def test_parsing_9x9(self):
self.assertEqual(coords.from_sgf('aa'), (0, 0))
self.assertEqual(coords.from_sgf('ac'), (2, 0))
self.assertEqual(coords.from_sgf('ca'), (0, 2))
self.assertEqual(coords.from_sgf(''), None)
self.assertEqual(coords.to_sgf(None), '')
self.assertEqual(
'aa',
coords.to_sgf(coords.from_sgf('aa')))
self.assertEqual(
'sa',
coords.to_sgf(coords.from_sgf('sa')))
self.assertEqual(
(1, 17),
coords.from_sgf(coords.to_sgf((1, 17))))
self.assertEqual(coords.from_kgs('A1'), (8, 0))
self.assertEqual(coords.from_kgs('A9'), (0, 0))
self.assertEqual(coords.from_kgs('C2'), (7, 2))
self.assertEqual(coords.from_kgs('J2'), (7, 8))
self.assertEqual(coords.from_pygtp((1, 1)), (8, 0))
self.assertEqual(coords.from_pygtp((1, 9)), (0, 0))
self.assertEqual(coords.from_pygtp((3, 2)), (7, 2))
self.assertEqual(coords.to_pygtp((8, 0)), (1, 1))
self.assertEqual(coords.to_pygtp((0, 0)), (1, 9))
self.assertEqual(coords.to_pygtp((7, 2)), (3, 2))
self.assertEqual(coords.to_kgs((0, 8)), 'J9')
self.assertEqual(coords.to_kgs((8, 0)), 'A1')
def describe(self):
sort_order = list(range(go.N * go.N + 1))
sort_order.sort(key=lambda i: (
self.child_N[i], self.child_action_score[i]), reverse=True)
soft_n = self.child_N / sum(self.child_N)
p_delta = soft_n - self.child_prior
p_rel = p_delta / self.child_prior
# Dump out some statistics
output = []
# try:
output.append("{q:.4f}\n".format(q=self.Q))
# except:
# output.append("{q:.4f}\n".format(q=self.Q[0]))
output.append(self.most_visited_path())
output.append(
"move: action Q U P P-Dir N soft-N p-delta p-rel\n")
output.append("\n".join(["{!s:6}: {: .3f}, {: .3f}, {:.3f}, {:.3f}, {:.3f}, {:4d} {:.4f} {: .5f} {: .2f}".format(
coords.to_kgs(coords.from_flat(key)),
self.child_action_score[key],
self.child_Q[key],
self.child_U[key],
self.child_prior[key],
self.original_prior[key],
int(self.child_N[key]),
soft_n[key],
p_delta[key],
p_rel[key])
for key in sort_order][:15]))
return ''.join(output)
def describe(self):
sort_order = list(range(go.N * go.N + 1))
sort_order.sort(key=lambda i: (
self.child_N[i], self.child_action_score[i]), reverse=True)
soft_n = self.child_N / sum(self.child_N)
p_delta = soft_n - self.child_prior
p_rel = p_delta / self.child_prior
# Dump out some statistics
output = []
output.append("{q:.4f}\n".format(q=self.Q))
output.append(self.most_visited_path())
output.append(
"move: action Q U P P-Dir N soft-N p-delta p-rel\n")
output.append("\n".join(["{!s:6}: {: .3f}, {: .3f}, {:.3f}, {:.3f}, {:.3f}, {:4d} {:.4f} {: .5f} {: .2f}".format(
coords.to_kgs(coords.from_flat(key)),
self.child_action_score[key],
self.child_Q[key],
self.child_U[key],
self.child_prior[key],
self.original_prior[key],
int(self.child_N[key]),
soft_n[key],
p_delta[key],
p_rel[key])
for key in sort_order][:15]))
return ''.join(output)
def test_parsing_9x9(self):
self.assertEqual(coords.from_sgf('aa'), (0, 0))
self.assertEqual(coords.from_sgf('ac'), (2, 0))
self.assertEqual(coords.from_sgf('ca'), (0, 2))
self.assertEqual(coords.from_sgf(''), None)
self.assertEqual(coords.to_sgf(None), '')
self.assertEqual('aa', coords.to_sgf(coords.from_sgf('aa')))
self.assertEqual('sa', coords.to_sgf(coords.from_sgf('sa')))
self.assertEqual((1, 17), coords.from_sgf(coords.to_sgf((1, 17))))
self.assertEqual(coords.from_kgs('A1'), (8, 0))
self.assertEqual(coords.from_kgs('A9'), (0, 0))
self.assertEqual(coords.from_kgs('C2'), (7, 2))
self.assertEqual(coords.from_kgs('J2'), (7, 8))
self.assertEqual(coords.to_kgs((0, 8)), 'J9')
self.assertEqual(coords.to_kgs((8, 0)), 'A1')
def describe(self):
sort_order = list(range(go.N * go.N + 1))
sort_order.sort(key=lambda i:
(self.child_N[i], self.child_action_score[i]),
reverse=True)
soft_n = self.child_N / max(1, sum(self.child_N))
prior = self.child_prior
p_delta = soft_n - prior
p_rel = np.divide(p_delta,
prior,
out=np.zeros_like(p_delta),
where=prior != 0)
# Dump out some statistics
output = []
output.append("{q:.4f}\n".format(q=self.Q))
output.append(self.most_visited_path())
output.append(
"move : action Q U P P-Dir N soft-N p-delta p-rel"
)
for key in sort_order[:15]:
if self.child_N[key] == 0:
break
output.append(
"\n{!s:4} : {: .3f} {: .3f} {:.3f} {:.3f} {:.3f} {:5d} {:.4f} {: .5f} {: .2f}"
.format(coords.to_kgs(coords.from_flat(key)),
self.child_action_score[key], self.child_Q[key],
self.child_U[key],
self.child_prior[key], self.original_prior[key],
int(self.child_N[key]), soft_n[key], p_delta[key],
p_rel[key]))
return ''.join(output)
def _minigui_report_position(self):
root = self._player.get_root()
position = root.position
board = []
for row in range(go.N):
for col in range(go.N):
stone = position.board[row, col]
if stone == go.BLACK:
board.append("X")
elif stone == go.WHITE:
board.append("O")
else:
board.append(".")
msg = {
"id": hex(id(root)),
"toPlay": "B" if position.to_play == 1 else "W",
"moveNum": position.n,
"stones": "".join(board),
"gameOver": position.is_game_over(),
"caps": position.caps,
}
if root.parent and root.parent.parent:
msg["parentId"] = hex(id(root.parent))
msg["q"] = float(root.parent.Q)
if position.recent:
msg["move"] = coords.to_kgs(position.recent[-1].move)
dbg("mg-position:%s" % json.dumps(msg, sort_keys=True))
def _dbg_game_state(self):
position = self._game.position
msg = {}
board = []
for row in range(go.N):
for col in range(go.N):
stone = position.board[row, col]
if stone == go.BLACK:
board.append("X")
elif stone == go.WHITE:
board.append("O")
else:
board.append(".")
msg["board"] = "".join(board)
msg["toPlay"] = "Black" if position.to_play == 1 else "White"
if position.recent:
msg["lastMove"] = coords.to_kgs(position.recent[-1].move)
else:
msg["lastMove"] = None
msg["n"] = position.n
if self._game.root.parent and self._game.root.parent.parent:
msg["q"] = self._game.root.parent.Q
else:
msg["q"] = 0
dbg("mg-gamestate:%s", json.dumps(msg, sort_keys=True))
def test_topleft(self):
self.assertEqual((0, 8), coords.from_sgf('ia'))
self.assertEqual((0, 8), coords.from_flat(8))
self.assertEqual((0, 8), coords.from_kgs('J9'))
self.assertEqual('ia', coords.to_sgf((0, 8)))
self.assertEqual(8, coords.to_flat((0, 8)))
self.assertEqual('J9', coords.to_kgs((0, 8)))
def test_pass(self):
self.assertEqual(coords.from_sgf(''), None)
self.assertEqual(coords.from_flat(81), None)
self.assertEqual(coords.from_kgs('pass'), None)
self.assertEqual(coords.to_sgf(None), '')
self.assertEqual(coords.to_flat(None), 81)
self.assertEqual(coords.to_kgs(None), 'pass')
def test_topleft(self):
self.assertEqual(coords.from_sgf('ia'), (0, 8))
self.assertEqual(coords.from_flat(8), (0, 8))
self.assertEqual(coords.from_kgs('J9'), (0, 8))
self.assertEqual(coords.to_sgf((0, 8)), 'ia')
self.assertEqual(coords.to_flat((0, 8)), 8)
self.assertEqual(coords.to_kgs((0, 8)), 'J9')
def test_upperleft(self):
self.assertEqual(coords.from_sgf('aa'), (0, 0))
self.assertEqual(coords.from_flat(0), (0, 0))
self.assertEqual(coords.from_kgs('A9'), (0, 0))
self.assertEqual(coords.to_sgf((0, 0)), 'aa')
self.assertEqual(coords.to_flat((0, 0)), 0)
self.assertEqual(coords.to_kgs((0, 0)), 'A9')
def mvp_gg(self):
""" Returns most visited path in go-gui VAR format e.g. 'b r3 w c17..."""
output = []
for node in self.most_visited_path_nodes():
if max(node.child_N) <= 1:
break
output.append(coords.to_kgs(coords.from_flat(node.fmove)))
return ' '.join(output)
def test_pass(self):
self.assertEqual(None, coords.from_sgf(''))
self.assertEqual(None, coords.from_flat(81))
self.assertEqual(None, coords.from_kgs('pass'))
self.assertEqual('', coords.to_sgf(None))
self.assertEqual(81, coords.to_flat(None))
self.assertEqual('pass', coords.to_kgs(None))
def test_upperleft(self):
self.assertEqual((0, 0), coords.from_sgf('aa'))
self.assertEqual((0, 0), coords.from_flat(0))
self.assertEqual((0, 0), coords.from_kgs('A9'))
self.assertEqual('aa', coords.to_sgf((0, 0)))
self.assertEqual(0, coords.to_flat((0, 0)))
self.assertEqual('A9', coords.to_kgs((0, 0)))
def most_visited_path(self):
output = []
node = self
for node in self.most_visited_path_nodes():
output.append("%s (%d) ==> " % (
coords.to_kgs(coords.from_flat(node.fmove)), node.N))
output.append("Q: {:.5f}\n".format(node.Q))
return ''.join(output)
def mvp_gg(self):
""" Returns most visited path in go-gui VAR format e.g. 'b r3 w c17..."""
node = self
output = []
while node.children and max(node.child_N) > 1:
next_kid = np.argmax(node.child_N)
node = node.children[next_kid]
output.append("%s" % coords.to_kgs(coords.from_flat(node.fmove)))
return ' '.join(output)
def test_topleft(self):
self.assertEqual(coords.from_sgf('ia'), (0, 8))
self.assertEqual(coords.from_flat(utils_test.BOARD_SIZE, 8), (0, 8))
self.assertEqual(coords.from_kgs(utils_test.BOARD_SIZE, 'J9'), (0, 8))
self.assertEqual(coords.from_pygtp(utils_test.BOARD_SIZE, (9, 9)), (0, 8))
self.assertEqual(coords.to_sgf((0, 8)), 'ia')
self.assertEqual(coords.to_flat(utils_test.BOARD_SIZE, (0, 8)), 8)
self.assertEqual(coords.to_kgs(utils_test.BOARD_SIZE, (0, 8)), 'J9')
self.assertEqual(coords.to_pygtp(utils_test.BOARD_SIZE, (0, 8)), (9, 9))
def test_upperleft(self):
self.assertEqual(coords.from_sgf('aa'), (0, 0))
self.assertEqual(coords.from_flat(utils_test.BOARD_SIZE, 0), (0, 0))
self.assertEqual(coords.from_kgs(utils_test.BOARD_SIZE, 'A9'), (0, 0))
self.assertEqual(coords.from_pygtp(utils_test.BOARD_SIZE, (1, 9)), (0, 0))
self.assertEqual(coords.to_sgf((0, 0)), 'aa')
self.assertEqual(coords.to_flat(utils_test.BOARD_SIZE, (0, 0)), 0)
self.assertEqual(coords.to_kgs(utils_test.BOARD_SIZE, (0, 0)), 'A9')
self.assertEqual(coords.to_pygtp(utils_test.BOARD_SIZE, (0, 0)), (1, 9))
def mvp_gg(self):
""" Returns most visited path in go-gui VAR format e.g. 'b r3 w c17..."""
node = self
output = []
while node.children and max(node.child_N) > 1:
next_kid = np.argmax(node.child_N)
node = node.children[next_kid]
output.append("%s" % coords.to_kgs(
coords.from_flat(node.fmove)))
return ' '.join(output)
def test_topleft(self):
self.assertEqual(coords.from_sgf('ia'), (0, 8))
self.assertEqual(coords.from_flat(8), (0, 8))
self.assertEqual(coords.from_kgs('J9'), (0, 8))
self.assertEqual(coords.from_pygtp((9, 9)), (0, 8))
self.assertEqual(coords.to_sgf((0, 8)), 'ia')
self.assertEqual(coords.to_flat((0, 8)), 8)
self.assertEqual(coords.to_kgs((0, 8)), 'J9')
self.assertEqual(coords.to_pygtp((0, 8)), (9, 9))
def test_upperleft(self):
self.assertEqual(coords.from_sgf('aa'), (0, 0))
self.assertEqual(coords.from_flat(0), (0, 0))
self.assertEqual(coords.from_kgs('A9'), (0, 0))
self.assertEqual(coords.from_pygtp((1, 9)), (0, 0))
self.assertEqual(coords.to_sgf((0, 0)), 'aa')
self.assertEqual(coords.to_flat((0, 0)), 0)
self.assertEqual(coords.to_kgs((0, 0)), 'A9')
self.assertEqual(coords.to_pygtp((0, 0)), (1, 9))
def test_pass(self):
self.assertEqual(coords.from_sgf(''), None)
self.assertEqual(coords.from_flat(utils_test.BOARD_SIZE, 81), None)
self.assertEqual(coords.from_kgs(utils_test.BOARD_SIZE, 'pass'), None)
self.assertEqual(coords.from_pygtp(utils_test.BOARD_SIZE, (0, 0)), None)
self.assertEqual(coords.to_sgf(None), '')
self.assertEqual(coords.to_flat(utils_test.BOARD_SIZE, None), 81)
self.assertEqual(coords.to_kgs(utils_test.BOARD_SIZE, None), 'pass')
self.assertEqual(coords.to_pygtp(utils_test.BOARD_SIZE, None), (0, 0))
def test_pass(self):
self.assertEqual(coords.from_sgf(''), None)
self.assertEqual(coords.from_flat(81), None)
self.assertEqual(coords.from_kgs('pass'), None)
self.assertEqual(coords.from_pygtp((0, 0)), None)
self.assertEqual(coords.to_sgf(None), '')
self.assertEqual(coords.to_flat(None), 81)
self.assertEqual(coords.to_kgs(None), 'pass')
self.assertEqual(coords.to_pygtp(None), (0, 0))
def test_pass(self):
self.assertEqual(coords.from_sgf(''), None)
self.assertEqual(coords.from_flat(utils_test.BOARD_SIZE, 81), None)
self.assertEqual(coords.from_kgs(utils_test.BOARD_SIZE, 'pass'), None)
self.assertEqual(coords.from_pygtp(utils_test.BOARD_SIZE, (0, 0)),
None)
self.assertEqual(coords.to_sgf(None), '')
self.assertEqual(coords.to_flat(utils_test.BOARD_SIZE, None), 81)
self.assertEqual(coords.to_kgs(utils_test.BOARD_SIZE, None), 'pass')
self.assertEqual(coords.to_pygtp(utils_test.BOARD_SIZE, None), (0, 0))
def play_move(self, c, color=None, mutate=False):
# Obeys CGOS Rules of Play. In short:
# No suicides
# Chinese/area scoring
# Positional superko (this is very crudely approximate at the moment.)
if color is None:
color = self.to_play
pos = self if mutate else copy.deepcopy(self)
if c is None:
pos = pos.pass_move(mutate=mutate)
return pos
if not self.is_move_legal(c):
raise IllegalMove('{} move at {} is illegal: \n{}'.format(
'Black' if self.to_play == BLACK else 'White',
coords.to_kgs(c), self))
potential_ko = is_koish(self.board_size, self.board, c)
place_stones(pos.board, color, [c])
captured_stones = pos.lib_tracker.add_stone(color, c)
place_stones(pos.board, EMPTY, captured_stones)
opp_color = color * -1
new_board_delta = np.zeros([self.board_size, self.board_size],
dtype=np.int8)
new_board_delta[c] = color
place_stones(new_board_delta, color, captured_stones)
if len(captured_stones) == 1 and potential_ko == opp_color:
new_ko = list(captured_stones)[0]
else:
new_ko = None
if pos.to_play == BLACK:
new_caps = (pos.caps[0] + len(captured_stones), pos.caps[1])
else:
new_caps = (pos.caps[0], pos.caps[1] + len(captured_stones))
pos.n += 1
pos.caps = new_caps
pos.ko = new_ko
pos.recent += (PlayerMove(color, c), )
# keep a rolling history of last 7 deltas - that's all we'll need to
# extract the last 8 board states.
pos.board_deltas = np.concatenate(
(new_board_delta.reshape(1, self.board_size,
self.board_size), pos.board_deltas[:6]))
pos.to_play *= -1
return pos
def _minigui_report_search_status(self, leaves):
"""Prints the current MCTS search status to stderr.
Reports the current search path, root node's child_Q, root node's
child_N, the most visited path in a format that can be parsed by
one of the STDERR_HANDLERS in minigui.ts.
Args:
leaves: list of leaf MCTSNodes returned by tree_search().
"""
root = self._player.get_root()
msg = {
"moveNum": root.position.n,
"toPlay": "B" if root.position.to_play == go.BLACK else "W",
}
if leaves:
path = []
leaf = leaves[0]
while leaf != root:
path.append(leaf.fmove)
leaf = leaf.parent
msg["search"] = [coords.to_kgs(coords.from_flat(m))
for m in reversed(path)]
else:
msg["search"] = []
dq = root.child_Q - root.Q
msg["dq"] = [int(round(x * 100)) for x in dq]
msg["n"] = [int(n) for n in root.child_N]
nodes = root.most_visited_path_nodes()
pv = [coords.to_kgs(coords.from_flat(m.fmove)) for m in nodes]
if pv != self._last_pv:
msg["pv"] = pv
self._last_pv = pv
dbg("mg-search:%s" % json.dumps(msg, sort_keys=True))
def play_move(self, c, color=None, mutate=False):
# Obeys CGOS Rules of Play. In short:
# No suicides
# Chinese/area scoring
# Positional superko (this is very crudely approximate at the moment.)
if color is None:
color = self.to_play
pos = self if mutate else copy.deepcopy(self)
if c is None:
pos = pos.pass_move(mutate=mutate)
return pos
if not self.is_move_legal(c):
raise IllegalMove("{} move at {} is illegal: \n{}".format(
"Black" if self.to_play == BLACK else "White",
coords.to_kgs(c), self))
potential_ko = is_koish(self.board, c)
place_stones(pos.board, color, [c])
captured_stones = pos.lib_tracker.add_stone(color, c)
place_stones(pos.board, EMPTY, captured_stones)
opp_color = color * -1
new_board_delta = np.zeros([N, N], dtype=np.int8)
new_board_delta[c] = color
place_stones(new_board_delta, color, captured_stones)
if len(captured_stones) == 1 and potential_ko == opp_color:
new_ko = list(captured_stones)[0]
else:
new_ko = None
if pos.to_play == BLACK:
new_caps = (pos.caps[0] + len(captured_stones), pos.caps[1])
else:
new_caps = (pos.caps[0], pos.caps[1] + len(captured_stones))
pos.n += 1
pos.caps = new_caps
pos.ko = new_ko
pos.recent += (PlayerMove(color, c),)
# keep a rolling history of last 7 deltas - that's all we'll need to
# extract the last 8 board states.
pos.board_deltas = np.concatenate((
new_board_delta.reshape(1, N, N),
pos.board_deltas[:6]))
pos.to_play *= -1
return pos
def test_upperleft(self):
self.assertEqual(coords.from_sgf('aa'), (0, 0))
self.assertEqual(coords.from_flat(utils_test.BOARD_SIZE, 0), (0, 0))
self.assertEqual(coords.from_kgs(utils_test.BOARD_SIZE, 'A9'), (0, 0))
self.assertEqual(coords.from_pygtp(utils_test.BOARD_SIZE, (1, 9)),
(0, 0))
self.assertEqual(coords.to_sgf((0, 0)), 'aa')
self.assertEqual(coords.to_flat(utils_test.BOARD_SIZE, (0, 0)), 0)
self.assertEqual(coords.to_kgs(utils_test.BOARD_SIZE, (0, 0)), 'A9')
self.assertEqual(coords.to_pygtp(utils_test.BOARD_SIZE, (0, 0)),
(1, 9))
def test_topleft(self):
self.assertEqual(coords.from_sgf('ia'), (0, 8))
self.assertEqual(coords.from_flat(utils_test.BOARD_SIZE, 8), (0, 8))
self.assertEqual(coords.from_kgs(utils_test.BOARD_SIZE, 'J9'), (0, 8))
self.assertEqual(coords.from_pygtp(utils_test.BOARD_SIZE, (9, 9)),
(0, 8))
self.assertEqual(coords.to_sgf((0, 8)), 'ia')
self.assertEqual(coords.to_flat(utils_test.BOARD_SIZE, (0, 8)), 8)
self.assertEqual(coords.to_kgs(utils_test.BOARD_SIZE, (0, 8)), 'J9')
self.assertEqual(coords.to_pygtp(utils_test.BOARD_SIZE, (0, 8)),
(9, 9))
def most_visited_path(self):
node = self
output = []
while node.children:
next_kid = np.argmax(node.child_N)
node = node.children.get(next_kid)
if node is None:
output.append("GAME END")
break
output.append(
"%s (%d) ==> " %
(coords.to_kgs(coords.from_flat(node.fmove)), node.N))
output.append("Q: {:.5f}\n".format(node.Q))
return ''.join(output)
def most_visited_path(self):
node = self
output = []
while node.children:
next_kid = np.argmax(node.child_N)
node = node.children.get(next_kid)
if node is None:
output.append("GAME END")
break
output.append("%s (%d) ==> " % (coords.to_kgs(
coords.from_flat(node.fmove)),
node.N))
output.append("Q: {:.5f}\n".format(node.Q))
return ''.join(output)
def most_visited_path(self):
node = self
output = []
while node.children:
next_kid = np.argmax(node.child_N)
node = node.children.get(next_kid)
if node is None:
output.append('GAME END')
break
output.append('{} ({}) ==> '.format(
coords.to_kgs(
self.board_size,
coords.from_flat(self.board_size, node.fmove)), node.N))
output.append('Q: {:.5f}\n'.format(node.Q))
return ''.join(output)
def most_visited_path(self):
node = self
output = []
while node.children:
next_kid = np.argmax(node.child_N)
node = node.children.get(next_kid)
if node is None:
output.append('GAME END')
break
output.append('{} ({}) ==> '.format(
coords.to_kgs(self.board_size,
coords.from_flat(self.board_size, node.fmove)),
node.N))
output.append('Q: {:.5f}\n'.format(node.Q))
return ''.join(output)
def extract_move_data(root_node, worker_id, completed_time, board_size):
current_node = root_node.next
move_data = []
move_num = 1
while current_node is not None:
props = current_node.properties
if 'B' in props:
to_play = 1
move_played = props['B'][0]
elif 'W' in props:
to_play = -1
move_played = props['W'][0]
else:
import pdb; pdb.set_trace()
move_played = coords.to_flat(coords.from_sgf(move_played))
post_Q, debug_rows = parse_comment_node(props['C'][0])
policy_prior = [0] * (board_size * board_size + 1)
policy_prior_orig = policy_prior[:]
mcts_visit_counts = policy_prior[:]
mcts_visit_counts_norm = policy_prior[:]
for debug_row in debug_rows:
move = debug_row.move
policy_prior[move] = debug_row.prior
policy_prior_orig[move] = debug_row.orig_prior
mcts_visit_counts[move] = debug_row.N
mcts_visit_counts_norm[move] = debug_row.soft_N
move_data.append({
'worker_id': worker_id,
'completed_time': completed_time,
'move_num': move_num,
'turn_to_play': to_play,
'move': move_played,
'move_kgs': coords.to_kgs(coords.from_flat(move_played)),
'prior_Q': None,
'post_Q': post_Q,
'policy_prior': policy_prior,
'policy_prior_orig': policy_prior_orig,
'mcts_visit_counts': mcts_visit_counts,
'mcts_visit_counts_norm': mcts_visit_counts_norm,
})
move_num += 1
current_node = current_node.next
return move_data
def cmd_genmove(self, color=None):
if color is not None:
self._accomodate_out_of_turn(color)
if self._courtesy_pass:
# If courtesy pass is True and the previous move was a pass, we'll
# pass too, regardless of score or our opinion on the game.
position = self._player.get_position()
if position.recent and position.recent[-1].move is None:
return "pass"
move = self._player.suggest_move(self._player.get_position())
if self._player.should_resign():
self._player.set_result(-1 * self._player.get_position().to_play,
was_resign=True)
return "resign"
self._player.play_move(move)
if self._player.get_root().is_done():
self._player.set_result(self._player.get_position().result(),
was_resign=False)
return coords.to_kgs(move)
def cmd_gamestate(self):
position = self._player.get_position()
root = self._player.get_root()
msg = {}
board = []
for row in range(go.N):
for col in range(go.N):
stone = position.board[row, col]
if stone == go.BLACK:
board.append("X")
elif stone == go.WHITE:
board.append("O")
else:
board.append(".")
msg["board"] = "".join(board)
if position.recent:
msg["lastMove"] = coords.to_kgs(position.recent[-1].move)
else:
msg["lastMove"] = None
msg["toPlay"] = "B" if position.to_play == 1 else "W"
msg["moveNum"] = position.n
msg["q"] = root.parent.Q if root.parent and root.parent.parent else 0
msg["gameOver"] = position.is_game_over()
dbg("mg-gamestate:%s" % json.dumps(msg, sort_keys=True))
def play(board_size, network, readouts, resign_threshold, simultaneous_leaves,
verbosity=0):
"""Plays out a self-play match.
Args:
board_size: the go board size
network: the DualNet model
readouts: the number of readouts in MCTS
resign_threshold: the threshold to resign at in the match
simultaneous_leaves: the number of simultaneous leaves in MCTS
verbosity: the verbosity of the self-play match
Returns:
the final position
the n x 362 tensor of floats representing the mcts search probabilities
the n-ary tensor of floats representing the original value-net estimate
where n is the number of moves in the game.
"""
player = MCTSPlayer(board_size, network, resign_threshold=resign_threshold,
verbosity=verbosity, num_parallel=simultaneous_leaves)
# Disable resign in 5% of games
if random.random() < 0.05:
player.resign_threshold = -1.0
player.initialize_game()
# Must run this once at the start, so that noise injection actually
# affects the first move of the game.
first_node = player.root.select_leaf()
prob, val = network.run(first_node.position)
first_node.incorporate_results(prob, val, first_node)
while True:
start = time.time()
player.root.inject_noise()
current_readouts = player.root.N
# we want to do "X additional readouts", rather than "up to X readouts".
while player.root.N < current_readouts + readouts:
player.tree_search()
if verbosity >= 3:
print(player.root.position)
print(player.root.describe())
if player.should_resign():
player.set_result(-1 * player.root.position.to_play, was_resign=True)
break
move = player.pick_move()
player.play_move(move)
if player.root.is_done():
player.set_result(player.root.position.result(), was_resign=False)
break
if (verbosity >= 2) or (
verbosity >= 1 and player.root.position.n % 10 == 9):
print("Q: {:.5f}".format(player.root.Q))
dur = time.time() - start
print("%d: %d readouts, %.3f s/100. (%.2f sec)" % (
player.root.position.n, readouts, dur / readouts * 100.0, dur))
if verbosity >= 3:
print("Played >>",
coords.to_kgs(coords.from_flat(player.root.fmove)))
if verbosity >= 2:
print("%s: %.3f" % (player.result_string, player.root.Q), file=sys.stderr)
print(player.root.position,
player.root.position.score(), file=sys.stderr)
return player
def fmt(move):
return '{}-{}'.format('b' if move.color == 1 else 'w',
coords.to_kgs(self.board_size, move.move))
def fmt(move): return "{}-{}".format('b' if move.color == 1 else 'w',
coords.to_kgs(move.move))
path = " ".join(fmt(move) for move in pos.recent[-diff:])
def heatmap(self, sort_order, node, prop):
return "\n".join(["{!s:6} {}".format(
coords.to_kgs(coords.from_flat(key)),
node.__dict__.get(prop)[key])
for key in sort_order if node.child_N[key] > 0][:20])
def fmt(move): return "{}-{}".format('b' if move.color == 1 else 'w',
coords.to_kgs(move.move))
path = " ".join(fmt(move) for move in pos.recent[-diff:])
def _heatmap(self, sort_order, node, prop):
return "\n".join([
"{!s:6} {}".format(coords.to_kgs(coords.from_flat(key)),
node.__dict__.get(prop)[key])
for key in sort_order if node.child_N[key] > 0
][:20])
def play(network):
''' Plays out a self-play match, returning a MCTSPlayer object containing:
- the final position
- the n x 362 tensor of floats representing the mcts search probabilities
- the n-ary tensor of floats representing the original value-net estimate
where n is the number of moves in the game'''
readouts = FLAGS.num_readouts # defined in strategies.py
# Disable resign in 5% of games
if random.random() < FLAGS.resign_disable_pct:
resign_threshold = -1.0
else:
resign_threshold = None
player = MCTSPlayer(network, resign_threshold=resign_threshold)
player.initialize_game()
# Must run this once at the start to expand the root node.
first_node = player.root.select_leaf()
prob, val = network.run(first_node.position)
first_node.incorporate_results(prob, val, first_node)
while True:
start = time.time()
player.root.inject_noise()
current_readouts = player.root.N
# we want to do "X additional readouts", rather than "up to X readouts".
while player.root.N < current_readouts + readouts:
player.tree_search()
if FLAGS.verbose >= 3:
print(player.root.position)
print(player.root.describe())
if player.should_resign():
player.set_result(-1 * player.root.position.to_play,
was_resign=True)
break
move = player.pick_move()
player.play_move(move)
if player.root.is_done():
player.set_result(player.root.position.result(), was_resign=False)
break
if (FLAGS.verbose >= 2) or (FLAGS.verbose >= 1
and player.root.position.n % 10 == 9):
print("Q: {:.5f}".format(player.root.Q))
dur = time.time() - start
print("%d: %d readouts, %.3f s/100. (%.2f sec)" %
(player.root.position.n, readouts, dur / readouts * 100.0,
dur),
flush=True)
if FLAGS.verbose >= 3:
print("Played >>",
coords.to_kgs(coords.from_flat(player.root.fmove)))
if FLAGS.verbose >= 2:
utils.dbg("%s: %.3f" % (player.result_string, player.root.Q))
utils.dbg(player.root.position, player.root.position.score())
return player
def play(network, readouts, resign_threshold, verbosity=0):
''' Plays out a self-play match, returning
- the final position
- the n x 362 tensor of floats representing the mcts search probabilities
- the n-ary tensor of floats representing the original value-net estimate
where n is the number of moves in the game'''
player = MCTSPlayer(network,
resign_threshold=resign_threshold,
verbosity=verbosity,
num_parallel=SIMULTANEOUS_LEAVES)
global_n = 0
# Disable resign in 5% of games
if random.random() < 0.05:
player.resign_threshold = -1.0
player.initialize_game()
# Must run this once at the start, so that noise injection actually
# affects the first move of the game.
first_node = player.root.select_leaf()
prob, val = network.run(first_node.position)
# print("prob", prob)
# print("val", val)
first_node.incorporate_results(prob, val, first_node)
while True:
start = time.time()
player.root.inject_noise()
current_readouts = player.root.N
# we want to do "X additional readouts", rather than "up to X readouts".
while player.root.N < current_readouts + readouts:
player.tree_search()
if (verbosity >= 3):
print(player.root.position)
print(player.root.describe())
if player.should_resign():
player.set_result(-1 * player.root.position.to_play,
was_resign=True)
break
move = player.pick_move()
player.play_move(move)
if player.root.is_done():
player.set_result(player.root.position.result(), was_resign=False)
break
if (verbosity >= 2) or (verbosity >= 1
and player.root.position.n % 10 == 9):
print("Q: {:.5f}".format(player.root.Q))
dur = time.time() - start
print("%d: %d readouts, %.3f s/100. (%.2f sec)" %
(player.root.position.n, readouts, dur / readouts * 100.0,
dur),
flush=True)
if verbosity >= 3:
print("Played >>",
coords.to_kgs(coords.from_flat(player.root.fmove)))
if verbosity >= 2:
print("%s: %.3f" % (player.result_string, player.root.Q),
file=sys.stderr)
print(player.root.position,
player.root.position.score(),
file=sys.stderr)
return player
def play_move(self, c, color=None, mutate=False):
"""Obeys CGOS Rules of Play.
In short:
No suicides
Chinese/area scoring
Positional superko (this is very crudely approximate at the moment.)
Args:
c: the coordinate to play from.
color: the color of the player to play.
mutate:
Returns:
The position of next move.
Raises:
IllegalMove: if the input c is an illegal move.
"""
if color is None:
color = self.to_play
pos = self if mutate else copy.deepcopy(self)
if c is None:
pos = pos.pass_move(mutate=mutate)
return pos
if not self.is_move_legal(c):
raise IllegalMove('{} move at {} is illegal: \n{}'.format(
'Black' if self.to_play == BLACK else 'White',
coords.to_kgs(self.board_size, c), self))
potential_ko = is_koish(self.board_size, self.board, c)
place_stones(pos.board, color, [c])
captured_stones = pos.lib_tracker.add_stone(color, c)
place_stones(pos.board, EMPTY, captured_stones)
opp_color = -1 * color
new_board_delta = np.zeros([self.board_size, self.board_size],
dtype=np.int8)
new_board_delta[c] = color
place_stones(new_board_delta, color, captured_stones)
if len(captured_stones) == 1 and potential_ko == opp_color:
new_ko = list(captured_stones)[0]
else:
new_ko = None
if pos.to_play == BLACK:
new_caps = (pos.caps[0] + len(captured_stones), pos.caps[1])
else:
new_caps = (pos.caps[0], pos.caps[1] + len(captured_stones))
pos.n += 1
pos.caps = new_caps
pos.ko = new_ko
pos.recent += (PlayerMove(color, c),)
# keep a rolling history of last 7 deltas - that's all we'll need to
# extract the last 8 board states.
pos.board_deltas = np.concatenate((
new_board_delta.reshape(1, self.board_size, self.board_size),
pos.board_deltas[:6]))
pos.to_play *= -1
return pos
def fmt(move):
return "{}-{}".format('b' if move.color == go.BLACK else 'w',
coords.to_kgs(move.move))
def fmt(move):
return '{}-{}'.format('b' if move.color == 1 else 'w',
coords.to_kgs(self.board_size, move.move))
