def test_with_abalone(self):
        dataset = 'abalone'
        dataset_path = os.path.join(os.path.dirname(__file__), '.datasets',
                                    dataset)
        D = CompetitionDataManager(dataset_path)
        configuration_space = get_configuration_space(
            D.info,
            include_estimators=['extra_trees'],
            include_preprocessors=['no_preprocessing'])

        errors = []
        for i in range(N_TEST_RUNS):
            configuration = configuration_space.sample_configuration()
            D_ = copy.deepcopy(D)
            evaluator = NestedCVEvaluator(D_, configuration,
                                          inner_cv_folds=2,
                                          outer_cv_folds=2)
            if not self._fit(evaluator):
                continue
            err = evaluator.predict()
            self.assertLess(err, 0.99)
            self.assertTrue(np.isfinite(err))
            errors.append(err)
        # This is a reasonable bound
        self.assertEqual(10, len(errors))
        self.assertLess(min(errors), 0.77)
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    def test_with_abalone(self):
        dataset = 'abalone'
        dataset_path = os.path.join(os.path.dirname(__file__), '.datasets',
                                    dataset)
        D = CompetitionDataManager(dataset_path)
        configuration_space = get_configuration_space(
            D.info,
            include_estimators=['extra_trees'],
            include_preprocessors=['no_preprocessing'])

        errors = []
        for i in range(N_TEST_RUNS):
            configuration = configuration_space.sample_configuration()
            D_ = copy.deepcopy(D)
            evaluator = NestedCVEvaluator(D_,
                                          configuration,
                                          inner_cv_folds=2,
                                          outer_cv_folds=2)
            if not self._fit(evaluator):
                continue
            err = evaluator.predict()
            self.assertLess(err, 0.99)
            self.assertTrue(np.isfinite(err))
            errors.append(err)
        # This is a reasonable bound
        self.assertEqual(10, len(errors))
        self.assertLess(min(errors), 0.77)
    def test_evaluate_multiclass_classification(self):
        X_train, Y_train, X_test, Y_test = get_dataset('iris')

        X_valid = X_test[:25, ]
        Y_valid = Y_test[:25, ]
        X_test = X_test[25:, ]
        Y_test = Y_test[25:, ]

        D = Dummy()
        D.info = {
            'metric': ACC_METRIC,
            'task': MULTICLASS_CLASSIFICATION,
            'is_sparse': False,
            'label_num': 3
        }
        D.data = {
            'X_train': X_train,
            'Y_train': Y_train,
            'X_valid': X_valid,
            'X_test': X_test
        }
        D.feat_type = ['numerical', 'Numerical', 'numerical', 'numerical']

        configuration_space = get_configuration_space(
            D.info,
            include_estimators=['lda'],
            include_preprocessors=['pca'])

        err = np.zeros([N_TEST_RUNS])
        num_models_better_than_random = 0
        for i in range(N_TEST_RUNS):
            print('Evaluate configuration: %d; result:' % i)
            configuration = configuration_space.sample_configuration()
            D_ = copy.deepcopy(D)
            evaluator = NestedCVEvaluator(D_, configuration,
                                          with_predictions=True,
                                          all_scoring_functions=True)

            if not self._fit(evaluator):
                continue
            e_, Y_optimization_pred, Y_valid_pred, Y_test_pred = \
                evaluator.predict()
            err[i] = e_[ACC_METRIC]
            print(err[i], configuration['classifier:__choice__'])
            print(e_['outer:bac_metric'], e_[BAC_METRIC])

            # Test the outer CV
            num_targets = len(np.unique(Y_train))
            self.assertTrue(np.isfinite(err[i]))
            self.assertGreaterEqual(err[i], 0.0)
            # Test that ten models were trained
            self.assertEqual(len(evaluator.outer_models), 5)
            self.assertTrue(all([model is not None
                                 for model in evaluator.outer_models]))

            self.assertEqual(Y_optimization_pred.shape[0], Y_train.shape[0])
            self.assertEqual(Y_optimization_pred.shape[1], num_targets)
            self.assertEqual(Y_valid_pred.shape[0], Y_valid.shape[0])
            self.assertEqual(Y_valid_pred.shape[1], num_targets)
            self.assertEqual(Y_test_pred.shape[0], Y_test.shape[0])
            self.assertEqual(Y_test_pred.shape[1], num_targets)
            # Test some basic statistics of the predictions
            if err[i] < 0.5:
                self.assertTrue(0.3 < Y_valid_pred.mean() < 0.36666)
                self.assertGreaterEqual(Y_valid_pred.std(), 0.1)
                self.assertTrue(0.3 < Y_test_pred.mean() < 0.36666)
                self.assertGreaterEqual(Y_test_pred.std(), 0.1)
                num_models_better_than_random += 1

            # Test the inner CV
            self.assertEqual(len(evaluator.inner_models), 5)
            for fold in range(5):
                self.assertEqual(len(evaluator.inner_models[fold]), 5)
                self.assertTrue(all([model is not None
                                     for model in evaluator.inner_models[fold]
                                     ]))
                self.assertGreaterEqual(len(evaluator.outer_indices[fold][0]),
                                        75)
                for inner_fold in range(5):
                    self.assertGreaterEqual(
                        len(evaluator.inner_indices[fold][inner_fold][0]), 60)

        self.assertGreater(num_models_better_than_random, 9)
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    def test_evaluate_multiclass_classification(self):
        X_train, Y_train, X_test, Y_test = get_dataset('iris')

        X_valid = X_test[:25, ]
        Y_valid = Y_test[:25, ]
        X_test = X_test[25:, ]
        Y_test = Y_test[25:, ]

        D = Dummy()
        D.info = {
            'metric': ACC_METRIC,
            'task': MULTICLASS_CLASSIFICATION,
            'is_sparse': False,
            'label_num': 3
        }
        D.data = {
            'X_train': X_train,
            'Y_train': Y_train,
            'X_valid': X_valid,
            'X_test': X_test
        }
        D.feat_type = ['numerical', 'Numerical', 'numerical', 'numerical']

        configuration_space = get_configuration_space(
            D.info, include_estimators=['lda'], include_preprocessors=['pca'])

        err = np.zeros([N_TEST_RUNS])
        num_models_better_than_random = 0
        for i in range(N_TEST_RUNS):
            print('Evaluate configuration: %d; result:' % i)
            configuration = configuration_space.sample_configuration()
            D_ = copy.deepcopy(D)
            evaluator = NestedCVEvaluator(D_,
                                          configuration,
                                          with_predictions=True,
                                          all_scoring_functions=True)

            if not self._fit(evaluator):
                continue
            e_, Y_optimization_pred, Y_valid_pred, Y_test_pred = \
                evaluator.predict()
            err[i] = e_[ACC_METRIC]
            print(err[i], configuration['classifier:__choice__'])
            print(e_['outer:bac_metric'], e_[BAC_METRIC])

            # Test the outer CV
            num_targets = len(np.unique(Y_train))
            self.assertTrue(np.isfinite(err[i]))
            self.assertGreaterEqual(err[i], 0.0)
            # Test that ten models were trained
            self.assertEqual(len(evaluator.outer_models), 5)
            self.assertTrue(
                all([model is not None for model in evaluator.outer_models]))

            self.assertEqual(Y_optimization_pred.shape[0], Y_train.shape[0])
            self.assertEqual(Y_optimization_pred.shape[1], num_targets)
            self.assertEqual(Y_valid_pred.shape[0], Y_valid.shape[0])
            self.assertEqual(Y_valid_pred.shape[1], num_targets)
            self.assertEqual(Y_test_pred.shape[0], Y_test.shape[0])
            self.assertEqual(Y_test_pred.shape[1], num_targets)
            # Test some basic statistics of the predictions
            if err[i] < 0.5:
                self.assertTrue(0.3 < Y_valid_pred.mean() < 0.36666)
                self.assertGreaterEqual(Y_valid_pred.std(), 0.1)
                self.assertTrue(0.3 < Y_test_pred.mean() < 0.36666)
                self.assertGreaterEqual(Y_test_pred.std(), 0.1)
                num_models_better_than_random += 1

            # Test the inner CV
            self.assertEqual(len(evaluator.inner_models), 5)
            for fold in range(5):
                self.assertEqual(len(evaluator.inner_models[fold]), 5)
                self.assertTrue(
                    all([
                        model is not None
                        for model in evaluator.inner_models[fold]
                    ]))
                self.assertGreaterEqual(len(evaluator.outer_indices[fold][0]),
                                        75)
                for inner_fold in range(5):
                    self.assertGreaterEqual(
                        len(evaluator.inner_indices[fold][inner_fold][0]), 60)

        self.assertGreater(num_models_better_than_random, 9)