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low prob assignment for zero transformation cases

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Jin Yong Yoo
2020-06-30 03:23:13 -04:00
parent 7651e2738c
commit 73c0fda293
1 changed files with 12 additions and 17 deletions
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29
textattack/search_methods/genetic_algorithm.py
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@@ -25,9 +25,7 @@ class GeneticAlgorithm(SearchMethod):
Higher temperature increases the sensitivity to lower probability candidates.
give_up_if_no_improvement (bool): If True, stop the search early if no candidate that improves the score is found.
max_crossover_retries (int): Maximum number of crossover retries if resulting child fails to pass the constraints.
Setting it to 0 means we immediately take one of the parents at random as the child.
compare_againt_original (bool): If True, the reference text for constraints is the original text.
Else, the reference text is the most recent text from which the new text is generated.
Setting it to 0 means we immediately take one of the parents at random as the child.
"""
def __init__(
@@ -44,7 +42,6 @@ class GeneticAlgorithm(SearchMethod):
self.temp = temp
self.give_up_if_no_improvement = give_up_if_no_improvement
self.max_crossover_retries = max_crossover_retries
self.compare_against_original = compare_against_original
# internal flag to indicate if search should end immediately
self._search_over = False
@@ -60,7 +57,7 @@ class GeneticAlgorithm(SearchMethod):
"""
num_words = pop_member.num_candidates_per_word.shape[0]
num_candidates_per_word = np.copy(pop_member.num_candidates_per_word)
non_zero_indices = np.sum(np.sign(pop_member.num_candidates_per_word))
non_zero_indices = np.count_nonzero(num_candidates_per_word)
if non_zero_indices == 0:
return
iterations = 0
@@ -68,16 +65,11 @@ class GeneticAlgorithm(SearchMethod):
w_select_probs = num_candidates_per_word / np.sum(num_candidates_per_word)
rand_idx = np.random.choice(num_words, 1, p=w_select_probs)[0]
if self.compare_against_original:
transformations = self.get_transformations(
pop_member.attacked_text,
original_text=original_result.attacked_text,
indices_to_modify=[rand_idx],
)
else:
transformations = self.get_transformations(
pop_member.attacked_text, indices_to_modify=[rand_idx],
)
transformations = self.get_transformations(
pop_member.attacked_text,
original_text=original_result.attacked_text,
indices_to_modify=[rand_idx]
)
if not len(transformations):
iterations += 1
@@ -179,12 +171,15 @@ class GeneticAlgorithm(SearchMethod):
)
num_candidates_per_word[diff_idx] += 1
total_candidates = np.sum(num_candidates_per_word)
# Just b/c there are no candidates now doesn't mean we never want to select the word for perturbation
# Therefore, we give small non-zero probability for words with no candidates
# Epsilon is some small number to approximately assign 1% probability
total_candidates = np.sum(num_candidates_per_word)
num_zero_elements = len(words) - np.count_zero(num_candidates_per_word)
epsilon = min(1, int(total_candidates / (100 - num_zero_elements)))
for i in range(len(num_candidates_per_word)):
if num_candidates_per_word[i] == 0:
num_candidates_per_word[i] = int(total_candidates * 0.01)
num_candidates_per_word[i] = epsilon
population = []
for _ in range(self.pop_size):