Source code for biogeme.likelihood.bootstrap

import logging

from joblib import Parallel, delayed
from tqdm import tqdm
from tqdm_joblib import tqdm_joblib

from biogeme.calculator import CompiledFormulaEvaluator
from biogeme.default_parameters import ParameterValue
from biogeme.model_elements import ModelElements
from biogeme.optimization import OptimizationAlgorithm
from biogeme.second_derivatives import SecondDerivativesMode
from .model_estimation import AlgorithmResults, model_estimation

logger = logging.getLogger(__name__)




def bootstrap(
    number_of_bootstrap_samples,
    the_algorithm: OptimizationAlgorithm,
    modeling_elements: ModelElements,
    parameters: dict[str, ParameterValue],
    starting_values: dict[str, float],
    second_derivatives_mode: SecondDerivativesMode,
    numerically_safe: bool,
    use_jit: bool,
    number_of_jobs: int,
) -> list[AlgorithmResults]:
    """
    Perform bootstrap estimation to assess the variability of model parameters.

    This function generates a specified number of bootstrap samples from the
    original dataset, estimates the model on each sample using the provided
    algorithm and parameters, and returns the collection of estimation results.

    :param number_of_bootstrap_samples: Number of bootstrap replications to perform.
    :param the_algorithm: The optimization algorithm used to estimate the model.
    :param modeling_elements: The components defining the model, including the database and log-likelihood expression.
    :param parameters: Configuration parameters used during estimation.
    :param starting_values: Dictionary of initial values for the model's free parameters.
    :param second_derivatives_mode: specifies how second derivatives are calculated.
    :param numerically_safe: improves the numerical stability of the calculations.
    :param use_jit: if True, performs just-in-time compilation.
    :param number_of_jobs: number of jobs for parallel execution of bootstrapping.

    :return: A list of tuples containing:
        - estimated parameter values (NumPy array),
        - diagnostic information from the optimizer (dictionary),
        - convergence status (boolean).
    """
    the_database = modeling_elements.database

    def run_one_bootstrap_estimation(_):
        bootstrap_modeling_elements = ModelElements(
            expressions=modeling_elements.expressions,
            database=the_database.bootstrap_sample(),
            number_of_draws=modeling_elements.number_of_draws,
            draws_management=None,
            user_defined_draws=modeling_elements.user_defined_draws,
            expressions_registry=None,
            use_jit=use_jit,
        )
        compiled_formula = CompiledFormulaEvaluator(
            model_elements=bootstrap_modeling_elements,
            second_derivatives_mode=second_derivatives_mode,
            numerically_safe=numerically_safe,
        )
        one_result = model_estimation(
            the_algorithm=the_algorithm,
            function_evaluator=compiled_formula,
            parameters=parameters,
            some_starting_values=starting_values,
            save_iterations_filename=None,
        )
        return one_result

    PARALLEL = True

    logger.info(f'Number of jobs for bootstrapping: {number_of_jobs}')
    if PARALLEL:
        with tqdm_joblib(
            tqdm(
                desc="Bootstraps",
                total=number_of_bootstrap_samples,
            )
        ) as progress_bar:
            results = Parallel(n_jobs=number_of_jobs)(
                delayed(run_one_bootstrap_estimation)(_)
                for _ in range(number_of_bootstrap_samples)
            )
        return results
    else:
        results = []
        for _ in tqdm(range(number_of_bootstrap_samples)):
            results.append(run_one_bootstrap_estimation(_))
        return results