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Pipeline: chaining estimators

.. currentmodule:: sklearn.pipeline

:class:`Pipeline` can be used to chain multiple estimators into one. This is useful as there is often a fixed sequence of steps in processing the data, for example feature selection, normalization and classification. :class:`Pipeline` serves two purposes here:

Convenience: You only have to call fit and predict once on your data to fit a whole sequence of estimators.

Joint parameter selection: You can :ref:`grid search <grid_search>` over parameters of all estimators in the pipeline at once.

All estimators in a pipeline, except the last one, must be transformers (i.e. must have a transform method). The last estimator may be any type (transformer, classifier, etc.).

Usage

The :class:`Pipeline` is build using a list of (key, value) pairs, where the key a string containing the name you want to give this step and value is an estimator object:

>>> from sklearn.pipeline import Pipeline
>>> from sklearn.svm import SVC
>>> from sklearn.decomposition import PCA
>>> estimators = [('reduce_dim', PCA()), ('svm', SVC())]
>>> clf = Pipeline(estimators)
>>> clf # doctest: +NORMALIZE_WHITESPACE
Pipeline(steps=[('reduce_dim', PCA(copy=True, n_components=None,
    whiten=False)), ('svm', SVC(C=1.0, cache_size=200, class_weight=None,
    coef0=0.0, degree=3, gamma=0.0, kernel='rbf', max_iter=-1,
    probability=False, random_state=None, shrinking=True, tol=0.001,
    verbose=False))])

The utility function :func:`make_pipeline` is a shorthand for constructing pipelines; it takes a variable number of estimators and returns a pipeline, filling in the names automatically:

>>> from sklearn.pipeline import make_pipeline
>>> from sklearn.naive_bayes import MultinomialNB
>>> from sklearn.preprocessing import Binarizer
>>> make_pipeline(Binarizer(), MultinomialNB()) # doctest: +NORMALIZE_WHITESPACE
Pipeline(steps=[('binarizer', Binarizer(copy=True, threshold=0.0)),
                ('multinomialnb', MultinomialNB(alpha=1.0,
                                                class_prior=None,
                                                fit_prior=True))])

The estimators of a pipeline are stored as a list in the steps attribute:

>>> clf.steps[0]
('reduce_dim', PCA(copy=True, n_components=None, whiten=False))

and as a dict in named_steps:

>>> clf.named_steps['reduce_dim']
PCA(copy=True, n_components=None, whiten=False)

Parameters of the estimators in the pipeline can be accessed using the <estimator>__<parameter> syntax:

>>> clf.set_params(svm__C=10) # doctest: +NORMALIZE_WHITESPACE
Pipeline(steps=[('reduce_dim', PCA(copy=True, n_components=None,
    whiten=False)), ('svm', SVC(C=10, cache_size=200, class_weight=None,
    coef0=0.0, degree=3, gamma=0.0, kernel='rbf', max_iter=-1,
    probability=False, random_state=None, shrinking=True, tol=0.001,
    verbose=False))])

This is particularly important for doing grid searches:

>>> from sklearn.grid_search import GridSearchCV
>>> params = dict(reduce_dim__n_components=[2, 5, 10],
...               svm__C=[0.1, 10, 100])
>>> grid_search = GridSearchCV(clf, param_grid=params)

Notes

Calling fit on the pipeline is the same as calling fit on each estimator in turn, transform the input and pass it on to the next step. The pipeline has all the methods that the last estimator in the pipeline has, i.e. if the last estimator is a classifier, the :class:`Pipeline` can be used as a classifier. If the last estimator is a transformer, again, so is the pipeline.

FeatureUnion: Combining feature extractors

.. currentmodule:: sklearn.pipeline

:class:`FeatureUnion` combines several transformer objects into a new transformer that combines their output. A :class:`FeatureUnion` takes a list of transformer objects. During fitting, each of these is fit to the data independently. For transforming data, the transformers are applied in parallel, and the sample vectors they output are concatenated end-to-end into larger vectors.

:class:`FeatureUnion` serves the same purposes as :class:`Pipeline` - convenience and joint parameter estimation and validation.

:class:`FeatureUnion` and :class:`Pipeline` can be combined to create complex models.

(A :class:`FeatureUnion` has no way of checking whether two transformers might produce identical features. It only produces a union when the feature sets are disjoint, and making sure they are is the caller's responsibility.)

Usage

A :class:`FeatureUnion` is built using a list of (key, value) pairs, where the key is the name you want to give to a given transformation (an arbitrary string; it only serves as an identifier) and value is an estimator object:

>>> from sklearn.pipeline import FeatureUnion
>>> from sklearn.decomposition import PCA
>>> from sklearn.decomposition import KernelPCA
>>> estimators = [('linear_pca', PCA()), ('kernel_pca', KernelPCA())]
>>> combined = FeatureUnion(estimators)
>>> combined # doctest: +NORMALIZE_WHITESPACE
FeatureUnion(n_jobs=1, transformer_list=[('linear_pca', PCA(copy=True,
    n_components=None, whiten=False)), ('kernel_pca', KernelPCA(alpha=1.0,
    coef0=1, degree=3, eigen_solver='auto', fit_inverse_transform=False,
    gamma=None, kernel='linear', kernel_params=None, max_iter=None,
    n_components=None, remove_zero_eig=False, tol=0))],
    transformer_weights=None)

Like pipelines, feature unions have a shorthand constructor called :func:`make_union` that does require manual naming of the components.