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')