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import copy |
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import matplotlib.pylab as plt |
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import numpy as np |
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import pandas as pd |
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import seaborn as sns |
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from gensim.models.keyedvectors import KeyedVectors |
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from scipy.stats import spearmanr |
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from sklearn.decomposition import PCA |
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from sklearn.metrics.pairwise import euclidean_distances |
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from sklearn.svm import LinearSVC |
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from tqdm import tqdm |
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from tabulate import tabulate |
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from ..consts import RANDOM_STATE |
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from .benchmark import evaluate_words_embedding |
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from .utils import ( |
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cosine_similarity, normalize, project_reject_vector, project_vector, |
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reject_vector, round_to_extreme, take_two_sides_extreme_sorted, |
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update_word_vector, |
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) |
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DIRECTION_METHODS = ['single', 'sum', 'pca'] |
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DEBIAS_METHODS = ['neutralize', 'hard', 'soft'] |
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FIRST_PC_THRESHOLD = 0.5 |
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MAX_NON_SPECIFIC_EXAMPLES = 1000 |
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class BiasWordsEmbedding: |
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"""Audit and Adjust a Bias in English Words Embedding. |
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:param model: Words embedding model of ``gensim.model.KeyedVectors`` |
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:param bool only_lower: Whether the words embedding contrains |
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only lower case words |
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:param bool verbose: Set vebosity |
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""" |
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def __init__(self, model, only_lower=False, verbose=False, |
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identify_direction=False): |
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if not isinstance(model, KeyedVectors): |
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raise TypeError('model should be of type KeyedVectors, not {}' |
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.format(type(model))) |
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# TODO: this is bad Python, ask someone about it |
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# probably should be a better design |
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# identify_direction doesn't have any meaning |
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# for the calss BiasWordsEmbedding |
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if self.__class__ == __class__ and identify_direction is not False: |
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raise ValueError('identify_direction must be False' |
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' for an instance of {}' |
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.format(__class__)) |
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self.model = model |
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# TODO: write unitest for when it is False |
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self.only_lower = only_lower |
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self._verbose = verbose |
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self.direction = None |
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self.positive_end = None |
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self.negative_end = None |
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def __copy__(self): |
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bias_words_embedding = self.__class__(self.model, |
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self.only_lower, |
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self._verbose, |
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identify_direction=False) |
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bias_words_embedding.direction = copy.deepcopy(self.direction) |
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bias_words_embedding.positive_end = copy.deepcopy(self.positive_end) |
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bias_words_embedding.negative_end = copy.deepcopy(self.negative_end) |
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return bias_words_embedding |
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def __deepcopy__(self, memo): |
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bias_words_embedding = copy.copy(self) |
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bias_words_embedding.model = copy.deepcopy(bias_words_embedding.model) |
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return bias_words_embedding |
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def __getitem__(self, key): |
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return self.model[key] |
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def __contains__(self, item): |
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return item in self.model |
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def _filter_words_by_model(self, words): |
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return [word for word in words if word in self] |
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def _is_direction_identified(self): |
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if self.direction is None: |
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raise RuntimeError('The direction was not identified' |
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' for this {} instance' |
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.format(self.__class__.__name__)) |
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# There is a mistake in the article |
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# it is written (section 5.1): |
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# "To identify the gender subspace, we took the ten gender pair difference |
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# vectors and computed its principal components (PCs)" |
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# however in the source code: |
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# https://github.com/tolga-b/debiaswe/blob/10277b23e187ee4bd2b6872b507163ef4198686b/debiaswe/we.py#L235-L245 |
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def _identify_subspace_by_pca(self, definitional_pairs, n_components): |
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matrix = [] |
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for word1, word2 in definitional_pairs: |
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vector1 = normalize(self[word1]) |
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vector2 = normalize(self[word2]) |
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center = (vector1 + vector2) / 2 |
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matrix.append(vector1 - center) |
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matrix.append(vector2 - center) |
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pca = PCA(n_components=n_components) |
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pca.fit(matrix) |
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if self._verbose: |
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table = enumerate(pca.explained_variance_ratio_, start=1) |
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headers = ['Principal Component', |
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'Explained Variance Ratio'] |
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print(tabulate(table, headers=headers)) |
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return pca |
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# TODO: add the SVD method from section 6 step 1 |
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# It seems there is a mistake there, I think it is the same as PCA |
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# just with repleacing it with SVD |
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def _identify_direction(self, positive_end, negative_end, |
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definitional, method='pca'): |
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if method not in DIRECTION_METHODS: |
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raise ValueError('method should be one of {}, {} was given'.format( |
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DIRECTION_METHODS, method)) |
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if positive_end == negative_end: |
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raise ValueError('positive_end and negative_end' |
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'should be different, and not the same "{}"' |
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.format(positive_end)) |
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if self._verbose: |
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print('Identify direction using {} method...'.format(method)) |
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direction = None |
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if method == 'single': |
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direction = normalize(normalize(self[definitional[0]]) |
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- normalize(self[definitional[1]])) |
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elif method == 'sum': |
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group1_sum_vector = np.sum([self[word] |
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for word in definitional[0]], axis=0) |
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group2_sum_vector = np.sum([self[word] |
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for word in definitional[1]], axis=0) |
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diff_vector = (normalize(group1_sum_vector) |
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- normalize(group2_sum_vector)) |
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direction = normalize(diff_vector) |
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elif method == 'pca': |
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pca = self._identify_subspace_by_pca(definitional, 10) |
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if pca.explained_variance_ratio_[0] < FIRST_PC_THRESHOLD: |
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raise RuntimeError('The Explained variance' |
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'of the first principal component should be' |
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'at least {}, but it is {}' |
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.format(FIRST_PC_THRESHOLD, |
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pca.explained_variance_ratio_[0])) |
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direction = pca.components_[0] |
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# if direction is oposite (e.g. we cannot control |
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# what the PCA will return) |
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ends_diff_projection = cosine_similarity((self[positive_end] |
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- self[negative_end]), |
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direction) |
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if ends_diff_projection < 0: |
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direction = -direction # pylint: disable=invalid-unary-operand-type |
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self.direction = direction |
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self.positive_end = positive_end |
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self.negative_end = negative_end |
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def project_on_direction(self, word): |
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"""Project the normalized vector of the word on the direction. |
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:param str word: The word tor project |
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:return float: The projection scalar |
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""" |
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self._is_direction_identified() |
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vector = self[word] |
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projection_score = self.model.cosine_similarities(self.direction, |
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[vector])[0] |
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return projection_score |
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def _calc_projection_scores(self, words): |
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self._is_direction_identified() |
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df = pd.DataFrame({'word': words}) |
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# TODO: maybe using cosine_similarities on all the vectors? |
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# it might be faster |
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df['projection'] = df['word'].apply(self.project_on_direction) |
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df = df.sort_values('projection', ascending=False) |
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return df |
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def plot_projection_scores(self, words, n_extreme=10, |
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ax=None, axis_projection_step=None): |
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"""Plot the projection scalar of words on the direction. |
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:param list words: The words tor project |
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:param int or None n_extreme: The number of extreme words to show |
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:return: The ax object of the plot |
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""" |
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self._is_direction_identified() |
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projections_df = self._calc_projection_scores(words) |
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projections_df['projection'] = projections_df['projection'].round(2) |
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if n_extreme is not None: |
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projections_df = take_two_sides_extreme_sorted(projections_df, |
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n_extreme=n_extreme) |
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if ax is None: |
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_, ax = plt.subplots(1) |
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if axis_projection_step is None: |
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axis_projection_step = 0.1 |
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cmap = plt.get_cmap('RdBu') |
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projections_df['color'] = ((projections_df['projection'] + 0.5) |
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.apply(cmap)) |
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most_extream_projection = (projections_df['projection'] |
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.abs() |
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.max() |
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.round(1)) |
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sns.barplot(x='projection', y='word', data=projections_df, |
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palette=projections_df['color']) |
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plt.xticks(np.arange(-most_extream_projection, |
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most_extream_projection + axis_projection_step, |
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axis_projection_step)) |
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plt.title('← {} {} {} →'.format(self.negative_end, |
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' ' * 20, |
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self.positive_end)) |
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plt.xlabel('Direction Projection') |
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plt.ylabel('Words') |
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return ax |
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def plot_dist_projections_on_direction(self, word_groups, ax=None): |
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"""Plot the projection scalars distribution on the direction. |
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:param dict word_groups word: The groups to projects |
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:return float: The ax object of the plot |
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""" |
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if ax is None: |
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_, ax = plt.subplots(1) |
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names = sorted(word_groups.keys()) |
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for name in names: |
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words = word_groups[name] |
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label = '{} (#{})'.format(name, len(words)) |
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vectors = [self[word] for word in words] |
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projections = self.model.cosine_similarities(self.direction, |
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vectors) |
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sns.distplot(projections, hist=False, label=label, ax=ax) |
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plt.axvline(0, color='k', linestyle='--') |
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plt.title('← {} {} {} →'.format(self.negative_end, |
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' ' * 20, |
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self.positive_end)) |
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plt.xlabel('Direction Projection') |
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plt.ylabel('Density') |
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ax.legend(loc='center left', bbox_to_anchor=(1, 0.5)) |
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return ax |
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@classmethod |
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def _calc_bias_across_words_embeddings(cls, |
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words_embedding_bias_dict, |
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words): |
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""" |
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Calculate to projections and rho of words for two words embeddings. |
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:param dict words_embedding_bias_dict: ``WordsEmbeddingBias`` objects |
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as values, |
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and their names as keys. |
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:param list words: Words to be projected. |
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:return tuple: Projections and spearman rho. |
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""" |
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# pylint: disable=W0212 |
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assert len(words_embedding_bias_dict) == 2, 'Support only in two'\ |
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'words embeddings' |
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intersection_words = [word for word in words |
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if all(word in web |
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for web in (words_embedding_bias_dict |
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.values()))] |
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projections = {name: web._calc_projection_scores(intersection_words)['projection'] # pylint: disable=C0301 |
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308
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for name, web in words_embedding_bias_dict.items()} |
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310
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df = pd.DataFrame(projections) |
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311
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df.index = intersection_words |
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312
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rho, _ = spearmanr(*df.transpose().values) |
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return df, rho |
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@classmethod |
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def plot_bias_across_words_embeddings(cls, words_embedding_bias_dict, |
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words, ax=None, scatter_kwargs=None): |
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""" |
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Plot the projections of same words of two words Embeddings. |
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322
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:param dict words_embedding_bias_dict: ``WordsEmbeddingBias`` objects |
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as values, |
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and their names as keys. |
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:param list words: Words to be projected. |
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:param scatter_kwargs: Kwargs for matplotlib.pylab.scatter. |
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:type scatter_kwargs: dict or None |
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:return: The ax object of the plot |
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""" |
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330
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# pylint: disable=W0212 |
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332
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df, rho = cls._calc_bias_across_words_embeddings(words_embedding_bias_dict, # pylint: disable=C0301 |
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words) |
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if ax is None: |
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_, ax = plt.subplots(1) |
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if scatter_kwargs is None: |
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scatter_kwargs = {} |
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340
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341
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name1, name2 = words_embedding_bias_dict.keys() |
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ax.scatter(x=name1, y=name2, data=df, **scatter_kwargs) |
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345
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plt.title('Bias Across Words Embeddings' |
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'(Spearman Rho = {:0.2f})'.format(rho)) |
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348
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negative_end = words_embedding_bias_dict[name1].negative_end |
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positive_end = words_embedding_bias_dict[name1].positive_end |
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plt.xlabel('← {} {} {} →'.format(negative_end, |
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name1, |
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positive_end)) |
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plt.ylabel('← {} {} {} →'.format(negative_end, |
|
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name2, |
|
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positive_end)) |
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356
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357
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ax_min = round_to_extreme(df.values.min()) |
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358
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|
ax_max = round_to_extreme(df.values.max()) |
|
359
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plt.xlim(ax_min, ax_max) |
|
360
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plt.ylim(ax_min, ax_max) |
|
361
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362
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return ax |
|
363
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364
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# TODO: refactor for speed and clarity |
|
365
|
|
|
def generate_analogies(self, n_analogies=100, multiple=False, |
|
366
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delta=1., restrict_vocab=30000): |
|
367
|
|
|
""" |
|
368
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|
|
Generate anologies based on the bias directionself. |
|
369
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|
370
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x - y ~ direction. |
|
371
|
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or a:x::b:y when a-b ~ direction. |
|
372
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|
373
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|
``delta`` is used for semantically coherent. Default vale of 1 |
|
374
|
|
|
corresponds to an angle <= pi/3. |
|
375
|
|
|
|
|
376
|
|
|
:param int n_analogies: Number of analogies to generate. |
|
377
|
|
|
:param bool multiple: Whether to allow multiple apprerences of a word |
|
378
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|
|
in the analogies. |
|
379
|
|
|
:param float delta: Threshold for semantic similarity. |
|
380
|
|
|
The maximal distance between x and y. |
|
381
|
|
|
:param int restrict_vocab: The vocabulary size to use. |
|
382
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|
|
:return: Data Frame of anologies (x, y), thier distances, |
|
383
|
|
|
and their cosine similarity scores |
|
384
|
|
|
""" |
|
385
|
|
|
|
|
386
|
|
|
# pylint: disable=C0301,R0914 |
|
387
|
|
|
|
|
388
|
|
|
self._is_direction_identified() |
|
389
|
|
|
|
|
390
|
|
|
restrict_vocab_vectors = self.model.vectors[:restrict_vocab] |
|
391
|
|
|
|
|
392
|
|
|
normalized_vectores = (restrict_vocab_vectors |
|
393
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|
|
/ np.linalg.norm(restrict_vocab_vectors, axis=1)[:, None]) |
|
394
|
|
|
|
|
395
|
|
|
pairs_distances = euclidean_distances(normalized_vectores, normalized_vectores) |
|
396
|
|
|
pairs_indices = np.array(np.nonzero( |
|
397
|
|
|
((pairs_distances < delta) |
|
398
|
|
|
& (pairs_distances != 0)))).T |
|
399
|
|
|
x_vecores = np.take(normalized_vectores, pairs_indices[:, 0], axis=0) |
|
400
|
|
|
y_vecores = np.take(normalized_vectores, pairs_indices[:, 1], axis=0) |
|
401
|
|
|
|
|
402
|
|
|
x_minus_y_vectors = x_vecores - y_vecores |
|
403
|
|
|
normalized_x_minus_y_vectors = (x_minus_y_vectors |
|
404
|
|
|
/ np.linalg.norm(x_minus_y_vectors, axis=1)[:, None]) |
|
405
|
|
|
|
|
406
|
|
|
cos_distances = normalized_x_minus_y_vectors @ self.direction |
|
407
|
|
|
|
|
408
|
|
|
sorted_cos_distances_indices = np.argsort(cos_distances)[::-1] |
|
409
|
|
|
|
|
410
|
|
|
sorted_cos_distances_indices_iter = iter(sorted_cos_distances_indices) |
|
411
|
|
|
|
|
412
|
|
|
analogies = [] |
|
413
|
|
|
generated_words = set() |
|
414
|
|
|
|
|
415
|
|
|
while len(analogies) < n_analogies: |
|
416
|
|
|
cos_distance_index = next(sorted_cos_distances_indices_iter) |
|
417
|
|
|
paris_index = pairs_indices[cos_distance_index] |
|
418
|
|
|
word_x, word_y = [self.model.index2word[index] |
|
419
|
|
|
for index in paris_index] |
|
420
|
|
|
|
|
421
|
|
|
if multiple or (not multiple |
|
422
|
|
|
and (word_x not in generated_words |
|
423
|
|
|
and word_y not in generated_words)): |
|
424
|
|
|
analogies.append({'x': word_x, |
|
425
|
|
|
'y': word_y, |
|
426
|
|
|
'score': cos_distances[cos_distance_index], |
|
427
|
|
|
'distance': pairs_distances[tuple(paris_index)]}) |
|
428
|
|
|
generated_words.add(word_x) |
|
429
|
|
|
generated_words.add(word_y) |
|
430
|
|
|
|
|
431
|
|
|
df = pd.DataFrame(analogies) |
|
432
|
|
|
df = df[['x', 'y', 'distance', 'score']] |
|
433
|
|
|
return df |
|
434
|
|
|
|
|
435
|
|
|
def calc_direct_bias(self, neutral_words, c=None): |
|
436
|
|
|
"""Calculate the direct bias. |
|
437
|
|
|
|
|
438
|
|
|
Based on the projection of neuteral words on the direction. |
|
439
|
|
|
|
|
440
|
|
|
:param list neutral_words: List of neutral words |
|
441
|
|
|
:param c: Strictness of bias measuring |
|
442
|
|
|
:type c: float or None |
|
443
|
|
|
:return: The direct bias |
|
444
|
|
|
""" |
|
445
|
|
|
|
|
446
|
|
|
if c is None: |
|
447
|
|
|
c = 1 |
|
448
|
|
|
|
|
449
|
|
|
projections = self._calc_projection_scores(neutral_words)['projection'] |
|
450
|
|
|
direct_bias_terms = np.abs(projections) ** c |
|
451
|
|
|
direct_bias = direct_bias_terms.sum() / len(neutral_words) |
|
452
|
|
|
|
|
453
|
|
|
return direct_bias |
|
454
|
|
|
|
|
455
|
|
|
def calc_indirect_bias(self, word1, word2): |
|
456
|
|
|
"""Calculate the indirect bias between two words. |
|
457
|
|
|
|
|
458
|
|
|
Based on the amount of shared projection of the words on the direction. |
|
459
|
|
|
|
|
460
|
|
|
Also called PairBias. |
|
461
|
|
|
:param str word1: First word |
|
462
|
|
|
:param str word2: Second word |
|
463
|
|
|
:type c: float or None |
|
464
|
|
|
:return The indirect bias between the two words |
|
465
|
|
|
""" |
|
466
|
|
|
|
|
467
|
|
|
self._is_direction_identified() |
|
468
|
|
|
|
|
469
|
|
|
vector1 = normalize(self[word1]) |
|
470
|
|
|
vector2 = normalize(self[word2]) |
|
471
|
|
|
|
|
472
|
|
|
perpendicular_vector1 = reject_vector(vector1, self.direction) |
|
473
|
|
|
perpendicular_vector2 = reject_vector(vector2, self.direction) |
|
474
|
|
|
|
|
475
|
|
|
inner_product = vector1 @ vector2 |
|
476
|
|
|
perpendicular_similarity = cosine_similarity(perpendicular_vector1, |
|
477
|
|
|
perpendicular_vector2) |
|
478
|
|
|
|
|
479
|
|
|
indirect_bias = ((inner_product - perpendicular_similarity) |
|
480
|
|
|
/ inner_product) |
|
481
|
|
|
return indirect_bias |
|
482
|
|
|
|
|
483
|
|
|
def generate_closest_words_indirect_bias(self, |
|
484
|
|
|
neutral_positive_end, |
|
485
|
|
|
neutral_negative_end, |
|
486
|
|
|
words=None, n_extreme=5): |
|
487
|
|
|
""" |
|
488
|
|
|
Generate closest words to a neutral direction and thier indirect bias. |
|
489
|
|
|
|
|
490
|
|
|
:param str neutral_positive_end: A word that define the positive side |
|
491
|
|
|
of the neutral direction. |
|
492
|
|
|
:param str neutral_negative_end: A word that define the negative side |
|
493
|
|
|
of the neutral direction. |
|
494
|
|
|
:param list words: List of words to project on the neutral direction. |
|
495
|
|
|
:param int n_extreme: The number for the most extreme words |
|
496
|
|
|
(positive and negative) to show. |
|
497
|
|
|
:return: Data Frame of the most extreme words |
|
498
|
|
|
with their projection scores and indirect biases. |
|
499
|
|
|
""" |
|
500
|
|
|
|
|
501
|
|
|
neutral_direction = normalize(self[neutral_positive_end] |
|
502
|
|
|
- self[neutral_negative_end]) |
|
503
|
|
|
|
|
504
|
|
|
vectors = [normalize(self[word]) for word in words] |
|
505
|
|
|
df = (pd.DataFrame([{'word': word, |
|
506
|
|
|
'projection': vector @ neutral_direction} |
|
507
|
|
|
for word, vector in zip(words, vectors)]) |
|
508
|
|
|
.sort_values('projection', ascending=False)) |
|
509
|
|
|
|
|
510
|
|
|
df = take_two_sides_extreme_sorted(df, n_extreme, |
|
511
|
|
|
'end', |
|
512
|
|
|
neutral_positive_end, |
|
513
|
|
|
neutral_negative_end) |
|
514
|
|
|
|
|
515
|
|
|
df['indirect_bias'] = df.apply(lambda r: |
|
516
|
|
|
self.calc_indirect_bias(r['word'], |
|
517
|
|
|
r['end']), |
|
518
|
|
|
axis=1) |
|
519
|
|
|
|
|
520
|
|
|
df = df.set_index(['end', 'word']) |
|
521
|
|
|
df = df[['projection', 'indirect_bias']] |
|
522
|
|
|
|
|
523
|
|
|
return df |
|
524
|
|
|
|
|
525
|
|
|
def _extract_neutral_words(self, specific_words): |
|
526
|
|
|
extended_specific_words = set() |
|
527
|
|
|
|
|
528
|
|
|
# because or specific_full data was trained on partial words embedding |
|
529
|
|
|
for word in specific_words: |
|
530
|
|
|
extended_specific_words.add(word) |
|
531
|
|
|
extended_specific_words.add(word.lower()) |
|
532
|
|
|
extended_specific_words.add(word.upper()) |
|
533
|
|
|
extended_specific_words.add(word.title()) |
|
534
|
|
|
|
|
535
|
|
|
neutral_words = [word for word in self.model.vocab |
|
536
|
|
|
if word not in extended_specific_words] |
|
537
|
|
|
|
|
538
|
|
|
return neutral_words |
|
539
|
|
|
|
|
540
|
|
|
def _neutralize(self, neutral_words): |
|
541
|
|
|
self._is_direction_identified() |
|
542
|
|
|
|
|
543
|
|
|
if self._verbose: |
|
544
|
|
|
neutral_words_iter = tqdm(neutral_words) |
|
545
|
|
|
else: |
|
546
|
|
|
neutral_words_iter = iter(neutral_words) |
|
547
|
|
|
|
|
548
|
|
|
for word in neutral_words_iter: |
|
549
|
|
|
neutralized_vector = reject_vector(self[word], |
|
550
|
|
|
self.direction) |
|
551
|
|
|
update_word_vector(self.model, word, neutralized_vector) |
|
552
|
|
|
|
|
553
|
|
|
self.model.init_sims(replace=True) |
|
554
|
|
|
|
|
555
|
|
|
def _equalize(self, equality_sets): |
|
556
|
|
|
# pylint: disable=R0914 |
|
557
|
|
|
|
|
558
|
|
|
self._is_direction_identified() |
|
559
|
|
|
|
|
560
|
|
|
if self._verbose: |
|
561
|
|
|
words_data = [] |
|
562
|
|
|
|
|
563
|
|
|
for equality_set_index, equality_set_words in enumerate(equality_sets): |
|
564
|
|
|
equality_set_vectors = [normalize(self[word]) |
|
565
|
|
|
for word in equality_set_words] |
|
566
|
|
|
center = np.mean(equality_set_vectors, axis=0) |
|
567
|
|
|
(projected_center, |
|
568
|
|
|
rejected_center) = project_reject_vector(center, |
|
569
|
|
|
self.direction) |
|
570
|
|
|
scaling = np.sqrt(1 - np.linalg.norm(rejected_center)**2) |
|
571
|
|
|
|
|
572
|
|
|
for word, vector in zip(equality_set_words, equality_set_vectors): |
|
573
|
|
|
projected_vector = project_vector(vector, self.direction) |
|
574
|
|
|
|
|
575
|
|
|
projected_part = normalize(projected_vector - projected_center) |
|
576
|
|
|
|
|
577
|
|
|
# In the code it is different of Bolukbasi |
|
578
|
|
|
# It behaves the same only for equality_sets |
|
579
|
|
|
# with size of 2 (pairs) - not sure! |
|
580
|
|
|
# However, my code is the same as the article |
|
581
|
|
|
# equalized_vector = rejected_center + scaling * self.direction |
|
582
|
|
|
# https://github.com/tolga-b/debiaswe/blob/10277b23e187ee4bd2b6872b507163ef4198686b/debiaswe/debias.py#L36-L37 |
|
583
|
|
|
# For pairs, projected_part_vector1 == -projected_part_vector2, |
|
584
|
|
|
# and this is the same as |
|
585
|
|
|
# projected_part_vector1 == self.direction |
|
586
|
|
|
equalized_vector = rejected_center + scaling * projected_part |
|
587
|
|
|
|
|
588
|
|
|
update_word_vector(self.model, word, equalized_vector) |
|
589
|
|
|
|
|
590
|
|
|
if self._verbose: |
|
591
|
|
|
words_data.append({ |
|
|
|
|
|
|
592
|
|
|
'equality_set_index': equality_set_index, |
|
593
|
|
|
'word': word, |
|
594
|
|
|
'scaling': scaling, |
|
595
|
|
|
'projected_scalar': vector @ self.direction, |
|
596
|
|
|
'equalized_projected_scalar': (equalized_vector |
|
597
|
|
|
@ self.direction), |
|
598
|
|
|
}) |
|
599
|
|
|
|
|
600
|
|
|
if self._verbose: |
|
601
|
|
|
print('Equalize Words Data ' |
|
602
|
|
|
'(all equal for 1-dim bias space (direction):') |
|
603
|
|
|
words_data_df = (pd.DataFrame(words_data) |
|
604
|
|
|
.set_index(['equality_set_index', 'word'])) |
|
605
|
|
|
print(tabulate(words_data_df, headers='keys')) |
|
606
|
|
|
|
|
607
|
|
|
self.model.init_sims(replace=True) |
|
608
|
|
|
|
|
609
|
|
|
def debias(self, method='hard', neutral_words=None, equality_sets=None, |
|
610
|
|
|
inplace=True): |
|
611
|
|
|
"""Debias the words embedding. |
|
612
|
|
|
|
|
613
|
|
|
:param str method: The method of debiasing. |
|
614
|
|
|
:param list neutral_words: List of neutral words |
|
615
|
|
|
for the neutralize step |
|
616
|
|
|
:param list equality_sets: List of equality sets, |
|
617
|
|
|
for the equalize step. |
|
618
|
|
|
The sets represent the direction. |
|
619
|
|
|
:param bool inplace: Whether to debias the object inplace |
|
620
|
|
|
or return a new one |
|
621
|
|
|
|
|
622
|
|
|
.. warning:: |
|
623
|
|
|
|
|
624
|
|
|
After calling `debias`, |
|
625
|
|
|
all the vectors of the words embedding |
|
626
|
|
|
will be normalized to unit length. |
|
627
|
|
|
|
|
628
|
|
|
""" |
|
629
|
|
|
|
|
630
|
|
|
# pylint: disable=W0212 |
|
631
|
|
|
if inplace: |
|
632
|
|
|
bias_words_embedding = self |
|
633
|
|
|
else: |
|
634
|
|
|
bias_words_embedding = copy.deepcopy(self) |
|
635
|
|
|
|
|
636
|
|
|
if method not in DEBIAS_METHODS: |
|
637
|
|
|
raise ValueError('method should be one of {}, {} was given'.format( |
|
638
|
|
|
DEBIAS_METHODS, method)) |
|
639
|
|
|
|
|
640
|
|
|
if method in ['hard', 'neutralize']: |
|
641
|
|
|
if self._verbose: |
|
642
|
|
|
print('Neutralize...') |
|
643
|
|
|
bias_words_embedding._neutralize(neutral_words) |
|
644
|
|
|
|
|
645
|
|
|
if method == 'hard': |
|
646
|
|
|
if self._verbose: |
|
647
|
|
|
print('Equalize...') |
|
648
|
|
|
bias_words_embedding._equalize(equality_sets) |
|
649
|
|
|
|
|
650
|
|
|
if inplace: |
|
651
|
|
|
return None |
|
652
|
|
|
else: |
|
653
|
|
|
return bias_words_embedding |
|
654
|
|
|
|
|
655
|
|
|
def evaluate_words_embedding(self, |
|
656
|
|
|
kwargs_word_pairs=None, |
|
657
|
|
|
kwargs_word_analogies=None): |
|
658
|
|
|
""" |
|
659
|
|
|
Evaluate word pairs tasks and word analogies tasks. |
|
660
|
|
|
|
|
661
|
|
|
:param model: Words embedding. |
|
662
|
|
|
:param kwargs_word_pairs: Kwargs for |
|
663
|
|
|
evaluate_word_pairs |
|
664
|
|
|
method. |
|
665
|
|
|
:type kwargs_word_pairs: dict or None |
|
666
|
|
|
:param kwargs_word_analogies: Kwargs for |
|
667
|
|
|
evaluate_word_analogies |
|
668
|
|
|
method. |
|
669
|
|
|
:type evaluate_word_analogies: dict or None |
|
670
|
|
|
:return: Tuple of DataFrame for the evaluation results. |
|
671
|
|
|
""" |
|
672
|
|
|
|
|
673
|
|
|
return evaluate_words_embedding(self.model, |
|
674
|
|
|
kwargs_word_pairs, |
|
675
|
|
|
kwargs_word_analogies) |
|
676
|
|
|
|
|
677
|
|
|
def learn_full_specific_words(self, seed_specific_words, |
|
678
|
|
|
max_non_specific_examples=None, debug=None): |
|
679
|
|
|
"""Learn specific words given a list of seed specific wordsself. |
|
680
|
|
|
|
|
681
|
|
|
Using Linear SVM. |
|
682
|
|
|
|
|
683
|
|
|
:param list seed_specific_words: List of seed specific words |
|
684
|
|
|
:param int max_non_specific_examples: The number of non-specifc words |
|
685
|
|
|
to sample for training |
|
686
|
|
|
:return: List of learned specific words and the classifier object |
|
687
|
|
|
""" |
|
688
|
|
|
|
|
689
|
|
|
if debug is None: |
|
690
|
|
|
debug = False |
|
691
|
|
|
|
|
692
|
|
|
if max_non_specific_examples is None: |
|
693
|
|
|
max_non_specific_examples = MAX_NON_SPECIFIC_EXAMPLES |
|
694
|
|
|
|
|
695
|
|
|
data = [] |
|
696
|
|
|
non_specific_example_count = 0 |
|
697
|
|
|
|
|
698
|
|
|
for word in self.model.vocab: |
|
699
|
|
|
is_specific = word in seed_specific_words |
|
700
|
|
|
|
|
701
|
|
|
if not is_specific: |
|
702
|
|
|
non_specific_example_count += 1 |
|
703
|
|
|
if non_specific_example_count <= max_non_specific_examples: |
|
704
|
|
|
data.append((self[word], is_specific)) |
|
705
|
|
|
else: |
|
706
|
|
|
data.append((self[word], is_specific)) |
|
707
|
|
|
|
|
708
|
|
|
np.random.seed(RANDOM_STATE) |
|
709
|
|
|
np.random.shuffle(data) |
|
710
|
|
|
|
|
711
|
|
|
X, y = zip(*data) |
|
712
|
|
|
|
|
713
|
|
|
X = np.array(X) |
|
714
|
|
|
X /= np.linalg.norm(X, axis=1)[:, None] |
|
715
|
|
|
|
|
716
|
|
|
y = np.array(y).astype('int') |
|
717
|
|
|
|
|
718
|
|
|
clf = LinearSVC(C=1, class_weight='balanced', |
|
719
|
|
|
random_state=RANDOM_STATE) |
|
720
|
|
|
|
|
721
|
|
|
clf.fit(X, y) |
|
722
|
|
|
|
|
723
|
|
|
full_specific_words = [] |
|
724
|
|
|
for word in self.model.vocab: |
|
725
|
|
|
vector = [normalize(self[word])] |
|
726
|
|
|
if clf.predict(vector): |
|
727
|
|
|
full_specific_words.append(word) |
|
728
|
|
|
|
|
729
|
|
|
if not debug: |
|
730
|
|
|
return full_specific_words, clf |
|
731
|
|
|
|
|
732
|
|
|
return full_specific_words, clf, X, y |
|
733
|
|
|
|
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