“东证期货杯”全国大学生统计建模大赛选题2：互联网金融时代下信用评分体系模型的构建源码（附题目，数据）

本文为第一次数据挖掘实战，数据预处理部分较乱，源码略去，直接给出处理后数据，所用算法为ID3

# -*- coding: utf-8 -*-
"""
Created on Sun Mar  4 17:08:06 2018

@author: CSH
"""

import math
import operator
from numpy import*
import pandas as pd
import csv

def calcShannonEnt(dataSet):
    numEntries=len(dataSet)
    labelCounts={}
    for featVec in dataSet:
        currentLabel=featVec[-1]
#        labelCounts[currentLabel]=labelCounts.get(currentLabel,0)+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*math.log(prob,2)
    return shannonEnt

# 读取csv文件方式1
csvFile = open("qudiaonanrowTrain1.csv", "r")
reader = csv.reader(csvFile)  # 返回的是迭代类型
myDat = []
for item in reader:
#    print(item)
    myDat.append(item[1:])
del(myDat[0]) 
#print(data)
csvFile.close()
#myDat=pd.read_csv('qudiaonanrowTrain1.tsv',encoding='ANSI',sep='\t')
mylabels=['AGENT','IS_LOCAL','VORK_PROVINC','EDU_LEVEL','MARRY_STATUS','SALARY','HAS_FUND']




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
#a=splitDataSet(myDat,0,1)
#print(a)

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
#a=chooseBestFeatureToSplit(myDat)
#print(a)
    
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=operator.itemgetter(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
myTree=createTree(myDat[:5000],mylabels)
#print(myTree)
#classLabel=-1   
def classify(inputTree,featLabels,testVec):
#    global classLabel 
    firstSides=list(inputTree.keys())
    firstStr=firstSides[0]
    secondDict=inputTree[firstStr]
    featIndex=featLabels.index(firstStr)
    for key in secondDict.keys():
        if testVec[featIndex]==key:
            if type(secondDict[key]).__name__=='dict':                
                classLabel=classify(secondDict[key],featLabels,testVec)
            else:
                classLabel=secondDict[key]
#                print(classLabel)
    return classLabel
#labels=createDataSet()[1]

myLabels=['AGENT','IS_LOCAL','VORK_PROVINC','EDU_LEVEL','MARRY_STATUS','SALARY','HAS_FUND']
#a=classify(myTree,myLabels,['APP','本地籍','320000.0','本科','已婚','3.0','0.0'])
#a=classify(myTree,myLabels,['wechat', '本地籍', '420000.0', '专科及以下', '未婚', '4.0', '1.0'])
#print(a)
    
# =============================================================================
# def storeTree(inputTree,filename):
#     import pickle
#     fw=open(filename,'wb')
#     pickle.dump(inputTree,fw)
#     fw.close()
# def grabTree(filename):
#     import pickle
#     fr=open(filename,'rb')
#     return pickle.load(fr)
# 
# storeTree(myTree,'classifierStorage.txt')
# b=grabTree('classifierStorage.txt')
# #print(b)
# =============================================================================



#del(myDat[2])
def testing(myTree,data_test,labels):  
    error=0.0;a=0
    testCount=shape(data_test)[0]
    for i in range(len(data_test)): 
        try:
            if classify(myTree,labels,data_test[i])!=data_test[i][-1]:  
                error+=1.0 
        except:
            a+=1
            continue
    print('errorRate %d' %float(error/testCount),a)  
    return float(error)


# =============================================================================
# # 读取csv文件方式2
# with open("mostnan.csv", "r") as csvfile:
#     reader2 = csv.reader(csvfile) # 读取csv文件，返回的是迭代类型
#     data = []
#     for item2 in reader2:
# #        print(item2)
#         data.append(item[1:])
#     del(data[0])
# csvFile.close()
# 
# =============================================================================
testing(myTree,myDat[5000:],myLabels)    

附：链接：https://pan.baidu.com/s/1LkwdWC67uBhB1kuWCikF-Q 密码：xbm3

       相关信用评分模型博客：https://blog.csdn.net/lll1528238733/article/category/7072659