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"""
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"]
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