| import numpy as np |
| from remedi.optimization import solve_regret, regret_components, softmin, finite_minimax |
|
|
| def test_factorized_comparator_scan_matches_dense_covariance(): |
| rng = np.random.default_rng(1) |
| b = rng.normal(size=(7,3)); d = rng.uniform(size=7) |
| p = rng.dirichlet(np.ones(7)); mean = rng.normal(size=7) |
| c = b@b.T+np.diag(d) |
| expected = [] |
| for e in np.eye(7): |
| z = p-e |
| expected.append(z@mean+1.2*np.sqrt(z@c@z)) |
| np.testing.assert_allclose(regret_components(p,mean,b,d,1.2), expected, atol=1e-10) |
|
|
| def test_zero_radius_has_analytic_gibbs_solution(): |
| mean = np.array([1.,2.,.5]); prior = np.array([.2,.3,.5]) |
| result = solve_regret(mean,np.zeros((3,1)),np.ones(3),beta=0,tau=.2,prior=prior) |
| np.testing.assert_allclose(result.probabilities,softmin(mean,.2,prior)) |
|
|
| def test_common_loss_offsets_and_common_error_factors_cancel(): |
| mean = np.array([.2,.3,.8]); b = np.array([[.1],[.2],[.4]]); d = np.full(3,.01) |
| a = solve_regret(mean,b,d,tau=.1) |
| c = solve_regret(mean+123,np.column_stack([b,np.full(3,7.)]),d,tau=.1) |
| np.testing.assert_allclose(a.probabilities,c.probabilities,atol=2e-4) |
|
|
| def test_constraint_generation_matches_full_program(): |
| rng = np.random.default_rng(8) |
| mean=rng.normal(size=10); b=rng.normal(size=(10,3))*.2; d=np.full(10,.02) |
| full=solve_regret(mean,b,d,tau=.2,constraint_generation=False) |
| active=solve_regret(mean,b,d,tau=.2) |
| np.testing.assert_allclose(full.probabilities,active.probabilities,atol=3e-4) |
| assert active.constraint_violation <= 1e-6 |
|
|
| def test_conditional_bound_contains_sampled_loss_vectors(): |
| rng=np.random.default_rng(3); mean=rng.normal(size=5); b=rng.normal(size=(5,2)); d=np.full(5,.05) |
| result=solve_regret(mean,b,d,beta=1.5,tau=.1) |
| factor=np.column_stack([b,np.diag(np.sqrt(d))]) |
| for _ in range(30): |
| u=rng.normal(size=7);u=u/np.linalg.norm(u)*rng.uniform(0,1.5) |
| loss=mean+factor@u |
| assert result.probabilities@loss-loss.min() <= result.worst_regret+1e-6 |
|
|
| def test_finite_scenario_minimax_is_offset_invariant(): |
| losses=np.array([[0.,3.,2.],[3.,0.,2.]]) |
| a=finite_minimax(losses,tau=.1) |
| b=finite_minimax(losses+np.array([[100.],[-20.]]),tau=.1) |
| np.testing.assert_allclose(a,b,atol=1e-5) |
|
|
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|
