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Allow use of truncated Dictionary for coherence measures #1349

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merged 34 commits into from
Jun 14, 2017
Merged

Allow use of truncated Dictionary for coherence measures #1349

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9d06a1f
#1342: Allow use of truncated `Dictionary` for coherence calculation …
May 22, 2017
f69a2ff
#1342: Do not produce sliding windows for texts with no relevant word…
May 22, 2017
26de547
#1342: Remove unused multiprocessing import in `coherencemodel` module.
May 22, 2017
dfe159b
add utility functions for strided windowing of texts (lists of string…
May 24, 2017
2e3852e
handle edge cases with window_size equal to or exceeding document siz…
May 24, 2017
ec7af1b
move code for building inverted index into a new text_analysis module…
May 24, 2017
3f8fb7f
complete migration to using the accumulators in the new text_analysis…
May 24, 2017
b12edef
fix bug in WordOccurrenceAccumulator so that co-occurrences of same w…
May 25, 2017
91b8a05
make wikicorpus parsing handle KeyboardInterrupt gracefully
May 25, 2017
c6224b7
add ParallelWordOccurrenceAccumulator and make default method for p_b…
May 26, 2017
f00d389
clean up, clarify, and optimize the indirect_confirmation_measure.cos…
May 27, 2017
327b739
#1342: Cleanup, documentation improvements, proper caching of accumul…
May 30, 2017
3c0752b
Merge branch 'develop' into develop
macks22 May 30, 2017
e06c7c3
#1342: Do not swallow `KeyboardInterrupt` naively in `WikiCorpus.get_…
May 30, 2017
2ca43f7
#1342: Formatting fixes (hanging indent in `coherencemodel` and non-e…
May 30, 2017
825b0e9
#1342: Improve `CoherenceModel` documentation and minor refactor for …
May 30, 2017
314a400
#1342: Optimize word occurrence accumulation and fix a bug with repea…
May 30, 2017
e785773
#1342: Minor bug fixes and improved logging in text_analysis module; …
May 31, 2017
5f78cdb
#1342: Optimize data structures being used for window set tracking an…
May 31, 2017
bbd2748
#1342: Fix accidental typo.
May 31, 2017
5fb0b95
#1342: Further optimize word co-occurrence accumulation by using a `c…
May 31, 2017
880b8d0
#1342: Clean up logging in `text_analysis` module and remove empty li…
Jun 1, 2017
1d32b8e
#1342: Remove unused traceback module.
Jun 1, 2017
8e04b41
#1342: Fixes for python3 compatibility.
Jun 1, 2017
e3ce402
#1342: Hopefully `six.viewitems` works for python3 compatibility?
Jun 1, 2017
7f7f55d
#1342: Realized the python3 compatibility issue was due to the Dictio…
Jun 1, 2017
343da69
#1342: Fixed a few bugs and added test coverage for the coherencemode…
Jun 2, 2017
1ce8a72
#1342: Further tests for persistence of accumulator.
Jun 2, 2017
96fd343
#1342: Add test case for `CorpusAccumulator`.
Jun 4, 2017
a631ab6
#1342: Formatting fixes for hanging indents and overly long lines.
Jun 5, 2017
5f58bda
#1342: Fix `indirect_confirmation_measure.cosine_similarity` to retur…
Jun 6, 2017
b941f3c
#1342: Fix `direct_confirmation_measure` functions to return individu…
Jun 7, 2017
75fcac8
#1342: Hanging indents and switch out `union` with `update` for uniqu…
Jun 8, 2017
96d1349
#1342: Clarify documentation in the `probability_estimation` module.
Jun 9, 2017
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74 changes: 46 additions & 28 deletions gensim/corpora/wikicorpus.py
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Original file line number Diff line number Diff line change
Expand Up @@ -20,12 +20,13 @@

import bz2
import logging
import re
from xml.etree.cElementTree import iterparse # LXML isn't faster, so let's go with the built-in solution
import multiprocessing
import re
import signal
from xml.etree.cElementTree import \
iterparse # LXML isn't faster, so let's go with the built-in solution

from gensim import utils

# cannot import whole gensim.corpora, because that imports wikicorpus...
from gensim.corpora.dictionary import Dictionary
from gensim.corpora.textcorpus import TextCorpus
Expand Down Expand Up @@ -249,6 +250,11 @@ def process_article(args):
return result, title, pageid


def init_to_ignore_interrupt():
"""Should only be used when master is prepared to handle termination of child processes."""
signal.signal(signal.SIGINT, signal.SIG_IGN)


class WikiCorpus(TextCorpus):
"""
Treat a wikipedia articles dump (\*articles.xml.bz2) as a (read-only) corpus.
Expand All @@ -260,7 +266,8 @@ class WikiCorpus(TextCorpus):
>>> MmCorpus.serialize('wiki_en_vocab200k.mm', wiki) # another 8h, creates a file in MatrixMarket format plus file with id->word

"""
def __init__(self, fname, processes=None, lemmatize=utils.has_pattern(), dictionary=None, filter_namespaces=('0',)):
def __init__(self, fname, processes=None, lemmatize=utils.has_pattern(), dictionary=None,
filter_namespaces=('0',)):
"""
Initialize the corpus. Unless a dictionary is provided, this scans the
corpus once, to determine its vocabulary.
Expand Down Expand Up @@ -299,28 +306,39 @@ def get_texts(self):
"""
articles, articles_all = 0, 0
positions, positions_all = 0, 0
texts = ((text, self.lemmatize, title, pageid) for title, text, pageid in extract_pages(bz2.BZ2File(self.fname), self.filter_namespaces))
pool = multiprocessing.Pool(self.processes)
# process the corpus in smaller chunks of docs, because multiprocessing.Pool
# is dumb and would load the entire input into RAM at once...
for group in utils.chunkize(texts, chunksize=10 * self.processes, maxsize=1):
for tokens, title, pageid in pool.imap(process_article, group): # chunksize=10):
articles_all += 1
positions_all += len(tokens)
# article redirects and short stubs are pruned here
if len(tokens) < ARTICLE_MIN_WORDS or any(title.startswith(ignore + ':') for ignore in IGNORED_NAMESPACES):
continue
articles += 1
positions += len(tokens)
if self.metadata:
yield (tokens, (pageid, title))
else:
yield tokens
pool.terminate()

logger.info(
"finished iterating over Wikipedia corpus of %i documents with %i positions"
" (total %i articles, %i positions before pruning articles shorter than %i words)",
articles, positions, articles_all, positions_all, ARTICLE_MIN_WORDS)
self.length = articles # cache corpus length
texts = \
((text, self.lemmatize, title, pageid)
for title, text, pageid
in extract_pages(bz2.BZ2File(self.fname), self.filter_namespaces))
pool = multiprocessing.Pool(self.processes, init_to_ignore_interrupt)

try:
# process the corpus in smaller chunks of docs, because multiprocessing.Pool
# is dumb and would load the entire input into RAM at once...
for group in utils.chunkize(texts, chunksize=10 * self.processes, maxsize=1):
for tokens, title, pageid in pool.imap(process_article, group):
articles_all += 1
positions_all += len(tokens)
# article redirects and short stubs are pruned here
if len(tokens) < ARTICLE_MIN_WORDS or any(title.startswith(ignore + ':') for ignore in IGNORED_NAMESPACES):
continue
articles += 1
positions += len(tokens)
if self.metadata:
yield (tokens, (pageid, title))
else:
yield tokens
except KeyboardInterrupt:
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What is this for? Masking user interrupts is an anti-pattern; deserves a detailed comment, at the very least.

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I've updated the code to handle the interrupt more appropriately. As for why I chose to handle the interrupt at all (at the risk of repeating the above comment): this pool may be active during many other phases of gensim execution if the underlying corpus/texts being iterated come from the wikicorpus. It was confusing for me to see stdout activity when issuing an interrupt during execution of an entirely different code path.

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@piskvorky piskvorky May 30, 2017 •
edited
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Sorry, not sure I understand.

When iterating over a WikiCorpus, gensim uses a multiprocessing.Pool (forks), yes.

Now when you do CTRL+C, what happened (old behaviour)? And what happens now (after your changes here)? Why is that preferable?

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Here is the code I am running:

import os
import logging
import gensim
from gensim.models.coherencemodel import CoherenceModel

logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.INFO)

home = '/Users/user/workshop/nlp'
id2word = gensim.corpora.Dictionary.load_from_text(os.path.join(home, 'data', 'wikipedia-may-17_wordids.txt.bz2'))
texts = gensim.corpora.WikiCorpus(os.path.join(home, 'data', 'enwiki-latest-pages-articles.xml.bz2'), dictionary=id2word).get_texts()
lda = gensim.models.LdaModel.load(os.path.join(home, 'models', 'lda-k100_en-wiki.model'))
topics = [[t[0] for t in lda.get_topic_terms(i, 20)] for i in range(100)]
topic_words = [[id2word[token_id] for token_id in t] for t in topics]
cm = gensim.models.coherencemodel.CoherenceModel(topics=topic_words, texts=texts, dictionary=id2word, coherence='c_v', topn=20, window_size=110)
topic_coherences = cm.get_coherence_per_topic()
print(topic_coherences)

Before:

2017-05-30 07:34:29,027 : INFO : loading LdaModel object from /Users/user/workshop/nlp/models/lda-k100_en-wiki.model
2017-05-30 07:34:29,028 : INFO : loading expElogbeta from /Users/user/workshop/nlp/models/lda-k100_en-wiki.model.expElogbeta.npy with mmap=None
2017-05-30 07:34:29,080 : INFO : setting ignored attribute id2word to None
2017-05-30 07:34:29,080 : INFO : setting ignored attribute state to None
2017-05-30 07:34:29,080 : INFO : setting ignored attribute dispatcher to None
2017-05-30 07:34:29,080 : INFO : loaded /Users/user/workshop/nlp/models/lda-k100_en-wiki.model
2017-05-30 07:34:29,081 : INFO : loading LdaModel object from /Users/user/workshop/nlp/models/lda-k100_en-wiki.model.state
2017-05-30 07:34:29,249 : INFO : loaded /Users/user/workshop/nlp/models/lda-k100_en-wiki.model.state
2017-05-30 07:34:32,603 : INFO : using ParallelWordOccurrenceAccumulator(processes=7, batch_size=16) to estimate probabilities from sliding windows
2017-05-30 07:34:33,101 : INFO : submitted 0 batches to accumulate stats from 0 documents (90752 virtual)
2017-05-30 07:34:33,298 : INFO : submitted 1 batches to accumulate stats from 16 documents (154816 virtual)
2017-05-30 07:34:33,462 : INFO : submitted 2 batches to accumulate stats from 32 documents (223497 virtual)
2017-05-30 07:34:33,709 : INFO : submitted 3 batches to accumulate stats from 48 documents (285340 virtual)
2017-05-30 07:34:33,790 : INFO : submitted 4 batches to accumulate stats from 64 documents (342337 virtual)
2017-05-30 07:34:34,297 : INFO : submitted 5 batches to accumulate stats from 80 documents (415139 virtual)
2017-05-30 07:34:34,872 : INFO : submitted 6 batches to accumulate stats from 96 documents (484709 virtual)
2017-05-30 07:34:35,093 : INFO : submitted 7 batches to accumulate stats from 112 documents (542834 virtual)
2017-05-30 07:34:35,381 : INFO : submitted 8 batches to accumulate stats from 128 documents (628469 virtual)
2017-05-30 07:34:35,443 : INFO : submitted 9 batches to accumulate stats from 144 documents (691420 virtual)
2017-05-30 07:34:35,764 : INFO : submitted 10 batches to accumulate stats from 160 documents (741122 virtual)
2017-05-30 07:34:35,983 : INFO : submitted 11 batches to accumulate stats from 176 documents (774924 virtual)
2017-05-30 07:34:36,234 : INFO : submitted 12 batches to accumulate stats from 192 documents (829056 virtual)
2017-05-30 07:34:36,682 : INFO : submitted 13 batches to accumulate stats from 208 documents (887935 virtual)
^C2017-05-30 07:34:48,466 : WARNING : stats accumulation interrupted; <= 887935 documents processed
Process PoolWorker-12:
Process PoolWorker-10:
Process PoolWorker-11:
Traceback (most recent call last):
Traceback (most recent call last):
Traceback (most recent call last):
  File "/Users/user/anaconda2/lib/python2.7/multiprocessing/process.py", line 258, in _bootstrap
  File "/Users/user/anaconda2/lib/python2.7/multiprocessing/process.py", line 258, in _bootstrap
  File "/Users/user/anaconda2/lib/python2.7/multiprocessing/process.py", line 258, in _bootstrap
Process PoolWorker-9:
Traceback (most recent call last):
Process PoolWorker-8:
Process PoolWorker-13:
  File "/Users/user/anaconda2/lib/python2.7/multiprocessing/process.py", line 258, in _bootstrap
Process PoolWorker-14:
Traceback (most recent call last):
Traceback (most recent call last):
  File "/Users/user/anaconda2/lib/python2.7/multiprocessing/process.py", line 258, in _bootstrap
Traceback (most recent call last):
  File "/Users/user/anaconda2/lib/python2.7/multiprocessing/process.py", line 258, in _bootstrap
  File "/Users/user/anaconda2/lib/python2.7/multiprocessing/process.py", line 258, in _bootstrap
    self.run()
    self.run()
  File "/Users/user/anaconda2/lib/python2.7/multiprocessing/process.py", line 114, in run
  File "/Users/user/anaconda2/lib/python2.7/multiprocessing/process.py", line 114, in run
    self.run()
  File "/Users/user/anaconda2/lib/python2.7/multiprocessing/process.py", line 114, in run
    self._target(*self._args, **self._kwargs)
  File "/Users/user/anaconda2/lib/python2.7/multiprocessing/pool.py", line 102, in worker
    self._target(*self._args, **self._kwargs)
  File "/Users/user/anaconda2/lib/python2.7/multiprocessing/pool.py", line 102, in worker
    self.run()
    self.run()
  File "/Users/user/anaconda2/lib/python2.7/multiprocessing/process.py", line 114, in run
  File "/Users/user/anaconda2/lib/python2.7/multiprocessing/process.py", line 114, in run
    self._target(*self._args, **self._kwargs)
    self.run()
  File "/Users/user/anaconda2/lib/python2.7/multiprocessing/process.py", line 114, in run
    self._target(*self._args, **self._kwargs)
    self._target(*self._args, **self._kwargs)
  File "/Users/user/anaconda2/lib/python2.7/multiprocessing/pool.py", line 102, in worker
  File "/Users/user/anaconda2/lib/python2.7/multiprocessing/pool.py", line 102, in worker
  File "/Users/user/anaconda2/lib/python2.7/multiprocessing/pool.py", line 102, in worker
    self.run()
    self._target(*self._args, **self._kwargs)
  File "/Users/user/anaconda2/lib/python2.7/multiprocessing/process.py", line 114, in run
  File "/Users/user/anaconda2/lib/python2.7/multiprocessing/pool.py", line 102, in worker
    self._target(*self._args, **self._kwargs)
  File "/Users/user/anaconda2/lib/python2.7/multiprocessing/pool.py", line 102, in worker
2017-05-30 07:34:48,467 : INFO : AccumulatingWorker interrupted after processing 8598 documents
2017-05-30 07:34:48,468 : INFO : AccumulatingWorker interrupted after processing 3630 documents
2017-05-30 07:34:48,467 : INFO : AccumulatingWorker interrupted after processing 7670 documents
2017-05-30 07:34:48,468 : INFO : AccumulatingWorker interrupted after processing 5859 documents
2017-05-30 07:34:48,468 : INFO : AccumulatingWorker interrupted after processing 9993 documents
2017-05-30 07:34:48,468 : INFO : AccumulatingWorker interrupted after processing 8987 documents
2017-05-30 07:34:48,469 : INFO : serializing accumulator to return to master...
2017-05-30 07:34:48,469 : INFO : serializing accumulator to return to master...
2017-05-30 07:34:48,468 : INFO : AccumulatingWorker interrupted after processing 5062 documents
2017-05-30 07:34:48,469 : INFO : serializing accumulator to return to master...
2017-05-30 07:34:48,469 : INFO : serializing accumulator to return to master...
2017-05-30 07:34:48,469 : INFO : serializing accumulator to return to master...
2017-05-30 07:34:48,469 : INFO : serializing accumulator to return to master...
    task = get()
    task = get()
  File "/Users/user/anaconda2/lib/python2.7/multiprocessing/queues.py", line 376, in get
    task = get()
    task = get()
  File "/Users/user/anaconda2/lib/python2.7/multiprocessing/queues.py", line 376, in get
    task = get()
    task = get()
  File "/Users/user/anaconda2/lib/python2.7/multiprocessing/queues.py", line 376, in get
  File "/Users/user/anaconda2/lib/python2.7/multiprocessing/queues.py", line 376, in get
  File "/Users/user/anaconda2/lib/python2.7/multiprocessing/queues.py", line 378, in get
  File "/Users/user/anaconda2/lib/python2.7/multiprocessing/queues.py", line 376, in get
    task = get()
  File "/Users/user/anaconda2/lib/python2.7/multiprocessing/queues.py", line 376, in get
    racquire()
    racquire()
    racquire()
    racquire()
    racquire()
    racquire()
    return recv()
KeyboardInterrupt
KeyboardInterrupt
KeyboardInterrupt
KeyboardInterrupt
KeyboardInterrupt
KeyboardInterrupt
KeyboardInterrupt
2017-05-30 07:34:48,469 : INFO : serializing accumulator to return to master...
2017-05-30 07:34:48,471 : INFO : accumulator serialized
2017-05-30 07:34:48,473 : INFO : accumulator serialized
2017-05-30 07:34:48,471 : INFO : accumulator serialized
2017-05-30 07:34:48,473 : INFO : accumulator serialized
2017-05-30 07:34:48,471 : INFO : accumulator serialized
2017-05-30 07:34:48,474 : INFO : accumulator serialized
2017-05-30 07:34:48,474 : INFO : accumulator serialized
2017-05-30 07:34:50,727 : INFO : 7 accumulators retrieved from output queue

After:

2017-05-30 07:29:17,246 : WARNING : Slow version of gensim.models.doc2vec is being used
2017-05-30 07:29:17,253 : INFO : 'pattern' package not found; tag filters are not available for English
2017-05-30 07:29:18,188 : INFO : loading LdaModel object from /Users/user/workshop/nlp/models/lda-k100_en-wiki.model
2017-05-30 07:29:18,198 : INFO : loading expElogbeta from /Users/user/workshop/nlp/models/lda-k100_en-wiki.model.expElogbeta.npy with mmap=None
2017-05-30 07:29:18,267 : INFO : setting ignored attribute id2word to None
2017-05-30 07:29:18,267 : INFO : setting ignored attribute state to None
2017-05-30 07:29:18,267 : INFO : setting ignored attribute dispatcher to None
2017-05-30 07:29:18,267 : INFO : loaded /Users/user/workshop/nlp/models/lda-k100_en-wiki.model
2017-05-30 07:29:18,267 : INFO : loading LdaModel object from /Users/user/workshop/nlp/models/lda-k100_en-wiki.model.state
2017-05-30 07:29:18,422 : INFO : loaded /Users/user/workshop/nlp/models/lda-k100_en-wiki.model.state
2017-05-30 07:29:20,667 : INFO : using ParallelWordOccurrenceAccumulator(processes=7, batch_size=16) to estimate probabilities from sliding windows
2017-05-30 07:29:21,117 : INFO : submitted 0 batches to accumulate stats from 0 documents (90752 virtual)
2017-05-30 07:29:21,312 : INFO : submitted 1 batches to accumulate stats from 16 documents (154816 virtual)
2017-05-30 07:29:21,441 : INFO : submitted 2 batches to accumulate stats from 32 documents (223497 virtual)
2017-05-30 07:29:21,715 : INFO : submitted 3 batches to accumulate stats from 48 documents (285340 virtual)
2017-05-30 07:29:21,783 : INFO : submitted 4 batches to accumulate stats from 64 documents (342337 virtual)
2017-05-30 07:29:22,270 : INFO : submitted 5 batches to accumulate stats from 80 documents (415139 virtual)
2017-05-30 07:29:22,634 : INFO : submitted 6 batches to accumulate stats from 96 documents (484709 virtual)
2017-05-30 07:29:22,701 : INFO : submitted 7 batches to accumulate stats from 112 documents (542834 virtual)
2017-05-30 07:29:22,995 : INFO : submitted 8 batches to accumulate stats from 128 documents (628469 virtual)
2017-05-30 07:29:23,118 : INFO : submitted 9 batches to accumulate stats from 144 documents (691420 virtual)
2017-05-30 07:29:23,238 : INFO : submitted 10 batches to accumulate stats from 160 documents (741122 virtual)
2017-05-30 07:29:23,336 : INFO : submitted 11 batches to accumulate stats from 176 documents (774924 virtual)
2017-05-30 07:29:23,471 : INFO : submitted 12 batches to accumulate stats from 192 documents (829056 virtual)
2017-05-30 07:29:23,665 : INFO : submitted 13 batches to accumulate stats from 208 documents (887935 virtual)
^C2017-05-30 07:29:58,875 : WARNING : stats accumulation interrupted; <= 887935 documents processed
2017-05-30 07:29:58,876 : INFO : AccumulatingWorker interrupted after processing 17775 documents
2017-05-30 07:29:58,876 : INFO : AccumulatingWorker interrupted after processing 27228 documents
2017-05-30 07:29:58,876 : INFO : AccumulatingWorker interrupted after processing 20910 documents
2017-05-30 07:29:58,876 : INFO : AccumulatingWorker interrupted after processing 26774 documents
2017-05-30 07:29:58,876 : INFO : AccumulatingWorker interrupted after processing 31392 documents
2017-05-30 07:29:58,876 : INFO : AccumulatingWorker interrupted after processing 32224 documents
2017-05-30 07:29:58,876 : INFO : AccumulatingWorker interrupted after processing 22497 documents
2017-05-30 07:29:58,877 : INFO : serializing accumulator to return to master...
2017-05-30 07:29:58,877 : INFO : serializing accumulator to return to master...
2017-05-30 07:29:58,877 : INFO : serializing accumulator to return to master...
2017-05-30 07:29:58,877 : INFO : serializing accumulator to return to master...
2017-05-30 07:29:58,877 : INFO : serializing accumulator to return to master...
2017-05-30 07:29:58,877 : INFO : serializing accumulator to return to master...
2017-05-30 07:29:58,877 : INFO : serializing accumulator to return to master...
2017-05-30 07:29:58,878 : INFO : accumulator serialized
2017-05-30 07:29:58,878 : INFO : accumulator serialized
2017-05-30 07:29:58,878 : INFO : accumulator serialized
2017-05-30 07:29:58,878 : INFO : accumulator serialized
2017-05-30 07:29:58,878 : INFO : accumulator serialized
2017-05-30 07:29:58,878 : INFO : accumulator serialized
2017-05-30 07:29:58,878 : INFO : accumulator serialized
2017-05-30 07:30:01,106 : INFO : 7 accumulators retrieved from output queue

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So the advantage is a cleaner log?
What are the disadvantages? (I am not familiar with this type of functionality, but I assume there must be some disadvantages, otherwise it would be the default behaviour).

logger.warn(
"user terminated iteration over Wikipedia corpus after %i documents with %i positions"
" (total %i articles, %i positions before pruning articles shorter than %i words)",
articles, positions, articles_all, positions_all, ARTICLE_MIN_WORDS)
else:
logger.info(
"finished iterating over Wikipedia corpus of %i documents with %i positions"
" (total %i articles, %i positions before pruning articles shorter than %i words)",
articles, positions, articles_all, positions_all, ARTICLE_MIN_WORDS)
self.length = articles # cache corpus length
finally:
pool.terminate()
# endclass WikiCorpus
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