吴裕雄 python 机器学习——集成学习AdaBoost算法回归模型

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_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AdaBoost算法回归模型

def test_AdaBoostRegressor(*data):

    '''

    测试 AdaBoostRegressor 的用法，绘制 AdaBoostRegressor 的预测性能随基础回归器数量的影响

    '''

    X_train,X_test,y_train,y_test=data

    regr=ensemble.AdaBoostRegressor()

    regr.fit(X_train,y_train)

    ## 绘图

    fig=plt.figure()

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

    estimators_num=len(regr.estimators_)

    X=range(1,estimators_num+1)

    ax.plot(list(X),list(regr.staged_score(X_train,y_train)),label="Traing score")

    ax.plot(list(X),list(regr.staged_score(X_test,y_test)),label="Testing score")

    ax.set_xlabel("estimator num")

    ax.set_ylabel("score")

    ax.legend(loc="best")

    ax.set_title("AdaBoostRegressor")

    plt.show()

# 获取分类数据

X_train,X_test,y_train,y_test=load_data_classification()

# 调用 test_AdaBoostRegressor

test_AdaBoostRegressor(X_train,X_test,y_train,y_test) 

def test_AdaBoostRegressor_base_regr(*data):

    '''

    测试 AdaBoostRegressor 的预测性能随基础回归器数量的和基础回归器类型的影响

    '''

    from sklearn.svm import  LinearSVR

    X_train,X_test,y_train,y_test=data

    fig=plt.figure()

    regrs=[ensemble.AdaBoostRegressor(), # 基础回归器为默认类型

    ensemble.AdaBoostRegressor(base_estimator=LinearSVR(epsilon=0.01,C=100))] # 基础回归器为 LinearSVR

    labels=["Decision Tree Regressor","Linear SVM Regressor"]

    for i ,regr in enumerate(regrs):

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

        regr.fit(X_train,y_train)

        ## 绘图

        estimators_num=len(regr.estimators_)

        X=range(1,estimators_num+1)

        ax.plot(list(X),list(regr.staged_score(X_train,y_train)),label="Traing score")

        ax.plot(list(X),list(regr.staged_score(X_test,y_test)),label="Testing score")

        ax.set_xlabel("estimator num")

        ax.set_ylabel("score")

        ax.legend(loc="lower right")

        ax.set_ylim(-1,1)

        ax.set_title("Base_Estimator:%s"%labels[i])

    plt.suptitle("AdaBoostRegressor")

    plt.show()

# 调用 test_AdaBoostRegressor_base_regr

test_AdaBoostRegressor_base_regr(X_train,X_test,y_train,y_test) 

def test_AdaBoostRegressor_learning_rate(*data):

    '''

    测试 AdaBoostRegressor 的预测性能随学习率的影响

    '''

    X_train,X_test,y_train,y_test=data

    learning_rates=np.linspace(0.01,1)

    fig=plt.figure()

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

    traing_scores=[]

    testing_scores=[]

    for learning_rate in learning_rates:

        regr=ensemble.AdaBoostRegressor(learning_rate=learning_rate,n_estimators=500)

        regr.fit(X_train,y_train)

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

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

    ax.plot(learning_rates,traing_scores,label="Traing score")

    ax.plot(learning_rates,testing_scores,label="Testing score")

    ax.set_xlabel("learning rate")

    ax.set_ylabel("score")

    ax.legend(loc="best")

    ax.set_title("AdaBoostRegressor")

    plt.show()

# 调用 test_AdaBoostRegressor_learning_rate

test_AdaBoostRegressor_learning_rate(X_train,X_test,y_train,y_test) 

def test_AdaBoostRegressor_loss(*data):

    '''

    测试 AdaBoostRegressor 的预测性能随损失函数类型的影响

    '''

    X_train,X_test,y_train,y_test=data

    losses=['linear','square','exponential']

    fig=plt.figure()

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

    for i ,loss in enumerate(losses):

        regr=ensemble.AdaBoostRegressor(loss=loss,n_estimators=30)

        regr.fit(X_train,y_train)

        ## 绘图

        estimators_num=len(regr.estimators_)

        X=range(1,estimators_num+1)

        ax.plot(list(X),list(regr.staged_score(X_train,y_train)),label="Traing score:loss=%s"%loss)

        ax.plot(list(X),list(regr.staged_score(X_test,y_test)),label="Testing score:loss=%s"%loss)

        ax.set_xlabel("estimator num")

        ax.set_ylabel("score")

        ax.legend(loc="lower right")

        ax.set_ylim(-1,1)

    plt.suptitle("AdaBoostRegressor")

    plt.show()

# 调用 test_AdaBoostRegressor_loss

test_AdaBoostRegressor_loss(X_train,X_test,y_train,y_test) 

吴裕雄 python 机器学习——集成学习AdaBoost算法回归模型的相关教程结束。