class_docstr: Base class for quadruplets learners.
class_docstr_tok: ["Base","class","for","quadruplets","learners","."]
docstr: Computes score on input quadruplets
  
  Returns the accuracy score of the following classification task: a record
  is correctly classified if the predicted similarity between the first two
  samples is higher than that of the last two.
  
  Parameters
  ----------
  quadruplets : array-like, shape=(n_quadruplets, 4, n_features) or                   (n_quadruplets, 4)
    3D Array of quadruplets to score, with each row corresponding to four
    points, or 2D array of indices of quadruplets if the metric learner
    uses a preprocessor.
  
  Returns
  -------
  score : float
    The quadruplets score.
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code: def score(self, quadruplets):
      """Computes score on input quadruplets
  
      Returns the accuracy score of the following classification task: a record
      is correctly classified if the predicted similarity between the first two
      samples is higher than that of the last two.
  
      Parameters
      ----------
      quadruplets : array-like, shape=(n_quadruplets, 4, n_features) or \
                    (n_quadruplets, 4)
        3D Array of quadruplets to score, with each row corresponding to four
        points, or 2D array of indices of quadruplets if the metric learner
        uses a preprocessor.
  
      Returns
      -------
      score : float
        The quadruplets score.
      """
      # Since the prediction is a vector of values in {-1, +1}, we need to
      # rescale them to {0, 1} to compute the accuracy using the mean (because
      # then 1 means a correctly classified result (pairs are in the right
      # order), and a 0 an incorrectly classified result (pairs are in the
      # wrong order).
      return self.predict(quadruplets).mean() / 2 + 0.5
  
imports: from sklearn.base import BaseEstimator, ClassifierMixin
  from sklearn.utils.extmath import stable_cumsum
  from sklearn.utils.validation import _is_arraylike, check_is_fitted
  from sklearn.metrics import roc_auc_score, roc_curve, precision_recall_curve
  import numpy
  from abc import ABCMeta, abstractmethod
  from ._util import ArrayIndexer, check_input, validate_vector
  import warnings
label_desc: Use image node_id 3 for calling the _QuadrupletsClassifierMixin obj's underlying member method code with example usage: obj.score(quadruplets) and returns: unknown