def test_concept_similarity(self):
cdb = CDB(config=self.config)
np.random.seed(11)
for i in range(500):
cui = "C" + str(i)
type_ids = {'T-' + str(i%10)}
cdb.add_concept(cui=cui, names=prepare_name('Name: ' + str(i), self.maker.nlp, {}, self.config), ontologies=set(),
name_status='P', type_ids=type_ids, description='', full_build=True)
vectors = {}
for cntx_type in self.config.linking['context_vector_sizes']:
vectors[cntx_type] = np.random.rand(300)
cdb.update_context_vector(cui, vectors, negative=False)
res = cdb.most_similar('C200', 'long', type_id_filter=['T-0'], min_cnt=1, topn=10, force_build=True)
assert len(res) == 10
cdb = CDB(config=config)
np.random.seed(11)
for i in range(500):
cui = "C" + str(i)
type_ids = {'T-' + str(i % 10)}
cdb.add_concept(cui=cui,
names=prepare_name('Name: ' + str(i), maker.nlp, {},
config),
ontologies=set(),
name_status='P',
type_ids=type_ids,
description='',
full_build=True)
vectors = {}
for cntx_type in config.linking['context_vector_sizes']:
vectors[cntx_type] = np.random.rand(300)
cdb.update_context_vector(cui, vectors, negative=False)
res = cdb.most_similar('C200',
'long',
type_id_filter=['T-0'],
min_cnt=1,
topn=10,
force_build=True)
assert len(res) == 10
# Test training reset
cdb.reset_training()
assert len(cdb.cui2context_vectors['C0']) == 0
assert cdb.cui2count_train['C0'] == 0
