Re-estimate the Pareto optimal models

The assisted specification algorithm generates a file containing the pareto optimal specification. This script is designed to re-estimate the Pareto optimal models. The catalog of specifications is defined in Specification of a catalog of models .

Michel Bierlaire, EPFL Sat Jun 28 2025, 12:35:57m

from biogeme.results_processing import compile_estimation_results

try:
    import matplotlib.pyplot as plt

    can_plot = True
except ModuleNotFoundError:
    can_plot = False
from biogeme.assisted import ParetoPostProcessing
from plot_b22multiple_models_spec import the_biogeme

PARETO_FILE_NAME = 'saved_results/b22multiple_models.pareto'

CSV_FILE = 'b22process_pareto.csv'
SEP_CSV = ','

The constructor of the Pareto post-processing object takes two arguments:

• the biogeme object,

• the name of the file where the algorithm has stored the estimated models.

the_pareto_post = ParetoPostProcessing(
    biogeme_object=the_biogeme,
    pareto_file_name=PARETO_FILE_NAME,
)

the_pareto_post.log_statistics()

all_results = the_pareto_post.reestimate(recycle=True)

summary, description = compile_estimation_results(all_results, use_short_names=True)
print(summary)

                                      Model_000000
Number of estimated parameters                   9
Sample size                                   6768
Final log likelihood                      -4810.59
Akaike Information Criterion               9639.18
Bayesian Information Criterion             9700.56
asc_train_ref (t-test)           -0.0737  (-0.705)
asc_train_diff_male (t-test)        -1.16  (-13.7)
asc_train_diff_with_ga (t-test)       2.11  (23.1)
b_time (t-test)                     -1.61  (-20.2)
b_cost (t-test)                     -1.49  (-18.4)
b_headway (t-test)               -0.00664  (-6.15)
asc_car_ref (t-test)               -0.696  (-6.52)
asc_car_diff_male (t-test)           0.474  (4.39)
asc_car_diff_with_ga (t-test)          -2  (-9.32)

print(f'Summary table available in {CSV_FILE}')
summary.to_csv(CSV_FILE, sep=SEP_CSV)

Summary table available in b22process_pareto.csv

Explanation of the short names of the models.

with open(CSV_FILE, 'a', encoding='utf-8') as f:
    print('\n\n', file=f)
    for k, v in description.items():
        if k != v:
            print(f'{k}: {v}')
            print(f'{k}{SEP_CSV}{v}', file=f)

Model_000000: asc:no_seg;train_cost_catalog:linear;train_headway_catalog:without_headway;train_tt_catalog:linear

The following plot illustrates all models that have been estimated. Each dot corresponds to a model. The x-coordinate corresponds to the Akaike Information Criterion (AIC). The y-coordinate corresponds to the Bayesian Information Criterion (BIC). Note that there is a third objective that does not appear on this picture: the number of parameters. If the shape of the dot is a circle, it means that it corresponds to a Pareto optimal model. If the shape is a cross, it means that the model has been Pareto optimal at some point during the algorithm and later removed as a new model dominating it has been found. If the shape is a start, it means that the model has been deemed invalid.

if can_plot:
    _ = the_pareto_post.plot(label_x='AIC', label_y='BIC')
    plt.show()

It is possible to plot two different objectives: AIC and number of parameters.

if can_plot:
    _ = the_pareto_post.plot(
        objective_x=0, objective_y=2, label_x='AIC', label_y='Number of parameters'
    )
    plt.show()

It is possible to plot two different objectives: BIC and number of parameters.

if can_plot:
    _ = the_pareto_post.plot(
        objective_x=1, objective_y=2, label_x='BIC', label_y='Number of parameters'
    )
    plt.show()