Python sklearn.ensemble.AdaBoostClassifier() Examples
The following are 30 code examples for showing how to use sklearn.ensemble.AdaBoostClassifier(). These examples are extracted from open source projects. You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example.
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Example 1
Project: stock-price-prediction Author: chinuy File: classifier.py License: MIT License | 6 votes |
def buildModel(dataset, method, parameters): """ Build final model for predicting real testing data """ features = dataset.columns[0:-1] if method == 'RNN': clf = performRNNlass(dataset[features], dataset['UpDown']) return clf elif method == 'RF': clf = RandomForestClassifier(n_estimators=1000, n_jobs=-1) elif method == 'KNN': clf = neighbors.KNeighborsClassifier() elif method == 'SVM': c = parameters[0] g = parameters[1] clf = SVC(C=c, gamma=g) elif method == 'ADA': clf = AdaBoostClassifier() return clf.fit(dataset[features], dataset['UpDown'])
Example 2
Project: Machine-Learning-for-Beginner-by-Python3 Author: Anfany File: AdaBoost_Classify.py License: MIT License | 6 votes |
def Train(data, modelcount, censhu, yanzhgdata): model = AdaBoostClassifier(DecisionTreeClassifier(max_depth=censhu), algorithm="SAMME", n_estimators=modelcount, learning_rate=0.8) model.fit(data[:, :-1], data[:, -1]) # 给出训练数据的预测值 train_out = model.predict(data[:, :-1]) # 计算MSE train_mse = fmse(data[:, -1], train_out)[0] # 给出验证数据的预测值 add_yan = model.predict(yanzhgdata[:, :-1]) # 计算f1度量 add_mse = fmse(yanzhgdata[:, -1], add_yan)[0] print(train_mse, add_mse) return train_mse, add_mse # 最终确定组合的函数
Example 3
Project: Machine-Learning-for-Beginner-by-Python3 Author: Anfany File: AdaBoost_Classify.py License: MIT License | 6 votes |
def recspre(estrs, predata, datadict, zhe): mo, ze = estrs.split('-') model = AdaBoostClassifier(DecisionTreeClassifier(max_depth=int(ze)), algorithm="SAMME", n_estimators=int(mo), learning_rate=0.8) model.fit(datadict[zhe]['train'][:, :-1], datadict[zhe]['train'][:, -1]) # 预测 yucede = model.predict(predata[:, :-1]) # 计算混淆矩阵 print(ConfuseMatrix(predata[:, -1], yucede)) return fmse(predata[:, -1], yucede) # 主函数
Example 4
Project: Mastering-Elasticsearch-7.0 Author: PacktPublishing File: test_weight_boosting.py License: MIT License | 6 votes |
def test_gridsearch(): # Check that base trees can be grid-searched. # AdaBoost classification boost = AdaBoostClassifier(base_estimator=DecisionTreeClassifier()) parameters = {'n_estimators': (1, 2), 'base_estimator__max_depth': (1, 2), 'algorithm': ('SAMME', 'SAMME.R')} clf = GridSearchCV(boost, parameters) clf.fit(iris.data, iris.target) # AdaBoost regression boost = AdaBoostRegressor(base_estimator=DecisionTreeRegressor(), random_state=0) parameters = {'n_estimators': (1, 2), 'base_estimator__max_depth': (1, 2)} clf = GridSearchCV(boost, parameters) clf.fit(boston.data, boston.target)
Example 5
Project: Mastering-Elasticsearch-7.0 Author: PacktPublishing File: test_weight_boosting.py License: MIT License | 6 votes |
def test_importances(): # Check variable importances. X, y = datasets.make_classification(n_samples=2000, n_features=10, n_informative=3, n_redundant=0, n_repeated=0, shuffle=False, random_state=1) for alg in ['SAMME', 'SAMME.R']: clf = AdaBoostClassifier(algorithm=alg) clf.fit(X, y) importances = clf.feature_importances_ assert_equal(importances.shape[0], 10) assert_equal((importances[:3, np.newaxis] >= importances[3:]).all(), True)
Example 6
Project: Mastering-Elasticsearch-7.0 Author: PacktPublishing File: test_weight_boosting.py License: MIT License | 6 votes |
def test_multidimensional_X(): """ Check that the AdaBoost estimators can work with n-dimensional data matrix """ from sklearn.dummy import DummyClassifier, DummyRegressor rng = np.random.RandomState(0) X = rng.randn(50, 3, 3) yc = rng.choice([0, 1], 50) yr = rng.randn(50) boost = AdaBoostClassifier(DummyClassifier(strategy='most_frequent')) boost.fit(X, yc) boost.predict(X) boost.predict_proba(X) boost = AdaBoostRegressor(DummyRegressor()) boost.fit(X, yr) boost.predict(X)
Example 7
Project: libfaceid Author: richmondu File: classifier.py License: MIT License | 6 votes |
def __init__(self, classifier=FaceClassifierModels.DEFAULT): self._clf = None if classifier == FaceClassifierModels.LINEAR_SVM: self._clf = SVC(C=1.0, kernel="linear", probability=True) elif classifier == FaceClassifierModels.NAIVE_BAYES: self._clf = GaussianNB() elif classifier == FaceClassifierModels.RBF_SVM: self._clf = SVC(C=1, kernel='rbf', probability=True, gamma=2) elif classifier == FaceClassifierModels.NEAREST_NEIGHBORS: self._clf = KNeighborsClassifier(1) elif classifier == FaceClassifierModels.DECISION_TREE: self._clf = DecisionTreeClassifier(max_depth=5) elif classifier == FaceClassifierModels.RANDOM_FOREST: self._clf = RandomForestClassifier(max_depth=5, n_estimators=10, max_features=1) elif classifier == FaceClassifierModels.NEURAL_NET: self._clf = MLPClassifier(alpha=1) elif classifier == FaceClassifierModels.ADABOOST: self._clf = AdaBoostClassifier() elif classifier == FaceClassifierModels.QDA: self._clf = QuadraticDiscriminantAnalysis() print("classifier={}".format(FaceClassifierModels(classifier)))
Example 8
Project: sia-cog Author: tech-quantum File: scikitlearn.py License: MIT License | 6 votes |
def getModels(): result = [] result.append("LinearRegression") result.append("BayesianRidge") result.append("ARDRegression") result.append("ElasticNet") result.append("HuberRegressor") result.append("Lasso") result.append("LassoLars") result.append("Rigid") result.append("SGDRegressor") result.append("SVR") result.append("MLPClassifier") result.append("KNeighborsClassifier") result.append("SVC") result.append("GaussianProcessClassifier") result.append("DecisionTreeClassifier") result.append("RandomForestClassifier") result.append("AdaBoostClassifier") result.append("GaussianNB") result.append("LogisticRegression") result.append("QuadraticDiscriminantAnalysis") return result
Example 9
Project: Awesome-Scripts Author: DedSecInside File: AdaBoost.py License: MIT License | 6 votes |
def main(): # prepare data trainingSet=[] testSet=[] accuracy = 0.0 split = 0.20 loadDataset('../Dataset/med.data', split, trainingSet, testSet) print('Train set: ' + repr(len(trainingSet))) print('Test set: ' + repr(len(testSet))) trainData = np.array(trainingSet)[:,0:np.array(trainingSet).shape[1] - 1] columns = trainData.shape[1] X = np.array(trainData) y = np.array(trainingSet)[:,columns] clf = AdaBoostClassifier() clf.fit(X, y) testData = np.array(testSet)[:,0:np.array(trainingSet).shape[1] - 1] X_test = np.array(testData) y_test = np.array(testSet)[:,columns] accuracy = clf.score(X_test,y_test) accuracy *= 100 print("Accuracy %:",accuracy)
Example 10
Project: DataMiningCompetitionFirstPrize Author: lzddzh File: ada_boosting.py License: MIT License | 6 votes |
def learn(x, y, test_x): # set sample weight weight_list = [] for j in range(len(y)): if y[j] == "0": weight_list.append(variables.weight_0_ada) if y[j] == "1000": weight_list.append(variables.weight_1000_ada) if y[j] == "1500": weight_list.append(variables.weight_1500_ada) if y[j] == "2000": weight_list.append(variables.weight_2000_ada) clf = AdaBoostClassifier(n_estimators=variables.n_estimators_ada, learning_rate=variables.learning_rate_ada).fit(x, y, np.asarray( weight_list)) prediction_list = clf.predict(test_x) prediction_list_prob = clf.predict_proba(test_x) return prediction_list, prediction_list_prob
Example 11
Project: jh-kaggle-util Author: jeffheaton File: models.py License: Apache License 2.0 | 6 votes |
def run_sklearn(): n_trees = 100 n_folds = 3 # https://www.analyticsvidhya.com/blog/2015/06/tuning-random-forest-model/ alg_list = [ ['rforest',RandomForestClassifier(n_estimators=1000, n_jobs=-1, verbose=1, max_depth=3)], ['extree',ExtraTreesClassifier(n_estimators = 1000,max_depth=3,n_jobs=-1)], ['adaboost',AdaBoostClassifier(base_estimator=None, n_estimators=600, learning_rate=1.0)], ['knn', sklearn.neighbors.KNeighborsClassifier(n_neighbors=5,n_jobs=-1)] ] start_time = time.time() for name,alg in alg_list: train = jhkaggle.train_sklearn.TrainSKLearn("1",name,alg,False) train.run() train = None
Example 12
Project: CDSS Author: HealthRex File: SupervisedClassifier.py License: GNU General Public License v3.0 | 6 votes |
def _train_adaboost(self, X, y): # Define hyperparams. # http://scikit-learn.org/stable/modules/ensemble.html#adaboost self._get_or_set_hyperparam('base_estimator') self._get_or_set_hyperparam('n_estimators') self._get_or_set_hyperparam('learning_rate') self._get_or_set_hyperparam('adaboost_algorithm') self._get_or_set_hyperparam('n_jobs') self._get_or_set_hyperparam('class_weight') self._get_or_set_hyperparam('scoring') # Build initial model. self._model = AdaBoostClassifier(\ base_estimator=DecisionTreeClassifier(class_weight='balanced'), n_estimators=self._hyperparams['n_estimators'], learning_rate=self._hyperparams['learning_rate'], algorithm=self._hyperparams['adaboost_algorithm'], random_state=self._hyperparams['random_state'] ) # Tune hyperparams. self._tune_hyperparams(self._hyperparam_search_space, X, y)
Example 13
Project: sklearn-onnx Author: onnx File: test_sklearn_adaboost_converter.py License: MIT License | 6 votes |
def test_ada_boost_classifier_samme_r(self): model, X_test = fit_classification_model(AdaBoostClassifier( n_estimators=10, algorithm="SAMME.R", random_state=42, base_estimator=DecisionTreeClassifier( max_depth=2, random_state=42)), 3) model_onnx = convert_sklearn( model, "AdaBoost classification", [("input", FloatTensorType((None, X_test.shape[1])))], target_opset=10 ) self.assertIsNotNone(model_onnx) dump_data_and_model( X_test, model, model_onnx, basename="SklearnAdaBoostClassifierSAMMER", allow_failure="StrictVersion(" "onnxruntime.__version__)" "<= StrictVersion('0.2.1')", )
Example 14
Project: sklearn-onnx Author: onnx File: test_sklearn_adaboost_converter.py License: MIT License | 6 votes |
def test_ada_boost_classifier_samme_r_decision_function(self): model, X_test = fit_classification_model(AdaBoostClassifier( n_estimators=10, algorithm="SAMME.R", random_state=42, base_estimator=DecisionTreeClassifier( max_depth=2, random_state=42)), 4) options = {id(model): {'raw_scores': True}} model_onnx = convert_sklearn( model, "AdaBoost classification", [("input", FloatTensorType((None, X_test.shape[1])))], target_opset=10, options=options, ) self.assertIsNotNone(model_onnx) dump_data_and_model( X_test, model, model_onnx, basename="SklearnAdaBoostClassifierSAMMERDecisionFunction", allow_failure="StrictVersion(" "onnxruntime.__version__)" "<= StrictVersion('0.2.1')", methods=['predict', 'decision_function'], )
Example 15
Project: sklearn-onnx Author: onnx File: test_sklearn_adaboost_converter.py License: MIT License | 6 votes |
def test_ada_boost_classifier_samme_r_logreg(self): model, X_test = fit_classification_model(AdaBoostClassifier( n_estimators=5, algorithm="SAMME.R", base_estimator=LogisticRegression( solver='liblinear')), 4) model_onnx = convert_sklearn( model, "AdaBoost classification", [("input", FloatTensorType((None, X_test.shape[1])))], target_opset=10 ) self.assertIsNotNone(model_onnx) dump_data_and_model( X_test, model, model_onnx, basename="SklearnAdaBoostClassifierSAMMERLogReg", allow_failure="StrictVersion(" "onnxruntime.__version__)" "<= StrictVersion('0.2.1')", )
Example 16
Project: sklearn-onnx Author: onnx File: test_sklearn_adaboost_converter.py License: MIT License | 6 votes |
def test_ada_boost_classifier_samme(self): model, X_test = fit_classification_model(AdaBoostClassifier( n_estimators=5, algorithm="SAMME", random_state=42, base_estimator=DecisionTreeClassifier( max_depth=6, random_state=42)), 2) model_onnx = convert_sklearn( model, "AdaBoostClSamme", [("input", FloatTensorType((None, X_test.shape[1])))], target_opset=10, ) self.assertIsNotNone(model_onnx) dump_data_and_model( X_test, model, model_onnx, basename="SklearnAdaBoostClassifierSAMMEDT", allow_failure="StrictVersion(" "onnxruntime.__version__)" "< StrictVersion('0.5.0')", )
Example 17
Project: sklearn-onnx Author: onnx File: test_sklearn_adaboost_converter.py License: MIT License | 6 votes |
def test_ada_boost_classifier_samme_decision_function(self): model, X_test = fit_classification_model(AdaBoostClassifier( n_estimators=5, algorithm="SAMME", random_state=42, base_estimator=DecisionTreeClassifier( max_depth=6, random_state=42)), 2) options = {id(model): {'raw_scores': True}} model_onnx = convert_sklearn( model, "AdaBoostClSamme", [("input", FloatTensorType((None, X_test.shape[1])))], target_opset=10, options=options, ) self.assertIsNotNone(model_onnx) dump_data_and_model( X_test, model, model_onnx, basename="SklearnAdaBoostClassifierSAMMEDTDecisionFunction", allow_failure="StrictVersion(" "onnxruntime.__version__)" "< StrictVersion('0.5.0')", methods=['predict', 'decision_function_binary'], )
Example 18
Project: sklearn-onnx Author: onnx File: test_sklearn_adaboost_converter.py License: MIT License | 6 votes |
def test_ada_boost_classifier_bool(self): model, X_test = fit_classification_model( AdaBoostClassifier(random_state=42), 3, is_bool=True) model_onnx = convert_sklearn( model, "AdaBoost classification", [("input", BooleanTensorType((None, X_test.shape[1])))], target_opset=10, ) self.assertIsNotNone(model_onnx) dump_data_and_model( X_test, model, model_onnx, basename="SklearnAdaBoostClassifierBool", allow_failure="StrictVersion(" "onnxruntime.__version__)" "<= StrictVersion('0.2.1')", )
Example 19
Project: abu Author: bbfamily File: ABuMLCreater.py License: GNU General Public License v3.0 | 6 votes |
def adaboost_classifier(self, assign=True, base_estimator=DecisionTreeClassifier(), **kwargs): """ 有监督学习分类器,实例化AdaBoostClassifier,默认使用: AdaBoostClassifier(base_estimator=base_estimator, n_estimators=100, random_state=1) 通过**kwargs即关键字参数透传AdaBoostClassifier,即: AdaBoostClassifier(**kwargs) :param base_estimator: 默认使用DecisionTreeClassifier() :param assign: 是否保存实例后的AdaBoostClassifier对象,默认True,self.clf = clf :param kwargs: 有参数情况下初始化: AdaBoostClassifier(**kwargs) 无参数情况下初始化: AdaBoostClassifier(n_estimators=100, random_state=1) :return: 实例化的AdaBoostClassifier对象 """ if kwargs is not None and len(kwargs) > 0: if 'base_estimator' not in kwargs: kwargs['base_estimator'] = base_estimator clf = AdaBoostClassifier(**kwargs) else: clf = AdaBoostClassifier(base_estimator=base_estimator, n_estimators=100, random_state=1) if assign: self.clf = clf return clf
Example 20
Project: easyML Author: aarshayj File: models_classification.py License: BSD 3-Clause "New" or "Revised" License | 6 votes |
def __init__( self,data_block, predictors=[],cv_folds=10, scoring_metric='accuracy',additional_display_metrics=[]): base_classification.__init__( self, alg=AdaBoostClassifier(), data_block=data_block, predictors=predictors,cv_folds=cv_folds, scoring_metric=scoring_metric, additional_display_metrics=additional_display_metrics ) self.model_output = pd.Series(self.default_parameters) self.model_output['Feature_Importance'] = "-" #Set parameters to default values: self.set_parameters(set_default=True)
Example 21
Project: AirTicketPredicting Author: junlulocky File: ClassificationAdaBoost.py License: MIT License | 6 votes |
def __init__(self, isTrain, isOutlierRemoval): super(ClassificationAdaBoost, self).__init__(isTrain, isOutlierRemoval) # data preprocessing self.dataPreprocessing() self.dt_stump = DecisionTreeClassifier(max_depth=10) self.ada = AdaBoostClassifier( base_estimator=self.dt_stump, learning_rate=1, n_estimators=7, algorithm="SAMME.R") # self.dt_stump = DecisionTreeClassifier(max_depth=14) # self.ada = AdaBoostClassifier( # base_estimator=self.dt_stump, # learning_rate=1, # n_estimators=50, # algorithm="SAMME")
Example 22
Project: DESlib Author: scikit-learn-contrib File: test_static_selection.py License: BSD 3-Clause "New" or "Revised" License | 6 votes |
def test_different_scorer(): X, y = make_classification(n_samples=100, random_state=42) X_val, y_val = make_classification(n_samples=25, random_state=123) pool = AdaBoostClassifier(n_estimators=10).fit(X, y) performances = [] for clf in pool: preds = clf.predict_proba(X_val) performances.append(log_loss(y_val.ravel(), preds[:, -1])) id_best = np.argsort(performances) ss = StaticSelection(pool_classifiers=pool, scoring='neg_log_loss') ss.fit(X_val, y_val) assert (id_best[:ss.n_classifiers_ensemble_] == ss.clf_indices_).all() # Test if static_selection can select the best classifier according to a # metric that needs to be minimized.
Example 23
Project: twitter-stock-recommendation Author: alvarobartt File: test_weight_boosting.py License: MIT License | 6 votes |
def test_gridsearch(): # Check that base trees can be grid-searched. # AdaBoost classification boost = AdaBoostClassifier(base_estimator=DecisionTreeClassifier()) parameters = {'n_estimators': (1, 2), 'base_estimator__max_depth': (1, 2), 'algorithm': ('SAMME', 'SAMME.R')} clf = GridSearchCV(boost, parameters) clf.fit(iris.data, iris.target) # AdaBoost regression boost = AdaBoostRegressor(base_estimator=DecisionTreeRegressor(), random_state=0) parameters = {'n_estimators': (1, 2), 'base_estimator__max_depth': (1, 2)} clf = GridSearchCV(boost, parameters) clf.fit(boston.data, boston.target)
Example 24
Project: twitter-stock-recommendation Author: alvarobartt File: test_weight_boosting.py License: MIT License | 6 votes |
def test_importances(): # Check variable importances. X, y = datasets.make_classification(n_samples=2000, n_features=10, n_informative=3, n_redundant=0, n_repeated=0, shuffle=False, random_state=1) for alg in ['SAMME', 'SAMME.R']: clf = AdaBoostClassifier(algorithm=alg) clf.fit(X, y) importances = clf.feature_importances_ assert_equal(importances.shape[0], 10) assert_equal((importances[:3, np.newaxis] >= importances[3:]).all(), True)
Example 25
Project: fake-news-detection Author: aldengolab File: model_loop.py License: MIT License | 5 votes |
def define_clfs_params(self): ''' Defines all relevant parameters and classes for classfier objects. Edit these if you wish to change parameters. ''' # These are the classifiers self.clfs = { 'RF': RandomForestClassifier(n_estimators = 50, n_jobs = -1), 'ET': ExtraTreesClassifier(n_estimators = 10, n_jobs = -1, criterion = 'entropy'), 'AB': AdaBoostClassifier(DecisionTreeClassifier(max_depth = [1, 5, 10, 15]), algorithm = "SAMME", n_estimators = 200), 'LR': LogisticRegression(penalty = 'l1', C = 1e5), 'SVM': svm.SVC(kernel = 'linear', probability = True, random_state = 0), 'GB': GradientBoostingClassifier(learning_rate = 0.05, subsample = 0.5, max_depth = 6, n_estimators = 10), 'NB': GaussianNB(), 'DT': DecisionTreeClassifier(), 'SGD': SGDClassifier(loss = 'log', penalty = 'l2'), 'KNN': KNeighborsClassifier(n_neighbors = 3) } # These are the parameters which will be run through self.params = { 'RF':{'n_estimators': [1,10,100,1000], 'max_depth': [10, 15,20,30,40,50,60,70,100], 'max_features': ['sqrt','log2'],'min_samples_split': [2,5,10], 'random_state': [1]}, 'LR': {'penalty': ['l1','l2'], 'C': [0.00001,0.0001,0.001,0.01,0.1,1,10], 'random_state': [1]}, 'SGD': {'loss': ['log'], 'penalty': ['l2','l1','elasticnet'], 'random_state': [1]}, 'ET': {'n_estimators': [1,10,100,1000], 'criterion' : ['gini', 'entropy'], 'max_depth': [1,3,5,10,15], 'max_features': ['sqrt','log2'],'min_samples_split': [2,5,10], 'random_state': [1]}, 'AB': {'algorithm': ['SAMME', 'SAMME.R'], 'n_estimators': [1,10,100,1000], 'random_state': [1]}, 'GB': {'n_estimators': [1,10,100,1000], 'learning_rate' : [0.001,0.01,0.05,0.1,0.5],'subsample' : [0.1,0.5,1.0], 'max_depth': [1,3,5,10,20,50,100], 'random_state': [1]}, 'NB': {}, 'DT': {'criterion': ['gini', 'entropy'], 'max_depth': [1,2,15,20,30,40,50], 'max_features': ['sqrt','log2'],'min_samples_split': [2,5,10], 'random_state': [1]}, 'SVM' :{'C' :[0.00001,0.0001,0.001,0.01,0.1,1,10],'kernel':['linear'], 'random_state': [1]}, 'KNN' :{'n_neighbors': [1,5,10,25,50,100],'weights': ['uniform','distance'],'algorithm': ['auto','ball_tree','kd_tree']} }
Example 26
Project: stock-price-prediction Author: chinuy File: classifier.py License: MIT License | 5 votes |
def performAdaBoostClass(X_train, y_train, X_test, y_test, parameters, savemodel): """ Ada Boosting binary Classification """ # n = parameters[0] # l = parameters[1] clf = AdaBoostClassifier() clf.fit(X_train, y_train) accuracy = clf.score(X_test, y_test) return accuracy
Example 27
Project: Machine-Learning-for-Beginner-by-Python3 Author: Anfany File: Blending_Classify_adult.py License: MIT License | 5 votes |
def Adaboost_First(self, data, max_depth=5, n_estimators=300): model = AdaBoostClassifier(DecisionTreeClassifier(max_depth=max_depth), algorithm="SAMME", n_estimators=n_estimators, learning_rate=0.8) model.fit(data['train'][:, :-1], data['train'][:, -1]) # 存储验证数据集结果和预测数据集结果的 # 训练数据集的预测结果 xul = model.predict(data['train'][:, :-1]) # 验证的预测结果 yanre = model.predict(data['test'][:, :-1]) # 预测的预测结果 prer = model.predict(data['predict'][:, :-1]) # 每计算一折后,要计算训练、验证、预测数据的误差 xx = self.F1(xul, data['train'][:, -1]) yy = self.F1(yanre, data['test'][:, -1]) pp = self.F1(prer, data['predict'][:, -1]) # 开始结合 self.yanzhneg_pr.append(yanre) self.yanzhneg_real = data['test'][:, -1] self.predi.append(prer) self.preal = data['predict'][:, -1] # 存储误差 self.error_dict['AdaBoost'] = [xx, yy, pp] return print('1层中的AdaBoost运行完毕') # GBDT
Example 28
Project: FAE Author: salan668 File: Classifier.py License: GNU General Public License v3.0 | 5 votes |
def __init__(self, **kwargs): super(AdaBoost, self).__init__() super(AdaBoost, self).SetModel(AdaBoostClassifier(random_state=RANDOM_SEED[CLASSIFIER_AB], **kwargs))
Example 29
Project: Mastering-Elasticsearch-7.0 Author: PacktPublishing File: test_weight_boosting.py License: MIT License | 5 votes |
def test_oneclass_adaboost_proba(): # Test predict_proba robustness for one class label input. # In response to issue #7501 # https://github.com/scikit-learn/scikit-learn/issues/7501 y_t = np.ones(len(X)) clf = AdaBoostClassifier().fit(X, y_t) assert_array_almost_equal(clf.predict_proba(X), np.ones((len(X), 1)))
Example 30
Project: Mastering-Elasticsearch-7.0 Author: PacktPublishing File: test_weight_boosting.py License: MIT License | 5 votes |
def test_classification_toy(): # Check classification on a toy dataset. for alg in ['SAMME', 'SAMME.R']: clf = AdaBoostClassifier(algorithm=alg, random_state=0) clf.fit(X, y_class) assert_array_equal(clf.predict(T), y_t_class) assert_array_equal(np.unique(np.asarray(y_t_class)), clf.classes_) assert_equal(clf.predict_proba(T).shape, (len(T), 2)) assert_equal(clf.decision_function(T).shape, (len(T),))