class_docstr: Base class for all metric-learners.
  
  Parameters
  ----------
  preprocessor : array-like, shape=(n_samples, n_features) or callable
    The preprocessor to call to get tuples from indices. If array-like,
    tuples will be gotten like this: X[indices].
class_docstr_tok: ["Base","class","for","all","metric-learners",".","Parameters","--","--","--","--","--","preprocessor",":","array-like",",","shape=","(","n_samples",",","n_features",")","or","callable","The","preprocessor","to","call","to","get","tuples","from","indices",".","If","array-like",",","tuples","will","be","gotten","like","this",":","X","[","indices","]","."]
docstr: Returns the score between pairs
  (can be a similarity, or a distance/metric depending on the algorithm)
  
  .. deprecated:: 0.7.0
      Refer to `pair_distance` and `pair_score`.
  
  .. warning::
      This method will be removed in 0.8.0. Please refer to `pair_distance`
      or `pair_score`. This change will occur in order to add learners
      that don't necessarily learn a Mahalanobis distance.
  
  Parameters
  ----------
  pairs : array-like, shape=(n_pairs, 2, n_features) or (n_pairs, 2)
    3D Array of pairs to score, with each row corresponding to two points,
    for 2D array of indices of pairs if the metric learner uses a
    preprocessor.
  
  Returns
  -------
  scores : `numpy.ndarray` of shape=(n_pairs,)
    The score of every pair.
  
  See Also
  --------
  get_metric : a method that returns a function to compute the metric between
    two points. The difference between `score_pairs` is that it works on two
    1D arrays and cannot use a preprocessor. Besides, the returned function
    is independent of the metric learner and hence is not modified if the
    metric learner is.
docstr_tok: ["Returns","the","score","between","pairs","(","can","be","a","similarity",",","or","a","distance\/metric","depending","on","the","algorithm",")","..","deprecated",":",":","0.7.0","Refer","to","`","pair_distance","`","and","`","pair_score","`",".","..","warning",":",":","This","method","will","be","removed","in","0.8.0",".","Please","refer","to","`","pair_distance","`","or","`","pair_score","`",".","This","change","will","occur","in","order","to","add","learners","that","do","n't","necessarily","learn","a","Mahalanobis","distance",".","Parameters","--","--","--","--","--","pairs",":","array-like",",","shape=","(","n_pairs",",","2",",","n_features",")","or","(","n_pairs",",","2",")","3D","Array","of","pairs","to","score",",","with","each","row","corresponding","to","two","points",",","for","2D","array","of","indices","of","pairs","if","the","metric","learner","uses","a","preprocessor",".","Returns","--","--","--","-","scores",":","`","numpy.ndarray","`","of","shape=","(","n_pairs",",",")","The","score","of","every","pair",".","See","Also","--","--","--","--","get_metric",":","a","method","that","returns","a","function","to","compute","the","metric","between","two","points",".","The","difference","between","`","score_pairs","`","is","that","it","works","on","two","1D","arrays","and","can","not","use","a","preprocessor",".","Besides",",","the","returned","function","is","independent","of","the","metric","learner","and","hence","is","not","modified","if","the","metric","learner","is","."]
code: def score_pairs(self, pairs):
      """
      Returns the score between pairs
      (can be a similarity, or a distance/metric depending on the algorithm)
  
      .. deprecated:: 0.7.0
          Refer to `pair_distance` and `pair_score`.
  
      .. warning::
          This method will be removed in 0.8.0. Please refer to `pair_distance`
          or `pair_score`. This change will occur in order to add learners
          that don't necessarily learn a Mahalanobis distance.
  
      Parameters
      ----------
      pairs : array-like, shape=(n_pairs, 2, n_features) or (n_pairs, 2)
        3D Array of pairs to score, with each row corresponding to two points,
        for 2D array of indices of pairs if the metric learner uses a
        preprocessor.
  
      Returns
      -------
      scores : `numpy.ndarray` of shape=(n_pairs,)
        The score of every pair.
  
      See Also
      --------
      get_metric : a method that returns a function to compute the metric between
        two points. The difference between `score_pairs` is that it works on two
        1D arrays and cannot use a preprocessor. Besides, the returned function
        is independent of the metric learner and hence is not modified if the
        metric learner is.
      """
  
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 2 for calling the BaseMetricLearner obj's underlying member method code with example usage: obj.score_pairs(pairs) without return types