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# `sklearn.pipeline`.Pipeline

class sklearn.pipeline.Pipeline(steps, *, memory=None, verbose=False)

  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`.

  For an example use case of `Pipeline` combined with `GridSearchCV`, refer to Selecting dimensionality reduction with Pipeline and GridSearchCV. The example Pipelining: chaining a PCA and a logistic regression shows how to grid search on a pipeline using `'__'` as a separator in the parameter names.

  Read more in the User Guide.

  New in version 0.5.

  Parameters:
    steps list of tuple

      List of (name, transform) tuples (implementing `fit`/`transform`) that are chained in sequential order. The last transform must be an estimator.

    memory str or object with the joblib.Memory interface, default=None

      Used to cache the fitted transformers of the pipeline. The last step will never be cached, even if it is a transformer. 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.

    verbose bool, default=False

      If True, the time elapsed while fitting each step will be printed as it is completed.

  Attributes:
    `named_steps` `Bunch`

      Access the steps by name.

    `classes_`ndarray of shape (n_classes,)

      The classes labels.

    `n_features_in_`int

      Number of features seen during first step `fit` method.

    `feature_names_in_`ndarray of shape (`n_features_in_`,)

      Names of features seen during first step `fit` method.

  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).score(X_test, y_test)
      0.88
      >>> # An estimator's parameter can be set using '__' syntax
      >>> pipe.set_params(svc__C=10).fit(X_train, y_train).score(X_test, y_test)
      0.76

  Methods

  `decision_function`(X) | Transform the data, and apply `decision_function` with the final estimator.
  `fit`(X[, y]) | Fit the model.
  `fit_predict`(X[, y]) | Transform the data, and apply `fit_predict` with the final estimator.
  `fit_transform`(X[, y]) | Fit the model and transform with the final estimator.
  `get_feature_names_out`([input_features]) | Get output feature names for transformation.
  `get_metadata_routing`() | Get metadata routing of this object.
  `get_params`([deep]) | Get parameters for this estimator.
  `inverse_transform`(Xt) | Apply `inverse_transform` for each step in a reverse order.
  `predict`(X, **predict_params) | Transform the data, and apply `predict` with the final estimator.
  `predict_log_proba`(X, **predict_log_proba_params) | Transform the data, and apply `predict_log_proba` with the final estimator.
  `predict_proba`(X, **predict_proba_params) | Transform the data, and apply `predict_proba` with the final estimator.
  `score`(X[, y, sample_weight]) | Transform the data, and apply `score` with the final estimator.
  `score_samples`(X) | Transform the data, and apply `score_samples` with the final estimator.
  `set_output`(*[, transform]) | Set the output container when `"transform"` and `"fit_transform"` are called.
  `set_params`(**kwargs) | Set the parameters of this estimator.
  `set_score_request`(*[, sample_weight]) | Request metadata passed to the `score` method.
  `transform`(X) | Transform the data, and apply `transform` with the final estimator.

  property classes_

    The classes labels. Only exist if the last step is a classifier.

  decision_function(X)

    Transform the data, and apply `decision_function` with the final estimator.

    Call `transform` of each transformer in the pipeline. The transformed data are finally passed to the final estimator that calls `decision_function` method. Only valid if the final estimator implements `decision_function`.

    Parameters:
      X iterable

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

    Returns:
      y_score ndarray of shape (n_samples, n_classes)

        Result of calling `decision_function` on the final estimator.

  property feature_names_in_

    Names of features seen during first step `fit` method.

  fit(X, y=None, **fit_params)

    Fit the model.

    Fit all the transformers one after the other and transform the data. Finally, fit the transformed 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 object

        Pipeline with fitted steps.

  fit_predict(X, y=None, **fit_params)

    Transform the data, and apply `fit_predict` with the final estimator.

    Call `fit_transform` of each transformer in the pipeline. The transformed data are finally passed to the final estimator that calls `fit_predict` method. Only valid 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 ndarray

        Result of calling `fit_predict` on the final estimator.

  fit_transform(X, y=None, **fit_params)

    Fit the model and transform with the final estimator.

    Fits all the transformers one after the other and transform 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 ndarray of shape (n_samples, n_transformed_features)

        Transformed samples.

  get_feature_names_out(input_features=None)

    Get output feature names for transformation.

    Transform input features using the pipeline.

    Parameters:
      input_features array-like of str or None, default=None

        Input features.

    Returns:
      feature_names_out ndarray of str objects

        Transformed feature names.

  get_metadata_routing()

    Get metadata routing of this object.

    Please check User Guide on how the routing mechanism works.

    Returns:
      routing MetadataRequest

        A `MetadataRequest` encapsulating routing information.

  get_params(deep=True)

    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:
      deep bool, default=True

        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(Xt)

    Apply `inverse_transform` for each step in a reverse order.

    All estimators in the pipeline must support `inverse_transform`.

    Parameters:
      Xt array-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:
      Xt ndarray of shape (n_samples, n_features)

        Inverse transformed data, that is, data in the original feature space.

  property n_features_in_

    Number of features seen during first step `fit` method.

  property named_steps

    Access the steps by name.

    Read-only attribute to access any step by given name. Keys are steps names and values are the steps objects.

  predict(X, **predict_params)

    Transform the data, and apply `predict` with the final estimator.

    Call `transform` of each transformer in the pipeline. The transformed data are finally passed to the final estimator that calls `predict` method. Only valid if the final estimator implements `predict`.

    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.

        New in version 0.20.

    Returns:
      y_pred ndarray

        Result of calling `predict` on the final estimator.

  predict_log_proba(X, **predict_log_proba_params)

    Transform the data, and apply `predict_log_proba` with the final estimator.

    Call `transform` of each transformer in the pipeline. The transformed data are finally passed to the final estimator that calls `predict_log_proba` method. Only valid if the final estimator implements `predict_log_proba`.

    Parameters:
      X iterable

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

      **predict_log_proba_params dict of string -> object

        Parameters to the `predict_log_proba` called at the end of all transformations in the pipeline.

    Returns:
      y_log_proba ndarray of shape (n_samples, n_classes)

        Result of calling `predict_log_proba` on the final estimator.

  predict_proba(X, **predict_proba_params)

    Transform the data, and apply `predict_proba` with the final estimator.

    Call `transform` of each transformer in the pipeline. The transformed data are finally passed to the final estimator that calls `predict_proba` method. Only valid if the final estimator implements `predict_proba`.

    Parameters:
      X iterable

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

      **predict_proba_params dict of string -> object

        Parameters to the `predict_proba` called at the end of all transformations in the pipeline.

    Returns:
      y_proba ndarray of shape (n_samples, n_classes)

        Result of calling `predict_proba` on the final estimator.

  score(X, y=None, sample_weight=None)

    Transform the data, and apply `score` with the final estimator.

    Call `transform` of each transformer in the pipeline. The transformed data are finally passed to the final estimator that calls `score` method. Only valid if the final estimator implements `score`.

    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

        Result of calling `score` on the final estimator.

  score_samples(X)

    Transform the data, and apply `score_samples` with the final estimator.

    Call `transform` of each transformer in the pipeline. The transformed data are finally passed to the final estimator that calls `score_samples` method. Only valid if the final estimator implements `score_samples`.

    Parameters:
      X iterable

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

    Returns:
      y_score ndarray of shape (n_samples,)

        Result of calling `score_samples` on the final estimator.

  set_output(*, transform=None)

    Set the output container when `"transform"` and `"fit_transform"` are called.

    Calling `set_output` will set the output of all estimators in `steps`.

    Parameters:
      transform{“default”, “pandas”}, default=None

        Configure output of `transform` and `fit_transform`.

        - `"default"`: Default output format of a transformer

        - `"pandas"`: DataFrame output

        - `None`: Transform configuration is unchanged

    Returns:
      self estimator instance

        Estimator instance.

  set_params(**kwargs)

    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`.

    Parameters:
      **kwargs dict

        Parameters of this estimator or parameters of estimators contained in `steps`. Parameters of the steps may be set using its name and the parameter name separated by a ‘__’.

    Returns:
      self object

        Pipeline class instance.

  set_score_request(*, sample_weight: bool | None | str = '$UNCHANGED$') → Pipeline

    Request metadata passed to the `score` method.

    Note that this method is only relevant if `enable_metadata_routing=True` (see `sklearn.set_config`). Please see User Guide on how the routing mechanism works.

    The options for each parameter are:

    - `True`: metadata is requested, and passed to `score` if provided. The request is ignored if metadata is not provided.

    - `False`: metadata is not requested and the meta-estimator will not pass it to `score`.

    - `None`: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

    - `str`: metadata should be passed to the meta-estimator with this given alias instead of the original name.

    The default (`sklearn.utils.metadata_routing.UNCHANGED`) retains the existing request. This allows you to change the request for some parameters and not others.

    New in version 1.3.

    Note

    This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a `Pipeline`. Otherwise it has no effect.

    Parameters:
      sample_weight str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

        Metadata routing for `sample_weight` parameter in `score`.

    Returns:
      self object

        The updated object.

  transform(X)

    Transform the data, and apply `transform` with the final estimator.

    Call `transform` of each transformer in the pipeline. The transformed data are finally passed to the final estimator that calls `transform` method. Only valid if the final estimator implements `transform`.

    This also works where final estimator is `None` in which case all prior transformations are applied.

    Parameters:
      X iterable

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

    Returns:
      Xt ndarray of shape (n_samples, n_transformed_features)

        Transformed data.

## Examples using `sklearn.pipeline.Pipeline`

Feature agglomeration vs. univariate selection

Feature agglomeration vs. univariate selection

Pipeline ANOVA SVM

Pipeline ANOVA SVM

Poisson regression and non-normal loss

Poisson regression and non-normal loss

Permutation Importance vs Random Forest Feature Importance (MDI)

Permutation Importance vs Random Forest Feature Importance (MDI)

Displaying Pipelines

Displaying Pipelines

Explicit feature map approximation for RBF kernels

Explicit feature map approximation for RBF kernels

Balance model complexity and cross-validated score

Balance model complexity and cross-validated score

Sample pipeline for text feature extraction and evaluation

Sample pipeline for text feature extraction and evaluation

Underfitting vs. Overfitting

Underfitting vs. Overfitting

Caching nearest neighbors

Caching nearest neighbors

Comparing Nearest Neighbors with and without Neighborhood Components Analysis

Comparing Nearest Neighbors with and without Neighborhood Components Analysis

Nearest Neighbors Classification

Nearest Neighbors Classification

Restricted Boltzmann Machine features for digit classification

Restricted Boltzmann Machine features for digit classification

Column Transformer with Heterogeneous Data Sources

Column Transformer with Heterogeneous Data Sources

Column Transformer with Mixed Types

Column Transformer with Mixed Types

Concatenating multiple feature extraction methods

Concatenating multiple feature extraction methods

Pipelining: chaining a PCA and a logistic regression

Pipelining: chaining a PCA and a logistic regression

Selecting dimensionality reduction with Pipeline and GridSearchCV

Selecting dimensionality reduction with Pipeline and GridSearchCV

Target Encoder’s Internal Cross fitting

Target Encoder's Internal Cross fitting

Semi-supervised Classification on a Text Dataset

Semi-supervised Classification on a Text Dataset

SVM-Anova: SVM with univariate feature selection

SVM-Anova: SVM with univariate feature selection