had to designate a maliable target

global_func/stratification_function.py

@@ -4,6 +4,7 @@ import numpy as np
 def stratification_function(portfolio: pd.DataFrame, lineup_target: int, exclude_cols: list, sport: str, sorting_choice: str, low_threshold: float, high_threshold: float):
     excluded_cols = ['salary', 'median', 'Own', 'Finish_percentile', 'Dupes', 'Stack', 'Size', 'Win%', 'Lineup Edge', 'Weighted Own', 'Geomean', 'Diversity', 'SE Score']
     player_columns = [col for col in portfolio.columns if col not in excluded_cols]
+    base_target = lineup_target
     
     # Work with indices instead of copying entire DataFrame
     if sorting_choice == 'Finish_percentile':
@@ -16,7 +17,7 @@ def stratification_function(portfolio: pd.DataFrame, lineup_target: int, exclude
     similarity_ceiling = portfolio[sorting_choice].quantile(high_threshold / 100)
     
     for range_var in range(1, 10):
-        target_similarities = np.linspace(similarity_floor, similarity_ceiling, lineup_target)
+        target_similarities = np.linspace(similarity_floor, similarity_ceiling, base_target)
         
         # Find the closest lineup to each target similarity score
         selected_indices = []
@@ -34,7 +35,7 @@ def stratification_function(portfolio: pd.DataFrame, lineup_target: int, exclude
             print(len(selected_indices))
             range_var = 10
         else:
-            lineup_target += 5 * range_var
+            base_target += 5 * range_var
     
     # Return view instead of copy
     return portfolio.loc[selected_indices].sort_values(by=sorting_choice, ascending=False)