parameters: (self, X, y=None, sample_weight=None)
return_statement: return self.steps[-1][-1].score(Xt, y, **score_params)
docstring: Apply transforms, and score with the final estimator
  
          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
docstring_summary: Apply transforms, and score with the final estimator
docstring_tokens: Apply transforms and score with the final estimator
function: def score(self, X, y=None, sample_weight=None):
          """Apply transforms, and score with the final estimator
  
          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
          """
          Xt = X
          for _, name, transform in self._iter(with_final=False):
              Xt = transform.transform(Xt)
          score_params = {}
          if sample_weight is not None:
              score_params['sample_weight'] = sample_weight
          return self.steps[-1][-1].score(Xt, y, **score_params)
function_tokens: def score ( self , X , y = None , sample_weight = None ) : Xt = X for _ , name , transform in self . _iter ( with_final = False ) : Xt = transform . transform ( Xt ) score_params = { } if sample_weight is not None : score_params [ 'sample_weight' ] = sample_weight return self . steps [ - 1 ] [ - 1 ] . score ( Xt , y , * * score_params )