def _counts2PMI(self):
words = list(self.words.keys())
contexts = list(self.contexts.keys())
iw = sorted(words)
ic = sorted(contexts)
wi = dict([(w, i) for i, w in enumerate(iw)])
ci = dict([(c, i) for i, c in enumerate(ic)])
counts = csr_matrix((len(wi), len(ci)), dtype=np.float32)
tmp_counts = dok_matrix((len(wi), len(ci)), dtype=np.float32)
update_threshold = 100000
i = 0
with open(self.count_pair_file) as f:
for line in f:
count, word, context = line.strip().split()
if word in wi and context in ci:
tmp_counts[wi[word], ci[context]] = int(count)
i += 1
if i == update_threshold:
counts = counts + tmp_counts.tocsr()
tmp_counts = dok_matrix((len(wi), len(ci)), dtype=np.float32)
i = 0
counts = counts + tmp_counts.tocsr()
pmi = self.calc_pmi(counts, self.cds)
save_matrix(self.pmi_file, pmi)
save_vocabulary(self.pmi_file + '.words.vocab', iw)
save_vocabulary(self.pmi_file + '.contexts.vocab', ic)
self.explicit = PositiveExplicit(self.pmi_file, normalize=False, neg=self.neg)
cf.saveDictionary(self.explicit,self.dict_name.split('/')[0]+'/'+self.dict_name.split('/')[1]+'_explicit_ppmi.bin')
def main():
args = docopt("""
Usage:
counts2pmi.py [options] <counts> <output_path>
Options:
--cds NUM Context distribution smoothing [default: 1.0]
""")
counts_path = args['<counts>']
vectors_path = args['<output_path>']
cds = float(args['--cds'])
#words = load_count_vocabulary(counts_path + '.words.vocab')
#contexts = load_count_vocabulary(counts_path + '.contexts.vocab')
#loader = np.load(counts_path+'.pairs.counts.npz')
#counts = csr_matrix((loader['data'], loader['indices'], loader['indptr']))
#counts, iw, ic = read_counts_matrix(counts_path)
#iw = sorted(words)
#ic = sorted(contexts)
counts, iw, ic = read_counts_matrix(counts_path)
pmi = calc_pmi(counts, cds, alpha=1.0)
#words = load_count_vocabulary(counts_path + '.words.vocab')
#contexts = load_count_vocabulary(counts_path + '.contexts.vocab')
#iw = sorted(words)
#ic = sorted(contexts)
save_matrix(vectors_path + '.count_matrix', counts)
save_matrix(vectors_path, pmi)
save_vocabulary(vectors_path + '.words.vocab', iw)
save_vocabulary(vectors_path + '.contexts.vocab', ic)
def main():
args = docopt("""
Usage:
counts2pmi.py [options] <counts> <output_path>
Options:
--cds NUM Context distribution smoothing [default: 1.0]
""")
counts_path = args['<counts>']
vectors_path = args['<output_path>']
cds = float(args['--cds'])
o = open(counts_path + '-new',"w")
for line in open(counts_path):
o.write(line.strip()+"\n")
o.close()
counts_path_new = counts_path + '-new'
counts, iw, ic = read_counts_matrxi_fast(counts_path, counts_path_new)
pmi = calc_pmi(counts, cds)
save_matrix(vectors_path, pmi)
save_vocabulary(vectors_path + '.words.vocab', iw)
save_vocabulary(vectors_path + '.contexts.vocab', ic)
savePmiNonzeroTerm_fast(counts,vectors_path + '.cooccurrence')
remain_index = pmi.data > 1
pmi.data = np.log(pmi.data)
savePmiNonzeroTerm_fast(pmi,vectors_path + '.PMI')
counts.data = counts.data * remain_index
counts.eliminate_zeros()
savePmiNonzeroTerm_fast(counts,vectors_path + '.PPMIcooccurrence')
pmi.data[pmi.data < 0] = 0
pmi.eliminate_zeros()
savePmiNonzeroTerm_fast(pmi,vectors_path + '.PPMI')
def main():
args = docopt("""
Usage:
counts2ica.py [options] <counts> <output_path>
Options:
--cps NUM Number of ICA components to obtain [default: 50]
""")
counts_path = args['<counts>']
vectors_path = args['<output_path>']
counts, iw, ic = read_counts_matrix(counts_path)
embeddings = calc_ica(counts, args['--cps'])
save_matrix(vectors_path, embeddings)
save_vocabulary(vectors_path + '.words.vocab', iw)
save_vocabulary(vectors_path + '.contexts.vocab', ic)
def main():
args = docopt("""
Usage:
counts2pmi.py [options] <counts> <output_path>
Options:
--cds NUM Context distribution smoothing [default: 1.0]
""")
counts_path = args['<counts>']
vectors_path = args['<output_path>']
cds = float(args['--cds'])
counts, iw, ic = read_counts_matrix(counts_path)
pmi = calc_pmi(counts, cds)
save_matrix(vectors_path, pmi)
save_vocabulary(vectors_path + '.words.vocab', iw)
save_vocabulary(vectors_path + '.contexts.vocab', ic)
def main():
args = docopt("""
Usage:
counts2pmi.py [options] <counts> <output_path>
Options:
--cds NUM Context distribution smoothing [default: 1.0]
""")
counts_path = args['<counts>']
vectors_path = args['<output_path>']
cds = float(args['--cds'])
counts, iw, ic = read_counts_matrix(counts_path)
pmi = calc_pmi(counts, cds)
save_matrix(vectors_path, pmi)
save_vocabulary(vectors_path + '.words.vocab', iw)
save_vocabulary(vectors_path + '.contexts.vocab', ic)
def main():
args = docopt("""
Usage:
counts2pmi.py [options] <words_vocab> <contexts_vocab> <counts> <output>
Options:
--cds NUM Context distribution smoothing [default: 1.0]
""")
print "**********************"
print "counts2ppmi"
counts_path = args['<counts>']
vectors_path = args['<output>']
words_path = args['<words_vocab>']
contexts_path = args['<contexts_vocab>']
cds = float(args['--cds'])
counts = read_counts_matrix(words_path, contexts_path, counts_path)
pmi = calc_pmi(counts, cds)
save_matrix(vectors_path, pmi)
def main():
args = docopt("""
Usage:
counts2pmi.py [options] <words_vocab> <contexts_vocab> <counts> <output>
Options:
--cds NUM Context distribution smoothing [default: 1.0]
""")
print "**********************"
print "counts2ppmi"
counts_path = args['<counts>']
vectors_path = args['<output>']
words_path = args['<words_vocab>']
contexts_path = args['<contexts_vocab>']
cds = float(args['--cds'])
counts = read_counts_matrix(words_path, contexts_path, counts_path)
pmi = calc_pmi(counts, cds)
save_matrix(vectors_path, pmi)