Wolpert, a stacked generalization framework

Wolpert is a scikit-learn 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:

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, 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 to know more.