python决策树DecisionTreeRegressor模型

运行环境：win10 64位 py 3.6 pycharm 2018.1.1

import numpy as np
from sklearn.tree import DecisionTreeRegressor
from sklearn import cross_validation
import  matplotlib.pyplot as plt

#自己造一点数据
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)
    y[::5] += 3*(0.5-np.random.rand(noise_num))
    return cross_validation.train_test_split(X,y,test_size=0.25,random_state=1)

#使用决策树拟合
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 ("Test 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()

X_train, X_test, y_train, y_test = creat_data(100)
print (X_train, X_test, y_train, y_test)
test_DecisionTreeRegressor(X_train, X_test, y_train, y_test)

#检测随机划分和最优划分的影响
def test_DecisionTreeRegressor_splitter(*data):
    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)))

X_train, X_test, y_train, y_test = creat_data(100)
test_DecisionTreeRegressor_splitter(X_train, X_test, y_train, y_test)

#考查决策树深度的影响
def test_DecisionTreeRegressor_depth(*data,maxdepth):
    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_scores')
    ax.set_xlabel("maxdepth")
    ax.set_ylabel("score")
    ax.set_title("Decision Tree Regression")
    ax.legend(framealpha=0.5)
    plt.show()
X_train, X_test, y_train, y_test = creat_data(100)
test_DecisionTreeRegressor_depth(X_train, X_test, y_train, y_test,maxdepth=15)