global_func/stratification_function.py

import pandas as pd
import numpy as np

def stratification_function(portfolio: pd.DataFrame, lineup_target: int, exclude_cols: list, sport: str, sorting_choice: 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()
    if sorting_choice == 'Finish_percentile':
        concat_portfolio = concat_portfolio.sort_values(by=sorting_choice, ascending=True).reset_index(drop=True)
    else:
        concat_portfolio = concat_portfolio.sort_values(by=sorting_choice, ascending=False).reset_index(drop=True)

    # Calculate target similarity scores for linear progression
    similarity_floor = concat_portfolio[sorting_choice].min()
    similarity_ceiling = concat_portfolio[sorting_choice].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[sorting_choice] - 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=sorting_choice, ascending=False)