def test_flatten(self):
self.assertEqual(coords.flatten_coords((0, 0)), 0)
self.assertEqual(coords.flatten_coords((0, 3)), 3)
self.assertEqual(coords.flatten_coords((3, 0)), 27)
self.assertEqual(coords.unflatten_coords(27), (3, 0))
self.assertEqual(coords.unflatten_coords(10), (1, 1))
self.assertEqual(coords.unflatten_coords(80), (8, 8))
self.assertEqual(coords.flatten_coords(coords.unflatten_coords(10)),
10)
self.assertEqual(
coords.unflatten_coords(coords.flatten_coords((5, 4))), (5, 4))
def test_proper_move_transform(self):
# Check that the reinterpretation of 362 = 19*19 + 1 during symmetry
# application is consistent with coords.unflatten_coords
move_array = np.arange(go.N ** 2 + 1)
coord_array = np.zeros([go.N, go.N])
for c in range(go.N ** 2):
coord_array[coords.unflatten_coords(c)] = c
for s in symmetries.SYMMETRIES:
with self.subTest(symmetry=s):
transformed_moves = apply_p(s, move_array)
transformed_board = apply_f(s, coord_array)
for new_coord, old_coord in enumerate(transformed_moves[:-1]):
self.assertEqual(
old_coord,
transformed_board[coords.unflatten_coords(new_coord)])
def test_proper_move_transform(self):
# Check that the reinterpretation of 362 = 19*19 + 1 during symmetry
# application is consistent with coords.unflatten_coords
move_array = np.arange(go.N**2 + 1)
coord_array = np.zeros([go.N, go.N])
for c in range(go.N**2):
coord_array[coords.unflatten_coords(c)] = c
for s in symmetries.SYMMETRIES:
with self.subTest(symmetry=s):
transformed_moves = apply_p(s, move_array)
transformed_board = apply_f(s, coord_array)
for new_coord, old_coord in enumerate(transformed_moves[:-1]):
self.assertEqual(
old_coord,
transformed_board[coords.unflatten_coords(new_coord)])
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_human_coord(coords.unflatten_coords(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 heatmap(self, sort_order, node, prop):
return "\n".join([
"{!s:6} {}".format(
coords.to_human_coord(coords.unflatten_coords(key)),
node.__dict__.get(prop)[key]) for key in sort_order
if node.child_N[key] > 0
][:20])
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 = -0.9999
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())
# Sets is_done to be True if player.should resign.
if player.should_resign(): # TODO: make this less side-effecty.
break
move = player.pick_move()
player.play_move(move)
if player.is_done():
# TODO: actually handle the result instead of ferrying it around as a property.
player.result = player.position.result()
break
if (verbosity >= 2) or (verbosity >= 1 and player.root.position.n % 10 == 9):
print("Q: {}".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_human_coord(coords.unflatten_coords(player.root.fmove)))
# TODO: break when i >= 2 * go.N * go.N (where is this being done now??...)
return player
def maybe_add_child(self, fcoord):
""" Adds child node for fcoord if it doesn't already exist, and returns it. """
if fcoord not in self.children:
new_position = self.position.play_move(
coords.unflatten_coords(fcoord))
self.children[fcoord] = MCTSNode(new_position,
fmove=fcoord,
parent=self)
return self.children[fcoord]
def test_upperleft(self):
self.assertEqual(coords.parse_sgf_coords('aa'), (0, 0))
self.assertEqual(coords.unflatten_coords(0), (0, 0))
self.assertEqual(coords.parse_kgs_coords('A9'), (0, 0))
self.assertEqual(coords.parse_pygtp_coords((1, 9)), (0, 0))
self.assertEqual(coords.unparse_sgf_coords((0, 0)), 'aa')
self.assertEqual(coords.flatten_coords((0, 0)), 0)
self.assertEqual(coords.to_human_coord((0, 0)), 'A9')
self.assertEqual(coords.unparse_pygtp_coords((0, 0)), (1, 9))
def test_topleft(self):
self.assertEqual(coords.parse_sgf_coords('ia'), (0, 8))
self.assertEqual(coords.unflatten_coords(8), (0, 8))
self.assertEqual(coords.parse_kgs_coords('J9'), (0, 8))
self.assertEqual(coords.parse_pygtp_coords((9, 9)), (0, 8))
self.assertEqual(coords.unparse_sgf_coords((0, 8)), 'ia')
self.assertEqual(coords.flatten_coords((0, 8)), 8)
self.assertEqual(coords.to_human_coord((0, 8)), 'J9')
self.assertEqual(coords.unparse_pygtp_coords((0, 8)), (9, 9))
def test_pass(self):
self.assertEqual(coords.parse_sgf_coords(''), None)
self.assertEqual(coords.unflatten_coords(81), None)
self.assertEqual(coords.parse_kgs_coords('pass'), None)
self.assertEqual(coords.parse_pygtp_coords((0, 0)), None)
self.assertEqual(coords.unparse_sgf_coords(None), '')
self.assertEqual(coords.flatten_coords(None), 81)
self.assertEqual(coords.to_human_coord(None), 'pass')
self.assertEqual(coords.unparse_pygtp_coords(None), (0, 0))
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_human_coord(coords.unflatten_coords(node.fmove)))
return ' '.join(output)
def test_legal_moves(self):
board = test_utils.load_board('''
.O.O.XOX.
O..OOOOOX
......O.O
OO.....OX
XO.....X.
.O.......
OX.....OO
XX...OOOX
.....O.X.
''')
position = Position(board=board, to_play=BLACK)
illegal_moves = parse_kgs_coords_set('A9 E9 J9')
legal_moves = parse_kgs_coords_set('A4 G1 J1 H7') | {None}
for move in illegal_moves:
with self.subTest(type='illegal', move=move):
self.assertFalse(position.is_move_legal(move))
for move in legal_moves:
with self.subTest(type='legal', move=move):
self.assertTrue(position.is_move_legal(move))
# check that the bulk legal test agrees with move-by-move illegal test.
bulk_legality = position.all_legal_moves()
for i, bulk_legal in enumerate(bulk_legality):
with self.subTest(type='bulk', move=unflatten_coords(i)):
self.assertEqual(bulk_legal,
position.is_move_legal(unflatten_coords(i)))
# flip the colors and check that everything is still (il)legal
position = Position(board=-board, to_play=WHITE)
for move in illegal_moves:
with self.subTest(type='illegal', move=move):
self.assertFalse(position.is_move_legal(move))
for move in legal_moves:
with self.subTest(type='legal', move=move):
self.assertTrue(position.is_move_legal(move))
bulk_legality = position.all_legal_moves()
for i, bulk_legal in enumerate(bulk_legality):
with self.subTest(type='bulk', move=unflatten_coords(i)):
self.assertEqual(bulk_legal,
position.is_move_legal(unflatten_coords(i)))
def test_legal_moves(self):
board = test_utils.load_board('''
.O.O.XOX.
O..OOOOOX
......O.O
OO.....OX
XO.....X.
.O.......
OX.....OO
XX...OOOX
.....O.X.
''')
position = Position(board=board, to_play=BLACK)
illegal_moves = parse_kgs_coords_set('A9 E9 J9')
legal_moves = parse_kgs_coords_set('A4 G1 J1 H7') | {None}
for move in illegal_moves:
with self.subTest(type='illegal', move=move):
self.assertFalse(position.is_move_legal(move))
for move in legal_moves:
with self.subTest(type='legal', move=move):
self.assertTrue(position.is_move_legal(move))
# check that the bulk legal test agrees with move-by-move illegal test.
bulk_legality = position.all_legal_moves()
for i, bulk_legal in enumerate(bulk_legality):
with self.subTest(type='bulk', move=unflatten_coords(i)):
self.assertEqual(
bulk_legal, position.is_move_legal(unflatten_coords(i)))
# flip the colors and check that everything is still (il)legal
position = Position(board=-board, to_play=WHITE)
for move in illegal_moves:
with self.subTest(type='illegal', move=move):
self.assertFalse(position.is_move_legal(move))
for move in legal_moves:
with self.subTest(type='legal', move=move):
self.assertTrue(position.is_move_legal(move))
bulk_legality = position.all_legal_moves()
for i, bulk_legal in enumerate(bulk_legality):
with self.subTest(type='bulk', move=unflatten_coords(i)):
self.assertEqual(
bulk_legal, position.is_move_legal(unflatten_coords(i)))
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_human_coord(
coords.unflatten_coords(node.fmove)), node.N))
output.append("Q: {:.5f}\n".format(node.Q))
return ''.join(output)
def pick_move(self):
'''Picks a move to play, based on MCTS readout statistics.
Highest N is most robust indicator. In the early stage of the game, pick
a move weighted by visit count; later on, pick the absolute max.'''
if self.root.position.n > self.temp_threshold:
fcoord = np.argmax(self.root.child_N)
else:
cdf = self.root.child_N.cumsum()
cdf /= cdf[-1]
selection = random.random()
fcoord = cdf.searchsorted(selection)
assert self.root.child_N[fcoord] != 0
return coords.unflatten_coords(fcoord)
def pick_move(self):
'''Picks a move to play, based on MCTS readout statistics.
Highest N is most robust indicator. In the early stage of the game, pick
a move weighted by visit count; later on, pick the absolute max.'''
if self.root.position.n > self.temp_threshold:
fcoord = np.argmax(self.root.child_N)
else:
cdf = self.root.child_N.cumsum()
cdf /= cdf[-1]
selection = random.random()
fcoord = cdf.searchsorted(selection)
assert self.root.child_N[fcoord] != 0
return coords.unflatten_coords(fcoord)
def heatmap(self, sort_order, node, prop):
return "\n".join(["{!s:6} {}".format(
coords.to_human_coord(coords.unflatten_coords(key)),
node.__dict__.get(prop)[key])
for key in sort_order if node.child_N[key] > 0][:20])
def eval_player(player, positions, moves, results): probs, values = batch_run_many(player, positions) policy_moves = [coords.unflatten_coords(c) for c in np.argmax(probs, axis=1)] top_move_agree = [moves[idx] == policy_moves[idx] for idx in range(len(moves))] square_err = (values - results)**2/4 return top_move_agree, square_err
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)
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_human_coord(coords.unflatten_coords(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
