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            "License :: OSI Approved :: MIT License",
            "Natural Language :: English",
            "Programming Language :: Python :: 2",
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        "description": "<p align=\"left\">\n<img width=15% src=\"https://dai.lids.mit.edu/wp-content/uploads/2018/06/Logo_DAI_highres.png\" alt=\u201cBTB\u201d />\n<i>An open source project from Data to AI Lab at MIT.</i>\n</p>\n\n![](https://raw.githubusercontent.com/HDI-Project/BTB/master/docs/_static/BTB-Icon-small.png)\n\nA simple, extensible backend for developing auto-tuning systems.\n\n[![PyPi](https://img.shields.io/pypi/v/baytune.svg)](https://pypi.python.org/pypi/baytune)\n[![Travis](https://travis-ci.org/HDI-Project/BTB.svg?branch=master)](https://travis-ci.org/HDI-Project/BTB)\n[![CodeCov](https://codecov.io/gh/HDI-Project/BTB/branch/master/graph/badge.svg)](https://codecov.io/gh/HDI-Project/BTB)\n\n# Overview\n\nBayesian Tuning and Bandits is a simple, extensible backend for developing auto-tuning systems such as AutoML systems. It is currently being used in [ATM](https://github.com/HDI-Project/ATM) (an AutoML system that allows tuning of classifiers) and MIT's system for the DARPA [Data driven discovery of models program](https://www.darpa.mil/program/data-driven-discovery-of-models).\n\n* Free software: MIT license\n* Documentation: https://HDI-Project.github.io/BTB\n* Homepage: https://github.com/HDI-Project/BTB\n\n*BTB is under active development. If you come across any issues, please report them [here](https://github.com/HDI-Project/BTB/issues/new).*\n\n## Installation\n\n### Install with pip\n\nThe easiest way to install BTB is using `pip`.\n\n```\npip install baytune\n```\n\n### Build from source\n\nYou can also clone the repository and build it from source.\n\n```\ngit clone git@github.com:HDI-Project/BTB.git\ncd BTB\nmake install\n```\n\n## Basic Usage\n\n### Tuners\n\nIn order to use a tuner we will create a ``Tuner`` instance indicating which parameters\nwe want to tune, their types and the range of values that we want to try.\n\n``` python\n>>> from btb.tuning import GP\n>>> from btb import HyperParameter, ParamTypes\n>>> tunables = [\n... ('n_estimators', HyperParameter(ParamTypes.INT, [10, 500])),\n... ('max_depth', HyperParameter(ParamTypes.INT, [3, 20]))\n... ]\n>>> tuner = GP(tunables)\n```\n\nThen we perform the following three steps in a loop.\n\n1. Let the Tuner propose a new set of parameters\n\n    ``` python\n    >>> parameters = tuner.propose()\n    >>> parameters\n    {'n_estimators': 297, 'max_depth': 3}\n    ```\n\n2. Fit and score a new model using these parameters\n\n    ``` python\n    >>> model = RandomForestClassifier(**parameters)\n    >>> model.fit(X_train, y_train)\n    RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n                max_depth=3, max_features='auto', max_leaf_nodes=None,\n                min_impurity_decrease=0.0, min_impurity_split=None,\n                min_samples_leaf=1, min_samples_split=2,\n                min_weight_fraction_leaf=0.0, n_estimators=297, n_jobs=1,\n                oob_score=False, random_state=None, verbose=0,\n                warm_start=False)\n    >>> score = model.score(X_test, y_test)\n    >>> score\n    0.77\n    ```\n\n3. Pass the used parameters and the score obtained back to the tuner\n\n    ``` python\n    tuner.add(parameters, score)\n    ```\n\nAt each iteration, the `Tuner` will use the information about the previous tests\nto evaluate and propose the set of parameter values that have the highest probability\nof obtaining the highest score.\n\nFor more detailed examples, check scripts from the `examples` folder.\n\n### Selectors\n\nThe selectors are intended to be used in combination with tuners in order to find\nout and decide which model seems to get the best results once it is properly fine tuned.\n\nIn order to use the selector we will create a ``Tuner`` instance for each model that\nwe want to try out, as well as the ``Selector`` instance.\n\n```\n>>> from sklearn.ensemble import RandomForestClassifier\n>>> from sklearn.svm import SVC\n>>> models = {\n...     'RF': RandomForestClassifier,\n...     'SVC': SVC\n... }\n>>> from btb.selection import UCB1\n>>> selector = UCB1(['RF', 'SVC'])\n>>> tuners = {\n...     'RF': GP([\n...         ('n_estimators', HyperParameter(ParamTypes.INT, [10, 500])),\n...         ('max_depth', HyperParameter(ParamTypes.INT, [3, 20]))\n...     ]),\n...     'SVC': GP([\n...         ('c', HyperParameter(ParamTypes.FLOAT_EXP, [0.01, 10.0])),\n...         ('gamma', HyperParameter(ParamTypes.FLOAT, [0.000000001, 0.0000001]))\n...     ])\n... }\n```\n\nThen we perform the following steps in a loop.\n\n1. Pass all the obtained scores to the selector and let it decide which model to test.\n\n    ``` python\n    >>> next_choice = selector.select({'RF': tuners['RF'].y, 'SVC': tuners['SVC'].y})\n    >>> next_choice\n    'RF'\n    ```\n\n2. Obtain a new set of parameters from the indicated tuner and create a model instance.\n\n    ``` python\n    >>> parameters = tuners[next_choice].propose()\n    >>> parameters\n    {'n_estimators': 289, 'max_depth': 18}\n    >>> model = models[next_choice](**parameters)\n    ```\n\n3. Evaluate the score of the new model instance and pass it back to the tuner\n\n    ``` python\n    >>> model.fit(X_train, y_train)\n    RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n                max_depth=18, max_features='auto', max_leaf_nodes=None,\n                min_impurity_decrease=0.0, min_impurity_split=None,\n                min_samples_leaf=1, min_samples_split=2,\n                min_weight_fraction_leaf=0.0, n_estimators=289, n_jobs=1,\n                oob_score=False, random_state=None, verbose=0,\n                warm_start=False)\n    >>> score = model.score(X_test, y_test)\n    >>> score\n    0.89\n    >>> tuners[next_choice].add(parameters, score)\n    ```\n\n## References\n\nIf you use BTB, please consider citing the following work:\n\n- Laura Gustafson. Bayesian Tuning and Bandits: An Extensible, Open Source Library for AutoML. Masters thesis, MIT EECS, June 2018. [(pdf)](https://dai.lids.mit.edu/wp-content/uploads/2018/05/Laura_MEng_Final.pdf)\n\n  ``` bibtex \n  @MastersThesis{Laura:2018,\n    title = \"Bayesian Tuning and Bandits: An Extensible, Open Source Library for AutoML\",\n    author = \"Laura Gustafson\",\n    month = \"May\",\n    year = \"2018\",\n    url = \"https://dai.lids.mit.edu/wp-content/uploads/2018/05/Laura_MEng_Final.pdf\",\n    type = \"M. Eng Thesis\",\n    address = \"Cambridge, MA\",\n    school = \"Massachusetts Institute of Technology\",\n  }\n```\n\n\n# History\n\n## 0.2.5\n\n### Bug Fixes\n\n* Issue #115: HyperParameter subclass instantiation not working properly\n\n## 0.2.4\n\n### Internal Improvements\n\n* Issue #62: Test for `None` in `HyperParameter.cast` instead of `HyperParameter.__init__`\n\n### Bug fixes\n\n* Issue #98: Categorical hyperparameters do not support `None` as input\n* Issue #89: Fix the computation of `avg_rewards` in `BestKReward`\n\n## 0.2.3\n\n### Bug Fixes\n\n* Issue #84: Error in GP tuning when only one parameter is present bug\n* Issue #96: Fix pickling of HyperParameters\n* Issue #98: Fix implementation of the GPEi tuner\n\n## 0.2.2\n\n### Internal Improvements\n\n* Updated documentation\n\n### Bug Fixes\n\n* Issue #94: Fix unicode `param_type` caused error on python 2.\n\n## 0.2.1\n\n### Bug fixes\n\n* Issue #74: `ParamTypes.STRING` tunables do not work\n\n## 0.2.0\n\n### New Features\n\n* New Recommendation module\n* New HyperParameter types\n* Improved documentation and examples\n* Fully tested Python 2.7, 3.4, 3.5 and 3.6 compatibility\n* HyperParameter copy and deepcopy support\n* Replace print statements with logging\n\n### Internal Improvements\n\n* Integrated with Travis-CI\n* Exhaustive unit testing\n* New implementation of HyperParameter\n* Tuner builds a grid of real values instead of indices\n* Resolve Issue #29: Make args explicit in `__init__` methods\n* Resolve Issue #34: make all imports explicit\n\n### Bug Fixes\n\n* Fix error from mixing string/numerical hyperparameters\n* Inverse transform for categorical hyperparameter returns single item\n\n## 0.1.2\n\n* Issue #47: Add missing requirements in v0.1.1 setup.py\n* Issue #46: Error on v0.1.1: 'GP' object has no attribute 'X'\n\n## 0.1.1\n\n* First release.\n\n\n",
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