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        "author": "Manuel Calzolari",
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            "Development Status :: 4 - Beta",
            "Intended Audience :: Developers",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: GNU Lesser General Public License v3 (LGPLv3)",
            "Programming Language :: Python :: 2",
            "Programming Language :: Python :: 2.7",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.4",
            "Programming Language :: Python :: 3.5",
            "Programming Language :: Python :: 3.6",
            "Programming Language :: Python :: 3.7",
            "Topic :: Scientific/Engineering",
            "Topic :: Software Development"
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        "description": "# sklearn-genetic\n\nGenetic feature selection module for scikit-learn\n\nGenetic algorithms mimic the process of natural selection to search for optimal values of a function.\n\n## Installation\n\n```bash\npip install sklearn-genetic\n```\n\n## Requirements\n* Python >= 2.7\n* scikit-learn >= 0.18\n* DEAP >= 1.0.2\n\n## Example\n\n```python\nfrom __future__ import print_function\nimport numpy as np\nfrom sklearn import datasets, linear_model\n\nfrom genetic_selection import GeneticSelectionCV\n\n\ndef main():\n    iris = datasets.load_iris()\n\n    # Some noisy data not correlated\n    E = np.random.uniform(0, 0.1, size=(len(iris.data), 20))\n\n    X = np.hstack((iris.data, E))\n    y = iris.target\n\n    estimator = linear_model.LogisticRegression(solver=\"liblinear\", multi_class=\"ovr\")\n\n    selector = GeneticSelectionCV(estimator,\n                                  cv=5,\n                                  verbose=1,\n                                  scoring=\"accuracy\",\n                                  max_features=5,\n                                  n_population=50,\n                                  crossover_proba=0.5,\n                                  mutation_proba=0.2,\n                                  n_generations=40,\n                                  crossover_independent_proba=0.5,\n                                  mutation_independent_proba=0.05,\n                                  tournament_size=3,\n                                  n_gen_no_change=10,\n                                  caching=True,\n                                  n_jobs=-1)\n    selector = selector.fit(X, y)\n\n    print(selector.support_)\n\n\nif __name__ == \"__main__\":\n    main()\n\n```",
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