Pipeline#

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

Pipeline of transforms and resamples with a final estimator.

Sequentially apply a list of transforms, sampling, and a final estimator. Intermediate steps of the pipeline must be transformers or resamplers, that is, they must implement fit, transform and sample methods. The samplers are only applied during fit. The final estimator only needs to implement fit. The transformers and samplers 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.

Parameters:

stepslist

List of (name, transform) tuples (implementing fit/transform/fit_resample) that are chained, in the order in which they are chained, with the last object an estimator.

transform_inputlist of str, default=None

The names of the metadata parameters that should be transformed by the pipeline before passing it to the step consuming it.

This enables transforming some input arguments to fit (other than X) to be transformed by the steps of the pipeline up to the step which requires them. Requirement is defined via metadata routing. For instance, this can be used to pass a validation set through the pipeline.

You can only set this if metadata routing is enabled, which you can enable using sklearn.set_config(enable_metadata_routing=True).

Added in version 1.6.

memoryInstance of joblib.Memory or str, 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: Helper function to make pipeline.

Notes

See Usage of pipeline embedding samplers

Warning

A surprising behaviour of the imbalanced-learn pipeline is that it breaks the scikit-learn contract where one expects estimmator.fit_transform(X, y) to be equivalent to estimator.fit(X, y).transform(X).

The semantic of fit_resample is to be applied only during the fit stage. Therefore, resampling will happen when calling fit_transform while it will only happen on the fit stage when calling fit and transform separately. Practically, fit_transform will lead to a resampled dataset while fit and transform will not.

Examples

>>> from collections import Counter
>>> from sklearn.datasets import make_classification
>>> from sklearn.model_selection import train_test_split as tts
>>> from sklearn.decomposition import PCA
>>> from sklearn.neighbors import KNeighborsClassifier as KNN
>>> from sklearn.metrics import classification_report
>>> from imblearn.over_sampling import SMOTE
>>> from imblearn.pipeline import Pipeline
>>> X, y = make_classification(n_classes=2, class_sep=2,
... weights=[0.1, 0.9], n_informative=3, n_redundant=1, flip_y=0,
... n_features=20, n_clusters_per_class=1, n_samples=1000, random_state=10)
>>> print(f'Original dataset shape {Counter(y)}')
Original dataset shape Counter({1: 900, 0: 100})
>>> pca = PCA()
>>> smt = SMOTE(random_state=42)
>>> knn = KNN()
>>> pipeline = Pipeline([('smt', smt), ('pca', pca), ('knn', knn)])
>>> X_train, X_test, y_train, y_test = tts(X, y, random_state=42)
>>> pipeline.fit(X_train, y_train)
Pipeline(...)
>>> y_hat = pipeline.predict(X_test)
>>> print(classification_report(y_test, y_hat))
              precision    recall  f1-score   support

           0       0.87      1.00      0.93        26
           1       1.00      0.98      0.99       224

    accuracy                           0.98       250
   macro avg       0.93      0.99      0.96       250
weighted avg       0.99      0.98      0.98       250

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])	Apply `fit_predict` of last step in pipeline after transforms.
`fit_resample`(X[, y])	Fit the model and sample 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.

Added 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 transforms/samplers one after the other and transform/sample the data, then fit the transformed/sampled 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:

selfPipeline: This estimator.

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

Apply 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:

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.

Added 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 of shape (n_samples,): The predicted target.

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

Fit the model and sample with the final estimator.

Fits all the transformers/samplers one after the other and transform/sample the data, then uses fit_resample 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.

**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:

Xtarray-like of shape (n_samples, n_transformed_features): Transformed samples.
ytarray-like of shape (n_samples, n_transformed_features): Transformed target.

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

Fit the model and transform with the final estimator.

Fits all the transformers/samplers one after the other and transform/sample 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.

**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:

Xtarray-like 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.

Added 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.

Added 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.

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 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.

Added 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.

Added 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.

Added 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”, “polars”}, 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

Added 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.

Added 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.

Added 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 `imblearn.pipeline.Pipeline`#

Multiclass classification with under-sampling

Example of topic classification in text documents

Customized sampler to implement an outlier rejections estimator

Benchmark over-sampling methods in a face recognition task

Fitting model on imbalanced datasets and how to fight bias

Compare sampler combining over- and under-sampling

Bagging classifiers using sampler

Evaluate classification by compiling a report

Metrics specific to imbalanced learning

Plotting Validation Curves

Compare over-sampling samplers

Usage of pipeline embedding samplers

Compare under-sampling samplers

Pipeline#

Examples using imblearn.pipeline.Pipeline#

This Page

Examples using `imblearn.pipeline.Pipeline`#