Add distribute_preset function and update app.py for preset selection

- Introduced a new `distribute_preset` function to manage portfolio distribution based on weighted ownership and finish percentiles.
- Updated `app.py` to include the new preset option 'Distributed (Spread Risk)' in the selection dropdown, replacing the previous 'Volatile' option.
- Adjusted logic to call the `distribute_preset` function when the new preset is selected, enhancing lineup generation capabilities.

app.py

@@ -22,6 +22,7 @@ from global_func.trim_portfolio import trim_portfolio
 from global_func.get_portfolio_names import get_portfolio_names
 from global_func.small_field_preset import small_field_preset
 from global_func.large_field_preset import large_field_preset
+from global_func.distribute_preset import distribute_preset
 
 freq_format = {'Finish_percentile': '{:.2%}', 'Lineup Edge': '{:.2%}', 'Win%': '{:.2%}'}
 stacking_sports = ['MLB', 'NHL', 'NFL']
@@ -1107,7 +1108,7 @@ with tab2:
             with st.expander('Presets'):
                 st.info("Still heavily in testing here, I'll announce when they are ready for use.")
                 with st.form(key='Small Field Preset'):
-                    preset_choice = st.selectbox("Preset", options=['Small Field (Heavy Own)', 'Large Field (Finish Percentile / Edge)', 'Volatile', 'Distributed'], index=0)
+                    preset_choice = st.selectbox("Preset", options=['Small Field (Heavy Own)', 'Large Field (Finish Percentile / Edge)', 'Distributed (Spread Risk)'], index=0)
                     lineup_target = st.number_input("Lineups to produce", value=150, min_value=1, step=1)
                     submitted = st.form_submit_button("Submit")
                     if submitted:
@@ -1115,10 +1116,8 @@ with tab2:
                             parsed_frame = small_field_preset(st.session_state['working_frame'], lineup_target, excluded_cols)
                         elif preset_choice == 'Large Field (Finish Percentile / Edge)':
                             parsed_frame = large_field_preset(st.session_state['working_frame'], lineup_target, excluded_cols)
-                        # elif preset_choice == 'Volatile':
-                        #     parsed_frame = volatile_preset(st.session_state['working_frame'], lineup_target)
-                        # elif preset_choice == 'Distributed':
-                        #     parsed_frame = distributed_preset(st.session_state['working_frame'], lineup_target)
+                        elif preset_choice == 'Distributed (Spread Risk)':
+                            parsed_frame = distribute_preset(st.session_state['working_frame'], lineup_target, excluded_cols)
                         st.session_state['working_frame'] = parsed_frame.reset_index(drop=True)
                         st.session_state['export_merge'] = st.session_state['working_frame'].copy()
 

global_func/distribute_preset.py

@@ -0,0 +1,27 @@
+import pandas as pd
+
+def distribute_preset(portfolio: pd.DataFrame, lineup_target: int, exclude_cols: list):
+    
+    for slack_var in range(1, 20):
+        concat_portfolio = pd.DataFrame(columns=portfolio.columns)
+
+        for finishing_range in range(1, 20):
+            rows_to_drop = []
+            working_portfolio = portfolio.copy()
+            working_portfolio = working_portfolio[(working_portfolio['Finish_percentile'] <= (finishing_range / 100)) & (working_portfolio['Finish_percentile'] >= ((finishing_range - 1) / 100))].sort_values(by='Median', ascending = True)
+            working_portfolio = working_portfolio.reset_index(drop=True)
+            curr_own_type_max = working_portfolio.loc[0, 'Weighted Own'] + (slack_var / 20 * working_portfolio.loc[0, 'Weighted Own'])
+
+            for i in range(1, len(working_portfolio)):
+                if working_portfolio.loc[i, 'Weighted'] > curr_own_type_max:
+                    rows_to_drop.append(i)
+                else:
+                    curr_own_type_max = working_portfolio.loc[i, 'Weighted'] + (slack_var / 20 * working_portfolio.loc[i, 'Weighted'])
+            
+            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='Finish_percentile', ascending=True)