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.

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.

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 sequential order. The last transform must be an estimator.

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

Used to cache the fitted transformers of the pipeline. The last step will never be cached, even if it is a transformer. 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).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)	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)	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_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)	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_metadata_routing()

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Returns:

routingMetadataRequest

A MetadataRequest encapsulating routing information.

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_output(*, transform=None)

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

• None: Transform configuration is unchanged

Returns:

selfestimator instance

Estimator instance.

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.

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

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)

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

Feature agglomeration vs. univariate selection

Pipeline ANOVA SVM

Pipeline ANOVA SVM

Poisson regression and non-normal loss

Poisson regression and non-normal loss

Permutation Importance vs Random Forest Feature Importance (MDI)

Permutation Importance vs Random Forest Feature Importance (MDI)

Displaying Pipelines

Displaying Pipelines

Explicit feature map approximation for RBF kernels

Explicit feature map approximation for RBF kernels

Balance model complexity and cross-validated score

Balance model complexity and cross-validated score

Sample pipeline for text feature extraction and evaluation

Sample pipeline for text feature extraction and evaluation

Underfitting vs. Overfitting

Underfitting vs. Overfitting

Caching nearest neighbors

Caching nearest neighbors

Comparing Nearest Neighbors with and without Neighborhood Components Analysis

Comparing Nearest Neighbors with and without Neighborhood Components Analysis

Nearest Neighbors Classification

Nearest Neighbors Classification

Restricted Boltzmann Machine features for digit classification

Restricted Boltzmann Machine features for digit classification

Column Transformer with Heterogeneous Data Sources

Column Transformer with Heterogeneous Data Sources

Column Transformer with Mixed Types

Column Transformer with Mixed Types

Concatenating multiple feature extraction methods

Concatenating multiple feature extraction methods

Pipelining: chaining a PCA and a logistic regression

Pipelining: chaining a PCA and a logistic regression

Selecting dimensionality reduction with Pipeline and GridSearchCV

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

Semi-supervised Classification on a Text Dataset

SVM-Anova: SVM with univariate feature selection

SVM-Anova: SVM with univariate feature selection