"""

23b. Binary probit model
========================

Bayesian estimation of a binary probit model.
Two alternatives: Train and Car.
All observations such that the Swissmetro was chosen haven been removed from the sample.


Michel Bierlaire, EPFL
Sat Jun 28 2025, 12:43:40

"""

from IPython.core.display_functions import display

from biogeme.bayesian_estimation import BayesianResults, get_pandas_estimated_parameters
from biogeme.biogeme import BIOGEME
from biogeme.expressions import Beta, Elem, NormalCdf, log
# %%
# See the data processing script: :ref:`swissmetro_binary`.
from swissmetro_binary import (
    CAR_CO_SCALED,
    CAR_TT_SCALED,
    CHOICE,
    TRAIN_COST_SCALED,
    TRAIN_TT_SCALED,
    database,
)

# %%
# Parameters to be estimated.
asc_car = Beta('asc_car', 0, None, None, 0)
b_time_car = Beta('b_time_car', 0, None, None, 0)
b_time_train = Beta('b_time_train', 0, None, None, 0)
b_cost_car = Beta('b_cost_car', 0, None, None, 0)
b_cost_train = Beta('b_cost_train', 0, None, None, 0)

# %%
# Definition of the utility functions.
# We estimate a binary probit model. There are only two alternatives.
v_train = b_time_train * TRAIN_TT_SCALED + b_cost_train * TRAIN_COST_SCALED
v_car = asc_car + b_time_car * CAR_TT_SCALED + b_cost_car * CAR_CO_SCALED

# %%
# Associate choice probability with the numbering of alternatives.
log_probability_dict = {
    1: log(NormalCdf(v_train - v_car)),
    3: log(NormalCdf(v_car - v_train)),
}

# %%
# Definition of the model. This is the contribution of each
# observation to the log likelihood function.
log_probability = Elem(log_probability_dict, CHOICE)

# %%
# Create the Biogeme object
the_biogeme = BIOGEME(database, log_probability)
the_biogeme.model_name = 'b23b_binary_probit'

# %%
# Estimate the parameters.
try:
    results = BayesianResults.from_netcdf(
        filename=f'saved_results/{the_biogeme.model_name}.nc'
    )
except FileNotFoundError:
    results = the_biogeme.bayesian_estimation()

# %%
print(results.short_summary())

# %%
# Get the results in a pandas table
pandas_results = get_pandas_estimated_parameters(
    estimation_results=results,
)
display(pandas_results)