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)
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