imblearn.pipeline.Pipeline

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

Pipeline of transforms and resamples with a final estimator.

Sequentially apply a list of transforms, samples 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 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_sample) 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.

Examples

>>> from collections import Counter
>>> from sklearn.datasets import make_classification
>>> from sklearn.cross_validation 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('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) 
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

avg / total       0.99      0.98      0.98       250

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.

Methods

decision_function(*args, **kwargs)	Apply transformers/samplers, and decision_function of the final
fit(X[, y])	Fit the model
fit_predict(*args, **kwargs)	Applies fit_predict of last step in pipeline after transforms.
fit_sample(*args, **kwargs)	Fit the model and sample with the final estimator
fit_transform(X[, y])	Fit the model and transform with the final estimator
get_params([deep])	Get parameters for this estimator.
predict(*args, **kwargs)	Apply transformers/samplers to the data, and predict with the final
predict_log_proba(*args, **kwargs)	Apply transformers/samplers, and predict_log_proba of the final
predict_proba(*args, **kwargs)	Apply transformers/samplers, and predict_proba of the final
sample(*args, **kwargs)	Sample the data with the final estimator
score(*args, **kwargs)	Apply transformers/samplers, and score with the final estimator
set_params(**kwargs)	Set the parameters of this estimator.

__init__(steps, memory=None)

Methods

__init__(steps[, memory])
decision_function(*args, **kwargs)	Apply transformers/samplers, and decision_function of the final
fit(X[, y])	Fit the model
fit_predict(*args, **kwargs)	Applies fit_predict of last step in pipeline after transforms.
fit_sample(*args, **kwargs)	Fit the model and sample with the final estimator
fit_transform(X[, y])	Fit the model and transform with the final estimator
get_params([deep])	Get parameters for this estimator.
predict(*args, **kwargs)	Apply transformers/samplers to the data, and predict with the final
predict_log_proba(*args, **kwargs)	Apply transformers/samplers, and predict_log_proba of the final
predict_proba(*args, **kwargs)	Apply transformers/samplers, and predict_proba of the final
sample(*args, **kwargs)	Sample the data with the final estimator
score(*args, **kwargs)	Apply transformers/samplers, and score with the final estimator
set_params(**kwargs)	Set the parameters of this estimator.

Attributes

classes_
inverse_transform	Apply inverse transformations in reverse order
named_steps
transform	Apply transformers/samplers, and transform with the final estimator

decision_function(*args, **kwargs)

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)

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(*args, **kwargs)

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_sample(*args, **kwargs)

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_sample 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)

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)

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(*args, **kwargs)

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.

Returns:

y_pred : array-like

predict_log_proba(*args, **kwargs)

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(*args, **kwargs)

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]

sample(*args, **kwargs)

Sample the data with the final estimator

Applies transformers/samplers to the data, and the sample method of the final estimator. Valid only if the final estimator implements sample.

Parameters:

X : iterable

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

score(*args, **kwargs)

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)

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]