def bolukbasi_debias_generalized(embedding,
words,
out_file,
excludes=None,
**kwargs):
# type: (WordEmbedding, Iterable[str], Path, Iterable[str], **Any) -> WordEmbedding
"""Debias a word embedding using a generalized version of Bolukbasi's algorithm.
Parameters:
embedding (WordEmbedding): The word embedding to debias.
words (Iterable[str]): A list of words that define the bias subspace.
out_file (Path): The path to save the new embedding to.
excludes (Iterable[str]): A collection of words to be excluded from the debiasing
**kwargs: Other keyword arguments.
Returns:
WordEmbedding: The debiased word embedding.
"""
if out_file.exists():
return WordEmbedding.load_word2vec_file(out_file)
matrix = recenter(
np.array([embedding[word] for word in words if word in embedding]))
bias_subspace = _define_pca_bias_subspace(matrix, **kwargs)
bias_subspace = bias_subspace[np.newaxis, :]
# debias by rejecting the subspace and reverting the excluded words
if excludes is None:
excludes = set()
new_vectors = reject(embedding.vectors, bias_subspace)
for word in excludes:
if word in embedding:
new_vectors[embedding.index(word)] = embedding[word]
new_vectors = normalize(new_vectors)
# create a word embedding from the new vectors
new_embedding = WordEmbedding.from_vectors(embedding.words, new_vectors)
new_embedding.source = out_file
new_embedding.save()
return new_embedding
def bolukbasi_debias_original(embedding,
word_pairs,
out_file,
excludes=None,
mirrors=None,
**kwargs):
# type: (WordEmbedding, Iterable[Tuple[str, str]], Path, Iterable[str], Iterable[Tuple[str, str]], **Any) -> WordEmbedding
"""Debias a word embedding using Bolukbasi's original algorithm.
Adapted from https://github.com/tolga-b/debiaswe/blob/master/debiaswe/debias.py#L19
Commit 10277b23e187ee4bd2b6872b507163ef4198686b on 2018-04-02
Parameters:
embedding (WordEmbedding): The word embedding to debias.
word_pairs (Iterable[Tuple[str, str]]):
A list of word pairs that define the bias subspace.
out_file (Path):
The path to save the new embedding to.
excludes (Iterable[str]):
A collection of words to be excluded from the debiasing
mirrors (Iterable[Tuple[str, str]]):
Specific words that should be equidistant.
**kwargs: Other keyword arguments.
Returns:
WordEmbedding: The debiased word embedding.
"""
if out_file.exists():
return WordEmbedding.load_word2vec_file(out_file)
# define the bias subspace
# recenter words
matrix = []
for male_word, female_word in word_pairs:
if male_word not in embedding or female_word not in embedding:
continue
matrix.extend(
recenter(np.array([embedding[male_word], embedding[female_word]])))
bias_subspace = define_bias_subspace(matrix, **kwargs)
bias_subspace = _align_gender_direction(embedding, bias_subspace,
word_pairs)
bias_subspace = bias_subspace[np.newaxis, :]
# debias by rejecting the subspace and reverting the excluded words
if excludes is None:
excludes = set()
new_vectors = reject(embedding.vectors, bias_subspace)
for word in excludes:
if word in embedding:
new_vectors[embedding.index(word)] = embedding[word]
new_vectors = normalize(new_vectors)
# FIXME does equalizing make sense in higher dimensions?
#new_vectors = _bolukbasi_equalize(embedding, new_vectors, bias_subspace, mirrors)
# create a word embedding from the new vectors
new_embedding = WordEmbedding.from_vectors(embedding.words, new_vectors)
new_embedding.source = out_file
new_embedding.save()
return new_embedding
