data colleciton

repelling/cec.ipynb

@@ -58,7 +58,8 @@
       "1116: 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, \n",
       "1117: 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, \n",
       "1118: 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, \n",
-      "1119: 0, 1, 2, 3, 4, "
+      "1119: 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, \n",
+      "1120: 0, "
      ]
     }
    ],

repelling/collect_bbob.py

@@ -0,0 +1,69 @@
+import argparse
+import time
+import sys
+
+import ioh
+import numpy as np
+import pandas as pd
+
+import modcma.c_maes as c_cmaes
+
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--fid", type=int, default=21)
+    parser.add_argument("--dim", type=int, default=2)
+    parser.add_argument("--n_instances", type=int, default=10)
+    parser.add_argument("--n_runs", type=int, default=100)
+    parser.add_argument("--elitist", action="store_true")
+    parser.add_argument("--repelling", action="store_true")
+    parser.add_argument("--logged", action="store_true")
+    parser.add_argument("--verbose", action="store_true")
+    parser.add_argument("--coverage", type=float, default=10.0)
+    parser.add_argument("--budget", type=int, default=10_000)
+    args = parser.parse_args()
+
+
+
+    algorithm_name = "CMA-ES"
+    if args.repelling:
+        algorithm_name += f"-repelling-c{args.coverage}"
+    if args.elitist:
+        algorithm_name += "-elitist"
+
+    if args.logged:
+        logger = ioh.logger.Analyzer(
+            root="data", algorithm_name=algorithm_name, folder_name=algorithm_name
+        )
+
+    modules = c_cmaes.parameters.Modules()
+    modules.restart_strategy = c_cmaes.options.RESTART
+    modules.center_placement = c_cmaes.options.UNIFORM
+    modules.bound_correction = c_cmaes.options.SATURATE
+    modules.repelling_restart = args.repelling
+    modules.elitist = args.elitist
+
+    centers = []
+    for instance in range(1, args.n_instances + 1):
+        problem = ioh.get_problem(args.fid, instance, args.dim)
+        if args.logged:
+            problem.attach_logger(logger)
+
+        for run in range(args.n_runs):
+            c_cmaes.utils.set_seed(42 * run)
+            settings = c_cmaes.parameters.Settings(
+                args.dim,
+                modules,
+                sigma0=2.0,
+                budget=args.dim * args.budget,
+                target=problem.optimum.y + 1e-8,
+            )
+
+            parameters = c_cmaes.Parameters(settings)
+            parameters.repelling.coverage = args.coverage
+            cma = c_cmaes.ModularCMAES(parameters)
+            cma.run(problem)
+            centers.append((instance, run, cma.p.stats.centers))
+    breakpoint()
+