pipeline.Pipeline()

class sklearn.pipeline.Pipeline(steps) [source]

Pipeline of transforms with a final estimator.

Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be ?transforms?, that is, they must implement fit and transform methods. The final estimator only needs to implement fit.

The purpose of the pipeline is to assemble several steps that can be cross-validated together while setting different parameters. For this, it enables setting parameters of the various steps using their names and the parameter name separated by a ?__?, as in the example below. A step?s estimator may be replaced entirely by setting the parameter with its name to another estimator, or a transformer removed by setting to None.

Read more in the User Guide.

Parameters:	steps : list List of (name, transform) tuples (implementing fit/transform) that are chained, in the order in which they are chained, with the last object an estimator.
Attributes:	named_steps : dict Read-only attribute to access any step parameter by user given name. Keys are step names and values are steps parameters.

Examples

>>> from sklearn import svm
>>> from sklearn.datasets import samples_generator
>>> from sklearn.feature_selection import SelectKBest
>>> from sklearn.feature_selection import f_regression
>>> from sklearn.pipeline import Pipeline
>>> # generate some data to play with
>>> X, y = samples_generator.make_classification(
...     n_informative=5, n_redundant=0, random_state=42)
>>> # ANOVA SVM-C
>>> anova_filter = SelectKBest(f_regression, k=5)
>>> clf = svm.SVC(kernel='linear')
>>> anova_svm = Pipeline([('anova', anova_filter), ('svc', clf)])
>>> # You can set the parameters using the names issued
>>> # For instance, fit using a k of 10 in the SelectKBest
>>> # and a parameter 'C' of the svm
>>> anova_svm.set_params(anova__k=10, svc__C=.1).fit(X, y)
...                                              
Pipeline(steps=[...])
>>> prediction = anova_svm.predict(X)
>>> anova_svm.score(X, y)                        
0.829...
>>> # getting the selected features chosen by anova_filter
>>> anova_svm.named_steps['anova'].get_support()
... 
array([False, False,  True,  True, False, False, True,  True, False,
       True,  False,  True,  True, False, True,  False, True, True,
       False, False], dtype=bool)

Methods

decision_function(\*args, \*\*kwargs)	Apply transforms, and decision_function of the final estimator
fit(X[, y])	Fit the model
fit_predict(\*args, \*\*kwargs)	Applies fit_predict of last step in pipeline after transforms.
fit_transform(X[, y])	Fit the model and transform with the final estimator
get_params([deep])	Get parameters for this estimator.
predict(\*args, \*\*kwargs)	Apply transforms to the data, and predict with the final estimator
predict_log_proba(\*args, \*\*kwargs)	Apply transforms, and predict_log_proba of the final estimator
predict_proba(\*args, \*\*kwargs)	Apply transforms, and predict_proba of the final estimator
score(\*args, \*\*kwargs)	Apply transforms, and score with the final estimator
set_params(\*\*kwargs)	Set the parameters of this estimator.

__init__(steps) [source]

decision_function(*args, **kwargs) [source]

Apply transforms, and decision_function of the final estimator

Parameters:	X : iterable Data to predict on. Must fulfill input requirements of first step of the pipeline.
Returns:	y_score : array-like, shape = [n_samples, n_classes]

fit(X, y=None, **fit_params) [source]

Fit the model

Fit all the transforms one after the other and transform the data, then fit the transformed data using the final estimator.

Parameters:	X : iterable Training data. Must fulfill input requirements of first step of the pipeline. y : iterable, default=None Training targets. Must fulfill label requirements for all steps of the pipeline. **fit_params : dict of string -> object Parameters passed to the fit method of each step, where each parameter name is prefixed such that parameter p for step s has key s__p.
Returns:	self : Pipeline This estimator

fit_predict(*args, **kwargs) [source]

Applies fit_predict of last step in pipeline after transforms.

Applies fit_transforms of a pipeline to the data, followed by the fit_predict method of the final estimator in the pipeline. Valid only if the final estimator implements fit_predict.

Parameters:	X : iterable Training data. Must fulfill input requirements of first step of the pipeline. y : iterable, default=None Training targets. Must fulfill label requirements for all steps of the pipeline. **fit_params : dict of string -> object Parameters passed to the fit method of each step, where each parameter name is prefixed such that parameter p for step s has key s__p.
Returns:	y_pred : array-like

fit_transform(X, y=None, **fit_params) [source]

Fit the model and transform with the final estimator

Fits all the transforms one after the other and transforms the data, then uses fit_transform on transformed data with the final estimator.

Parameters:	X : iterable Training data. Must fulfill input requirements of first step of the pipeline. y : iterable, default=None Training targets. Must fulfill label requirements for all steps of the pipeline. **fit_params : dict of string -> object Parameters passed to the fit method of each step, where each parameter name is prefixed such that parameter p for step s has key s__p.
Returns:	Xt : array-like, shape = [n_samples, n_transformed_features] Transformed samples

get_params(deep=True) [source]

Get parameters for this estimator.

Parameters:	deep: boolean, optional : If True, will return the parameters for this estimator and contained subobjects that are estimators.
Returns:	params : mapping of string to any Parameter names mapped to their values.

inverse_transform

Apply inverse transformations in reverse order

All estimators in the pipeline must support inverse_transform.

Parameters:	Xt : array-like, shape = [n_samples, n_transformed_features] Data samples, where n_samples is the number of samples and n_features is the number of features. Must fulfill input requirements of last step of pipeline?s inverse_transform method.
Returns:	Xt : array-like, shape = [n_samples, n_features]

predict(*args, **kwargs) [source]

Apply transforms to the data, and predict with the final estimator

Parameters:	X : iterable Data to predict on. Must fulfill input requirements of first step of the pipeline.
Returns:	y_pred : array-like

predict_log_proba(*args, **kwargs) [source]

Apply transforms, and predict_log_proba of the final estimator

Parameters:	X : iterable Data to predict on. Must fulfill input requirements of first step of the pipeline.
Returns:	y_score : array-like, shape = [n_samples, n_classes]

predict_proba(*args, **kwargs) [source]

Apply transforms, and predict_proba of the final estimator

Parameters:	X : iterable Data to predict on. Must fulfill input requirements of first step of the pipeline.
Returns:	y_proba : array-like, shape = [n_samples, n_classes]

score(*args, **kwargs) [source]

Apply transforms, and score with the final estimator

Parameters:	X : iterable Data to predict on. Must fulfill input requirements of first step of the pipeline. y : iterable, default=None Targets used for scoring. Must fulfill label requirements for all steps of the pipeline.
Returns:	score : float

set_params(**kwargs) [source]

Set the parameters of this estimator.

Valid parameter keys can be listed with get_params().

Returns:	self :

transform

Apply transforms, and transform with the final estimator

This also works where final estimator is None: all prior transformations are applied.

Parameters:	X : iterable Data to transform. Must fulfill input requirements of first step of the pipeline.
Returns:	Xt : array-like, shape = [n_samples, n_transformed_features]

Examples using sklearn.pipeline.Pipeline

Concatenating multiple feature extraction methods

Feature Union with Heterogeneous Data Sources

Imputing missing values before building an estimator

Selecting dimensionality reduction with Pipeline and GridSearchCV

Pipelining: chaining a PCA and a logistic regression

Explicit feature map approximation for RBF kernels

Feature agglomeration vs. univariate selection

Sample pipeline for text feature extraction and evaluation

Underfitting vs. Overfitting

Restricted Boltzmann Machine features for digit classification

SVM-Anova: SVM with univariate feature selection

Classification of text documents using sparse features