global_func/exposure_spread.py

import random
import numpy as np
import math

#### Goal is to choose a player and adjust the amount of lineups that have them
#### First thing you need to do is find comparable players in the projections, so any player in the projections that is within $500 of the player and within 10% of the projection
#### Take that list of players and create a list that can be accessed for random insertion into the portfolio
#### Find the player and the amount of rows that contain them and then find an exposure rate which is the percentage of total rows
#### Use the exposure target argument and try to replace the player from as many rows as necessary to be at or just under the target

def exposure_spread(working_frame, exposure_player, exposure_target, exposure_stack_bool, projections_df):
    # Find comparable players in the projections
    comparable_players = projections_df[projections_df['player_names'] == exposure_player]
    comp_salary_high = comparable_players['salary'][0]
    comp_salary_low = comparable_players['salary'][0] - 500
    comp_projection_high = comparable_players['median'][0]
    comp_projection_low = comparable_players['median'][0] - (comparable_players['median'][0] * .9)

    comparable_players = projections_df[
        (projections_df['salary'] >= comp_salary_low) &
        (projections_df['salary'] <= comp_salary_high) &
        (projections_df['median'] >= comp_projection_low) &
        (projections_df['median'] <= comp_projection_high)
    ]
    
    # Create a list of comparable players
    comparable_player_list = comparable_players['player_names'].tolist()

    # find the exposure rate of the player in the working frame
    player_mask = working_frame[working_frame.columns].apply(
                                    lambda row: exposure_player in list(row), axis=1
                                )
    player_exposure = player_mask.sum() / len(working_frame)

    # find the number of lineups that need to be removed to reach the target exposure
    lineups_to_remove = (player_exposure - exposure_target) * len(working_frame)

    # isolate the rows that contain the player
    player_rows = working_frame[player_mask]
    print(player_rows.head(10))
    print(working_frame.head(10))

    # for each row to the the number of lineups to remove, replace with random choice from comparable player list
    for row in player_rows.index:
        for change in range(math.ceil(lineups_to_remove)):
            insert_player = random.choice(comparable_player_list)
            # Find which column contains the exposure_player
            row_data = working_frame.iloc[row]
            for col in working_frame.columns:
                if row_data[col] == exposure_player:
                    working_frame.at[row, col] = insert_player
                    break
            break

    return working_frame