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How to use test_set_domain method in Kiwi
Best Python code snippet using Kiwi_python
test_optimizers.py
Source:test_optimizers.py 
...38 def test_set_initial(self):39 self.optimizer.set_initial([1, 1])40 self.assertTupleEqual(self.optimizer._initial.shape, (1, 2), msg="Invalid shape of initial points array")41 self.assertTrue(np.allclose(self.optimizer._initial, 1), msg="Specified initial point not loaded.")42 def test_set_domain(self):43 self.optimizer.domain = gpflowopt.domain.UnitCube(3)44 self.assertNotEqual(self.optimizer.domain, self.domain)45 self.assertEqual(self.optimizer.domain, gpflowopt.domain.UnitCube(3))46 self.assertTrue(np.allclose(self.optimizer.get_initial(), 0.5))47class TestCandidateOptimizer(_TestOptimizer, GPflowOptTestCase):48 def setUp(self):49 super(TestCandidateOptimizer, self).setUp()50 design = gpflowopt.design.FactorialDesign(4, self.domain)51 self.optimizer = gpflowopt.optim.CandidateOptimizer(self.domain, design.generate())52 def test_default_initial(self):53 self.assertTupleEqual(self.optimizer._initial.shape, (0, 2), msg="Invalid shape of initial points array")54 def test_set_initial(self):55 # When run separately this test works, however when calling nose to run all tests on python 2.7 this records56 # no warnings57 with warnings.catch_warnings(record=True) as w:58 self.optimizer.set_initial([1, 1])59 assert len(w) == 160 assert issubclass(w[-1].category, UserWarning)61 def test_object_integrity(self):62 self.assertTupleEqual(self.optimizer.candidates.shape, (16, 2), msg="Invalid shape of candidate property.")63 self.assertTupleEqual(self.optimizer._get_eval_points().shape, (16, 2))64 self.assertTupleEqual(self.optimizer.get_initial().shape, (0, 2), msg="Invalid shape of initial points")65 self.assertFalse(self.optimizer.gradient_enabled(), msg="CandidateOptimizer supports no gradients.")66 def test_set_domain(self):67 with self.assertRaises(AssertionError):68 super(TestCandidateOptimizer, self).test_set_domain()69 self.optimizer.domain = gpflowopt.domain.UnitCube(2)70 self.assertNotEqual(self.optimizer.domain, self.domain)71 self.assertEqual(self.optimizer.domain, gpflowopt.domain.UnitCube(2))72 rescaled_candidates = gpflowopt.design.FactorialDesign(4, gpflowopt.domain.UnitCube(2)).generate()73 self.assertTrue(np.allclose(self.optimizer.candidates, rescaled_candidates))74 def test_optimize(self):75 self.optimizer.candidates = np.vstack((self.optimizer.candidates, np.zeros((1,2))))76 result = self.optimizer.optimize(parabola2d)77 self.assertTrue(result.success, msg="Optimization should succeed.")78 self.assertTrue(np.allclose(result.x, 0), msg="Optimum should be identified")79 self.assertTrue(np.allclose(result.fun, 0), msg="Function value in optimum is 0")80 self.assertEqual(result.nfev, 17, msg="Number of function evaluations equals candidates + initial points")81 def test_optimize_second(self):82 result = self.optimizer.optimize(parabola2d)83 self.assertGreater(result.fun, 0, msg="Optimum is not amongst candidates and initial points")84 self.assertLess(result.fun, 2, msg="Function value not reachable within domain")85class TestSciPyOptimizer(_TestOptimizer, GPflowOptTestCase):86 def setUp(self):87 super(TestSciPyOptimizer, self).setUp()88 self.optimizer = gpflowopt.optim.SciPyOptimizer(self.domain, maxiter=10)89 def test_object_integrity(self):90 self.assertDictEqual(self.optimizer.config, {'tol': None, 'method': 'L-BFGS-B',91 'options': {'maxiter': 10, 'disp': False}},92 msg="Config dict contains invalid entries.")93 self.assertTrue(self.optimizer.gradient_enabled(), msg="Gradient is supported.")94 def test_optimize(self):95 self.optimizer.set_initial([-1, -1])96 result = self.optimizer.optimize(parabola2d)97 self.assertTrue(result.success)98 self.assertLessEqual(result.nit, 10, "Only 10 Iterations permitted")99 self.assertLessEqual(result.nfev, 20, "Max 20 evaluations permitted")100 self.assertTrue(np.allclose(result.x, 0), msg="Optimizer failed to find optimum")101 self.assertTrue(np.allclose(result.fun, 0), msg="Incorrect function value returned")102 def test_optimizer_interrupt(self):103 self.optimizer.set_initial([-1, -1])104 result = self.optimizer.optimize(KeyboardRaiser(2, parabola2d))105 self.assertFalse(result.success, msg="After one evaluation, a keyboard interrupt is raised, "106 "non-succesfull result expected.")107 self.assertFalse(np.allclose(result.x, 0), msg="After one iteration, the optimum will not be found")108class TestStagedOptimizer(_TestOptimizer, GPflowOptTestCase):109 def setUp(self):110 super(TestStagedOptimizer, self).setUp()111 self.optimizer = gpflowopt.optim.StagedOptimizer([gpflowopt.optim.MCOptimizer(self.domain, 5),112 gpflowopt.optim.MCOptimizer(self.domain, 5),113 gpflowopt.optim.SciPyOptimizer(self.domain, maxiter=10)])114 def test_default_initial(self):115 self.assertTupleEqual(self.optimizer.optimizers[0]._initial.shape, (0,2))116 def test_set_initial(self):117 self.optimizer.set_initial([1, 1])118 self.assertTupleEqual(self.optimizer.optimizers[0]._initial.shape, (0, 2))119 self.assertTupleEqual(self.optimizer.optimizers[1]._initial.shape, (0, 2))120 self.assertTupleEqual(self.optimizer.optimizers[2]._initial.shape, (1, 2))121 self.assertTupleEqual(self.optimizer.get_initial().shape, (0, 2))122 def test_object_integrity(self):123 self.assertEqual(len(self.optimizer.optimizers), 3, msg="Two optimizers expected in optimizerlist")124 self.assertFalse(self.optimizer.gradient_enabled(), msg="MCOptimizer supports no gradients => neither "125 "does stagedoptimizer.")126 def test_optimize(self):127 with warnings.catch_warnings():128 warnings.filterwarnings("ignore", category=UserWarning)129 result = self.optimizer.optimize(parabola2d)130 self.assertTrue(result.success)131 self.assertLessEqual(result.nfev, 20, "Only 20 Iterations permitted")132 self.assertTrue(np.allclose(result.x, 0), msg="Optimizer failed to find optimum")133 self.assertTrue(np.allclose(result.fun, 0), msg="Incorrect function value returned")134 def test_optimizer_interrupt(self):135 with warnings.catch_warnings():136 warnings.filterwarnings("ignore", category=UserWarning)137 result = self.optimizer.optimize(KeyboardRaiser(0, parabola2d))138 self.assertFalse(result.success, msg="non-succesfull result expected.")139 self.assertEqual(result.nstages, 1, msg="Stage 2 should be in progress during interrupt")140 self.assertEqual(result.nfev, 0)141 result = self.optimizer.optimize(KeyboardRaiser(3, parabola2d))142 self.assertFalse(result.success, msg="non-succesfull result expected.")143 self.assertFalse(np.allclose(result.x, 0.0), msg="The optimum will not be found")144 self.assertEqual(result.nstages, 2, msg="Stage 2 should be in progress during interrupt")145 self.assertEqual(result.nfev, 5)146 result = self.optimizer.optimize(KeyboardRaiser(12, parabola2d))147 self.assertFalse(result.success, msg="non-succesfull result expected.")148 self.assertEqual(result.nfev, 12)149 self.assertFalse(np.allclose(result.x[0, :], 0.0), msg="The optimum should not be found yet")150 self.assertEqual(result.nstages, 3, msg="Stage 3 should be in progress during interrupt")151 def test_set_domain(self):152 super(TestStagedOptimizer, self).test_set_domain()153 for opt in self.optimizer.optimizers:154 self.assertEqual(opt.domain, gpflowopt.domain.UnitCube(3))155class TestBayesianOptimizer(_TestOptimizer, GPflowOptTestCase):156 def setUp(self):157 super(TestBayesianOptimizer, self).setUp()158 acquisition = gpflowopt.acquisition.ExpectedImprovement(create_parabola_model(self.domain))159 self.optimizer = gpflowopt.BayesianOptimizer(self.domain, acquisition)160 def test_default_initial(self):161 self.assertTupleEqual(self.optimizer._initial.shape, (0, 2), msg="Invalid shape of initial points array")162 def test_optimize(self):163 for verbose in [False, True]:164 with self.test_session():165 acquisition = gpflowopt.acquisition.ExpectedImprovement(create_parabola_model(self.domain))166 optimizer = gpflowopt.BayesianOptimizer(self.domain, acquisition, verbose=verbose)167 result = optimizer.optimize(lambda X: parabola2d(X)[0], n_iter=20)168 self.assertTrue(result.success)169 self.assertEqual(result.nfev, 20, "Only 20 evaluations permitted")170 self.assertTrue(np.allclose(result.x, 0), msg="Optimizer failed to find optimum")171 self.assertTrue(np.allclose(result.fun, 0), msg="Incorrect function value returned")172 def test_optimize_multi_objective(self):173 for verbose in [False, True]:174 with self.test_session():175 m1, m2 = create_vlmop2_model()176 acquisition = gpflowopt.acquisition.ExpectedImprovement(m1) + gpflowopt.acquisition.ExpectedImprovement(m2)177 optimizer = gpflowopt.BayesianOptimizer(self.domain, acquisition, verbose=verbose)178 result = optimizer.optimize(vlmop2, n_iter=2)179 self.assertTrue(result.success)180 self.assertEqual(result.nfev, 2, "Only 2 evaluations permitted")181 self.assertTupleEqual(result.x.shape, (7, 2))182 self.assertTupleEqual(result.fun.shape, (7, 2))183 _, dom = gpflowopt.pareto.non_dominated_sort(result.fun)184 self.assertTrue(np.all(dom == 0))185 def test_optimize_constraint(self):186 for verbose in [False, True]:187 with self.test_session():188 acquisition = gpflowopt.acquisition.ProbabilityOfFeasibility(create_parabola_model(self.domain), threshold=-1)189 optimizer = gpflowopt.BayesianOptimizer(self.domain, acquisition, verbose=verbose)190 result = optimizer.optimize(lambda X: parabola2d(X)[0], n_iter=1)191 self.assertFalse(result.success)192 self.assertEqual(result.message, 'No evaluations satisfied all the constraints')193 self.assertEqual(result.nfev, 1, "Only 1 evaluations permitted")194 self.assertTupleEqual(result.x.shape, (17, 2))195 self.assertTupleEqual(result.fun.shape, (17, 0))196 self.assertTupleEqual(result.constraints.shape, (17, 1))197 acquisition = gpflowopt.acquisition.ProbabilityOfFeasibility(create_parabola_model(self.domain), threshold=0.3)198 optimizer = gpflowopt.BayesianOptimizer(self.domain, acquisition, verbose=verbose)199 result = optimizer.optimize(lambda X: parabola2d(X)[0], n_iter=1)200 self.assertTrue(result.success)201 self.assertEqual(result.nfev, 1, "Only 1 evaluation permitted")202 self.assertTupleEqual(result.x.shape, (5, 2))203 self.assertTupleEqual(result.fun.shape, (5, 0))204 self.assertTupleEqual(result.constraints.shape, (5, 1))205 def test_optimizer_interrupt(self):206 with self.test_session():207 result = self.optimizer.optimize(KeyboardRaiser(3, lambda X: parabola2d(X)[0]), n_iter=20)208 self.assertFalse(result.success, msg="After 2 evaluations, a keyboard interrupt is raised, "209 "failed result expected.")210 self.assertTrue(np.allclose(result.x, 0.0), msg="The optimum will not be identified nonetheless")211 def test_failsafe(self):212 with self.test_session():213 X, Y = self.optimizer.acquisition.data[0], self.optimizer.acquisition.data[1]214 # Provoke cholesky faillure215 self.optimizer.acquisition.optimize_restarts = 1216 self.optimizer.acquisition.models[0].likelihood.variance.transform = gpflow.transforms.Identity()217 self.optimizer.acquisition.models[0].likelihood.variance = -5.0218 self.optimizer.acquisition.models[0]._needs_recompile = True219 with self.assertRaises(RuntimeError) as e:220 with self.optimizer.failsafe():221 self.optimizer.acquisition.set_data(X, Y)222 self.optimizer.acquisition.evaluate(X)223 fname = 'failed_bopt_{0}.npz'.format(id(e.exception))224 self.assertTrue(os.path.isfile(fname))225 with np.load(fname) as data:226 np.testing.assert_almost_equal(data['X'], X)227 np.testing.assert_almost_equal(data['Y'], Y)228 os.remove(fname)229 def test_set_domain(self):230 with self.test_session():231 with self.assertRaises(AssertionError):232 super(TestBayesianOptimizer, self).test_set_domain()233 domain = gpflowopt.domain.ContinuousParameter("x1", -2.0, 2.0) + \234 gpflowopt.domain.ContinuousParameter("x2", -2.0, 2.0)235 self.optimizer.domain = domain236 expected = gpflowopt.design.LatinHyperCube(16, self.domain).generate() / 4 + 0.5237 self.assertTrue(np.allclose(expected, self.optimizer.acquisition.models[0].wrapped.X.value))238class TestBayesianOptimizerConfigurations(GPflowOptTestCase):239 def setUp(self):240 self.domain = gpflowopt.domain.ContinuousParameter("x1", 0.0, 1.0) + \241 gpflowopt.domain.ContinuousParameter("x2", 0.0, 1.0)242 self.acquisition = gpflowopt.acquisition.ExpectedImprovement(create_parabola_model(self.domain))243 def test_initial_design(self):244 with self.test_session():245 design = gpflowopt.design.RandomDesign(5, self.domain)246 optimizer = gpflowopt.BayesianOptimizer(self.domain, self.acquisition, initial=design)...
test1.py
Source:test1.py
1# coding: utf-82import pandas as pd3from sklearn.datasets.samples_generator import make_blobs4from sklearn.ensemble import RandomForestClassifier5import random6from tl_algs import peters, tnb, trbag, tl_baseline, burak, tca, tca_plus7RAND_SEED = 2016 8random.seed(RAND_SEED) # change this to see new random data!9# randomly generate some data10X, domain_index = make_blobs(n_samples=15, centers=3, n_features=2, cluster_std=5)11# randomly assigning domain and label12all_instances = pd.DataFrame({"x_coord" : [x[0] for x in X],13 "y_coord" : [x[1] for x in X],14 "domain_index" : domain_index,15 "label" : [random.choice([True,False]) for _ in X]},16 columns = ['x_coord','y_coord','domain_index', 'label']17 )18#arbitrarily set domain index 0 as target19test_set_domain = 020# we are going to set the first three instances as test data21# note that this means that some of the training set has target instances!22test_set = all_instances[all_instances.domain_index == test_set_domain].sample(3, random_state=RAND_SEED)23test_set_X = test_set.loc[:, ["x_coord", "y_coord"]].reset_index(drop=True)24test_set_y = test_set.loc[:, ["label"]].reset_index(drop=True)25# gather all non-test indexes 26train_pool = all_instances.iloc[all_instances.index.difference(test_set.index), ] 27train_pool_X = train_pool.loc[:, ["x_coord", "y_coord"]].reset_index(drop=True)28train_pool_y = train_pool["label"].reset_index(drop=True)29train_pool_domain = train_pool.domain_index30# We don't have much training data, but we got some predictions with confidence levels!31transfer_learners = [32 tca_plus.TCAPlus(test_set_X=test_set_X, 33 test_set_domain=test_set_domain, 34 train_pool_X=train_pool_X, 35 train_pool_y=train_pool_y, 36 train_pool_domain=train_pool_domain, 37 Base_Classifier=RandomForestClassifier,38 rand_seed=RAND_SEED39 ),40 tca.TCA(test_set_X=test_set_X, 41 test_set_domain=test_set_domain, 42 train_pool_X=train_pool_X, 43 train_pool_y=train_pool_y, 44 train_pool_domain=train_pool_domain, 45 Base_Classifier=RandomForestClassifier,46 rand_seed=RAND_SEED47 ),48 tl_baseline.Source_Baseline(49 test_set_X=test_set_X, 50 test_set_domain=test_set_domain, 51 train_pool_X=train_pool_X, 52 train_pool_y=train_pool_y, 53 train_pool_domain=train_pool_domain, 54 Base_Classifier=RandomForestClassifier,55 rand_seed=RAND_SEED56 ),57 burak.Burak(58 test_set_X=test_set_X, 59 test_set_domain=test_set_domain, 60 train_pool_X=train_pool_X, 61 train_pool_y=train_pool_y, 62 train_pool_domain=train_pool_domain,63 cluster_factor = 15,64 k = 2,65 Base_Classifier=RandomForestClassifier,66 rand_seed=RAND_SEED67 ),68 peters.Peters(test_set_X=test_set_X, 69 test_set_domain=test_set_domain, 70 train_pool_X=train_pool_X, 71 train_pool_y=train_pool_y, 72 train_pool_domain=train_pool_domain, 73 cluster_factor=15,74 Base_Classifier=RandomForestClassifier,75 rand_seed=RAND_SEED76 ),77 tnb.TransferNaiveBayes(test_set_X=test_set_X, 78 test_set_domain=test_set_domain, 79 train_pool_X=train_pool_X, 80 train_pool_y=train_pool_y, 81 train_pool_domain=train_pool_domain, 82 rand_seed=RAND_SEED83 ),84 trbag.TrBag(test_set_X=test_set_X, 85 test_set_domain=test_set_domain, 86 train_pool_X=train_pool_X, 87 train_pool_y=train_pool_y, 88 train_pool_domain=train_pool_domain, 89 Base_Classifier=RandomForestClassifier,90 sample_size=test_set_y.shape[0],91 rand_seed=RAND_SEED92 ),93 tl_baseline.Hybrid_Baseline(test_set_X=test_set_X, 94 test_set_domain=test_set_domain, 95 train_pool_X=train_pool_X, 96 train_pool_y=train_pool_y, 97 train_pool_domain=train_pool_domain, 98 Base_Classifier=RandomForestClassifier,99 rand_seed=RAND_SEED100 ),101 tl_baseline.Target_Baseline(test_set_X=test_set_X, 102 test_set_domain=test_set_domain, 103 train_pool_X=train_pool_X, 104 train_pool_y=train_pool_y, 105 train_pool_domain=train_pool_domain, 106 Base_Classifier=RandomForestClassifier,107 rand_seed=RAND_SEED108 )109]110for transfer_learner in transfer_learners:...
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