def _test(y_pred, y, n_iters, metric_device):
metric_device = torch.device(metric_device)
ck = CohenKappa(device=metric_device)
torch.manual_seed(10 + rank)
ck.reset()
ck.update((y_pred, y))
if n_iters > 1:
batch_size = y.shape[0] // n_iters + 1
for i in range(n_iters):
idx = i * batch_size
ck.update(
(y_pred[idx:idx + batch_size], y[idx:idx + batch_size]))
# 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 = ck.compute()
assert isinstance(res, float)
assert cohen_kappa_score(np_y, np_y_pred) == pytest.approx(res)
def test_input_types():
ck = CohenKappa()
ck.reset()
output1 = (torch.rand(4,
3), torch.randint(0,
2,
size=(4, 3),
dtype=torch.long))
ck.update(output1)
with pytest.raises(
ValueError,
match=
r"Incoherent types between input y_pred and stored predictions"):
ck.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"):
ck.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"):
ck.update((torch.randint(0, 2, size=(10, )).long(),
torch.randint(0, 2, size=(10, 5)).long()))
def test_cohen_kappa_all_weights(weights):
size = 100
np_y_pred = np.random.randint(0, 2, size=(size, 1), dtype=np.long)
np_y = np.random.randint(0, 2, size=(size, 1), dtype=np.long)
np_ck = cohen_kappa_score(np_y, np_y_pred)
ck_metric = CohenKappa(weights=weights)
y_pred = torch.from_numpy(np_y_pred)
y = torch.from_numpy(np_y)
ck_metric.reset()
ck_metric.update((y_pred, y))
ck = ck_metric.compute()
assert ck == pytest.approx(np_ck)
def _test(metric_device):
metric_device = torch.device(metric_device)
ck_metric = CohenKappa(device=metric_device)
torch.manual_seed(10 + rank)
y_pred = torch.randint(0, 2, size=(100, 1), device=device)
y = torch.randint(0, 2, size=(100, 1), device=device)
ck_metric.update((y_pred, y))
# gather y_pred, y
y_pred = idist.all_gather(y_pred)
y = idist.all_gather(y)
np_y_pred = y_pred.cpu().numpy()
np_y = y.cpu().numpy()
np_ck = cohen_kappa_score(np_y, np_y_pred)
res = ck_metric.compute()
assert res == pytest.approx(np_ck)
def test_multilabel_inputs():
ck = CohenKappa()
with pytest.raises(ValueError, match=r"multilabel-indicator is not supported"):
ck.reset()
ck.update((torch.randint(0, 2, size=(10, 4)).long(), torch.randint(0, 2, size=(10, 4)).long()))
ck.compute()
with pytest.raises(ValueError, match=r"multilabel-indicator is not supported"):
ck.reset()
ck.update((torch.randint(0, 2, size=(10, 6)).long(), torch.randint(0, 2, size=(10, 6)).long()))
ck.compute()
with pytest.raises(ValueError, match=r"multilabel-indicator is not supported"):
ck.reset()
ck.update((torch.randint(0, 2, size=(10, 8)).long(), torch.randint(0, 2, size=(10, 8)).long()))
ck.compute()
