def em_step(self, iteration):
ffile = open(self.ffilename)
efile = open(self.efilename)
afile = open(self.afilename)
alignments = Alignment.reader_pharaoh(ffile, efile, afile)
dirname = os.path.join(self.outputdir,
'iter_%s' % str(iteration + 1).rjust(3, '0'))
os.mkdir(dirname)
if logger.level >= 1:
logger.writeln('\niteration %s' % (iteration + 1))
likelihood = 0
starttime = time.time()
for i, alignment in enumerate(alignments, 1):
if i % FLAGS.emtrain_log_interval == 0:
logger.writeln('%s sentences at %s secs/sent' %
(i, (time.time() - starttime) / i))
starttime = time.time()
extractor = Extractor(
maxabslen=100000,
maxlen=10000,
minhole=1,
maxvars=100000,
lexical_weighter=self.lexical_weighter,
forbid_adjacent=self.forbid_adjacent,
maximize_derivation=self.maximize_derivation,
require_aligned_terminal=self.require_aligned_terminal)
hg = extractor.extract_hypergraph(alignment)
if hg is None:
continue
# compute expected counts
self.compute_expected_counts(hg)
likelihood += hg.root.inside
treefilename = os.path.join(dirname,
'tree_%s' % str(i).rjust(8, '0'))
self.write_viterbi_tree(hg, treefilename)
#for edge in hg.edges():
# logger.writeln('%s %s' % (self.counter.get_prob(edge.rule),
# edge.rule))
if logger.level >= 1:
logger.writeln('likelihood: %s' % likelihood)
if logger.level >= 1:
logger.writeln('normalizing...')
self.counter.normalize_vbdp(self.alpha, self.threshold)
if logger.level >= 1:
logger.writeln('prob table size: %s' % len(self.counter.prob))
def em_step(self, iteration):
ffile = open(self.ffilename)
efile = open(self.efilename)
afile = open(self.afilename)
alignments = Alignment.reader_pharaoh(ffile, efile, afile)
percent_counter = PercentCounter(total=self.corpus_size)
dirname = os.path.join(self.outputdir,
'iter_%s' % str(iteration + 1).rjust(3, '0'))
os.mkdir(dirname)
if logger.level >= 1:
logger.writeln('\niteration %s' % (iteration + 1))
likelihood = 0
for i, alignment in enumerate(alignments):
percent_counter.print_percent(i)
# if logger.level >= 1:
# logger.writeln()
# logger.writeln('>>> sentence_pair_%s' % i)
extractor = Extractor(lexical_weighter=self.lexical_weighter,
maximize_derivation=self.maximize_derivation)
hg = extractor.extract_hypergraph(alignment)
if hg is None:
continue
# compute expected counts
self.compute_expected_counts(hg)
likelihood += hg.root.inside
treefilename = os.path.join(dirname,
'tree_%s' % str(i + 1).rjust(8, '0'))
self.write_viterbi_tree(hg, treefilename)
#for edge in hg.edges():
# logger.writeln('%s %s' % (self.counter.get_prob(edge.rule),
# edge.rule))
if logger.level >= 1:
logger.writeln('likelihood: %s' % likelihood)
if logger.level >= 1:
logger.writeln('normalizing...')
self.counter.normalize_vbdp(self.alpha, self.threshold)
if logger.level >= 1:
logger.writeln('prob table size: %s' % len(self.counter.prob))
#!/usr/bin/env python3
import sys
from extractor import Extractor
from alignment import Alignment
import hypergraph
import logger
if __name__ == '__main__':
ffilename = sys.argv[1]
efilename = sys.argv[2]
afilename = sys.argv[3]
n = int(sys.argv[4])
ffile = open(ffilename)
efile = open(efilename)
afile = open(afilename)
alignments = Alignment.reader_pharaoh(ffile, efile, afile)
for i, alignment in enumerate(alignments):
if i == n:
extractor = Extractor(maximize_derivation=True)
hg = extractor.extract_hypergraph(alignment)
hg.show()
