Add FLEX5 ownership calculations in predict_dupes.py to enhance ownership metrics and improve portfolio analysis.

global_func/predict_dupes.py

@@ -130,7 +130,8 @@ def predict_dupes(portfolio, maps_dict, site_var, type_var, Contest_Size, streng
                     portfolio.iloc[:,1].map(maps_dict['own_map']),
                     portfolio.iloc[:,2].map(maps_dict['own_map']),
                     portfolio.iloc[:,3].map(maps_dict['own_map']),
-                    portfolio.iloc[:,4].map(maps_dict['own_map'])
+                    portfolio.iloc[:,4].map(maps_dict['own_map']),
+                    portfolio.iloc[:,5].map(maps_dict['own_map'])
                 ])
             flex_rank = flex_ownerships.rank(pct=True)
             
@@ -140,12 +141,14 @@ def predict_dupes(portfolio, maps_dict, site_var, type_var, Contest_Size, streng
             portfolio['FLEX2_Own_percent_rank'] = portfolio.iloc[:,2].map(maps_dict['own_map']).map(lambda x: flex_rank[flex_ownerships == x].iloc[0])
             portfolio['FLEX3_Own_percent_rank'] = portfolio.iloc[:,3].map(maps_dict['own_map']).map(lambda x: flex_rank[flex_ownerships == x].iloc[0])
             portfolio['FLEX4_Own_percent_rank'] = portfolio.iloc[:,4].map(maps_dict['own_map']).map(lambda x: flex_rank[flex_ownerships == x].iloc[0])
+            portfolio['FLEX5_Own_percent_rank'] = portfolio.iloc[:,5].map(maps_dict['own_map']).map(lambda x: flex_rank[flex_ownerships == x].iloc[0])
 
             portfolio['CPT_Own'] = portfolio.iloc[:,0].map(maps_dict['cpt_own_map']).astype('float32') / 100
             portfolio['FLEX1_Own'] = portfolio.iloc[:,1].map(maps_dict['own_map']).astype('float32') / 100
             portfolio['FLEX2_Own'] = portfolio.iloc[:,2].map(maps_dict['own_map']).astype('float32') / 100
             portfolio['FLEX3_Own'] = portfolio.iloc[:,3].map(maps_dict['own_map']).astype('float32') / 100
             portfolio['FLEX4_Own'] = portfolio.iloc[:,4].map(maps_dict['own_map']).astype('float32') / 100
+            portfolio['FLEX5_Own'] = portfolio.iloc[:,5].map(maps_dict['own_map']).astype('float32') / 100
             
             portfolio['own_product'] = (portfolio[own_columns].product(axis=1)) * max(Contest_Size / 10000, 1)
             portfolio['own_average'] = (portfolio['Own'].max() * .33) / 100