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Mixture with lognormal distribution
Example of a mixture of logit models, using Monte-Carlo integration. The mixing distribution is distributed as a log normal.
- author:
Michel Bierlaire, EPFL
- date:
Mon Apr 10 12:11:53 2023
import biogeme.biogeme_logging as blog
import biogeme.biogeme as bio
from biogeme import models
from biogeme.expressions import (
Beta,
exp,
log,
MonteCarlo,
bioDraws,
)
See the data processing script: Data preparation for Swissmetro.
from swissmetro_data import (
database,
CHOICE,
SM_AV,
CAR_AV_SP,
TRAIN_AV_SP,
TRAIN_TT_SCALED,
TRAIN_COST_SCALED,
SM_TT_SCALED,
SM_COST_SCALED,
CAR_TT_SCALED,
CAR_CO_SCALED,
)
logger = blog.get_screen_logger(level=blog.INFO)
logger.info('Example b17lognormal_mixture.py')
Example b17lognormal_mixture.py
Parameters to be estimated.
ASC_CAR = Beta('ASC_CAR', 0, None, None, 0)
ASC_TRAIN = Beta('ASC_TRAIN', 0, None, None, 0)
ASC_SM = Beta('ASC_SM', 0, None, None, 1)
B_COST = Beta('B_COST', 0, None, None, 0)
Define a random parameter, normally distributed, designed to be used for Monte-Carlo simulation.
B_TIME = Beta('B_TIME', 0, None, None, 0)
It is advised not to use 0 as starting value for the following parameter.
B_TIME_S = Beta('B_TIME_S', 1, -2, 2, 0)
Define a random parameter, log normally distributed, designed to be used for Monte-Carlo simulation.
B_TIME_RND = -exp(B_TIME + B_TIME_S * bioDraws('B_TIME_RND', 'NORMAL'))
Definition of the utility functions.
V1 = ASC_TRAIN + B_TIME_RND * TRAIN_TT_SCALED + B_COST * TRAIN_COST_SCALED
V2 = ASC_SM + B_TIME_RND * SM_TT_SCALED + B_COST * SM_COST_SCALED
V3 = ASC_CAR + B_TIME_RND * CAR_TT_SCALED + B_COST * CAR_CO_SCALED
Associate utility functions with the numbering of alternatives.
V = {1: V1, 2: V2, 3: V3}
Associate the availability conditions with the alternatives.
av = {1: TRAIN_AV_SP, 2: SM_AV, 3: CAR_AV_SP}
Conditional to B_TIME_RND, we have a logit model (called the kernel).
prob = models.logit(V, av, CHOICE)
We integrate over B_TIME_RND using Monte-Carlo.
logprob = log(MonteCarlo(prob))
# Create the Biogeme object. As the objective is to illustrate the
# syntax, we calculate the Monte-Carlo approximation with a small
# number of draws. To achieve that, we provide a parameter file
# different from the default one.
the_biogeme = bio.BIOGEME(database, logprob, parameter_file='few_draws.toml')
the_biogeme.modelName = '17lognormal_mixture'
File few_draws.toml has been parsed.
Estimate the parameters.
results = the_biogeme.estimate()
*** Initial values of the parameters are obtained from the file __17lognormal_mixture.iter
Cannot read file __17lognormal_mixture.iter. Statement is ignored.
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_TRAIN B_COST B_TIME B_TIME_S Function Relgrad Radius Rho
0 0.18 -0.4 -1 0.36 0.98 5.3e+03 0.018 10 1 ++
1 0.16 -0.36 -1.3 0.55 1.1 5.2e+03 0.0026 1e+02 1.1 ++
2 0.14 -0.37 -1.4 0.54 1.2 5.2e+03 8.8e-05 1e+03 1 ++
3 0.14 -0.37 -1.4 0.54 1.2 5.2e+03 3.3e-07 1e+03 1 ++
Results saved in file 17lognormal_mixture.html
Results saved in file 17lognormal_mixture.pickle
print(results.short_summary())
Results for model 17lognormal_mixture
Nbr of parameters: 5
Sample size: 6768
Excluded data: 3960
Final log likelihood: -5239.842
Akaike Information Criterion: 10489.68
Bayesian Information Criterion: 10523.78
pandas_results = results.getEstimatedParameters()
pandas_results
Total running time of the script: (0 minutes 6.844 seconds)