def test_basic(self, Xy_dummy):
eps = 1e-5
X, _, _, _ = Xy_dummy
dtype, device = X.dtype, X.device
n_samples, n_channels, lookback, n_assets = X.shape
rets = X.mean(dim=(1, 2))
with pytest.raises(ValueError):
SparsemaxAllocator(n_assets, temperature=None)(rets,
temperature=None)
weights = SparsemaxAllocator(n_assets, temperature=2)(rets)
assert torch.allclose(
weights,
SparsemaxAllocator(n_assets, temperature=None)(
rets, 2 * torch.ones(n_samples, dtype=dtype, device=device)))
assert weights.shape == (n_samples, n_assets)
assert weights.dtype == X.dtype
assert weights.device == X.device
assert torch.all(-eps <= weights) and torch.all(weights <= 1 + eps)
assert torch.allclose(weights.sum(dim=1),
torch.ones(n_samples).to(dtype=dtype,
device=device),
atol=eps)
def test_contstrained(self, max_weight):
rets = torch.tensor([[1.7909, -2, -0.6818, -0.4972, 0.0333]])
w_const = SparsemaxAllocator(5, temperature=1, max_weight=max_weight)(rets)
w_unconst = SparsemaxAllocator(5, temperature=1)(rets)
assert not torch.allclose(w_const, w_unconst)
assert w_const.max().item() == pytest.approx(max_weight, abs=1e-5)
def test_known(self):
rets = torch.tensor([[1.7909, 0.3637, -0.6818, -0.4972, 0.0333],
[0.6655, -0.9960, 1.1463, 1.9849, -0.1662]])
true_weights = torch.tensor([[1.0000, 0.0000, 0.0000, 0.0000, 0.0000],
[0.0000, 0.0000, 0.0807, 0.9193, 0.0000]])
assert torch.allclose(SparsemaxAllocator(5, temperature=1)(rets), true_weights, atol=1e-4)
def test_uniform(self):
rets = torch.ones(2, 5)
weights = SparsemaxAllocator(5, temperature=1)(rets)
assert torch.allclose(weights, rets / 5)
def test_errors(self):
with pytest.raises(ValueError):
SparsemaxAllocator(n_assets=2, max_weight=0.3)
max_weights = [0.2, 0.5, 1]
torch.manual_seed(seed)
logits = torch.rand(size=(1, n_assets)) - 0.5
fig, axs = plt.subplots(len(temperatures),
len(max_weights),
sharex=True,
sharey=True,
figsize=(15, 5))
cbar_ax = fig.add_axes([.91, .3, .03, .4])
for r, temperature in enumerate(temperatures):
for c, max_weight in enumerate(max_weights):
sparsemax = SparsemaxAllocator(n_assets,
max_weight=max_weight,
temperature=temperature)
softmax = SoftmaxAllocator(n_assets=n_assets,
temperature=temperature,
max_weight=max_weight,
formulation='variational')
w_sparsemax = sparsemax(logits).detach().numpy()
w_softmax = softmax(logits).detach().numpy()
df = pd.DataFrame(np.concatenate([w_softmax, w_sparsemax], axis=0),
index=['softmax', 'sparsemax'])
axs[r, c].set_title('temp={}, max_weight={}'.format(
temperature, max_weight))
