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.. _pipeline:
==============================
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.
For estimators to be usable within a pipeline, all except the last one need to have
a ``transform`` function. Otherwise, the dataset can not be passed through this
estimator.
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', probability=False,
shrinking=True, tol=0.001, verbose=False))])
The estimators of the 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) # 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', probability=False, 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)
.. topic:: Examples:
* :ref:`example_feature_selection_pipeline.py`
* :ref:`example_grid_search_text_feature_extraction.py`
* :ref:`example_plot_digits_pipe.py`
* :ref:`example_plot_kernel_approximation.py`
* :ref:`example_svm_plot_svm_anova.py`
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 pipline 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.
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