kermitting soon

util/app.py

@@ -134,10 +134,10 @@ def predict_champion(player_opgg_url, *champions):
 
         training_df = convert_df(training_df)
         #print("type(training_df): ", type(training_df), "\n")
-        print("check_datatypes(training_df) BEFORE feature eng. :", check_datatypes(training_df), "\n")
+        print("check_datatypes(training_df) BEFORE feature eng. :\n", check_datatypes(training_df), "\n")
 
         training_df = apply_feature_engineering(training_df)
-        print("check_datatypes(training_df) :", check_datatypes(training_df), "\n")
+        print("check_datatypes(training_df) AFTER feature eng. \n:", check_datatypes(training_df), "\n")
         
         # Get feature columns
         feature_columns = [col for col in training_df.columns 

util/app_training_df_getter.py

@@ -378,7 +378,7 @@ def create_app_user_training_df(url):
         if recent_stats is None or recent_stats.empty:
             raise ValueError("recent_stats is empty. type(recent_stats): ", type(recent_stats) , "  recent_stats: \n", recent_stats)
         
-        print("Recent matches columns:", recent_stats.columns.tolist())
+        #print("Recent matches columns:", recent_stats.columns.tolist())
         
         # Check if player_id exists before trying to process it
         #if 'player_id' not in recent_stats.columns:
@@ -386,7 +386,7 @@ def create_app_user_training_df(url):
         
         # Process player_id
         recent_stats['player_id'] = recent_stats['player_id'].str.replace(" #", "-", regex=False)
-        print("Processed player_ids:", recent_stats['player_id'].head())
+        #print("Processed player_ids:", recent_stats['player_id'].head())
 
         # Get player stats
         print("Fetching player stats...")
@@ -406,7 +406,7 @@ def create_app_user_training_df(url):
         if merged_stats is None or merged_stats.empty:
             raise ValueError("Failed to merge stats")
         
-        print("Merged stats columns:", merged_stats.columns.tolist())
+        #print("Merged stats columns:", merged_stats.columns.tolist())
 
         # Create features
         print("Creating champion features...")

util/helper.py

@@ -902,7 +902,7 @@ def calculate_role_specialization(df):
 
 def calculate_champion_loyalty(df):
     df = df.copy()
-    print("df.dtypes: \n", df.dtypes, "\n")
+    #print("df.dtypes: \n", df.dtypes, "\n")
 
     
     def get_loyalty_scores(row):
@@ -986,21 +986,21 @@ def calculate_champion_loyalty(df):
                     
                     loyalty_score += combined_weight 
             
-            print("Start calculate confidence score...\n")
+            #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
             confidence_score += 0.2 if pd.notna(row['most_champ_2']) else 0  # Increased weight for second
             confidence_score += sum(0.1 for i in range(1, 4) if pd.notna(row[f'season_champ_{i}']))
             confidence_score += sum(0.05 for i in range(4, 8) if pd.notna(row[f'season_champ_{i}']))
-            print("...END calculate confidence score\n")
+            #print("...END calculate confidence score\n")
 
             recent_games = sum((row[f'W_{i}'] + row[f'L_{i}']) if pd.notna(row[f'most_champ_{i}']) else 0 
                              for i in range(1, 3))  # Only top 2
             confidence_score += min(0.1, recent_games / 100)
             
-            print(f"loyalty_score, confidence score: [{loyalty_score}], [{confidence_score}]  \n")
-            print("===================== exiting: get_loyalty_scores()===================")
+            # print(f"loyalty_score, confidence score: [{loyalty_score}], [{confidence_score}]  \n")
+            # print("===================== exiting: get_loyalty_scores()===================")
             return {
                 'loyalty_score': round(min(loyalty_score, 1.0), 3),
                 'confidence_score': round(min(confidence_score, 1.0), 3),