`sklearn.pipeline`.Pipeline#

class sklearn.pipeline.Pipeline(steps, *, memory=None, verbose=False)[source]#

A sequence of data transformers with an optional final predictor.

Pipeline allows you to sequentially apply a list of transformers to preprocess the data and, if desired, conclude the sequence with a final predictor for predictive modeling.

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.

For an example use case of Pipeline combined with GridSearchCV, refer to Selecting dimensionality reduction with Pipeline and GridSearchCV. The example Pipelining: chaining a PCA and a logistic regression shows how to grid search on a pipeline using '__' as a separator in the parameter names.

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).score(X_test, y_test)
0.88
>>> # An estimator's parameter can be set using '__' syntax
>>> pipe.set_params(svc__C=10).fit(X_train, y_train).score(X_test, y_test)
0.76

Methods

`decision_function`(X, **params)	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_metadata_routing`()	Get metadata routing of this object.
`get_params`([deep])	Get parameters for this estimator.
`inverse_transform`(Xt, **params)	Apply `inverse_transform` for each step in a reverse order.
`predict`(X, **params)	Transform the data, and apply `predict` with the final estimator.
`predict_log_proba`(X, **params)	Transform the data, and apply `predict_log_proba` with the final estimator.
`predict_proba`(X, **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_output`(*[, transform])	Set the output container when `"transform"` and `"fit_transform"` are called.
`set_params`(**kwargs)	Set the parameters of this estimator.
`set_score_request`(*[, sample_weight])	Request metadata passed to the `score` method.
`transform`(X, **params)	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, **params)[source]#

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.
**paramsdict of string -> object: Parameters requested and accepted by steps. Each step must have requested certain metadata for these parameters to be forwarded to them.

New in version 1.4: Only available if enable_metadata_routing=True. See Metadata Routing User Guide for more details.

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, **params)[source]#

Fit the model.

Fit all the transformers one after the other and sequentially 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.

**paramsdict of str -> object

If enable_metadata_routing=False (default):

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.
If enable_metadata_routing=True:

Parameters requested and accepted by steps. Each step must have requested certain metadata for these parameters to be forwarded to them.

Changed in version 1.4: Parameters are now passed to the transform method of the intermediate steps as well, if requested, and if enable_metadata_routing=True is set via set_config.

See Metadata Routing User Guide for more details.

Returns:

selfobject: Pipeline with fitted steps.

fit_predict(X, y=None, **params)[source]#

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.

**paramsdict of str -> object

If enable_metadata_routing=False (default):

Parameters to the predict called at the end of all transformations in the pipeline.
If enable_metadata_routing=True:

Parameters requested and accepted by steps. Each step must have requested certain metadata for these parameters to be forwarded to them.

New in version 0.20.

Changed in version 1.4: Parameters are now passed to the transform method of the intermediate steps as well, if requested, and if enable_metadata_routing=True.

See Metadata Routing User Guide for more details.

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.

Returns:

y_predndarray: Result of calling fit_predict on the final estimator.

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

Fit the model and transform with the final estimator.

Fit all the transformers one after the other and sequentially transform the data. Only valid if the final estimator either implements fit_transform or fit and transform.

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.

**paramsdict of str -> object

If enable_metadata_routing=False (default):

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.
If enable_metadata_routing=True:

Parameters requested and accepted by steps. Each step must have requested certain metadata for these parameters to be forwarded to them.

Changed in version 1.4: Parameters are now passed to the transform method of the intermediate steps as well, if requested, and if enable_metadata_routing=True.

See Metadata Routing User Guide for more details.

Returns:

Xtndarray of shape (n_samples, n_transformed_features): Transformed samples.

get_feature_names_out(input_features=None)[source]#

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_metadata_routing()[source]#

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Returns:

routingMetadataRouter: A MetadataRouter encapsulating routing information.

get_params(deep=True)[source]#

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, **params)[source]#

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.
**paramsdict of str -> object: Parameters requested and accepted by steps. Each step must have requested certain metadata for these parameters to be forwarded to them.

New in version 1.4: Only available if enable_metadata_routing=True. See Metadata Routing User Guide for more details.

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, **params)[source]#

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.

**paramsdict of str -> object

If enable_metadata_routing=False (default):

Parameters to the predict called at the end of all transformations in the pipeline.
If enable_metadata_routing=True:

Parameters requested and accepted by steps. Each step must have requested certain metadata for these parameters to be forwarded to them.

New in version 0.20.

Changed in version 1.4: Parameters are now passed to the transform method of the intermediate steps as well, if requested, and if enable_metadata_routing=True is set via set_config.

See Metadata Routing User Guide for more details.

Returns:

y_predndarray: Result of calling predict on the final estimator.

predict_log_proba(X, **params)[source]#

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.

**paramsdict of str -> object

If enable_metadata_routing=False (default):

Parameters to the predict_log_proba called at the end of all transformations in the pipeline.
If enable_metadata_routing=True:

Parameters requested and accepted by steps. Each step must have requested certain metadata for these parameters to be forwarded to them.

New in version 0.20.

Changed in version 1.4: Parameters are now passed to the transform method of the intermediate steps as well, if requested, and if enable_metadata_routing=True.

See Metadata Routing User Guide for more details.

Returns:

y_log_probandarray of shape (n_samples, n_classes): Result of calling predict_log_proba on the final estimator.

predict_proba(X, **params)[source]#

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.

**paramsdict of str -> object

If enable_metadata_routing=False (default):

Parameters to the predict_proba called at the end of all transformations in the pipeline.
If enable_metadata_routing=True:

Parameters requested and accepted by steps. Each step must have requested certain metadata for these parameters to be forwarded to them.

New in version 0.20.

Changed in version 1.4: Parameters are now passed to the transform method of the intermediate steps as well, if requested, and if enable_metadata_routing=True.

See Metadata Routing User Guide for more details.

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, **params)[source]#

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.
**paramsdict of str -> object: Parameters requested and accepted by steps. Each step must have requested certain metadata for these parameters to be forwarded to them.

New in version 1.4: Only available if enable_metadata_routing=True. See Metadata Routing User Guide for more details.

Returns:

scorefloat: Result of calling score on the final estimator.

score_samples(X)[source]#

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_output(*, transform=None)[source]#

Set the output container when "transform" and "fit_transform" are called.

Calling set_output will set the output of all estimators in steps.

Parameters:

transform{“default”, “pandas”}, default=None

Configure output of transform and fit_transform.

"default": Default output format of a transformer
"pandas": DataFrame output
"polars": Polars output
None: Transform configuration is unchanged

New in version 1.4: "polars" option was added.

Returns:

selfestimator instance: Estimator instance.

set_params(**kwargs)[source]#

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.

set_score_request(*, sample_weight: bool | None | str = '$UNCHANGED$') → Pipeline[source]#

Request metadata passed to the score method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config). Please see User Guide on how the routing mechanism works.

The options for each parameter are:

True: metadata is requested, and passed to score if provided. The request is ignored if metadata is not provided.
False: metadata is not requested and the meta-estimator will not pass it to score.
None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.
str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

New in version 1.3.

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a Pipeline. Otherwise it has no effect.

Parameters:

sample_weightstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED: Metadata routing for sample_weight parameter in score.

Returns:

selfobject: The updated object.

transform(X, **params)[source]#

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.
**paramsdict of str -> object: Parameters requested and accepted by steps. Each step must have requested certain metadata for these parameters to be forwarded to them.

New in version 1.4: Only available if enable_metadata_routing=True. See Metadata Routing User Guide for more details.

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)

Displaying Pipelines

Explicit feature map approximation for RBF kernels

Balance model complexity and cross-validated score

Sample pipeline for text feature extraction and evaluation

Underfitting vs. Overfitting

Caching nearest neighbors

Comparing Nearest Neighbors with and without Neighborhood Components Analysis

Nearest Neighbors Classification

Restricted Boltzmann Machine features for digit classification

Column Transformer with Heterogeneous Data Sources

Column Transformer with Mixed Types

Concatenating multiple feature extraction methods

Pipelining: chaining a PCA and a logistic regression

Selecting dimensionality reduction with Pipeline and GridSearchCV

Target Encoder’s Internal Cross fitting

Target Encoder's Internal Cross fitting

Semi-supervised Classification on a Text Dataset

SVM-Anova: SVM with univariate feature selection

sklearn.pipeline.Pipeline#

Examples using sklearn.pipeline.Pipeline#

`sklearn.pipeline`.Pipeline#

Examples using `sklearn.pipeline.Pipeline`#