def test_values(
distr: PiecewiseLinear,
target: List[float],
expected_target_cdf: List[float],
expected_target_crps: List[float],
):
target = mx.nd.array(target).reshape(shape=(len(target),))
expected_target_cdf = np.array(expected_target_cdf).reshape(
(len(expected_target_cdf),)
)
expected_target_crps = np.array(expected_target_crps).reshape(
(len(expected_target_crps),)
)
assert all(np.isclose(distr.cdf(target).asnumpy(), expected_target_cdf))
assert all(np.isclose(distr.crps(target).asnumpy(), expected_target_crps))
# compare with empirical cdf from samples
num_samples = 100_000
samples = distr.sample(num_samples).asnumpy()
assert np.isfinite(samples).all()
emp_cdf, edges = empirical_cdf(samples)
calc_cdf = distr.cdf(mx.nd.array(edges)).asnumpy()
assert np.allclose(calc_cdf[1:, :], emp_cdf, atol=1e-2)
def test_simple_symmetric():
gamma = mx.nd.array([-1.0])
slopes = mx.nd.array([[2.0, 2.0]])
knot_spacings = mx.nd.array([[0.5, 0.5]])
distr = PiecewiseLinear(gamma=gamma,
slopes=slopes,
knot_spacings=knot_spacings)
assert distr.cdf(mx.nd.array([-2.0])).asnumpy().item() == 0.0
assert distr.cdf(mx.nd.array([+2.0])).asnumpy().item() == 1.0
expected_crps = np.array([1.0 + 2.0 / 3.0])
assert np.allclose(
distr.crps(mx.nd.array([-2.0])).asnumpy(), expected_crps)
assert np.allclose(distr.crps(mx.nd.array([2.0])).asnumpy(), expected_crps)
def test_values(
distr: PiecewiseLinear,
target: List[float],
expected_target_cdf: List[float],
expected_target_crps: List[float],
):
target = mx.nd.array(target).reshape(shape=(len(target), ))
expected_target_cdf = np.array(expected_target_cdf).reshape(
(len(expected_target_cdf), ))
expected_target_crps = np.array(expected_target_crps).reshape(
(len(expected_target_crps), ))
assert all(np.isclose(distr._cdf(target).asnumpy(), expected_target_cdf))
assert all(np.isclose(distr.crps(target).asnumpy(), expected_target_crps))
def test_shapes(batch_shape: Tuple, num_pieces: int, num_samples: int,
serialize_fn):
gamma = mx.nd.ones(shape=(*batch_shape, ))
slopes = mx.nd.ones(shape=(*batch_shape, num_pieces)) # all positive
knot_spacings = (mx.nd.ones(shape=(*batch_shape, num_pieces)) / num_pieces
) # positive and sum to 1
target = mx.nd.ones(shape=batch_shape) # shape of gamma
distr = PiecewiseLinear(gamma=gamma,
slopes=slopes,
knot_spacings=knot_spacings)
distr = serialize_fn(distr)
# assert that the parameters and target have proper shapes
assert gamma.shape == target.shape
assert knot_spacings.shape == slopes.shape
assert len(gamma.shape) + 1 == len(knot_spacings.shape)
# assert that batch_shape is computed properly
assert distr.batch_shape == batch_shape
# assert that shapes of original parameters are correct
assert distr.b.shape == slopes.shape
assert distr.knot_positions.shape == knot_spacings.shape
# assert that the shape of crps is correct
assert distr.crps(target).shape == batch_shape
# assert that the quantile shape is correct when computing the quantile values at knot positions - used for a_tilde
assert distr.quantile_internal(knot_spacings, axis=-2).shape == (
*batch_shape,
num_pieces,
)
# assert that the samples and the quantile values shape when num_samples is None is correct
samples = distr.sample()
assert samples.shape == batch_shape
assert distr.quantile_internal(samples).shape == batch_shape
# assert that the samples and the quantile values shape when num_samples is not None is correct
samples = distr.sample(num_samples)
assert samples.shape == (num_samples, *batch_shape)
assert distr.quantile_internal(samples, axis=0).shape == (
num_samples,
*batch_shape,
)