def align_samples(model, lang1, lang2, art):
"""TODO: Docstring for align_samples.
:model: TODO
:lang1: TODO
:lang2: TODO
:art: TODO
:returns: TODO
"""
model = YalignModel.load(model)
try:
doc1 = text_to_document(remove_extra_spaces(art['orig'][lang1]))
doc2 = text_to_document(remove_extra_spaces(art['orig'][lang2]))
pairs = model.align(doc1, doc2)
except:
log.error('Error aligning %s', art['path'])
else:
# to_text returns <str>
pairs = [(p1.to_text(), p2.to_text()) for p1, p2 in pairs]
text1 = ''.join(p[0] + '\n' for p in pairs)
text2 = ''.join(p[1] + '\n' for p in pairs)
path1 = os.path.join(art['path'], '{}_yalign'.format(lang1))
path2 = os.path.join(art['path'], '{}_yalign'.format(lang2))
with open(path1, 'w') as f:
f.write(text1)
with open(path2, 'w') as f:
f.write(text2)
finally:
del model
gc.collect()
def _init_model(model_name, mode):
"""@todo: Docstring for _init_model.
:model_name: @todo
:returns: @todo
"""
global model
model = YalignModel.load(model_name)
model.mode = mode
def get_similarity(lang1, lang2, sample_path, threshold, penalty, model,
len_flt, sim_flt):
"""@todo: Docstring for get_similarity.
:sample_path: @todo
:threshold: @todo
:penalty: @todo
:model: @todo
:returns: @todo
"""
with model_lock:
model_conf_path = os.path.join(model, 'metadata.json')
update_model(model_conf_path, dict(threshold=threshold, penalty=penalty))
model = YalignModel.load(model)
similarity = []
for sample in iter_samples(sample_path):
try:
doc1 = text_to_document(remove_extra_spaces(sample['orig'][lang1]))
doc2 = text_to_document(remove_extra_spaces(sample['orig'][lang2]))
pairs = model.align(doc1, doc2)
except:
similarity.append(0)
else:
pairs = [(p[0].to_text(), p[1].to_text()) for p in pairs]
text1 = [p[0] for p in pairs]
text1 = [t+'\n' for t in text1]
text1 = ''.join(text1)
text2 = [p[1] for p in pairs]
text2 = [t+'\n' for t in text2]
text2 = ''.join(text2)
try:
text2 = text2.decode('utf-8')
except UnicodeEncodeError:
del model
gc.collect()
return 0
similarity.append(line_sim(text2, sample['human'][lang2], len_flt, sim_flt))
del model
gc.collect()
return sum(similarity)/len(similarity)