class_docstr: A stopping criterion which stops as soon as the predictions do not change during two
  subsequent checks [ZWH08]_.
  
  Compared to the original paper, this implementation offers a threshold parameter which lessens
  the stopping requirement so that a percentage of samples are allowed to change.
  The default setting (:code:`threshold=0.0`) will result in the original algorithm.
  
  .. versionadded:: 1.1.0
code: def stop(
      self,
      active_learner=None,
      predictions=None,
      proba=None,
      indices_stopping=None,
  ):
      check_window_based_predictions(predictions, self.last_predictions)
  
      if self.last_predictions is None:
          self.last_predictions = predictions
          return False
      else:
          if issparse(predictions):
  
              def compare_rows(mat_one, mat_two, row):
                  row_one = get_row(mat_one, row)
                  row_two = get_row(mat_two, row)
                  same_shape = row_one.shape == row_two.shape
                  return same_shape and (row_one == row_two).all()
  
              def get_row(mat, row):
                  item = slice(mat.indptr[row], mat.indptr[row + 1])
                  return mat.indices[item]
  
              unchanged = np.array(
                  [
                      compare_rows(
                          self.last_predictions, predictions, i
                      )
                      for i in range(self.last_predictions.shape[0])
                  ],
                  dtype=bool,
              )
          else:
              unchanged = np.equal(self.last_predictions, predictions)
          self.last_predictions = predictions
  
          if unchanged.sum() >= predictions.shape[0] * (
              1 - self.threshold
          ):
              return True
  
          return False
  
imports: import numpy
  from scipy.sparse import issparse
  from small_text.stopping_criteria.base import StoppingCriterion, check_window_based_predictions
label_desc: Use image node_id 2 for calling the ClassificationChange obj's underlying member method code with example usage: obj.stop(active_learner, predictions, proba, indices_stopping) and returns: False, False, True, unknown, mat