def test_fit_transform(self):
np.testing.assert_array_equal(
RDF2VecTransformer().fit_transform(KNOWLEDGE_GRAPH,
ENTITIES_SUBSET),
RDF2VecTransformer().fit(
KNOWLEDGE_GRAPH, ENTITIES_SUBSET).transform(ENTITIES_SUBSET),
)
def test_load_save_transformer(self):
RDF2VecTransformer(
walkers=[RandomWalker(2, None),
WeisfeilerLehmanWalker(2, 2)]).save()
transformer = RDF2VecTransformer.load()
assert len(transformer.walkers) == 2
assert isinstance(transformer.walkers[0], RandomWalker)
assert isinstance(transformer.walkers[1], WeisfeilerLehmanWalker)
os.remove("transformer_data")
def test_transform(self):
transformer = RDF2VecTransformer()
with pytest.raises(NotFittedError):
transformer.transform(ENTITIES_SUBSET)
transformer.fit(KNOWLEDGE_GRAPH, ENTITIES_SUBSET)
features = transformer.transform(ENTITIES_SUBSET)
assert type(features) == list
def test_fit_transform(self, setup, kg, walker, sampler, is_inverse,
is_split):
if "UniformSampler" in str(sampler):
sampler = sampler()
else:
sampler = sampler(is_inverse, is_split)
assert RDF2VecTransformer(
walkers=[walker(2, 5, sampler, random_state=42)]).fit_transform(
kg, [f"{URL}#{entity}" for entity in ROOTS_WITHOUT_URL])
def test_fit(self):
transformer = RDF2VecTransformer()
with pytest.raises(ValueError):
transformer.fit(KNOWLEDGE_GRAPH, ["does", "not", "exist"])
transformer.fit(KNOWLEDGE_GRAPH, ENTITIES_SUBSET)
def test_fit_transform(self, walker, sampler):
transformer = RDF2VecTransformer(walkers=[walker(2, 5, sampler())])
assert transformer.fit_transform(KNOWLEDGE_GRAPH, ENTITIES_SUBSET)
def test_fail_load_transformer(self):
pickle.dump([0, 1, 2], open("tmp", "wb"))
with pytest.raises(ValueError):
RDF2VecTransformer.load("tmp")
os.remove("tmp")