inp, outp = sys.argv[1:3]
if not os.path.isdir(os.path.dirname(outp)):
raise SystemExit("Error: The output directory must be different than input. Create a new folder and try again")
if len(sys.argv) > 3:
keep_words = int(sys.argv[3])
else:
keep_words = DEFAULT_DICT_SIZE
online = 'online' in program
lemmatize = 'lemma' in program
debug = 'nodebug' not in program
if online:
dictionary = HashDictionary(id_range=keep_words, debug=debug)
dictionary.allow_update = True
wiki = WikiCorpus(inp, lemmatize=lemmatize, dictionary=dictionary)
MmCorpus.serialize(outp + '_bow.mm', wiki, progress_cnt=10000, metadata=True)
dictionary.filter_extremes(no_below=20, no_above=0.1, keep_n=DEFAULT_DICT_SIZE)
dictionary.save_as_text(outp + '_wordids.txt.bz2')
wiki.save(outp + '_corpus.pkl.bz2')
dictionary.allow_update = False
else:
wiki = WikiCorpus(inp, lemmatize=lemmatize)
wiki.dictionary.filter_extremes(no_below=20, no_above=0.1, keep_n=DEFAULT_DICT_SIZE)
MmCorpus.serialize(outp + '_bow.mm', wiki, progress_cnt=10000, metadata=True)
wiki.dictionary.save_as_text(outp + '_wordids.txt.bz2')
dictionary = Dictionary.load_from_text(outp + '_wordids.txt.bz2')
del wiki
mm = MmCorpus(outp + '_bow.mm')
# check and process input arguments
if len(sys.argv) < 3:
print(globals()['__doc__'] % locals())
sys.exit(1)
inp, outp = sys.argv[1:3]
if len(sys.argv) > 3:
keep_words = int(sys.argv[3])
else:
keep_words = DEFAULT_DICT_SIZE
online = 'online' in program
lemmatize = 'lemma' in program
debug = 'nodebug' not in program
if online:
dictionary = HashDictionary(id_range=keep_words, debug=debug)
dictionary.allow_update = True # start collecting document frequencies
wiki = WikiCorpus(inp, lemmatize=lemmatize, dictionary=dictionary)
MmCorpus.serialize(outp + '_bow.mm', wiki, progress_cnt=10000) # ~4h on my macbook pro without lemmatization, 3.1m articles (august 2012)
# with HashDictionary, the token->id mapping is only fully instantiated now, after `serialize`
dictionary.filter_extremes(no_below=20, no_above=0.1, keep_n=DEFAULT_DICT_SIZE)
dictionary.save_as_text(outp + '_wordids.txt.bz2')
wiki.save(outp + '_corpus.pkl.bz2')
dictionary.allow_update = False
else:
wiki = WikiCorpus(inp, lemmatize=lemmatize) # takes about 9h on a macbook pro, for 3.5m articles (june 2011)
mywiki = myWikiCorpus(inp, lemmatize=lemmatize)
# only keep the most frequent words (out of total ~8.2m unique tokens)
wiki.dictionary.filter_extremes(no_below=20, no_above=0.1, keep_n=DEFAULT_DICT_SIZE)
# save dictionary and bag-of-words (term-document frequency matrix)
MmCorpus.serialize(outp + '_bow.mm', wiki, progress_cnt=10000) # another ~9h
MmCorpus.serialize(outp + '_bowm.mm', mywiki, progress_cnt=10000) # another ~9h
def process_corpus(input_filename=WIKI_CORPUS,
output_dir=GENSIM_DIR,
online=False,
to_lemmatize=LEMMING,
debug=True):
program = 'GensimWikiCorpus'
logger = logging.getLogger(program)
inp = input_filename
# twice because model will be saved into directory/prefixfilenames
outp = os.path.join(output_dir, WIKI_STATS + '/' + WIKI_STATS)
if not os.path.isdir(os.path.dirname(outp)):
os.makedirs(outp)
keep_words = DEFAULT_DICT_SIZE
if online:
dictionary = HashDictionary(id_range=keep_words, debug=debug)
dictionary.allow_update = True # start collecting document frequencies
wiki = JsonWikiCorpus(inp,
to_lemmatize=to_lemmatize,
dictionary=dictionary)
MmCorpus.serialize(
outp + '_bow.mm', wiki, progress_cnt=10000
) # ~4h on my macbook pro without lemmatization, 3.1m articles (august 2012)
# with HashDictionary, the token->id mapping is only fully instantiated now, after `serialize`
dictionary.filter_extremes(no_below=20,
no_above=0.1,
keep_n=DEFAULT_DICT_SIZE)
dictionary.save_as_text(outp + '_wordids.txt.bz2')
wiki.save(outp + '_corpus.pkl.bz2')
dictionary.allow_update = False
else:
wiki = JsonWikiCorpus(
inp, to_lemmatize=to_lemmatize
) # takes about 9h on a macbook pro, for 3.5m articles (june 2011)
# only keep the most frequent words (out of total ~8.2m unique tokens)
wiki.dictionary.filter_extremes(no_below=20,
no_above=0.1,
keep_n=DEFAULT_DICT_SIZE)
# save dictionary and bag-of-words (term-document frequency matrix)
MmCorpus.serialize(outp + '_bow.mm', wiki,
progress_cnt=10000) # another ~9h
wiki.dictionary.save_as_text(outp + '_wordids.txt.bz2')
# load back the id->word mapping directly from file
# this seems to save more memory, compared to keeping the wiki.dictionary object from above
dictionary = Dictionary.load_from_text(outp + '_wordids.txt.bz2')
del wiki
# initialize corpus reader and word->id mapping
mm = MmCorpus(outp + '_bow.mm')
# build tfidf, ~50min
tfidf = TfidfModel(mm, id2word=dictionary, normalize=True)
tfidf.save(outp + '.tfidf_model')
# save tfidf vectors in matrix market format
# ~4h; result file is 15GB! bzip2'ed down to 4.5GB
MmCorpus.serialize(outp + '_tfidf.mm', tfidf[mm], progress_cnt=10000)
logger.info("finished running %s" % program)
