updated analysis script

analysis/proc_task_cond_result.py

@@ -36,10 +36,10 @@ def event_key_to_str(event_key):
         return f'{CODE_TO_EVENT[event_key[0]]}_{ITEM_ID_TO_NAME[event_key[1]]}'
 
     elif event_key[0] == EventCode.GO_FARTHEST:
-        return '2_PROGRESS_TO_CENTER'
+        return '3_PROGRESS_TO_CENTER'
 
     elif event_key[0] == EventCode.AGENT_CULLED:
-        return '1_AGENT_LIFESPAN'
+        return '2_AGENT_LIFESPAN'
 
     else:
         return CODE_TO_EVENT[event_key[0]]
@@ -67,7 +67,6 @@ def gather_agent_events_by_task(data_dir):
     return data_by_task
 
 def get_event_stats(task_name, task_data):
-    results = {'0_NAME': task_name}
     num_agents = len(task_data)
     assert num_agents > 0, 'There should be at least one agent'
 
@@ -78,6 +77,7 @@ def get_event_stats(task_name, task_data):
     cnt_harvest = 0
     cnt_list = 0
 
+    results = {'0_NAME': task_name, '1_COUNT': num_agents}
     event_data = defaultdict(list)
     for data in task_data:
         for event, val in data.items():
@@ -90,7 +90,11 @@ def get_event_stats(task_name, task_data):
             results[event_key_to_str(event)] = np.mean(vals)  # AVG skill level
         elif event[0] == EventCode.AGENT_CULLED:
             life_span = np.mean(vals)
-            results[event_key_to_str(event)] = life_span
+            results['2_AGENT_LIFESPAN_AVG'] = life_span
+            results['2_AGENT_LIFESPAN_SD'] = np.std(vals)
+        elif event[0] == EventCode.GO_FARTHEST:
+            results['3_PROGRESS_TO_CENTER_AVG'] = np.mean(vals)
+            results['3_PROGRESS_TO_CENTER_SD'] = np.std(vals)
         else:
             results[event_key_to_str(event)] = sum(vals) / num_agents
 
@@ -107,19 +111,21 @@ def get_event_stats(task_name, task_data):
         if event[0] == EventCode.LIST_ITEM:
             cnt_list += sum(vals)
 
-    results['3_NORM_ATTACK'] = cnt_attack / life_span
-    results['3_NORM_BUY'] = cnt_buy / life_span
-    results['3_NORM_CONSUME'] = cnt_consume / life_span
-    results['3_NORM_EQUIP'] = cnt_equip / life_span
-    results['3_NORM_HARVEST'] = cnt_harvest / life_span
-    results['3_NORM_LIST'] = cnt_list / life_span
+    results['4_NORM_ATTACK'] = cnt_attack / life_span
+    results['4_NORM_BUY'] = cnt_buy / life_span
+    results['4_NORM_CONSUME'] = cnt_consume / life_span
+    results['4_NORM_EQUIP'] = cnt_equip / life_span
+    results['4_NORM_HARVEST'] = cnt_harvest / life_span
+    results['4_NORM_LIST'] = cnt_list / life_span
 
     return results
 
 if __name__ == '__main__':
-    parser = argparse.ArgumentParser(description='Process replay data')
-    parser.add_argument('policy_store_dir', type=str, help='Path to the policy directory')
-    args = parser.parse_args()
+    # parser = argparse.ArgumentParser(description='Process replay data')
+    # parser.add_argument('policy_store_dir', type=str, help='Path to the policy directory')
+    # args = parser.parse_args()
+
+    args = argparse.Namespace(policy_store_dir='pol_task_cond2')
 
     # Gather the event data by tasks, across multiple replays
     data_by_task = gather_agent_events_by_task(args.policy_store_dir)