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

Posted 2021-11-26 tszr

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)