Source code for textattack.search_methods.improved_genetic_algorithm


Reimplementation of search method from Xiaosen Wang, Hao Jin, Kun He (2019).

Natural Language Adversarial Attack and Defense in Word Level.

import numpy as np

from textattack.search_methods import GeneticAlgorithm, PopulationMember

[docs]class ImprovedGeneticAlgorithm(GeneticAlgorithm): """Attacks a model with word substiutitions using a genetic algorithm. Args: pop_size (int): The population size. Defaults to 20. max_iters (int): The maximum number of iterations to use. Defaults to 50. 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. max_replace_times_per_index (int): The maximum times words at the same index can be replaced in improved genetic algorithm. """ 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, max_replace_times_per_index=5, ): 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, ) self.max_replace_times_per_index = max_replace_times_per_index 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 _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_replacements_left"] 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_replacements_left = np.copy(pop_member1.attributes["num_replacements_left"]) # To better simulate the reproduction and biological crossover, # IGA randomly cut the text from two parents and concat two fragments into a new text # rather than randomly choose a word of each position from the two parents. crossover_point = np.random.randint(0, pop_member1.num_words) for i in range(crossover_point, pop_member1.num_words): indices_to_replace.append(i) words_to_replace.append(pop_member2.words[i]) num_replacements_left[i] = pop_member2.attributes["num_replacements_left"][ i ] new_text = pop_member1.attacked_text.replace_words_at_indices( indices_to_replace, words_to_replace ) return new_text, {"num_replacements_left": num_replacements_left} 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]
[docs] def extra_repr_keys(self): return super().extra_repr_keys() + ["max_replace_times_per_index"]