print "external_embedding", d["external_embedding"]
stored_opt = Namespace(**d)
print "Running model with this configuration", stored_opt
parser = lysfastparse.bcovington.covington.CovingtonBILSTM(
words, lemmas, cpos, pos, feats, rels, w2i, l2i, stored_opt, None)
parser.Load(path_model)
with codecs.open(f_temp.name) as f_temp:
lookup_conll_data = lysfastparse.utils.lookup_conll_extra_data(f_temp)
testpath = f_temp.name
ts = time.time()
pred = list(parser.Predict(testpath))
te = time.time()
print "Took " + str(te - ts) + " seconds"
lysfastparse.bcovington.utils_bcovington.write_conll(testpath, pred)
lysfastparse.utils.dump_lookup_extra_into_conll(
testpath, lookup_conll_data)
lysfastparse.utils.transform_to_single_root(testpath)
with codecs.open(path_outfile, "w") as f_out:
with codecs.open(f_temp.name) as f_out_aux:
f_out.write(f_out_aux.read())
os.unlink(f_temp.name)
words, w2i, c2i, pos, rels, stored_opt = pickle.load(paramsfp)
stored_opt.external_embedding = None
print('Loading pre-trained model')
parser = oldslavdep.OldSlavDep(words, pos, rels, w2i, c2i, stored_opt)
parser.Load(args.model)
testoutpath = os.path.join(args.output, args.conll_test_output)
print('Predicting POS tags and parsing dependencies')
# ts = time.time()
# test_pred = list(parser.Predict(options.conll_test))
# te = time.time()
# print 'Finished in', te-ts, 'seconds.'
# utils.write_conll(testoutpath, test_pred)
with open(testoutpath, 'w') as fh:
for sentence in parser.Predict(args.conll_test):
for entry in sentence[1:]:
fh.write(str(entry) + '\n')
fh.write('\n')
else:
print("Training file: " + args.conll_train)
if args.conll_dev != "N/A":
print("Development file: " + args.conll_dev)
highestScore = 0.0
eId = 0
if os.path.isfile(os.path.join(args.output, args.params)) and \
os.path.isfile(os.path.join(args.output, os.path.basename(args.model))) :
if path_tmp_file_oov is not None:
os.unlink(path_tmp_file_oov)
with codecs.open(args.conll_dev) as f_conll_dev:
lookup_conll_data = lysfastparse.utils.lookup_conll_extra_data(f_conll_dev)
log_results_file = codecs.open(os.path.join(args.output.rsplit("/",1)[0], args.output.rsplit("/",1)[1]+'.dev_results'),"a")
for epoch in xrange(args.epochs):
print 'Starting epoch', epoch
parser.Train(args.conll_train)
devpath = os.path.join(args.output, 'dev_epoch_' + str(epoch+1) + '.conll')
lysfastparse.bcovington.utils_bcovington.write_conll(devpath, parser.Predict(args.conll_dev))
lysfastparse.utils.dump_lookup_extra_into_conll(devpath, lookup_conll_data)
lysfastparse.utils.transform_to_single_root(devpath)
print 'Executing conll17_eval'
os.system('python '+config[YAML_CONLL17_EVAL]+' '+args.conll_dev + ' '+devpath+ ' > ' + devpath + '.txt ')
with codecs.open(devpath+".txt") as f_devpath:
content = f_devpath.readlines()
las_lines = [l for l in content if
l.startswith("LAS F1 Score")]
if len(las_lines) != 1:
warnings.warn("Cannot determine LAS F1 Score from file")
else:
las = float(las_lines[0].split(":")[1])
