Source code for textattack.search_methods.alzantot_genetic_algorithm


Reimplementation of search method from Generating Natural Language Adversarial Examples

by Alzantot et. al `<>`_  from `<>`_

import numpy as np

from textattack.search_methods import GeneticAlgorithm, PopulationMember

[docs]class AlzantotGeneticAlgorithm(GeneticAlgorithm): """Attacks a model with word substiutitions using a genetic algorithm. Args: pop_size (int): The population size. Defaults to 60. max_iters (int): The maximum number of iterations to use. Defaults to 20. temp (float): Temperature for softmax function used to normalize probability dist when sampling parents. 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. post_crossover_check (bool): If True, check if child produced from crossover step passes the constraints. max_crossover_retries (int): Maximum number of crossover retries if resulting child fails to pass the constraints. Applied only when `post_crossover_check` is set to `True`. Setting it to 0 means we immediately take one of the parents at random as the child upon failure. """ def __init__( self, pop_size=60, max_iters=20, temp=0.3, give_up_if_no_improvement=False, post_crossover_check=True, max_crossover_retries=20, ): super().__init__( pop_size=pop_size, max_iters=max_iters, temp=temp, give_up_if_no_improvement=give_up_if_no_improvement, post_crossover_check=post_crossover_check, max_crossover_retries=max_crossover_retries, ) 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 _get_word_select_prob_weights(self, pop_member): """Get the attribute of `pop_member` that is used for determining probability of each word being selected for perturbation.""" return pop_member.attributes["num_candidate_transformations"] def _crossover_operation(self, pop_member1, pop_member2): """Actual operation that takes `pop_member1` text and `pop_member2` text and mixes the two to generate crossover between `pop_member1` and `pop_member2`. Args: pop_member1 (PopulationMember): The first population member. pop_member2 (PopulationMember): The second population member. Returns: Tuple of `AttackedText` and a dictionary of attributes. """ indices_to_replace = [] words_to_replace = [] num_candidate_transformations = np.copy( pop_member1.attributes["num_candidate_transformations"] ) for i in range(pop_member1.num_words): if np.random.uniform() < 0.5: indices_to_replace.append(i) words_to_replace.append(pop_member2.words[i]) num_candidate_transformations[i] = pop_member2.attributes[ "num_candidate_transformations" ][i] new_text = pop_member1.attacked_text.replace_words_at_indices( indices_to_replace, words_to_replace ) return ( new_text, {"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]` """ 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