Adjust ownership average calculation in predict_dupes function to a new ratio of 0.33, and add a debug print statement to display key portfolio metrics, enhancing data verification and accuracy in ownership assessments.

global_func/predict_dupes.py

@@ -471,7 +471,7 @@ def predict_dupes(portfolio, maps_dict, site_var, type_var, Contest_Size, streng
                 portfolio['FLEX5_Own'] = portfolio.iloc[:,5].map(maps_dict['own_map']).astype('float32') / 100
 
                 portfolio['own_product'] = (portfolio[own_columns].product(axis=1))
-                portfolio['own_average'] = (portfolio['Own'].max() * .66) / 100
+                portfolio['own_average'] = (portfolio['Own'].max() * .33) / 100
                 portfolio['own_sum'] = portfolio[own_columns].sum(axis=1)
                 portfolio['avg_own_rank'] = portfolio[dup_count_columns].mean(axis=1)
                 
@@ -520,9 +520,11 @@ def predict_dupes(portfolio, maps_dict, site_var, type_var, Contest_Size, streng
                 portfolio['Team_Own'] = portfolio.iloc[:,6].map(maps_dict['own_map']).astype('float32') / 100
 
                 portfolio['own_product'] = (portfolio[own_columns].product(axis=1))
-                portfolio['own_average'] = (portfolio['Own'].max() * .66) / 100
+                portfolio['own_average'] = (portfolio['Own'].max() * .33) / 100
                 portfolio['own_sum'] = portfolio[own_columns].sum(axis=1)
                 portfolio['avg_own_rank'] = portfolio[dup_count_columns].mean(axis=1)
+
+                print(portfolio[['Own', 'own_product', 'own_average', 'own_sum', 'avg_own_rank']].head(10))
                 
                 # Calculate dupes formula
                 portfolio['dupes_calc'] = (portfolio['own_product'] * portfolio['avg_own_rank']) * Contest_Size + ((portfolio['salary'] - (50000 - portfolio['Own'])) / 100) - ((50000 - portfolio['salary']) / 100)
@@ -593,6 +595,7 @@ def predict_dupes(portfolio, maps_dict, site_var, type_var, Contest_Size, streng
     portfolio['Finish_percentile'] = portfolio['Finish_percentile'] + .005 + (.005 * (Contest_Size / 10000))
     portfolio['Finish_percentile'] = portfolio['Finish_percentile'] * percentile_multiplier
     portfolio['Win%'] = portfolio['Win%'] * (1 - portfolio['Finish_percentile'])
+    portfolio['Win%'] = portfolio['Win%'].clip(lower=0, upper=max_allowed_win)
     
     portfolio['low_own_count'] = portfolio[own_columns].apply(lambda row: (row < 0.10).sum(), axis=1)
     portfolio['Finish_percentile'] = portfolio.apply(lambda row: row['Finish_percentile'] if row['low_own_count'] <= 0 else row['Finish_percentile'] / row['low_own_count'], axis=1)