Convert Score ['High', 'Medium', 'Low'] to [3, 2, 1]

Author: chris0765

collector/spot-dataset/azure/lambda/current_collector/load_sps.py

@@ -213,9 +213,9 @@ def execute_spot_placement_score_task_by_parameter_pool_df(api_calls_df, desired
                                     score.get("sku", ""), {}).get("InstanceTier"),
                                 "InstanceType": SS_Resources.region_map_and_instance_map_tmp['instance_map'].get(
                                     score.get("sku", ""), {}).get("InstanceTypeOld"),
-                                "Score": score.get("score"),
-                                "T3": desired_count if score.get("score") == 3 else 0,
-                                "T2": desired_count if score.get("score") == 2 else 0
+                                "Score": map_score_to_int(score.get("score")),
+                                "T3": desired_count if map_score_to_int(score.get("score")) == 3 else 0,
+                                "T2": desired_count if map_score_to_int(score.get("score")) >= 2 else 0
                             }
                             if availability_zones is True:
                                 score_data["AvailabilityZone"] = score.get("availabilityZone", "Single")
@@ -259,6 +259,21 @@ def execute_spot_placement_score_task_by_parameter_pool_df(api_calls_df, desired
     return sps_res_df
 
 
+    return sps_res_df
+
+
+def map_score_to_int(score_val):
+    if isinstance(score_val, int):
+        return score_val
+    
+    score_map = {
+        "High": 3,
+        "Medium": 2,
+        "Low": 1
+    }
+    return score_map.get(score_val, 0)
+
+
 def execute_spot_placement_score_api(region_chunk, instance_type_chunk, desired_count, max_retries=12):
     '''
     실제 SPS API호출 메서드.

collector/spot-dataset/azure/lambda/current_collector/utils/compare_data.py

@@ -61,12 +61,6 @@ def compare_max_instance(previous_df, new_df, target_capacity):
         if col not in previous_df.columns:
             previous_df[col] = 0
 
-    print(f"[DEBUG] compare_max_instance - target_capacity: {target_capacity}")
-    print(f"[DEBUG] new_df columns: {new_df.columns}")
-    print(f"[DEBUG] new_df Score sample: {new_df['Score'].head(5).tolist()}")
-    print(f"[DEBUG] previous_df columns: {previous_df.columns}")
-    print(f"[DEBUG] previous_df Score sample: {previous_df['Score'].head(5).tolist() if 'Score' in previous_df else 'No Score col'}")
-
     merged_df = pd.merge(
         new_df,
         previous_df[["InstanceType", "Region", "AvailabilityZone", "DesiredCount", "Score", "T3", "T2"]],
@@ -120,12 +114,7 @@ def compare_max_instance(previous_df, new_df, target_capacity):
         # AWS comment: "Fix SPS to Single node SPS" - this logic seems specific to assuming single node fallback.
         # But let's copy logic:
         # FIXED: Do not blindly overwrite if Score_prev is NaN (e.g. first run). Use combine_first or fillna.
-        print(f"[DEBUG] Before assignment - Score sample: {merged_df['Score'].head(5).tolist()}")
-        print(f"[DEBUG] Before assignment - Score_prev sample: {merged_df['Score_prev'].head(5).tolist()}")
-        
         merged_df["Score"] = merged_df["Score_prev"].fillna(merged_df["Score"])
-        
-        print(f"[DEBUG] After assignment - Score sample: {merged_df['Score'].head(5).tolist()}")
 
     # Convert to standard types to prevent '3' vs '3.0' change detection mismatch
     for col in ["T2", "T3"]: