python DEA:强/弱处置性假设下的考虑非期望产出的非径向距离函数

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Matlab DEA 程序包
pyDEA安装
非径向距离函数
考虑非期望产出的非径向距离函数

这一版更新比较简单，增加了强处置性假设下的非径向距离函数（NDDF）计算。比较强\弱处置假设下的NDDF，可以计算相应的环境规制成本[ Technological Forecasting & Social Change 96 (2015) 62–70].

import numpy as np
import os
import pandas as pd
import pickle
import pulp
import timedef read_excel(iteration):dea_inout = pd.read_excel(os.path.join(dir_data, xls), sheet_name=str(iteration), header=0)return dea_inoutclass DEAProblem:def __init__(self, inputs, outputs, bad_outs, weight_vector,  disp='weak disposability', directional_factor=None, returns='CRS',in_weights=[0, None], out_weights=[0, None],badout_weights=[0, None]):self.inputs = inputsself.outputs = outputsself.bad_outs = bad_outsself.returns = returnsself.disp = dispself.weight_vector = weight_vector # weight vector in directional distance function      self.J, self.I = self.inputs.shape  # no of DMUs, inputs_, self.R = self.outputs.shape  # no of outputs_, self.S = self.bad_outs.shape # no of bad outputsself._i = range(self.I)  # inputsself._r = range(self.R)  # outputsself._s = range(self.S)  # bad_outputself._j = range(self.J)  # DMUsif directional_factor == None:self.gx = self.inputsself.gy = self.outputsself.gb = self.bad_outselse:self.gx = directional_factor[:self.I]self.gy = directional_factor[self.I:(self.I+self.J)]self.gy = directional_factor[(self.I+self.J):]self._in_weights = in_weights  # input weight restrictionsself._out_weights = out_weights  # output weight restrictionsself._badout_weights = badout_weights # bad output weight restrictions# creates dictionary of pulp.LpProblem objects for the DMUsself.dmus = self._create_problems()def _create_problems(self):"""Iterate over the DMU and create a dictionary of LP problems, onefor each DMU."""dmu_dict = {}for j0 in self._j:dmu_dict[j0] = self._make_problem(j0)return dmu_dictdef _make_problem(self, j0):"""Create a pulp.LpProblem for a DMU."""# Set up pulpprob = pulp.LpProblem("".join(["DMU_", str(j0)]), pulp.LpMaximize)self.weights = pulp.LpVariable.dicts("Weight", (self._j),lowBound=self._in_weights[0])self.betax = pulp.LpVariable.dicts("scalingFactor_x", (self._i),lowBound=0,upBound=1)self.betay = pulp.LpVariable.dicts("scalingFacotr_y", (self._r),lowBound=0)self.betab = pulp.LpVariable.dicts("scalingFacotr_b", (self._s),lowBound=0, upBound=1)# Set returns to scaleif self.returns == "VRS":prob += pulp.lpSum([weight for weight in self.weights]) == 1# Set up objective function      prob += pulp.lpSum([(self.weight_vector[i]*self.betax[i]) for i in self._i]+[(self.weight_vector[self.I+r]*self.betay[r]) for r in self._r]+[(self.weight_vector[self.I+self.R+s]*self.betab[s]) for s in self._s])# Set up constraintsfor i in self._i:prob += pulp.lpSum([(self.weights[j0]*self.inputs.values[j0][i]) for j0 in self._j]) <= self.inputs.values[j0][i]-self.betax[i]*self.gx.values[j0][i]for r in self._r:prob += pulp.lpSum([(self.weights[j0]*self.outputs.values[j0][r]) for j0 in self._j]) >= self.outputs.values[j0][r]+self.betay[r]*self.gy.values[j0][r]if  self.disp == "weak disposability":  for s in self._s:   # weak disposabilityprob += pulp.lpSum([(self.weights[j0]*self.bad_outs.values[j0][s]) for j0 in self._j]) == self.bad_outs.values[j0][s]-self.betab[s]*self.gb.values[j0][s]elif self.disp =="strong disposability":for s in self._s:prob += pulp.lpSum([(self.weights[j0]*self.bad_outs.values[j0][s]) for j0 in self._j]) >= self.bad_outs.values[j0][s]-self.betab[s]*self.gb.values[j0][s] return probdef solve(self):"""Iterate over the dictionary of DMUs' problems, solve them, and collatethe results into a pandas dataframe."""sol_status = {}sol_weights = {}sol_efficiency = {}for ind, problem in list(self.dmus.items()):problem.solve()sol_status[ind] = pulp.LpStatus[problem.status]sol_weights[ind] = {}for v in problem.variables():sol_weights[ind][v.name] = v.varValuesol_efficiency[ind] = pulp.value(problem.objective)return sol_status, sol_efficiency, sol_weightsdef inout_data(iteration, data_columnslist):dea_data = read_excel(iteration)data_df = dea_data.loc[:, data_columnslist]return data_dfdef results_df(inputs, outputs, undesirable_output, weight, names, disp):solve = DEAProblem(inputs, outputs, undesirable_output, weight,disp).solve()status = pd.DataFrame.from_dict(solve[0], orient="index", columns=["status"])efficiency = pd.DataFrame.from_dict(solve[1], orient="index", columns=["efficiency"])weight = pd.DataFrame.from_dict(solve[2], orient="index")results = pd.concat([names, status, efficiency, weight], axis=1)return results.round(decimals=4)dir_data = r"~\Data_input"
xls = r"DEA_inout.xlsx"input_columns = ["a", "b", "c"]
output_columns = ["d"]
undesirable_outputs = ["e", "f"]
weight = [0, 0, 1 / 3, 1 / 3, 1 / 6, 1 / 6]
names = ["DMU name"]
iteration = 1000

Cite the work

Yang, F.; Choi, Y.: Lee, H. Life-cycle data envelopment analysis to measure efficiency and cost-effectiveness of environmental regulation in China’s transport sector. Ecological indicators 2021. https://doi.org/10.1016/j.ecolind.2021.107717
Choi, Y.; Yang, F.; Lee, H. On the Unbalanced Atmospheric Environmental Performance of Major Cities in China. Sustainability 2020, 12, 5391. https://doi.org/10.3390/su12135391

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