Check that solve_batch gives the same results as solve for each instance in the batch.
(solver, reg_type)
| 26 | @pytest.mark.parametrize("solver", ["sinkhorn", "log_sinkhorn"]) |
| 27 | @pytest.mark.parametrize("reg_type", ["kl", "entropy"]) |
| 28 | def test_solve_batch(solver, reg_type): |
| 29 | """Check that solve_batch gives the same results as solve for each instance in the batch.""" |
| 30 | batchsize = 4 |
| 31 | n = 16 |
| 32 | rng = np.random.RandomState(0) |
| 33 | |
| 34 | M = rng.rand(batchsize, n, n) |
| 35 | |
| 36 | reg = 0.1 |
| 37 | max_iter = 10000 |
| 38 | tol = 1e-5 |
| 39 | |
| 40 | res = solve_batch( |
| 41 | M, |
| 42 | a=None, |
| 43 | b=None, |
| 44 | reg=reg, |
| 45 | max_iter=max_iter, |
| 46 | tol=tol, |
| 47 | solver=solver, |
| 48 | reg_type=reg_type, |
| 49 | grad="detach", |
| 50 | ) |
| 51 | plan_batch = res.plan |
| 52 | values_batch = res.value_linear |
| 53 | |
| 54 | for i in range(batchsize): |
| 55 | M_i = M[i] |
| 56 | res_i = solve( |
| 57 | M_i, a=None, b=None, reg=reg, max_iter=max_iter, tol=tol, reg_type=reg_type |
| 58 | ) |
| 59 | plan_i = res_i.plan |
| 60 | value_i = res_i.value_linear |
| 61 | np.testing.assert_allclose(plan_i, plan_batch[i], atol=1e-05) |
| 62 | np.testing.assert_allclose(value_i, values_batch[i], atol=1e-4) |
| 63 | |
| 64 | |
| 65 | def test_bregman_batch(): |
nothing calls this directly
no test coverage detected