吴裕雄 python 机器学习——集成学习梯度提升决策树GradientBoostingRegressor回归模型

import numpy as np

import matplotlib.pyplot as plt

from sklearn import datasets,ensemble

from sklearn.model_selection import train_test_split

def load_data_regression():

    '''

    加载用于回归问题的数据集

    '''

    #使用 scikit-learn 自带的一个糖尿病病人的数据集

    diabetes = datasets.load_diabetes()

    # 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(diabetes.data,diabetes.target,test_size=0.25,random_state=0) 

#集成学习梯度提升决策树GradientBoostingRegressor回归模型

def test_GradientBoostingRegressor(*data):

    X_train,X_test,y_train,y_test=data

    regr=ensemble.GradientBoostingRegressor()

    regr.fit(X_train,y_train)

    print("Training score:%f"%regr.score(X_train,y_train))

    print("Testing score:%f"%regr.score(X_test,y_test))

# 获取分类数据

X_train,X_test,y_train,y_test=load_data_regression()

# 调用 test_GradientBoostingRegressor

test_GradientBoostingRegressor(X_train,X_test,y_train,y_test) 

def test_GradientBoostingRegressor_num(*data):

    '''

    测试 GradientBoostingRegressor 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(1,200,step=2)

    fig=plt.figure()

    ax=fig.add_subplot(1,1,1)

    testing_scores=[]

    training_scores=[]

    for num in nums:

        regr=ensemble.GradientBoostingRegressor(n_estimators=num)

        regr.fit(X_train,y_train)

        training_scores.append(regr.score(X_train,y_train))

        testing_scores.append(regr.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("GradientBoostingRegressor")

    plt.show()

# 调用 test_GradientBoostingRegressor_num

test_GradientBoostingRegressor_num(X_train,X_test,y_train,y_test) 

def test_GradientBoostingRegressor_maxdepth(*data):

    '''

    测试 GradientBoostingRegressor 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:

        regr=ensemble.GradientBoostingRegressor(max_depth=maxdepth,max_leaf_nodes=None)

        regr.fit(X_train,y_train)

        training_scores.append(regr.score(X_train,y_train))

        testing_scores.append(regr.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(-1,1.05)

    plt.suptitle("GradientBoostingRegressor")

    plt.show()

# 调用 test_GradientBoostingRegressor_maxdepth

test_GradientBoostingRegressor_maxdepth(X_train,X_test,y_train,y_test) 

def test_GradientBoostingRegressor_learning(*data):

    '''

    测试 GradientBoostingRegressor 的预测性能随 learning_rate 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:

        regr=ensemble.GradientBoostingRegressor(learning_rate=learning)

        regr.fit(X_train,y_train)

        training_scores.append(regr.score(X_train,y_train))

        testing_scores.append(regr.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(-1,1.05)

    plt.suptitle("GradientBoostingRegressor")

    plt.show()

# 调用 test_GradientBoostingRegressor_learning

test_GradientBoostingRegressor_learning(X_train,X_test,y_train,y_test) 

def test_GradientBoostingRegressor_subsample(*data):

    '''

    测试 GradientBoostingRegressor 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,num=20)

    testing_scores=[]

    training_scores=[]

    for subsample in subsamples:

        regr=ensemble.GradientBoostingRegressor(subsample=subsample)

        regr.fit(X_train,y_train)

        training_scores.append(regr.score(X_train,y_train))

        testing_scores.append(regr.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(-1,1.05)

    plt.suptitle("GradientBoostingRegressor")

    plt.show()

# 调用 test_GradientBoostingRegressor_subsample

test_GradientBoostingRegressor_subsample(X_train,X_test,y_train,y_test) 

def test_GradientBoostingRegressor_loss(*data):

    '''

    测试 GradientBoostingRegressor 的预测性能随不同的损失函数和 alpha 参数的影响

    '''

    X_train,X_test,y_train,y_test=data

    fig=plt.figure()

    nums=np.arange(1,200,step=2)

    ########## 绘制 huber ######

    ax=fig.add_subplot(2,1,1)

    alphas=np.linspace(0.01,1.0,endpoint=False,num=5)

    for alpha in alphas:

        testing_scores=[]

        training_scores=[]

        for num in nums:

            regr=ensemble.GradientBoostingRegressor(n_estimators=num,loss='huber',alpha=alpha)

            regr.fit(X_train,y_train)

            training_scores.append(regr.score(X_train,y_train))

            testing_scores.append(regr.score(X_test,y_test))

        ax.plot(nums,training_scores,label="Training Score:alpha=%f"%alpha)

        ax.plot(nums,testing_scores,label="Testing Score:alpha=%f"%alpha)

    ax.set_xlabel("estimator num")

    ax.set_ylabel("score")

    ax.legend(loc="lower right",framealpha=0.4)

    ax.set_ylim(0,1.05)

    ax.set_title("loss=%huber")

    plt.suptitle("GradientBoostingRegressor")

    #### 绘制 ls  和 lad

    ax=fig.add_subplot(2,1,2)

    for loss in ['ls','lad']:

        testing_scores=[]

        training_scores=[]

        for num in nums:

            regr=ensemble.GradientBoostingRegressor(n_estimators=num,loss=loss)

            regr.fit(X_train,y_train)

            training_scores.append(regr.score(X_train,y_train))

            testing_scores.append(regr.score(X_test,y_test))

        ax.plot(nums,training_scores,label="Training Score:loss=%s"%loss)

        ax.plot(nums,testing_scores,label="Testing Score:loss=%s"%loss)

    ax.set_xlabel("estimator num")

    ax.set_ylabel("score")

    ax.legend(loc="lower right",framealpha=0.4)

    ax.set_ylim(0,1.05)

    ax.set_title("loss=ls,lad")

    plt.suptitle("GradientBoostingRegressor")

    plt.show()

# 调用 test_GradientBoostingRegressor_loss

test_GradientBoostingRegressor_loss(X_train,X_test,y_train,y_test) 

def test_GradientBoostingRegressor_max_features(*data):

    '''

    测试 GradientBoostingRegressor 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:

        regr=ensemble.GradientBoostingRegressor(max_features=features)

        regr.fit(X_train,y_train)

        training_scores.append(regr.score(X_train,y_train))

        testing_scores.append(regr.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("GradientBoostingRegressor")

    plt.show()

# 调用 test_GradientBoostingRegressor_max_features

test_GradientBoostingRegressor_max_features(X_train,X_test,y_train,y_test) 

吴裕雄 python 机器学习——集成学习梯度提升决策树GradientBoostingRegressor回归模型的相关教程结束。