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#!/usr/bin/env python
import sys
import os
import re
import gzip
import argparse
import logging
import signal
import multiprocessing as mp
import cdec.sa
from cdec.sa._sa import monitor_cpu
extractor, prefix = None, None
online, compress = False, False
vocab = None
def make_extractor(args):
global extractor, prefix, online, compress
signal.signal(signal.SIGINT, signal.SIG_IGN) # Let parent process catch Ctrl+C
load_features(args.features)
extractor = cdec.sa.GrammarExtractor(args.config, online, vocab)
prefix = args.grammars
online = args.online
compress = args.compress
def load_features(features):
for featdef in features:
logging.info('Loading additional feature definitions from %s', featdef)
prefix = os.path.dirname(featdef)
sys.path.append(prefix)
__import__(os.path.basename(featdef).replace('.py', ''))
sys.path.remove(prefix)
def extract(inp):
global extractor, prefix, online, compress
i, sentence = inp
sentence = sentence[:-1]
fields = re.split('\s*\|\|\|\s*', sentence)
suffix = ''
# 3 fields for online mode, 1 for normal
if online:
if len(fields) < 3:
sys.stderr.write('Error: online mode requires references and alignments.'
' Not adding sentence to training data: {}\n'.format(sentence))
sentence = fields[0]
else:
sentence, reference, alignment = fields[0:3]
if len(fields) > 3:
suffix = ' ||| ' + ' ||| '.join(fields[3:])
else:
if len(fields) > 1:
sentence = fields[0]
suffix = ' ||| ' + ' ||| '.join(fields[1:])
grammar_file = os.path.join(prefix, 'grammar.'+str(i))
if compress: grammar_file += '.gz'
with (gzip.open if compress else open)(grammar_file, 'w') as output:
for rule in extractor.grammar(sentence):
output.write(str(rule)+'\n')
# Add training instance _after_ extracting grammars
if online:
extractor.add_instance(sentence, reference, alignment)
grammar_file = os.path.abspath(grammar_file)
return '<seg grammar="{}" id="{}">{}</seg>{}'.format(grammar_file, i, sentence, suffix)
def main():
global online, vocab
logging.basicConfig(level=logging.INFO)
parser = argparse.ArgumentParser(description='Extract grammars from a compiled corpus.')
parser.add_argument('-c', '--config', required=True,
help='extractor configuration')
parser.add_argument('-g', '--grammars', required=True,
help='grammar output path')
parser.add_argument('-j', '--jobs', type=int, default=1,
help='number of parallel extractors')
parser.add_argument('-s', '--chunksize', type=int, default=10,
help='number of sentences / chunk')
parser.add_argument('-f', '--features', nargs='*', default=[],
help='additional feature definitions')
parser.add_argument('-o', '--online', action='store_true',
help='online grammar extraction')
parser.add_argument('-e', '--except-vocab', default=None,
help='add LM and Lex except features (use with -o, pass vocab.gz)')
parser.add_argument('-z', '--compress', action='store_true',
help='compress grammars with gzip')
args = parser.parse_args()
if not os.path.exists(args.grammars):
os.mkdir(args.grammars)
for featdef in args.features:
if not featdef.endswith('.py'):
sys.stderr.write('Error: feature definition file <{}>'
' should be a python module\n'.format(featdef))
sys.exit(1)
online = args.online
vocab = args.except_vocab
start_time = monitor_cpu()
if args.jobs > 1:
logging.info('Starting %d workers; chunk size: %d', args.jobs, args.chunksize)
pool = mp.Pool(args.jobs, make_extractor, (args,))
try:
for output in pool.imap(extract, enumerate(sys.stdin), args.chunksize):
print(output)
except KeyboardInterrupt:
pool.terminate()
else:
make_extractor(args)
for output in map(extract, enumerate(sys.stdin)):
print(output)
stop_time = monitor_cpu()
logging.info("Overall extraction step took %f seconds", stop_time - start_time)
if __name__ == '__main__':
main()
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