finalized dupe math, maintaining on staging

app.py

@@ -1171,11 +1171,10 @@ if selected_tab == 'Manage Portfolio':
             st.session_state['working_frame']['median'] = st.session_state['working_frame']['median'].astype('float32')
             st.session_state['working_frame']['salary'] = st.session_state['working_frame']['salary'].astype('uint16')
             
-            st.session_state['base_frame'], check_frame = predict_dupes(st.session_state['working_frame'], st.session_state['map_dict'], site_var, type_var, Contest_Size, strength_var, sport_var, salary_max)
+            st.session_state['base_frame'] = predict_dupes(st.session_state['working_frame'], st.session_state['map_dict'], site_var, type_var, Contest_Size, strength_var, sport_var, salary_max)
             st.session_state['working_frame'] = st.session_state['base_frame'].copy()
             # st.session_state['highest_owned_teams'] = st.session_state['projections_df'][~st.session_state['projections_df']['position'].isin(['P', 'SP'])].groupby('team')['ownership'].sum().sort_values(ascending=False).head(3).index.tolist()
             # st.session_state['highest_owned_pitchers'] = st.session_state['projections_df'][st.session_state['projections_df']['position'].isin(['P', 'SP'])]['player_names'].sort_values(by='ownership', ascending=False).head(3).tolist()
-            # st.table(check_frame)
 
             #set some maxes for trimming variables
             if 'trimming_dict_maxes' not in st.session_state:

global_func/predict_dupes.py

@@ -207,13 +207,6 @@ def predict_dupes(portfolio, maps_dict, site_var, type_var, Contest_Size, streng
             0, 
             np.round(portfolio['Dupes'], 0) - 1
         )
-
-        print(portfolio['own_product'])
-        print(portfolio['avg_own_rank'])
-        print(portfolio['salary'])
-        print(portfolio['Own'])
-        print(portfolio['dupes_calc'])
-        print(portfolio['Dupes'])
     elif type_var == 'Classic':
         if sport_var == 'CS2':
             dup_count_columns = ['CPT_Own_percent_rank', 'FLEX1_Own_percent_rank', 'FLEX2_Own_percent_rank', 'FLEX3_Own_percent_rank', 'FLEX4_Own_percent_rank', 'FLEX5_Own_percent_rank']
@@ -431,7 +424,7 @@ def predict_dupes(portfolio, maps_dict, site_var, type_var, Contest_Size, streng
     
     # Calculate similarity score based on actual player selection
     portfolio['Diversity'] = calculate_player_similarity_score_vectorized(portfolio, player_columns)
-    check_portfolio = portfolio.copy()
+    # check_portfolio = portfolio.copy()
     portfolio = portfolio.drop(columns=dup_count_columns)
     portfolio = portfolio.drop(columns=own_columns)
     portfolio = portfolio.drop(columns=calc_columns)
@@ -440,8 +433,6 @@ def predict_dupes(portfolio, maps_dict, site_var, type_var, Contest_Size, streng
     int16_columns_nstacks = ['Dupes', 'salary']
     float32_columns = ['median', 'Own', 'Finish_percentile', 'Win%', 'Lineup Edge', 'Weighted Own', 'Geomean', 'Diversity']
 
-    print(portfolio.columns)
-    print(portfolio.head(10))
     try:
         portfolio[int16_columns_stacks] = portfolio[int16_columns_stacks].astype('uint16')
     except:
@@ -455,5 +446,4 @@ def predict_dupes(portfolio, maps_dict, site_var, type_var, Contest_Size, streng
             portfolio[float32_columns] = portfolio[float32_columns].astype('float32')
         except:
             pass
-    #printing a check frame
-    return portfolio, check_portfolio
+    return portfolio