def test_conve_bce_combo():
# no exception
model = ConvE(loss='bce')
# no exception
model = TransE(loss='nll')
# Invalid combination. Hence exception.
with pytest.raises(ValueError):
model = TransE(loss='bce')
# Invalid combination. Hence exception.
with pytest.raises(ValueError):
model = ConvE(loss='nll')
def test_conve_evaluation_protocol():
X = load_wn18()
model = ConvE(batches_count=200,
seed=22,
epochs=1,
k=10,
embedding_model_params={
'conv_filters': 16,
'conv_kernel_size': 3
},
optimizer='adam',
optimizer_params={'lr': 0.01},
loss='bce',
loss_params={},
regularizer=None,
regularizer_params={
'p': 2,
'lambda': 1e-5
},
verbose=True,
low_memory=True)
model.fit(X['train'])
y1 = model.predict(X['test'][:5])
save_model(model, 'model.tmp')
del model
model = restore_model('model.tmp')
y2 = model.predict(X['test'][:5])
assert np.all(y1 == y2)
os.remove('model.tmp')
def test_conve_fit_predict_save_restore():
X = np.array([['a', 'y', 'b'], ['b', 'y', 'a'], ['a', 'y', 'c'],
['c', 'y', 'a'], ['a', 'y', 'd'], ['c', 'y', 'd'],
['b', 'y', 'c'], ['f', 'y', 'e']])
X_test = np.array([['f', 'y', 'a'], ['f', 'y', 'b']])
model = ConvE(batches_count=1,
seed=22,
epochs=1,
k=10,
embedding_model_params={
'conv_filters': 16,
'conv_kernel_size': 3
},
optimizer='adam',
optimizer_params={'lr': 0.01},
loss='bce',
loss_params={},
regularizer=None,
regularizer_params={
'p': 2,
'lambda': 1e-5
},
verbose=True,
low_memory=True)
model.fit(X)
y1 = model.predict(X_test)
print(y1)
save_model(model, 'model.tmp')
del model
model = restore_model('model.tmp')
y2 = model.predict(X_test)
assert np.all(y1 == y2)
os.remove('model.tmp')