def test_tversky():
shape = (4, 32, 32, 32, 1)
y_pred = np.random.rand(*shape).astype(np.float64)
y_true = np.random.randint(2, size=shape).astype(np.float64)
# Test that tversky and dice are same when alpha = beta = 0.5
dice = metrics.dice(y_true, y_pred).numpy()
tversky = metrics.tversky(y_true,
y_pred,
axis=(1, 2, 3),
alpha=0.5,
beta=0.5).numpy()
assert_allclose(dice, tversky)
# Test that tversky and jaccard are same when alpha = beta = 1.0
jaccard = metrics.jaccard(y_true, y_pred).numpy()
tversky = metrics.tversky(y_true,
y_pred,
axis=(1, 2, 3),
alpha=1.,
beta=1.).numpy()
assert_allclose(jaccard, tversky)
with pytest.raises(ValueError):
metrics.tversky([0., 0., 1.], [1., 0., 1.], axis=0)
def test_jaccard():
x = np.zeros(4)
y = np.zeros(4)
out = metrics.jaccard(x, y, axis=None).numpy()
assert_allclose(out, 1)
x = np.ones(4)
y = np.ones(4)
out = metrics.jaccard(x, y, axis=None).numpy()
assert_allclose(out, 1)
x = [0., 0., 1., 1.]
y = [1., 1., 1., 1.]
out = metrics.jaccard(x, y, axis=None).numpy()
ref = 1. - scipy.spatial.distance.jaccard(x, y)
assert_allclose(out, ref)
dice_out = metrics.dice(x, y, axis=None).numpy()
assert_allclose(out, dice_out / (2. - dice_out))
x = [0., 0., 1., 1.]
y = [1., 1., 0., 0.]
out = metrics.jaccard(x, y, axis=None).numpy()
ref = 1. - scipy.spatial.distance.jaccard(x, y)
assert_allclose(out, ref, atol=1e-07)
assert_allclose(out, 0, atol=1e-07)
x = np.ones((4, 32, 32, 32, 1), dtype=np.float32)
y = x.copy()
x[:2, :10, 10:] = 0
y[:2, :3, 20:] = 0
y[3:, 10:] = 0
jaccards = np.empty(x.shape[0])
for i in range(x.shape[0]):
jaccards[i] = 1. - scipy.spatial.distance.jaccard(
x[i].flatten(), y[i].flatten())
assert_allclose(metrics.jaccard(x, y, axis=(1, 2, 3, 4)), jaccards)
def jaccard(y_true, y_pred, axis=(1, 2, 3, 4)):
return 1.0 - metrics.jaccard(y_true=y_true, y_pred=y_pred, axis=axis)