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    "info": {
        "author": "Luca Cappelletti",
        "author_email": "cappelletti.luca94@gmail.com",
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        "classifiers": [
            "Development Status :: 3 - Alpha",
            "License :: OSI Approved :: MIT License",
            "Programming Language :: Python :: 3"
        ],
        "description": "gaussian_process\n=========================================================================================\n|travis| |sonar_quality| |sonar_maintainability| |codacy| |code_climate_maintainability| |pip| |downloads|\n\nWrapper for `\"sklearn.gp_minimize\"` for a simpler parameter specification using nested dictionaries.\n\nHow do I install this package?\n----------------------------------------------\nAs usual, just download it using pip:\n\n.. code:: shell\n\n    pip install gaussian_process\n\nTests Coverage\n----------------------------------------------\nSince some software handling coverages sometime get slightly different results, here's three of them:\n\n|coveralls| |sonar_coverage| |code_climate_coverage|\n\nKeras model optimization using a gaussian process\n-------------------------------------------------------------\n\n.. code:: python\n\n    import silence_tensorflow\n    from keras.models import Sequential\n    from keras.layers import Dense, Dropout\n    from keras.datasets import boston_housing\n    from extra_keras_utils import set_seed\n    from typing import Callable, Dict\n    import numpy as np\n    from holdouts_generator import holdouts_generator, random_holdouts\n    from gaussian_process import TQDMGaussianProcess, Space, GaussianProcess\n\n\n    class MLP:\n        def __init__(self, holdouts:Callable):\n            self._holdouts = holdouts\n        \n        def mlp(self, dense_layers:Dict, dropout_rate:float)->Sequential:\n            return Sequential([\n                *[Dense(**kwargs) for kwargs in dense_layers],\n                Dropout(dropout_rate),\n                Dense(1, activation=\"relu\"),\n            ])\n\n        def model_score(self, train:np.ndarray, test:np.ndarray, structure:Dict, fit:Dict):\n            model = self.mlp(**structure)\n            model.compile(\n                optimizer=\"nadam\",\n                loss=\"mse\"\n            )\n\n            return model.fit(\n                *train,\n                epochs=1,\n                validation_data=test,\n                verbose=0,\n                **fit\n            ).history[\"val_loss\"][-1]\n\n\n        def score(self, structure:Dict, fit:Dict):\n            return -np.mean([\n                self.model_score(training, test, structure, fit) for (training, test), _ in self._holdouts()\n            ])\n\n    if __name__ == \"__main__\":\n        set_seed(42)\n\n        generator = holdouts_generator(\n            *boston_housing.load_data()[0],\n            holdouts=random_holdouts([0.1], [2])\n        )\n\n        mlp = MLP(generator)\n\n        space = Space({\n            \"structure\":{\n                \"dense_layers\":[{\n                    \"units\":(8,16,32),\n                    \"activation\":(\"relu\", \"selu\")\n                },\n                {\n                    \"units\":[8,16,32],\n                    \"activation\":(\"relu\", \"selu\")\n                }],\n                \"dropout_rate\":[0.0,1.0]\n            },\n            \"fit\":{\n                \"batch_size\":[100,1000]\n            }\n        })\n\n        gp = GaussianProcess(mlp.score, space)\n        \n        n_calls = 3\n        results = gp.minimize(\n            n_calls=n_calls,\n            n_random_starts=1,\n            callback=[TQDMGaussianProcess(n_calls=n_calls)],\n            random_state=42\n        )\n        results = gp.minimize(\n            n_calls=n_calls,\n            n_random_starts=1,\n            callback=[TQDMGaussianProcess(n_calls=n_calls)],\n            random_state=42\n        )\n        print(gp.best_parameters)\n        print(gp.best_optimized_parameters)\n        gp.clear_cache()\n\n.. |travis| image:: https://travis-ci.org/LucaCappelletti94/gaussian_process.png\n   :target: https://travis-ci.org/LucaCappelletti94/gaussian_process\n   :alt: Travis CI build\n\n.. |sonar_quality| image:: https://sonarcloud.io/api/project_badges/measure?project=LucaCappelletti94_gaussian_process&metric=alert_status\n    :target: https://sonarcloud.io/dashboard/index/LucaCappelletti94_gaussian_process\n    :alt: SonarCloud Quality\n\n.. |sonar_maintainability| image:: https://sonarcloud.io/api/project_badges/measure?project=LucaCappelletti94_gaussian_process&metric=sqale_rating\n    :target: https://sonarcloud.io/dashboard/index/LucaCappelletti94_gaussian_process\n    :alt: SonarCloud Maintainability\n\n.. |sonar_coverage| image:: https://sonarcloud.io/api/project_badges/measure?project=LucaCappelletti94_gaussian_process&metric=coverage\n    :target: https://sonarcloud.io/dashboard/index/LucaCappelletti94_gaussian_process\n    :alt: SonarCloud Coverage\n\n.. |coveralls| image:: https://coveralls.io/repos/github/LucaCappelletti94/gaussian_process/badge.svg?branch=master\n    :target: https://coveralls.io/github/LucaCappelletti94/gaussian_process?branch=master\n    :alt: Coveralls Coverage\n\n.. |pip| image:: https://badge.fury.io/py/gaussian_process.svg\n    :target: https://badge.fury.io/py/gaussian_process\n    :alt: Pypi project\n\n.. |downloads| image:: https://pepy.tech/badge/gaussian_process\n    :target: https://pepy.tech/badge/gaussian_process\n    :alt: Pypi total project downloads \n\n.. |codacy|  image:: https://api.codacy.com/project/badge/Grade/0a674ed703f44793a27936462ca05080\n    :target: https://www.codacy.com/app/LucaCappelletti94/gaussian_process?utm_source=github.com&utm_medium=referral&utm_content=LucaCappelletti94/gaussian_process&utm_campaign=Badge_Grade\n    :alt: Codacy Maintainability\n\n.. |code_climate_maintainability| image:: https://api.codeclimate.com/v1/badges/aabe32e918c9ba7cd773/maintainability\n    :target: https://codeclimate.com/github/LucaCappelletti94/gaussian_process/maintainability\n    :alt: Maintainability\n\n.. |code_climate_coverage| image:: https://api.codeclimate.com/v1/badges/aabe32e918c9ba7cd773/test_coverage\n    :target: https://codeclimate.com/github/LucaCappelletti94/gaussian_process/test_coverage\n    :alt: Code Climate Coverate",
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