吴裕雄 python 机器学习——回归决策树模型

import numpy as np
import matplotlib.pyplot as plt

from sklearn import datasets
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier,DecisionTreeRegressor

def creat_data(n):
    np.random.seed(0)
    X = 5 * np.random.rand(n, 1)
    y = np.sin(X).ravel()
    noise_num=(int)(n/5)
    # 每第5个样本，就在该样本的值上添加噪音
    y[::5] += 3 * (0.5 - np.random.rand(noise_num))
    return train_test_split(X, y,test_size=0.25,random_state=1)

#决策树DecisionTreeRegressor模型
def test_DecisionTreeRegressor(*data):
    X_train,X_test,y_train,y_test=data
    regr = DecisionTreeRegressor()
    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)))
    #绘图
    fig=plt.figure()
    ax=fig.add_subplot(1,1,1)
    X = np.arange(0.0, 5.0, 0.01)[:, np.newaxis]
    Y = regr.predict(X)
    ax.scatter(X_train, y_train, label="train sample",c='g')
    ax.scatter(X_test, y_test, label="test sample",c='r')
    ax.plot(X, Y, label="predict_value", linewidth=2,alpha=0.5)
    ax.set_xlabel("data")
    ax.set_ylabel("target")
    ax.set_title("Decision Tree Regression")
    ax.legend(framealpha=0.5)
    plt.show()

# 产生用于回归问题的数据集
X_train,X_test,y_train,y_test=creat_data(100)
# 调用 test_DecisionTreeRegressor
test_DecisionTreeRegressor(X_train,X_test,y_train,y_test)

def test_DecisionTreeRegressor_splitter(*data):
    '''
    测试 DecisionTreeRegressor 预测性能随划分类型的影响
    '''
    X_train,X_test,y_train,y_test=data
    splitters=['best','random']
    for splitter in splitters:
        regr = DecisionTreeRegressor(splitter=splitter)
        regr.fit(X_train, y_train)
        print("Splitter %s"%splitter)
        print("Training score:%f"%(regr.score(X_train,y_train)))
        print("Testing score:%f"%(regr.score(X_test,y_test)))

# 调用 test_DecisionTreeRegressor_splitter
test_DecisionTreeRegressor_splitter(X_train,X_test,y_train,y_test)

def test_DecisionTreeRegressor_depth(*data,maxdepth):
    '''
    测试 DecisionTreeRegressor 预测性能随  max_depth 的影响
    '''
    X_train,X_test,y_train,y_test=data
    depths=np.arange(1,maxdepth)
    training_scores=[]
    testing_scores=[]
    for depth in depths:
        regr = DecisionTreeRegressor(max_depth=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))
    # 绘图
    fig=plt.figure()
    ax=fig.add_subplot(1,1,1)
    ax.plot(depths,training_scores,label="traing score")
    ax.plot(depths,testing_scores,label="testing score")
    ax.set_xlabel("maxdepth")
    ax.set_ylabel("score")
    ax.set_title("Decision Tree Regression")
    ax.legend(framealpha=0.5)
    plt.show()

# 调用 test_DecisionTreeRegressor_depth
test_DecisionTreeRegressor_depth(X_train,X_test,y_train,y_test,maxdepth=20)