def test_torch_autoencoder_save_load(): X = utils.randmatrix(20, 50) mod = torch_autoencoder.TorchAutoencoder(hidden_dim=5, max_iter=2) mod.fit(X) mod.predict(X) with tempfile.NamedTemporaryFile(mode='wb') as f: name = f.name mod.to_pickle(name) mod2 = torch_autoencoder.TorchAutoencoder.from_pickle(name) mod2.predict(X) mod2.fit(X)
def test_torch_autoencoder(pandas): """Just makes sure that this code will run; it doesn't check that it is creating good models. """ X = utils.randmatrix(20, 50) if pandas: X = pd.DataFrame(X) ae = torch_autoencoder.TorchAutoencoder(hidden_dim=5, max_iter=100) H = ae.fit(X) ae.predict(X) H_is_pandas = isinstance(H, pd.DataFrame) assert H_is_pandas == pandas
[ torch_shallow_neural_classifier.TorchShallowNeuralClassifier( hidden_dim=5, hidden_activation=nn.ReLU(), max_iter=1, eta=1.0), { 'hidden_dim': 10, 'hidden_activation': nn.ReLU(), 'max_iter': 10, 'eta': 0.1 } ], [ np_autoencoder.Autoencoder(hidden_dim=5, max_iter=1, eta=1.0), {'hidden_dim': 10, 'max_iter': 10, 'eta': 0.1} ], [ torch_autoencoder.TorchAutoencoder( hidden_dim=5, hidden_activation=nn.ReLU(), max_iter=1, eta=1.0), { 'hidden_dim': 10, 'hidden_activation': nn.ReLU(), 'max_iter': 10, 'eta': 0.1 } ] ]) def test_parameter_setting(model, params): model.set_params(**params) for p, val in params.items(): assert getattr(model, p) == val @pytest.mark.parametrize("model_class", [