Spaces:
Sleeping
Sleeping
| """ | |
| Determine if maintaining the same predicted label | |
| --------------------------------------------------------------------- | |
| """ | |
| from .classification_goal_function import ClassificationGoalFunction | |
| class InputReduction(ClassificationGoalFunction): | |
| """Attempts to reduce the input down to as few words as possible while | |
| maintaining the same predicted label. | |
| From Feng, Wallace, Grissom, Iyyer, Rodriguez, Boyd-Graber. (2018). | |
| Pathologies of Neural Models Make Interpretations Difficult. | |
| https://arxiv.org/abs/1804.07781 | |
| """ | |
| def __init__(self, *args, target_num_words=1, **kwargs): | |
| self.target_num_words = target_num_words | |
| super().__init__(*args, **kwargs) | |
| def _is_goal_complete(self, model_output, attacked_text): | |
| return ( | |
| self.ground_truth_output == model_output.argmax() | |
| and attacked_text.num_words <= self.target_num_words | |
| ) | |
| def _should_skip(self, model_output, attacked_text): | |
| return self.ground_truth_output != model_output.argmax() | |
| def _get_score(self, model_output, attacked_text): | |
| # Give the lowest score possible to inputs which don't maintain the ground truth label. | |
| if self.ground_truth_output != model_output.argmax(): | |
| return 0 | |
| cur_num_words = attacked_text.num_words | |
| initial_num_words = self.initial_attacked_text.num_words | |
| # The main goal is to reduce the number of words (num_words_score) | |
| # Higher model score for the ground truth label is used as a tiebreaker (model_score) | |
| num_words_score = max( | |
| (initial_num_words - cur_num_words) / initial_num_words, 0 | |
| ) | |
| model_score = model_output[self.ground_truth_output] | |
| return min(num_words_score + model_score / initial_num_words, 1) | |
| def extra_repr_keys(self): | |
| if self.maximizable: | |
| return ["maximizable"] | |
| else: | |
| return ["maximizable", "target_num_words"] | |