def test_basic(self, Xy_dummy, transform_type, hidden_size):
eps = 1e-4
X, _, _, _ = Xy_dummy
n_samples, n_channels, lookback, n_assets = X.shape
dtype = X.dtype
device = X.device
network = KeynesNet(n_channels,
hidden_size=hidden_size,
transform_type=transform_type,
n_groups=2) #
network.to(device=device, dtype=dtype)
weights = network(X)
assert isinstance(network.hparams, dict)
assert network.hparams
assert torch.is_tensor(weights)
assert weights.shape == (n_samples, n_assets)
assert X.device == weights.device
assert X.dtype == weights.dtype
assert torch.allclose(weights.sum(dim=1),
torch.ones(n_samples).to(dtype=dtype,
device=device),
atol=eps)
def test_n_params(self, n_input_channels, hidden_size, n_groups, transform_type):
network = KeynesNet(n_input_channels=n_input_channels,
hidden_size=hidden_size,
n_groups=n_groups,
transform_type=transform_type)
expected = 0
expected += n_input_channels * 2 # instance norm
if transform_type == 'Conv':
expected += n_input_channels * 3 * hidden_size + hidden_size
else:
expected += 4 * ((n_input_channels * hidden_size) + (hidden_size * hidden_size) + 2 * hidden_size)
expected += 2 * hidden_size # group_norm
expected += 1 # temperature
actual = sum(p.numel() for p in network.parameters() if p.requires_grad)
assert expected == actual
def test_error(self):
with pytest.raises(ValueError):
KeynesNet(2, transform_type='FAKE', hidden_size=10, n_groups=2)
with pytest.raises(ValueError):
KeynesNet(2, hidden_size=10, n_groups=3)