Configuration parameters ======================== Biogeme can be configured using a parameter file. By default, the file is named ``biogeme.toml``. If it does not exist, Biogeme creates one containing the default values. .. list-table:: :header-rows: 1 * - Name - Description - Default - Section - Type * - largest_neighborhood - int: size of the largest neighborhood considered by the Variable Neighborhood Search (VNS) algorithm. - 20 - AssistedSpecification - int * - maximum_attempts - int: an attempts consists in selecting a solution in the Pareto set, and trying to improve it. The parameter imposes an upper bound on the total number of attempts, irrespectively if they are successful or not. - 100 - AssistedSpecification - int * - maximum_number_parameters - int: maximum number of parameters allowed in a model. Each specification with a higher number is deemed invalid and not estimated. - 50 - AssistedSpecification - int * - number_of_neighbors - int: maximum number of neighbors that are visited by the VNS algorithm. - 20 - AssistedSpecification - int * - bayesian_draws - Number of draws per chain from the posterior distribution - 2000 - Bayesian - int * - calculate_likelihood - Calculates likelihood-based statistics from the posterior draws - True - Bayesian - bool * - calculate_loo - Calculates the Leave-One-Out Cross-Validation (LOO) - True - Bayesian - bool * - calculate_waic - Calculates the Widely Applicable Information Criterion (WAIC) - False - Bayesian - bool * - chains - Number of independent Markov chains to run in parallel. - 4 - Bayesian - int * - mcmc_sampling_strategy - Defines how MCMC sampling is performed: 'automatic' (selected based on hardware), 'numpyro-parallel' (one chain per device), 'numpyro-vectorized' (all chains on one device), 'pymc' (default PyMC sampler on CPU) - automatic - Bayesian - str * - sample_from_prior - bool: if "True", samples from the prior distributions are generated. This may help in the diagnostic of indentification issues. - True - Bayesian - bool * - target_accept - Target acceptance probability for the No-U-Turn Sampler (NUTS) algorithm. Higher values like 0.9 or 0.95 often work better for problematic posteriors. - 0.9 - Bayesian - float * - warmup - Number of warm-up / burn-in iterations per chain that are used only to adapt the sampler, not to estimate the posterior. - 2000 - Bayesian - int * - version - Version of Biogeme that created the TOML file. Do not modify this value. - 3.3.5 - Biogeme - str * - number_of_jobs - int: The maximum number of concurrently running jobs. If -1 is given, joblib tries to use all CPUs. - 2 - Bootstrap - int * - analytical_hessian_mode - Implementation of the analytical Hessian: full, chunked, or automatic. The compatibility default is full. - full - Estimation - str * - bootstrap_samples - int: number of re-estimations for bootstrap sampling. - 100 - Estimation - int * - calculating_second_derivatives - Defines how to calculate the second derivatives: analytical,finite_differences,never. - analytical - Estimation - str * - hessian_memory_fraction - Maximum fraction of effective process memory allocated to analytical Hessian intermediates. Resource-manager limits, such as a Slurm memory allocation, are honored when available. - 0.25 - Estimation - float * - hessian_observation_batch_size - Number of observations evaluated simultaneously by the chunked analytical Hessian. - 100 - Estimation - int * - hessian_parameter_block_size - Number of parameter directions evaluated simultaneously by the chunked analytical Hessian. - 4 - Estimation - int * - large_data_set - If the number of observations is larger than this value, the data set is deemed large, and the default estimation algorithm will not use second derivatives. - 100000 - Estimation - int * - max_number_parameters_to_report - int: maximum number of parameters to report during the estimation. - 15 - Estimation - int * - maximum_number_catalog_expressions - If the expression contains catalogs, the parameter sets an upper bound of the total number of possible combinations that can be estimated in the same loop. - 100 - Estimation - int * - optimization_algorithm - str: optimization algorithm to be used for estimation. Valid values: ['automatic', 'scipy', 'LS-newton', 'TR-newton', 'LS-BFGS', 'TR-BFGS', 'simple_bounds', 'simple_bounds_newton', 'simple_bounds_BFGS'] - automatic - Estimation - str * - save_iterations - bool: If True, the current iterate is saved after each iteration, in a file named ``__[modelName].iter``, where ``[modelName]`` is the name given to the model. If such a file exists, the starting values for the estimation are replaced by the values saved in the file. - True - Estimation - bool * - monte_carlo_diagnostic_auto - Run the post-estimation Monte Carlo draw-stability diagnostic automatically after estimate_or_load. Disabled by default. - False - MonteCarlo - bool * - monte_carlo_diagnostic_draw_factors - Comma-separated factors applied to the estimation draw count for the post-estimation stability diagnostic. - 0.25,0.5,1.0,2.0,4.0 - MonteCarlo - str * - monte_carlo_diagnostic_gradient_tolerance - Maximum infinity norm of the gradient discrepancy considered stable by the Monte Carlo diagnostic. - 1e-05 - MonteCarlo - float * - monte_carlo_diagnostic_max_draws - Maximum number of draws in one diagnostic evaluation. - 1000000 - MonteCarlo - int * - monte_carlo_diagnostic_minimum_level_factor - Minimum draw-count factor above the original count required for a conclusive stability result. - 2.0 - MonteCarlo - float * - monte_carlo_diagnostic_objective_tolerance - Maximum absolute objective discrepancy considered stable by the Monte Carlo diagnostic. - 0.001 - MonteCarlo - float * - monte_carlo_diagnostic_replications - Number of independent diagnostic draw designs per level. - 2 - MonteCarlo - int * - monte_carlo_diagnostic_safety_factor - Safety multiplier applied to the forecast runtime of pending diagnostic evaluations. - 1.5 - MonteCarlo - float * - monte_carlo_diagnostic_time_budget - Hard wall-clock budget in seconds for the draw-stability diagnostic. - 1800 - MonteCarlo - int * - number_of_draws - int: Number of draws for Monte-Carlo integration. - 10000 - MonteCarlo - int * - seed - int: Seed used for the pseudo-random number generation. It is useful only when each run should generate the exact same result. If 0, a new seed is used at each run. - 0 - MonteCarlo - int * - number_of_threads - int: Number of threads/processors to be used. If the parameter is 0, the number of available threads is calculated using cpu_count(). - 0 - MultiThreading - int * - generate_html - bool: "True" if the HTML file with the results must be generated. - True - Output - bool * - generate_netcdf - bool: "True" if the netcdf file with the Bayesian estimation results must be generated. - True - Output - bool * - generate_yaml - bool: "True" if the yaml file with the results must be generated. - True - Output - bool * - identification_threshold - float: if the smallest eigenvalue of the second derivative matrix is lesser or equal to this parameter, the model is considered not identified. The corresponding eigenvector is then reported to identify the parameters involved in the issue. - 1e-05 - Output - float * - only_robust_stats - bool: "True" if only the robust statistics need to be reported. If "False", the statistics from the Rao-Cramer bound are also reported. - True - Output - bool * - save_validation_results - bool: "True" if the validation results are saved in CSV files. - True - Output - bool * - enlarging_factor - If an iteration is very successful, the radius of the trust region is multiplied by this factor - 10 - SimpleBounds - float * - infeasible_cg - If True, the conjugate gradient algorithm may generate infeasible solutions until termination. The result will then be projected on the feasible domain. If False, the algorithm stops as soon as an infeasible iterate is generated - False - SimpleBounds - bool * - initial_radius - Initial radius of the trust region - 1 - SimpleBounds - float * - max_iterations - int: maximum number of iterations - 1000 - SimpleBounds - int * - second_derivatives - float: proportion (between 0 and 1) of iterations when the analytical Hessian is calculated - 1.0 - SimpleBounds - float * - steptol - The algorithm stops when the relative change in x is below this threshold. Basically, if p significant digits of x are needed, steptol should be set to 1.0e-p. - 3.666852862501036e-11 - SimpleBounds - float * - tolerance - float: the algorithm stops when this precision is reached - 6.055454452393343e-06 - SimpleBounds - float * - missing_data - number: If one variable has this value, it is assumed that a data is missing and an exception will be triggered. - 99999 - Specification - int * - numerically_safe - If true, Biogeme prioritizes numerical stability over speed. Likelihood expressions use finite masks for unavailable alternatives and empty nests so that values and automatic derivatives remain finite. - False - Specification - bool * - use_jit - If True, the model is compiled using jit (just-in-time) to speed up the calculation. For complex models, compilation time may exceed the gain due to compilation, so that it is worth turning it off. - True - Specification - bool * - dogleg - bool: choice of the method to solve the trust region subproblem. True: dogleg. False: truncated conjugate gradient. - True - TrustRegion - bool The structure of the ``biogeme.toml`` file is as follows. .. literalinclude:: biogeme.toml :language: none :linenos: