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| """ | |
| Metrics on perturbed words | |
| --------------------------------------------------------------------- | |
| """ | |
| import numpy as np | |
| from textattack.attack_results import FailedAttackResult, SkippedAttackResult | |
| from textattack.metrics import Metric | |
| class WordsPerturbed(Metric): | |
| def __init__(self): | |
| self.total_attacks = 0 | |
| self.all_num_words = None | |
| self.perturbed_word_percentages = None | |
| self.num_words_changed_until_success = 0 | |
| self.all_metrics = {} | |
| def calculate(self, results): | |
| """Calculates all metrics related to perturbed words in an attack. | |
| Args: | |
| results (``AttackResult`` objects): | |
| Attack results for each instance in dataset | |
| """ | |
| self.results = results | |
| self.total_attacks = len(self.results) | |
| self.all_num_words = np.zeros(len(self.results)) | |
| self.perturbed_word_percentages = np.zeros(len(self.results)) | |
| self.num_words_changed_until_success = np.zeros(2**16) | |
| self.max_words_changed = 0 | |
| for i, result in enumerate(self.results): | |
| self.all_num_words[i] = len(result.original_result.attacked_text.words) | |
| if isinstance(result, FailedAttackResult) or isinstance( | |
| result, SkippedAttackResult | |
| ): | |
| continue | |
| num_words_changed = len( | |
| result.original_result.attacked_text.all_words_diff( | |
| result.perturbed_result.attacked_text | |
| ) | |
| ) | |
| self.num_words_changed_until_success[num_words_changed - 1] += 1 | |
| self.max_words_changed = max( | |
| self.max_words_changed or num_words_changed, num_words_changed | |
| ) | |
| if len(result.original_result.attacked_text.words) > 0: | |
| perturbed_word_percentage = ( | |
| num_words_changed | |
| * 100.0 | |
| / len(result.original_result.attacked_text.words) | |
| ) | |
| else: | |
| perturbed_word_percentage = 0 | |
| self.perturbed_word_percentages[i] = perturbed_word_percentage | |
| self.all_metrics["avg_word_perturbed"] = self.avg_number_word_perturbed_num() | |
| self.all_metrics["avg_word_perturbed_perc"] = self.avg_perturbation_perc() | |
| self.all_metrics["max_words_changed"] = self.max_words_changed | |
| self.all_metrics[ | |
| "num_words_changed_until_success" | |
| ] = self.num_words_changed_until_success | |
| return self.all_metrics | |
| def avg_number_word_perturbed_num(self): | |
| average_num_words = self.all_num_words.mean() | |
| average_num_words = round(average_num_words, 2) | |
| return average_num_words | |
| def avg_perturbation_perc(self): | |
| self.perturbed_word_percentages = self.perturbed_word_percentages[ | |
| self.perturbed_word_percentages > 0 | |
| ] | |
| average_perc_words_perturbed = self.perturbed_word_percentages.mean() | |
| average_perc_words_perturbed = round(average_perc_words_perturbed, 2) | |
| return average_perc_words_perturbed | |