remove debug code

modcmac_code/utils/utils.py

@@ -125,7 +125,7 @@ def generate_proportionally_weighted_states(n_components, start_states, probs, r
     return np.array(random.choices(start_states, weights=adjusted_weights, k=n_components))
 
 
-def weibull_interpolation(P, P_start, ndeterioration, lambda_=92, kappa=2):
+def weibull_interpolation(P, P_start, ndeterioration, lambda_=92, kappa=2.5):
     """
     Adjust the transition matrix over time using a Weibull distribution, where the probability
     of moving to a higher state increases, and the probability of staying in the same state decreases.
@@ -178,7 +178,6 @@ def weibull_interpolation(P, P_start, ndeterioration, lambda_=92, kappa=2):
 
         P[t] = P_copy
 
-    print(list(P[-1].round(3)))
     return P
 
 

run_MODCMAC.py

@@ -33,6 +33,7 @@ def parse_arguments():
     parser.add_argument("--device", type=str, default="cpu", help="Device.", choices=["cpu", "cuda", "mps"])
     parser.add_argument("--save_folder", type=str, default="./models", help="Folder to save models.")
     parser.add_argument("--do_eval", action='store_true', help="Flag to do evaluation.")
+    parser.add_argument('--eval_steps', type=int, default=5, help='Number of evaluation steps to perform.')
     parser.add_argument("--path_pnet", type=str, default=None, help="Path to policy network.")
     parser.add_argument("--path_vnet", type=str, default=None, help="Path to value network.")
     parser.add_argument("--no_log", action="store_false", help="Flag to not log the run")
@@ -125,7 +126,7 @@ def signal_handler(sig, frame):
         agent.train(training_steps=args.num_steps)
     else:
         agent.load_model(args.path_pnet, args.path_vnet)
-        cost_array, risk_array, uti_array, scoring_table = agent.do_eval(5)
+        cost_array, risk_array, uti_array, scoring_table = agent.do_eval(args.eval_steps)
         print("Cost: ", np.mean(cost_array))
         print("Risk: ", np.mean(risk_array))
         print("Utility: ", np.mean(uti_array))