docstr: Evaluate predicted target values for X relative to y_true.
  
  Parameters
  ----------
  estimator : object
      Trained estimator to use for scoring. Must have a predict_proba
      method; the output of that is used to compute the score.
  
  X : array-like or sparse matrix
      Test data that will be fed to estimator.predict.
  
  y_true : array-like
      Gold standard target values for X.
  
  sample_weight : array-like, optional (default=None)
      Sample weights.
  
  Returns
  -------
  score : float
      Score function applied to prediction of estimator on X.
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code: def __call__(self, estimator, X, y_true, sample_weight=None):
      """Evaluate predicted target values for X relative to y_true.
  
      Parameters
      ----------
      estimator : object
          Trained estimator to use for scoring. Must have a predict_proba
          method; the output of that is used to compute the score.
  
      X : array-like or sparse matrix
          Test data that will be fed to estimator.predict.
  
      y_true : array-like
          Gold standard target values for X.
  
      sample_weight : array-like, optional (default=None)
          Sample weights.
  
      Returns
      -------
      score : float
          Score function applied to prediction of estimator on X.
      """
      super(_PredictScorer, self).__call__(
          estimator, X, y_true, sample_weight=sample_weight
      )
      y_pred = estimator.predict(X)
      if sample_weight is not None:
          return self._sign * self._score_func(
              y_true,
              y_pred,
              sample_weight=sample_weight,
              **self._kwargs,
          )
      else:
          return self._sign * self._score_func(
              y_true, y_pred, **self._kwargs
          )
  
imports: import warnings
  from abc import ABCMeta, abstractmethod
  import numpy
  from ..None import six
  from .base import is_regressor
  from .type_of_target import type_of_target
label_desc: Use image node_id 1 for calling the _PredictScorer obj's underlying member method code with example usage: obj.__call__(estimator, X, y_true, sample_weight) and returns: unknown, unknown