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 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 

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.      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  
decision_function(X) 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.  
predict(X, **predict_params) Apply transforms to the data, and predict with the final estimator  
predict_log_proba(X) Apply transforms, and predict_log_proba of the final estimator  
predict_proba(X) Apply transforms, and predict_proba of the final estimator  
score(X[, y, sample_weight]) Apply transforms, and score with the final estimator  
score_samples(X) 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 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 parameter p for step s has key s__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 parameter p for step s has key s__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 parameter p for step s has key s__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 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.     
  
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 and n_features is the number of features. Must fulfill input requirements of last step of pipeline’s inverse_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 the score 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 in steps.  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) 
   
 
 Examples using sklearn.pipeline.Pipeline
 
  Feature agglomeration vs. univariate selection  

  Poisson regression and non-normal loss  

  Permutation Importance vs Random Forest Feature Importance (MDI)  

  Scalable learning with polynomial kernel aproximation  

  Explicit feature map approximation for RBF kernels  

  Underfitting vs. Overfitting  

  Sample pipeline for text feature extraction and evaluation  

  Balance model complexity and cross-validated score  

  Caching nearest neighbors  

  Comparing Nearest Neighbors with and without Neighborhood Components Analysis  

  Restricted Boltzmann Machine features for digit classification  

  Concatenating multiple feature extraction methods  

  Pipelining: chaining a PCA and a logistic regression  

  Selecting dimensionality reduction with Pipeline and GridSearchCV  

  Column Transformer with Mixed Types  

  Column Transformer with Heterogeneous Data Sources  

  Semi-supervised Classification on a Text Dataset  

  SVM-Anova: SVM with univariate feature selection  

  Classification of text documents using sparse features