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)