Metadata-Version: 1.1
Name: wolpert
Version: 0.1a2
Summary: Stacked generalization framework
Home-page: https://github.com/caioaao/wolpert
Author: Caio Oliveira
Author-email: UNKNOWN
License: new BSD
Description: |build-status| |docs| |package-status|
        
        Wolpert, a stacked generalization framework
        ===========================================
        
        Wolpert is a `scikit-learn <http://scikit-learn.org>`_ compatible framework for easily building stacked ensembles. It supports:
        
        * Different stacking strategies
        * Multi-layer models
        * Different weights for each transformer
        * Easy to make it distributed
        
        Quickstart
        ==========
        
        Install
        -------
        
        The easiest way to install is using pip. Just run ``pip install wolpert`` and you're ready to go.
        
        Building a simple model
        -----------------------
        
        First 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>`_:
        
        .. testcode::
        
             from sklearn.ensemble import RandomForestClassifier
             from sklearn.svm import SVC
             from sklearn.neighbors import KNeighborsClassifier
             from sklearn.linear_model import LogisticRegression
             from wolpert.pipeline import make_stack_layer, StackingPipeline
             
             layer0 = make_stack_layer(SVC(), KNeighborsClassifier(),
                                       RandomForestClassifier(),
                                       blending_wrapper='holdout')
                                       
             clf = StackingPipeline([('l0', layer0),
                                     ('l1', LogisticRegression())])
        
        And 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::
        
            clf.fit(Xtrain, ytrain)
            ypreds = clf.predict_proba(Xtest)
        
        This 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.
        
        .. |build-status| image:: https://circleci.com/gh/caioaao/wolpert.png?style=shield
            :alt: CircleCI
            :scale: 100%
            :target: https://circleci.com/gh/caioaao/wolpert
        
        .. |docs| image:: https://readthedocs.org/projects/wolpert/badge/?verion=latest
            :alt: Documentation status
            :scale: 100%
            :target: https://wolpert.readthedocs.io/en/latest/?badge=latest
        
        .. |package-status| image:: https://badge.fury.io/py/wolpert.svg
            :alt: PyPI version
            :scale: 100%
            :target: https://badge.fury.io/py/wolpert
        
Platform: UNKNOWN
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved
Classifier: Programming Language :: Python
Classifier: Topic :: Software Development
Classifier: Topic :: Scientific/Engineering
Classifier: Operating System :: Microsoft :: Windows
Classifier: Operating System :: POSIX
Classifier: Operating System :: Unix
Classifier: Operating System :: MacOS
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
