def get_alignments(src_file,
tg_file,
trained_model=None,
src_train='',
tg_train='',
align_model='align_model',
label='alignments'):
if trained_model is None:
trained_model = train_alignments(src_train, tg_train, align_model)
if trained_model == '':
sys.stderr.write('No alignment model trained\n')
return []
aligner = Aligner(trained_model + '.fwd_params',
trained_model + '.fwd_err',
trained_model + '.rev_params',
trained_model + '.rev_err')
src = open(src_file)
tg = open(tg_file)
align_file = src_file + '_' + os.path.basename(tg_file) + '.aligned'
aligned = open(align_file, 'w')
for src_line, tg_line in zip(src, tg):
aligned.write(
aligner.align(src_line[:-1].decode('utf-8') + u' ||| ' +
tg_line[:-1].decode('utf-8')) + u'\n')
aligned.close()
aligner.close()
return (label, align_file)
def get_alignments(self, src, tg, align_model):
alignments = [[[] for j in range(len(tg[i]))] for i in range(len(tg))]
aligner = Aligner(align_model+'.fwd_params', align_model+'.fwd_err', align_model+'.rev_params', align_model+'.rev_err')
for idx, (src_list, tg_list) in enumerate(zip(src, tg)):
align_string = aligner.align(' '.join(src_list) + ' ||| ' + ' '.join(tg_list))
pairs = align_string.split()
for p_str in pairs:
p = p_str.split('-')
alignments[idx][int(p[1])].append(int(p[0]))
aligner.close()
return alignments
def align_files(src_file, tg_file, align_model, align_file):
'''
align 2 files and put the alignments in a new file
:align_model: - alignment model prefix
:align_file: - new file to store the alignments
'''
aligner = Aligner(align_model+'.fwd_params', align_model+'.fwd_err', align_model+'.rev_params', align_model+'.rev_err')
align_out = open(align_file, 'w')
for src_line, tg_line in zip(open(src_file), open(tg_file)):
align_out.write('%s\n' % aligner.align(src_line[:-1].decode('utf-8') + u' ||| ' + tg_line[:-1].decode('utf-8')))
aligner.close()
align_out.close()
def align_files(src_file, tg_file, align_model, align_file):
'''
align 2 files and put the alignments in a new file
:align_model: - alignment model prefix
:align_file: - new file to store the alignments
'''
aligner = Aligner(align_model + '.fwd_params', align_model + '.fwd_err',
align_model + '.rev_params', align_model + '.rev_err')
align_out = open(align_file, 'w')
for src_line, tg_line in zip(open(src_file), open(tg_file)):
align_out.write(
'%s\n' % aligner.align(src_line[:-1].decode('utf-8') + u' ||| ' +
tg_line[:-1].decode('utf-8')))
aligner.close()
align_out.close()
def align_sentence(src_line, tg_line, align_model):
# TODO: there is an error here if one or both fields are missing -- we cannot align a sentence without both src_line and tg_line
# throw an error prompting the user to specify another dataset or context creator
# if not src_line or not tg_line:
cur_alignments = [[] for i in range(len(tg_line))]
aligner = Aligner(align_model+'.fwd_params', align_model+'.fwd_err', align_model+'.rev_params', align_model+'.rev_err')
align_str = aligner.align(' '.join(src_line)+u' ||| '+' '.join(tg_line))
# parse the return value from the aligner
for pair in align_str.split():
pair = pair.split('-')
cur_alignments[int(pair[1])].append(int(pair[0]))
aligner.close()
return cur_alignments
def force_alignments(src_file, tg_file, trained_model):
alignments = []
aligner = Aligner(trained_model+'.fwd_params',trained_model+'.fwd_err',trained_model+'.rev_params',trained_model+'.rev_err')
src = open(src_file)
tg = open(tg_file)
for src_line, tg_line in zip(src, tg):
align_str = aligner.align( src_line[:-1].decode('utf-8')+u' ||| '+tg_line[:-1].decode('utf-8') )
cur_alignments = [ [] for i in range(len(tg_line.split())) ]
for pair in align_str.split():
pair = pair.split('-')
cur_alignments[int(pair[1])].append( pair[0] )
alignments.append(cur_alignments)
src.close()
tg.close()
aligner.close()
return alignments
def get_alignments(src_file, tg_file, trained_model=None, src_train='', tg_train='', align_model='align_model', label='alignments'):
if trained_model is None:
trained_model = train_alignments(src_train, tg_train, align_model)
if trained_model == '':
sys.stderr.write('No alignment model trained\n')
return []
aligner = Aligner(trained_model+'.fwd_params', trained_model+'.fwd_err', trained_model+'.rev_params', trained_model+'.rev_err')
src = open(src_file)
tg = open(tg_file)
align_file = src_file+'_'+os.path.basename(tg_file)+'.aligned'
aligned = open(align_file, 'w')
for src_line, tg_line in zip(src, tg):
aligned.write(aligner.align(src_line[:-1].decode('utf-8')+u' ||| '+tg_line[:-1].decode('utf-8'))+u'\n')
aligned.close()
aligner.close()
return (label, align_file)
def align_sentence(src_line, tg_line, align_model):
# TODO: there is an error here if one or both fields are missing -- we cannot align a sentence without both src_line and tg_line
# throw an error prompting the user to specify another dataset or context creator
# if not src_line or not tg_line:
cur_alignments = [[] for i in range(len(tg_line))]
aligner = Aligner(align_model + '.fwd_params', align_model + '.fwd_err',
align_model + '.rev_params', align_model + '.rev_err')
align_str = aligner.align(' '.join(src_line) + u' ||| ' +
' '.join(tg_line))
# parse the return value from the aligner
for pair in align_str.split():
pair = pair.split('-')
cur_alignments[int(pair[1])].append(int(pair[0]))
aligner.close()
return cur_alignments
def force_alignments(src_file, tg_file, trained_model):
alignments = []
aligner = Aligner(trained_model + '.fwd_params',
trained_model + '.fwd_err',
trained_model + '.rev_params',
trained_model + '.rev_err')
src = open(src_file)
tg = open(tg_file)
for src_line, tg_line in zip(src, tg):
align_str = aligner.align(src_line[:-1].decode('utf-8') + u' ||| ' +
tg_line[:-1].decode('utf-8'))
cur_alignments = [[] for i in range(len(tg_line.split()))]
for pair in align_str.split():
pair = pair.split('-')
cur_alignments[int(pair[1])].append(pair[0])
alignments.append(cur_alignments)
src.close()
tg.close()
aligner.close()
return alignments
