"""

12. Mixture of logit with panel data
====================================

Example of a mixture of logit models, using Monte-Carlo integration.
 The datafile is organized as panel data.

Michel Bierlaire, EPFL
Sat Jun 21 2025, 16:54:51
"""

import biogeme.biogeme_logging as blog
from IPython.core.display_functions import display
from biogeme.biogeme import BIOGEME
from biogeme.expressions import Beta, Draws, MonteCarlo, PanelLikelihoodTrajectory, log
from biogeme.models import logit
from biogeme.results_processing import (
    EstimationResults,
    get_pandas_estimated_parameters,
)

# %%
# See the data processing script: :ref:`swissmetro_panel`.
from swissmetro_panel import (
    CAR_AV_SP,
    CAR_CO_SCALED,
    CAR_TT_SCALED,
    CHOICE,
    SM_AV,
    SM_COST_SCALED,
    SM_TT_SCALED,
    TRAIN_AV_SP,
    TRAIN_COST_SCALED,
    TRAIN_TT_SCALED,
    database,
)

# from biogeme.results_processing import get_pandas_estimated_parameters

logger = blog.get_screen_logger(level=blog.INFO)
logger.info('Example b12_panel.py')

# %%
# Parameters to be estimated.
b_cost = Beta('b_cost', 0, None, 0, 0)

# %%
# Define a random parameter, normally distributed across individuals,
# designed to be used for Monte-Carlo simulation.
b_time = Beta('b_time', 0, None, 0, 0)

# %%
# It is advised not to use 0 as starting value for the following parameter.
b_time_s = Beta('b_time_s', 1, 1.0e-5, None, 0)
b_time_rnd = b_time + b_time_s * Draws('b_time_rnd', 'NORMAL_ANTI')

# %%
# We do the same for the constants, to address serial correlation.
asc_car = Beta('asc_car', 0, None, None, 0)
asc_car_s = Beta('asc_car_s', 1, 1.0e-5, None, 0)
asc_car_rnd = asc_car + asc_car_s * Draws('asc_car_rnd', 'NORMAL_ANTI')

asc_train = Beta('asc_train', 0, None, None, 0)
asc_train_s = Beta('asc_train_s', 1, 1.0e-5, None, 0)
asc_train_rnd = asc_train + asc_train_s * Draws('asc_train_rnd', 'NORMAL_ANTI')

asc_sm = Beta('asc_sm', 0, None, None, 0)
asc_sm_s = Beta('asc_sm_s', 1, 1.0e-5, None, 0)
asc_sm_rnd = asc_sm + asc_sm_s * Draws('asc_sm_rnd', 'NORMAL_ANTI')

# %%
# Definition of the utility functions.
v_train = asc_train_rnd + b_time_rnd * TRAIN_TT_SCALED + b_cost * TRAIN_COST_SCALED
v_swissmetro = asc_sm_rnd + b_time_rnd * SM_TT_SCALED + b_cost * SM_COST_SCALED
v_car = asc_car_rnd + b_time_rnd * CAR_TT_SCALED + b_cost * CAR_CO_SCALED

# %%
# Associate utility functions with the numbering of alternatives.
v = {1: v_train, 2: v_swissmetro, 3: v_car}

# %%
# Associate the availability conditions with the alternatives.
av = {1: TRAIN_AV_SP, 2: SM_AV, 3: CAR_AV_SP}

# %%
# Conditional on the random parameters, the likelihood of one observation is
# given by the logit model (called the kernel).
choice_probability_one_observation = logit(v, av, CHOICE)

# %%
# Conditional on the random parameters, the likelihood of all observations for
# one individual (the trajectory) is the product of the likelihood of
# each observation.
conditional_trajectory_probability = PanelLikelihoodTrajectory(
    choice_probability_one_observation
)

# %%
# We integrate over the random parameters using Monte-Carlo
log_probability = log(MonteCarlo(conditional_trajectory_probability))

# %%
# As the objective is to illustrate the
# syntax, we calculate the Monte-Carlo approximation with a small
# number of draws.
the_biogeme = BIOGEME(
    database,
    log_probability,
    number_of_draws=5_000,
    seed=1223,
    calculating_second_derivatives='never',
)
the_biogeme.model_name = 'b12_panel'


# %%
# Estimate the parameters.
try:
    results = EstimationResults.from_yaml_file(
        filename=f'saved_results/{the_biogeme.model_name}.yaml'
    )
except FileNotFoundError:
    results = the_biogeme.estimate()

# %%
print(results.short_summary())

# %%
pandas_results = get_pandas_estimated_parameters(estimation_results=results)
display(pandas_results)

# %%
# Here is a list of the columns of the "flat" database fthat has been generated by Biogeme
for col in the_biogeme.model_elements.database.dataframe.columns:
    print(col)

# %%
# And here is a list of the variables that have been renamed in the model specification.
for var in the_biogeme.model_elements.expressions_registry.variables:
    print(var)