def test_interval_transform_raises():
with pytest.raises(ValueError, match="Lower and upper interval bounds cannot both be None"):
tr.Interval(None, None)
with pytest.raises(ValueError, match="Interval bounds must be constant values"):
tr.Interval(at.constant(5) + 1, None)
assert tr.Interval(at.constant(5), None)
def test_upperbound():
trans = tr.Interval(None, 0.0)
check_transform(trans, Rminusbig)
check_jacobian_det(trans, Rminusbig, elemwise=True)
check_jacobian_det(trans, Vector(Rminusbig, 2), at.dvector, [-1, -1], elemwise=True)
vals = get_values(trans)
close_to_logical(vals < 0, True, tol)
def test_lowerbound():
trans = tr.Interval(0.0, None)
check_transform(trans, Rplusbig)
check_jacobian_det(trans, Rplusbig, elemwise=True)
check_jacobian_det(trans, Vector(Rplusbig, 2), at.dvector, [0, 0], elemwise=True)
vals = get_values(trans)
close_to_logical(vals > 0, True, tol)
def test_interval():
for a, b in [(-4, 5.5), (0.1, 0.7), (-10, 4.3)]:
domain = Unit * np.float64(b - a) + np.float64(a)
trans = tr.Interval(a, b)
check_transform(trans, domain)
check_jacobian_det(trans, domain, elemwise=True)
vals = get_values(trans)
close_to_logical(vals > a, True, tol)
close_to_logical(vals < b, True, tol)
def test_triangular(self, lower, c, upper, size):
def transform_params(*inputs):
_, _, _, lower, _, upper = inputs
lower = at.as_tensor_variable(lower) if lower is not None else None
upper = at.as_tensor_variable(upper) if upper is not None else None
return lower, upper
interval = tr.Interval(bounds_fn=transform_params)
model = self.build_model(
pm.Triangular, {"lower": lower, "c": c, "upper": upper}, size=size, transform=interval
)
self.check_transform_elementwise_logp(model)
def test_uniform_ordered(self, lower, upper, size):
def transform_params(*inputs):
_, _, _, lower, upper = inputs
lower = at.as_tensor_variable(lower) if lower is not None else None
upper = at.as_tensor_variable(upper) if upper is not None else None
return lower, upper
interval = tr.Interval(bounds_fn=transform_params)
initval = np.sort(np.abs(np.random.rand(*size)))
model = self.build_model(
pm.Uniform,
{"lower": lower, "upper": upper},
size=size,
initval=initval,
transform=tr.Chain([interval, tr.ordered]),
)
self.check_vectortransform_elementwise_logp(model)