Pipeline¶
- class imblearn.pipeline.Pipeline(steps, *, 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.
- 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
orsteps
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_steps
Bunch
Read-only attribute to access any step parameter by user given name. Keys are step names and values are steps parameters.
- named_steps
See also
make_pipeline
Helper function to make pipeline.
Notes
See Usage of pipeline embedding samplers
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
Apply transforms, and decision_function of 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_params
([deep])Get parameters for this estimator.
predict
(X, **predict_params)Apply transforms to the data, and predict with the final estimator
Apply transforms, and predict_log_proba of the final estimator
Apply transforms, and predict_proba of the final estimator
score
(X[, y, sample_weight])Apply transforms, and score with the final estimator
Apply transforms, and score_samples of the final estimator.
set_params
(**kwargs)Set the parameters of this estimator.
- decision_function(X)[source]¶
Apply transforms, and decision_function of the final estimator
- Parameters
- Xiterable
Data to predict on. Must fulfill input requirements of first step of the pipeline.
- Returns
- y_scorearray-like of shape (n_samples, n_classes)
- fit(X, y=None, **fit_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.
- **fit_paramsdict of str -> object
Parameters passed to the
fit
method of each step, where each parameter name is prefixed such that parameterp
for steps
has keys__p
.
- Returns
- selfPipeline
This estimator.
- fit_predict(X, y=None, **fit_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.
- **fit_paramsdict of string -> object
Parameters passed to the
fit
method of each step, where each parameter name is prefixed such that parameterp
for steps
has keys__p
.
- Returns
- y_predndarray of shape (n_samples,)
The predicted target.
- fit_resample(X, y=None, **fit_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.
- **fit_paramsdict of string -> object
Parameters passed to the
fit
method of each step, where each parameter name is prefixed such that parameterp
for steps
has keys__p
.
- 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, **fit_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.
- **fit_paramsdict of string -> object
Parameters passed to the
fit
method of each step, where each parameter name is prefixed such that parameterp
for steps
has keys__p
.
- Returns
- Xtarray-like of shape (n_samples, n_transformed_features)
Transformed samples.
- 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 thePipeline
.- 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.
- property inverse_transform¶
Apply inverse transformations in 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 andn_features
is the number of features. Must fulfill input requirements of last step of pipeline’sinverse_transform
method.
- Returns
- Xtarray-like of shape (n_samples, n_features)
- predict(X, **predict_params)[source]¶
Apply transforms to the data, and predict with the final estimator
- 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_predarray-like
- predict_log_proba(X)[source]¶
Apply transforms, and predict_log_proba of the final estimator
- Parameters
- Xiterable
Data to predict on. Must fulfill input requirements of first step of the pipeline.
- Returns
- y_scorearray-like of shape (n_samples, n_classes)
- predict_proba(X)[source]¶
Apply transforms, and predict_proba of the final estimator
- Parameters
- Xiterable
Data to predict on. Must fulfill input requirements of first step of the pipeline.
- Returns
- y_probaarray-like of shape (n_samples, n_classes)
- score(X, y=None, sample_weight=None)[source]¶
Apply transforms, and score with the final estimator
- 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 thescore
method of the final estimator.
- Returns
- scorefloat
- score_samples(X)[source]¶
Apply transforms, and score_samples of the final estimator.
- Parameters
- Xiterable
Data to predict on. Must fulfill input requirements of first step of the pipeline.
- Returns
- y_scorendarray of shape (n_samples,)
- 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 insteps
.- Returns
- self
- property transform¶
Apply transforms, and transform with the final estimator
This also works where final estimator is
None
: all prior transformations are applied.- Parameters
- Xiterable
Data to transform. Must fulfill input requirements of first step of the pipeline.
- Returns
- Xtarray-like of shape (n_samples, n_transformed_features)