def test_intial_point(self):
for dtype in (torch.float, torch.double):
A = torch.tensor(
[[0.0, -1.0, 0.0], [0.0, -1.0, 0.0], [0.0, 4.0, 0.0]],
device=self.device,
dtype=dtype,
)
b = torch.tensor([[0.0], [-1.0], [1.0]], device=self.device, dtype=dtype)
x0 = torch.tensor(
[0.1, 0.1, 0.1], device=self.device, dtype=dtype
).unsqueeze(-1)
# testing for infeasibility of the initial point and
# infeasibility of the original LP (status 2 of the linprog output).
for initial_point in [x0, None]:
with self.assertRaises(ValueError):
PolytopeSampler(
inequality_constraints=(A, b), initial_point=initial_point
)
class Result:
status = 1
message = "mock status 1"
# testing for only status 1 of the LP
with mock.patch("scipy.optimize.linprog") as mock_linprog:
mock_linprog.return_value = Result()
with self.assertRaises(ValueError):
PolytopeSampler(inequality_constraints=(A, b))
def test_sample_polytope_with_constraints(self):
for dtype in (torch.float, torch.double):
A = torch.tensor(
[[0.0, -1.0, 0.0], [0.0, 0.0, -1.0], [0.0, 4.0, 1.0]],
device=self.device,
dtype=dtype,
)
b = torch.tensor([[0.0], [0.0], [1.0]], device=self.device, dtype=dtype)
C = torch.tensor([1.0, -1, 0.0], device=self.device, dtype=dtype).reshape(
(1, 3)
)
d = torch.tensor([0.0], device=self.device, dtype=dtype).reshape((1, 1))
x0 = torch.tensor(
[0.1, 0.1, 0.1], device=self.device, dtype=dtype
).unsqueeze(-1)
for initial_point in [x0, None]:
sampler = PolytopeSampler(
inequality_constraints=(A, b),
equality_constraints=(C, d),
initial_point=initial_point,
n_burnin=0,
)
samples = sampler.draw(n=10, seed=1)
inequality_satisfied = ((A @ samples.t() - b) > 0).sum().item() == 0
equality_satisfied = (C @ samples.t() - d).abs().sum().item() < 10e-6
self.assertTrue(inequality_satisfied)
self.assertTrue(equality_satisfied)
def test_sample_polytope(self):
for dtype in (torch.float, torch.double):
A = torch.tensor(
[[0.0, -1.0, 0.0], [0.0, 0.0, -1.0], [0.0, 4.0, 1.0]],
device=self.device,
dtype=dtype,
)
b = torch.tensor([[0.0], [0.0], [1.0]],
device=self.device,
dtype=dtype)
x0 = torch.tensor([0.1, 0.1, 0.1], device=self.device,
dtype=dtype).unsqueeze(-1)
for initial_point in [x0, None]:
sampler = PolytopeSampler(
inequality_constraints=(A, b),
initial_point=initial_point,
n_burnin=0,
)
samples = sampler.draw(n=10, seed=1)
self.assertEqual(((A @ samples.t() - b) > 0).sum().item(), 0)
# make sure we can draw mulitple samples
more_samples = sampler.draw(n=5)
self.assertEqual(((A @ more_samples.t() - b) > 0).sum().item(),
0)