parameters: (self, X, y=None, **fit_params)
docstring: 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
          ----------
          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 of shape  (n_samples, n_transformed_features)
              Transformed samples
docstring_summary: Fit the model and transform with the final estimator
docstring_tokens: Fit the model and transform with the final estimator
function: def fit_transform(self, X, y=None, **fit_params):
          """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
          ----------
          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 of shape  (n_samples, n_transformed_features)
              Transformed samples
          """
          last_step = self._final_estimator
          Xt, fit_params = self._fit(X, y, **fit_params)
          with _print_elapsed_time('Pipeline',
                                   self._log_message(len(self.steps) - 1)):
              if last_step == 'passthrough':
                  return Xt
              if hasattr(last_step, 'fit_transform'):
                  return last_step.fit_transform(Xt, y, **fit_params)
              else:
                  return last_step.fit(Xt, y, **fit_params).transform(Xt)
function_tokens: def fit_transform ( self , X , y = None , * * fit_params ) : last_step = self . _final_estimator Xt , fit_params = self . _fit ( X , y , * * fit_params ) with _print_elapsed_time ( 'Pipeline' , self . _log_message ( len ( self . steps ) - 1 ) ) : if last_step == 'passthrough' : return Xt if hasattr ( last_step , 'fit_transform' ) : return last_step . fit_transform ( Xt , y , * * fit_params ) else : return last_step . fit ( Xt , y , * * fit_params ) . transform ( Xt )