sklearn.pipeline.Pipeline

class sklearn.pipeline.Pipeline(steps, *, memory=None, verbose=False)

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 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 it to 'passthrough' or None.

Read more in the User Guide.

New in version 0.5.

Parameters

stepslist of tuple

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.

memorystr or object with the joblib.Memory interface, default=None

Used to cache the fitted transformers of the pipeline. By default, no caching is performed. If a string is given, it is the path to the caching directory. Enabling caching triggers a clone of the transformers before fitting. Therefore, the transformer instance given to the pipeline cannot be inspected directly. Use the attribute named_steps or steps to inspect estimators within the pipeline. Caching the transformers is advantageous when fitting is time consuming.

verbosebool, default=False

If True, the time elapsed while fitting each step will be printed as it is completed.

Attributes

named_stepsBunch

Access the steps by name.

classes_ndarray of shape (n_classes,)

The classes labels.

n_features_in_int

Number of features seen during first step fit method.

feature_names_in_ndarray of shape (n_features_in_,)

Names of features seen during first step fit method.

See also

make_pipeline

Convenience function for simplified pipeline construction.

Examples

>>> from sklearn.svm import SVC
>>> from sklearn.preprocessing import StandardScaler
>>> from sklearn.datasets import make_classification
>>> from sklearn.model_selection import train_test_split
>>> from sklearn.pipeline import Pipeline
>>> X, y = make_classification(random_state=0)
>>> X_train, X_test, y_train, y_test = train_test_split(X, y,
...                                                     random_state=0)
>>> pipe = Pipeline([('scaler', StandardScaler()), ('svc', SVC())])
>>> # The pipeline can be used as any other estimator
>>> # and avoids leaking the test set into the train set
>>> pipe.fit(X_train, y_train)
Pipeline(steps=[('scaler', StandardScaler()), ('svc', SVC())])
>>> pipe.score(X_test, y_test)
0.88

Methods

decision_function(X)	Transform the data, and apply decision_function with the final estimator.
fit(X[, y])	Fit the model.
fit_predict(X[, y])	Transform the data, and apply fit_predict with the final estimator.
fit_transform(X[, y])	Fit the model and transform with the final estimator.
get_feature_names_out([input_features])	Get output feature names for transformation.
get_params([deep])	Get parameters for this estimator.
inverse_transform(Xt)	Apply inverse_transform for each step in a reverse order.
predict(X, **predict_params)	Transform the data, and apply predict with the final estimator.
predict_log_proba(X, **predict_log_proba_params)	Transform the data, and apply predict_log_proba with the final estimator.
predict_proba(X, **predict_proba_params)	Transform the data, and apply predict_proba with the final estimator.
score(X[, y, sample_weight])	Transform the data, and apply score with the final estimator.
score_samples(X)	Transform the data, and apply score_samples with the final estimator.
set_params(**kwargs)	Set the parameters of this estimator.
transform(X)	Transform the data, and apply transform with the final estimator.

property classes_

The classes labels. Only exist if the last step is a classifier.

decision_function(X)

Transform the data, and apply decision_function with the final estimator.

Call transform of each transformer in the pipeline. The transformed data are finally passed to the final estimator that calls decision_function method. Only valid if the final estimator implements decision_function.

Parameters

Xiterable

Data to predict on. Must fulfill input requirements of first step of the pipeline.

Returns

y_scorendarray of shape (n_samples, n_classes)

Result of calling decision_function on the final estimator.

property feature_names_in_

Names of features seen during first step fit method.

fit(X, y=None, **fit_params)

Fit the model.

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

Parameters

Xiterable

Training data. Must fulfill input requirements of first step of the pipeline.

yiterable, default=None

Training targets. Must fulfill label requirements for all steps of the pipeline.

**fit_paramsdict 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

selfobject

Pipeline with fitted steps.

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

Transform the data, and apply fit_predict with the final estimator.

Call fit_transform of each transformer in the pipeline. The transformed data are finally passed to the final estimator that calls fit_predict method. Only valid if the final estimator implements fit_predict.

Parameters

Xiterable

Training data. Must fulfill input requirements of first step of the pipeline.

yiterable, default=None

Training targets. Must fulfill label requirements for all steps of the pipeline.

**fit_paramsdict 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_predndarray

Result of calling fit_predict on the final estimator.

fit_transform(X, y=None, **fit_params)

Fit the model and transform with the final estimator.

Fits all the transformers one after the other and transform the data. Then uses fit_transform on transformed data with the final estimator.

Parameters

Xiterable

Training data. Must fulfill input requirements of first step of the pipeline.

yiterable, default=None

Training targets. Must fulfill label requirements for all steps of the pipeline.

**fit_paramsdict 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

Xtndarray of shape (n_samples, n_transformed_features)

Transformed samples.

get_feature_names_out(input_features=None)

Get output feature names for transformation.

Transform input features using the pipeline.

Parameters

input_featuresarray-like of str or None, default=None

Input features.

Returns

feature_names_outndarray of str objects

Transformed feature names.

get_params(deep=True)

Get parameters for this estimator.

Returns the parameters given in the constructor as well as the estimators contained within the steps of the Pipeline.

Parameters

deepbool, default=True

If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns

paramsmapping of string to any

Parameter names mapped to their values.

inverse_transform(Xt)

Apply inverse_transform for each step in a reverse order.

All estimators in the pipeline must support inverse_transform.

Parameters

Xtarray-like of 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

Xtndarray of shape (n_samples, n_features)

Inverse transformed data, that is, data in the original feature space.

property n_features_in_

Number of features seen during first step fit method.

property named_steps

Access the steps by name.

Read-only attribute to access any step by given name. Keys are steps names and values are the steps objects.

predict(X, **predict_params)

Transform the data, and apply predict with the final estimator.

Call transform of each transformer in the pipeline. The transformed data are finally passed to the final estimator that calls predict method. Only valid if the final estimator implements predict.

Parameters

Xiterable

Data to predict on. Must fulfill input requirements of first step of the pipeline.

**predict_paramsdict of string -> object

Parameters to the predict called at the end of all transformations in the pipeline. Note that while this may be used to return uncertainties from some models with return_std or return_cov, uncertainties that are generated by the transformations in the pipeline are not propagated to the final estimator.

New in version 0.20.

Returns

y_predndarray

Result of calling predict on the final estimator.

predict_log_proba(X, **predict_log_proba_params)

Transform the data, and apply predict_log_proba with the final estimator.

Call transform of each transformer in the pipeline. The transformed data are finally passed to the final estimator that calls predict_log_proba method. Only valid if the final estimator implements predict_log_proba.

Parameters

Xiterable

Data to predict on. Must fulfill input requirements of first step of the pipeline.

**predict_log_proba_paramsdict of string -> object

Parameters to the predict_log_proba called at the end of all transformations in the pipeline.

Returns

y_log_probandarray of shape (n_samples, n_classes)

Result of calling predict_log_proba on the final estimator.

predict_proba(X, **predict_proba_params)

Transform the data, and apply predict_proba with the final estimator.

Call transform of each transformer in the pipeline. The transformed data are finally passed to the final estimator that calls predict_proba method. Only valid if the final estimator implements predict_proba.

Parameters

Xiterable

Data to predict on. Must fulfill input requirements of first step of the pipeline.

**predict_proba_paramsdict of string -> object

Parameters to the predict_proba called at the end of all transformations in the pipeline.

Returns

y_probandarray of shape (n_samples, n_classes)

Result of calling predict_proba on the final estimator.

score(X, y=None, sample_weight=None)

Transform the data, and apply score with the final estimator.

Call transform of each transformer in the pipeline. The transformed data are finally passed to the final estimator that calls score method. Only valid if the final estimator implements score.

Parameters

Xiterable

Data to predict on. Must fulfill input requirements of first step of the pipeline.

yiterable, default=None

Targets used for scoring. Must fulfill label requirements for all steps of the pipeline.

sample_weightarray-like, default=None

If not None, this argument is passed as sample_weight keyword argument to the score method of the final estimator.

Returns

scorefloat

Result of calling score on the final estimator.

score_samples(X)

Transform the data, and apply score_samples with the final estimator.

Call transform of each transformer in the pipeline. The transformed data are finally passed to the final estimator that calls score_samples method. Only valid if the final estimator implements score_samples.

Parameters

Xiterable

Data to predict on. Must fulfill input requirements of first step of the pipeline.

Returns

y_scorendarray of shape (n_samples,)

Result of calling score_samples on the final estimator.

set_params(**kwargs)

Set the parameters of this estimator.

Valid parameter keys can be listed with get_params(). Note that you can directly set the parameters of the estimators contained in steps.

Parameters

**kwargsdict

Parameters of this estimator or parameters of estimators contained in steps. Parameters of the steps may be set using its name and the parameter name separated by a ‘__’.

Returns

selfobject

Pipeline class instance.

transform(X)

Transform the data, and apply transform with the final estimator.

Call transform of each transformer in the pipeline. The transformed data are finally passed to the final estimator that calls transform method. Only valid if the final estimator implements transform.

This also works where final estimator is None in which case all prior transformations are applied.

Parameters

Xiterable

Data to transform. Must fulfill input requirements of first step of the pipeline.

Returns

Xtndarray of shape (n_samples, n_transformed_features)

Transformed data.

Examples using sklearn.pipeline.Pipeline

Feature agglomeration vs. univariate selection

Pipeline ANOVA SVM

Poisson regression and non-normal loss

Permutation Importance vs Random Forest Feature Importance (MDI)

Scalable learning with polynomial kernel approximation

Displaying Pipelines

Explicit feature map approximation for RBF kernels

Underfitting vs. Overfitting

Sample pipeline for text feature extraction and evaluation

Balance model complexity and cross-validated score

Caching nearest neighbors

Comparing Nearest Neighbors with and without Neighborhood Components Analysis

Restricted Boltzmann Machine features for digit classification

Concatenating multiple feature extraction methods

Pipelining: chaining a PCA and a logistic regression

Selecting dimensionality reduction with Pipeline and GridSearchCV

Column Transformer with Mixed Types

Column Transformer with Heterogeneous Data Sources

Semi-supervised Classification on a Text Dataset

SVM-Anova: SVM with univariate feature selection

Classification of text documents using sparse features