def update(self, logits: torch.Tensor,
targets: torch.Tensor) -> List[float]:
"""
Update metric value with map for new data and return intermediate metrics values.
Args:
logits (torch.Tensor): tensor of logits
targets (torch.Tensor): tensor of targets
Returns:
list of map@k values
"""
values = mrr(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
def test_mrr():
"""
Test mrr
"""
y_pred1 = [4.0, 2.0, 3.0, 1.0]
y_pred2 = [1.0, 2.0, 3.0, 4.0]
y_true1 = [0, 0, 1.0, 1.0]
y_true2 = [0, 0, 1.0, 1.0]
k_list = [1, 3]
y_pred_torch = torch.Tensor([y_pred1, y_pred2])
y_true_torch = torch.Tensor([y_true1, y_true2])
mrr_results = mrr(y_pred_torch, y_true_torch, k_list)
mrr_at1 = mrr_results[0]
mrr_at3 = mrr_results[1]
assert mrr_at1 == 0.5
assert mrr_at3 == 0.75