def build_ta_index(self):
clock = Timer()
ta_index = ""
index = 0
for book in iter_data_to_stream(self.fname):
if index % 50 == 0:
print "Processing bookindex #" + str(index)
clock.print_lap()
title = book[0]
author = book[1]
ta_row = str(index) + "_" + str(title) + "_" + str(author) + "\n"
index += 1
ta_index += ta_row
print "\n Done!\n"
clock.print_lap()
clock = None
print "\n Writing to file " + str(self.ta_indexfile) + " ...\n"
with open(self.ta_indexfile, "w") as text_file:
text_file.write(ta_index)
# testing
# print (tokenized_corpus.read_ta_index(12))
print "\n Starting document stream."
doc_stream = (tokenized_corpus.get_token_stream())
print " doc_stream type: " + str(type(doc_stream))
print "\n"
clock = Timer()
print "\n Creating dictionary."
id2word_pgfin = gensim.corpora.Dictionary(doc_stream)
print (id2word_pgfin)
clock.print_lap()
# filter tokens: discard those in less than 2 documents and those in more than 40%
# leaves about 10% with test set of 20
id2word_pgfin.filter_extremes(no_below=10, no_above=0.7)
print (id2word_pgfin)
print "\n Saving dictionary to disk.\n"
# id2word_pgfin.save('./data/pgfintestdata20.dictionary')
id2word_pgfin.save('./data/pgfin.dictionary')
clock.print_lap()
# create a stream of bag-of-words vectors
pgfin_bow_corpus = CorpusPGFinBOW(datafile, id2word_pgfin)