Python SimpleClassificationPipeline.fit方法代码示例

# 需要导入模块: from autosklearn.pipeline.classification import SimpleClassificationPipeline [as 别名]
# 或者: from autosklearn.pipeline.classification.SimpleClassificationPipeline import fit [as 别名]
    def test_predict_batched(self):
        cs = SimpleClassificationPipeline.get_hyperparameter_search_space()
        default = cs.get_default_configuration()
        cls = SimpleClassificationPipeline(default)

        # Multiclass
        X_train, Y_train, X_test, Y_test = get_dataset(dataset="digits")
        cls.fit(X_train, Y_train)
        X_test_ = X_test.copy()
        prediction_ = cls.predict(X_test_)
        cls_predict = mock.Mock(wraps=cls.pipeline_)
        cls.pipeline_ = cls_predict
        prediction = cls.predict(X_test, batch_size=20)
        self.assertEqual((1647,), prediction.shape)
        self.assertEqual(83, cls_predict.predict.call_count)
        assert_array_almost_equal(prediction_, prediction)

        # Multilabel
        X_train, Y_train, X_test, Y_test = get_dataset(dataset="digits")
        Y_train = np.array(list([(list([1 if i != y else 0 for i in range(10)])) for y in Y_train]))
        cls.fit(X_train, Y_train)
        X_test_ = X_test.copy()
        prediction_ = cls.predict(X_test_)
        cls_predict = mock.Mock(wraps=cls.pipeline_)
        cls.pipeline_ = cls_predict
        prediction = cls.predict(X_test, batch_size=20)
        self.assertEqual((1647, 10), prediction.shape)
        self.assertEqual(83, cls_predict.predict.call_count)
        assert_array_almost_equal(prediction_, prediction)

# 需要导入模块: from autosklearn.pipeline.classification import SimpleClassificationPipeline [as 别名]
# 或者: from autosklearn.pipeline.classification.SimpleClassificationPipeline import fit [as 别名]
    def test_configurations_signed_data(self):
        # Use a limit of ~4GiB
        limit = 4000 * 1024 * 1024
        resource.setrlimit(resource.RLIMIT_AS, (limit, limit))

        cs = SimpleClassificationPipeline.get_hyperparameter_search_space(
            dataset_properties={'signed': True})

        print(cs)

        for i in range(10):
            config = cs.sample_configuration()
            config._populate_values()
            if config['classifier:passive_aggressive:n_iter'] is not None:
                config._values['classifier:passive_aggressive:n_iter'] = 5
            if config['classifier:sgd:n_iter'] is not None:
                config._values['classifier:sgd:n_iter'] = 5

            X_train, Y_train, X_test, Y_test = get_dataset(dataset='digits')
            cls = SimpleClassificationPipeline(config, random_state=1)
            print(config)
            try:
                cls.fit(X_train, Y_train)
                X_test_ = X_test.copy()
                predictions = cls.predict(X_test)
                self.assertIsInstance(predictions, np.ndarray)
                predicted_probabiliets = cls.predict_proba(X_test_)
                self.assertIsInstance(predicted_probabiliets, np.ndarray)
            except ValueError as e:
                if "Floating-point under-/overflow occurred at epoch" in \
                       e.args[0] or \
                       "removed all features" in e.args[0] or \
                                "all features are discarded" in e.args[0]:
                    continue
                else:
                    print(config)
                    print(traceback.format_exc())
                    raise e
            except RuntimeWarning as e:
                if "invalid value encountered in sqrt" in e.args[0]:
                    continue
                elif "divide by zero encountered in" in e.args[0]:
                    continue
                elif "invalid value encountered in divide" in e.args[0]:
                    continue
                elif "invalid value encountered in true_divide" in e.args[0]:
                    continue
                else:
                    print(config)
                    print(traceback.format_exc())
                    raise e
            except UserWarning as e:
                if "FastICA did not converge" in e.args[0]:
                    continue
                else:
                    print(config)
                    print(traceback.format_exc())
                    raise e
            except MemoryError as e:
                continue

# 需要导入模块: from autosklearn.pipeline.classification import SimpleClassificationPipeline [as 别名]
# 或者: from autosklearn.pipeline.classification.SimpleClassificationPipeline import fit [as 别名]
    def test_predict_proba_batched(self):
        cs = SimpleClassificationPipeline.get_hyperparameter_search_space()
        default = cs.get_default_configuration()

        # Multiclass
        cls = SimpleClassificationPipeline(default)
        X_train, Y_train, X_test, Y_test = get_dataset(dataset='digits')
        cls.fit(X_train, Y_train)
        X_test_ = X_test.copy()
        prediction_ = cls.predict_proba(X_test_)
        # The object behind the last step in the pipeline
        cls_predict = mock.Mock(wraps=cls.pipeline_.steps[-1][1])
        cls.pipeline_.steps[-1] = ("estimator", cls_predict)
        prediction = cls.predict_proba(X_test, batch_size=20)
        self.assertEqual((1647, 10), prediction.shape)
        self.assertEqual(84, cls_predict.predict_proba.call_count)
        assert_array_almost_equal(prediction_, prediction)

        # Multilabel
        cls = SimpleClassificationPipeline(default)
        X_train, Y_train, X_test, Y_test = get_dataset(dataset='digits')
        Y_train_ = np.zeros((Y_train.shape[0], 10))
        for i, y in enumerate(Y_train):
            Y_train_[i][y] = 1
        Y_train = Y_train_
        cls.fit(X_train, Y_train)
        X_test_ = X_test.copy()
        prediction_ = cls.predict_proba(X_test_)
        cls_predict = mock.Mock(wraps=cls.pipeline_.steps[-1][1])
        cls.pipeline_.steps[-1] = ("estimator", cls_predict)
        prediction = cls.predict_proba(X_test, batch_size=20)
        self.assertIsInstance(prediction, np.ndarray)
        self.assertEqual(prediction.shape, ((1647, 10)))
        self.assertEqual(84, cls_predict.predict_proba.call_count)
        assert_array_almost_equal(prediction_, prediction)

# 需要导入模块: from autosklearn.pipeline.classification import SimpleClassificationPipeline [as 别名]
# 或者: from autosklearn.pipeline.classification.SimpleClassificationPipeline import fit [as 别名]
    def test_predict_proba_batched(self):
        # Multiclass
        cls = SimpleClassificationPipeline(include={'classifier': ['sgd']})
        X_train, Y_train, X_test, Y_test = get_dataset(dataset='digits')

        cls.fit(X_train, Y_train)
        X_test_ = X_test.copy()
        prediction_ = cls.predict_proba(X_test_)
        # The object behind the last step in the pipeline
        cls_predict = unittest.mock.Mock(wraps=cls.steps[-1][1].predict_proba)
        cls.steps[-1][-1].predict_proba = cls_predict
        prediction = cls.predict_proba(X_test, batch_size=20)
        self.assertEqual((1647, 10), prediction.shape)
        self.assertEqual(84, cls_predict.call_count)
        assert_array_almost_equal(prediction_, prediction)

        # Multilabel
        cls = SimpleClassificationPipeline(include={'classifier': ['lda']})
        X_train, Y_train, X_test, Y_test = get_dataset(dataset='digits')
        Y_train = np.array(list([(list([1 if i != y else 0 for i in range(10)]))
                                 for y in Y_train]))
        cls.fit(X_train, Y_train)
        X_test_ = X_test.copy()
        prediction_ = cls.predict_proba(X_test_)
        # The object behind the last step in the pipeline
        cls_predict = unittest.mock.Mock(wraps=cls.steps[-1][1].predict_proba)
        cls.steps[-1][-1].predict_proba = cls_predict
        prediction = cls.predict_proba(X_test, batch_size=20)
        self.assertEqual((1647, 10), prediction.shape)
        self.assertEqual(84, cls_predict.call_count)
        assert_array_almost_equal(prediction_, prediction)

# 需要导入模块: from autosklearn.pipeline.classification import SimpleClassificationPipeline [as 别名]
# 或者: from autosklearn.pipeline.classification.SimpleClassificationPipeline import fit [as 别名]
    def test_predict_proba_batched(self):
        cs = SimpleClassificationPipeline.get_hyperparameter_search_space()
        default = cs.get_default_configuration()

        # Multiclass
        cls = SimpleClassificationPipeline(default)
        X_train, Y_train, X_test, Y_test = get_dataset(dataset="digits")
        cls.fit(X_train, Y_train)
        X_test_ = X_test.copy()
        prediction_ = cls.predict_proba(X_test_)
        # The object behind the last step in the pipeline
        cls_predict = mock.Mock(wraps=cls.pipeline_.steps[-1][1])
        cls.pipeline_.steps[-1] = ("estimator", cls_predict)
        prediction = cls.predict_proba(X_test, batch_size=20)
        self.assertEqual((1647, 10), prediction.shape)
        self.assertEqual(84, cls_predict.predict_proba.call_count)
        assert_array_almost_equal(prediction_, prediction)

        # Multilabel
        cls = SimpleClassificationPipeline(default)
        X_train, Y_train, X_test, Y_test = get_dataset(dataset="digits")
        Y_train = np.array(list([(list([1 if i != y else 0 for i in range(10)])) for y in Y_train]))
        cls.fit(X_train, Y_train)
        X_test_ = X_test.copy()
        prediction_ = cls.predict_proba(X_test_)
        cls_predict = mock.Mock(wraps=cls.pipeline_.steps[-1][1])
        cls.pipeline_.steps[-1] = ("estimator", cls_predict)
        prediction = cls.predict_proba(X_test, batch_size=20)
        self.assertIsInstance(prediction, np.ndarray)
        self.assertEqual(prediction.shape, ((1647, 10)))
        self.assertEqual(84, cls_predict.predict_proba.call_count)
        assert_array_almost_equal(prediction_, prediction)

# 需要导入模块: from autosklearn.pipeline.classification import SimpleClassificationPipeline [as 别名]
# 或者: from autosklearn.pipeline.classification.SimpleClassificationPipeline import fit [as 别名]
    def test_predict_proba_batched_sparse(self):
        cs = SimpleClassificationPipeline.get_hyperparameter_search_space(
            dataset_properties={'sparse': True})

        config = Configuration(cs,
                               values={"balancing:strategy": "none",
                                       "classifier:__choice__": "random_forest",
                                       "imputation:strategy": "mean",
                                       "one_hot_encoding:minimum_fraction": 0.01,
                                       "one_hot_encoding:use_minimum_fraction": 'True',
                                       "preprocessor:__choice__": "no_preprocessing",
                                       'classifier:random_forest:bootstrap': 'True',
                                       'classifier:random_forest:criterion': 'gini',
                                       'classifier:random_forest:max_depth': 'None',
                                       'classifier:random_forest:min_samples_split': 2,
                                       'classifier:random_forest:min_samples_leaf': 2,
                                       'classifier:random_forest:min_weight_fraction_leaf': 0.0,
                                       'classifier:random_forest:max_features': 0.5,
                                       'classifier:random_forest:max_leaf_nodes': 'None',
                                       'classifier:random_forest:n_estimators': 100,
                                       "rescaling:__choice__": "min/max"})

        # Multiclass
        cls = SimpleClassificationPipeline(config)
        X_train, Y_train, X_test, Y_test = get_dataset(dataset='digits',
                                                       make_sparse=True)
        cls.fit(X_train, Y_train)
        X_test_ = X_test.copy()
        prediction_ = cls.predict_proba(X_test_)
        # The object behind the last step in the pipeline
        cls_predict = mock.Mock(wraps=cls.pipeline_.steps[-1][1])
        cls.pipeline_.steps[-1] = ("estimator", cls_predict)
        prediction = cls.predict_proba(X_test, batch_size=20)
        self.assertEqual((1647, 10), prediction.shape)
        self.assertEqual(84, cls_predict.predict_proba.call_count)
        assert_array_almost_equal(prediction_, prediction)

        # Multilabel
        cls = SimpleClassificationPipeline(config)
        X_train, Y_train, X_test, Y_test = get_dataset(dataset='digits',
                                                       make_sparse=True)
        Y_train_ = np.zeros((Y_train.shape[0], 10))
        for i, y in enumerate(Y_train):
            Y_train_[i][y] = 1
        Y_train = Y_train_
        cls.fit(X_train, Y_train)
        X_test_ = X_test.copy()
        prediction_ = cls.predict_proba(X_test_)
        cls_predict = mock.Mock(wraps=cls.pipeline_.steps[-1][1])
        cls.pipeline_.steps[-1] = ("estimator", cls_predict)
        prediction = cls.predict_proba(X_test, batch_size=20)
        self.assertEqual(prediction.shape, ((1647, 10)))
        self.assertIsInstance(prediction, np.ndarray)
        self.assertEqual(84, cls_predict.predict_proba.call_count)
        assert_array_almost_equal(prediction_, prediction)

# 需要导入模块: from autosklearn.pipeline.classification import SimpleClassificationPipeline [as 别名]
# 或者: from autosklearn.pipeline.classification.SimpleClassificationPipeline import fit [as 别名]
    def test_predict_proba_batched_sparse(self):
        cs = SimpleClassificationPipeline.get_hyperparameter_search_space(dataset_properties={"sparse": True})

        config = Configuration(
            cs,
            values={
                "balancing:strategy": "none",
                "classifier:__choice__": "random_forest",
                "imputation:strategy": "mean",
                "one_hot_encoding:minimum_fraction": 0.01,
                "one_hot_encoding:use_minimum_fraction": "True",
                "preprocessor:__choice__": "no_preprocessing",
                "classifier:random_forest:bootstrap": "True",
                "classifier:random_forest:criterion": "gini",
                "classifier:random_forest:max_depth": "None",
                "classifier:random_forest:min_samples_split": 2,
                "classifier:random_forest:min_samples_leaf": 2,
                "classifier:random_forest:min_weight_fraction_leaf": 0.0,
                "classifier:random_forest:max_features": 0.5,
                "classifier:random_forest:max_leaf_nodes": "None",
                "classifier:random_forest:n_estimators": 100,
                "rescaling:__choice__": "min/max",
            },
        )

        # Multiclass
        cls = SimpleClassificationPipeline(config)
        X_train, Y_train, X_test, Y_test = get_dataset(dataset="digits", make_sparse=True)
        cls.fit(X_train, Y_train)
        X_test_ = X_test.copy()
        prediction_ = cls.predict_proba(X_test_)
        # The object behind the last step in the pipeline
        cls_predict = mock.Mock(wraps=cls.pipeline_.steps[-1][1])
        cls.pipeline_.steps[-1] = ("estimator", cls_predict)
        prediction = cls.predict_proba(X_test, batch_size=20)
        self.assertEqual((1647, 10), prediction.shape)
        self.assertEqual(84, cls_predict.predict_proba.call_count)
        assert_array_almost_equal(prediction_, prediction)

        # Multilabel
        cls = SimpleClassificationPipeline(config)
        X_train, Y_train, X_test, Y_test = get_dataset(dataset="digits", make_sparse=True)
        Y_train = np.array(list([(list([1 if i != y else 0 for i in range(10)])) for y in Y_train]))
        cls.fit(X_train, Y_train)
        X_test_ = X_test.copy()
        prediction_ = cls.predict_proba(X_test_)
        cls_predict = mock.Mock(wraps=cls.pipeline_.steps[-1][1])
        cls.pipeline_.steps[-1] = ("estimator", cls_predict)
        prediction = cls.predict_proba(X_test, batch_size=20)
        self.assertEqual(prediction.shape, ((1647, 10)))
        self.assertIsInstance(prediction, np.ndarray)
        self.assertEqual(84, cls_predict.predict_proba.call_count)
        assert_array_almost_equal(prediction_, prediction)

# 需要导入模块: from autosklearn.pipeline.classification import SimpleClassificationPipeline [as 别名]
# 或者: from autosklearn.pipeline.classification.SimpleClassificationPipeline import fit [as 别名]
    def test_configurations_sparse(self):
        # Use a limit of ~4GiB
        limit = 4000 * 1024 * 1024
        resource.setrlimit(resource.RLIMIT_AS, (limit, limit))

        cs = SimpleClassificationPipeline.get_hyperparameter_search_space(
            dataset_properties={'sparse': True})
        print(cs)
        for i in range(10):
            config = cs.sample_configuration()
            config._populate_values()
            if 'classifier:passive_aggressive:n_iter' in config and \
                    config['classifier:passive_aggressive:n_iter'] is not None:
                config._values['classifier:passive_aggressive:n_iter'] = 5
            if 'classifier:sgd:n_iter' in config and \
                    config['classifier:sgd:n_iter'] is not None:
                config._values['classifier:sgd:n_iter'] = 5

            print(config)
            X_train, Y_train, X_test, Y_test = get_dataset(dataset='digits',
                                                           make_sparse=True)
            cls = SimpleClassificationPipeline(config, random_state=1)
            try:
                cls.fit(X_train, Y_train)
                predictions = cls.predict(X_test)
            except ValueError as e:
                if "Floating-point under-/overflow occurred at epoch" in \
                       e.args[0] or \
                        "removed all features" in e.args[0] or \
                                "all features are discarded" in e.args[0]:
                    continue
                else:
                    print(config)
                    traceback.print_tb(sys.exc_info()[2])
                    raise e
            except RuntimeWarning as e:
                if "invalid value encountered in sqrt" in e.args[0]:
                    continue
                elif "divide by zero encountered in" in e.args[0]:
                    continue
                elif "invalid value encountered in divide" in e.args[0]:
                    continue
                elif "invalid value encountered in true_divide" in e.args[0]:
                    continue
                else:
                    print(config)
                    raise e
            except UserWarning as e:
                if "FastICA did not converge" in e.args[0]:
                    continue
                else:
                    print(config)
                    raise e

# 需要导入模块: from autosklearn.pipeline.classification import SimpleClassificationPipeline [as 别名]
# 或者: from autosklearn.pipeline.classification.SimpleClassificationPipeline import fit [as 别名]
    def test_predict_batched_sparse(self):
        cs = SimpleClassificationPipeline.get_hyperparameter_search_space(
            dataset_properties={'sparse': True})
        config = Configuration(cs,
            values={"balancing:strategy": "none",
                    "classifier:__choice__": "random_forest",
                    "imputation:strategy": "mean",
                    "one_hot_encoding:minimum_fraction": 0.01,
                    "one_hot_encoding:use_minimum_fraction": "True",
                    "preprocessor:__choice__": "no_preprocessing",
                    'classifier:random_forest:bootstrap': 'True',
                    'classifier:random_forest:criterion': 'gini',
                    'classifier:random_forest:max_depth': 'None',
                    'classifier:random_forest:min_samples_split': 2,
                    'classifier:random_forest:min_samples_leaf': 2,
                    'classifier:random_forest:max_features': 0.5,
                    'classifier:random_forest:max_leaf_nodes': 'None',
                    'classifier:random_forest:n_estimators': 100,
                    'classifier:random_forest:min_weight_fraction_leaf': 0.0,
                    "rescaling:__choice__": "min/max"})
        cls = SimpleClassificationPipeline(config)

        # Multiclass
        X_train, Y_train, X_test, Y_test = get_dataset(dataset='digits',
                                                       make_sparse=True)
        cls.fit(X_train, Y_train)
        X_test_ = X_test.copy()
        prediction_ = cls.predict(X_test_)
        cls_predict = mock.Mock(wraps=cls.pipeline_)
        cls.pipeline_ = cls_predict
        prediction = cls.predict(X_test, batch_size=20)
        self.assertEqual((1647,), prediction.shape)
        self.assertEqual(83, cls_predict.predict.call_count)
        assert_array_almost_equal(prediction_, prediction)

        # Multilabel
        X_train, Y_train, X_test, Y_test = get_dataset(dataset='digits',
                                                       make_sparse=True)
        Y_train = np.array([(y, 26 - y) for y in Y_train])
        cls.fit(X_train, Y_train)
        X_test_ = X_test.copy()
        prediction_ = cls.predict(X_test_)
        cls_predict = mock.Mock(wraps=cls.pipeline_)
        cls.pipeline_ = cls_predict
        prediction = cls.predict(X_test, batch_size=20)
        self.assertEqual((1647, 2), prediction.shape)
        self.assertEqual(83, cls_predict.predict.call_count)
        assert_array_almost_equal(prediction_, prediction)

# 需要导入模块: from autosklearn.pipeline.classification import SimpleClassificationPipeline [as 别名]
# 或者: from autosklearn.pipeline.classification.SimpleClassificationPipeline import fit [as 别名]
    def test_predict_batched_sparse(self):
        cls = SimpleClassificationPipeline(dataset_properties={'sparse': True},
                                           include={'classifier': ['sgd']})

        # Multiclass
        X_train, Y_train, X_test, Y_test = get_dataset(dataset='digits',
                                                       make_sparse=True)
        cls.fit(X_train, Y_train)
        X_test_ = X_test.copy()
        prediction_ = cls.predict_proba(X_test_)
        # The object behind the last step in the pipeline
        cls_predict = unittest.mock.Mock(wraps=cls.steps[-1][1].predict_proba)
        cls.steps[-1][-1].predict_proba = cls_predict
        prediction = cls.predict_proba(X_test, batch_size=20)
        self.assertEqual((1647, 10), prediction.shape)
        self.assertEqual(84, cls_predict.call_count)
        assert_array_almost_equal(prediction_, prediction)

# 需要导入模块: from autosklearn.pipeline.classification import SimpleClassificationPipeline [as 别名]
# 或者: from autosklearn.pipeline.classification.SimpleClassificationPipeline import fit [as 别名]
 def test_default_configuration(self):
     for i in range(2):
         X_train, Y_train, X_test, Y_test = get_dataset(dataset='iris')
         auto = SimpleClassificationPipeline()
         auto = auto.fit(X_train, Y_train)
         predictions = auto.predict(X_test)
         self.assertAlmostEqual(0.94,
             sklearn.metrics.accuracy_score(predictions, Y_test))
         scores = auto.predict_proba(X_test)

# 需要导入模块: from autosklearn.pipeline.classification import SimpleClassificationPipeline [as 别名]
# 或者: from autosklearn.pipeline.classification.SimpleClassificationPipeline import fit [as 别名]
 def test_default_configuration_multilabel(self):
     for i in range(2):
         cs = SimpleClassificationPipeline.get_hyperparameter_search_space(dataset_properties={"multilabel": True})
         default = cs.get_default_configuration()
         X_train, Y_train, X_test, Y_test = get_dataset(dataset="iris", make_multilabel=True)
         auto = SimpleClassificationPipeline(default)
         auto = auto.fit(X_train, Y_train)
         predictions = auto.predict(X_test)
         self.assertAlmostEqual(0.9599999999999995, sklearn.metrics.accuracy_score(predictions, Y_test))
         scores = auto.predict_proba(X_test)

# 需要导入模块: from autosklearn.pipeline.classification import SimpleClassificationPipeline [as 别名]
# 或者: from autosklearn.pipeline.classification.SimpleClassificationPipeline import fit [as 别名]
 def test_default_configuration_multilabel(self):
     for i in range(2):
         dataset_properties = {'multilabel': True}
         classifier = SimpleClassificationPipeline(
             dataset_properties=dataset_properties)
         cs = classifier.get_hyperparameter_search_space()
         default = cs.get_default_configuration()
         X_train, Y_train, X_test, Y_test = get_dataset(dataset='iris',
                                                        make_multilabel=True)
         classifier.set_hyperparameters(default)
         classifier = classifier.fit(X_train, Y_train)
         predictions = classifier.predict(X_test)
         self.assertAlmostEqual(0.94,
                                sklearn.metrics.accuracy_score(predictions,
                                                               Y_test))
         scores = classifier.predict_proba(X_test)

# 需要导入模块: from autosklearn.pipeline.classification import SimpleClassificationPipeline [as 别名]
# 或者: from autosklearn.pipeline.classification.SimpleClassificationPipeline import fit [as 别名]
    def _test_configurations(self, configurations_space, make_sparse=False,
                             data=None, init_params=None,
                             dataset_properties=None):
        # Use a limit of ~3GiB
        limit = 3072 * 1024 * 1024
        resource.setrlimit(resource.RLIMIT_AS, (limit, limit))

        print(configurations_space)

        for i in range(10):
            config = configurations_space.sample_configuration()
            config._populate_values()

            # Restrict configurations which could take too long on travis-ci
            restrictions = {'classifier:passive_aggressive:n_iter': 5,
                            'classifier:sgd:n_iter': 5,
                            'classifier:adaboost:n_estimators': 50,
                            'classifier:adaboost:max_depth': 1,
                            'preprocessor:kernel_pca:n_components': 10,
                            'preprocessor:kitchen_sinks:n_components': 50,
                            'classifier:proj_logit:max_epochs': 1,
                            'classifier:libsvm_svc:degree': 2,
                            'regressor:libsvm_svr:degree': 2,
                            'preprocessor:truncatedSVD:target_dim': 10,
                            'preprocessor:polynomial:degree': 2,
                            'classifier:lda:n_components': 10,
                            'preprocessor:nystroem_sampler:n_components': 50,
                            'preprocessor:feature_agglomeration:n_clusters': 2,
                            'classifier:gradient_boosting:max_depth': 2,
                            'classifier:gradient_boosting:n_estimators': 50}

            for restrict_parameter in restrictions:
                restrict_to = restrictions[restrict_parameter]
                if restrict_parameter in config and \
                        config[restrict_parameter] is not None:
                    config._values[restrict_parameter] = restrict_to

            print(config)

            if data is None:
                X_train, Y_train, X_test, Y_test = get_dataset(
                    dataset='digits', make_sparse=make_sparse, add_NaNs=True)
            else:
                X_train = data['X_train'].copy()
                Y_train = data['Y_train'].copy()
                X_test = data['X_test'].copy()
                Y_test = data['Y_test'].copy()

            init_params_ = copy.deepcopy(init_params)
            cls = SimpleClassificationPipeline(random_state=1,
                                               dataset_properties=dataset_properties,
                                               init_params=init_params_,)
            cls.set_hyperparameters(config, init_params=init_params_)
            try:
                cls.fit(X_train, Y_train, )
                predictions = cls.predict(X_test.copy())
                predictions = cls.predict_proba(X_test)
            except MemoryError as e:
                continue
            except ValueError as e:
                if "Floating-point under-/overflow occurred at epoch" in \
                        e.args[0]:
                    continue
                elif "removed all features" in e.args[0]:
                    continue
                elif "all features are discarded" in e.args[0]:
                    continue
                elif "Numerical problems in QDA" in e.args[0]:
                    continue
                elif 'Bug in scikit-learn' in e.args[0]:
                    continue
                elif 'The condensed distance matrix must contain only finite ' \
                     'values.' in e.args[0]:
                    continue
                else:
                    print(config)
                    print(traceback.format_exc())
                    raise e
            except RuntimeWarning as e:
                if "invalid value encountered in sqrt" in e.args[0]:
                    continue
                elif "divide by zero encountered in" in e.args[0]:
                    continue
                elif "invalid value encountered in divide" in e.args[0]:
                    continue
                elif "invalid value encountered in true_divide" in e.args[0]:
                    continue
                else:
                    print(traceback.format_exc())
                    print(config)
                    raise e
            except UserWarning as e:
                if "FastICA did not converge" in e.args[0]:
                    continue
                else:
                    print(traceback.format_exc())
                    print(config)
                    raise e

# 需要导入模块: from autosklearn.pipeline.classification import SimpleClassificationPipeline [as 别名]
# 或者: from autosklearn.pipeline.classification.SimpleClassificationPipeline import fit [as 别名]
    def test_weighting_effect(self):
        data = sklearn.datasets.make_classification(
            n_samples=200, n_features=10, n_redundant=2, n_informative=2,
            n_repeated=2, n_clusters_per_class=2, weights=[0.8, 0.2],
            random_state=1)

        for name, clf, acc_no_weighting, acc_weighting, places in \
                [('adaboost', AdaboostClassifier, 0.810, 0.735, 3),
                 ('decision_tree', DecisionTree, 0.780, 0.643, 3),
                 ('extra_trees', ExtraTreesClassifier, 0.780, 0.8, 3),
                 ('gradient_boosting', GradientBoostingClassifier,
                  0.737, 0.684, 3),
                 ('random_forest', RandomForest, 0.780, 0.789, 3),
                 ('libsvm_svc', LibSVM_SVC, 0.769, 0.72, 3),
                 ('liblinear_svc', LibLinear_SVC, 0.762, 0.735, 3),
                 ('passive_aggressive', PassiveAggressive, 0.642, 0.449, 3),
                 ('sgd', SGD, 0.818, 0.575, 2)
                ]:
            for strategy, acc in [
                ('none', acc_no_weighting),
                ('weighting', acc_weighting)
            ]:
                # Fit
                data_ = copy.copy(data)
                X_train = data_[0][:100]
                Y_train = data_[1][:100]
                X_test = data_[0][100:]
                Y_test = data_[1][100:]

                include = {'classifier': [name],
                           'preprocessor': ['no_preprocessing']}
                classifier = SimpleClassificationPipeline(
                    random_state=1, include=include)
                cs = classifier.get_hyperparameter_search_space()
                default = cs.get_default_configuration()
                default._values['balancing:strategy'] = strategy
                classifier = SimpleClassificationPipeline(
                    default, random_state=1, include=include)
                predictor = classifier.fit(X_train, Y_train)
                predictions = predictor.predict(X_test)
                self.assertAlmostEqual(
                    sklearn.metrics.f1_score(predictions, Y_test), acc,
                    places=places, msg=(name, strategy))

                # fit_transformer and fit_estimator
                data_ = copy.copy(data)
                X_train = data_[0][:100]
                Y_train = data_[1][:100]
                X_test = data_[0][100:]
                Y_test = data_[1][100:]

                classifier = SimpleClassificationPipeline(
                    default, random_state=1, include=include)
                classifier.set_hyperparameters(configuration=default)
                Xt, fit_params = classifier.fit_transformer(X_train, Y_train)
                classifier.fit_estimator(Xt, Y_train, **fit_params)
                predictions = classifier.predict(X_test)
                self.assertAlmostEqual(
                    sklearn.metrics.f1_score(predictions, Y_test), acc,
                    places=places)

        for name, pre, acc_no_weighting, acc_weighting in \
                [('extra_trees_preproc_for_classification',
                    ExtraTreesPreprocessorClassification, 0.810, 0.563),
                 ('liblinear_svc_preprocessor', LibLinear_Preprocessor,
                    0.837, 0.567)]:
            for strategy, acc in [('none', acc_no_weighting),
                                  ('weighting', acc_weighting)]:
                data_ = copy.copy(data)
                X_train = data_[0][:100]
                Y_train = data_[1][:100]
                X_test = data_[0][100:]
                Y_test = data_[1][100:]

                include = {'classifier': ['sgd'], 'preprocessor': [name]}

                classifier = SimpleClassificationPipeline(
                    random_state=1, include=include)
                cs = classifier.get_hyperparameter_search_space()
                default = cs.get_default_configuration()
                default._values['balancing:strategy'] = strategy
                classifier.set_hyperparameters(default)
                predictor = classifier.fit(X_train, Y_train)
                predictions = predictor.predict(X_test)
                self.assertAlmostEqual(
                    sklearn.metrics.f1_score(predictions, Y_test), acc,
                    places=3, msg=(name, strategy))

                # fit_transformer and fit_estimator
                data_ = copy.copy(data)
                X_train = data_[0][:100]
                Y_train = data_[1][:100]
                X_test = data_[0][100:]
                Y_test = data_[1][100:]

                default._values['balancing:strategy'] = strategy
                classifier = SimpleClassificationPipeline(
                    default, random_state=1, include=include)
                Xt, fit_params = classifier.fit_transformer(X_train, Y_train)
                classifier.fit_estimator(Xt, Y_train, **fit_params)
#.........这里部分代码省略.........