Source code for textattack.goal_functions.classification.untargeted_classification


Determine successful in untargeted Classification

from .classification_goal_function import ClassificationGoalFunction

[docs]class UntargetedClassification(ClassificationGoalFunction): """An untargeted attack on classification models which attempts to minimize the score of the correct label until it is no longer the predicted label. Args: target_max_score (float): If set, goal is to reduce model output to below this score. Otherwise, goal is to change the overall predicted class. """ def __init__(self, *args, target_max_score=None, **kwargs): self.target_max_score = target_max_score super().__init__(*args, **kwargs) def _is_goal_complete(self, model_output, _): if self.target_max_score: return model_output[self.ground_truth_output] < self.target_max_score elif (model_output.numel() == 1) and isinstance( self.ground_truth_output, float ): return abs(self.ground_truth_output - model_output.item()) >= 0.5 else: return model_output.argmax() != self.ground_truth_output def _get_score(self, model_output, _): # If the model outputs a single number and the ground truth output is # a float, we assume that this is a regression task. if (model_output.numel() == 1) and isinstance(self.ground_truth_output, float): return abs(model_output.item() - self.ground_truth_output) else: return 1 - model_output[self.ground_truth_output]