def create_bow_tag(corpora, language, pos_tags, outdir='.', alph_suffix=''):
# Step 1: extract unigram distributions for words
unigram_alph = CPPUniAlphabet()
unigram_alph.fromfile(
file(os.path.join(outdir, 'unigram%s_alph.txt' % (alph_suffix, ))))
unigram_alph.growing = False
bigram_alph = CPPUniAlphabet()
bigram_alph.fromfile(
file(os.path.join(outdir, 'bigram%s_alph.txt' % (alph_suffix, ))))
bigram_alph.growing = False
infix = '_'.join(prefix_l)
if infix != '': infix = '_' + infix
if opts.limit != -1:
prefix_l.append('%d' % (opts.limit / 1000))
word_matrix = None
word_alphs = get_word_alphs_by_pos(language)
for word_pos in pos_tags:
word_alph = word_alphs[word_pos]
word_feat_alph = CPPUniAlphabet()
for corpus_name in corpora:
corpus = Corpus(corpus_name)
att = corpus.attribute(opts.attr_name, 'p')
att_find = corpus.attribute('tb_lemma', 'p')
att_sent = corpus.attribute('s', 's')
pair_alphs = get_pair_alphs_by_pos(opts.language)
word_alphs = get_word_alphs_by_pos(opts.language)
print "word features for %s in %s" % (word_pos, corpus_name)
wmat = gather_word_vectors(list(word_alph), att, att_find,
att_sent, unigram_alph, bigram_alph,
word_feat_alph,
forward_mapping_by_pos(word_pos),
opts.limit)
if word_matrix is None:
word_matrix = wmat
else:
word_matrix += wmat
word_feat_alph.tofile_utf8(
file(
os.path.join(opts.outdir, 'word_bow%s%s_alph.txt' % (
infix,
word_pos,
)), 'w'))
word_matrix.write_binary(
file(
os.path.join(opts.outdir, 'word_bow%s%s_mtx.bin' % (
infix,
word_pos,
)), 'w'))
def create_bow_pair(corpora, language, pos_pairs, outdir='.', alph_suffix=''):
unigram_alph = CPPUniAlphabet()
unigram_alph.fromfile(
file(os.path.join(outdir, 'unigram%s_alph.txt' % (alph_suffix, ))))
unigram_alph.growing = False
bigram_alph = CPPUniAlphabet()
bigram_alph.fromfile(
file(os.path.join(outdir, 'bigram%s_alph.txt' % (alph_suffix, ))))
bigram_alph.growing = False
if opts.limit != -1:
prefix_l.append('%d' % (opts.limit / 1000))
pair_alphs = get_pair_alphs_by_pos(language)
for word_pos in pos_pairs:
pair_alph = pair_alphs[word_pos]
pair_feat_alph = CPPUniAlphabet()
word_matrix = None
for corpus_name in corpora:
print "word pair features for %s" % (pos_pair, )
pair_feat_alph = CPPUniAlphabet()
for corpus_name in corpora:
wmat = gather_pair_vectors(
[x.split('_', 1) for x in pair_alph], att, att_find,
att_sent, unigram_alph, bigram_alph, pair_feat_alph,
forward_mapping_by_pos(pos_pair[0]),
forward_mapping_by_pos(pos_pair[1]), opts.limit)
if word_matrix is None:
word_matrix = wmat
else:
word_matrix += wmat
pair_feat_alph.tofile_utf8(
file('pair_bow%s%s_alph.txt' % (
infix,
pos_pair,
), 'w'))
word_matrix.write_binary(
file('pair_bow%s%s_mtx.bin' % (
infix,
pos_pair,
), 'w'))
def load_target_alph(self):
alph=CPPUniAlphabet()
alph.fromfile(self.open_by_pat('target_alph'))
return alph
def load_component_alph(self,name):
alph=CPPUniAlphabet()
alph.fromfile(self.open_by_pat('component_alph',matrix_name=name))
if self.max_range is None:
self.max_range=len(alph)
return alph
def load_component_alph(self):
name=self.matrix_name
alph=CPPUniAlphabet()
alph.fromfile(self.open_by_pat('component_alph',matrix_name=name))
return alph