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app.py
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138 lines (124 loc) · 4.17 KB
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# encoding=utf-8
from flask import Flask
from flask import render_template
from flask import request
import xgboost as xgb
import json
import pymysql
import numpy as np
import pandas as pd
app = Flask(__name__)
import torch
import db
@app.route('/', methods=['GET'])
def index():
return render_template('index.html')
@app.route('/predict_xgb', methods=['GET', 'POST'])
def predict_xg():
city, brand, output_volume, launch_year, kilometres, is_import, old_price, gear_type = parse_data(request)
g1 = 1
if gear_type == '自动挡': # 自动挡0，手动挡1
g1 = 0
else:
g1 = 1
l = [city, brand, output_volume, launch_year, kilometres, is_import, old_price, g1]
result = get_encoder()
city_l = np.array(json.loads(result[0]))
brand_l = np.array(json.loads(result[1]))
output_volume_l = np.array(json.loads(result[2]))
re_assign(2, output_volume_l, l)
re_assign(0, city_l, l)
re_assign(1, brand_l, l)
t = []
for v in l:
if isinstance(v, str):
t.append(float(v))
else:
t.append(v)
tu = tuple(t)
t = list()
t.append(tu)
print(pd)
t = pd.DataFrame(t, columns=['city', 'brand', 'output_volume', 'launch_year', 'kilometres', 'is_import', 'old_price', 'gear_type', 'gear_type'])
model = xgb.Booster(model_file='xgboost.model')
t = xgb.DMatrix(t)
result = model.predict(t)
result = float(result[0])
print(type(result))
return {'result': result}
@app.route('/add', methods=['GET', 'POST'])
def add():
return 'success'
@app.route('/predict_torch', methods=['GET', 'POST'])
def predict_torch():
city, brand, output_volume, launch_year, kilometres, is_import, old_price, gear_type = parse_data(request)
g1 = 1
if gear_type == '自动挡': # 自动挡0，手动挡1
g1 = 0
else:
g1 = 1
result = get_encoder()
a = [city, brand, output_volume, launch_year, kilometres, is_import, old_price, g1]
city_l = np.array(json.loads(result[0]))
brand_l = np.array(json.loads(result[1]))
re_assign(0, city_l, a)
re_assign(1, brand_l, a)
t = []
for v in a:
if isinstance(v, str):
t.append(float(v))
else:
t.append(v)
a = np.array(t)
result = get_statistics()
mean_ = np.array(json.loads(result[0]))
var_ = np.array(json.loads(result[1]))
b = (a - mean_)/np.sqrt(var_)
b = torch.from_numpy(b).float()
model = torch.load('torch.model')
y_b = model(b).detach().numpy()
print(y_b)
return {'result': float(y_b[0])}
# 非数值类型数据转换为数值类型（以原转换为准则）
def re_assign(index, ls, tbd):
for i in range(len(ls)):
tbd[index] = i + 1
if ls[i] == tbd[index]:
tbd[index] = i
break
def get_cursor():
c = db.config()
conn = pymysql.connect(host=c['host'], port=c['port'], user=c['user'], password=c['password'], charset=c['charset'], database=c['database'])
cursor = conn.cursor()
return cursor, conn
def get_encoder():
cursor, conn = get_cursor()
sql = 'select city, brand, output_volume from t_encoder order by id desc limit 1'
cursor.execute(sql)
result = cursor.fetchone()
close(conn, cursor)
return result
def get_statistics():
cursor, conn = get_cursor()
sql = 'select mean, var from t_statistics order by id desc limit 1'
cursor.execute(sql)
result = cursor.fetchone()
close(conn, cursor)
return result
def close(conn, cursor):
cursor.close()
conn.close()
def parse_data(req):
data = req.get_data()
data = json.loads(data)
city = data.get('city')
brand = data.get('brand')
output_volume = data.get('output_volume')
launch_year = data.get('launch_year')
kilometres = data.get('kilometres')
is_import = data.get('is_import')
old_price = data.get('old_price')
gear_type = data.get('gear_type')
return city, brand, output_volume, launch_year, kilometres, is_import, old_price, gear_type
if __name__ == '__main__':
app.run(debug=True) # 启动应用