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fix string-doctags missing from most_similar results #560

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merged 3 commits into from
Jan 14, 2016
Merged

fix string-doctags missing from most_similar results #560

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511d1dc
fix string-doctags missing from most_similar results
gojomo Dec 9, 2015
bcfd6b2
test for #560 (return string doctags); rm stray prints
gojomo Jan 12, 2016
f5b4804
note for #560
gojomo Jan 12, 2016
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1 change: 1 addition & 0 deletions CHANGELOG.txt
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Original file line number Diff line number Diff line change
Expand Up @@ -9,6 +9,7 @@ Changes
* Better internal handling of job batching in word2vec (#535)
- up to 300% speed up when training on very short documents (~tweets)
* Word2vec allows non-strict unicode error handling (ignore or replace) (Gordon Mohr, #466)
* Fix `DocvecsArray.index_to_doctag` so `most_similar()` returns string doctags (Gordon Mohr, #560)
* On-demand loading of the `pattern` library in utils.lemmatize (Jan Zikes, #461)
- `utils.HAS_PATTERN` flag moved to `utils.has_pattern()`
* Forwards compatibility for NumPy > 1.10 (Matti Lyra, #494, #513)
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2 changes: 1 addition & 1 deletion gensim/models/doc2vec.py
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Expand Up @@ -319,7 +319,7 @@ def _key_index(self, i_index, missing=None):

def index_to_doctag(self, i_index):
"""Return string key for given i_index, if available. Otherwise return raw int doctag (same int)."""
candidate_offset = self.max_rawint - i_index - 1
candidate_offset = i_index - self.max_rawint - 1
if 0 <= candidate_offset < len(self.offset2doctag):
return self.offset2doctag[candidate_offset]
else:
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4 changes: 2 additions & 2 deletions gensim/test/test_doc2vec.py
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Expand Up @@ -103,6 +103,8 @@ def test_string_doctags(self):
self.assertTrue(all(model.docvecs['_*0'] == model.docvecs[0]))
self.assertTrue(max(d.offset for d in model.docvecs.doctags.values()) < len(model.docvecs.doctags))
self.assertTrue(max(model.docvecs._int_index(str_key) for str_key in model.docvecs.doctags.keys()) < len(model.docvecs.doctag_syn0))
# verify docvecs.most_similar() returns string doctags rather than indexes
self.assertEqual(model.docvecs.offset2doctag[0], model.docvecs.most_similar([model.docvecs[0]])[0][0])

def test_empty_errors(self):
# no input => "RuntimeError: you must first build vocabulary before training the model"
Expand Down Expand Up @@ -242,8 +244,6 @@ def test_mixed_tag_types(self):
model = doc2vec.Doc2Vec()
model.build_vocab(mixed_tag_corpus)
expected_length = len(sentences) + len(model.docvecs.doctags) # 9 sentences, 7 unique first tokens
print(model.docvecs.doctags)
print(model.docvecs.count)
self.assertEquals(len(model.docvecs.doctag_syn0), expected_length)

def models_equal(self, model, model2):
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