pyblp.ProblemResults.to_dict

ProblemResults.to_dict(attributes=('step', 'optimization_time', 'cumulative_optimization_time', 'total_time', 'cumulative_total_time', 'converged', 'cumulative_converged', 'optimization_iterations', 'cumulative_optimization_iterations', 'objective_evaluations', 'cumulative_objective_evaluations', 'fp_converged', 'cumulative_fp_converged', 'fp_iterations', 'cumulative_fp_iterations', 'contraction_evaluations', 'cumulative_contraction_evaluations', 'parameters', 'parameter_covariances', 'parameter_sensitivity', 'theta', 'sigma', 'sigma_squared', 'pi', 'rho', 'beta', 'gamma', 'sigma_se', 'sigma_squared_se', 'pi_se', 'rho_se', 'beta_se', 'gamma_se', 'sigma_bounds', 'pi_bounds', 'rho_bounds', 'beta_bounds', 'gamma_bounds', 'sigma_labels', 'pi_labels', 'rho_labels', 'beta_labels', 'gamma_labels', 'theta_labels', 'delta', 'tilde_costs', 'clipped_shares', 'clipped_costs', 'xi', 'omega', 'xi_fe', 'omega_fe', 'micro', 'micro_values', 'micro_covariances', 'moments', 'moments_jacobian', 'moments_covariances', 'simulation_covariances', 'objective', 'xi_by_theta_jacobian', 'omega_by_theta_jacobian', 'micro_by_theta_jacobian', 'gradient', 'projected_gradient', 'projected_gradient_norm', 'hessian', 'reduced_hessian', 'reduced_hessian_eigenvalues', 'W', 'updated_W'))

Convert these results into a dictionary that maps attribute names to values.

Parameters

attributes (sequence of str, optional) – Name of attributes that will be added to the dictionary. By default, all ProblemResults attributes are added except for ProblemResults.problem and ProblemResults.last_results.

Returns

Mapping from attribute names to values.

Return type

dict

Examples