- 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)
- # 集成学习随机森林 RandomForestRegressor 回归模型
- def test_RandomForestRegressor(*data):
- X_train,X_test,y_train,y_test=data
- regr=ensemble.RandomForestRegressor()
- regr.fit(X_train,y_train)
- print("Traing 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_RandomForestRegressor
- test_RandomForestRegressor(X_train,X_test,y_train,y_test)
- def test_RandomForestRegressor_num(*data):
- '''
- 测试 RandomForestRegressor 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(1,100,step=2)
- fig=plt.figure()
- ax=fig.add_subplot(1,1,1)
- testing_scores=[]
- training_scores=[]
- for num in nums:
- regr=ensemble.RandomForestRegressor(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(-1,1)
- plt.suptitle("RandomForestRegressor")
- plt.show()
- # 调用 test_RandomForestRegressor_num
- test_RandomForestRegressor_num(X_train,X_test,y_train,y_test)
- def test_RandomForestRegressor_max_depth(*data):
- '''
- 测试 RandomForestRegressor 的预测性能随 max_depth 参数的影响
- '''
- X_train,X_test,y_train,y_test=data
- maxdepths=range(1,20)
- fig=plt.figure()
- ax=fig.add_subplot(1,1,1)
- testing_scores=[]
- training_scores=[]
- for max_depth in maxdepths:
- regr=ensemble.RandomForestRegressor(max_depth=max_depth)
- 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(0,1.05)
- plt.suptitle("RandomForestRegressor")
- plt.show()
- # 调用 test_RandomForestRegressor_max_depth
- test_RandomForestRegressor_max_depth(X_train,X_test,y_train,y_test)
- def test_RandomForestRegressor_max_features(*data):
- '''
- 测试 RandomForestRegressor 的预测性能随 max_features 参数的影响
- '''
- X_train,X_test,y_train,y_test=data
- max_features=np.linspace(0.01,1.0)
- fig=plt.figure()
- ax=fig.add_subplot(1,1,1)
- testing_scores=[]
- training_scores=[]
- for max_feature in max_features:
- regr=ensemble.RandomForestRegressor(max_features=max_feature)
- 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="Testing Score")
- ax.set_xlabel("max_feature")
- ax.set_ylabel("score")
- ax.legend(loc="lower right")
- ax.set_ylim(0,1.05)
- plt.suptitle("RandomForestRegressor")
- plt.show()
- # 调用 test_RandomForestRegressor_max_features
- test_RandomForestRegressor_max_features(X_train,X_test,y_train,y_test)
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