class_docstr: Base class for triplets learners.
class_docstr_tok: ["Base","class","for","triplets","learners","."]
docstr: Computes score on input triplets.
  
  Returns the accuracy score of the following classification task: a triplet
  (X_a, X_b, X_c) is correctly classified if the predicted similarity between
  the first pair (X_a, X_b) is higher than that of the second pair (X_a, X_c)
  
  Parameters
  ----------
  triplets : array-like, shape=(n_triplets, 3, n_features) or                   (n_triplets, 3)
    3D array of triplets to score, with each row corresponding to three
    points, or 2D array of indices of triplets if the metric learner
    uses a preprocessor.
  
  Returns
  -------
  score : float
    The triplets score.
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code: def score(self, triplets):
      """Computes score on input triplets.
  
      Returns the accuracy score of the following classification task: a triplet
      (X_a, X_b, X_c) is correctly classified if the predicted similarity between
      the first pair (X_a, X_b) is higher than that of the second pair (X_a, X_c)
  
      Parameters
      ----------
      triplets : array-like, shape=(n_triplets, 3, n_features) or \
                    (n_triplets, 3)
        3D array of triplets to score, with each row corresponding to three
        points, or 2D array of indices of triplets if the metric learner
        uses a preprocessor.
  
      Returns
      -------
      score : float
        The triplets 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(triplets).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 _TripletsClassifierMixin obj's underlying member method code with example usage: obj.score(triplets) and returns: unknown