Nested logit

Estimation of a nested logit model using sampling of alternatives.

Michel Bierlaire Sat Jul 26 2025, 13:01:22

import pandas as pd
from alternatives import ID_COLUMN, all_alternatives, alternatives, asian, partitions
from compare import compare
from IPython.core.display_functions import display
from specification_sampling import V, combined_variables

import biogeme.biogeme_logging as blog
from biogeme.biogeme import BIOGEME
from biogeme.expressions import Beta
from biogeme.nests import NestsForNestedLogit, OneNestForNestedLogit
from biogeme.results_processing import (
    EstimationResults,
    get_pandas_estimated_parameters,
)
from biogeme.sampling_of_alternatives import (
    ChoiceSetsGeneration,
    GenerateModel,
    SamplingContext,
    generate_segment_size,
)
from biogeme.tools import timeit

logger = blog.get_screen_logger(level=blog.INFO)

SAMPLE_SIZE = 20  # out of 100
SAMPLE_SIZE_MEV = 33  # out of 33
CHOICE_COLUMN = 'nested_0'
PARTITION = 'downtown'
MEV_PARTITION = 'uniform_asian'
MODEL_NAME = f'nested_{PARTITION}_{SAMPLE_SIZE}'
FILE_NAME = f'{MODEL_NAME}.dat'

the_partition = partitions.get(PARTITION)
if the_partition is None:
    raise ValueError(f'Unknown partition: {PARTITION}')

segment_sizes = generate_segment_size(SAMPLE_SIZE, the_partition.number_of_segments())

We use all alternatives in the nest.

mev_partition = partitions.get(MEV_PARTITION)
if mev_partition is None:
    raise ValueError(f'Unknown partition: {MEV_PARTITION}')
mev_segment_sizes = [SAMPLE_SIZE_MEV]

observations = pd.read_csv('obs_choice.dat')

context = SamplingContext(
    the_partition=the_partition,
    sample_sizes=segment_sizes,
    individuals=observations,
    choice_column=CHOICE_COLUMN,
    alternatives=alternatives,
    id_column=ID_COLUMN,
    biogeme_file_name=FILE_NAME,
    utility_function=V,
    combined_variables=combined_variables,
    mev_partition=mev_partition,
    mev_sample_sizes=mev_segment_sizes,
)

logger.info(context.reporting())

the_data_generation = ChoiceSetsGeneration(context=context)
the_model_generation = GenerateModel(context=context)

biogeme_database = the_data_generation.sample_and_merge(recycle=False)

Definition of the nest.

mu_asian = Beta('mu_asian', 1.0, 1.0, None, 0)
nest_asian = OneNestForNestedLogit(
    nest_param=mu_asian, list_of_alternatives=asian, name='asian'
)
nests = NestsForNestedLogit(
    choice_set=all_alternatives,
    tuple_of_nests=(nest_asian,),
)

log_probability = the_model_generation.get_nested_logit(nests)

the_biogeme = BIOGEME(biogeme_database, log_probability)
the_biogeme.model_name = MODEL_NAME

Calculate the null log likelihood for reporting.

the_biogeme.calculate_null_loglikelihood(
    {i: 1 for i in range(context.total_sample_size)}
)

Estimate the parameters.

try:
    results = EstimationResults.from_yaml_file(
        filename=f'saved_results/{the_biogeme.model_name}.yaml'
    )
except FileNotFoundError:
    with timeit(f'Estimate of model {the_biogeme.model_name}'):
        results = the_biogeme.estimate()

print(results.short_summary())

parameters_tables = get_pandas_estimated_parameters(estimation_results=results)
estimated_parameters = parameters_tables['Estimated parameters']
display(estimated_parameters)

df, msg = compare(estimated_parameters)

print(df)

print(msg)