model_path = os.getcwd() + options.model
utils_path = os.getcwd() + '/bmstparser/src/utils/' # 'src/utils/'
# Added to run from IntelliJ
if options.predictFlag:
# Added to run from IntelliJ
test_file = os.getcwd() + options.conll_test
params_file = os.getcwd() + options.params
# Added to run from IntelliJ
with open(params_file, 'rb') as paramsfp:
words, enum_word, pos, rels, onto, cpos, stored_opt = pickle.load(
paramsfp)
print('Initializing lstm mstparser:')
parser = mstlstm.MSTParserLSTM(words, pos, rels, enum_word, stored_opt,
onto, cpos)
parser.load(model_path)
conllu = (os.path.splitext(test_file.lower())[1] == '.conllu')
testpath = os.path.join(
output_file,
'test_pred.conll' if not conllu else 'test_pred.conllu')
ts = time.time()
test_res = list(parser.predict(test_file))
te = time.time()
print('Finished predicting test.', te - ts, 'seconds.')
utils.write_conll(testpath, test_res)
if not conllu:
os.system('perl ' + utils_path + 'eval.pl -g ' + test_file +
' -s ' + testpath + ' > ' + testpath + '.txt')
parser.add_option("--dynet-gpus",
action="store_true",
dest="dynet-gpus",
default=False,
help='Use GPU instead of cpu.')
(options, args) = parser.parse_args()
print options
print 'Using external embedding:', options.external_embedding
if options.predictFlag:
with open(options.params, 'r') as paramsfp:
w2i, pos, rels, chars, stored_opt = pickle.load(paramsfp)
stored_opt.external_embedding = options.external_embedding
print stored_opt
print 'Initializing lstm mstparser:'
parser = mstlstm.MSTParserLSTM(pos, rels, w2i, chars, stored_opt)
parser.Load(options.model)
ts = time.time()
print 'loading buckets'
test_buckets = [list()]
test_data = list(utils.read_conll(open(options.conll_test, 'r')))
for d in test_data:
test_buckets[0].append(d)
print 'parsing'
test(parser, test_buckets, options.conll_test, options.conll_output)
te = time.time()
print 'Finished predicting test.', te - ts, 'seconds.'
else:
print 'Preparing vocab'
w2i, pos, rels, chars = utils.vocab(options.conll_train)
if not os.path.isdir(options.output): os.mkdir(options.output)
default=True)
parser.add_option("--dynet-seed", type="int", dest="seed", default=0)
parser.add_option("--dynet-mem", type="int", dest="mem", default=0)
(options, args) = parser.parse_args()
print 'Using external embedding:', options.external_embedding
if options.predictFlag:
with open(options.params, 'r') as paramsfp:
words, w2i, pos, rels, stored_opt = pickle.load(paramsfp)
stored_opt.external_embedding = options.external_embedding
print 'Initializing lstm mstparser:'
parser = mstlstm.MSTParserLSTM(words, pos, rels, w2i, stored_opt)
parser.Load(options.model)
conllu = (os.path.splitext(options.conll_test.lower())[1] == '.conllu')
tespath = os.path.join(
options.output,
'test_pred.conll' if not conllu else 'test_pred.conllu')
ts = time.time()
test_res = list(parser.Predict(options.conll_test))
te = time.time()
print 'Finished predicting test.', te - ts, 'seconds.'
utils.write_conll(tespath, test_res)
if not conllu:
os.system('perl conll/eval.pl -g ' + options.conll_test + ' -s ' +
dest="train_multilingual",
help="Train it based on multilingual source?",
default=False)
parser.add_option("--out_file_name",
type="string",
dest="out_file_name",
default="unknown.conllu")
(options, args) = parser.parse_args()
print 'Using external embedding:', options.external_embedding
if options.predictFlag:
print 'Initializing lstm mstparser:'
parser = mstlstm.MSTParserLSTM(options)
print "Start loading a model"
parser.Load(options.model)
print "Finished loading a model"
# languageVec_dic = read_languageVec(options.lang_vec_file) ## read language_vec.csv file
# conllu = (os.path.splitext(options.conll_test.lower())[1] == '.conllu')
# tespath = os.path.join(options.output, languageVec_dic[options.conll_test_language].lang_code+'.conll' if not conllu else languageVec_dic[options.conll_test_language].lang_code+'.conllu')
testpath = options.output + options.out_file_name
ts = time.time()
test_res = list(parser.Predict(options.conll_test))
te = time.time()
print 'Finished predicting test.', te - ts, 'seconds.'
utils.write_conll(testpath, test_res)