class sklearn.pipeline.Pipeline(steps, memory=None)
[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 transformers in the pipeline can be cached using memory
argument.
The purpose of the pipeline is to assemble several steps that can be crossvalidated 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: 


Attributes: 

See also
sklearn.pipeline.make_pipeline
>>> 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 SVMC >>> 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(memory=None, steps=[('anova', SelectKBest(...)), ('svc', SVC(...))]) >>> prediction = anova_svm.predict(X) >>> anova_svm.score(X, y) 0.83 >>> # 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]) >>> # Another way to get 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])
decision_function (X)  Apply transforms, and decision_function of the final estimator 
fit (X[, y])  Fit the model 
fit_predict (X[, y])  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 (X, **predict_params)  Apply transforms to the data, and predict with the final estimator 
predict_log_proba (X)  Apply transforms, and predict_log_proba of the final estimator 
predict_proba (X)  Apply transforms, and predict_proba of the final estimator 
score (X[, y, sample_weight])  Apply transforms, and score with the final estimator 
set_params (**kwargs)  Set the parameters of this estimator. 
__init__(steps, memory=None)
[source]
decision_function(X)
[source]
Apply transforms, and decision_function of the final estimator
Parameters: 


Returns: 

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: 


Returns: 

fit_predict(X, y=None, **fit_params)
[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: 


Returns: 

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: 


Returns: 

get_params(deep=True)
[source]
Get parameters for this estimator.
Parameters: 


Returns: 

inverse_transform
Apply inverse transformations in reverse order
All estimators in the pipeline must support inverse_transform
.
Parameters: 


Returns: 

predict(X, **predict_params)
[source]
Apply transforms to the data, and predict with the final estimator
Parameters: 


Returns: 

predict_log_proba(X)
[source]
Apply transforms, and predict_log_proba of the final estimator
Parameters: 


Returns: 

predict_proba(X)
[source]
Apply transforms, and predict_proba of the final estimator
Parameters: 


Returns: 

score(X, y=None, sample_weight=None)
[source]
Apply transforms, and score with the final estimator
Parameters: 


Returns: 

set_params(**kwargs)
[source]
Set the parameters of this estimator.
Valid parameter keys can be listed with get_params()
.
Returns: 


transform
Apply transforms, and transform with the final estimator
This also works where final estimator is None
: all prior transformations are applied.
Parameters: 


Returns: 

sklearn.pipeline.Pipeline
© 2007–2018 The scikitlearn developers
Licensed under the 3clause BSD License.
http://scikitlearn.org/stable/modules/generated/sklearn.pipeline.Pipeline.html