idmtools_calibra.algorithms.pbnb.optim_tool_pbnb module

class idmtools_calibra.algorithms.pbnb.optim_tool_pbnb.OptimToolPBnB(params, s_running_file_name, s_problem_type, constrain_sample_fn=<function OptimToolPBnB.<lambda>>, f_delta=0.6, f_alpha=0.5, i_k_b=3, i_n_branching=4, i_c=120, i_replication=1, i_stopping_max_k=10, i_max_num_simulation_per_run=1500, f_elite_worst_sampling_para=4e-05)

Bases: NextPointAlgorithm

get_samples_for_iteration(iteration)

fun_generate_samples_from_df(df_samples)

fun_probability_branching_and_bound(iteration)

logging_saver(iteration)

print_results_for_iteration()

set_results_for_iteration(iteration, results)

get_state()

set_state(state, iteration)

end_condition()

get_results_to_cache(results)

get_final_samples()

update_summary_table(iteration_state, previous_results)

copy from NExtPointAlgorithm, output: all_results, summary_table

get_param_names()

cleanup()