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#!/usr/bin/env python
#
# 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.
import argparse
import os
import subprocess
import sys
# Feature field in N-best format
FEAT_FIELD = 2
# Location of mert, kbmira, etc. in relation to this script
BIN_DIR = os.path.join(os.path.dirname(os.path.dirname(os.path.dirname(__file__))), 'bin')
def main():
# Args
parser = argparse.ArgumentParser(description='Learn N-best rescoring weights')
parser.add_argument('--nbest', metavar='nbest', \
help='Dev set N-best list augmented with new features', required=True)
parser.add_argument('--ref', metavar='ref', \
help='Dev set reference translation', required=True)
parser.add_argument('--working-dir', metavar='rescore-work', \
help='Optimizer working directory', required=True)
parser.add_argument('--bin-dir', metavar='DIR', \
help='Moses bin dir, containing kbmira, evaluator, etc.', default=BIN_DIR)
# Since we're starting with uniform weights and only running kbmira once,
# run a gratuitous number of iterations. (mert-moses.pl default is 60
# iterations for each Moses run)
parser.add_argument('--iterations', metavar='N', type=int, \
help='Number of K-best MIRA iterations to run (default: 300)', default=300)
args = parser.parse_args()
# Find executables
extractor = os.path.join(args.bin_dir, 'extractor')
kbmira = os.path.join(args.bin_dir, 'kbmira')
for exe in (extractor, kbmira):
if not os.path.exists(exe):
sys.stderr.write('Error: cannot find executable "{}" in "{}", please specify --bin-dir\n'.format(exe, args.bin_dir))
sys.exit(1)
# rescore-work dir
if not os.path.exists(args.working_dir):
os.mkdir(args.working_dir)
# Feature names and numbers of weights from N-best list
# Assume all features are dense (present for each entry)
init_weights = []
fields = [f.strip() for f in open(args.nbest).readline().split('|||')]
feats = fields[FEAT_FIELD].split()
for i in range(len(feats)):
if feats[i].endswith('='):
n_weights = 0
j = i + 1
while j < len(feats):
if feats[j].endswith('='):
break
n_weights += 1
j += 1
# Start all weights at 0
init_weights.append([feats[i], [0] * n_weights])
# Extract score and feature data from N-best list
extractor_cmd = [extractor, \
'--sctype', 'BLEU', '--scconfig', 'case:true', \
'--scfile', os.path.join(args.working_dir, 'scores.dat'), \
'--ffile', os.path.join(args.working_dir, 'features.dat'), \
'-r', args.ref, \
'-n', args.nbest]
subprocess.call(extractor_cmd)
# Write dense feature list
with open(os.path.join(args.working_dir, 'init.dense'), 'w') as out:
for (feat, weights) in init_weights:
for w in weights:
out.write('{} {}\n'.format(feat, w))
# Run K-best MIRA optimizer
kbmira_cmd = [kbmira, \
'--dense-init', os.path.join(args.working_dir, 'init.dense'), \
'--ffile', os.path.join(args.working_dir, 'features.dat'), \
'--scfile', os.path.join(args.working_dir, 'scores.dat'), \
'-o', os.path.join(args.working_dir, 'mert.out'), \
'--iters', str(args.iterations)]
subprocess.call(kbmira_cmd)
# Read optimized weights, sum for normalization
opt_weights = []
total = 0
with open(os.path.join(args.working_dir, 'mert.out')) as inp:
# Same structure as original weight list
for (feat, weights) in init_weights:
opt_weights.append([feat, []])
for _ in weights:
w = float(inp.readline().split()[1])
opt_weights[-1][1].append(w)
# Sum for normalization
total += abs(w)
# Normalize weights
for (_, weights) in opt_weights:
for i in range(len(weights)):
weights[i] /= total
# Generate rescore.ini
with open(os.path.join(args.working_dir, 'rescore.ini'), 'w') as out:
out.write('# For use with Moses N-best rescorer "scripts/nbest-rescore/rescore.py"\n')
out.write('\n')
out.write('[weight]\n')
for (feat, weights) in opt_weights:
out.write('{} {}\n'.format(feat, ' '.join(str(w) for w in weights)))
if __name__ == '__main__':
main()
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