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Mixtures of logit with Monte-Carlo 500 draws
Estimation of a mixtures of logit models where the integral is approximated using MonteCarlo integration.
- author:
Michel Bierlaire, EPFL
- date:
Thu Apr 13 22:42:06 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_500.py')
Example b07estimation_monte_carlo_500.py
R = 500
the_draws = bioDraws('B_TIME_RND', 'NORMAL')
the_biogeme = get_biogeme(the_draws=the_draws, number_of_draws=R)
the_biogeme.modelName = 'b07estimation_monte_carlo_500'
File /var/folders/rp/ppksq7xd6_x7p0jb0t73x7vw0000gq/T/tmpd6pmuo9z/e6e6ba48-d807-45ec-b73e-25410c07336b has been parsed.
results = the_biogeme.estimate()
*** Initial values of the parameters are obtained from the file __b07estimation_monte_carlo_500.iter
Parameter values restored from __b07estimation_monte_carlo_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 0.017 -0.56 -1 -1.6 0.93 5.2e+03 0.011 10 1.1 ++
1 0.099 -0.43 -1.2 -2 1.4 5.2e+03 0.006 1e+02 1.1 ++
2 0.13 -0.41 -1.3 -2.2 1.6 5.2e+03 0.00083 1e+03 1.1 ++
3 0.13 -0.41 -1.3 -2.2 1.6 5.2e+03 2.3e-05 1e+04 1 ++
4 0.13 -0.41 -1.3 -2.2 1.6 5.2e+03 1.3e-08 1e+04 1 ++
print(results.short_summary())
Results for model b07estimation_monte_carlo_500
Nbr of parameters: 5
Sample size: 6768
Excluded data: 3960
Final log likelihood: -5215.252
Akaike Information Criterion: 10440.5
Bayesian Information Criterion: 10474.6
pandas_results = results.getEstimatedParameters()
pandas_results
Total running time of the script: (0 minutes 58.164 seconds)