a9979a8a54bae31a710eb4117cde5b8af4fbdb28,metric_learn/itml.py,ITML_Supervised,fit,#ITML_Supervised#Any#Any#Any#,212

Before Change

      num_classes = len(np.unique(y))
      num_constraints = 20 * num_classes**2

    c = Constraints.random_subset(y, self.num_labeled,
                                  random_state=random_state)
    pos_neg = c.positive_negative_pairs(num_constraints,
                                        random_state=random_state)
    pairs, y = wrap_pairs(X, pos_neg)
    return _BaseITML._fit(self, pairs, y, bounds=self.bounds)

After Change

    random_state : numpy.random.RandomState, optional
        If provided, controls random number generation.
    
    if self.num_labeled != "deprecated":
      warnings.warn(""num_labeled" parameter is not used."
                    " It has been deprecated in version 0.5.0 and will be"
                    "removed in 0.6.0", DeprecationWarning)
    X, y = self._prepare_inputs(X, y, ensure_min_samples=2)
    num_constraints = self.num_constraints
    if num_constraints is None:
      num_classes = len(np.unique(y))

Instances