def main():
args = docopt("""
Usage:
pmi2svd.py [options] <pmi_path> <output_path>
Options:
--dim NUM Dimensionality of eigenvectors [default: 500]
--neg NUM Number of negative samples; subtracts its log from PMI [default: 1]
""")
pmi_path = args['<pmi_path>']
output_path = args['<output_path>']
dim = int(args['--dim'])
neg = int(args['--neg'])
explicit = PositiveExplicit(pmi_path, normalize=False, neg=neg)
start = time.time()
ut, s, vt = sparsesvd(explicit.m.tocsc(), dim)
print("Time elapsed for SVD: %f" % (time.time() - start))
np.save(output_path + '.ut.npy', ut)
np.save(output_path + '.s.npy', s)
np.save(output_path + '.vt.npy', vt)
save_vocabulary(output_path + '.words.vocab', explicit.iw)
save_vocabulary(output_path + '.contexts.vocab', explicit.ic)
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 create_representation(args):
rep_type = args['<representation>']
path = args['<representation_path>']
neg = int(args['--neg'])
w_c = args['--w+c']
eig = float(args['--eig'])
if rep_type == 'PPMI':
if w_c:
raise Exception('w+c is not implemented for PPMI.')
else:
return PositiveExplicit(path, True, neg)
elif rep_type == 'SVD':
if w_c:
return EnsembleEmbedding(SVDEmbedding(path, False, eig, False), SVDEmbedding(path, False, eig, True), True)
else:
return SVDEmbedding(path, True, eig)
elif rep_type == 'GLOVE':
return GLOVEEmbedding(path, True)
else:
if w_c:
return EnsembleEmbedding(Embedding(path + '.words', False), Embedding(path + '.contexts', False), True)
else:
return Embedding(path + '.words', True)
def main():
args = docopt("""
Usage:
pmi2svd.py [options] <repres> <pmi_path> <output_path>
Options:
--dim NUM Dimensionality of eigenvectors [default: 500]
--neg NUM Number of negative samples; subtracts its log from PMI [default: 1]
--k NUM [default: 1]
""")
repres = args['<repres>']
pmi_path = args['<pmi_path>']
output_path = args['<output_path>']
dim = int(args['--dim'])
neg = int(args['--neg'])
k = int(args['--k'])
if (repres == "BPMI"):
explicit = BinExplicit(pmi_path, normalize=False)
elif (repres == "PMI"):
explicit = NoExplicit(pmi_path, normalize=False, k=k)
elif (repres == "NPMI"):
explicit = NegExplicit(pmi_path, normalize=False)
else:
explicit = PositiveExplicit(pmi_path, normalize=False, neg=neg)
ut, s, vt = sparsesvd(explicit.m.tocsc(), dim)
np.save(output_path + '.ut.npy', ut)
np.save(output_path + '.s.npy', s)
np.save(output_path + '.vt.npy', vt)
save_vocabulary(output_path + '.words.vocab', explicit.iw)
save_vocabulary(output_path + '.contexts.vocab', explicit.ic)
def main():
args = docopt("""
Usage:
ppmi2svd.py [options] <ppmi> <output>
Options:
--dim NUM Dimensionality of eigenvectors [default: 300]
--neg NUM Number of negative samples; subtracts its log from PMI [default: 1]
""")
ppmi_path = args['<ppmi>']
output_path = args['<output>']
dim = int(args['--dim'])
neg = int(args['--neg'])
explicit = PositiveExplicit(ppmi_path, normalize=False, neg=neg)
ut, s, vt = sparsesvd(explicit.m.tocsc(), dim)
np.save(output_path + '.ut.npy', ut)
np.save(output_path + '.s.npy', s)
np.save(output_path + '.vt.npy', vt)
def folder2chi(folder):
return PositiveExplicit(join(folder, "chi")).similarity_first_order