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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>_::

 from sklearn.ensemble import RandomForestClassifier
 from sklearn.svm import SVC
 from sklearn.neighbors import KNeighborsClassifier
 from sklearn.linear_model import LogisticRegression
 from wolpert 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.

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