import numpy as npimport matplotlib.pyplot as pltfrom sklearn import datasets,ensemblefrom sklearn.model_selection import train_test_splitdef load_data_classification(): ''' 加载用于分类问题的数据集 ''' # 使用 scikit-learn 自带的 digits 数据集 digits=datasets.load_digits() # 分层采样拆分成训练集和测试集，测试集大小为原始数据集大小的 1/4 return train_test_split(digits.data,digits.target,test_size=0.25,random_state=0,stratify=digits.target) #集成学习梯度提升决策树GradientBoostingClassifier分类模型def test_GradientBoostingClassifier(*data): X_train,X_test,y_train,y_test=data clf=ensemble.GradientBoostingClassifier() clf.fit(X_train,y_train) print("Traing Score:%f"%clf.score(X_train,y_train)) print("Testing Score:%f"%clf.score(X_test,y_test))# 获取分类数据X_train,X_test,y_train,y_test=load_data_classification() # 调用 test_GradientBoostingClassifiertest_GradientBoostingClassifier(X_train,X_test,y_train,y_test) 
def test_GradientBoostingClassifier_num(*data): ''' 测试 GradientBoostingClassifier 的预测性能随 n_estimators 参数的影响 ''' X_train,X_test,y_train,y_test=data nums=np.arange(1,100,step=2) fig=plt.figure() ax=fig.add_subplot(1,1,1) testing_scores=[] training_scores=[] for num in nums: clf=ensemble.GradientBoostingClassifier(n_estimators=num) clf.fit(X_train,y_train) training_scores.append(clf.score(X_train,y_train)) testing_scores.append(clf.score(X_test,y_test)) ax.plot(nums,training_scores,label="Training Score") ax.plot(nums,testing_scores,label="Testing Score") ax.set_xlabel("estimator num") ax.set_ylabel("score") ax.legend(loc="lower right") ax.set_ylim(0,1.05) plt.suptitle("GradientBoostingClassifier") plt.show() # 调用 test_GradientBoostingClassifier_numtest_GradientBoostingClassifier_num(X_train,X_test,y_train,y_test) 
def test_GradientBoostingClassifier_maxdepth(*data): ''' 测试 GradientBoostingClassifier 的预测性能随 max_depth 参数的影响 ''' X_train,X_test,y_train,y_test=data maxdepths=np.arange(1,20) fig=plt.figure() ax=fig.add_subplot(1,1,1) testing_scores=[] training_scores=[] for maxdepth in maxdepths: clf=ensemble.GradientBoostingClassifier(max_depth=maxdepth,max_leaf_nodes=None) clf.fit(X_train,y_train) training_scores.append(clf.score(X_train,y_train)) testing_scores.append(clf.score(X_test,y_test)) ax.plot(maxdepths,training_scores,label="Training Score") ax.plot(maxdepths,testing_scores,label="Testing Score") ax.set_xlabel("max_depth") ax.set_ylabel("score") ax.legend(loc="lower right") ax.set_ylim(0,1.05) plt.suptitle("GradientBoostingClassifier") plt.show() # 调用 test_GradientBoostingClassifier_maxdepthtest_GradientBoostingClassifier_maxdepth(X_train,X_test,y_train,y_test) 
def test_GradientBoostingClassifier_learning(*data): ''' 测试 GradientBoostingClassifier 的预测性能随学习率参数的影响 ''' X_train,X_test,y_train,y_test=data learnings=np.linspace(0.01,1.0) fig=plt.figure() ax=fig.add_subplot(1,1,1) testing_scores=[] training_scores=[] for learning in learnings: clf=ensemble.GradientBoostingClassifier(learning_rate=learning) clf.fit(X_train,y_train) training_scores.append(clf.score(X_train,y_train)) testing_scores.append(clf.score(X_test,y_test)) ax.plot(learnings,training_scores,label="Training Score") ax.plot(learnings,testing_scores,label="Testing Score") ax.set_xlabel("learning_rate") ax.set_ylabel("score") ax.legend(loc="lower right") ax.set_ylim(0,1.05) plt.suptitle("GradientBoostingClassifier") plt.show() # 调用 test_GradientBoostingClassifier_learningtest_GradientBoostingClassifier_learning(X_train,X_test,y_train,y_test) 
def test_GradientBoostingClassifier_subsample(*data): ''' 测试 GradientBoostingClassifier 的预测性能随 subsample 参数的影响 ''' X_train,X_test,y_train,y_test=data fig=plt.figure() ax=fig.add_subplot(1,1,1) subsamples=np.linspace(0.01,1.0) testing_scores=[] training_scores=[] for subsample in subsamples: clf=ensemble.GradientBoostingClassifier(subsample=subsample) clf.fit(X_train,y_train) training_scores.append(clf.score(X_train,y_train)) testing_scores.append(clf.score(X_test,y_test)) ax.plot(subsamples,training_scores,label="Training Score") ax.plot(subsamples,testing_scores,label="Training Score") ax.set_xlabel("subsample") ax.set_ylabel("score") ax.legend(loc="lower right") ax.set_ylim(0,1.05) plt.suptitle("GradientBoostingClassifier") plt.show() # 调用 test_GradientBoostingClassifier_subsampletest_GradientBoostingClassifier_subsample(X_train,X_test,y_train,y_test) 
def test_GradientBoostingClassifier_max_features(*data): ''' 测试 GradientBoostingClassifier 的预测性能随 max_features 参数的影响 ''' X_train,X_test,y_train,y_test=data fig=plt.figure() ax=fig.add_subplot(1,1,1) max_features=np.linspace(0.01,1.0) testing_scores=[] training_scores=[] for features in max_features: clf=ensemble.GradientBoostingClassifier(max_features=features) clf.fit(X_train,y_train) training_scores.append(clf.score(X_train,y_train)) testing_scores.append(clf.score(X_test,y_test)) ax.plot(max_features,training_scores,label="Training Score") ax.plot(max_features,testing_scores,label="Training Score") ax.set_xlabel("max_features") ax.set_ylabel("score") ax.legend(loc="lower right") ax.set_ylim(0,1.05) plt.suptitle("GradientBoostingClassifier") plt.show() # 调用 test_GradientBoostingClassifier_max_featurestest_GradientBoostingClassifier_max_features(X_train,X_test,y_train,y_test) 