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    "info": {
        "author": "Dayvid Victor <victor.dvro@gmail.com>, Thyago Porpino <thyago.porpino@gmail.com>",
        "author_email": "brew-python-devs@googlegroups.com",
        "bugtrack_url": null,
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            "Development Status :: 2 - Pre-Alpha",
            "Intended Audience :: Developers",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: MIT License",
            "Natural Language :: English",
            "Programming Language :: Python",
            "Programming Language :: Python :: 3",
            "Topic :: Scientific/Engineering"
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        "description": "=============================\nbrew\n=============================\n\n.. image:: https://badge.fury.io/py/brew.png\n    :target: http://badge.fury.io/py/brew\n\n.. image:: https://travis-ci.org/viisar/brew.png?branch=master\n    :target: https://travis-ci.org/viisar/brew\n\n.. image:: https://landscape.io/github/viisar/brew/master/landscape.svg?style=flat\n   :target: https://landscape.io/github/viisar/brew/master\n   :alt: Code Health\n\n.. image:: https://coveralls.io/repos/github/viisar/brew/badge.svg?branch=master\n    :target: https://coveralls.io/github/viisar/brew?branch=master\n\n.. image:: https://badges.gitter.im/Join%20Chat.svg\n   :alt: Join the chat at https://gitter.im/viisar/brew\n   :target: https://gitter.im/viisar/brew?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge\n\n\n**brew: A Multiple Classifier Systems API**\n\n| This project was started in 2014 by *Dayvid Victor* and *Thyago Porpino*\n| for the Multiple Classifier Systems class at Federal University of Pernambuco.\n\n\n| The aim of this project is to provide an easy API for Ensembling, Stacking, \n| Blending, Ensemble Generation, Ensemble Pruning, Dynamic Classifier Selection, \n| and Dynamic Ensemble Selection.\n\nFeatures\n===========\n* General: Ensembling, Stacking and Blending.\n* Ensemble Classifier Generators: Bagging, Random Subspace, SMOTE-Bagging, ICS-Bagging, SMOTE-ICS-Bagging.\n* Dynamic Selection: Overall Local Accuracy (OLA), Local Class Accuracy (LCA), Multiple Classifier Behavior (MCB), K-Nearest Oracles Eliminate (KNORA-E), K-Nearest Oracles Union (KNORA-U), A Priori Dynamic Selection, A Posteriori Dynamic Selection, Dynamic Selection KNN (DSKNN).\n* Ensemble Combination Rules: majority vote, min, max, mean and median.\n* Ensemble Diversity Metrics: Entropy Measure E, Kohavi Wolpert Variance, Q Statistics, Correlation Coefficient p, Disagreement Measure, Agreement Measure, Double Fault Measure.\n* Ensemble Pruning: Ensemble Pruning via Individual Contribution (EPIC).\n\nExample\n============\n\n.. code-block:: python\n\n        import numpy as np\n        import matplotlib.pyplot as plt\n        import matplotlib.gridspec as gridspec\n        import itertools\n\n        import sklearn\n\n        from sklearn.linear_model import LogisticRegression\n        from sklearn.svm import SVC\n        from sklearn.ensemble import RandomForestClassifier\n\n        from brew.base import Ensemble, EnsembleClassifier\n        from brew.stacking.stacker import EnsembleStack, EnsembleStackClassifier\n        from brew.combination.combiner import Combiner\n\n        from mlxtend.data import iris_data\n        from mlxtend.evaluate import plot_decision_regions\n\n        # Initializing Classifiers\n        clf1 = LogisticRegression(random_state=0)\n        clf2 = RandomForestClassifier(random_state=0)\n        clf3 = SVC(random_state=0, probability=True)\n\n        # Creating Ensemble\n        ensemble = Ensemble([clf1, clf2, clf3])\n        eclf = EnsembleClassifier(ensemble=ensemble, combiner=Combiner('mean'))\n\n        # Creating Stacking\n        layer_1 = Ensemble([clf1, clf2, clf3])\n        layer_2 = Ensemble([sklearn.clone(clf1)])\n\n        stack = EnsembleStack(cv=3)\n\n        stack.add_layer(layer_1)\n        stack.add_layer(layer_2)\n\n        sclf = EnsembleStackClassifier(stack)\n\n        clf_list = [clf1, clf2, clf3, eclf, sclf]\n        lbl_list = ['Logistic Regression', 'Random Forest', 'RBF kernel SVM', 'Ensemble', 'Stacking']\n\n        # Loading some example data\n        X, y = iris_data()\n        X = X[:,[0, 2]]\n\n        # Plotting Decision Regions\n        gs = gridspec.GridSpec(2, 3)\n        fig = plt.figure(figsize=(10, 8))\n\n        itt = itertools.product([0, 1, 2], repeat=2)\n\n        for clf, lab, grd in zip(clf_list, lbl_list, itt):\n            clf.fit(X, y)\n            ax = plt.subplot(gs[grd[0], grd[1]])\n            fig = plot_decision_regions(X=X, y=y, clf=clf, legend=2)\n            plt.title(lab)\n        plt.show()\n\n\n.. image:: https://raw.githubusercontent.com/viisar/brew/master/docs/sources/img/iris_decision_regions_2d.png\n    :alt: decision regions plots\n    :align: center\n\n\nDependencies\n============\n- Python 2.7+\n- scikit-learn >= 0.15.2\n- Numpy >= 1.6.1\n- SciPy >= 0.9\n- Matplotlib >= 0.99.1 (examples, only)\n- mlxtend (examples, only)\n\n\nInstalling\n==========\n\nYou can easily install brew using ``pip``::\n\n    pip install brew\n\nor, if you prefer an up-to-date version, get it from here::\n\n    pip install git+https://github.com/viisar/brew.git\n\n\nImportant References\n====================\n\n- Kuncheva, Ludmila I. Combining pattern classifiers: methods and algorithms. John Wiley & Sons, 2014.\n- Zhou, Zhi-Hua. Ensemble methods: foundations and algorithms. CRC Press, 2012.\n\n\n\n\nDocumentation\n-------------\n\nThe full documentation is at http://brew.rtfd.org.\n\n\n\nHistory\n-------\n\n0.1.0 (2014-11-12)\n++++++++++++++++++\n\n* First release on PyPI.",
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