imblearn.pipeline.Pipeline

class imblearn.pipeline.Pipeline(steps, memory=None)[source][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.

Parameters:
steps : list

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.

memory : Instance of joblib.Memory or string, optional (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.

See also

make_pipeline
helper function to make pipeline.

Notes

See Pipeline Object

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 # doctest: +NORMALIZE_WHITESPACE
>>> 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('Original dataset shape {}'.format(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) # doctest: +ELLIPSIS
Pipeline(...)
>>> y_hat = pipeline.predict(X_test)
>>> print(classification_report(y_test, y_hat))
              precision    recall  f1-score   support
<BLANKLINE>
           0       0.87      1.00      0.93        26
           1       1.00      0.98      0.99       224
<BLANKLINE>
   micro avg       0.98      0.98      0.98       250
   macro avg       0.93      0.99      0.96       250
weighted avg       0.99      0.98      0.98       250
<BLANKLINE>
Attributes:
named_steps : dict

Read-only attribute to access any step parameter by user given name. Keys are step names and values are steps parameters.

__init__(steps, memory=None)[source][source]

Initialize self. See help(type(self)) for accurate signature.

decision_function(X)[source][source]

Apply transformers/samplers, and decision_function of the final estimator

Parameters:
X : iterable

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

Returns:
y_score : array-like, shape = [n_samples, n_classes]
fit(X, y=None, **fit_params)[source][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:
X : iterable

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

y : iterable, default=None

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

**fit_params : dict 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:
self : Pipeline

This estimator

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

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

y : iterable, default=None

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

**fit_params : dict 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_pred : array-like
fit_resample(X, y=None, **fit_params)[source][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:
X : iterable

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

y : iterable, default=None

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

**fit_params : dict 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:
Xt : array-like, shape = [n_samples, n_transformed_features]

Transformed samples

yt : array-like, shape = [n_samples, n_transformed_features]

Transformed target

fit_transform(X, y=None, **fit_params)[source][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:
X : iterable

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

y : iterable, default=None

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

**fit_params : dict 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:
Xt : array-like, shape = [n_samples, n_transformed_features]

Transformed samples

get_params(deep=True)[source][source]

Get parameters for this estimator.

Parameters:
deep : boolean, optional

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

Returns:
params : mapping of string to any

Parameter names mapped to their values.

inverse_transform

Apply inverse transformations in reverse order

All estimators in the pipeline must support inverse_transform.

Parameters:
Xt : array-like, 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:
Xt : array-like, shape = [n_samples, n_features]
predict(X, **predict_params)[source][source]

Apply transformers/samplers to the data, and predict with the final estimator

Parameters:
X : iterable

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

**predict_params : dict 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.

Returns:
y_pred : array-like
predict_log_proba(X)[source][source]

Apply transformers/samplers, and predict_log_proba of the final estimator

Parameters:
X : iterable

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

Returns:
y_score : array-like, shape = [n_samples, n_classes]
predict_proba(X)[source][source]

Apply transformers/samplers, and predict_proba of the final estimator

Parameters:
X : iterable

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

Returns:
y_proba : array-like, shape = [n_samples, n_classes]
score(X, y=None, sample_weight=None)[source][source]

Apply transformers/samplers, and score with the final estimator

Parameters:
X : iterable

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

y : iterable, default=None

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

sample_weight : array-like, default=None

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

Returns:
score : float
set_params(**kwargs)[source][source]

Set the parameters of this estimator.

Valid parameter keys can be listed with get_params().

Returns:
self
transform

Apply transformers/samplers, and transform with the final estimator

This also works where final estimator is None: all prior transformations are applied.

Parameters:
X : iterable

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

Returns:
Xt : array-like, shape = [n_samples, n_transformed_features]