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 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_lr(self):
model_path = os.path.join(TEST_DATA, 'test_lr_model.pickle')
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, -1)
assert p >= 0.8
assert r >= 0.6
assert f1 >= 0.7
def main(argv):
model_path = None
model_type = "nn"
source_ont_file = None
target_ont_file = None
input_alignment_file = None
output_alignment_file = None
align_strat = "best"
cuda_device = -1
sys.stdout.write('\n')
sys.stdout.write('-------------------------\n')
sys.stdout.write('OntoEMMA version 0.1 \n')
sys.stdout.write('-------------------------\n')
sys.stdout.write('https://github.com/allenai/ontoemma\n')
sys.stdout.write('An ML-based ontology matcher to produce entity alignments between knowledgebases\n')
sys.stdout.write('\n')
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")
try:
# TODO(waleeda): use argparse instead of getopt to parse command line arguments.
opts, args = getopt.getopt(
argv, "hs:t:i:o:m:p:g:a:", ["source=", "target=", "input=", "output=", "model_path=", "model_type=", "cuda_device=", "alignment_strategy="]
)
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('-s <source_ontology_file>\n')
sys.stdout.write('-t <target_ontology_file>\n')
sys.stdout.write('-i <input_alignment_file>\n')
sys.stdout.write('-o <output_alignment_file>\n')
sys.stdout.write('-m <model_location>\n')
sys.stdout.write('-p <model_type>')
sys.stdout.write('-g <cuda_device>')
sys.stdout.write('-a <alignment_strategy>')
sys.stdout.write('Example usage: \n')
sys.stdout.write(
' ./run_ontoemma.py -s source_ont.json -t target_ont.json -i gold_alignment.tsv -o generated_alignment.tsv -m model_serialization_dir -p nn\n'
)
sys.stdout.write('-------------------------\n')
sys.stdout.write('Accepted KB file formats: json, pickle, owl\n')
sys.stdout.write('Accepted alignment file formats: rdf, tsv\n')
sys.stdout.write('Accepted model types (defaults to nn):\n')
sys.stdout.write('\tnn (neural network)\n')
sys.stdout.write('\tlr (logistic regression)\n')
sys.stdout.write('Accepted alignment strategies (defaults to best):\n')
sys.stdout.write('\tbest (best match per entity above threshold)\n')
sys.stdout.write('\tall (all matches per entity above threshold)\n')
sys.stdout.write('\tmodh (modified hungarian algorithm for assignment)\n')
sys.stdout.write('Pretrained models can be found at:\n')
sys.stdout.write(' /net/nfs.corp/s2-research/scigraph/ontoemma/')
sys.stdout.write('-------------------------\n')
sys.stdout.write('\n')
sys.exit(0)
elif opt in ("-s", "--source"):
source_ont_file = os.path.abspath(arg)
sys.stdout.write('Source ontology file is %s\n' % source_ont_file)
elif opt in ("-t", "--target"):
target_ont_file = os.path.abspath(arg)
sys.stdout.write('Target ontology file is %s\n' % target_ont_file)
elif opt in ("-i", "--input"):
input_alignment_file = os.path.abspath(arg)
sys.stdout.write(
'Input alignment file is %s\n' % input_alignment_file
)
elif opt in ("-o", "--output"):
output_alignment_file = os.path.abspath(arg)
sys.stdout.write(
'Output alignment file is %s\n' % output_alignment_file
)
elif opt in ("-m", "--model"):
model_path = os.path.abspath(arg)
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 ("-a", "--alignment_method"):
if arg in emma.constants.IMPLEMENTED_ALIGNMENT_STRATEGY:
align_strat = arg
sys.stdout.write(
'Alignment selection strategy is %s\n' % arg.upper()
)
else:
sys.stdout.write('Error: Unknown alignment selection strategy')
elif opt in ("-g", "--cuda_device"):
cuda_device = int(arg)
sys.stdout.write(
'Using CUDA device %i\n' % cuda_device
)
sys.stdout.write('\n')
if source_ont_file is not None and target_ont_file is not None:
matcher = OntoEmma()
matcher.align(
model_type, model_path,
source_ont_file, target_ont_file,
input_alignment_file, output_alignment_file,
align_strat, cuda_device
)