【python和机器学习入门2】决策树3——使用决策树预测隐形眼镜类型
参考博客：决策树实战篇之为自己配个隐形眼镜 （po主Jack-Cui,《——大部分内容转载自
参考书籍：《机器学习实战》——第三章3.4
《——决策树基础知识见前两篇 ，
摘要：本篇用一个预测隐形眼镜类型的例子讲述如何建树、可视化，并介绍了用sklearn构建决策树的代码
目录
1 数据处理
2 完整代码
3 Matplotlib可视化
4 sklearn构建决策树
1 数据处理
隐形眼镜数据集是非常著名的数据集，它包含很多患者眼部状态的观察条件以及医生推荐的隐形眼镜类型。隐形眼镜类型包括硬材质(hard)、软材质(soft)以及不适合佩戴隐形眼镜(no lenses)。给出一个数据集，使用决策树预测患者的隐形眼镜类型（共三类：hard／soft／no lenses）
lenses.txt数据如下图，共24组数据，5列属性，第5列为隐形眼镜类型，即我们需要预测的分类。
数据labels为[age、prescript、astigmatic、tearRate、class]
即[年龄、症状，是否散光，眼泪数量，最终的分类标签]
'''创建数据集'''
def createDataSet():
fr = open('lenses.txt')
dataSet = [rl.strip().split('\t') for rl in fr.readlines()]
print dataSet
labels = ['age','prescript','astigmatic','tearRate'] #特征属性
return dataSet, labels #返回数据集和特征属性2 完整代码
#!/usr/bin/env python
#_*_coding:utf-8_*_
import numpy as np
import json
import operator
from math import log
'''创建数据集'''
def createDataSet():
fr = open('lenses.txt')
dataSet = [rl.strip().split('\t') for rl in fr.readlines()]
labels = ['age','prescript','astigmatic','tearRate'] #特征属性
return dataSet, labels #返回数据集和特征属性
'''经验熵'''
def calShannonEnt(dataset):
m = len(dataset)
lableCount = {}
'''计数'''
for data in dataset:
currentLabel = data[-1]
if currentLabel not in lableCount.keys():
lableCount[currentLabel] = 0
lableCount[currentLabel] += 1
'''遍历字典求和'''
entropy = 0
for label in lableCount:
p = float(lableCount[label]) / m
entropy -= p * log(p,2)
return entropy
'''第i个特征根据取值value划分子数据集'''
def splitdataset(dataset,axis,value):
subSet = []
for data in dataset:
if(data[axis] == value):
data_x = data[:axis]
data_x.extend(data[axis+1:])
subSet.append(data_x)
return subSet
'''遍历数据集求最优IG和特征'''
def chooseBestFeatureToSpit(dataSet):
feature_num = len(dataSet[0])-1
origin_ent = calShannonEnt(dataSet)
infoGain = 0.0
best_infogain = 0.0
for i in range(feature_num):
fi_all = [data[i] for data in dataSet]
fi_all = set(fi_all)
#print fi_all
subset_Ent = 0
'''遍历所有可能value'''
for value in fi_all:
#划分子集
#print i,value
subset = splitdataset(dataSet,i,value)
#print subset
#计算子集熵
p = float(len(subset)) / len(dataSet)
subset_Ent += p * calShannonEnt(subset)
#计算信息增益
infoGain = origin_ent - subset_Ent
#记录最大IG
#print "第 %d 个特征的信息增益为 %f" % (i,infoGain)
if(infoGain > best_infogain):
best_feature = i
best_infogain = infoGain
return best_feature
'''计数并返回最多类别'''
def majorityCnt(classList):
classCount = {}
for class_ in classList:
if(class_ not in classCount.keys()):
classCount[class_] = 0
classCount[class_] += 1
classSort = sorted(classCount.iteritems(),key = operator.itemgetter(1),reverse=True)
return classSort[0][0]
'''向下递归创建树 '''
def createTree(dataSet,labels,feaLabels):
'''数据集所有类别'''
classList = [example[-1] for example in dataSet]
'''判断是否属于2个终止类型'''
'''1 全属一个类'''
if(len(classList) == classList.count(classList[0])):
return classList[0]
'''2 只剩1个特征属性'''
if(len(dataSet[0]) == 1):
majorClass = majorityCnt(classList)
return majorClass
'''继续划分'''
best_feature = chooseBestFeatureToSpit(dataSet)#最优划分特征 下标号
best_feaLabel = labels[best_feature]
feaLabels.append(best_feaLabel) #存储最优特征
del(labels[best_feature])#特征属性中删去最优特征《——ID3消耗特征
feaValue = [example[best_feature] for example in dataSet]
feaValue = set(feaValue) #获取最优特征的属性值列表
deci_tree = {best_feaLabel:{}}#子树的根的key是此次划分的最优特征名，value是再往下递归划分的子树
for value in feaValue:
subLabel = labels[:] #因为每个value都需要label，copy以免递归更改
subset = splitdataset(dataSet,best_feature,value)
deci_tree[best_feaLabel][value] = createTree(subset,subLabel,feaLabels)
#print deci_tree
return deci_tree
if __name__ == '__main__':
dataSet, labels = createDataSet()
feaLabels = []
mytree = createTree(dataSet,labels,feaLabels)
print json.dumps(mytree,ensure_ascii=False)
建树结果
{"tearRate": {"reduced": "no lenses", "normal": {"astigmatic": {"yes": {"prescript": {"hyper": {"age": {"pre": "no lenses", "presbyopic": "no lenses", "young": "hard"}}, "myope": "hard"}}, "no": {"age": {"pre": "soft", "presbyopic": {"prescript": {"hyper": "soft", "myope": "no lenses"}}, "young": "soft"}}}}}}
3 Matplotlib可视化
上面建树的字典展示看起来很不直观，接下来用matplotlib将结果可视化一下
环境 maxos 10.12.3 python2.7
模块下载，python2
pip install matplotlib
如果是python3
pip3 install matplotlib
代码引入模块
import matplotlib
import matplotlib.pyplot as plt需要用到的函数：
- getNumLeafs：获取决策树叶子结点的数目
- getTreeDepth：获取决策树的层数
- plotNode：绘制结点
- plotMidText：标注有向边属性值
- plotTree：绘制决策树
- createPlot：创建绘制面板
def getNumLeafs(myTree):
numLeafs = 0 #初始化叶子
firstStr = next(iter(myTree)) #python3中myTree.keys()返回的是dict_keys,不在是list,所以不能使用myTree.keys()[0]的方法获取结点属性，可以使用list(myTree.keys())[0]
secondDict = myTree[firstStr] #获取下一组字典
for key in secondDict.keys():
if type(secondDict[key]).__name__=='dict': #测试该结点是否为字典，如果不是字典，代表此结点为叶子结点
numLeafs += getNumLeafs(secondDict[key])
else: numLeafs +=1
return numLeafs
def getTreeDepth(myTree):
maxDepth = 0 #初始化决策树深度
firstStr = next(iter(myTree)) #python3中myTree.keys()返回的是dict_keys,不在是list,所以不能使用myTree.keys()[0]的方法获取结点属性，可以使用list(myTree.keys())[0]
secondDict = myTree[firstStr] #获取下一个字典
for key in secondDict.keys():
if type(secondDict[key]).__name__=='dict': #测试该结点是否为字典，如果不是字典，代表此结点为叶子结点
thisDepth = 1 + getTreeDepth(secondDict[key])
else: thisDepth = 1
if thisDepth > maxDepth: maxDepth = thisDepth #更新层数
return maxDepth
def plotNode2(nodeTxt, centerPt, parentPt, nodeType):
arrow_args = dict(arrowstyle="<-") #定义箭头格式
#font = FontProperties(fname=r"c:\windows\fonts\simsun.ttc", size=14) #设置中文字体
createPlot.ax1.annotate(nodeTxt, xy=parentPt, xycoords='axes fraction', #绘制结点
xytext=centerPt, textcoords='axes fraction',
va="center", ha="center", bbox=nodeType, arrowprops=arrow_args)
def plotMidText(cntrPt, parentPt, txtString):
xMid = (parentPt[0]-cntrPt[0])/2.0 + cntrPt[0] #计算标注位置
yMid = (parentPt[1]-cntrPt[1])/2.0 + cntrPt[1]
createPlot.ax1.text(xMid, yMid, txtString, va="center", ha="center", rotation=30)
def plotTree(myTree, parentPt, nodeTxt):
decisionNode = dict(boxstyle="sawtooth", fc="0.8") #设置结点格式
leafNode = dict(boxstyle="round4", fc="0.8") #设置叶结点格式
numLeafs = getNumLeafs(myTree) #获取决策树叶结点数目，决定了树的宽度
depth = getTreeDepth(myTree) #获取决策树层数
firstStr = next(iter(myTree)) #下个字典
cntrPt = (plotTree.xOff + (1.0 + float(numLeafs))/2.0/plotTree.totalW, plotTree.yOff) #中心位置
plotMidText(cntrPt, parentPt, nodeTxt) #标注有向边属性值
plotNode2(firstStr, cntrPt, parentPt, decisionNode) #绘制结点
secondDict = myTree[firstStr] #下一个字典，也就是继续绘制子结点
plotTree.yOff = plotTree.yOff - 1.0/plotTree.totalD #y偏移
for key in secondDict.keys():
if type(secondDict[key]).__name__=='dict': #测试该结点是否为字典，如果不是字典，代表此结点为叶子结点
plotTree(secondDict[key],cntrPt,str(key)) #不是叶结点，递归调用继续绘制
else: #如果是叶结点，绘制叶结点，并标注有向边属性值
plotTree.xOff = plotTree.xOff + 1.0/plotTree.totalW
plotNode2(secondDict[key], (plotTree.xOff, plotTree.yOff), cntrPt, leafNode)
plotMidText((plotTree.xOff, plotTree.yOff), cntrPt, str(key))
plotTree.yOff = plotTree.yOff + 1.0/plotTree.totalD
def createPlot(inTree):
fig = plt.figure(1, facecolor='white') #创建fig
fig.clf() #清空fig
axprops = dict(xticks=[], yticks=[])
createPlot.ax1 = plt.subplot(111, frameon=False, **axprops) #去掉x、y轴
plotTree.totalW = float(getNumLeafs(inTree)) #获取决策树叶结点数目
plotTree.totalD = float(getTreeDepth(inTree)) #获取决策树层数
plotTree.xOff = -0.5/plotTree.totalW; plotTree.yOff = 1.0; #x偏移
plotTree(inTree, (0.5,1.0), '') #绘制决策树
plt.show() #显示绘制结果
if __name__ == '__main__':
dataSet, labels = createDataSet()
feaLabels = []
mytree = createTree(dataSet,labels,feaLabels)
# print json.dumps(mytree,ensure_ascii=False)
createPlot(mytree)
4 sklearn构建决策树