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# -*- coding: utf-8 -*-

# writer : lgy

# data : 2017-08-18

import sys

sys.path.append("../")

reload(sys)

from tools.ReadCsvFile import ReadCsvFile

from tools.preProcess import PreProcess

from sklearn.tree import DecisionTreeClassifier

from tools.WriteResult import WriteResult

import logging

from tools import Config

import time

# import Process

import pickle

logging.basicConfig(filename=Config.ResultDataPath+'logger.log',format='%(asctime)s : %(levelname)s : %(message)s', level=logging.INFO)

rf = ReadCsvFile()

train_data = rf.ReadTrainFile()

preP = PreProcess()

train_X,train_lab,loc_dic = preP.preProcessTrainData(train_data)

train_Xc = preP.getFeatureScaler(train_X)

def TrainModel(modelName='DecisionTreeModel'):

dt_model = None

try:

with open(Config.ResultDataPath+modelName+".model","rb") as fp:

# dt_model = ReadCsvFile.ReadValueFromFile(modelName)

dt_model = pickle.load(fp)

print "load model success"

logging.info("load model success")

except:

print "Train model"

logging.info("Train model")

dt_model = DecisionTreeClassifier()

dt_model.fit(train_Xc[:20000],train_lab[:20000])

print "save model"

logging.info("save model")

with open(Config.ResultDataPath+modelName+".model","wb") as fp:

pickle.dump(dt_model,fp)

# wr = WriteResult()

# wr.WriteValueToFile(dt_model,modelName)

print "精确率: {0}".format(dt_model.score(train_Xc[10000:11000],train_lab[10000:11000]))

return dt_model

model = TrainModel()

def topindex(result,topn=3):

indexs = {}

for i in xrange(topn):

for j in xrange(len(result)):

if j in indexs:

continue

else:

if len(indexs.keys()) == 3:

for index in indexs:

print index,indexs[index],";",

print

min_index = min(indexs.iteritems(),key=lambda X:X[1])

# print min_index

if result[j] > result[min_index[0]]:

indexs.pop(min_index[0])

indexs[j] = result[j]

else:

indexs[j] = result[j]

return indexs.keys()

# for test

def score():

print "start caculate rate... ..."

result_X = model.predict_proba(train_Xc[10000:11000])

labs = train_lab[10000:11000]

right = 0

for i in xrange(len(labs)):

# print result_X[i]

result = topindex(result_X[i])

print result

if labs[i] in result:

right += 1

print right

print "精确率：{0}".format(float(right)/len(labs))

score()

def predict(model):

rf = ReadCsvFile()

preP = PreProcess()

print "Read data predict ... ... "

test_data = rf.ReadTestFile()

test_X,test_loc_dic = preP.preProcessTestData(test_data)

test_Xc = preP.getFeatureScaler(test_X)

test_X = None

length = len(test_Xc)

print "test data len:",length

print "predict result ... ... "

for i in xrange(0,length):

# pre_result = None

start_index = i * 100

end_index = (i+1) * 100

if end_index > length:

end_index = length

start_time = time.time()

# pre_result = predict(model,test_Xc[start_index:end_index])

pre_result = model.predict_proba(test_Xc[start_index:end_index])

end_time = time.time()

for re in pre_result:

print re

# print "predict from %d to %d. \n cost time :%lf s\n" % (start_index, end_index,end_time-start_time)

# start_time = time.time()

# result = Process.transformResult(pre_result, test_loc_dic)

# logging.info("predict from %d to %d. \ncost time :%lf s" % (start_index, end_index,end_time-start_time))

# wr = WriteResult()

# wr.WriteResultAnswer(test_data[start_index:end_index], result,Config.ResultDataPath+"result_d_1.csv")

# end_time = time.time()

# logging.info("transform index from %d to %d. \ncost time :%lf s" % (start_index, end_index,end_time-start_time))

# print "transform index from %d to %d. \n cost time :%lf s\n" % (start_index, end_index,end_time-start_time)

# predict(model)