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Mixtures of logit with Monte-Carlo 500 MLHS draws
Estimation of a mixtures of logit models where the integral is approximated using MonteCarlo integration with MLHS draws.
- author:
Michel Bierlaire, EPFL
- date:
Thu Apr 13 23:37:44 2023
import biogeme.biogeme_logging as blog
from biogeme.expressions import bioDraws
from b07estimation_specification import get_biogeme
logger = blog.get_screen_logger(level=blog.INFO)
logger.info('Example b07estimation_monte_carlo_mlhs_500.py')
Example b07estimation_monte_carlo_mlhs_500.py
R = 500
the_draws = bioDraws('B_TIME_RND', 'NORMAL_MLHS')
the_biogeme = get_biogeme(the_draws=the_draws, number_of_draws=R)
the_biogeme.modelName = 'b07estimation_monte_carlo_mlhs_500'
File /var/folders/rp/ppksq7xd6_x7p0jb0t73x7vw0000gq/T/tmpya5lp_yq/079077ab-a391-4ce5-9594-61feaa529541 has been parsed.
results = the_biogeme.estimate()
*** Initial values of the parameters are obtained from the file __b07estimation_monte_carlo_mlhs_500.iter
Parameter values restored from __b07estimation_monte_carlo_mlhs_500.iter
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 0.1 -0.43 -1.2 -2.1 1.4 5.2e+03 0.0074 10 1.1 ++
1 0.13 -0.41 -1.3 -2.2 1.6 5.2e+03 0.00096 1e+02 1.1 ++
2 0.13 -0.4 -1.3 -2.2 1.6 5.2e+03 1.6e-05 1e+03 1 ++
3 0.13 -0.4 -1.3 -2.2 1.6 5.2e+03 4.6e-09 1e+03 1 ++
print(results.short_summary())
Results for model b07estimation_monte_carlo_mlhs_500
Nbr of parameters: 5
Sample size: 6768
Excluded data: 3960
Final log likelihood: -5217.204
Akaike Information Criterion: 10444.41
Bayesian Information Criterion: 10478.51
pandas_results = results.getEstimatedParameters()
pandas_results
Total running time of the script: (0 minutes 51.636 seconds)