from BPModelDataProcessor import BPModelDataProcessor
from MongoDataProcessor import MongoDataProcessor
from BPModelTrainer import BPModelTrainer
import Utility as util
import numpy as np
import time
from pymongo import MongoClient
from ZScaleNormalizer import ZScaleNormalizer
loanType = 'CarLoan'
version = 1.0
mongoAddress = ['192.168.1.125','192.168.1.126','192.168.1.127']
dbSource = MongoClient(mongoAddress, 27017)
dataBase = dbSource.RANK
mongoDataProcessor = MongoDataProcessor('1019', address=mongoAddress)
bpModelDataPrcocessor = BPModelDataProcessor()
logPath = './log.txt'
# Step1: get all tags list
allTags = mongoDataProcessor.getMetaTagList()
util.printToFile(','.join(allTags), logPath, 'w')
# Step2: get Flatten Header/Data
flattenHeader, flattenData = mongoDataProcessor.getFlattenTagData(additionalTags=['creditScore'])
with open('Data/FlattenData.csv', 'w') as f:
f.write(','.join(flattenHeader)+'\n')
for dataRow in flattenData:
print >> f, ','.join(dataRow) # encode('utf-8')
# Step3: get Target Header/Data
targetHeader, targetData = mongoDataProcessor.getTargetTagData(targetTags=['creditScore'])
