import os, argparse import shutil open_ml_problems_folder = 'OpenML_datasets' surrogate_data_path = 'data/surrogate/data/' # fixed test params as first part k_params = [30, 50, 100] # 100, 150, 200 k_random = [0] # 0, 1 k_reinit = [0] # 0, 1 every_ls = 5 n_times = 5 def main(): parser = argparse.ArgumentParser(description="Find best features for each OpenML problems") parser.add_argument('--ils', type=int, help='number of total iteration for ils algorithm', required=True) parser.add_argument('--ls', type=int, help='number of iteration for Local Search algorithm', required=True) args = parser.parse_args() p_ils = args.ils p_ls = args.ls open_ml_problems = sorted(os.listdir(open_ml_problems_folder)) for ml_problem in open_ml_problems: # for each problem prepare specific pre-computed real solution file ml_problem_name = ml_problem.replace('.csv', '') ml_problem_path = os.path.join(open_ml_problems_folder, ml_problem) # ml_surrogate_command = f"python find_best_attributes_surrogate_openML_multi_specific.py " \ # f"--data {ml_problem_path} " \ # f"--ils {p_ils} " \ # f"--ls {p_ls} " \ # f"--output {ml_problem_name} " \ # f"--generate_only 1" # print(f'Running extraction real evaluations data for {ml_problem_name}') # os.system(ml_surrogate_command) # real_evaluation_data_file_path = os.path.join(surrogate_data_path, ml_problem_name) # for each multi param: # - copy precomputed real_evaluation_data_file # - run new instance using specific data for k in k_params: for k_r in k_random: for k_init in k_reinit: # if not use of k_reinit and use of random, then run multiple times this instance to do mean later if k_init == 0 and k_r == 1: for i in range(n_times): str_index = str(i) while len(str_index) < 3: str_index = "0" + str_index output_problem_name = f'{ml_problem_name}_everyLS_{every_ls}_k{k}_random{k_r}_reinit{k_init}_{str_index}' # copy pre-computed real evaluation data for this instance # current_output_real_eval_path = os.path.join(surrogate_data_path, output_problem_name) # shutil.copy2(real_evaluation_data_file_path, current_output_real_eval_path) ml_surrogate_multi_command = f"python find_best_attributes_surrogate_openML_multi_specific.py " \ f"--data {ml_problem_path} " \ f"--ils {p_ils} " \ f"--ls {p_ls} " \ f"--every_ls {every_ls} " \ f"--k_division {k} " \ f"--k_random {k_r} " \ f"--k_dynamic {k_init} " \ f"--output {output_problem_name}" print(f'Running extraction data for {ml_problem_name} with [ils: {p_ils}, ls: {p_ls}, k: {k}, k_r: {k_r}, i: {i}]') os.system(ml_surrogate_multi_command) else: output_problem_name = f'{ml_problem_name}_everyLS_{every_ls}_k{k}_random{k_r}_reinit{k_init}' # copy pre-computed real evaluation data for this instance # current_output_real_eval_path = os.path.join(surrogate_data_path, output_problem_name) # shutil.copy2(real_evaluation_data_file_path, current_output_real_eval_path) ml_surrogate_multi_command = f"python find_best_attributes_surrogate_openML_multi_specific.py " \ f"--data {ml_problem_path} " \ f"--ils {p_ils} " \ f"--ls {p_ls} " \ f"--every_ls {every_ls} " \ f"--k_division {k} " \ f"--k_random {k_r} " \ f"--k_dynamic {k_init} " \ f"--output {output_problem_name}" print(f'Running extraction data for {ml_problem_name} with [ils: {p_ils}, ls: {p_ls}, k: {k}, k_r: {k_r}]') os.system(ml_surrogate_multi_command) if __name__ == "__main__": main()