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data_functions.py
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data_functions.py
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# -*- coding: utf-8 -*-
"""
Created on Sat Nov 23 15:42:00 2019
@author: Michael K
"""
import numpy as np
def run_alg(team_a_list, team_b_list, data, champ_inds):
num = pick_num(team_a_list, team_b_list)
flag = False
if not is_a_picking(num, flag):
data = flipinds(data)
flag = True
print("Flipping data for B side")
temp = team_a_list.copy()
team_a_list = team_b_list.copy()
team_b_list = temp.copy()
winspace = create_constrained_space(data.copy(),
team_a_list,
team_b_list)
losspace = create_constrained_space(flipinds(data),
team_a_list,
team_b_list)
# print(len(winspace))
# print(len(losspace))
champs = generate_champ_list(team_a_list, team_b_list,
champ_inds)
prob = create_champ_probability(winspace, losspace,
num, champs, champ_inds, flag)
final_prob = create_final_prob(prob)
return alternate_amax(np.asarray(final_prob))
def flipinds(space):
temp = np.asarray(space)
return np.stack([temp[:, 1, :], temp[:, 0, :]], 1)
def pick_num(team_a_list, team_b_list):
num_picks = len(team_a_list) + len(team_b_list)
selection_num = num_picks + 1
return selection_num
def create_constrained_space(space, team_a_list, team_b_list):
out = space
if len(team_a_list) > 0:
for A in team_a_list:
out = [el for el in out if A in el[0]]
if len(team_b_list) > 0:
for B in team_b_list:
out = [el for el in out if B in el[1]]
return out
def generate_champ_list(team_a_list, team_b_list, champ_inds):
valid = champ_inds.copy()
valid = [el for el in valid if el not in team_a_list]
return [el for el in valid if el not in team_b_list]
def get_amax(probs, top_n):
temp = np.asarray(probs)
inds = np.argsort(temp[:, 1]).tolist()
champs = temp[inds[-top_n:], 0]
probs = temp[inds[-top_n:], 1]
return [np.flip(champs), np.flip(probs)]
def alternate_amax(probs):
inds = np.lexsort((probs[:, 2], probs[:, 1]))
return probs[inds,:]
def get_winloss(winspace, lossspace, champ_list, flag):
out = []
for num in champ_list:
# last pick is always team B.
if not flag:
win_games = [el for el in winspace if num in el[1]]
loss_games = [el for el in lossspace if num in el[1]]
else:
win_games = [el for el in winspace if num in el[0]]
loss_games = [el for el in lossspace if num in el[0]]
out.append([num, len(win_games), len(loss_games)])
return np.asarray(out)
def marginalize(probs):
temp = np.asarray(probs)
wins = np.sum(temp[:, 1])
losses = np.sum(temp[:, 2])
# normalization code left if necessary.
'''
normalization = wins + losses
if normalization == 0:
return [0,0]
else:
return [wins/normalization, losses/normalization]
'''
return [wins, losses]
def is_a_picking(numb, flag):
if (numb == 1) or (numb == 4) or (numb == 5) or (numb == 8) or (numb == 9):
return not flag
else:
return flag
def create_final_prob(inmat):
out = []
for el in inmat:
if (el[1] + el[2]) > 0:
numgames = el[1] + el[2]
winrate = el[1]/numgames
else:
numgames = 0
winrate = 0
out.append([el[0], winrate, numgames])
return out
def create_champ_probability(winspace, lossspace,
num, champ_list,
champ_inds, flag):
if num == 10:
return get_winloss(winspace, lossspace,
champ_list, flag)
elif num == 11:
return 0
else:
out = []
for champ in champ_list:
if is_a_picking(num, flag):
new_a = [champ]
newwin = create_constrained_space(winspace, new_a, [])
newloss = create_constrained_space(lossspace, new_a, [])
if len(newwin) == 0 and len(newloss) == 0:
out.append([champ, 0, 0])
else:
newchamps = generate_champ_list(new_a, [], champ_list)
temp = create_champ_probability(newwin, newloss,
num+1, newchamps,
champ_inds, flag)
marg = marginalize(temp)
out.append([champ, marg[0], marg[1]])
else:
new_b = [champ]
newwin = create_constrained_space(winspace, [], new_b)
newloss = create_constrained_space(lossspace, [], new_b)
if len(newwin) == 0 and len(newloss) == 0:
out.append([champ, 0, 0])
else:
newchamps = generate_champ_list([], new_b, champ_list)
temp = create_champ_probability(newwin, newloss,
num+1, newchamps,
champ_inds, flag)
marg = marginalize(temp)
out.append([champ, marg[0], marg[1]])
return np.asarray(out)