{"id":20106219,"url":"https://github.com/himkt/optuna-versioned-gh-pages","last_synced_at":"2025-08-22T20:06:25.260Z","repository":{"id":51112241,"uuid":"370041669","full_name":"himkt/optuna-versioned-gh-pages","owner":"himkt","description":null,"archived":false,"fork":false,"pushed_at":"2021-05-23T12:42:36.000Z","size":19043,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":2,"default_branch":"master","last_synced_at":"2025-01-13T05:08:47.625Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/himkt.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":"CONTRIBUTING.md","funding":null,"license":"LICENSE","code_of_conduct":"CODE_OF_CONDUCT.md","threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null}},"created_at":"2021-05-23T12:03:06.000Z","updated_at":"2021-05-23T12:39:09.000Z","dependencies_parsed_at":"2022-09-09T17:13:11.642Z","dependency_job_id":null,"html_url":"https://github.com/himkt/optuna-versioned-gh-pages","commit_stats":null,"previous_names":[],"tags_count":3,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/himkt%2Foptuna-versioned-gh-pages","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/himkt%2Foptuna-versioned-gh-pages/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/himkt%2Foptuna-versioned-gh-pages/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/himkt%2Foptuna-versioned-gh-pages/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/himkt","download_url":"https://codeload.github.com/himkt/optuna-versioned-gh-pages/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":241549089,"owners_count":19980475,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":[],"created_at":"2024-11-13T17:49:29.552Z","updated_at":"2025-03-02T17:47:35.384Z","avatar_url":"https://github.com/himkt.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"\u003cdiv align=\"center\"\u003e\u003cimg src=\"https://raw.githubusercontent.com/optuna/optuna/master/docs/image/optuna-logo.png\" width=\"800\"/\u003e\u003c/div\u003e\n\n# Optuna: A hyperparameter optimization framework\n\n[![Python](https://img.shields.io/badge/python-3.6%20%7C%203.7%20%7C%203.8%20%7C%203.9-blue)](https://www.python.org)\n[![pypi](https://img.shields.io/pypi/v/optuna.svg)](https://pypi.python.org/pypi/optuna)\n[![conda](https://img.shields.io/conda/vn/conda-forge/optuna.svg)](https://anaconda.org/conda-forge/optuna)\n[![GitHub license](https://img.shields.io/badge/license-MIT-blue.svg)](https://github.com/optuna/optuna)\n[![CircleCI](https://circleci.com/gh/optuna/optuna.svg?style=svg)](https://circleci.com/gh/optuna/optuna)\n[![Read the Docs](https://readthedocs.org/projects/optuna/badge/?version=stable)](https://optuna.readthedocs.io/en/stable/)\n[![Codecov](https://codecov.io/gh/optuna/optuna/branch/master/graph/badge.svg)](https://codecov.io/gh/optuna/optuna/branch/master)\n[![Gitter chat](https://badges.gitter.im/optuna/gitter.svg)](https://gitter.im/optuna/optuna)\n\n[**Website**](https://optuna.org/)\n| [**Docs**](https://optuna.readthedocs.io/en/stable/)\n| [**Install Guide**](https://optuna.readthedocs.io/en/stable/installation.html)\n| [**Tutorial**](https://optuna.readthedocs.io/en/stable/tutorial/index.html)\n\n*Optuna* is an automatic hyperparameter optimization software framework, particularly designed\nfor machine learning. It features an imperative, *define-by-run* style user API. Thanks to our\n*define-by-run* API, the code written with Optuna enjoys high modularity, and the user of\nOptuna can dynamically construct the search spaces for the hyperparameters.\n\n## Key Features\n\nOptuna has modern functionalities as follows:\n\n- [Lightweight, versatile, and platform agnostic architecture](https://optuna.readthedocs.io/en/stable/tutorial/10_key_features/001_first.html)\n  - Handle a wide variety of tasks with a simple installation that has few requirements.\n- [Pythonic search spaces](https://optuna.readthedocs.io/en/stable/tutorial/10_key_features/002_configurations.html)\n  - Define search spaces using familiar Python syntax including conditionals and loops.\n- [Efficient optimization algorithms](https://optuna.readthedocs.io/en/stable/tutorial/10_key_features/003_efficient_optimization_algorithms.html)\n  - Adopt state-of-the-art algorithms for sampling hyperparameters and efficiently pruning unpromising trials.\n- [Easy parallelization](https://optuna.readthedocs.io/en/stable/tutorial/10_key_features/004_distributed.html)\n  - Scale studies to tens or hundreds or workers with little or no changes to the code.\n- [Quick visualization](https://optuna.readthedocs.io/en/stable/tutorial/10_key_features/005_visualization.html)\n  - Inspect optimization histories from a variety of plotting functions.\n\n\n## Basic Concepts\n\nWe use the terms *study* and *trial* as follows:\n\n- Study: optimization based on an objective function\n- Trial: a single execution of the objective function\n\nPlease refer to sample code below. The goal of a *study* is to find out the optimal set of\nhyperparameter values (e.g., `classifier` and `svm_c`) through multiple *trials* (e.g.,\n`n_trials=100`). Optuna is a framework designed for the automation and the acceleration of the\noptimization *studies*.\n\n[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](http://colab.research.google.com/github/optuna/optuna-examples/blob/main/quickstart.ipynb)\n\n```python\nimport ...\n\n# Define an objective function to be minimized.\ndef objective(trial):\n\n    # Invoke suggest methods of a Trial object to generate hyperparameters.\n    regressor_name = trial.suggest_categorical('classifier', ['SVR', 'RandomForest'])\n    if regressor_name == 'SVR':\n        svr_c = trial.suggest_float('svr_c', 1e-10, 1e10, log=True)\n        regressor_obj = sklearn.svm.SVR(C=svr_c)\n    else:\n        rf_max_depth = trial.suggest_int('rf_max_depth', 2, 32)\n        regressor_obj = sklearn.ensemble.RandomForestRegressor(max_depth=rf_max_depth)\n\n    X, y = sklearn.datasets.load_boston(return_X_y=True)\n    X_train, X_val, y_train, y_val = sklearn.model_selection.train_test_split(X, y, random_state=0)\n\n    regressor_obj.fit(X_train, y_train)\n    y_pred = regressor_obj.predict(X_val)\n\n    error = sklearn.metrics.mean_squared_error(y_val, y_pred)\n\n    return error  # An objective value linked with the Trial object.\n\nstudy = optuna.create_study()  # Create a new study.\nstudy.optimize(objective, n_trials=100)  # Invoke optimization of the objective function.\n```\n\n## Examples\n\nExamples can be found in [optuna/optuna-examples](https://github.com/optuna/optuna-examples).\n\n## Integrations\n\n[Integrations modules](https://optuna.readthedocs.io/en/stable/tutorial/10_key_features/003_efficient_optimization_algorithms.html#integration-modules-for-pruning), which allow pruning, or early stopping, of unpromising trials are available for the following libraries:\n\n* [AllenNLP](https://github.com/optuna/optuna-examples/tree/main/allennlp)\n* [Catalyst](https://github.com/optuna/optuna-examples/tree/main/pytorch/catalyst_simple.py)\n* [Catboost](https://github.com/optuna/optuna-examples/tree/main/catboost/catboost_simple.py)\n* [Chainer](https://github.com/optuna/optuna-examples/tree/main/chainer/chainer_integration.py)\n* FastAI ([V1](https://github.com/optuna/optuna-examples/tree/main/fastai/fastaiv1_simple.py), [V2](https://github.com/optuna/optuna-examples/tree/main/fastai/fastaiv2_simple.py))\n* [Keras](https://github.com/optuna/optuna-examples/tree/main/keras/keras_integration.py)\n* [LightGBM](https://github.com/optuna/optuna-examples/tree/main/lightgbm/lightgbm_integration.py)\n* [MXNet](https://github.com/optuna/optuna-examples/tree/main/mxnet/mxnet_integration.py)\n* [PyTorch](https://github.com/optuna/optuna-examples/tree/main/pytorch/pytorch_simple.py)\n* [PyTorch Ignite](https://github.com/optuna/optuna-examples/tree/main/pytorch/pytorch_ignite_simple.py)\n* [PyTorch Lightning](https://github.com/optuna/optuna-examples/tree/main/pytorch/pytorch_lightning_simple.py)\n* [TensorFlow](https://github.com/optuna/optuna-examples/tree/main/tensorflow/tensorflow_estimator_integration.py)\n* [tf.keras](https://github.com/optuna/optuna-examples/tree/main/tfkeras/tfkeras_integration.py)\n* [XGBoost](https://github.com/optuna/optuna-examples/tree/main/xgboost/xgboost_integration.py)\n\n\n## Web Dashboard (experimental)\n\nThe new Web dashboard is under the development at [optuna-dashboard](https://github.com/optuna/optuna-dashboard).\nIt is still experimental, but much better in many regards.\nFeature requests and bug reports welcome!\n\n| Manage studies | Visualize with interactive graphs |\n| -------------- | --------------------------------- |\n| ![manage-studies](https://user-images.githubusercontent.com/5564044/97099702-4107be80-16cf-11eb-9d97-f5ceec98ce52.gif) | ![optuna-realtime-graph](https://user-images.githubusercontent.com/5564044/97099797-66e19300-16d0-11eb-826c-6977e3941fb0.gif) |\n\nInstall `optuna-dashboard` via pip:\n\n```\n$ pip install optuna-dashboard\n$ optuna-dashboard sqlite:///db.sqlite3\n...\nListening on http://localhost:8080/\nHit Ctrl-C to quit.\n```\n\n## Installation\n\nOptuna is available at [the Python Package Index](https://pypi.org/project/optuna/) and on [Anaconda Cloud](https://anaconda.org/conda-forge/optuna).\n\n```bash\n# PyPI\n$ pip install optuna\n```\n\n```bash\n# Anaconda Cloud\n$ conda install -c conda-forge optuna\n```\n\nOptuna supports Python 3.6 or newer.\n\nAlso, we also provide Optuna docker images on [DockerHub](https://hub.docker.com/r/optuna/optuna).\n\n## Communication\n\n- [GitHub Issues] for bug reports, feature requests and questions.\n- [Gitter] for interactive chat with developers.\n- [Stack Overflow] for questions.\n\n[GitHub issues]: https://github.com/optuna/optuna/issues\n[Gitter]: https://gitter.im/optuna/optuna\n[Stack Overflow]: https://stackoverflow.com/questions/tagged/optuna\n\n\n## Contribution\n\nAny contributions to Optuna are more than welcome!\n\nIf you are new to Optuna, please check the [good first issues](https://github.com/optuna/optuna/labels/good%20first%20issue). They are relatively simple, well-defined and are often good starting points for you to get familiar with the contribution workflow and other developers.\n\nIf you already have contributed to Optuna, we recommend the other [contribution-welcome issues](https://github.com/optuna/optuna/labels/contribution-welcome).\n\nFor general guidelines how to contribute to the project, take a look at [CONTRIBUTING.md](./CONTRIBUTING.md).\n\n\n## Reference\n\nTakuya Akiba, Shotaro Sano, Toshihiko Yanase, Takeru Ohta, and Masanori Koyama. 2019.\nOptuna: A Next-generation Hyperparameter Optimization Framework. In KDD ([arXiv](https://arxiv.org/abs/1907.10902)).\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fhimkt%2Foptuna-versioned-gh-pages","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fhimkt%2Foptuna-versioned-gh-pages","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fhimkt%2Foptuna-versioned-gh-pages/lists"}