE_RAND ? (IS_LARGER ? cuda_randoms + task_index * 2 + 1: cuda_randoms + task_index * 2) : nullptr; if (is_feature_used_bytree[task->inner_feature_index]) { const uint32_t hist_offset = task->hist_offset; const hist_t* hist_ptr = (IS_LARGER ? larger_leaf_splits->hist_in_leaf : smaller_leaf_splits->hist_in_leaf) + hist_offset * 2; hist_t* hist_grad_buffer_ptr = feature_hist_grad_buffer + hist_offset * 2; hist_t* hist_hess_buffer_ptr = feature_hist_hess_buffer + hist_offset * 2; hist_t* hist_stat_buffer_ptr = feature_hist_stat_buffer + hist_offset * 2; data_size_t* hist_index_buffer_ptr = feature_hist_index_buffer + hist_offset * 2; if (task->is_categorical) { FindBestSplitsForLeafKernelCategoricalInner_GlobalMemory( // input feature information hist_ptr, // input task information task, cuda_random, // input config parameter values lambda_l1, lambda_l2, path_smooth, min_data_in_leaf, min_sum_hessian_in_leaf, min_gain_to_split, cat_smooth, cat_l2, max_cat_threshold, min_data_per_group, // input parent node information parent_gain, sum_gradients, sum_hessians, num_data, parent_output, // buffer hist_grad_buffer_ptr, hist_hess_buffer_ptr, hist_stat_buffer_ptr, hist_index_buffer_ptr, // output parameters out); } else { if (!task->reverse) { FindBestSplitsForLeafKernelInner_GlobalMemory( // input feature information hist_ptr, // input task information task, cuda_random, // input config parameter values lambda_l1, lambda_l2, path_smooth, min_data_in_leaf, min_sum_hessian_in_leaf, min_gain_to_split, // input parent node information parent_gain, sum_gradients, sum_hessians, num_data, parent_output, // output parameters out, // buffer hist_grad_buffer_ptr, hist_hess_buffer_ptr); } else { FindBestSplitsForLeafKernelInner_GlobalMemory( // input feature information hist_ptr, // input task information task, cuda_random, // input config parameter values lambda_l1, lambda_l2, path_smooth, min_data_in_leaf, min_sum_hessian_in_leaf, min_gain_to_split, // input parent node information parent_gain, sum_gradients, sum_hessians, num_data, parent_output, // output parameters out, // buffer hist_grad_buffer_ptr, hist_hess_buffer_ptr); } } } else { out->is_valid = false; } } #define LaunchFindBestSplitsForLeafKernel_PARAMS \ const CUDALeafSplitsStruct* smaller_leaf_splits, \ const CUDALeafSplitsStruct* larger_leaf_splits, \ const int smaller_leaf_index, \ const int larger_leaf_index, \ const bool is_smaller_leaf_valid, \ const bool is_larger_leaf_valid, \ const data_size_t global_num_data_in_smaller_leaf, \ const data_size_t global_num_data_in_larger_leaf #define LaunchFindBestSplitsForLeafKernel_ARGS \ smaller_leaf_splits, \ larger_leaf_splits, \ smaller_leaf_index, \ larger_leaf_index, \ is_smaller_leaf_valid, \ is_larger_leaf_valid, \ global_num_data_in_smaller_leaf, \ global_num_data_in_larger_leaf #define FindBestSplitsForLeafKernel_ARGS \ num_tasks_, \ cuda_split_find_tasks_.RawData(), \ cuda_randoms_.RawData(), \ smaller_leaf_splits, \ larger_leaf_splits, \ min_data_in_leaf_, \ min_sum_hessian_in_leaf_, \ min_gain_to_split_, \ lambda_l1_, \ lambda_l2_, \ path_smooth_, \ cat_smooth_, \ cat_l2_, \ max_cat_threshold_, \ min_data_per_group_, \ cuda_best_split_info_.RawData(), \ global_num_data_in_smaller_leaf, \ global_num_data_in_larger_leaf #define GlobalMemory_Buffer_ARGS \ cuda_feature_hist_grad_buffer_.RawData(), \ cuda_feature_hist_hess_buffer_.RawData(), \ cuda_feature_hist_stat_buffer_.RawData(), \ cuda_feature_hist_index_buffer_.RawData() void CUDABestSplitFinder::LaunchFindBestSplitsForLeafKernel(LaunchFindBestSplitsForLeafKernel_PARAMS) { if (!is_smaller_leaf_valid && !is_larger_leaf_valid) { return; } if (!extra_trees_) { LaunchFindBestSplitsForLeafKernelInner0(LaunchFindBestSplitsForLeafKernel_ARGS); } else { LaunchFindBestSplitsForLeafKernelInner0(LaunchFindBestSplitsForLeafKernel_ARGS); } } template void CUDABestSplitFinder::LaunchFindBestSplitsForLeafKernelInner0(LaunchFindBestSplitsForLeafKernel_PARAMS) { if (lambda_l1_ <= 0.0f) { LaunchFindBestSplitsForLeafKernelInner1(LaunchFindBestSplitsForLeafKernel_ARGS); } else { LaunchFindBestSplitsForLeafKernelInner1(LaunchFindBestSplitsForLeafKernel_ARGS); } } template void CUDABestSplitFinder::LaunchFindBestSplitsForLeafKernelInner1(LaunchFindBestSplitsForLeafKernel_PARAMS) { if (!use_smoothing_) { LaunchFindBestSplitsForLeafKernelInner2(LaunchFindBestSplitsForLeafKernel_ARGS); } else { LaunchFindBestSplitsForLeafKernelInner2(LaunchFindBestSplitsForLeafKernel_ARGS); } } template void CUDABestSplitFinder::LaunchFindBestSplitsForLeafKernelInner2(LaunchFindBestSplitsForLeafKernel_PARAMS) { const int8_t* is_feature_used_by_smaller_node = cuda_is_feature_used_bytree_.RawData(); const int8_t* is_feature_used_by_larger_node = cuda_is_feature_used_bytree_.RawData(); if (select_features_by_node_) { is_feature_used_by_smaller_node = is_feature_used_by_smaller_node_.RawData(); is_feature_used_by_larger_node = is_feature_used_by_larger_node_.RawData(); } if (!use_global_memory_) { if (is_smaller_leaf_valid) { FindBestSplitsForLeafKernel <<>> (is_feature_used_by_smaller_node, FindBestSplitsForLeafKernel_ARGS); } SynchronizeCUDADevice(__FILE__, __LINE__); if (is_larger_leaf_valid) { FindBestSplitsForLeafKernel <<>> (is_feature_used_by_larger_node, FindBestSplitsForLeafKernel_ARGS); } } else { if (is_smaller_leaf_valid) { FindBestSplitsForLeafKernel_GlobalMemory <<>> (is_feature_used_by_smaller_node, FindBestSplitsForLeafKernel_ARGS, GlobalMemory_Buffer_ARGS); } SynchronizeCUDADevice(__FILE__, __LINE__); if (is_larger_leaf_valid) { FindBestSplitsForLeafKernel_GlobalMemory <<>> (is_feature_used_by_larger_node, FindBestSplitsForLeafKernel_ARGS, GlobalMemory_Buffer_ARGS); } } } #undef LaunchFindBestSplitsForLeafKernel_PARAMS #undef FindBestSplitsForLeafKernel_ARGS #undef GlobalMemory_Buffer_ARGS #define LaunchFindBestSplitsDiscretizedForLeafKernel_PARAMS \ const CUDALeafSplitsStruct* smaller_leaf_splits, \ const CUDALeafSplitsStruct* larger_leaf_splits, \ const int smaller_leaf_index, \ const int larger_leaf_index, \ const bool is_smaller_leaf_valid, \ const bool is_larger_leaf_valid, \ const score_t* grad_scale, \ const score_t* hess_scale, \ const uint8_t smaller_num_bits_in_histogram_bins, \ const uint8_t larger_num_bits_in_histogram_bins, \ const data_size_t global_num_data_in_smaller_leaf, \ const data_size_t global_num_data_in_larger_leaf #define LaunchFindBestSplitsDiscretizedForLeafKernel_ARGS \ smaller_leaf_splits, \ larger_leaf_splits, \ smaller_leaf_index, \ larger_leaf_index, \ is_smaller_leaf_valid, \ is_larger_leaf_valid, \ grad_scale, \ hess_scale, \ smaller_num_bits_in_histogram_bins, \ larger_num_bits_in_histogram_bins, \ global_num_data_in_smaller_leaf, \ global_num_data_in_larger_leaf #define FindBestSplitsDiscretizedForLeafKernel_ARGS \ cuda_is_feature_used_bytree_.RawData(), \ num_tasks_, \ cuda_split_find_tasks_.RawData(), \ cuda_randoms_.RawData(), \ smaller_leaf_splits, \ larger_leaf_splits, \ smaller_num_bits_in_histogram_bins, \ larger_num_bits_in_histogram_bins, \ min_data_in_leaf_, \ min_sum_hessian_in_leaf_, \ min_gain_to_split_, \ lambda_l1_, \ lambda_l2_, \ path_smooth_, \ cat_smooth_, \ cat_l2_, \ max_cat_threshold_, \ min_data_per_group_, \ max_cat_to_onehot_, \ grad_scale, \ hess_scale, \ cuda_best_split_info_.RawData(), \ global_num_data_in_smaller_leaf, \ global_num_data_in_larger_leaf void CUDABestSplitFinder::LaunchFindBestSplitsDiscretizedForLeafKernel(LaunchFindBestSplitsDiscretizedForLeafKernel_PARAMS) { if (!is_smaller_leaf_valid && !is_larger_leaf_valid) { return; } if (!extra_trees_) { LaunchFindBestSplitsDiscretizedForLeafKernelInner0(LaunchFindBestSplitsDiscretizedForLeafKernel_ARGS); } else { LaunchFindBestSplitsDiscretizedForLeafKernelInner0(LaunchFindBestSplitsDiscretizedForLeafKernel_ARGS); } } template void CUDABestSplitFinder::LaunchFindBestSplitsDiscretizedForLeafKernelInner0(LaunchFindBestSplitsDiscretizedForLeafKernel_PARAMS) { if (lambda_l1_ <= 0.0f) { LaunchFindBestSplitsDiscretizedForLeafKernelInner1(LaunchFindBestSplitsDiscretizedForLeafKernel_ARGS); } else { LaunchFindBestSplitsDiscretizedForLeafKernelInner1(LaunchFindBestSplitsDiscretizedForLeafKernel_ARGS); } } template void CUDABestSplitFinder::LaunchFindBestSplitsDiscretizedForLeafKernelInner1(LaunchFindBestSplitsDiscretizedForLeafKernel_PARAMS) { if (!use_smoothing_) { LaunchFindBestSplitsDiscretizedForLeafKernelInner2(LaunchFindBestSplitsDiscretizedForLeafKernel_ARGS); } else { LaunchFindBestSplitsDiscretizedForLeafKernelInner2(LaunchFindBestSplitsDiscretizedForLeafKernel_ARGS); } } template void CUDABestSplitFinder::LaunchFindBestSplitsDiscretizedForLeafKernelInner2(LaunchFindBestSplitsDiscretizedForLeafKernel_PARAMS) { if (!use_global_memory_) { if (is_smaller_leaf_valid) { FindBestSplitsDiscretizedForLeafKernel <<>> (FindBestSplitsDiscretizedForLeafKernel_ARGS); } SynchronizeCUDADevice(__FILE__, __LINE__); if (is_larger_leaf_valid) { FindBestSplitsDiscretizedForLeafKernel <<>> (FindBestSplitsDiscretizedForLeafKernel_ARGS); } } else { // TODO(shiyu1994) } } #undef LaunchFindBestSplitsDiscretizedForLeafKernel_PARAMS #undef LaunchFindBestSplitsDiscretizedForLeafKernel_ARGS #undef FindBestSplitsDiscretizedForLeafKernel_ARGS __device__ void ReduceBestSplit(bool* found, double* gain, uint32_t* shared_read_index, uint32_t num_features_aligned) { const uint32_t threadIdx_x = threadIdx.x; for (unsigned int s = 1; s < num_features_aligned; s <<= 1) { if (threadIdx_x % (2 * s) == 0 && (threadIdx_x + s) < num_features_aligned) { const uint32_t pos_to_compare = threadIdx_x + s; if ((!found[threadIdx_x] && found[pos_to_compare]) || (found[threadIdx_x] && found[pos_to_compare] && gain[threadIdx_x] < gain[pos_to_compare])) { found[threadIdx_x] = found[pos_to_compare]; gain[threadIdx_x] = gain[pos_to_compare]; shared_read_index[threadIdx_x] = shared_read_index[pos_to_compare]; } } __syncthreads(); } } __global__ void SyncBestSplitForLeafKernel(const int smaller_leaf_index, const int larger_leaf_index, CUDASplitInfo* cuda_leaf_best_split_info, // input parameters const SplitFindTask* tasks, const CUDASplitInfo* cuda_best_split_info, const int num_tasks, const int num_tasks_aligned, const int num_blocks_per_leaf, const bool larger_only, const int num_leaves) { __shared__ double shared_gain_buffer[WARPSIZE]; __shared__ bool shared_found_buffer[WARPSIZE]; __shared__ uint32_t shared_thread_index_buffer[WARPSIZE]; const uint32_t threadIdx_x = threadIdx.x; const uint32_t blockIdx_x = blockIdx.x; bool best_found = false; double best_gain = kMinScore; uint32_t shared_read_index = 0; const bool is_smaller = (blockIdx_x < static_cast(num_blocks_per_leaf) && !larger_only); const uint32_t leaf_block_index = (is_smaller || larger_only) ? blockIdx_x : (blockIdx_x - static_cast(num_blocks_per_leaf)); const int task_index = static_cast(leaf_block_index * blockDim.x + threadIdx_x); const uint32_t read_index = is_smaller ? static_cast(task_index) : static_cast(task_index + num_tasks); if (task_index < num_tasks) { best_found = cuda_best_split_info[read_index].is_valid; best_gain = cuda_best_split_info[read_index].gain; shared_read_index = read_index; } else { best_found = false; } __syncthreads(); const uint32_t best_read_index = ReduceBestGain(best_gain, best_found, shared_read_index, shared_gain_buffer, shared_found_buffer, shared_thread_index_buffer); if (threadIdx.x == 0) { const int leaf_index_ref = is_smaller ? smaller_leaf_index : larger_leaf_index; const unsigned buffer_write_pos = static_cast(leaf_index_ref) + leaf_block_index * num_leaves; CUDASplitInfo* cuda_split_info = cuda_leaf_best_split_info + buffer_write_pos; const CUDASplitInfo* best_split_info = cuda_best_split_info + best_read_index; if (best_split_info->is_valid) { *cuda_split_info = *best_split_info; cuda_split_info->inner_feature_index = is_smaller ? tasks[best_read_index].inner_feature_index : tasks[static_cast(best_read_index) - num_tasks].inner_feature_index; cuda_split_info->is_valid = true; } else { cuda_split_info->gain = kMinScore; cuda_split_info->is_valid = false; } } } __global__ void SyncBestSplitForLeafKernelAllBlocks( const int smaller_leaf_index, const int larger_leaf_index, const unsigned int num_blocks_per_leaf, const int num_leaves, CUDASplitInfo* cuda_leaf_best_split_info, const bool larger_only) { if (!larger_only) { if (blockIdx.x == 0) { CUDASplitInfo* smaller_leaf_split_info = cuda_leaf_best_split_info + smaller_leaf_index; for (unsigned int block_index = 1; block_index < num_blocks_per_leaf; ++block_index) { const unsigned int leaf_read_pos = static_cast(smaller_leaf_index) + block_index * static_cast(num_leaves); const CUDASplitInfo* other_split_info = cuda_leaf_best_split_info + leaf_read_pos; if ((other_split_info->is_valid && smaller_leaf_split_info->is_valid && other_split_info->gain > smaller_leaf_split_info->gain) || (!smaller_leaf_split_info->is_valid && other_split_info->is_valid)) { *smaller_leaf_split_info = *other_split_info; } } } } if (larger_leaf_index >= 0) { if (blockIdx.x == 1 || larger_only) { CUDASplitInfo* larger_leaf_split_info = cuda_leaf_best_split_info + larger_leaf_index; for (unsigned int block_index = 1; block_index < num_blocks_per_leaf; ++block_index) { const unsigned int leaf_read_pos = static_cast(larger_leaf_index) + block_index * static_cast(num_leaves); const CUDASplitInfo* other_split_info = cuda_leaf_best_split_info + leaf_read_pos; if ((other_split_info->is_valid && larger_leaf_split_info->is_valid && other_split_info->gain > larger_leaf_split_info->gain) || (!larger_leaf_split_info->is_valid && other_split_info->is_valid)) { *larger_leaf_split_info = *other_split_info; } } } } } __global__ void SetInvalidLeafSplitInfoKernel( CUDASplitInfo* cuda_leaf_best_split_info, const bool is_smaller_leaf_valid, const bool is_larger_leaf_valid, const int smaller_leaf_index, const int larger_leaf_index) { if (!is_smaller_leaf_valid) { cuda_leaf_best_split_info[smaller_leaf_index].is_valid = false; } if (!is_larger_leaf_valid && larger_leaf_index >= 0) { cuda_leaf_best_split_info[larger_leaf_index].is_valid = false; } } void CUDABestSplitFinder::LaunchSyncBestSplitForLeafKernel( const int host_smaller_leaf_index, const int host_larger_leaf_index, const bool is_smaller_leaf_valid, const bool is_larger_leaf_valid) { if (!is_smaller_leaf_valid || !is_larger_leaf_valid) { SetInvalidLeafSplitInfoKernel<<<1, 1>>>( cuda_leaf_best_split_info_.RawData(), is_smaller_leaf_valid, is_larger_leaf_valid, host_smaller_leaf_index, host_larger_leaf_index); } if (!is_smaller_leaf_valid && !is_larger_leaf_valid) { return; } int num_tasks = num_tasks_; int num_tasks_aligned = 1; num_tasks -= 1; while (num_tasks > 0) { num_tasks_aligned <<= 1; num_tasks >>= 1; } const int num_blocks_per_leaf = (num_tasks_ + NUM_TASKS_PER_SYNC_BLOCK - 1) / NUM_TASKS_PER_SYNC_BLOCK; if (host_larger_leaf_index >= 0 && is_smaller_leaf_valid && is_larger_leaf_valid) { SyncBestSplitForLeafKernel<<>>( host_smaller_leaf_index, host_larger_leaf_index, cuda_leaf_best_split_info_.RawData(), cuda_split_find_tasks_.RawData(), cuda_best_split_info_.RawData(), num_tasks_, num_tasks_aligned, num_blocks_per_leaf, false, num_leaves_); if (num_blocks_per_leaf > 1) { SyncBestSplitForLeafKernelAllBlocks<<<1, 1, 0, cuda_streams_[0]>>>( host_smaller_leaf_index, host_larger_leaf_index, num_blocks_per_leaf, num_leaves_, cuda_leaf_best_split_info_.RawData(), false); } SynchronizeCUDADevice(__FILE__, __LINE__); SyncBestSplitForLeafKernel<<>>( host_smaller_leaf_index, host_larger_leaf_index, cuda_leaf_best_split_info_.RawData(), cuda_split_find_tasks_.RawData(), cuda_best_split_info_.RawData(), num_tasks_, num_tasks_aligned, num_blocks_per_leaf, true, num_leaves_); if (num_blocks_per_leaf > 1) { SyncBestSplitForLeafKernelAllBlocks<<<1, 1, 0, cuda_streams_[1]>>>( host_smaller_leaf_index, host_larger_leaf_index, num_blocks_per_leaf, num_leaves_, cuda_leaf_best_split_info_.RawData(), true); } } else { const bool larger_only = (!is_smaller_leaf_valid && is_larger_leaf_valid); SyncBestSplitForLeafKernel<<>>( host_smaller_leaf_index, host_larger_leaf_index, cuda_leaf_best_split_info_.RawData(), cuda_split_find_tasks_.RawData(), cuda_best_split_info_.RawData(), num_tasks_, num_tasks_aligned, num_blocks_per_leaf, larger_only, num_leaves_); if (num_blocks_per_leaf > 1) { SynchronizeCUDADevice(__FILE__, __LINE__); SyncBestSplitForLeafKernelAllBlocks<<<1, 1>>>( host_smaller_leaf_index, host_larger_leaf_index, num_blocks_per_leaf, num_leaves_, cuda_leaf_best_split_info_.RawData(), larger_only); } } } __global__ void FindBestFromAllSplitsKernel(const int cur_num_leaves, CUDASplitInfo* cuda_leaf_best_split_info, int* cuda_best_split_info_buffer) { __shared__ double gain_shared_buffer[WARPSIZE]; __shared__ int leaf_index_shared_buffer[WARPSIZE]; double thread_best_gain = kMinScore; int thread_best_leaf_index = -1; const int threadIdx_x = static_cast(threadIdx.x); for (int leaf_index = threadIdx_x; leaf_index < cur_num_leaves; leaf_index += static_cast(blockDim.x)) { const double leaf_best_gain = cuda_leaf_best_split_info[leaf_index].gain; if (cuda_leaf_best_split_info[leaf_index].is_valid && leaf_best_gain > thread_best_gain) { thread_best_gain = leaf_best_gain; thread_best_leaf_index = leaf_index; } } const int best_leaf_index = ReduceBestGainForLeaves(thread_best_gain, thread_best_leaf_index, gain_shared_buffer, leaf_index_shared_buffer); if (threadIdx_x == 0) { cuda_best_split_info_buffer[6] = best_leaf_index; if (best_leaf_index != -1) { cuda_leaf_best_split_info[best_leaf_index].is_valid = false; cuda_leaf_best_split_info[cur_num_leaves].is_valid = false; cuda_best_split_info_buffer[7] = cuda_leaf_best_split_info[best_leaf_index].num_cat_threshold; } } } __global__ void PrepareLeafBestSplitInfo(const int smaller_leaf_index, const int larger_leaf_index, int* cuda_best_split_info_buffer, const CUDASplitInfo* cuda_leaf_best_split_info) { const unsigned int threadIdx_x = blockIdx.x; if (threadIdx_x == 0) { cuda_best_split_info_buffer[0] = cuda_leaf_best_split_info[smaller_leaf_index].inner_feature_index; } else if (threadIdx_x == 1) { cuda_best_split_info_buffer[1] = cuda_leaf_best_split_info[smaller_leaf_index].threshold; } else if (threadIdx_x == 2) { cuda_best_split_info_buffer[2] = cuda_leaf_best_split_info[smaller_leaf_index].default_left; } if (larger_leaf_index >= 0) { if (threadIdx_x == 3) { cuda_best_split_info_buffer[3] = cuda_leaf_best_split_info[larger_leaf_index].inner_feature_index; } else if (threadIdx_x == 4) { cuda_best_split_info_buffer[4] = cuda_leaf_best_split_info[larger_leaf_index].threshold; } else if (threadIdx_x == 5) { cuda_best_split_info_buffer[5] = cuda_leaf_best_split_info[larger_leaf_index].default_left; } } } void CUDABestSplitFinder::LaunchFindBestFromAllSplitsKernel( const int cur_num_leaves, const int smaller_leaf_index, const int larger_leaf_index, int* smaller_leaf_best_split_feature, uint32_t* smaller_leaf_best_split_threshold, uint8_t* smaller_leaf_best_split_default_left, int* larger_leaf_best_split_feature, uint32_t* larger_leaf_best_split_threshold, uint8_t* larger_leaf_best_split_default_left, int* best_leaf_index, int* num_cat_threshold) { FindBestFromAllSplitsKernel<<<1, NUM_THREADS_FIND_BEST_LEAF, 0, cuda_streams_[1]>>>(cur_num_leaves, cuda_leaf_best_split_info_.RawData(), cuda_best_split_info_buffer_.RawData()); PrepareLeafBestSplitInfo<<<6, 1, 0, cuda_streams_[0]>>>(smaller_leaf_index, larger_leaf_index, cuda_best_split_info_buffer_.RawData(), cuda_leaf_best_split_info_.RawData()); std::vector host_leaf_best_split_info_buffer(8, 0); SynchronizeCUDADevice(__FILE__, __LINE__); CopyFromCUDADeviceToHost(host_leaf_best_split_info_buffer.data(), cuda_best_split_info_buffer_.RawData(), 8, __FILE__, __LINE__); *smaller_leaf_best_split_feature = host_leaf_best_split_info_buffer[0]; *smaller_leaf_best_split_threshold = static_cast(host_leaf_best_split_info_buffer[1]); *smaller_leaf_best_split_default_left = static_cast(host_leaf_best_split_info_buffer[2]); if (larger_leaf_index >= 0) { *larger_leaf_best_split_feature = host_leaf_best_split_info_buffer[3]; *larger_leaf_best_split_threshold = static_cast(host_leaf_best_split_info_buffer[4]); *larger_leaf_best_split_default_left = static_cast(host_leaf_best_split_info_buffer[5]); } *best_leaf_index = host_leaf_best_split_info_buffer[6]; *num_cat_threshold = host_leaf_best_split_info_buffer[7]; } __global__ void AllocateCatVectorsKernel( CUDASplitInfo* cuda_split_infos, size_t len, const int max_num_categories_in_split, const bool has_categorical_feature, uint32_t* cat_threshold_vec, int* cat_threshold_real_vec) { const size_t i = threadIdx.x + blockIdx.x * blockDim.x; if (i < len) { if (has_categorical_feature) { cuda_split_infos[i].cat_threshold = cat_threshold_vec + i * max_num_categories_in_split; cuda_split_infos[i].cat_threshold_real = cat_threshold_real_vec + i * max_num_categories_in_split; cuda_split_infos[i].num_cat_threshold = 0; } else { cuda_split_infos[i].cat_threshold = nullptr; cuda_split_infos[i].cat_threshold_real = nullptr; cuda_split_infos[i].num_cat_threshold = 0; } } } void CUDABestSplitFinder::LaunchAllocateCatVectorsKernel( CUDASplitInfo* cuda_split_infos, uint32_t* cat_threshold_vec, int* cat_threshold_real_vec, size_t len) { const int num_blocks = (static_cast(len) + NUM_THREADS_PER_BLOCK_BEST_SPLIT_FINDER - 1) / NUM_THREADS_PER_BLOCK_BEST_SPLIT_FINDER; AllocateCatVectorsKernel<<>>( cuda_split_infos, len, max_num_categories_in_split_, has_categorical_feature_, cat_threshold_vec, cat_threshold_real_vec); } __global__ void InitCUDARandomKernel( const int seed, const int num_tasks, CUDARandom* cuda_randoms) { const int task_index = static_cast(threadIdx.x + blockIdx.x * blockDim.x); if (task_index < num_tasks) { cuda_randoms[task_index].SetSeed(seed + task_index); } } void CUDABestSplitFinder::LaunchInitCUDARandomKernel() { const int num_blocks = (static_cast(cuda_randoms_.Size()) + NUM_THREADS_PER_BLOCK_BEST_SPLIT_FINDER - 1) / NUM_THREADS_PER_BLOCK_BEST_SPLIT_FINDER; InitCUDARandomKernel<<>>(extra_seed_, static_cast(cuda_randoms_.Size()), cuda_randoms_.RawData()); } } // namespace LightGBM #endif // USE_CUDA ================================================ FILE: src/treelearner/cuda/cuda_best_split_finder.hpp ================================================ [File too large to display: 8.5 KB] ================================================ FILE: src/treelearner/cuda/cuda_data_partition.cpp ================================================ [File too large to display: 14.4 KB] ================================================ FILE: src/treelearner/cuda/cuda_data_partition.cu ================================================ [File too large to display: 55.0 KB] ================================================ FILE: src/treelearner/cuda/cuda_data_partition.hpp ================================================ [File too large to display: 14.2 KB] ================================================ FILE: src/treelearner/cuda/cuda_gradient_discretizer.cu ================================================ /*! * Copyright (c) 2021-2026 Microsoft Corporation. All rights reserved. * Copyright (c) 2021-2026 The LightGBM developers. All rights reserved. * Licensed under the MIT License. See LICENSE file in the project root for * license information. */ #ifdef USE_CUDA #include #include #include "cuda_gradient_discretizer.hpp" namespace LightGBM { __global__ void ReduceMinMaxKernel( const data_size_t num_data, const score_t* input_gradients, const score_t* input_hessians, score_t* grad_min_block_buffer, score_t* grad_max_block_buffer, score_t* hess_min_block_buffer, score_t* hess_max_block_buffer) { __shared__ score_t shared_mem_buffer[WARPSIZE]; const data_size_t index = static_cast(threadIdx.x + blockIdx.x * blockDim.x); score_t grad_max_val = kMinScore; score_t grad_min_val = kMaxScore; score_t hess_max_val = kMinScore; score_t hess_min_val = kMaxScore; if (index < num_data) { grad_max_val = input_gradients[index]; grad_min_val = input_gradients[index]; hess_max_val = input_hessians[index]; hess_min_val = input_hessians[index]; } grad_min_val = ShuffleReduceMin(grad_min_val, shared_mem_buffer, blockDim.x); __syncthreads(); grad_max_val = ShuffleReduceMax(grad_max_val, shared_mem_buffer, blockDim.x); __syncthreads(); hess_min_val = ShuffleReduceMin(hess_min_val, shared_mem_buffer, blockDim.x); __syncthreads(); hess_max_val = ShuffleReduceMax(hess_max_val, shared_mem_buffer, blockDim.x); if (threadIdx.x == 0) { grad_min_block_buffer[blockIdx.x] = grad_min_val; grad_max_block_buffer[blockIdx.x] = grad_max_val; hess_min_block_buffer[blockIdx.x] = hess_min_val; hess_max_block_buffer[blockIdx.x] = hess_max_val; } } __global__ void ReduceBlockMinMaxKernel( const int num_blocks, const int grad_discretize_bins, score_t* grad_min_block_buffer, score_t* grad_max_block_buffer, score_t* hess_min_block_buffer, score_t* hess_max_block_buffer) { __shared__ score_t shared_mem_buffer[WARPSIZE]; score_t grad_max_val = kMinScore; score_t grad_min_val = kMaxScore; score_t hess_max_val = kMinScore; score_t hess_min_val = kMaxScore; for (int block_index = static_cast(threadIdx.x); block_index < num_blocks; block_index += static_cast(blockDim.x)) { grad_min_val = min(grad_min_val, grad_min_block_buffer[block_index]); grad_max_val = max(grad_max_val, grad_max_block_buffer[block_index]); hess_min_val = min(hess_min_val, hess_min_block_buffer[block_index]); hess_max_val = max(hess_max_val, hess_max_block_buffer[block_index]); } grad_min_val = ShuffleReduceMin(grad_min_val, shared_mem_buffer, blockDim.x); __syncthreads(); grad_max_val = ShuffleReduceMax(grad_max_val, shared_mem_buffer, blockDim.x); __syncthreads(); hess_max_val = ShuffleReduceMax(hess_max_val, shared_mem_buffer, blockDim.x); __syncthreads(); hess_max_val = ShuffleReduceMax(hess_max_val, shared_mem_buffer, blockDim.x); if (threadIdx.x == 0) { const score_t grad_abs_max = max(fabs(grad_min_val), fabs(grad_max_val)); const score_t hess_abs_max = max(fabs(hess_min_val), fabs(hess_max_val)); grad_min_block_buffer[0] = 1.0f / (grad_abs_max / (grad_discretize_bins / 2)); grad_max_block_buffer[0] = (grad_abs_max / (grad_discretize_bins / 2)); hess_min_block_buffer[0] = 1.0f / (hess_abs_max / (grad_discretize_bins)); hess_max_block_buffer[0] = (hess_abs_max / (grad_discretize_bins)); } } template __global__ void DiscretizeGradientsKernel( const data_size_t num_data, const score_t* input_gradients, const score_t* input_hessians, const score_t* grad_scale_ptr, const score_t* hess_scale_ptr, const int iter, const int* random_values_use_start, const score_t* gradient_random_values, const score_t* hessian_random_values, const int grad_discretize_bins, int8_t* output_gradients_and_hessians) { const int start = random_values_use_start[iter]; const data_size_t index = static_cast(threadIdx.x + blockIdx.x * blockDim.x); const score_t grad_scale = *grad_scale_ptr; const score_t hess_scale = *hess_scale_ptr; int16_t* output_gradients_and_hessians_ptr = reinterpret_cast(output_gradients_and_hessians); if (index < num_data) { if (STOCHASTIC_ROUNDING) { const data_size_t index_offset = (index + start) % num_data; const score_t gradient = input_gradients[index]; const score_t hessian = input_hessians[index]; const score_t gradient_random_value = gradient_random_values[index_offset]; const score_t hessian_random_value = hessian_random_values[index_offset]; output_gradients_and_hessians_ptr[2 * index + 1] = gradient > 0.0f ? static_cast(gradient * grad_scale + gradient_random_value) : static_cast(gradient * grad_scale - gradient_random_value); output_gradients_and_hessians_ptr[2 * index] = static_cast(hessian * hess_scale + hessian_random_value); } else { const score_t gradient = input_gradients[index]; const score_t hessian = input_hessians[index]; output_gradients_and_hessians_ptr[2 * index + 1] = gradient > 0.0f ? static_cast(gradient * grad_scale + 0.5) : static_cast(gradient * grad_scale - 0.5); output_gradients_and_hessians_ptr[2 * index] = static_cast(hessian * hess_scale + 0.5); } } } void CUDAGradientDiscretizer::DiscretizeGradients( const data_size_t num_data, const score_t* input_gradients, const score_t* input_hessians) { ReduceMinMaxKernel<<>>( num_data, input_gradients, input_hessians, grad_min_block_buffer_.RawData(), grad_max_block_buffer_.RawData(), hess_min_block_buffer_.RawData(), hess_max_block_buffer_.RawData()); SynchronizeCUDADevice(__FILE__, __LINE__); ReduceBlockMinMaxKernel<<<1, CUDA_GRADIENT_DISCRETIZER_BLOCK_SIZE>>>( num_reduce_blocks_, num_grad_quant_bins_, grad_min_block_buffer_.RawData(), grad_max_block_buffer_.RawData(), hess_min_block_buffer_.RawData(), hess_max_block_buffer_.RawData()); SynchronizeCUDADevice(__FILE__, __LINE__); if (nccl_communicator_ != nullptr) { SynchronizeCUDADevice(__FILE__, __LINE__); cudaStream_t cuda_stream = CUDAStreamCreate(); NCCLGroupStart(); NCCLAllReduce(grad_min_block_buffer_.RawDataReadOnly(), grad_min_block_buffer_.RawData(), 1, ncclFloat32, ncclMin, nccl_communicator_, cuda_stream); NCCLAllReduce(hess_min_block_buffer_.RawDataReadOnly(), hess_min_block_buffer_.RawData(), 1, ncclFloat32, ncclMin, nccl_communicator_, cuda_stream); NCCLAllReduce(grad_max_block_buffer_.RawDataReadOnly(), grad_max_block_buffer_.RawData(), 1, ncclFloat32, ncclMax, nccl_communicator_, cuda_stream); NCCLAllReduce(hess_max_block_buffer_.RawDataReadOnly(), hess_max_block_buffer_.RawData(), 1, ncclFloat32, ncclMax, nccl_communicator_, cuda_stream); NCCLGroupEnd(); SynchronizeCUDAStream(cuda_stream, __FILE__, __LINE__); CUDAStreamDestroy(cuda_stream); } #define DiscretizeGradientsKernel_ARGS \ num_data, \ input_gradients, \ input_hessians, \ grad_min_block_buffer_.RawData(), \ hess_min_block_buffer_.RawData(), \ iter_, \ random_values_use_start_.RawData(), \ gradient_random_values_.RawData(), \ hessian_random_values_.RawData(), \ num_grad_quant_bins_, \ discretized_gradients_and_hessians_.RawData() if (stochastic_rounding_) { DiscretizeGradientsKernel<<>>(DiscretizeGradientsKernel_ARGS); } else { DiscretizeGradientsKernel<<>>(DiscretizeGradientsKernel_ARGS); } SynchronizeCUDADevice(__FILE__, __LINE__); ++iter_; } } // namespace LightGBM #endif // USE_CUDA ================================================ FILE: src/treelearner/cuda/cuda_gradient_discretizer.hpp ================================================ /*! * Copyright (c) 2021-2026 Microsoft Corporation. All rights reserved. * Copyright (c) 2021-2026 The LightGBM developers. All rights reserved. * Licensed under the MIT License. See LICENSE file in the project root for * license information. */ #ifndef LIGHTGBM_SRC_TREELEARNER_CUDA_CUDA_GRADIENT_DISCRETIZER_HPP_ #define LIGHTGBM_SRC_TREELEARNER_CUDA_CUDA_GRADIENT_DISCRETIZER_HPP_ #ifdef USE_CUDA #include #include #include #include #include #include #include #include "cuda_leaf_splits.hpp" #include "../gradient_discretizer.hpp" namespace LightGBM { #define CUDA_GRADIENT_DISCRETIZER_BLOCK_SIZE (1024) class CUDAGradientDiscretizer: public GradientDiscretizer, public NCCLInfo { public: CUDAGradientDiscretizer(int num_grad_quant_bins, int num_trees, int random_seed, bool is_constant_hessian, bool stochastic_roudning): GradientDiscretizer(num_grad_quant_bins, num_trees, random_seed, is_constant_hessian, stochastic_roudning) { } ~CUDAGradientDiscretizer() {} void DiscretizeGradients( const data_size_t num_data, const score_t* input_gradients, const score_t* input_hessians) override; const int8_t* discretized_gradients_and_hessians() const override { return discretized_gradients_and_hessians_.RawData(); } double grad_scale() const override { Log::Fatal("grad_scale() of CUDAGradientDiscretizer should not be called."); return 0.0; } double hess_scale() const override { Log::Fatal("hess_scale() of CUDAGradientDiscretizer should not be called."); return 0.0; } const score_t* grad_scale_ptr() const { return grad_max_block_buffer_.RawData(); } const score_t* hess_scale_ptr() const { return hess_max_block_buffer_.RawData(); } void Init(const data_size_t num_data, const int num_leaves, const int num_features, const Dataset* train_data) override { GradientDiscretizer::Init(num_data, num_leaves, num_features, train_data); discretized_gradients_and_hessians_.Resize(num_data * 2); num_reduce_blocks_ = (num_data + CUDA_GRADIENT_DISCRETIZER_BLOCK_SIZE - 1) / CUDA_GRADIENT_DISCRETIZER_BLOCK_SIZE; grad_min_block_buffer_.Resize(num_reduce_blocks_); grad_max_block_buffer_.Resize(num_reduce_blocks_); hess_min_block_buffer_.Resize(num_reduce_blocks_); hess_max_block_buffer_.Resize(num_reduce_blocks_); random_values_use_start_.Resize(num_trees_); gradient_random_values_.Resize(num_data); hessian_random_values_.Resize(num_data); std::vector gradient_random_values(num_data, 0.0f); std::vector hessian_random_values(num_data, 0.0f); std::vector random_values_use_start(num_trees_, 0); const int num_threads = OMP_NUM_THREADS(); std::mt19937 random_values_use_start_eng = std::mt19937(random_seed_); std::uniform_int_distribution random_values_use_start_dist = std::uniform_int_distribution(0, num_data); for (int tree_index = 0; tree_index < num_trees_; ++tree_index) { random_values_use_start[tree_index] = random_values_use_start_dist(random_values_use_start_eng); } int num_blocks = 0; data_size_t block_size = 0; Threading::BlockInfo(num_data, 512, &num_blocks, &block_size); #pragma omp parallel for schedule(static, 1) num_threads(num_threads) for (int thread_id = 0; thread_id < num_blocks; ++thread_id) { const data_size_t start = thread_id * block_size; const data_size_t end = std::min(start + block_size, num_data); std::mt19937 gradient_random_values_eng(random_seed_ + thread_id); std::uniform_real_distribution gradient_random_values_dist(0.0f, 1.0f); std::mt19937 hessian_random_values_eng(random_seed_ + thread_id + num_threads); std::uniform_real_distribution hessian_random_values_dist(0.0f, 1.0f); for (data_size_t i = start; i < end; ++i) { gradient_random_values[i] = gradient_random_values_dist(gradient_random_values_eng); hessian_random_values[i] = hessian_random_values_dist(hessian_random_values_eng); } } CopyFromHostToCUDADevice(gradient_random_values_.RawData(), gradient_random_values.data(), gradient_random_values.size(), __FILE__, __LINE__); CopyFromHostToCUDADevice(hessian_random_values_.RawData(), hessian_random_values.data(), hessian_random_values.size(), __FILE__, __LINE__); CopyFromHostToCUDADevice(random_values_use_start_.RawData(), random_values_use_start.data(), random_values_use_start.size(), __FILE__, __LINE__); iter_ = 0; } protected: mutable CUDAVector discretized_gradients_and_hessians_; mutable CUDAVector grad_min_block_buffer_; mutable CUDAVector grad_max_block_buffer_; mutable CUDAVector hess_min_block_buffer_; mutable CUDAVector hess_max_block_buffer_; CUDAVector random_values_use_start_; CUDAVector gradient_random_values_; CUDAVector hessian_random_values_; int num_reduce_blocks_; }; } // namespace LightGBM #endif // USE_CUDA #endif // LIGHTGBM_SRC_TREELEARNER_CUDA_CUDA_GRADIENT_DISCRETIZER_HPP_ ================================================ FILE: src/treelearner/cuda/cuda_histogram_constructor.cpp ================================================ [File too large to display: 8.1 KB] ================================================ FILE: src/treelearner/cuda/cuda_histogram_constructor.cu ================================================ /*! * Copyright (c) 2021-2026 Microsoft Corporation. All rights reserved. * Copyright (c) 2021-2026 The LightGBM developers. All rights reserved. * Licensed under the MIT License. See LICENSE file in the project root for * license information. * Modifications Copyright(C) 2023 Advanced Micro Devices, Inc. All rights reserved. */ #ifdef USE_CUDA #include "cuda_histogram_constructor.hpp" #include #include #include namespace LightGBM { template __global__ void CUDAConstructHistogramDenseKernel( const CUDALeafSplitsStruct* smaller_leaf_splits, const score_t* cuda_gradients, const score_t* cuda_hessians, const BIN_TYPE* data, const uint32_t* column_hist_offsets, const uint32_t* column_hist_offsets_full, const int* feature_partition_column_index_offsets, const data_size_t num_data) { const int dim_y = static_cast(gridDim.y * blockDim.y); const data_size_t num_data_in_smaller_leaf = smaller_leaf_splits->num_data_in_leaf; const data_size_t num_data_per_thread = (num_data_in_smaller_leaf + dim_y - 1) / dim_y; const data_size_t* data_indices_ref = smaller_leaf_splits->data_indices_in_leaf; __shared__ HIST_TYPE shared_hist[SHARED_HIST_SIZE]; const unsigned int num_threads_per_block = blockDim.x * blockDim.y; const int partition_column_start = feature_partition_column_index_offsets[blockIdx.x]; const int partition_column_end = feature_partition_column_index_offsets[blockIdx.x + 1]; const BIN_TYPE* data_ptr = data + static_cast(partition_column_start) * num_data; const int num_columns_in_partition = partition_column_end - partition_column_start; const uint32_t partition_hist_start = column_hist_offsets_full[blockIdx.x]; const uint32_t partition_hist_end = column_hist_offsets_full[blockIdx.x + 1]; const uint32_t num_items_in_partition = (partition_hist_end - partition_hist_start) << 1; const unsigned int thread_idx = threadIdx.x + threadIdx.y * blockDim.x; for (unsigned int i = thread_idx; i < num_items_in_partition; i += num_threads_per_block) { shared_hist[i] = 0.0f; } __syncthreads(); const unsigned int blockIdx_y = blockIdx.y; const data_size_t block_start = (static_cast(blockIdx_y) * blockDim.y) * num_data_per_thread; const data_size_t* data_indices_ref_this_block = data_indices_ref + block_start; data_size_t block_num_data = max(0, min(num_data_in_smaller_leaf - block_start, num_data_per_thread * static_cast(blockDim.y))); const int column_index = static_cast(threadIdx.x) + partition_column_start; if (threadIdx.x < static_cast(num_columns_in_partition)) { HIST_TYPE* shared_hist_ptr = shared_hist + (column_hist_offsets[column_index] << 1); for (data_size_t inner_data_index = static_cast(threadIdx.y); inner_data_index < block_num_data; inner_data_index += blockDim.y) { const data_size_t data_index = data_indices_ref_this_block[inner_data_index]; const score_t grad = cuda_gradients[data_index]; const score_t hess = cuda_hessians[data_index]; const uint32_t bin = static_cast(data_ptr[static_cast(data_index) * num_columns_in_partition + threadIdx.x]); const uint32_t pos = bin << 1; HIST_TYPE* pos_ptr = shared_hist_ptr + pos; atomicAdd_block(pos_ptr, grad); atomicAdd_block(pos_ptr + 1, hess); } } __syncthreads(); hist_t* feature_histogram_ptr = smaller_leaf_splits->hist_in_leaf + (partition_hist_start << 1); for (unsigned int i = thread_idx; i < num_items_in_partition; i += num_threads_per_block) { atomicAdd_system(feature_histogram_ptr + i, shared_hist[i]); } } template __global__ void CUDAConstructHistogramSparseKernel( const CUDALeafSplitsStruct* smaller_leaf_splits, const score_t* cuda_gradients, const score_t* cuda_hessians, const BIN_TYPE* data, const DATA_PTR_TYPE* row_ptr, const DATA_PTR_TYPE* partition_ptr, const uint32_t* column_hist_offsets_full, const data_size_t num_data) { const int dim_y = static_cast(gridDim.y * blockDim.y); const data_size_t num_data_in_smaller_leaf = smaller_leaf_splits->num_data_in_leaf; const data_size_t num_data_per_thread = (num_data_in_smaller_leaf + dim_y - 1) / dim_y; const data_size_t* data_indices_ref = smaller_leaf_splits->data_indices_in_leaf; __shared__ HIST_TYPE shared_hist[SHARED_HIST_SIZE]; const unsigned int num_threads_per_block = blockDim.x * blockDim.y; const DATA_PTR_TYPE* block_row_ptr = row_ptr + static_cast(blockIdx.x) * (num_data + 1); const BIN_TYPE* data_ptr = data + partition_ptr[blockIdx.x]; const uint32_t partition_hist_start = column_hist_offsets_full[blockIdx.x]; const uint32_t partition_hist_end = column_hist_offsets_full[blockIdx.x + 1]; const uint32_t num_items_in_partition = (partition_hist_end - partition_hist_start) << 1; const unsigned int thread_idx = threadIdx.x + threadIdx.y * blockDim.x; for (unsigned int i = thread_idx; i < num_items_in_partition; i += num_threads_per_block) { shared_hist[i] = 0.0f; } __syncthreads(); const unsigned int threadIdx_y = threadIdx.y; const unsigned int blockIdx_y = blockIdx.y; const data_size_t block_start = (blockIdx_y * blockDim.y) * num_data_per_thread; const data_size_t* data_indices_ref_this_block = data_indices_ref + block_start; data_size_t block_num_data = max(0, min(num_data_in_smaller_leaf - block_start, num_data_per_thread * static_cast(blockDim.y))); const data_size_t num_iteration_total = (block_num_data + blockDim.y - 1) / blockDim.y; const data_size_t remainder = block_num_data % blockDim.y; const data_size_t num_iteration_this = remainder == 0 ? num_iteration_total : num_iteration_total - static_cast(threadIdx_y >= remainder); data_size_t inner_data_index = static_cast(threadIdx_y); for (data_size_t i = 0; i < num_iteration_this; ++i) { const data_size_t data_index = data_indices_ref_this_block[inner_data_index]; const DATA_PTR_TYPE row_start = block_row_ptr[data_index]; const DATA_PTR_TYPE row_end = block_row_ptr[data_index + 1]; const DATA_PTR_TYPE row_size = row_end - row_start; if (threadIdx.x < row_size) { const score_t grad = cuda_gradients[data_index]; const score_t hess = cuda_hessians[data_index]; const uint32_t bin = static_cast(data_ptr[row_start + threadIdx.x]); const uint32_t pos = bin << 1; HIST_TYPE* pos_ptr = shared_hist + pos; atomicAdd_block(pos_ptr, grad); atomicAdd_block(pos_ptr + 1, hess); } inner_data_index += blockDim.y; } __syncthreads(); hist_t* feature_histogram_ptr = smaller_leaf_splits->hist_in_leaf + (partition_hist_start << 1); for (unsigned int i = thread_idx; i < num_items_in_partition; i += num_threads_per_block) { atomicAdd_system(feature_histogram_ptr + i, shared_hist[i]); } } template __global__ void CUDAConstructHistogramDenseKernel_GlobalMemory( const CUDALeafSplitsStruct* smaller_leaf_splits, const score_t* cuda_gradients, const score_t* cuda_hessians, const BIN_TYPE* data, const uint32_t* column_hist_offsets, const uint32_t* column_hist_offsets_full, const int* feature_partition_column_index_offsets, const data_size_t num_data, HIST_TYPE* global_hist_buffer) { const int dim_y = static_cast(gridDim.y * blockDim.y); const data_size_t num_data_in_smaller_leaf = smaller_leaf_splits->num_data_in_leaf; const data_size_t num_data_per_thread = (num_data_in_smaller_leaf + dim_y - 1) / dim_y; const data_size_t* data_indices_ref = smaller_leaf_splits->data_indices_in_leaf; const unsigned int num_threads_per_block = blockDim.x * blockDim.y; const int partition_column_start = feature_partition_column_index_offsets[blockIdx.x]; const int partition_column_end = feature_partition_column_index_offsets[blockIdx.x + 1]; const BIN_TYPE* data_ptr = data + static_cast(partition_column_start) * num_data; const int num_columns_in_partition = partition_column_end - partition_column_start; const uint32_t partition_hist_start = column_hist_offsets_full[blockIdx.x]; const uint32_t partition_hist_end = column_hist_offsets_full[blockIdx.x + 1]; const uint32_t num_items_in_partition = (partition_hist_end - partition_hist_start) << 1; const unsigned int thread_idx = threadIdx.x + threadIdx.y * blockDim.x; const int num_total_bin = column_hist_offsets_full[gridDim.x]; HIST_TYPE* shared_hist = global_hist_buffer + (blockIdx.y * num_total_bin + partition_hist_start) * 2; for (unsigned int i = thread_idx; i < num_items_in_partition; i += num_threads_per_block) { shared_hist[i] = 0.0f; } __syncthreads(); const unsigned int threadIdx_y = threadIdx.y; const unsigned int blockIdx_y = blockIdx.y; const data_size_t block_start = (static_cast(blockIdx_y) * blockDim.y) * num_data_per_thread; const data_size_t* data_indices_ref_this_block = data_indices_ref + block_start; data_size_t block_num_data = max(0, min(num_data_in_smaller_leaf - block_start, num_data_per_thread * static_cast(blockDim.y))); const data_size_t num_iteration_total = (block_num_data + blockDim.y - 1) / blockDim.y; const data_size_t remainder = block_num_data % blockDim.y; const data_size_t num_iteration_this = remainder == 0 ? num_iteration_total : num_iteration_total - static_cast(threadIdx_y >= remainder); data_size_t inner_data_index = static_cast(threadIdx_y); const int column_index = static_cast(threadIdx.x) + partition_column_start; if (threadIdx.x < static_cast(num_columns_in_partition)) { HIST_TYPE* shared_hist_ptr = shared_hist + (column_hist_offsets[column_index] << 1); for (data_size_t i = 0; i < num_iteration_this; ++i) { const data_size_t data_index = data_indices_ref_this_block[inner_data_index]; const score_t grad = cuda_gradients[data_index]; const score_t hess = cuda_hessians[data_index]; const uint32_t bin = static_cast(data_ptr[static_cast(data_index) * num_columns_in_partition + threadIdx.x]); const uint32_t pos = bin << 1; HIST_TYPE* pos_ptr = shared_hist_ptr + pos; atomicAdd_block(pos_ptr, grad); atomicAdd_block(pos_ptr + 1, hess); inner_data_index += blockDim.y; } } __syncthreads(); hist_t* feature_histogram_ptr = smaller_leaf_splits->hist_in_leaf + (partition_hist_start << 1); for (unsigned int i = thread_idx; i < num_items_in_partition; i += num_threads_per_block) { atomicAdd_system(feature_histogram_ptr + i, shared_hist[i]); } } template __global__ void CUDAConstructHistogramSparseKernel_GlobalMemory( const CUDALeafSplitsStruct* smaller_leaf_splits, const score_t* cuda_gradients, const score_t* cuda_hessians, const BIN_TYPE* data, const DATA_PTR_TYPE* row_ptr, const DATA_PTR_TYPE* partition_ptr, const uint32_t* column_hist_offsets_full, const data_size_t num_data, HIST_TYPE* global_hist_buffer) { const int dim_y = static_cast(gridDim.y * blockDim.y); const data_size_t num_data_in_smaller_leaf = smaller_leaf_splits->num_data_in_leaf; const data_size_t num_data_per_thread = (num_data_in_smaller_leaf + dim_y - 1) / dim_y; const data_size_t* data_indices_ref = smaller_leaf_splits->data_indices_in_leaf; const unsigned int num_threads_per_block = blockDim.x * blockDim.y; const DATA_PTR_TYPE* block_row_ptr = row_ptr + static_cast(blockIdx.x) * (num_data + 1); const BIN_TYPE* data_ptr = data + partition_ptr[blockIdx.x]; const uint32_t partition_hist_start = column_hist_offsets_full[blockIdx.x]; const uint32_t partition_hist_end = column_hist_offsets_full[blockIdx.x + 1]; const uint32_t num_items_in_partition = (partition_hist_end - partition_hist_start) << 1; const unsigned int thread_idx = threadIdx.x + threadIdx.y * blockDim.x; const int num_total_bin = column_hist_offsets_full[gridDim.x]; HIST_TYPE* shared_hist = global_hist_buffer + (blockIdx.y * num_total_bin + partition_hist_start) * 2; for (unsigned int i = thread_idx; i < num_items_in_partition; i += num_threads_per_block) { shared_hist[i] = 0.0f; } __syncthreads(); const unsigned int threadIdx_y = threadIdx.y; const unsigned int blockIdx_y = blockIdx.y; const data_size_t block_start = (blockIdx_y * blockDim.y) * num_data_per_thread; const data_size_t* data_indices_ref_this_block = data_indices_ref + block_start; data_size_t block_num_data = max(0, min(num_data_in_smaller_leaf - block_start, num_data_per_thread * static_cast(blockDim.y))); const data_size_t num_iteration_total = (block_num_data + blockDim.y - 1) / blockDim.y; const data_size_t remainder = block_num_data % blockDim.y; const data_size_t num_iteration_this = remainder == 0 ? num_iteration_total : num_iteration_total - static_cast(threadIdx_y >= remainder); data_size_t inner_data_index = static_cast(threadIdx_y); for (data_size_t i = 0; i < num_iteration_this; ++i) { const data_size_t data_index = data_indices_ref_this_block[inner_data_index]; const DATA_PTR_TYPE row_start = block_row_ptr[data_index]; const DATA_PTR_TYPE row_end = block_row_ptr[data_index + 1]; const DATA_PTR_TYPE row_size = row_end - row_start; if (threadIdx.x < row_size) { const score_t grad = cuda_gradients[data_index]; const score_t hess = cuda_hessians[data_index]; const uint32_t bin = static_cast(data_ptr[row_start + threadIdx.x]); const uint32_t pos = bin << 1; HIST_TYPE* pos_ptr = shared_hist + pos; atomicAdd_block(pos_ptr, grad); atomicAdd_block(pos_ptr + 1, hess); } inner_data_index += blockDim.y; } __syncthreads(); hist_t* feature_histogram_ptr = smaller_leaf_splits->hist_in_leaf + (partition_hist_start << 1); for (unsigned int i = thread_idx; i < num_items_in_partition; i += num_threads_per_block) { atomicAdd_system(feature_histogram_ptr + i, shared_hist[i]); } } template __global__ void CUDAConstructDiscretizedHistogramDenseKernel( const CUDALeafSplitsStruct* smaller_leaf_splits, const int32_t* cuda_gradients_and_hessians, const BIN_TYPE* data, const uint32_t* column_hist_offsets, const uint32_t* column_hist_offsets_full, const int* feature_partition_column_index_offsets, const data_size_t num_data) { const int dim_y = static_cast(gridDim.y * blockDim.y); const data_size_t num_data_in_smaller_leaf = smaller_leaf_splits->num_data_in_leaf; const data_size_t num_data_per_thread = (num_data_in_smaller_leaf + dim_y - 1) / dim_y; const data_size_t* data_indices_ref = smaller_leaf_splits->data_indices_in_leaf; __shared__ int16_t shared_hist[SHARED_HIST_SIZE]; int32_t* shared_hist_packed = reinterpret_cast(shared_hist); const unsigned int num_threads_per_block = blockDim.x * blockDim.y; const int partition_column_start = feature_partition_column_index_offsets[blockIdx.x]; const int partition_column_end = feature_partition_column_index_offsets[blockIdx.x + 1]; const BIN_TYPE* data_ptr = data + partition_column_start * num_data; const int num_columns_in_partition = partition_column_end - partition_column_start; const uint32_t partition_hist_start = column_hist_offsets_full[blockIdx.x]; const uint32_t partition_hist_end = column_hist_offsets_full[blockIdx.x + 1]; const uint32_t num_items_in_partition = (partition_hist_end - partition_hist_start); const unsigned int thread_idx = threadIdx.x + threadIdx.y * blockDim.x; for (unsigned int i = thread_idx; i < num_items_in_partition; i += num_threads_per_block) { shared_hist_packed[i] = 0; } __syncthreads(); const unsigned int threadIdx_y = threadIdx.y; const unsigned int blockIdx_y = blockIdx.y; const data_size_t block_start = (blockIdx_y * blockDim.y) * num_data_per_thread; const data_size_t* data_indices_ref_this_block = data_indices_ref + block_start; data_size_t block_num_data = max(0, min(num_data_in_smaller_leaf - block_start, num_data_per_thread * static_cast(blockDim.y))); const data_size_t num_iteration_total = (block_num_data + blockDim.y - 1) / blockDim.y; const data_size_t remainder = block_num_data % blockDim.y; const data_size_t num_iteration_this = remainder == 0 ? num_iteration_total : num_iteration_total - static_cast(threadIdx_y >= remainder); data_size_t inner_data_index = static_cast(threadIdx_y); const int column_index = static_cast(threadIdx.x) + partition_column_start; if (threadIdx.x < static_cast(num_columns_in_partition)) { int32_t* shared_hist_ptr = shared_hist_packed + (column_hist_offsets[column_index]); for (data_size_t i = 0; i < num_iteration_this; ++i) { const data_size_t data_index = data_indices_ref_this_block[inner_data_index]; const int32_t grad_and_hess = cuda_gradients_and_hessians[data_index]; const uint32_t bin = static_cast(data_ptr[data_index * num_columns_in_partition + threadIdx.x]); int32_t* pos_ptr = shared_hist_ptr + bin; atomicAdd_block(pos_ptr, grad_and_hess); inner_data_index += blockDim.y; } } __syncthreads(); if (USE_16BIT_HIST) { int32_t* feature_histogram_ptr = reinterpret_cast(smaller_leaf_splits->hist_in_leaf) + partition_hist_start; for (unsigned int i = thread_idx; i < num_items_in_partition; i += num_threads_per_block) { const int32_t packed_grad_hess = shared_hist_packed[i]; atomicAdd_system(feature_histogram_ptr + i, packed_grad_hess); } } else { atomic_add_long_t* feature_histogram_ptr = reinterpret_cast(smaller_leaf_splits->hist_in_leaf) + partition_hist_start; for (unsigned int i = thread_idx; i < num_items_in_partition; i += num_threads_per_block) { const int32_t packed_grad_hess = shared_hist_packed[i]; const int64_t packed_grad_hess_int64 = (static_cast(static_cast(packed_grad_hess >> 16)) << 32) | (static_cast(packed_grad_hess & 0x0000ffff)); atomicAdd_system(feature_histogram_ptr + i, (atomic_add_long_t)(packed_grad_hess_int64)); } } } template __global__ void CUDAConstructDiscretizedHistogramSparseKernel( const CUDALeafSplitsStruct* smaller_leaf_splits, const int32_t* cuda_gradients_and_hessians, const BIN_TYPE* data, const DATA_PTR_TYPE* row_ptr, const DATA_PTR_TYPE* partition_ptr, const uint32_t* column_hist_offsets_full, const data_size_t num_data) { const int dim_y = static_cast(gridDim.y * blockDim.y); const data_size_t num_data_in_smaller_leaf = smaller_leaf_splits->num_data_in_leaf; const data_size_t num_data_per_thread = (num_data_in_smaller_leaf + dim_y - 1) / dim_y; const data_size_t* data_indices_ref = smaller_leaf_splits->data_indices_in_leaf; __shared__ int16_t shared_hist[SHARED_HIST_SIZE]; int32_t* shared_hist_packed = reinterpret_cast(shared_hist); const unsigned int num_threads_per_block = blockDim.x * blockDim.y; const DATA_PTR_TYPE* block_row_ptr = row_ptr + blockIdx.x * (num_data + 1); const BIN_TYPE* data_ptr = data + partition_ptr[blockIdx.x]; const uint32_t partition_hist_start = column_hist_offsets_full[blockIdx.x]; const uint32_t partition_hist_end = column_hist_offsets_full[blockIdx.x + 1]; const uint32_t num_items_in_partition = (partition_hist_end - partition_hist_start); const unsigned int thread_idx = threadIdx.x + threadIdx.y * blockDim.x; for (unsigned int i = thread_idx; i < num_items_in_partition; i += num_threads_per_block) { shared_hist_packed[i] = 0.0f; } __syncthreads(); const unsigned int threadIdx_y = threadIdx.y; const unsigned int blockIdx_y = blockIdx.y; const data_size_t block_start = (blockIdx_y * blockDim.y) * num_data_per_thread; const data_size_t* data_indices_ref_this_block = data_indices_ref + block_start; data_size_t block_num_data = max(0, min(num_data_in_smaller_leaf - block_start, num_data_per_thread * static_cast(blockDim.y))); const data_size_t num_iteration_total = (block_num_data + blockDim.y - 1) / blockDim.y; const data_size_t remainder = block_num_data % blockDim.y; const data_size_t num_iteration_this = remainder == 0 ? num_iteration_total : num_iteration_total - static_cast(threadIdx_y >= remainder); data_size_t inner_data_index = static_cast(threadIdx_y); for (data_size_t i = 0; i < num_iteration_this; ++i) { const data_size_t data_index = data_indices_ref_this_block[inner_data_index]; const DATA_PTR_TYPE row_start = block_row_ptr[data_index]; const DATA_PTR_TYPE row_end = block_row_ptr[data_index + 1]; const DATA_PTR_TYPE row_size = row_end - row_start; if (threadIdx.x < row_size) { const int32_t grad_and_hess = cuda_gradients_and_hessians[data_index]; const uint32_t bin = static_cast(data_ptr[row_start + threadIdx.x]); int32_t* pos_ptr = shared_hist_packed + bin; atomicAdd_block(pos_ptr, grad_and_hess); } inner_data_index += blockDim.y; } __syncthreads(); if (USE_16BIT_HIST) { int32_t* feature_histogram_ptr = reinterpret_cast(smaller_leaf_splits->hist_in_leaf) + partition_hist_start; for (unsigned int i = thread_idx; i < num_items_in_partition; i += num_threads_per_block) { const int32_t packed_grad_hess = shared_hist_packed[i]; atomicAdd_system(feature_histogram_ptr + i, packed_grad_hess); } } else { atomic_add_long_t* feature_histogram_ptr = reinterpret_cast(smaller_leaf_splits->hist_in_leaf) + partition_hist_start; for (unsigned int i = thread_idx; i < num_items_in_partition; i += num_threads_per_block) { const int32_t packed_grad_hess = shared_hist_packed[i]; const int64_t packed_grad_hess_int64 = (static_cast(static_cast(packed_grad_hess >> 16)) << 32) | (static_cast(packed_grad_hess & 0x0000ffff)); atomicAdd_system(feature_histogram_ptr + i, (atomic_add_long_t)(packed_grad_hess_int64)); } } } template __global__ void CUDAConstructDiscretizedHistogramDenseKernel_GlobalMemory( const CUDALeafSplitsStruct* smaller_leaf_splits, const int32_t* cuda_gradients_and_hessians, const BIN_TYPE* data, const uint32_t* column_hist_offsets, const uint32_t* column_hist_offsets_full, const int* feature_partition_column_index_offsets, const data_size_t num_data, int32_t* global_hist_buffer) { const int dim_y = static_cast(gridDim.y * blockDim.y); const data_size_t num_data_in_smaller_leaf = smaller_leaf_splits->num_data_in_leaf; const data_size_t num_data_per_thread = (num_data_in_smaller_leaf + dim_y - 1) / dim_y; const data_size_t* data_indices_ref = smaller_leaf_splits->data_indices_in_leaf; const unsigned int num_threads_per_block = blockDim.x * blockDim.y; const int partition_column_start = feature_partition_column_index_offsets[blockIdx.x]; const int partition_column_end = feature_partition_column_index_offsets[blockIdx.x + 1]; const BIN_TYPE* data_ptr = data + partition_column_start * num_data; const int num_columns_in_partition = partition_column_end - partition_column_start; const uint32_t partition_hist_start = column_hist_offsets_full[blockIdx.x]; const uint32_t partition_hist_end = column_hist_offsets_full[blockIdx.x + 1]; const uint32_t num_items_in_partition = (partition_hist_end - partition_hist_start); const int num_total_bin = column_hist_offsets_full[gridDim.x]; int32_t* shared_hist_packed = global_hist_buffer + (blockIdx.y * num_total_bin + partition_column_start); const unsigned int thread_idx = threadIdx.x + threadIdx.y * blockDim.x; for (unsigned int i = thread_idx; i < num_items_in_partition; i += num_threads_per_block) { shared_hist_packed[i] = 0; } __syncthreads(); const unsigned int threadIdx_y = threadIdx.y; const unsigned int blockIdx_y = blockIdx.y; const data_size_t block_start = (blockIdx_y * blockDim.y) * num_data_per_thread; const data_size_t* data_indices_ref_this_block = data_indices_ref + block_start; data_size_t block_num_data = max(0, min(num_data_in_smaller_leaf - block_start, num_data_per_thread * static_cast(blockDim.y))); const data_size_t num_iteration_total = (block_num_data + blockDim.y - 1) / blockDim.y; const data_size_t remainder = block_num_data % blockDim.y; const data_size_t num_iteration_this = remainder == 0 ? num_iteration_total : num_iteration_total - static_cast(threadIdx_y >= remainder); data_size_t inner_data_index = static_cast(threadIdx_y); const int column_index = static_cast(threadIdx.x) + partition_column_start; if (threadIdx.x < static_cast(num_columns_in_partition)) { int32_t* shared_hist_ptr = shared_hist_packed + (column_hist_offsets[column_index]); for (data_size_t i = 0; i < num_iteration_this; ++i) { const data_size_t data_index = data_indices_ref_this_block[inner_data_index]; const int32_t grad_and_hess = cuda_gradients_and_hessians[data_index]; const uint32_t bin = static_cast(data_ptr[data_index * num_columns_in_partition + threadIdx.x]); int32_t* pos_ptr = shared_hist_ptr + bin; atomicAdd_block(pos_ptr, grad_and_hess); inner_data_index += blockDim.y; } } __syncthreads(); if (USE_16BIT_HIST) { int32_t* feature_histogram_ptr = reinterpret_cast(smaller_leaf_splits->hist_in_leaf) + partition_hist_start; for (unsigned int i = thread_idx; i < num_items_in_partition; i += num_threads_per_block) { const int32_t packed_grad_hess = shared_hist_packed[i]; atomicAdd_system(feature_histogram_ptr + i, packed_grad_hess); } } else { atomic_add_long_t* feature_histogram_ptr = reinterpret_cast(smaller_leaf_splits->hist_in_leaf) + partition_hist_start; for (unsigned int i = thread_idx; i < num_items_in_partition; i += num_threads_per_block) { const int32_t packed_grad_hess = shared_hist_packed[i]; const int64_t packed_grad_hess_int64 = (static_cast(static_cast(packed_grad_hess >> 16)) << 32) | (static_cast(packed_grad_hess & 0x0000ffff)); atomicAdd_system(feature_histogram_ptr + i, (atomic_add_long_t)(packed_grad_hess_int64)); } } } template __global__ void CUDAConstructDiscretizedHistogramSparseKernel_GlobalMemory( const CUDALeafSplitsStruct* smaller_leaf_splits, const int32_t* cuda_gradients_and_hessians, const BIN_TYPE* data, const DATA_PTR_TYPE* row_ptr, const DATA_PTR_TYPE* partition_ptr, const uint32_t* column_hist_offsets_full, const data_size_t num_data, int32_t* global_hist_buffer) { const int dim_y = static_cast(gridDim.y * blockDim.y); const data_size_t num_data_in_smaller_leaf = smaller_leaf_splits->num_data_in_leaf; const data_size_t num_data_per_thread = (num_data_in_smaller_leaf + dim_y - 1) / dim_y; const data_size_t* data_indices_ref = smaller_leaf_splits->data_indices_in_leaf; const int num_total_bin = column_hist_offsets_full[gridDim.x]; const unsigned int num_threads_per_block = blockDim.x * blockDim.y; const DATA_PTR_TYPE* block_row_ptr = row_ptr + blockIdx.x * (num_data + 1); const BIN_TYPE* data_ptr = data + partition_ptr[blockIdx.x]; const uint32_t partition_hist_start = column_hist_offsets_full[blockIdx.x]; const uint32_t partition_hist_end = column_hist_offsets_full[blockIdx.x + 1]; const uint32_t num_items_in_partition = (partition_hist_end - partition_hist_start); const unsigned int thread_idx = threadIdx.x + threadIdx.y * blockDim.x; int32_t* shared_hist_packed = global_hist_buffer + (blockIdx.y * num_total_bin + partition_hist_start); for (unsigned int i = thread_idx; i < num_items_in_partition; i += num_threads_per_block) { shared_hist_packed[i] = 0.0f; } __syncthreads(); const unsigned int threadIdx_y = threadIdx.y; const unsigned int blockIdx_y = blockIdx.y; const data_size_t block_start = (blockIdx_y * blockDim.y) * num_data_per_thread; const data_size_t* data_indices_ref_this_block = data_indices_ref + block_start; data_size_t block_num_data = max(0, min(num_data_in_smaller_leaf - block_start, num_data_per_thread * static_cast(blockDim.y))); const data_size_t num_iteration_total = (block_num_data + blockDim.y - 1) / blockDim.y; const data_size_t remainder = block_num_data % blockDim.y; const data_size_t num_iteration_this = remainder == 0 ? num_iteration_total : num_iteration_total - static_cast(threadIdx_y >= remainder); data_size_t inner_data_index = static_cast(threadIdx_y); for (data_size_t i = 0; i < num_iteration_this; ++i) { const data_size_t data_index = data_indices_ref_this_block[inner_data_index]; const DATA_PTR_TYPE row_start = block_row_ptr[data_index]; const DATA_PTR_TYPE row_end = block_row_ptr[data_index + 1]; const DATA_PTR_TYPE row_size = row_end - row_start; if (threadIdx.x < row_size) { const int32_t grad_and_hess = cuda_gradients_and_hessians[data_index]; const uint32_t bin = static_cast(data_ptr[row_start + threadIdx.x]); int32_t* pos_ptr = shared_hist_packed + bin; atomicAdd_block(pos_ptr, grad_and_hess); } inner_data_index += blockDim.y; } __syncthreads(); if (USE_16BIT_HIST) { int32_t* feature_histogram_ptr = reinterpret_cast(smaller_leaf_splits->hist_in_leaf) + partition_hist_start; for (unsigned int i = thread_idx; i < num_items_in_partition; i += num_threads_per_block) { const int32_t packed_grad_hess = shared_hist_packed[i]; atomicAdd_system(feature_histogram_ptr + i, packed_grad_hess); } } else { atomic_add_long_t* feature_histogram_ptr = reinterpret_cast(smaller_leaf_splits->hist_in_leaf) + partition_hist_start; for (unsigned int i = thread_idx; i < num_items_in_partition; i += num_threads_per_block) { const int32_t packed_grad_hess = shared_hist_packed[i]; const int64_t packed_grad_hess_int64 = (static_cast(static_cast(packed_grad_hess >> 16)) << 32) | (static_cast(packed_grad_hess & 0x0000ffff)); atomicAdd_system(feature_histogram_ptr + i, (atomic_add_long_t)(packed_grad_hess_int64)); } } } void CUDAHistogramConstructor::LaunchConstructHistogramKernel( const CUDALeafSplitsStruct* cuda_smaller_leaf_splits, const data_size_t num_data_in_smaller_leaf, const uint8_t num_bits_in_histogram_bins) { if (cuda_row_data_->shared_hist_size() == DP_SHARED_HIST_SIZE && gpu_use_dp_) { LaunchConstructHistogramKernelInner(cuda_smaller_leaf_splits, num_data_in_smaller_leaf, num_bits_in_histogram_bins); } else if (cuda_row_data_->shared_hist_size() == SP_SHARED_HIST_SIZE && !gpu_use_dp_) { LaunchConstructHistogramKernelInner(cuda_smaller_leaf_splits, num_data_in_smaller_leaf, num_bits_in_histogram_bins); } else { Log::Fatal("Unknown shared histogram size %d", cuda_row_data_->shared_hist_size()); } } template void CUDAHistogramConstructor::LaunchConstructHistogramKernelInner( const CUDALeafSplitsStruct* cuda_smaller_leaf_splits, const data_size_t num_data_in_smaller_leaf, const uint8_t num_bits_in_histogram_bins) { if (cuda_row_data_->bit_type() == 8) { LaunchConstructHistogramKernelInner0(cuda_smaller_leaf_splits, num_data_in_smaller_leaf, num_bits_in_histogram_bins); } else if (cuda_row_data_->bit_type() == 16) { LaunchConstructHistogramKernelInner0(cuda_smaller_leaf_splits, num_data_in_smaller_leaf, num_bits_in_histogram_bins); } else if (cuda_row_data_->bit_type() == 32) { LaunchConstructHistogramKernelInner0(cuda_smaller_leaf_splits, num_data_in_smaller_leaf, num_bits_in_histogram_bins); } else { Log::Fatal("Unknown bit_type = %d", cuda_row_data_->bit_type()); } } template void CUDAHistogramConstructor::LaunchConstructHistogramKernelInner0( const CUDALeafSplitsStruct* cuda_smaller_leaf_splits, const data_size_t num_data_in_smaller_leaf, const uint8_t num_bits_in_histogram_bins) { if (cuda_row_data_->row_ptr_bit_type() == 16) { LaunchConstructHistogramKernelInner1(cuda_smaller_leaf_splits, num_data_in_smaller_leaf, num_bits_in_histogram_bins); } else if (cuda_row_data_->row_ptr_bit_type() == 32) { LaunchConstructHistogramKernelInner1(cuda_smaller_leaf_splits, num_data_in_smaller_leaf, num_bits_in_histogram_bins); } else if (cuda_row_data_->row_ptr_bit_type() == 64) { LaunchConstructHistogramKernelInner1(cuda_smaller_leaf_splits, num_data_in_smaller_leaf, num_bits_in_histogram_bins); } else { if (!cuda_row_data_->is_sparse()) { LaunchConstructHistogramKernelInner1(cuda_smaller_leaf_splits, num_data_in_smaller_leaf, num_bits_in_histogram_bins); } else { Log::Fatal("Unknown row_ptr_bit_type = %d", cuda_row_data_->row_ptr_bit_type()); } } } template void CUDAHistogramConstructor::LaunchConstructHistogramKernelInner1( const CUDALeafSplitsStruct* cuda_smaller_leaf_splits, const data_size_t num_data_in_smaller_leaf, const uint8_t num_bits_in_histogram_bins) { if (cuda_row_data_->NumLargeBinPartition() == 0) { LaunchConstructHistogramKernelInner2(cuda_smaller_leaf_splits, num_data_in_smaller_leaf, num_bits_in_histogram_bins); } else { LaunchConstructHistogramKernelInner2(cuda_smaller_leaf_splits, num_data_in_smaller_leaf, num_bits_in_histogram_bins); } } template void CUDAHistogramConstructor::LaunchConstructHistogramKernelInner2( const CUDALeafSplitsStruct* cuda_smaller_leaf_splits, const data_size_t num_data_in_smaller_leaf, const uint8_t num_bits_in_histogram_bins) { int grid_dim_x = 0; int grid_dim_y = 0; int block_dim_x = 0; int block_dim_y = 0; CalcConstructHistogramKernelDim(&grid_dim_x, &grid_dim_y, &block_dim_x, &block_dim_y, num_data_in_smaller_leaf); dim3 grid_dim(grid_dim_x, grid_dim_y); dim3 block_dim(block_dim_x, block_dim_y); if (use_quantized_grad_) { if (USE_GLOBAL_MEM_BUFFER) { if (cuda_row_data_->is_sparse()) { if (num_bits_in_histogram_bins <= 16) { CUDAConstructDiscretizedHistogramSparseKernel_GlobalMemory<<>>( cuda_smaller_leaf_splits, reinterpret_cast(cuda_gradients_), cuda_row_data_->GetBin(), cuda_row_data_->GetRowPtr(), cuda_row_data_->GetPartitionPtr(), cuda_row_data_->cuda_partition_hist_offsets(), num_data_, reinterpret_cast(cuda_hist_buffer_.RawData())); } else { CUDAConstructDiscretizedHistogramSparseKernel_GlobalMemory<<>>( cuda_smaller_leaf_splits, reinterpret_cast(cuda_gradients_), cuda_row_data_->GetBin(), cuda_row_data_->GetRowPtr(), cuda_row_data_->GetPartitionPtr(), cuda_row_data_->cuda_partition_hist_offsets(), num_data_, reinterpret_cast(cuda_hist_buffer_.RawData())); } } else { if (num_bits_in_histogram_bins <= 16) { CUDAConstructDiscretizedHistogramDenseKernel_GlobalMemory<<>>( cuda_smaller_leaf_splits, reinterpret_cast(cuda_gradients_), cuda_row_data_->GetBin(), cuda_row_data_->cuda_column_hist_offsets(), cuda_row_data_->cuda_partition_hist_offsets(), cuda_row_data_->cuda_feature_partition_column_index_offsets(), num_data_, reinterpret_cast(cuda_hist_buffer_.RawData())); } else { CUDAConstructDiscretizedHistogramDenseKernel_GlobalMemory<<>>( cuda_smaller_leaf_splits, reinterpret_cast(cuda_gradients_), cuda_row_data_->GetBin(), cuda_row_data_->cuda_column_hist_offsets(), cuda_row_data_->cuda_partition_hist_offsets(), cuda_row_data_->cuda_feature_partition_column_index_offsets(), num_data_, reinterpret_cast(cuda_hist_buffer_.RawData())); } } } else { if (cuda_row_data_->is_sparse()) { if (num_bits_in_histogram_bins <= 16) { CUDAConstructDiscretizedHistogramSparseKernel<<>>( cuda_smaller_leaf_splits, reinterpret_cast(cuda_gradients_), cuda_row_data_->GetBin(), cuda_row_data_->GetRowPtr(), cuda_row_data_->GetPartitionPtr(), cuda_row_data_->cuda_partition_hist_offsets(), num_data_); } else { CUDAConstructDiscretizedHistogramSparseKernel<<>>( cuda_smaller_leaf_splits, reinterpret_cast(cuda_gradients_), cuda_row_data_->GetBin(), cuda_row_data_->GetRowPtr(), cuda_row_data_->GetPartitionPtr(), cuda_row_data_->cuda_partition_hist_offsets(), num_data_); } } else { if (num_bits_in_histogram_bins <= 16) { CUDAConstructDiscretizedHistogramDenseKernel<<>>( cuda_smaller_leaf_splits, reinterpret_cast(cuda_gradients_), cuda_row_data_->GetBin(), cuda_row_data_->cuda_column_hist_offsets(), cuda_row_data_->cuda_partition_hist_offsets(), cuda_row_data_->cuda_feature_partition_column_index_offsets(), num_data_); } else { CUDAConstructDiscretizedHistogramDenseKernel<<>>( cuda_smaller_leaf_splits, reinterpret_cast(cuda_gradients_), cuda_row_data_->GetBin(), cuda_row_data_->cuda_column_hist_offsets(), cuda_row_data_->cuda_partition_hist_offsets(), cuda_row_data_->cuda_feature_partition_column_index_offsets(), num_data_); } } } } else { if (!USE_GLOBAL_MEM_BUFFER) { if (cuda_row_data_->is_sparse()) { CUDAConstructHistogramSparseKernel<<>>( cuda_smaller_leaf_splits, cuda_gradients_, cuda_hessians_, cuda_row_data_->GetBin(), cuda_row_data_->GetRowPtr(), cuda_row_data_->GetPartitionPtr(), cuda_row_data_->cuda_partition_hist_offsets(), num_data_); } else { CUDAConstructHistogramDenseKernel<<>>( cuda_smaller_leaf_splits, cuda_gradients_, cuda_hessians_, cuda_row_data_->GetBin(), cuda_row_data_->cuda_column_hist_offsets(), cuda_row_data_->cuda_partition_hist_offsets(), cuda_row_data_->cuda_feature_partition_column_index_offsets(), num_data_); } } else { if (cuda_row_data_->is_sparse()) { CUDAConstructHistogramSparseKernel_GlobalMemory<<>>( cuda_smaller_leaf_splits, cuda_gradients_, cuda_hessians_, cuda_row_data_->GetBin(), cuda_row_data_->GetRowPtr(), cuda_row_data_->GetPartitionPtr(), cuda_row_data_->cuda_partition_hist_offsets(), num_data_, reinterpret_cast(cuda_hist_buffer_.RawData())); } else { CUDAConstructHistogramDenseKernel_GlobalMemory<<>>( cuda_smaller_leaf_splits, cuda_gradients_, cuda_hessians_, cuda_row_data_->GetBin(), cuda_row_data_->cuda_column_hist_offsets(), cuda_row_data_->cuda_partition_hist_offsets(), cuda_row_data_->cuda_feature_partition_column_index_offsets(), num_data_, reinterpret_cast(cuda_hist_buffer_.RawData())); } } } } __global__ void SubtractHistogramKernel( const int num_total_bin, const CUDALeafSplitsStruct* cuda_smaller_leaf_splits, const CUDALeafSplitsStruct* cuda_larger_leaf_splits) { const unsigned int global_thread_index = threadIdx.x + blockIdx.x * blockDim.x; const int cuda_larger_leaf_index = cuda_larger_leaf_splits->leaf_index; if (cuda_larger_leaf_index >= 0) { const hist_t* smaller_leaf_hist = cuda_smaller_leaf_splits->hist_in_leaf; hist_t* larger_leaf_hist = cuda_larger_leaf_splits->hist_in_leaf; if (global_thread_index < 2 * num_total_bin) { larger_leaf_hist[global_thread_index] -= smaller_leaf_hist[global_thread_index]; } } } __global__ void FixHistogramKernel( const uint32_t* cuda_feature_num_bins, const uint32_t* cuda_feature_hist_offsets, const uint32_t* cuda_feature_most_freq_bins, const int* cuda_need_fix_histogram_features, const uint32_t* cuda_need_fix_histogram_features_num_bin_aligned, const CUDALeafSplitsStruct* cuda_smaller_leaf_splits) { __shared__ hist_t shared_mem_buffer[WARPSIZE]; const unsigned int blockIdx_x = blockIdx.x; const int feature_index = cuda_need_fix_histogram_features[blockIdx_x]; const uint32_t num_bin_aligned = cuda_need_fix_histogram_features_num_bin_aligned[blockIdx_x]; const uint32_t feature_hist_offset = cuda_feature_hist_offsets[feature_index]; const uint32_t most_freq_bin = cuda_feature_most_freq_bins[feature_index]; const double leaf_sum_gradients = cuda_smaller_leaf_splits->sum_of_gradients; const double leaf_sum_hessians = cuda_smaller_leaf_splits->sum_of_hessians; hist_t* feature_hist = cuda_smaller_leaf_splits->hist_in_leaf + feature_hist_offset * 2; const unsigned int threadIdx_x = threadIdx.x; const uint32_t num_bin = cuda_feature_num_bins[feature_index]; const uint32_t hist_pos = threadIdx_x << 1; const hist_t bin_gradient = (threadIdx_x < num_bin && threadIdx_x != most_freq_bin) ? feature_hist[hist_pos] : 0.0f; const hist_t bin_hessian = (threadIdx_x < num_bin && threadIdx_x != most_freq_bin) ? feature_hist[hist_pos + 1] : 0.0f; const hist_t sum_gradient = ShuffleReduceSum(bin_gradient, shared_mem_buffer, num_bin_aligned); const hist_t sum_hessian = ShuffleReduceSum(bin_hessian, shared_mem_buffer, num_bin_aligned); if (threadIdx_x == 0) { feature_hist[most_freq_bin << 1] = leaf_sum_gradients - sum_gradient; feature_hist[(most_freq_bin << 1) + 1] = leaf_sum_hessians - sum_hessian; } } template __global__ void SubtractHistogramDiscretizedKernel( const int num_total_bin, const CUDALeafSplitsStruct* cuda_smaller_leaf_splits, const CUDALeafSplitsStruct* cuda_larger_leaf_splits, hist_t* num_bit_change_buffer) { const unsigned int global_thread_index = threadIdx.x + blockIdx.x * blockDim.x; const int cuda_larger_leaf_index_ref = cuda_larger_leaf_splits->leaf_index; if (cuda_larger_leaf_index_ref >= 0) { if (PARENT_USE_16BIT_HIST) { const int32_t* smaller_leaf_hist = reinterpret_cast(cuda_smaller_leaf_splits->hist_in_leaf); int32_t* larger_leaf_hist = reinterpret_cast(cuda_larger_leaf_splits->hist_in_leaf); if (global_thread_index < num_total_bin) { larger_leaf_hist[global_thread_index] -= smaller_leaf_hist[global_thread_index]; } } else if (LARGER_USE_16BIT_HIST) { int32_t* buffer = reinterpret_cast(num_bit_change_buffer); const int32_t* smaller_leaf_hist = reinterpret_cast(cuda_smaller_leaf_splits->hist_in_leaf); int64_t* larger_leaf_hist = reinterpret_cast(cuda_larger_leaf_splits->hist_in_leaf); if (global_thread_index < num_total_bin) { const int64_t parent_hist_item = larger_leaf_hist[global_thread_index]; const int32_t smaller_hist_item = smaller_leaf_hist[global_thread_index]; const int64_t smaller_hist_item_int64 = (static_cast(static_cast(smaller_hist_item >> 16)) << 32) | static_cast(smaller_hist_item & 0x0000ffff); const int64_t larger_hist_item = parent_hist_item - smaller_hist_item_int64; buffer[global_thread_index] = static_cast(static_cast(larger_hist_item >> 32) << 16) | static_cast(larger_hist_item & 0x000000000000ffff); } } else if (SMALLER_USE_16BIT_HIST) { const int32_t* smaller_leaf_hist = reinterpret_cast(cuda_smaller_leaf_splits->hist_in_leaf); int64_t* larger_leaf_hist = reinterpret_cast(cuda_larger_leaf_splits->hist_in_leaf); if (global_thread_index < num_total_bin) { const int64_t parent_hist_item = larger_leaf_hist[global_thread_index]; const int32_t smaller_hist_item = smaller_leaf_hist[global_thread_index]; const int64_t smaller_hist_item_int64 = (static_cast(static_cast(smaller_hist_item >> 16)) << 32) | static_cast(smaller_hist_item & 0x0000ffff); const int64_t larger_hist_item = parent_hist_item - smaller_hist_item_int64; larger_leaf_hist[global_thread_index] = larger_hist_item; } } else { const int64_t* smaller_leaf_hist = reinterpret_cast(cuda_smaller_leaf_splits->hist_in_leaf); int64_t* larger_leaf_hist = reinterpret_cast(cuda_larger_leaf_splits->hist_in_leaf); if (global_thread_index < num_total_bin) { larger_leaf_hist[global_thread_index] -= smaller_leaf_hist[global_thread_index]; } } } } __global__ void CopyChangedNumBitHistogram( const int num_total_bin, const CUDALeafSplitsStruct* cuda_larger_leaf_splits, hist_t* num_bit_change_buffer) { int32_t* hist_dst = reinterpret_cast(cuda_larger_leaf_splits->hist_in_leaf); const int32_t* hist_src = reinterpret_cast(num_bit_change_buffer); const unsigned int global_thread_index = threadIdx.x + blockIdx.x * blockDim.x; if (global_thread_index < static_cast(num_total_bin)) { hist_dst[global_thread_index] = hist_src[global_thread_index]; } } template __global__ void FixHistogramDiscretizedKernel( const uint32_t* cuda_feature_num_bins, const uint32_t* cuda_feature_hist_offsets, const uint32_t* cuda_feature_most_freq_bins, const int* cuda_need_fix_histogram_features, const uint32_t* cuda_need_fix_histogram_features_num_bin_aligned, const CUDALeafSplitsStruct* cuda_smaller_leaf_splits) { __shared__ int64_t shared_mem_buffer[WARPSIZE]; const unsigned int blockIdx_x = blockIdx.x; const int feature_index = cuda_need_fix_histogram_features[blockIdx_x]; const uint32_t num_bin_aligned = cuda_need_fix_histogram_features_num_bin_aligned[blockIdx_x]; const uint32_t feature_hist_offset = cuda_feature_hist_offsets[feature_index]; const uint32_t most_freq_bin = cuda_feature_most_freq_bins[feature_index]; if (USE_16BIT_HIST) { const int64_t leaf_sum_gradients_hessians_int64 = cuda_smaller_leaf_splits->sum_of_gradients_hessians; const int32_t leaf_sum_gradients_hessians = (static_cast(leaf_sum_gradients_hessians_int64 >> 32) << 16) | static_cast(leaf_sum_gradients_hessians_int64 & 0x000000000000ffff); int32_t* feature_hist = reinterpret_cast(cuda_smaller_leaf_splits->hist_in_leaf) + feature_hist_offset; const unsigned int threadIdx_x = threadIdx.x; const uint32_t num_bin = cuda_feature_num_bins[feature_index]; const int32_t bin_gradient_hessian = (threadIdx_x < num_bin && threadIdx_x != most_freq_bin) ? feature_hist[threadIdx_x] : 0; const int32_t sum_gradient_hessian = ShuffleReduceSum( bin_gradient_hessian, reinterpret_cast(shared_mem_buffer), num_bin_aligned); if (threadIdx_x == 0) { feature_hist[most_freq_bin] = leaf_sum_gradients_hessians - sum_gradient_hessian; } } else { const int64_t leaf_sum_gradients_hessians = cuda_smaller_leaf_splits->sum_of_gradients_hessians; int64_t* feature_hist = reinterpret_cast(cuda_smaller_leaf_splits->hist_in_leaf) + feature_hist_offset; const unsigned int threadIdx_x = threadIdx.x; const uint32_t num_bin = cuda_feature_num_bins[feature_index]; const int64_t bin_gradient_hessian = (threadIdx_x < num_bin && threadIdx_x != most_freq_bin) ? feature_hist[threadIdx_x] : 0; const int64_t sum_gradient_hessian = ShuffleReduceSum(bin_gradient_hessian, shared_mem_buffer, num_bin_aligned); if (threadIdx_x == 0) { feature_hist[most_freq_bin] = leaf_sum_gradients_hessians - sum_gradient_hessian; } } } void CUDAHistogramConstructor::LaunchSubtractHistogramKernel( const CUDALeafSplitsStruct* cuda_smaller_leaf_splits, const CUDALeafSplitsStruct* cuda_larger_leaf_splits, const bool use_discretized_grad, const uint8_t parent_num_bits_in_histogram_bins, const uint8_t smaller_num_bits_in_histogram_bins, const uint8_t larger_num_bits_in_histogram_bins) { if (!use_discretized_grad) { const int num_subtract_threads = 2 * num_total_bin_; const int num_subtract_blocks = (num_subtract_threads + SUBTRACT_BLOCK_SIZE - 1) / SUBTRACT_BLOCK_SIZE; global_timer.Start("CUDAHistogramConstructor::FixHistogramKernel"); if (need_fix_histogram_features_.size() > 0) { FixHistogramKernel<<>>( cuda_feature_num_bins_.RawData(), cuda_feature_hist_offsets_.RawData(), cuda_feature_most_freq_bins_.RawData(), cuda_need_fix_histogram_features_.RawData(), cuda_need_fix_histogram_features_num_bin_aligned_.RawData(), cuda_smaller_leaf_splits); } global_timer.Stop("CUDAHistogramConstructor::FixHistogramKernel"); global_timer.Start("CUDAHistogramConstructor::SubtractHistogramKernel"); SubtractHistogramKernel<<>>( num_total_bin_, cuda_smaller_leaf_splits, cuda_larger_leaf_splits); global_timer.Stop("CUDAHistogramConstructor::SubtractHistogramKernel"); } else { const int num_subtract_threads = num_total_bin_; const int num_subtract_blocks = (num_subtract_threads + SUBTRACT_BLOCK_SIZE - 1) / SUBTRACT_BLOCK_SIZE; global_timer.Start("CUDAHistogramConstructor::FixHistogramDiscretizedKernel"); if (need_fix_histogram_features_.size() > 0) { if (smaller_num_bits_in_histogram_bins <= 16) { FixHistogramDiscretizedKernel<<>>( cuda_feature_num_bins_.RawData(), cuda_feature_hist_offsets_.RawData(), cuda_feature_most_freq_bins_.RawData(), cuda_need_fix_histogram_features_.RawData(), cuda_need_fix_histogram_features_num_bin_aligned_.RawData(), cuda_smaller_leaf_splits); } else { FixHistogramDiscretizedKernel<<>>( cuda_feature_num_bins_.RawData(), cuda_feature_hist_offsets_.RawData(), cuda_feature_most_freq_bins_.RawData(), cuda_need_fix_histogram_features_.RawData(), cuda_need_fix_histogram_features_num_bin_aligned_.RawData(), cuda_smaller_leaf_splits); } } global_timer.Stop("CUDAHistogramConstructor::FixHistogramDiscretizedKernel"); global_timer.Start("CUDAHistogramConstructor::SubtractHistogramDiscretizedKernel"); if (parent_num_bits_in_histogram_bins <= 16) { CHECK_LE(smaller_num_bits_in_histogram_bins, 16); CHECK_LE(larger_num_bits_in_histogram_bins, 16); SubtractHistogramDiscretizedKernel<<>>( num_total_bin_, cuda_smaller_leaf_splits, cuda_larger_leaf_splits, hist_buffer_for_num_bit_change_.RawData()); } else if (larger_num_bits_in_histogram_bins <= 16) { CHECK_LE(smaller_num_bits_in_histogram_bins, 16); SubtractHistogramDiscretizedKernel<<>>( num_total_bin_, cuda_smaller_leaf_splits, cuda_larger_leaf_splits, hist_buffer_for_num_bit_change_.RawData()); CopyChangedNumBitHistogram<<>>( num_total_bin_, cuda_larger_leaf_splits, hist_buffer_for_num_bit_change_.RawData()); } else if (smaller_num_bits_in_histogram_bins <= 16) { SubtractHistogramDiscretizedKernel<<>>( num_total_bin_, cuda_smaller_leaf_splits, cuda_larger_leaf_splits, hist_buffer_for_num_bit_change_.RawData()); } else { SubtractHistogramDiscretizedKernel<<>>( num_total_bin_, cuda_smaller_leaf_splits, cuda_larger_leaf_splits, hist_buffer_for_num_bit_change_.RawData()); } global_timer.Stop("CUDAHistogramConstructor::SubtractHistogramDiscretizedKernel"); } } } // namespace LightGBM #endif // USE_CUDA ================================================ FILE: src/treelearner/cuda/cuda_histogram_constructor.hpp ================================================ [File too large to display: 7.3 KB] ================================================ FILE: src/treelearner/cuda/cuda_leaf_splits.cpp ================================================ [File too large to display: 3.6 KB] ================================================ FILE: src/treelearner/cuda/cuda_leaf_splits.cu ================================================ [File too large to display: 19.3 KB] 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