def test_preferences(self): d = UniformPreferenceDict(3) a, b = (0, 1, 2), (3, 4, 5) d[a, b] = 1 e, f = (9, 9, 9), (1, 1, 1) d[e, f] = -1 self.assertEqual(sorted(d.preferences()), [(0, 1, 2), (1, 1, 1), (3, 4, 5), (9, 9, 9)])
def should_return_correct_preference_key_items(): # given a = (0, 1, 2) b = (3, 4, 5) e = (9, 9, 9) f = (1, 1, 1) preferences = UniformPreferenceDict(len(a)) # when preferences[a, b] = 1 preferences[e, f] = -1 # then assert sorted(preferences.preferences()) == [a, f, b, e]
def test_interface_with_direct_optimizer_scalar_lengthscale(self): with self.test_session(): # set RNG seed np.random.seed(RNG_SEED) tf.set_random_seed(RNG_SEED) # set up bounds = [ (-3, 6), (-3, 6), ] optimizer = DirectOptimizer(bounds) model = BinaryPreferenceModel( lengthscale=1.0, **CONFIG ) data = UniformPreferenceDict(2) a = (0, 0) b = (1, 1) c = (2, 2) d = (3, 3) data[a, b] = 1 data[a, c] = 1 data[b, c] = 1 data[b, d] = 1 data[c, d] = 1 # construct acquirer acquirer = ExpectedImprovementAcquirer(data, model, optimizer) # test that `next` needs to be called before `best` with self.assertRaises(ValueError): acquirer.best # test that `next` needs to be called before `valuations` with self.assertRaises(ValueError): acquirer.valuations # test `next` a1, a2 = a b1, b2 = b xn = xn1, xn2 = acquirer.next eps = 0.5 self.assertTrue( (a1 - eps < xn1 < b1) and (a2 - eps < xn2 < b2) ) self.assertTrue(np.allclose(xn, acquirer.next)) # test `best` best = acquirer.best self.assertTrue(np.allclose(a, best)) self.assertTrue(np.allclose(best, acquirer.best)) # test `valuations` valuations = acquirer.valuations x, f = zip(*valuations) self.assertEqual(len(x), len(data.preferences())) self.assertTrue(all(a < b for a, b in zip(x, x[1:]))) self.assertTrue(all(a > b for a, b in zip(f, f[1:]))) # test `update` e = (-1, -1) acquirer.update(e, a, 1) acquirer.update(e, b, 1) # test `next` l1, l2 = (x[0] for x in bounds) xn1, xn2 = acquirer.next self.assertTrue( (l1 < xn1 < a1) and (l2 < xn2 < a2), ) # test `best` best = acquirer.best self.assertTrue(np.allclose(e, best)) # test `valuations` valuations = acquirer.valuations x, f = zip(*valuations) self.assertEqual(len(x), len(data.preferences())) self.assertTrue(all(a < b for a, b in zip(x, x[1:]))) self.assertTrue(all(a > b for a, b in zip(f, f[1:])))
def test_interface_with_grid_search_optimizer_ard_lengthscale(self): with self.test_session(): # set RNG seed np.random.seed(RNG_SEED) tf.set_random_seed(RNG_SEED) # set up bounds = [ (-3, 6), (-3, 6), ] optimizer = GridSearchOptimizer(bounds) model = BinaryPreferenceModel( ard=True, **CONFIG ) data = UniformPreferenceDict(2) a = (0, 0) b = (1, 0) c = (2, 0) d = (3, 0) data[a, b] = 1 data[a, c] = 1 data[b, c] = 1 data[b, d] = 1 data[c, d] = 1 ab = (0.5, 0) bc = (1.5, 0) cd = (2.5, 0) data[a, ab] = 1 data[ab, b] = 1 data[b, bc] = 1 data[bc, c] = 1 data[c, cd] = 1 data[cd, d] = 1 data[ab, bc] = 1 data[ab, cd] = 1 data[bc, cd] = 1 # construct acquirer acquirer = ExpectedImprovementAcquirer(data, model, optimizer) # test that `next` needs to be called before `best` with self.assertRaises(ValueError): acquirer.best # test that `next` needs to be called before `valuations` with self.assertRaises(ValueError): acquirer.valuations # test `next` a1, a2 = a b1, b2 = b xn = xn1, xn2 = acquirer.next eps = 1.0 self.assertTrue( (a1 - eps < xn1 < b1), ) self.assertTrue(np.allclose(xn, acquirer.next)) # test `best` best = acquirer.best self.assertTrue(np.allclose(a, best)) self.assertTrue(np.allclose(best, acquirer.best)) # test `valuations` valuations = acquirer.valuations x, f = zip(*valuations) self.assertEqual(len(x), len(data.preferences())) self.assertTrue(all(a < b for a, b in zip(x, x[1:]))) self.assertTrue(all(a > b for a, b in zip(f, f[1:]))) # test `lengthscale` s1, s2 = model.lengthscale self.assertTrue(s1 < s2) # test `update` e = (-1, 0) acquirer.update(e, a, 1) acquirer.update(e, ab, 1) acquirer.update(e, b, 1) acquirer.update(e, bc, 1) # test `next` l1, l2 = (x[0] for x in bounds) xn1, xn2 = acquirer.next self.assertTrue( (l1 <= xn1 < a1), ) # test `best` best = acquirer.best self.assertTrue(best < b) # test `valuations` valuations = acquirer.valuations x, f = zip(*valuations) self.assertEqual(len(x), len(data.preferences())) self.assertTrue(all(a < b for a, b in zip(x, x[1:]))) self.assertTrue(all(a > b for a, b in zip(f, f[1:]))) # test `lengthscale` s1, s2 = model.lengthscale self.assertTrue(s1 < s2)
def test_interface_with_grid_search_optimizer_vector_lengthscale(self): with self.test_session(): # set RNG seed np.random.seed(RNG_SEED) tf.set_random_seed(RNG_SEED) # set up bounds = [ (-3, 6), (-3, 6), ] optimizer = GridSearchOptimizer(bounds) lengthscale = np.array([1, 10], np.float32) model = BinaryPreferenceModel(lengthscale=lengthscale, **CONFIG) data = UniformPreferenceDict(2) a = (0, 0) b = (1, 0) c = (2, 0) d = (3, 0) data[a, b] = 1 data[a, c] = 1 data[b, c] = 1 data[b, d] = 1 data[c, d] = 1 # construct acquirer acquirer = ExpectedImprovementAcquirer(data, model, optimizer) # test that `next` needs to be called before `best` with self.assertRaises(ValueError): acquirer.best # pylint: disable=pointless-statement # test that `next` needs to be called before `valuations` with self.assertRaises(ValueError): acquirer.valuations # pylint: disable=pointless-statement # test `next` a1, _ = a b1, _ = b xn = xn1, _ = acquirer.next eps = 0.5 self.assertTrue((a1 - eps < xn1 < b1)) self.assertTrue(np.allclose(xn, acquirer.next)) # test `best` best = acquirer.best self.assertTrue(np.allclose(a, best)) self.assertTrue(np.allclose(best, acquirer.best)) # test `valuations` valuations = acquirer.valuations x, f = zip(*valuations) self.assertEqual(len(x), len(data.preferences())) self.assertTrue(all(a < b for a, b in zip(x, x[1:]))) self.assertTrue(all(a > b for a, b in zip(f, f[1:]))) # test `lengthscale` self.assertAllClose(model.lengthscale, lengthscale) # test `update` e = (-1, 0) acquirer.update(e, a, 1) acquirer.update(e, b, 1) # test `next` l1, _ = (x[0] for x in bounds) xn1, _ = acquirer.next self.assertTrue((l1 < xn1 < a1)) # test `best` best = acquirer.best self.assertTrue(np.allclose(e, best)) # test `valuations` valuations = acquirer.valuations x, f = zip(*valuations) self.assertEqual(len(x), len(data.preferences())) self.assertTrue(all(a < b for a, b in zip(x, x[1:]))) self.assertTrue(all(a > b for a, b in zip(f, f[1:])))