code: def set_classifier_mask(self, v, base_mask=True):
          
          base = self._base
          v = tonparray(v)
          a = np.unique(v)
          if a[0] != -1 or a[1] != 1:
              raise RuntimeError("The labels must be -1 and 1 (%s)" % a)
          mask = np.zeros_like(v)
          cnt = min([(v == x).sum() for x in a]) * base._tr_fraction
          cnt = int(round(cnt))
          for i in a:
              index = np.where((v == i) & base_mask)[0]
              np.random.shuffle(index)
              mask[index[:cnt]] = True
          base._mask = SparseArray.fromlist(mask)
          return SparseArray.fromlist(v)
docs: Computes the mask used to create the training and validation set