andrewjlm / mftransformers

scikit-learn TransformerMixins for various matrix factorizations

mftransformers

One of my favorite scikit-learn features is the Pipeline class. Pipelines allow you to quickly combine multiple feature selection, classifiers or estimators into a single unit. Using an example from the scikit-learn documentation, we could set up a simple pipeline that performs an unsupervised dimension reduction using PCA and then performs classification using logistic regression:

pca = decomposition.PCA()
pipe = Pipeline(steps=[('pca', pca), ('logistic', logistic)])

By doing this we've essentially created a compound estimator that we can operate on as a whole (instead of working our data through each estimator individually). Continuing with the example from the scikit-learn documentation, we can use a grid search to set the parameters for number of components and inverse regularization strength and then fit the model:

estimator = GridSearchCV(pipe,
                         dict(pca__n_components=n_components,
                              logistic__C=Cs))
estimator.fit(X_digits, y_digits)

See the (excellent) scikit-learn documentation for more details.

While scikit-learn includes PCA it lacks other matrix factorization methods. The purpose of this package is to implement those methods in a way that is compatible with the scikit-learn pipeline system.