def test_roc_auc_score_2():
np.random.seed(1)
size = 100
np_y_pred = np.random.rand(size, 1)
np_y = np.zeros((size, ), dtype=np.long)
np_y[size // 2:] = 1
np.random.shuffle(np_y)
np_roc_auc = roc_auc_score(np_y, np_y_pred)
roc_auc_metric = ROC_AUC()
y_pred = torch.from_numpy(np_y_pred)
y = torch.from_numpy(np_y)
roc_auc_metric.reset()
n_iters = 10
batch_size = size // n_iters
for i in range(n_iters):
idx = i * batch_size
roc_auc_metric.update(
(y_pred[idx:idx + batch_size], y[idx:idx + batch_size]))
roc_auc = roc_auc_metric.compute()
assert roc_auc == np_roc_auc
def _test(y_pred, y, batch_size, metric_device):
metric_device = torch.device(metric_device)
roc_auc = ROC_AUC(device=metric_device)
torch.manual_seed(10 + rank)
roc_auc.reset()
if batch_size > 1:
n_iters = y.shape[0] // batch_size + 1
for i in range(n_iters):
idx = i * batch_size
roc_auc.update((y_pred[idx : idx + batch_size], y[idx : idx + batch_size]))
else:
roc_auc.update((y_pred, y))
# gather y_pred, y
y_pred = idist.all_gather(y_pred)
y = idist.all_gather(y)
np_y = y.cpu().numpy()
np_y_pred = y_pred.cpu().numpy()
res = roc_auc.compute()
assert isinstance(res, float)
assert roc_auc_score(np_y, np_y_pred) == pytest.approx(res)
def test_input_types():
roc_auc = ROC_AUC()
roc_auc.reset()
output1 = (torch.rand(4,
3), torch.randint(0,
2,
size=(4, 3),
dtype=torch.long))
roc_auc.update(output1)
with pytest.raises(
ValueError,
match=
r"Incoherent types between input y_pred and stored predictions"):
roc_auc.update(
(torch.randint(0, 5, size=(4, 3)), torch.randint(0, 2,
size=(4, 3))))
with pytest.raises(
ValueError,
match=r"Incoherent types between input y and stored targets"):
roc_auc.update(
(torch.rand(4, 3), torch.randint(0, 2,
size=(4, 3)).to(torch.int32)))
with pytest.raises(
ValueError,
match=
r"Incoherent types between input y_pred and stored predictions"):
roc_auc.update((torch.randint(0, 2, size=(10, )).long(),
torch.randint(0, 2, size=(10, 5)).long()))
def test_check_compute_fn():
y_pred = torch.zeros((8, 13))
y_pred[:, 1] = 1
y_true = torch.zeros_like(y_pred)
output = (y_pred, y_true)
em = ROC_AUC(check_compute_fn=True)
em.reset()
with pytest.warns(EpochMetricWarning, match=r"Probably, there can be a problem with `compute_fn`"):
em.update(output)
em = ROC_AUC(check_compute_fn=False)
em.update(output)
def test_roc_auc_score():
size = 100
np_y_pred = np.random.rand(size, 1)
np_y = np.zeros((size, ), dtype=np.long)
np_y[size // 2:] = 1
np_roc_auc = roc_auc_score(np_y, np_y_pred)
roc_auc_metric = ROC_AUC()
y_pred = torch.from_numpy(np_y_pred)
y = torch.from_numpy(np_y)
roc_auc_metric.reset()
roc_auc_metric.update((y_pred, y))
roc_auc = roc_auc_metric.compute()
assert roc_auc == np_roc_auc