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Fix WordEmbeddingsKeyedVectors.most_similar #2356

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merged 3 commits into from
Jan 27, 2019
Merged

Fix WordEmbeddingsKeyedVectors.most_similar #2356

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01c5336
Make WordEmbeddingsKeyedVectors.most_similar(..topn=O) return empty list
Witiko Jan 25, 2019
f4a2d00
Fix the example code for SoftCosineSimilarity
Witiko Jan 25, 2019
06781e2
Retain undocumented WordEmbeddingsKeyedVectors.most_similar(..topn=None)
Witiko Jan 25, 2019
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5 changes: 4 additions & 1 deletion gensim/models/keyedvectors.py
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Original file line number Diff line number Diff line change
Expand Up @@ -511,6 +511,9 @@ def most_similar(self, positive=None, negative=None, topn=10, restrict_vocab=Non
Sequence of (word, similarity).

"""
if topn is not None and topn < 1:
return []

if positive is None:
positive = []
if negative is None:
Expand Down Expand Up @@ -550,7 +553,7 @@ def most_similar(self, positive=None, negative=None, topn=10, restrict_vocab=Non

limited = self.vectors_norm if restrict_vocab is None else self.vectors_norm[:restrict_vocab]
dists = dot(limited, mean)
if not topn:
if topn is None:
return dists
best = matutils.argsort(dists, topn=topn + len(all_words), reverse=True)
# ignore (don't return) words from the input
Expand Down
6 changes: 3 additions & 3 deletions gensim/similarities/docsim.py
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Expand Up @@ -866,13 +866,13 @@ class SoftCosineSimilarity(interfaces.SimilarityABC):
>>> from gensim.test.utils import common_texts
>>> from gensim.corpora import Dictionary
>>> from gensim.models import Word2Vec, WordEmbeddingSimilarityIndex
>>> from gensim.similarities import SoftCosineSimilarity, TermSimilarityMatrix
>>> from gensim.similarities import SoftCosineSimilarity, SparseTermSimilarityMatrix
>>>
>>> model = Word2Vec(common_texts, size=20, min_count=1) # train word-vectors
>>> termsim_index = WordEmbeddingSimilarityIndex(model)
>>> termsim_index = WordEmbeddingSimilarityIndex(model.wv)
>>> dictionary = Dictionary(common_texts)
>>> bow_corpus = [dictionary.doc2bow(document) for document in common_texts]
>>> similarity_matrix = TermSimilarityMatrix(termsim_index, dictionary) # construct similarity matrix
>>> similarity_matrix = SparseTermSimilarityMatrix(termsim_index, dictionary) # construct similarity matrix
>>> docsim_index = SoftCosineSimilarity(bow_corpus, similarity_matrix, num_best=10)
>>>
>>> query = 'graph trees computer'.split() # make a query
Expand Down