def test_cohen_kappa_all_weights_with_output_transform(weights):
np.random.seed(1)
size = 100
np_y_pred = np.random.randint(0, 2, size=(size, 1), dtype=np.long)
np_y = np.zeros((size, ), dtype=np.long)
np_y[size // 2:] = 1
np.random.shuffle(np_y)
ck_value_sk = cohen_kappa_score(np_y, np_y_pred)
batch_size = 10
def update_fn(engine, batch):
idx = (engine.state.iteration - 1) * batch_size
y_true_batch = np_y[idx:idx + batch_size]
y_pred_batch = np_y_pred[idx:idx + batch_size]
return idx, torch.from_numpy(y_pred_batch), torch.from_numpy(
y_true_batch)
engine = Engine(update_fn)
ck_metric = CohenKappa(output_transform=lambda x: (x[1], x[2]),
weights=weights)
ck_metric.attach(engine, "cohen_kappa")
data = list(range(size // batch_size))
ck_value = engine.run(data, max_epochs=1).metrics["cohen_kappa"]
assert ck_value == pytest.approx(ck_value_sk)
def _test(n_epochs, metric_device):
metric_device = torch.device(metric_device)
n_iters = 80
s = 16
n_classes = 2
offset = n_iters * s
y_true = torch.randint(0, n_classes, size=(offset * idist.get_world_size(),)).to(device)
y_preds = torch.randint(0, n_classes, size=(offset * idist.get_world_size(),)).to(device)
def update(engine, i):
return (
y_preds[i * s + rank * offset : (i + 1) * s + rank * offset],
y_true[i * s + rank * offset : (i + 1) * s + rank * offset],
)
engine = Engine(update)
ck = CohenKappa(device=metric_device)
ck.attach(engine, "ck")
data = list(range(n_iters))
engine.run(data=data, max_epochs=n_epochs)
assert "ck" in engine.state.metrics
res = engine.state.metrics["ck"]
if isinstance(res, torch.Tensor):
res = res.cpu().numpy()
true_res = cohen_kappa_score(y_true.cpu().numpy(), y_preds.cpu().numpy())
assert pytest.approx(res) == true_res
def test_no_update():
ck = CohenKappa()
with pytest.raises(
NotComputableError, match=r"EpochMetric must have at least one example before it can be computed"
):
ck.compute()
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_check_shape():
ck = CohenKappa()
with pytest.raises(ValueError, match=r"Predictions should be of shape"):
ck._check_shape((torch.tensor(0), torch.tensor(0)))
with pytest.raises(ValueError, match=r"Predictions should be of shape"):
ck._check_shape((torch.rand(4, 3, 1), torch.rand(4, 3)))
with pytest.raises(ValueError, match=r"Targets should be of shape"):
ck._check_shape((torch.rand(4, 3), torch.rand(4, 3, 1)))
def test_check_shape():
ck = CohenKappa()
with pytest.raises(ValueError, match=r"Predictions should be of shape"):
ck._check_shape((torch.randint(0, 2, size=(10, 1, 5, 12)).long(),
torch.randint(0, 2, size=(10, 5, 6)).long()))
with pytest.raises(ValueError, match=r"Predictions should be of shape"):
ck._check_shape((torch.randint(0, 2, size=(10, 1, 6)).long(),
torch.randint(0, 2, size=(10, 5, 6)).long()))
with pytest.raises(ValueError, match=r"Targets should be of shape"):
ck._check_shape((torch.randint(0, 2, size=(10, 1)).long(),
torch.randint(0, 2, size=(10, 5, 2)).long()))
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(y_pred, y, batch_size):
def update_fn(engine, batch):
idx = (engine.state.iteration - 1) * batch_size
y_true_batch = np_y[idx : idx + batch_size]
y_pred_batch = np_y_pred[idx : idx + batch_size]
return idx, torch.from_numpy(y_pred_batch), torch.from_numpy(y_true_batch)
engine = Engine(update_fn)
ck_metric = CohenKappa(output_transform=lambda x: (x[1], x[2]), weights=weights)
ck_metric.attach(engine, "ck")
np_y = y.numpy()
np_y_pred = y_pred.numpy()
np_ck = cohen_kappa_score(np_y, np_y_pred, weights=weights)
data = list(range(y_pred.shape[0] // batch_size))
ck = engine.run(data, max_epochs=1).metrics["ck"]
assert isinstance(ck, float)
assert np_ck == pytest.approx(ck)
def test_binary_input_N(weights):
ck = CohenKappa(weights)
def _test(y_pred, y, n_iters):
ck.reset()
ck.update((y_pred, y))
np_y = y.numpy()
np_y_pred = y_pred.numpy()
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]))
res = ck.compute()
assert isinstance(res, float)
assert cohen_kappa_score(np_y, np_y_pred,
weights=weights) == pytest.approx(res)
def get_test_cases():
test_cases = [
(torch.randint(0, 2, size=(10, )).long(),
torch.randint(0, 2, size=(10, )).long(), 1),
(torch.randint(0, 2, size=(100, )).long(),
torch.randint(0, 2, size=(100, )).long(), 1),
(torch.randint(0, 2, size=(10, 1)).long(),
torch.randint(0, 2, size=(10, 1)).long(), 1),
(torch.randint(0, 2, size=(100, 1)).long(),
torch.randint(0, 2, size=(100, 1)).long(), 1),
# updated batches
(torch.randint(0, 2, size=(10, )).long(),
torch.randint(0, 2, size=(10, )).long(), 16),
(torch.randint(0, 2, size=(100, )).long(),
torch.randint(0, 2, size=(100, )).long(), 16),
(torch.randint(0, 2, size=(10, 1)).long(),
torch.randint(0, 2, size=(10, 1)).long(), 16),
(torch.randint(0, 2, size=(100, 1)).long(),
torch.randint(0, 2, size=(100, 1)).long(), 16),
]
return test_cases
for _ in range(10):
# check multiple random inputs as random exact occurencies are rare
test_cases = get_test_cases()
for y_pred, y, n_iters in test_cases:
_test(y_pred, y, n_iters)
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)))
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_cohen_kappa_wrong_weights_type():
with pytest.raises(ValueError, match=r"Kappa Weighting type must be"):
ck = CohenKappa(weights=7)
with pytest.raises(ValueError, match=r"Kappa Weighting type must be"):
ck = CohenKappa(weights="dd")
def test_no_sklearn(mock_no_sklearn):
with pytest.raises(
RuntimeError,
match=r"This contrib module requires sklearn to be installed."):
CohenKappa()
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()
