机器学习之回归决策树DecisionTreeRegressor

机器学习之回归决策树DecisionTreeRegressor
# -*- coding: utf-8 -*-
"""
Created on Fri Nov 23 20:00:23 2018

@author: muli
"""

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


def creat_data(n):
    '''
    产生用于回归问题的数据集

    :param n:  数据集容量
    :return: 返回一个元组，
     元素依次为：训练样本集、测试样本集、训练样本集对应的值、测试样本集对应的值
    '''
    np.random.seed(0)
    # 产生 n*1 的矩阵，随机值为[0,1)之间
    X = 5 * np.random.rand(n, 1)
    # 正弦值
    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 cross_validation.train_test_split(X, y,
		test_size=0.25,random_state=1) # 拆分原始数据集为训练集和测试集，其中测试集大小为元素数据集大小的 1/4
    

def test_DecisionTreeRegressor(*data):
    '''
    测试 DecisionTreeRegressor 的用法

    :param data: 可变参数。它是一个元组，这里要求其元素依次为：训练样本集、测试样本集、训练样本的值、测试样本的值
    :return: None
    '''
    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, 0.05)[:, np.newaxis]
    # 看预测 回归树的效果
    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()
    
    
def test_DecisionTreeRegressor_splitter(*data):
    '''
    检验 随机划分与最优划分的影响

    :param data: 可变参数。
    它是一个元组，这里要求其元素依次为：训练样本集、测试样本集、训练样本的值、测试样本的值
    :return: None
    '''
    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)))
        print("----------------------")
    

def test_DecisionTreeRegressor_depth(*data,maxdepth):
    '''
    预测性能随  max_depth 的影响

    :param data:  可变参数。它是一个元组，这里要求其元素依次为：训练样本集、测试样本集、训练样本的值、测试样本的值
    :param maxdepth: 一个整数，它作为 DecisionTreeRegressor 的 max_depth 参数
    :return: None
    '''
    X_train,X_test,y_train,y_test=data
    # maxdepth表示最大深度
    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()

  
if __name__=='__main__':
    X_train,X_test,y_train,y_test=creat_data(100) # 产生用于回归问题的数据集
#    test_DecisionTreeRegressor(X_train,X_test,y_train,y_test) # 调用 test_DecisionTreeRegressor
#    test_DecisionTreeRegressor_splitter(X_train,X_test,y_train,y_test) # 调用 test_DecisionTreeRegressor_splitter
    test_DecisionTreeRegressor_depth(X_train,X_test,y_train,y_test,maxdepth=20) # 调用 test_DecisionTreeRegressor_depth
机器学习之回归决策树DecisionTreeRegressor

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