def test_asset_specific(self):
n_samples, n_channels, lookback, n_assets = 2, 3, 4, 5
X = torch.rand(n_samples, n_channels, lookback, n_assets)
tform = torch.randn(n_samples, lookback, n_assets, dtype=X.dtype)
layer = Warp()
x_warped = layer(X, tform)
assert X.shape == x_warped.shape
def test_no_change(self, mode, padding_mode):
# scale=1
n_samples, n_channels, lookback, n_assets = 2, 3, 4, 5
X = torch.rand(n_samples, n_channels, lookback, n_assets)
tform = torch.stack(n_samples * [torch.linspace(-1, end=1, steps=lookback)], dim=0)
layer = Warp(mode=mode, padding_mode=padding_mode)
x_warped = layer(X, tform)
assert torch.allclose(x_warped, X, atol=1e-5)
def test_equality_with_warp(self):
n_samples, n_channels, lookback, n_assets = 2, 3, 4, 5
X = torch.rand(n_samples, n_channels, lookback, n_assets)
scale = torch.ones(n_samples, dtype=X.dtype) * 0.5
tform = torch.stack(n_samples * [torch.linspace(0, end=1, steps=lookback)], dim=0)
layer_zoom = Zoom()
layer_warp = Warp()
x_zoomed = layer_zoom(X, scale)
x_warped = layer_warp(X, tform)
assert torch.allclose(x_zoomed, x_warped)
def test_basic(self, Xy_dummy, mode, padding_mode):
X, _, _, _ = Xy_dummy
dtype, device = X.dtype, X.device
n_samples, _, lookback, n_assets = X.shape
layer = Warp(mode=mode, padding_mode=padding_mode)
tform_ = torch.rand(n_samples, lookback, dtype=dtype, device=device)
tform_cumsum = tform_.cumsum(dim=-1)
tform = 2 * (tform_cumsum / tform_cumsum.max(dim=1, keepdim=True)[0] - 0.5)
x_warped = layer(X, tform)
assert torch.is_tensor(x_warped)
assert x_warped.shape == X.shape
assert x_warped.dtype == X.dtype
assert x_warped.device == X.device
def test_n_parameters(self):
n_parameters = sum(p.numel() for p in Warp().parameters()
if p.requires_grad)
assert n_parameters == 0
def test_error(self):
with pytest.raises(ValueError):
Warp()(torch.rand(1, 2, 3, 4), torch.ones(4, ))
sin_single(lookback, freq=4 / lookback))[None, None, :, None]
x = torch.as_tensor(x_np)
grid = torch.linspace(0, end=1, steps=lookback)[None, :].to(dtype=x.dtype)
transform_dict = {
'identity': lambda x: 2 * (x - 0.5),
'zoom': lambda x: x,
'backwards': lambda x: -2 * (x - 0.5),
'slowdown\_start': lambda x: 2 * (x**3 - 0.5),
'slowdown\_end': lambda x: 2 * (x**(1 / 3) - 0.5),
}
n_tforms = len(transform_dict)
_, axs = plt.subplots(n_tforms,
2,
figsize=(16, 3 * n_tforms),
sharex=True,
sharey=True)
layer = Warp()
for i, (tform_name, tform_lambda) in enumerate(transform_dict.items()):
tform = tform_lambda(grid)
x_warped = layer(x, tform)
axs[i, 0].plot(tform.numpy().squeeze(), linewidth=3, color='red')
axs[i, 1].plot(x_warped.numpy().squeeze(), linewidth=3, color='blue')
axs[i, 0].set_title(r'$\bf{}$ tform'.format(tform_name))
axs[i, 1].set_title(r'$\bf{}$ warped'.format(tform_name))