def test_fbeta_score(pred, target, beta, exp_score):
score = fbeta_score(torch.tensor(pred),
torch.tensor(target),
beta,
reduction='none')
assert torch.allclose(score, torch.tensor(exp_score))
score = fbeta_score(to_onehot(torch.tensor(pred)),
torch.tensor(target),
beta,
reduction='none')
assert torch.allclose(score, torch.tensor(exp_score))
def test_tbd_remove_in_v1_2_0_metrics():
from pytorch_lightning.metrics.classification import Fbeta
from pytorch_lightning.metrics.functional.classification import f1_score, fbeta_score
with pytest.deprecated_call(match='will be removed in v1.2'):
Fbeta(2)
with pytest.deprecated_call(match='will be removed in v1.2'):
fbeta_score(torch.tensor([0, 1, 2, 3]), torch.tensor([0, 1, 2, 1]),
0.2)
with pytest.deprecated_call(match='will be removed in v1.2'):
f1_score(torch.tensor([0, 1, 0, 1]), torch.tensor([0, 1, 0, 0]))
def forward(self, pred: torch.Tensor, target: torch.Tensor) -> torch.Tensor:
"""
Actual metric computation
Args:
pred: predicted labels
target: groundtruth labels
Return:
torch.Tensor: classification score
"""
return fbeta_score(pred=pred, target=target,
beta=self.beta, num_classes=self.num_classes,
reduction=self.reduction)
