File size: 10,825 Bytes
158b61b |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
#
# This file is part of moses. Its use is licensed under the GNU Lesser General
# Public License version 2.1 or, at your option, any later version.
from __future__ import print_function, unicode_literals
import logging
import argparse
import subprocess
import sys
import os
import codecs
# ../bilingual-lm
sys.path.append(os.path.join(os.path.dirname(sys.path[0]), 'bilingual-lm'))
import train_nplm
import extract_vocab
import extract_syntactic_ngrams
logging.basicConfig(
format='%(asctime)s %(levelname)s: %(message)s',
datefmt='%Y-%m-%d %H:%M:%S', level=logging.DEBUG)
parser = argparse.ArgumentParser()
parser.add_argument(
"--working-dir", dest="working_dir", metavar="PATH")
parser.add_argument(
"--corpus", '-text', dest="corpus_stem", metavar="PATH", help="Input file.")
parser.add_argument(
"--nplm-home", dest="nplm_home", metavar="PATH", required=True,
help="Location of NPLM.")
parser.add_argument(
"--epochs", dest="epochs", type=int, metavar="INT",
help="Number of training epochs (default: %(default)s).")
parser.add_argument(
"--up-context-size", dest="up_context_size", type=int, metavar="INT",
help="Size of ancestor context (default: %(default)s).")
parser.add_argument(
"--left-context-size", dest="left_context_size", type=int, metavar="INT",
help="Size of sibling context (left) (default: %(default)s).")
parser.add_argument(
"--right-context-size", dest="right_context_size", type=int,
metavar="INT",
help="Size of sibling context (right) (default: %(default)s).")
parser.add_argument(
"--mode", dest="mode", choices=['head', 'label'], required=True,
help="Type of RDLM to train (both are required for decoding).")
parser.add_argument(
"--minibatch-size", dest="minibatch_size", type=int, metavar="INT",
help="Minibatch size (default: %(default)s).")
parser.add_argument(
"--noise", dest="noise", type=int, metavar="INT",
help="Number of noise samples for NCE (default: %(default)s).")
parser.add_argument(
"--hidden", dest="hidden", type=int, metavar="INT",
help=(
"Size of hidden layer (0 for single hidden layer) "
"(default: %(default)s)"))
parser.add_argument(
"--input-embedding", dest="input_embedding", type=int, metavar="INT",
help="Size of input embedding layer (default: %(default)s).")
parser.add_argument(
"--output-embedding", dest="output_embedding", type=int, metavar="INT",
help="Size of output embedding layer (default: %(default)s).")
parser.add_argument(
"--threads", "-t", dest="threads", type=int, metavar="INT",
help="Number of threads (default: %(default)s).")
parser.add_argument(
"--output-model", dest="output_model", metavar="PATH",
help="Name of output model (default: %(default)s).")
parser.add_argument(
"--output-dir", dest="output_dir", metavar="PATH",
help="Output directory (default: same as working-dir).")
parser.add_argument(
"--config-options-file", dest="config_options_file", metavar="PATH")
parser.add_argument(
"--log-file", dest="log_file", metavar="PATH",
help="Log file to write to (default: %(default)s).")
parser.add_argument(
"--validation-corpus", dest="validation_corpus", metavar="PATH",
help="Validation file (default: %(default)s).")
parser.add_argument(
"--activation-function", dest="activation_fn",
choices=['identity', 'rectifier', 'tanh', 'hardtanh'],
help="Activation function (default: %(default)s).")
parser.add_argument(
"--learning-rate", dest="learning_rate", type=float, metavar="FLOAT",
help="Learning rate (default: %(default)s).")
parser.add_argument(
"--input-words-file", dest="input_words_file", metavar="PATH",
help="Input vocabulary (default: %(default)s).")
parser.add_argument(
"--output-words-file", dest="output_words_file", metavar="PATH",
help="Output vocabulary (default: %(default)s).")
parser.add_argument(
"--input-vocab-size", dest="input_vocab_size", type=int, metavar="INT",
help="Input vocabulary size (default: %(default)s).")
parser.add_argument(
"--output-vocab-size", dest="output_vocab_size", type=int, metavar="INT",
help="Output vocabulary size (default: %(default)s).")
parser.add_argument(
"--mmap", dest="mmap", action="store_true",
help="Use memory-mapped file (for lower memory consumption).")
parser.add_argument(
"--train-host", dest="train_host",
help="Execute nplm training on this host, via ssh")
parser.add_argument("--extra-settings", dest="extra_settings",
help="Extra settings to be passed to NPLM")
parser.set_defaults(
working_dir="working",
corpus_stem="train",
nplm_home="/home/bhaddow/tools/nplm",
epochs=2,
up_context_size=2,
left_context_size=3,
right_context_size=0,
minibatch_size=1000,
noise=100,
hidden=0,
mode='head',
input_embedding=150,
output_embedding=750,
threads=4,
output_model="train",
output_dir=None,
config_options_file="config",
log_file="log",
validation_corpus=None,
activation_fn="rectifier",
learning_rate=1,
input_words_file=None,
output_words_file=None,
input_vocab_size=500000,
output_vocab_size=500000)
def prepare_vocabulary(options):
vocab_prefix = os.path.join(options.working_dir, 'vocab')
extract_vocab_options = extract_vocab.create_parser().parse_args(
['--input', options.corpus_stem, '--output', vocab_prefix])
extract_vocab.main(extract_vocab_options)
if options.input_words_file is None:
options.input_words_file = vocab_prefix + '.input'
orig = vocab_prefix + '.all'
filtered_vocab = open(orig).readlines()
if options.input_vocab_size:
filtered_vocab = filtered_vocab[:options.input_vocab_size]
open(options.input_words_file, 'w').writelines(filtered_vocab)
if options.output_words_file is None:
options.output_words_file = vocab_prefix + '.output'
if options.mode == 'label':
blacklist = [
'<null',
'<root',
'<start_head',
'<dummy',
'<head_head',
'<stop_head',
]
orig = vocab_prefix + '.special'
filtered_vocab = open(orig).readlines()
orig = vocab_prefix + '.nonterminals'
filtered_vocab += open(orig).readlines()
filtered_vocab = [
word
for word in filtered_vocab
if not any(word.startswith(prefix) for prefix in blacklist)]
if options.output_vocab_size:
filtered_vocab = filtered_vocab[:options.output_vocab_size]
else:
orig = vocab_prefix + '.all'
filtered_vocab = open(orig).readlines()[:options.output_vocab_size]
open(options.output_words_file, 'w').writelines(filtered_vocab)
def main(options):
if options.output_dir is None:
options.output_dir = options.working_dir
else:
# Create output dir if necessary
if not os.path.exists(options.output_dir):
os.makedirs(options.output_dir)
options.ngram_size = (
2 * options.up_context_size +
2 * options.left_context_size +
2 * options.right_context_size
)
if options.mode == 'head':
options.ngram_size += 2
elif options.mode == 'label':
options.ngram_size += 1
if options.input_words_file is None or options.output_words_file is None:
sys.stderr.write(
"Either input vocabulary or output vocabulary not specified: "
"extracting vocabulary from training text.\n")
prepare_vocabulary(options)
numberized_file = os.path.basename(options.corpus_stem) + '.numberized'
train_file = numberized_file
if options.mmap:
train_file += '.mmap'
extract_options = extract_syntactic_ngrams.create_parser().parse_args([
'--input', options.corpus_stem,
'--output', os.path.join(options.working_dir, numberized_file),
'--vocab', options.input_words_file,
'--output_vocab', options.output_words_file,
'--right_context', str(options.right_context_size),
'--left_context', str(options.left_context_size),
'--up_context', str(options.up_context_size),
'--mode', options.mode
])
sys.stderr.write('extracting syntactic n-grams\n')
extract_syntactic_ngrams.main(extract_options)
if options.validation_corpus:
extract_options.input = open(options.validation_corpus)
options.validation_file = os.path.join(
options.working_dir, os.path.basename(options.validation_corpus))
extract_options.output = open(
options.validation_file + '.numberized', 'w')
sys.stderr.write('extracting syntactic n-grams (validation file)\n')
extract_syntactic_ngrams.main(extract_options)
extract_options.output.close()
else:
options.validation_file = None
if options.mmap:
try:
os.remove(os.path.join(options.working_dir, train_file))
except OSError:
pass
mmap_cmd = [os.path.join(options.nplm_home, 'src', 'createMmap'),
'--input_file',
os.path.join(options.working_dir, numberized_file),
'--output_file',
os.path.join(options.working_dir, train_file)
]
sys.stderr.write('creating memory-mapped file\n')
sys.stderr.write('executing: ' + ', '.join(mmap_cmd) + '\n')
ret = subprocess.call(mmap_cmd)
if ret:
raise Exception("creating memory-mapped file failed")
sys.stderr.write('training neural network\n')
train_nplm.main(options)
sys.stderr.write('averaging null words\n')
ret = subprocess.call([
os.path.join(sys.path[0], 'average_null_embedding.py'),
options.nplm_home,
os.path.join(
options.output_dir,
options.output_model + '.model.nplm.' + str(options.epochs)),
os.path.join(
options.working_dir,
numberized_file),
os.path.join(options.output_dir, options.output_model + '.model.nplm')
])
if ret:
raise Exception("averaging null words failed")
if __name__ == "__main__":
if sys.version_info < (3, 0):
sys.stderr = codecs.getwriter('UTF-8')(sys.stderr)
sys.stdout = codecs.getwriter('UTF-8')(sys.stdout)
sys.stdin = codecs.getreader('UTF-8')(sys.stdin)
options = parser.parse_known_args()[0]
if parser.parse_known_args()[1]:
sys.stderr.write('Warning: unknown arguments: {0}\n'.format(parser.parse_known_args()[1]))
main(options)
|