def test_supervised_opf_predict():
opf = supervised.SupervisedOPF()
try:
_ = opf.predict(X)
except:
opf.fit(X, Y)
preds = opf.predict(X)
assert len(preds) == 100
try:
opf.fit(X, Y)
opf.subgraph.trained = False
_ = opf.predict(X)
except:
opf.fit(X, Y)
preds = opf.predict(X)
assert len(preds) == 100
opf.pre_computed_distance = True
opf.pre_distances = np.ones((100, 100))
opf.fit(X, Y)
preds = opf.predict(X)
assert len(preds) == 100
def test_supervised_opf_prune():
opf = supervised.SupervisedOPF()
X_train, X_val, Y_train, Y_val = splitter.split(X,
Y,
percentage=0.1,
random_state=1)
opf.prune(X_train, Y_train, X_val, Y_val, n_iterations=5)
assert opf.subgraph.n_nodes == 10
def test_supervised_opf_learn():
opf = supervised.SupervisedOPF()
X_train, X_val, Y_train, Y_val = splitter.split(X,
Y,
percentage=0.1,
random_state=1)
opf.learn(X_train, Y_train, X_val, Y_val, n_iterations=5)
assert isinstance(opf, supervised.SupervisedOPF)
def test_supervised_opf_fit():
opf = supervised.SupervisedOPF()
opf.fit(X, Y)
assert opf.subgraph.trained == True
opf.pre_computed_distance = True
try:
opf.pre_distances = np.ones((99, 99))
opf.fit(X, Y)
except:
opf.pre_distances = np.ones((100, 100))
opf.fit(X, Y)
assert opf.subgraph.trained == True