Note
Go to the end to download the full example code
Illustration of additional features of Biogeme
- Same model as b01logit, using bioLinearUtility, segmentations
and features.
- author:
Michel Bierlaire, EPFL
- date:
Sun Apr 9 17:03:31 2023
import biogeme.biogeme_logging as blog
import biogeme.biogeme as bio
from biogeme import models
import biogeme.segmentation as seg
from biogeme.expressions import Beta, bioLinearUtility
See the data processing script: Data preparation for Swissmetro.
from swissmetro_data import (
database,
CHOICE,
GA,
CAR_AV_SP,
TRAIN_AV_SP,
TRAIN_TT_SCALED,
TRAIN_COST_SCALED,
SM_TT_SCALED,
SM_COST_SCALED,
CAR_TT_SCALED,
CAR_CO_SCALED,
MALE,
SM_AV,
)
logger = blog.get_screen_logger(level=blog.INFO)
logger.info('Example b01logit_bis.py')
Example b01logit_bis.py
Parameters to be estimated.
ASC_CAR = Beta('ASC_CAR', 0, None, None, 0)
ASC_TRAIN = Beta('ASC_TRAIN', 0, None, None, 0)
Starting value. We use starting values estimated from a previous run
B_TIME = Beta('B_TIME', -1.28, None, None, 0)
B_COST = Beta('B_COST', -1.08, None, None, 0)
Define segmentations.
gender_segmentation = database.generate_segmentation(
variable=MALE, mapping={0: 'female', 1: 'male'}
)
GA_segmentation = database.generate_segmentation(
variable=GA, mapping={0: 'without_ga', 1: 'with_ga'}
)
segmentations_for_asc = [
gender_segmentation,
GA_segmentation,
]
Segmentation of the constants.
ASC_TRAIN_segmentation = seg.Segmentation(ASC_TRAIN, segmentations_for_asc)
segmented_ASC_TRAIN = ASC_TRAIN_segmentation.segmented_beta()
ASC_CAR_segmentation = seg.Segmentation(ASC_CAR, segmentations_for_asc)
segmented_ASC_CAR = ASC_CAR_segmentation.segmented_beta()
Definition of the utility functions.
terms1 = [(B_TIME, TRAIN_TT_SCALED), (B_COST, TRAIN_COST_SCALED)]
V1 = segmented_ASC_TRAIN + bioLinearUtility(terms1)
terms2 = [(B_TIME, SM_TT_SCALED), (B_COST, SM_COST_SCALED)]
V2 = bioLinearUtility(terms2)
terms3 = [(B_TIME, CAR_TT_SCALED), (B_COST, CAR_CO_SCALED)]
V3 = segmented_ASC_CAR + bioLinearUtility(terms3)
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}
Definition of the model.
This is the contribution of each observation to the log likelihood function.
logprob = models.loglogit(V, av, CHOICE)
User notes.
These notes will be included as such in the report file.
USER_NOTES = (
'Example of a logit model with three alternatives: Train, Car and'
' Swissmetro. Same as 01logit and '
'introducing some options and features. In particular, bioLinearUtility,'
' and automatic segmentation of parameters.'
)
Create the Biogeme object.
the_biogeme = bio.BIOGEME(
database, logprob, userNotes=USER_NOTES, parameter_file='b01logit_bis.toml'
)
File b01logit_bis.toml has been parsed.
Calculate the null log likelihood for reporting.
As we have used starting values different from 0, the initial model is not the equal probability model.
the_biogeme.calculateNullLoglikelihood(av)
the_biogeme.modelName = 'b01logit_bis'
Turn off saving iterations.
the_biogeme.saveIterations = False
Estimate the parameters.
the_biogeme.bootstrap_samples = 100
results = the_biogeme.estimate(run_bootstrap=True)
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_male ASC_CAR_with_ga ASC_TRAIN ASC_TRAIN_male ASC_TRAIN_with_ B_COST B_TIME Function Relgrad Radius Rho
0 -0.38 -0.046 -0.2 -0.21 -0.78 1 -1 -1.3 5.1e+03 0.053 10 1.1 ++
1 -0.61 0.42 -0.42 -0.53 -1 1.8 -1.1 -1.1 4.9e+03 0.0091 1e+02 1 ++
2 -0.61 0.41 -0.41 -0.53 -1.1 1.9 -1.1 -1.2 4.9e+03 0.00031 1e+03 1 ++
3 -0.61 0.41 -0.41 -0.53 -1.1 1.9 -1.1 -1.2 4.9e+03 6.6e-07 1e+03 1 ++
Re-estimate the model 100 times for bootstrapping
0%| | 0/100 [00:00<?, ?it/s]Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_male ASC_CAR_with_ga ASC_TRAIN ASC_TRAIN_male ASC_TRAIN_with_ B_COST B_TIME Function Relgrad Radius Rho
0 -0.52 0.3 -0.08 -0.56 -1.1 2 -1.1 -1.2 5e+03 0.00026 10 0.96 ++
1 -0.52 0.3 -0.08 -0.56 -1.1 2 -1.1 -1.2 5e+03 2.1e-06 10 1 ++
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_male ASC_CAR_with_ga ASC_TRAIN ASC_TRAIN_male ASC_TRAIN_with_ B_COST B_TIME Function Relgrad Radius Rho
0 -0.59 0.5 -0.51 -0.48 -0.94 1.8 -1.2 -1.4 4.9e+03 0.00073 10 1 ++
1 -0.59 0.5 -0.51 -0.48 -0.94 1.8 -1.2 -1.4 4.9e+03 1.9e-06 10 1 ++
2%|▏ | 2/100 [00:00<00:06, 15.35it/s]Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_male ASC_CAR_with_ga ASC_TRAIN ASC_TRAIN_male ASC_TRAIN_with_ B_COST B_TIME Function Relgrad Radius Rho
0 -0.57 0.41 -0.52 -0.55 -1.2 2 -0.94 -1.2 4.9e+03 0.00056 10 1 ++
1 -0.57 0.41 -0.52 -0.55 -1.2 2 -0.94 -1.2 4.9e+03 2.1e-06 10 1 ++
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_male ASC_CAR_with_ga ASC_TRAIN ASC_TRAIN_male ASC_TRAIN_with_ B_COST B_TIME Function Relgrad Radius Rho
0 -0.58 0.38 -0.062 -0.49 -1.1 1.9 -1.1 -1.2 5e+03 0.00036 10 0.92 ++
1 -0.58 0.38 -0.062 -0.49 -1.1 1.9 -1.1 -1.2 5e+03 5.2e-06 10 1 ++
4%|▍ | 4/100 [00:00<00:06, 14.67it/s]Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_male ASC_CAR_with_ga ASC_TRAIN ASC_TRAIN_male ASC_TRAIN_with_ B_COST B_TIME Function Relgrad Radius Rho
0 -0.86 0.62 -0.56 -0.53 -1.2 2 -1.1 -1.2 4.9e+03 0.00051 10 1 ++
1 -0.86 0.62 -0.56 -0.53 -1.2 2 -1.1 -1.2 4.9e+03 3.5e-06 10 1 ++
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_male ASC_CAR_with_ga ASC_TRAIN ASC_TRAIN_male ASC_TRAIN_with_ B_COST B_TIME Function Relgrad Radius Rho
0 -0.48 0.26 -0.31 -0.64 -0.97 1.8 -1 -1.2 5e+03 0.00021 10 0.99 ++
1 -0.48 0.26 -0.31 -0.64 -0.97 1.8 -1 -1.2 5e+03 6.7e-07 10 1 ++
6%|▌ | 6/100 [00:00<00:06, 14.75it/s]Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_male ASC_CAR_with_ga ASC_TRAIN ASC_TRAIN_male ASC_TRAIN_with_ B_COST B_TIME Function Relgrad Radius Rho
0 -0.74 0.51 -0.55 -0.64 -1 1.9 -1.1 -1.2 4.9e+03 0.00013 10 1 ++
1 -0.74 0.51 -0.55 -0.64 -1 1.9 -1.1 -1.2 4.9e+03 3.9e-07 10 1 ++
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_male ASC_CAR_with_ga ASC_TRAIN ASC_TRAIN_male ASC_TRAIN_with_ B_COST B_TIME Function Relgrad Radius Rho
0 -0.71 0.61 -0.73 -0.43 -1.1 2 -1 -1.4 4.8e+03 0.002 10 1 ++
1 -0.71 0.63 -0.76 -0.42 -1.1 2 -1.1 -1.4 4.8e+03 1e-05 1e+02 1 ++
2 -0.71 0.63 -0.76 -0.42 -1.1 2 -1.1 -1.4 4.8e+03 8.2e-10 1e+02 1 ++
8%|▊ | 8/100 [00:00<00:06, 13.98it/s]Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_male ASC_CAR_with_ga ASC_TRAIN ASC_TRAIN_male ASC_TRAIN_with_ B_COST B_TIME Function Relgrad Radius Rho
0 -0.55 0.29 -0.41 -0.62 -1.1 1.9 -1 -1 5e+03 0.00065 10 0.97 ++
1 -0.55 0.29 -0.41 -0.62 -1.1 1.9 -1 -1 5e+03 1.7e-06 10 1 ++
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_male ASC_CAR_with_ga ASC_TRAIN ASC_TRAIN_male ASC_TRAIN_with_ B_COST B_TIME Function Relgrad Radius Rho
0 -0.66 0.52 -0.44 -0.48 -1 1.7 -1 -1.2 5e+03 0.00016 10 1 ++
1 -0.66 0.52 -0.44 -0.48 -1 1.7 -1 -1.2 5e+03 1.2e-07 10 1 ++
10%|█ | 10/100 [00:00<00:06, 14.33it/s]Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_male ASC_CAR_with_ga ASC_TRAIN ASC_TRAIN_male ASC_TRAIN_with_ B_COST B_TIME Function Relgrad Radius Rho
0 -0.66 0.48 -0.59 -0.53 -0.99 1.8 -1 -1.1 5e+03 0.00043 10 0.98 ++
1 -0.66 0.48 -0.59 -0.53 -0.99 1.8 -1 -1.1 5e+03 1e-06 10 1 ++
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_male ASC_CAR_with_ga ASC_TRAIN ASC_TRAIN_male ASC_TRAIN_with_ B_COST B_TIME Function Relgrad Radius Rho
0 -0.57 0.4 -0.62 -0.52 -1.2 1.9 -1 -1.2 4.9e+03 0.00028 10 1 ++
1 -0.57 0.4 -0.62 -0.52 -1.2 1.9 -1 -1.2 4.9e+03 4.8e-07 10 1 ++
12%|█▏ | 12/100 [00:00<00:06, 14.56it/s]Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_male ASC_CAR_with_ga ASC_TRAIN ASC_TRAIN_male ASC_TRAIN_with_ B_COST B_TIME Function Relgrad Radius Rho
0 -0.71 0.46 -0.07 -0.6 -1.1 1.9 -1.2 -1.1 5e+03 0.00042 10 0.99 ++
1 -0.71 0.46 -0.07 -0.6 -1.1 1.9 -1.2 -1.1 5e+03 1.3e-06 10 1 ++
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_male ASC_CAR_with_ga ASC_TRAIN ASC_TRAIN_male ASC_TRAIN_with_ B_COST B_TIME Function Relgrad Radius Rho
0 -0.64 0.46 -0.53 -0.46 -1.1 1.8 -1.1 -1.3 4.8e+03 0.00073 10 1 ++
1 -0.64 0.46 -0.53 -0.46 -1.1 1.8 -1.1 -1.3 4.8e+03 1.3e-06 10 1 ++
14%|█▍ | 14/100 [00:00<00:05, 14.94it/s]Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_male ASC_CAR_with_ga ASC_TRAIN ASC_TRAIN_male ASC_TRAIN_with_ B_COST B_TIME Function Relgrad Radius Rho
0 -0.77 0.59 -0.43 -0.62 -0.91 1.9 -1.1 -1.2 5e+03 0.00026 10 0.99 ++
1 -0.77 0.59 -0.43 -0.62 -0.91 1.9 -1.1 -1.2 5e+03 3.4e-07 10 1 ++
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_male ASC_CAR_with_ga ASC_TRAIN ASC_TRAIN_male ASC_TRAIN_with_ B_COST B_TIME Function Relgrad Radius Rho
0 -0.73 0.47 -0.12 -0.72 -1 2.1 -1.1 -1.1 5e+03 0.0003 10 0.99 ++
1 -0.73 0.47 -0.12 -0.72 -1 2.1 -1.1 -1.1 5e+03 6.5e-07 10 1 ++
16%|█▌ | 16/100 [00:01<00:05, 14.98it/s]Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_male ASC_CAR_with_ga ASC_TRAIN ASC_TRAIN_male ASC_TRAIN_with_ B_COST B_TIME Function Relgrad Radius Rho
0 -0.57 0.4 -0.35 -0.52 -0.98 1.9 -1.1 -1.3 4.9e+03 0.00033 10 1 ++
1 -0.57 0.4 -0.35 -0.52 -0.98 1.9 -1.1 -1.3 4.9e+03 2.4e-07 10 1 ++
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_male ASC_CAR_with_ga ASC_TRAIN ASC_TRAIN_male ASC_TRAIN_with_ B_COST B_TIME Function Relgrad Radius Rho
0 -0.65 0.34 -0.29 -0.63 -1.1 2.1 -1.1 -1.2 4.8e+03 0.00022 10 1 ++
1 -0.65 0.34 -0.29 -0.63 -1.1 2.1 -1.1 -1.2 4.8e+03 1.3e-07 10 1 ++
18%|█▊ | 18/100 [00:01<00:05, 15.20it/s]Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_male ASC_CAR_with_ga ASC_TRAIN ASC_TRAIN_male ASC_TRAIN_with_ B_COST B_TIME Function Relgrad Radius Rho
0 -0.75 0.58 -0.81 -0.58 -1 1.8 -0.97 -1.1 5e+03 0.00054 10 0.99 ++
1 -0.75 0.58 -0.81 -0.58 -1 1.8 -0.97 -1.1 5e+03 4.2e-06 10 1 ++
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_male ASC_CAR_with_ga ASC_TRAIN ASC_TRAIN_male ASC_TRAIN_with_ B_COST B_TIME Function Relgrad Radius Rho
0 -0.62 0.34 -0.44 -0.55 -1.1 1.9 -1 -1.1 5e+03 0.00011 10 1 ++
1 -0.62 0.34 -0.44 -0.55 -1.1 1.9 -1 -1.1 5e+03 5.3e-08 10 1 ++
20%|██ | 20/100 [00:01<00:05, 15.20it/s]Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_male ASC_CAR_with_ga ASC_TRAIN ASC_TRAIN_male ASC_TRAIN_with_ B_COST B_TIME Function Relgrad Radius Rho
0 -0.64 0.49 -0.22 -0.55 -1.1 1.9 -1.1 -1.2 4.9e+03 0.00014 10 0.98 ++
1 -0.64 0.49 -0.22 -0.55 -1.1 1.9 -1.1 -1.2 4.9e+03 6.8e-07 10 1 ++
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_male ASC_CAR_with_ga ASC_TRAIN ASC_TRAIN_male ASC_TRAIN_with_ B_COST B_TIME Function Relgrad Radius Rho
0 -0.57 0.39 -0.22 -0.61 -0.96 1.9 -1.1 -1.1 5e+03 0.00028 10 0.98 ++
1 -0.57 0.39 -0.22 -0.61 -0.96 1.9 -1.1 -1.1 5e+03 9e-07 10 1 ++
22%|██▏ | 22/100 [00:01<00:05, 15.18it/s]Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_male ASC_CAR_with_ga ASC_TRAIN ASC_TRAIN_male ASC_TRAIN_with_ B_COST B_TIME Function Relgrad Radius Rho
0 -0.51 0.26 -0.33 -0.6 -1.1 2.1 -0.95 -1.1 5e+03 0.00043 10 0.98 ++
1 -0.51 0.26 -0.33 -0.6 -1.1 2.1 -0.95 -1.1 5e+03 9.4e-07 10 1 ++
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_male ASC_CAR_with_ga ASC_TRAIN ASC_TRAIN_male ASC_TRAIN_with_ B_COST B_TIME Function Relgrad Radius Rho
0 -0.61 0.4 -0.29 -0.5 -1 1.8 -1.2 -1.3 4.9e+03 0.00038 10 1 ++
1 -0.61 0.4 -0.29 -0.5 -1 1.8 -1.2 -1.3 4.9e+03 7e-07 10 1 ++
24%|██▍ | 24/100 [00:01<00:04, 15.46it/s]Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_male ASC_CAR_with_ga ASC_TRAIN ASC_TRAIN_male ASC_TRAIN_with_ B_COST B_TIME Function Relgrad Radius Rho
0 -0.36 0.037 -0.17 -0.53 -1.2 1.9 -1 -0.94 5.1e+03 0.0015 10 0.96 ++
1 -0.38 0.059 -0.19 -0.52 -1.2 1.9 -1 -0.96 5.1e+03 8.6e-06 1e+02 1 ++
2 -0.38 0.059 -0.19 -0.52 -1.2 1.9 -1 -0.96 5.1e+03 3.6e-10 1e+02 1 ++
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_male ASC_CAR_with_ga ASC_TRAIN ASC_TRAIN_male ASC_TRAIN_with_ B_COST B_TIME Function Relgrad Radius Rho
0 -0.29 0.21 -0.56 -0.57 -0.98 1.9 -0.91 -1.2 5e+03 0.0009 10 0.97 ++
1 -0.29 0.21 -0.56 -0.57 -0.98 1.9 -0.91 -1.2 5e+03 3.6e-06 10 1 ++
26%|██▌ | 26/100 [00:01<00:04, 14.95it/s]Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_male ASC_CAR_with_ga ASC_TRAIN ASC_TRAIN_male ASC_TRAIN_with_ B_COST B_TIME Function Relgrad Radius Rho
0 -0.72 0.58 -0.57 -0.53 -1 2 -1 -1.3 4.9e+03 0.00027 10 1 ++
1 -0.72 0.58 -0.57 -0.53 -1 2 -1 -1.3 4.9e+03 3.6e-07 10 1 ++
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_male ASC_CAR_with_ga ASC_TRAIN ASC_TRAIN_male ASC_TRAIN_with_ B_COST B_TIME Function Relgrad Radius Rho
0 -0.31 0.16 -0.15 -0.31 -1.2 1.9 -1.1 -1.3 4.9e+03 0.00057 10 1 ++
1 -0.31 0.16 -0.15 -0.31 -1.2 1.9 -1.1 -1.3 4.9e+03 3e-06 10 1 ++
28%|██▊ | 28/100 [00:01<00:04, 15.24it/s]Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_male ASC_CAR_with_ga ASC_TRAIN ASC_TRAIN_male ASC_TRAIN_with_ B_COST B_TIME Function Relgrad Radius Rho
0 -0.56 0.35 -0.34 -0.66 -1 1.9 -1.2 -1.2 4.9e+03 0.00017 10 1 ++
1 -0.56 0.35 -0.34 -0.66 -1 1.9 -1.2 -1.2 4.9e+03 1.8e-07 10 1 ++
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_male ASC_CAR_with_ga ASC_TRAIN ASC_TRAIN_male ASC_TRAIN_with_ B_COST B_TIME Function Relgrad Radius Rho
0 -0.57 0.37 -0.76 -0.56 -1.1 1.8 -0.97 -1.1 5e+03 0.00031 10 0.99 ++
1 -0.57 0.37 -0.76 -0.56 -1.1 1.8 -0.97 -1.1 5e+03 1.5e-06 10 1 ++
30%|███ | 30/100 [00:02<00:04, 15.24it/s]Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_male ASC_CAR_with_ga ASC_TRAIN ASC_TRAIN_male ASC_TRAIN_with_ B_COST B_TIME Function Relgrad Radius Rho
0 -0.44 0.22 -0.72 -0.47 -1.1 1.8 -1.1 -1.2 5e+03 0.00028 10 1 ++
1 -0.44 0.22 -0.72 -0.47 -1.1 1.8 -1.1 -1.2 5e+03 2.6e-06 10 1 ++
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_male ASC_CAR_with_ga ASC_TRAIN ASC_TRAIN_male ASC_TRAIN_with_ B_COST B_TIME Function Relgrad Radius Rho
0 -0.6 0.43 -0.44 -0.52 -1.1 1.8 -1.1 -1.2 5e+03 4.7e-05 10 1 ++
1 -0.6 0.43 -0.44 -0.52 -1.1 1.8 -1.1 -1.2 5e+03 8.2e-09 10 1 ++
32%|███▏ | 32/100 [00:02<00:04, 15.46it/s]Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_male ASC_CAR_with_ga ASC_TRAIN ASC_TRAIN_male ASC_TRAIN_with_ B_COST B_TIME Function Relgrad Radius Rho
0 -0.69 0.53 -0.39 -0.67 -1 2 -1.1 -1.1 5e+03 0.00045 10 0.99 ++
1 -0.69 0.53 -0.39 -0.67 -1 2 -1.1 -1.1 5e+03 5.1e-07 10 1 ++
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_male ASC_CAR_with_ga ASC_TRAIN ASC_TRAIN_male ASC_TRAIN_with_ B_COST B_TIME Function Relgrad Radius Rho
0 -0.66 0.57 -0.44 -0.49 -1.1 1.9 -0.97 -1.2 5e+03 0.00037 10 0.98 ++
1 -0.66 0.57 -0.44 -0.49 -1.1 1.9 -0.97 -1.2 5e+03 5.9e-07 10 1 ++
34%|███▍ | 34/100 [00:02<00:04, 15.57it/s]Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_male ASC_CAR_with_ga ASC_TRAIN ASC_TRAIN_male ASC_TRAIN_with_ B_COST B_TIME Function Relgrad Radius Rho
0 -0.69 0.41 -0.39 -0.53 -1.2 1.9 -1.1 -1 5e+03 0.00046 10 0.98 ++
1 -0.69 0.41 -0.39 -0.53 -1.2 1.9 -1.1 -1 5e+03 5.5e-07 10 1 ++
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_male ASC_CAR_with_ga ASC_TRAIN ASC_TRAIN_male ASC_TRAIN_with_ B_COST B_TIME Function Relgrad Radius Rho
0 -0.78 0.54 -0.72 -0.57 -1.1 2 -1 -1.1 5e+03 0.00037 10 1 ++
1 -0.78 0.54 -0.72 -0.57 -1.1 2 -1 -1.1 5e+03 5.5e-06 10 1 ++
36%|███▌ | 36/100 [00:02<00:04, 15.78it/s]Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_male ASC_CAR_with_ga ASC_TRAIN ASC_TRAIN_male ASC_TRAIN_with_ B_COST B_TIME Function Relgrad Radius Rho
0 -0.72 0.54 -0.39 -0.44 -1.1 1.9 -1.1 -1.2 4.9e+03 0.00016 10 1 ++
1 -0.72 0.54 -0.39 -0.44 -1.1 1.9 -1.1 -1.2 4.9e+03 1.5e-07 10 1 ++
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_male ASC_CAR_with_ga ASC_TRAIN ASC_TRAIN_male ASC_TRAIN_with_ B_COST B_TIME Function Relgrad Radius Rho
0 -0.59 0.34 -0.13 -0.69 -1.1 2 -1.1 -1.2 4.8e+03 0.00055 10 1 ++
1 -0.59 0.34 -0.13 -0.69 -1.1 2 -1.1 -1.2 4.8e+03 1.5e-06 10 1 ++
38%|███▊ | 38/100 [00:02<00:03, 15.90it/s]Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_male ASC_CAR_with_ga ASC_TRAIN ASC_TRAIN_male ASC_TRAIN_with_ B_COST B_TIME Function Relgrad Radius Rho
0 -0.72 0.48 -0.55 -0.61 -1.1 1.7 -0.97 -1 5e+03 0.00038 10 0.98 ++
1 -0.72 0.48 -0.55 -0.61 -1.1 1.7 -0.97 -1 5e+03 9e-07 10 1 ++
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_male ASC_CAR_with_ga ASC_TRAIN ASC_TRAIN_male ASC_TRAIN_with_ B_COST B_TIME Function Relgrad Radius Rho
0 -0.59 0.37 -0.34 -0.48 -1.2 1.8 -1.1 -1.1 5e+03 0.00033 10 0.99 ++
1 -0.59 0.37 -0.34 -0.48 -1.2 1.8 -1.1 -1.1 5e+03 2.8e-07 10 1 ++
40%|████ | 40/100 [00:02<00:03, 16.02it/s]Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_male ASC_CAR_with_ga ASC_TRAIN ASC_TRAIN_male ASC_TRAIN_with_ B_COST B_TIME Function Relgrad Radius Rho
0 -0.58 0.44 -0.67 -0.41 -1.1 1.7 -0.92 -1.3 4.9e+03 0.00045 10 1 ++
1 -0.58 0.44 -0.67 -0.41 -1.1 1.7 -0.92 -1.3 4.9e+03 9.7e-07 10 1 ++
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_male ASC_CAR_with_ga ASC_TRAIN ASC_TRAIN_male ASC_TRAIN_with_ B_COST B_TIME Function Relgrad Radius Rho
0 -0.57 0.13 -0.038 -0.86 -0.95 1.9 -1.1 -0.88 5e+03 0.0014 10 0.97 ++
1 -0.58 0.14 -0.064 -0.86 -0.95 1.9 -1.1 -0.9 5e+03 7.2e-06 1e+02 1 ++
2 -0.58 0.14 -0.064 -0.86 -0.95 1.9 -1.1 -0.9 5e+03 6.5e-10 1e+02 1 ++
42%|████▏ | 42/100 [00:02<00:03, 15.33it/s]Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_male ASC_CAR_with_ga ASC_TRAIN ASC_TRAIN_male ASC_TRAIN_with_ B_COST B_TIME Function Relgrad Radius Rho
0 -0.6 0.45 -0.57 -0.58 -1.1 2 -1 -1.2 4.9e+03 0.00022 10 1 ++
1 -0.6 0.45 -0.57 -0.58 -1.1 2 -1 -1.2 4.9e+03 2.3e-07 10 1 ++
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_male ASC_CAR_with_ga ASC_TRAIN ASC_TRAIN_male ASC_TRAIN_with_ B_COST B_TIME Function Relgrad Radius Rho
0 -0.65 0.41 -0.44 -0.61 -1.1 1.9 -1.2 -1.2 4.9e+03 0.00023 10 1 ++
1 -0.65 0.41 -0.44 -0.61 -1.1 1.9 -1.2 -1.2 4.9e+03 2.8e-07 10 1 ++
44%|████▍ | 44/100 [00:02<00:03, 15.48it/s]Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_male ASC_CAR_with_ga ASC_TRAIN ASC_TRAIN_male ASC_TRAIN_with_ B_COST B_TIME Function Relgrad Radius Rho
0 -0.57 0.38 -0.52 -0.45 -1.3 2 -1 -1.1 5e+03 0.00023 10 1 ++
1 -0.57 0.38 -0.52 -0.45 -1.3 2 -1 -1.1 5e+03 2.2e-07 10 1 ++
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_male ASC_CAR_with_ga ASC_TRAIN ASC_TRAIN_male ASC_TRAIN_with_ B_COST B_TIME Function Relgrad Radius Rho
0 -0.82 0.51 -0.28 -0.76 -1 2 -1.1 -1.1 4.9e+03 0.00028 10 1 ++
1 -0.82 0.51 -0.28 -0.76 -1 2 -1.1 -1.1 4.9e+03 3.2e-07 10 1 ++
46%|████▌ | 46/100 [00:03<00:03, 15.64it/s]Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_male ASC_CAR_with_ga ASC_TRAIN ASC_TRAIN_male ASC_TRAIN_with_ B_COST B_TIME Function Relgrad Radius Rho
0 -0.6 0.43 -0.41 -0.43 -1.1 1.9 -1.1 -1.3 4.9e+03 0.00027 10 1 ++
1 -0.6 0.43 -0.41 -0.43 -1.1 1.9 -1.1 -1.3 4.9e+03 1.7e-07 10 1 ++
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_male ASC_CAR_with_ga ASC_TRAIN ASC_TRAIN_male ASC_TRAIN_with_ B_COST B_TIME Function Relgrad Radius Rho
0 -0.58 0.31 -0.19 -0.77 -0.83 1.8 -0.97 -0.97 5.1e+03 0.0015 10 0.95 ++
1 -0.59 0.32 -0.22 -0.77 -0.84 1.8 -0.98 -0.98 5.1e+03 8.1e-06 1e+02 1 ++
2 -0.59 0.32 -0.22 -0.77 -0.84 1.8 -0.98 -0.98 5.1e+03 3.8e-10 1e+02 1 ++
48%|████▊ | 48/100 [00:03<00:03, 15.13it/s]Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_male ASC_CAR_with_ga ASC_TRAIN ASC_TRAIN_male ASC_TRAIN_with_ B_COST B_TIME Function Relgrad Radius Rho
0 -0.59 0.36 -0.59 -0.61 -1 2 -1.1 -1.2 4.9e+03 0.00024 10 1 ++
1 -0.59 0.36 -0.59 -0.61 -1 2 -1.1 -1.2 4.9e+03 1.6e-06 10 1 ++
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_male ASC_CAR_with_ga ASC_TRAIN ASC_TRAIN_male ASC_TRAIN_with_ B_COST B_TIME Function Relgrad Radius Rho
0 -0.55 0.37 -0.5 -0.57 -1 1.8 -1 -1.2 4.9e+03 8.7e-05 10 1 ++
1 -0.55 0.37 -0.5 -0.57 -1 1.8 -1 -1.2 4.9e+03 3.6e-08 10 1 ++
50%|█████ | 50/100 [00:03<00:03, 14.86it/s]Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_male ASC_CAR_with_ga ASC_TRAIN ASC_TRAIN_male ASC_TRAIN_with_ B_COST B_TIME Function Relgrad Radius Rho
0 -0.56 0.36 -0.29 -0.41 -1.1 1.7 -1.2 -1.2 5e+03 0.00024 10 1 ++
1 -0.56 0.36 -0.29 -0.41 -1.1 1.7 -1.2 -1.2 5e+03 2.8e-07 10 1 ++
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_male ASC_CAR_with_ga ASC_TRAIN ASC_TRAIN_male ASC_TRAIN_with_ B_COST B_TIME Function Relgrad Radius Rho
0 -0.68 0.5 -0.36 -0.64 -0.98 1.9 -1.2 -1.3 4.9e+03 0.00043 10 1 ++
1 -0.68 0.5 -0.36 -0.64 -0.98 1.9 -1.2 -1.3 4.9e+03 8.4e-07 10 1 ++
52%|█████▏ | 52/100 [00:03<00:03, 15.17it/s]Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_male ASC_CAR_with_ga ASC_TRAIN ASC_TRAIN_male ASC_TRAIN_with_ B_COST B_TIME Function Relgrad Radius Rho
0 -0.57 0.36 -0.32 -0.51 -1.1 1.9 -1.2 -1.3 4.9e+03 0.00046 10 1 ++
1 -0.57 0.36 -0.32 -0.51 -1.1 1.9 -1.2 -1.3 4.9e+03 1.1e-06 10 1 ++
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_male ASC_CAR_with_ga ASC_TRAIN ASC_TRAIN_male ASC_TRAIN_with_ B_COST B_TIME Function Relgrad Radius Rho
0 -0.66 0.45 -0.38 -0.4 -1.2 1.8 -1 -1.2 5e+03 0.00023 10 1 ++
1 -0.66 0.45 -0.38 -0.4 -1.2 1.8 -1 -1.2 5e+03 1.4e-07 10 1 ++
54%|█████▍ | 54/100 [00:03<00:02, 15.41it/s]Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_male ASC_CAR_with_ga ASC_TRAIN ASC_TRAIN_male ASC_TRAIN_with_ B_COST B_TIME Function Relgrad Radius Rho
0 -0.63 0.37 -0.025 -0.6 -1.1 2 -1.3 -1.1 4.9e+03 0.00041 10 1 ++
1 -0.63 0.37 -0.025 -0.6 -1.1 2 -1.3 -1.1 4.9e+03 1.2e-06 10 1 ++
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_male ASC_CAR_with_ga ASC_TRAIN ASC_TRAIN_male ASC_TRAIN_with_ B_COST B_TIME Function Relgrad Radius Rho
0 -0.68 0.54 -0.76 -0.41 -1.2 1.7 -1.1 -1.3 4.9e+03 0.00025 10 1 ++
1 -0.68 0.54 -0.76 -0.41 -1.2 1.7 -1.1 -1.3 4.9e+03 1.4e-06 10 1 ++
56%|█████▌ | 56/100 [00:03<00:02, 15.63it/s]Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_male ASC_CAR_with_ga ASC_TRAIN ASC_TRAIN_male ASC_TRAIN_with_ B_COST B_TIME Function Relgrad Radius Rho
0 -0.49 0.25 -0.32 -0.57 -1.1 2 -1.1 -1.2 4.9e+03 0.0001 10 0.99 ++
1 -0.49 0.25 -0.32 -0.57 -1.1 2 -1.1 -1.2 4.9e+03 9.1e-08 10 1 ++
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_male ASC_CAR_with_ga ASC_TRAIN ASC_TRAIN_male ASC_TRAIN_with_ B_COST B_TIME Function Relgrad Radius Rho
0 -0.75 0.45 -0.35 -0.74 -1.1 2 -1.1 -0.96 5e+03 0.00094 10 0.97 ++
1 -0.75 0.45 -0.35 -0.74 -1.1 2 -1.1 -0.96 5e+03 2.4e-06 10 1 ++
58%|█████▊ | 58/100 [00:03<00:02, 15.77it/s]Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_male ASC_CAR_with_ga ASC_TRAIN ASC_TRAIN_male ASC_TRAIN_with_ B_COST B_TIME Function Relgrad Radius Rho
0 -0.48 0.37 -0.42 -0.49 -1 1.9 -0.96 -1.2 5e+03 0.00023 10 0.99 ++
1 -0.48 0.37 -0.42 -0.49 -1 1.9 -0.96 -1.2 5e+03 3.5e-07 10 1 ++
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_male ASC_CAR_with_ga ASC_TRAIN ASC_TRAIN_male ASC_TRAIN_with_ B_COST B_TIME Function Relgrad Radius Rho
0 -0.5 0.32 -0.31 -0.44 -1.1 1.9 -1.1 -1.3 4.9e+03 0.00033 10 1 ++
1 -0.5 0.32 -0.31 -0.44 -1.1 1.9 -1.1 -1.3 4.9e+03 2.9e-07 10 1 ++
60%|██████ | 60/100 [00:03<00:02, 15.84it/s]Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_male ASC_CAR_with_ga ASC_TRAIN ASC_TRAIN_male ASC_TRAIN_with_ B_COST B_TIME Function Relgrad Radius Rho
0 -0.75 0.49 -0.28 -0.65 -1.1 1.9 -1.1 -1.1 4.9e+03 0.00017 10 1 ++
1 -0.75 0.49 -0.28 -0.65 -1.1 1.9 -1.1 -1.1 4.9e+03 7.9e-08 10 1 ++
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_male ASC_CAR_with_ga ASC_TRAIN ASC_TRAIN_male ASC_TRAIN_with_ B_COST B_TIME Function Relgrad Radius Rho
0 -0.46 0.28 -0.28 -0.42 -1.1 2 -1.1 -1.3 4.9e+03 0.00049 10 1 ++
1 -0.46 0.28 -0.28 -0.42 -1.1 2 -1.1 -1.3 4.9e+03 8e-07 10 1 ++
62%|██████▏ | 62/100 [00:04<00:02, 15.90it/s]Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_male ASC_CAR_with_ga ASC_TRAIN ASC_TRAIN_male ASC_TRAIN_with_ B_COST B_TIME Function Relgrad Radius Rho
0 -0.68 0.4 -0.45 -0.51 -1.1 1.8 -1.1 -1.1 4.9e+03 0.0001 10 1 ++
1 -0.68 0.4 -0.45 -0.51 -1.1 1.8 -1.1 -1.1 4.9e+03 3.9e-08 10 1 ++
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_male ASC_CAR_with_ga ASC_TRAIN ASC_TRAIN_male ASC_TRAIN_with_ B_COST B_TIME Function Relgrad Radius Rho
0 -0.69 0.45 -0.33 -0.58 -1.2 1.9 -1.2 -1.1 5e+03 0.00029 10 1 ++
1 -0.69 0.45 -0.33 -0.58 -1.2 1.9 -1.2 -1.1 5e+03 2.6e-07 10 1 ++
64%|██████▍ | 64/100 [00:04<00:02, 16.06it/s]Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_male ASC_CAR_with_ga ASC_TRAIN ASC_TRAIN_male ASC_TRAIN_with_ B_COST B_TIME Function Relgrad Radius Rho
0 -0.59 0.45 -0.5 -0.49 -1.1 1.9 -1.1 -1.2 4.9e+03 0.00012 10 1 ++
1 -0.59 0.45 -0.5 -0.49 -1.1 1.9 -1.1 -1.2 4.9e+03 4.5e-08 10 1 ++
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_male ASC_CAR_with_ga ASC_TRAIN ASC_TRAIN_male ASC_TRAIN_with_ B_COST B_TIME Function Relgrad Radius Rho
0 -0.66 0.46 -0.26 -0.64 -1.1 1.9 -1.2 -1.2 4.9e+03 0.00019 10 1 ++
1 -0.66 0.46 -0.26 -0.64 -1.1 1.9 -1.2 -1.2 4.9e+03 2e-07 10 1 ++
66%|██████▌ | 66/100 [00:04<00:02, 16.06it/s]Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_male ASC_CAR_with_ga ASC_TRAIN ASC_TRAIN_male ASC_TRAIN_with_ B_COST B_TIME Function Relgrad Radius Rho
0 -0.5 0.26 -0.21 -0.56 -1 1.8 -1.2 -1.2 5e+03 0.00034 10 1 ++
1 -0.5 0.26 -0.21 -0.56 -1 1.8 -1.2 -1.2 5e+03 5.2e-07 10 1 ++
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_male ASC_CAR_with_ga ASC_TRAIN ASC_TRAIN_male ASC_TRAIN_with_ B_COST B_TIME Function Relgrad Radius Rho
0 -0.65 0.44 -0.63 -0.52 -1.3 2.1 -1.2 -1.2 4.8e+03 0.00073 10 1 ++
1 -0.66 0.45 -0.69 -0.52 -1.3 2.1 -1.2 -1.2 4.8e+03 6.6e-06 1e+02 1 ++
2 -0.66 0.45 -0.69 -0.52 -1.3 2.1 -1.2 -1.2 4.8e+03 3.5e-09 1e+02 1 ++
68%|██████▊ | 68/100 [00:04<00:02, 15.41it/s]Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_male ASC_CAR_with_ga ASC_TRAIN ASC_TRAIN_male ASC_TRAIN_with_ B_COST B_TIME Function Relgrad Radius Rho
0 -0.57 0.32 -0.33 -0.62 -1.1 1.8 -1.1 -1.1 5e+03 0.00034 10 0.98 ++
1 -0.57 0.32 -0.33 -0.62 -1.1 1.8 -1.1 -1.1 5e+03 3.9e-07 10 1 ++
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_male ASC_CAR_with_ga ASC_TRAIN ASC_TRAIN_male ASC_TRAIN_with_ B_COST B_TIME Function Relgrad Radius Rho
0 -0.46 0.29 -0.098 -0.47 -1.1 1.9 -1.2 -1.2 5e+03 0.00058 10 1 ++
1 -0.46 0.29 -0.098 -0.47 -1.1 1.9 -1.2 -1.2 5e+03 1.9e-06 10 1 ++
70%|███████ | 70/100 [00:04<00:01, 15.52it/s]Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_male ASC_CAR_with_ga ASC_TRAIN ASC_TRAIN_male ASC_TRAIN_with_ B_COST B_TIME Function Relgrad Radius Rho
0 -0.61 0.47 -0.34 -0.38 -1.2 1.8 -1.1 -1.3 4.9e+03 0.00032 10 1 ++
1 -0.61 0.47 -0.34 -0.38 -1.2 1.8 -1.1 -1.3 4.9e+03 1.7e-07 10 1 ++
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_male ASC_CAR_with_ga ASC_TRAIN ASC_TRAIN_male ASC_TRAIN_with_ B_COST B_TIME Function Relgrad Radius Rho
0 -0.52 0.3 -0.48 -0.53 -1.2 1.9 -1 -1.2 4.9e+03 0.00043 10 1 ++
1 -0.52 0.3 -0.48 -0.53 -1.2 1.9 -1 -1.2 4.9e+03 1.1e-06 10 1 ++
72%|███████▏ | 72/100 [00:04<00:01, 15.53it/s]Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_male ASC_CAR_with_ga ASC_TRAIN ASC_TRAIN_male ASC_TRAIN_with_ B_COST B_TIME Function Relgrad Radius Rho
0 -0.59 0.46 -0.31 -0.39 -1.1 1.8 -1.1 -1.3 4.9e+03 0.00049 10 1 ++
1 -0.59 0.46 -0.31 -0.39 -1.1 1.8 -1.1 -1.3 4.9e+03 5.4e-07 10 1 ++
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_male ASC_CAR_with_ga ASC_TRAIN ASC_TRAIN_male ASC_TRAIN_with_ B_COST B_TIME Function Relgrad Radius Rho
0 -0.56 0.21 -0.59 -0.61 -1.2 1.9 -1 -1 4.9e+03 0.00031 10 0.99 ++
1 -0.56 0.21 -0.59 -0.61 -1.2 1.9 -1 -1 4.9e+03 1.4e-06 10 1 ++
74%|███████▍ | 74/100 [00:04<00:01, 15.74it/s]Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_male ASC_CAR_with_ga ASC_TRAIN ASC_TRAIN_male ASC_TRAIN_with_ B_COST B_TIME Function Relgrad Radius Rho
0 -0.62 0.5 -1 -0.41 -1.1 1.8 -0.95 -1.3 5e+03 0.00055 10 1 ++
1 -0.62 0.5 -1.1 -0.41 -1.1 1.8 -0.95 -1.3 5e+03 2.2e-05 1e+02 1 ++
2 -0.62 0.5 -1.1 -0.41 -1.1 1.8 -0.95 -1.3 5e+03 4.4e-08 1e+02 1 ++
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_male ASC_CAR_with_ga ASC_TRAIN ASC_TRAIN_male ASC_TRAIN_with_ B_COST B_TIME Function Relgrad Radius Rho
0 -0.58 0.37 -0.41 -0.59 -1.1 2 -1.1 -1.2 4.9e+03 0.00011 10 1 ++
1 -0.58 0.37 -0.41 -0.59 -1.1 2 -1.1 -1.2 4.9e+03 5.2e-08 10 1 ++
76%|███████▌ | 76/100 [00:04<00:01, 15.28it/s]Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_male ASC_CAR_with_ga ASC_TRAIN ASC_TRAIN_male ASC_TRAIN_with_ B_COST B_TIME Function Relgrad Radius Rho
0 -0.57 0.3 -0.13 -0.65 -1.1 2 -1.2 -1.1 4.9e+03 0.00028 10 0.99 ++
1 -0.57 0.3 -0.13 -0.65 -1.1 2 -1.2 -1.1 4.9e+03 4.7e-07 10 1 ++
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_male ASC_CAR_with_ga ASC_TRAIN ASC_TRAIN_male ASC_TRAIN_with_ B_COST B_TIME Function Relgrad Radius Rho
0 -0.72 0.43 -0.47 -0.55 -1.2 2 -1.1 -1.1 4.9e+03 0.00011 10 1 ++
1 -0.72 0.43 -0.47 -0.55 -1.2 2 -1.1 -1.1 4.9e+03 1.8e-07 10 1 ++
78%|███████▊ | 78/100 [00:05<00:01, 15.70it/s]Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_male ASC_CAR_with_ga ASC_TRAIN ASC_TRAIN_male ASC_TRAIN_with_ B_COST B_TIME Function Relgrad Radius Rho
0 -0.66 0.53 -0.62 -0.5 -1.1 1.8 -1 -1.2 5e+03 0.00013 10 1 ++
1 -0.66 0.53 -0.62 -0.5 -1.1 1.8 -1 -1.2 5e+03 1.3e-07 10 1 ++
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_male ASC_CAR_with_ga ASC_TRAIN ASC_TRAIN_male ASC_TRAIN_with_ B_COST B_TIME Function Relgrad Radius Rho
0 -0.67 0.51 -0.49 -0.44 -1.1 2 -1.1 -1.3 4.9e+03 0.00038 10 1 ++
1 -0.67 0.51 -0.49 -0.44 -1.1 2 -1.1 -1.3 4.9e+03 5.2e-07 10 1 ++
80%|████████ | 80/100 [00:05<00:01, 15.79it/s]Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_male ASC_CAR_with_ga ASC_TRAIN ASC_TRAIN_male ASC_TRAIN_with_ B_COST B_TIME Function Relgrad Radius Rho
0 -0.75 0.62 -0.39 -0.55 -1 1.9 -0.95 -1.1 5e+03 0.00043 10 0.98 ++
1 -0.75 0.62 -0.39 -0.55 -1 1.9 -0.95 -1.1 5e+03 9.5e-07 10 1 ++
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_male ASC_CAR_with_ga ASC_TRAIN ASC_TRAIN_male ASC_TRAIN_with_ B_COST B_TIME Function Relgrad Radius Rho
0 -0.45 0.23 -0.33 -0.55 -1 1.8 -1.1 -1.2 5e+03 0.00018 10 0.97 ++
1 -0.45 0.23 -0.33 -0.55 -1 1.8 -1.1 -1.2 5e+03 4e-07 10 1 ++
82%|████████▏ | 82/100 [00:05<00:01, 15.91it/s]Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_male ASC_CAR_with_ga ASC_TRAIN ASC_TRAIN_male ASC_TRAIN_with_ B_COST B_TIME Function Relgrad Radius Rho
0 -0.62 0.48 -0.76 -0.42 -1.1 1.9 -0.98 -1.2 5e+03 0.00022 10 1 ++
1 -0.62 0.48 -0.76 -0.42 -1.1 1.9 -0.98 -1.2 5e+03 1.6e-06 10 1 ++
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_male ASC_CAR_with_ga ASC_TRAIN ASC_TRAIN_male ASC_TRAIN_with_ B_COST B_TIME Function Relgrad Radius Rho
0 -0.63 0.29 -0.24 -0.53 -1.2 2 -1.2 -1 5e+03 0.0006 10 0.99 ++
1 -0.63 0.29 -0.24 -0.53 -1.2 2 -1.2 -1 5e+03 1e-06 10 1 ++
84%|████████▍ | 84/100 [00:05<00:01, 15.95it/s]Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_male ASC_CAR_with_ga ASC_TRAIN ASC_TRAIN_male ASC_TRAIN_with_ B_COST B_TIME Function Relgrad Radius Rho
0 -0.6 0.26 -0.019 -0.65 -1.1 2 -1.2 -1.1 4.9e+03 0.00042 10 1 ++
1 -0.6 0.26 -0.019 -0.65 -1.1 2 -1.2 -1.1 4.9e+03 1.2e-06 10 1 ++
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_male ASC_CAR_with_ga ASC_TRAIN ASC_TRAIN_male ASC_TRAIN_with_ B_COST B_TIME Function Relgrad Radius Rho
0 -0.57 0.31 -0.51 -0.63 -1.1 1.8 -0.95 -1 5e+03 0.00058 10 0.96 ++
1 -0.57 0.31 -0.51 -0.63 -1.1 1.8 -0.95 -1 5e+03 2e-06 10 1 ++
86%|████████▌ | 86/100 [00:05<00:00, 16.21it/s]Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_male ASC_CAR_with_ga ASC_TRAIN ASC_TRAIN_male ASC_TRAIN_with_ B_COST B_TIME Function Relgrad Radius Rho
0 -0.52 0.35 -0.56 -0.6 -0.97 2 -1.1 -1.2 4.9e+03 0.00024 10 1 ++
1 -0.52 0.35 -0.56 -0.6 -0.97 2 -1.1 -1.2 4.9e+03 1.1e-07 10 1 ++
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_male ASC_CAR_with_ga ASC_TRAIN ASC_TRAIN_male ASC_TRAIN_with_ B_COST B_TIME Function Relgrad Radius Rho
0 -0.56 0.46 -0.15 -0.33 -1.2 1.9 -1.2 -1.4 4.9e+03 0.001 10 1 ++
1 -0.56 0.46 -0.15 -0.33 -1.2 1.9 -1.2 -1.4 4.9e+03 3.6e-06 10 1 ++
88%|████████▊ | 88/100 [00:05<00:00, 16.30it/s]Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_male ASC_CAR_with_ga ASC_TRAIN ASC_TRAIN_male ASC_TRAIN_with_ B_COST B_TIME Function Relgrad Radius Rho
0 -0.62 0.6 -0.56 -0.42 -1.1 1.8 -1 -1.4 4.8e+03 0.0016 10 1 ++
1 -0.62 0.61 -0.56 -0.42 -1.2 1.8 -1 -1.4 4.8e+03 6.2e-06 1e+02 1 ++
2 -0.62 0.61 -0.56 -0.42 -1.2 1.8 -1 -1.4 4.8e+03 9.2e-11 1e+02 1 ++
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_male ASC_CAR_with_ga ASC_TRAIN ASC_TRAIN_male ASC_TRAIN_with_ B_COST B_TIME Function Relgrad Radius Rho
0 -0.53 0.38 -0.4 -0.37 -1.1 1.9 -1.2 -1.3 4.9e+03 0.00033 10 1 ++
1 -0.53 0.38 -0.4 -0.37 -1.1 1.9 -1.2 -1.3 4.9e+03 4.7e-07 10 1 ++
90%|█████████ | 90/100 [00:05<00:00, 15.59it/s]Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_male ASC_CAR_with_ga ASC_TRAIN ASC_TRAIN_male ASC_TRAIN_with_ B_COST B_TIME Function Relgrad Radius Rho
0 -0.61 0.39 -0.33 -0.46 -1.1 1.9 -1.1 -1.2 5e+03 0.00011 10 1 ++
1 -0.61 0.39 -0.33 -0.46 -1.1 1.9 -1.1 -1.2 5e+03 5.3e-08 10 1 ++
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_male ASC_CAR_with_ga ASC_TRAIN ASC_TRAIN_male ASC_TRAIN_with_ B_COST B_TIME Function Relgrad Radius Rho
0 -0.54 0.39 -0.25 -0.49 -1.2 2 -1.1 -1.2 5e+03 0.00023 10 0.99 ++
1 -0.54 0.39 -0.25 -0.49 -1.2 2 -1.1 -1.2 5e+03 5.8e-07 10 1 ++
92%|█████████▏| 92/100 [00:05<00:00, 15.33it/s]Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_male ASC_CAR_with_ga ASC_TRAIN ASC_TRAIN_male ASC_TRAIN_with_ B_COST B_TIME Function Relgrad Radius Rho
0 -0.48 0.37 -1 -0.44 -1.1 2 -1 -1.3 4.9e+03 0.00083 10 1.1 ++
1 -0.48 0.37 -1.2 -0.44 -1.1 2 -1 -1.3 4.9e+03 5.1e-05 1e+02 1 ++
2 -0.48 0.37 -1.2 -0.44 -1.1 2 -1 -1.3 4.9e+03 2.4e-07 1e+02 1 ++
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_male ASC_CAR_with_ga ASC_TRAIN ASC_TRAIN_male ASC_TRAIN_with_ B_COST B_TIME Function Relgrad Radius Rho
0 -0.59 0.39 -0.57 -0.51 -1.2 1.9 -1 -1.2 4.9e+03 9.5e-05 10 1 ++
1 -0.59 0.39 -0.57 -0.51 -1.2 1.9 -1 -1.2 4.9e+03 8.4e-08 10 1 ++
94%|█████████▍| 94/100 [00:06<00:00, 14.93it/s]Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_male ASC_CAR_with_ga ASC_TRAIN ASC_TRAIN_male ASC_TRAIN_with_ B_COST B_TIME Function Relgrad Radius Rho
0 -0.52 0.32 -0.51 -0.5 -1.1 1.9 -1.1 -1.2 4.9e+03 9.4e-05 10 1 ++
1 -0.52 0.32 -0.51 -0.5 -1.1 1.9 -1.1 -1.2 4.9e+03 4.1e-08 10 1 ++
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_male ASC_CAR_with_ga ASC_TRAIN ASC_TRAIN_male ASC_TRAIN_with_ B_COST B_TIME Function Relgrad Radius Rho
0 -0.64 0.42 -0.073 -0.47 -1.1 2 -1 -1.1 5e+03 0.00031 10 0.99 ++
1 -0.64 0.42 -0.073 -0.47 -1.1 2 -1 -1.1 5e+03 1.6e-06 10 1 ++
96%|█████████▌| 96/100 [00:06<00:00, 15.28it/s]Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_male ASC_CAR_with_ga ASC_TRAIN ASC_TRAIN_male ASC_TRAIN_with_ B_COST B_TIME Function Relgrad Radius Rho
0 -0.71 0.4 -0.16 -0.56 -1.2 1.8 -1.1 -1 5e+03 0.00033 10 0.98 ++
1 -0.71 0.4 -0.16 -0.56 -1.2 1.8 -1.1 -1 5e+03 8.6e-07 10 1 ++
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_male ASC_CAR_with_ga ASC_TRAIN ASC_TRAIN_male ASC_TRAIN_with_ B_COST B_TIME Function Relgrad Radius Rho
0 -0.49 0.32 -0.37 -0.41 -1.1 1.7 -1.1 -1.2 5e+03 0.00015 10 0.99 ++
1 -0.49 0.32 -0.37 -0.41 -1.1 1.7 -1.1 -1.2 5e+03 1.1e-07 10 1 ++
98%|█████████▊| 98/100 [00:06<00:00, 15.47it/s]Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_male ASC_CAR_with_ga ASC_TRAIN ASC_TRAIN_male ASC_TRAIN_with_ B_COST B_TIME Function Relgrad Radius Rho
0 -0.75 0.54 -0.62 -0.51 -1 1.9 -1.1 -1.3 4.9e+03 0.00071 10 1 ++
1 -0.75 0.54 -0.62 -0.51 -1 1.9 -1.1 -1.3 4.9e+03 3.1e-06 10 1 ++
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_male ASC_CAR_with_ga ASC_TRAIN ASC_TRAIN_male ASC_TRAIN_with_ B_COST B_TIME Function Relgrad Radius Rho
0 -0.3 0.16 -0.23 -0.59 -1 1.8 -1.1 -1.2 5e+03 0.00072 10 0.96 ++
1 -0.3 0.16 -0.23 -0.59 -1 1.8 -1.1 -1.2 5e+03 4.4e-06 10 1 ++
100%|██████████| 100/100 [00:06<00:00, 15.79it/s]
100%|██████████| 100/100 [00:06<00:00, 15.45it/s]
Results saved in file b01logit_bis.html
Results saved in file b01logit_bis.pickle
Get the results in a pandas table.
print('Parameters')
print('----------')
pandas_results = results.getEstimatedParameters()
pandas_results
Parameters
----------
Get general statistics.
print('General statistics')
print('------------------')
stats = results.getGeneralStatistics()
for description, (value, formatting) in stats.items():
print(f'{description}: {value:{formatting}}')
General statistics
------------------
Number of estimated parameters: 8
Sample size: 6768
Excluded observations: 3960
Null log likelihood: -6964.663
Init log likelihood: -5533.155
Final log likelihood: -4943.895
Likelihood ratio test for the null model: 4041.535
Rho-square for the null model: 0.29
Rho-square-bar for the null model: 0.289
Likelihood ratio test for the init. model: 1178.519
Rho-square for the init. model: 0.106
Rho-square-bar for the init. model: 0.105
Akaike Information Criterion: 9903.791
Bayesian Information Criterion: 9958.351
Final gradient norm: 5.3376E-03
Bootstrapping time: 0:00:06.475044
Nbr of threads: 12
Messages from the optimization algorithm.
print('Optimization algorithm')
print('----------------------')
for description, message in results.data.optimizationMessages.items():
print(f'{description}:\t{message}')
Optimization algorithm
----------------------
Relative gradient: 6.556090729381411e-07
Cause of termination: Relative gradient = 6.6e-07 <= 6.1e-06
Number of function evaluations: 5
Number of gradient evaluations: 5
Number of hessian evaluations: 4
Algorithm: Newton with trust region for simple bound constraints
Number of iterations: 4
Proportion of Hessian calculation: 4/4 = 100.0%
Optimization time: 0:00:00.080396
Generate the file in Alogit format.
results.writeF12(robustStdErr=True)
print(f'Estimation results in ALogit format generated: {results.data.F12FileName}')
Results saved in file b01logit_bis.F12
Estimation results in ALogit format generated: b01logit_bis.F12
Generate LaTeX code with the results.
results.writeLaTeX()
print(f'Estimation results in LaTeX format generated: {results.data.latexFileName}')
Results saved in file b01logit_bis.tex
Estimation results in LaTeX format generated: b01logit_bis.tex
Total running time of the script: (0 minutes 6.800 seconds)