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Make token2id mapping reproducible #1715

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merged 5 commits into from
Nov 23, 2017
Merged

Make token2id mapping reproducible #1715

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d290ff3
Make token2id mapping reproducible
formi23 Nov 14, 2017
c67ced2
Use iteritems instead of items
formi23 Nov 19, 2017
ba36a1b
Use sorted for deterministic token2id mapping
formi23 Nov 20, 2017
2a53556
Replace frequency-token sort with token sort
formi23 Nov 23, 2017
31f0903
remove key from sort
menshikh-iv Nov 23, 2017
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8 changes: 4 additions & 4 deletions gensim/corpora/dictionary.py
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Original file line number Diff line number Diff line change
Expand Up @@ -148,9 +148,9 @@ def doc2bow(self, document, allow_update=False, return_missing=False):

token2id = self.token2id
if allow_update or return_missing:
missing = {w: freq for w, freq in iteritems(counter) if w not in token2id}
missing = sorted((x for x in iteritems(counter) if x[0] not in token2id), key=lambda x: (x[1], x[0]))
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Sorting by (freq, w), are you sure that this is correct?

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@formi23 formi23 Nov 21, 2017 •
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@menshikh-iv, that's a good point. Either way works and ensures reproducibility. It can be assumed that sorting integers is faster than strings, but then the branching for words with the same frequency seems to introduce overhead (see the figure, where freq indicates frequency-word sorting and string indicates word-frequency sorting, i, f, c, and d methods are as in the previous example).
string-integer

Since the words in token2id guaranteed to be unique but the frequencies are not (and will likely overlap given patterns within natural language), sorting by token then frequency is probably the better choice but will vary on a dataset-by-dataset basis. What's your preference?

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@menshikh-iv menshikh-iv Nov 22, 2017 •
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IMO (w, freq) better than (freq, w) (just because of the uniqueness of the tokens). For bigger corpus, the situation will be even worse for (freq, w) because frequencies obey zipf's law -> more and more duplicates by freq in "tail" of distribution.

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Made the changes.

if allow_update:
for w in missing:
for w, _ in missing:
# new id = number of ids made so far;
# NOTE this assumes there are no gaps in the id sequence!
token2id[w] = len(token2id)
Expand All @@ -169,7 +169,7 @@ def doc2bow(self, document, allow_update=False, return_missing=False):
# return tokenids, in ascending id order
result = sorted(iteritems(result))
if return_missing:
return result, missing
return result, dict(missing)
else:
return result

Expand Down Expand Up @@ -266,7 +266,7 @@ def compactify(self):
logger.debug("rebuilding dictionary, shrinking gaps")

# build mapping from old id -> new id
idmap = dict(izip(itervalues(self.token2id), xrange(len(self.token2id))))
idmap = dict(izip(sorted(itervalues(self.token2id)), xrange(len(self.token2id))))

# reassign mappings to new ids
self.token2id = {token: idmap[tokenid] for token, tokenid in iteritems(self.token2id)}
Expand Down