File size: 1,450 Bytes
936a186 2f8b929 936a186 206da8c efb1867 119b2bf 936a186 2f8b929 efb1867 d38df13 2f8b929 efb1867 2f8b929 efb1867 2f8b929 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 |
import pandas as pd
import numpy as np
def large_field_preset(portfolio: pd.DataFrame, lineup_target: int, exclude_cols: list, sport: str):
excluded_cols = ['salary', 'median', 'Own', 'Finish_percentile', 'Dupes', 'Stack', 'Size', 'Win%', 'Lineup Edge', 'Weighted Own', 'Geomean', 'Diversity']
player_columns = [col for col in portfolio.columns if col not in excluded_cols]
concat_portfolio = portfolio.copy()
concat_portfolio = concat_portfolio.sort_values(by='Diversity', ascending=True).reset_index(drop=True)
# Calculate target similarity scores for linear progression
similarity_floor = concat_portfolio['Diversity'].min()
similarity_ceiling = concat_portfolio['Diversity'].max()
# Create evenly spaced target similarity scores
target_similarities = np.linspace(similarity_floor, similarity_ceiling, lineup_target)
# Find the closest lineup to each target similarity score
selected_indices = []
for target_sim in target_similarities:
# Find the index of the closest similarity score
closest_idx = (concat_portfolio['Diversity'] - target_sim).abs().idxmin()
if closest_idx not in selected_indices: # Avoid duplicates
selected_indices.append(closest_idx)
# Select the lineups
concat_portfolio = concat_portfolio.loc[selected_indices].reset_index(drop=True)
return concat_portfolio.sort_values(by='median', ascending=False)
|