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 pltfrom sklearn import datasets,ensemble
from 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)