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improve augmentation; merge in fix-docs

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Jack Morris
2020-05-20 16:32:25 -04:00
parent 0230d0b7dd 93741a7b8b
commit f030a16c71
137 changed files with 2760 additions and 1587 deletions
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67
textattack/search_methods/genetic_algorithm.py
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@@ -1,5 +1,6 @@
"""
Algorithm from Generating Natural Language Adversarial Examples by Alzantot et. al
Reimplementatio of search method from Generating Natural Language Adversarial Examples
by Alzantot et. al
`<arxiv.org/abs/1804.07998>`_
`<github.com/nesl/nlp_adversarial_examples>`_
"""
@@ -8,26 +9,19 @@ import numpy as np
import torch
from copy import deepcopy
from .attack import Attack
from textattack.attack_results import FailedAttackResult, SuccessfulAttackResult
from textattack.transformations import WordSwap
from textattack.search_methods import SearchMethod
from textattack.shared.validators import transformation_consists_of_word_swaps
class GeneticAlgorithm(Attack):
class GeneticAlgorithm(SearchMethod):
"""
Attacks a model using a genetic algorithm.
Attacks a model with word substiutitions using a genetic algorithm.
Args:
goal_function: A function for determining how well a perturbation is doing at achieving the attack's goal.
transformation: The type of transformation to use. Should be a subclass of WordSwap.
pop_size (:obj:`int`, optional): The population size. Defauls to 20.
max_iters (:obj:`int`, optional): The maximum number of iterations to use. Defaults to 50.
Raises:
ValueError: If the transformation is not a subclass of WordSwap.
"""
def __init__(self, goal_function, transformation, constraints=[], pop_size=20, max_iters=50, temp=0.3,
give_up_if_no_improvement=False):
if not isinstance(transformation, WordSwap):
raise ValueError(f'Transformation is of type {type(transformation)}, should be a subclass of WordSwap')
super().__init__(goal_function, transformation, constraints=constraints)
def __init__(self, pop_size=20, max_iters=50, temp=0.3, give_up_if_no_improvement=False):
self.max_iters = max_iters
self.pop_size = pop_size
self.temp = temp
@@ -37,20 +31,22 @@ class GeneticAlgorithm(Attack):
"""
Select the best replacement for word at position (idx)
in (pop_member) to maximize score.
Args:
pop_member: The population member being perturbed.
idx: The index at which to replace a word.
Returns:
Whether a replacement which decreased the score was found.
"""
transformations = self.get_transformations(pop_member.tokenized_text,
original_text=self.original_tokenized_text,
indices_to_replace=[idx])
indices_to_modify=[idx])
if not len(transformations):
return False
new_x_results = self.goal_function.get_results(transformations, self.correct_output)
new_x_results = self.get_goal_results(transformations, self.correct_output)
new_x_scores = torch.Tensor([r.score for r in new_x_results])
orig_score = self.goal_function.get_results([pop_member.tokenized_text], self.correct_output)[0].score
orig_score = self.get_goal_results([pop_member.tokenized_text], self.correct_output)[0].score
new_x_scores = new_x_scores - orig_score
if new_x_scores.max() > 0:
pop_member.tokenized_text = transformations[new_x_scores.argmax()]
@@ -126,8 +122,7 @@ class GeneticAlgorithm(Attack):
words = tokenized_text.words
neighbors_list = [[] for _ in range(len(words))]
transformations = self.get_transformations(tokenized_text,
original_text=self.original_tokenized_text,
apply_constraints=False)
original_text=self.original_tokenized_text)
for transformed_text in transformations:
diff_idx = tokenized_text.first_word_diff_index(transformed_text)
neighbors_list[diff_idx].append(transformed_text.words[diff_idx])
@@ -135,15 +130,14 @@ class GeneticAlgorithm(Attack):
neighbors_len = np.array([len(x) for x in neighbors_list])
return neighbors_len
def attack_one(self, tokenized_text, correct_output):
self.original_tokenized_text = tokenized_text
self.correct_output = correct_output
original_result = self.goal_function.get_results([tokenized_text], correct_output)[0]
neighbors_len = self._get_neighbors_len(tokenized_text)
def _perform_search(self, initial_result):
self.original_tokenized_text = initial_result.tokenized_text
self.correct_output = initial_result.output
neighbors_len = self._get_neighbors_len(self.original_tokenized_text)
pop = self._generate_population(neighbors_len)
cur_score = original_result.score
cur_score = initial_result.score
for i in range(self.max_iters):
pop_results = self.goal_function.get_results([pm.tokenized_text for pm in pop], correct_output)
pop_results = self.get_goal_results([pm.tokenized_text for pm in pop], self.correct_output)
for idx, result in enumerate(pop_results):
pop[idx].result = pop_results[idx]
pop = sorted(pop, key=lambda x: -x.result.score)
@@ -154,10 +148,7 @@ class GeneticAlgorithm(Attack):
select_probs = (logits / logits.sum()).cpu().numpy()
if pop[0].result.succeeded:
return SuccessfulAttackResult(
original_result,
pop[0].result
)
return pop[0].result
if pop[0].result.score > cur_score:
cur_score = pop[0].result.score
@@ -177,13 +168,23 @@ class GeneticAlgorithm(Attack):
pop = elite + children
return FailedAttackResult(original_result, pop[0].result)
return pop[0].result
def check_transformation_compatibility(self, transformation):
"""
The genetic algorithm is specifically designed for word substitutions.
"""
return transformation_consists_of_word_swaps(transformation)
def extra_repr_keys(self):
return ['pop_size', 'max_iters', 'temp', 'give_up_if_no_improvement']
class PopulationMember:
"""
A member of the population during the course of the genetic algorithm.
Args:
tokenized_text: The tokenized text of the population member.
tokenized_text: The ``TokenizedText`` 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):