code: def r2_score(y_true, y_pred, sample_weight=None, multioutput=None):
      """R^2 (coefficient of determination) regression score function.
  
      Best possible score is 1.0 and it can be negative (because the
      model can be arbitrarily worse). A constant model that always
      predicts the expected value of y, disregarding the input features,
      would get a R^2 score of 0.0.
  
      Read more in the :ref:`User Guide <r2_score>`.
  
      Parameters
      ----------
      y_true : array-like of shape = (n_samples) or (n_samples, n_outputs)
          Ground truth (correct) target values.
  
      y_pred : array-like of shape = (n_samples) or (n_samples, n_outputs)
          Estimated target values.
  
      sample_weight : array-like of shape = (n_samples), optional
          Sample weights.
  
      multioutput : string in ['raw_values', 'uniform_average', \
  'variance_weighted'] or None or array-like of shape (n_outputs)
  
          Defines aggregating of multiple output scores.
          Array-like value defines weights used to average scores.
          Default value corresponds to 'variance_weighted', this behaviour is
          deprecated since version 0.17 and will be changed to 'uniform_average'
          starting from 0.19.
  
          'raw_values' :
              Returns a full set of scores in case of multioutput input.
  
          'uniform_average' :
              Scores of all outputs are averaged with uniform weight.
  
          'variance_weighted' :
              Scores of all outputs are averaged, weighted by the variances
              of each individual output.
  
      Returns
      -------
      z : float or ndarray of floats
          The R^2 score or ndarray of scores if 'multioutput' is
          'raw_values'.
  
      Notes
      -----
      This is not a symmetric function.
  
      Unlike most other scores, R^2 score may be negative (it need not actually
      be the square of a quantity R).
  
      References
      ----------
      .. [1] `Wikipedia entry on the Coefficient of determination
              <https://en.wikipedia.org/wiki/Coefficient_of_determination>`_
  
      Examples
      --------
      >>> from sklearn.metrics import r2_score
      >>> y_true = [3, -0.5, 2, 7]
      >>> y_pred = [2.5, 0.0, 2, 8]
      >>> r2_score(y_true, y_pred)  # doctest: +ELLIPSIS
      0.948...
      >>> y_true = [[0.5, 1], [-1, 1], [7, -6]]
      >>> y_pred = [[0, 2], [-1, 2], [8, -5]]
      >>> r2_score(y_true, y_pred, multioutput='variance_weighted')  # doctest: +ELLIPSIS
      0.938...
      >>> y_true = [1,2,3]
      >>> y_pred = [1,2,3]
      >>> r2_score(y_true, y_pred)
      1.0
      >>> y_true = [1,2,3]
      >>> y_pred = [2,2,2]
      >>> r2_score(y_true, y_pred)
      0.0
      >>> y_true = [1,2,3]
      >>> y_pred = [3,2,1]
      >>> r2_score(y_true, y_pred)
      -3.0
      """
      y_type, y_true, y_pred, multioutput = _check_reg_targets(
          y_true, y_pred, multioutput
      )
  
      if sample_weight is not None:
          sample_weight = column_or_1d(sample_weight)
          weight = sample_weight[:, np.newaxis]
      else:
          weight = 1.0
  
      numerator = (weight * (y_true - y_pred) ** 2).sum(
          axis=0, dtype=np.float64
      )
      denominator = (
          weight
          * (y_true - np.average(y_true, axis=0, weights=sample_weight))
          ** 2
      ).sum(axis=0, dtype=np.float64)
      nonzero_denominator = denominator != 0
      nonzero_numerator = numerator != 0
      valid_score = nonzero_denominator & nonzero_numerator
      output_scores = np.ones([y_true.shape[1]])
      output_scores[valid_score] = 1 - (
          numerator[valid_score] / denominator[valid_score]
      )
      # arbitrary set to zero to avoid -inf scores, having a constant
      # y_true is not interesting for scoring a regression anyway
      output_scores[nonzero_numerator & ~nonzero_denominator] = 0.0
      if multioutput is None and y_true.shape[1] != 1:
          warnings.warn(
              "Default 'multioutput' behavior now corresponds to "
              "'variance_weighted' value which is deprecated since "
              "0.17, it will be changed to 'uniform_average' "
              "starting from 0.19.",
              DeprecationWarning,
          )
          multioutput = "variance_weighted"
      if isinstance(multioutput, string_types):
          if multioutput == "raw_values":
              # return scores individually
              return output_scores
          elif multioutput == "uniform_average":
              # passing None as weights results is uniform mean
              avg_weights = None
          elif multioutput == "variance_weighted":
              avg_weights = denominator
              # avoid fail on constant y or one-element arrays
              if not np.any(nonzero_denominator):
                  if not np.any(nonzero_numerator):
                      return 1.0
                  else:
                      return 0.0
      else:
          avg_weights = multioutput
  
      return np.average(output_scores, weights=avg_weights)
  
file_path: catboost/contrib/python/scikit-learn/py2/sklearn/metrics/regression.py
imports: from __future__ import division
  import numpy
  from ..utils.validation import check_array, check_consistent_length
  from ..utils.validation import column_or_1d
  from ..externals.six import string_types
  import warnings
label_desc: Use image node_id 6 for calling a global function with example usage: r2_score(y_true, y_pred, sample_weight, multioutput) and returns: np, output_scores, int, int