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.
docstr: num_classes : int
      Number of classes.
  window_size : int, default=3
      Defines number of iterations for which the predictions are taken into account, i.e.
      this stopping criterion only sees the last `window_size`-many states of the prediction
      array passed to `stop()`.
  threshold : float, default=0.05
      The criterion stops when the predicted F-score falls below this threshold.
code: def __init__(self, num_classes, window_size=3, threshold=0.05):
      """
      num_classes : int
          Number of classes.
      window_size : int, default=3
          Defines number of iterations for which the predictions are taken into account, i.e.
          this stopping criterion only sees the last `window_size`-many states of the prediction
          array passed to `stop()`.
      threshold : float, default=0.05
          The criterion stops when the predicted F-score falls below this threshold.
      """
      self.num_classes = num_classes
  
      if num_classes != 2:
          raise ValueError(
              "DeltaFScore is only applicable for binary classifications "
              "(requires num_class=2)"
          )
  
      self.window_size = window_size
      self.threshold = threshold
  
      self.last_predictions = None
      self.delta_history = []
  
file_path: small-text/small_text/stopping_criteria/base.py
imports: import numpy
  from abc import ABC, abstractmethod
label_desc: Use image node_id 1 to create a new DeltaFScore object from inherited base classes: StoppingCriterion with example: obj = DeltaFScore(num_classes, window_size, threshold)