sklearn.pipeline
.Pipeline¶
- class sklearn.pipeline.Pipeline(steps, *, memory=None, verbose=False)[source]¶
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
.Read more in the User Guide.
New in version 0.5.
- Parameters
- stepslist
List of (name, transform) tuples (implementing fit/transform) that are chained, in the order in which they are chained, with the last object an estimator.
- memorystr or object with the joblib.Memory interface, 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
Dictionary-like object, with the following attributes. Read-only attribute to access any step parameter by user given name. Keys are step names and values are steps parameters.
- classes_ndarray of shape (n_classes,)
The classes labels. Only exist if the last step of the pipeline is a classifier.
- n_features_in_int
Number of features seen during fit. Only defined if the underlying first estimator in
steps
exposes such an attribute when fit.New in version 0.24.
- named_steps
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) Pipeline(steps=[('scaler', StandardScaler()), ('svc', SVC())]) >>> pipe.score(X_test, y_test) 0.88
Methods
Apply transforms, and decision_function of the final estimator
fit
(X[, y])Fit the model
fit_predict
(X[, y])Applies fit_predict of last step in pipeline after transforms.
fit_transform
(X[, y])Fit the model and transform with the final estimator
get_params
([deep])Get parameters for this estimator.
Apply inverse transformations in reverse order
predict
(X, **predict_params)Apply transforms to the data, and predict with the final estimator
predict_log_proba
(X, **predict_log_proba_params)Apply transforms, and predict_log_proba of the final estimator
predict_proba
(X, **predict_proba_params)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.
transform
(X)Apply transforms, and transform with the final 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 one after the other and transform the data, then 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 parameterp
for steps
has keys__p
.
- Returns
- selfPipeline
This estimator
- fit_predict(X, y=None, **fit_params)[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
- 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_predarray-like
- fit_transform(X, y=None, **fit_params)[source]¶
Fit the model and transform with the final estimator
Fits all the transforms one after the other and transforms 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.
- inverse_transform(Xt)[source]¶
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, **predict_log_proba_params)[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.
- **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_scorearray-like of shape (n_samples, n_classes)
- predict_proba(X, **predict_proba_params)[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.
- **predict_proba_paramsdict of string -> object
Parameters to the
predict_proba
called at the end of all transformations in 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
- transform(X)[source]¶
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)