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update attackedtext references, need to update tokenization

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This commit is contained in:
Jack Morris
2020-06-16 21:56:33 -04:00
parent 9bf213f7fd
commit d25bf44f52
22 changed files with 189 additions and 166 deletions
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36
textattack/search_methods/genetic_algorithm.py
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@@ -45,14 +45,14 @@ class GeneticAlgorithm(SearchMethod):
Whether a replacement which increased the score was found.
"""
transformations = self.get_transformations(
pop_member.tokenized_text,
original_text=self.original_tokenized_text,
pop_member.attacked_text,
original_text=self.original_attacked_text,
indices_to_modify=[idx],
)
if not len(transformations):
return False
orig_result, self.search_over = self.get_goal_results(
[pop_member.tokenized_text], self.correct_output
[pop_member.attacked_text], self.correct_output
)
if self.search_over:
return False
@@ -62,7 +62,7 @@ class GeneticAlgorithm(SearchMethod):
new_x_scores = torch.Tensor([r.score for r in new_x_results])
new_x_scores = new_x_scores - orig_result[0].score
if len(new_x_scores) and new_x_scores.max() > 0:
pop_member.tokenized_text = transformations[new_x_scores.argmax()]
pop_member.attacked_text = transformations[new_x_scores.argmax()]
return True
return False
@@ -99,7 +99,7 @@ class GeneticAlgorithm(SearchMethod):
pop = []
for _ in range(self.pop_size):
pop_member = PopulationMember(
self.original_tokenized_text, deepcopy(neighbors_len), initial_result
self.original_attacked_text, deepcopy(neighbors_len), initial_result
)
self._perturb(pop_member)
pop.append(pop_member)
@@ -116,8 +116,8 @@ class GeneticAlgorithm(SearchMethod):
"""
indices_to_replace = []
words_to_replace = []
x1_text = pop_member1.tokenized_text
x2_words = pop_member2.tokenized_text.words
x1_text = pop_member1.attacked_text
x2_words = pop_member2.attacked_text.words
new_neighbors_len = deepcopy(pop_member1.neighbors_len)
for i in range(len(x1_text.words)):
if np.random.uniform() < 0.5:
@@ -129,35 +129,35 @@ class GeneticAlgorithm(SearchMethod):
)
return PopulationMember(new_text, deepcopy(new_neighbors_len))
def _get_neighbors_len(self, tokenized_text):
def _get_neighbors_len(self, attacked_text):
"""
Generates this neighbors_len list
Args:
tokenized_text: The original text
attacked_text: The original text
Returns:
A list of number of candidate neighbors for each word
"""
words = tokenized_text.words
words = attacked_text.words
neighbors_list = [[] for _ in range(len(words))]
transformations = self.get_transformations(
tokenized_text, original_text=self.original_tokenized_text
attacked_text, original_text=self.original_attacked_text
)
for transformed_text in transformations:
diff_idx = tokenized_text.first_word_diff_index(transformed_text)
diff_idx = attacked_text.first_word_diff_index(transformed_text)
neighbors_list[diff_idx].append(transformed_text.words[diff_idx])
neighbors_list = [np.array(x) for x in neighbors_list]
neighbors_len = np.array([len(x) for x in neighbors_list])
return neighbors_len
def _perform_search(self, initial_result):
self.original_tokenized_text = initial_result.tokenized_text
self.original_attacked_text = initial_result.attacked_text
self.correct_output = initial_result.output
neighbors_len = self._get_neighbors_len(self.original_tokenized_text)
neighbors_len = self._get_neighbors_len(self.original_attacked_text)
pop = self._generate_population(neighbors_len, initial_result)
cur_score = initial_result.score
for i in range(self.max_iters):
pop_results, self.search_over = self.get_goal_results(
[pm.tokenized_text for pm in pop], self.correct_output
[pm.attacked_text for pm in pop], self.correct_output
)
if self.search_over:
if not len(pop_results):
@@ -213,11 +213,11 @@ class PopulationMember:
A member of the population during the course of the genetic algorithm.
Args:
tokenized_text: The ``AttackedText`` of the population member.
attacked_text: The ``AttackedText`` of the population member.
neighbors_len: A list of the number of candidate neighbors list for each word.
"""
def __init__(self, tokenized_text, neighbors_len, result=None):
self.tokenized_text = tokenized_text
def __init__(self, attacked_text, neighbors_len, result=None):
self.attacked_text = attacked_text
self.neighbors_len = neighbors_len
self.result = result