pids2 = replays.playerid2.astype(np.int)
scores12 = (replays.score1 - replays.score2).astype(np.int)
ratings = np.empty((nplayers,2),dtype=np.float)
ub = np.array([1500., 500., 500., 50., 0.1])
lb = np.array([1500., 500., 50., 1., 0.01])
N=100000
results = np.zeros((N,6),dtype=np.double) * np.nan
for n in range(N):
print n, '\r',
env = np.random.uniform(0.,1.,5)
env *= ub-lb
env += lb
ratings[:,0] = env[0]
ratings[:,1] = env[1]
r1,r2,nGames1,nGames2,L = ts.rate_1vs1(env,pids1,pids2,scores12,ratings)
idx = (nGames1>10) & (nGames2>10)
L = np.sum(np.log(L[idx]))
results[n,0:-1] = env
results[n,-1] = L
if n%100 == 99:
save_results(results, 'learntrueskill_solution_space.txt')
save_results(results, 'learntrueskill_solution_space.txt')
env = results[-1,0:5]
ratings[:,0] = env[0]
ratings[:,1] = env[1]
r1,r2,nGames1,nGames2,L = ts.rate_1vs1(env,pids1,pids2,scores12,ratings)
def eval_pyts(env, pid1, pid2, scores):
nplayers = 1 + max(np.max(pid1), np.max(pid2))
ratings = np.zeros((nplayers, 2), dtype=np.float)
ratings[:, 0] = env[0]
ratings[:, 1] = env[1]
return pyts.rate_1vs1(env, pid1, pid2, scores, ratings) + (ratings, )
nPlayersExamine = 5
figs_progression = [plt.subplots() for n in range(nPlayersExamine)]
fig_filenames = {}
probabilities = [[] for n in range(nPlayersExamine)]
all_probabilities = []
aliases = [None for n in range(nPlayersExamine)]
environments = (np.array([1500., 500., 250., 5.,
0.1]), np.array([1500., 500., 240., 10., 0.045]),
np.array([1500., 500., 240., 18., 0.045]))
for nenv, env in enumerate(environments):
ratings[:, 0] = env[0]
ratings[:, 1] = env[1]
r1, r2, nGames1, nGames2, L = ts.rate_1vs1(env, pids1, pids2, scores12,
ratings)
all_probabilities += [L]
finalGameCount = np.zeros(nplayers, dtype=np.int)
finalGameCount[pids1] = nGames1
iSorNGames = np.argsort(finalGameCount)[::-1]
for nplayer, ((f1, ax1),
PID) in enumerate(zip(figs_progression, iSorNGames)):
idx1 = pids1 == PID
idx2 = pids2 == PID
aliases[nplayer] = ','.join(playerAliases[PID][1:])
mu = idx1 * r1[:, 0] + idx2 * r2[:, 0]
sigma = idx1 * r1[:, 1] + idx2 * r2[:, 1]
mu = mu[idx1 | idx2]
sigma = sigma[idx1 | idx2]
def eval_pyts(env,pid1,pid2,scores):
nplayers = 1+max(np.max(pid1),np.max(pid2))
ratings = np.zeros((nplayers,2),dtype=np.float)
ratings[:,0] = env[0]
ratings[:,1] = env[1]
return pyts.rate_1vs1(env,pid1,pid2,scores,ratings) + (ratings,)
