FastAPI+Pyomo实现线性回归解决饮食问题-海口c网

FastAPI+Pyomo实现线性回归解决饮食问题

article/2025/7/5 15:25:20

之前在 FastAPI介绍-CSDN博客中介绍过FastAPI，在 Pyomo中线性规划接口的使用-CSDN博客中使用Pyomo解决饮食问题，这里将两者组合，即FastAPI在服务器端启动，通过Pyomo实现线性回归；客户端通过浏览器获取饮食的最优解。

这里服务器端的diet.json是已存在的，后期调整为从客户端传过来，以及其它可变参数也从客户端通过json格式传给服务器端。

实现如下：

from fastapi import FastAPI
from pyomo.environ import *
import mathdef parse_json(file):model = ConcreteModel()data = DataPortal()data.load(filename=file)model.F = Set(initialize=data['sets']['F'])model.N = Set(initialize=data['sets']['N'])model.c = Param(model.F, initialize=data['params']['c'], within=PositiveReals)def parse_a(model, food, nutr):return data['params']['a'][food][nutr]model.a = Param(model.F, model.N, initialize=parse_a, within=NonNegativeReals)model.V = Param(model.F, initialize=data['params']['V'], within=PositiveReals)model.Nmin = Param(model.N, initialize=data['params']['Nmin'], within=NonNegativeReals, default=0.0)def parse_Nmax(model, nutr):val = data['params']['Nmax'][nutr]return val if val != "inf" else math.inf model.Nmax = Param(model.N, initialize=parse_Nmax, within=NonNegativeReals)model.Vmax = Param(initialize=data['params']['Vmax'], within=PositiveReals)return modeldef linear_programming_diet(file, number):model = parse_json(file)model.x = Var(model.F, within=NonNegativeIntegers)model.y = Var(model.F, within=Binary)model.cost = Objective(expr=sum(model.c[i]*model.x[i] for i in model.F), sense=minimize)def nutrient_rule(model, j):value = sum(model.a[i,j]*model.x[i] for i in model.F)return inequality(model.Nmin[j], value, model.Nmax[j])model.nutrient_limit = Constraint(model.N, rule=nutrient_rule)def volume_rule(model):return sum(model.V[i]*model.x[i] for i in model.F) <= model.Vmaxmodel.volume = Constraint(rule=volume_rule)def select_rule(model):return sum(model.y[i] for i in model.F) == numbermodel.select = Constraint(rule=select_rule)def linking_upper_rule(model, f):return model.x[f] <= model.y[f] * 1e6model.linking_upper = Constraint(model.F, rule=linking_upper_rule)def linking_lower_rule(model, f):return model.x[f] >= model.y[f]model.linking_lower = Constraint(model.F, rule=linking_lower_rule)solver = SolverFactory('glpk')results = solver.solve(model)if results.solver.termination_condition != TerminationCondition.optimal:return {"result":"no optimal solution"}results = {}results["total const"] = f"{value(model.cost):.2f}"foods = {}count = 0for f in model.F:v = int(value(model.x[f]))if v != 0:foods[f] = vcount += 1results["selected food"] = foodsif count != number:return {"result":"solution result is wrong, number of food types does not match"}nutrients = {}	for n in model.N:actual = sum(value(model.a[f,n] * model.x[f]) for f in model.F)nutrients[n] = f"{actual:.2f}"results["nutrients"] = nutrientsreturn resultsapp = FastAPI()@app.get("/diet")
def diet_optimization():return linear_programming_diet("../test_data/diet.json", 5)

通过以下命令启动：