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add of runner of solution

Jérôme BUISINE 1 year ago
parent
commit
e537b4e8f0
1 changed files with 103 additions and 0 deletions
  1. 103 0
      run_openML_surrogate_multi_specific.py

+ 103 - 0
run_openML_surrogate_multi_specific.py

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+import os, argparse
+import shutil
+
+open_ml_problems_folder = 'OpenML_v2'
+surrogate_data_path = 'data/surrogate/data/'
+
+k_params = [100, 150, 200]
+k_random = [0, 1]
+k_reinit = [0, 1]
+every_ls = 50
+
+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:
+                    if 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}_{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"--output {output_problem_name}"
+                                            #f"--k_dynamic {k_init} " \
+                            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}'
+
+                        # 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"--output {output_problem_name}"
+                                        #f"--k_dynamic {k_init} " \
+                        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()