包络分析代码实现python,包络数据分析,Python

#!/usr/bin/env python # coding: utf-8 # In[1]: import gurobipy import pandas as pd # 分页显示数据, 设置为 False 不允许分页 pd.set_option('display.expand_frame_repr', False) # 最多显示的列数, 设置为 None 显示全部列 pd.set_option('display.max_columns', None) # 最多显示的行数, 设置为 None 显示全部行 pd.set_option('display.max_rows', None) class DEA(object): def __init__(self, DMUs_Name, X, Y, AP=False): self.m1, self.m1_name, self.m2, self.m2_name, self.AP = X.shape[1], X.columns.tolist(), Y.shape[1], Y.columns.tolist(), AP self.DMUs, self.X, self.Y = gurobipy.multidict({DMU: [X.loc[DMU].tolist(), Y.loc[DMU].tolist()] for DMU in DMUs_Name}) print(f'DEA(AP={AP}) MODEL RUNING...') def __CCR(self): for k in self.DMUs: MODEL = gurobipy.Model() OE, lambdas, s_negitive, s_positive = MODEL.addVar(), MODEL.addVars(self.DMUs), MODEL.addVars(self.m1), MODEL.addVars(self.m2) MODEL.update() MODEL.setObjectiveN(OE, index=0, priority=1) MODEL.setObjectiveN(-(sum(s_negitive) + sum(s_positive)), index=1, priority=0) MODEL.addConstrs(gurobipy.quicksum(lambdas[i] * self.X[i][j] for i in self.DMUs if i != k or not self.AP) + s_negitive[j] == OE * self.X[k][j] for j in range(self.m1)) MODEL.addConstrs(gurobipy.quicksum(lambdas[i] * self.Y[i][j] for i in self.DMUs if i != k or not self.AP) - s_positive[j] == self.Y[k][j] for j in range(self.m2)) MODEL.setParam('OutputFlag', 0) MODEL.optimize() self.Result.at[k, ('效益分析', '综合技术效益(CCR)')] = MODEL.objVal self.Result.at[k, ('规模报酬分析', '有效性')] = '非 DEA 有效' if MODEL.objVal < 1 else 'DEA 弱有效' if s_negitive.sum().getValue() + s_positive.sum().getValue() else 'DEA 强有效' self.Result.at[k, ('规模报酬分析', '类型')] = '规模报酬固定' if lambdas.sum().getValue() == 1 else '规模报酬递增' if lambdas.sum().getValue() < 1 else '规模报酬递减' for m in range(self.m1): self.Result.at[k, ('差额变数分析', f'{self.m1_name[m]}')] = s_negitive[m].X self.Result.at[k, ('投入冗余率', f'{self.m1_name[m]}')] = 'N/A' if self.X[k][m] == 0 else s_negitive[m].X / self.X[k][m] for m in range(self.m2): self.Result.at[k, ('差额变数分析', f'{self.m2_name[m]}')] = s_positive[m].X self.Result.at[k, ('产出不足率', f'{self.m2_name[m]}')] = 'N/A' if self.Y[k][m] == 0 else s_positive[m].X / self.Y[k][m] return self.Result def __BCC(self): for k in self.DMUs: MODEL = gurobipy.Model() TE, lambdas = MODEL.addVar(), MODEL.addVars(self.DMUs) MODEL.update() MODEL.setObjective(TE, sense=gurobipy.GRB.MINIMIZE) MODEL.addConstrs(gurobipy.quicksum(lambdas[i] * self.X[i][j] for i in self.DMUs if i != k or not self.AP) <= TE * self.X[k][j] for j in range(self.m1)) MODEL.addConstrs(gurobipy.quicksum(lambdas[i] * self.Y[i][j] for i in self.DMUs if i != k or not self.AP) >= self.Y[k][j] for j in range(self.m2)) MODEL.addConstr(gurobipy.quicksum(lambdas[i] for i in self.DMUs if i != k or not self.AP) == 1) MODEL.setParam('OutputFlag', 0) MODEL.optimize() self.Result.at[k, ('效益分析', '技术效益(BCC)')] = MODEL.objVal if MODEL.status == gurobipy.GRB.Status.OPTIMAL else 'N/A' return self.Result def dea(self): columns_Page = ['效益分析'] * 3 + ['规模报酬分析'] * 2 + ['差额变数分析'] * (self.m1 + self.m2) + ['投入冗余率'] * self.m1 + ['产出不足率'] * self.m2 columns_Group = ['技术效益(BCC)', '规模效益(CCR/BCC)', '综合技术效益(CCR)','有效性', '类型'] + (self.m1_name + self.m2_name) * 2 self.Result = pd.DataFrame(index=self.DMUs, columns=[columns_Page, columns_Group]) self.__CCR() self.__BCC() self.Result.loc[:, ('效益分析', '规模效益(CCR/BCC)')] = self.Result.loc[:, ('效益分析', '综合技术效益(CCR)')] / self.Result.loc[:,('效益分析', '技术效益(BCC)')] return self.Result def analysis(self, file_name=None): Result = self.dea() file_name = 'DEA 数据包络分析报告.xlsx' if file_name is None else f'\\{file_name}.xlsx' Result.to_excel(file_name, 'DEA 数据包络分析报告') # In[4]: data=pd.read_excel('E:\data.xlsx',encoding='gbk') X = data[['营业成本', '销售费用', '管理费用','财务费用归一化']] Y = data[['营业收入', '总资产','总资产周转率','净利润归一化']] dea = DEA(DMUs_Name=data.index, X=X, Y=Y) dea.analysis() # dea 分析并输出表格 print(dea.dea()) # dea 分析，不输出结果