def _initialize_population(self, initial_result, pop_size):
"""
Initialize a population of size `pop_size` with `initial_result`
Args:
initial_result (GoalFunctionResult): Original text
pop_size (int): size of population
Returns:
population as `list[PopulationMember]`
"""
words = initial_result.attacked_text.words
# For IGA, `num_replacements_left` represents the number of times the word at each index can be modified
num_replacements_left = np.array(
[self.max_replace_times_per_index] * len(words)
)
population = []
# IGA initializes the first population by replacing each word by its optimal synonym
for idx in range(len(words)):
pop_member = PopulationMember(
initial_result.attacked_text,
initial_result,
attributes={"num_replacements_left": np.copy(num_replacements_left)},
)
pop_member = self._perturb(pop_member, initial_result, index=idx)
population.append(pop_member)
return population[:pop_size]
def _crossover(self, pop_member1, pop_member2, original_text):
"""Generates a crossover between pop_member1 and pop_member2.
If the child fails to satisfy the constraints, we re-try crossover for a fix number of times,
before taking one of the parents at random as the resulting child.
Args:
pop_member1 (PopulationMember): The first population member.
pop_member2 (PopulationMember): The second population member.
original_text (AttackedText): Original text
Returns:
A population member containing the crossover.
"""
x1_text = pop_member1.attacked_text
x2_text = pop_member2.attacked_text
num_tries = 0
passed_constraints = False
while num_tries < self.max_crossover_retries + 1:
new_text, attributes = self._crossover_operation(
pop_member1, pop_member2)
replaced_indices = new_text.attack_attrs["newly_modified_indices"]
new_text.attack_attrs["modified_indices"] = (
x1_text.attack_attrs["modified_indices"] -
replaced_indices) | (x2_text.attack_attrs["modified_indices"]
& replaced_indices)
if "last_transformation" in x1_text.attack_attrs:
new_text.attack_attrs[
"last_transformation"] = x1_text.attack_attrs[
"last_transformation"]
elif "last_transformation" in x2_text.attack_attrs:
new_text.attack_attrs[
"last_transformation"] = x2_text.attack_attrs[
"last_transformation"]
if self.post_crossover_check:
passed_constraints = self._post_crossover_check(
new_text, x1_text, x2_text, original_text)
if not self.post_crossover_check or passed_constraints:
break
num_tries += 1
if self.post_crossover_check and not passed_constraints:
# If we cannot find a child that passes the constraints,
# we just randomly pick one of the parents to be the child for the next iteration.
pop_mem = pop_member1 if np.random.uniform() < 0.5 else pop_member2
return pop_mem
else:
new_results, self._search_over = self.get_goal_results([new_text])
return PopulationMember(new_text,
result=new_results[0],
attributes=attributes)
def _modify_population_member(self, pop_member, new_text, new_result, word_idx):
"""Modify `pop_member` by returning a new copy with `new_text`,
`new_result`, and `num_replacements_left` altered appropriately for
given `word_idx`"""
num_replacements_left = np.copy(pop_member.attributes["num_replacements_left"])
num_replacements_left[word_idx] -= 1
return PopulationMember(
new_text,
result=new_result,
attributes={"num_replacements_left": num_replacements_left},
)
def _modify_population_member(self, pop_member, new_text, new_result,
word_idx):
"""Modify `pop_member` by returning a new copy with `new_text`,
`new_result`, and `num_candidate_transformations` altered appropriately
for given `word_idx`"""
num_candidate_transformations = np.copy(
pop_member.attributes["num_candidate_transformations"])
num_candidate_transformations[word_idx] = 0
return PopulationMember(
new_text,
result=new_result,
attributes={
"num_candidate_transformations": num_candidate_transformations
},
)
def _initialize_population(self, initial_result, pop_size):
"""
Initialize a population of size `pop_size` with `initial_result`
Args:
initial_result (GoalFunctionResult): Original text
pop_size (int): size of population
Returns:
population as `list[PopulationMember]`
"""
best_neighbors, prob_list = self._get_best_neighbors(
initial_result, initial_result)
population = []
for _ in range(pop_size):
# Mutation step
random_result = np.random.choice(best_neighbors, 1, p=prob_list)[0]
population.append(
PopulationMember(random_result.attacked_text, random_result))
return population
def _initialize_population(self, initial_result, pop_size):
"""
Initialize a population of size `pop_size` with `initial_result`
Args:
initial_result (GoalFunctionResult): Original text
pop_size (int): size of population
Returns:
population as `list[PopulationMember]`
"""
words = initial_result.attacked_text.words
num_candidate_transformations = np.zeros(len(words))
transformed_texts = self.get_transformations(
initial_result.attacked_text,
original_text=initial_result.attacked_text)
for transformed_text in transformed_texts:
diff_idx = next(
iter(transformed_text.attack_attrs["newly_modified_indices"]))
num_candidate_transformations[diff_idx] += 1
# Just b/c there are no replacements now doesn't mean we never want to select the word for perturbation
# Therefore, we give small non-zero probability for words with no replacements
# Epsilon is some small number to approximately assign small probability
min_num_candidates = np.amin(num_candidate_transformations)
epsilon = max(1, int(min_num_candidates * 0.1))
for i in range(len(num_candidate_transformations)):
num_candidate_transformations[i] = max(
num_candidate_transformations[i], epsilon)
population = []
for _ in range(pop_size):
pop_member = PopulationMember(
initial_result.attacked_text,
initial_result,
attributes={
"num_candidate_transformations":
np.copy(num_candidate_transformations)
},
)
# Perturb `pop_member` in-place
pop_member = self._perturb(pop_member, initial_result)
population.append(pop_member)
return population
def _turn(self, source_text, target_text, prob, original_text):
"""
Based on given probabilities, "move" to `target_text` from `source_text`
Args:
source_text (PopulationMember): Text we start from.
target_text (PopulationMember): Text we want to move to.
prob (np.array[float]): Turn probability for each word.
original_text (AttackedText): Original text for constraint check if `self.post_turn_check=True`.
Returns:
New `Position` that we moved to (or if we fail to move, same as `source_text`)
"""
assert len(source_text.words) == len(
target_text.words), "Word length mismatch for turn operation."
assert len(source_text.words) == len(
prob), "Length mismatch for words and probability list."
len_x = len(source_text.words)
num_tries = 0
passed_constraints = False
while num_tries < self.max_turn_retries + 1:
indices_to_replace = []
words_to_replace = []
for i in range(len_x):
if np.random.uniform() < prob[i]:
indices_to_replace.append(i)
words_to_replace.append(target_text.words[i])
new_text = source_text.attacked_text.replace_words_at_indices(
indices_to_replace, words_to_replace)
indices_to_replace = set(indices_to_replace)
new_text.attack_attrs["modified_indices"] = (
source_text.attacked_text.attack_attrs["modified_indices"] -
indices_to_replace) | (
target_text.attacked_text.attack_attrs["modified_indices"]
& indices_to_replace)
if "last_transformation" in source_text.attacked_text.attack_attrs:
new_text.attack_attrs[
"last_transformation"] = source_text.attacked_text.attack_attrs[
"last_transformation"]
if not self.post_turn_check or (new_text.words
== source_text.words):
break
if "last_transformation" in new_text.attack_attrs:
passed_constraints = self._check_constraints(
new_text,
source_text.attacked_text,
original_text=original_text)
else:
passed_constraints = True
if passed_constraints:
break
num_tries += 1
if self.post_turn_check and not passed_constraints:
# If we cannot find a turn that passes the constraints, we do not move.
return source_text
else:
return PopulationMember(new_text)