python实现决策树

参考：《机器学习实战》- Machine Learning in Action

一、 基本思想

 我们所熟知的决策树的形状可能如下：

 使用决策树算法的目的就是生成类似于上图的分类效果。所以算法的主要步骤就是如何去选择结点。

 划分数据集的最大原则是：将无序的数据变得更加有序。我们可以使用多种方法划分数据集，但是每种方法都有各自的优缺点。集合信息的度量方式称为香农熵。

 伪代码如下：

检测数据集中的每个子项是否属于同一分类；
    if so return 类标签；
    else
        寻找划分数据集的最好特征
        划分数据集
        创建分支节点
            for 每个划分的子集
                调用createBranch并增加返回结果到分支节点中
        return 分支节点

一般而言，计算距离会采用欧式距离。

二、 代码

# -*- coding:utf8 -*-
import operator
from math import log

#计算信息熵
def calcShannonEnt(dataSet):
	numEntries = len(dataSet)
	labelCounts = {}
	for featVec in dataSet:
		currentLabel = featVec[-1]
		if currentLabel not in labelCounts.keys():
			labelCounts[currentLabel] = 0
		labelCounts[currentLabel] += 1

	shannonEnt = 0.0
	for key in labelCounts:
		prob = float(labelCounts[key])/numEntries
		shannonEnt -= prob*log(prob, 2)
	return shannonEnt

#按照给定特征划分数据集
def splitDataSet(dataSet, axis, value):
	retDataSet = []
	for featVec in dataSet:
		if featVec[axis] == value:
			reducedFeatVec = featVec[:axis]
			reducedFeatVec.extend(featVec[axis+1:])
			retDataSet.append(reducedFeatVec)

	return retDataSet

#选择最好的数据集划分方式
def chooseBestFeatureToSplit(dataSet):
	numFeatures = len(dataSet[0]) - 1
	baseEntropy = calcShannonEnt(dataSet)
	bestInfoGain = 0.0
	bestFeature = -1
	for i in range(numFeatures):
		featList = [example[i] for example in dataSet]
		uniqueVals = set(featList)
		newEntropy = 0.0
		for value in uniqueVals:
			subDataSet = splitDataSet(dataSet, i, value)
			prob = len(subDataSet)/float(len(dataSet))
			newEntropy += prob * calcShannonEnt(subDataSet)
		infoGain = baseEntropy - newEntropy
		if (infoGain > bestInfoGain):
			bestInfoGain = infoGain
			bestFeature = i
	return bestFeature


#构造决策树
def majorityCnt(classList):
	classCount = {}
	for vote in classList:
		if vote not in classCount.keys():
			classCount[vote] = 0
		classCount[vote] += 1
		sortedClassCount = sorted(classCount.items(), \
			key=lambda item:item[1], reverse=True)

	return sortedClassCount[0][0]

def createTree(dataSet, labels):
	classList = [example[-1] for example in dataSet]
	if classList.count(classList[0]) == len(classList):
		return classList[0]
	if len(dataSet[0]) == 1:
		return majorityCnt(classList)
	bestFeat = chooseBestFeatureToSplit(dataSet)
	bestFeatLabel = labels[bestFeat]
	myTree = {bestFeatLabel:{}}
	del labels[bestFeat]
	featValues = [example[bestFeat] for example in dataSet]
	uniqueVals = set(featValues)
	for value in uniqueVals:
		subLabels = labels[:]
		myTree[bestFeatLabel][value] = createTree(splitDataSet(dataSet, bestFeat, value), subLabels)

	return myTree