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        "author": "Caio Oliveira",
        "author_email": "",
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        "classifiers": [
            "Intended Audience :: Developers",
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            "License :: OSI Approved",
            "Operating System :: MacOS",
            "Operating System :: Microsoft :: Windows",
            "Operating System :: POSIX",
            "Operating System :: Unix",
            "Programming Language :: Python",
            "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",
            "Topic :: Scientific/Engineering",
            "Topic :: Software Development"
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        "description": "|build-status| |docs| |package-status|\n\nWolpert, a stacked generalization framework\n===========================================\n\nWolpert is a `scikit-learn <http://scikit-learn.org>`_ compatible framework for easily building stacked ensembles. It supports:\n\n* Different stacking strategies\n* Multi-layer models\n* Different weights for each transformer\n* Easy to make it distributed\n\nQuickstart\n==========\n\nInstall\n-------\n\nThe easiest way to install is using pip. Just run ``pip install wolpert`` and you're ready to go.\n\nBuilding a simple model\n-----------------------\n\nFirst we need the layers of our model. The simplest way is using the helper function `make_stack_layer <http://wolpert.readthedocs.io/en/latest/generated/wolpert.pipeline.html#wolpert.pipeline.make_stack_layer>`_::\n\n     from sklearn.ensemble import RandomForestClassifier\n     from sklearn.svm import SVC\n     from sklearn.neighbors import KNeighborsClassifier\n     from sklearn.linear_model import LogisticRegression\n     from wolpert import make_stack_layer, StackingPipeline\n\n     layer0 = make_stack_layer(SVC(), KNeighborsClassifier(),\n                               RandomForestClassifier(),\n                               blending_wrapper='holdout')\n\n     clf = StackingPipeline([('l0', layer0),\n                             ('l1', LogisticRegression())])\n\nAnd that's it! And ``StackingPipeline`` inherits a scikit learn class: the `Pipeline <http://scikit-learn.org/stable/modules/generated/sklearn.pipeline.Pipeline.html>`_, so it works just the same::\n\n    clf.fit(Xtrain, ytrain)\n    ypreds = clf.predict_proba(Xtest)\n\nThis is just the basic example, but there are several ways of building a stacked ensemble with this framework. Make sure to check the `User Guide <http://wolpert.readthedocs.io/en/latest/user_guide.html>`_ to know more.\n\n.. |build-status| image:: https://circleci.com/gh/caioaao/wolpert.png?style=shield\n    :alt: CircleCI\n    :scale: 100%\n    :target: https://circleci.com/gh/caioaao/wolpert\n\n.. |docs| image:: https://readthedocs.org/projects/wolpert/badge/?verion=latest\n    :alt: Documentation status\n    :scale: 100%\n    :target: https://wolpert.readthedocs.io/en/latest/?badge=latest\n\n.. |package-status| image:: https://badge.fury.io/py/wolpert.svg\n    :alt: PyPI version\n    :scale: 100%\n    :target: https://badge.fury.io/py/wolpert\n\n\n",
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        "license": "new BSD",
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        "name": "wolpert",
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