{
    "info": {
        "author": "Jasper Koops",
        "author_email": "jasperkoops91@gmail.com",
        "bugtrack_url": null,
        "classifiers": [
            "License :: OSI Approved :: MIT License",
            "Operating System :: OS Independent",
            "Programming Language :: Python :: 3.7"
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        "description": "[![Build Status](https://travis-ci.org/Jasper-Koops/easy-gscv.svg?branch=master)](https://travis-ci.org/Jasper-Koops/easy-gscv)\n[![codecov](https://codecov.io/gh/Jasper-Koops/easy-gscv/branch/master/graph/badge.svg)](https://codecov.io/gh/Jasper-Koops/easy-gscv)\n[![Checked with mypy](http://www.mypy-lang.org/static/mypy_badge.svg)](http://mypy-lang.org/)\n\n## Easy Grid Search / Cross Validation\n\n*From data to score in 4 lines of code.*\n\nThis library allows you to quickly train machine learning classifiers by\nautomatically splitting the dataset and using both\n[grid search](https://en.wikipedia.org/wiki/Hyperparameter_optimization) and [cross validation](https://en.wikipedia.org/wiki/Cross-validation_(statistics)) in the training process. Users can either pass define the parameters themselves or let the **GSCV** object\nchoose them automatically (based on the classifier).\n\nThis library is an extension of the [scikit-learn](http://scikit-learn.org/stable/index.html) project.\n\n[View on pypi](https://pypi.org/project/easy-gscv/)\n\n\n### Example:\n\n```\nfrom sklearn.neural_network import MLPClassifier\nfrom sklearn import datasets\nfrom easy_gscv.classifiers import GSCV\n\n# Create test dataset\niris = datasets.load_iris()\nX = iris.data\ny = iris.target\nclf = MLPClassifier()\n\n# Create model instance\ngscv_model = GSCV(clf(), X, y)\n\n# Get score\ngscv_model.score()\n\n```\n\n\n## install\n\n*requires python 3.7+*\n\n```\npip install easy-gscv\n```\n\n\n## create\n\n```\nfrom easy_gscv.models import GSCV\nclf = LogisticRegression()\ngscv_model = GSCV(\n    clf(), X, y, cv=15, n_jobs=-1, params={\n        'C': [10, 100],\n        'penalty': ['l2']\n    }\n)\n```\n\nNo need to create separate train / test datasets, the model does this\nautomatically on initialization.\nIf no parameters are provided the grid search is performed on a default set.\nBut these can be overridden.\n\nThe number of folds to be used for cross validation can be specified\nby using the `cv` keyword.\nTo speed up the training process you can use the `n_jobs` parameter to\nset the number of cpu cores to use (or set it to `-1` to use all available.)\n\nThe model accepts either sklearn classifiers or string values.\nYou can get a list of valid classifiers by calling the 'classifiers' property. Passing string arguments to the GSCV object in turn saves\nyou from having to import sklearn classifiers yourself.\n\n```\ngscv_model = GSCV('RandomForestClassifier',, X, y)\ngscv_model.classifiers\n\n'KNeighborsClassifier',\n'RandomForestClassifier',\n'GradientBoostingClassifier',\n'MLPClassifier',\n'LogisticRegression',\n```\n\n\n## score\n\n```\ngscv_model.score()\n```\n\nThe grid search is performed on the training data. Use the `score` method to evaluate\nhow well the model can be generalized by scoring it against the test dataset.\n\n\n## get_best_estimator\n\n```\ngscv_model.get_best_estimator()\n```\n\nReturns the best scoring sklearn classifier (based on training data).\nAs its a valid scikit-learn classifier, you can use it do anything that\nyou could do with sklearn classifier.\n\nThe following classifiers are currently supported. With the eventual goal of\nsupporting all scikit-learn classifiers in the future.\n\n* KNeighborsClassifier\n* RandomForestClassifier\n* GradientBoostingClassifier\n* MLPClassifier\n* LogisticRegression\n\n\n## get_fit_details\n\nAs cross validation returns an average, it can be helpful to\nget a more detailed overview of the best scoring classifier.\n\nThis method returns a table like the one displayed below, which\nthen can be used to further refine the choice or parameters for\nsubsequent runs.\n\n```\nclf = KNeighborsClassifier()\ngscv_model = GSCV(clf(), X, y)\ngscv_model.get_fit_details()\n\n0.965 (+/-0.026) for {'weights': 'uniform', 'n_neighbors': 3}\n0.977 (+/-0.013) for {'weights': 'distance', 'n_neighbors': 3}\n0.979 (+/-0.011) for {'weights': 'uniform', 'n_neighbors': 5}\n0.979 (+/-0.011) for {'weights': 'distance', 'n_neighbors': 5}\n0.976 (+/-0.018) for {'weights': 'uniform', 'n_neighbors': 8}\n0.975 (+/-0.018) for {'weights': 'distance', 'n_neighbors': 8}\n0.971 (+/-0.022) for {'weights': 'uniform', 'n_neighbors': 12}\n0.973 (+/-0.024) for {'weights': 'distance', 'n_neighbors': 12}\n0.973 (+/-0.025) for {'weights': 'uniform', 'n_neighbors': 15}\n\n```\n\n\n",
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