def saveWTL(config, players, wtl):
for i in range(wtl.shape[0]):
for j in range(wtl.shape[1]):
util.saveWTL(config, util.getPlayerName(players[i]),
util.getPlayerName(players[j]), wtl[i, j, 0],
wtl[i, j, 1], wtl[i, j, 2])
util.mergeStagedWTL(config)
def run_games(config):
game = Othello()
model = ""
x = config.iterations
while(x != 0):
x -= 1
models = sorted(glob.glob(config.data.model_location+"*.h5"))
if model == "":
model = models[-1]
print("Loading new model: %s" % util.getPlayerName(model))
ai = AIPlayer(config.buffer_size, config.game.simulation_num_per_move, model=model)
elif models[-1] != model:
model = models[-1]
print("Loading new model: %s" % util.getPlayerName(model))
ai.load(model)
start=time()
for j in range(config.nb_game_in_file):
util.print_progress_bar(j, config.nb_game_in_file, start=start)
side = -1
turn = 1
while not game.game_over():
ai.tau = config.game.tau_1
if config.game.tau_swap < turn:
ai.tau = config.game.tau_2
t = ai.pick_move(game, side)
game.play_move(t[0], t[1], side)
side *= -1
turn += 1
ai.update_buffer(game.get_winner())
game.reset_board()
#print("Average Game Time: ", (time()-start)/(config.nb_game_in_file))
util.print_progress_bar(config.nb_game_in_file, config.nb_game_in_file, start=start)
save_games(config, ai.buffer)
t.join()
def savePerformance(config, model_1, model_2, wins, ties, losses):
m1 = config.model_1
if m1 == "newest":
m1 = util.getPlayerName(model_1)
m2 = config.model_2
if m2 == "newest":
m2 = util.getPlayerName(model_2)
util.saveWTL(config, m1, m2, wins, ties, losses)
util.mergeStagedWTL(config)
def savePerformance(config, model_1, model_2, wins, ties, losses):
m1 = config.model_1
if m1 == "newest":
m1 = util.getPlayerName(model_1)
m2 = config.model_2
if m2 == "newest":
m2 = util.getPlayerName(model_2)
util.saveWTL(config, m1, m2, wins, ties, losses)
util.mergeStagedWTL(config)
def load_player(player, player_name, current, config):
if player_name == "newest":
model = sorted(glob.glob(config.data.model_location+"*.h5"))[-1]
if model != current:
print("Loading new model: %s" % util.getPlayerName(model))
player.load(model)
return model
else:
return current
def load_player(player, player_name, current, config):
if player_name == "newest":
model = sorted(glob.glob(config.data.model_location + "*.h5"))[-1]
if model != current:
print("Loading new model: %s" % util.getPlayerName(model))
player.load(model)
return model
else:
return current
def calc_ranking(config):
models = sorted(glob.glob(config.data.model_location+"*.h5"))
players = []
for i, model in enumerate(models):
if i % config.model_skip == 0 or i == len(models):
players.append(model)
wtl = np.zeros((len(players), len(players), 3))
win_matrix = np.zeros((len(players),len(players)))
game = Othello()
challenger1 = AIPlayer(0, config.game.simulation_num_per_move, train=False, model=players[-1], tau=config.game.tau_1)
challenger2 = AIPlayer(0, config.game.simulation_num_per_move, train=False, model=players[0], tau=config.game.tau_1)
total_games = (config.game_num_per_model * (len(players)))//2
played_games = 0
finished = False
start = time()
print("Ranking with %d players and %d games per player" % (len(players), config.game_num_per_model))
if config.game_num_per_model < len(players):
print("We suggest that you increase games per player to be greater than players")
for i in itertools.count():
ranks = getRankings(win_matrix)
if len(ranks) == 0:
msg = "No Clear Best Yet"
else:
msg = "Current Best is "+util.getPlayerName(players[ranks[-1]])
if config.print_best:
print(msg.ljust(90))
for j in range(len(players)):
util.print_progress_bar(played_games, total_games, start=start)
challenger1_index = getLeastPlayed(win_matrix, j)
AIPlayer.clear()
challenger1.load(players[challenger1_index])
challenger2.load(players[j])
if random.random() < 0.5:
challenger1_side = -1
p1 = challenger1
p2 = challenger2
else:
challenger1_side = 1
p1 = challenger2
p2 = challenger1
side = -1
turn = 1
while not game.game_over():
tau = config.game.tau_1
if config.game.tau_swap < turn:
tau = config.game.tau_2
p1.tau = tau
p2.tau = tau
if side == -1:
t = p1.pick_move(game, side)
else:
t = p2.pick_move(game, side)
game.play_move(t[0], t[1], side)
side *= -1
turn += 1
if game.get_winner() == challenger1_side:
win_matrix[challenger1_index,j] += 1
wtl[challenger1_index, j,0] += 1
elif game.get_winner() == -1*challenger1_side:
win_matrix[j, challenger1_index] += 1
wtl[challenger1_index, j,2] += 1
else:
win_matrix[challenger1_index,j] += 0.5
win_matrix[j, challenger1_index] += 0.5
wtl[challenger1_index, j, 1] += 1
game.reset_board()
played_games += 1
if played_games >= total_games:
finished = True
break
saveWTL(config, players, wtl)
wtl = np.zeros((len(players), len(players), 3))
if finished:
break
util.print_progress_bar(total_games, total_games, start=start)
print("\n",[util.getPlayerName(player) for player in players])
print("\nWin Matrix(row beat column):")
print(win_matrix)
try:
with np.errstate(divide='ignore', invalid='ignore'):
params = choix.ilsr_pairwise_dense(win_matrix)
print("\nRankings:")
for i, player in enumerate(np.argsort(params)[::-1]):
print("%d. %s (expected %d) with %0.2f rating"%
(i+1, util.getPlayerName(players[player]), len(players)-player, params[player]))
print("\n(Rating Diff, Winrate) -> (0.5, 62%), (1, 73%), (2, 88%), (3, 95%), (5, 99%)")
except Exception:
print("\nNot Enough data to calculate rankings")
def saveWTL(config, players, wtl):
for i in range(wtl.shape[0]):
for j in range(wtl.shape[1]):
util.saveWTL(config, util.getPlayerName(players[i]), util.getPlayerName(players[j]), wtl[i,j,0], wtl[i,j,1], wtl[i,j,2])
util.mergeStagedWTL(config)
def calc_ranking(config):
models = sorted(glob.glob(config.data.model_location + "*.h5"))
players = []
for i, model in enumerate(models):
if i % config.model_skip == 0 or i == len(models):
players.append(model)
wtl = np.zeros((len(players), len(players), 3))
win_matrix = np.zeros((len(players), len(players)))
game = Othello()
challenger1 = AIPlayer(0,
config.game.simulation_num_per_move,
train=False,
model=players[-1],
tau=config.game.tau_1)
challenger2 = AIPlayer(0,
config.game.simulation_num_per_move,
train=False,
model=players[0],
tau=config.game.tau_1)
total_games = (config.game_num_per_model * (len(players))) // 2
played_games = 0
finished = False
start = time()
print("Ranking with %d players and %d games per player" %
(len(players), config.game_num_per_model))
if config.game_num_per_model < len(players):
print(
"We suggest that you increase games per player to be greater than players"
)
for i in itertools.count():
ranks = getRankings(win_matrix)
if len(ranks) == 0:
msg = "No Clear Best Yet"
else:
msg = "Current Best is " + util.getPlayerName(players[ranks[-1]])
if config.print_best:
print(msg.ljust(90))
for j in range(len(players)):
util.print_progress_bar(played_games, total_games, start=start)
challenger1_index = getLeastPlayed(win_matrix, j)
AIPlayer.clear()
challenger1.load(players[challenger1_index])
challenger2.load(players[j])
if random.random() < 0.5:
challenger1_side = -1
p1 = challenger1
p2 = challenger2
else:
challenger1_side = 1
p1 = challenger2
p2 = challenger1
side = -1
turn = 1
while not game.game_over():
tau = config.game.tau_1
if config.game.tau_swap < turn:
tau = config.game.tau_2
p1.tau = tau
p2.tau = tau
if side == -1:
t = p1.pick_move(game, side)
else:
t = p2.pick_move(game, side)
game.play_move(t[0], t[1], side)
side *= -1
turn += 1
if game.get_winner() == challenger1_side:
win_matrix[challenger1_index, j] += 1
wtl[challenger1_index, j, 0] += 1
elif game.get_winner() == -1 * challenger1_side:
win_matrix[j, challenger1_index] += 1
wtl[challenger1_index, j, 2] += 1
else:
win_matrix[challenger1_index, j] += 0.5
win_matrix[j, challenger1_index] += 0.5
wtl[challenger1_index, j, 1] += 1
game.reset_board()
played_games += 1
if played_games >= total_games:
finished = True
break
saveWTL(config, players, wtl)
wtl = np.zeros((len(players), len(players), 3))
if finished:
break
util.print_progress_bar(total_games, total_games, start=start)
print("\n", [util.getPlayerName(player) for player in players])
print("\nWin Matrix(row beat column):")
print(win_matrix)
try:
with np.errstate(divide='ignore', invalid='ignore'):
params = choix.ilsr_pairwise_dense(win_matrix)
print("\nRankings:")
for i, player in enumerate(np.argsort(params)[::-1]):
print("%d. %s (expected %d) with %0.2f rating" %
(i + 1, util.getPlayerName(
players[player]), len(players) - player, params[player]))
print(
"\n(Rating Diff, Winrate) -> (0.5, 62%), (1, 73%), (2, 88%), (3, 95%), (5, 99%)"
)
except Exception:
print("\nNot Enough data to calculate rankings")