Source code for textattack.transformations.word_swaps.word_swap_change_location

Word Swap by Changing Location
import more_itertools as mit
import numpy as np

from import NAMED_ENTITIES

from .word_swap import WordSwap

[docs]def idx_to_words(ls, words): """Given a list generated from cluster_idx, return a list that contains sub-list (the first element being the idx, and the second element being the words corresponding to the idx)""" output = [] for sub_ls in ls: word = words[sub_ls[0]] for idx in sub_ls[1:]: word = " ".join([word, words[idx]]) output.append([sub_ls, word]) return output
[docs]class WordSwapChangeLocation(WordSwap): def __init__(self, n=3, confidence_score=0.7, language="en", **kwargs): """Transformation that changes recognized locations of a sentence to another location that is given in the location map. :param n: Number of new locations to generate :param confidence_score: Location will only be changed if it's above the confidence score >>> from textattack.transformations import WordSwapChangeLocation >>> from textattack.augmentation import Augmenter >>> transformation = WordSwapChangeLocation() >>> augmenter = Augmenter(transformation=transformation) >>> s = 'I am in Dallas.' >>> augmenter.augment(s) """ super().__init__(**kwargs) self.n = n self.confidence_score = confidence_score self.language = language def _get_transformations(self, current_text, indices_to_modify): words = current_text.words location_idx = [] if self.language == "en": model_name = "ner" elif self.language == "fra" or self.language == "french": model_name = "flair/ner-french" else: model_name = "flair/ner-multi-fast" for i in indices_to_modify: tag = current_text.ner_of_word_index(i, model_name) if "LOC" in tag.value and tag.score > self.confidence_score: location_idx.append(i) # Combine location idx and words to a list ([0] is idx, [1] is location name) # For example, [1,2] to [ [1,2] , ["New York"] ] location_idx = [list(group) for group in mit.consecutive_groups(location_idx)] location_words = idx_to_words(location_idx, words) transformed_texts = [] for location in location_words: idx = location[0] word = location[1].capitalize() replacement_words = self._get_new_location(word) for r in replacement_words: if r == word: continue text = current_text # if original location is more than a single word, remain only the starting word if len(idx) > 1: index = idx[1] for i in idx[1:]: text = text.delete_word_at_index(index) # replace the starting word with new location text = text.replace_word_at_index(idx[0], r) transformed_texts.append(text) return transformed_texts def _get_new_location(self, word): """Return a list of new locations, with the choice of country, nationality, and city.""" language = "" if self.language == "esp" or self.language == "spanish": language = "-spanish" elif self.language == "fra" or self.language == "french": language = "-french" if word in NAMED_ENTITIES["country" + language]: return np.random.choice(NAMED_ENTITIES["country" + language], self.n) elif word in NAMED_ENTITIES["nationality" + language]: return np.random.choice(NAMED_ENTITIES["nationality" + language], self.n) elif word in NAMED_ENTITIES["city"]: return np.random.choice(NAMED_ENTITIES["city"], self.n) return []