redistributed bonus

app.py

@@ -491,126 +491,11 @@ def redistribute_lost_points(match_id):
     updated_dataset.push_to_hub("Jay-Rajput/DIS_IPL_Leads", split="train")
 
 
-# def update_leaderboard_and_outcomes(match_id, winning_team, man_of_the_match, outcome_only=False):
-#     outcomes = load_dataset("Jay-Rajput/DIS_IPL_Outcomes", split="train")
-#     outcomes_df = pd.DataFrame(outcomes)
-
-#     # Update or add match outcome
-#     outcome_exists = False
-#     for idx, outcome in outcomes_df.iterrows():
-#         if outcome['match_id'] == match_id:
-#             outcomes_df.at[idx, 'winning_team'] = winning_team
-#             outcomes_df.at[idx, 'man_of_the_match'] = man_of_the_match
-#             outcome_exists = True
-#             break
-#     if not outcome_exists:
-#         new_outcome = {"match_id": match_id, "winning_team": winning_team, "man_of_the_match": man_of_the_match}
-#         outcomes_df = pd.concat([outcomes_df, pd.DataFrame([new_outcome])], ignore_index=True)
-#         outcomes = Dataset.from_pandas(outcomes_df)
-
-#     if not outcome_only:
-#         predictions = fetch_latest_predictions(match_id)
-#         users = load_dataset("Jay-Rajput/DIS_IPL_Leads", split="train")
-#         users_df = pd.DataFrame(users)
-
-#         # Capture previous leaderboard (top 5 users and their points)
-#         prev_scores = [(user, users_df[user][0]['points']) for user in users_df.columns]
-#         prev_scores.sort(key=lambda x: x[1], reverse=True)
-#         prev_top_5 = prev_scores[:5]
-#         top5_usernames = [user for user, _ in prev_top_5]
-
-#         lost_points_by_top5 = 0
-#         user_outcomes = {}
-
-#         # Step 1: Apply current match outcomes
-#         for user_name in users_df.columns:
-#             user_data = users_df[user_name][0]
-#             user_points = user_data['points']
-#             user_initial_points = user_points
-
-#             if user_name in set(predictions['user_name']):
-#                 prediction = predictions[predictions['user_name'] == user_name].iloc[0]
-#                 predicted_winner = prediction['predicted_winner']
-#                 predicted_motm = prediction['predicted_motm']
-#                 bid_points = prediction['bid_points']
-
-#                 if predicted_winner == winning_team:
-#                     user_points += 2000 + bid_points
-#                     result_indicator = "🟢"
-#                     if predicted_motm == man_of_the_match:
-#                         user_points += 500
-#                 else:
-#                     user_points -= 200 + bid_points
-#                     result_indicator = "🔴"
-#                     if user_name in top5_usernames:
-#                         lost_points_by_top5 += (200 + bid_points)
-#             else:
-#                 user_points -= 200
-#                 result_indicator = "⚪"
-#                 if user_name in top5_usernames:
-#                     lost_points_by_top5 += 200
-
-#             user_points = max(user_points, 0)
-#             user_outcomes[user_name] = {
-#                 "updated_points": user_points,
-#                 "result_indicator": result_indicator,
-#                 "initial_points": user_initial_points
-#             }
-
-#         # Step 2: Build new leaderboard after applying outcome
-#         new_leaderboard = [(u, d["updated_points"]) for u, d in user_outcomes.items()]
-#         new_leaderboard.sort(key=lambda x: x[1], reverse=True)
-        
-#         # Step 3: Redistribute lost points with reverse leaderboard weighting
-#         bonus_distribution = {}
-
-#         remaining_users = [u for u in user_outcomes if u not in top5_usernames]
-#         if remaining_users and lost_points_by_top5 > 0:
-#             sorted_remaining = sorted(remaining_users, key=lambda u: user_outcomes[u]['updated_points'])
-#             weights = {u: 1 / (i + 1) for i, u in enumerate(sorted_remaining)}
-#             total_weight = sum(weights.values())
-
-#             cumulative_bonus = 0
-#             for i, user in enumerate(sorted_remaining):
-#                 if i == len(sorted_remaining) - 1:
-#                     bonus = lost_points_by_top5 - cumulative_bonus
-#                 else:
-#                     share_fraction = weights[user] / total_weight
-#                     bonus = int(lost_points_by_top5 * share_fraction)
-#                     cumulative_bonus += bonus
-#                 bonus_distribution[user] = bonus
-
-#         # Step 4: Apply the appropriate update
-#         for user in users_df.columns:
-#             bonus = bonus_distribution.get(user, 0)
-#             final_points = user_outcomes[user]["updated_points"] + bonus
-
-#             users_df[user][0]['points'] = final_points
-#             users_df[user][0]['redistributed_bonus'] = bonus
-
-#             result = user_outcomes[user]["result_indicator"]
-#             if "last_5_results" not in users_df[user][0]:
-#                 users_df[user][0]["last_5_results"] = []
-#             users_df[user][0]["last_5_results"].insert(0, result)
-#             users_df[user][0]["last_5_results"] = users_df[user][0]["last_5_results"][:5]
-
-#         # Save updated leaderboard
-#         users.to_json(USERS_JSON)
-#         updated_dataset = Dataset.from_pandas(users_df)
-#         updated_dataset.push_to_hub("Jay-Rajput/DIS_IPL_Leads", split="train")
-
-#     # Save match outcome
-#     outcomes.to_json(OUTCOMES)
-#     outcomes.push_to_hub("Jay-Rajput/DIS_IPL_Outcomes", split="train")
-
-
 def update_leaderboard_and_outcomes(match_id, winning_team, man_of_the_match, outcome_only=False):
-
-    # Load existing match outcomes
-    outcomes = load_dataset("Jay-Rajput/DIS_IPL_Outcomes", split="train")  
+    outcomes = load_dataset("Jay-Rajput/DIS_IPL_Outcomes", split="train")
     outcomes_df = pd.DataFrame(outcomes)
 
-    # Directly update or add the match outcome
+    # Update or add match outcome
     outcome_exists = False
     for idx, outcome in outcomes_df.iterrows():
         if outcome['match_id'] == match_id:
@@ -623,64 +508,182 @@ def update_leaderboard_and_outcomes(match_id, winning_team, man_of_the_match, ou
         outcomes_df = pd.concat([outcomes_df, pd.DataFrame([new_outcome])], ignore_index=True)
         outcomes = Dataset.from_pandas(outcomes_df)
 
-    if not outcome_only:  # Update user scores only if outcome_only is False
-        # Load predictions only if necessary
+    if not outcome_only:
         predictions = fetch_latest_predictions(match_id)
-
-        # Load users' data only if necessary
         users = load_dataset("Jay-Rajput/DIS_IPL_Leads", split="train")
         users_df = pd.DataFrame(users)
 
-        # Update user points based on prediction accuracy
-        users_with_predictions = set(predictions['user_name'])
+        # Capture previous leaderboard (top 3 users and their points)
+        prev_scores = [(user, users_df[user][0]['points']) for user in users_df.columns]
+        prev_scores.sort(key=lambda x: x[1], reverse=True)
+        prev_top_3 = prev_scores[:3]
+        top3_usernames = [user for user, _ in prev_top_3]
+
+        lost_points_by_top3 = 0
+        user_outcomes = {}
+
+        # Step 1: Apply current match outcomes
         for user_name in users_df.columns:
-            user_points = users_df[user_name][0]['points']
-            if user_name in users_with_predictions:
+            user_data = users_df[user_name][0]
+            user_points = user_data['points']
+            user_initial_points = user_points
+
+            if user_name in set(predictions['user_name']):
                 prediction = predictions[predictions['user_name'] == user_name].iloc[0]
                 predicted_winner = prediction['predicted_winner']
                 predicted_motm = prediction['predicted_motm']
                 bid_points = prediction['bid_points']
 
-                # Update points based on prediction accuracy
                 if predicted_winner == winning_team:
                     user_points += 2000 + bid_points
-                    result_indicator = "🟢"  # Correct Prediction
+                    result_indicator = "🟢"
                     if predicted_motm == man_of_the_match:
                         user_points += 500
                 else:
                     user_points -= 200 + bid_points
-                    result_indicator = "🔴"  # Wrong Prediction
+                    result_indicator = "🔴"
+                    if user_name in top3_usernames:
+                        lost_points_by_top3 += (200 + bid_points)
             else:
-                # Deduct 200 points for not submitting a prediction
                 user_points -= 200
-                result_indicator = "⚪"  # No Prediction
-                
-            # Ensure user_points is never negative
-            user_points = max(user_points, 0)
-
-            # Update user's points in the DataFrame
-            users_df[user_name][0]['points'] = user_points
-            users[user_name][0]['points'] = user_points
-            
-            # Maintain last 5 prediction results
-            if "last_5_results" not in users_df[user_name][0]:
-                users_df[user_name][0]["last_5_results"] = []
-            users_df[user_name][0]["last_5_results"].insert(0, result_indicator)  # Insert at beginning
-            users_df[user_name][0]["last_5_results"] = users_df[user_name][0]["last_5_results"][:5]  # Keep only last 5
-            
-            if "last_5_results" not in users[user_name][0]:
-                users[user_name][0]["last_5_results"] = []
-            users[user_name][0]["last_5_results"].insert(0, result_indicator)  # Insert at beginning
-            users[user_name][0]["last_5_results"] = users[user_name][0]["last_5_results"][:5]  # Keep only last 5
+                result_indicator = "⚪"
+                if user_name in top3_usernames:
+                    lost_points_by_top3 += 200
 
+            user_points = max(user_points, 0)
+            user_outcomes[user_name] = {
+                "updated_points": user_points,
+                "result_indicator": result_indicator,
+                "initial_points": user_initial_points
+            }
+
+        # Step 2: Build new leaderboard after applying outcome
+        new_leaderboard = [(u, d["updated_points"]) for u, d in user_outcomes.items()]
+        new_leaderboard.sort(key=lambda x: x[1], reverse=True)
+        third_place_points = new_leaderboard[2][1] if len(new_leaderboard) >= 3 else 0
+
+        # Step 3: Redistribute lost points using difference-from-3rd-place logic
+        redistribution_pool = lost_points_by_top3
+        redistribution_weights = {}
+        redistribution_total_weight = 0
+
+        for user, data in user_outcomes.items():
+            if user not in top3_usernames:
+                diff_from_3rd = max(third_place_points - data['updated_points'], 0)
+                redistribution_weights[user] = diff_from_3rd
+                redistribution_total_weight += diff_from_3rd
+
+        bonus_distribution = {}
+        for user, weight in redistribution_weights.items():
+            if redistribution_total_weight == 0:
+                bonus = 0
+            else:
+                bonus = int(redistribution_pool * (weight / redistribution_total_weight))
+            bonus_distribution[user] = bonus
+
+        # Step 4: Apply bonus and update dataset
+        for user in users_df.columns:
+            base_points = user_outcomes[user]["updated_points"]
+            bonus = bonus_distribution.get(user, 0)
+            final_points = base_points + bonus
+
+            users_df[user][0]['points'] = final_points
+            users_df[user][0]['redistributed_bonus'] = bonus
+
+            # Maintain last 5 results
+            result = user_outcomes[user]["result_indicator"]
+            if "last_5_results" not in users_df[user][0]:
+                users_df[user][0]["last_5_results"] = []
+            users_df[user][0]["last_5_results"].insert(0, result)
+            users_df[user][0]["last_5_results"] = users_df[user][0]["last_5_results"][:5]
+
+        # Save updated leaderboard
         users.to_json(USERS_JSON)
         updated_dataset = Dataset.from_pandas(users_df)
         updated_dataset.push_to_hub("Jay-Rajput/DIS_IPL_Leads", split="train")
 
+    # Save match outcome
     outcomes.to_json(OUTCOMES)
     outcomes.push_to_hub("Jay-Rajput/DIS_IPL_Outcomes", split="train")
 
 
+# def update_leaderboard_and_outcomes(match_id, winning_team, man_of_the_match, outcome_only=False):
+
+#     # Load existing match outcomes
+#     outcomes = load_dataset("Jay-Rajput/DIS_IPL_Outcomes", split="train")  
+#     outcomes_df = pd.DataFrame(outcomes)
+
+#     # Directly update or add the match outcome
+#     outcome_exists = False
+#     for idx, outcome in outcomes_df.iterrows():
+#         if outcome['match_id'] == match_id:
+#             outcomes_df.at[idx, 'winning_team'] = winning_team
+#             outcomes_df.at[idx, 'man_of_the_match'] = man_of_the_match
+#             outcome_exists = True
+#             break
+#     if not outcome_exists:
+#         new_outcome = {"match_id": match_id, "winning_team": winning_team, "man_of_the_match": man_of_the_match}
+#         outcomes_df = pd.concat([outcomes_df, pd.DataFrame([new_outcome])], ignore_index=True)
+#         outcomes = Dataset.from_pandas(outcomes_df)
+
+#     if not outcome_only:  # Update user scores only if outcome_only is False
+#         # Load predictions only if necessary
+#         predictions = fetch_latest_predictions(match_id)
+
+#         # Load users' data only if necessary
+#         users = load_dataset("Jay-Rajput/DIS_IPL_Leads", split="train")
+#         users_df = pd.DataFrame(users)
+
+#         # Update user points based on prediction accuracy
+#         users_with_predictions = set(predictions['user_name'])
+#         for user_name in users_df.columns:
+#             user_points = users_df[user_name][0]['points']
+#             if user_name in users_with_predictions:
+#                 prediction = predictions[predictions['user_name'] == user_name].iloc[0]
+#                 predicted_winner = prediction['predicted_winner']
+#                 predicted_motm = prediction['predicted_motm']
+#                 bid_points = prediction['bid_points']
+
+#                 # Update points based on prediction accuracy
+#                 if predicted_winner == winning_team:
+#                     user_points += 2000 + bid_points
+#                     result_indicator = "🟢"  # Correct Prediction
+#                     if predicted_motm == man_of_the_match:
+#                         user_points += 500
+#                 else:
+#                     user_points -= 200 + bid_points
+#                     result_indicator = "🔴"  # Wrong Prediction
+#             else:
+#                 # Deduct 200 points for not submitting a prediction
+#                 user_points -= 200
+#                 result_indicator = "⚪"  # No Prediction
+                
+#             # Ensure user_points is never negative
+#             user_points = max(user_points, 0)
+
+#             # Update user's points in the DataFrame
+#             users_df[user_name][0]['points'] = user_points
+#             users[user_name][0]['points'] = user_points
+            
+#             # Maintain last 5 prediction results
+#             if "last_5_results" not in users_df[user_name][0]:
+#                 users_df[user_name][0]["last_5_results"] = []
+#             users_df[user_name][0]["last_5_results"].insert(0, result_indicator)  # Insert at beginning
+#             users_df[user_name][0]["last_5_results"] = users_df[user_name][0]["last_5_results"][:5]  # Keep only last 5
+            
+#             if "last_5_results" not in users[user_name][0]:
+#                 users[user_name][0]["last_5_results"] = []
+#             users[user_name][0]["last_5_results"].insert(0, result_indicator)  # Insert at beginning
+#             users[user_name][0]["last_5_results"] = users[user_name][0]["last_5_results"][:5]  # Keep only last 5
+
+#         users.to_json(USERS_JSON)
+#         updated_dataset = Dataset.from_pandas(users_df)
+#         updated_dataset.push_to_hub("Jay-Rajput/DIS_IPL_Leads", split="train")
+
+#     outcomes.to_json(OUTCOMES)
+#     outcomes.push_to_hub("Jay-Rajput/DIS_IPL_Outcomes", split="train")
+
+
 # Function to fetch matches for a given date
 def fetch_matches_by_date(matches, selected_date):
     return [match for match in matches if datetime.strptime(match['date'], '%Y-%m-%d').date() == selected_date]