def test_v1_5_metric_auc_auroc():
AUC.__init__._warned = False
with pytest.deprecated_call(match='It will be removed in v1.5.0'):
AUC()
ROC.__init__._warned = False
with pytest.deprecated_call(match='It will be removed in v1.5.0'):
ROC()
AUROC.__init__._warned = False
with pytest.deprecated_call(match='It will be removed in v1.5.0'):
AUROC()
x = torch.tensor([0, 1, 2, 3])
y = torch.tensor([0, 1, 2, 2])
auc._warned = False
with pytest.deprecated_call(match='It will be removed in v1.5.0'):
assert auc(x, y) == torch.tensor(4.)
preds = torch.tensor([0, 1, 2, 3])
target = torch.tensor([0, 1, 1, 1])
roc._warned = False
with pytest.deprecated_call(match='It will be removed in v1.5.0'):
fpr, tpr, thrs = roc(preds, target, pos_label=1)
assert torch.equal(fpr, torch.tensor([0., 0., 0., 0., 1.]))
assert torch.allclose(tpr, torch.tensor([0.0000, 0.3333, 0.6667, 1.0000, 1.0000]), atol=1e-4)
assert torch.equal(thrs, torch.tensor([4, 3, 2, 1, 0]))
preds = torch.tensor([0.13, 0.26, 0.08, 0.19, 0.34])
target = torch.tensor([0, 0, 1, 1, 1])
auroc._warned = False
with pytest.deprecated_call(match='It will be removed in v1.5.0'):
assert auroc(preds, target) == torch.tensor(0.5)
def forward(self, logits: torch.FloatTensor, labels: torch.LongTensor):
multi_prcs = self.multi_prc(pred=logits.softmax(dim=1),
target=labels,
sample_weight=None)
avg_auprc = 0.
for precision_, recall_, _ in multi_prcs:
avg_auprc += auc(x=precision_, y=recall_, reorder=True)
return torch.Tensor([avg_auprc / self.num_classes])
def forward(self, logits: torch.FloatTensor, labels: torch.LongTensor):
multi_rocs = self.multi_roc(pred=logits.softmax(dim=1),
target=labels,
sample_weight=None)
avg_auroc = 0.
for fpr, tpr, _ in multi_rocs:
avg_auroc += auc(x=fpr, y=tpr, reorder=True)
return torch.Tensor([avg_auroc / self.num_classes])
def auprc_pytorch(y_true, y_scores):
""" Compute AUPRC for 1 class
Args:
y_true (np.array): one hot encoded labels
y_scores (np.array): model prediction
Return:
auc (float): the Area Under the Recall Precision curve
"""
device = y_true.device
precision, recall, thresholds = plm.precision_recall_curve(
y_scores, y_true)
# To ensure the curve goes from (0,1) to (1,0) included
recall = torch.cat(
(torch.ones([1], device=device), recall, torch.zeros([1],
device=device)))
precision = torch.cat((torch.zeros([1], device=device), precision,
torch.ones([1], device=device)))
return plm.auc(recall, precision)
def compute(self) -> torch.Tensor:
preds, targets = self._get_preds_and_targets()
if torch.unique(targets).numel() == 1:
return torch.tensor(np.nan)
prec, recall, _ = precision_recall_curve(preds, targets)
return auc(recall, prec) # type: ignore
