James McCool
commited on
Commit
·
d38df13
1
Parent(s):
1b1db4f
Refactor large_field_preset function to streamline the portfolio selection process by removing unnecessary iterations and simplifying the logic for team-based filtering, enhancing efficiency and lineup accuracy.
Browse files
global_func/large_field_preset.py
CHANGED
@@ -4,67 +4,24 @@ def large_field_preset(portfolio: pd.DataFrame, lineup_target: int, exclude_cols
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for slack_var in range(1, 20):
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concat_portfolio = pd.DataFrame(columns=portfolio.columns)
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player_columns = [col for col in concat_portfolio.columns if col not in concat_portfolio]
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remove_list = []
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max_iterations = 5
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for each_iteration in range(max_iterations):
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player_stats = []
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concat_portfolio = pd.DataFrame(columns=portfolio.columns)
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for team in portfolio['Stack'].unique():
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rows_to_drop = []
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working_portfolio = portfolio.copy()
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if remove_list:
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if len(remove_list) > 0:
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remove_mask = working_portfolio[player_columns].apply(
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lambda row: not any(player in list(row) for player in remove_list), axis=1
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)
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working_portfolio = working_portfolio[remove_mask]
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working_portfolio = working_portfolio[working_portfolio['Stack'] == team].sort_values(by='Finish_percentile', ascending = True)
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working_portfolio = working_portfolio.reset_index(drop=True)
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if len(working_portfolio) == 0:
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continue
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curr_own_type_max = working_portfolio.loc[0, 'Own'] + (slack_var / 20 * working_portfolio.loc[0, 'Own'])
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for i in range(1, len(working_portfolio)):
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if working_portfolio.loc[i, 'Own'] > curr_own_type_max:
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rows_to_drop.append(i)
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else:
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curr_own_type_max = working_portfolio.loc[i, 'Own'] + (slack_var / 20 * working_portfolio.loc[i, 'Own'])
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working_portfolio = working_portfolio.drop(rows_to_drop).reset_index(drop=True)
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concat_portfolio = pd.concat([concat_portfolio, working_portfolio])
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player_names = set()
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for col in concat_portfolio.columns:
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if col not in exclude_cols:
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player_names.update(concat_portfolio[col].unique())
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for player in player_names:
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player_mask = concat_portfolio[player_columns].apply(
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lambda row: player in list(row), axis=1
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)
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if player_mask.any():
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player_stats.append({
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'Player': player,
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'Lineup Count': player_mask.sum(),
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'Exposure': player_mask.sum() / len(concat_portfolio),
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})
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player_exposure = pd.DataFrame(player_stats)
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player_exposure = player_exposure[player_exposure['Exposure'] > .50]
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remove_list = player_exposure['Player'].tolist()
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if len(remove_list) == 0:
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break
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return concat_portfolio.sort_values(by='Finish_percentile', ascending=True).head(lineup_target)
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return concat_portfolio.sort_values(by='Finish_percentile', ascending=True)
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for slack_var in range(1, 20):
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concat_portfolio = pd.DataFrame(columns=portfolio.columns)
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for team in portfolio['Stack'].unique():
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rows_to_drop = []
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working_portfolio = portfolio.copy()
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working_portfolio = working_portfolio[working_portfolio['Stack'] == team].sort_values(by='Finish_percentile', ascending = True)
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working_portfolio = working_portfolio.reset_index(drop=True)
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curr_own_type_max = working_portfolio.loc[0, 'Own'] + (slack_var / 20 * working_portfolio.loc[0, 'Own'])
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for i in range(1, len(working_portfolio)):
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if working_portfolio.loc[i, 'Own'] > curr_own_type_max:
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rows_to_drop.append(i)
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else:
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curr_own_type_max = working_portfolio.loc[i, 'Own'] + (slack_var / 20 * working_portfolio.loc[i, 'Own'])
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working_portfolio = working_portfolio.drop(rows_to_drop).reset_index(drop=True)
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concat_portfolio = pd.concat([concat_portfolio, working_portfolio])
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if len(concat_portfolio) >= lineup_target:
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return concat_portfolio.sort_values(by='Finish_percentile', ascending=True).head(lineup_target)
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return concat_portfolio.sort_values(by='Finish_percentile', ascending=True)
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