def main():
logging.basicConfig(level=logging.INFO, format='%(message)s')
parser = argparse.ArgumentParser(description='Train segmentation model')
parser.add_argument('-i', '--n_iter', type=int, required=True,
help='Number of iterations')
parser.add_argument('--alpha_p', type=float, default=0.001,
help='Smoothing parameter for prefix Dirichlet prior')
parser.add_argument('--alpha_s', type=float, default=0.001,
help='Smoothing parameter for suffix Dirichlet prior')
parser.add_argument('--strength', '-t', type=float, default=1e-6,
help='DP prior stength')
parser.add_argument('--collapse', action='store_true')
args = parser.parse_args()
word_vocabulary = Vocabulary(start_stop=False)
corpus = [word_vocabulary[line.decode('utf8').strip()] for line in sys.stdin]
# Compute all the possible prefixes, suffixes
prefix_vocabulary, suffix_vocabulary = affixes(word_vocabulary)
logging.info('%d tokens / %d types / %d prefixes / %d suffixes',
len(corpus), len(word_vocabulary), len(prefix_vocabulary), len(suffix_vocabulary))
model = SegmentationModel(args.strength, args.alpha_p, args.alpha_s,
word_vocabulary, prefix_vocabulary, suffix_vocabulary, args.collapse)
t_start = time.time()
run_sampler(model, args.n_iter, corpus)
runtime = time.time() - t_start
logging.info('Sampler ran for %f seconds', runtime)
show_analyses(model)
def main():
logging.basicConfig(level=logging.INFO)
# Read the training corpus
word_vocabulary = Vocabulary(start_stop=False)
analyses = {}
for line in sys.stdin:
word, analysis, _ = line.decode('utf8').split('\t')
morphemes = analysis.split('+')
if len(morphemes) not in (1, 2):
raise Exception('wtf?')
prefix = morphemes[0]
suffix = '' if len(morphemes) == 1 else morphemes[1]
word_vocabulary[word]
analyses[word] = (prefix, suffix)
# Compute all the possible prefixes, suffixes
prefix_vocabulary, suffix_vocabulary = affixes(word_vocabulary)
logging.info('%d types / %d prefixes / %d suffixes',
len(word_vocabulary), len(prefix_vocabulary), len(suffix_vocabulary))
prefix_counts = Counter()
suffix_counts = Counter()
for word, (prefix, suffix) in analyses.iteritems():
prefix_counts[prefix_vocabulary[prefix]] += 1
suffix_counts[suffix_vocabulary[suffix]] += 1
## The base
base = MultinomialProduct(len(prefix_vocabulary), 0.001, len(suffix_vocabulary), 0.001)
## Updating the counts
base.update(prefix_counts, suffix_counts)
print base.log_likelihood()
def main():
logging.basicConfig(level=logging.INFO, format='%(message)s')
parser = argparse.ArgumentParser(description='Train segmentation model')
parser.add_argument('-i', '--n_iter', type=int, required=True,
help='Number of iterations')
parser.add_argument('--alpha_p', type=float, default=0.001,
help='Smoothing parameter for prefix Dirichlet prior')
parser.add_argument('--alpha_s', type=float, default=0.001,
help='Smoothing parameter for suffix Dirichlet prior')
parser.add_argument('--strength', '-t', type=float, default=1e-6,
help='DP prior stength')
parser.add_argument('--processors', '-p', type=int, default=mp.cpu_count(),
help='Number of processors to use')
parser.add_argument('-mh', type=int, required=True,
help='Number of MH steps per global iteration')
parser.add_argument('--collapse', action='store_true',
help='Use approximate collapsed base')
args = parser.parse_args()
word_vocabulary = Vocabulary(start_stop=False)
corpus = [word_vocabulary[line.decode('utf8').strip()] for line in sys.stdin]
prefix_vocabulary, suffix_vocabulary = affixes(word_vocabulary)
logging.info('%d tokens / %d types / %d prefixes / %d suffixes',
len(corpus), len(word_vocabulary), len(prefix_vocabulary), len(suffix_vocabulary))
logging.info('Starting %d processes', args.processors)
model = ParallelSegmentationModel(args.strength, args.alpha_p, args.alpha_s, corpus, word_vocabulary,
prefix_vocabulary, suffix_vocabulary, args.processors, args.mh, args.collapse)
t_start = time.time()
run_sampler(model, args.n_iter)
runtime = time.time() - t_start
logging.info('Sampler ran for %f seconds', runtime)
model.shutdown()
for word in word_vocabulary:
p, s = model.decode(word)
print(u'{}\t{}\t{}'.format(word, p, s).encode('utf8'))