Refactor portfolio distribution logic in distribute_preset.py to group by 'Stack' and update sorting criteria to 'median'. This change improves the accuracy of portfolio filtering by ensuring the correct metrics are used for both selection and final output.

global_func/distribute_preset.py

@@ -5,35 +5,23 @@ def distribute_preset(portfolio: pd.DataFrame, lineup_target: int, exclude_cols:
     for slack_var in range(1, 20):
         concat_portfolio = pd.DataFrame(columns=portfolio.columns)
 
-        for finishing_range in range(1, 20):
-            finish_threshold = (finishing_range / 100)
-            lower_threshold = ((finishing_range - 1) / 100)
+        for team in portfolio['Stack'].unique():
             rows_to_drop = []
             working_portfolio = portfolio.copy()
-            if finishing_range == 1:
-                working_portfolio = working_portfolio[working_portfolio['Finish_percentile'] <= finish_threshold]
-            elif finishing_range == 20:
-                working_portfolio = working_portfolio[working_portfolio['Finish_percentile'] >= lower_threshold]
-            else:
-                working_portfolio = working_portfolio[working_portfolio['Finish_percentile'] <= finish_threshold]
-                working_portfolio = working_portfolio[working_portfolio['Finish_percentile'] >= lower_threshold]
-            working_portfolio = working_portfolio.sort_values(by='Finish_percentile', ascending = False)
+            working_portfolio = working_portfolio[working_portfolio['Stack'] == team].sort_values(by='Similarity Score', ascending = True)
             working_portfolio = working_portfolio.reset_index(drop=True)
-            if len(working_portfolio) == 0:
-                continue
-            elif len(working_portfolio) >= 1:
-                curr_own_type_max = working_portfolio.loc[0, 'Similarity Score'] + (slack_var / 20 * working_portfolio.loc[0, 'Similarity Score'])
+            curr_own_type_max = working_portfolio.loc[0, 'Similarity Score'] + (slack_var / 20 * working_portfolio.loc[0, 'Similarity Score'])
 
-                for i in range(1, len(working_portfolio)):
-                    if working_portfolio.loc[i, 'Similarity Score'] < curr_own_type_max:
-                        rows_to_drop.append(i)
-                    else:
-                        curr_own_type_max = working_portfolio.loc[i, 'Similarity Score'] + (slack_var / 20 * working_portfolio.loc[i, 'Similarity Score'])
-                
-                working_portfolio = working_portfolio.drop(rows_to_drop).reset_index(drop=True)
-                concat_portfolio = pd.concat([concat_portfolio, working_portfolio.head(10)])
+            for i in range(1, len(working_portfolio)):
+                if working_portfolio.loc[i, 'Similarity Score'] > curr_own_type_max:
+                    rows_to_drop.append(i)
+                else:
+                    curr_own_type_max = working_portfolio.loc[i, 'Similarity Score'] + (slack_var / 20 * working_portfolio.loc[i, 'Similarity Score'])
+            
+            working_portfolio = working_portfolio.drop(rows_to_drop).reset_index(drop=True)
+            concat_portfolio = pd.concat([concat_portfolio, working_portfolio])
 
         if len(concat_portfolio) >= lineup_target:
-            return concat_portfolio.sort_values(by='Finish_percentile', ascending=True).head(lineup_target)
+            return concat_portfolio.sort_values(by='median', ascending=True).head(lineup_target)
         
-    return concat_portfolio.sort_values(by='Finish_percentile', ascending=True)
+    return concat_portfolio.sort_values(by='median', ascending=True)