python使用sklearn实现决策树的方法示例

时间：2021-01-06 11:21:51|栏目：Python代码|点击：次

1. 基本环境

安装 anaconda 环境，由于国内登陆不了他的官网 https://www.continuum.io/downloads, 不过可以使用国内的镜像站点： https://mirrors.tuna.tsinghua.edu.cn/anaconda/archive/

添加绘图工具 Graphviz http://www.graphviz.org/Download_windows.php

安装后，将bin 目录内容添加到环境变量path 即可

参考blog ： https://www.jb51.net/article/169878.htm

官网技术文档： http://scikit-learn.org/stable/modules/tree.html#tree-algorithms-id3-c4-5-c5-0-and-cart

2. 遇到的一些问题

csv 文件读取 https://docs.python.org/3.5/library/csv.html?highlight=csv#module-csv

https://docs.python.org/2/library/csv.html?highlight=csv#module-csv

3. 实现

数据文件：

这里写图片描述

这是一个给定 4 个属性， age， income， student, credit_rating 以及一个标记属性 class_buys_computer 的数据集，我们需要根据这个数据集进行分析并构建一颗决策树

代码实现：

核心就是调用 tree 的 DecisionTreeClassifier 方法对数据进行训练得到一颗决策树

# -*- coding: utf-8 -*-
"""
Created on Sun Dec 25 11:25:40 2016

@author: Administrator
"""

from sklearn.feature_extraction import DictVectorizer
import csv
from sklearn import tree
from sklearn import preprocessing
from sklearn.externals.six import StringIO
import pydotplus
from IPython.display import Image

# Read in the csv file and put features into list of dict and list of class label
allElectornicsData = open('AllElectronics.csv', 'r')
reader = csv.reader(allElectornicsData)
# headers = reader.next()  python2.7 supported  本质获取csv 文件的第一行数据
#headers = reader.__next__()  python 3.5.2 
headers = next(reader)

print(headers)

featureList = []
labelList = []

for row in reader:
  labelList.append(row[len(row) - 1])
  rowDict = {}
  for i in range(1, len(row) - 1):
    rowDict[headers[i]] = row[i]
  featureList.append(rowDict)

print(featureList)
print(labelList)

# Vetorize features
vec = DictVectorizer()
dummyX = vec.fit_transform(featureList).toarray()

print("dummyX: " + str(dummyX))
print(vec.get_feature_names())
print("labelList: " + str(labelList))

# vectorize class labels
lb = preprocessing.LabelBinarizer()
dummyY = lb.fit_transform(labelList)
print("dummyY: ", str(dummyY))

# Using decision tree for classification    ===========【此处调用为算法核心】============
#clf = tree.DecisionTreeClassifier(criterion='entropy')
clf = tree.DecisionTreeClassifier(criterion='gini')
clf = clf.fit(dummyX, dummyY)
print("clf: ", str(clf))

# Visualize model
# dot -Tpdf iris.dot -o ouput.pdf
with open("allElectronicInformationGainOri.dot", 'w') as f:
  f = tree.export_graphviz(clf, feature_names = vec.get_feature_names(), out_file = f)


# predict
oneRowX = dummyX[0, :]
print("oneRowX: " + str(oneRowX))

newRowX = oneRowX
newRowX[0] = 1
newRowX[2] = 0
print("newRowX: " + str(newRowX))

predictedY = clf.predict(newRowX)
print("predictedY: " + str(predictedY))

输出结果：

ID3 算法

这里写图片描述