def test_lr(self):
config_file = os.path.join(TEST_DATA, 'test_lr_config_file.json')
model_path = os.path.join(TEST_DATA, 'test_lr_model.pickle')
if os.path.exists(model_path):
os.remove(model_path)
matcher = OntoEmma()
matcher.train('lr', model_path, config_file)
assert (os.path.exists(model_path))
source_ont_file = os.path.join(TEST_DATA, 'test_source_ont.json')
target_ont_file = os.path.join(TEST_DATA, 'test_target_ont.json')
input_alignment_file = os.path.join(TEST_DATA,
'test_input_alignment.tsv')
output_alignment_file = os.path.join(TEST_DATA,
'test_output_alignment.tsv')
matcher = OntoEmma()
p, r, f1 = matcher.align('lr', model_path, source_ont_file,
target_ont_file, input_alignment_file,
output_alignment_file, "best", -1)
assert p >= 0.8
assert r >= 0.6
assert f1 >= 0.7
def test_nn(self):
config_file = os.path.join(TEST_DATA, 'test_nn_config_file.json')
model_path = os.path.join(TEST_DATA, 'test_nn_model')
if os.path.exists(model_path):
shutil.rmtree(model_path)
matcher = OntoEmma()
matcher.train('nn', model_path, config_file)
assert (os.path.exists(os.path.join(model_path, 'model.tar.gz')))
model_path = os.path.join(TEST_DATA, 'test_nn_model', 'model.tar.gz')
source_ont_file = os.path.join(TEST_DATA, 'test_source_ont.json')
target_ont_file = os.path.join(TEST_DATA, 'test_target_ont.json')
input_alignment_file = os.path.join(TEST_DATA,
'test_input_alignment.tsv')
output_alignment_file = os.path.join(TEST_DATA,
'test_output_alignment.tsv')
matcher = OntoEmma()
p, r, f1 = matcher.align('nn', model_path, source_ont_file,
target_ont_file, input_alignment_file,
output_alignment_file, -1)
assert p >= 0.0
assert r >= 0.0
assert f1 >= 0.0
def main(argv):
model_path = None
model_type = "nn"
config_file = None
evaluate_flag = False
evaluation_data_file = None
cuda_device = -1
try:
nltk.data.find("corpora/stopwords")
except LookupError:
try:
_create_unverified_https_context = ssl._create_unverified_context
except AttributeError:
pass
else:
ssl._create_default_https_context = _create_unverified_https_context
nltk.download("stopwords")
nltk.download("wordnet")
try:
# TODO(waleeda): use argparse instead of getopt to parse command line arguments.
opts, args = getopt.getopt(argv, "hec:m:p:d:g:", [
"config=", "model_path=", "model_type=", "evaluation_data_file=",
"cuda_device="
])
except getopt.GetoptError:
sys.stdout.write('Unknown option... -h or --help for help.\n')
sys.exit(1)
for opt, arg in opts:
if opt in ("-h", "--help"):
sys.stdout.write('Options: \n')
sys.stdout.write('-c <configuration_file>\n')
sys.stdout.write('-m <model_location>\n')
sys.stdout.write('-p <model_type>\n')
sys.stdout.write('-e # evaluation mode\n')
sys.stdout.write('-d <evaluation_data_file>\n')
sys.stdout.write('-g <cuda_device>\n\n')
sys.stdout.write('Example usages: \n')
sys.stdout.write(
' ./train_ontoemma.py -c configuration_file.json -m model_file_path -p nn\n'
)
sys.stdout.write(
' ./train_ontoemma.py -e -m model_file_path -d evaluation_data_path -g 5\n'
)
sys.stdout.write('-------------------------\n')
sys.stdout.write(
'Accepted model types: nn (neural network), lr (logistic regression)\n'
)
sys.stdout.write('-------------------------\n')
sys.stdout.write('\n')
sys.exit(0)
elif opt in ("-e", "--evaluate"):
evaluate_flag = True
sys.stdout.write('Evaluation mode\n')
elif opt in ("-c", "--config"):
config_file = os.path.abspath(arg)
sys.stdout.write('Configuration file is %s\n' % config_file)
elif opt in ("-m", "--model"):
model_path = os.path.abspath(arg)
sys.stdout.write('Model output path is %s\n' % model_path)
elif opt in ("-p", "--model-type"):
if arg in emma.constants.IMPLEMENTED_MODEL_TYPES:
model_type = arg
sys.stdout.write(
'Model type is %s\n' %
emma.constants.IMPLEMENTED_MODEL_TYPES[model_type])
else:
sys.stdout.write('Error: Unknown model type...\n')
sys.exit(1)
elif opt in ("-d", "--eval-data-file"):
evaluation_data_file = os.path.abspath(arg)
sys.stdout.write('Evaluation data file is %s\n' %
evaluation_data_file)
elif opt in ("-g", "--cuda-device"):
cuda_device = int(arg)
sys.stdout.write('Using CUDA device %i\n' % cuda_device)
sys.stdout.write('\n')
matcher = OntoEmma()
if evaluate_flag:
matcher.evaluate(model_type, model_path, evaluation_data_file,
cuda_device)
else:
matcher.train(model_type, model_path, config_file)