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.
1 maintainer
Metadata
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Release files for wolpert 0.1.1
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