addressing a reoptimization issue

app.py

@@ -7,6 +7,7 @@ from collections import Counter
 import io
 
 ## import global functions
+## Need to make sure that I take all functions from here and give them a home
 from global_func.clean_player_name import clean_player_name
 from global_func.load_file import load_file
 from global_func.load_ss_file import load_ss_file

global_func/optimize_lineup.py

@@ -13,6 +13,22 @@ def get_effective_salary(player_name: str, column_name: str, map_dict: dict, typ
     return base_salary
 
 
+def calculate_lineup_objective(
+    row: pd.Series,
+    player_columns: list,
+    player_pool: pd.DataFrame,
+    optimize_by: str = 'median'
+) -> float:
+    """Calculate the total objective value of a lineup."""
+    total = 0.0
+    for col in player_columns:
+        player_name = row[col]
+        player_data = player_pool[player_pool['player_names'] == player_name]
+        if not player_data.empty:
+            total += player_data.iloc[0].get(optimize_by, player_data.iloc[0].get('median', 0))
+    return total
+
+
 def optimize_single_lineup(
     row: pd.Series,
     player_columns: list,
@@ -46,6 +62,9 @@ def optimize_single_lineup(
     Returns:
         Tuple of (optimized_row, achieved_objective_value)
     """
+    # Calculate the original lineup's objective value BEFORE any changes
+    original_objective = calculate_lineup_objective(row, player_columns, player_pool, optimize_by)
+    
     # Create a copy of the row to modify
     optimized_row = row.copy()
     
@@ -74,9 +93,9 @@ def optimize_single_lineup(
             # This position is open for optimization
             open_columns.append(col)
     
-    # If no open columns, return with locked objective value
+    # If no open columns, return with original objective value (keep original intact)
     if not open_columns:
-        return optimized_row, locked_objective_value
+        return row.copy(), original_objective
     
     # Calculate remaining salary budget
     remaining_salary = salary_max - locked_salary
@@ -85,9 +104,9 @@ def optimize_single_lineup(
     remaining_objective_budget = None
     if max_objective_value is not None:
         remaining_objective_budget = max_objective_value - locked_objective_value
-        # If we can't meet the constraint, return original row
+        # If we can't meet the constraint, return original row unchanged
         if remaining_objective_budget < 0:
-            return optimized_row, locked_objective_value
+            return row.copy(), original_objective
     
     # Filter player pool: exclude locked teams and already-locked players
     available_players = player_pool[
@@ -99,13 +118,14 @@ def optimize_single_lineup(
     available_players = available_players.drop_duplicates(subset=['player_names'], keep='first')
     
     if available_players.empty:
-        return optimized_row, locked_objective_value
+        # No available players - keep original lineup
+        return row.copy(), original_objective
     
     # Build the optimization model
     solver = pywraplp.Solver.CreateSolver('CBC')
     if not solver:
-        # Fallback if solver not available
-        return optimized_row, locked_objective_value
+        # Fallback if solver not available - keep original lineup
+        return row.copy(), original_objective
     
     # Create decision variables: x[player_idx, col_idx] = 1 if player is assigned to column
     player_list = available_players.to_dict('records')
@@ -179,6 +199,12 @@ def optimize_single_lineup(
                     achieved_objective += player.get(optimize_by, player.get('median', 0))
                     break
     
+    # CRITICAL: Only return optimized lineup if it's actually better than original
+    # If optimization resulted in a worse lineup, keep the original
+    if achieved_objective < original_objective:
+        # Optimization made things worse - keep the original lineup
+        return row.copy(), original_objective
+    
     return optimized_row, achieved_objective