[ { "path": ".github/ISSUE_TEMPLATE/bug_report.md", "content": "---\nname: Bug report\nabout: Create a report to help us improve\ntitle: ''\nlabels: bug, enhancement\nassignees: ''\n\n---\n\n**Describe the bug**\nA clear and concise description of what the bug is.\nEx: Using `scipy==1.8` with `bayesian-optimization==1.2.0` results in `TypeError: 'float' object is not subscriptable`.\n\n\n\n**To Reproduce**\nA concise, self-contained code snippet that reproduces the bug you would like to report.\n\nEx:\n```python\nfrom bayes_opt import BayesianOptimization\n\nblack_box_function = lambda x, y: -x ** 2 - (y - 1) ** 2 + 1\n\npbounds = {'x': (2, 4), 'y': (-3, 3)}\n\noptimizer = BayesianOptimization(\n f=black_box_function,\n pbounds=pbounds\n)\noptimizer.maximize()\n```\n\n**Expected behavior**\nA clear and concise description of what you expected to happen.\n\n**Screenshots**\nIf applicable, add screenshots to help explain your problem.\n\n**Environment (please complete the following information):**\n - OS: [e.g. Arch Linux, macOS, Windows]\n - `python` Version [e.g. 3.8.9]\n - `numpy` Version [e.g. 1.21.6]\n - `scipy` Version [e.g. 1.8.0]\n - `bayesian-optimization` Version [e.g. 1.2.0]\n\n**Additional context**\nAdd any other context about the problem here.\n" }, { "path": ".github/ISSUE_TEMPLATE/feature_request.md", "content": "---\nname: Feature request\nabout: Suggest an idea for this project\ntitle: ''\nlabels: enhancement\nassignees: ''\n\n---\n\n**Is your feature request related to a problem? Please describe.**\nA clear and concise description of what the problem is.\n\n**Describe the solution you'd like**\nA clear and concise description of what you want to happen.\n\n**References or alternative approaches**\nIf this feature was described in literature, please add references here. Additionally, feel free to add descriptions of any alternative solutions or features you've considered.\n\n**Additional context**\nAdd any other context or screenshots about the feature request here.\n\n**Are you able and willing to implement this feature yourself and open a pull request?**\n- [ ] Yes, I can provide this feature.\n" }, { "path": ".github/workflows/build_docs.yml", "content": "name: docs\n\non:\n release:\n types: [published]\n push:\n branches:\n - master\n pull_request:\n\nconcurrency:\n group: ${{ github.workflow }}\n\njobs:\n build-docs-and-publish:\n runs-on: ubuntu-latest\n permissions:\n contents: write\n steps:\n - uses: actions/checkout@v3\n - name: Install uv\n uses: astral-sh/setup-uv@v6\n with:\n python-version: \"3.10\"\n - name: Get tag\n uses: olegtarasov/get-tag@v2.1\n - name: Install pandoc\n run: sudo apt-get install -y pandoc\n - name: Install package and test dependencies\n run: uv sync --extra dev\n - name: build sphinx docs\n run: |\n cd docsrc\n uv run make github\n - name: Determine directory to publish docs to\n id: docs-publish-dir\n uses: jannekem/run-python-script-action@v1\n with:\n script: |\n import os, re\n github_ref = os.environ.get('GITHUB_REF')\n m = re.match(r'^refs/tags/v([0-9]+\\.[0-9]+\\.[0-9]+(-dev\\.[0-9]+)?)$',\n github_ref)\n if m:\n target = m.group(1)\n elif github_ref == 'refs/heads/master':\n target = 'master'\n else:\n target = ''\n set_output('target', target)\n - name: Deploy\n uses: peaceiris/actions-gh-pages@v3\n if: steps.docs-publish-dir.outputs.target != ''\n with:\n github_token: ${{ secrets.GITHUB_TOKEN }}\n publish_dir: ./docs/html\n destination_dir: ${{ steps.docs-publish-dir.outputs.target }}\n keep_files: false\n outputs:\n docs-target: ${{ steps.docs-publish-dir.outputs.target }}\n update-versions:\n name: Update docs versions JSON\n needs: build-docs-and-publish\n if: needs.build-docs-and-publish.outputs.docs-target != ''\n runs-on: Ubuntu-latest\n permissions:\n contents: write\n steps:\n - uses: actions/checkout@v3\n with:\n ref: gh-pages\n - name: Write versions to JSON file\n uses: jannekem/run-python-script-action@v1\n with:\n script: |\n import json\n import re\n\n # dependency of sphinx, so should be installed\n from packaging import version as version_\n from pathlib import Path\n\n cwd = Path.cwd()\n\n versions = sorted((item.name for item in cwd.iterdir()\n if item.is_dir() and not item.name.startswith('.')),\n reverse=True)\n\n # Filter out master and dev versions\n parseable_versions = []\n for version in versions:\n try:\n version_.parse(version)\n except version_.InvalidVersion:\n continue\n parseable_versions.append(version)\n\n if parseable_versions:\n max_version = max(parseable_versions, key=version_.parse)\n else:\n max_version = None\n target_dir = Path('gh-pages')\n target_dir.mkdir(parents=True)\n\n versions = [\n dict(\n version=version,\n title=version + ' (stable)' if version == max_version else version,\n aliases=['stable'] if version == max_version else [],\n ) for version in versions\n ]\n target_file = target_dir / 'versions.json'\n with target_file.open('w') as f:\n json.dump(versions, f)\n\n - name: Publish versions JSON to GitHub pages\n uses: peaceiris/actions-gh-pages@v3\n with:\n github_token: ${{ secrets.GITHUB_TOKEN }}\n publish_dir: gh-pages\n keep_files: true\n" }, { "path": ".github/workflows/format_and_lint.yml", "content": "name: Code format and lint\n\non:\n push:\n branches: [ \"master\" ]\n pull_request:\n\npermissions:\n contents: read\n\njobs:\n check:\n runs-on: ubuntu-latest\n steps:\n - uses: actions/checkout@v3\n - name: Install uv\n uses: astral-sh/setup-uv@v6\n with:\n python-version: \"3.9\"\n - name: Install dependencies\n run: uv sync --extra dev\n - name: Run pre-commit\n run: uv run pre-commit run --all-files --show-diff-on-failure --color=always\n" }, { "path": ".github/workflows/python-publish.yml", "content": "# This workflow will upload a Python Package using uv when a release is created\n# Note that you must manually update the version number in pyproject.toml before attempting this.\n\nname: Upload Python Package\n\non:\n release:\n types: [published]\n\npermissions:\n contents: read\n\njobs:\n deploy:\n runs-on: ubuntu-latest\n steps:\n - uses: actions/checkout@v4\n - uses: astral-sh/setup-uv@v6\n - run: uv build\n - name: Publish to pypi\n env:\n PYPI_API_TOKEN: ${{ secrets.PYPI_API_TOKEN }}\n run: uv publish --token \"$PYPI_API_TOKEN\"\n" }, { "path": ".github/workflows/run_tests.yml", "content": "# This workflow will install Python dependencies and run tests in multiple versions of Python\n# For more information see: https://help.github.com/actions/language-and-framework-guides/using-python-with-github-actions\n\nname: tests\n\non:\n push:\n branches: [ \"master\" ]\n pull_request:\n\npermissions:\n contents: read\n\njobs:\n build:\n name: Python ${{ matrix.python-version }} - numpy ${{ matrix.numpy-version }}\n runs-on: ubuntu-latest\n strategy:\n matrix:\n python-version: [\"3.9\", \"3.10\", \"3.11\", \"3.12\", \"3.13\", \"3.14\"]\n numpy-version: [\">=1.25,<2\", \">=2\"]\n exclude:\n - python-version: \"3.13\" # numpy<2 is not supported on 3.13+\n numpy-version: \">=1.25,<2\"\n - python-version: \"3.14\"\n numpy-version: \">=1.25,<2\"\n\n steps:\n - uses: actions/checkout@v3\n - name: Install uv and set the python version\n uses: astral-sh/setup-uv@v6\n with:\n python-version: ${{ matrix.python-version }}\n - name: Install test dependencies\n run: |\n uv sync --extra dev\n uv pip install \"numpy${{ matrix.numpy-version}}\"\n - name: Run pytest\n run: uv run --no-sync pytest --cov-report xml --cov=bayes_opt/\n - name: Upload coverage to Codecov\n uses: codecov/codecov-action@v4\n with:\n token: ${{ secrets.CODECOV_TOKEN }}\n" }, { "path": ".gitignore", "content": ".ipynb_checkpoints\n*.pyc\n*.egg-info/\nbuild/\ndist/\nscratch/\n.idea/\n.DS_Store\nbo_eg*.png\ngif/\n\n# Unit test / coverage reports\nhtmlcov/\n.tox/\n.coverage\n.coverage.*\n.cache\nnosetests.xml\ncoverage.xml\n*,cover\n.hypothesis/\n\n# Environments\n.env\n.venv\nenv/\nvenv/\nENV/\nenv.bak/\nvenv.bak/\n*temp*\n\ndocs/*\ndocsrc/.ipynb_checkpoints/*\ndocsrc/*.ipynb\ndocsrc/static/*\ndocsrc/README.md\n\npoetry.lock\nuv.lock\n\n# Add log files and optimizer state files to gitignore\nexamples/logs.log\nexamples/optimizer_state.json\n" }, { "path": ".pre-commit-config.yaml", "content": "repos:\n - hooks:\n - id: ruff\n name: ruff-lint\n - id: ruff-format\n name: ruff-format\n args: [--check]\n repo: https://github.com/astral-sh/ruff-pre-commit\n rev: v0.12.3" }, { "path": "LICENSE", "content": "The MIT License (MIT)\n\nCopyright (c) 2014 Fernando M. F. Nogueira\n\nPermission 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:\n\nThe above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.\n\nTHE 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." }, { "path": "README.md", "content": "
\n

\n
\n\n# Bayesian Optimization\n\n![tests](https://github.com/bayesian-optimization/BayesianOptimization/actions/workflows/run_tests.yml/badge.svg)\n[![docs - stable](https://img.shields.io/badge/docs-stable-blue)](https://bayesian-optimization.github.io/BayesianOptimization/index.html)\n[![Codecov](https://codecov.io/github/bayesian-optimization/BayesianOptimization/badge.svg?branch=master&service=github)](https://codecov.io/github/bayesian-optimization/BayesianOptimization?branch=master)\n[![Pypi](https://img.shields.io/pypi/v/bayesian-optimization.svg)](https://pypi.python.org/pypi/bayesian-optimization)\n![PyPI - Python Version](https://img.shields.io/pypi/pyversions/bayesian-optimization)\n\n\nPure Python implementation of bayesian global optimization with gaussian\nprocesses.\n\n\nThis is a constrained global optimization package built upon bayesian inference\nand gaussian processes, that attempts to find the maximum value of an unknown\nfunction in as few iterations as possible. This technique is particularly\nsuited for optimization of high cost functions and situations where the balance\nbetween exploration and exploitation is important.\n\n## Installation\n\n* pip (via PyPI):\n\n```console\n$ pip install bayesian-optimization\n```\n\n* Conda (via conda-forge):\n\n```console\n$ conda install -c conda-forge bayesian-optimization\n```\n\n## How does it work?\n\nSee the [documentation](https://bayesian-optimization.github.io/BayesianOptimization/) for how to use this package.\n\nBayesian optimization works by constructing a posterior distribution of functions (gaussian process) that best describes the function you want to optimize. As the number of observations grows, the posterior distribution improves, and the algorithm becomes more certain of which regions in parameter space are worth exploring and which are not, as seen in the picture below.\n\n![BayesianOptimization in action](docsrc/static/bo_example.png)\n\nAs you iterate over and over, the algorithm balances its needs of exploration and exploitation taking into account what it knows about the target function. At each step a Gaussian Process is fitted to the known samples (points previously explored), and the posterior distribution, combined with a exploration strategy (such as UCB (Upper Confidence Bound), or EI (Expected Improvement)), are used to determine the next point that should be explored (see the gif below).\n\n![BayesianOptimization in action](docsrc/static/bayesian_optimization.gif)\n\nThis process is designed to minimize the number of steps required to find a combination of parameters that are close to the optimal combination. To do so, this method uses a proxy optimization problem (finding the maximum of the acquisition function) that, albeit still a hard problem, is cheaper (in the computational sense) and common tools can be employed. Therefore Bayesian Optimization is most adequate for situations where sampling the function to be optimized is a very expensive endeavor. See the references for a proper discussion of this method.\n\nThis project is under active development. If you run into trouble, find a bug or notice\nanything that needs correction, please let us know by filing an issue.\n\n\n## Basic tour of the Bayesian Optimization package\n\n### 1. Specifying the function to be optimized\n\nThis is a function optimization package, therefore the first and most important ingredient is, of course, the function to be optimized.\n\n**DISCLAIMER:** We know exactly how the output of the function below depends on its parameter. Obviously this is just an example, and you shouldn't expect to know it in a real scenario. However, it should be clear that you don't need to. All you need in order to use this package (and more generally, this technique) is a function `f` that takes a known set of parameters and outputs a real number.\n\n\n```python\ndef black_box_function(x, y):\n \"\"\"Function with unknown internals we wish to maximize.\n\n This is just serving as an example, for all intents and\n purposes think of the internals of this function, i.e.: the process\n which generates its output values, as unknown.\n \"\"\"\n return -x ** 2 - (y - 1) ** 2 + 1\n```\n\n### 2. Getting Started\n\nAll we need to get started is to instantiate a `BayesianOptimization` object specifying a function to be optimized `f`, and its parameters with their corresponding bounds, `pbounds`. This is a constrained optimization technique, so you must specify the minimum and maximum values that can be probed for each parameter in order for it to work\n\n\n```python\nfrom bayes_opt import BayesianOptimization\n\n# Bounded region of parameter space\npbounds = {'x': (2, 4), 'y': (-3, 3)}\n\noptimizer = BayesianOptimization(\n f=black_box_function,\n pbounds=pbounds,\n random_state=1,\n)\n```\n\nThe BayesianOptimization object will work out of the box without much tuning needed. The main method you should be aware of is `maximize`, which does exactly what you think it does.\n\nThere are many parameters you can pass to maximize, nonetheless, the most important ones are:\n- `n_iter`: How many steps of bayesian optimization you want to perform. The more steps the more likely to find a good maximum you are.\n- `init_points`: How many steps of **random** exploration you want to perform. Random exploration can help by diversifying the exploration space.\n\n\n```python\noptimizer.maximize(\n init_points=2,\n n_iter=3,\n)\n```\n\n | iter | target | x | y |\n -------------------------------------------------\n | 1 | -7.135 | 2.834 | 1.322 |\n | 2 | -7.78 | 2.0 | -1.186 |\n | 3 | -19.0 | 4.0 | 3.0 |\n | 4 | -16.3 | 2.378 | -2.413 |\n | 5 | -4.441 | 2.105 | -0.005822 |\n =================================================\n\n\nThe best combination of parameters and target value found can be accessed via the property `optimizer.max`.\n\n\n```python\nprint(optimizer.max)\n>>> {'target': -4.441293113411222, 'params': {'y': -0.005822117636089974, 'x': 2.104665051994087}}\n```\n\n\nWhile the list of all parameters probed and their corresponding target values is available via the property `optimizer.res`.\n\n\n```python\nfor i, res in enumerate(optimizer.res):\n print(\"Iteration {}: \\n\\t{}\".format(i, res))\n\n>>> Iteration 0:\n>>> {'target': -7.135455292718879, 'params': {'y': 1.3219469606529488, 'x': 2.8340440094051482}}\n>>> Iteration 1:\n>>> {'target': -7.779531005607566, 'params': {'y': -1.1860045642089614, 'x': 2.0002287496346898}}\n>>> Iteration 2:\n>>> {'target': -19.0, 'params': {'y': 3.0, 'x': 4.0}}\n>>> Iteration 3:\n>>> {'target': -16.29839645063864, 'params': {'y': -2.412527795983739, 'x': 2.3776144540856503}}\n>>> Iteration 4:\n>>> {'target': -4.441293113411222, 'params': {'y': -0.005822117636089974, 'x': 2.104665051994087}}\n```\n\n\n## Minutiae\n\n### Citation\n\nIf you used this package in your research, please cite it:\n\n```\n@Misc{,\n author = {Fernando Nogueira},\n title = {{Bayesian Optimization}: Open source constrained global optimization tool for {Python}},\n year = {2014--},\n url = \" https://github.com/bayesian-optimization/BayesianOptimization\"\n}\n```\nIf you used any of the advanced functionalities, please additionally cite the corresponding publication:\n\nFor the `SequentialDomainTransformer`:\n```\n@article{\n author = {Stander, Nielen and Craig, Kenneth},\n year = {2002},\n month = {06},\n pages = {},\n title = {On the robustness of a simple domain reduction scheme for simulation-based optimization},\n volume = {19},\n journal = {International Journal for Computer-Aided Engineering and Software (Eng. Comput.)},\n doi = {10.1108/02644400210430190}\n}\n```\n\nFor constrained optimization:\n```\n@inproceedings{gardner2014bayesian,\n title={Bayesian optimization with inequality constraints.},\n author={Gardner, Jacob R and Kusner, Matt J and Xu, Zhixiang Eddie and Weinberger, Kilian Q and Cunningham, John P},\n booktitle={ICML},\n volume={2014},\n pages={937--945},\n year={2014}\n}\n```\n\nFor optimization over non-float parameters:\n```\n@article{garrido2020dealing,\n title={Dealing with categorical and integer-valued variables in bayesian optimization with gaussian processes},\n author={Garrido-Merch{\\'a}n, Eduardo C and Hern{\\'a}ndez-Lobato, Daniel},\n journal={Neurocomputing},\n volume={380},\n pages={20--35},\n year={2020},\n publisher={Elsevier}\n}\n```\n" }, { "path": "bayes_opt/__init__.py", "content": "\"\"\"Pure Python implementation of bayesian global optimization with gaussian processes.\"\"\"\n\nfrom __future__ import annotations\n\nimport importlib.metadata\n\nfrom bayes_opt import acquisition\nfrom bayes_opt.bayesian_optimization import BayesianOptimization\nfrom bayes_opt.constraint import ConstraintModel\nfrom bayes_opt.domain_reduction import SequentialDomainReductionTransformer\nfrom bayes_opt.logger import ScreenLogger\nfrom bayes_opt.target_space import TargetSpace\n\n__version__ = importlib.metadata.version(\"bayesian-optimization\")\n\n\n__all__ = [\n \"BayesianOptimization\",\n \"ConstraintModel\",\n \"ScreenLogger\",\n \"SequentialDomainReductionTransformer\",\n \"TargetSpace\",\n \"acquisition\",\n]\n" }, { "path": "bayes_opt/acquisition.py", "content": "\"\"\"Acquisition functions for Bayesian Optimization.\n\nThe acquisition functions in this module can be grouped the following way:\n\n- One of the base acquisition functions\n (:py:class:`UpperConfidenceBound`,\n :py:class:`ProbabilityOfImprovement` and\n :py:class:`ExpectedImprovement`) is always dictating the basic\n behavior of the suggestion step. They can be used alone or combined with a meta acquisition function.\n- :py:class:`GPHedge` is a meta acquisition function that combines multiple\n base acquisition functions and determines the most suitable one for a particular problem.\n- :py:class:`ConstantLiar` is a meta acquisition function that can be\n used for parallelized optimization and discourages sampling near a previously suggested, but not yet\n evaluated, point.\n- :py:class:`AcquisitionFunction` is the base class for all\n acquisition functions. You can implement your own acquisition function by subclassing it. See the\n `Acquisition Functions notebook <../acquisition.html>`__ to understand the many ways this class can be\n modified.\n\"\"\"\n\nfrom __future__ import annotations\n\nimport abc\nimport warnings\nfrom copy import deepcopy\nfrom typing import TYPE_CHECKING, Any, Literal, NoReturn\n\nimport numpy as np\nfrom numpy.random import RandomState\nfrom packaging import version\nfrom scipy import __version__ as scipy_version\nfrom scipy.optimize._differentialevolution import DifferentialEvolutionSolver, minimize\nfrom scipy.special import softmax\nfrom scipy.stats import norm\nfrom sklearn.gaussian_process import GaussianProcessRegressor\n\nfrom bayes_opt.exception import (\n ConstraintNotSupportedError,\n NoValidPointRegisteredError,\n TargetSpaceEmptyError,\n)\nfrom bayes_opt.target_space import TargetSpace\nfrom bayes_opt.util import ensure_rng\n\nif TYPE_CHECKING:\n from collections.abc import Callable, Sequence\n\n from numpy.typing import NDArray\n from scipy.optimize import OptimizeResult\n\n from bayes_opt.constraint import ConstraintModel\n\n Float = np.floating[Any]\n\n\nclass AcquisitionFunction(abc.ABC):\n \"\"\"Base class for acquisition functions.\n\n Parameters\n ----------\n random_state : int, RandomState, default None\n Set the random state for reproducibility.\n \"\"\"\n\n def __init__(self, random_state: int | RandomState | None = None) -> None:\n if random_state is not None:\n msg = (\n \"Providing a random_state to an acquisition function during initialization is deprecated \"\n \"and will be ignored. The random_state is instead provided automatically during the \"\n \"suggest() call.\"\n )\n warnings.warn(msg, DeprecationWarning, stacklevel=2)\n self.i = 0\n\n @abc.abstractmethod\n def base_acq(self, *args: Any, **kwargs: Any) -> NDArray[Float]:\n \"\"\"Provide access to the base acquisition function.\"\"\"\n\n def _fit_gp(self, gp: GaussianProcessRegressor, target_space: TargetSpace) -> None:\n # Sklearn's GP throws a large number of warnings at times, but\n # we don't really need to see them here.\n with warnings.catch_warnings():\n warnings.simplefilter(\"ignore\")\n gp.fit(target_space.params, target_space.target)\n if target_space.constraint is not None:\n target_space.constraint.fit(target_space.params, target_space._constraint_values)\n\n def get_acquisition_params(self) -> dict[str, Any]:\n \"\"\"\n Get the parameters of the acquisition function.\n\n Returns\n -------\n dict\n The parameters of the acquisition function.\n \"\"\"\n error_msg = (\n \"Custom AcquisitionFunction subclasses must implement their own get_acquisition_params method.\"\n )\n raise NotImplementedError(error_msg)\n\n def set_acquisition_params(self, params: dict[str, Any]) -> None:\n \"\"\"\n Set the parameters of the acquisition function.\n\n Parameters\n ----------\n params : dict\n The parameters of the acquisition function.\n \"\"\"\n error_msg = (\n \"Custom AcquisitionFunction subclasses must implement their own set_acquisition_params method.\"\n )\n raise NotImplementedError(error_msg)\n\n def suggest(\n self,\n gp: GaussianProcessRegressor,\n target_space: TargetSpace,\n n_random: int = 10_000,\n n_smart: int = 10,\n fit_gp: bool = True,\n random_state: int | RandomState | None = None,\n ) -> NDArray[Float]:\n \"\"\"Suggest a promising point to probe next.\n\n Parameters\n ----------\n gp : GaussianProcessRegressor\n A fitted Gaussian Process.\n\n target_space : TargetSpace\n The target space to probe.\n\n n_random : int, default 10_000\n Number of random samples to use.\n\n n_smart : int, default 10\n Controls the number of runs for the smart optimization. If all parameters are continuous,\n this is the number of random starting points for the L-BFGS-B optimizer. If there are\n discrete parameters, n_smart of the best points are used as starting points for the\n differential evolution optimizer, with the remaining points being random samples.\n\n fit_gp : bool, default True\n Whether to fit the Gaussian Process to the target space.\n Set to False if the GP is already fitted.\n\n random_state : int, RandomState, default None\n Random state to use for the optimization.\n\n Returns\n -------\n np.ndarray\n Suggested point to probe next.\n \"\"\"\n random_state = ensure_rng(random_state)\n if len(target_space) == 0:\n msg = (\n \"Cannot suggest a point without previous samples. Use \"\n \" target_space.random_sample() to generate a point and \"\n \" target_space.probe(*) to evaluate it.\"\n )\n raise TargetSpaceEmptyError(msg)\n self.i += 1\n if fit_gp:\n self._fit_gp(gp=gp, target_space=target_space)\n\n acq = self._get_acq(gp=gp, constraint=target_space.constraint)\n return self._acq_min(acq, target_space, n_random=n_random, n_smart=n_smart, random_state=random_state)\n\n def _get_acq(\n self, gp: GaussianProcessRegressor, constraint: ConstraintModel | None = None\n ) -> Callable[[NDArray[Float]], NDArray[Float]]:\n \"\"\"Prepare the acquisition function for minimization.\n\n Transforms a base_acq Callable, which takes `mean` and `std` as\n input, into an acquisition function that only requires an array of\n parameters.\n Handles GP predictions and constraints.\n\n Parameters\n ----------\n gp : GaussianProcessRegressor\n A fitted Gaussian Process.\n\n constraint : ConstraintModel, default None\n A fitted constraint model, if constraints are present and the\n acquisition function supports them.\n\n Returns\n -------\n Callable\n Function to minimize.\n \"\"\"\n dim = gp.X_train_.shape[1]\n if constraint is not None:\n\n def acq(x: NDArray[Float]) -> NDArray[Float]:\n x = x.reshape(-1, dim)\n with warnings.catch_warnings():\n warnings.simplefilter(\"ignore\")\n mean: NDArray[Float]\n std: NDArray[Float]\n p_constraints: NDArray[Float]\n mean, std = gp.predict(x, return_std=True)\n p_constraints = constraint.predict(x)\n return -1 * self.base_acq(mean, std) * p_constraints\n else:\n\n def acq(x: NDArray[Float]) -> NDArray[Float]:\n x = x.reshape(-1, dim)\n with warnings.catch_warnings():\n warnings.simplefilter(\"ignore\")\n mean: NDArray[Float]\n std: NDArray[Float]\n mean, std = gp.predict(x, return_std=True)\n return -1 * self.base_acq(mean, std)\n\n return acq\n\n def _acq_min(\n self,\n acq: Callable[[NDArray[Float]], NDArray[Float]],\n space: TargetSpace,\n random_state: RandomState,\n n_random: int = 10_000,\n n_smart: int = 10,\n ) -> NDArray[Float]:\n \"\"\"Find the maximum of the acquisition function.\n\n Uses a combination of random sampling (cheap) and either 'L-BFGS-B' or differential evolution\n optimization (smarter, but expensive). First samples `n_random` (1e5) points at random, then\n uses the best points as starting points for the smart optimizer.\n\n Parameters\n ----------\n acq : Callable\n Acquisition function to use. Should accept an array of parameters `x`.\n\n space : TargetSpace\n The target space over which to optimize.\n\n random_state : RandomState\n Random state to use for the optimization.\n\n n_random : int\n Number of random samples to use.\n\n n_smart : int\n Controls the number of runs for the smart optimization. If all parameters are continuous,\n this is the number of random starting points for the L-BFGS-B optimizer. Otherwise, n_smart\n of the best points are used as starting points for the differential evolution optimizer, with\n the remaining points being random samples.\n\n Returns\n -------\n np.ndarray\n Parameters maximizing the acquisition function.\n\n \"\"\"\n if n_random == 0 and n_smart == 0:\n error_msg = \"Either n_random or n_smart needs to be greater than 0.\"\n raise ValueError(error_msg)\n x_min_r, min_acq_r, x_seeds = self._random_sample_minimize(\n acq, space, random_state, n_random=max(n_random, n_smart), n_x_seeds=n_smart\n )\n if n_smart:\n x_min_s, min_acq_s = self._smart_minimize(acq, space, x_seeds=x_seeds, random_state=random_state)\n # Either n_random or n_smart is not 0 => at least one of x_min_r and x_min_s is not None\n if min_acq_r > min_acq_s:\n return x_min_s\n return x_min_r\n\n def _random_sample_minimize(\n self,\n acq: Callable[[NDArray[Float]], NDArray[Float]],\n space: TargetSpace,\n random_state: RandomState,\n n_random: int,\n n_x_seeds: int = 0,\n ) -> tuple[NDArray[Float] | None, float, NDArray[Float]]:\n \"\"\"Random search to find the minimum of `acq` function.\n\n Parameters\n ----------\n acq : Callable\n Acquisition function to use. Should accept an array of parameters `x`.\n\n space : TargetSpace\n The target space over which to optimize.\n\n random_state : RandomState\n Random state to use for the optimization.\n\n n_random : int\n Number of random samples to use.\n\n n_x_seeds : int\n Number of top points to return, for use as starting points for L-BFGS-B.\n\n Returns\n -------\n x_min : np.ndarray\n Random sample minimizing the acquisition function.\n\n min_acq : float\n Acquisition function value at `x_min`\n \"\"\"\n if n_random == 0:\n return None, np.inf, space.random_sample(n_x_seeds, random_state=random_state)\n x_tries = space.random_sample(n_random, random_state=random_state)\n ys = acq(x_tries)\n x_min = x_tries[ys.argmin()]\n min_acq = ys.min()\n if n_x_seeds != 0:\n idxs = np.argsort(ys)[:n_x_seeds]\n x_seeds = x_tries[idxs]\n else:\n x_seeds = []\n return x_min, min_acq, x_seeds\n\n def _smart_minimize(\n self,\n acq: Callable[[NDArray[Float]], NDArray[Float]],\n space: TargetSpace,\n x_seeds: NDArray[Float],\n random_state: RandomState,\n ) -> tuple[NDArray[Float] | None, float]:\n \"\"\"Random search to find the minimum of `acq` function.\n\n Parameters\n ----------\n acq : Callable\n Acquisition function to use. Should accept an array of parameters `x`.\n\n space : TargetSpace\n The target space over which to optimize.\n\n x_seeds : np.ndarray\n Starting points for the smart optimizer.\n If all parameters are continuous, this is the number of random starting points for the L-BFGS-B\n optimizer. Otherwise, n_smart of the best points are used as starting points for the differential\n evolution optimizer, with the remaining points being random samples.\n\n random_state : RandomState\n Random state to use for the optimization.\n\n Returns\n -------\n x_min : np.ndarray\n Minimal result of the L-BFGS-B optimizer.\n\n min_acq : float\n Acquisition function value at `x_min`\n \"\"\"\n continuous_dimensions = space.continuous_dimensions\n continuous_bounds = space.bounds[continuous_dimensions]\n\n min_acq: float | None = None\n x_try: NDArray[Float]\n x_min: NDArray[Float]\n\n # Case of continous optimization\n if all(continuous_dimensions):\n for x_try in x_seeds:\n res: OptimizeResult = minimize(acq, x_try, bounds=continuous_bounds, method=\"L-BFGS-B\")\n if not res.success:\n continue\n\n # Store it if better than previous minimum(maximum).\n if min_acq is None or np.squeeze(res.fun) < min_acq:\n x_try = res.x\n x_min = x_try\n min_acq = np.squeeze(res.fun)\n # Case of mixed-integer optimization\n else:\n xinit = space.random_sample(15 * len(space.bounds), random_state=random_state)\n if len(x_seeds) > 0:\n n_seeds = min(len(x_seeds), len(xinit))\n xinit[:n_seeds] = x_seeds[:n_seeds]\n\n de_parameters = {\"func\": acq, \"bounds\": space.bounds, \"polish\": False, \"init\": xinit}\n if version.parse(scipy_version) < version.parse(\"1.15.0\"):\n de_parameters[\"seed\"] = random_state\n else:\n de_parameters[\"rng\"] = random_state\n\n de = DifferentialEvolutionSolver(**de_parameters)\n res_de: OptimizeResult = de.solve()\n # Check if success\n if not res_de.success:\n msg = f\"Differential evolution optimization failed. Message: {res_de.message}\"\n raise RuntimeError(msg)\n\n x_min = res_de.x\n min_acq = np.squeeze(res_de.fun)\n\n # Refine the identification of continous parameters with deterministic search\n if any(continuous_dimensions):\n x_try = x_min.copy()\n\n def continuous_acq(x: NDArray[Float], x_try=x_try) -> NDArray[Float]:\n x_try[continuous_dimensions] = x\n return acq(x_try)\n\n res: OptimizeResult = minimize(\n continuous_acq, x_min[continuous_dimensions], bounds=continuous_bounds\n )\n if res.success and np.squeeze(res.fun) < min_acq:\n x_try[continuous_dimensions] = res.x\n x_min = x_try\n min_acq = np.squeeze(res.fun)\n\n if min_acq is None:\n min_acq = np.inf\n x_min = np.array([np.nan] * space.bounds.shape[0])\n\n # Clip output to make sure it lies within the bounds. Due to floating\n # point technicalities this is not always the case.\n return np.clip(x_min, space.bounds[:, 0], space.bounds[:, 1]), min_acq\n\n\nclass UpperConfidenceBound(AcquisitionFunction):\n r\"\"\"Upper Confidence Bound acquisition function.\n\n The upper confidence bound is calculated as\n\n .. math::\n \\text{UCB}(x) = \\mu(x) + \\kappa \\sigma(x).\n\n Parameters\n ----------\n kappa : float, default 2.576\n Governs the exploration/exploitation tradeoff. Lower prefers\n exploitation, higher prefers exploration.\n\n exploration_decay : float, default None\n Decay rate for kappa. If None, no decay is applied.\n\n exploration_decay_delay : int, default None\n Delay for decay. If None, decay is applied from the start.\n\n \"\"\"\n\n def __init__(\n self,\n kappa: float = 2.576,\n exploration_decay: float | None = None,\n exploration_decay_delay: int | None = None,\n random_state: int | RandomState | None = None,\n ) -> None:\n if kappa < 0:\n error_msg = \"kappa must be greater than or equal to 0.\"\n raise ValueError(error_msg)\n if exploration_decay is not None and not (0 < exploration_decay <= 1):\n error_msg = \"exploration_decay must be greater than 0 and less than or equal to 1.\"\n raise ValueError(error_msg)\n if exploration_decay_delay is not None and (\n not isinstance(exploration_decay_delay, int) or exploration_decay_delay < 0\n ):\n error_msg = \"exploration_decay_delay must be an integer greater than or equal to 0.\"\n raise ValueError(error_msg)\n\n super().__init__(random_state=random_state)\n self.kappa = kappa\n self.exploration_decay = exploration_decay\n self.exploration_decay_delay = exploration_decay_delay\n\n def base_acq(self, mean: NDArray[Float], std: NDArray[Float]) -> NDArray[Float]:\n \"\"\"Calculate the upper confidence bound.\n\n Parameters\n ----------\n mean : np.ndarray\n Mean of the predictive distribution.\n\n std : np.ndarray\n Standard deviation of the predictive distribution.\n\n Returns\n -------\n np.ndarray\n Acquisition function value.\n \"\"\"\n return mean + self.kappa * std\n\n def suggest(\n self,\n gp: GaussianProcessRegressor,\n target_space: TargetSpace,\n n_random: int = 10_000,\n n_smart: int = 10,\n fit_gp: bool = True,\n random_state: int | RandomState | None = None,\n ) -> NDArray[Float]:\n \"\"\"Suggest a promising point to probe next.\n\n Parameters\n ----------\n gp : GaussianProcessRegressor\n A fitted Gaussian Process.\n\n target_space : TargetSpace\n The target space to probe.\n\n n_random : int, default 10_000\n Number of random samples to use.\n\n n_smart : int, default 10\n Number of starting points for the L-BFGS-B optimizer.\n\n fit_gp : bool, default True\n Whether to fit the Gaussian Process to the target space.\n Set to False if the GP is already fitted.\n\n random_state : int, RandomState, default None\n Random state to use for the optimization.\n\n Returns\n -------\n np.ndarray\n Suggested point to probe next.\n \"\"\"\n if target_space.constraint is not None:\n msg = (\n f\"Received constraints, but acquisition function {type(self)} \"\n \"does not support constrained optimization.\"\n )\n raise ConstraintNotSupportedError(msg)\n x_max = super().suggest(\n gp=gp,\n target_space=target_space,\n n_random=n_random,\n n_smart=n_smart,\n fit_gp=fit_gp,\n random_state=random_state,\n )\n self.decay_exploration()\n return x_max\n\n def decay_exploration(self) -> None:\n \"\"\"Decay kappa by a constant rate.\n\n Adjust exploration/exploitation trade-off by reducing kappa.\n\n Note\n ----\n\n This method is called automatically at the end of each ``suggest()`` call.\n \"\"\"\n if self.exploration_decay is not None and (\n self.exploration_decay_delay is None or self.exploration_decay_delay <= self.i\n ):\n self.kappa = self.kappa * self.exploration_decay\n\n def get_acquisition_params(self) -> dict[str, Any]:\n \"\"\"Get the current acquisition function parameters.\n\n Returns\n -------\n dict\n Dictionary containing the current acquisition function parameters.\n \"\"\"\n return {\n \"kappa\": self.kappa,\n \"exploration_decay\": self.exploration_decay,\n \"exploration_decay_delay\": self.exploration_decay_delay,\n }\n\n def set_acquisition_params(self, params: dict[str, Any]) -> None:\n \"\"\"Set the acquisition function parameters.\n\n Parameters\n ----------\n params : dict\n Dictionary containing the acquisition function parameters.\n \"\"\"\n self.kappa = params[\"kappa\"]\n self.exploration_decay = params[\"exploration_decay\"]\n self.exploration_decay_delay = params[\"exploration_decay_delay\"]\n\n\nclass ProbabilityOfImprovement(AcquisitionFunction):\n r\"\"\"Probability of Improvement acqusition function.\n\n Calculated as\n\n .. math:: \\text{POI}(x) = \\Phi\\left( \\frac{\\mu(x)-y_{\\text{max}} - \\xi }{\\sigma(x)} \\right)\n\n where :math:`\\Phi` is the CDF of the normal distribution.\n\n Parameters\n ----------\n xi : float, positive\n Governs the exploration/exploitation tradeoff. Lower prefers\n exploitation, higher prefers exploration.\n\n exploration_decay : float, default None\n Decay rate for xi. If None, no decay is applied.\n\n exploration_decay_delay : int, default None\n Delay for decay. If None, decay is applied from the start.\n\n random_state : int, RandomState, default None\n Set the random state for reproducibility.\n \"\"\"\n\n def __init__(\n self,\n xi: float,\n exploration_decay: float | None = None,\n exploration_decay_delay: int | None = None,\n random_state: int | RandomState | None = None,\n ) -> None:\n if xi < 0:\n error_msg = \"xi must be greater than or equal to 0.\"\n raise ValueError(error_msg)\n if exploration_decay is not None and not (0 < exploration_decay <= 1):\n error_msg = \"exploration_decay must be greater than 0 and less than or equal to 1.\"\n raise ValueError(error_msg)\n if exploration_decay_delay is not None and (\n not isinstance(exploration_decay_delay, int) or exploration_decay_delay < 0\n ):\n error_msg = \"exploration_decay_delay must be an integer greater than or equal to 0.\"\n raise ValueError(error_msg)\n\n super().__init__(random_state=random_state)\n self.xi = xi\n self.exploration_decay = exploration_decay\n self.exploration_decay_delay = exploration_decay_delay\n self.y_max = None\n\n def base_acq(self, mean: NDArray[Float], std: NDArray[Float]) -> NDArray[Float]:\n \"\"\"Calculate the probability of improvement.\n\n Parameters\n ----------\n mean : np.ndarray\n Mean of the predictive distribution.\n\n std : np.ndarray\n Standard deviation of the predictive distribution.\n\n Returns\n -------\n np.ndarray\n Acquisition function value.\n\n Raises\n ------\n ValueError\n If y_max is not set.\n \"\"\"\n if self.y_max is None:\n msg = (\n \"y_max is not set. If you are calling this method outside \"\n \"of suggest(), you must set y_max manually.\"\n )\n raise ValueError(msg)\n z = (mean - self.y_max - self.xi) / std\n return norm.cdf(z)\n\n def suggest(\n self,\n gp: GaussianProcessRegressor,\n target_space: TargetSpace,\n n_random: int = 10_000,\n n_smart: int = 10,\n fit_gp: bool = True,\n random_state: int | RandomState | None = None,\n ) -> NDArray[Float]:\n \"\"\"Suggest a promising point to probe next.\n\n Parameters\n ----------\n gp : GaussianProcessRegressor\n A fitted Gaussian Process.\n\n target_space : TargetSpace\n The target space to probe.\n\n n_random : int, default 10_000\n Number of random samples to use.\n\n n_smart : int, default 10\n Number of starting points for the L-BFGS-B optimizer.\n\n fit_gp : bool, default True\n Whether to fit the Gaussian Process to the target space.\n Set to False if the GP is already fitted.\n\n random_state : int, RandomState, default None\n Random state to use for the optimization.\n\n Returns\n -------\n np.ndarray\n Suggested point to probe next.\n \"\"\"\n y_max = target_space._target_max()\n if y_max is None and not target_space.empty:\n # If target space is empty, let base class handle the error\n msg = (\n \"Cannot suggest a point without an allowed point. Use \"\n \"target_space.random_sample() to generate a point until \"\n \" at least one point that satisfies the constraints is found.\"\n )\n raise NoValidPointRegisteredError(msg)\n self.y_max = y_max\n x_max = super().suggest(\n gp=gp,\n target_space=target_space,\n n_random=n_random,\n n_smart=n_smart,\n fit_gp=fit_gp,\n random_state=random_state,\n )\n self.decay_exploration()\n return x_max\n\n def decay_exploration(self) -> None:\n r\"\"\"Decay xi by a constant rate.\n\n Adjust exploration/exploitation trade-off by reducing xi.\n\n Note\n ----\n\n This method is called automatically at the end of each ``suggest()`` call.\n \"\"\"\n if self.exploration_decay is not None and (\n self.exploration_decay_delay is None or self.exploration_decay_delay <= self.i\n ):\n self.xi = self.xi * self.exploration_decay\n\n def get_acquisition_params(self) -> dict[str, Any]:\n \"\"\"Get the current acquisition function parameters.\n\n Returns\n -------\n dict\n Dictionary containing the current acquisition function parameters.\n \"\"\"\n return {\n \"xi\": self.xi,\n \"exploration_decay\": self.exploration_decay,\n \"exploration_decay_delay\": self.exploration_decay_delay,\n }\n\n def set_acquisition_params(self, params: dict[str, Any]) -> None:\n \"\"\"Set the acquisition function parameters.\n\n Parameters\n ----------\n params : dict\n Dictionary containing the acquisition function parameters.\n \"\"\"\n self.xi = params[\"xi\"]\n self.exploration_decay = params[\"exploration_decay\"]\n self.exploration_decay_delay = params[\"exploration_decay_delay\"]\n\n\nclass ExpectedImprovement(AcquisitionFunction):\n r\"\"\"Expected Improvement acqusition function.\n\n Similar to Probability of Improvement (`ProbabilityOfImprovement`), but also considers the\n magnitude of improvement.\n Calculated as\n\n .. math::\n \\text{EI}(x) = (\\mu(x)-y_{\\text{max}} - \\xi) \\Phi\\left(\n \\frac{\\mu(x)-y_{\\text{max}} - \\xi }{\\sigma(x)} \\right)\n + \\sigma(x) \\phi\\left(\n \\frac{\\mu(x)-y_{\\text{max}} - \\xi }{\\sigma(x)} \\right)\n\n where :math:`\\Phi` is the CDF and :math:`\\phi` the PDF of the normal\n distribution.\n\n Parameters\n ----------\n xi : float, positive\n Governs the exploration/exploitation tradeoff. Lower prefers\n exploitation, higher prefers exploration.\n\n exploration_decay : float, default None\n Decay rate for xi. If None, no decay is applied.\n\n exploration_decay_delay : int, default None\n Delay for decay. If None, decay is applied from the start.\n\n random_state : int, RandomState, default None\n Set the random state for reproducibility.\n \"\"\"\n\n def __init__(\n self,\n xi: float,\n exploration_decay: float | None = None,\n exploration_decay_delay: int | None = None,\n random_state: int | RandomState | None = None,\n ) -> None:\n if xi < 0:\n error_msg = \"xi must be greater than or equal to 0.\"\n raise ValueError(error_msg)\n if exploration_decay is not None and not (0 < exploration_decay <= 1):\n error_msg = \"exploration_decay must be greater than 0 and less than or equal to 1.\"\n raise ValueError(error_msg)\n if exploration_decay_delay is not None and (\n not isinstance(exploration_decay_delay, int) or exploration_decay_delay < 0\n ):\n error_msg = \"exploration_decay_delay must be an integer greater than or equal to 0.\"\n raise ValueError(error_msg)\n\n super().__init__(random_state=random_state)\n self.xi = xi\n self.exploration_decay = exploration_decay\n self.exploration_decay_delay = exploration_decay_delay\n self.y_max = None\n\n def base_acq(self, mean: NDArray[Float], std: NDArray[Float]) -> NDArray[Float]:\n \"\"\"Calculate the expected improvement.\n\n Parameters\n ----------\n mean : np.ndarray\n Mean of the predictive distribution.\n\n std : np.ndarray\n Standard deviation of the predictive distribution.\n\n Returns\n -------\n np.ndarray\n Acquisition function value.\n\n Raises\n ------\n ValueError\n If y_max is not set.\n \"\"\"\n if self.y_max is None:\n msg = (\n \"y_max is not set. If you are calling this method outside \"\n \"of suggest(), ensure y_max is set, or set it manually.\"\n )\n raise ValueError(msg)\n a = mean - self.y_max - self.xi\n z = a / std\n return a * norm.cdf(z) + std * norm.pdf(z)\n\n def suggest(\n self,\n gp: GaussianProcessRegressor,\n target_space: TargetSpace,\n n_random: int = 10_000,\n n_smart: int = 10,\n fit_gp: bool = True,\n random_state: int | RandomState | None = None,\n ) -> NDArray[Float]:\n \"\"\"Suggest a promising point to probe next.\n\n Parameters\n ----------\n gp : GaussianProcessRegressor\n A fitted Gaussian Process.\n\n target_space : TargetSpace\n The target space to probe.\n\n n_random : int, default 10_000\n Number of random samples to use.\n\n n_smart : int, default 10\n Number of starting points for the L-BFGS-B optimizer.\n\n fit_gp : bool, default True\n Whether to fit the Gaussian Process to the target space.\n Set to False if the GP is already fitted.\n\n random_state : int, RandomState, default None\n Random state to use for the optimization.\n\n Returns\n -------\n np.ndarray\n Suggested point to probe next.\n \"\"\"\n y_max = target_space._target_max()\n if y_max is None and not target_space.empty:\n # If target space is empty, let base class handle the error\n msg = (\n \"Cannot suggest a point without an allowed point. Use \"\n \"target_space.random_sample() to generate a point until \"\n \" at least one point that satisfies the constraints is found.\"\n )\n raise NoValidPointRegisteredError(msg)\n self.y_max = y_max\n\n x_max = super().suggest(\n gp=gp,\n target_space=target_space,\n n_random=n_random,\n n_smart=n_smart,\n fit_gp=fit_gp,\n random_state=random_state,\n )\n self.decay_exploration()\n return x_max\n\n def decay_exploration(self) -> None:\n r\"\"\"Decay xi by a constant rate.\n\n Adjust exploration/exploitation trade-off by reducing xi.\n\n Note\n ----\n\n This method is called automatically at the end of each ``suggest()`` call.\n \"\"\"\n if self.exploration_decay is not None and (\n self.exploration_decay_delay is None or self.exploration_decay_delay <= self.i\n ):\n self.xi = self.xi * self.exploration_decay\n\n def get_acquisition_params(self) -> dict[str, Any]:\n \"\"\"Get the current acquisition function parameters.\n\n Returns\n -------\n dict\n Dictionary containing the current acquisition function parameters.\n \"\"\"\n return {\n \"xi\": self.xi,\n \"exploration_decay\": self.exploration_decay,\n \"exploration_decay_delay\": self.exploration_decay_delay,\n }\n\n def set_acquisition_params(self, params: dict[str, Any]) -> None:\n \"\"\"Set the acquisition function parameters.\n\n Parameters\n ----------\n params : dict\n Dictionary containing the acquisition function parameters.\n \"\"\"\n self.xi = params[\"xi\"]\n self.exploration_decay = params[\"exploration_decay\"]\n self.exploration_decay_delay = params[\"exploration_decay_delay\"]\n\n\nclass ConstantLiar(AcquisitionFunction):\n \"\"\"Constant Liar acquisition function.\n\n Used for asynchronous optimization. It operates on a copy of the target space\n that includes the previously suggested points that have not been evaluated yet.\n A GP fitted to this target space is less likely to suggest the same point again,\n since the variance of the predictive distribution is lower at these points.\n This is discourages the optimization algorithm from suggesting the same point\n to multiple workers.\n\n Parameters\n ----------\n base_acquisition : AcquisitionFunction\n The acquisition function to use.\n\n strategy : float or str, default 'max'\n Strategy to use for the constant liar. If a float, the constant liar\n will always register dummies with this value. If 'min'/'mean'/'max',\n the constant liar will register dummies with the minimum/mean/maximum\n target value in the target space.\n\n random_state : int, RandomState, default None\n Set the random state for reproducibility.\n\n atol : float, default 1e-5\n Absolute tolerance to eliminate a dummy point.\n\n rtol : float, default 1e-8\n Relative tolerance to eliminate a dummy point.\n \"\"\"\n\n def __init__(\n self,\n base_acquisition: AcquisitionFunction,\n strategy: Literal[\"min\", \"mean\", \"max\"] | float = \"max\",\n random_state: int | RandomState | None = None,\n atol: float = 1e-5,\n rtol: float = 1e-8,\n ) -> None:\n super().__init__(random_state)\n self.base_acquisition = base_acquisition\n self.dummies = []\n if not isinstance(strategy, float) and strategy not in [\"min\", \"mean\", \"max\"]:\n error_msg = f\"Received invalid argument {strategy} for strategy.\"\n raise ValueError(error_msg)\n self.strategy: Literal[\"min\", \"mean\", \"max\"] | float = strategy\n self.atol = atol\n self.rtol = rtol\n\n def base_acq(self, *args: Any, **kwargs: Any) -> NDArray[Float]:\n \"\"\"Calculate the acquisition function.\n\n Calls the base acquisition function's `base_acq` method.\n\n Returns\n -------\n np.ndarray\n Acquisition function value.\n \"\"\"\n return self.base_acquisition.base_acq(*args, **kwargs)\n\n def _copy_target_space(self, target_space: TargetSpace) -> TargetSpace:\n \"\"\"Create a copy of the target space.\n\n Parameters\n ----------\n target_space : TargetSpace\n The target space to copy.\n\n Returns\n -------\n TargetSpace\n A copy of the target space.\n \"\"\"\n keys = target_space.keys\n pbounds = {key: bound for key, bound in zip(keys, target_space.bounds)}\n target_space_copy = TargetSpace(\n None, pbounds=pbounds, allow_duplicate_points=target_space._allow_duplicate_points\n )\n if target_space._constraint is not None:\n target_space_copy.set_constraint(deepcopy(target_space.constraint))\n\n target_space_copy._params = deepcopy(target_space._params)\n target_space_copy._target = deepcopy(target_space._target)\n\n return target_space_copy\n\n def _remove_expired_dummies(self, target_space: TargetSpace) -> None:\n \"\"\"Remove expired dummy points from the list of dummies.\n\n Once a worker has evaluated a dummy point, the dummy is discarded. To\n accomplish this, we compare every dummy point to the current target\n space's parameters and remove it if it is close to any of them.\n\n Parameters\n ----------\n target_space : TargetSpace\n The target space to compare the dummies to.\n \"\"\"\n dummies = []\n for dummy in self.dummies:\n close = np.isclose(dummy, target_space.params, rtol=self.rtol, atol=self.atol)\n if not close.all(axis=1).any():\n dummies.append(dummy)\n self.dummies = dummies\n\n def suggest(\n self,\n gp: GaussianProcessRegressor,\n target_space: TargetSpace,\n n_random: int = 10_000,\n n_smart: int = 10,\n fit_gp: bool = True,\n random_state: int | RandomState | None = None,\n ) -> NDArray[Float]:\n \"\"\"Suggest a promising point to probe next.\n\n Parameters\n ----------\n gp : GaussianProcessRegressor\n A fitted Gaussian Process.\n\n target_space : TargetSpace\n The target space to probe.\n\n n_random : int, default 10_000\n Number of random samples to use.\n\n n_smart : int, default 10\n Number of starting points for the L-BFGS-B optimizer.\n\n fit_gp : bool, default True\n Unused, since the GP is always fitted to the dummy target space.\n Remains for compatibility with the base class.\n\n random_state : int, RandomState, default None\n Random state to use for the optimization.\n\n Returns\n -------\n np.ndarray\n Suggested point to probe next.\n \"\"\"\n if len(target_space) == 0:\n msg = (\n \"Cannot suggest a point without previous samples. Use \"\n \" target_space.random_sample() to generate a point and \"\n \" target_space.probe(*) to evaluate it.\"\n )\n raise TargetSpaceEmptyError(msg)\n\n if target_space.constraint is not None:\n msg = (\n f\"Received constraints, but acquisition function {type(self)} \"\n \"does not support constrained optimization.\"\n )\n raise ConstraintNotSupportedError(msg)\n\n # Check if any dummies have been evaluated and remove them\n self._remove_expired_dummies(target_space)\n\n # Create a copy of the target space\n dummy_target_space = self._copy_target_space(target_space)\n\n dummy_target: float\n # Choose the dummy target value\n if isinstance(self.strategy, float):\n dummy_target = self.strategy\n elif self.strategy == \"min\":\n dummy_target = target_space.target.min()\n elif self.strategy == \"mean\":\n dummy_target = target_space.target.mean()\n elif self.strategy != \"max\":\n error_msg = f\"Received invalid argument {self.strategy} for strategy.\"\n raise ValueError(error_msg)\n else:\n dummy_target = target_space.target.max()\n\n # Register the dummies to the dummy target space\n for dummy in self.dummies:\n dummy_target_space.register(dummy, dummy_target)\n\n # Fit the GP to the dummy target space and suggest a point\n self._fit_gp(gp=gp, target_space=dummy_target_space)\n x_max = self.base_acquisition.suggest(\n gp,\n dummy_target_space,\n n_random=n_random,\n n_smart=n_smart,\n fit_gp=False,\n random_state=random_state,\n )\n\n # Register the suggested point as a dummy\n self.dummies.append(x_max)\n\n return x_max\n\n def get_acquisition_params(self) -> dict[str, Any]:\n \"\"\"Get the current acquisition function parameters.\n\n Returns\n -------\n dict\n Dictionary containing the current acquisition function parameters.\n \"\"\"\n return {\n \"dummies\": [dummy.tolist() for dummy in self.dummies],\n \"base_acquisition_params\": self.base_acquisition.get_acquisition_params(),\n \"strategy\": self.strategy,\n \"atol\": self.atol,\n \"rtol\": self.rtol,\n }\n\n def set_acquisition_params(self, params: dict[str, Any]) -> None:\n \"\"\"Set the acquisition function parameters.\n\n Parameters\n ----------\n params : dict\n Dictionary containing the acquisition function parameters.\n \"\"\"\n self.dummies = [np.array(dummy) for dummy in params[\"dummies\"]]\n self.base_acquisition.set_acquisition_params(params[\"base_acquisition_params\"])\n self.strategy = params[\"strategy\"]\n self.atol = params[\"atol\"]\n self.rtol = params[\"rtol\"]\n\n\nclass GPHedge(AcquisitionFunction):\n \"\"\"GPHedge acquisition function.\n\n At each suggestion step, GPHedge samples suggestions from each base\n acquisition function acq_i. Then a candidate is selected from the\n suggestions based on the on the cumulative rewards of each acq_i.\n After evaluating the candidate, the gains are updated (in the next\n iteration) based on the updated expectation value of the candidates.\n\n For more information, see:\n Brochu et al., \"Portfolio Allocation for Bayesian Optimization\",\n https://arxiv.org/abs/1009.5419\n\n Parameters\n ----------\n base_acquisitions : Sequence[AcquisitionFunction]\n Sequence of base acquisition functions.\n\n random_state : int, RandomState, default None\n Set the random state for reproducibility.\n \"\"\"\n\n def __init__(\n self, base_acquisitions: Sequence[AcquisitionFunction], random_state: int | RandomState | None = None\n ) -> None:\n super().__init__(random_state)\n self.base_acquisitions = list(base_acquisitions)\n self.n_acq = len(self.base_acquisitions)\n self.gains = np.zeros(self.n_acq)\n self.previous_candidates = None\n\n def base_acq(self, *args: Any, **kwargs: Any) -> NoReturn:\n \"\"\"Raise an error, since the base acquisition function is ambiguous.\"\"\"\n msg = (\n \"GPHedge base acquisition function is ambiguous.\"\n \" You may use self.base_acquisitions[i].base_acq(mean, std)\"\n \" to get the base acquisition function for the i-th acquisition.\"\n )\n raise TypeError(msg)\n\n def _sample_idx_from_softmax_gains(self, random_state: RandomState) -> int:\n \"\"\"Sample an index weighted by the softmax of the gains.\n\n Parameters\n ----------\n random_state : RandomState\n Random state to use for the sampling.\n\n Returns\n -------\n int\n Index of the selected base acquisition function.\n \"\"\"\n cumsum_softmax_g = np.cumsum(softmax(self.gains))\n r = random_state.rand()\n return np.argmax(r <= cumsum_softmax_g) # Returns the first True value\n\n def _update_gains(self, gp: GaussianProcessRegressor) -> None:\n \"\"\"Update the gains of the base acquisition functions.\n\n Parameters\n ----------\n gp : GaussianProcessRegressor\n A fitted Gaussian Process.\n \"\"\"\n with warnings.catch_warnings():\n warnings.simplefilter(\"ignore\")\n rewards = gp.predict(self.previous_candidates)\n self.gains += rewards\n self.previous_candidates = None\n\n def suggest(\n self,\n gp: GaussianProcessRegressor,\n target_space: TargetSpace,\n n_random: int = 10_000,\n n_smart: int = 10,\n fit_gp: bool = True,\n random_state: int | RandomState | None = None,\n ) -> NDArray[Float]:\n \"\"\"Suggest a promising point to probe next.\n\n Parameters\n ----------\n gp : GaussianProcessRegressor\n A fitted Gaussian Process.\n\n target_space : TargetSpace\n The target space to probe.\n\n n_random : int, default 10_000\n Number of random samples to use.\n\n n_smart : int, default 10\n Number of starting points for the L-BFGS-B optimizer.\n\n fit_gp : bool, default True\n Whether to fit the Gaussian Process to the target space.\n Set to False if the GP is already fitted.\n\n random_state : int, RandomState, default None\n Random state to use for the optimization.\n\n Returns\n -------\n np.ndarray\n Suggested point to probe next.\n \"\"\"\n if len(target_space) == 0:\n msg = (\n \"Cannot suggest a point without previous samples. Use \"\n \" target_space.random_sample() to generate a point and \"\n \" target_space.probe(*) to evaluate it.\"\n )\n raise TargetSpaceEmptyError(msg)\n self.i += 1\n random_state = ensure_rng(random_state)\n if fit_gp:\n self._fit_gp(gp=gp, target_space=target_space)\n\n # Update the gains of the base acquisition functions\n if self.previous_candidates is not None:\n self._update_gains(gp)\n\n # Suggest a point using each base acquisition function\n x_max = [\n base_acq.suggest(\n gp=gp,\n target_space=target_space,\n n_random=n_random // self.n_acq,\n n_smart=n_smart // self.n_acq,\n fit_gp=False,\n random_state=random_state,\n )\n for base_acq in self.base_acquisitions\n ]\n self.previous_candidates = np.array(x_max)\n idx = self._sample_idx_from_softmax_gains(random_state=random_state)\n return x_max[idx]\n\n def get_acquisition_params(self) -> dict[str, Any]:\n \"\"\"Get the current acquisition function parameters.\n\n Returns\n -------\n dict\n Dictionary containing the current acquisition function parameters.\n \"\"\"\n return {\n \"base_acquisitions_params\": [acq.get_acquisition_params() for acq in self.base_acquisitions],\n \"gains\": self.gains.tolist(),\n \"previous_candidates\": self.previous_candidates.tolist()\n if self.previous_candidates is not None\n else None,\n }\n\n def set_acquisition_params(self, params: dict[str, Any]) -> None:\n \"\"\"Set the acquisition function parameters.\n\n Parameters\n ----------\n params : dict\n Dictionary containing the acquisition function parameters.\n \"\"\"\n for acq, acq_params in zip(self.base_acquisitions, params[\"base_acquisitions_params\"]):\n acq.set_acquisition_params(acq_params)\n\n self.gains = np.array(params[\"gains\"])\n self.previous_candidates = (\n np.array(params[\"previous_candidates\"]) if params[\"previous_candidates\"] is not None else None\n )\n" }, { "path": "bayes_opt/bayesian_optimization.py", "content": "\"\"\"Main module.\n\nHolds the `BayesianOptimization` class, which handles the maximization of a\nfunction over a specific target space.\n\"\"\"\n\nfrom __future__ import annotations\n\nimport json\nfrom collections import deque\nfrom collections.abc import Iterable\nfrom os import PathLike\nfrom pathlib import Path\nfrom typing import TYPE_CHECKING, Any\nfrom warnings import warn\n\nimport numpy as np\nfrom scipy.optimize import NonlinearConstraint\nfrom sklearn.gaussian_process import GaussianProcessRegressor\nfrom sklearn.gaussian_process.kernels import Matern\n\nfrom bayes_opt import acquisition\nfrom bayes_opt.domain_reduction import DomainTransformer\nfrom bayes_opt.logger import ScreenLogger\nfrom bayes_opt.parameter import wrap_kernel\nfrom bayes_opt.target_space import TargetSpace\nfrom bayes_opt.util import ensure_rng\n\nif TYPE_CHECKING:\n from collections.abc import Callable, Mapping\n\n from numpy.random import RandomState\n from numpy.typing import NDArray\n\n from bayes_opt.acquisition import AcquisitionFunction\n from bayes_opt.constraint import ConstraintModel\n from bayes_opt.domain_reduction import DomainTransformer\n from bayes_opt.parameter import BoundsMapping, ParamsType\n\n Float = np.floating[Any]\n\n\nclass BayesianOptimization:\n \"\"\"Handle optimization of a target function over a specific target space.\n\n This class takes the function to optimize as well as the parameters bounds\n in order to find which values for the parameters yield the maximum value\n using bayesian optimization.\n\n Parameters\n ----------\n f: function or None.\n Function to be maximized.\n\n pbounds: dict\n Dictionary with parameters names as keys and a tuple with minimum\n and maximum values.\n\n acquisition_function: AcquisitionFunction, optional(default=None)\n The acquisition function to use for suggesting new points to evaluate.\n If None, defaults to UpperConfidenceBound for unconstrained problems\n and ExpectedImprovement for constrained problems.\n\n constraint: NonlinearConstraint.\n Note that the names of arguments of the constraint function and of\n f need to be the same.\n\n random_state: int or numpy.random.RandomState, optional(default=None)\n If the value is an integer, it is used as the seed for creating a\n numpy.random.RandomState. Otherwise the random state provided is used.\n When set to None, an unseeded random state is generated.\n\n verbose: int, optional(default=2)\n The level of verbosity.\n\n bounds_transformer: DomainTransformer, optional(default=None)\n If provided, the transformation is applied to the bounds.\n\n allow_duplicate_points: bool, optional (default=False)\n If True, the optimizer will allow duplicate points to be registered.\n This behavior may be desired in high noise situations where repeatedly probing\n the same point will give different answers. In other situations, the acquisition\n may occasionally generate a duplicate point.\n \"\"\"\n\n def __init__(\n self,\n f: Callable[..., float] | None,\n pbounds: Mapping[str, tuple[float, float]],\n acquisition_function: AcquisitionFunction | None = None,\n constraint: NonlinearConstraint | None = None,\n random_state: int | RandomState | None = None,\n verbose: int = 2,\n bounds_transformer: DomainTransformer | None = None,\n allow_duplicate_points: bool = False,\n ):\n self._random_state = ensure_rng(random_state)\n self._allow_duplicate_points = allow_duplicate_points\n self._queue: deque[ParamsType] = deque()\n\n if acquisition_function is None:\n if constraint is None:\n self._acquisition_function = acquisition.UpperConfidenceBound(kappa=2.576)\n else:\n self._acquisition_function = acquisition.ExpectedImprovement(xi=0.01)\n else:\n self._acquisition_function = acquisition_function\n\n # Data structure containing the function to be optimized, the\n # bounds of its domain, and a record of the evaluations we have\n # done so far\n self._space = TargetSpace(\n f,\n pbounds,\n constraint=constraint,\n random_state=random_state,\n allow_duplicate_points=self._allow_duplicate_points,\n )\n if constraint is None:\n self.is_constrained = False\n else:\n self.is_constrained = True\n\n # Internal GP regressor\n self._gp = GaussianProcessRegressor(\n kernel=wrap_kernel(Matern(nu=2.5), transform=self._space.kernel_transform),\n alpha=1e-6,\n normalize_y=True,\n n_restarts_optimizer=5,\n random_state=self._random_state,\n )\n\n self._verbose = verbose\n self._bounds_transformer = bounds_transformer\n if self._bounds_transformer:\n if not isinstance(self._bounds_transformer, DomainTransformer):\n msg = \"The transformer must be an instance of DomainTransformer\"\n raise TypeError(msg)\n self._bounds_transformer.initialize(self._space)\n\n self._sorting_warning_already_shown = False # TODO: remove in future version\n\n # Initialize logger\n self.logger = ScreenLogger(verbose=self._verbose, is_constrained=self.is_constrained)\n\n @property\n def space(self) -> TargetSpace:\n \"\"\"Return the target space associated with the optimizer.\"\"\"\n return self._space\n\n @property\n def acquisition_function(self) -> AcquisitionFunction:\n \"\"\"Return the acquisition function associated with the optimizer.\"\"\"\n return self._acquisition_function\n\n @property\n def constraint(self) -> ConstraintModel | None:\n \"\"\"Return the constraint associated with the optimizer, if any.\"\"\"\n if self.is_constrained:\n return self._space.constraint\n return None\n\n @property\n def max(self) -> dict[str, Any] | None:\n \"\"\"Get maximum target value found and corresponding parameters.\n\n See `TargetSpace.max` for more information.\n \"\"\"\n return self._space.max()\n\n @property\n def res(self) -> list[dict[str, Any]]:\n \"\"\"Get all target values and constraint fulfillment for all parameters.\n\n See `TargetSpace.res` for more information.\n \"\"\"\n return self._space.res()\n\n def predict(\n self,\n params: dict[str, Any] | Iterable[dict[str, Any]],\n return_std=False,\n return_cov=False,\n fit_gp=True,\n ) -> float | NDArray[Float] | tuple[float | NDArray[Float], float | NDArray[Float]]:\n \"\"\"Predict the target function value at given parameters.\n\n Parameters\n ---------\n params: dict or iterable of dicts\n The parameters where the prediction is made.\n\n return_std: bool, optional(default=False)\n If True, the standard deviation of the prediction is returned.\n\n return_cov: bool, optional(default=False)\n If True, the covariance of the prediction is returned.\n\n fit_gp: bool, optional(default=True)\n If True, the internal Gaussian Process model is fitted before\n making the prediction.\n\n Returns\n -------\n mean: float or np.ndarray\n The predicted mean of the target function at the given parameters.\n When params is a dict, returns a scalar. When params is an iterable,\n returns a 1D array.\n\n std_or_cov: float or np.ndarray (only if return_std or return_cov is True)\n The predicted standard deviation or covariance of the target function\n at the given parameters.\n \"\"\"\n # Validate param types\n if isinstance(params, dict):\n params_array = self._space.params_to_array(params).reshape(1, -1)\n single_param = True\n elif isinstance(params, Iterable) and not isinstance(params, str):\n # convert iterable of dicts to 2D array\n params_array = np.array([self._space.params_to_array(p) for p in params])\n single_param = False\n else:\n msg = f\"params must be a dict or iterable of dicts, got {type(params).__name__}\"\n raise TypeError(msg)\n\n # Validate mutual exclusivity of return_std and return_cov\n if return_std and return_cov:\n msg = \"return_std and return_cov cannot both be True\"\n raise ValueError(msg)\n\n if fit_gp:\n if len(self._space) == 0:\n msg = (\n \"The Gaussian Process model cannot be fitted with zero observations. To use predict(), \"\n \"without fitting the GP, set fit_gp=False. The predictions will then be made using the \"\n \"GP prior.\"\n )\n raise RuntimeError(msg)\n self.acquisition_function._fit_gp(self._gp, self._space)\n\n res = self._gp.predict(params_array, return_std=return_std, return_cov=return_cov)\n\n if return_std or return_cov:\n mean, std_or_cov = res\n else:\n mean = res\n\n # Shape semantics: dict input returns scalars, list input returns arrays\n # Ensure list input always returns arrays (convert scalar to 1D if needed)\n if not single_param and mean.ndim == 0:\n mean = np.atleast_1d(mean)\n # ruff complains when nesting conditionals, so this three-way split is necessary\n if not single_param and (return_std or return_cov) and std_or_cov.ndim == 0:\n std_or_cov = np.atleast_1d(std_or_cov)\n\n if single_param and mean.ndim > 0:\n mean = mean[0]\n if single_param and return_std and std_or_cov.ndim > 0:\n std_or_cov = std_or_cov[0]\n\n if return_std or return_cov:\n return mean, std_or_cov\n return mean\n\n def register(\n self, params: ParamsType, target: float, constraint_value: float | NDArray[Float] | None = None\n ) -> None:\n \"\"\"Register an observation with known target.\n\n Parameters\n ----------\n params: dict or list\n The parameters associated with the observation.\n\n target: float\n Value of the target function at the observation.\n\n constraint_value: float or None\n Value of the constraint function at the observation, if any.\n \"\"\"\n # TODO: remove in future version\n if isinstance(params, np.ndarray) and not self._sorting_warning_already_shown:\n msg = (\n \"You're attempting to register an np.ndarray. In previous versions, the optimizer internally\"\n \" sorted parameters by key and expected any registered array to respect this order.\"\n \" In the current and any future version the order as given by the pbounds dictionary will be\"\n \" used. If you wish to retain sorted parameters, please manually sort your pbounds\"\n \" dictionary before constructing the optimizer.\"\n )\n warn(msg, stacklevel=1)\n self._sorting_warning_already_shown = True\n self._space.register(params, target, constraint_value)\n self.logger.log_optimization_step(\n self._space.keys, self._space.res()[-1], self._space.params_config, self.max\n )\n\n def probe(self, params: ParamsType, lazy: bool = True) -> None:\n \"\"\"Evaluate the function at the given points.\n\n Useful to guide the optimizer.\n\n Parameters\n ----------\n params: dict or list\n The parameters where the optimizer will evaluate the function.\n\n lazy: bool, optional(default=True)\n If True, the optimizer will evaluate the points when calling\n maximize(). Otherwise it will evaluate it at the moment.\n \"\"\"\n # TODO: remove in future version\n if isinstance(params, np.ndarray) and not self._sorting_warning_already_shown:\n msg = (\n \"You're attempting to register an np.ndarray. In previous versions, the optimizer internally\"\n \" sorted parameters by key and expected any registered array to respect this order.\"\n \" In the current and any future version the order as given by the pbounds dictionary will be\"\n \" used. If you wish to retain sorted parameters, please manually sort your pbounds\"\n \" dictionary before constructing the optimizer.\"\n )\n warn(msg, stacklevel=1)\n self._sorting_warning_already_shown = True\n params = self._space.array_to_params(params)\n if lazy:\n self._queue.append(params)\n else:\n self._space.probe(params)\n self.logger.log_optimization_step(\n self._space.keys, self._space.res()[-1], self._space.params_config, self.max\n )\n\n def random_sample(self, n: int = 1) -> list[dict[str, float | NDArray[Float]]]:\n \"\"\"Generate a random sample of parameters from the target space.\n\n Parameters\n ----------\n n: int, optional(default=1)\n Number of random samples to generate.\n\n Returns\n -------\n list of dict\n List of randomly sampled parameters.\n \"\"\"\n return [\n self._space.array_to_params(self._space.random_sample(random_state=self._random_state))\n for _ in range(n)\n ]\n\n def suggest(self) -> dict[str, float | NDArray[Float]]:\n \"\"\"Suggest a promising point to probe next.\"\"\"\n if len(self._space) == 0:\n return self.random_sample(1)[0]\n\n # Finding argmax of the acquisition function.\n suggestion = self._acquisition_function.suggest(\n gp=self._gp, target_space=self._space, fit_gp=True, random_state=self._random_state\n )\n\n return self._space.array_to_params(suggestion)\n\n def _prime_queue(self, init_points: int) -> None:\n \"\"\"Ensure the queue is not empty.\n\n Parameters\n ----------\n init_points: int\n Number of parameters to prime the queue with.\n \"\"\"\n if not self._queue and self._space.empty:\n init_points = max(init_points, 1)\n\n self._queue.extend(self.random_sample(init_points))\n\n def maximize(self, init_points: int = 5, n_iter: int = 25) -> None:\n r\"\"\"\n Maximize the given function over the target space.\n\n Parameters\n ----------\n init_points : int, optional(default=5)\n Number of random points to probe before starting the optimization.\n\n n_iter: int, optional(default=25)\n Number of iterations where the method attempts to find the maximum\n value.\n\n Warning\n -------\n The maximize loop only fits the GP when suggesting a new point to\n probe based on the acquisition function. This means that the GP may\n not be fitted on all points registered to the target space when the\n method completes. If you intend to use the GP model after the\n optimization routine, make sure to call predict() with fit_gp=True.\n\n \"\"\"\n # Log optimization start\n self.logger.log_optimization_start(self._space.keys)\n\n # Prime the queue with random points\n self._prime_queue(init_points)\n\n iteration = 0\n while self._queue or iteration < n_iter:\n try:\n x_probe = self._queue.popleft()\n except IndexError:\n x_probe = self.suggest()\n iteration += 1\n self.probe(x_probe, lazy=False)\n\n if self._bounds_transformer and iteration > 0:\n # The bounds transformer should only modify the bounds after\n # the init_points points (only for the true iterations)\n self.set_bounds(self._bounds_transformer.transform(self._space))\n\n # Log optimization end\n self.logger.log_optimization_end()\n\n def set_bounds(self, new_bounds: BoundsMapping) -> None:\n \"\"\"Modify the bounds of the search space.\n\n Parameters\n ----------\n new_bounds : dict\n A dictionary with the parameter name and its new bounds\n \"\"\"\n self._space.set_bounds(new_bounds)\n\n def set_gp_params(self, **params: Any) -> None:\n \"\"\"Set parameters of the internal Gaussian Process Regressor.\"\"\"\n if \"kernel\" in params:\n params[\"kernel\"] = wrap_kernel(kernel=params[\"kernel\"], transform=self._space.kernel_transform)\n self._gp.set_params(**params)\n\n def save_state(self, path: str | PathLike[str]) -> None:\n \"\"\"Save complete state for reconstruction of the optimizer.\n\n Parameters\n ----------\n path : str or PathLike\n Path to save the optimization state\n \"\"\"\n random_state = None\n if self._random_state is not None:\n state_dict = self._random_state.get_state(legacy=False)\n random_state = {\n \"bit_generator\": state_dict[\"bit_generator\"],\n \"state\": state_dict[\"state\"][\"key\"].tolist(),\n \"pos\": state_dict[\"state\"][\"pos\"],\n \"has_gauss\": state_dict[\"has_gauss\"],\n \"cached_gaussian\": state_dict[\"gauss\"],\n }\n\n # Get constraint values if they exist\n constraint_values = self._space._constraint_values.tolist() if self.is_constrained else None\n acquisition_params = self._acquisition_function.get_acquisition_params()\n state = {\n \"pbounds\": {key: self._space._bounds[i].tolist() for i, key in enumerate(self._space.keys)},\n # Add current transformed bounds if using bounds transformer\n \"transformed_bounds\": (self._space.bounds.tolist() if self._bounds_transformer else None),\n \"keys\": self._space.keys,\n \"params\": np.array(self._space.params).tolist(),\n \"target\": self._space.target.tolist(),\n \"constraint_values\": constraint_values,\n \"gp_params\": {\n \"kernel\": self._gp.kernel.get_params(),\n \"alpha\": self._gp.alpha,\n \"normalize_y\": self._gp.normalize_y,\n \"n_restarts_optimizer\": self._gp.n_restarts_optimizer,\n },\n \"allow_duplicate_points\": self._allow_duplicate_points,\n \"verbose\": self._verbose,\n \"random_state\": random_state,\n \"acquisition_params\": acquisition_params,\n }\n\n with Path(path).open(\"w\") as f:\n json.dump(state, f, indent=2)\n\n def load_state(self, path: str | PathLike[str]) -> None:\n \"\"\"Load optimizer state from a JSON file.\n\n Parameters\n ----------\n path : str or PathLike\n Path to the JSON file containing the optimizer state.\n \"\"\"\n with Path(path).open(\"r\") as file:\n state = json.load(file)\n\n params_array = np.asarray(state[\"params\"], dtype=np.float64)\n target_array = np.asarray(state[\"target\"], dtype=np.float64)\n constraint_array = (\n np.array(state[\"constraint_values\"]) if state[\"constraint_values\"] is not None else None\n )\n\n for i in range(len(params_array)):\n params = self._space.array_to_params(params_array[i])\n target = target_array[i]\n constraint = constraint_array[i] if constraint_array is not None else None\n self.register(params=params, target=target, constraint_value=constraint)\n\n self._acquisition_function.set_acquisition_params(state[\"acquisition_params\"])\n\n if state.get(\"transformed_bounds\") and self._bounds_transformer:\n new_bounds = {\n key: bounds for key, bounds in zip(self._space.keys, np.array(state[\"transformed_bounds\"]))\n }\n self._space.set_bounds(new_bounds)\n self._bounds_transformer.initialize(self._space)\n\n # Construct the GP kernel\n kernel = Matern(**state[\"gp_params\"][\"kernel\"])\n # Re-construct the GP parameters\n gp_params = {k: v for k, v in state[\"gp_params\"].items() if k != \"kernel\"}\n gp_params[\"kernel\"] = kernel\n\n # Set the GP parameters\n self.set_gp_params(**gp_params)\n\n if len(self._space):\n self._gp.fit(self._space.params, self._space.target)\n\n if state[\"random_state\"] is not None:\n random_state_tuple = (\n state[\"random_state\"][\"bit_generator\"],\n np.array(state[\"random_state\"][\"state\"], dtype=np.uint32),\n state[\"random_state\"][\"pos\"],\n state[\"random_state\"][\"has_gauss\"],\n state[\"random_state\"][\"cached_gaussian\"],\n )\n self._random_state.set_state(random_state_tuple)\n" }, { "path": "bayes_opt/constraint.py", "content": "\"\"\"Constraint handling.\"\"\"\n\nfrom __future__ import annotations\n\nfrom typing import TYPE_CHECKING, Any\n\nimport numpy as np\nfrom scipy.stats import norm\nfrom sklearn.gaussian_process import GaussianProcessRegressor\nfrom sklearn.gaussian_process.kernels import Matern\n\nfrom bayes_opt.parameter import wrap_kernel\n\nif TYPE_CHECKING:\n from collections.abc import Callable\n\n from numpy.random import RandomState\n from numpy.typing import NDArray\n\n Float = np.floating[Any]\n\n\nclass ConstraintModel:\n \"\"\"Model constraints using GP regressors.\n\n This class takes the function to optimize as well as the parameters bounds\n in order to find which values for the parameters yield the maximum value\n using bayesian optimization.\n\n Parameters\n ----------\n fun : None or Callable -> float or np.ndarray\n The constraint function. Should be float-valued or array-valued (if\n multiple constraints are present). Needs to take the same parameters\n as the optimization target with the same argument names.\n\n lb : float or np.ndarray\n The lower bound on the constraints. Should have the same\n dimensionality as the return value of the constraint function.\n\n ub : float or np.ndarray\n The upper bound on the constraints. Should have the same\n dimensionality as the return value of the constraint function.\n\n random_state : np.random.RandomState or int or None, default=None\n Random state to use.\n\n Note\n ----\n In case of multiple constraints, this model assumes conditional\n independence. This means that the overall probability of fulfillment is a\n simply the product of the individual probabilities.\n \"\"\"\n\n def __init__(\n self,\n fun: Callable[..., float] | Callable[..., NDArray[Float]] | None,\n lb: float | NDArray[Float],\n ub: float | NDArray[Float],\n transform: Callable[[Any], Any] | None = None,\n random_state: int | RandomState | None = None,\n ) -> None:\n self.fun = fun\n\n self._lb = np.atleast_1d(lb)\n self._ub = np.atleast_1d(ub)\n\n if np.any(self._lb >= self._ub):\n msg = \"Lower bounds must be less than upper bounds.\"\n raise ValueError(msg)\n\n self._model = [\n GaussianProcessRegressor(\n kernel=wrap_kernel(Matern(nu=2.5), transform) if transform is not None else Matern(nu=2.5),\n alpha=1e-6,\n normalize_y=True,\n n_restarts_optimizer=5,\n random_state=random_state,\n )\n for _ in range(len(self._lb))\n ]\n\n @property\n def lb(self) -> NDArray[Float]:\n \"\"\"Return lower bounds.\"\"\"\n return self._lb\n\n @property\n def ub(self) -> NDArray[Float]:\n \"\"\"Return upper bounds.\"\"\"\n return self._ub\n\n @property\n def model(self) -> list[GaussianProcessRegressor]:\n \"\"\"Return GP regressors of the constraint function.\"\"\"\n return self._model\n\n def eval(self, **kwargs: Any) -> float | NDArray[Float]: # noqa: D417\n r\"\"\"Evaluate the constraint function.\n\n Parameters\n ----------\n \\*\\*kwargs : any\n Function arguments to evaluate the constraint function on.\n\n\n Returns\n -------\n Value of the constraint function.\n\n Raises\n ------\n TypeError\n If the kwargs' keys don't match the function argument names.\n \"\"\"\n if self.fun is None:\n error_msg = \"No constraint function was provided.\"\n raise ValueError(error_msg)\n\n try:\n return self.fun(**kwargs)\n except TypeError as e:\n msg = (\n \"Encountered TypeError when evaluating constraint \"\n \"function. This could be because your constraint function \"\n \"doesn't use the same keyword arguments as the target \"\n f\"function. Original error message:\\n\\n{e}\"\n )\n e.args = (msg,)\n raise\n\n def fit(self, X: NDArray[Float], Y: NDArray[Float]) -> None:\n \"\"\"Fit internal GPRs to the data.\n\n Parameters\n ----------\n X : np.ndarray of shape (n_samples, n_features)\n Parameters of the constraint function.\n Y : np.ndarray of shape (n_samples, n_constraints)\n Values of the constraint function.\n\n\n Returns\n -------\n None\n \"\"\"\n if len(self._model) == 1:\n self._model[0].fit(X, Y)\n else:\n for i, gp in enumerate(self._model):\n gp.fit(X, Y[:, i])\n\n def predict(self, X: NDArray[Float]) -> NDArray[Float]:\n r\"\"\"Calculate the probability that the constraint is fulfilled at `X`.\n\n Note that this does not try to approximate the values of the\n constraint function (for this, see `ConstraintModel.approx()`.), but\n probability that the constraint function is fulfilled. That is, this\n function calculates\n\n .. math::\n p = \\text{Pr}\\left\\{c^{\\text{low}} \\leq \\tilde{c}(x) \\leq\n c^{\\text{up}} \\right\\} = \\int_{c^{\\text{low}}}^{c^{\\text{up}}}\n \\mathcal{N}(c, \\mu(x), \\sigma^2(x)) \\, dc.\n\n with :math:`\\mu(x)`, :math:`\\sigma^2(x)` the mean and variance at\n :math:`x` as given by the GP and :math:`c^{\\text{low}}`,\n :math:`c^{\\text{up}}` the lower and upper bounds of the constraint\n respectively.\n\n Note\n ----\n\n In case of multiple constraints, we assume conditional independence.\n This means we calculate the probability of constraint fulfilment\n individually, with the joint probability given as their product.\n\n Parameters\n ----------\n X : np.ndarray of shape (n_samples, n_features)\n Parameters for which to predict the probability of constraint\n fulfilment.\n\n\n Returns\n -------\n np.ndarray of shape (n_samples,)\n Probability of constraint fulfilment.\n\n \"\"\"\n X_shape = X.shape\n X = X.reshape((-1, self._model[0].n_features_in_))\n\n result: NDArray[Float]\n y_mean: NDArray[Float]\n y_std: NDArray[Float]\n p_lower: NDArray[Float]\n p_upper: NDArray[Float]\n if len(self._model) == 1:\n y_mean, y_std = self._model[0].predict(X, return_std=True)\n\n p_lower = (\n norm(loc=y_mean, scale=y_std).cdf(self._lb[0]) if self._lb[0] != -np.inf else np.array([0])\n )\n p_upper = (\n norm(loc=y_mean, scale=y_std).cdf(self._ub[0]) if self._ub[0] != np.inf else np.array([1])\n )\n result = p_upper - p_lower\n return result.reshape(X_shape[:-1])\n\n result = np.ones(X.shape[0])\n for j, gp in enumerate(self._model):\n y_mean, y_std = gp.predict(X, return_std=True)\n p_lower = (\n norm(loc=y_mean, scale=y_std).cdf(self._lb[j]) if self._lb[j] != -np.inf else np.array([0])\n )\n p_upper = (\n norm(loc=y_mean, scale=y_std).cdf(self._ub[j]) if self._ub[j] != np.inf else np.array([1])\n )\n result = result * (p_upper - p_lower)\n return result.reshape(X_shape[:-1])\n\n def approx(self, X: NDArray[Float]) -> NDArray[Float]:\n \"\"\"\n Approximate the constraint function using the internal GPR model.\n\n Parameters\n ----------\n X : np.ndarray of shape (n_samples, n_features)\n Parameters for which to estimate the constraint function value.\n\n Returns\n -------\n np.ndarray of shape (n_samples, n_constraints)\n Constraint function value estimates.\n \"\"\"\n X_shape = X.shape\n X = X.reshape((-1, self._model[0].n_features_in_))\n if len(self._model) == 1:\n return self._model[0].predict(X).reshape(X_shape[:-1])\n\n result = np.column_stack([gp.predict(X) for gp in self._model])\n return result.reshape(*X_shape[:-1], len(self._lb))\n\n def allowed(self, constraint_values: NDArray[Float]) -> NDArray[np.bool_]:\n \"\"\"Check whether `constraint_values` fulfills the specified limits.\n\n Parameters\n ----------\n constraint_values : np.ndarray of shape (n_samples, n_constraints)\n The values of the constraint function.\n\n\n Returns\n -------\n np.ndarrray of shape (n_samples,)\n Specifying wheter the constraints are fulfilled.\n\n \"\"\"\n if self._lb.size == 1:\n return np.less_equal(self._lb, constraint_values) & np.less_equal(constraint_values, self._ub)\n\n return np.all(constraint_values <= self._ub, axis=-1) & np.all(constraint_values >= self._lb, axis=-1)\n" }, { "path": "bayes_opt/domain_reduction.py", "content": "\"\"\"Implement domain transformation.\n\nIn particular, this provides a base transformer class and a sequential domain\nreduction transformer as based on Stander and Craig's \"On the robustness of a\nsimple domain reduction scheme for simulation-based optimization\"\n\"\"\"\n\nfrom __future__ import annotations\n\nfrom abc import ABC, abstractmethod\nfrom collections.abc import Iterable, Mapping, Sequence\nfrom typing import TYPE_CHECKING, Any\nfrom warnings import warn\n\nimport numpy as np\n\nfrom bayes_opt.parameter import FloatParameter\nfrom bayes_opt.target_space import TargetSpace\n\nif TYPE_CHECKING:\n from numpy.typing import NDArray\n\n Float = np.floating[Any]\n\n\nclass DomainTransformer(ABC):\n \"\"\"Base class.\"\"\"\n\n @abstractmethod\n def __init__(self, **kwargs: Any) -> None:\n \"\"\"To override with specific implementation.\"\"\"\n\n @abstractmethod\n def initialize(self, target_space: TargetSpace) -> None:\n \"\"\"To override with specific implementation.\"\"\"\n\n @abstractmethod\n def transform(self, target_space: TargetSpace) -> dict[str, NDArray[Float]]:\n \"\"\"To override with specific implementation.\"\"\"\n\n\nclass SequentialDomainReductionTransformer(DomainTransformer):\n \"\"\"Reduce the searchable space.\n\n A sequential domain reduction transformer based on the work by Stander, N. and Craig, K:\n \"On the robustness of a simple domain reduction scheme for simulation-based optimization\"\n\n Parameters\n ----------\n gamma_osc : float, default=0.7\n Parameter used to scale (typically dampen) oscillations.\n\n gamma_pan : float, default=1.0\n Parameter used to scale (typically unitary) panning.\n\n eta : float, default=0.9\n Zooming parameter used to shrink the region of interest.\n\n minimum_window : float or np.ndarray or dict, default=0.0\n Minimum window size for each parameter. If a float is provided,\n the same value is used for all parameters.\n \"\"\"\n\n def __init__(\n self,\n parameters: Iterable[str] | None = None,\n gamma_osc: float = 0.7,\n gamma_pan: float = 1.0,\n eta: float = 0.9,\n minimum_window: NDArray[Float] | Sequence[float] | Mapping[str, float] | float = 0.0,\n ) -> None:\n # TODO: Ensure that this is only applied to continuous parameters\n self.parameters = parameters\n self.gamma_osc = gamma_osc\n self.gamma_pan = gamma_pan\n self.eta = eta\n\n self.minimum_window_value = minimum_window\n\n def initialize(self, target_space: TargetSpace) -> None:\n \"\"\"Initialize all of the parameters.\n\n Parameters\n ----------\n target_space : TargetSpace\n TargetSpace this DomainTransformer operates on.\n \"\"\"\n if isinstance(self.minimum_window_value, Mapping):\n self.minimum_window_value = [self.minimum_window_value[key] for key in target_space.keys]\n else:\n self.minimum_window_value = self.minimum_window_value\n\n any_not_float = any([not isinstance(p, FloatParameter) for p in target_space._params_config.values()])\n if any_not_float:\n msg = \"Domain reduction is only supported for all-FloatParameter optimization.\"\n raise ValueError(msg)\n # Set the original bounds\n self.original_bounds = np.copy(target_space.bounds)\n self.bounds = [self.original_bounds]\n\n self.minimum_window: NDArray[Float] | Sequence[float]\n # Set the minimum window to an array of length bounds\n if isinstance(self.minimum_window_value, (Sequence, np.ndarray)):\n if len(self.minimum_window_value) != len(target_space.bounds):\n error_msg = \"Length of minimum_window must be the same as the number of parameters\"\n raise ValueError(error_msg)\n self.minimum_window = self.minimum_window_value\n else:\n self.minimum_window = [self.minimum_window_value] * len(target_space.bounds)\n\n # Set initial values\n self.previous_optimal = np.mean(target_space.bounds, axis=1)\n self.current_optimal = np.mean(target_space.bounds, axis=1)\n self.r = target_space.bounds[:, 1] - target_space.bounds[:, 0]\n\n self.previous_d = 2.0 * (self.current_optimal - self.previous_optimal) / self.r\n\n self.current_d = 2.0 * (self.current_optimal - self.previous_optimal) / self.r\n\n self.c = self.current_d * self.previous_d\n self.c_hat = np.sqrt(np.abs(self.c)) * np.sign(self.c)\n\n self.gamma = 0.5 * (self.gamma_pan * (1.0 + self.c_hat) + self.gamma_osc * (1.0 - self.c_hat))\n\n self.contraction_rate = self.eta + np.abs(self.current_d) * (self.gamma - self.eta)\n\n self.r = self.contraction_rate * self.r\n\n # check if the minimum window fits in the original bounds\n self._window_bounds_compatibility(self.original_bounds)\n\n def _update(self, target_space: TargetSpace) -> None:\n \"\"\"Update contraction rate, window size, and window center.\n\n Parameters\n ----------\n target_space : TargetSpace\n TargetSpace this DomainTransformer operates on.\n \"\"\"\n # setting the previous\n self.previous_optimal = self.current_optimal\n self.previous_d = self.current_d\n\n self.current_optimal = target_space.params_to_array(target_space.max()[\"params\"])\n\n self.current_d = 2.0 * (self.current_optimal - self.previous_optimal) / self.r\n\n self.c = self.current_d * self.previous_d\n\n self.c_hat = np.sqrt(np.abs(self.c)) * np.sign(self.c)\n\n self.gamma = 0.5 * (self.gamma_pan * (1.0 + self.c_hat) + self.gamma_osc * (1.0 - self.c_hat))\n\n self.contraction_rate = self.eta + np.abs(self.current_d) * (self.gamma - self.eta)\n\n self.r = self.contraction_rate * self.r\n\n def _trim(self, new_bounds: NDArray[Float], global_bounds: NDArray[Float]) -> NDArray[Float]:\n \"\"\"\n Adjust the new_bounds and verify that they adhere to global_bounds and minimum_window.\n\n Parameters\n ----------\n new_bounds : np.ndarray\n The proposed new_bounds that (may) need adjustment.\n\n global_bounds : np.ndarray\n The maximum allowable bounds for each parameter.\n\n Returns\n -------\n new_bounds : np.ndarray\n The adjusted bounds after enforcing constraints.\n \"\"\"\n # sort bounds\n new_bounds = np.sort(new_bounds)\n\n pbounds: NDArray[Float]\n # Validate each parameter's bounds against the global_bounds\n for i, pbounds in enumerate(new_bounds):\n # If the one of the bounds is outside the global bounds, reset the bound to the global bound\n # This is expected to happen when the window is near the global bounds, no warning is issued\n if pbounds[0] < global_bounds[i, 0]:\n pbounds[0] = global_bounds[i, 0]\n\n if pbounds[1] > global_bounds[i, 1]:\n pbounds[1] = global_bounds[i, 1]\n\n # If a lower bound is greater than the associated global upper bound,\n # reset it to the global lower bound\n if pbounds[0] > global_bounds[i, 1]:\n pbounds[0] = global_bounds[i, 0]\n warn(\n \"\\nDomain Reduction Warning:\\n\"\n \"A parameter's lower bound is greater than the global upper bound.\"\n \"The offensive boundary has been reset.\"\n \"Be cautious of subsequent reductions.\",\n stacklevel=2,\n )\n\n # If an upper bound is less than the associated global lower bound,\n # reset it to the global upper bound\n if pbounds[1] < global_bounds[i, 0]:\n pbounds[1] = global_bounds[i, 1]\n warn(\n \"\\nDomain Reduction Warning:\\n\"\n \"A parameter's lower bound is greater than the global upper bound.\"\n \"The offensive boundary has been reset.\"\n \"Be cautious of subsequent reductions.\",\n stacklevel=2,\n )\n\n # Adjust new_bounds to ensure they respect the minimum window width for each parameter\n for i, pbounds in enumerate(new_bounds):\n current_window_width = abs(pbounds[0] - pbounds[1])\n\n # If the window width is less than the minimum allowable width, adjust it\n # Note that when minimum_window < width of the global bounds one side\n # always has more space than required\n if current_window_width < self.minimum_window[i]:\n width_deficit = (self.minimum_window[i] - current_window_width) / 2.0\n available_left_space = abs(global_bounds[i, 0] - pbounds[0])\n available_right_space = abs(global_bounds[i, 1] - pbounds[1])\n\n # determine how much to expand on the left and right\n expand_left = min(width_deficit, available_left_space)\n expand_right = min(width_deficit, available_right_space)\n\n # calculate the deficit on each side\n expand_left_deficit = width_deficit - expand_left\n expand_right_deficit = width_deficit - expand_right\n\n # shift the deficit to the side with more space\n adjust_left = expand_left + max(expand_right_deficit, 0)\n adjust_right = expand_right + max(expand_left_deficit, 0)\n\n # adjust the bounds\n pbounds[0] -= adjust_left\n pbounds[1] += adjust_right\n\n return new_bounds\n\n def _window_bounds_compatibility(self, global_bounds: NDArray[Float]) -> None:\n \"\"\"Check if global bounds are compatible with the minimum window sizes.\n\n Parameters\n ----------\n global_bounds : np.ndarray\n The maximum allowable bounds for each parameter.\n\n Raises\n ------\n ValueError\n If global bounds are not compatible with the minimum window size.\n \"\"\"\n entry: NDArray[Float]\n for i, entry in enumerate(global_bounds):\n global_window_width = abs(entry[1] - entry[0])\n if global_window_width < self.minimum_window[i]:\n error_msg = \"Global bounds are not compatible with the minimum window size.\"\n raise ValueError(error_msg)\n\n def _create_bounds(self, parameters: Iterable[str], bounds: NDArray[Float]) -> dict[str, NDArray[Float]]:\n \"\"\"Create a dictionary of bounds for each parameter.\n\n Parameters\n ----------\n parameters : Iterable[str]\n The parameters for which to create the bounds.\n\n bounds : np.ndarray\n The bounds for each parameter.\n \"\"\"\n return {param: bounds[i, :] for i, param in enumerate(parameters)}\n\n def transform(self, target_space: TargetSpace) -> dict[str, NDArray[Float]]:\n \"\"\"Transform the bounds of the target space.\n\n Parameters\n ----------\n target_space : TargetSpace\n TargetSpace this DomainTransformer operates on.\n\n Returns\n -------\n dict\n The new bounds of each parameter.\n \"\"\"\n self._update(target_space)\n\n new_bounds = np.array([self.current_optimal - 0.5 * self.r, self.current_optimal + 0.5 * self.r]).T\n\n new_bounds = self._trim(new_bounds, self.original_bounds)\n self.bounds.append(new_bounds)\n return self._create_bounds(target_space.keys, new_bounds)\n" }, { "path": "bayes_opt/exception.py", "content": "\"\"\"This module contains custom exceptions for Bayesian Optimization.\"\"\"\n\nfrom __future__ import annotations\n\n__all__ = [\n \"BayesianOptimizationError\",\n \"ConstraintNotSupportedError\",\n \"NoValidPointRegisteredError\",\n \"NotUniqueError\",\n \"TargetSpaceEmptyError\",\n]\n\n\nclass BayesianOptimizationError(Exception):\n \"\"\"Base class for exceptions in the Bayesian Optimization.\"\"\"\n\n\nclass NotUniqueError(BayesianOptimizationError):\n \"\"\"A point is non-unique.\"\"\"\n\n\nclass ConstraintNotSupportedError(BayesianOptimizationError):\n \"\"\"Raised when constrained optimization is not supported.\"\"\"\n\n\nclass NoValidPointRegisteredError(BayesianOptimizationError):\n \"\"\"Raised when an acquisition function depends on previous points but none are registered.\"\"\"\n\n\nclass TargetSpaceEmptyError(BayesianOptimizationError):\n \"\"\"Raised when the target space is empty.\"\"\"\n" }, { "path": "bayes_opt/logger.py", "content": "\"\"\"Contains classes and functions for logging.\"\"\"\n\nfrom __future__ import annotations\n\nfrom collections.abc import Mapping\nfrom typing import TYPE_CHECKING, Any\n\nfrom colorama import Fore, just_fix_windows_console\n\nif TYPE_CHECKING:\n from bayes_opt.parameter import ParamsType\n\njust_fix_windows_console()\n\n\nclass ScreenLogger:\n \"\"\"Logger that outputs text, e.g. to log to a terminal.\n\n Parameters\n ----------\n verbose : int\n Verbosity level of the logger.\n\n is_constrained : bool\n Whether the logger is associated with a constrained optimization\n instance.\n \"\"\"\n\n _default_cell_size = 9\n _default_precision = 4\n _color_new_max = Fore.MAGENTA\n _color_regular_message = Fore.RESET\n _color_reset = Fore.RESET\n\n def __init__(self, verbose: int = 2, is_constrained: bool = False) -> None:\n self._verbose = verbose\n self._is_constrained = is_constrained\n self._header_length = None\n self._iterations = 0\n self._previous_max = None\n self._previous_max_params = None\n self._start_time = None\n self._previous_time = None\n\n @property\n def verbose(self) -> int:\n \"\"\"Return the verbosity level.\"\"\"\n return self._verbose\n\n @verbose.setter\n def verbose(self, v: int) -> None:\n \"\"\"Set the verbosity level.\n\n Parameters\n ----------\n v : int\n New verbosity level of the logger.\n \"\"\"\n self._verbose = v\n\n @property\n def is_constrained(self) -> bool:\n \"\"\"Return whether the logger is constrained.\"\"\"\n return self._is_constrained\n\n def _format_number(self, x: float) -> str:\n \"\"\"Format a number.\n\n Parameters\n ----------\n x : number\n Value to format.\n\n Returns\n -------\n A stringified, formatted version of `x`.\n \"\"\"\n s = f\"{x:.5e}\" if abs(x) >= 10000000.0 else str(x)\n\n if len(s) > self._default_cell_size:\n # Convert to str representation of scientific notation\n result = \"\"\n width = self._default_cell_size\n # Keep negative sign, exponent, and as many decimal places as possible\n if x < 0:\n result += \"-\"\n width -= 1\n s = s[1:]\n if \"e\" in s:\n e_pos = s.find(\"e\")\n end = s[e_pos:]\n width -= len(end)\n if \".\" in s:\n dot_pos = s.find(\".\") + 1\n result += s[:dot_pos]\n width -= dot_pos\n if width > 0:\n result += s[dot_pos : dot_pos + width]\n if \"e\" in s:\n result += end\n result = result.ljust(self._default_cell_size)\n else:\n result = s.ljust(self._default_cell_size)\n return result\n\n def _format_bool(self, x: bool) -> str:\n \"\"\"Format a boolean.\n\n Parameters\n ----------\n x : boolean\n Value to format.\n\n Returns\n -------\n A stringified, formatted version of `x`.\n \"\"\"\n x_ = (\"T\" if x else \"F\") if self._default_cell_size < 5 else str(x)\n return f\"{x_:<{self._default_cell_size}}\"\n\n def _format_str(self, str_: str) -> str:\n \"\"\"Format a str.\n\n Parameters\n ----------\n str_ : str\n Value to format.\n\n Returns\n -------\n A stringified, formatted version of `x`.\n \"\"\"\n s = f\"{str_:^{self._default_cell_size}}\"\n if len(s) > self._default_cell_size:\n return s[: self._default_cell_size - 3] + \"...\"\n return s\n\n def _print_step(\n self,\n keys: list[str],\n result: dict[str, Any],\n params_config: Mapping[str, ParamsType],\n color: str = _color_regular_message,\n ) -> str:\n \"\"\"Print a step.\n\n Parameters\n ----------\n result : dict[str, Any]\n The result dictionary for the most recent step.\n\n keys : list[str]\n The parameter keys.\n\n params_config : Mapping[str, ParamsType]\n The configuration to map the key to the parameter for correct formatting.\n\n color : str, optional\n Color to use for the output.\n (Default value = _color_regular_message, equivalent to Fore.RESET)\n\n Returns\n -------\n A stringified, formatted version of the most recent optimization step.\n \"\"\"\n # iter, target, allowed [, *params]\n cells: list[str | None] = [None] * (3 + len(keys))\n\n cells[:2] = self._format_number(self._iterations), self._format_number(result[\"target\"])\n if self._is_constrained:\n cells[2] = self._format_bool(result[\"allowed\"])\n params = result.get(\"params\", {})\n cells[3:] = [\n self._format_number(val)\n if isinstance(val, (int, float))\n else params_config[key].to_string(val, self._default_cell_size)\n for key, val in params.items()\n ]\n return \"| \" + \" | \".join(color + x + self._color_reset for x in cells if x is not None) + \" |\"\n\n def _print_header(self, keys: list[str]) -> str:\n \"\"\"Print the header of the log.\n\n Parameters\n ----------\n keys : list[str]\n The parameter keys.\n\n Returns\n -------\n A stringified, formatted version of the most header.\n \"\"\"\n # iter, target, allowed [, *params]\n cells: list[str | None] = [None] * (3 + len(keys))\n\n cells[:2] = self._format_str(\"iter\"), self._format_str(\"target\")\n if self._is_constrained:\n cells[2] = self._format_str(\"allowed\")\n cells[3:] = [self._format_str(key) for key in keys]\n\n line = \"| \" + \" | \".join(x for x in cells if x is not None) + \" |\"\n self._header_length = len(line)\n return line + \"\\n\" + (\"-\" * self._header_length)\n\n def _is_new_max(self, current_max: dict[str, Any] | None) -> bool:\n \"\"\"Check if the step to log produced a new maximum.\n\n Parameters\n ----------\n current_max : dict[str, Any] | None\n The current maximum target value and its parameters.\n\n Returns\n -------\n boolean\n \"\"\"\n if current_max is None:\n # During constrained optimization, there might not be a maximum\n # value since the optimizer might've not encountered any points\n # that fulfill the constraints.\n return False\n if self._previous_max is None:\n self._previous_max = current_max[\"target\"]\n return current_max[\"target\"] > self._previous_max\n\n def _update_tracker(self, current_max: dict[str, Any] | None) -> None:\n \"\"\"Update the tracker.\n\n Parameters\n ----------\n current_max : dict[str, Any] | None\n The current maximum target value and its parameters.\n \"\"\"\n self._iterations += 1\n\n if current_max is None:\n return\n\n if self._previous_max is None or current_max[\"target\"] > self._previous_max:\n self._previous_max = current_max[\"target\"]\n self._previous_max_params = current_max[\"params\"]\n\n def log_optimization_start(self, keys: list[str]) -> None:\n \"\"\"Log the start of the optimization process.\n\n Parameters\n ----------\n keys : list[str]\n The parameter keys.\n \"\"\"\n if self._verbose:\n line = self._print_header(keys) + \"\\n\"\n print(line, end=\"\")\n\n def log_optimization_step(\n self,\n keys: list[str],\n result: dict[str, Any],\n params_config: Mapping[str, ParamsType],\n current_max: dict[str, Any] | None,\n ) -> None:\n \"\"\"Log an optimization step.\n\n Parameters\n ----------\n keys : list[str]\n The parameter keys.\n\n result : dict[str, Any]\n The result dictionary for the most recent step.\n\n params_config : Mapping[str, ParamsType]\n The configuration to map the key to the parameter for correct formatting.\n\n current_max : dict[str, Any] | None\n The current maximum target value and its parameters.\n \"\"\"\n is_new_max = self._is_new_max(current_max)\n self._update_tracker(current_max)\n if self._verbose == 0:\n return\n\n if self._verbose == 2 or is_new_max:\n color = self._color_new_max if is_new_max else self._color_regular_message\n line = self._print_step(keys, result, params_config, color=color) + \"\\n\"\n if self._verbose:\n print(line, end=\"\")\n\n def log_optimization_end(self) -> None:\n \"\"\"Log the end of the optimization process.\"\"\"\n if self._verbose and self._header_length is not None:\n line = \"=\" * self._header_length + \"\\n\"\n print(line, end=\"\")\n" }, { "path": "bayes_opt/parameter.py", "content": "\"\"\"Parameter classes for Bayesian optimization.\"\"\"\n\nfrom __future__ import annotations\n\nimport abc\nfrom collections.abc import Sequence\nfrom inspect import signature\nfrom numbers import Number\nfrom typing import TYPE_CHECKING, Any, Callable, Union\n\nimport numpy as np\nfrom sklearn.gaussian_process import kernels\n\nfrom bayes_opt.util import ensure_rng\n\nif TYPE_CHECKING:\n from collections.abc import Mapping\n\n from numpy.typing import NDArray\n\n Float = np.floating[Any]\n Int = np.integer[Any]\n\n FloatBoundsWithoutType = tuple[float, float]\n FloatBoundsWithType = tuple[float, float, type[float]]\n FloatBounds = Union[FloatBoundsWithoutType, FloatBoundsWithType]\n IntBounds = tuple[Union[int, float], Union[int, float], type[int]]\n CategoricalBounds = Sequence[Any]\n Bounds = Union[FloatBounds, IntBounds, CategoricalBounds]\n BoundsMapping = Mapping[str, Bounds]\n\n # FIXME: categorical parameters can be of any type.\n # This will make static type checking for parameters difficult.\n ParamsType = Union[Mapping[str, Any], Sequence[Any], NDArray[Float]]\n\n\ndef is_numeric(value: Any) -> bool:\n \"\"\"Check if a value is numeric.\"\"\"\n return isinstance(value, Number) or (\n isinstance(value, np.generic)\n and (np.isdtype(value.dtype, np.number) or np.issubdtype(value.dtype, np.number))\n )\n\n\nclass BayesParameter(abc.ABC):\n \"\"\"Base class for Bayesian optimization parameters.\n\n Parameters\n ----------\n name : str\n The name of the parameter.\n \"\"\"\n\n def __init__(self, name: str, bounds: NDArray[Any]) -> None:\n self.name = name\n self._bounds = bounds\n\n @property\n def bounds(self) -> NDArray[Any]:\n \"\"\"The bounds of the parameter in float space.\"\"\"\n return self._bounds\n\n @property\n @abc.abstractmethod\n def is_continuous(self) -> bool:\n \"\"\"Whether the parameter is continuous.\"\"\"\n\n def random_sample(\n self, n_samples: int, random_state: np.random.RandomState | int | None\n ) -> NDArray[Float]:\n \"\"\"Generate random samples from the parameter.\n\n Parameters\n ----------\n n_samples : int\n The number of samples to generate.\n\n random_state : np.random.RandomState | int | None\n The random state to use for sampling.\n\n Returns\n -------\n np.ndarray\n The samples.\n \"\"\"\n random_state = ensure_rng(random_state)\n return random_state.uniform(self.bounds[0], self.bounds[1], n_samples)\n\n @abc.abstractmethod\n def to_float(self, value: Any) -> float | NDArray[Float]:\n \"\"\"Convert a parameter value to a float.\n\n Parameters\n ----------\n value : Any\n The value to convert, should be the canonical representation of the parameter.\n \"\"\"\n\n @abc.abstractmethod\n def to_param(self, value: float | NDArray[Float]) -> Any:\n \"\"\"Convert a float value to a parameter.\n\n Parameters\n ----------\n value : np.ndarray\n The value to convert, should be a float.\n\n Returns\n -------\n Any\n The canonical representation of the parameter.\n \"\"\"\n\n @abc.abstractmethod\n def kernel_transform(self, value: NDArray[Float]) -> NDArray[Float]:\n \"\"\"Transform a parameter value for use in a kernel.\n\n Parameters\n ----------\n value : np.ndarray\n The value(s) to transform, should be a float.\n\n Returns\n -------\n np.ndarray\n \"\"\"\n\n def to_string(self, value: Any, str_len: int) -> str:\n \"\"\"Represent a parameter value as a string.\n\n Parameters\n ----------\n value : Any\n The value to represent.\n\n str_len : int\n The maximum length of the string representation.\n\n Returns\n -------\n str\n \"\"\"\n s = f\"{value:<{str_len}}\"\n\n if len(s) > str_len:\n return s[: str_len - 3] + \"...\"\n return s\n\n @property\n @abc.abstractmethod\n def dim(self) -> int:\n \"\"\"The dimensionality of the parameter.\"\"\"\n\n\nclass FloatParameter(BayesParameter):\n \"\"\"A parameter with float values.\n\n Parameters\n ----------\n name : str\n The name of the parameter.\n\n bounds : tuple[float, float]\n The bounds of the parameter.\n \"\"\"\n\n def __init__(self, name: str, bounds: tuple[float, float]) -> None:\n super().__init__(name, np.array(bounds))\n\n @property\n def is_continuous(self) -> bool:\n \"\"\"Whether the parameter is continuous.\"\"\"\n return True\n\n def to_float(self, value: float) -> float:\n \"\"\"Convert a parameter value to a float.\n\n Parameters\n ----------\n value : Any\n The value to convert, should be the canonical representation of the parameter.\n \"\"\"\n return value\n\n def to_param(self, value: float | NDArray[Float]) -> float:\n \"\"\"Convert a float value to a parameter.\n\n Parameters\n ----------\n value : np.ndarray\n The value to convert, should be a float.\n\n Returns\n -------\n Any\n The canonical representation of the parameter.\n \"\"\"\n return value.flatten()[0]\n\n def to_string(self, value: float, str_len: int) -> str:\n \"\"\"Represent a parameter value as a string.\n\n Parameters\n ----------\n value : Any\n The value to represent.\n\n str_len : int\n The maximum length of the string representation.\n\n Returns\n -------\n str\n \"\"\"\n s = f\"{value:<{str_len}.{str_len}}\"\n if len(s) > str_len:\n if \".\" in s and \"e\" not in s:\n return s[:str_len]\n return s[: str_len - 3] + \"...\"\n return s\n\n def kernel_transform(self, value: NDArray[Float]) -> NDArray[Float]:\n \"\"\"Transform a parameter value for use in a kernel.\n\n Parameters\n ----------\n value : np.ndarray\n The value(s) to transform, should be a float.\n\n Returns\n -------\n np.ndarray\n \"\"\"\n return value\n\n @property\n def dim(self) -> int:\n \"\"\"The dimensionality of the parameter.\"\"\"\n return 1\n\n\nclass IntParameter(BayesParameter):\n \"\"\"A parameter with int values.\n\n Parameters\n ----------\n name : str\n The name of the parameter.\n\n bounds : tuple[int, int]\n The bounds of the parameter.\n \"\"\"\n\n def __init__(self, name: str, bounds: tuple[int, int]) -> None:\n super().__init__(name, np.array(bounds))\n\n @property\n def is_continuous(self) -> bool:\n \"\"\"Whether the parameter is continuous.\"\"\"\n return False\n\n def random_sample(\n self, n_samples: int, random_state: np.random.RandomState | int | None\n ) -> NDArray[Float]:\n \"\"\"Generate random samples from the parameter.\n\n Parameters\n ----------\n n_samples : int\n The number of samples to generate.\n\n random_state : np.random.RandomState | int | None\n The random state to use for sampling.\n\n Returns\n -------\n np.ndarray\n The samples.\n \"\"\"\n random_state = ensure_rng(random_state)\n return random_state.randint(self.bounds[0], self.bounds[1] + 1, n_samples).astype(float)\n\n def to_float(self, value: int | float) -> float:\n \"\"\"Convert a parameter value to a float.\n\n Parameters\n ----------\n value : Any\n The value to convert, should be the canonical representation of the parameter.\n \"\"\"\n return float(value)\n\n def to_param(self, value: int | float | NDArray[Int] | NDArray[Float]) -> int:\n \"\"\"Convert a float value to a parameter.\n\n Parameters\n ----------\n value : np.ndarray\n The value to convert, should be a float.\n\n Returns\n -------\n Any\n The canonical representation of the parameter.\n \"\"\"\n return int(np.round(np.squeeze(value)))\n\n def kernel_transform(self, value: NDArray[Float]) -> NDArray[Float]:\n \"\"\"Transform a parameter value for use in a kernel.\n\n Parameters\n ----------\n value : np.ndarray\n The value(s) to transform, should be a float.\n\n Returns\n -------\n np.ndarray\n \"\"\"\n return np.round(value)\n\n @property\n def dim(self) -> int:\n \"\"\"The dimensionality of the parameter.\"\"\"\n return 1\n\n\nclass CategoricalParameter(BayesParameter):\n \"\"\"A parameter with categorical values.\n\n Parameters\n ----------\n name : str\n The name of the parameter.\n\n categories : Sequence[Any]\n The categories of the parameter.\n \"\"\"\n\n def __init__(self, name: str, categories: Sequence[Any]) -> None:\n if len(categories) != len(set(categories)):\n msg = \"Categories must be unique.\"\n raise ValueError(msg)\n if len(categories) < 2:\n msg = \"At least two categories are required.\"\n raise ValueError(msg)\n\n self.categories = categories\n lower = np.zeros(self.dim)\n upper = np.ones(self.dim)\n bounds = np.vstack((lower, upper)).T\n super().__init__(name, bounds)\n\n @property\n def is_continuous(self) -> bool:\n \"\"\"Whether the parameter is continuous.\"\"\"\n return False\n\n def random_sample(\n self, n_samples: int, random_state: np.random.RandomState | int | None\n ) -> NDArray[Float]:\n \"\"\"Generate random float-format samples from the parameter.\n\n Parameters\n ----------\n n_samples : int\n The number of samples to generate.\n\n random_state : np.random.RandomState | int | None\n The random state to use for sampling.\n\n Returns\n -------\n np.ndarray\n The samples.\n \"\"\"\n random_state = ensure_rng(random_state)\n res = random_state.randint(0, len(self.categories), n_samples)\n one_hot = np.zeros((n_samples, len(self.categories)))\n one_hot[np.arange(n_samples), res] = 1\n return one_hot.astype(float)\n\n def to_float(self, value: Any) -> NDArray[Float]:\n \"\"\"Convert a parameter value to a float.\n\n Parameters\n ----------\n value : Any\n The value to convert, should be the canonical representation of the parameter.\n \"\"\"\n res = np.zeros(len(self.categories))\n one_hot_index = [i for i, val in enumerate(self.categories) if val == value]\n res[one_hot_index] = 1\n return res.astype(float)\n\n def to_param(self, value: float | NDArray[Float]) -> Any:\n \"\"\"Convert a float value to a parameter.\n\n Parameters\n ----------\n value : np.ndarray\n The value to convert, should be a float.\n\n Returns\n -------\n Any\n The canonical representation of the parameter.\n \"\"\"\n return self.categories[int(np.argmax(value))]\n\n def to_string(self, value: Any, str_len: int) -> str:\n \"\"\"Represent a parameter value as a string.\n\n Parameters\n ----------\n value : Any\n The value to represent.\n\n str_len : int\n The maximum length of the string representation.\n\n Returns\n -------\n str\n \"\"\"\n if not isinstance(value, str):\n value = repr(value)\n s = f\"{value:<{str_len}}\"\n\n if len(s) > str_len:\n return s[: str_len - 3] + \"...\"\n return s\n\n def kernel_transform(self, value: NDArray[Float]) -> NDArray[Float]:\n \"\"\"Transform a parameter value for use in a kernel.\n\n Parameters\n ----------\n value : np.ndarray\n The value(s) to transform, should be a float.\n\n Returns\n -------\n np.ndarray\n \"\"\"\n value = np.atleast_2d(value)\n res = np.zeros(value.shape)\n res[:, np.argmax(value, axis=1)] = 1\n return res\n\n @property\n def dim(self) -> int:\n \"\"\"The dimensionality of the parameter.\"\"\"\n return len(self.categories)\n\n\ndef wrap_kernel(kernel: kernels.Kernel, transform: Callable[[Any], Any]) -> kernels.Kernel:\n \"\"\"Wrap a kernel to transform input data before passing it to the kernel.\n\n Parameters\n ----------\n kernel : kernels.Kernel\n The kernel to wrap.\n\n transform : Callable\n The transformation function to apply to the input data.\n\n Returns\n -------\n kernels.Kernel\n The wrapped kernel.\n\n Notes\n -----\n See https://arxiv.org/abs/1805.03463 for more information.\n \"\"\"\n kernel_type = type(kernel)\n\n class WrappedKernel(kernel_type):\n @_copy_signature(getattr(kernel_type.__init__, \"deprecated_original\", kernel_type.__init__))\n def __init__(self, **kwargs: Any) -> None:\n super().__init__(**kwargs)\n\n def __call__(self, X: Any, Y: Any = None, eval_gradient: bool = False) -> Any:\n X = transform(X)\n Y = transform(Y) if Y is not None else None\n return super().__call__(X, Y, eval_gradient)\n\n def __reduce__(self) -> str | tuple[Any, ...]:\n return (wrap_kernel, (kernel, transform))\n\n wrapped_instance = WrappedKernel.__new__(WrappedKernel)\n wrapped_instance.__dict__.update(kernel.__dict__)\n wrapped_instance._transform = transform\n return wrapped_instance\n\n\ndef _copy_signature(source_fct: Callable[..., Any]) -> Callable[[Callable[..., Any]], Callable[..., Any]]:\n \"\"\"Clone a signature from a source function to a target function.\n\n via\n https://stackoverflow.com/a/58989918/\n \"\"\"\n\n def copy(target_fct: Callable[..., Any]) -> Callable[..., Any]:\n target_fct.__signature__ = signature(source_fct)\n return target_fct\n\n return copy\n" }, { "path": "bayes_opt/py.typed", "content": "" }, { "path": "bayes_opt/target_space.py", "content": "\"\"\"Manages the optimization domain and holds points.\"\"\"\n\nfrom __future__ import annotations\n\nfrom copy import deepcopy\nfrom typing import TYPE_CHECKING, Any\nfrom warnings import warn\n\nimport numpy as np\nfrom colorama import Fore\n\nfrom bayes_opt.constraint import ConstraintModel\nfrom bayes_opt.exception import NotUniqueError\nfrom bayes_opt.parameter import BayesParameter, CategoricalParameter, FloatParameter, IntParameter, is_numeric\nfrom bayes_opt.util import ensure_rng\n\nif TYPE_CHECKING:\n from collections.abc import Callable, Mapping\n\n from numpy.random import RandomState\n from numpy.typing import NDArray\n from scipy.optimize import NonlinearConstraint\n\n from bayes_opt.parameter import BoundsMapping, ParamsType\n\n Float = np.floating[Any]\n Int = np.integer[Any]\n\n\ndef _hashable(x: NDArray[Float]) -> tuple[float, ...]:\n \"\"\"Ensure that a point is hashable by a python dict.\"\"\"\n return tuple(map(float, x))\n\n\nclass TargetSpace:\n \"\"\"Holds the param-space coordinates (X) and target values (Y).\n\n Allows for constant-time appends.\n\n Parameters\n ----------\n target_func : function or None.\n Function to be maximized.\n\n pbounds : dict\n Dictionary with parameters names as keys and a tuple with minimum\n and maximum values.\n\n random_state : int, RandomState, or None\n optionally specify a seed for a random number generator\n\n allow_duplicate_points: bool, optional (default=False)\n If True, the optimizer will allow duplicate points to be registered.\n This behavior may be desired in high noise situations where repeatedly probing\n the same point will give different answers. In other situations, the acquisition\n may occasionally generate a duplicate point.\n\n Examples\n --------\n >>> def target_func(p1, p2):\n >>> return p1 + p2\n >>> pbounds = {\"p1\": (0, 1), \"p2\": (1, 100)}\n >>> space = TargetSpace(target_func, pbounds, random_state=0)\n >>> x = np.array([4, 5])\n >>> y = target_func(x)\n >>> space.register(x, y)\n >>> assert self.max()[\"target\"] == 9\n >>> assert self.max()[\"params\"] == {\"p1\": 1.0, \"p2\": 2.0}\n \"\"\"\n\n def __init__(\n self,\n target_func: Callable[..., float] | None,\n pbounds: BoundsMapping,\n constraint: NonlinearConstraint | None = None,\n random_state: int | RandomState | None = None,\n allow_duplicate_points: bool | None = False,\n ) -> None:\n self._allow_duplicate_points = allow_duplicate_points or False\n self.n_duplicate_points = 0\n\n # The function to be optimized\n self.target_func = target_func\n\n # Get the name of the parameters\n self._keys: list[str] = list(pbounds.keys())\n\n self._params_config = self.make_params(pbounds)\n self._dim = sum([self._params_config[key].dim for key in self._keys])\n\n self._masks = self.make_masks()\n self._bounds = self.calculate_bounds()\n\n # preallocated memory for X and Y points\n self._params: NDArray[Float] = np.empty(shape=(0, self.dim))\n self._target: NDArray[Float] = np.empty(shape=(0,))\n\n # keep track of unique points we have seen so far\n self._cache: dict[tuple[float, ...], float | tuple[float, float | NDArray[Float]]] = {}\n\n self._constraint: ConstraintModel | None = None\n if constraint is None:\n self._constraint = None\n else:\n self._constraint = ConstraintModel(\n constraint.fun,\n constraint.lb,\n constraint.ub,\n transform=self.kernel_transform,\n random_state=random_state,\n )\n\n # preallocated memory for constraint fulfillment\n self._constraint_values: NDArray[Float]\n if self._constraint.lb.size == 1:\n self._constraint_values = np.empty(shape=(0), dtype=float)\n else:\n self._constraint_values = np.empty(shape=(0, self._constraint.lb.size), dtype=float)\n\n def __contains__(self, x: NDArray[Float]) -> bool:\n \"\"\"Check if this parameter has already been registered.\n\n Returns\n -------\n bool\n \"\"\"\n return _hashable(x) in self._cache\n\n def __len__(self) -> int:\n \"\"\"Return number of observations registered.\n\n Returns\n -------\n int\n \"\"\"\n return len(self._target)\n\n @property\n def empty(self) -> bool:\n \"\"\"Check if anything has been registered.\n\n Returns\n -------\n bool\n \"\"\"\n return len(self) == 0\n\n @property\n def params(self) -> NDArray[Float]:\n \"\"\"Get the parameter values registered to this TargetSpace.\n\n Returns\n -------\n np.ndarray\n \"\"\"\n return self._params\n\n @property\n def target(self) -> NDArray[Float]:\n \"\"\"Get the target function values registered to this TargetSpace.\n\n Returns\n -------\n np.ndarray\n \"\"\"\n return self._target\n\n @property\n def dim(self) -> int:\n \"\"\"Get the number of parameter names.\n\n Returns\n -------\n int\n \"\"\"\n return self._dim\n\n @property\n def keys(self) -> list[str]:\n \"\"\"Get the keys (or parameter names).\n\n Returns\n -------\n list of str\n \"\"\"\n return self._keys\n\n @property\n def params_config(self) -> dict[str, BayesParameter]:\n \"\"\"Get the parameters configuration.\"\"\"\n return self._params_config\n\n @property\n def bounds(self) -> NDArray[Float]:\n \"\"\"Get the bounds of this TargetSpace.\n\n Returns\n -------\n np.ndarray\n \"\"\"\n return self._bounds\n\n @property\n def constraint(self) -> ConstraintModel | None:\n \"\"\"Get the constraint model.\n\n Returns\n -------\n ConstraintModel\n \"\"\"\n return self._constraint\n\n @property\n def masks(self) -> dict[str, NDArray[np.bool_]]:\n \"\"\"Get the masks for the parameters.\n\n Returns\n -------\n dict\n \"\"\"\n return self._masks\n\n @property\n def continuous_dimensions(self) -> NDArray[np.bool_]:\n \"\"\"Get the continuous parameters.\n\n Returns\n -------\n dict\n \"\"\"\n result = np.zeros(self.dim, dtype=bool)\n masks = self.masks\n for key in self.keys:\n result[masks[key]] = self._params_config[key].is_continuous\n return result\n\n def make_params(self, pbounds: BoundsMapping) -> dict[str, BayesParameter]:\n \"\"\"Create a dictionary of parameters from a dictionary of bounds.\n\n Parameters\n ----------\n pbounds : dict\n A dictionary with the parameter names as keys and a tuple with minimum\n and maximum values.\n\n Returns\n -------\n dict\n A dictionary with the parameter names as keys and the corresponding\n parameter objects as values.\n \"\"\"\n any_is_not_float = False # TODO: remove in an upcoming release\n params: dict[str, BayesParameter] = {}\n for key in pbounds:\n pbound = pbounds[key]\n\n if isinstance(pbound, BayesParameter):\n res = pbound\n if not isinstance(pbound, FloatParameter):\n any_is_not_float = True\n elif (len(pbound) == 2 and is_numeric(pbound[0]) and is_numeric(pbound[1])) or (\n len(pbound) == 3 and pbound[-1] is float\n ):\n res = FloatParameter(name=key, bounds=(float(pbound[0]), float(pbound[1])))\n elif len(pbound) == 3 and pbound[-1] is int:\n res = IntParameter(name=key, bounds=(int(pbound[0]), int(pbound[1])))\n any_is_not_float = True\n else:\n # assume categorical variable with pbound as list of possible values\n res = CategoricalParameter(name=key, categories=pbound)\n any_is_not_float = True\n params[key] = res\n if any_is_not_float:\n msg = (\n \"Non-float parameters are experimental and may not work as expected.\"\n \" Exercise caution when using them and please report any issues you encounter.\"\n )\n warn(msg, stacklevel=4)\n return params\n\n def make_masks(self) -> dict[str, NDArray[np.bool_]]:\n \"\"\"Create a dictionary of masks for the parameters.\n\n The mask can be used to select the corresponding parameters from an array.\n\n Returns\n -------\n dict\n A dictionary with the parameter names as keys and the corresponding\n mask as values.\n \"\"\"\n masks = {}\n pos = 0\n for key in self._keys:\n mask = np.zeros(self._dim)\n mask[pos : pos + self._params_config[key].dim] = 1\n masks[key] = mask.astype(bool)\n pos = pos + self._params_config[key].dim\n return masks\n\n def calculate_bounds(self) -> NDArray[Float]:\n \"\"\"Calculate the float bounds of the parameter space.\"\"\"\n bounds = np.empty((self._dim, 2))\n for key in self._keys:\n bounds[self.masks[key]] = self._params_config[key].bounds\n return bounds\n\n def params_to_array(self, params: Mapping[str, float | NDArray[Float]]) -> NDArray[Float]:\n \"\"\"Convert a dict representation of parameters into an array version.\n\n Parameters\n ----------\n params : dict\n a single point, with len(x) == self.dim.\n\n Returns\n -------\n np.ndarray\n Representation of the parameters as an array.\n \"\"\"\n if set(params) != set(self.keys):\n error_msg = f\"Parameters' keys ({params}) do not match the expected set of keys ({self.keys}).\"\n raise ValueError(error_msg)\n return self._to_float(params)\n\n @property\n def constraint_values(self) -> NDArray[Float]:\n \"\"\"Get the constraint values registered to this TargetSpace.\n\n Returns\n -------\n np.ndarray\n \"\"\"\n if self._constraint is None:\n error_msg = \"TargetSpace belongs to an unconstrained optimization\"\n raise AttributeError(error_msg)\n\n return self._constraint_values\n\n def kernel_transform(self, value: NDArray[Float]) -> NDArray[Float]:\n \"\"\"Transform floating-point suggestions to values used in the kernel.\n\n Vectorized.\n \"\"\"\n value = np.atleast_2d(value)\n res = [self._params_config[p].kernel_transform(value[:, self.masks[p]]) for p in self._keys]\n return np.hstack(res)\n\n def array_to_params(self, x: NDArray[Float]) -> dict[str, float | NDArray[Float]]:\n \"\"\"Convert an array representation of parameters into a dict version.\n\n Parameters\n ----------\n x : np.ndarray\n a single point, with len(x) == self.dim.\n\n Returns\n -------\n dict\n Representation of the parameters as dictionary.\n \"\"\"\n if len(x) != self._dim:\n error_msg = (\n f\"Size of array ({len(x)}) is different than the expected number of parameters ({self._dim}).\"\n )\n raise ValueError(error_msg)\n return self._to_params(x)\n\n def _to_float(self, value: Mapping[str, float | NDArray[Float]]) -> NDArray[Float]:\n if set(value) != set(self.keys):\n msg = f\"Parameters' keys ({value}) do not match the expected set of keys ({self.keys}).\"\n raise ValueError(msg)\n res = np.zeros(self._dim)\n for key in self._keys:\n p = self._params_config[key]\n res[self.masks[key]] = p.to_float(value[key])\n return res\n\n def _to_params(self, value: NDArray[Float]) -> dict[str, float | NDArray[Float]]:\n res: dict[str, float | NDArray[Float]] = {}\n for key in self._keys:\n p = self._params_config[key]\n mask = self.masks[key]\n res[key] = p.to_param(value[mask])\n return res\n\n @property\n def mask(self) -> NDArray[np.bool_]:\n \"\"\"Return a boolean array of valid points.\n\n Points are valid if they satisfy both the constraint and boundary conditions.\n\n Returns\n -------\n np.ndarray\n \"\"\"\n mask = np.ones_like(self.target, dtype=bool)\n\n # mask points that don't satisfy the constraint\n if self._constraint is not None:\n mask &= self._constraint.allowed(self._constraint_values)\n\n # mask points that are outside the bounds\n if self._bounds is not None:\n within_bounds = np.all(\n (self._bounds[:, 0] <= self._params) & (self._params <= self._bounds[:, 1]), axis=1\n )\n mask &= within_bounds\n\n return mask\n\n def _as_array(self, x: Any) -> NDArray[Float]:\n try:\n x = np.asarray(x, dtype=float)\n except TypeError:\n x = self.params_to_array(x)\n\n x = x.ravel()\n if x.size != self.dim:\n msg = f\"Size of array ({len(x)}) is different than the expected number of ({self.dim}).\"\n raise ValueError(msg)\n return x\n\n def register(\n self, params: ParamsType, target: float, constraint_value: float | NDArray[Float] | None = None\n ) -> None:\n \"\"\"Append a point and its target value to the known data.\n\n Parameters\n ----------\n params : np.ndarray\n a single point, with len(x) == self.dim.\n\n target : float\n target function value\n\n constraint_value : float or np.ndarray or None\n Constraint function value\n\n Raises\n ------\n NotUniqueError:\n if the point is not unique\n\n Notes\n -----\n runs in amortized constant time\n\n Examples\n --------\n >>> target_func = lambda p1, p2: p1 + p2\n >>> pbounds = {\"p1\": (0, 1), \"p2\": (1, 100)}\n >>> space = TargetSpace(target_func, pbounds)\n >>> len(space)\n 0\n >>> x = np.array([0, 0])\n >>> y = 1\n >>> space.register(x, y)\n >>> len(space)\n 1\n \"\"\"\n x = self._as_array(params)\n\n if x in self:\n if self._allow_duplicate_points:\n self.n_duplicate_points = self.n_duplicate_points + 1\n\n print(\n Fore.RED + f\"Data point {x} is not unique. {self.n_duplicate_points}\"\n \" duplicates registered. Continuing ...\" + Fore.RESET\n )\n else:\n error_msg = (\n f\"Data point {x} is not unique. You can set\"\n ' \"allow_duplicate_points=True\" to avoid this error'\n )\n raise NotUniqueError(error_msg)\n\n # if x is not within the bounds of the parameter space, warn the user\n if self._bounds is not None and not np.all((self._bounds[:, 0] <= x) & (x <= self._bounds[:, 1])):\n for key in self.keys:\n if not np.all(\n (self._params_config[key].bounds[..., 0] <= x[self.masks[key]])\n & (x[self.masks[key]] <= self._params_config[key].bounds[..., 1])\n ):\n msg = (\n f\"\\nData point {x} is outside the bounds of the parameter {key}.\"\n f\"\\n\\tBounds:\\n{self._params_config[key].bounds}\"\n )\n warn(msg, stacklevel=2)\n\n # Make copies of the data, so as not to modify the originals incase something fails\n # during the registration process. This prevents out-of-sync data.\n params_copy: NDArray[Float] = np.concatenate([self._params, x.reshape(1, -1)])\n target_copy: NDArray[Float] = np.concatenate([self._target, [target]])\n cache_copy = self._cache.copy() # shallow copy suffices\n\n if self._constraint is None:\n # Insert data into unique dictionary\n cache_copy[_hashable(x.ravel())] = target\n else:\n if constraint_value is None:\n msg = (\n \"When registering a point to a constrained TargetSpace\"\n \" a constraint value needs to be present.\"\n )\n raise ValueError(msg)\n # Insert data into unique dictionary\n cache_copy[_hashable(x.ravel())] = (target, constraint_value)\n constraint_values_copy: NDArray[Float] = np.concatenate(\n [self._constraint_values, [constraint_value]]\n )\n self._constraint_values = constraint_values_copy\n\n # Operations passed, update the variables\n self._params = params_copy\n self._target = target_copy\n self._cache = cache_copy\n\n def probe(self, params: ParamsType) -> float | tuple[float, float | NDArray[Float]]:\n \"\"\"Evaluate the target function on a point and register the result.\n\n Notes\n -----\n If `params` has been previously seen and duplicate points are not allowed,\n returns a cached value of `result`.\n\n Parameters\n ----------\n params : np.ndarray\n a single point, with len(x) == self.dim\n\n Returns\n -------\n result : float | Tuple(float, float)\n target function value, or Tuple(target function value, constraint value)\n\n Example\n -------\n >>> target_func = lambda p1, p2: p1 + p2\n >>> pbounds = {\"p1\": (0, 1), \"p2\": (1, 100)}\n >>> space = TargetSpace(target_func, pbounds)\n >>> space.probe([1, 5])\n >>> assert self.max()[\"target\"] == 6\n >>> assert self.max()[\"params\"] == {\"p1\": 1.0, \"p2\": 5.0}\n \"\"\"\n x = self._as_array(params)\n if x in self and not self._allow_duplicate_points:\n return self._cache[_hashable(x.ravel())]\n\n dict_params = self.array_to_params(x)\n if self.target_func is None:\n error_msg = \"No target function has been provided.\"\n raise ValueError(error_msg)\n target = self.target_func(**dict_params)\n\n if self._constraint is None:\n self.register(x, target)\n return target\n\n constraint_value = self._constraint.eval(**dict_params)\n self.register(x, target, constraint_value)\n return target, constraint_value\n\n def random_sample(\n self, n_samples: int = 0, random_state: np.random.RandomState | int | None = None\n ) -> NDArray[Float]:\n \"\"\"\n Sample a random point from within the bounds of the space.\n\n Parameters\n ----------\n n_samples : int, optional\n Number of samples to draw. If 0, a single sample is drawn,\n and a 1D array is returned. If n_samples > 0, an array of\n shape (n_samples, dim) is returned.\n\n random_state : np.random.RandomState | int | None\n The random state to use for sampling.\n\n Returns\n -------\n data: ndarray\n [1 x dim] array with dimensions corresponding to `self._keys`\n\n Examples\n --------\n >>> target_func = lambda p1, p2: p1 + p2\n >>> pbounds = {\"p1\": (0, 1), \"p2\": (1, 100)}\n >>> space = TargetSpace(target_func, pbounds, random_state=0)\n >>> space.random_sample()\n array([[ 0.54488318, 55.33253689]])\n \"\"\"\n random_state = ensure_rng(random_state)\n flatten = n_samples == 0\n n_samples = max(1, n_samples)\n data = np.empty((n_samples, self._dim))\n for key, mask in self.masks.items():\n smpl = self._params_config[key].random_sample(n_samples, random_state)\n data[:, mask] = smpl.reshape(n_samples, self._params_config[key].dim)\n if flatten:\n return data.ravel()\n return data\n\n def _target_max(self) -> float | None:\n \"\"\"Get the maximum target value within the current parameter bounds.\n\n If there is a constraint present, the maximum value that fulfills the\n constraint within the parameter bounds is returned.\n\n Returns\n -------\n max: float\n The maximum target value.\n \"\"\"\n if len(self.target) == 0:\n return None\n\n if len(self.target[self.mask]) == 0:\n return None\n\n return self.target[self.mask].max()\n\n def max(self) -> dict[str, Any] | None:\n \"\"\"Get maximum target value found and corresponding parameters.\n\n If there is a constraint present, the maximum value that fulfills the\n constraint within the parameter bounds is returned.\n\n Returns\n -------\n res: dict\n A dictionary with the keys 'target' and 'params'. The value of\n 'target' is the maximum target value, and the value of 'params' is\n a dictionary with the parameter names as keys and the parameter\n values as values.\n \"\"\"\n target_max = self._target_max()\n if target_max is None:\n return None\n\n target = self.target[self.mask]\n params = self.params[self.mask]\n target_max_idx = np.argmax(target)\n\n res = {\"target\": target_max, \"params\": self.array_to_params(params[target_max_idx])}\n\n if self._constraint is not None:\n constraint_values = self.constraint_values[self.mask]\n res[\"constraint\"] = constraint_values[target_max_idx]\n\n return res\n\n def res(self) -> list[dict[str, Any]]:\n \"\"\"Get all target values and constraint fulfillment for all parameters.\n\n Returns\n -------\n res: list\n A list of dictionaries with the keys 'target', 'params', and\n 'constraint'. The value of 'target' is the target value, the value\n of 'params' is a dictionary with the parameter names as keys and the\n parameter values as values, and the value of 'constraint' is the\n constraint fulfillment.\n\n Notes\n -----\n Does not report if points are within the bounds of the parameter space.\n \"\"\"\n if self._constraint is None:\n params = [self.array_to_params(p) for p in self.params]\n\n return [{\"target\": target, \"params\": param} for target, param in zip(self.target, params)]\n\n params = [self.array_to_params(p) for p in self.params]\n\n return [\n {\"target\": target, \"constraint\": constraint_value, \"params\": param, \"allowed\": allowed}\n for target, constraint_value, param, allowed in zip(\n self.target,\n self._constraint_values,\n params,\n self._constraint.allowed(self._constraint_values),\n )\n ]\n\n def set_bounds(self, new_bounds: BoundsMapping) -> None:\n \"\"\"Change the lower and upper search bounds.\n\n Parameters\n ----------\n new_bounds : dict\n A dictionary with the parameter name and its new bounds\n \"\"\"\n new_params_config = self.make_params(new_bounds)\n\n dims = 0\n params_config = deepcopy(self._params_config)\n for key in self.keys:\n if key in new_bounds:\n if not isinstance(new_params_config[key], type(self._params_config[key])):\n msg = (\n f\"Parameter type {type(new_params_config[key])} of\"\n \" new bounds does not match parameter type\"\n f\" {type(self._params_config[key])} of old bounds\"\n )\n raise ValueError(msg)\n params_config[key] = new_params_config[key]\n dims = dims + params_config[key].dim\n if dims != self.dim:\n msg = f\"Dimensions of new bounds ({dims}) does not match dimensions of old bounds ({self.dim}).\"\n raise ValueError(msg)\n self._params_config = params_config\n self._bounds = self.calculate_bounds()\n" }, { "path": "bayes_opt/util.py", "content": "\"\"\"Contains utility functions.\"\"\"\n\nfrom __future__ import annotations\n\nimport numpy as np\n\n\ndef ensure_rng(random_state: int | np.random.RandomState | None = None) -> np.random.RandomState:\n \"\"\"Create a random number generator based on an optional seed.\n\n Parameters\n ----------\n random_state : np.random.RandomState or int or None, default=None\n Random state to use. if `None`, will create an unseeded random state.\n If `int`, creates a state using the argument as seed. If a\n `np.random.RandomState` simply returns the argument.\n\n Returns\n -------\n np.random.RandomState\n\n \"\"\"\n if random_state is None:\n random_state = np.random.RandomState()\n elif isinstance(random_state, int):\n random_state = np.random.RandomState(random_state)\n elif not isinstance(random_state, np.random.RandomState):\n error_msg = \"random_state should be an instance of np.random.RandomState, an int, or None.\"\n raise TypeError(error_msg)\n return random_state\n" }, { "path": "docsrc/Makefile", "content": "# Minimal makefile for Sphinx documentation\n#\n\n# You can set these variables from the command line, and also\n# from the environment for the first two.\nSPHINXOPTS ?=\nSPHINXBUILD ?= sphinx-build\nSOURCEDIR = .\nBUILDDIR = ../docs\n\n# Put it first so that \"make\" without argument is like \"make help\".\n# help:\n# \t@$(SPHINXBUILD) -M help \"$(SOURCEDIR)\" \"$(BUILDDIR)\" $(SPHINXOPTS) $(O)\n\n.PHONY: help Makefile\n\ngithub:\n# \t@cp ../README.md .\n\t@make html\n\t@cp -a ../docs/html/. ../docs\n\t@cp -r ../docsrc/ ../docs\n\n# Catch-all target: route all unknown targets to Sphinx using the new\n# \"make mode\" option. $(O) is meant as a shortcut for $(SPHINXOPTS).\n%: Makefile\n\t@$(SPHINXBUILD) -M $@ \"$(SOURCEDIR)\" \"$(BUILDDIR)\" $(SPHINXOPTS) $(O)" }, { "path": "docsrc/conf.py", "content": "# Configuration file for the Sphinx documentation builder.\n#\n# This file only contains a selection of the most common options. For a full\n# list see the documentation:\n# https://www.sphinx-doc.org/en/master/usage/configuration.html\n\n# -- Path setup --------------------------------------------------------------\n\n# If extensions (or modules to document with autodoc) are in another directory,\n# add these directories to sys.path here. If the directory is relative to the\n# documentation root, use os.path.abspath to make it absolute, like shown here.\n#\nimport os\nimport sys\nimport time\nimport shutil\nfrom glob import glob\nfrom pathlib import Path\n# sys.path.insert(0, os.path.abspath('.'))\nsys.path.insert(0, os.path.abspath('..'))\n\n# copy the latest example files:\nthis_file_loc = Path(__file__).parent\nnotebooks = glob(str(this_file_loc.parent / 'examples' / '*.ipynb'))\nfor notebook in notebooks:\n shutil.copy(notebook, this_file_loc)\n\n\n# -- Project information -----------------------------------------------------\n\nproject = 'bayesian-optimization'\nauthor = 'Fernando Nogueira'\n\n\n# -- General configuration ---------------------------------------------------\n\n# Add any Sphinx extension module names here, as strings. They can be\n# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom\n# ones.\nextensions = [\n 'sphinx.ext.autodoc',\n 'sphinx.ext.coverage',\n 'sphinx.ext.githubpages',\n 'nbsphinx',\n 'IPython.sphinxext.ipython_console_highlighting',\n 'sphinx.ext.mathjax',\n \"sphinx.ext.napoleon\",\n 'sphinx_autodoc_typehints',\n 'sphinx.ext.intersphinx',\n 'sphinx_immaterial',\n]\n\nsource_suffix = {\n '.rst': 'restructuredtext',\n}\n# Add any paths that contain templates here, relative to this directory.\ntemplates_path = ['_templates']\n\n# List of patterns, relative to source directory, that match files and\n# directories to ignore when looking for source files.\n# This pattern also affects html_static_path and html_extra_path.\nexclude_patterns = []\n\n# Link types to the corresponding documentations\nintersphinx_mapping = {\n 'python': ('https://docs.python.org/3', None),\n 'numpy': ('https://numpy.org/doc/stable', None),\n 'scipy': ('https://docs.scipy.org/doc/scipy/reference', None),\n 'sklearn': ('https://scikit-learn.org/stable', None),\n}\n\n\nnapoleon_use_rtype = False\n\n# -- Options for HTML output -------------------------------------------------\n\n# The theme to use for HTML and HTML Help pages. See the documentation for\n# a list of builtin themes.\n#\n\nhtml_title = \"Bayesian Optimization\"\nhtml_theme = \"sphinx_immaterial\"\ncopyright = f\"{time.strftime('%Y')}, Fernando Nogueira and the bayesian-optimization developers\"\n\n# material theme options (see theme.conf for more information)\nhtml_theme_options = {\n \"icon\": {\n \"repo\": \"fontawesome/brands/github\",\n \"edit\": \"material/file-edit-outline\",\n },\n \"site_url\": \"https://bayesian-optimization.github.io/BayesianOptimization/\",\n \"repo_url\": \"https://github.com/bayesian-optimization/BayesianOptimization/\",\n \"repo_name\": \"bayesian-optimization\",\n \"edit_uri\": \"blob/master/docsrc\",\n \"globaltoc_collapse\": True,\n \"features\": [\n \"navigation.expand\",\n # \"navigation.tabs\",\n # \"toc.integrate\",\n \"navigation.sections\",\n # \"navigation.instant\",\n # \"header.autohide\",\n \"navigation.top\",\n # \"navigation.tracking\",\n # \"search.highlight\",\n \"search.share\",\n \"toc.follow\",\n \"toc.sticky\",\n \"content.tabs.link\",\n \"announce.dismiss\",\n ],\n \"palette\": [\n {\n \"media\": \"(prefers-color-scheme: light)\",\n \"scheme\": \"default\",\n \"primary\": \"light-blue\",\n \"accent\": \"light-green\",\n \"toggle\": {\n \"icon\": \"material/lightbulb-outline\",\n \"name\": \"Switch to dark mode\",\n },\n },\n {\n \"media\": \"(prefers-color-scheme: dark)\",\n \"scheme\": \"slate\",\n \"primary\": \"deep-orange\",\n \"accent\": \"lime\",\n \"toggle\": {\n \"icon\": \"material/lightbulb\",\n \"name\": \"Switch to light mode\",\n },\n },\n ],\n # BEGIN: version_dropdown\n \"version_dropdown\": True,\n \"version_json\": '../versions.json',\n # END: version_dropdown\n \"scope\": \"/\", # share preferences across subsites\n \"toc_title_is_page_title\": True,\n # BEGIN: social icons\n \"social\": [\n {\n \"icon\": \"fontawesome/brands/github\",\n \"link\": \"https://github.com/bayesian-optimization/BayesianOptimization\",\n \"name\": \"Source on github.com\",\n },\n {\n \"icon\": \"fontawesome/brands/python\",\n \"link\": \"https://pypi.org/project/bayesian-optimization/\",\n },\n {\n \"icon\": \"fontawesome/brands/python\",\n \"link\": \"https://anaconda.org/conda-forge/bayesian-optimization\",\n }\n ],\n # END: social icons\n}\n\nhtml_favicon = 'func.ico'\n\n# Add any paths that contain custom static files (such as style sheets) here,\n# relative to this directory. They are copied after the builtin static files,\n# so a file named \"default.css\" will overwrite the builtin \"default.css\".\n# html_static_path = ['_static']\n\n## extensions configuration\n### sphinx-autodoc-typehints\ntypehints_use_signature = True\n\"\"\"\nIf True, typehints for parameters in the signature are shown.\n\nsee more: https://github.com/tox-dev/sphinx-autodoc-typehints/blob/main/README.md#options\n\"\"\"\ntypehints_use_signature_return = True\n\"\"\"\nIf True, return annotations in the signature are shown.\n\nsee more: https://github.com/tox-dev/sphinx-autodoc-typehints/blob/main/README.md#options\n\"\"\"\n### autodoc\nautodoc_typehints = \"both\"\n\"\"\"\nThis value controls how to represent typehints. The setting takes the following values:\n - `signature`: Show typehints in the signature\n - `description`: Show typehints as content of the function or method\n The typehints of overloaded functions or methods will still be represented in the signature.\n - `none`: Do not show typehints\n - `both`: Show typehints in the signature and as content of the function or method\n\nOverloaded functions or methods will not have typehints included in the description\nbecause it is impossible to accurately represent all possible overloads as a list of parameters.\n\nsee more: https://www.sphinx-doc.org/en/master/usage/extensions/autodoc.html#confval-autodoc_typehints\n\"\"\"\nautodoc_typehints_format = \"short\"\n\"\"\"\nThis value controls the format of typehints. The setting takes the following values:\n - `fully-qualified`: Show the module name and its name of typehints\n - `short`: Suppress the leading module names of the typehints\n (e.g. io.StringIO -> StringIO)\n\nsee more: https://www.sphinx-doc.org/en/master/usage/extensions/autodoc.html#confval-autodoc_typehints_format\n\"\"\"\n" }, { "path": "docsrc/index.rst", "content": ".. toctree::\n :hidden:\n\n Quickstart \n\n.. toctree::\n :hidden:\n :maxdepth: 3\n :caption: Example Notebooks:\n\n Basic Tour \n Advanced Tour \n Constrained Bayesian Optimization \n Parameter Types \n Sequential Domain Reduction \n Acquisition Functions \n Exploration vs. Exploitation \n Visualization of a 1D-Optimization \n\n.. toctree::\n :hidden:\n :maxdepth: 2\n :caption: API reference:\n\n reference/bayes_opt\n reference/acquisition\n reference/constraint\n reference/domain_reduction\n reference/target_space\n reference/parameter\n reference/exception\n reference/other\n\n.. raw:: html\n\n
\n

\n
\n\nBayesian Optimization\n=====================\n\n|tests| |Codecov| |Pypi| |PyPI - Python Version|\n\nPure Python implementation of bayesian global optimization with gaussian\nprocesses.\n\nThis is a constrained global optimization package built upon bayesian\ninference and gaussian processes, that attempts to find the maximum value\nof an unknown function in as few iterations as possible. This technique\nis particularly suited for optimization of high cost functions and\nsituations where the balance between exploration and exploitation is\nimportant.\n\nInstallation\n------------\n\npip (via PyPI)\n~~~~~~~~~~~~~~\n\n.. code:: console\n\n $ pip install bayesian-optimization\n\nConda (via conda-forge)\n~~~~~~~~~~~~~~~~~~~~~~~\n\n.. code:: console\n\n $ conda install -c conda-forge bayesian-optimization\n\nHow does it work?\n-----------------\n\nBayesian optimization works by constructing a posterior distribution of\nfunctions (gaussian process) that best describes the function you want\nto optimize. As the number of observations grows, the posterior\ndistribution improves, and the algorithm becomes more certain of which\nregions in parameter space are worth exploring and which are not, as\nseen in the picture below.\n\n.. image:: ./static/bo_example.png\n :alt: BayesianOptimization in action\n\nAs you iterate over and over, the algorithm balances its needs of\nexploration and exploitation taking into account what it knows about the\ntarget function. At each step a Gaussian Process is fitted to the known\nsamples (points previously explored), and the posterior distribution,\ncombined with a exploration strategy (such as UCB (Upper Confidence\nBound), or EI (Expected Improvement)), are used to determine the next\npoint that should be explored (see the gif below).\n\n.. image:: ./static/bayesian_optimization.gif\n :alt: BayesianOptimization in action\n\nThis process is designed to minimize the number of steps required to\nfind a combination of parameters that are close to the optimal\ncombination. To do so, this method uses a proxy optimization problem\n(finding the maximum of the acquisition function) that, albeit still a\nhard problem, is cheaper (in the computational sense) and common tools\ncan be employed. Therefore Bayesian Optimization is most adequate for\nsituations where sampling the function to be optimized is a very\nexpensive endeavor. See the references for a proper discussion of this\nmethod.\n\nThis project is under active development, if you find a bug, or anything\nthat needs correction, please let us know by filing an\n`issue on GitHub `__\n.\n\n\nQuick Index\n-----------\n\nSee below for a quick tour over the basics of the Bayesian Optimization\npackage. More detailed information, other advanced features, and tips on\nusage/implementation can be found in the\n`examples `__\nsection. We suggest that you:\n\n- Follow the `basic tour\n notebook `__\n to learn how to use the package's most important features.\n- Take a look at the `advanced tour\n notebook `__\n to learn how to make the package more flexible or how to use observers.\n- To learn more about acquisition functions, a central building block\n of bayesian optimization, see the `acquisition functions\n notebook `__\n- If you want to optimize over integer-valued or categorical\n parameters, see the `parameter types\n notebook `__.\n- Check out this\n `notebook `__\n with a step by step visualization of how this method works.\n- To understand how to use bayesian optimization when additional\n constraints are present, see the `constrained optimization\n notebook `__.\n- Explore the `domain reduction\n notebook `__\n to learn more about how search can be sped up by dynamically changing\n parameters' bounds.\n- Explore this\n `notebook `__\n exemplifying the balance between exploration and exploitation and how\n to control it.\n- Go over this\n `script `__\n for examples of how to tune parameters of Machine Learning models\n using cross validation and bayesian optimization.\n- Finally, take a look at this\n `script `__\n for ideas on how to implement bayesian optimization in a distributed\n fashion using this package.\n\n\nCitation\n--------\n\nIf you used this package in your research, please cite it:\n\n::\n\n @Misc{,\n author={Fernando Nogueira},\n title={{Bayesian Optimization}: Open source constrained global optimization tool for {Python}},\n year={2014--},\n url=\"https://github.com/bayesian-optimization/BayesianOptimization\"\n }\n\nIf you used any of the advanced functionalities, please additionally\ncite the corresponding publication:\n\nFor the ``SequentialDomainTransformer``:\n\n::\n\n @article{\n author={Stander, Nielen and Craig, Kenneth},\n year={2002},\n month={06},\n pages={},\n title={On the robustness of a simple domain reduction scheme for simulation-based optimization},\n volume={19},\n journal={International Journal for Computer-Aided Engineering and Software (Eng. Comput.)},\n doi={10.1108/02644400210430190}\n }\n\nFor constrained optimization:\n\n::\n\n @inproceedings{gardner2014bayesian,\n title={Bayesian optimization with inequality constraints.},\n author={Gardner, Jacob R and Kusner, Matt J and Xu, Zhixiang Eddie and Weinberger, Kilian Q and Cunningham, John P},\n booktitle={ICML},\n volume={2014},\n pages={937--945},\n year={2014}\n }\n\nFor optimization over non-float parameters:\n\n::\n\n @article{garrido2020dealing,\n title={Dealing with categorical and integer-valued variables in bayesian optimization with gaussian processes},\n author={Garrido-Merch{\\'a}n, Eduardo C and Hern{\\'a}ndez-Lobato, Daniel},\n journal={Neurocomputing},\n volume={380},\n pages={20--35},\n year={2020},\n publisher={Elsevier}\n }\n\n.. |tests| image:: https://github.com/bayesian-optimization/BayesianOptimization/actions/workflows/run_tests.yml/badge.svg\n.. |Codecov| image:: https://codecov.io/github/bayesian-optimization/BayesianOptimization/badge.svg?branch=master&service=github\n :target: https://codecov.io/github/bayesian-optimization/BayesianOptimization?branch=master\n.. |Pypi| image:: https://img.shields.io/pypi/v/bayesian-optimization.svg\n :target: https://pypi.python.org/pypi/bayesian-optimization\n.. |PyPI - Python Version| image:: https://img.shields.io/pypi/pyversions/bayesian-optimization\n\n" }, { "path": "docsrc/make.bat", "content": "@ECHO OFF\n\npushd %~dp0\n\nREM Command file for Sphinx documentation\n\nif \"%SPHINXBUILD%\" == \"\" (\n\tset SPHINXBUILD=sphinx-build\n)\nset SOURCEDIR=.\nset BUILDDIR=../docs\n\n%SPHINXBUILD% >NUL 2>NUL\nif errorlevel 9009 (\n\techo.\n\techo.The 'sphinx-build' command was not found. Make sure you have Sphinx\n\techo.installed, then set the SPHINXBUILD environment variable to point\n\techo.to the full path of the 'sphinx-build' executable. Alternatively you\n\techo.may add the Sphinx directory to PATH.\n\techo.\n\techo.If you don't have Sphinx installed, grab it from\n\techo.https://www.sphinx-doc.org/\n\texit /b 1\n)\n\nif \"%1\" == \"\" goto help\n\nif \"%1\" == \"github\" (\n %SPHINXBUILD% -M html %SOURCEDIR% %BUILDDIR% %SPHINXOPTS%\n robocopy %BUILDDIR%/html ../docs /E > nul\n echo.Generated files copied to ../docs\n goto end\n)\n\n\n%SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O%\ngoto end\n\n\n:help\n%SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O%\n\n:end\npopd" }, { "path": "docsrc/reference/acquisition/ConstantLiar.rst", "content": ":py:class:`bayes_opt.acquisition.ConstantLiar`\n----------------------------------------------\n\n.. autoclass:: bayes_opt.acquisition.ConstantLiar\n :members:\n" }, { "path": "docsrc/reference/acquisition/ExpectedImprovement.rst", "content": ":py:class:`bayes_opt.acquisition.ExpectedImprovement`\n-----------------------------------------------------\n\n.. autoclass:: bayes_opt.acquisition.ExpectedImprovement\n :members:\n" }, { "path": "docsrc/reference/acquisition/GPHedge.rst", "content": ":py:class:`bayes_opt.acquisition.GPHedge`\n-----------------------------------------\n\n.. autoclass:: bayes_opt.acquisition.GPHedge\n :members:\n" }, { "path": "docsrc/reference/acquisition/ProbabilityOfImprovement.rst", "content": ":py:class:`bayes_opt.acquisition.ProbabilityOfImprovement`\n----------------------------------------------------------\n\n.. autoclass:: bayes_opt.acquisition.ProbabilityOfImprovement\n :members:\n" }, { "path": "docsrc/reference/acquisition/UpperConfidenceBound.rst", "content": ":py:class:`bayes_opt.acquisition.UpperConfidenceBound`\n------------------------------------------------------\n\n.. autoclass:: bayes_opt.acquisition.UpperConfidenceBound\n :members:\n" }, { "path": "docsrc/reference/acquisition.rst", "content": ":py:mod:`bayes_opt.acquisition`\n-------------------------------\n\n.. automodule:: bayes_opt.acquisition\n :members: AcquisitionFunction\n\n.. toctree::\n :hidden:\n\n acquisition/UpperConfidenceBound\n acquisition/ProbabilityOfImprovement\n acquisition/ExpectedImprovement\n acquisition/GPHedge\n acquisition/ConstantLiar\n" }, { "path": "docsrc/reference/bayes_opt.rst", "content": ":py:class:`bayes_opt.BayesianOptimization`\n------------------------------------------\n\n.. autoclass:: bayes_opt.BayesianOptimization\n :members:\n" }, { "path": "docsrc/reference/constraint.rst", "content": ":py:class:`bayes_opt.ConstraintModel`\n------------------------------------------------\n\nSee the `Constrained Optimization notebook <../constraints.html#2.-Advanced-Constrained-Optimization>`__ for a complete example.\n\n.. autoclass:: bayes_opt.constraint.ConstraintModel\n :members:\n" }, { "path": "docsrc/reference/domain_reduction.rst", "content": ":py:class:`bayes_opt.SequentialDomainReductionTransformer`\n----------------------------------------------------------\n\nSee the `Sequential Domain Reduction notebook <../domain_reduction.html>`__ for a complete example.\n\n.. autoclass:: bayes_opt.SequentialDomainReductionTransformer\n :members:" }, { "path": "docsrc/reference/exception.rst", "content": ":py:mod:`bayes_opt.exception`\n-------------------------------\n\n.. automodule:: bayes_opt.exception\n :members: \n" }, { "path": "docsrc/reference/other.rst", "content": "Other\n-----\n\n.. autoclass:: bayes_opt.ScreenLogger\n :members:\n" }, { "path": "docsrc/reference/parameter.rst", "content": ":py:mod:`bayes_opt.parameter`\n--------------------------------\n\n.. automodule:: bayes_opt.parameter\n :members: \n" }, { "path": "docsrc/reference/target_space.rst", "content": ":py:class:`bayes_opt.TargetSpace`\n---------------------------------\n\n.. autoclass:: bayes_opt.TargetSpace\n :members:\n" }, { "path": "docsrc/requirements.txt", "content": "sphinx\nnbsphinx\nsphinx_rtd_theme" }, { "path": "examples/acquisition_functions.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Acquisition functions\\n\",\n \"\\n\",\n \"An acquisition function dictates the exploration policy of the optimizer. In practice, there are many different acquisition policies and which one is the best might depend on your situation. This package comes with several acquisition functions out-of-the-box, but it is also very easy to write your acquisition function. This notebook showcases how to do that using various examples.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Let's begin with a few import statements and some function definitions we will use later.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from bayes_opt import BayesianOptimization\\n\",\n \"from bayes_opt import acquisition\\n\",\n \"import numpy as np\\n\",\n \"\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"from matplotlib import gridspec\\n\",\n \"\\n\",\n \"SEED = 42\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def target(x):\\n\",\n \" return np.exp(-(x - 2)**2) + np.exp(-(x - 6)**2/10) + 1/ (x**2 + 1)\\n\",\n \"\\n\",\n \"x = np.linspace(-2, 10, 1000).reshape(-1, 1)\\n\",\n \"y = target(x)\\n\",\n \"\\n\",\n \"def posterior(optimizer, grid):\\n\",\n \" mu, sigma = optimizer._gp.predict(grid, return_std=True)\\n\",\n \" return mu, sigma\\n\",\n \"\\n\",\n \"def plot_gp(optimizer, x, y):\\n\",\n \" acquisition_function_ = optimizer.acquisition_function\\n\",\n \" fig = plt.figure(figsize=(16, 10))\\n\",\n \" steps = len(optimizer.space)\\n\",\n \" fig.suptitle(\\n\",\n \" 'Gaussian Process and Utility Function After {} Steps'.format(steps),\\n\",\n \" fontsize=30\\n\",\n \" )\\n\",\n \"\\n\",\n \" gs = gridspec.GridSpec(2, 1, height_ratios=[3, 1])\\n\",\n \" axis = plt.subplot(gs[0])\\n\",\n \" acq = plt.subplot(gs[1])\\n\",\n \"\\n\",\n \" x_obs = np.array([[res[\\\"params\\\"][\\\"x\\\"]] for res in optimizer.res])\\n\",\n \" y_obs = np.array([res[\\\"target\\\"] for res in optimizer.res])\\n\",\n \"\\n\",\n \" acquisition_function_._fit_gp(optimizer._gp, optimizer._space)\\n\",\n \" mu, sigma = posterior(optimizer, x)\\n\",\n \"\\n\",\n \" axis.plot(x, y, linewidth=3, label='Target')\\n\",\n \" axis.plot(x_obs.flatten(), y_obs, 'D', markersize=8, label=u'Observations', color='r')\\n\",\n \" axis.plot(x, mu, '--', color='k', label='Prediction')\\n\",\n \"\\n\",\n \" axis.fill(np.concatenate([x, x[::-1]]),\\n\",\n \" np.concatenate([mu - 1.9600 * sigma, (mu + 1.9600 * sigma)[::-1]]),\\n\",\n \" alpha=.6, fc='c', ec='None', label='95% confidence interval')\\n\",\n \"\\n\",\n \" axis.set_xlim((-2, 10))\\n\",\n \" axis.set_ylim((None, None))\\n\",\n \" axis.set_ylabel('f(x)', fontdict={'size':20})\\n\",\n \" axis.set_xlabel('x', fontdict={'size':20})\\n\",\n \"\\n\",\n \" utility = -1 * acquisition_function_._get_acq(gp=optimizer._gp)(x)\\n\",\n \" x = x.flatten()\\n\",\n \"\\n\",\n \" acq.plot(x, utility, label='Utility Function', color='purple')\\n\",\n \" acq.plot(x[np.argmax(utility)], np.max(utility), '*', markersize=15,\\n\",\n \" label=u'Next Best Guess', markerfacecolor='gold', markeredgecolor='k', markeredgewidth=1)\\n\",\n \" acq.set_xlim((-2, 10))\\n\",\n \" #acq.set_ylim((0, np.max(utility) + 0.5))\\n\",\n \" acq.set_ylabel('Utility', fontdict={'size':20})\\n\",\n \" acq.set_xlabel('x', fontdict={'size':20})\\n\",\n \"\\n\",\n \" axis.legend(loc=2, bbox_to_anchor=(1.01, 1), borderaxespad=0.)\\n\",\n \" acq.legend(loc=2, bbox_to_anchor=(1.01, 1), borderaxespad=0.)\\n\",\n \" return fig, fig.axes\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Most (simple) acquisition operate directly on the estimates of mean and standard deviation of the GP. For this, all it takes is to implement a `.base_acq` function, which combines the `mean`/`std` values into an acquisition policy. One very simple such function could simple disregard the `std` and always opt to investigate the maximum of the GP `mean`.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"class GreedyAcquisition(acquisition.AcquisitionFunction):\\n\",\n \" def __init__(self):\\n\",\n \" super().__init__()\\n\",\n \"\\n\",\n \" def base_acq(self, mean, std):\\n\",\n \" return mean # disregard std\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"| iter | target | x |\\n\",\n \"-------------------------------------\\n\",\n \"| \\u001b[39m1 \\u001b[39m | \\u001b[39m1.2141683\\u001b[39m | \\u001b[39m2.4944814\\u001b[39m |\\n\",\n \"| \\u001b[39m2 \\u001b[39m | \\u001b[39m0.3240816\\u001b[39m | \\u001b[39m9.4085716\\u001b[39m |\\n\",\n \"| \\u001b[39m3 \\u001b[39m | \\u001b[39m0.9616628\\u001b[39m | \\u001b[39m6.7839273\\u001b[39m |\\n\",\n \"| \\u001b[39m4 \\u001b[39m | \\u001b[39m1.2141034\\u001b[39m | \\u001b[39m2.4945789\\u001b[39m |\\n\",\n \"| \\u001b[35m5 \\u001b[39m | \\u001b[35m1.2213315\\u001b[39m | \\u001b[35m2.4836524\\u001b[39m |\\n\",\n \"| \\u001b[35m6 \\u001b[39m | \\u001b[35m1.3813338\\u001b[39m | \\u001b[35m2.1554093\\u001b[39m |\\n\",\n \"| \\u001b[35m7 \\u001b[39m | \\u001b[35m1.3904450\\u001b[39m | \\u001b[35m1.8852594\\u001b[39m |\\n\",\n \"| \\u001b[35m8 \\u001b[39m | \\u001b[35m1.4018873\\u001b[39m | \\u001b[35m2.0042391\\u001b[39m |\\n\",\n \"| \\u001b[39m9 \\u001b[39m | \\u001b[39m1.4018855\\u001b[39m | \\u001b[39m2.0045401\\u001b[39m |\\n\",\n \"| \\u001b[39m10 \\u001b[39m | \\u001b[39m1.4018849\\u001b[39m | \\u001b[39m2.0046339\\u001b[39m |\\n\",\n \"=====================================\\n\"\n ]\n },\n {\n \"data\": {\n \"image/png\": \"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\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"acquisition_function = GreedyAcquisition()\\n\",\n \"optimizer = BayesianOptimization(target, {'x': (-2, 10)}, acquisition_function=acquisition_function, random_state=SEED)\\n\",\n \"optimizer.maximize(init_points=3, n_iter=7)\\n\",\n \"\\n\",\n \"plot_gp(optimizer, x, y);\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"As we can see, the greedy policy is _too_ greedy and does not explore enough to find the maximum.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"What if our acquisition function does not depend solely on `mean` and `std`? In that case, we can intervene at a deeper level, and overwrite the `._get_acq` function. The built-in version of `._get_acq` is a higher-order function which returns a function accepting an array-like `x` and performing the following:\\n\",\n \"1. Evaluate the GP mean $\\\\mu$ and std $\\\\sigma$ at points `x`.\\n\",\n \"2. If applicapable, evaluate the constraint fulfilment probability $p_c$ at x.\\n\",\n \"3. Return `-1 * base_acq(mean, std)` or `-1 * base_acq(mean, std) * p_c`\\n\",\n \"\\n\",\n \"An example of such an acquisition function is Thompson Sampling. If you consider a Gaussian Process as a prior over functions, Thompson Sampling works by sampling a function from this prior and then selecting the argmax as next point of interest. This is a somewhat noisy version of the greedy acquisition, which will encourage some exploration.\\n\",\n \"\\n\",\n \"While we usually find the argmax of the acquisition function through a combination of random sampling and gradient-based optimization, we will skip the gradient-based optimization here, as it is quite expensive and would require us to fix the multivariate normal. We can do this by additionally overwriting `.suggest`, specifically by changing the default argument of `n_l_bfgs_b` to `0`.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import warnings\\n\",\n \"\\n\",\n \"class ThompsonSampling(acquisition.AcquisitionFunction):\\n\",\n \" def __init__(self):\\n\",\n \" super().__init__()\\n\",\n \"\\n\",\n \" def base_acq(self, y_mean, y_cov):\\n\",\n \" assert y_cov.shape[0] == y_cov.shape[1], \\\"y_cov must be a square matrix.\\\"\\n\",\n \" return self.random_state.multivariate_normal(y_mean, y_cov)\\n\",\n \"\\n\",\n \" def _get_acq(self, gp, constraint=None):\\n\",\n \" if constraint is not None:\\n\",\n \" msg = (\\n\",\n \" f\\\"Received constraints, but acquisition function {type(self)} \\\"\\n\",\n \" + \\\"does not support constrained optimization.\\\"\\n\",\n \" )\\n\",\n \" raise acquisition.ConstraintNotSupportedError(msg)\\n\",\n \"\\n\",\n \" # overwrite the base method since we require cov not std\\n\",\n \" dim = gp.X_train_.shape[1]\\n\",\n \" def acq(x):\\n\",\n \" x = x.reshape(-1, dim)\\n\",\n \" with warnings.catch_warnings():\\n\",\n \" warnings.simplefilter(\\\"ignore\\\")\\n\",\n \" mean, cov = gp.predict(x, return_cov=True)\\n\",\n \" return -1 * self.base_acq(mean, cov)\\n\",\n \" return acq\\n\",\n \"\\n\",\n \" def suggest(self, gp, target_space, n_random=1_000, n_l_bfgs_b=0, fit_gp: bool = True, random_state=None):\\n\",\n \" # reduce n_random and n_l_bfgs_b to reduce the computational load\\n\",\n \" self.random_state = random_state\\n\",\n \" return super().suggest(gp, target_space, n_random, n_l_bfgs_b, fit_gp, random_state)\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"| iter | target | x |\\n\",\n \"-------------------------------------\\n\",\n \"| \\u001b[39m1 \\u001b[39m | \\u001b[39m1.2141683\\u001b[39m | \\u001b[39m2.4944814\\u001b[39m |\\n\",\n \"| \\u001b[39m2 \\u001b[39m | \\u001b[39m0.3240816\\u001b[39m | \\u001b[39m9.4085716\\u001b[39m |\\n\",\n \"| \\u001b[39m3 \\u001b[39m | \\u001b[39m0.9616628\\u001b[39m | \\u001b[39m6.7839273\\u001b[39m |\\n\",\n \"| \\u001b[39m4 \\u001b[39m | \\u001b[39m1.0070261\\u001b[39m | \\u001b[39m-0.159783\\u001b[39m |\\n\",\n \"| \\u001b[39m5 \\u001b[39m | \\u001b[39m1.0327122\\u001b[39m | \\u001b[39m-0.075615\\u001b[39m |\\n\",\n \"| \\u001b[35m6 \\u001b[39m | \\u001b[35m1.3085636\\u001b[39m | \\u001b[35m1.6588374\\u001b[39m |\\n\",\n \"| \\u001b[39m7 \\u001b[39m | \\u001b[39m0.9518282\\u001b[39m | \\u001b[39m1.0057953\\u001b[39m |\\n\",\n \"| \\u001b[39m8 \\u001b[39m | \\u001b[39m1.0260256\\u001b[39m | \\u001b[39m5.8443843\\u001b[39m |\\n\",\n \"| \\u001b[39m9 \\u001b[39m | \\u001b[39m0.8310726\\u001b[39m | \\u001b[39m3.0843178\\u001b[39m |\\n\",\n \"| \\u001b[35m10 \\u001b[39m | \\u001b[35m1.3409715\\u001b[39m | \\u001b[35m1.7284422\\u001b[39m |\\n\",\n \"=====================================\\n\",\n \"Adding GP samples to the plot... this can take up to several minutes.\\n\"\n ]\n },\n {\n \"data\": {\n \"image/png\": \"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\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"acquisition_function = ThompsonSampling()\\n\",\n \"optimizer = BayesianOptimization(target, {'x': (-2, 10)}, acquisition_function=acquisition_function, random_state=SEED)\\n\",\n \"optimizer.maximize(init_points=3, n_iter=7)\\n\",\n \"\\n\",\n \"fig, axs = plot_gp(optimizer, x, y);\\n\",\n \"\\n\",\n \"print(\\\"Adding GP samples to the plot... this can take up to several minutes.\\\")\\n\",\n \"\\n\",\n \"x_reduced = np.linspace(-2, 10, 100).reshape(-1, 1)\\n\",\n \"for i in range(99):\\n\",\n \" y_sample = -1 * acquisition_function._get_acq(gp=optimizer._gp)(x_reduced)\\n\",\n \" y_sample_argmax = (x_reduced[np.argmax(y_sample)], np.max(y_sample))\\n\",\n \" axs[1].plot(x_reduced, y_sample, 'k', alpha=0.1, label='other samples from the GP' if i == 0 else None)\\n\",\n \" axs[1].plot(*y_sample_argmax, 'r.', alpha=0.4, markersize=10, label='other argmaxes' if i == 0 else None)\\n\",\n \"\\n\",\n \"axs[1].legend(loc=2, bbox_to_anchor=(1.01, 1), borderaxespad=0.);\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Let's push things even further!\\n\",\n \"\\n\",\n \"In some situations, you might be working in a setting where evaluations are not equally expensive. For example, let's say that you're running an expensive mechanical experiment, and performing the experiment is cheap, but adjusting its parameters is an expensive process. In particular, the further away it is from the current set-up, the more expensive it becomes. In such a situation, you could factor in the expense by running a specialized version of Expected Improvement (EI) called EIpu (EI per unit cost). Here we divide the EI by a specific cost function.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"| iter | target | x |\\n\",\n \"-------------------------------------\\n\",\n \"| \\u001b[39m1 \\u001b[39m | \\u001b[39m1.2141683\\u001b[39m | \\u001b[39m2.4944814\\u001b[39m |\\n\",\n \"| \\u001b[39m2 \\u001b[39m | \\u001b[39m0.3240816\\u001b[39m | \\u001b[39m9.4085716\\u001b[39m |\\n\",\n \"| \\u001b[39m3 \\u001b[39m | \\u001b[39m0.9616628\\u001b[39m | \\u001b[39m6.7839273\\u001b[39m |\\n\",\n \"| \\u001b[39m4 \\u001b[39m | \\u001b[39m0.9616653\\u001b[39m | \\u001b[39m6.7839105\\u001b[39m |\\n\",\n \"| \\u001b[39m5 \\u001b[39m | \\u001b[39m1.0206224\\u001b[39m | \\u001b[39m6.2150299\\u001b[39m |\\n\",\n \"| \\u001b[39m6 \\u001b[39m | \\u001b[39m0.8162246\\u001b[39m | \\u001b[39m4.3517586\\u001b[39m |\\n\",\n \"| \\u001b[35m7 \\u001b[39m | \\u001b[35m1.4016724\\u001b[39m | \\u001b[35m1.9847738\\u001b[39m |\\n\",\n \"| \\u001b[39m8 \\u001b[39m | \\u001b[39m1.2149407\\u001b[39m | \\u001b[39m1.4942299\\u001b[39m |\\n\",\n \"| \\u001b[39m9 \\u001b[39m | \\u001b[39m1.0164502\\u001b[39m | \\u001b[39m0.2826411\\u001b[39m |\\n\",\n \"| \\u001b[39m10 \\u001b[39m | \\u001b[39m0.6101292\\u001b[39m | \\u001b[39m-0.816206\\u001b[39m |\\n\",\n \"=====================================\\n\"\n ]\n },\n {\n \"data\": {\n \"image/png\": \"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\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"class ExpectedImprovementPerUnitCost(acquisition.ExpectedImprovement):\\n\",\n \" def __init__(self, xi, exploration_decay=None, exploration_decay_delay=None) -> None:\\n\",\n \" super().__init__(xi, exploration_decay, exploration_decay_delay)\\n\",\n \" self.last_x = None\\n\",\n \"\\n\",\n \" def cost(self, x):\\n\",\n \" if self.last_x is None:\\n\",\n \" return 1\\n\",\n \" return np.mean((self.last_x - np.atleast_2d(x))**2, axis=1) + 1.\\n\",\n \"\\n\",\n \" def _get_acq(self, gp, constraint=None):\\n\",\n \" super_acq = super()._get_acq(gp, constraint)\\n\",\n \" acq = lambda x: super_acq(x) / self.cost(x)\\n\",\n \" return acq\\n\",\n \"\\n\",\n \" def suggest(self, gp, target_space, n_random=10000, n_l_bfgs_b=10, fit_gp: bool = True, random_state=None):\\n\",\n \" # let's get the most recently evaluated point from the target_space\\n\",\n \" self.last_x = target_space.params[-1]\\n\",\n \" return super().suggest(gp, target_space, n_random, n_l_bfgs_b, fit_gp, random_state)\\n\",\n \"\\n\",\n \"acquisition_function = ExpectedImprovementPerUnitCost(1e-4)\\n\",\n \"optimizer = BayesianOptimization(target, {'x': (-2, 10)}, acquisition_function=acquisition_function, random_state=SEED)\\n\",\n \"optimizer.maximize(init_points=3, n_iter=7)\\n\",\n \"\\n\",\n \"fig, axs = plot_gp(optimizer, x, y)\\n\",\n \"\\n\",\n \"ax2 = axs[1].twinx()\\n\",\n \"ax2.plot(x, acquisition_function.cost(x), label='Cost to access', color='orange')\\n\",\n \"\\n\",\n \"axs[1].plot(x, -1 * super(acquisition.ExpectedImprovement, acquisition_function)._get_acq(gp=optimizer._gp)(x), 'k--', label='Expected Improvement')\\n\",\n \"\\n\",\n \"lines, labels = axs[1].get_legend_handles_labels()\\n\",\n \"labels[0] = 'Expected Improvement per Unit Cost'\\n\",\n \"lines2, labels2 = ax2.get_legend_handles_labels()\\n\",\n \"axs[1].legend().remove()\\n\",\n \"ax2.legend(lines + lines2, labels + labels2, loc=2, bbox_to_anchor=(1.01, 1), borderaxespad=0.);\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Saving and loading with custom acquisition functions\\n\",\n \"\\n\",\n \"If saving and loading is desired functionality, the acquisition function needs to have a `get_acquisition_params` and `set_acquisition_params` method.\\n\",\n \"\\n\",\n \"Default acquisition functions have a `get_acquisition_params` and `set_acquisition_params` method that is used to save and load the acquisition function state when the optimizer is being saved and loaded. When writing and using a custom acquisition function, those methods need to also be defined. In order to have perfect replicability, all parameters that the acquisition function depends on need to be saved and loaded. The get_acquisition_params method should return a dictionary containing all acquisition function parameters. The set_acquisition_params method should take that same dictionary containing the acquisition function parameters and update the respective parameters.\\n\",\n \"\\n\",\n \"_serialize_random_state and _deserialize_random_state are provided as part of the base class and should be included in the dictionary returned by `get_acquisition_params`. See any of the default acquisition functions for reference.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"bayesianoptimization\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.12.3\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n" }, { "path": "examples/advanced-tour.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Advanced tour of the Bayesian Optimization package\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from bayes_opt import BayesianOptimization\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 1. Suggest-Evaluate-Register Paradigm\\n\",\n \"\\n\",\n \"Internally the `maximize` method is simply a wrapper around the methods `suggest`, `probe`, and `register`. If you need more control over your optimization loops the Suggest-Evaluate-Register paradigm should give you that extra flexibility.\\n\",\n \"\\n\",\n \"For an example of running the `BayesianOptimization` in a distributed fashion (where the function being optimized is evaluated concurrently in different cores/machines/servers), checkout the `async_optimization.py` script in the examples folder.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Let's start by defining our function, bounds, and instantiating an optimization object.\\n\",\n \"def black_box_function(x, y):\\n\",\n \" return -x ** 2 - (y - 1) ** 2 + 1\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"One extra ingredient we will need is an `AcquisitionFunction`, such as `acquisition.UpperConfidenceBound`. In case it is not clear why, take a look at the literature to understand better how this method works.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from bayes_opt import acquisition\\n\",\n \"\\n\",\n \"acq = acquisition.UpperConfidenceBound(kappa=2.5)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Notice that the evaluation of the blackbox function will NOT be carried out by the optimizer object. We are simulating a situation where this function could be being executed in a different machine, maybe it is written in another language, or it could even be the result of a chemistry experiment. Whatever the case may be, you can take charge of it and as long as you don't invoke the `probe` or `maximize` methods directly, the optimizer object will ignore the blackbox function.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"optimizer = BayesianOptimization(\\n\",\n \" f=None,\\n\",\n \" acquisition_function=acq,\\n\",\n \" pbounds={'x': (-2, 2), 'y': (-3, 3)},\\n\",\n \" verbose=2,\\n\",\n \" random_state=1,\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The `suggest` method of our optimizer can be called at any time. What you get back is a suggestion for the next parameter combination the optimizer wants to probe.\\n\",\n \"\\n\",\n \"Notice that while the optimizer hasn't observed any points, the suggestions will be random. However, they will stop being random and improve in quality the more points are observed.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Next point to probe is: {'x': np.float64(-0.331911981189704), 'y': np.float64(1.3219469606529486)}\\n\"\n ]\n }\n ],\n \"source\": [\n \"next_point_to_probe = optimizer.suggest()\\n\",\n \"print(\\\"Next point to probe is:\\\", next_point_to_probe)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"You are now free to evaluate your function at the suggested point however/whenever you like.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Found the target value to be: 0.7861845912690544\\n\"\n ]\n }\n ],\n \"source\": [\n \"target = black_box_function(**next_point_to_probe)\\n\",\n \"print(\\\"Found the target value to be:\\\", target)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Last thing left to do is to tell the optimizer what target value was observed.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"| \\u001b[39m2 \\u001b[39m | \\u001b[39m0.7862 \\u001b[39m | \\u001b[39m-0.331911\\u001b[39m | \\u001b[39m1.3219469\\u001b[39m |\\n\"\n ]\n }\n ],\n \"source\": [\n \"optimizer.register(\\n\",\n \" params=next_point_to_probe,\\n\",\n \" target=target,\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 1.1 The maximize loop\\n\",\n \"\\n\",\n \"And that's it. By repeating the steps above you recreate the internals of the `maximize` method. This should give you all the flexibility you need to log progress, hault execution, perform concurrent evaluations, etc.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"| \\u001b[39m3 \\u001b[39m | \\u001b[39m-18.41 \\u001b[39m | \\u001b[39m1.9506186\\u001b[39m | \\u001b[39m-2.950721\\u001b[39m |\\n\",\n \"-18.413111112960056 {'x': np.float64(1.9506186451101901), 'y': np.float64(-2.9507212017944955)}\\n\",\n \"| \\u001b[39m4 \\u001b[39m | \\u001b[39m0.7603 \\u001b[39m | \\u001b[39m-0.379805\\u001b[39m | \\u001b[39m1.3089202\\u001b[39m |\\n\",\n \"0.7603162209132889 {'x': np.float64(-0.37980530851809036), 'y': np.float64(1.3089202270946163)}\\n\",\n \"| \\u001b[39m5 \\u001b[39m | \\u001b[39m-6.841 \\u001b[39m | \\u001b[39m-1.990473\\u001b[39m | \\u001b[39m2.9694974\\u001b[39m |\\n\",\n \"-6.840906127161674 {'x': np.float64(-1.9904737772920469), 'y': np.float64(2.9694974661254085)}\\n\",\n \"| \\u001b[39m6 \\u001b[39m | \\u001b[39m-6.879 \\u001b[39m | \\u001b[39m1.9740210\\u001b[39m | \\u001b[39m2.9954409\\u001b[39m |\\n\",\n \"-6.8785435274794136 {'x': np.float64(1.9740210595375953), 'y': np.float64(2.995440899646362)}\\n\",\n \"| \\u001b[39m7 \\u001b[39m | \\u001b[39m-7.124 \\u001b[39m | \\u001b[39m-1.985509\\u001b[39m | \\u001b[39m-1.044851\\u001b[39m |\\n\",\n \"-7.123667302755344 {'x': np.float64(-1.9855094780813816), 'y': np.float64(-1.0448519298972099)}\\n\",\n \"{'target': np.float64(0.7861845912690544), 'params': {'x': np.float64(-0.331911981189704), 'y': np.float64(1.3219469606529486)}}\\n\"\n ]\n }\n ],\n \"source\": [\n \"for _ in range(5):\\n\",\n \" next_point = optimizer.suggest()\\n\",\n \" target = black_box_function(**next_point)\\n\",\n \" optimizer.register(params=next_point, target=target)\\n\",\n \"\\n\",\n \" print(target, next_point)\\n\",\n \"print(optimizer.max)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 2. Tuning the underlying Gaussian Process\\n\",\n \"\\n\",\n \"The bayesian optimization algorithm works by performing a gaussian process regression of the observed combination of parameters and their associated target values. The predicted parameter $\\\\rightarrow$ target hyper-surface (and its uncertainty) is then used to guide the next best point to probe.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 2.1 Passing parameter to the GP\\n\",\n \"\\n\",\n \"Depending on the problem it could be beneficial to change the default parameters of the underlying GP. You can use the `optimizer.set_gp_params` method to do this:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"| iter | target | x | y |\\n\",\n \"-------------------------------------------------\\n\",\n \"| \\u001b[39m2 \\u001b[39m | \\u001b[39m0.7862 \\u001b[39m | \\u001b[39m-0.331911\\u001b[39m | \\u001b[39m1.3219469\\u001b[39m |\\n\",\n \"| \\u001b[39m3 \\u001b[39m | \\u001b[39m-18.34 \\u001b[39m | \\u001b[39m1.9021640\\u001b[39m | \\u001b[39m-2.965222\\u001b[39m |\\n\",\n \"| \\u001b[35m4 \\u001b[39m | \\u001b[35m0.8731 \\u001b[39m | \\u001b[35m-0.298167\\u001b[39m | \\u001b[35m1.1948749\\u001b[39m |\\n\",\n \"| \\u001b[39m5 \\u001b[39m | \\u001b[39m-6.497 \\u001b[39m | \\u001b[39m1.9876938\\u001b[39m | \\u001b[39m2.8830942\\u001b[39m |\\n\",\n \"| \\u001b[39m6 \\u001b[39m | \\u001b[39m-4.286 \\u001b[39m | \\u001b[39m-1.995643\\u001b[39m | \\u001b[39m-0.141769\\u001b[39m |\\n\",\n \"| \\u001b[39m7 \\u001b[39m | \\u001b[39m-6.781 \\u001b[39m | \\u001b[39m-1.953302\\u001b[39m | \\u001b[39m2.9913127\\u001b[39m |\\n\",\n \"=================================================\\n\"\n ]\n }\n ],\n \"source\": [\n \"optimizer = BayesianOptimization(\\n\",\n \" f=black_box_function,\\n\",\n \" pbounds={'x': (-2, 2), 'y': (-3, 3)},\\n\",\n \" verbose=2,\\n\",\n \" random_state=1,\\n\",\n \")\\n\",\n \"optimizer.set_gp_params(alpha=1e-3, n_restarts_optimizer=5)\\n\",\n \"optimizer.maximize(\\n\",\n \" init_points=1,\\n\",\n \" n_iter=5\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 2.2 Tuning the `alpha` parameter\\n\",\n \"\\n\",\n \"When dealing with functions with discrete parameters,or particularly erratic target space it might be beneficial to increase the value of the `alpha` parameter. This parameters controls how much noise the GP can handle, so increase it whenever you think that extra flexibility is needed.\"\n ]\n },\n {\n \"attachments\": {},\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 2.3 Changing kernels\\n\",\n \"\\n\",\n \"By default this package uses the Matern 2.5 kernel. Depending on your use case you may find that tuning the GP kernel could be beneficial. You're on your own here since these are very specific solutions to very specific problems. You should start with the [scikit learn docs](https://scikit-learn.org/stable/modules/gaussian_process.html#kernels-for-gaussian-processes).\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"bayesian-optimization-t6LLJ9me-py3.10\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.13.1\"\n },\n \"nbdime-conflicts\": {\n \"local_diff\": [\n {\n \"diff\": [\n {\n \"diff\": [\n {\n \"key\": 0,\n \"op\": \"addrange\",\n \"valuelist\": [\n \"3.1.0\"\n ]\n },\n {\n \"key\": 0,\n \"length\": 1,\n \"op\": \"removerange\"\n }\n ],\n \"key\": \"version\",\n \"op\": \"patch\"\n }\n ],\n \"key\": \"language_info\",\n \"op\": \"patch\"\n }\n ],\n \"remote_diff\": [\n {\n \"diff\": [\n {\n \"diff\": [\n {\n \"key\": 0,\n \"op\": \"addrange\",\n \"valuelist\": [\n \"3.10.12\"\n ]\n },\n {\n \"key\": 0,\n \"length\": 1,\n \"op\": \"removerange\"\n }\n ],\n \"key\": \"version\",\n \"op\": \"patch\"\n }\n ],\n \"key\": \"language_info\",\n \"op\": \"patch\"\n }\n ]\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n" }, { "path": "examples/async_optimization.py", "content": "import time\nimport random\n\nfrom bayes_opt import BayesianOptimization\nfrom bayes_opt.util import UtilityFunction\n\nimport asyncio\nimport threading\nfrom colorama import Fore\n\ntry:\n import json\n import tornado.ioloop\n import tornado.httpserver\n from tornado.web import RequestHandler\n import requests\nexcept ImportError:\n raise ImportError(\n \"In order to run this example you must have the libraries: \" +\n \"`tornado` and `requests` installed.\"\n )\n\n\ndef black_box_function(x, y):\n \"\"\"Function with unknown internals we wish to maximize.\n\n This is just serving as an example, however, for all intents and\n purposes think of the internals of this function, i.e.: the process\n which generates its outputs values, as unknown.\n \"\"\"\n time.sleep(random.randint(1, 7))\n return -x ** 2 - (y - 1) ** 2 + 1\n\n\nclass BayesianOptimizationHandler(RequestHandler):\n \"\"\"Basic functionality for NLP handlers.\"\"\"\n _bo = BayesianOptimization(\n f=black_box_function,\n pbounds={\"x\": (-4, 4), \"y\": (-3, 3)}\n )\n _uf = UtilityFunction(kind=\"ucb\", kappa=3, xi=1)\n\n def post(self):\n \"\"\"Deal with incoming requests.\"\"\"\n body = tornado.escape.json_decode(self.request.body)\n\n try:\n self._bo.register(\n params=body[\"params\"],\n target=body[\"target\"],\n )\n print(\"BO has registered: {} points.\".format(len(self._bo.space)), end=\"\\n\\n\")\n except KeyError:\n pass\n finally:\n suggested_params = self._bo.suggest(self._uf)\n\n self.write(json.dumps(suggested_params))\n\n\ndef run_optimization_app():\n asyncio.set_event_loop(asyncio.new_event_loop())\n handlers = [\n (r\"/bayesian_optimization\", BayesianOptimizationHandler),\n ]\n server = tornado.httpserver.HTTPServer(\n tornado.web.Application(handlers)\n )\n server.listen(9009)\n tornado.ioloop.IOLoop.instance().start()\n\n\ndef run_optimizer():\n global optimizers_config\n config = optimizers_config.pop()\n name = config[\"name\"]\n colour = config[\"colour\"]\n\n register_data = {}\n max_target = None\n for _ in range(10):\n status = name + \" wants to register: {}.\\n\".format(register_data)\n\n resp = requests.post(\n url=\"http://localhost:9009/bayesian_optimization\",\n json=register_data,\n ).json()\n target = black_box_function(**resp)\n\n register_data = {\n \"params\": resp,\n \"target\": target,\n }\n\n if max_target is None or target > max_target:\n max_target = target\n\n status += name + \" got {} as target.\\n\".format(target)\n status += name + \" will to register next: {}.\\n\".format(register_data)\n print(colour + status, end=\"\\n\")\n\n global results\n results.append((name, max_target))\n print(colour + name + \" is done!\", end=\"\\n\\n\")\n\n\nif __name__ == \"__main__\":\n ioloop = tornado.ioloop.IOLoop.instance()\n optimizers_config = [\n {\"name\": \"optimizer 1\", \"colour\": Fore.RED},\n {\"name\": \"optimizer 2\", \"colour\": Fore.GREEN},\n {\"name\": \"optimizer 3\", \"colour\": Fore.BLUE},\n ]\n\n app_thread = threading.Thread(target=run_optimization_app)\n app_thread.daemon = True\n app_thread.start()\n\n targets = (\n run_optimizer,\n run_optimizer,\n run_optimizer\n )\n optimizer_threads = []\n for target in targets:\n optimizer_threads.append(threading.Thread(target=target))\n optimizer_threads[-1].daemon = True\n optimizer_threads[-1].start()\n\n results = []\n for optimizer_thread in optimizer_threads:\n optimizer_thread.join()\n\n for result in results:\n print(result[0], \"found a maximum value of: {}\".format(result[1]))\n\n ioloop.stop()\n" }, { "path": "examples/async_optimization_dummies.py", "content": "\"\"\"Originally by @rhizhiy\nhttps://github.com/bayesian-optimization/BayesianOptimization/issues/347#issuecomment-1273465096\n\"\"\"\nimport time\nfrom typing import Callable\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom bayes_opt import BayesianOptimization\nfrom bayes_opt import acquisition\nfrom scipy.optimize import rosen\n\nASYNC_METHOD = 'lie_max'\nassert ASYNC_METHOD in ['lie_max', 'none']\ndef _closest_distance(point, points):\n return min(np.linalg.norm(point - p) for p in points if p is not point)\n\n\ndef optimize(\n func: Callable[..., float], num_iter: int, bounds: dict[str, tuple[float, float]], num_workers=0\n):\n init_samples = int(np.sqrt(num_iter))\n init_kappa = 10\n kappa_decay = (0.1 / init_kappa) ** (1 / num_iter)\n\n acquisition_function = acquisition.UpperConfidenceBound(\n kappa=init_kappa,\n exploration_decay=kappa_decay,\n exploration_decay_delay=0\n )\n\n if ASYNC_METHOD == 'lie_max':\n acquisition_function = acquisition.ConstantLiar(acquisition_function, 'max')\n\n\n optimizer = BayesianOptimization(\n f=None,\n acquisition_function=acquisition_function,\n pbounds=bounds,\n verbose=0\n )\n\n\n init_queue = [optimizer.suggest() for _ in range(init_samples)]\n result_queue = []\n while len(optimizer.res) < num_iter:\n sample = init_queue.pop(0) if init_queue else optimizer.suggest()\n loss = func(list(sample.values())) * -1\n result_queue.append((sample, loss))\n if len(result_queue) >= num_workers:\n optimizer.register(*result_queue.pop(0))\n return optimizer.res\n\n\nbounds = {\"x\": [-5, 5], \"y\": [-5, 5]}\n\nall_times = {}\nall_results = {}\nworkers_each = [1, 2, 4, 8,]# 16]\nprint(f\"Simulating parallel optimization for {workers_each} workers, this can take some time.\")\nprint(f\"Async method: {ASYNC_METHOD}.\")\nfor num_workers in workers_each:\n print(f\"\\tChecking {num_workers} workers\")\n results = []\n start = time.perf_counter()\n results = optimize(rosen, 200, bounds, num_workers)\n end = time.perf_counter()\n delta = end - start\n all_times[num_workers] = delta\n samples = [res[\"params\"] for res in results]\n all_results[num_workers] = samples\n\nfig, axs = plt.subplots(2, 2)\nif ASYNC_METHOD == 'lie_max':\n acquisition_function_str = \"Constant Max Liar (UCB)\"\nelse:\n acquisition_function_str = \"UCB\"\n\nfig.suptitle(f\"Acquisition function: {acquisition_function_str}\")\nfig.set_figheight(8)\nfig.set_figwidth(8)\naxs = [item for sublist in axs for item in sublist]\nfor idx, (num_workers, samples) in enumerate(all_results.items()):\n if num_workers > 8:\n continue\n samples = [np.array(list(sample.values())) for sample in samples]\n axs[idx].scatter(*zip(*samples), s=1)\n axs[idx].set_title(f\"{num_workers=}\")\n avg_min_distance = np.mean([_closest_distance(sample, samples) for sample in samples])\n print(f\"{num_workers=}, mean_min_distance={avg_min_distance:.3f}, time={all_times[num_workers]:.3f}\")\nfig.tight_layout()\nplt.savefig(f\"corrected_async_{ASYNC_METHOD}.png\")\n" }, { "path": "examples/basic-tour.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Basic tour of the Bayesian Optimization package\\n\",\n \"\\n\",\n \"This is a constrained global optimization package built upon bayesian inference and gaussian process, that attempts to find the maximum value of an unknown function in as few iterations as possible. This technique is particularly suited for optimization of high cost functions, situations where the balance between exploration and exploitation is important.\\n\",\n \"\\n\",\n \"Bayesian optimization works by constructing a posterior distribution of functions (gaussian process) that best describes the function you want to optimize. As the number of observations grows, the posterior distribution improves, and the algorithm becomes more certain of which regions in parameter space are worth exploring and which are not, as seen in the picture below.\\n\",\n \"\\n\",\n \"As you iterate over and over, the algorithm balances its needs of exploration and exploitation taking into account what it knows about the target function. At each step a Gaussian Process is fitted to the known samples (points previously explored), and the posterior distribution, combined with a exploration strategy (such as UCB (Upper Confidence Bound), or EI (Expected Improvement)), are used to determine the next point that should be explored (see the gif below).\\n\",\n \"\\n\",\n \"This process is designed to minimize the number of steps required to find a combination of parameters that are close to the optimal combination. To do so, this method uses a proxy optimization problem (finding the maximum of the acquisition function) that, albeit still a hard problem, is cheaper (in the computational sense) and common tools can be employed. Therefore Bayesian Optimization is most adequate for situations where sampling the function to be optimized is a very expensive endeavor. See the references for a proper discussion of this method.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 1. Specifying the function to be optimized\\n\",\n \"\\n\",\n \"This is a function optimization package, therefore the first and most important ingredient is, of course, the function to be optimized.\\n\",\n \"\\n\",\n \"**DISCLAIMER:** We know exactly how the output of the function below depends on its parameter. Obviously this is just an example, and you shouldn't expect to know it in a real scenario. However, it should be clear that you don't need to. All you need in order to use this package (and more generally, this technique) is a function `f` that takes a known set of parameters and outputs a real number.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def black_box_function(x, y):\\n\",\n \" \\\"\\\"\\\"Function with unknown internals we wish to maximize.\\n\",\n \"\\n\",\n \" This is just serving as an example, for all intents and\\n\",\n \" purposes think of the internals of this function, i.e.: the process\\n\",\n \" which generates its output values, as unknown.\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" return -x ** 2 - (y - 1) ** 2 + 1\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 2. Getting Started\\n\",\n \"\\n\",\n \"All we need to get started is to instantiate a `BayesianOptimization` object specifying a function to be optimized `f`, and its parameters with their corresponding bounds, `pbounds`. This is a constrained optimization technique, so you must specify the minimum and maximum values that can be probed for each parameter in order for it to work\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from bayes_opt import BayesianOptimization\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Bounded region of parameter space\\n\",\n \"pbounds = {'x': (2, 4), 'y': (-3, 3)}\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"optimizer = BayesianOptimization(\\n\",\n \" f=black_box_function,\\n\",\n \" pbounds=pbounds,\\n\",\n \" verbose=2, # verbose = 1 prints only when a maximum is observed, verbose = 0 is silent\\n\",\n \" random_state=1,\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The BayesianOptimization object will work out of the box without much tuning needed. The main method you should be aware of is `maximize`, which does exactly what you think it does.\\n\",\n \"\\n\",\n \"There are many parameters you can pass to maximize, nonetheless, the most important ones are:\\n\",\n \"- `n_iter`: How many steps of bayesian optimization you want to perform. The more steps the more likely to find a good maximum you are.\\n\",\n \"- `init_points`: How many steps of **random** exploration you want to perform. Random exploration can help by diversifying the exploration space.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"| iter | target | x | y |\\n\",\n \"-------------------------------------------------\\n\",\n \"| \\u001b[39m1 \\u001b[39m | \\u001b[39m-7.135 \\u001b[39m | \\u001b[39m2.8340440\\u001b[39m | \\u001b[39m1.3219469\\u001b[39m |\\n\",\n \"| \\u001b[39m2 \\u001b[39m | \\u001b[39m-7.78 \\u001b[39m | \\u001b[39m2.0002287\\u001b[39m | \\u001b[39m-1.186004\\u001b[39m |\\n\",\n \"| \\u001b[39m3 \\u001b[39m | \\u001b[39m-7.157 \\u001b[39m | \\u001b[39m2.8375977\\u001b[39m | \\u001b[39m1.3238498\\u001b[39m |\\n\",\n \"| \\u001b[35m4 \\u001b[39m | \\u001b[35m-6.633 \\u001b[39m | \\u001b[35m2.7487090\\u001b[39m | \\u001b[35m1.2790562\\u001b[39m |\\n\",\n \"| \\u001b[35m5 \\u001b[39m | \\u001b[35m-5.751 \\u001b[39m | \\u001b[35m2.5885326\\u001b[39m | \\u001b[35m1.2246876\\u001b[39m |\\n\",\n \"=================================================\\n\"\n ]\n }\n ],\n \"source\": [\n \"optimizer.maximize(\\n\",\n \" init_points=2,\\n\",\n \" n_iter=3,\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The best combination of parameters and target value found can be accessed via the property `bo.max`.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"{'target': np.float64(-5.750985875689304), 'params': {'x': np.float64(2.5885326650623566), 'y': np.float64(1.2246876000015976)}}\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(optimizer.max)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"While the list of all parameters probed and their corresponding target values is available via the property `bo.res`.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Iteration 0: \\n\",\n \"\\t{'target': np.float64(-7.135455292718879), 'params': {'x': np.float64(2.8340440094051482), 'y': np.float64(1.3219469606529486)}}\\n\",\n \"Iteration 1: \\n\",\n \"\\t{'target': np.float64(-7.779531005607566), 'params': {'x': np.float64(2.0002287496346898), 'y': np.float64(-1.1860045642089614)}}\\n\",\n \"Iteration 2: \\n\",\n \"\\t{'target': np.float64(-7.156839989425082), 'params': {'x': np.float64(2.8375977943744273), 'y': np.float64(1.3238498831039895)}}\\n\",\n \"Iteration 3: \\n\",\n \"\\t{'target': np.float64(-6.633273772355583), 'params': {'x': np.float64(2.7487090390562576), 'y': np.float64(1.2790562505410115)}}\\n\",\n \"Iteration 4: \\n\",\n \"\\t{'target': np.float64(-5.750985875689304), 'params': {'x': np.float64(2.5885326650623566), 'y': np.float64(1.2246876000015976)}}\\n\"\n ]\n }\n ],\n \"source\": [\n \"for i, res in enumerate(optimizer.res):\\n\",\n \" print(\\\"Iteration {}: \\\\n\\\\t{}\\\".format(i, res))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 2.1 Changing bounds\\n\",\n \"\\n\",\n \"During the optimization process you may realize the bounds chosen for some parameters are not adequate. For these situations you can invoke the method `set_bounds` to alter them. You can pass any combination of **existing** parameters and their associated new bounds.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"optimizer.set_bounds(new_bounds={\\\"x\\\": (-2, 3)})\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"| iter | target | x | y |\\n\",\n \"-------------------------------------------------\\n\",\n \"| \\u001b[35m6 \\u001b[39m | \\u001b[35m-4.438 \\u001b[39m | \\u001b[35m2.3269441\\u001b[39m | \\u001b[35m1.1533794\\u001b[39m |\\n\",\n \"| \\u001b[35m7 \\u001b[39m | \\u001b[35m-2.42 \\u001b[39m | \\u001b[35m1.8477442\\u001b[39m | \\u001b[35m0.9230233\\u001b[39m |\\n\",\n \"| \\u001b[35m8 \\u001b[39m | \\u001b[35m-0.2088 \\u001b[39m | \\u001b[35m1.0781674\\u001b[39m | \\u001b[35m1.2152869\\u001b[39m |\\n\",\n \"| \\u001b[35m9 \\u001b[39m | \\u001b[35m0.7797 \\u001b[39m | \\u001b[35m-0.298812\\u001b[39m | \\u001b[35m1.3619705\\u001b[39m |\\n\",\n \"| \\u001b[39m10 \\u001b[39m | \\u001b[39m-3.391 \\u001b[39m | \\u001b[39m-0.655060\\u001b[39m | \\u001b[39m2.9904883\\u001b[39m |\\n\",\n \"=================================================\\n\"\n ]\n }\n ],\n \"source\": [\n \"optimizer.maximize(\\n\",\n \" init_points=0,\\n\",\n \" n_iter=5,\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 3. Guiding the optimization\\n\",\n \"\\n\",\n \"It is often the case that we have an idea of regions of the parameter space where the maximum of our function might lie. For these situations the `BayesianOptimization` object allows the user to specify specific points to be probed. By default these will be explored lazily (`lazy=True`), meaning these points will be evaluated only the next time you call `maximize`. This probing process happens before the gaussian process takes over.\\n\",\n \"\\n\",\n \"Parameters can be passed as dictionaries such as below:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"optimizer.probe(\\n\",\n \" params={\\\"x\\\": 0.5, \\\"y\\\": 0.7},\\n\",\n \" lazy=True,\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Or as an iterable. Beware that the order has to match the order of the initial `pbounds` dictionary. You can usee `optimizer.space.keys` for guidance\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"['x', 'y']\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(optimizer.space.keys)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"optimizer.probe(\\n\",\n \" params=[-0.3, 0.1],\\n\",\n \" lazy=True,\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"| iter | target | x | y |\\n\",\n \"-------------------------------------------------\\n\",\n \"| \\u001b[39m11 \\u001b[39m | \\u001b[39m0.66 \\u001b[39m | \\u001b[39m0.5 \\u001b[39m | \\u001b[39m0.7 \\u001b[39m |\\n\",\n \"| \\u001b[39m12 \\u001b[39m | \\u001b[39m0.1 \\u001b[39m | \\u001b[39m-0.3 \\u001b[39m | \\u001b[39m0.1 \\u001b[39m |\\n\",\n \"=================================================\\n\"\n ]\n }\n ],\n \"source\": [\n \"optimizer.maximize(init_points=0, n_iter=0)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 4. Saving and loading the optimizer\\n\",\n \"\\n\",\n \"The optimizer state can be saved to a file and loaded from a file. This is useful for continuing an optimization from a previous state, or for analyzing the optimization history without running the optimizer again.\\n\",\n \"\\n\",\n \"Note: if you are using your own custom acquisition function, you will need to save and load the acquisition function state as well. This is done by calling the `get_acquisition_params` and `set_acquisition_params` methods of the acquisition function. See the acquisition function documentation for more information.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 4.1 Saving the optimizer state\\n\",\n \"\\n\",\n \"The optimizer state can be saved to a file using the `save_state` method.\\n\",\n \"optimizer.save_state(\\\"./optimizer_state.json\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 22,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"optimizer.save_state(\\\"optimizer_state.json\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 4.2 Loading the optimizer state\\n\",\n \"\\n\",\n \"To load with a previously saved state, pass the path of your saved state file to the `load_state_path` parameter. Note that if you've changed the bounds of your parameters, you'll need to pass the updated bounds to the new optimizer.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 23,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"new_optimizer = BayesianOptimization(\\n\",\n \" f=black_box_function,\\n\",\n \" pbounds={\\\"x\\\": (-2, 3), \\\"y\\\": (-3, 3)},\\n\",\n \" random_state=1,\\n\",\n \" verbose=0\\n\",\n \")\\n\",\n \"\\n\",\n \"new_optimizer.load_state(\\\"./optimizer_state.json\\\")\\n\",\n \"\\n\",\n \"# Continue optimization\\n\",\n \"new_optimizer.maximize(\\n\",\n \" init_points=0,\\n\",\n \" n_iter=5\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"This provides a simpler alternative to the logging system shown in section 4, especially when you want to continue optimization from a previous state.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Next Steps\\n\",\n \"\\n\",\n \"This tour should be enough to cover most usage scenarios of this package. If, however, you feel like you need to know more, please checkout the `advanced-tour` notebook. There you will be able to find other, more advanced features of this package that could be what you're looking for. Also, browse the examples folder for implementation tips and ideas.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.13.1\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n" }, { "path": "examples/constraints.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Constrained Optimization\\n\",\n \"\\n\",\n \"Constrained optimization refers to situations in which you must for instance maximize \\\"f\\\", a function of \\\"x\\\" and \\\"y\\\", but the solution must lie in a region where for instance \\\"xx. To do this, we are simply going to return a 'bad' value from the objective function whenever this constrain is violated. What constitutes a 'bad' value is objective function specific - in general, it's a good idea for the 'bad' value you use to be similar in magnitude to the worst value that the objective function naturally has.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"the best solution with y>x is {'target': -4.021127762033562, 'params': {'x': 2.0008684793556073, 'y': 2.0087879313090253}}\\n\"\n ]\n }\n ],\n \"source\": [\n \"def black_box_function_with_constraints(x, y):\\n\",\n \" if y <= x:\\n\",\n \" return -10\\n\",\n \" else:\\n\",\n \" return -x ** 2 - (y - 1) ** 2 + 1\\n\",\n \" \\n\",\n \"optimizer = BayesianOptimization(\\n\",\n \" f=black_box_function_with_constraints,\\n\",\n \" pbounds=pbounds,\\n\",\n \" random_state=0,\\n\",\n \" verbose=0\\n\",\n \")\\n\",\n \"\\n\",\n \"optimizer.maximize(\\n\",\n \" init_points=2,\\n\",\n \" n_iter=100\\n\",\n \")\\n\",\n \"\\n\",\n \"print(f'the best solution with y>x is {optimizer.max}')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Ok, this seems to have worked pretty well; our constraints have been respected and the target value isn't even **that** much worse!\\n\",\n \"\\n\",\n \"In certain other cases, you may be able to reformulate your objective function such that the constraint is explicitly embedded. For instance, consider the constraint `x+y=4`. Since this implies that `y=4-x`, we could simply reformulate the objective function explicitly:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"the best solution with y=4-x is {'target': -4.0, 'params': {'x': 2.0}}\\n\"\n ]\n }\n ],\n \"source\": [\n \"def surrogate_objective(x):\\n\",\n \" y=4-x\\n\",\n \" return black_box_function_no_constraints(x,y)\\n\",\n \"\\n\",\n \"pbounds = {'x': (2, 4)}\\n\",\n \"# note that in general, we would have to update pbounds such that the values that x were allowed to take on\\n\",\n \"# respected the bounds of y. In this case (4-4=0)<=y<=(4-2=2) already respect our original bounds -3<=y<=3\\n\",\n \"\\n\",\n \"optimizer = BayesianOptimization(\\n\",\n \" f=surrogate_objective,\\n\",\n \" pbounds=pbounds,\\n\",\n \" random_state=0,\\n\",\n \" verbose=0,\\n\",\n \" allow_duplicate_points=True\\n\",\n \")\\n\",\n \"\\n\",\n \"optimizer.maximize(\\n\",\n \" init_points=2,\\n\",\n \" n_iter=10\\n\",\n \")\\n\",\n \"\\n\",\n \"print(f'the best solution with y=4-x is {optimizer.max}')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"> Note: in this last example, we have set `allow_duplicate_points=True`. The reason we are getting some duplicate points in this example is probably because the space is now so constrained that the optimizer quickly hones in on only one 'interesting' point and repeatedly probes it. The default behavior in these cases is `allow_duplicate_points=False` which will raise an error when a duplicate is registered.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 2. Advanced Constrained Optimization\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"In some situations, certain regions of the solution domain may be infeasible or not allowed. In addition, you may not know whether specific parameter combinations fall into these regions until you have evaluated the constraint function at these points. In other words, checking for feasibility is as expensive, or close to as expensive as evaluating the objective function. This notebook demonstrates how you can handle these situations by modelling the constraints as a Gaussian process. This approach is based on a paper by [Gardner et. al., 2014](http://proceedings.mlr.press/v32/gardner14.pdf).\\n\",\n \"\\n\",\n \"Note that if the constrained regions are known/if the constraint function is cheap to evaluate, then other approaches are preferable due to the computational complexity of modelling using Gaussian processes.\\n\",\n \"In this case, at the time of writing the best approach is to return a low number to the optimizer when it tries to evaluate these regions\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 2.1 Simple, single constraint\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import numpy as np\\n\",\n \"from bayes_opt import BayesianOptimization\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"from scipy.optimize import NonlinearConstraint\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We illustrate the use of advanced constrained bayesian optimization on the examples Gardner et al. used in their paper.\\n\",\n \"\\n\",\n \"Define the target function ($f$ or `target_function`) we want to optimize along with a constraint function ($c$ or `constraint_function`) and constraint limit ($c^{lim}$ or `constraint_limit`). The mathematical problem we are trying to solve is\\n\",\n \"$$\\n\",\n \" \\\\max f(x, y)\\n\",\n \"$$\\n\",\n \"$$\\n\",\n \" \\\\text{subj. to} \\\\: \\\\: c(x, y) \\\\leq c^{\\\\text{lim}}\\n\",\n \"$$\\n\",\n \"Note that the constraint function should have the same parameter names as the target function (i.e. in this case `x` and `y`).\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def target_function(x, y):\\n\",\n \" # Gardner is looking for the minimum, but this packages looks for maxima, thus the sign switch\\n\",\n \" return np.cos(2*x)*np.cos(y) + np.sin(x)\\n\",\n \"\\n\",\n \"def constraint_function(x, y):\\n\",\n \" return np.cos(x) * np.cos(y) - np.sin(x) * np.sin(y)\\n\",\n \"\\n\",\n \"constraint_limit = 0.5\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"A `scipy.NonlinearConstraint` object stores the constraint configuration. Since we do not have a lower bound on our problem, provide `-np.inf` as `lb` argument.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"constraint = NonlinearConstraint(constraint_function, -np.inf, constraint_limit)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Create a `BayesianOptimization` model as you would usually, providing the `ConstraintModel` instance as additional keyword argument. \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Bounded region of parameter space\\n\",\n \"pbounds = {'x': (0, 6), 'y': (0, 6)}\\n\",\n \"\\n\",\n \"optimizer = BayesianOptimization(\\n\",\n \" f=target_function,\\n\",\n \" constraint=constraint,\\n\",\n \" pbounds=pbounds,\\n\",\n \" verbose=0, # verbose = 1 prints only when a maximum is observed, verbose = 0 is silent\\n\",\n \" random_state=1,\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Run the optimization as usual -- the optimizer automatically estimates the probability for the constraint to be fulfilled and modifies the acquisition function accordingly. This means, that the optimizer avoids sampling points that are likely unfeasible.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"optimizer.maximize(\\n\",\n \" init_points=3,\\n\",\n \" n_iter=10,\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The best combination of parameters, target value and constraint function value found by the optimizer can be accessed via the property `optimizer.max`.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"{'target': 1.9734131381980626, 'params': {'x': 1.64727683929071, 'y': 3.2975037081163068}, 'constraint': 0.230305457787476}\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(optimizer.max)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def plot_constrained_opt(pbounds, target_function, optimizer):\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" Plots a number of interesting contours to visualize constrained 2-dimensional optimization.\\n\",\n \" \\\"\\\"\\\"\\n\",\n \"\\n\",\n \" # Set a few parameters\\n\",\n \" n_constraints = optimizer.constraint.lb.size\\n\",\n \" n_plots_per_row = 2+n_constraints\\n\",\n \"\\n\",\n \" # Construct the subplot titles\\n\",\n \" if n_constraints==1:\\n\",\n \" c_labels = [\\\"constraint\\\"]\\n\",\n \" else:\\n\",\n \" c_labels = [f\\\"constraint {i+1}\\\" for i in range(n_constraints)]\\n\",\n \" labels_top = [\\\"target\\\"] + c_labels + [\\\"masked target\\\"]\\n\",\n \" labels_bot = [\\\"target estimate\\\"] + [c + \\\" estimate\\\" for c in c_labels] + [\\\"acquisition function\\\"]\\n\",\n \" labels = [labels_top, labels_bot]\\n\",\n \"\\n\",\n \" # Setup the grid to plot on\\n\",\n \" x = np.linspace(pbounds['x'][0], pbounds['x'][1], 1000)\\n\",\n \" y = np.linspace(pbounds['y'][0], pbounds['y'][1], 1000)\\n\",\n \" xy = np.array([[x_i, y_j] for y_j in y for x_i in x])\\n\",\n \" X, Y = np.meshgrid(x, y)\\n\",\n \"\\n\",\n \" # Evaluate the actual functions on the grid\\n\",\n \" Z = target_function(X, Y)\\n\",\n \" # This reshaping is a bit painful admittedly, but it's a consequence of np.meshgrid\\n\",\n \" C = optimizer.constraint.fun(X, Y).reshape((n_constraints,) + Z.shape).swapaxes(0, -1)\\n\",\n \" \\n\",\n \" \\n\",\n \" fig, axs = plt.subplots(2, n_plots_per_row, constrained_layout=True, figsize=(12,8))\\n\",\n \"\\n\",\n \" for i in range(2):\\n\",\n \" for j in range(n_plots_per_row):\\n\",\n \" axs[i, j].set_aspect(\\\"equal\\\")\\n\",\n \" axs[i, j].set_title(labels[i][j])\\n\",\n \" \\n\",\n \" \\n\",\n \" # Extract & unpack the optimization results\\n\",\n \" max_ = optimizer.max\\n\",\n \" res = optimizer.res\\n\",\n \" x_ = np.array([r[\\\"params\\\"]['x'] for r in res])\\n\",\n \" y_ = np.array([r[\\\"params\\\"]['y'] for r in res])\\n\",\n \" c_ = np.array([r[\\\"constraint\\\"] for r in res])\\n\",\n \" a_ = np.array([r[\\\"allowed\\\"] for r in res])\\n\",\n \"\\n\",\n \"\\n\",\n \" Z_est = optimizer._gp.predict(xy).reshape(Z.shape)\\n\",\n \" C_est = optimizer.constraint.approx(xy).reshape(Z.shape + (n_constraints,))\\n\",\n \" P_allowed = optimizer.constraint.predict(xy).reshape(Z.shape)\\n\",\n \"\\n\",\n \" Acq = np.where(Z_est >0, Z_est * P_allowed, Z_est / (0.5 + P_allowed))\\n\",\n \" \\n\",\n \" \\n\",\n \" target_vbounds = np.min([Z, Z_est]), np.max([Z, Z_est])\\n\",\n \" constraint_vbounds = np.min([C, C_est]), np.max([C, C_est])\\n\",\n \"\\n\",\n \"\\n\",\n \" axs[0,0].contourf(X, Y, Z, cmap=plt.cm.coolwarm, vmin=target_vbounds[0], vmax=target_vbounds[1])\\n\",\n \" for i in range(n_constraints):\\n\",\n \" axs[0,1+i].contourf(X, Y, C[:,:,i], cmap=plt.cm.coolwarm, vmin=constraint_vbounds[0], vmax=constraint_vbounds[1])\\n\",\n \" Z_mask = Z\\n\",\n \"\\n\",\n \" Z_mask[~np.squeeze(optimizer.constraint.allowed(C))] = np.nan\\n\",\n \" axs[0,n_plots_per_row-1].contourf(X, Y, Z_mask, cmap=plt.cm.coolwarm, vmin=target_vbounds[0], vmax=target_vbounds[1])\\n\",\n \"\\n\",\n \" axs[1,0].contourf(X, Y, Z_est, cmap=plt.cm.coolwarm, vmin=target_vbounds[0], vmax=target_vbounds[1])\\n\",\n \" for i in range(n_constraints):\\n\",\n \" axs[1,1+i].contourf(X, Y, C_est[:, :, i], cmap=plt.cm.coolwarm, vmin=constraint_vbounds[0], vmax=constraint_vbounds[1])\\n\",\n \" axs[1,n_plots_per_row-1].contourf(X, Y, Acq, cmap=plt.cm.coolwarm, vmin=0, vmax=1)\\n\",\n \"\\n\",\n \" for i in range(2):\\n\",\n \" for j in range(n_plots_per_row):\\n\",\n \" axs[i,j].scatter(x_[a_], y_[a_], c='white', s=80, edgecolors='black')\\n\",\n \" axs[i,j].scatter(x_[~a_], y_[~a_], c='red', s=80, edgecolors='black')\\n\",\n \" axs[i,j].scatter(max_[\\\"params\\\"]['x'], max_[\\\"params\\\"]['y'], s=80, c='green', edgecolors='black')\\n\",\n \"\\n\",\n \" return fig, axs\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We will now visualize the constrained optimization.\\n\",\n \"\\n\",\n \"In the following figure you will see two rows of plots with 3 quadratic plots each. The top row contains - in order -- contour visualizations of the target function, constraint function and the target function (masked such that only areas where the constraint is fulfilled are plotted). Additionally we have visualized the points sampled by the optimizer -- the optimal point is plotted in green, allowed (but non-optimal) points in white, and disallowed points in red. The bottom row shows -- in order -- the approximation of the target function using the gaussian regressors, the approximation of the constraint function and a visualization of the acquisition function, i.e. the function that guides the next point to sample.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": \"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\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plot_constrained_opt(pbounds, target_function, optimizer);\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Simulation 2\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We proceed with another slightly different problem of the same form. Here, we place a constraint from above and below on a function value.\\n\",\n \"$$\\n\",\n \" \\\\max f(x, y)\\n\",\n \"$$\\n\",\n \"$$\\n\",\n \" \\\\text{subj. to} \\\\: \\\\: c^{\\\\text{low}} \\\\leq c(x, y) \\\\leq c^{\\\\text{up}}\\n\",\n \"$$\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def target_function(x, y):\\n\",\n \" # Gardner is looking for the minimum, but this packages looks for maxima, thus the sign switch\\n\",\n \" return np.sin(x) + y\\n\",\n \"\\n\",\n \"def constraint_function(x, y):\\n\",\n \" return np.sin(x) * np.sin(y)\\n\",\n \"\\n\",\n \"# Note that the constraint limit in case of one-dimensional constraints can be both an array of shape (1,) or a number.\\n\",\n \"constraint_lower = np.array([-0.9])\\n\",\n \"constraint_upper = np.array([-0.3])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"constraint = NonlinearConstraint(constraint_function, constraint_lower, constraint_upper)\\n\",\n \"\\n\",\n \"optimizer = BayesianOptimization(\\n\",\n \" f=target_function,\\n\",\n \" constraint=constraint,\\n\",\n \" pbounds=pbounds,\\n\",\n \" verbose=0, # verbose = 1 prints only when a maximum is observed, verbose = 0 is silent\\n\",\n \" random_state=1,\\n\",\n \")\\n\",\n \"\\n\",\n \"optimizer.maximize(\\n\",\n \" init_points=3,\\n\",\n \" n_iter=10,\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": \"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\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plot_constrained_opt(pbounds, target_function, optimizer);\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 2.2 Multiple Constraints\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Occasionally, one might need to fulfill multiple constraints. In this case, simply employ a multi-dimensional surrogate constraint function, i.e., in case of `n` constraints, a function returning an array of shape `(n,)`. Similarly, the `constraint_limit` should be an array of shape `(n,)`. The problem we are solving is\\n\",\n \"$$\\n\",\n \" \\\\max f(x, y)\\n\",\n \"$$\\n\",\n \"$$\\n\",\n \" \\\\text{subj. to} \\\\: \\\\: c_1^{\\\\text{low}} \\\\leq c_1(x, y) < c_1^{\\\\text{up}}\\n\",\n \"$$\\n\",\n \"$$\\n\",\n \" \\\\text{subj. to} \\\\: \\\\: c_2^{\\\\text{low}} \\\\leq c_2(x, y) < c_2^{\\\\text{up}}\\n\",\n \"$$\\n\",\n \"$$\\n\",\n \" \\\\dots\\n\",\n \"$$\\n\",\n \"$$\\n\",\n \" \\\\text{subj. to} \\\\: \\\\: c_n^{\\\\text{low}} \\\\leq c_n(x, y) < c_n^{\\\\text{up}}\\n\",\n \"$$ \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def target_function(x, y):\\n\",\n \" # Gardner is looking for the minimum, but this packages looks for maxima, thus the sign switch\\n\",\n \" return np.cos(2*x)*np.cos(y) + np.sin(x)\\n\",\n \"\\n\",\n \"def constraint_function_2_dim(x, y):\\n\",\n \" return np.array([\\n\",\n \" - np.cos(x) * np.cos(y) + np.sin(x) * np.sin(y),\\n\",\n \" - np.cos(x) * np.cos(-y) + np.sin(x) * np.sin(-y)])\\n\",\n \"\\n\",\n \"constraint_lower = np.array([-np.inf, -np.inf])\\n\",\n \"constraint_upper = np.array([0.6, 0.6])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Construct the problem is you would in the single-constraint case and run the optimization. Note that internally the optimizer assumes conditional independence between multiple constraints.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"constraint = NonlinearConstraint(constraint_function_2_dim, constraint_lower, constraint_upper)\\n\",\n \"optimizer = BayesianOptimization(\\n\",\n \" f=target_function,\\n\",\n \" constraint=constraint,\\n\",\n \" pbounds=pbounds,\\n\",\n \" verbose=0, # verbose = 1 prints only when a maximum is observed, verbose = 0 is silent\\n\",\n \" random_state=1,\\n\",\n \")\\n\",\n \"\\n\",\n \"optimizer.maximize(\\n\",\n \" init_points=3,\\n\",\n \" n_iter=10,\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": \"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\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plot_constrained_opt(pbounds, target_function, optimizer);\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.10.0\"\n },\n \"vscode\": {\n \"interpreter\": {\n \"hash\": \"49851069de08cc5bbf068d7713ecb1523f4cab708013d75e8e72826d85a7e48d\"\n }\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n" }, { "path": "examples/domain_reduction.ipynb", "content": "{\n \"cells\": [\n {\n \"attachments\": {},\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Sequential Domain Reduction\\n\",\n \"\\n\",\n \"## Background\\n\",\n \"Sequential domain reduction is a process where the bounds of the optimization problem are mutated (typically contracted) to reduce the time required to converge to an optimal value. The advantage of this method is typically seen when a cost function is particularly expensive to calculate, or if the optimization routine oscillates heavily. \\n\",\n \"\\n\",\n \"## Basics\\n\",\n \"\\n\",\n \"The basic steps are a *pan* and a *zoom*. These two steps are applied at one time, therefore updating the problem search space every iteration.\\n\",\n \"\\n\",\n \"**Pan**: recentering the region of interest around the most optimal point found.\\n\",\n \"\\n\",\n \"**Zoom**: contract the region of interest.\\n\",\n \"\\n\",\n \"![](../docsrc/static/sdr.png)\\n\",\n \"\\n\",\n \"\\n\",\n \"## Parameters\\n\",\n \"\\n\",\n \"There are three parameters for the built-in `SequentialDomainReductionTransformer` object:\\n\",\n \"\\n\",\n \"\\n\",\n \"$\\\\gamma_{osc}:$ shrinkage parameter for oscillation. Typically [0.5-0.7]. Default = 0.7\\n\",\n \"\\n\",\n \"$\\\\gamma_{pan}:$ panning parameter. Typically 1.0. Default = 1.0\\n\",\n \"\\n\",\n \"$\\\\eta:$ zoom parameter. Default = 0.9\\n\",\n \"\\n\",\n \"\\n\",\n \"More information can be found in this reference document:\\n\",\n \"\\n\",\n \"---\\n\",\n \"\\n\",\n \"Title: \\\"On the robustness of a simple domain reduction scheme for simulation‐based optimization\\\" \\n\",\n \"\\n\",\n \"Date: 2002 \\n\",\n \"\\n\",\n \"Author: Stander, N. and Craig, K. \\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"---\\n\",\n \"---\\n\",\n \"Let's start by importing the packages we'll be needing\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import numpy as np\\n\",\n \"from bayes_opt import BayesianOptimization\\n\",\n \"from bayes_opt import SequentialDomainReductionTransformer\\n\",\n \"import matplotlib.pyplot as plt\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Now let's create an example cost function. This is the [Ackley function](https://en.wikipedia.org/wiki/Ackley_function), which is quite non-linear. \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def ackley(**kwargs):\\n\",\n \" x = np.fromiter(kwargs.values(), dtype=float)\\n\",\n \" arg1 = -0.2 * np.sqrt(0.5 * (x[0] ** 2 + x[1] ** 2))\\n\",\n \" arg2 = 0.5 * (np.cos(2. * np.pi * x[0]) + np.cos(2. * np.pi * x[1]))\\n\",\n \" return -1.0 * (-20. * np.exp(arg1) - np.exp(arg2) + 20. + np.e)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We will use the standard bounds for this problem.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"\\n\",\n \"pbounds = {'x': (-5, 5), 'y': (-5, 5)}\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"This is where we define our `bound_transformer` , the Sequential Domain Reduction Transformer\\n\",\n \"\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"bounds_transformer = SequentialDomainReductionTransformer(minimum_window=0.5)\"\n ]\n },\n {\n \"attachments\": {},\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Now we can set up two identical optimization problems, except one has the `bound_transformer` variable set.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"mutating_optimizer = BayesianOptimization(\\n\",\n \" f=ackley,\\n\",\n \" pbounds=pbounds,\\n\",\n \" verbose=0,\\n\",\n \" random_state=1,\\n\",\n \" bounds_transformer=bounds_transformer\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"mutating_optimizer.maximize(\\n\",\n \" init_points=2,\\n\",\n \" n_iter=50,\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"standard_optimizer = BayesianOptimization(\\n\",\n \" f=ackley,\\n\",\n \" pbounds=pbounds,\\n\",\n \" verbose=0,\\n\",\n \" random_state=1,\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"standard_optimizer.maximize(\\n\",\n \" init_points=2,\\n\",\n \" n_iter=50,\\n\",\n \")\"\n ]\n },\n {\n \"attachments\": {},\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"After both have completed we can plot to see how the objectives performed. It's quite obvious to see that the Sequential Domain Reduction technique contracted onto the optimal point relatively quick.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 9,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": \"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\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.plot(mutating_optimizer.space.target, label='Mutated Optimizer')\\n\",\n \"plt.plot(standard_optimizer.space.target, label='Standard Optimizer')\\n\",\n \"plt.legend()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Now let's plot the actual contraction of one of the variables (`x`)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# example x-bound shrinking - we need to shift the x-axis by the init_points as the bounds\\n\",\n \"# transformer only mutates when searching - not in the initial phase.\\n\",\n \"x_min_bound = [b[0][0] for b in bounds_transformer.bounds]\\n\",\n \"x_max_bound = [b[0][1] for b in bounds_transformer.bounds]\\n\",\n \"x = [x[0] for x in mutating_optimizer.space.params]\\n\",\n \"bounds_transformers_iteration = list(range(2, len(x)))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 11,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": \"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\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.plot(bounds_transformers_iteration, x_min_bound[1:], label='x lower bound')\\n\",\n \"plt.plot(bounds_transformers_iteration, x_max_bound[1:], label='x upper bound')\\n\",\n \"plt.plot(x[1:], label='x')\\n\",\n \"plt.legend()\\n\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.10.12\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n" }, { "path": "examples/duplicate_point.py", "content": "import numpy as np\nfrom bayes_opt import BayesianOptimization\nfrom bayes_opt import acquisition\n\n\ndef f(x):\n return np.exp(-(x - 2) ** 2) + np.exp(-(x - 6) ** 2 / 10) + 1/ (x ** 2 + 1)\n\n\nif __name__ == '__main__':\n acq = acquisition.UpperConfidenceBound(kappa=5) # kappa determines explore/Exploitation ratio\n optimizer = BayesianOptimization(f=None, pbounds={'x': (-2, 2)}, verbose=2, random_state=1,\n allow_duplicate_points=True)\n optimizer.set_gp_params(normalize_y=True, alpha=2.5e-3, n_restarts_optimizer=20) # tuning of the gaussian parameters...\n for point in range(20):\n next_point_to_probe = optimizer.suggest()\n NextPointValues = np.array(list(next_point_to_probe.values()))\n mean,std = optimizer._gp.predict(NextPointValues.reshape(1, -1),return_std=True)\n target = f(**next_point_to_probe)\n optimizer.register(params=next_point_to_probe, target=target)" }, { "path": "examples/exploitation_vs_exploration.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Exploitation vs Exploration\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"\\n\",\n \"\\n\",\n \"import numpy as np\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"\\n\",\n \"from bayes_opt import BayesianOptimization\\n\",\n \"from bayes_opt import acquisition\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Target function\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": \"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\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"np.random.seed(42)\\n\",\n \"xs = np.linspace(-2, 10, 10000)\\n\",\n \"\\n\",\n \"def f(x):\\n\",\n \" return np.exp(-(x - 2) ** 2) + np.exp(-(x - 6) ** 2 / 10) + 1/ (x ** 2 + 1)\\n\",\n \"\\n\",\n \"plt.plot(xs, f(xs))\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Utility function for plotting\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def plot_bo(f, bo):\\n\",\n \" x = np.linspace(-2, 10, 10000)\\n\",\n \" mean, sigma = bo._gp.predict(x.reshape(-1, 1), return_std=True)\\n\",\n \" \\n\",\n \" plt.figure(figsize=(16, 9))\\n\",\n \" plt.plot(x, f(x))\\n\",\n \" plt.plot(x, mean)\\n\",\n \" plt.fill_between(x, mean + sigma, mean - sigma, alpha=0.1)\\n\",\n \" plt.scatter(bo.space.params.flatten(), bo.space.target, c=\\\"red\\\", s=50, zorder=10)\\n\",\n \" plt.show()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Acquisition Function \\\"Upper Confidence Bound\\\"\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Prefer exploitation (kappa=0.1)\\n\",\n \"\\n\",\n \"Note that most points are around the peak(s).\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": \"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\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"acquisition_function = acquisition.UpperConfidenceBound(kappa=0.1)\\n\",\n \"\\n\",\n \"bo = BayesianOptimization(\\n\",\n \" f=f,\\n\",\n \" acquisition_function=acquisition_function,\\n\",\n \" pbounds={\\\"x\\\": (-2, 10)},\\n\",\n \" verbose=0,\\n\",\n \" random_state=987234,\\n\",\n \")\\n\",\n \"\\n\",\n \"bo.maximize(n_iter=10)\\n\",\n \"\\n\",\n \"plot_bo(f, bo)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Prefer exploration (kappa=10)\\n\",\n \"\\n\",\n \"Note that the points are more spread out across the whole range.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": \"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\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"acquisition_function = acquisition.UpperConfidenceBound(kappa=10.)\\n\",\n \"\\n\",\n \"bo = BayesianOptimization(\\n\",\n \" f=f,\\n\",\n \" acquisition_function=acquisition_function,\\n\",\n \" pbounds={\\\"x\\\": (-2, 10)},\\n\",\n \" verbose=0,\\n\",\n \" random_state=987234,\\n\",\n \")\\n\",\n \"\\n\",\n \"bo.maximize(n_iter=10)\\n\",\n \"\\n\",\n \"plot_bo(f, bo)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Acquisition Function \\\"Expected Improvement\\\"\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Prefer exploitation (xi=0.0)\\n\",\n \"\\n\",\n \"Note that most points are around the peak(s).\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {\n \"scrolled\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": \"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\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"acquisition_function = acquisition.ExpectedImprovement(xi=0.0)\\n\",\n \"\\n\",\n \"bo = BayesianOptimization(\\n\",\n \" f=f,\\n\",\n \" acquisition_function=acquisition_function,\\n\",\n \" pbounds={\\\"x\\\": (-2, 10)},\\n\",\n \" verbose=0,\\n\",\n \" random_state=987234,\\n\",\n \")\\n\",\n \"\\n\",\n \"bo.maximize(n_iter=10)\\n\",\n \"\\n\",\n \"plot_bo(f, bo)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Prefer exploration (xi=0.1)\\n\",\n \"\\n\",\n \"Note that the points are more spread out across the whole range.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {\n \"scrolled\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": \"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\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"acquisition_function = acquisition.ExpectedImprovement(xi=0.1)\\n\",\n \"\\n\",\n \"bo = BayesianOptimization(\\n\",\n \" f=f,\\n\",\n \" acquisition_function=acquisition_function,\\n\",\n \" pbounds={\\\"x\\\": (-2, 10)},\\n\",\n \" verbose=0,\\n\",\n \" random_state=987234,\\n\",\n \")\\n\",\n \"\\n\",\n \"bo.maximize(n_iter=10)\\n\",\n \"\\n\",\n \"plot_bo(f, bo)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Acquisition Function \\\"Probability of Improvement\\\"\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Prefer exploitation (xi=1e-4)\\n\",\n \"\\n\",\n \"Note that most points are around the peak(s).\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": \"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\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"acquisition_function = acquisition.ProbabilityOfImprovement(xi=1e-4)\\n\",\n \"\\n\",\n \"bo = BayesianOptimization(\\n\",\n \" f=f,\\n\",\n \" acquisition_function=acquisition_function,\\n\",\n \" pbounds={\\\"x\\\": (-2, 10)},\\n\",\n \" verbose=0,\\n\",\n \" random_state=987234,\\n\",\n \")\\n\",\n \"\\n\",\n \"bo.maximize(n_iter=10)\\n\",\n \"\\n\",\n \"plot_bo(f, bo)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Prefer exploration (xi=0.1)\\n\",\n \"\\n\",\n \"Note that the points are more spread out across the whole range.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": \"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\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"acquisition_function = acquisition.ProbabilityOfImprovement(xi=0.1)\\n\",\n \"\\n\",\n \"bo = BayesianOptimization(\\n\",\n \" f=f,\\n\",\n \" acquisition_function=acquisition_function,\\n\",\n \" pbounds={\\\"x\\\": (-2, 10)},\\n\",\n \" verbose=0,\\n\",\n \" random_state=987234,\\n\",\n \")\\n\",\n \"\\n\",\n \"bo.maximize(n_iter=10)\\n\",\n \"\\n\",\n \"plot_bo(f, bo)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.10.0\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n" }, { "path": "examples/parameter_types.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Optimizing over non-float Parameters\\n\",\n \"\\n\",\n \"Sometimes, you need to optimize a target that is not just a function of floating-point values, but relies on integer or categorical parameters. This notebook shows how such problems are handled by following an approach from [\\\"Dealing with categorical and integer-valued variables in Bayesian Optimization with Gaussian processes\\\" by Garrido-Merchán and Hernández-Lobato](https://arxiv.org/abs/1805.03463). One simple way of handling an integer-valued parameter is to run the optimization as normal, but then round to the nearest integer after a point has been suggested. This method is similar, except that the rounding is performed in the _kernel_. Why does this matter? It means that the kernel is aware that two parameters, that map the to same point but are potentially distinct before this transformation are the same.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import warnings\\n\",\n \"import numpy as np\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"from bayes_opt import BayesianOptimization\\n\",\n \"from bayes_opt import acquisition\\n\",\n \"\\n\",\n \"from sklearn.gaussian_process.kernels import Matern\\n\",\n \"\\n\",\n \"# suppress warnings about this being an experimental feature\\n\",\n \"warnings.filterwarnings(action=\\\"ignore\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 1. Simple integer-valued function\\n\",\n \"Let's look at a simple, one-dimensional, integer-valued target function and compare a typed optimizer and a continuous optimizer.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": \"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\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"def target_function_1d(x):\\n\",\n \" return np.sin(np.round(x)) - np.abs(np.round(x) / 5)\\n\",\n \"\\n\",\n \"c_pbounds = {'x': (-10, 10)}\\n\",\n \"bo_cont = BayesianOptimization(target_function_1d, c_pbounds, verbose=0, random_state=1)\\n\",\n \"\\n\",\n \"# one way of constructing an integer-valued parameter is to add a third element to the tuple\\n\",\n \"d_pbounds = {'x': (-10, 10, int)}\\n\",\n \"bo_disc = BayesianOptimization(target_function_1d, d_pbounds, verbose=0, random_state=1)\\n\",\n \"\\n\",\n \"fig, axs = plt.subplots(2, 1, figsize=(10, 6), sharex=True, sharey=True)\\n\",\n \"\\n\",\n \"bo_cont.maximize(init_points=2, n_iter=10)\\n\",\n \"bo_cont.acquisition_function._fit_gp(bo_cont._gp, bo_cont.space)\\n\",\n \"\\n\",\n \"y_mean, y_std = bo_cont._gp.predict(np.linspace(-10, 10, 1000).reshape(-1, 1), return_std=True)\\n\",\n \"axs[0].set_title('Continuous')\\n\",\n \"axs[0].plot(np.linspace(-10, 10, 1000), target_function_1d(np.linspace(-10, 10, 1000)), 'k--', label='True function')\\n\",\n \"axs[0].plot(np.linspace(-10, 10, 1000), y_mean, label='Predicted mean')\\n\",\n \"axs[0].fill_between(np.linspace(-10, 10, 1000), y_mean - y_std, y_mean + y_std, alpha=0.3, label='Predicted std')\\n\",\n \"axs[0].plot(bo_cont.space.params, bo_cont.space.target, 'ro')\\n\",\n \"\\n\",\n \"bo_disc.maximize(init_points=2, n_iter=10)\\n\",\n \"bo_disc.acquisition_function._fit_gp(bo_disc._gp, bo_disc.space)\\n\",\n \"\\n\",\n \"y_mean, y_std = bo_disc._gp.predict(np.linspace(-10, 10, 1000).reshape(-1, 1), return_std=True)\\n\",\n \"axs[1].set_title('Discrete')\\n\",\n \"axs[1].plot(np.linspace(-10, 10, 1000), target_function_1d(np.linspace(-10, 10, 1000)), 'k--', label='True function')\\n\",\n \"axs[1].plot(np.linspace(-10, 10, 1000), y_mean, label='Predicted mean')\\n\",\n \"axs[1].fill_between(np.linspace(-10, 10, 1000), y_mean - y_std, y_mean + y_std, alpha=0.3, label='Predicted std')\\n\",\n \"axs[1].plot(bo_disc.space.params, bo_disc.space.target, 'ro')\\n\",\n \"\\n\",\n \"for ax in axs:\\n\",\n \" ax.grid(True)\\n\",\n \"fig.tight_layout()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We can see, that the discrete optimizer is aware that the function is discrete and does not try to predict values between the integers. The continuous optimizer tries to predict values between the integers, despite the fact that these are known.\\n\",\n \"We can also see that the discrete optimizer predicts blocky mean and standard deviations, which is a result of the discrete nature of the function.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 2. Mixed-parameter optimization\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def discretized_function(x, y):\\n\",\n \" y = np.round(y)\\n\",\n \" return (-1*np.cos(x)**np.abs(y) + -1*np.cos(y)) + 0.1 * (x + y) - 0.01 * (x**2 + y**2)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Bounded region of parameter space\\n\",\n \"c_pbounds = {'x': (-5, 5), 'y': (-5, 5)}\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"labels = [\\\"All-float Optimizer\\\", \\\"Typed Optimizer\\\"]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"continuous_optimizer = BayesianOptimization(\\n\",\n \" f=discretized_function,\\n\",\n \" acquisition_function=acquisition.ExpectedImprovement(xi=0.01, random_state=1),\\n\",\n \" pbounds=c_pbounds,\\n\",\n \" verbose=2,\\n\",\n \" random_state=1,\\n\",\n \")\\n\",\n \"\\n\",\n \"continuous_optimizer.set_gp_params(kernel=Matern(nu=2.5, length_scale=np.ones(2)))\\n\",\n \"\\n\",\n \"d_pbounds = {'x': (-5, 5), 'y': (-5, 5, int)}\\n\",\n \"discrete_optimizer = BayesianOptimization(\\n\",\n \" f=discretized_function,\\n\",\n \" acquisition_function=acquisition.ExpectedImprovement(xi=0.01, random_state=1),\\n\",\n \" pbounds=d_pbounds,\\n\",\n \" verbose=2,\\n\",\n \" random_state=1,\\n\",\n \")\\n\",\n \"\\n\",\n \"discrete_optimizer.set_gp_params(kernel=Matern(nu=2.5, length_scale=np.ones(2)));\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"==================== All-float Optimizer ====================\\n\",\n \"\\n\",\n \"| iter | target | x | y |\\n\",\n \"-------------------------------------------------\\n\",\n \"| \\u001b[39m2 \\u001b[39m | \\u001b[39m0.03061 \\u001b[39m | \\u001b[39m-0.829779\\u001b[39m | \\u001b[39m2.2032449\\u001b[39m |\\n\",\n \"| \\u001b[39m3 \\u001b[39m | \\u001b[39m-0.6535 \\u001b[39m | \\u001b[39m-4.998856\\u001b[39m | \\u001b[39m-1.976674\\u001b[39m |\\n\",\n \"| \\u001b[35m4 \\u001b[39m | \\u001b[35m0.8025 \\u001b[39m | \\u001b[35m-0.829779\\u001b[39m | \\u001b[35m2.6549696\\u001b[39m |\\n\",\n \"| \\u001b[35m5 \\u001b[39m | \\u001b[35m0.9203 \\u001b[39m | \\u001b[35m-0.981065\\u001b[39m | \\u001b[35m2.6644394\\u001b[39m |\\n\",\n \"| \\u001b[35m6 \\u001b[39m | \\u001b[35m1.008 \\u001b[39m | \\u001b[35m-1.652553\\u001b[39m | \\u001b[35m2.7133425\\u001b[39m |\\n\",\n \"| \\u001b[39m7 \\u001b[39m | \\u001b[39m0.9926 \\u001b[39m | \\u001b[39m-1.119714\\u001b[39m | \\u001b[39m2.8358733\\u001b[39m |\\n\",\n \"| \\u001b[35m8 \\u001b[39m | \\u001b[35m1.322 \\u001b[39m | \\u001b[35m-2.418942\\u001b[39m | \\u001b[35m3.4600371\\u001b[39m |\\n\",\n \"| \\u001b[39m9 \\u001b[39m | \\u001b[39m-0.5063 \\u001b[39m | \\u001b[39m-3.092074\\u001b[39m | \\u001b[39m3.7368226\\u001b[39m |\\n\",\n \"| \\u001b[39m10 \\u001b[39m | \\u001b[39m-0.6432 \\u001b[39m | \\u001b[39m-4.089558\\u001b[39m | \\u001b[39m-0.560384\\u001b[39m |\\n\",\n \"| \\u001b[39m11 \\u001b[39m | \\u001b[39m1.267 \\u001b[39m | \\u001b[39m-2.360726\\u001b[39m | \\u001b[39m3.3725022\\u001b[39m |\\n\",\n \"| \\u001b[39m12 \\u001b[39m | \\u001b[39m0.4649 \\u001b[39m | \\u001b[39m-2.247113\\u001b[39m | \\u001b[39m3.7419056\\u001b[39m |\\n\",\n \"| \\u001b[39m13 \\u001b[39m | \\u001b[39m1.0 \\u001b[39m | \\u001b[39m-1.740988\\u001b[39m | \\u001b[39m3.4854116\\u001b[39m |\\n\",\n \"| \\u001b[39m14 \\u001b[39m | \\u001b[39m0.986 \\u001b[39m | \\u001b[39m1.2164322\\u001b[39m | \\u001b[39m4.4938459\\u001b[39m |\\n\",\n \"| \\u001b[39m15 \\u001b[39m | \\u001b[39m-2.27 \\u001b[39m | \\u001b[39m-2.213867\\u001b[39m | \\u001b[39m0.3585570\\u001b[39m |\\n\",\n \"| \\u001b[39m16 \\u001b[39m | \\u001b[39m-1.853 \\u001b[39m | \\u001b[39m1.7935035\\u001b[39m | \\u001b[39m-0.377351\\u001b[39m |\\n\",\n \"=================================================\\n\",\n \"Max: 1.321554535694256\\n\",\n \"\\n\",\n \"\\n\",\n \"==================== Typed Optimizer ====================\\n\",\n \"\\n\",\n \"| iter | target | x | y |\\n\",\n \"-------------------------------------------------\\n\",\n \"| \\u001b[39m2 \\u001b[39m | \\u001b[39m0.8025 \\u001b[39m | \\u001b[39m-0.829779\\u001b[39m | \\u001b[39m3 \\u001b[39m |\\n\",\n \"| \\u001b[39m3 \\u001b[39m | \\u001b[39m-2.75 \\u001b[39m | \\u001b[39m-4.998856\\u001b[39m | \\u001b[39m0 \\u001b[39m |\\n\",\n \"| \\u001b[39m4 \\u001b[39m | \\u001b[39m0.8007 \\u001b[39m | \\u001b[39m-0.827713\\u001b[39m | \\u001b[39m3 \\u001b[39m |\\n\",\n \"| \\u001b[39m5 \\u001b[39m | \\u001b[39m-0.749 \\u001b[39m | \\u001b[39m2.2682240\\u001b[39m | \\u001b[39m-5 \\u001b[39m |\\n\",\n \"| \\u001b[39m6 \\u001b[39m | \\u001b[39m0.2379 \\u001b[39m | \\u001b[39m-2.449054\\u001b[39m | \\u001b[39m4 \\u001b[39m |\\n\",\n \"| \\u001b[39m7 \\u001b[39m | \\u001b[39m0.7479 \\u001b[39m | \\u001b[39m4.995975 \\u001b[39m | \\u001b[39m2 \\u001b[39m |\\n\",\n \"| \\u001b[35m8 \\u001b[39m | \\u001b[35m0.8296 \\u001b[39m | \\u001b[35m4.9889151\\u001b[39m | \\u001b[35m-3 \\u001b[39m |\\n\",\n \"| \\u001b[39m9 \\u001b[39m | \\u001b[39m-1.199 \\u001b[39m | \\u001b[39m-1.446743\\u001b[39m | \\u001b[39m-5 \\u001b[39m |\\n\",\n \"| \\u001b[39m10 \\u001b[39m | \\u001b[39m-0.2539 \\u001b[39m | \\u001b[39m-4.999373\\u001b[39m | \\u001b[39m2 \\u001b[39m |\\n\",\n \"| \\u001b[35m11 \\u001b[39m | \\u001b[35m1.429 \\u001b[39m | \\u001b[35m4.9898854\\u001b[39m | \\u001b[35m3 \\u001b[39m |\\n\",\n \"| \\u001b[39m12 \\u001b[39m | \\u001b[39m0.2149 \\u001b[39m | \\u001b[39m4.9849287\\u001b[39m | \\u001b[39m5 \\u001b[39m |\\n\",\n \"| \\u001b[39m13 \\u001b[39m | \\u001b[39m-0.1697 \\u001b[39m | \\u001b[39m-4.992664\\u001b[39m | \\u001b[39m-3 \\u001b[39m |\\n\",\n \"| \\u001b[39m14 \\u001b[39m | \\u001b[39m1.137 \\u001b[39m | \\u001b[39m4.9998822\\u001b[39m | \\u001b[39m4 \\u001b[39m |\\n\",\n \"| \\u001b[39m15 \\u001b[39m | \\u001b[39m0.3508 \\u001b[39m | \\u001b[39m4.9904899\\u001b[39m | \\u001b[39m-2 \\u001b[39m |\\n\",\n \"| \\u001b[35m16 \\u001b[39m | \\u001b[35m1.741 \\u001b[39m | \\u001b[35m3.9775298\\u001b[39m | \\u001b[35m3 \\u001b[39m |\\n\",\n \"=================================================\\n\",\n \"Max: 1.7409514743244399\\n\",\n \"\\n\",\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"for lbl, optimizer in zip(labels, [continuous_optimizer, discrete_optimizer]):\\n\",\n \" print(f\\\"==================== {lbl} ====================\\\\n\\\")\\n\",\n \" optimizer.maximize(\\n\",\n \" init_points=2,\\n\",\n \" n_iter=13\\n\",\n \" )\\n\",\n \" print(f\\\"Max: {optimizer.max['target']}\\\\n\\\\n\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": \"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\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"x = np.linspace(c_pbounds['x'][0], c_pbounds['x'][1], 1000)\\n\",\n \"y = np.linspace(c_pbounds['y'][0], c_pbounds['y'][1], 1000)\\n\",\n \"\\n\",\n \"X, Y = np.meshgrid(x, y)\\n\",\n \"\\n\",\n \"Z = discretized_function(X, Y)\\n\",\n \"\\n\",\n \"params = [{'x': x_i, 'y': y_j} for y_j in y for x_i in x]\\n\",\n \"array_params = [continuous_optimizer._space.params_to_array(p) for p in params]\\n\",\n \"c_pred = continuous_optimizer._gp.predict(array_params).reshape(X.shape)\\n\",\n \"d_pred = discrete_optimizer._gp.predict(array_params).reshape(X.shape)\\n\",\n \"\\n\",\n \"vmin = np.min([np.min(Z), np.min(c_pred), np.min(d_pred)])\\n\",\n \"vmax = np.max([np.max(Z), np.max(c_pred), np.max(d_pred)])\\n\",\n \"\\n\",\n \"fig, axs = plt.subplots(1, 3)\\n\",\n \"\\n\",\n \"axs[0].set_title('Actual function')\\n\",\n \"axs[0].contourf(X, Y, Z, cmap=plt.cm.coolwarm, vmin=vmin, vmax=vmax)\\n\",\n \"\\n\",\n \"\\n\",\n \"axs[1].set_title(labels[0])\\n\",\n \"axs[1].contourf(X, Y, c_pred, cmap=plt.cm.coolwarm, vmin=vmin, vmax=vmax)\\n\",\n \"axs[1].scatter(continuous_optimizer._space.params[:,0], continuous_optimizer._space.params[:,1], c='k')\\n\",\n \"\\n\",\n \"axs[2].set_title(labels[1])\\n\",\n \"axs[2].contourf(X, Y, d_pred, cmap=plt.cm.coolwarm, vmin=vmin, vmax=vmax)\\n\",\n \"axs[2].scatter(discrete_optimizer._space.params[:,0], discrete_optimizer._space.params[:,1], c='k')\\n\",\n \"\\n\",\n \"def make_plot_fancy(ax: plt.Axes):\\n\",\n \" ax.set_aspect(\\\"equal\\\")\\n\",\n \" ax.set_xlabel('x (float)')\\n\",\n \" ax.set_xticks([-5.0, -2.5, 0., 2.5, 5.0])\\n\",\n \" ax.set_ylabel('y (int)')\\n\",\n \" ax.set_yticks([-4, -2, 0, 2, 4])\\n\",\n \"\\n\",\n \"for ax in axs:\\n\",\n \" make_plot_fancy(ax)\\n\",\n \"\\n\",\n \"plt.tight_layout()\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 3. Categorical variables\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We can also handle categorical variables! This is done under-the-hood by constructing parameters in a one-hot-encoding representation, with a transformation in the kernel rounding to the nearest one-hot representation. If you want to use this, you can specify a collection of strings as options.\\n\",\n \"\\n\",\n \"NB: As internally, the categorical variables are within a range of `[0, 1]` and the GP used for BO is by default isotropic, you might want to ensure your other features are similarly scaled to a range of `[0, 1]` or use an anisotropic GP.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def f1(x1, x2):\\n\",\n \" return -1*(x1 - np.sqrt(x1**2 + x2**2) * np.cos(np.sqrt(x1**2 + x2**2))**2 + 0.5 * np.sqrt(x1**2 + x2**2))\\n\",\n \"\\n\",\n \"def f2(x1, x2):\\n\",\n \" return -1*(x2 - np.sqrt(x1**2 + x2**2) * np.sin(np.sqrt(x1**2 + x2**2))**2 + 0.5 * np.sqrt(x1**2 + x2**2))\\n\",\n \"\\n\",\n \"def SPIRAL(x1, x2, k):\\n\",\n \" \\\"\\\"\\\"cf Ladislav-Luksan\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" if k=='1':\\n\",\n \" return f1(10 * x1, 10 * x2)\\n\",\n \" elif k=='2':\\n\",\n \" return f2(10 * x1, 10 * x2)\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"| iter | target | x1 | x2 | k |\\n\",\n \"-------------------------------------------------------------\\n\",\n \"| \\u001b[39m2 \\u001b[39m | \\u001b[39m-2.052 \\u001b[39m | \\u001b[39m-0.165955\\u001b[39m | \\u001b[39m0.4406489\\u001b[39m | \\u001b[39m2 \\u001b[39m |\\n\",\n \"| \\u001b[35m3 \\u001b[39m | \\u001b[35m13.49 \\u001b[39m | \\u001b[35m-0.743751\\u001b[39m | \\u001b[35m0.9980810\\u001b[39m | \\u001b[35m1 \\u001b[39m |\\n\",\n \"| \\u001b[39m4 \\u001b[39m | \\u001b[39m-15.7 \\u001b[39m | \\u001b[39m-0.763608\\u001b[39m | \\u001b[39m0.9785936\\u001b[39m | \\u001b[39m2 \\u001b[39m |\\n\",\n \"| \\u001b[39m5 \\u001b[39m | \\u001b[39m-0.3783 \\u001b[39m | \\u001b[39m-0.093284\\u001b[39m | \\u001b[39m0.1023150\\u001b[39m | \\u001b[39m2 \\u001b[39m |\\n\",\n \"| \\u001b[39m6 \\u001b[39m | \\u001b[39m3.247 \\u001b[39m | \\u001b[39m-0.806061\\u001b[39m | \\u001b[39m-0.713343\\u001b[39m | \\u001b[39m1 \\u001b[39m |\\n\",\n \"| \\u001b[39m7 \\u001b[39m | \\u001b[39m3.551 \\u001b[39m | \\u001b[39m-0.196292\\u001b[39m | \\u001b[39m0.2582785\\u001b[39m | \\u001b[39m1 \\u001b[39m |\\n\",\n \"| \\u001b[39m8 \\u001b[39m | \\u001b[39m6.097 \\u001b[39m | \\u001b[39m-0.611686\\u001b[39m | \\u001b[39m-0.354793\\u001b[39m | \\u001b[39m1 \\u001b[39m |\\n\",\n \"| \\u001b[39m9 \\u001b[39m | \\u001b[39m0.878 \\u001b[39m | \\u001b[39m-0.109889\\u001b[39m | \\u001b[39m-0.701415\\u001b[39m | \\u001b[39m1 \\u001b[39m |\\n\",\n \"| \\u001b[39m10 \\u001b[39m | \\u001b[39m-13.98 \\u001b[39m | \\u001b[39m0.9422961\\u001b[39m | \\u001b[39m-0.625876\\u001b[39m | \\u001b[39m1 \\u001b[39m |\\n\",\n \"| \\u001b[39m11 \\u001b[39m | \\u001b[39m-5.565 \\u001b[39m | \\u001b[39m0.8618152\\u001b[39m | \\u001b[39m0.5454722\\u001b[39m | \\u001b[39m2 \\u001b[39m |\\n\",\n \"| \\u001b[39m12 \\u001b[39m | \\u001b[39m-5.01 \\u001b[39m | \\u001b[39m0.2751833\\u001b[39m | \\u001b[39m-0.369982\\u001b[39m | \\u001b[39m1 \\u001b[39m |\\n\",\n \"| \\u001b[39m13 \\u001b[39m | \\u001b[39m-8.922 \\u001b[39m | \\u001b[39m-0.635194\\u001b[39m | \\u001b[39m0.6307052\\u001b[39m | \\u001b[39m2 \\u001b[39m |\\n\",\n \"| \\u001b[39m14 \\u001b[39m | \\u001b[39m1.136 \\u001b[39m | \\u001b[39m-0.182129\\u001b[39m | \\u001b[39m-0.271731\\u001b[39m | \\u001b[39m2 \\u001b[39m |\\n\",\n \"| \\u001b[39m15 \\u001b[39m | \\u001b[39m-10.96 \\u001b[39m | \\u001b[39m-0.639808\\u001b[39m | \\u001b[39m0.6662940\\u001b[39m | \\u001b[39m2 \\u001b[39m |\\n\",\n \"| \\u001b[39m16 \\u001b[39m | \\u001b[39m4.995 \\u001b[39m | \\u001b[39m-0.214441\\u001b[39m | \\u001b[39m0.5711843\\u001b[39m | \\u001b[39m1 \\u001b[39m |\\n\",\n \"| \\u001b[39m17 \\u001b[39m | \\u001b[39m-6.05 \\u001b[39m | \\u001b[39m-0.202833\\u001b[39m | \\u001b[39m0.5261994\\u001b[39m | \\u001b[39m2 \\u001b[39m |\\n\",\n \"| \\u001b[39m18 \\u001b[39m | \\u001b[39m1.284 \\u001b[39m | \\u001b[39m0.0906321\\u001b[39m | \\u001b[39m-0.584503\\u001b[39m | \\u001b[39m1 \\u001b[39m |\\n\",\n \"| \\u001b[39m19 \\u001b[39m | \\u001b[39m-2.206 \\u001b[39m | \\u001b[39m0.1402396\\u001b[39m | \\u001b[39m0.1141631\\u001b[39m | \\u001b[39m1 \\u001b[39m |\\n\",\n \"| \\u001b[39m20 \\u001b[39m | \\u001b[39m0.5288 \\u001b[39m | \\u001b[39m-0.134786\\u001b[39m | \\u001b[39m0.0990788\\u001b[39m | \\u001b[39m1 \\u001b[39m |\\n\",\n \"| \\u001b[39m21 \\u001b[39m | \\u001b[39m-9.041 \\u001b[39m | \\u001b[39m0.8403564\\u001b[39m | \\u001b[39m0.4792001\\u001b[39m | \\u001b[39m2 \\u001b[39m |\\n\",\n \"=============================================================\\n\"\n ]\n }\n ],\n \"source\": [\n \"pbounds = {'x1': (-1, 1), 'x2': (-1, 1), 'k': ('1', '2')}\\n\",\n \"\\n\",\n \"categorical_optimizer = BayesianOptimization(\\n\",\n \" f=SPIRAL,\\n\",\n \" acquisition_function=acquisition.ExpectedImprovement(1e-2),\\n\",\n \" pbounds=pbounds,\\n\",\n \" verbose=2,\\n\",\n \" random_state=1,\\n\",\n \")\\n\",\n \"discrete_optimizer.set_gp_params(alpha=1e-3)\\n\",\n \"\\n\",\n \"categorical_optimizer.maximize(\\n\",\n \" init_points=2,\\n\",\n \" n_iter=18,\\n\",\n \" )\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"res = categorical_optimizer._space.res()\\n\",\n \"k1 = np.array([[p['params']['x1'], p['params']['x2']] for p in res if p['params']['k']=='1'])\\n\",\n \"k2 = np.array([[p['params']['x1'], p['params']['x2']] for p in res if p['params']['k']=='2'])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": \"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\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"x1 = np.linspace(pbounds['x1'][0], pbounds['x1'][1], 1000)\\n\",\n \"x2 = np.linspace(pbounds['x2'][0], pbounds['x2'][1], 1000)\\n\",\n \"\\n\",\n \"X1, X2 = np.meshgrid(x1, x2)\\n\",\n \"Z1 = SPIRAL(X1, X2, '1')\\n\",\n \"Z2 = SPIRAL(X1, X2, '2')\\n\",\n \"\\n\",\n \"fig, axs = plt.subplots(1, 2)\\n\",\n \"\\n\",\n \"vmin = np.min([np.min(Z1), np.min(Z2)])\\n\",\n \"vmax = np.max([np.max(Z1), np.max(Z2)])\\n\",\n \"\\n\",\n \"axs[0].contourf(X1, X2, Z1, vmin=vmin, vmax=vmax)\\n\",\n \"axs[0].set_aspect(\\\"equal\\\")\\n\",\n \"axs[0].scatter(k1[:,0], k1[:,1], c='k')\\n\",\n \"axs[1].contourf(X1, X2, Z2, vmin=vmin, vmax=vmax)\\n\",\n \"axs[1].scatter(k2[:,0], k2[:,1], c='k')\\n\",\n \"axs[1].set_aspect(\\\"equal\\\")\\n\",\n \"axs[0].set_title('k=1')\\n\",\n \"axs[1].set_title('k=2')\\n\",\n \"fig.tight_layout()\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 4. Use in ML\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"A typical usecase for integer and categorical parameters is optimizing the hyperparameters of a machine learning model. Below you can find an example where the hyperparameters of an SVM are optimized.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"| iter | target | kernel | log10_C |\\n\",\n \"-------------------------------------------------\\n\",\n \"| \\u001b[39m2 \\u001b[39m | \\u001b[39m-0.2361 \\u001b[39m | \\u001b[39mpoly2 \\u001b[39m | \\u001b[39m0.9943696\\u001b[39m |\\n\",\n \"| \\u001b[39m3 \\u001b[39m | \\u001b[39m-0.2864 \\u001b[39m | \\u001b[39mrbf \\u001b[39m | \\u001b[39m-0.999771\\u001b[39m |\\n\",\n \"| \\u001b[39m4 \\u001b[39m | \\u001b[39m-0.2625 \\u001b[39m | \\u001b[39mpoly3 \\u001b[39m | \\u001b[39m0.7449728\\u001b[39m |\\n\",\n \"| \\u001b[35m5 \\u001b[39m | \\u001b[35m-0.2361 \\u001b[39m | \\u001b[35mpoly2 \\u001b[39m | \\u001b[35m0.9944598\\u001b[39m |\\n\",\n \"| \\u001b[39m6 \\u001b[39m | \\u001b[39m-0.298 \\u001b[39m | \\u001b[39mpoly3 \\u001b[39m | \\u001b[39m-0.999625\\u001b[39m |\\n\",\n \"| \\u001b[35m7 \\u001b[39m | \\u001b[35m-0.2361 \\u001b[39m | \\u001b[35mpoly2 \\u001b[39m | \\u001b[35m0.9945010\\u001b[39m |\\n\",\n \"| \\u001b[35m8 \\u001b[39m | \\u001b[35m-0.2152 \\u001b[39m | \\u001b[35mrbf \\u001b[39m | \\u001b[35m0.9928960\\u001b[39m |\\n\",\n \"| \\u001b[39m9 \\u001b[39m | \\u001b[39m-0.2153 \\u001b[39m | \\u001b[39mrbf \\u001b[39m | \\u001b[39m0.9917667\\u001b[39m |\\n\",\n \"| \\u001b[39m10 \\u001b[39m | \\u001b[39m-0.2362 \\u001b[39m | \\u001b[39mpoly2 \\u001b[39m | \\u001b[39m0.9897298\\u001b[39m |\\n\",\n \"| \\u001b[39m11 \\u001b[39m | \\u001b[39m-0.2362 \\u001b[39m | \\u001b[39mpoly2 \\u001b[39m | \\u001b[39m0.9874217\\u001b[39m |\\n\",\n \"=================================================\\n\"\n ]\n }\n ],\n \"source\": [\n \"from sklearn.datasets import load_breast_cancer\\n\",\n \"from sklearn.svm import SVC\\n\",\n \"from sklearn.metrics import log_loss\\n\",\n \"from sklearn.model_selection import train_test_split\\n\",\n \"from bayes_opt import BayesianOptimization\\n\",\n \"\\n\",\n \"data = load_breast_cancer()\\n\",\n \"X_train, y_train = data['data'], data['target']\\n\",\n \"X_train, X_val, y_train, y_val = train_test_split(X_train, y_train, test_size=0.2, random_state=1)\\n\",\n \"kernels = ['rbf', 'poly']\\n\",\n \"\\n\",\n \"def f_target(kernel, log10_C):\\n\",\n \" if kernel == 'poly2':\\n\",\n \" kernel = 'poly'\\n\",\n \" degree = 2\\n\",\n \" elif kernel == 'poly3':\\n\",\n \" kernel = 'poly'\\n\",\n \" degree = 3\\n\",\n \" elif kernel == 'rbf':\\n\",\n \" degree = 3 # not used, equal to default\\n\",\n \"\\n\",\n \" C = 10**log10_C\\n\",\n \"\\n\",\n \" model = SVC(C=C, kernel=kernel, degree=degree, probability=True, random_state=1)\\n\",\n \" model.fit(X_train, y_train)\\n\",\n \"\\n\",\n \" # Package looks for maximum, so we return -1 * log_loss\\n\",\n \" loss = -1 * log_loss(y_val, model.predict_proba(X_val))\\n\",\n \" return loss\\n\",\n \"\\n\",\n \"\\n\",\n \"params_svm ={\\n\",\n \" 'kernel': ['rbf', 'poly2', 'poly3'],\\n\",\n \" 'log10_C':(-1, +1),\\n\",\n \"}\\n\",\n \"\\n\",\n \"optimizer = BayesianOptimization(\\n\",\n \" f_target,\\n\",\n \" params_svm,\\n\",\n \" random_state=1,\\n\",\n \" verbose=2\\n\",\n \")\\n\",\n \"\\n\",\n \"kernel = Matern(nu=2.5, length_scale=np.ones(optimizer.space.dim))\\n\",\n \"discrete_optimizer.set_gp_params(kernel=kernel)\\n\",\n \"optimizer.maximize(init_points=2, n_iter=8)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 5. Defining your own Parameter\\n\",\n \"\\n\",\n \"Maybe you want to optimize over another form of parameters, which does not align with `float`, `int` or categorical. For this purpose, you can create your own, custom parameter. A simple example is a parameter that is discrete, but still admits a distance representation (like an integer) while not being uniformly spaced.\\n\",\n \"\\n\",\n \"However, you can go further even and encode constraints and even symmetries in your parameter. Let's consider the problem of finding a triangle which maximizes an area given its sides $a, b, c$ with a constraint that the perimeter is fixed, i.e. $a + b + c=s$.\\n\",\n \"\\n\",\n \"We will create a parameter that encodes such a triangle, and via it's kernel transform ensures that the sides sum to the required length $s$. As you might expect, the solution to this problem is an equilateral triangle, i.e. $a=b=c=s/3$.\\n\",\n \"\\n\",\n \"To define the parameter, we need to subclass `BayesParameter` and define a few important functions/properties.\\n\",\n \"\\n\",\n \"- `is_continuous` is a property which denotes whether a parameter is continuous. When optimizing the acquisition function, non-continuous parameters will not be optimized using gradient-based methods, but only via random sampling.\\n\",\n \"- `random_sample` is a function that samples randomly from the space of the parameter.\\n\",\n \"- `to_float` transforms the canonical representation of a parameter into float values for the target space to store. There is a one-to-one correspondence between valid float representations produced by this function and canonical representations of the parameter. This function is most important when working with parameters that use a non-numeric canonical representation, such as categorical parameters.\\n\",\n \"- `to_param` performs the inverse of `to_float`: Given a float-based representation, it creates a canonical representation. This function should perform binning whenever appropriate, e.g. in the case of the `IntParameter`, this function would round any float values supplied to it.\\n\",\n \"- `kernel_transform` is the most important function of the Parameter and defines how to represent a value in the kernel space. In contrast to `to_float`, this function expects both the input, as well as the output to be float-representations of the value.\\n\",\n \"- `to_string` produces a stringified version of the parameter, which allows users to define custom pretty-print rules for ththe ScreenLogger use.\\n\",\n \"- `dim` is a property which defines the dimensionality of the parameter. In most cases, this will be 1, but e.g. for categorical parameters it is equivalent to the cardinality of the category space. \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from bayes_opt.logger import ScreenLogger\\n\",\n \"from bayes_opt.parameter import BayesParameter\\n\",\n \"from bayes_opt.util import ensure_rng\\n\",\n \"\\n\",\n \"\\n\",\n \"class FixedPerimeterTriangleParameter(BayesParameter):\\n\",\n \" def __init__(self, name: str, bounds, perimeter) -> None:\\n\",\n \" super().__init__(name, bounds)\\n\",\n \" self.perimeter = perimeter\\n\",\n \"\\n\",\n \" @property\\n\",\n \" def is_continuous(self):\\n\",\n \" return True\\n\",\n \"\\n\",\n \" def random_sample(self, n_samples: int, random_state):\\n\",\n \" random_state = ensure_rng(random_state)\\n\",\n \" samples = []\\n\",\n \" while len(samples) < n_samples:\\n\",\n \" samples_ = random_state.dirichlet(np.ones(3), n_samples)\\n\",\n \" samples_ = samples_ * self.perimeter # scale samples by perimeter\\n\",\n \"\\n\",\n \" samples_ = samples_[np.all((self.bounds[:, 0] <= samples_) & (samples_ <= self.bounds[:, 1]), axis=-1)]\\n\",\n \" samples.extend(np.atleast_2d(samples_))\\n\",\n \" samples = np.array(samples[:n_samples])\\n\",\n \" return samples\\n\",\n \"\\n\",\n \" def to_float(self, value):\\n\",\n \" return value\\n\",\n \"\\n\",\n \" def to_param(self, value):\\n\",\n \" return value * self.perimeter / sum(value)\\n\",\n \"\\n\",\n \" def kernel_transform(self, value):\\n\",\n \" return value * self.perimeter / np.sum(value, axis=-1, keepdims=True)\\n\",\n \"\\n\",\n \" def to_string(self, value, str_len: int) -> str:\\n\",\n \" len_each = (str_len - 2) // 3\\n\",\n \" str_ = '|'.join([f\\\"{float(np.round(value[i], 4))}\\\"[:len_each] for i in range(3)])\\n\",\n \" return str_.ljust(str_len)\\n\",\n \"\\n\",\n \" @property\\n\",\n \" def dim(self):\\n\",\n \" return 3 # as we have three float values, each representing the length of one side.\\n\",\n \"\\n\",\n \"def area_of_triangle(sides):\\n\",\n \" a, b, c = sides\\n\",\n \" s = np.sum(sides, axis=-1) # perimeter\\n\",\n \" A = np.sqrt(s * (s-a) * (s-b) * (s-c))\\n\",\n \" return A\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"| iter | target | sides |\\n\",\n \"-------------------------------------------------------\\n\",\n \"| \\u001b[39m2 \\u001b[39m | \\u001b[39m0.4572 \\u001b[39m | \\u001b[39m0.29|0.70|0.00 \\u001b[39m |\\n\",\n \"| \\u001b[35m3 \\u001b[39m | \\u001b[35m0.5096 \\u001b[39m | \\u001b[35m0.58|0.25|0.15 \\u001b[39m |\\n\",\n \"| \\u001b[39m4 \\u001b[39m | \\u001b[39m0.5081 \\u001b[39m | \\u001b[39m0.58|0.25|0.15 \\u001b[39m |\\n\",\n \"| \\u001b[35m5 \\u001b[39m | \\u001b[35m0.5386 \\u001b[39m | \\u001b[35m0.44|0.28|0.26 \\u001b[39m |\\n\",\n \"| \\u001b[39m6 \\u001b[39m | \\u001b[39m0.5279 \\u001b[39m | \\u001b[39m0.38|0.14|0.47 \\u001b[39m |\\n\",\n \"| \\u001b[39m7 \\u001b[39m | \\u001b[39m0.5328 \\u001b[39m | \\u001b[39m0.18|0.36|0.45 \\u001b[39m |\\n\",\n \"| \\u001b[39m8 \\u001b[39m | \\u001b[39m0.4366 \\u001b[39m | \\u001b[39m0.02|0.22|0.74 \\u001b[39m |\\n\",\n \"| \\u001b[39m9 \\u001b[39m | \\u001b[39m0.4868 \\u001b[39m | \\u001b[39m0.00|0.61|0.37 \\u001b[39m |\\n\",\n \"| \\u001b[39m10 \\u001b[39m | \\u001b[39m0.4977 \\u001b[39m | \\u001b[39m0.56|0.01|0.42 \\u001b[39m |\\n\",\n \"| \\u001b[35m11 \\u001b[39m | \\u001b[35m0.5418 \\u001b[39m | \\u001b[35m0.29|0.40|0.30 \\u001b[39m |\\n\",\n \"| \\u001b[39m12 \\u001b[39m | \\u001b[39m0.3361 \\u001b[39m | \\u001b[39m0.06|0.87|0.06 \\u001b[39m |\\n\",\n \"| \\u001b[39m13 \\u001b[39m | \\u001b[39m0.06468 \\u001b[39m | \\u001b[39m0.99|0.00|0.00 \\u001b[39m |\\n\",\n \"| \\u001b[39m14 \\u001b[39m | \\u001b[39m0.01589 \\u001b[39m | \\u001b[39m0.0|0.00|0.99 \\u001b[39m |\\n\",\n \"| \\u001b[39m15 \\u001b[39m | \\u001b[39m0.4999 \\u001b[39m | \\u001b[39m0.21|0.16|0.61 \\u001b[39m |\\n\",\n \"| \\u001b[39m16 \\u001b[39m | \\u001b[39m0.499 \\u001b[39m | \\u001b[39m0.53|0.46|0.00 \\u001b[39m |\\n\",\n \"| \\u001b[39m17 \\u001b[39m | \\u001b[39m0.4937 \\u001b[39m | \\u001b[39m0.00|0.41|0.58 \\u001b[39m |\\n\",\n \"| \\u001b[39m18 \\u001b[39m | \\u001b[39m0.5233 \\u001b[39m | \\u001b[39m0.33|0.51|0.14 \\u001b[39m |\\n\",\n \"| \\u001b[39m19 \\u001b[39m | \\u001b[39m0.5204 \\u001b[39m | \\u001b[39m0.17|0.54|0.28 \\u001b[39m |\\n\",\n \"| \\u001b[39m20 \\u001b[39m | \\u001b[39m0.5235 \\u001b[39m | \\u001b[39m0.51|0.15|0.32 \\u001b[39m |\\n\",\n \"| \\u001b[39m21 \\u001b[39m | \\u001b[39m0.5412 \\u001b[39m | \\u001b[39m0.31|0.27|0.41 \\u001b[39m |\\n\",\n \"| \\u001b[39m22 \\u001b[39m | \\u001b[39m0.4946 \\u001b[39m | \\u001b[39m0.41|0.00|0.57 \\u001b[39m |\\n\",\n \"| \\u001b[39m23 \\u001b[39m | \\u001b[39m0.5355 \\u001b[39m | \\u001b[39m0.41|0.39|0.19 \\u001b[39m |\\n\",\n \"| \\u001b[35m24 \\u001b[39m | \\u001b[35m0.5442 \\u001b[39m | \\u001b[35m0.35|0.32|0.32 \\u001b[39m |\\n\",\n \"| \\u001b[39m25 \\u001b[39m | \\u001b[39m0.5192 \\u001b[39m | \\u001b[39m0.16|0.28|0.54 \\u001b[39m |\\n\",\n \"| \\u001b[39m26 \\u001b[39m | \\u001b[39m0.5401 \\u001b[39m | \\u001b[39m0.39|0.23|0.36 \\u001b[39m |\\n\",\n \"=======================================================\\n\"\n ]\n }\n ],\n \"source\": [\n \"param = FixedPerimeterTriangleParameter(\\n\",\n \" name='sides',\\n\",\n \" bounds=np.array([[0., 1.], [0., 1.], [0., 1.]]),\\n\",\n \" perimeter=1.\\n\",\n \")\\n\",\n \"\\n\",\n \"pbounds = {'sides': param}\\n\",\n \"optimizer = BayesianOptimization(\\n\",\n \" area_of_triangle,\\n\",\n \" pbounds,\\n\",\n \" random_state=1,\\n\",\n \")\\n\",\n \"\\n\",\n \"# Increase the cell size to accommodate the three float values\\n\",\n \"optimizer.logger._verbose = 2\\n\",\n \"optimizer.logger._default_cell_size = 15\\n\",\n \"optimizer.logger._is_constrained = False\\n\",\n \"\\n\",\n \"optimizer.maximize(init_points=2, n_iter=23)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"This seems to work decently well, but we can improve it significantly if we consider the symmetries inherent in the problem: This problem is permutation invariant, i.e. we do not care which side specifically is denoted as $a$, $b$ or $c$. Instead, we can, without loss of generality, decide that the shortest side will always be denoted as $a$, and the longest always as $c$. If we enhance our kernel transform with this symmetry, the performance improves significantly. This can be easily done by sub-classing the previously created triangle parameter.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"| iter | target | sides |\\n\",\n \"-------------------------------------------------------\\n\",\n \"| \\u001b[39m2 \\u001b[39m | \\u001b[39m0.4572 \\u001b[39m | \\u001b[39m0.00|0.29|0.70 \\u001b[39m |\\n\",\n \"| \\u001b[35m3 \\u001b[39m | \\u001b[35m0.5096 \\u001b[39m | \\u001b[35m0.15|0.25|0.58 \\u001b[39m |\\n\",\n \"| \\u001b[39m4 \\u001b[39m | \\u001b[39m0.498 \\u001b[39m | \\u001b[39m0.06|0.33|0.60 \\u001b[39m |\\n\",\n \"| \\u001b[35m5 \\u001b[39m | \\u001b[35m0.5097 \\u001b[39m | \\u001b[35m0.13|0.27|0.58 \\u001b[39m |\\n\",\n \"| \\u001b[35m6 \\u001b[39m | \\u001b[35m0.5358 \\u001b[39m | \\u001b[35m0.19|0.36|0.43 \\u001b[39m |\\n\",\n \"| \\u001b[35m7 \\u001b[39m | \\u001b[35m0.5443 \\u001b[39m | \\u001b[35m0.33|0.33|0.33 \\u001b[39m |\\n\",\n \"| \\u001b[39m8 \\u001b[39m | \\u001b[39m0.5405 \\u001b[39m | \\u001b[39m0.28|0.28|0.42 \\u001b[39m |\\n\",\n \"| \\u001b[39m9 \\u001b[39m | \\u001b[39m0.5034 \\u001b[39m | \\u001b[39m0.01|0.49|0.49 \\u001b[39m |\\n\",\n \"| \\u001b[39m10 \\u001b[39m | \\u001b[39m0.4977 \\u001b[39m | \\u001b[39m0.01|0.42|0.56 \\u001b[39m |\\n\",\n \"| \\u001b[39m11 \\u001b[39m | \\u001b[39m0.5427 \\u001b[39m | \\u001b[39m0.27|0.36|0.36 \\u001b[39m |\\n\",\n \"=======================================================\\n\"\n ]\n }\n ],\n \"source\": [\n \"class SortingFixedPerimeterTriangleParameter(FixedPerimeterTriangleParameter):\\n\",\n \" def __init__(self, name: str, bounds, perimeter) -> None:\\n\",\n \" super().__init__(name, bounds, perimeter)\\n\",\n \"\\n\",\n \" def to_param(self, value):\\n\",\n \" value = np.sort(value, axis=-1)\\n\",\n \" return super().to_param(value)\\n\",\n \"\\n\",\n \" def kernel_transform(self, value):\\n\",\n \" value = np.sort(value, axis=-1)\\n\",\n \" return super().kernel_transform(value)\\n\",\n \"\\n\",\n \"param = SortingFixedPerimeterTriangleParameter(\\n\",\n \" name='sides',\\n\",\n \" bounds=np.array([[0., 1.], [0., 1.], [0., 1.]]),\\n\",\n \" perimeter=1.\\n\",\n \")\\n\",\n \"\\n\",\n \"pbounds = {'sides': param}\\n\",\n \"optimizer = BayesianOptimization(\\n\",\n \" area_of_triangle,\\n\",\n \" pbounds,\\n\",\n \" random_state=1,\\n\",\n \")\\n\",\n \"\\n\",\n \"optimizer.logger.verbose = 2\\n\",\n \"optimizer.logger._default_cell_size = 15\\n\",\n \"\\n\",\n \"optimizer.maximize(init_points=2, n_iter=8)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"bayesian-optimization-tb9vsVm6-py3.9\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.13.1\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n" }, { "path": "examples/sklearn_example.py", "content": "from sklearn.datasets import make_classification\nfrom sklearn.model_selection import cross_val_score\nfrom sklearn.ensemble import RandomForestClassifier as RFC\nfrom sklearn.svm import SVC\n\nfrom bayes_opt import BayesianOptimization\nfrom colorama import Fore\n\ndef get_data():\n \"\"\"Synthetic binary classification dataset.\"\"\"\n data, targets = make_classification(\n n_samples=1000,\n n_features=45,\n n_informative=12,\n n_redundant=7,\n random_state=134985745,\n )\n return data, targets\n\n\ndef svc_cv(C, gamma, data, targets):\n \"\"\"SVC cross validation.\n\n This function will instantiate a SVC classifier with parameters C and\n gamma. Combined with data and targets this will in turn be used to perform\n cross validation. The result of cross validation is returned.\n\n Our goal is to find combinations of C and gamma that maximizes the roc_auc\n metric.\n \"\"\"\n estimator = SVC(C=C, gamma=gamma, random_state=2)\n cval = cross_val_score(estimator, data, targets, scoring='roc_auc', cv=4)\n return cval.mean()\n\n\ndef rfc_cv(n_estimators, min_samples_split, max_features, data, targets):\n \"\"\"Random Forest cross validation.\n\n This function will instantiate a random forest classifier with parameters\n n_estimators, min_samples_split, and max_features. Combined with data and\n targets this will in turn be used to perform cross validation. The result\n of cross validation is returned.\n\n Our goal is to find combinations of n_estimators, min_samples_split, and\n max_features that minimizes the log loss.\n \"\"\"\n estimator = RFC(\n n_estimators=n_estimators,\n min_samples_split=min_samples_split,\n max_features=max_features,\n random_state=2\n )\n cval = cross_val_score(estimator, data, targets,\n scoring='neg_log_loss', cv=4)\n return cval.mean()\n\n\ndef optimize_svc(data, targets):\n \"\"\"Apply Bayesian Optimization to SVC parameters.\"\"\"\n def svc_crossval(expC, expGamma):\n \"\"\"Wrapper of SVC cross validation.\n\n Notice how we transform between regular and log scale. While this\n is not technically necessary, it greatly improves the performance\n of the optimizer.\n \"\"\"\n C = 10 ** expC\n gamma = 10 ** expGamma\n return svc_cv(C=C, gamma=gamma, data=data, targets=targets)\n\n optimizer = BayesianOptimization(\n f=svc_crossval,\n pbounds={\"expC\": (-3, 2), \"expGamma\": (-4, -1)},\n random_state=1234,\n verbose=2\n )\n optimizer.maximize(n_iter=10)\n\n print(\"Final result:\", optimizer.max)\n\n\ndef optimize_rfc(data, targets):\n \"\"\"Apply Bayesian Optimization to Random Forest parameters.\"\"\"\n def rfc_crossval(n_estimators, min_samples_split, max_features):\n \"\"\"Wrapper of RandomForest cross validation.\n\n Notice how we ensure n_estimators and min_samples_split are casted\n to integer before we pass them along. Moreover, to avoid max_features\n taking values outside the (0, 1) range, we also ensure it is capped\n accordingly.\n \"\"\"\n return rfc_cv(\n n_estimators=int(n_estimators),\n min_samples_split=int(min_samples_split),\n max_features=max(min(max_features, 0.999), 1e-3),\n data=data,\n targets=targets,\n )\n\n optimizer = BayesianOptimization(\n f=rfc_crossval,\n pbounds={\n \"n_estimators\": (10, 250),\n \"min_samples_split\": (2, 25),\n \"max_features\": (0.1, 0.999),\n },\n random_state=1234,\n verbose=2\n )\n optimizer.maximize(n_iter=10)\n\n print(\"Final result:\", optimizer.max)\n\nif __name__ == \"__main__\":\n data, targets = get_data()\n\n print(Fore.YELLOW + \"--- Optimizing SVM ---\")\n optimize_svc(data, targets)\n\n print(Fore.GREEN(\"--- Optimizing Random Forest ---\"))\n optimize_rfc(data, targets)\n" }, { "path": "examples/typed_hyperparameter_tuning.py", "content": "import numpy as np\nfrom bayes_opt import BayesianOptimization, acquisition\nfrom sklearn.ensemble import GradientBoostingClassifier\nfrom sklearn.datasets import load_digits\nfrom sklearn.model_selection import KFold\nfrom sklearn.metrics import log_loss\nimport matplotlib.pyplot as plt\n\nN_FOLDS = 10\nN_START = 2\nN_ITER = 25 - N_START\n# Load data\ndata = load_digits()\n \n\n# Define the hyperparameter space\ncontinuous_pbounds = {\n 'log_learning_rate': (-10, 0),\n 'max_depth': (1, 6),\n 'min_samples_split': (2, 6)\n}\n\ndiscrete_pbounds = {\n 'log_learning_rate': (-10, 0),\n 'max_depth': (1, 6, int),\n 'min_samples_split': (2, 6, int)\n}\n\nkfold = KFold(n_splits=N_FOLDS, shuffle=True, random_state=42)\n\nres_continuous = []\nres_discrete = []\n\nMETRIC_SIGN = -1\n\nfor i, (train_idx, test_idx) in enumerate(kfold.split(data.data)):\n print(f'Fold {i + 1}/{N_FOLDS}')\n def gboost(log_learning_rate, max_depth, min_samples_split):\n clf = GradientBoostingClassifier(\n n_estimators=10,\n max_depth=int(max_depth),\n learning_rate=np.exp(log_learning_rate),\n min_samples_split=int(min_samples_split),\n random_state=42 + i\n )\n clf.fit(data.data[train_idx], data.target[train_idx])\n #return clf.score(data.data[test_idx], data.target[test_idx])\n return METRIC_SIGN * log_loss(data.target[test_idx], clf.predict_proba(data.data[test_idx]), labels=list(range(10)))\n \n continuous_optimizer = BayesianOptimization(\n f=gboost,\n pbounds=continuous_pbounds,\n acquisition_function=acquisition.ExpectedImprovement(xi=1e-2, random_state=42),\n verbose=0,\n random_state=42,\n )\n\n discrete_optimizer = BayesianOptimization(\n f=gboost,\n pbounds=discrete_pbounds,\n acquisition_function=acquisition.ExpectedImprovement(xi=1e-2, random_state=42),\n verbose=0,\n random_state=42,\n )\n continuous_optimizer.maximize(init_points=2, n_iter=N_ITER)\n discrete_optimizer.maximize(init_points=2, n_iter=N_ITER)\n res_continuous.append(METRIC_SIGN * continuous_optimizer.space.target)\n res_discrete.append(METRIC_SIGN * discrete_optimizer.space.target)\n\nscore_continuous = []\nscore_discrete = []\n\nfor fold in range(N_FOLDS):\n best_in_fold = min(np.min(res_continuous[fold]), np.min(res_discrete[fold]))\n score_continuous.append(np.minimum.accumulate((res_continuous[fold] - best_in_fold)))\n score_discrete.append(np.minimum.accumulate((res_discrete[fold] - best_in_fold)))\n\nmean_continuous = np.mean(score_continuous, axis=0)\nquantiles_continuous = np.quantile(score_continuous, [0.1, 0.9], axis=0)\nmean_discrete = np.mean(score_discrete, axis=0)\nquantiles_discrete = np.quantile(score_discrete, [0.1, 0.9], axis=0)\n\n\nplt.figure(figsize=(10, 5))\nplt.plot((mean_continuous), label='Continuous best seen')\nplt.fill_between(range(N_ITER + N_START), quantiles_continuous[0], quantiles_continuous[1], alpha=0.3)\nplt.plot((mean_discrete), label='Discrete best seen')\nplt.fill_between(range(N_ITER + N_START), quantiles_discrete[0], quantiles_discrete[1], alpha=0.3)\n\nplt.xlabel('Number of iterations')\nplt.ylabel('Score')\nplt.legend(loc='best')\nplt.grid()\nplt.savefig('discrete_vs_continuous.png')\n" }, { "path": "examples/visualization.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from bayes_opt import BayesianOptimization\\n\",\n \"from bayes_opt import acquisition\\n\",\n \"import numpy as np\\n\",\n \"\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"from matplotlib import gridspec\\n\",\n \"%matplotlib inline\"\n ]\n },\n {\n \"attachments\": {},\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Visualization\\n\",\n \"\\n\",\n \"Lets create a target 1-D function with multiple local maxima to test and visualize how the [BayesianOptimization](https://github.com/fmfn/BayesianOptimization) package works. The target function we will try to maximize is the following:\\n\",\n \"\\n\",\n \"$f(x) = e^{-(x - 2)^2} + e^{-\\\\frac{(x - 6)^2}{10}} + \\\\frac{1}{x^2 + 1}, $ its maximum is at $x = 2$ and we will restrict the interval of interest to $x \\\\in (-2, 10)$.\\n\",\n \"\\n\",\n \"Notice that, in practice, this function is unknown, the only information we have is obtained by sequentially probing it at different points. Bayesian Optimization works by constructing a posterior distribution of functions that best fit the data observed and choosing the next probing point by balancing exploration and exploitation.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def target(x):\\n\",\n \" return np.exp(-(x - 2)**2) + np.exp(-(x - 6)**2/10) + 1/ (x**2 + 1)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": \"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\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"x = np.linspace(-2, 10, 10000).reshape(-1, 1)\\n\",\n \"y = target(x)\\n\",\n \"\\n\",\n \"plt.plot(x, y);\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Create a BayesianOptimization Object\\n\",\n \"\\n\",\n \"Enter the target function to be maximized, its variable(s) and their corresponding ranges. A minimum number of 2 initial guesses is necessary to kick start the algorithms, these can either be random or user defined.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"acquisition_function = acquisition.UpperConfidenceBound(kappa=5.)\\n\",\n \"optimizer = BayesianOptimization(target, {'x': (-2, 10)}, acquisition_function=acquisition_function, random_state=27)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"In this example we will use the Upper Confidence Bound (UCB) as our utility function. It has the free parameter\\n\",\n \"$\\\\kappa$ which control the balance between exploration and exploitation; we will set $\\\\kappa=5$ which, in this case, makes the algorithm quite bold.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"| iter | target | x |\\n\",\n \"-------------------------------------\\n\",\n \"| \\u001b[39m1 \\u001b[39m | \\u001b[39m0.8198 \\u001b[39m | \\u001b[39m3.109 \\u001b[39m |\\n\",\n \"| \\u001b[39m2 \\u001b[39m | \\u001b[39m0.746 \\u001b[39m | \\u001b[39m7.775 \\u001b[39m |\\n\",\n \"=====================================\\n\"\n ]\n }\n ],\n \"source\": [\n \"optimizer.maximize(init_points=2, n_iter=0)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Plotting and visualizing the algorithm at each step\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Let's first define a couple functions to make plotting easier\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def posterior(optimizer, grid):\\n\",\n \" mu, sigma = optimizer._gp.predict(grid, return_std=True)\\n\",\n \" return mu, sigma\\n\",\n \"\\n\",\n \"def plot_gp(optimizer, x, y):\\n\",\n \" fig = plt.figure(figsize=(16, 10))\\n\",\n \" steps = len(optimizer.space)\\n\",\n \" fig.suptitle(\\n\",\n \" 'Gaussian Process and Utility Function After {} Steps'.format(steps),\\n\",\n \" fontsize=30\\n\",\n \" )\\n\",\n \" \\n\",\n \" gs = gridspec.GridSpec(2, 1, height_ratios=[3, 1]) \\n\",\n \" axis = plt.subplot(gs[0])\\n\",\n \" acq = plt.subplot(gs[1])\\n\",\n \" \\n\",\n \" x_obs = np.array([[res[\\\"params\\\"][\\\"x\\\"]] for res in optimizer.res])\\n\",\n \" y_obs = np.array([res[\\\"target\\\"] for res in optimizer.res])\\n\",\n \" \\n\",\n \" optimizer.acquisition_function._fit_gp(optimizer._gp, optimizer._space)\\n\",\n \" mu, sigma = posterior(optimizer, x)\\n\",\n \"\\n\",\n \" axis.plot(x, y, linewidth=3, label='Target')\\n\",\n \" axis.plot(x_obs.flatten(), y_obs, 'D', markersize=8, label=u'Observations', color='r')\\n\",\n \" axis.plot(x, mu, '--', color='k', label='Prediction')\\n\",\n \"\\n\",\n \" axis.fill(np.concatenate([x, x[::-1]]), \\n\",\n \" np.concatenate([mu - 1.9600 * sigma, (mu + 1.9600 * sigma)[::-1]]),\\n\",\n \" alpha=.6, fc='c', ec='None', label='95% confidence interval')\\n\",\n \" \\n\",\n \" axis.set_xlim((-2, 10))\\n\",\n \" axis.set_ylim((None, None))\\n\",\n \" axis.set_ylabel('f(x)', fontdict={'size':20})\\n\",\n \" axis.set_xlabel('x', fontdict={'size':20})\\n\",\n \" \\n\",\n \" \\n\",\n \" utility_function = acquisition.UpperConfidenceBound(kappa=5)\\n\",\n \" utility = -1 * utility_function._get_acq(gp=optimizer._gp)(x)\\n\",\n \" x = x.flatten()\\n\",\n \"\\n\",\n \" acq.plot(x, utility, label='Utility Function', color='purple')\\n\",\n \" acq.plot(x[np.argmax(utility)], np.max(utility), '*', markersize=15, \\n\",\n \" label=u'Next Best Guess', markerfacecolor='gold', markeredgecolor='k', markeredgewidth=1)\\n\",\n \" acq.set_xlim((-2, 10))\\n\",\n \" #acq.set_ylim((0, np.max(utility) + 0.5))\\n\",\n \" acq.set_ylabel('Utility', fontdict={'size':20})\\n\",\n \" acq.set_xlabel('x', fontdict={'size':20})\\n\",\n \" \\n\",\n \" axis.legend(loc=2, bbox_to_anchor=(1.01, 1), borderaxespad=0.)\\n\",\n \" acq.legend(loc=2, bbox_to_anchor=(1.01, 1), borderaxespad=0.)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Two random points\\n\",\n \"\\n\",\n \"After we probe two points at random, we can fit a Gaussian Process and start the bayesian optimization procedure. Two points should give us a uneventful posterior with the uncertainty growing as we go further from the observations.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": \"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\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plot_gp(optimizer, x, y)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### After one step of GP (and two random points)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"| iter | target | x |\\n\",\n \"-------------------------------------\\n\",\n \"| \\u001b[35m3 \\u001b[39m | \\u001b[35m1.154 \\u001b[39m | \\u001b[35m2.582 \\u001b[39m |\\n\",\n \"=====================================\\n\"\n ]\n },\n {\n \"data\": {\n \"image/png\": \"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\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"optimizer.maximize(init_points=0, n_iter=1)\\n\",\n \"plot_gp(optimizer, x, y)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### After two steps of GP (and two random points)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"| iter | target | x |\\n\",\n \"-------------------------------------\\n\",\n \"| \\u001b[35m4 \\u001b[39m | \\u001b[35m1.158 \\u001b[39m | \\u001b[35m2.576 \\u001b[39m |\\n\",\n \"=====================================\\n\"\n ]\n },\n {\n \"data\": {\n \"image/png\": \"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\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"optimizer.maximize(init_points=0, n_iter=1)\\n\",\n \"plot_gp(optimizer, x, y)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### After three steps of GP (and two random points)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"| iter | target | x |\\n\",\n \"-------------------------------------\\n\",\n \"| \\u001b[35m5 \\u001b[39m | \\u001b[35m1.374 \\u001b[39m | \\u001b[35m1.819 \\u001b[39m |\\n\",\n \"=====================================\\n\"\n ]\n },\n {\n \"data\": {\n \"image/png\": \"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\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"optimizer.maximize(init_points=0, n_iter=1)\\n\",\n \"plot_gp(optimizer, x, y)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### After four steps of GP (and two random points)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"| iter | target | x |\\n\",\n \"-------------------------------------\\n\",\n \"| \\u001b[39m6 \\u001b[39m | \\u001b[39m1.033 \\u001b[39m | \\u001b[39m0.2094 \\u001b[39m |\\n\",\n \"=====================================\\n\"\n ]\n },\n {\n \"data\": {\n \"image/png\": \"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\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"optimizer.maximize(init_points=0, n_iter=1)\\n\",\n \"plot_gp(optimizer, x, y)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### After five steps of GP (and two random points)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"| iter | target | x |\\n\",\n \"-------------------------------------\\n\",\n \"| \\u001b[39m7 \\u001b[39m | \\u001b[39m0.2017 \\u001b[39m | \\u001b[39m-2.0 \\u001b[39m |\\n\",\n \"=====================================\\n\"\n ]\n },\n {\n \"data\": {\n \"image/png\": \"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\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"optimizer.maximize(init_points=0, n_iter=1)\\n\",\n \"plot_gp(optimizer, x, y)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### After six steps of GP (and two random points)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"| iter | target | x |\\n\",\n \"-------------------------------------\\n\",\n \"| \\u001b[39m8 \\u001b[39m | \\u001b[39m0.2122 \\u001b[39m | \\u001b[39m9.998 \\u001b[39m |\\n\",\n \"=====================================\\n\"\n ]\n },\n {\n \"data\": {\n \"image/png\": \"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\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"optimizer.maximize(init_points=0, n_iter=1)\\n\",\n \"plot_gp(optimizer, x, y)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### After seven steps of GP (and two random points)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"| iter | target | x |\\n\",\n \"-------------------------------------\\n\",\n \"| \\u001b[39m9 \\u001b[39m | \\u001b[39m1.021 \\u001b[39m | \\u001b[39m5.705 \\u001b[39m |\\n\",\n \"=====================================\\n\"\n ]\n },\n {\n \"data\": {\n \"image/png\": \"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\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"optimizer.maximize(init_points=0, n_iter=1)\\n\",\n \"plot_gp(optimizer, x, y)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Stopping\\n\",\n \"\\n\",\n \"After just a few points the algorithm was able to get pretty close to the true maximum. It is important to notice that the trade off between exploration (exploring the parameter space) and exploitation (probing points near the current known maximum) is fundamental to a succesful bayesian optimization procedure. The utility function being used here (Upper Confidence Bound - UCB) has a free parameter $\\\\kappa$ that allows the user to make the algorithm more or less conservative. Additionally, the larger the initial set of random points explored, the less likely the algorithm is to get stuck in local minima due to being too conservative.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.1.-1\"\n },\n \"nbdime-conflicts\": {\n \"local_diff\": [\n {\n \"diff\": [\n {\n \"diff\": [\n {\n \"key\": 0,\n \"op\": \"addrange\",\n \"valuelist\": [\n \"3.10.0\"\n ]\n },\n {\n \"key\": 0,\n \"length\": 1,\n \"op\": \"removerange\"\n }\n ],\n \"key\": \"version\",\n \"op\": \"patch\"\n }\n ],\n \"key\": \"language_info\",\n \"op\": \"patch\"\n }\n ],\n \"remote_diff\": [\n {\n \"diff\": [\n {\n \"diff\": [\n {\n \"key\": 0,\n \"op\": \"addrange\",\n \"valuelist\": [\n \"3.11.3\"\n ]\n },\n {\n \"key\": 0,\n \"length\": 1,\n \"op\": \"removerange\"\n }\n ],\n \"key\": \"version\",\n \"op\": \"patch\"\n }\n ],\n \"key\": \"language_info\",\n \"op\": \"patch\"\n }\n ]\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n" }, { "path": "pyproject.toml", "content": "[project]\nname = \"bayesian-optimization\"\nversion = \"3.2.1\"\ndescription = \"Bayesian Optimization package\"\nauthors = [{ name = \"Fernando Nogueira\", email = \"fmfnogueira@gmail.com\" }]\nlicense = { file = \"LICENSE\" }\nreadme = \"README.md\"\nrequires-python = \">=3.9\"\nclassifiers = [\n \"License :: OSI Approved :: MIT License\",\n \"Programming Language :: Python\",\n \"Programming Language :: Python :: 3\",\n \"Programming Language :: Python :: 3.9\",\n \"Programming Language :: Python :: 3.10\",\n \"Programming Language :: Python :: 3.11\",\n \"Programming Language :: Python :: 3.12\",\n \"Programming Language :: Python :: 3.13\",\n \"Programming Language :: Python :: 3.14\",\n]\ndependencies = [\n \"colorama>=0.4.6\",\n \"numpy>=1.25; python_version<'3.13'\",\n \"numpy>=2.1.3; python_version>='3.13'\",\n \"packaging>=20.0\",\n \"scikit-learn>=1.0.0\",\n \"scipy>=1.0.0; python_version<'3.13'\",\n \"scipy>=1.14.1; python_version>='3.13'\",\n]\n\n[project.optional-dependencies]\ndev = [\n \"coverage>=7.4.1\",\n \"jupyter>=1.0.0\",\n \"matplotlib>=3.0\",\n \"nbconvert>=7.14.2\",\n \"nbformat>=5.9.2\",\n \"nbsphinx>=0.9.4\",\n \"pre-commit>=3.7.1\",\n \"pytest>=8.0.0\",\n \"pytest-cov>=4.1.0\",\n \"ruff>=0.12.3\",\n \"sphinx-immaterial>=0.12.0\",\n \"sphinx>=7.0.0; python_version<'3.10'\",\n \"sphinx>=8.0.0; python_version>='3.10'\",\n \"sphinx-autodoc-typehints>=2.3.0; python_version<'3.10'\",\n \"sphinx-autodoc-typehints>=2.4.0; python_version>='3.10'\",\n]\n\n[build-system]\nrequires = [\"uv_build>=0.7.21,<0.8.0\"]\nbuild-backend = \"uv_build\"\n\n[tool.uv.build-backend]\nmodule-name = \"bayes_opt\"\nmodule-root = \"\"\n\n[tool.coverage.report]\nexclude_lines = [\n \"pragma: no cover\",\n \"if TYPE_CHECKING:\",\n]\n" }, { "path": "ruff.toml", "content": "line-length = 110\nunsafe-fixes = false\ntarget-version = \"py39\"\nextend = \"./pyproject.toml\"\nexclude = [\n # docs\n \"docsrc/**/*\",\n # examples\n \"examples/**/*\",\n]\n\n[lint]\nextend-select = [\n \"D\", # pydocstyle\n \"D417\", # undocumented-param\n \"I\", # isort\n \"UP\", # pyupgrade\n \"G\", # flake8-logging-format\n \"LOG\", # flake8-logging\n \"PT\", # flake8-pytest-style\n \"E\", # pycodestyle\n \"W\", # pycodestyle\n \"PGH\", # pygrep-hooks\n \"B\", # flake8-bugbear\n \"SIM\", # flake8-simplify\n \"S\", # flake8-bandit\n \"DTZ\", # flake8-datetimez\n \"EM\", # flake8-errmsg \n \"LOG\", # flake8-logging \n \"G\", # flake8-logging-format\n \"PIE\", # flake8-pie\n \"Q\", # flake8-quotes\n \"RET\", # flake8-return\n \"TID\", # flake8-tidy-imports \n \"PTH\", # flake8-use-pathlib\n \"F\", # Pyflakes\n \"NPY\", # NumPy-specific rules\n \"PERF\", # Perflint\n \"FURB\", # refurb\n \"RUF\", # Ruff-specific rules\n \"ISC\", # flake8-implicit-str-concat\n \"TRY002\", # raise-vanilla-class\n]\nignore = [\n \"PT011\", # TODO\n # pydocstyle numpy default\n \"D203\",\n \"D212\",\n \"D213\",\n \"D214\",\n \"D215\",\n \"D404\",\n \"D405\",\n \"D406\",\n \"D407\",\n \"D408\",\n \"D409\",\n \"D410\",\n \"D411\",\n \"D413\",\n \"D415\",\n \"D416\",\n # ruff format\n \"W191\", # tab-indentation\n \"E111\", # indentation-with-invalid-multiple\n \"E114\", # indentation-with-invalid-multiple-comment\n \"E117\", # over-indented\n \"D206\", # indent-with-spaces\n \"D300\", # triple-single-quotes\n \"Q000\", # bad-quotes-inline-string\n \"Q001\", # bad-quotes-multiline-string\n \"Q002\", # bad-quotes-docstring\n \"Q003\", # avoidable-escaped-quote\n \"COM812\", # missing-trailing-comma\n \"COM819\", # prohibited-trailing-comma\n \"ISC001\", # single-line-implicit-string-concatenation\n \"ISC002\", # multi-line-implicit-string-concatenation\n]\nfixable = [\n \"I\",\n \"UP\",\n \"ISC\",\n \"G\",\n \"LOG\",\n \"PT\",\n \"E\",\n \"W\",\n \"PGH\",\n \"B\",\n \"SIM\",\n \"S\",\n \"LOG\",\n \"G\",\n \"PIE\",\n \"Q\",\n \"RET\",\n \"TID\",\n \"PTH\",\n \"F\",\n \"NPY\",\n \"PERF\",\n \"FURB\",\n \"RUF\",\n]\n\n[lint.per-file-ignores]\n\"tests/test_*.py\" = [\"S101\", \"D\"]\n\n[format]\nindent-style = \"space\"\nquote-style = \"double\"\nskip-magic-trailing-comma = true\ndocstring-code-format = true\ndocstring-code-line-length = \"dynamic\"\n\n[lint.pylint]\nmax-args = 10\n\n[lint.isort]\nknown-local-folder = [\"bayes_opt\"]\nrequired-imports = [\"from __future__ import annotations\"]\n# ruff format\nforce-single-line = false\nforce-wrap-aliases = false\nsplit-on-trailing-comma = false\n\n[lint.pydocstyle]\nconvention = \"numpy\"\n\n[lint.flake8-pytest-style]\nfixture-parentheses = false\n" }, { "path": "scripts/check.sh", "content": "#!/usr/bin/env sh\nset -ex\n\nuv run ruff format --check bayes_opt tests\nuv run ruff check bayes_opt tests\n" }, { "path": "scripts/check_precommit.sh", "content": "#!/usr/bin/env sh\nset -ex\n\nuv run pre-commit install\nuv run pre-commit run --all-files --show-diff-on-failure\n" }, { "path": "scripts/format.sh", "content": "#!/usr/bin/env sh\nset -ex\n\nuv run ruff format bayes_opt tests\nuv run ruff check bayes_opt --fix\n" }, { "path": "tests/test_acquisition.py", "content": "from __future__ import annotations\n\nimport sys\n\nimport numpy as np\nimport pytest\nfrom scipy.optimize import NonlinearConstraint\nfrom scipy.spatial.distance import pdist\nfrom sklearn.gaussian_process import GaussianProcessRegressor\n\nfrom bayes_opt import BayesianOptimization, acquisition, exception\nfrom bayes_opt.acquisition import (\n ConstantLiar,\n ExpectedImprovement,\n GPHedge,\n ProbabilityOfImprovement,\n UpperConfidenceBound,\n)\nfrom bayes_opt.constraint import ConstraintModel\nfrom bayes_opt.target_space import TargetSpace\n\n\n# Test fixtures\n@pytest.fixture\ndef target_func_x_and_y():\n return lambda x, y: -((x - 1) ** 2) - (y - 2) ** 2\n\n\n@pytest.fixture\ndef pbounds():\n return {\"x\": (-5, 5), \"y\": (-5, 5)}\n\n\n@pytest.fixture\ndef constraint(constraint_func):\n return NonlinearConstraint(fun=constraint_func, lb=-1.0, ub=4.0)\n\n\n@pytest.fixture\ndef target_func():\n return lambda x: sum(x)\n\n\n@pytest.fixture\ndef random_state():\n return np.random.RandomState(0)\n\n\n@pytest.fixture\ndef gp(random_state):\n return GaussianProcessRegressor(random_state=random_state)\n\n\n@pytest.fixture\ndef target_space(target_func):\n return TargetSpace(target_func=target_func, pbounds={\"x\": (1, 4), \"y\": (0, 3.0)})\n\n\n@pytest.fixture\ndef constraint_func():\n return lambda x, y: x + y\n\n\n@pytest.fixture\ndef constrained_target_space(target_func):\n constraint_model = ConstraintModel(fun=lambda params: params[\"x\"] + params[\"y\"], lb=0.0, ub=1.0)\n return TargetSpace(\n target_func=target_func, pbounds={\"x\": (1, 4), \"y\": (0, 3)}, constraint=constraint_model\n )\n\n\nclass MockAcquisition(acquisition.AcquisitionFunction):\n def __init__(self):\n super().__init__()\n\n def _get_acq(self, gp, constraint=None):\n def mock_acq(x: np.ndarray):\n return (3 - x[..., 0]) ** 2 + (1 - x[..., 1]) ** 2\n\n return mock_acq\n\n def base_acq(self, mean, std):\n pass\n\n def get_acquisition_params(self) -> dict:\n return {}\n\n def set_acquisition_params(self, params: dict) -> None:\n pass\n\n\ndef test_acquisition_optimization(gp, target_space):\n acq = MockAcquisition()\n target_space.register(params={\"x\": 2.5, \"y\": 0.5}, target=3.0)\n res = acq.suggest(gp=gp, target_space=target_space)\n assert np.array([3.0, 1.0]) == pytest.approx(res)\n\n with pytest.raises(ValueError):\n acq.suggest(gp=gp, target_space=target_space, n_random=0, n_smart=0)\n\n\ndef test_acquisition_optimization_only_random(gp, target_space, random_state):\n acq = MockAcquisition()\n target_space.register(params={\"x\": 2.5, \"y\": 0.5}, target=3.0)\n res = acq.suggest(gp=gp, target_space=target_space, n_smart=0, n_random=10_000, random_state=random_state)\n # very lenient comparison as we're just considering random samples\n assert np.array([3.0, 1.0]) == pytest.approx(res, abs=1e-1, rel=1e-1)\n\n # make sure that the best random sample is in the seeds\n acq_f = acq._get_acq(gp=gp, constraint=target_space.constraint)\n x_min, _, x_seeds = acq._random_sample_minimize(\n acq_f, target_space, random_state=random_state, n_random=10_000, n_x_seeds=3\n )\n assert x_min in x_seeds\n\n\ndef test_acquisition_optimization_only_l_bfgs_b(gp, target_space):\n acq = MockAcquisition()\n target_space.register(params={\"x\": 2.5, \"y\": 0.5}, target=3.0)\n res = acq.suggest(gp=gp, target_space=target_space, n_smart=10, n_random=0)\n assert np.array([3.0, 1.0]) == pytest.approx(res)\n\n\ndef test_upper_confidence_bound(gp, target_space, random_state):\n acq = acquisition.UpperConfidenceBound(exploration_decay=0.5, exploration_decay_delay=2, kappa=1.0)\n assert acq.kappa == 1.0\n\n # Test that the suggest method raises an error if the GP is unfitted\n with pytest.raises(\n exception.TargetSpaceEmptyError, match=\"Cannot suggest a point without previous samples\"\n ):\n acq.suggest(gp=gp, target_space=target_space)\n\n target_space.register(params={\"x\": 2.5, \"y\": 0.5}, target=3.0)\n acq.suggest(gp=gp, target_space=target_space, random_state=random_state)\n assert acq.kappa == 1.0\n acq.suggest(gp=gp, target_space=target_space, random_state=random_state)\n assert acq.kappa == 0.5\n\n\ndef test_smart_minimize_fails(target_space, random_state):\n acq = acquisition.UpperConfidenceBound()\n\n def fun(x):\n try:\n return np.nan * np.zeros_like(x[:, 0])\n except IndexError:\n return np.nan\n\n _, min_acq_l = acq._smart_minimize(\n fun, space=target_space, x_seeds=np.array([[2.5, 0.5]]), random_state=random_state\n )\n assert min_acq_l == np.inf\n\n\ndef test_upper_confidence_bound_with_constraints(gp, constrained_target_space):\n acq = acquisition.UpperConfidenceBound()\n\n constrained_target_space.register(params={\"x\": 2.5, \"y\": 0.5}, target=3.0, constraint_value=0.5)\n with pytest.raises(exception.ConstraintNotSupportedError):\n acq.suggest(gp=gp, target_space=constrained_target_space)\n\n\ndef test_probability_of_improvement(gp, target_space, random_state):\n acq = acquisition.ProbabilityOfImprovement(exploration_decay=0.5, exploration_decay_delay=2, xi=0.01)\n assert acq.xi == 0.01\n with pytest.raises(ValueError, match=\"y_max is not set\"):\n acq.base_acq(0.0, 0.0)\n\n target_space.register(params={\"x\": 2.5, \"y\": 0.5}, target=3.0)\n acq.suggest(gp=gp, target_space=target_space, random_state=random_state)\n assert acq.xi == 0.01\n acq.suggest(gp=gp, target_space=target_space, random_state=random_state)\n assert acq.xi == 0.005\n\n # no decay\n acq = acquisition.ProbabilityOfImprovement(exploration_decay=None, xi=0.01)\n assert acq.xi == 0.01\n acq.suggest(gp=gp, target_space=target_space, random_state=random_state)\n assert acq.xi == 0.01\n acq.suggest(gp=gp, target_space=target_space, random_state=random_state)\n assert acq.xi == 0.01\n\n\ndef test_probability_of_improvement_with_constraints(gp, constrained_target_space, random_state):\n acq = acquisition.ProbabilityOfImprovement(exploration_decay=0.5, exploration_decay_delay=2, xi=0.01)\n assert acq.xi == 0.01\n with pytest.raises(ValueError, match=\"y_max is not set\"):\n acq.base_acq(0.0, 0.0)\n\n with pytest.raises(exception.TargetSpaceEmptyError):\n acq.suggest(gp=gp, target_space=constrained_target_space, random_state=random_state)\n\n constrained_target_space.register(params={\"x\": 2.5, \"y\": 0.5}, target=3.0, constraint_value=3.0)\n with pytest.raises(exception.NoValidPointRegisteredError):\n acq.suggest(gp=gp, target_space=constrained_target_space, random_state=random_state)\n\n constrained_target_space.register(params={\"x\": 1.0, \"y\": 0.0}, target=1.0, constraint_value=1.0)\n acq.suggest(gp=gp, target_space=constrained_target_space, random_state=random_state)\n\n\ndef test_expected_improvement(gp, target_space, random_state):\n acq = acquisition.ExpectedImprovement(exploration_decay=0.5, exploration_decay_delay=2, xi=0.01)\n assert acq.xi == 0.01\n\n with pytest.raises(ValueError, match=\"y_max is not set\"):\n acq.base_acq(0.0, 0.0)\n\n target_space.register(params={\"x\": 2.5, \"y\": 0.5}, target=3.0)\n acq.suggest(gp=gp, target_space=target_space, random_state=random_state)\n assert acq.xi == 0.01\n acq.suggest(gp=gp, target_space=target_space, random_state=random_state)\n assert acq.xi == 0.005\n\n acq = acquisition.ExpectedImprovement(exploration_decay=None, xi=0.01)\n assert acq.xi == 0.01\n acq.suggest(gp=gp, target_space=target_space, random_state=random_state)\n assert acq.xi == 0.01\n acq.suggest(gp=gp, target_space=target_space, random_state=random_state)\n assert acq.xi == 0.01\n\n\ndef test_expected_improvement_with_constraints(gp, constrained_target_space, random_state):\n acq = acquisition.ExpectedImprovement(exploration_decay=0.5, exploration_decay_delay=2, xi=0.01)\n assert acq.xi == 0.01\n with pytest.raises(ValueError, match=\"y_max is not set\"):\n acq.base_acq(0.0, 0.0)\n\n with pytest.raises(exception.TargetSpaceEmptyError):\n acq.suggest(gp=gp, target_space=constrained_target_space, random_state=random_state)\n\n constrained_target_space.register(params={\"x\": 2.5, \"y\": 0.5}, target=3.0, constraint_value=3.0)\n with pytest.raises(exception.NoValidPointRegisteredError):\n acq.suggest(gp=gp, target_space=constrained_target_space, random_state=random_state)\n\n constrained_target_space.register(params={\"x\": 1.0, \"y\": 0.0}, target=1.0, constraint_value=1.0)\n acq.suggest(gp=gp, target_space=constrained_target_space, random_state=random_state)\n\n\n@pytest.mark.parametrize(\"strategy\", [0.0, \"mean\", \"min\", \"max\"])\ndef test_constant_liar(gp, target_space, target_func, random_state, strategy):\n base_acq = acquisition.UpperConfidenceBound()\n acq = acquisition.ConstantLiar(base_acquisition=base_acq, strategy=strategy)\n\n target_space.register(params={\"x\": 2.5, \"y\": 0.5}, target=3.0)\n target_space.register(params={\"x\": 1.0, \"y\": 1.5}, target=2.5)\n base_samples = np.array([base_acq.suggest(gp=gp, target_space=target_space) for _ in range(10)])\n samples = []\n\n assert len(acq.dummies) == 0\n for _ in range(10):\n samples.append(acq.suggest(gp=gp, target_space=target_space, random_state=random_state))\n assert len(acq.dummies) == len(samples)\n\n samples = np.array(samples)\n print(samples)\n\n base_distance = pdist(base_samples, \"sqeuclidean\").mean()\n distance = pdist(samples, \"sqeuclidean\").mean()\n\n assert base_distance < distance\n\n for i in range(10):\n target_space.register(params={\"x\": samples[i][0], \"y\": samples[i][1]}, target=target_func(samples[i]))\n\n acq.suggest(gp=gp, target_space=target_space, random_state=random_state)\n\n assert len(acq.dummies) == 1\n\n\ndef test_constant_liar_invalid_strategy():\n with pytest.raises(ValueError):\n acquisition.ConstantLiar(acquisition.UpperConfidenceBound, strategy=\"definitely-an-invalid-strategy\")\n\n\ndef test_constant_liar_with_constraints(gp, constrained_target_space, random_state):\n base_acq = acquisition.UpperConfidenceBound()\n acq = acquisition.ConstantLiar(base_acquisition=base_acq)\n\n with pytest.raises(exception.TargetSpaceEmptyError):\n acq.suggest(gp=gp, target_space=constrained_target_space, random_state=random_state)\n\n constrained_target_space.register(params={\"x\": 2.5, \"y\": 0.5}, target=3.0, constraint_value=0.5)\n with pytest.raises(exception.ConstraintNotSupportedError):\n acq.suggest(gp=gp, target_space=constrained_target_space, random_state=random_state)\n\n mean = random_state.rand(10)\n std = random_state.rand(10)\n assert (base_acq.base_acq(mean, std) == acq.base_acq(mean, std)).all()\n\n\ndef test_gp_hedge(random_state):\n acq = acquisition.GPHedge(base_acquisitions=[acquisition.UpperConfidenceBound()])\n with pytest.raises(TypeError, match=\"GPHedge base acquisition function is ambiguous\"):\n acq.base_acq(0.0, 0.0)\n\n base_acq1 = acquisition.UpperConfidenceBound()\n base_acq2 = acquisition.ProbabilityOfImprovement(xi=0.01)\n base_acquisitions = [base_acq1, base_acq2]\n acq = acquisition.GPHedge(base_acquisitions=base_acquisitions)\n\n mean = random_state.rand(10)\n std = random_state.rand(10)\n\n base_acq2.y_max = 1.0\n assert (acq.base_acquisitions[0].base_acq(mean, std) == base_acq1.base_acq(mean, std)).all()\n assert (acq.base_acquisitions[1].base_acq(mean, std) == base_acq2.base_acq(mean, std)).all()\n\n\ndef test_gphedge_update_gains(random_state):\n base_acq1 = acquisition.UpperConfidenceBound()\n base_acq2 = acquisition.ProbabilityOfImprovement(xi=0.01)\n base_acquisitions = [base_acq1, base_acq2]\n\n acq = acquisition.GPHedge(base_acquisitions=base_acquisitions)\n\n class MockGP1:\n def __init__(self, n):\n self.gains = np.zeros(n)\n\n def predict(self, x):\n rng = np.random.default_rng()\n res = rng.random(x.shape[0], np.float64)\n self.gains += res\n return res\n\n mock_gp = MockGP1(len(base_acquisitions))\n for _ in range(10):\n acq.previous_candidates = np.zeros(len(base_acquisitions))\n acq._update_gains(mock_gp)\n assert (mock_gp.gains == acq.gains).all()\n\n\ndef test_gphedge_softmax_sampling(random_state):\n base_acq1 = acquisition.UpperConfidenceBound()\n base_acq2 = acquisition.ProbabilityOfImprovement(xi=0.01)\n base_acquisitions = [base_acq1, base_acq2]\n\n acq = acquisition.GPHedge(base_acquisitions=base_acquisitions)\n\n class MockGP2:\n def __init__(self, good_index=0):\n self.good_index = good_index\n\n def predict(self, x):\n print(x)\n res = -np.inf * np.ones_like(x)\n res[self.good_index] = 1.0\n return res\n\n for good_index in [0, 1]:\n acq = acquisition.GPHedge(base_acquisitions=base_acquisitions)\n acq.previous_candidates = np.zeros(len(base_acquisitions))\n acq._update_gains(MockGP2(good_index=good_index))\n assert good_index == acq._sample_idx_from_softmax_gains(random_state=random_state)\n\n\ndef test_gphedge_integration(gp, target_space, random_state):\n base_acq1 = acquisition.UpperConfidenceBound()\n base_acq2 = acquisition.ProbabilityOfImprovement(xi=0.01)\n base_acquisitions = [base_acq1, base_acq2]\n\n acq = acquisition.GPHedge(base_acquisitions=base_acquisitions)\n assert acq.base_acquisitions == base_acquisitions\n with pytest.raises(exception.TargetSpaceEmptyError):\n acq.suggest(gp=gp, target_space=target_space, random_state=random_state)\n target_space.register(params={\"x\": 2.5, \"y\": 0.5}, target=3.0)\n\n for _ in range(5):\n p = acq.suggest(gp=gp, target_space=target_space, random_state=random_state)\n target_space.register(p, sum(p))\n\n\n@pytest.mark.parametrize(\"kappa\", [-1.0, -sys.float_info.epsilon, -np.inf])\ndef test_upper_confidence_bound_invalid_kappa_error(kappa: float):\n with pytest.raises(ValueError, match=\"kappa must be greater than or equal to 0.\"):\n acquisition.UpperConfidenceBound(kappa=kappa)\n\n\n@pytest.mark.parametrize(\"exploration_decay\", [-0.1, 0.0, 1.1, 2.0, np.inf])\ndef test_upper_confidence_bound_invalid_exploration_decay_error(exploration_decay: float):\n with pytest.raises(\n ValueError, match=\"exploration_decay must be greater than 0 and less than or equal to 1.\"\n ):\n acquisition.UpperConfidenceBound(kappa=1.0, exploration_decay=exploration_decay)\n\n\n@pytest.mark.parametrize(\"exploration_decay_delay\", [-1, -10, \"not_an_int\", 1.5])\ndef test_upper_confidence_bound_invalid_exploration_decay_delay_error(exploration_decay_delay):\n with pytest.raises(\n ValueError, match=\"exploration_decay_delay must be an integer greater than or equal to 0.\"\n ):\n acquisition.UpperConfidenceBound(kappa=1.0, exploration_decay_delay=exploration_decay_delay)\n\n\n@pytest.mark.parametrize(\"xi\", [-0.1, -1.0, -np.inf])\ndef test_probability_of_improvement_invalid_xi_error(xi: float):\n with pytest.raises(ValueError, match=\"xi must be greater than or equal to 0.\"):\n acquisition.ProbabilityOfImprovement(xi=xi)\n\n\n@pytest.mark.parametrize(\"exploration_decay\", [-0.1, 0.0, 1.1, 2.0, np.inf])\ndef test_probability_of_improvement_invalid_exploration_decay_error(exploration_decay: float):\n with pytest.raises(\n ValueError, match=\"exploration_decay must be greater than 0 and less than or equal to 1.\"\n ):\n acquisition.ProbabilityOfImprovement(xi=0.01, exploration_decay=exploration_decay)\n\n\n@pytest.mark.parametrize(\"exploration_decay_delay\", [-1, -10, \"not_an_int\", 1.5])\ndef test_probability_of_improvement_invalid_exploration_decay_delay_error(exploration_decay_delay):\n with pytest.raises(\n ValueError, match=\"exploration_decay_delay must be an integer greater than or equal to 0.\"\n ):\n acquisition.ProbabilityOfImprovement(xi=0.01, exploration_decay_delay=exploration_decay_delay)\n\n\n@pytest.mark.parametrize(\"xi\", [-0.1, -1.0, -np.inf])\ndef test_expected_improvement_invalid_xi_error(xi: float):\n with pytest.raises(ValueError, match=\"xi must be greater than or equal to 0.\"):\n acquisition.ExpectedImprovement(xi=xi)\n\n\n@pytest.mark.parametrize(\"exploration_decay\", [-0.1, 0.0, 1.1, 2.0, np.inf])\ndef test_expected_improvement_invalid_exploration_decay_error(exploration_decay: float):\n with pytest.raises(\n ValueError, match=\"exploration_decay must be greater than 0 and less than or equal to 1.\"\n ):\n acquisition.ExpectedImprovement(xi=0.01, exploration_decay=exploration_decay)\n\n\n@pytest.mark.parametrize(\"exploration_decay_delay\", [-1, -10, \"not_an_int\", 1.5])\ndef test_expected_improvement_invalid_exploration_decay_delay_error(exploration_decay_delay):\n with pytest.raises(\n ValueError, match=\"exploration_decay_delay must be an integer greater than or equal to 0.\"\n ):\n acquisition.ExpectedImprovement(xi=0.01, exploration_decay_delay=exploration_decay_delay)\n\n\ndef verify_optimizers_match(optimizer1, optimizer2):\n \"\"\"Helper function to verify two optimizers match.\"\"\"\n assert len(optimizer1.space) == len(optimizer2.space)\n assert optimizer1.max[\"target\"] == optimizer2.max[\"target\"]\n assert optimizer1.max[\"params\"] == optimizer2.max[\"params\"]\n\n np.testing.assert_array_equal(optimizer1.space.params, optimizer2.space.params)\n np.testing.assert_array_equal(optimizer1.space.target, optimizer2.space.target)\n\n if optimizer1.is_constrained:\n np.testing.assert_array_equal(\n optimizer1.space._constraint_values, optimizer2.space._constraint_values\n )\n assert optimizer1.space._constraint.lb == optimizer2.space._constraint.lb\n assert optimizer1.space._constraint.ub == optimizer2.space._constraint.ub\n\n rng = np.random.default_rng()\n assert rng.bit_generator.state[\"state\"][\"state\"] == rng.bit_generator.state[\"state\"][\"state\"]\n\n kernel_params1 = optimizer1._gp.kernel.get_params()\n kernel_params2 = optimizer2._gp.kernel.get_params()\n for k in kernel_params1:\n assert (np.array(kernel_params1[k]) == np.array(kernel_params2[k])).all()\n\n suggestion1 = optimizer1.suggest()\n suggestion2 = optimizer2.suggest()\n assert suggestion1 == suggestion2, f\"\\nSuggestion 1: {suggestion1}\\nSuggestion 2: {suggestion2}\"\n\n\n@pytest.mark.parametrize(\n (\"acquisition_fn_factory\", \"state_filename\"),\n [\n (lambda: UpperConfidenceBound(kappa=2.576), \"ucb_state.json\"),\n (lambda: ProbabilityOfImprovement(xi=0.01), \"pi_state.json\"),\n (lambda: ExpectedImprovement(xi=0.01), \"ei_state.json\"),\n (lambda: ConstantLiar(base_acquisition=UpperConfidenceBound(kappa=2.576)), \"cl_state.json\"),\n (\n lambda: GPHedge(\n base_acquisitions=[\n UpperConfidenceBound(kappa=2.576),\n ProbabilityOfImprovement(xi=0.01),\n ExpectedImprovement(xi=0.01),\n ]\n ),\n \"gphedge_state.json\",\n ),\n ],\n)\ndef test_integration_acquisition_functions(\n acquisition_fn_factory, state_filename, target_func_x_and_y, pbounds, tmp_path\n):\n \"\"\"Parametrized integration test for acquisition functions.\"\"\"\n acquisition_function = acquisition_fn_factory()\n\n optimizer = BayesianOptimization(\n f=target_func_x_and_y,\n pbounds=pbounds,\n acquisition_function=acquisition_function,\n random_state=1,\n verbose=0,\n )\n optimizer.maximize(init_points=2, n_iter=3)\n\n state_path = tmp_path / state_filename\n optimizer.save_state(state_path)\n\n new_optimizer = BayesianOptimization(\n f=target_func_x_and_y,\n pbounds=pbounds,\n acquisition_function=acquisition_fn_factory(),\n random_state=1,\n verbose=0,\n )\n new_optimizer.load_state(state_path)\n\n verify_optimizers_match(optimizer, new_optimizer)\n\n\ndef test_integration_constrained(target_func_x_and_y, pbounds, constraint, tmp_path):\n \"\"\"Test save/load integration with constraints.\"\"\"\n acquisition_function = ExpectedImprovement(xi=0.01)\n\n optimizer = BayesianOptimization(\n f=target_func_x_and_y,\n pbounds=pbounds,\n acquisition_function=acquisition_function,\n constraint=constraint,\n random_state=1,\n verbose=0,\n )\n optimizer.maximize(init_points=2, n_iter=3)\n\n state_path = tmp_path / \"constrained_state.json\"\n optimizer.save_state(state_path)\n\n new_optimizer = BayesianOptimization(\n f=target_func_x_and_y,\n pbounds=pbounds,\n acquisition_function=ExpectedImprovement(xi=0.01),\n constraint=constraint,\n random_state=1,\n verbose=0,\n )\n new_optimizer.load_state(state_path)\n\n verify_optimizers_match(optimizer, new_optimizer)\n\n\ndef test_custom_acquisition_without_get_params():\n \"\"\"Test that a custom acquisition function without get_acquisition_params raises NotImplementedError.\"\"\"\n\n class CustomAcqWithoutGetParams(acquisition.AcquisitionFunction):\n def __init__(self):\n super().__init__()\n\n def base_acq(self, mean, std):\n return mean + std\n\n def set_acquisition_params(self, params):\n pass\n\n acq = CustomAcqWithoutGetParams()\n with pytest.raises(\n NotImplementedError,\n match=\"Custom AcquisitionFunction subclasses must implement their own get_acquisition_params method\",\n ):\n acq.get_acquisition_params()\n\n\ndef test_custom_acquisition_without_set_params():\n \"\"\"Test that a custom acquisition function without set_acquisition_params raises NotImplementedError.\"\"\"\n\n class CustomAcqWithoutSetParams(acquisition.AcquisitionFunction):\n def __init__(self):\n super().__init__()\n\n def base_acq(self, mean, std):\n return mean + std\n\n def get_acquisition_params(self):\n return {}\n\n acq = CustomAcqWithoutSetParams()\n with pytest.raises(\n NotImplementedError,\n match=\"Custom AcquisitionFunction subclasses must implement their own set_acquisition_params method\",\n ):\n acq.set_acquisition_params(params={})\n" }, { "path": "tests/test_bayesian_optimization.py", "content": "from __future__ import annotations\n\nimport pickle\nfrom pathlib import Path\n\nimport numpy as np\nimport pytest\nfrom scipy.optimize import NonlinearConstraint\n\nfrom bayes_opt import BayesianOptimization, acquisition\nfrom bayes_opt.acquisition import AcquisitionFunction\nfrom bayes_opt.domain_reduction import SequentialDomainReductionTransformer\nfrom bayes_opt.exception import NotUniqueError\nfrom bayes_opt.parameter import BayesParameter\nfrom bayes_opt.target_space import TargetSpace\nfrom bayes_opt.util import ensure_rng\n\n\nclass FixedPerimeterTriangleParameter(BayesParameter):\n def __init__(self, name: str, bounds, perimeter) -> None:\n super().__init__(name, bounds)\n self.perimeter = perimeter\n\n @property\n def is_continuous(self):\n return True\n\n def random_sample(self, n_samples: int, random_state):\n random_state = ensure_rng(random_state)\n samples = []\n while len(samples) < n_samples:\n samples_ = random_state.dirichlet(np.ones(3), n_samples)\n samples_ = samples_ * self.perimeter # scale samples by perimeter\n\n samples_ = samples_[\n np.all((self.bounds[:, 0] <= samples_) & (samples_ <= self.bounds[:, 1]), axis=-1)\n ]\n samples.extend(np.atleast_2d(samples_))\n return np.array(samples[:n_samples])\n\n def to_float(self, value):\n return value\n\n def to_param(self, value):\n return value * self.perimeter / sum(value)\n\n def kernel_transform(self, value):\n return value * self.perimeter / np.sum(value, axis=-1, keepdims=True)\n\n def to_string(self, value, str_len: int) -> str:\n len_each = (str_len - 2) // 3\n str_ = \"|\".join([f\"{float(np.round(value[i], 4))}\"[:len_each] for i in range(3)])\n return str_.ljust(str_len)\n\n @property\n def dim(self):\n return 3 # as we have three float values, each representing the length of one side.\n\n\ndef area_of_triangle(sides):\n a, b, c = sides\n s = np.sum(sides, axis=-1) # perimeter\n return np.sqrt(s * (s - a) * (s - b) * (s - c))\n\n\ndef target_func(**kwargs):\n # arbitrary target func\n return sum(kwargs.values())\n\n\nPBOUNDS = {\"p1\": (0, 10), \"p2\": (0, 10)}\n\n\ndef test_properties():\n optimizer = BayesianOptimization(target_func, PBOUNDS, random_state=1)\n assert isinstance(optimizer.space, TargetSpace)\n assert isinstance(optimizer.acquisition_function, AcquisitionFunction)\n # constraint present tested in test_constraint.py\n assert optimizer.constraint is None\n\n\ndef test_register():\n optimizer = BayesianOptimization(target_func, PBOUNDS, random_state=1)\n assert len(optimizer.space) == 0\n\n optimizer.register(params={\"p1\": 1, \"p2\": 2}, target=3)\n assert len(optimizer.res) == 1\n assert len(optimizer.space) == 1\n\n optimizer.space.register(params=np.array([5, 4]), target=9)\n assert len(optimizer.res) == 2\n assert len(optimizer.space) == 2\n\n with pytest.raises(NotUniqueError):\n optimizer.register(params={\"p1\": 1, \"p2\": 2}, target=3)\n with pytest.raises(NotUniqueError):\n optimizer.register(params={\"p1\": 5, \"p2\": 4}, target=9)\n\n\ndef test_probe_lazy():\n optimizer = BayesianOptimization(target_func, PBOUNDS, random_state=1)\n\n optimizer.probe(params={\"p1\": 1, \"p2\": 2}, lazy=True)\n assert len(optimizer.space) == 0\n assert len(optimizer._queue) == 1\n\n optimizer.probe(params={\"p1\": 6, \"p2\": 2}, lazy=True)\n assert len(optimizer.space) == 0\n assert len(optimizer._queue) == 2\n\n optimizer.probe(params={\"p1\": 6, \"p2\": 2}, lazy=True)\n assert len(optimizer.space) == 0\n assert len(optimizer._queue) == 3\n\n\ndef test_probe_eager():\n optimizer = BayesianOptimization(target_func, PBOUNDS, random_state=1, allow_duplicate_points=True)\n\n optimizer.probe(params={\"p1\": 1, \"p2\": 2}, lazy=False)\n assert len(optimizer.space) == 1\n assert len(optimizer._queue) == 0\n assert optimizer.max[\"target\"] == 3\n assert optimizer.max[\"params\"] == {\"p1\": 1, \"p2\": 2}\n\n optimizer.probe(params={\"p1\": 3, \"p2\": 3}, lazy=False)\n assert len(optimizer.space) == 2\n assert len(optimizer._queue) == 0\n assert optimizer.max[\"target\"] == 6\n assert optimizer.max[\"params\"] == {\"p1\": 3, \"p2\": 3}\n\n optimizer.probe(params={\"p1\": 3, \"p2\": 3}, lazy=False)\n assert len(optimizer.space) == 3\n assert len(optimizer._queue) == 0\n assert optimizer.max[\"target\"] == 6\n assert optimizer.max[\"params\"] == {\"p1\": 3, \"p2\": 3}\n\n\ndef test_suggest_at_random():\n acq = acquisition.ProbabilityOfImprovement(xi=0)\n optimizer = BayesianOptimization(target_func, PBOUNDS, acq, random_state=1)\n\n for _ in range(50):\n sample = optimizer.space.params_to_array(optimizer.suggest())\n assert len(sample) == optimizer.space.dim\n assert all(sample >= optimizer.space.bounds[:, 0])\n assert all(sample <= optimizer.space.bounds[:, 1])\n\n\ndef test_suggest_with_one_observation():\n acq = acquisition.UpperConfidenceBound(kappa=5)\n optimizer = BayesianOptimization(target_func, PBOUNDS, acq, random_state=1)\n\n optimizer.register(params={\"p1\": 1, \"p2\": 2}, target=3)\n\n for _ in range(5):\n sample = optimizer.space.params_to_array(optimizer.suggest())\n assert len(sample) == optimizer.space.dim\n assert all(sample >= optimizer.space.bounds[:, 0])\n assert all(sample <= optimizer.space.bounds[:, 1])\n\n # suggestion = optimizer.suggest(util)\n # for _ in range(5):\n # new_suggestion = optimizer.suggest(util)\n # assert suggestion == new_suggestion\n\n\ndef test_prime_queue_all_empty():\n optimizer = BayesianOptimization(target_func, PBOUNDS, random_state=1)\n assert len(optimizer._queue) == 0\n assert len(optimizer.space) == 0\n\n optimizer._prime_queue(init_points=0)\n assert len(optimizer._queue) == 1\n assert len(optimizer.space) == 0\n\n\ndef test_prime_queue_empty_with_init():\n optimizer = BayesianOptimization(target_func, PBOUNDS, random_state=1)\n assert len(optimizer._queue) == 0\n assert len(optimizer.space) == 0\n\n optimizer._prime_queue(init_points=5)\n assert len(optimizer._queue) == 5\n assert len(optimizer.space) == 0\n\n\ndef test_prime_queue_with_register():\n optimizer = BayesianOptimization(target_func, PBOUNDS, random_state=1)\n assert len(optimizer._queue) == 0\n assert len(optimizer.space) == 0\n\n optimizer.register(params={\"p1\": 1, \"p2\": 2}, target=3)\n optimizer._prime_queue(init_points=0)\n assert len(optimizer._queue) == 0\n assert len(optimizer.space) == 1\n\n\ndef test_prime_queue_with_register_and_init():\n optimizer = BayesianOptimization(target_func, PBOUNDS, random_state=1)\n assert len(optimizer._queue) == 0\n assert len(optimizer.space) == 0\n\n optimizer.register(params={\"p1\": 1, \"p2\": 2}, target=3)\n optimizer._prime_queue(init_points=3)\n assert len(optimizer._queue) == 3\n assert len(optimizer.space) == 1\n\n\ndef test_set_bounds():\n pbounds = {\"p1\": (0, 1), \"p3\": (0, 3), \"p2\": (0, 2), \"p4\": (0, 4)}\n optimizer = BayesianOptimization(target_func, pbounds, random_state=1)\n\n # Ignore unknown keys\n optimizer.set_bounds({\"other\": (7, 8)})\n assert all(optimizer.space.bounds[:, 0] == np.array([0, 0, 0, 0]))\n assert all(optimizer.space.bounds[:, 1] == np.array([1, 3, 2, 4]))\n\n # Update bounds accordingly\n optimizer.set_bounds({\"p2\": (1, 8)})\n assert all(optimizer.space.bounds[:, 0] == np.array([0, 0, 1, 0]))\n assert all(optimizer.space.bounds[:, 1] == np.array([1, 3, 8, 4]))\n\n\ndef test_set_gp_params():\n optimizer = BayesianOptimization(target_func, PBOUNDS, random_state=1)\n assert optimizer._gp.alpha == 1e-6\n assert optimizer._gp.n_restarts_optimizer == 5\n\n optimizer.set_gp_params(alpha=1e-2)\n assert optimizer._gp.alpha == 1e-2\n assert optimizer._gp.n_restarts_optimizer == 5\n\n optimizer.set_gp_params(n_restarts_optimizer=7)\n assert optimizer._gp.alpha == 1e-2\n assert optimizer._gp.n_restarts_optimizer == 7\n\n\ndef test_maximize():\n acq = acquisition.UpperConfidenceBound()\n optimizer = BayesianOptimization(\n target_func, PBOUNDS, acq, random_state=np.random.RandomState(1), allow_duplicate_points=True\n )\n\n # Test initial maximize with no init_points and n_iter\n optimizer.maximize(init_points=0, n_iter=0)\n assert not optimizer._queue\n assert len(optimizer.space) == 1 # Even with no init_points, we should have at least one point\n\n # Test after setting GP parameters\n optimizer.set_gp_params(alpha=1e-2)\n optimizer.maximize(init_points=2, n_iter=0)\n assert not optimizer._queue\n assert len(optimizer.space) == 3 # Previously had 1, add 2 more from init_points\n assert optimizer._gp.alpha == 1e-2\n\n # Test with additional iterations\n optimizer.maximize(init_points=0, n_iter=2)\n assert not optimizer._queue\n assert len(optimizer.space) == 5 # Previously had 3, add 2 more from n_iter\n\n\ndef test_define_wrong_transformer():\n with pytest.raises(TypeError):\n BayesianOptimization(\n target_func, PBOUNDS, random_state=np.random.RandomState(1), bounds_transformer=3\n )\n\n\ndef test_single_value_objective():\n \"\"\"\n As documented [here](https://github.com/scipy/scipy/issues/16898)\n scipy is changing the way they handle 1D objectives inside minimize.\n This is a simple test to make sure our tests fail if scipy updates this\n in future\n \"\"\"\n pbounds = {\"x\": (-10, 10)}\n\n optimizer = BayesianOptimization(f=lambda x: x * 3, pbounds=pbounds, verbose=2, random_state=1)\n optimizer.maximize(init_points=2, n_iter=3)\n\n\ndef test_pickle():\n \"\"\"\n several users have asked that the BO object be 'pickalable'\n This tests that this is the case\n \"\"\"\n optimizer = BayesianOptimization(f=None, pbounds={\"x\": (-10, 10)}, verbose=2, random_state=1)\n test_dump = Path(\"test_dump.obj\")\n with test_dump.open(\"wb\") as filehandler:\n pickle.dump(optimizer, filehandler)\n test_dump.unlink()\n\n\ndef test_duplicate_points():\n \"\"\"\n The default behavior of this code is to not enable duplicate points in the target space,\n however there are situations in which you may want this, particularly optimization in high\n noise situations. In that case one can set allow_duplicate_points to be True.\n This tests the behavior of the code around duplicate points under several scenarios\n \"\"\"\n # test manual registration of duplicate points (should generate error)\n acq = acquisition.UpperConfidenceBound(kappa=5.0) # kappa determines explore/Exploitation ratio\n optimizer = BayesianOptimization(f=None, pbounds={\"x\": (-2, 2)}, acquisition_function=acq, random_state=1)\n next_point_to_probe = optimizer.suggest()\n target = 1\n # register once (should work)\n optimizer.register(params=next_point_to_probe, target=target)\n # register twice (should throw error)\n try:\n optimizer.register(params=next_point_to_probe, target=target)\n duplicate_point_error = None # should be overwritten below\n except Exception as e:\n duplicate_point_error = e\n\n assert isinstance(duplicate_point_error, NotUniqueError)\n\n # OK, now let's test that it DOESNT fail when allow_duplicate_points=True\n optimizer = BayesianOptimization(\n f=None, pbounds={\"x\": (-2, 2)}, random_state=1, allow_duplicate_points=True\n )\n optimizer.register(params=next_point_to_probe, target=target)\n # and again (should throw warning)\n optimizer.register(params=next_point_to_probe, target=target)\n\n\ndef test_save_load_state(tmp_path):\n \"\"\"Test saving and loading optimizer state.\"\"\"\n # Initialize and run original optimizer\n optimizer = BayesianOptimization(f=target_func, pbounds=PBOUNDS, random_state=1, verbose=0)\n optimizer.maximize(init_points=2, n_iter=3)\n\n # Save state\n state_path = tmp_path / \"optimizer_state.json\"\n optimizer.save_state(state_path)\n\n # Create new optimizer and load state\n new_optimizer = BayesianOptimization(f=target_func, pbounds=PBOUNDS, random_state=1, verbose=0)\n new_optimizer.load_state(state_path)\n\n # Test that key properties match\n assert len(optimizer.space) == len(new_optimizer.space)\n assert optimizer.max[\"target\"] == new_optimizer.max[\"target\"]\n assert optimizer.max[\"params\"] == new_optimizer.max[\"params\"]\n np.testing.assert_array_equal(optimizer.space.params, new_optimizer.space.params)\n np.testing.assert_array_equal(optimizer.space.target, new_optimizer.space.target)\n\n\ndef test_save_load_w_categorical_params(tmp_path):\n \"\"\"Test saving and loading optimizer state with categorical parameters.\"\"\"\n\n def str_target_func(param1: str, param2: str) -> float:\n # Simple function that maps strings to numbers\n value_map = {\"low\": 1.0, \"medium\": 2.0, \"high\": 3.0}\n return value_map[param1] + value_map[param2]\n\n str_pbounds = {\"param1\": [\"low\", \"medium\", \"high\"], \"param2\": [\"low\", \"medium\", \"high\"]}\n\n optimizer = BayesianOptimization(f=str_target_func, pbounds=str_pbounds, random_state=1, verbose=0)\n\n optimizer.maximize(init_points=2, n_iter=3)\n\n state_path = tmp_path / \"optimizer_state.json\"\n optimizer.save_state(state_path)\n\n new_optimizer = BayesianOptimization(f=str_target_func, pbounds=str_pbounds, random_state=1, verbose=0)\n new_optimizer.load_state(state_path)\n\n assert len(optimizer.space) == len(new_optimizer.space)\n assert optimizer.max[\"target\"] == new_optimizer.max[\"target\"]\n assert optimizer.max[\"params\"] == new_optimizer.max[\"params\"]\n for i in range(len(optimizer.space)):\n assert isinstance(optimizer.res[i][\"params\"][\"param1\"], str)\n assert isinstance(optimizer.res[i][\"params\"][\"param2\"], str)\n assert isinstance(new_optimizer.res[i][\"params\"][\"param1\"], str)\n assert isinstance(new_optimizer.res[i][\"params\"][\"param2\"], str)\n assert optimizer.res[i][\"params\"] == new_optimizer.res[i][\"params\"]\n\n\ndef test_suggest_point_returns_same_point(tmp_path):\n \"\"\"Check that suggest returns same point after save/load.\"\"\"\n optimizer = BayesianOptimization(f=target_func, pbounds=PBOUNDS, random_state=1, verbose=0)\n optimizer.maximize(init_points=2, n_iter=3)\n\n state_path = tmp_path / \"optimizer_state.json\"\n optimizer.save_state(state_path)\n\n new_optimizer = BayesianOptimization(f=target_func, pbounds=PBOUNDS, random_state=1, verbose=0)\n new_optimizer.load_state(state_path)\n\n # Both optimizers should suggest the same point\n suggestion1 = optimizer.suggest()\n suggestion2 = new_optimizer.suggest()\n assert suggestion1 == suggestion2\n\n\ndef test_save_load_random_state(tmp_path):\n \"\"\"Test that random state is properly preserved.\"\"\"\n # Initialize optimizer\n optimizer = BayesianOptimization(f=target_func, pbounds=PBOUNDS, random_state=1, verbose=0)\n\n # Register a point before saving\n optimizer.probe(params={\"p1\": 1, \"p2\": 2}, lazy=False)\n\n # Save state\n state_path = tmp_path / \"optimizer_state.json\"\n optimizer.save_state(state_path)\n\n # Create new optimizer with same configuration\n new_optimizer = BayesianOptimization(f=target_func, pbounds=PBOUNDS, random_state=1, verbose=0)\n new_optimizer.load_state(state_path)\n\n # Both optimizers should suggest the same point\n suggestion1 = optimizer.suggest()\n suggestion2 = new_optimizer.suggest()\n assert suggestion1 == suggestion2\n\n\ndef test_save_load_unused_optimizer(tmp_path):\n \"\"\"Test saving and loading optimizer state with unused optimizer.\"\"\"\n optimizer = BayesianOptimization(f=target_func, pbounds=PBOUNDS, random_state=1, verbose=0)\n\n # Test that saving without samples does not raise an error\n optimizer.save_state(tmp_path / \"unprobed_optimizer_state.json\")\n\n # Check that we load the original state\n first_suggestion = optimizer.suggest()\n optimizer.load_state(tmp_path / \"unprobed_optimizer_state.json\")\n assert optimizer.suggest() == first_suggestion\n\n # Save an optimizer state with a probed point\n optimizer.probe(params={\"p1\": 1, \"p2\": 2}, lazy=False)\n optimizer.save_state(tmp_path / \"optimizer_state.json\")\n\n new_optimizer = BayesianOptimization(f=target_func, pbounds=PBOUNDS, random_state=1, verbose=0)\n new_optimizer.load_state(tmp_path / \"optimizer_state.json\")\n\n assert len(optimizer.space) == len(new_optimizer.space)\n assert optimizer.max[\"target\"] == new_optimizer.max[\"target\"]\n assert optimizer.max[\"params\"] == new_optimizer.max[\"params\"]\n np.testing.assert_array_equal(optimizer.space.params, new_optimizer.space.params)\n np.testing.assert_array_equal(optimizer.space.target, new_optimizer.space.target)\n\n \"\"\"Test saving and loading optimizer state with constraints.\"\"\"\n\n def constraint_func(x: float, y: float) -> float:\n return x + y # Simple constraint: sum of parameters should be within bounds\n\n constraint = NonlinearConstraint(fun=constraint_func, lb=0.0, ub=3.0)\n\n # Initialize optimizer with constraint\n optimizer = BayesianOptimization(\n f=target_func, pbounds={\"x\": (-1, 3), \"y\": (0, 5)}, constraint=constraint, random_state=1, verbose=0\n )\n\n # Register some points, some that satisfy constraint and some that don't\n optimizer.register(\n params={\"x\": 1.0, \"y\": 1.0}, # Satisfies constraint: sum = 2.0\n target=2.0,\n constraint_value=2.0,\n )\n optimizer.register(\n params={\"x\": 2.0, \"y\": 2.0}, # Violates constraint: sum = 4.0\n target=4.0,\n constraint_value=4.0,\n )\n optimizer.register(\n params={\"x\": 0.5, \"y\": 0.5}, # Satisfies constraint: sum = 1.0\n target=1.0,\n constraint_value=1.0,\n )\n\n state_path = tmp_path / \"optimizer_state.json\"\n optimizer.save_state(state_path)\n\n new_optimizer = BayesianOptimization(\n f=target_func, pbounds={\"x\": (-1, 3), \"y\": (0, 5)}, constraint=constraint, random_state=1, verbose=0\n )\n new_optimizer.load_state(state_path)\n\n # Test that key properties match\n assert len(optimizer.space) == len(new_optimizer.space)\n assert optimizer.max[\"target\"] == new_optimizer.max[\"target\"]\n assert optimizer.max[\"params\"] == new_optimizer.max[\"params\"]\n np.testing.assert_array_equal(optimizer.space.params, new_optimizer.space.params)\n np.testing.assert_array_equal(optimizer.space.target, new_optimizer.space.target)\n\n # Test that constraint values were properly saved and loaded\n np.testing.assert_array_equal(optimizer.space._constraint_values, new_optimizer.space._constraint_values)\n\n # Test that both optimizers suggest the same point (should respect constraints)\n suggestion1 = optimizer.suggest()\n suggestion2 = new_optimizer.suggest()\n assert suggestion1 == suggestion2\n\n # Verify that suggested point satisfies constraint\n constraint_value = constraint_func(**suggestion1)\n assert 0.0 <= constraint_value <= 3.0, \"Suggested point violates constraint\"\n\n\ndef test_save_load_w_domain_reduction(tmp_path):\n \"\"\"Test saving and loading optimizer state with domain reduction transformer.\"\"\"\n # Initialize optimizer with bounds transformer\n bounds_transformer = SequentialDomainReductionTransformer()\n optimizer = BayesianOptimization(\n f=target_func, pbounds=PBOUNDS, random_state=1, verbose=0, bounds_transformer=bounds_transformer\n )\n\n # Run some iterations to trigger domain reduction\n optimizer.maximize(init_points=2, n_iter=3)\n\n # Save state\n state_path = tmp_path / \"optimizer_state.json\"\n optimizer.save_state(state_path)\n\n # Create new optimizer with same configuration\n new_bounds_transformer = SequentialDomainReductionTransformer()\n new_optimizer = BayesianOptimization(\n f=target_func, pbounds=PBOUNDS, random_state=1, verbose=0, bounds_transformer=new_bounds_transformer\n )\n new_optimizer.load_state(state_path)\n\n # Both optimizers should probe the same point\n point = {\"p1\": 1.5, \"p2\": 0.5}\n probe1 = optimizer.probe(point)\n probe2 = new_optimizer.probe(point)\n assert probe1 == probe2\n\n # Both optimizers should suggest the same point\n suggestion1 = optimizer.suggest()\n suggestion2 = new_optimizer.suggest()\n assert suggestion1 == suggestion2\n\n # Verify that the transformed bounds match\n assert optimizer._space.bounds.tolist() == new_optimizer._space.bounds.tolist()\n\n\ndef test_save_load_w_custom_parameter(tmp_path):\n \"\"\"Test saving and loading optimizer state with custom parameter types.\"\"\"\n\n # Create parameter and bounds\n param = FixedPerimeterTriangleParameter(\n name=\"sides\", bounds=np.array([[0.0, 1.0], [0.0, 1.0], [0.0, 1.0]]), perimeter=1.0\n )\n pbounds = {\"sides\": param}\n\n # Print initial pbounds\n print(\"\\nOriginal pbounds:\")\n print(pbounds)\n\n # Initialize first optimizer\n optimizer = BayesianOptimization(f=area_of_triangle, pbounds=pbounds, random_state=1, verbose=0)\n\n # Run iterations and immediately save state\n optimizer.maximize(init_points=2, n_iter=5)\n\n # Force GP update before saving\n optimizer._gp.fit(optimizer.space.params, optimizer.space.target)\n\n # Save state\n state_path = tmp_path / \"optimizer_state.json\"\n optimizer.save_state(state_path)\n\n # Create new optimizer and load state\n new_optimizer = BayesianOptimization(f=area_of_triangle, pbounds=pbounds, random_state=1, verbose=0)\n new_optimizer.load_state(state_path)\n\n # Test that key properties match\n assert len(optimizer.space) == len(new_optimizer.space)\n assert optimizer.max[\"target\"] == new_optimizer.max[\"target\"]\n np.testing.assert_array_almost_equal(\n optimizer.max[\"params\"][\"sides\"], new_optimizer.max[\"params\"][\"sides\"], decimal=10\n )\n\n # Test that all historical data matches\n for i in range(len(optimizer.space)):\n np.testing.assert_array_almost_equal(\n optimizer.space.params[i], new_optimizer.space.params[i], decimal=10\n )\n assert optimizer.space.target[i] == new_optimizer.space.target[i]\n np.testing.assert_array_almost_equal(\n optimizer.res[i][\"params\"][\"sides\"], new_optimizer.res[i][\"params\"][\"sides\"], decimal=10\n )\n assert optimizer.res[i][\"target\"] == new_optimizer.res[i][\"target\"]\n\n # Test that multiple subsequent suggestions match\n for _ in range(5):\n suggestion1 = optimizer.suggest()\n suggestion2 = new_optimizer.suggest()\n np.testing.assert_array_almost_equal(suggestion1[\"sides\"], suggestion2[\"sides\"], decimal=7)\n\n\ndef test_predict():\n \"\"\"Test the predict method of the optimizer.\"\"\"\n optimizer = BayesianOptimization(f=target_func, pbounds=PBOUNDS, random_state=1, verbose=0)\n\n # Register some points\n optimizer.register(params={\"p1\": 1, \"p2\": 2}, target=3)\n optimizer.register(params={\"p1\": 4, \"p2\": 5}, target=9)\n optimizer.register(params={\"p1\": 7, \"p2\": 8}, target=15)\n\n # Points to predict\n test_points = [{\"p1\": 2, \"p2\": 3}, {\"p1\": 5, \"p2\": 6}, {\"p1\": 8, \"p2\": 9}]\n\n # Perform predictions\n means = optimizer.predict(test_points)\n\n # Check that means have correct length\n assert len(means) == len(test_points)\n\n # Also test with return_std=True to get std\n means, stds = optimizer.predict(test_points, return_std=True)\n assert len(means) == len(test_points)\n assert len(stds) == len(test_points)\n\n\ndef test_predict_example():\n \"\"\"Test the predict method with known outputs.\"\"\"\n optimizer = BayesianOptimization(f=target_func, pbounds=PBOUNDS, random_state=1, verbose=0)\n\n # Register some points\n optimizer.register(params={\"p1\": 0, \"p2\": 0}, target=0)\n optimizer.register(params={\"p1\": 10, \"p2\": 10}, target=20)\n\n # Point to predict\n test_point = {\"p1\": 0, \"p2\": 0}\n\n # Perform prediction\n means = optimizer.predict([test_point])\n assert np.isclose(means, 0, atol=1e-3)\n\n # Test with return_std=True\n means, stds = optimizer.predict([test_point], return_std=True)\n assert np.isclose(means, 0, atol=1e-3)\n assert stds < 0.02 # std should be small but not tiny due to GP uncertainty\n\n test_point = {\"p1\": 10, \"p2\": 10}\n means = optimizer.predict([test_point])\n assert np.isclose(means, 20, atol=1e-3)\n\n means, stds = optimizer.predict([test_point], return_std=True)\n assert np.isclose(means, 20, atol=1e-3)\n assert stds < 0.02\n\n\ndef test_predict_no_fit():\n \"\"\"Test the predict method when GP has not been fitted.\"\"\"\n optimizer = BayesianOptimization(f=target_func, pbounds=PBOUNDS, random_state=1, verbose=0)\n\n # Perform prediction with fit_gp=True should raise error when no data\n with pytest.raises(RuntimeError):\n optimizer.predict({\"p1\": 5, \"p2\": 5}, fit_gp=True)\n\n # Predict without fitting GP using single dict - get scalar mean by default\n mean = optimizer.predict({\"p1\": 5, \"p2\": 5}, fit_gp=False)\n # Since GP is not fitted, mean should be close to 0\n assert np.isclose(mean, 0, atol=1e-4)\n\n # Get std when not fitting GP\n mean, std = optimizer.predict({\"p1\": 5, \"p2\": 5}, fit_gp=False, return_std=True)\n # Since GP is not fitted, std should be large\n assert std > 1e-2\n\n # Test with list - returns array\n means = optimizer.predict([{\"p1\": 5, \"p2\": 5}], fit_gp=False)\n # With a list, even single point returns array\n assert len(means) == 1\n assert np.isclose(means[0], 0, atol=1e-4)\n\n\ndef test_predict_return_cov():\n \"\"\"Test the predict method with return_cov=True.\"\"\"\n optimizer = BayesianOptimization(f=target_func, pbounds=PBOUNDS, random_state=1, verbose=0)\n\n optimizer.register(params={\"p1\": 1, \"p2\": 2}, target=3)\n optimizer.register(params={\"p1\": 4, \"p2\": 5}, target=9)\n\n test_points = [{\"p1\": 2, \"p2\": 3}, {\"p1\": 5, \"p2\": 6}]\n\n means, cov = optimizer.predict(test_points, return_cov=True)\n\n assert len(means) == len(test_points)\n assert cov.shape == (len(test_points), len(test_points))\n\n\ndef test_predict_integer_params():\n \"\"\"Test the predict method with integer parameters.\"\"\"\n int_pbounds = {\"p1\": (0, 10, int), \"p2\": (0, 10, int)}\n optimizer = BayesianOptimization(f=target_func, pbounds=int_pbounds, random_state=1, verbose=0)\n optimizer.register(params={\"p1\": 1, \"p2\": 2}, target=3)\n optimizer.register(params={\"p1\": 4, \"p2\": 5}, target=9)\n test_points = [{\"p1\": 2, \"p2\": 3}, {\"p1\": 5, \"p2\": 6}]\n means = optimizer.predict(test_points)\n assert len(means) == len(test_points)\n\n # Test with return_std\n means, stds = optimizer.predict(test_points, return_std=True)\n assert len(means) == len(test_points)\n assert len(stds) == len(test_points)\n\n float_points = [{\"p1\": 2.7, \"p2\": 3.3}, {\"p1\": 5.9, \"p2\": 6.1}]\n means_float = optimizer.predict(float_points)\n assert len(means_float) == len(float_points)\n\n means_float, stds_float = optimizer.predict(float_points, return_std=True)\n assert len(means_float) == len(float_points)\n assert len(stds_float) == len(float_points)\n # Check that rounding float inputs gives similar predictions as integer inputs\n\n for i in range(len(test_points)):\n rounded_point = {k: round(v) for k, v in float_points[i].items()}\n mean_rounded = optimizer.predict([rounded_point])\n assert np.isclose(means_float[i], mean_rounded, atol=1e-1)\n\n # Also check with std\n for i in range(len(test_points)):\n rounded_point = {k: round(v) for k, v in float_points[i].items()}\n mean_rounded, std_rounded = optimizer.predict([rounded_point], return_std=True)\n assert np.isclose(means_float[i], mean_rounded, atol=1e-1)\n assert np.isclose(stds_float[i], std_rounded, atol=1e-1)\n\n\ndef test_predict_categorical_params():\n \"\"\"Test the predict method with categorical parameters.\"\"\"\n\n def cat_target_func(param1: str, param2: str) -> float:\n value_map = {\"low\": 1.0, \"medium\": 2.0, \"high\": 3.0}\n return value_map[param1] + value_map[param2]\n\n cat_pbounds = {\"param1\": [\"low\", \"medium\", \"high\"], \"param2\": [\"low\", \"medium\", \"high\"]}\n\n optimizer = BayesianOptimization(f=cat_target_func, pbounds=cat_pbounds, random_state=1, verbose=0)\n\n optimizer.register(params={\"param1\": \"low\", \"param2\": \"low\"}, target=2.0)\n optimizer.register(params={\"param1\": \"high\", \"param2\": \"high\"}, target=6.0)\n\n test_points = [{\"param1\": \"medium\", \"param2\": \"medium\"}, {\"param1\": \"low\", \"param2\": \"high\"}]\n\n means = optimizer.predict(test_points)\n\n assert len(means) == len(test_points)\n assert np.isclose(means[0], 4.0, atol=1.0)\n assert np.isclose(means[1], 4.0, atol=1.0)\n\n # Test with return_std\n means, stds = optimizer.predict(test_points, return_std=True)\n assert len(means) == len(test_points)\n assert len(stds) == len(test_points)\n assert stds[0] > 0.0\n assert stds[1] > 0.0\n\n\ndef test_predict_no_points_registered():\n \"\"\"Test the predict method when no points have been registered.\"\"\"\n optimizer = BayesianOptimization(f=target_func, pbounds=PBOUNDS, random_state=1, verbose=0)\n\n test_points = [{\"p1\": 2, \"p2\": 3}, {\"p1\": 5, \"p2\": 6}]\n\n means = optimizer.predict(test_points, fit_gp=False)\n\n assert len(means) == len(test_points)\n for mean in means:\n assert np.isclose(mean, 0, atol=1e-4)\n\n # Test with return_std to get uncertainty\n means, stds = optimizer.predict(test_points, fit_gp=False, return_std=True)\n assert len(means) == len(test_points)\n assert len(stds) == len(test_points)\n for std in stds:\n assert std > 1e-2\n\n\ndef test_predict_custom_parameter():\n \"\"\"Test the predict method with a custom parameter type.\"\"\"\n\n param = FixedPerimeterTriangleParameter(\n name=\"sides\", bounds=np.array([[0.0, 1.0], [0.0, 1.0], [0.0, 1.0]]), perimeter=1.0\n )\n pbounds = {\"sides\": param}\n\n optimizer = BayesianOptimization(f=area_of_triangle, pbounds=pbounds, random_state=1, verbose=0)\n\n optimizer.register(\n params={\"sides\": np.array([0.3, 0.4, 0.3])}, target=area_of_triangle(np.array([0.3, 0.4, 0.3]))\n )\n optimizer.register(\n params={\"sides\": np.array([0.2, 0.5, 0.3])}, target=area_of_triangle(np.array([0.2, 0.5, 0.3]))\n )\n\n test_points = [{\"sides\": np.array([0.25, 0.5, 0.25])}, {\"sides\": np.array([0.4, 0.4, 0.2])}]\n\n means = optimizer.predict(test_points)\n\n assert len(means) == len(test_points)\n for i, point in enumerate(test_points):\n expected_area = area_of_triangle(point[\"sides\"])\n assert np.isclose(means[i], expected_area, atol=0.1)\n\n # Test with return_std\n means, stds = optimizer.predict(test_points, return_std=True)\n assert len(means) == len(test_points)\n assert len(stds) == len(test_points)\n for i in range(len(test_points)):\n assert stds[i] > 0.0\n\n\ndef test_predict_invalid_params_type():\n \"\"\"Test that predict raises TypeError for invalid params type.\"\"\"\n optimizer = BayesianOptimization(f=target_func, pbounds=PBOUNDS, random_state=1, verbose=0)\n optimizer.register(params={\"p1\": 1, \"p2\": 2}, target=3)\n\n # Test with invalid type (string)\n with pytest.raises(TypeError, match=\"params must be a dict or iterable of dicts\"):\n optimizer.predict(\"invalid\", fit_gp=False)\n\n\ndef test_predict_with_tuple():\n \"\"\"Test that predict works with tuple of dicts.\"\"\"\n optimizer = BayesianOptimization(f=target_func, pbounds=PBOUNDS, random_state=1, verbose=0)\n optimizer.register(params={\"p1\": 1, \"p2\": 2}, target=3)\n\n # Tuple of dicts should work as a valid iterable\n result = optimizer.predict(({\"p1\": 1, \"p2\": 2},), fit_gp=False)\n assert isinstance(result, np.ndarray)\n\n\ndef test_predict_return_std_and_cov_mutually_exclusive():\n \"\"\"Test that predict raises ValueError when both return_std and return_cov are True.\"\"\"\n optimizer = BayesianOptimization(f=target_func, pbounds=PBOUNDS, random_state=1, verbose=0)\n optimizer.register(params={\"p1\": 1, \"p2\": 2}, target=3)\n optimizer.register(params={\"p1\": 4, \"p2\": 5}, target=9)\n\n # Test with both return_std and return_cov as True\n with pytest.raises(ValueError, match=\"return_std and return_cov cannot both be True\"):\n optimizer.predict({\"p1\": 2, \"p2\": 3}, return_std=True, return_cov=True, fit_gp=False)\n\n # Test with list\n with pytest.raises(ValueError, match=\"return_std and return_cov cannot both be True\"):\n optimizer.predict([{\"p1\": 2, \"p2\": 3}], return_std=True, return_cov=True, fit_gp=False)\n\n\ndef test_predict_shape_semantics_dict_vs_list():\n \"\"\"Test that dict input returns scalars and list input returns arrays.\"\"\"\n optimizer = BayesianOptimization(f=target_func, pbounds=PBOUNDS, random_state=1, verbose=0)\n optimizer.register(params={\"p1\": 1, \"p2\": 2}, target=3)\n optimizer.register(params={\"p1\": 4, \"p2\": 5}, target=9)\n\n # Test dict input returns scalar\n mean_dict = optimizer.predict({\"p1\": 2, \"p2\": 3}, fit_gp=False)\n assert mean_dict.ndim == 0, \"dict input should return scalar (0-d array)\"\n\n # Test list with single dict returns 1D array\n mean_list_single = optimizer.predict([{\"p1\": 2, \"p2\": 3}], fit_gp=False)\n assert mean_list_single.ndim == 1, \"list with single dict should return 1D array\"\n assert len(mean_list_single) == 1, \"list with single dict should have length 1\"\n\n # Test list with multiple dicts returns 1D array\n mean_list_multi = optimizer.predict([{\"p1\": 2, \"p2\": 3}, {\"p1\": 5, \"p2\": 6}], fit_gp=False)\n assert mean_list_multi.ndim == 1, \"list with multiple dicts should return 1D array\"\n assert len(mean_list_multi) == 2, \"list with two dicts should have length 2\"\n\n\ndef test_predict_shape_semantics_with_std():\n \"\"\"Test shape semantics with return_std=True.\"\"\"\n optimizer = BayesianOptimization(f=target_func, pbounds=PBOUNDS, random_state=1, verbose=0)\n optimizer.register(params={\"p1\": 1, \"p2\": 2}, target=3)\n optimizer.register(params={\"p1\": 4, \"p2\": 5}, target=9)\n\n # Test dict input returns tuple of scalars\n mean_dict, std_dict = optimizer.predict({\"p1\": 2, \"p2\": 3}, return_std=True, fit_gp=False)\n assert mean_dict.ndim == 0, \"dict mean should be scalar\"\n assert std_dict.ndim == 0, \"dict std should be scalar\"\n\n # Test list with single dict returns tuple of 1D arrays\n mean_list, std_list = optimizer.predict([{\"p1\": 2, \"p2\": 3}], return_std=True, fit_gp=False)\n assert mean_list.ndim == 1, \"list mean should be 1D array\"\n assert std_list.ndim == 1, \"list std should be 1D array\"\n assert len(mean_list) == 1, \"list mean should have length 1\"\n assert len(std_list) == 1, \"list std should have length 1\"\n\n # Test list with multiple dicts returns tuple of 1D arrays\n mean_list, std_list = optimizer.predict(\n [{\"p1\": 2, \"p2\": 3}, {\"p1\": 5, \"p2\": 6}], return_std=True, fit_gp=False\n )\n assert mean_list.ndim == 1, \"list mean should be 1D array\"\n assert std_list.ndim == 1, \"list std should be 1D array\"\n assert len(mean_list) == 2, \"list mean should have length 2\"\n assert len(std_list) == 2, \"list std should have length 2\"\n\n\ndef test_predict_shape_semantics_with_cov():\n \"\"\"Test shape semantics with return_cov=True.\"\"\"\n optimizer = BayesianOptimization(f=target_func, pbounds=PBOUNDS, random_state=1, verbose=0)\n optimizer.register(params={\"p1\": 1, \"p2\": 2}, target=3)\n optimizer.register(params={\"p1\": 4, \"p2\": 5}, target=9)\n\n # Test dict input returns tuple of scalar and 2D covariance\n mean_dict, cov_dict = optimizer.predict({\"p1\": 2, \"p2\": 3}, return_cov=True, fit_gp=False)\n assert mean_dict.ndim == 0, \"dict mean should be scalar\"\n assert cov_dict.ndim == 2, \"dict cov should be 2D\"\n\n # Test list input returns tuple of 1D array and 2D covariance\n mean_list, cov_list = optimizer.predict(\n [{\"p1\": 2, \"p2\": 3}, {\"p1\": 5, \"p2\": 6}], return_cov=True, fit_gp=False\n )\n assert mean_list.ndim == 1, \"list mean should be 1D array\"\n assert cov_list.ndim == 2, \"list cov should be 2D\"\n assert len(mean_list) == 2, \"list mean should have length 2\"\n assert cov_list.shape == (2, 2), \"cov shape should match number of points\"\n" }, { "path": "tests/test_constraint.py", "content": "from __future__ import annotations\n\nimport numpy as np\nimport pytest\nfrom scipy.optimize import NonlinearConstraint\n\nfrom bayes_opt import BayesianOptimization, ConstraintModel\n\n\n@pytest.fixture\ndef target_function():\n return lambda x, y: np.cos(2 * x) * np.cos(y) + np.sin(x)\n\n\n@pytest.fixture\ndef constraint_function():\n return lambda x, y: np.cos(x) * np.cos(y) - np.sin(x) * np.sin(y)\n\n\ndef test_constraint_property(target_function, constraint_function):\n constraint_limit_upper = 0.5\n constraint = NonlinearConstraint(constraint_function, -np.inf, constraint_limit_upper)\n pbounds = {\"x\": (0, 6), \"y\": (0, 6)}\n\n optimizer = BayesianOptimization(\n f=target_function, constraint=constraint, pbounds=pbounds, verbose=0, random_state=1\n )\n assert isinstance(optimizer.constraint, ConstraintModel)\n assert isinstance(optimizer.space.constraint, ConstraintModel)\n\n\ndef test_single_constraint_upper(target_function, constraint_function):\n constraint_limit_upper = 0.5\n\n constraint = NonlinearConstraint(constraint_function, -np.inf, constraint_limit_upper)\n pbounds = {\"x\": (0, 6), \"y\": (0, 6)}\n\n optimizer = BayesianOptimization(\n f=target_function, constraint=constraint, pbounds=pbounds, verbose=0, random_state=1\n )\n\n optimizer.maximize(init_points=2, n_iter=10)\n\n assert constraint_function(**optimizer.max[\"params\"]) <= constraint_limit_upper\n\n\ndef test_single_constraint_lower(target_function, constraint_function):\n constraint_limit_lower = -0.5\n\n constraint = NonlinearConstraint(constraint_function, constraint_limit_lower, np.inf)\n pbounds = {\"x\": (0, 6), \"y\": (0, 6)}\n\n optimizer = BayesianOptimization(\n f=target_function, constraint=constraint, pbounds=pbounds, verbose=0, random_state=1\n )\n\n optimizer.maximize(init_points=2, n_iter=10)\n\n assert constraint_function(**optimizer.max[\"params\"]) >= constraint_limit_lower\n\n\ndef test_single_constraint_lower_upper(target_function, constraint_function):\n constraint_limit_lower = -0.5\n constraint_limit_upper = 0.5\n\n constraint = NonlinearConstraint(constraint_function, constraint_limit_lower, constraint_limit_upper)\n pbounds = {\"x\": (0, 6), \"y\": (0, 6)}\n\n optimizer = BayesianOptimization(\n f=target_function, constraint=constraint, pbounds=pbounds, verbose=0, random_state=1\n )\n\n assert optimizer.constraint.lb == constraint.lb\n assert optimizer.constraint.ub == constraint.ub\n\n optimizer.maximize(init_points=2, n_iter=10)\n\n # Check limits\n assert constraint_function(**optimizer.max[\"params\"]) <= constraint_limit_upper\n assert constraint_function(**optimizer.max[\"params\"]) >= constraint_limit_lower\n\n # Exclude the last sampled point, because the constraint is not fitted on that.\n res = np.array(\n [[r[\"target\"], r[\"constraint\"], r[\"params\"][\"x\"], r[\"params\"][\"y\"]] for r in optimizer.res[:-1]]\n )\n\n xy = res[:, [2, 3]]\n x = res[:, 2]\n y = res[:, 3]\n\n # Check accuracy of approximation for sampled points\n assert constraint_function(x, y) == pytest.approx(optimizer.constraint.approx(xy), rel=1e-4, abs=1e-4)\n\n\ndef test_multiple_constraints(target_function):\n def constraint_function_2_dim(x, y):\n return np.array(\n [-np.cos(x) * np.cos(y) + np.sin(x) * np.sin(y), -np.cos(x) * np.cos(-y) + np.sin(x) * np.sin(-y)]\n )\n\n constraint_limit_lower = np.array([-np.inf, -np.inf])\n constraint_limit_upper = np.array([0.6, 0.6])\n\n conmod = NonlinearConstraint(constraint_function_2_dim, constraint_limit_lower, constraint_limit_upper)\n pbounds = {\"x\": (0, 6), \"y\": (0, 6)}\n\n optimizer = BayesianOptimization(\n f=target_function, constraint=conmod, pbounds=pbounds, verbose=0, random_state=1\n )\n\n optimizer.maximize(init_points=2, n_iter=10)\n\n constraint_at_max = constraint_function_2_dim(**optimizer.max[\"params\"])\n assert np.all(\n (constraint_at_max <= constraint_limit_upper) & (constraint_at_max >= constraint_limit_lower)\n )\n\n params = optimizer.res[0][\"params\"]\n x, y = params[\"x\"], params[\"y\"]\n\n assert constraint_function_2_dim(x, y) == pytest.approx(\n optimizer.constraint.approx(np.array([x, y])), rel=1e-3, abs=1e-3\n )\n\n\ndef test_kwargs_not_the_same(target_function):\n def target_function(x, y):\n return np.cos(2 * x) * np.cos(y) + np.sin(x)\n\n def constraint_function(a, b):\n return np.cos(a) * np.cos(b) - np.sin(a) * np.sin(b)\n\n constraint_limit_upper = 0.5\n\n constraint = NonlinearConstraint(constraint_function, -np.inf, constraint_limit_upper)\n pbounds = {\"x\": (0, 6), \"y\": (0, 6)}\n\n optimizer = BayesianOptimization(\n f=target_function, constraint=constraint, pbounds=pbounds, verbose=0, random_state=1\n )\n with pytest.raises(TypeError, match=\"Encountered TypeError when evaluating\"):\n optimizer.maximize(init_points=2, n_iter=10)\n\n\ndef test_lower_less_than_upper(target_function):\n def target_function(x, y):\n return np.cos(2 * x) * np.cos(y) + np.sin(x)\n\n def constraint_function_2_dim(x, y):\n return np.array(\n [-np.cos(x) * np.cos(y) + np.sin(x) * np.sin(y), -np.cos(x) * np.cos(-y) + np.sin(x) * np.sin(-y)]\n )\n\n constraint_limit_lower = np.array([0.6, -np.inf])\n constraint_limit_upper = np.array([0.3, 0.6])\n\n conmod = NonlinearConstraint(constraint_function_2_dim, constraint_limit_lower, constraint_limit_upper)\n pbounds = {\"x\": (0, 6), \"y\": (0, 6)}\n\n with pytest.raises(ValueError):\n BayesianOptimization(f=target_function, constraint=conmod, pbounds=pbounds, verbose=0, random_state=1)\n\n\ndef test_null_constraint_function():\n constraint = ConstraintModel(None, np.array([0, 0]), np.array([1, 1]))\n with pytest.raises(ValueError, match=\"No constraint function was provided.\"):\n constraint.eval()\n" }, { "path": "tests/test_logger.py", "content": "from __future__ import annotations\n\nimport io\nfrom unittest.mock import patch\n\nfrom colorama import Fore\n\nfrom bayes_opt import BayesianOptimization\nfrom bayes_opt.logger import ScreenLogger\n\n\ndef target_func(**kwargs):\n \"\"\"Simple target function for testing.\"\"\"\n return sum(kwargs.values())\n\n\nPBOUNDS = {\"p1\": (0, 10), \"p2\": (0, 10)}\n\n\ndef test_initialization():\n \"\"\"Test logger initialization with default and custom parameters.\"\"\"\n # Default parameters\n logger = ScreenLogger()\n assert logger.verbose == 2\n assert not logger.is_constrained\n\n # Custom parameters\n logger = ScreenLogger(verbose=0, is_constrained=True)\n assert logger.verbose == 0\n assert logger.is_constrained\n\n\ndef test_verbose_property():\n \"\"\"Test the verbose property getter and setter.\"\"\"\n logger = ScreenLogger(verbose=1)\n assert logger.verbose == 1\n\n logger.verbose = 0\n assert logger.verbose == 0\n\n logger.verbose = 2\n assert logger.verbose == 2\n\n\ndef test_is_constrained_property():\n \"\"\"Test the is_constrained property getter.\"\"\"\n logger = ScreenLogger(is_constrained=False)\n assert not logger.is_constrained\n\n logger = ScreenLogger(is_constrained=True)\n assert logger.is_constrained\n\n\ndef test_format_number():\n \"\"\"Test the _format_number method.\"\"\"\n logger = ScreenLogger()\n\n # Test integer formatting\n assert len(logger._format_number(42)) == logger._default_cell_size\n\n # Test float formatting with precision\n float_str = logger._format_number(3.14159)\n assert len(float_str) == logger._default_cell_size\n assert \"3.14\" in float_str # default precision is 4\n\n # Test long integer truncation\n long_int = 12345678901234\n formatted = logger._format_number(long_int)\n assert len(formatted) == logger._default_cell_size\n assert formatted == \"1.234e+13\"\n\n # Test long float truncation\n long_float = 1234.5678901234\n formatted = logger._format_number(long_float)\n assert len(formatted) == logger._default_cell_size\n assert formatted == \"1234.5678\"\n\n # Test negative long float truncation\n long_float = -1234.5678901234\n formatted = logger._format_number(long_float)\n assert len(formatted) == logger._default_cell_size\n assert formatted == \"-1234.567\"\n\n # Test scientific notation truncation\n sci_float = 12345678901234.5678901234\n formatted = logger._format_number(sci_float)\n assert len(formatted) == logger._default_cell_size\n assert formatted == \"1.234e+13\"\n\n sci_float = 1.11111111e-5\n formatted = logger._format_number(sci_float)\n assert formatted == \"1.111e-05\"\n\n sci_float_neg = -1.11111111e-5\n formatted = logger._format_number(sci_float_neg)\n assert formatted == \"-1.11e-05\"\n\n sci_float = -12345678901234.5678901234\n formatted = logger._format_number(sci_float)\n assert len(formatted) == logger._default_cell_size\n assert formatted == \"-1.23e+13\"\n\n # Test long scientific notation truncation\n sci_float = -12345678901234.534e132\n formatted = logger._format_number(sci_float)\n assert len(formatted) == logger._default_cell_size\n assert formatted == \"-1.2e+145\"\n\n\ndef test_format_bool():\n \"\"\"Test the _format_bool method.\"\"\"\n logger = ScreenLogger()\n\n # Test True formatting\n true_str = logger._format_bool(True)\n assert len(true_str) == logger._default_cell_size\n assert \"True\" in true_str\n\n # Test False formatting\n false_str = logger._format_bool(False)\n assert len(false_str) == logger._default_cell_size\n assert \"False\" in false_str\n\n # Test with small cell size\n small_cell_logger = ScreenLogger()\n small_cell_logger._default_cell_size = 3\n assert small_cell_logger._format_bool(True) == \"T \"\n assert small_cell_logger._format_bool(False) == \"F \"\n\n\ndef test_format_str():\n \"\"\"Test the _format_str method.\"\"\"\n logger = ScreenLogger()\n\n # Test normal string\n normal_str = logger._format_str(\"test\")\n assert len(normal_str) == logger._default_cell_size\n assert \"test\" in normal_str\n\n # Test long string truncation\n long_str = \"this_is_a_very_long_string_that_should_be_truncated\"\n formatted = logger._format_str(long_str)\n assert len(formatted) == logger._default_cell_size\n assert \"...\" in formatted\n\n\ndef test_step():\n \"\"\"Test the _print_step method.\"\"\"\n optimizer = BayesianOptimization(target_func, PBOUNDS, random_state=1)\n\n logger = ScreenLogger()\n\n # Register a point so we have something to log\n optimizer.register(params={\"p1\": 1.5, \"p2\": 2.5}, target=4.0)\n\n # Test default color\n step_str = logger._print_step(\n optimizer._space.keys, optimizer._space.res()[-1], optimizer._space.params_config\n )\n assert \"|\" in step_str\n assert \"1\" in step_str # iteration\n assert \"4.0\" in step_str # target value\n\n # Test with custom color\n custom_color = Fore.RED\n step_str_colored = logger._print_step(\n optimizer._space.keys, optimizer._space.res()[-1], optimizer._space.params_config, color=custom_color\n )\n assert custom_color in step_str_colored\n\n\ndef test_print_header():\n \"\"\"Test the _print_header method.\"\"\"\n optimizer = BayesianOptimization(target_func, PBOUNDS, random_state=1)\n logger = ScreenLogger()\n\n header_str = logger._print_header(optimizer._space.keys)\n\n # Check if header contains expected column names\n assert \"iter\" in header_str\n assert \"target\" in header_str\n assert \"p1\" in header_str\n assert \"p2\" in header_str\n\n # Check if divider line is included\n assert \"-\" * 10 in header_str\n\n # Check with constrained logger\n constrained_logger = ScreenLogger(is_constrained=True)\n constrained_header = constrained_logger._print_header(optimizer._space.keys)\n assert \"allowed\" in constrained_header\n\n\ndef test_is_new_max():\n \"\"\"Test the _is_new_max method.\"\"\"\n optimizer = BayesianOptimization(target_func, PBOUNDS, random_state=1)\n logger = ScreenLogger()\n\n # No observations yet\n assert not logger._is_new_max(None)\n\n # Add first observation\n optimizer.register(params={\"p1\": 1, \"p2\": 2}, target=3)\n\n current_max = optimizer.max\n logger._is_new_max(current_max)\n assert not logger._is_new_max(current_max)\n\n # Add lower observation (not a new max)\n optimizer.register(params={\"p1\": 0.5, \"p2\": 1}, target=1.5)\n assert not logger._is_new_max(optimizer.max)\n\n # Add higher observation (new max)\n optimizer.register(params={\"p1\": 2, \"p2\": 2}, target=4)\n assert logger._is_new_max(optimizer.max)\n\n\ndef test_update_tracker():\n \"\"\"Test the _update_tracker method.\"\"\"\n optimizer = BayesianOptimization(target_func, PBOUNDS, random_state=1)\n logger = ScreenLogger()\n\n # Initial state\n assert logger._iterations == 0\n assert logger._previous_max is None\n\n # Update with first observation\n optimizer.register(params={\"p1\": 1, \"p2\": 2}, target=3)\n logger._update_tracker(optimizer.max)\n assert logger._iterations == 1\n assert logger._previous_max == 3\n assert logger._previous_max_params == {\"p1\": 1, \"p2\": 2}\n\n # Update with lower observation\n optimizer.register(params={\"p1\": 0.5, \"p2\": 1}, target=1.5)\n logger._update_tracker(optimizer.max)\n assert logger._iterations == 2\n assert logger._previous_max == 3 # Unchanged\n assert logger._previous_max_params == {\"p1\": 1, \"p2\": 2} # Unchanged\n\n # Update with higher observation\n optimizer.register(params={\"p1\": 2, \"p2\": 2}, target=4)\n logger._update_tracker(optimizer.max)\n assert logger._iterations == 3\n assert logger._previous_max == 4 # Updated\n assert logger._previous_max_params == {\"p1\": 2, \"p2\": 2} # Updated\n\n\n@patch(\"sys.stdout\", new_callable=io.StringIO)\ndef test_log_optimization_start(mock_stdout):\n \"\"\"Test the log_optimization_start method.\"\"\"\n optimizer = BayesianOptimization(target_func, PBOUNDS, random_state=1)\n\n # Test with verbose=0 (should not print)\n logger = ScreenLogger(verbose=0)\n logger.log_optimization_start(optimizer._space.keys)\n assert mock_stdout.getvalue() == \"\"\n\n # Test with verbose=1 (should print)\n logger.verbose = 1\n logger.log_optimization_start(optimizer._space.keys)\n output = mock_stdout.getvalue()\n assert \"iter\" in output\n assert \"target\" in output\n assert \"p1\" in output\n assert \"p2\" in output\n\n\n@patch(\"sys.stdout\", new_callable=io.StringIO)\ndef test_log_optimization_step(mock_stdout):\n \"\"\"Test the log_optimization_step method.\"\"\"\n optimizer = BayesianOptimization(target_func, PBOUNDS, random_state=1)\n\n logger = ScreenLogger()\n\n # Create logger with verbose=1 specifically, as this is the only verbose level\n # that doesn't print for non-max points according to the implementation:\n # if self._verbose == 2 or is_new_max:\n logger.verbose = 1\n\n # Clear any output that might have happened\n mock_stdout.truncate(0)\n mock_stdout.seek(0)\n\n # Register a point and log it\n optimizer.register(params={\"p1\": 1, \"p2\": 2}, target=3)\n current_max = optimizer.max\n logger._is_new_max(current_max) # Initialize previous_max\n\n # For a point that is not a new max with verbose=1, should not print\n mock_stdout.truncate(0)\n mock_stdout.seek(0)\n logger.log_optimization_step(\n optimizer._space.keys, optimizer._space.res()[-1], optimizer._space.params_config, optimizer.max\n )\n assert mock_stdout.getvalue() == \"\" # Nothing printed for non-max point with verbose=1\n\n # Register a higher value, which should trigger output with verbose=1\n optimizer.register(params={\"p1\": 2, \"p2\": 2}, target=4)\n mock_stdout.truncate(0)\n mock_stdout.seek(0)\n logger.log_optimization_step(\n optimizer._space.keys, optimizer._space.res()[-1], optimizer._space.params_config, optimizer.max\n )\n max_output = mock_stdout.getvalue()\n assert max_output != \"\" # Something printed for new max point with verbose=1\n assert \"4.0\" in max_output # Should show target value\n\n # Test with verbose=2 (should print even for non-max points)\n logger.verbose = 2\n optimizer.register(params={\"p1\": 1, \"p2\": 1}, target=1)\n mock_stdout.truncate(0)\n mock_stdout.seek(0)\n logger.log_optimization_step(\n optimizer._space.keys, optimizer._space.res()[-1], optimizer._space.params_config, optimizer.max\n )\n non_max_output = mock_stdout.getvalue()\n assert non_max_output != \"\" # Something printed for non-max point with verbose=2\n assert \"1.0\" in non_max_output # Should show target value\n\n\n@patch(\"sys.stdout\", new_callable=io.StringIO)\ndef test_log_optimization_end(mock_stdout):\n \"\"\"Test the log_optimization_end method.\"\"\"\n optimizer = BayesianOptimization(target_func, PBOUNDS, random_state=1)\n\n # Initialize header length\n logger = ScreenLogger(verbose=2)\n logger.log_optimization_start(optimizer._space.keys)\n\n # Clear previous output\n mock_stdout.truncate(0)\n mock_stdout.seek(0)\n\n # Test with verbose=0 (should not print)\n logger.verbose = 0\n logger.log_optimization_end()\n assert mock_stdout.getvalue() == \"\"\n\n # Test with verbose=1 (should print)\n logger.verbose = 1\n mock_stdout.truncate(0)\n mock_stdout.seek(0)\n logger.log_optimization_end()\n output = mock_stdout.getvalue()\n assert \"=\" in output # Should show a line of equals signs\n" }, { "path": "tests/test_notebooks_run.py", "content": "\"\"\"\ncollect all notebooks in examples, and check that they run without error\n\"\"\"\n\nfrom __future__ import annotations\n\nfrom pathlib import Path\n\nimport nbformat\nimport pytest\nfrom nbclient.exceptions import CellExecutionError\nfrom nbconvert.preprocessors import ExecutePreprocessor\n\nthis_file_loc = Path(__file__).parent\n_NOTEBOOKS_NOT_TO_RUN = frozenset([\"put_notebooks_to_skip_here\"])\n\n\n# get all notebooks:\n@pytest.mark.parametrize(\"notebook\", this_file_loc.with_name(\"examples\").glob(\"*.ipynb\"))\ndef test_all_notebooks_run(notebook: Path):\n as_string = str(notebook)\n if any([nb in as_string for nb in _NOTEBOOKS_NOT_TO_RUN]):\n pytest.skip(f\"skipping [{notebook!s}]\")\n\n print(f\"running: {notebook}...\")\n with notebook.open(encoding=\"utf8\") as f:\n nb = nbformat.read(f, as_version=4)\n ep = ExecutePreprocessor(timeout=600, kernel_name=\"python3\")\n try:\n ep.preprocess(nb, {\"metadata\": {\"path\": notebook.parent}})\n print(\"success!\")\n except CellExecutionError as e:\n # Wrap the original error with the notebook name\n pytest.fail(f\"Error executing notebook {notebook}: {e!s}\")\n" }, { "path": "tests/test_parameter.py", "content": "from __future__ import annotations\n\nimport numpy as np\nimport pytest\nfrom scipy.optimize import NonlinearConstraint\nfrom sklearn.gaussian_process import GaussianProcessRegressor, kernels\n\nfrom bayes_opt import BayesianOptimization\nfrom bayes_opt.parameter import CategoricalParameter, FloatParameter, IntParameter, wrap_kernel\nfrom bayes_opt.target_space import TargetSpace\n\n\ndef test_float_parameters():\n def target_func(**kwargs):\n # arbitrary target func\n return sum(kwargs.values())\n\n pbounds = {\"p1\": (0, 1), \"p2\": (1, 2)}\n space = TargetSpace(target_func, pbounds)\n\n assert space.dim == len(pbounds)\n assert space.empty\n assert space.keys == [\"p1\", \"p2\"]\n\n assert isinstance(space._params_config[\"p1\"], FloatParameter)\n assert isinstance(space._params_config[\"p2\"], FloatParameter)\n\n assert all(space.bounds[:, 0] == np.array([0, 1]))\n assert all(space.bounds[:, 1] == np.array([1, 2]))\n assert (space.bounds == space.bounds).all()\n\n point1 = {\"p1\": 0.2, \"p2\": 1.5}\n target1 = 1.7\n space.probe(point1)\n\n point2 = {\"p1\": 0.5, \"p2\": 1.0}\n target2 = 1.5\n space.probe(point2)\n\n assert (space.params[0] == np.fromiter(point1.values(), dtype=float)).all()\n assert (space.params[1] == np.fromiter(point2.values(), dtype=float)).all()\n\n assert (space.target == np.array([target1, target2])).all()\n\n p1 = space._params_config[\"p1\"]\n assert p1.to_float(0.2) == 0.2\n assert p1.to_float(np.array(2.3)) == 2.3\n assert p1.to_float(3) == 3.0\n\n\ndef test_int_parameters():\n def target_func(**kwargs):\n assert [isinstance(kwargs[key], int) for key in kwargs]\n # arbitrary target func\n return sum(kwargs.values())\n\n pbounds = {\"p1\": (0, 5, int), \"p3\": (-1, 3, int)}\n space = TargetSpace(target_func, pbounds)\n\n assert space.dim == len(pbounds)\n assert space.empty\n assert space.keys == [\"p1\", \"p3\"]\n\n assert isinstance(space._params_config[\"p1\"], IntParameter)\n assert isinstance(space._params_config[\"p3\"], IntParameter)\n\n point1 = {\"p1\": 2, \"p3\": 0}\n target1 = 2\n space.probe(point1)\n\n point2 = {\"p1\": 1, \"p3\": -1}\n target2 = 0\n space.probe(point2)\n\n assert (space.params[0] == np.fromiter(point1.values(), dtype=float)).all()\n assert (space.params[1] == np.fromiter(point2.values(), dtype=float)).all()\n\n assert (space.target == np.array([target1, target2])).all()\n\n p1 = space._params_config[\"p1\"]\n assert p1.to_float(0) == 0.0\n assert p1.to_float(np.array(2)) == 2.0\n assert p1.to_float(3) == 3.0\n\n assert p1.kernel_transform(0) == 0.0\n assert p1.kernel_transform(2.3) == 2.0\n assert p1.kernel_transform(np.array([1.3, 3.6, 7.2])) == pytest.approx(np.array([1, 4, 7]))\n\n\ndef test_cat_parameters():\n fruit_ratings = {\"apple\": 1.0, \"banana\": 2.0, \"mango\": 5.0, \"honeydew melon\": -10.0, \"strawberry\": np.pi}\n\n def target_func(fruit: str):\n return fruit_ratings[fruit]\n\n fruits = (\"apple\", \"banana\", \"mango\", \"honeydew melon\", \"strawberry\")\n pbounds = {\"fruit\": (\"apple\", \"banana\", \"mango\", \"honeydew melon\", \"strawberry\")}\n space = TargetSpace(target_func, pbounds)\n\n assert space.dim == len(fruits)\n assert space.empty\n assert space.keys == [\"fruit\"]\n\n assert isinstance(space._params_config[\"fruit\"], CategoricalParameter)\n\n assert space.bounds.shape == (len(fruits), 2)\n assert (space.bounds[:, 0] == np.zeros(len(fruits))).all()\n assert (space.bounds[:, 1] == np.ones(len(fruits))).all()\n\n point1 = {\"fruit\": \"banana\"}\n target1 = 2.0\n space.probe(point1)\n\n point2 = {\"fruit\": \"honeydew melon\"}\n target2 = -10.0\n space.probe(point2)\n\n assert (space.params[0] == np.array([0, 1, 0, 0, 0])).all()\n assert (space.params[1] == np.array([0, 0, 0, 1, 0])).all()\n\n assert (space.target == np.array([target1, target2])).all()\n\n p1 = space._params_config[\"fruit\"]\n for i, fruit in enumerate(fruits):\n assert (p1.to_float(fruit) == np.eye(5)[i]).all()\n\n assert (p1.kernel_transform(np.array([0.8, 0.2, 0.3, 0.5, 0.78])) == np.array([1, 0, 0, 0, 0])).all()\n assert (p1.kernel_transform(np.array([0.78, 0.2, 0.3, 0.5, 0.8])) == np.array([0, 0, 0, 0, 1.0])).all()\n\n\ndef test_cateogrical_valid_bounds():\n pbounds = {\"fruit\": (\"apple\", \"banana\", \"mango\", \"honeydew melon\", \"banana\", \"strawberry\")}\n with pytest.raises(ValueError):\n TargetSpace(None, pbounds)\n\n pbounds = {\"fruit\": (\"apple\",)}\n with pytest.raises(ValueError):\n TargetSpace(None, pbounds)\n\n\ndef test_to_string():\n pbounds = {\"p1\": (0, 1), \"p2\": (1, 2)}\n space = TargetSpace(None, pbounds)\n\n assert space._params_config[\"p1\"].to_string(0.2, 5) == \"0.2 \"\n assert space._params_config[\"p2\"].to_string(1.5, 5) == \"1.5 \"\n assert space._params_config[\"p1\"].to_string(0.2, 3) == \"0.2\"\n assert space._params_config[\"p2\"].to_string(np.pi, 5) == \"3.141\"\n assert space._params_config[\"p1\"].to_string(1e-5, 6) == \"1e-05 \"\n assert space._params_config[\"p2\"].to_string(-1e-5, 6) == \"-1e-05\"\n assert space._params_config[\"p1\"].to_string(1e-15, 5) == \"1e-15\"\n assert space._params_config[\"p1\"].to_string(-1.2e-15, 7) == \"-1.2...\"\n\n pbounds = {\"p1\": (0, 5, int), \"p3\": (-1, 3, int)}\n space = TargetSpace(None, pbounds)\n\n assert space._params_config[\"p1\"].to_string(2, 5) == \"2 \"\n assert space._params_config[\"p3\"].to_string(0, 5) == \"0 \"\n assert space._params_config[\"p1\"].to_string(2, 3) == \"2 \"\n assert space._params_config[\"p3\"].to_string(-1, 5) == \"-1 \"\n assert space._params_config[\"p1\"].to_string(123456789, 6) == \"123...\"\n\n pbounds = {\"fruit\": (\"apple\", \"banana\", \"mango\", \"honeydew melon\", \"strawberry\")}\n space = TargetSpace(None, pbounds)\n\n assert space._params_config[\"fruit\"].to_string(\"apple\", 5) == \"apple\"\n assert space._params_config[\"fruit\"].to_string(\"banana\", 5) == \"ba...\"\n assert space._params_config[\"fruit\"].to_string(\"mango\", 5) == \"mango\"\n assert space._params_config[\"fruit\"].to_string(\"honeydew melon\", 10) == \"honeyde...\"\n assert space._params_config[\"fruit\"].to_string(\"strawberry\", 10) == \"strawberry\"\n\n\ndef test_preconstructed_parameter():\n pbounds = {\"p1\": (0, 1), \"p2\": (1, 2), \"p3\": IntParameter(\"p3\", (-1, 3))}\n\n def target_func(p1, p2, p3):\n return p1 + p2 + p3\n\n optimizer1 = BayesianOptimization(target_func, pbounds)\n\n pbounds = {\"p1\": (0, 1), \"p2\": (1, 2), \"p3\": (-1, 3, int)}\n optimizer2 = BayesianOptimization(target_func, pbounds)\n\n assert optimizer1.space.keys == optimizer2.space.keys\n assert (optimizer1.space.bounds == optimizer2.space.bounds).all()\n assert optimizer1.space._params_config[\"p3\"].to_float(2) == 2.0\n\n\ndef test_integration_mixed_optimization():\n fruit_ratings = {\"apple\": 1.0, \"banana\": 2.0, \"mango\": 5.0, \"honeydew melon\": -10.0, \"strawberry\": np.pi}\n\n pbounds = {\n \"p1\": (0, 1),\n \"p2\": (1, 2),\n \"p3\": (-1, 3, int),\n \"fruit\": (\"apple\", \"banana\", \"mango\", \"honeydew melon\", \"strawberry\"),\n }\n\n def target_func(p1, p2, p3, fruit):\n return p1 + p2 + p3 + fruit_ratings[fruit]\n\n optimizer = BayesianOptimization(target_func, pbounds)\n optimizer.maximize(init_points=2, n_iter=10)\n\n\ndef test_integration_mixed_optimization_with_constraints():\n fruit_ratings = {\"apple\": 1.0, \"banana\": 2.0, \"mango\": 5.0, \"honeydew melon\": -10.0, \"strawberry\": np.pi}\n\n pbounds = {\n \"p1\": (0, 1),\n \"p2\": (1, 2),\n \"p3\": (-1, 3, int),\n \"fruit\": (\"apple\", \"banana\", \"mango\", \"honeydew melon\", \"strawberry\"),\n }\n\n def target_func(p1, p2, p3, fruit):\n return p1 + p2 + p3 + fruit_ratings[fruit]\n\n def constraint_func(p1, p2, p3, fruit):\n return (p1 + p2 + p3 - fruit_ratings[fruit]) ** 2\n\n constraint = NonlinearConstraint(constraint_func, 0, 4.0)\n\n optimizer = BayesianOptimization(target_func, pbounds, constraint=constraint)\n init_points = [\n {\"p1\": 0.5, \"p2\": 1.5, \"p3\": 1, \"fruit\": \"banana\"},\n {\"p1\": 0.5, \"p2\": 1.5, \"p3\": 2, \"fruit\": \"mango\"},\n ]\n for p in init_points:\n optimizer.register(p, target=target_func(**p), constraint_value=constraint_func(**p))\n optimizer.maximize(init_points=0, n_iter=2)\n\n\ndef test_wrapped_kernel_fit():\n pbounds = {\"p1\": (0, 1), \"p2\": (1, 10, int)}\n space = TargetSpace(None, pbounds)\n\n space.register(space.random_sample(0), 1.0)\n space.register(space.random_sample(1), 5.0)\n\n kernel = wrap_kernel(kernels.Matern(nu=2.5, length_scale=1e5), space.kernel_transform)\n gp = GaussianProcessRegressor(kernel=kernel, alpha=1e-6, n_restarts_optimizer=5)\n\n gp.fit(space.params, space.target)\n\n assert gp.kernel_.length_scale != 1e5\n\n\ndef test_combined_wrapped_kernel_fit():\n pbounds = {\"p1\": (0, 1), \"p2\": (1, 10, int)}\n space = TargetSpace(None, pbounds)\n\n space.register(space.random_sample(0), 1.0)\n space.register(space.random_sample(1), 5.0)\n\n kernel_fct = kernels.Matern(nu=2.5, length_scale=1e5) + kernels.WhiteKernel(noise_level=1.0)\n kernel = wrap_kernel(kernel_fct, space.kernel_transform)\n gp = GaussianProcessRegressor(kernel=kernel, alpha=1e-6, n_restarts_optimizer=5)\n\n gp.fit(space.params, space.target)\n" }, { "path": "tests/test_seq_domain_red.py", "content": "from __future__ import annotations\n\nimport numpy as np\nimport pytest\n\nfrom bayes_opt import BayesianOptimization, SequentialDomainReductionTransformer\nfrom bayes_opt.target_space import TargetSpace\n\n\ndef black_box_function(x, y):\n \"\"\"Function with unknown internals we wish to maximize.\n\n This is just serving as an example, for all intents and\n purposes think of the internals of this function, i.e.: the process\n which generates its output values, as unknown.\n \"\"\"\n return -(x**2) - (y - 1) ** 2 + 1\n\n\ndef test_bound_x_maximize():\n class Tracker:\n def __init__(self):\n self.start_count = 0\n self.step_count = 0\n self.end_count = 0\n\n def update_start(self, event, instance):\n self.start_count += 1\n\n def update_step(self, event, instance):\n self.step_count += 1\n\n def update_end(self, event, instance):\n self.end_count += 1\n\n def reset(self):\n self.__init__()\n\n bounds_transformer = SequentialDomainReductionTransformer()\n pbounds = {\"x\": (-10, 10), \"y\": (-10, 10)}\n n_iter = 10\n\n standard_optimizer = BayesianOptimization(\n f=black_box_function, pbounds=pbounds, verbose=2, random_state=1\n )\n\n standard_optimizer.maximize(init_points=2, n_iter=n_iter)\n\n mutated_optimizer = BayesianOptimization(\n f=black_box_function,\n pbounds=pbounds,\n verbose=2,\n random_state=1,\n bounds_transformer=bounds_transformer,\n )\n\n mutated_optimizer.maximize(init_points=2, n_iter=n_iter)\n\n assert len(standard_optimizer.space) == len(mutated_optimizer.space)\n assert not (standard_optimizer._space.bounds == mutated_optimizer._space.bounds).any()\n\n\ndef test_minimum_window_is_kept():\n bounds_transformer = SequentialDomainReductionTransformer(minimum_window=1.0)\n pbounds = {\"x\": (-0.5, 0.5), \"y\": (-1.0, 0.0)}\n mutated_optimizer = BayesianOptimization(\n f=black_box_function,\n pbounds=pbounds,\n verbose=0,\n random_state=1,\n bounds_transformer=bounds_transformer,\n )\n\n mutated_optimizer.maximize(init_points=2, n_iter=10)\n window_width = np.diff(bounds_transformer.bounds)\n assert np.isclose(np.min(window_width), 1.0)\n\n\ndef test_minimum_window_array_is_kept():\n window_ranges = [1.0, 0.5]\n bounds_transformer = SequentialDomainReductionTransformer(minimum_window=window_ranges)\n pbounds = {\"x\": (-0.5, 0.5), \"y\": (-1.0, 0.0)}\n mutated_optimizer = BayesianOptimization(\n f=black_box_function,\n pbounds=pbounds,\n verbose=0,\n random_state=1,\n bounds_transformer=bounds_transformer,\n )\n\n mutated_optimizer.maximize(init_points=2, n_iter=10)\n window_widths = np.diff(bounds_transformer.bounds)\n assert np.all(np.isclose(np.squeeze(np.min(window_widths, axis=0)), window_ranges))\n\n\ndef test_trimming_bounds():\n \"\"\"Test if the bounds are trimmed correctly within the bounds\"\"\"\n\n def dummy_function(x1, x2, x3, x4, x5):\n return 0.0\n\n min_window = 1.0\n bounds_transformer = SequentialDomainReductionTransformer(minimum_window=min_window)\n pbounds = {\"x1\": (-1, 0.6), \"x2\": (-1, 0.5), \"x3\": (-0.4, 0.6), \"x4\": (0.3, 1.3), \"x5\": (-1, 0.8)}\n target_sp = TargetSpace(target_func=dummy_function, pbounds=pbounds)\n bounds_transformer.initialize(target_sp)\n new_bounds = np.concatenate((np.ones((5, 1)) * 0.1, np.ones((5, 1))), axis=1)\n global_bounds = np.asarray(list(pbounds.values()))\n\n trimmed_bounds = bounds_transformer._trim(new_bounds, global_bounds)\n # check that the bounds are trimmed to the minimum window\n # raises ValueError if the bounds are not trimmed correctly\n bounds_transformer._window_bounds_compatibility(trimmed_bounds)\n\n\ndef test_exceeded_bounds():\n \"\"\"Raises Value Error if the bounds are exceeded.\"\"\"\n window_ranges = [1.01, 0.72]\n bounds_transformer = SequentialDomainReductionTransformer(minimum_window=window_ranges)\n pbounds = {\"x\": (-0.5, 0.5), \"y\": (-0.7, 0.0)}\n with pytest.raises(ValueError):\n _ = BayesianOptimization(\n f=black_box_function,\n pbounds=pbounds,\n verbose=0,\n random_state=1,\n bounds_transformer=bounds_transformer,\n )\n\n\ndef test_trim_when_both_new_bounds_exceed_global_bounds():\n \"\"\"Test if the global bounds are respected when both new bounds for a given parameter\n are beyond the global bounds.\"\"\"\n\n # initialize a bounds transformer\n bounds_transformer = SequentialDomainReductionTransformer(minimum_window=10)\n pbounds = {\"x\": (-10, 10), \"y\": (-10, 10)}\n target_sp = TargetSpace(target_func=black_box_function, pbounds=pbounds)\n bounds_transformer.initialize(target_sp)\n global_bounds = np.asarray(list(pbounds.values()))\n\n def verify_bounds_in_range(new_bounds, global_bounds):\n \"\"\"Check if the new bounds are within the global bounds.\"\"\"\n test = True\n for i, pbounds in enumerate(new_bounds):\n if pbounds[0] < global_bounds[i, 0] or pbounds[0] > global_bounds[i, 1]:\n test = False\n if pbounds[1] > global_bounds[i, 1] or pbounds[1] < global_bounds[i, 0]:\n test = False\n return test\n\n # test if the sorting of the bounds is correct\n new_bounds = np.array([[5, -5], [-10, 10]])\n trimmed_bounds = bounds_transformer._trim(new_bounds, global_bounds)\n assert (trimmed_bounds == np.array([[-5, 5], [-10, 10]])).all()\n\n # test if both (upper/lower) bounds for a parameter exceed the global bounds\n new_bounds = np.array([[-50, -20], [20, 50]])\n with pytest.warns(UserWarning, match=\"A parameter's lower bound is greater than the global upper bound\"):\n trimmed_bounds = bounds_transformer._trim(new_bounds, global_bounds)\n assert verify_bounds_in_range(trimmed_bounds, global_bounds)\n\n # test if both (upper/lower) bounds for a parameter exceed the global bounds\n # while they are out of order\n new_bounds = np.array([[-20, -50], [-10, 10]])\n with pytest.warns(UserWarning, match=\"A parameter's lower bound is greater than the global upper bound\"):\n trimmed_bounds = bounds_transformer._trim(new_bounds, global_bounds)\n assert verify_bounds_in_range(trimmed_bounds, global_bounds)\n\n\ndef test_minimum_window_dict_ordering():\n \"\"\"Tests if dictionary input for minimum_window is reordered the same as pbounds\"\"\"\n window_ranges = {\"y\": 1, \"x\": 3, \"w\": 1e5}\n bounds_transformer = SequentialDomainReductionTransformer(minimum_window=window_ranges)\n pbounds = {\"y\": (-1, 1), \"w\": (-1e6, 1e6), \"x\": (-10, 10)}\n\n _ = BayesianOptimization(\n f=None, pbounds=pbounds, verbose=0, random_state=1, bounds_transformer=bounds_transformer\n )\n\n\ndef test_mixed_parameters():\n \"\"\"Ensure that the transformer errors when providing non-float parameters\"\"\"\n pbounds = {\"x\": (-10, 10), \"y\": (-10, 10), \"z\": (1, 10, int)}\n target_space = TargetSpace(target_func=black_box_function, pbounds=pbounds)\n with pytest.raises(ValueError):\n _ = SequentialDomainReductionTransformer().initialize(target_space)\n" }, { "path": "tests/test_target_space.py", "content": "from __future__ import annotations\n\nimport numpy as np\nimport pytest\nfrom scipy.optimize import NonlinearConstraint\n\nfrom bayes_opt.exception import NotUniqueError\nfrom bayes_opt.target_space import TargetSpace\n\n\ndef target_func(**kwargs):\n # arbitrary target func\n return sum(kwargs.values())\n\n\nPBOUNDS = {\"p1\": (0, 1), \"p2\": (1, 100)}\n\n\ndef test_keys_and_bounds_in_same_order():\n pbounds = {\"p1\": (0, 1), \"p3\": (0, 3), \"p2\": (0, 2), \"p4\": (0, 4)}\n space = TargetSpace(target_func, pbounds)\n\n assert space.dim == len(pbounds)\n assert space.empty\n assert space.keys == [\"p1\", \"p3\", \"p2\", \"p4\"]\n assert all(space.bounds[:, 0] == np.array([0, 0, 0, 0]))\n assert all(space.bounds[:, 1] == np.array([1, 3, 2, 4]))\n\n\ndef test_params_to_array():\n space = TargetSpace(target_func, PBOUNDS)\n\n assert all(space.params_to_array({\"p1\": 2, \"p2\": 3}) == np.array([2, 3]))\n assert all(space.params_to_array({\"p2\": 2, \"p1\": 9}) == np.array([9, 2]))\n with pytest.raises(ValueError):\n space.params_to_array({\"p2\": 1})\n with pytest.raises(ValueError):\n space.params_to_array({\"p2\": 1, \"p1\": 7, \"other\": 4})\n with pytest.raises(ValueError):\n space.params_to_array({\"other\": 1})\n\n\ndef test_array_to_params():\n space = TargetSpace(target_func, PBOUNDS)\n\n assert space.array_to_params(np.array([2, 3])) == {\"p1\": 2, \"p2\": 3}\n with pytest.raises(ValueError):\n space.array_to_params(np.array([2]))\n with pytest.raises(ValueError):\n space.array_to_params(np.array([2, 3, 5]))\n\n\ndef test_to_float():\n space = TargetSpace(target_func, PBOUNDS)\n\n x = space._to_float({\"p2\": 0, \"p1\": 1})\n assert x.shape == (2,)\n assert all(x == np.array([1, 0]))\n\n with pytest.raises(ValueError):\n x = space._to_float([0, 1])\n with pytest.raises(ValueError):\n x = space._to_float([2, 1, 7])\n with pytest.raises(ValueError):\n x = space._to_float({\"p2\": 1, \"p1\": 2, \"other\": 7})\n with pytest.raises(ValueError):\n x = space._to_float({\"p2\": 1})\n with pytest.raises(ValueError):\n x = space._to_float({\"other\": 7})\n\n\ndef test_register():\n PBOUNDS = {\"p1\": (0, 10), \"p2\": (1, 100)}\n space = TargetSpace(target_func, PBOUNDS)\n\n assert len(space) == 0\n # registering with dict\n space.register(params={\"p1\": 1, \"p2\": 2}, target=3)\n assert len(space) == 1\n assert all(space.params[0] == np.array([1, 2]))\n assert all(space.target == np.array([3]))\n\n # registering with dict out of order\n space.register(params={\"p2\": 4, \"p1\": 5}, target=9)\n assert len(space) == 2\n assert all(space.params[1] == np.array([5, 4]))\n assert all(space.target == np.array([3, 9]))\n\n # registering with array\n space.register(params=np.array([0, 1]), target=1)\n assert len(space) == 3\n assert all(space.params[2] == np.array([0, 1]))\n assert all(space.target == np.array([3, 9, 1]))\n\n with pytest.raises(NotUniqueError):\n space.register(params={\"p1\": 1, \"p2\": 2}, target=3)\n with pytest.raises(NotUniqueError):\n space.register(params={\"p1\": 5, \"p2\": 4}, target=9)\n\n\ndef test_register_with_constraint():\n constraint = NonlinearConstraint(lambda x: x, -2, 2)\n space = TargetSpace(target_func, PBOUNDS, constraint=constraint)\n\n assert len(space) == 0\n # registering with dict\n space.register(params={\"p1\": 1, \"p2\": 2}, target=3, constraint_value=0.0)\n assert len(space) == 1\n assert all(space.params[0] == np.array([1, 2]))\n assert all(space.target == np.array([3]))\n assert all(space.constraint_values == np.array([0]))\n\n # registering with array\n space.register(params={\"p1\": 0.5, \"p2\": 4}, target=4.5, constraint_value=2)\n assert len(space) == 2\n assert all(space.params[1] == np.array([0.5, 4]))\n assert all(space.target == np.array([3, 4.5]))\n assert all(space.constraint_values == np.array([0, 2]))\n\n with pytest.raises(ValueError):\n space.register(params={\"p1\": 0.2, \"p2\": 2}, target=2.2)\n\n\ndef test_register_point_beyond_bounds():\n PBOUNDS = {\"p1\": (0, 1), \"p2\": (1, 10)}\n space = TargetSpace(target_func, PBOUNDS)\n\n with pytest.warns(UserWarning, match=\"is outside the bounds of the parameter\"):\n space.register(params={\"p1\": 0.5, \"p2\": 20}, target=2.5)\n\n\ndef test_probe():\n PBOUNDS = {\"p1\": (0, 10), \"p2\": (1, 100)}\n space = TargetSpace(target_func, PBOUNDS, allow_duplicate_points=True)\n\n assert len(space) == 0\n # probing with dict\n space.probe(params={\"p1\": 1, \"p2\": 2})\n assert len(space) == 1\n assert all(space.params[-1] == np.array([1, 2]))\n assert all(space.target == np.array([3]))\n\n # probing with array\n space.probe(np.array([5, 4]))\n assert len(space) == 2\n assert all(space.params[1] == np.array([5, 4]))\n assert all(space.target == np.array([3, 9]))\n\n # probing same point with dict\n space.probe(params={\"p1\": 1, \"p2\": 2})\n assert len(space) == 3\n assert all(space.params[2] == np.array([1, 2]))\n assert all(space.target == np.array([3, 9, 3]))\n\n # probing same point with array\n space.probe(np.array([5, 4]))\n assert len(space) == 4\n assert all(space.params[1] == np.array([5, 4]))\n assert all(space.target == np.array([3, 9, 3, 9]))\n\n space = TargetSpace(target_func, PBOUNDS, allow_duplicate_points=False)\n\n # register wrong target to check probe doesn't recompute a duplicate point\n space.register(params={\"p1\": 1, \"p2\": 2}, target=5)\n\n # probing same point with dict\n target_ = space.probe(params={\"p1\": 1, \"p2\": 2})\n assert target_ == 5\n\n # probing same point with array\n target_ = space.probe(np.array([1, 2]))\n assert target_ == 5\n\n\ndef test_random_sample():\n pbounds = {\"p1\": (0, 1), \"p3\": (0, 3), \"p2\": (0, 2), \"p4\": (0, 4)}\n space = TargetSpace(target_func, pbounds, random_state=8)\n\n for _ in range(50):\n random_sample = space.random_sample()\n assert len(random_sample) == space.dim\n assert all(random_sample >= space.bounds[:, 0])\n assert all(random_sample <= space.bounds[:, 1])\n\n\ndef test_y_max():\n space = TargetSpace(target_func, PBOUNDS)\n assert space._target_max() is None\n space.probe(params={\"p1\": 1, \"p2\": 7})\n space.probe(params={\"p1\": 0.5, \"p2\": 1})\n space.probe(params={\"p1\": 0, \"p2\": 1})\n assert space._target_max() == 8\n\n\ndef test_y_max_with_constraint():\n PBOUNDS = {\"p1\": (0, 10), \"p2\": (1, 100)}\n constraint = NonlinearConstraint(lambda p1, p2: p1 - p2, -2, 2)\n space = TargetSpace(target_func, PBOUNDS, constraint)\n assert space._target_max() is None\n space.probe(params={\"p1\": 1, \"p2\": 2}) # Feasible\n space.probe(params={\"p1\": 5, \"p2\": 1}) # Unfeasible\n space.probe(params={\"p1\": 0, \"p2\": 1}) # Feasible\n assert space._target_max() == 3\n\n\ndef test_y_max_within_pbounds():\n PBOUNDS = {\"p1\": (0, 2), \"p2\": (1, 100)}\n space = TargetSpace(target_func, PBOUNDS)\n assert space._target_max() is None\n space.probe(params={\"p1\": 1, \"p2\": 2})\n space.probe(params={\"p1\": 0, \"p2\": 1})\n with pytest.warns(UserWarning, match=\"is outside the bounds of the parameter\"):\n space.probe(params={\"p1\": 5, \"p2\": 1})\n assert space._target_max() == 3\n\n\ndef test_max():\n PBOUNDS = {\"p1\": (0, 10), \"p2\": (1, 100)}\n space = TargetSpace(target_func, PBOUNDS)\n\n assert space.max() is None\n space.probe(params={\"p1\": 1, \"p2\": 2})\n space.probe(params={\"p1\": 5, \"p2\": 4})\n space.probe(params={\"p1\": 2, \"p2\": 3})\n space.probe(params={\"p1\": 1, \"p2\": 6})\n assert space.max() == {\"params\": {\"p1\": 5, \"p2\": 4}, \"target\": 9}\n\n\ndef test_max_with_constraint():\n PBOUNDS = {\"p1\": (0, 10), \"p2\": (1, 100)}\n constraint = NonlinearConstraint(lambda p1, p2: p1 - p2, -2, 2)\n space = TargetSpace(target_func, PBOUNDS, constraint=constraint)\n\n assert space.max() is None\n space.probe(params={\"p1\": 1, \"p2\": 2}) # Feasible\n space.probe(params={\"p1\": 5, \"p2\": 8}) # Unfeasible\n space.probe(params={\"p1\": 2, \"p2\": 3}) # Feasible\n space.probe(params={\"p1\": 1, \"p2\": 6}) # Unfeasible\n assert space.max() == {\"params\": {\"p1\": 2, \"p2\": 3}, \"target\": 5, \"constraint\": -1}\n\n\ndef test_max_with_constraint_identical_target_value():\n PBOUNDS = {\"p1\": (0, 10), \"p2\": (1, 100)}\n constraint = NonlinearConstraint(lambda p1, p2: p1 - p2, -2, 2)\n space = TargetSpace(target_func, PBOUNDS, constraint=constraint)\n\n assert space.max() is None\n space.probe(params={\"p1\": 1, \"p2\": 2}) # Feasible\n space.probe(params={\"p1\": 0, \"p2\": 5}) # Unfeasible, target value is 5, should not be selected\n space.probe(params={\"p1\": 5, \"p2\": 8}) # Unfeasible\n space.probe(params={\"p1\": 2, \"p2\": 3}) # Feasible, target value is also 5\n space.probe(params={\"p1\": 1, \"p2\": 6}) # Unfeasible\n assert space.max() == {\"params\": {\"p1\": 2, \"p2\": 3}, \"target\": 5, \"constraint\": -1}\n\n\ndef test_max_categorical() -> None:\n PBOUNDS = {\n \"first_float\": (0.0, 1.0),\n \"categorical_value\": (\"a\", \"b\", \"c\", \"d\"),\n \"second_float\": (0.0, 1.0),\n }\n\n def _f(first_float: float, categorical_value: str, second_float: float) -> float:\n return second_float if categorical_value == \"c\" else first_float\n\n space = TargetSpace(_f, PBOUNDS)\n space.probe(params={\"first_float\": 0.1, \"categorical_value\": \"a\", \"second_float\": 0.1})\n space.probe(params={\"first_float\": 0.1, \"categorical_value\": \"b\", \"second_float\": 0.9})\n space.probe(params={\"first_float\": 0.1, \"categorical_value\": \"c\", \"second_float\": 0.8})\n space.probe(params={\"first_float\": 0.1, \"categorical_value\": \"d\", \"second_float\": 0.9})\n\n expected = {\"first_float\": 0.1, \"categorical_value\": \"c\", \"second_float\": 0.8}\n assert space.max()[\"params\"] == expected\n\n\ndef test_res():\n PBOUNDS = {\"p1\": (0, 10), \"p2\": (1, 100)}\n space = TargetSpace(target_func, PBOUNDS)\n\n assert space.res() == []\n space.probe(params={\"p1\": 1, \"p2\": 2})\n space.probe(params={\"p1\": 5, \"p2\": 4})\n space.probe(params={\"p1\": 2, \"p2\": 3})\n space.probe(params={\"p1\": 1, \"p2\": 6})\n\n expected_res = [\n {\"params\": {\"p1\": 1, \"p2\": 2}, \"target\": 3},\n {\"params\": {\"p1\": 5, \"p2\": 4}, \"target\": 9},\n {\"params\": {\"p1\": 2, \"p2\": 3}, \"target\": 5},\n {\"params\": {\"p1\": 1, \"p2\": 6}, \"target\": 7},\n ]\n assert len(space.res()) == 4\n assert space.res() == expected_res\n\n\ndef test_res_categorical() -> None:\n PBOUNDS = {\"p1\": (0, 10), \"p2\": [\"a\", \"b\", \"c\"]}\n\n def _f(p1: float, p2: str) -> float:\n return p1 + len(p2)\n\n space = TargetSpace(_f, PBOUNDS)\n\n assert space.res() == []\n space.probe(params={\"p1\": 1, \"p2\": \"a\"})\n space.probe(params={\"p1\": 5, \"p2\": \"b\"})\n space.probe(params={\"p1\": 2, \"p2\": \"c\"})\n space.probe(params={\"p1\": 2, \"p2\": \"a\"})\n\n expected_res = [\n {\"params\": {\"p1\": 1, \"p2\": \"a\"}, \"target\": 2},\n {\"params\": {\"p1\": 5, \"p2\": \"b\"}, \"target\": 6},\n {\"params\": {\"p1\": 2, \"p2\": \"c\"}, \"target\": 3},\n {\"params\": {\"p1\": 2, \"p2\": \"a\"}, \"target\": 3},\n ]\n assert len(space.res()) == 4\n assert space.res() == expected_res\n\n\ndef test_res_categorical_with_constraints() -> None:\n PBOUNDS = {\"p1\": (0, 10), \"p2\": [\"a\", \"b\", \"c\"]}\n\n def _f(p1: float, p2: str) -> float:\n return p1 + len(p2)\n\n space = TargetSpace(_f, PBOUNDS, constraint=NonlinearConstraint(lambda p1, p2: p1 - 2, 0, 5))\n\n assert space.res() == []\n space.probe(params={\"p1\": 1, \"p2\": \"a\"})\n space.probe(params={\"p1\": 5, \"p2\": \"b\"})\n space.probe(params={\"p1\": 2, \"p2\": \"c\"})\n space.probe(params={\"p1\": 2, \"p2\": \"a\"})\n\n expected_res = [\n {\"params\": {\"p1\": 1, \"p2\": \"a\"}, \"target\": 2, \"allowed\": False, \"constraint\": -1},\n {\"params\": {\"p1\": 5, \"p2\": \"b\"}, \"target\": 6, \"allowed\": True, \"constraint\": 3},\n {\"params\": {\"p1\": 2, \"p2\": \"c\"}, \"target\": 3, \"allowed\": True, \"constraint\": 0},\n {\"params\": {\"p1\": 2, \"p2\": \"a\"}, \"target\": 3, \"allowed\": True, \"constraint\": 0},\n ]\n assert len(space.res()) == 4\n assert space.res() == expected_res\n\n\ndef test_set_bounds():\n pbounds = {\"p1\": (0, 1), \"p3\": (0, 3), \"p2\": (0, 2), \"p4\": (0, 4)}\n space = TargetSpace(target_func, pbounds)\n\n # Ignore unknown keys\n space.set_bounds({\"other\": (7, 8)})\n assert all(space.bounds[:, 0] == np.array([0, 0, 0, 0]))\n assert all(space.bounds[:, 1] == np.array([1, 3, 2, 4]))\n\n # Update bounds accordingly\n space.set_bounds({\"p2\": (1, 8)})\n assert all(space.bounds[:, 0] == np.array([0, 0, 1, 0]))\n assert all(space.bounds[:, 1] == np.array([1, 3, 8, 4]))\n\n\ndef test_no_target_func():\n target_space = TargetSpace(None, PBOUNDS)\n with pytest.raises(ValueError, match=\"No target function has been provided.\"):\n target_space.probe({\"p1\": 1, \"p2\": 2})\n\n\ndef test_change_typed_bounds():\n pbounds = {\n \"p1\": (0, 1),\n \"p2\": (1, 2),\n \"p3\": (-1, 3, int),\n \"fruit\": (\"apple\", \"banana\", \"mango\", \"honeydew melon\", \"strawberry\"),\n }\n\n space = TargetSpace(None, pbounds)\n\n with pytest.raises(ValueError):\n space.set_bounds({\"fruit\": (\"apple\", \"banana\", \"mango\", \"honeydew melon\")})\n\n with pytest.raises(ValueError):\n space.set_bounds({\"p3\": (-1, 2, float)})\n" }, { "path": "tests/test_util.py", "content": "from __future__ import annotations\n\nimport numpy as np\n\nfrom bayes_opt.util import ensure_rng\n\n\ndef test_ensure_rng():\n \"\"\"Test that ensure_rng properly handles different inputs.\"\"\"\n\n # Test with None (should return a new RandomState)\n rng1 = ensure_rng(None)\n assert isinstance(rng1, np.random.RandomState)\n\n # Test with int (should return a new RandomState seeded with that int)\n rng2 = ensure_rng(123)\n assert isinstance(rng2, np.random.RandomState)\n\n # Test with RandomState (should return the same RandomState)\n rng3 = np.random.RandomState(456)\n rng4 = ensure_rng(rng3)\n assert rng3 is rng4\n\n # Test that different seeds produce different random numbers\n rng5 = ensure_rng(1)\n rng6 = ensure_rng(2)\n assert rng5.random() != rng6.random()\n" } ]