def test_sample_ndcg():
"""
Tests for catalyst.ndcg metric.
"""
y_pred = [0.5, 0.2, 0.1]
y_true = [1.0, 0.0, 1.0]
outputs = torch.Tensor([y_pred])
targets = torch.Tensor([y_true])
true_ndcg_at2 = 1.0 / (1.0 + 1 / math.log2(3))
comp_ndcg_at2 = ndcg(outputs, targets, topk=[2])[0]
assert np.isclose(true_ndcg_at2, comp_ndcg_at2)
y_pred1 = [0.5, 0.2, 0.1]
y_pred2 = [0.5, 0.2, 0.1]
y_true1 = [1.0, 0.0, 1.0]
y_true2 = [1.0, 0.0, 1.0]
outputs = torch.Tensor([y_pred1, y_pred2])
targets = torch.Tensor([y_true1, y_true2])
true_ndcg_at2 = 1.0 / (1.0 + 1 / math.log2(3))
comp_ndcg_at2 = ndcg(outputs, targets, topk=[2])[0]
assert np.isclose(true_ndcg_at2, comp_ndcg_at2)
def test_zero_ndcg():
"""
Tests for catalyst.ndcg metric.
"""
y_pred = [6, 5, 4, 3, 2, 1, 0]
y_true = [0, 0, 0, 0, 0, 0, 1]
ndcg_at1, ndcg_at3, ndcg_at7 = ndcg(torch.tensor([y_pred]),
torch.tensor([y_true]),
topk=[1, 3, 7])
assert torch.isclose(ndcg_at1, torch.tensor(0.0))
assert torch.isclose(ndcg_at3, torch.tensor(0.0))
assert torch.isclose(ndcg_at7, torch.tensor(0.3), atol=0.05)
def update(self, logits: torch.Tensor, targets: torch.Tensor) -> List[float]:
"""
Update metric value with ndcg for new data and return intermediate metrics values.
Args:
logits (torch.Tensor): tensor of logits
targets (torch.Tensor): tensor of targets
Returns:
list of ndcg@k values
"""
values = ndcg(logits, targets, topk=self.topk_args)
values = [v.item() for v in values]
for value, metric in zip(values, self.additive_metrics):
metric.update(value, len(targets))
return values