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        "description": "<div align=\"center\"><img src=\"https://raw.githubusercontent.com/pfnet/optuna/master/docs/image/optuna-logo.png\" width=\"800\"/></div>\n\n# Optuna: A hyperparameter optimization framework\n\n[![pypi](https://img.shields.io/pypi/v/optuna.svg)](https://pypi.python.org/pypi/optuna)\n[![GitHub license](https://img.shields.io/badge/license-MIT-blue.svg)](https://github.com/pfnet/optuna)\n[![CircleCI](https://circleci.com/gh/pfnet/optuna.svg?style=svg)](https://circleci.com/gh/pfnet/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/pfnet/optuna/branch/master/graph/badge.svg)](https://codecov.io/gh/pfnet/optuna/branch/master)\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\n## Key Features\n\nOptuna has modern functionalities as follows:\n\n- Parallel distributed optimization\n- Pruning of unpromising trials\n- Lightweight, versatile, and platform agnostic architecture\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\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_loguniform('svr_c', 1e-10, 1e10)\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  # A 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\n## Installation\n\nTo install Optuna, use `pip` as follows:\n\n```\n$ pip install optuna\n```\n\nOptuna supports Python 2.7 and Python 3.5 or newer.\n\n\n## Contribution\n\nAny contributions to Optuna are welcome! When you send a pull request, please follow the\n[contribution guide](./CONTRIBUTING.md).\n\n\n## License\n\nMIT License (see [LICENSE](./LICENSE)).\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)).",
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