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#!/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.
"""Train feed-forward neural network LM with NPLM tool.
The resulting model can be used in Moses as feature function NeuralLM.
"""
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(sys.path[0], 'bilingual-lm'))
import train_nplm
import averageNullEmbedding
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(
"--order", dest="order", type=int, metavar="INT",
help="N-gram order of language model (default: %(default)s).")
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(
"--words-file", dest="words_file", metavar="PATH",
help="Output vocabulary file (default: %(default)s).")
parser.add_argument(
"--vocab-size", dest="vocab_size", type=int, metavar="INT",
help="Vocabulary size (default: %(default)s).")
parser.set_defaults(
working_dir="working",
corpus_stem="train",
nplm_home="/home/bhaddow/tools/nplm",
epochs=2,
order=5,
minibatch_size=1000,
noise=100,
hidden=0,
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,
words_file='vocab',
vocab_size=500000)
def main(options):
options.ngram_size = options.order
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)
extraction_cmd = [os.path.join(options.nplm_home, 'src', 'prepareNeuralLM'),
'--train_text', options.corpus_stem,
'--ngramize', '1',
'--ngram_size', str(options.ngram_size),
'--vocab_size', str(options.vocab_size),
'--write_words_file', os.path.join(options.working_dir, options.words_file),
'--train_file', os.path.join(options.working_dir, os.path.basename(options.corpus_stem) + '.numberized')
]
sys.stderr.write('extracting n-grams\n')
ret = subprocess.call(extraction_cmd)
if ret:
raise Exception("preparing neural LM failed")
if options.validation_corpus:
extraction_cmd = [os.path.join(options.nplm_home, 'src', 'prepareNeuralLM'),
'--train_text', options.validation_corpus,
'--ngramize', '1',
'--ngram_size', str(options.ngram_size),
'--vocab_size', str(options.vocab_size),
'--words_file', os.path.join(options.working_dir, options.words_file),
'--train_file', os.path.join(options.working_dir, os.path.basename(options.validation_corpus) + '.numberized')
]
sys.stderr.write('extracting n-grams (validation file)\n')
ret = subprocess.call(extraction_cmd)
if ret:
raise Exception("preparing neural LM failed")
else:
options.validation_file = None
options.input_words_file = options.words_file
options.output_words_file = options.words_file
options.input_vocab_size = options.vocab_size
options.output_vocab_size = options.vocab_size
sys.stderr.write('training neural network\n')
train_nplm.main(options)
sys.stderr.write('averaging null words\n')
average_options = averageNullEmbedding.parser.parse_args(
['-i', os.path.join(options.output_dir, options.output_model + '.model.nplm.' + str(options.epochs)),
'-o', os.path.join(options.output_dir, options.output_model + '.model.nplm'),
'-t', os.path.join(options.working_dir, os.path.basename(options.corpus_stem) + '.numberized'),
'-p', os.path.join(options.nplm_home, 'python')])
averageNullEmbedding.main(average_options)
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)
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