biogeme巢式logit选择学习

1|0导入库

import pandas as pd
import csv
from biogeme import models
import biogeme.biogeme as bio
import biogeme.database as db
from biogeme.expressions import Beta, Variable
import biogeme.results as res
import biogeme.exceptions as excep
import sys

1|1输入选择集

df=pd.read_csv('choice.csv')
database=db.Database('choice',df)##输入选择集创建数据库

globals().update(database.variables)##定义全局变量

database.variables

{'Unnamed: 0.2': Unnamed: 0.2,
'Unnamed: 0.1': Unnamed: 0.1,
'Unnamed: 0': Unnamed: 0,
'id': id,
'hc_time_all': hc_time_all,
'hc_cishu': hc_cishu,
'hc_fare': hc_fare,
'path_length': path_length,
'car_fare': car_fare,
'car_time': car_time,
'car_cishu': car_cishu,
'bus_time': bus_time,
'metro_fare': metro_fare,
'metro_cishu': metro_cishu,
'metro_time': metro_time,
'bus_fare': bus_fare,
'bus_cishu': bus_cishu,
'choice': choice}

1|2定义变量

#首先定义常数项
ASC_CAR = Beta('ASC_CAR',0,None,None,0)
ASC_BUS = Beta('ASC_BUS',0,None,None,0)
ASC_METRO = Beta('ASC_METRO',0,None,None,0)
ASC_HC = Beta('ASC_HC',0,None,None,0)
#定义与效用有关参数的系数
BETA_TIME_CAR = Beta('BETA_TIME_CAR',0,None,None,0)
BETA_FARE_CAR = Beta('BETA_FARE_CAR',0,None,None,0)
BETA_LENGTH = Beta('BETA_LENGTH',0,None,None,0)
BETA_TIME_BUS =Beta('BETA_TIME_BUS',0,None,None,0)
BETA_FARE_BUS = Beta('BETA_FARE_BUS',0,None,None,0)
BEAT_CISHU_BUS = Beta('BETA_CISHU_BUS',0,None,None,0)
BETA_TIME_METRO = Beta('BETA_TIME_METRO',0,None,None,0)
BETA_FARE_METRO = Beta('BETA_FARE_METRO',0,None,None,0)
BETA_CISHU_METRO = Beta('BETA_CISHU_METRO',0,None,None,0)
BETA_TIME_HC = Beta('BETA_TIME_HC',0,None,None,0)
BETA_FARE_HC = Beta('BETA_FARE_HC',0,None,None,0)
BETA_CISHU_HC = Beta('BETA_CISHU_HC',0,None,None,0)

1|3定义效用函数

V_CAR = (
ASC_CAR
+BETA_TIME_CAR*car_time
+BETA_FARE_CAR*car_fare
+BETA_LENGTH*path_length
)
V_BUS = (
ASC_BUS
+BETA_TIME_BUS*bus_time
+BETA_FARE_BUS*bus_fare
+BEAT_CISHU_BUS*bus_cishu
)
V_METRO = (
ASC_METRO
+BETA_TIME_METRO*metro_time
+BETA_FARE_METRO*metro_fare
+BETA_CISHU_METRO*metro_cishu
)
V_HC = (
ASC_HC
+BETA_TIME_HC*hc_time_all
+BETA_FARE_HC*hc_fare
+BETA_CISHU_METRO*hc_cishu
)

1|4效用对应方案

V = {
0:V_CAR,
1:V_BUS,
2:V_METRO,
3:V_HC
}

1|5定义树形结构

PUBLIC = Beta('PUBLIC',1,1,None,0)

CAR_NEST = 1.0,[0]
PUBLIC_NEST = PUBLIC,[1,2,3]
nests = CAR_NEST,PUBLIC_NEST

1|6计算logprob

logprob = models.lognested(V,None,nests,choice)

1|7估计参数

biogeme = bio.BIOGEME(database, logprob)

biogeme.modelName = 'mode_choice_estimation'
results=biogeme.estimate()
Results=results.getEstimatedParameters()
print(Results)

[16:12:00] < Warning > Numerical problem with the second derivative matrix. Norm = inf. Replaced by the identity matrix.
[16:12:01] < Warning > Numerical problem with the second derivative matrix. Norm = inf. Replaced by the identity matrix.
[16:12:01] < Warning > Numerical problem with the second derivative matrix. Norm = inf. Replaced by the identity matrix.
[16:12:02] < Warning > Numerical problem with the second derivative matrix. Norm = inf. Replaced by the identity matrix.
[16:12:03] < Warning > Numerical problem with the second derivative matrix. Norm = inf. Replaced by the identity matrix.
[16:12:04] < Warning > Numerical problem with the second derivative matrix. Norm = inf. Replaced by the identity matrix.
[16:12:06] < Warning > Numerical problem with the second derivative matrix. Norm = inf. Replaced by the identity matrix.
[16:12:10] < Warning > Numerical problem with the second derivative matrix. Norm = inf. Replaced by the identity matrix.
[16:12:12] < Warning > Numerical problem with the second derivative matrix. Norm = inf. Replaced by the identity matrix.
[16:12:15] < Warning > Numerical problem with the second derivative matrix. Norm = inf. Replaced by the identity matrix.
[16:12:17] < Warning > Numerical problem with the second derivative matrix. Norm = inf. Replaced by the identity matrix.
[16:12:18] < Warning > Numerical problem with the second derivative matrix. Norm = inf. Replaced by the identity matrix.
[16:12:20] < Warning > Numerical problem with the second derivative matrix. Norm = inf. Replaced by the identity matrix.
[16:12:23] < Warning > Numerical problem with the second derivative matrix. Norm = inf. Replaced by the identity matrix.
[16:12:24] < Warning > Numerical problem with the second derivative matrix. Norm = inf. Replaced by the identity matrix.
[16:12:25] < Warning > Numerical problem with the second derivative matrix. Norm = inf. Replaced by the identity matrix.
[16:12:26] < Warning > Numerical problem with the second derivative matrix. Norm = inf. Replaced by the identity matrix.
[16:12:27] < Warning > Numerical problem with the second derivative matrix. Norm = inf. Replaced by the identity matrix.
[16:12:28] < Warning > Numerical problem with the second derivative matrix. Norm = inf. Replaced by the identity matrix.
[16:12:29] < Warning > Numerical problem with the second derivative matrix. Norm = inf. Replaced by the identity matrix.
[16:12:30] < Warning > Numerical problem with the second derivative matrix. Norm = inf. Replaced by the identity matrix.
[16:12:31] < Warning > Numerical problem with the second derivative matrix. Norm = inf. Replaced by the identity matrix.
[16:12:31] < Warning > Numerical problem with the second derivative matrix. Norm = inf. Replaced by the identity matrix.
[16:12:32] < Warning > Numerical problem with the second derivative matrix. Norm = inf. Replaced by the identity matrix.
[16:12:33] < Warning > Numerical problem with the second derivative matrix. Norm = inf. Replaced by the identity matrix.
Value Rob. Std err Rob. t-test Rob. p-value
ASC_BUS -7.965898e-01 0.0 1.797693e+308 0.0
ASC_CAR -1.250052e+00 0.0 1.797693e+308 0.0
ASC_HC 9.795643e-01 0.0 1.797693e+308 0.0
ASC_METRO 1.067077e+00 0.0 1.797693e+308 0.0
BETA_CISHU_BUS -1.776695e+00 0.0 1.797693e+308 0.0
BETA_CISHU_METRO -6.685987e-03 0.0 1.797693e+308 0.0
BETA_FARE_BUS 8.880739e-01 0.0 1.797693e+308 0.0
BETA_FARE_CAR 2.888911e-07 0.0 1.797693e+308 0.0
BETA_FARE_HC 3.266817e-04 0.0 1.797693e+308 0.0
BETA_FARE_METRO -3.187853e-02 0.0 1.797693e+308 0.0
BETA_LENGTH 1.094267e-04 0.0 1.797693e+308 0.0
BETA_TIME_BUS -8.687563e-07 0.0 1.797693e+308 0.0
BETA_TIME_CAR -8.526598e-04 0.0 1.797693e+308 0.0
BETA_TIME_HC 4.400077e-06 0.0 1.797693e+308 0.0
BETA_TIME_METRO -3.083993e-07 0.0 1.797693e+308 0.0
PUBLIC 6.521609e+02 0.0 1.797693e+308 0.0

1|8获取每个备选方案的选择概率

prob_CAR = models.nested(V, None, nests, 0)
prob_BUS = models.nested(V, None, nests, 1)
prob_METRO = models.nested(V, None, nests, 2)
prob_HC = models.nested(V, None, nests, 3)

1|9读取估计结果

# Read the estimation results from the file
try:
results = res.bioResults(pickleFile='mode_choice_estimation.pickle')
except excep.biogemeError:
sys.exit(
'Run first the script 02simulation.py '
'in order to generate the '
'file 02estimation.pickle.'
)

1|10进行模拟，计算选择概率和效用值

simulate_formulas = {
'Utility CAR': V_CAR.getValue_c(
betas=results.getBetaValues(), database=database, prepareIds=True
),
'Utility BUS': V_BUS.getValue_c(
betas=results.getBetaValues(), database=database, prepareIds=True
),
'Utility METRO': V_METRO.getValue_c(
betas=results.getBetaValues(), database=database, prepareIds=True
),
'Utility HC': V_HC.getValue_c(
betas=results.getBetaValues(), database=database, prepareIds=True
),
'Prob. CAR': prob_CAR.getValue_c(
betas=results.getBetaValues(), database=database, prepareIds=True
),
'Prob. BUS': prob_BUS.getValue_c(
betas=results.getBetaValues(), database=database, prepareIds=True
),
'Prob. METRO': prob_METRO.getValue_c(
betas=results.getBetaValues(), database=database, prepareIds=True
),
'Prob. HC': prob_HC.getValue_c(
betas=results.getBetaValues(), database=database, prepareIds=True
),
}
simulated_values = pd.DataFrame.from_dict(
simulate_formulas,
)

simulated_values

	Utility CAR	Utility BUS	Utility METRO	Utility HC	Prob. CAR	Prob. BUS	Prob. METRO	Prob. HC
0	-1.934563	1.258228	1.318423	1.282484	0.037193	0.006809	0.907109	0.048888
1	-1.934563	1.258228	1.318434	1.282424	0.037193	0.006805	0.907380	0.048621
2	-1.934563	1.274554	1.306116	1.282484	0.037573	0.060507	0.786654	0.115265
3	-1.934563	1.274554	1.306116	1.282424	0.037574	0.060543	0.787116	0.114767
4	-1.934563	1.274554	1.306092	1.282484	0.037574	0.060606	0.786367	0.115453
...	...	...	...	...	...	...	...	...
2425	-1.909643	1.258093	1.309319	1.289848	0.038368	0.012255	0.787545	0.161833
2426	-1.909643	1.257291	1.308803	1.289848	0.038384	0.011891	0.782124	0.167601
2427	-1.909643	1.274355	1.309319	1.289848	0.038352	0.044393	0.760898	0.156357
2428	-1.909643	1.266182	1.308803	1.289848	0.038378	0.024174	0.772013	0.165435
2429	-1.909643	1.266453	1.309319	1.289848	0.038362	0.023878	0.777908	0.159852

2430 rows × 8 columns

1|11另一种模拟方法，加入biogeme

1|0模拟结果相同，但速度更快

simulate = {
'Utility CAR': V_CAR,
'Utility BUS': V_BUS,
'Utility METRO': V_METRO,
'Utility HC': V_HC,
'Prob. CAR': prob_CAR,
'Prob. BUS': prob_BUS,
'Prob. METRO': prob_METRO,
'Prob. HC': prob_HC,
}

biogeme = bio.BIOGEME(database, simulate)
biogeme_simulation = biogeme.simulate(results.getBetaValues())

biogeme_simulation

	Utility CAR	Utility BUS	Utility METRO	Utility HC	Prob. CAR	Prob. BUS	Prob. METRO	Prob. HC
0	-1.934563	1.258228	1.318423	1.282484	0.037193	0.006809	0.907109	0.048888
1	-1.934563	1.258228	1.318434	1.282424	0.037193	0.006805	0.907380	0.048621
2	-1.934563	1.274554	1.306116	1.282484	0.037573	0.060507	0.786654	0.115265
3	-1.934563	1.274554	1.306116	1.282424	0.037574	0.060543	0.787116	0.114767
4	-1.934563	1.274554	1.306092	1.282484	0.037574	0.060606	0.786367	0.115453
...	...	...	...	...	...	...	...	...
2425	-1.909643	1.258093	1.309319	1.289848	0.038368	0.012255	0.787545	0.161833
2426	-1.909643	1.257291	1.308803	1.289848	0.038384	0.011891	0.782124	0.167601
2427	-1.909643	1.274355	1.309319	1.289848	0.038352	0.044393	0.760898	0.156357
2428	-1.909643	1.266182	1.308803	1.289848	0.038378	0.024174	0.772013	0.165435
2429	-1.909643	1.266453	1.309319	1.289848	0.038362	0.023878	0.777908	0.159852

2430 rows × 8 columns

__EOF__

本文作者：quanbb0818
本文链接：https://www.cnblogs.com/quanbb0818/p/16848829.html
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posted @ 2022-11-01 19:03 权BB 阅读(254) 评论(0) 编辑收藏举报

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biogeme巢式logit选择学习

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quanbb0818

biogeme巢式logit选择学习

1|0导入库

1|1输入选择集

1|2定义变量

1|3定义效用函数

1|4效用对应方案

1|5定义树形结构

1|6计算logprob

1|7估计参数

1|8获取每个备选方案的选择概率

1|9读取估计结果

1|10进行模拟，计算选择概率和效用值

1|11另一种模拟方法，加入biogeme

1|0模拟结果相同，但速度更快

公告

quanbb0818

biogeme巢式logit选择学习

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