Update app.py

app.py

@@ -146,14 +146,14 @@ def run_seed_frame(seed_depth1, Strength_var, strength_grow, Teams_used, Total_R
     while RunsVar <= seed_depth_def:
         if RunsVar <= 3:
             FieldStrength = Strength_var_def
-            FinalPortfolio, maps_dict = get_correlated_portfolio_for_sim(Total_Runs_def * .25, sharp_split, field_growth)
-            FinalPortfolio2, maps_dict2 = get_uncorrelated_portfolio_for_sim(Total_Runs_def * .25, sharp_split, field_growth)
+            FinalPortfolio, maps_dict = get_correlated_portfolio_for_sim(Total_Runs_def * .1, sharp_split, field_growth)
+            FinalPortfolio2, maps_dict2 = get_uncorrelated_portfolio_for_sim(Total_Runs_def * .1, sharp_split, field_growth)
             FinalPortfolio_init = pd.concat([FinalPortfolio, FinalPortfolio2], axis=0)
             maps_dict.update(maps_dict2)
         elif RunsVar > 3 and RunsVar <= 4:
             FieldStrength += (strength_grow_def + ((30 - len(Teams_used_def)) * .001))
-            FinalPortfolio3, maps_dict3 = get_correlated_portfolio_for_sim(Total_Runs_def * .25, sharp_split, field_growth)
-            FinalPortfolio4, maps_dict4 = get_uncorrelated_portfolio_for_sim(Total_Runs_def * .25, sharp_split, field_growth)
+            FinalPortfolio3, maps_dict3 = get_correlated_portfolio_for_sim(Total_Runs_def * .1, sharp_split, field_growth)
+            FinalPortfolio4, maps_dict4 = get_uncorrelated_portfolio_for_sim(Total_Runs_def * .1, sharp_split, field_growth)
             FinalPortfolio_merge_3 = pd.concat([FinalPortfolio_init, FinalPortfolio3], axis=0)
             FinalPortfolio_merge_4 = pd.concat([FinalPortfolio_merge_3, FinalPortfolio4], axis=0)
             FinalPortfolio_step_2 = FinalPortfolio_merge_4.drop_duplicates(subset = ['Projection', 'Own'],keep = 'last').reset_index(drop = True)
@@ -161,8 +161,8 @@ def run_seed_frame(seed_depth1, Strength_var, strength_grow, Teams_used, Total_R
             maps_dict.update(maps_dict4)
         elif RunsVar > 4:
             FieldStrength = 1
-            FinalPortfolio5, maps_dict5 = get_correlated_portfolio_for_sim(Total_Runs_def * .25, sharp_split, field_growth)
-            FinalPortfolio6, maps_dict6 = get_uncorrelated_portfolio_for_sim(Total_Runs_def * .25, sharp_split, field_growth)
+            FinalPortfolio5, maps_dict5 = get_correlated_portfolio_for_sim(Total_Runs_def * .1, sharp_split, field_growth)
+            FinalPortfolio6, maps_dict6 = get_uncorrelated_portfolio_for_sim(Total_Runs_def * .1, sharp_split, field_growth)
             FinalPortfolio_merge_5 = pd.concat([FinalPortfolio_step_2, FinalPortfolio5], axis=0)
             FinalPortfolio_merge_6 = pd.concat([FinalPortfolio_merge_5, FinalPortfolio6], axis=0)
             FinalPortfolio_export = FinalPortfolio_merge_6.drop_duplicates(subset = ['Projection', 'Own'],keep = 'last').reset_index(drop = True)
@@ -956,13 +956,16 @@ with tab2:
                         'team_check_map':dict(zip(cleaport_players.Player,nerf_frame.Team))
                         }
                     
-                    FinalPortfolio, maps_dict = run_seed_frame(10, Strength_var, strength_grow, Teams_used, 1000000, field_growth)
+                    FinalPortfolio, maps_dict = run_seed_frame(5, Strength_var, strength_grow, Teams_used, 1000000, field_growth)
                     
                     Sim_Winners = sim_contest(2500, FinalPortfolio, CleanPortfolio, maps_dict, up_dict, insert_port)
                     
                     # Initial setup
                     Sim_Winner_Frame = pd.DataFrame(np.concatenate(Sim_Winners), columns=FinalPortfolio.columns.tolist() + ['Fantasy'])
                     Sim_Winner_Frame['GPP_Proj'] = (Sim_Winner_Frame['Projection'] + Sim_Winner_Frame['Fantasy']) / 2
+                    Sim_Winner_Frame = Sim_Winner_Frame.sort_values(by='GPP_Proj', ascending=False).copy()
+                    Sim_Winner_Frame['unique_id'] = Sim_Winner_Frame['Projection'].astype(str) + Sim_Winner_Frame['Salary'].astype(str) + Sim_Winner_Frame['Own'].astype(str)
+                    Sim_Winner_Frame = Sim_Winner_Frame.assign(win_count=Sim_Winner_Frame['unique_id'].map(Sim_Winner_Frame['unique_id'].value_counts()))
                     
                     # Type Casting
                     type_cast_dict = {'Salary': int, 'Projection': np.float16, 'Fantasy': np.float16, 'GPP_Proj': np.float16}
@@ -971,10 +974,14 @@ with tab2:
                     del FinalPortfolio, insert_port, type_cast_dict
                     
                     # Sorting
-                    st.session_state.Sim_Winner_Frame = Sim_Winner_Frame.sort_values(by='GPP_Proj', ascending=False)
+                    st.session_state.Sim_Winner_Frame = Sim_Winner_Frame.sort_values(by='win_count', ascending=False).copy().drop_duplicates(subset='unique_id').head(100)
+                    st.session_state.Sim_Winner_Frame.drop(columns='unique_id', inplace=True)
                     
                     # Data Copying
-                    st.session_state.Sim_Winner_Export = Sim_Winner_Frame
+                    st.session_state.Sim_Winner_Export = Sim_Winner_Frame.copy()
+                    
+                    # Data Copying
+                    st.session_state.Sim_Winner_Display = Sim_Winner_Frame.copy()
                     
                     # Conditional Replacement
                     columns_to_replace = ['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST']
@@ -989,7 +996,7 @@ with tab2:
                     
                     del replace_dict, Sim_Winner_Frame, Sim_Winners
      
-                    st.session_state.player_freq = pd.DataFrame(np.column_stack(np.unique(st.session_state.Sim_Winner_Frame.iloc[:,0:9].values, return_counts=True)),
+                    st.session_state.player_freq = pd.DataFrame(np.column_stack(np.unique(st.session_state.Sim_Winner_Display.iloc[:,0:9].values, return_counts=True)),
                                                 columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
                     st.session_state.player_freq['Freq'] = st.session_state.player_freq['Freq'].astype(int)
                     st.session_state.player_freq['Position'] = st.session_state.player_freq['Player'].map(maps_dict['Pos_map'])
@@ -1001,7 +1008,7 @@ with tab2:
                     for checkVar in range(len(team_list)):
                                         st.session_state.player_freq['Team'] = st.session_state.player_freq['Team'].replace(item_list, team_list)
                     
-                    st.session_state.qb_freq = pd.DataFrame(np.column_stack(np.unique(st.session_state.Sim_Winner_Frame.iloc[:,0:1].values, return_counts=True)),
+                    st.session_state.qb_freq = pd.DataFrame(np.column_stack(np.unique(st.session_state.Sim_Winner_Display.iloc[:,0:1].values, return_counts=True)),
                                                 columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
                     st.session_state.qb_freq['Freq'] = st.session_state.qb_freq['Freq'].astype(int)
                     st.session_state.qb_freq['Position'] = st.session_state.qb_freq['Player'].map(maps_dict['Pos_map'])
@@ -1013,7 +1020,7 @@ with tab2:
                     for checkVar in range(len(team_list)):
                                         st.session_state.qb_freq['Team'] = st.session_state.qb_freq['Team'].replace(item_list, team_list)
     
-                    st.session_state.rb_freq = pd.DataFrame(np.column_stack(np.unique(st.session_state.Sim_Winner_Frame.iloc[:,[1, 2]].values, return_counts=True)),
+                    st.session_state.rb_freq = pd.DataFrame(np.column_stack(np.unique(st.session_state.Sim_Winner_Display.iloc[:,[1, 2]].values, return_counts=True)),
                                                columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
                     st.session_state.rb_freq['Freq'] = st.session_state.rb_freq['Freq'].astype(int)
                     st.session_state.rb_freq['Position'] = st.session_state.rb_freq['Player'].map(maps_dict['Pos_map'])
@@ -1025,7 +1032,7 @@ with tab2:
                     for checkVar in range(len(team_list)):
                                         st.session_state.rb_freq['Team'] = st.session_state.rb_freq['Team'].replace(item_list, team_list)
                     
-                    st.session_state.wr_freq = pd.DataFrame(np.column_stack(np.unique(st.session_state.Sim_Winner_Frame.iloc[:,[3, 4, 5]].values, return_counts=True)),
+                    st.session_state.wr_freq = pd.DataFrame(np.column_stack(np.unique(st.session_state.Sim_Winner_Display.iloc[:,[3, 4, 5]].values, return_counts=True)),
                                                columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
                     st.session_state.wr_freq['Freq'] = st.session_state.wr_freq['Freq'].astype(int)
                     st.session_state.wr_freq['Position'] = st.session_state.wr_freq['Player'].map(maps_dict['Pos_map'])
@@ -1037,7 +1044,7 @@ with tab2:
                     for checkVar in range(len(team_list)):
                                         st.session_state.wr_freq['Team'] = st.session_state.wr_freq['Team'].replace(item_list, team_list)
                     
-                    st.session_state.te_freq = pd.DataFrame(np.column_stack(np.unique(st.session_state.Sim_Winner_Frame.iloc[:,[6]].values, return_counts=True)),
+                    st.session_state.te_freq = pd.DataFrame(np.column_stack(np.unique(st.session_state.Sim_Winner_Display.iloc[:,[6]].values, return_counts=True)),
                                                columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
                     st.session_state.te_freq['Freq'] = st.session_state.te_freq['Freq'].astype(int)
                     st.session_state.te_freq['Position'] = st.session_state.te_freq['Player'].map(maps_dict['Pos_map'])
@@ -1049,7 +1056,7 @@ with tab2:
                     for checkVar in range(len(team_list)):
                                         st.session_state.te_freq['Team'] = st.session_state.te_freq['Team'].replace(item_list, team_list)
                     
-                    st.session_state.flex_freq = pd.DataFrame(np.column_stack(np.unique(st.session_state.Sim_Winner_Frame.iloc[:,[7]].values, return_counts=True)),
+                    st.session_state.flex_freq = pd.DataFrame(np.column_stack(np.unique(st.session_state.Sim_Winner_Display.iloc[:,[7]].values, return_counts=True)),
                                                columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
                     st.session_state.flex_freq['Freq'] = st.session_state.flex_freq['Freq'].astype(int)
                     st.session_state.flex_freq['Position'] = st.session_state.flex_freq['Player'].map(maps_dict['Pos_map'])
@@ -1061,7 +1068,7 @@ with tab2:
                     for checkVar in range(len(team_list)):
                                         st.session_state.flex_freq['Team'] = st.session_state.flex_freq['Team'].replace(item_list, team_list)
                     
-                    st.session_state.dst_freq = pd.DataFrame(np.column_stack(np.unique(st.session_state.Sim_Winner_Frame.iloc[:,8:9].values, return_counts=True)),
+                    st.session_state.dst_freq = pd.DataFrame(np.column_stack(np.unique(st.session_state.Sim_Winner_Display.iloc[:,8:9].values, return_counts=True)),
                                                columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
                     st.session_state.dst_freq['Freq'] = st.session_state.dst_freq['Freq'].astype(int)
                     st.session_state.dst_freq['Position'] = st.session_state.dst_freq['Player'].map(maps_dict['Pos_map'])
@@ -1089,7 +1096,7 @@ with tab2:
                 st.dataframe(st.session_state.Sim_Winner_Display.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').background_gradient(cmap='RdYlGn_r', subset=['Own']).format(precision=2), use_container_width = True)
             if 'Sim_Winner_Export' in st.session_state:
                 st.download_button(
-                    label="Export Tables",
+                    label="Export Full Frame",
                     data=st.session_state.Sim_Winner_Export.to_csv().encode('utf-8'),
                     file_name='NFL_consim_export.csv',
                     mime='text/csv',
@@ -1168,4 +1175,6 @@ del dkid_dict, fdid_dict
 del static_exposure, overall_exposure
 del insert_port1, Contest_Size, sharp_split, Strength_var, scaling_var, Sort_function, Sim_function, strength_grow, field_growth
 del raw_baselines
-del freq_format
+del freq_format
+
+gc.collect()