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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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