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", [
