Re-estimation of best models

After running the assisted specification algorithm for the 432 specifications in everything_spec.py, we use post-processing to re-estimate all Pareto optimal models, and display some information about the algorithm. See Bierlaire and Ortelli (2023).

Michel Bierlaire, EPFL Sun Apr 27 2025, 18:38:57

from IPython.core.display_functions import display

from biogeme.biogeme import BIOGEME
from biogeme.results_processing import get_pandas_estimated_parameters

try:
    import matplotlib.pyplot as plt

    can_plot = True
except ModuleNotFoundError:
    can_plot = False
import biogeme.biogeme_logging as blog
from biogeme.assisted import ParetoPostProcessing

from everything_spec import model_catalog, database

logger = blog.get_screen_logger(level=blog.INFO)
logger.info('Example b08selected_specification')

PARETO_FILE_NAME = 'saved_results/b07everything_assisted.pareto'

Create the biogeme object from the catalog.

the_biogeme = BIOGEME(database, model_catalog)
the_biogeme.model_name = 'b09post_processing'

Create the post-processing object.

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

Re-estimate the models.

all_results = post_processing.reestimate(recycle=True)

We retrieve the first estimation results for illustration.

spec, results = next(iter(all_results.items()))

print(spec)

print(results.short_summary())

estimated_parameters = get_pandas_estimated_parameters(estimation_results=results)
display(estimated_parameters)

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:
    _ = post_processing.plot(
        label_x='Nbr of parameters',
        label_y='Negative log likelihood',
        objective_x=1,
        objective_y=0,
    )