def test_fit():
reg = GPARRegressor(replace=False,
impute=False,
normalise_y=True,
transform_y=squishing_transform)
x = np.linspace(0, 5, 10)
y = reg.sample(x, p=2)
# TODO: Remove this once greedy search is implemented.
yield raises, NotImplementedError, lambda: reg.fit(x, y, greedy=True)
# Test that data is correctly transformed if it has an output with zero
# variance.
reg.fit(x, y, iters=0)
yield ok, (~B.isnan(reg.y)).numpy().all()
y_pathological = y.copy()
y_pathological[:, 0] = 1
reg.fit(x, y_pathological, iters=0)
yield ok, (~B.isnan(reg.y)).numpy().all()
# Test transformation and normalisation of outputs.
z = B.linspace(-1, 1, 10, dtype=torch.float64)
z = B.stack([z, 2 * z], axis=1)
yield allclose, reg._untransform_y(reg._transform_y(z)), z
yield allclose, reg._unnormalise_y(reg._normalise_y(z)), z
# Test that fitting runs without issues.
vs = reg.vs.detach()
yield lambda x_, y_: reg.fit(x_, y_, fix=False), x, y
reg.vs = vs
yield lambda x_, y_: reg.fit(x, y, fix=True), x, y
def test_condition_and_fit():
reg = GPARRegressor(replace=False, impute=False,
normalise_y=True, transform_y=squishing_transform)
x = np.linspace(0, 5, 10)
y = reg.sample(x, p=2)
# Test that data is correctly normalised.
reg.condition(x, y)
approx(B.mean(reg.y, axis=0), B.zeros(reg.p))
approx(B.std(reg.y, axis=0), B.ones(reg.p))
# Test that data is correctly normalised if it has an output with zero
# variance.
y_pathological = y.copy()
y_pathological[:, 0] = 1
reg.condition(x, y_pathological)
assert (~B.isnan(reg.y)).numpy().all()
# Test transformation and normalisation of outputs.
z = torch.linspace(-1, 1, 10, dtype=torch.float64)
z = B.stack(z, 2 * z, axis=1)
allclose(reg._untransform_y(reg._transform_y(z)), z)
allclose(reg._unnormalise_y(reg._normalise_y(z)), z)
# Test that fitting runs without issues.
vs = reg.vs.copy(detach=True)
reg.fit(x, y, fix=False)
reg.vs = vs
reg.fit(x, y, fix=True)
# TODO: Remove this once greedy search is implemented.
with pytest.raises(NotImplementedError):
reg.fit(x, y, greedy=True)