biogeme.toml

# Default parameter file for Biogeme 3.2.11a1
# Automatically created on 2022-12-06

[Estimation]
save_iterations = "True" # 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.
optimization_algorithm = "simple_bounds" # str: optimization algorithm to be used
                                         # for estimation. Valid values:
                                         # ['scipy', 'LS-newton', 'TR-newton',
                                         # 'LS-BFGS', 'TR-BFGS', 'simple_bounds']

[Specification]
skip_audit = "False" # bool: If True, does not check the validity of the formulas.
                     # It may save significant amount of time for large models
                     # and large data sets. 
suggest_scales = "True" # bool: If True, Biogeme suggests the scaling of the
                        # variables in the database.
missing_data = 99999 # number: If one variable has this value, it is assumed that
                     # a data is missing and an exception will be triggered.

[MonteCarlo]
number_of_draws = 1000 # int: Number of draws for Monte-Carlo integration.
seed = 0 # 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.

[SimpleBounds]
second_derivatives = 1.0 # float: proportion (between 0 and 1) of iterations when
                         # the analytical Hessian is calculated
tolerance = 6.06273418136464e-06 # float: the algorithm stops when this precision
                                 # is reached
max_iterations = 100 # int: maximum number of iterations
infeasible_cg = "False" # If True, the conjugate gradient algorithm may generate
                        # infeasible solutiona 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

[Output]
generate_html = "True" # bool: "True" if the HTML file with the results must be
                       # generated.
generate_pickle = "True" # bool: "True" if the pickle file with the results must be
                         # generated.

[TrustRegion]
dogleg = "True" # bool: choice of the method to solve the trust region subproblem.
                # True: dogleg. False: truncated conjugate gradient.

[MultiThreading]
number_of_threads = 0 # int: Number of threads/processors to be used. If the
                      # parameter is 0, the number of available threads is
                      # calculated using cpu_count().