kermitting soon

util/helper.py

@@ -903,16 +903,16 @@ def calculate_role_specialization(df):
 def calculate_champion_loyalty(df):
     print("========================== Inside: calculate_champion_loyalty ====================\n")
     df = df.copy()
-    print("df.dtypes: ", df.dtypes, "\n")
+    print("df.dtypes: \n", df.dtypes, "\n")
 
     
     def get_loyalty_scores(row):
         try:
             # Convert potentially non-numeric values to numbers !!!!!!!!!!!!!! chatGPT EDITED 
-            row['W_1'] = pd.to_numeric(row['W_1'], errors='coerce') if 'W_1' in row else 0
-            row['L_1'] = pd.to_numeric(row['L_1'], errors='coerce') if 'L_1' in row else 0
-            row['W_2'] = pd.to_numeric(row['W_2'], errors='coerce') if 'W_2' in row else 0
-            row['L_2'] = pd.to_numeric(row['L_2'], errors='coerce') if 'L_2' in row else 0
+            # row['W_1'] = pd.to_numeric(row['W_1'], errors='coerce') if 'W_1' in row else 0
+            # row['L_1'] = pd.to_numeric(row['L_1'], errors='coerce') if 'L_1' in row else 0
+            # row['W_2'] = pd.to_numeric(row['W_2'], errors='coerce') if 'W_2' in row else 0
+            # row['L_2'] = pd.to_numeric(row['L_2'], errors='coerce') if 'L_2' in row else 0
 
             # Get champions lists, handle potential NaN/None values (only top 2)
             recent_champs = [
@@ -950,6 +950,7 @@ def calculate_champion_loyalty(df):
                 }
             
             # Calculate games played for recent champions (only top 2)
+            print("Start calculate games played for recent champions (only top 2)...\n")
             recent_games = [
                 (row['W_1'] + row['L_1']) if pd.notna(row['most_champ_1']) else 0,
                 (row['W_2'] + row['L_2']) if pd.notna(row['most_champ_2']) else 0
@@ -983,6 +984,7 @@ def calculate_champion_loyalty(df):
                     
                     loyalty_score += combined_weight
             
+            print("Start calculate confidence score...\n")
             # Calculate confidence score (adjusted for 2 champions)
             confidence_score = 0
             confidence_score += 0.5 if pd.notna(row['most_champ_1']) else 0  # Increased weight for main