class_docstr: A stopping criterion which stops if the predicted change of the F-score falls below
  a threshold [AB19]_.
  
  .. note:: This criterion is only applicable for binary classification.
  
  .. versionchanged:: 1.3.3
     The implementation now correctly only considers the change in agreement of the predicted labels
     belonging to the positive class.
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:
          agreement = (
              ((self.last_predictions == 1) & (predictions == 1))
              .astype(int)
              .sum()
          )
          disagreement_old_positive = (
              ((self.last_predictions == 1) & (predictions == 0))
              .astype(int)
              .sum()
          )
          disagreement_new_positive = (
              ((self.last_predictions == 0) & (predictions == 1))
              .astype(int)
              .sum()
          )
  
          denominator = (
              2 * agreement
              + disagreement_old_positive
              + disagreement_new_positive
          )
          delta_f = 1 - 2 * agreement / denominator
  
          self.delta_history.append(delta_f)
          self.last_predictions = predictions
  
          if len(self.delta_history) < self.window_size:
              return False
  
          self.delta_history = self.delta_history[-self.window_size :]
  
          if np.all(np.array(self.delta_history) < self.threshold):
              return True
          else:
              return False
  
file_path: small-text/small_text/stopping_criteria/base.py
imports: import numpy
  from abc import ABC, abstractmethod
label_desc: Use image node_id 2 for calling the DeltaFScore obj's underlying member method code with example usage: obj.stop(active_learner, predictions, proba, indices_stopping) and returns: False, False, True, False