'\nCONV POINTS: ' + str(conv_points) + \ '\nTIME: ' + str(comp_time) # specify path path = 'results_and_plots/optimization/logicle5_1/' # file name is equal to starting points file_name = str(n_starts) # save save_optimization_to_file(result=result, n_start=n_starts, nominal_par=petab_problem.x_nominal, par_names=model.getParameterIds()[:22], opt_lh=obj(petab_problem.x_nominal), file_name=file_name, conv_points=conv_points, comp_time=comp_time, opt_interval=[problem.lb, problem.ub], startpoints=startpoints, options=options, path=path) # SAVE GUESSES _________________________________________________________________________________________________________ # save converged points to use them as starting points for # the regularization path_guess = 'guesses/logicle5_1/' save_guesses(result=result, n_starts=n_starts,
# save the startpoints save_startpoints(result=result, path='startpoints/', file_name='log10') # SAVE OPTIMIZATION RESULTS ____________________________________________________________________________________________ options = 'MODEL: caro model, ' \ '\nSCALE: log10' + \ '\nSTARTS: ' + str(n_starts) + \ '\nCONV POINTS: ' + str(conv_points) + \ '\nTIME: ' + str(comp_time) # specify path path = 'results_and_plots/optimization/log10/' # file name is equal to starting points file_name = str(n_starts) # save save_optimization_to_file(result=result, n_start=n_starts, nominal_par=petab_problem.x_nominal, par_names=['$\\xi_1$', '$\\xi_2$', '$\\xi_3$', '$\sigma$'], opt_lh=obj(petab_problem.x_nominal), file_name=file_name, conv_points=conv_points, comp_time=comp_time, opt_interval=[problem.lb, problem.ub], startpoints=startpoints, options=options, path=path)