Refactor change_mask logic in reassess_edge.py to filter based solely on median differences, improving accuracy in edge assessments for player lineups.

global_func/reassess_edge.py

@@ -75,7 +75,7 @@ def reassess_edge(refactored_frame: pd.DataFrame, original_frame: pd.DataFrame,
     refactored_df['median_diff'] = refactored_df['median'] - orig_df['median']
     refactored_df['own_diff'] = refactored_df['Own'] - orig_df['Own']
 
-    change_mask = refactored_df[refactored_df['salary_diff'] != 0 | refactored_df['median_diff'] != 0 | refactored_df['own_diff'] != 0]
+    change_mask = refactored_df[refactored_df['median_diff'] != 0]
 
     num_players = len([col for col in refactored_df.columns if col not in ['salary', 'median', 'Own', 'Finish_percentile', 'Win%', 'Edge', 'Weighted Own', 'Geomean', 'salary_diff', 'median_diff', 'own_diff']])
     own_columns = [f'player_{i}_own' for i in range(1, num_players + 1)]