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fix docstring

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This commit is contained in:
Jin Yong Yoo
2020-07-14 02:17:20 -04:00
parent a0c6b78ad9
commit 3a48ad3e7b
1 changed files with 9 additions and 7 deletions
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16
textattack/search_methods/particle_swarm_optimization.py
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@@ -25,10 +25,10 @@ class ParticleSwarmOptimization(SearchMethod):
probability of the particles ranges from 0.047 (sigmoid(-3)) to 0.953 (sigmoid(3))."
Args:
pop_size (:obj:`int`, optional): The population size. Defauls to 60.
pop_size (:obj:`int`, optional): The population size. Defaults to 60.
max_iters (:obj:`int`, optional): The maximum number of iterations to use. Defaults to 20.
post_turn_check (bool): If True, check if new position reached by moving passes the constraints.
max_turn_retries (int): Maximum number of movement retries if new position after turning fails to pass the constraints.
post_turn_check (:obj:`bool`, optional): If `True`, check if new position reached by moving passes the constraints. Defaults to `True`
max_turn_retries (:obj:`bool`, optional): Maximum number of movement retries if new position after turning fails to pass the constraints.
Applied only when `post_movement_check` is set to `True`.
Setting it to 0 means we immediately take the old position as the new position upon failure.
"""
@@ -178,7 +178,9 @@ class ParticleSwarmOptimization(SearchMethod):
current_text, original_text=original_text
)
for transformed_text in transformations:
diff_idx = current_text.first_word_diff_index(transformed_text)
diff_idx = next(
iter(transformed_text.attack_attrs["newly_modified_indices"])
)
neighbors_list[diff_idx].append(transformed_text)
return neighbors_list
@@ -187,12 +189,12 @@ class ParticleSwarmOptimization(SearchMethod):
neighbors_list = self._get_neighbors_list(
current_position.attacked_text, original_text
)
best_neighbors, prob_list = self._get_best_neighbors(
candidate_list, prob_list = self._get_best_neighbors(
neighbors_list, current_position
)
if self._search_over:
return current_position
random_candidate = np.random.choice(best_neighbors, 1, p=prob_list)[0]
random_candidate = np.random.choice(candidate_list, 1, p=prob_list)[0]
return random_candidate
def _generate_population(self, initial_position):
@@ -347,7 +349,7 @@ class Position:
Each position represents transformed version of original text
Args:
attacked_text (:obj:`AttackedText`): `AttackedText` for the transformed text
result (:obs:`GoalFunctionResult`, optional): `GoalFunctionResult` for the transformed text
result (:obj:`GoalFunctionResult`, optional): `GoalFunctionResult` for the transformed text
"""
def __init__(self, attacked_text, result=None):