"""
Goal Function for seq2sick
-------------------------------------------------------
"""
import functools
import numpy as np
from textattack.shared.utils import words_from_text
from .text_to_text_goal_function import TextToTextGoalFunction
[docs]class NonOverlappingOutput(TextToTextGoalFunction):
"""Ensures that none of the words at a position are equal.
Defined in seq2sick (https://arxiv.org/pdf/1803.01128.pdf), equation
(3).
"""
[docs] def clear_cache(self):
if self.use_cache:
self._call_model_cache.clear()
get_words_cached.cache_clear()
word_difference_score.cache_clear()
def _is_goal_complete(self, model_output, _):
return self._get_score(model_output, self.ground_truth_output) == 1.0
def _get_score(self, model_output, _):
num_words_diff = word_difference_score(model_output, self.ground_truth_output)
if num_words_diff == 0:
return 0.0
else:
return num_words_diff / len(get_words_cached(self.ground_truth_output))
[docs]@functools.lru_cache(maxsize=2**12)
def get_words_cached(s):
return np.array(words_from_text(s))
[docs]@functools.lru_cache(maxsize=2**12)
def word_difference_score(s1, s2):
"""Returns the number of words that are non-overlapping between s1 and
s2."""
s1_words = get_words_cached(s1)
s2_words = get_words_cached(s2)
min_length = min(len(s1_words), len(s2_words))
if min_length == 0:
return 0
s1_words = s1_words[:min_length]
s2_words = s2_words[:min_length]
return (s1_words != s2_words).sum()