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real_error.py
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# -*- coding: utf-8 -*-
"""
Created on Wed Jul 17 10:34:28 2019
@author: Lenovo
"""
import sys
import pandas as pd
import numpy as np
import time
from datetime import datetime, timedelta
import pymysql
from sqlalchemy import create_engine
import logging.config
#logging.config.fileConfig("LSTM_llyc.dataset")
#logger = logging.getLogger()
#logger.info("开始计算当前交通流量值》》》》》》》》》》")
#def rq_zh(d):
## today = datetime.now() #取今天
# today = (datetime.now()- timedelta(days=14)) #取昨天
# yeday = (today - timedelta(weeks=d)).strftime("%Y-%m-%d")
# return yeday
#dats = []
#for i in range(0,8):
# dat = rq_zh(i)
# dats.append(dat)
#dates = tuple(dats)
def forecast_real():
today = datetime.now() #取今天
yeday = (today - timedelta(days=1)).strftime("%Y-%m-%d") #取昨天，因为只能拿到昨天的数据
dates = (yeday)
con = pymysql.connect('52.1.123.6','root','123456','keenIts')
sql = "select * from t_transportation_flow WHERE point_number in ('#GS002','#GS004','#GS005','#GS006','#GS007',\
'#GS008','#GS009','#GS010','#GS011','#GS012','#GS017','#GS024','#GS031','#GS033','#GS035') and date in \
(\"%s\")"%(dates)
data = pd.read_sql(sql,con)
con.close()
xq = []
tm = data['date']
for i in tm:
dt = pd.to_datetime(i)
dt1 = dt.strftime('%A')
xq.append(dt1)
tm = [int(x[0:2]) for x in data['time_part']]
data['Tm'] = tm
data['xq'] = xq
data['rq'] = data.date
size_mapping = {
'Monday': str(1),
'Tuesday': 2,
'Wednesday': 3,
'Thursday':4,
'Friday':5,
'Saturday':6,
'Sunday':7}
data['xq'] = data['xq'].map(size_mapping)
data['flow_e'] = data['flow_e_l'] + data['flow_e_s'] + data['flow_e_r']
data['flow_w'] = data['flow_w_l'] + data['flow_w_s'] + data['flow_w_r']
data['flow_s'] = data['flow_s_l'] + data['flow_s_s'] + data['flow_s_r']
data['flow_n'] = data['flow_n_l'] + data['flow_n_s'] + data['flow_n_r']
#10分钟的交通流量
sjd = [int(x[12:14]) for x in data['time_part']]
data['sjd'] = sjd
size_sjd = {
5:1,
10:1,
15:2,
20:2,
25:3,
30:3,
35:4,
40:4,
45:5,
50:5,
55:6,
0:6
}
data['sjd'] = data['sjd'].map(size_sjd)
# Tm = datetime.now().strftime("%Y-%m-%d %H:%M")
# th = Tm[11:13]
# sd = Tm[14:16]
#
# if (int(th[0]==0 and int(th[1]==0))):
# th = 24
# else:
# th = int(th)
#
# t = 0
# if (int(sd[0]) == 0 and int(sd[1]) in range(5)) | (int(sd[0]) == 0 and int(sd[1]) in range(6,10)):
# t += 1
# if (int(sd[0]) == 1 and int(sd[1]) in range(5)) | (int(sd[0]) == 1 and int(sd[1]) in range(6,10)):
# t += 2
# if (int(sd[0]) == 2 and int(sd[1]) in range(5)) | (int(sd[0]) == 2 and int(sd[1]) in range(6,10)):
# t += 3
# if (int(sd[0]) == 3 and int(sd[1]) in range(5)) | (int(sd[0]) == 3 and int(sd[1]) in range(6,10)):
# t += 4
# if (int(sd[0]) == 4 and int(sd[1]) in range(5)) | (int(sd[0]) == 4 and int(sd[1]) in range(6,10)):
# t += 5
# if (int(sd[0]) == 5 and int(sd[1]) in range(5)) | (int(sd[0]) == 5 and int(sd[1]) in range(6,10)):
# t += 6
#
# data1 = data[(data['sjd']==t)&(data['time_hour']==th)|(data['time_hour']==th-1)]
#取当前时间所在10分钟的前6个10分钟
def rq_zh(d):
# today = datetime.now() #取今天
today = datetime.now()-timedelta(days=1)
Tm = (today - timedelta(minutes=d)).strftime("%Y-%m-%d %H:%M")
th = Tm[11:13]
sd = Tm[14:16]
if (int(th[0]==0 and int(th[1]==0))):
th = 24
else:
th = int(th)
t = 0
if (int(sd[0]) == 0 and int(sd[1]) in range(5)) | (int(sd[0]) == 0 and int(sd[1]) in range(6,10)):
t += 1
if (int(sd[0]) == 1 and int(sd[1]) in range(5)) | (int(sd[0]) == 1 and int(sd[1]) in range(6,10)):
t += 2
if (int(sd[0]) == 2 and int(sd[1]) in range(5)) | (int(sd[0]) == 2 and int(sd[1]) in range(6,10)):
t += 3
if (int(sd[0]) == 3 and int(sd[1]) in range(5)) | (int(sd[0]) == 3 and int(sd[1]) in range(6,10)):
t += 4
if (int(sd[0]) == 4 and int(sd[1]) in range(5)) | (int(sd[0]) == 4 and int(sd[1]) in range(6,10)):
t += 5
if (int(sd[0]) == 5 and int(sd[1]) in range(5)) | (int(sd[0]) == 5 and int(sd[1]) in range(6,10)):
t += 6
return th,t
dats = []
for i in [10,20,30,40,50,60]:
dat = rq_zh(i)
dats.append(dat)
# data1 = data[(data['time_hour'].isin([i[0]+1 for i in dats]))&(data['sjd'].isin([i[1] for i in dats]))]
data1 = []
for i in dats:
dat1 = data[(data['time_hour'].isin([i[0],i[0]+1]))&(data['sjd']==i[1])]
data1.append(dat1)
data1 = pd.concat(data1)
ds = []
for r in set(data1['date']):
d1 = data1[data1['date']==r]
for lk in set(d1['point_number']):
d2 = d1[d1['point_number']==lk]
for t in set(d2['Tm']):
d3 = d2[d2['Tm']==t]
for sj in set(d3['sjd']):
d4 = d3[d3['sjd']==sj]
def zj(col):
zz = d4[col].sum()
return zz
flow_all = zj('flow_all')
flow_e_l = zj('flow_e_l')
flow_e_s = zj('flow_e_s')
flow_e_r = zj('flow_e_r')
flow_w_l = zj('flow_w_l')
flow_w_s = zj('flow_w_s')
flow_w_r = zj('flow_w_r')
flow_s_l = zj('flow_s_l')
flow_s_s = zj('flow_s_s')
flow_s_r = zj('flow_s_r')
flow_n_l = zj('flow_n_l')
flow_n_s = zj('flow_n_s')
flow_n_r = zj('flow_n_r')
fl_e = zj('flow_e')
fl_w = zj('flow_w')
fl_s = zj('flow_s')
fl_n = zj('flow_n')
dsum = round(pd.DataFrame([flow_all,flow_e_l,flow_e_s,flow_e_r,flow_w_l,flow_w_s,
flow_w_r,flow_s_l,flow_s_s,flow_s_r,flow_n_l,
flow_n_s,flow_n_r,fl_e,fl_w,fl_s,fl_n]))
dsum1 = pd.DataFrame(dsum).T
dsum1.columns = ['flow_all','flow_e_l','flow_e_s','flow_e_r','flow_w_l','flow_w_s',
'flow_w_r','flow_s_l','flow_s_s','flow_s_r','flow_n_l',
'flow_n_s','flow_n_r','flow_e','flow_w','flow_s','flow_n']
dsum1['Tm'] = t
dsum1['lk'] = lk
dsum1['rq'] = r
dsum1['sjd'] = sj
ds.append(dsum1)
try:
lk = pd.concat(ds,ignore_index=True)
except ValueError:
print(" raise ValueError('No objects to concatenate')")
else:
print("objects to concatenate success")
lk['time_p'] = lk['Tm'].astype(str) + ":" + lk['sjd'].astype(str) + str(0)
lk['Time slice'] = lk['rq'].astype(str) + ":" + lk['time_p']
xq = []
tm = lk['rq']
for i in tm:
dt = pd.to_datetime(i)
dt1 = dt.strftime('%A')
xq.append(dt1)
lk['xq'] = xq
size_mapping = {
'Monday': str(1),
'Tuesday': 2,
'Wednesday': 3,
'Thursday':4,
'Friday':5,
'Saturday':6,
'Sunday':7}
lk['xq'] = lk['xq'].map(size_mapping)
lk1 = lk
lk1['sj'] = lk1['rq'].astype('str')+" "+lk1['time_p']
#交换列顺序
cols = list(lk)
cols.insert(0,cols.pop(cols.index('lk')))
cols.insert(1,cols.pop(cols.index('sj')))
cols.insert(2,cols.pop(cols.index('sjd')))
lk1 = lk1.loc[:,cols]
tz = lk1[lk1.columns[:20]]
tzz=[]
for lk in set(tz['lk']):
tz1=tz[tz['lk']==lk]
tz2=round(pd.DataFrame(tz1[tz1.columns[3:]].mean()).T)
tz2['date']=set(lk1['rq'])
tz2['point_number']=lk
tzz.append(tz2)
tzz=pd.concat(tzz)
#交换列顺序
cols = list(tzz)
cols.insert(0,cols.pop(cols.index('point_number')))
cols.insert(1,cols.pop(cols.index('date')))
tzz = tzz.loc[:,cols]
tzz = tzz.sort_values(by=['point_number'])
tzz.to_csv(r'F:\GZ\DM\Spyder\LSTM_realTmpredict_rhyc\tzz.csv')
print("当前交通流量值计算完毕》》》》》》》》》》》》")
time.sleep(5)
sql1 = "select * from flow_forecast_TimeSeries where point_number in ('#GS002','#GS004','#GS005','#GS006','#GS007','#GS008','#GS009','#GS010','#GS011','#GS012','#GS017','#GS024','#GS031','#GS033','#GS035') and date in (\"%s\")"%(dates)
con = pymysql.connect('52.1.123.6','root','123456','keenIts')
dat1 = pd.read_sql(sql1,con)
dat1 = dat1.sort_values(by=['point_number'])
con.close()
# dat1.to_csv(r'F:\GZ\DM\Spyder\Flow_realTmpredict_rhyc\dat1.csv')
print("开始计算预测误差率并存入数据库》》》》》》》")
time.sleep(1)
if data.empty == True:
forecast_error = pd.DataFrame(np.zeros((15,16)))
forecast_error.columns = ['point_number','date', 'time_part', 'flow_all', 'flow_e_l', 'flow_e_s','flow_e_r',
'flow_w_l', 'flow_w_s', 'flow_w_r', 'flow_s_l', 'flow_s_s','flow_s_r', 'flow_n_l', 'flow_n_s', 'flow_n_r']
else:
# dat1=dat1[dat1['point_number'].isin(set(tzz['point_number']))] #除去无数据的点情况
p_nms = [x for x in set(tzz['point_number']) if x in set(dat1['point_number'])]
dat1=dat1[dat1['point_number'].isin(p_nms)] #除去无数据的点情况
tzz = tzz[tzz['point_number'].isin(p_nms)]
forecast_error = pd.DataFrame((dat1[dat1.columns[4:]].values-tzz[tzz.columns[2:15]].values)/tzz[tzz.columns[2:15]].values,dtype='str')
forecast_error.columns = ['flow_all', 'flow_e_l', 'flow_e_s','flow_e_r', 'flow_w_l', 'flow_w_s',
'flow_w_r', 'flow_s_l', 'flow_s_s','flow_s_r', 'flow_n_l', 'flow_n_s', 'flow_n_r']
forecast_error['point_number'] = tzz['point_number'].values
forecast_error['date']=dat1['date'].values
forecast_error['time_part']=dat1['time_part'].values
cols = list(forecast_error)
cols.insert(0,cols.pop(cols.index('point_number')))
cols.insert(1,cols.pop(cols.index('date')))
cols.insert(2,cols.pop(cols.index('time_part')))
forecast_error = forecast_error.loc[:,cols]
forecast_error[forecast_error==0]==np.nan
forecast_error[forecast_error=='inf']==np.nan
# forecast_error=forecast_error[forecast_error['point_number']!=np.NaN]
forecast_error.to_csv('forecast_error.csv')
print(forecast_error)
#预测结果写入数据库
engine=create_engine("mysql+pymysql://root:[email protected]:3306/keenIts?charset=utf8")
forecast_error.to_sql(name='flow_forecast_TimeSeries_error',con=engine,if_exists='append',index=False,index_label=False)
print("预测误差率计算完毕！！！！！！！！")
forecast_real()