class Game:
"""Represents a game of go, including an implementation of capture rules."""
DEFAULT_PROPERTIES = {"GM": 1, "FF": 4}
def __init__(
self,
katrain,
engine: Union[Dict, KataGoEngine],
move_tree: GameNode = None,
analyze_fast=False,
game_properties: Optional[Dict] = None,
sgf_filename=None,
):
self.katrain = katrain
self._lock = threading.Lock()
if not isinstance(engine, Dict):
engine = {"B": engine, "W": engine}
self.engines = engine
self.game_id = datetime.strftime(datetime.now(), "%Y-%m-%d %H %M %S")
self.sgf_filename = sgf_filename
self.insert_mode = False
self.insert_after = None
self.region_of_interest = None
self.external_game = False # not generated by katrain at some point
if move_tree:
self.root = move_tree
self.external_game = PROGRAM_NAME not in self.root.get_property(
"AP", "")
self.komi = self.root.komi
handicap = int(self.root.handicap)
num_starting_moves_black = 0
node = self.root
while node.children:
node = node.children[0]
if node.player == "B":
num_starting_moves_black += 1
else:
break
if (
handicap >= 2 and not self.root.placements
and not (num_starting_moves_black == handicap)
and not (self.root.children
and self.root.children[0].placements)
): # not really according to sgf, and not sure if still needed, last clause for fox
self.root.place_handicap_stones(handicap)
else:
board_size = katrain.config("game/size")
rules = katrain.config("game/rules")
self.komi = katrain.config("game/komi")
self.root = GameNode(
properties={
**Game.DEFAULT_PROPERTIES,
**{
"SZ": board_size,
"KM": self.komi,
"DT": self.game_id,
"RU": rules
},
**(game_properties or {}),
})
handicap = katrain.config("game/handicap")
if handicap:
self.root.place_handicap_stones(handicap)
if not self.root.get_property("RU"):
self.root.set_property("RU", katrain.config("game/rules"))
self.set_current_node(self.root)
self.main_time_used = 0
# restore shortcuts
shortcut_id_to_node = {
node.get_property("KTSID", None): node
for node in self.root.nodes_in_tree
}
for node in self.root.nodes_in_tree:
shortcut_id = node.get_property("KTSF", None)
if shortcut_id and shortcut_id in shortcut_id_to_node:
shortcut_id_to_node[shortcut_id].add_shortcut(node)
threading.Thread(
target=lambda: self.analyze_all_nodes(
-1_000_000, analyze_fast=analyze_fast, even_if_present=False),
daemon=True,
).start() # return faster, but bypass Kivy Clock
def analyze_all_nodes(self,
priority=0,
analyze_fast=False,
even_if_present=True):
for node in self.root.nodes_in_tree:
# forced, or not present, or something went wrong in loading
if even_if_present or not node.analysis_from_sgf or not node.load_analysis(
):
node.clear_analysis()
node.analyze(self.engines[node.next_player],
priority=priority,
analyze_fast=analyze_fast)
# -- move tree functions --
def _calculate_groups(self):
board_size_x, board_size_y = self.board_size
with self._lock:
self.board = [[-1 for _x in range(board_size_x)]
for _y in range(board_size_y)
] # type: List[List[int]] # board pos -> chain id
self.chains = [] # type: List[List[Move]] # chain id -> chain
self.prisoners = [] # type: List[Move]
self.last_capture = [] # type: List[Move]
try:
for node in self.current_node.nodes_from_root:
for m in node.move_with_placements:
self._validate_move_and_update_chains(
m, True
) # ignore ko since we didn't know if it was forced
except IllegalMoveException as e:
raise Exception(f"Unexpected illegal move ({str(e)})")
def _validate_move_and_update_chains(self, move: Move, ignore_ko: bool):
board_size_x, board_size_y = self.board_size
def neighbours(moves):
return {
self.board[m.coords[1] + dy][m.coords[0] + dx]
for m in moves for dy, dx in [(-1, 0), (1, 0), (0, -1), (0, 1)]
if 0 <= m.coords[0] + dx < board_size_x and 0 <= m.coords[1] +
dy < board_size_y
}
ko_or_snapback = len(
self.last_capture) == 1 and self.last_capture[0] == move
self.last_capture = []
if move.is_pass:
return
if self.board[move.coords[1]][move.coords[0]] != -1:
raise IllegalMoveException("Space occupied")
nb_chains = list({
c
for c in neighbours([move])
if c >= 0 and self.chains[c][0].player == move.player
})
if nb_chains:
this_chain = nb_chains[0]
self.board = [[
nb_chains[0] if sq in nb_chains else sq for sq in line
] for line in self.board] # merge chains connected by this move
for oc in nb_chains[1:]:
self.chains[nb_chains[0]] += self.chains[oc]
self.chains[oc] = []
self.chains[nb_chains[0]].append(move)
else:
this_chain = len(self.chains)
self.chains.append([move])
self.board[move.coords[1]][move.coords[0]] = this_chain
opp_nb_chains = {
c
for c in neighbours([move])
if c >= 0 and self.chains[c][0].player != move.player
}
for c in opp_nb_chains:
if -1 not in neighbours(self.chains[c]):
self.last_capture += self.chains[c]
for om in self.chains[c]:
self.board[om.coords[1]][om.coords[0]] = -1
self.chains[c] = []
if ko_or_snapback and len(self.last_capture) == 1 and not ignore_ko:
raise IllegalMoveException("Ko")
self.prisoners += self.last_capture
if -1 not in neighbours(self.chains[this_chain]): # TODO: NZ rules?
raise IllegalMoveException("Suicide")
def set_insert_mode(self, mode):
if mode == "toggle":
mode = not self.insert_mode
if mode == self.insert_mode:
return
self.insert_mode = mode
if mode:
children = self.current_node.ordered_children
if not children:
self.insert_mode = False
else:
self.insert_after = self.current_node.ordered_children[0]
self.katrain.controls.set_status(
i18n._("starting insert mode"), STATUS_INFO)
else:
copy_from_node = self.insert_after
copy_to_node = self.current_node
num_copied = 0
if copy_to_node != self.insert_after.parent:
above_insertion_root = self.insert_after.parent.nodes_from_root
already_inserted_moves = [
n.move for n in copy_to_node.nodes_from_root
if n not in above_insertion_root and n.move
]
try:
while True:
if copy_from_node.move not in already_inserted_moves:
for m in copy_from_node.move_with_placements:
self._validate_move_and_update_chains(m, True)
# this inserts
copy_to_node = GameNode(
parent=copy_to_node,
properties=copy.deepcopy(
copy_from_node.properties))
num_copied += 1
if not copy_from_node.children:
break
copy_from_node = copy_from_node.ordered_children[0]
except IllegalMoveException:
pass # illegal move = stop
self._calculate_groups() # recalculate groups
self.katrain.controls.set_status(
i18n._("ending insert mode").format(num_copied=num_copied),
STATUS_INFO)
self.analyze_all_nodes(analyze_fast=True,
even_if_present=False)
else:
self.katrain.controls.set_status("", STATUS_INFO)
self.katrain.controls.move_tree.insert_node = self.insert_after if self.insert_mode else None
self.katrain.controls.move_tree.redraw()
self.katrain.update_state(redraw_board=True)
# Play a Move from the current position, raise IllegalMoveException if invalid.
def play(self, move: Move, ignore_ko: bool = False, analyze=True):
board_size_x, board_size_y = self.board_size
if not move.is_pass and not (0 <= move.coords[0] < board_size_x
and 0 <= move.coords[1] < board_size_y):
raise IllegalMoveException(
f"Move {move} outside of board coordinates")
try:
self._validate_move_and_update_chains(move, ignore_ko)
except IllegalMoveException:
self._calculate_groups()
raise
with self._lock:
played_node = self.current_node.play(move)
self.current_node = played_node
if analyze:
if self.region_of_interest:
played_node.analyze(self.engines[played_node.next_player],
analyze_fast=True)
played_node.analyze(self.engines[played_node.next_player],
region_of_interest=self.region_of_interest)
else:
played_node.analyze(self.engines[played_node.next_player])
return played_node
def set_current_node(self, node):
if self.insert_mode:
self.katrain.controls.set_status(
i18n._("finish inserting before navigating"), STATUS_ERROR)
return
self.current_node = node
self._calculate_groups()
def undo(self, n_times=1, stop_on_mistake=None):
# allow undo/delete only in insert mode
cn = self.current_node # avoid race conditions
if self.insert_mode: # in insert mode, undo = delete
if n_times == 1 and cn not in self.insert_after.nodes_from_root:
cn.parent.children = [c for c in cn.parent.children if c != cn]
self.current_node = cn.parent
self._calculate_groups()
return
break_on_branch = False
break_on_main_branch = False
last_branching_node = cn
if n_times == "branch":
n_times = 9999
break_on_branch = True
elif n_times == "main-branch":
n_times = 9999
break_on_main_branch = True
for move in range(n_times):
if (stop_on_mistake is not None and cn.points_lost is not None
and cn.points_lost >= stop_on_mistake
and self.katrain.players_info[cn.player].player_type !=
PLAYER_AI):
self.set_current_node(cn.parent)
return
previous_cn = cn
if cn.shortcut_from:
cn = cn.shortcut_from
elif not cn.is_root:
cn = cn.parent
else:
break # root
if break_on_branch and len(cn.children) > 1:
break
elif break_on_main_branch and cn.ordered_children[
0] != previous_cn: # implies > 1 child
last_branching_node = cn
if break_on_main_branch:
cn = last_branching_node
if cn is not self.current_node:
self.set_current_node(cn)
def redo(self, n_times=1, stop_on_mistake=None):
if self.insert_mode:
return
cn = self.current_node # avoid race conditions
for move in range(n_times):
if cn.children:
child = cn.ordered_children[0]
shortcut_to = [m for m, v in cn.shortcuts_to if child == v
] # are we about to go to a shortcut node?
if shortcut_to:
child = shortcut_to[0]
cn = child
if (move > 0 and stop_on_mistake is not None
and cn.points_lost is not None
and cn.points_lost >= stop_on_mistake
and self.katrain.players_info[cn.player].player_type !=
PLAYER_AI):
self.set_current_node(cn.parent)
return
if stop_on_mistake is None:
self.set_current_node(cn)
def cycle_children(self, direction):
cn = self.current_node # avoid race conditions
if cn.parent and len(cn.parent.children) > 1:
ordered_children = cn.parent.ordered_children
ix = (ordered_children.index(cn) + len(ordered_children) +
direction) % len(ordered_children)
self.set_current_node(ordered_children[ix])
@property
def board_size(self):
return self.root.board_size
@property
def stones(self):
with self._lock:
return sum(self.chains, [])
@property
def end_result(self):
if self.current_node.end_state:
return self.current_node.end_state
if self.current_node.parent and self.current_node.is_pass and self.current_node.parent.is_pass:
return self.manual_score or i18n._("board-game-end")
@property
def prisoner_count(
self,
) -> Dict: # returns prisoners that are of a certain colour as {B: black stones captures, W: white stones captures}
return {
player: sum([m.player == player for m in self.prisoners])
for player in Move.PLAYERS
}
@property
def manual_score(self):
rules = self.engines["B"].get_rules(self.root)
if not self.current_node.ownership or rules != "japanese":
if not self.current_node.score:
return None
self.katrain.log(
f"rules '{rules}' are not japanese, or no ownership available ({not self.current_node.ownership}) -> no manual score available",
OUTPUT_DEBUG,
)
return self.current_node.format_score(
round(2 * self.current_node.score) / 2) + "?"
board_size_x, board_size_y = self.board_size
ownership_grid = var_to_grid(self.current_node.ownership,
(board_size_x, board_size_y))
stones = {m.coords: m.player for m in self.stones}
lo_threshold = 0.15
hi_threshold = 0.85
max_unknown = 10
max_dame = 4 * (board_size_x + board_size_y)
def japanese_score_square(square, owner):
player = stones.get(square, None)
if ((player == "B" and owner > hi_threshold)
or (player == "W" and owner < -hi_threshold)
or abs(owner) < lo_threshold):
return 0 # dame or own stones
if player is None and abs(owner) >= hi_threshold:
return round(owner) # surrounded empty intersection
if (player == "B"
and owner < -hi_threshold) or (player == "W"
and owner > hi_threshold):
return 2 * round(owner) # captured stone
return math.nan # unknown!
scored_squares = [
japanese_score_square((x, y), ownership_grid[y][x])
for y in range(board_size_y) for x in range(board_size_x)
]
num_sq = {
t: sum([s == t for s in scored_squares])
for t in [-2, -1, 0, 1, 2]
}
num_unkn = sum(math.isnan(s) for s in scored_squares)
prisoners = self.prisoner_count
score = sum([t * n for t, n in num_sq.items()
]) + prisoners["W"] - prisoners["B"] - self.komi
self.katrain.log(
f"Manual Scoring: {num_sq} score by square with {num_unkn} unknown, {prisoners} captures, and {self.komi} komi -> score = {score}",
OUTPUT_DEBUG,
)
if num_unkn > max_unknown or (num_sq[0] - len(stones)) > max_dame:
return None
return self.current_node.format_score(score)
def __repr__(self):
return ("\n".join("".join(self.chains[c][0].player if c >= 0 else "-"
for c in line) for line in self.board) +
f"\ncaptures: {self.prisoner_count}")
def update_root_properties(self):
def player_name(player_info):
if player_info.name and player_info.player_type == PLAYER_HUMAN:
return player_info.name
else:
return f"{i18n._(player_info.player_type)} ({i18n._(player_info.player_subtype)})"
root_properties = self.root.properties
x_properties = {}
for bw in "BW":
if not self.external_game:
x_properties["P" +
bw] = player_name(self.katrain.players_info[bw]
) + SGF_INTERNAL_COMMENTS_MARKER
player_info = self.katrain.players_info[bw]
if player_info.player_type == PLAYER_AI:
x_properties[bw + "R"] = rank_label(
player_info.calculated_rank)
if "+" in str(self.end_result):
x_properties["RE"] = self.end_result
self.root.properties = {
**root_properties,
**{k: [v]
for k, v in x_properties.items()}
}
def generate_filename(self):
self.update_root_properties()
player_names = {
bw: re.sub(r"[\u200b\u3164'<>:\"/\\|?*]", "",
self.root.get_property("P" + bw, bw))
for bw in "BW"
}
base_game_name = f"{PROGRAM_NAME}_{player_names['B']} vs {player_names['W']}"
return f"{base_game_name} {self.game_id}.sgf"
def write_sgf(self, filename: str, trainer_config: Optional[Dict] = None):
if trainer_config is None:
trainer_config = self.katrain.config("trainer", {})
save_feedback = trainer_config.get("save_feedback", False)
eval_thresholds = trainer_config["eval_thresholds"]
save_analysis = trainer_config.get("save_analysis", False)
self.update_root_properties()
show_dots_for = {
bw: trainer_config.get("eval_show_ai", True)
or self.katrain.players_info[bw].human
for bw in "BW"
}
sgf = self.root.sgf(
save_comments_player=show_dots_for,
save_comments_class=save_feedback,
eval_thresholds=eval_thresholds,
save_analysis=save_analysis,
)
self.sgf_filename = filename
os.makedirs(os.path.dirname(filename), exist_ok=True)
with open(filename, "w", encoding="utf-8") as f:
f.write(sgf)
return i18n._("sgf written").format(file_name=filename)
def set_region_of_interest(self, region_of_interest):
x1, x2, y1, y2 = region_of_interest
xmin, xmax = min(x1, x2), max(x1, x2)
ymin, ymax = min(y1, y2), max(y1, y2)
szx, szy = self.board_size
if not (xmin == xmax and ymin == ymax) and not (
xmax - xmin + 1 >= szx and ymax - ymin + 1 >= szy):
self.region_of_interest = [xmin, xmax, ymin, ymax]
else:
self.region_of_interest = None
self.katrain.controls.set_status("", OUTPUT_INFO)
def analyze_extra(self, mode, **kwargs):
stones = {s.coords for s in self.stones}
cn = self.current_node
engine = self.engines[cn.next_player]
Clock.schedule_once(self.katrain.analysis_controls.hints.activate, 0)
if mode == "extra":
if kwargs.get("continuous", False):
visits = min(
1_000_000_000,
max(engine.config["max_visits"],
math.ceil(cn.analysis_visits_requested * 1.25)))
else:
visits = cn.analysis_visits_requested + engine.config[
"max_visits"]
self.katrain.controls.set_status(
i18n._("extra analysis").format(visits=visits),
STATUS_ANALYSIS)
self.katrain.controls.set_status(
i18n._("extra analysis").format(visits=visits),
STATUS_ANALYSIS)
cn.analyze(engine,
visits=visits,
priority=-1_000,
region_of_interest=self.region_of_interest,
time_limit=False)
return
if mode == "game":
nodes = self.root.nodes_in_tree
if "visits" in kwargs:
visits = kwargs["visits"]
else:
min_visits = min(node.analysis_visits_requested
for node in nodes)
visits = min_visits + engine.config["max_visits"]
for node in nodes:
node.analyze(engine,
visits=visits,
priority=-1_000_000,
time_limit=False,
report_every=None)
self.katrain.controls.set_status(
i18n._("game re-analysis").format(visits=visits),
STATUS_ANALYSIS)
return
elif mode == "sweep":
board_size_x, board_size_y = self.board_size
if cn.analysis_exists:
policy_grid = (var_to_grid(self.current_node.policy,
size=(board_size_x, board_size_y))
if self.current_node.policy else None)
analyze_moves = sorted(
[
Move(coords=(x, y), player=cn.next_player)
for x in range(board_size_x)
for y in range(board_size_y)
if (policy_grid is None and
(x, y) not in stones) or policy_grid[y][x] >= 0
],
key=lambda mv: -policy_grid[mv.coords[1]][mv.coords[0]],
)
else:
analyze_moves = [
Move(coords=(x, y), player=cn.next_player)
for x in range(board_size_x) for y in range(board_size_y)
if (x, y) not in stones
]
visits = engine.config["fast_visits"]
self.katrain.controls.set_status(
i18n._("sweep analysis").format(visits=visits),
STATUS_ANALYSIS)
priority = -1_000_000_000
elif mode in ["equalize", "alternative", "local"]:
if not cn.analysis_complete and mode != "local":
self.katrain.controls.set_status(
i18n._("wait-before-extra-analysis"), STATUS_INFO,
self.current_node)
return
if mode == "alternative": # also do a quick update on current candidates so it doesn't look too weird
self.katrain.controls.set_status(
i18n._("alternative analysis"), STATUS_ANALYSIS)
cn.analyze(engine,
priority=-500,
time_limit=False,
find_alternatives="alternative")
visits = engine.config["fast_visits"]
else: # equalize
visits = max(d["visits"]
for d in cn.analysis["moves"].values())
self.katrain.controls.set_status(
i18n._("equalizing analysis").format(visits=visits),
STATUS_ANALYSIS)
priority = -1_000
analyze_moves = [
Move.from_gtp(gtp, player=cn.next_player)
for gtp, _ in cn.analysis["moves"].items()
]
else:
raise ValueError("Invalid analysis mode")
for move in analyze_moves:
if cn.analysis["moves"].get(move.gtp(),
{"visits": 0})["visits"] < visits:
cn.analyze(
engine,
priority=priority,
visits=visits,
refine_move=move,
time_limit=False
) # explicitly requested so take as long as you need
def play_to_end(self):
cn = self.current_node
count = 0
if not cn.analysis_exists:
self.katrain.controls.set_status(
i18n._("wait-before-extra-analysis"), STATUS_INFO, cn)
return
def analyze_and_play_policy(node):
nonlocal count, cn
cand = node.candidate_moves
if self.katrain.game is not self:
return # a new game happened
if cand:
move = Move.from_gtp(cand[0]["move"], player=node.next_player)
else:
polmoves = node.policy_ranking
move = polmoves[0][1] if polmoves else Move(None)
if move.is_pass:
if self.current_node == cn:
self.set_current_node(node)
return
count += 1
new_node = GameNode(parent=node, move=move)
if node != cn:
node.remove_shortcut()
cn.add_shortcut(new_node)
self.katrain.controls.move_tree.redraw_tree_trigger()
def set_analysis(result, _partial):
new_node.set_analysis(result)
analyze_and_play_policy(new_node)
self.engines[node.next_player].request_analysis(
new_node,
callback=set_analysis,
priority=-1000,
analyze_fast=True)
analyze_and_play_policy(cn)
def analyze_undo(self, node):
train_config = self.katrain.config("trainer")
move = node.move
if node != self.current_node or node.auto_undo is not None or not node.analysis_complete or not move:
return
points_lost = node.points_lost
thresholds = train_config["eval_thresholds"]
num_undo_prompts = train_config["num_undo_prompts"]
i = 0
while i < len(thresholds) and points_lost < thresholds[i]:
i += 1
num_undos = num_undo_prompts[i] if i < len(num_undo_prompts) else 0
if num_undos == 0:
undo = False
elif num_undos < 1: # probability
undo = int(node.undo_threshold < num_undos) and len(
node.parent.children) == 1
else:
undo = len(node.parent.children) <= num_undos
node.auto_undo = undo
if undo:
self.undo(1)
self.katrain.controls.set_status(
i18n._("teaching undo message").format(
move=move.gtp(), points_lost=points_lost), STATUS_TEACHING)
self.katrain.update_state()
class BaseGame:
"""Represents a game of go, including an implementation of capture rules."""
DEFAULT_PROPERTIES = {"GM": 1, "FF": 4}
def __init__(
self,
katrain,
move_tree: GameNode = None,
game_properties: Optional[Dict] = None,
sgf_filename=None,
):
self.katrain = katrain
self._lock = threading.Lock()
self.game_id = datetime.strftime(datetime.now(), "%Y-%m-%d %H %M %S")
self.sgf_filename = sgf_filename
self.insert_mode = False
self.external_game = False # not generated by katrain at some point
if move_tree:
self.root = move_tree
self.external_game = PROGRAM_NAME not in self.root.get_property(
"AP", "")
self.komi = self.root.komi
handicap = int(self.root.handicap)
num_starting_moves_black = 0
node = self.root
while node.children:
node = node.children[0]
if node.player == "B":
num_starting_moves_black += 1
else:
break
if (
handicap >= 2 and not self.root.placements
and not (num_starting_moves_black == handicap)
and not (self.root.children
and self.root.children[0].placements)
): # not really according to sgf, and not sure if still needed, last clause for fox
self.root.place_handicap_stones(handicap)
else:
board_size = katrain.config("game/size")
rules = katrain.config("game/rules")
self.komi = katrain.config("game/komi")
self.root = GameNode(
properties={
**Game.DEFAULT_PROPERTIES,
**{
"SZ": board_size,
"KM": self.komi,
"DT": self.game_id,
"RU": rules
},
**(game_properties or {}),
})
handicap = katrain.config("game/handicap")
if handicap:
self.root.place_handicap_stones(handicap)
if not self.root.get_property("RU"):
self.root.set_property("RU", katrain.config("game/rules"))
self.set_current_node(self.root)
self.main_time_used = 0
# restore shortcuts
shortcut_id_to_node = {
node.get_property("KTSID", None): node
for node in self.root.nodes_in_tree
}
for node in self.root.nodes_in_tree:
shortcut_id = node.get_property("KTSF", None)
if shortcut_id and shortcut_id in shortcut_id_to_node:
shortcut_id_to_node[shortcut_id].add_shortcut(node)
# -- move tree functions --
def _init_state(self):
board_size_x, board_size_y = self.board_size
self.board = [[-1 for _x in range(board_size_x)]
for _y in range(board_size_y)
] # type: List[List[int]] # board pos -> chain id
self.chains = [] # type: List[List[Move]] # chain id -> chain
self.prisoners = [] # type: List[Move]
self.last_capture = [] # type: List[Move]
def _calculate_groups(self):
with self._lock:
self._init_state()
try:
for node in self.current_node.nodes_from_root:
for m in node.move_with_placements:
self._validate_move_and_update_chains(
m, True
) # ignore ko since we didn't know if it was forced
if node.clear_placements: # handle AE by playing all moves left from empty board
clear_coords = {
c.coords
for c in node.clear_placements
}
stones = [
m for c in self.chains for m in c
if m.coords not in clear_coords
]
self._init_state()
for m in stones:
self._validate_move_and_update_chains(m, True)
except IllegalMoveException as e:
raise Exception(f"Unexpected illegal move ({str(e)})")
def _validate_move_and_update_chains(self, move: Move, ignore_ko: bool):
board_size_x, board_size_y = self.board_size
def neighbours(moves):
return {
self.board[m.coords[1] + dy][m.coords[0] + dx]
for m in moves for dy, dx in [(-1, 0), (1, 0), (0, -1), (0, 1)]
if 0 <= m.coords[0] + dx < board_size_x and 0 <= m.coords[1] +
dy < board_size_y
}
ko_or_snapback = len(
self.last_capture) == 1 and self.last_capture[0] == move
self.last_capture = []
if move.is_pass:
return
if self.board[move.coords[1]][move.coords[0]] != -1:
raise IllegalMoveException("Space occupied")
# merge chains connected by this move, or create a new one
nb_chains = list({
c
for c in neighbours([move])
if c >= 0 and self.chains[c][0].player == move.player
})
if nb_chains:
this_chain = nb_chains[0]
self.board = [[
nb_chains[0] if sq in nb_chains else sq for sq in line
] for line in self.board]
for oc in nb_chains[1:]:
self.chains[nb_chains[0]] += self.chains[oc]
self.chains[oc] = []
self.chains[nb_chains[0]].append(move)
else:
this_chain = len(self.chains)
self.chains.append([move])
self.board[move.coords[1]][move.coords[0]] = this_chain
# check captures
opp_nb_chains = {
c
for c in neighbours([move])
if c >= 0 and self.chains[c][0].player != move.player
}
for c in opp_nb_chains:
if -1 not in neighbours(self.chains[c]): # no liberties
self.last_capture += self.chains[c]
for om in self.chains[c]:
self.board[om.coords[1]][om.coords[0]] = -1
self.chains[c] = []
if ko_or_snapback and len(self.last_capture) == 1 and not ignore_ko:
raise IllegalMoveException("Ko")
self.prisoners += self.last_capture
# suicide: check rules and throw exception if needed
if -1 not in neighbours(self.chains[this_chain]):
rules = self.rules
if len(
self.chains[this_chain]
) == 1: # even in new zealand rules, single stone suicide is not allowed
raise IllegalMoveException("Single stone suicide")
elif (isinstance(rules, str)
and rules in ["tromp-taylor", "new zealand"]) or (isinstance(
rules, dict) and rules.get("suicide", False)):
self.last_capture += self.chains[this_chain]
for om in self.chains[this_chain]:
self.board[om.coords[1]][om.coords[0]] = -1
self.chains[this_chain] = []
self.prisoners += self.last_capture
else: # suicide not allowed by rules
raise IllegalMoveException("Suicide")
# Play a Move from the current position, raise IllegalMoveException if invalid.
def play(self, move: Move, ignore_ko: bool = False):
board_size_x, board_size_y = self.board_size
if not move.is_pass and not (0 <= move.coords[0] < board_size_x
and 0 <= move.coords[1] < board_size_y):
raise IllegalMoveException(
f"Move {move} outside of board coordinates")
try:
self._validate_move_and_update_chains(move, ignore_ko)
except IllegalMoveException:
self._calculate_groups()
raise
with self._lock:
played_node = self.current_node.play(move)
self.current_node = played_node
return played_node
# Insert a list of moves from root, often just adding one.
def sync_branch(self, moves: List[Move]):
node = self.root
with self._lock:
for move in moves:
node = node.play(move)
return node
def set_current_node(self, node):
self.current_node = node
self._calculate_groups()
def undo(self, n_times=1, stop_on_mistake=None):
break_on_branch = False
cn = self.current_node # avoid race conditions
break_on_main_branch = False
last_branching_node = cn
if n_times == "branch":
n_times = 9999
break_on_branch = True
elif n_times == "main-branch":
n_times = 9999
break_on_main_branch = True
for move in range(n_times):
if (stop_on_mistake is not None and cn.points_lost is not None
and cn.points_lost >= stop_on_mistake
and self.katrain.players_info[cn.player].player_type !=
PLAYER_AI):
self.set_current_node(cn.parent)
return
previous_cn = cn
if cn.shortcut_from:
cn = cn.shortcut_from
elif not cn.is_root:
cn = cn.parent
else:
break # root
if break_on_branch and len(cn.children) > 1:
break
elif break_on_main_branch and cn.ordered_children[
0] != previous_cn: # implies > 1 child
last_branching_node = cn
if break_on_main_branch:
cn = last_branching_node
if cn is not self.current_node:
self.set_current_node(cn)
def redo(self, n_times=1, stop_on_mistake=None):
cn = self.current_node # avoid race conditions
for move in range(n_times):
if cn.children:
child = cn.ordered_children[0]
shortcut_to = [m for m, v in cn.shortcuts_to if child == v
] # are we about to go to a shortcut node?
if shortcut_to:
child = shortcut_to[0]
cn = child
if (move > 0 and stop_on_mistake is not None
and cn.points_lost is not None
and cn.points_lost >= stop_on_mistake
and self.katrain.players_info[cn.player].player_type !=
PLAYER_AI):
self.set_current_node(cn.parent)
return
if stop_on_mistake is None:
self.set_current_node(cn)
@property
def board_size(self):
return self.root.board_size
@property
def stones(self):
with self._lock:
return sum(self.chains, [])
@property
def end_result(self):
if self.current_node.end_state:
return self.current_node.end_state
if self.current_node.parent and self.current_node.is_pass and self.current_node.parent.is_pass:
return self.manual_score or i18n._("board-game-end")
@property
def prisoner_count(
self,
) -> Dict: # returns prisoners that are of a certain colour as {B: black stones captures, W: white stones captures}
return {
player: sum([m.player == player for m in self.prisoners])
for player in Move.PLAYERS
}
@property
def rules(self):
return KataGoEngine.get_rules(self.root.ruleset)
@property
def manual_score(self):
rules = self.rules
if (not self.current_node.ownership
or str(rules).lower() not in ["jp", "japanese"]
or not self.current_node.parent
or not self.current_node.parent.ownership):
if not self.current_node.score:
return None
return self.current_node.format_score(
round(2 * self.current_node.score) / 2) + "?"
board_size_x, board_size_y = self.board_size
mean_ownership = [(c + p) / 2 for c, p in zip(
self.current_node.ownership, self.current_node.parent.ownership)]
ownership_grid = var_to_grid(mean_ownership,
(board_size_x, board_size_y))
stones = {m.coords: m.player for m in self.stones}
lo_threshold = 0.15
hi_threshold = 0.85
max_unknown = 10
max_dame = 4 * (board_size_x + board_size_y)
def japanese_score_square(square, owner):
player = stones.get(square, None)
if ((player == "B" and owner > hi_threshold)
or (player == "W" and owner < -hi_threshold)
or abs(owner) < lo_threshold):
return 0 # dame or own stones
if player is None and abs(owner) >= hi_threshold:
return round(owner) # surrounded empty intersection
if (player == "B"
and owner < -hi_threshold) or (player == "W"
and owner > hi_threshold):
return 2 * round(owner) # captured stone
return math.nan # unknown!
scored_squares = [
japanese_score_square((x, y), ownership_grid[y][x])
for y in range(board_size_y) for x in range(board_size_x)
]
num_sq = {
t: sum([s == t for s in scored_squares])
for t in [-2, -1, 0, 1, 2]
}
num_unkn = sum(math.isnan(s) for s in scored_squares)
prisoners = self.prisoner_count
score = sum([t * n for t, n in num_sq.items()
]) + prisoners["W"] - prisoners["B"] - self.komi
self.katrain.log(
f"Manual Scoring: {num_sq} score by square with {num_unkn} unknown, {prisoners} captures, and {self.komi} komi -> score = {score}",
OUTPUT_DEBUG,
)
if num_unkn > max_unknown or (num_sq[0] - len(stones)) > max_dame:
return None
return self.current_node.format_score(score)
def __repr__(self):
return ("\n".join("".join(self.chains[c][0].player if c >= 0 else "-"
for c in line) for line in self.board) +
f"\ncaptures: {self.prisoner_count}")
def update_root_properties(self):
def player_name(player_info):
if player_info.name and player_info.player_type == PLAYER_HUMAN:
return player_info.name
else:
return f"{i18n._(player_info.player_type)} ({i18n._(player_info.player_subtype)}){SGF_INTERNAL_COMMENTS_MARKER}"
root_properties = self.root.properties
x_properties = {}
for bw in "BW":
if not self.external_game:
x_properties["P" + bw] = player_name(
self.katrain.players_info[bw])
player_info = self.katrain.players_info[bw]
if player_info.player_type == PLAYER_AI:
x_properties[bw + "R"] = rank_label(
player_info.calculated_rank)
if "+" in str(self.end_result):
x_properties["RE"] = self.end_result
self.root.properties = {
**root_properties,
**{k: [v]
for k, v in x_properties.items()}
}
def generate_filename(self):
self.update_root_properties()
player_names = {
bw: re.sub(r"[\u200b\u3164'<>:\"/\\|?*]", "",
self.root.get_property("P" + bw, bw))
for bw in "BW"
}
base_game_name = f"{PROGRAM_NAME}_{player_names['B']} vs {player_names['W']}"
return f"{base_game_name} {self.game_id}.sgf"
def write_sgf(self, filename: str, trainer_config: Optional[Dict] = None):
if trainer_config is None:
trainer_config = self.katrain.config("trainer", {})
save_feedback = trainer_config.get("save_feedback", False)
eval_thresholds = trainer_config["eval_thresholds"]
save_analysis = trainer_config.get("save_analysis", False)
save_marks = trainer_config.get("save_marks", False)
self.update_root_properties()
show_dots_for = {
bw: trainer_config.get("eval_show_ai", True)
or self.katrain.players_info[bw].human
for bw in "BW"
}
sgf = self.root.sgf(
save_comments_player=show_dots_for,
save_comments_class=save_feedback,
eval_thresholds=eval_thresholds,
save_analysis=save_analysis,
save_marks=save_marks,
)
self.sgf_filename = filename
os.makedirs(os.path.dirname(filename), exist_ok=True)
with open(filename, "w", encoding="utf-8") as f:
f.write(sgf)
return i18n._("sgf written").format(file_name=filename)
class Game:
"""Represents a game of go, including an implementation of capture rules."""
DEFAULT_PROPERTIES = {
"GM": 1,
"FF": 4,
"AP": f"KaTrain:{HOMEPAGE}",
"CA": "UTF-8"
}
def __init__(
self,
katrain,
engine: Union[Dict, KataGoEngine],
move_tree: GameNode = None,
analyze_fast=False,
game_properties: Optional[Dict] = None,
):
self.katrain = katrain
self._lock = threading.Lock()
if not isinstance(engine, Dict):
engine = {"B": engine, "W": engine}
self.engines = engine
self.game_id = datetime.strftime(datetime.now(), "%Y-%m-%d %H %M %S")
if move_tree:
self.root = move_tree
self.komi = self.root.komi
handicap = int(self.root.get_property("HA", 0))
if (
handicap >= 2 and not self.root.placements
and not (not self.root.move_with_placements
and self.root.children
and self.root.children[0].placements)
): # not really according to sgf, and not sure if still needed, last clause for fox
self.root.place_handicap_stones(handicap)
else:
board_size = katrain.config("game/size")
self.komi = katrain.config("game/komi")
self.root = GameNode(
properties={
**Game.DEFAULT_PROPERTIES,
**{
"SZ": board_size,
"KM": self.komi,
"DT": self.game_id
},
**(game_properties or {}),
})
handicap = katrain.config("game/handicap")
if handicap:
self.root.place_handicap_stones(handicap)
if not self.root.get_property("RU"):
self.root.set_property("RU", katrain.config("game/rules"))
self.set_current_node(self.root)
threading.Thread(
target=lambda: self.analyze_all_nodes(-1_000_000,
analyze_fast=analyze_fast),
daemon=True).start() # return faster, but bypass Kivy Clock
def analyze_all_nodes(self, priority=0, analyze_fast=False):
for node in self.root.nodes_in_tree:
node.analyze(self.engines[node.next_player],
priority=priority,
analyze_fast=analyze_fast)
# -- move tree functions --
def _calculate_groups(self):
board_size_x, board_size_y = self.board_size
with self._lock:
self.board = [[-1 for _x in range(board_size_x)]
for _y in range(board_size_y)
] # type: List[List[int]] # board pos -> chain id
self.chains = [] # type: List[List[Move]] # chain id -> chain
self.prisoners = [] # type: List[Move]
self.last_capture = [] # type: List[Move]
try:
for node in self.current_node.nodes_from_root:
for m in node.move_with_placements:
self._validate_move_and_update_chains(
m, True
) # ignore ko since we didn't know if it was forced
except IllegalMoveException as e:
raise Exception(f"Unexpected illegal move ({str(e)})")
def _validate_move_and_update_chains(self, move: Move, ignore_ko: bool):
board_size_x, board_size_y = self.board_size
def neighbours(moves):
return {
self.board[m.coords[1] + dy][m.coords[0] + dx]
for m in moves for dy, dx in [(-1, 0), (1, 0), (0, -1), (0, 1)]
if 0 <= m.coords[0] + dx < board_size_x and 0 <= m.coords[1] +
dy < board_size_y
}
ko_or_snapback = len(
self.last_capture) == 1 and self.last_capture[0] == move
self.last_capture = []
if move.is_pass:
return
if self.board[move.coords[1]][move.coords[0]] != -1:
raise IllegalMoveException("Space occupied")
nb_chains = list({
c
for c in neighbours([move])
if c >= 0 and self.chains[c][0].player == move.player
})
if nb_chains:
this_chain = nb_chains[0]
self.board = [[
nb_chains[0] if sq in nb_chains else sq for sq in line
] for line in self.board] # merge chains connected by this move
for oc in nb_chains[1:]:
self.chains[nb_chains[0]] += self.chains[oc]
self.chains[oc] = []
self.chains[nb_chains[0]].append(move)
else:
this_chain = len(self.chains)
self.chains.append([move])
self.board[move.coords[1]][move.coords[0]] = this_chain
opp_nb_chains = {
c
for c in neighbours([move])
if c >= 0 and self.chains[c][0].player != move.player
}
for c in opp_nb_chains:
if -1 not in neighbours(self.chains[c]):
self.last_capture += self.chains[c]
for om in self.chains[c]:
self.board[om.coords[1]][om.coords[0]] = -1
self.chains[c] = []
if ko_or_snapback and len(self.last_capture) == 1 and not ignore_ko:
raise IllegalMoveException("Ko")
self.prisoners += self.last_capture
if -1 not in neighbours(self.chains[this_chain]): # TODO: NZ rules?
raise IllegalMoveException("Suicide")
# Play a Move from the current position, raise IllegalMoveException if invalid.
def play(self, move: Move, ignore_ko: bool = False, analyze=True):
board_size_x, board_size_y = self.board_size
if not move.is_pass and not (0 <= move.coords[0] < board_size_x
and 0 <= move.coords[1] < board_size_y):
raise IllegalMoveException(
f"Move {move} outside of board coordinates")
try:
self._validate_move_and_update_chains(move, ignore_ko)
except IllegalMoveException:
self._calculate_groups()
raise
with self._lock:
played_node = self.current_node.play(move)
self.current_node = played_node
if analyze:
played_node.analyze(self.engines[played_node.next_player])
return played_node
def set_current_node(self, node):
self.current_node = node
self._calculate_groups()
def undo(self, n_times=1):
cn = self.current_node # avoid race conditions
for _ in range(n_times):
if not cn.is_root:
cn = cn.parent
self.set_current_node(cn)
def redo(self, n_times=1):
cn = self.current_node # avoid race conditions
for _ in range(n_times):
if cn.children:
cn = cn.ordered_children[0]
self.set_current_node(cn)
def cycle_children(self, direction):
cn = self.current_node # avoid race conditions
if cn.parent and len(cn.parent.children) > 1:
ordered_children = cn.parent.ordered_children
ix = (ordered_children.index(cn) + len(ordered_children) +
direction) % len(ordered_children)
self.set_current_node(ordered_children[ix])
@property
def board_size(self):
return self.root.board_size
@property
def stones(self):
with self._lock:
return sum(self.chains, [])
@property
def end_result(self):
if self.current_node.end_state:
return self.current_node.end_state
if self.current_node.parent and self.current_node.is_pass and self.current_node.parent.is_pass:
return self.manual_score or i18n._("board-game-end")
@property
def prisoner_count(
self,
) -> Dict: # returns prisoners that are of a certain colour as {B: black stones captures, W: white stones captures}
return {
player: sum([m.player == player for m in self.prisoners])
for player in Move.PLAYERS
}
@property
def manual_score(self):
rules = self.engines["B"].get_rules(self.root)
if not self.current_node.ownership or rules != "japanese":
if not self.current_node.score:
return None
self.katrain.log(
f"rules '{rules}' are not japanese, or no ownership available ({not self.current_node.ownership}) -> no manual score available",
OUTPUT_DEBUG,
)
return self.current_node.format_score(
round(2 * self.current_node.score) / 2) + "?"
board_size_x, board_size_y = self.board_size
ownership_grid = var_to_grid(self.current_node.ownership,
(board_size_x, board_size_y))
stones = {m.coords: m.player for m in self.stones}
lo_threshold = 0.15
hi_threshold = 0.85
max_unknown = 10
max_dame = 4 * (board_size_x + board_size_y)
def japanese_score_square(square, owner):
player = stones.get(square, None)
if ((player == "B" and owner > hi_threshold)
or (player == "W" and owner < -hi_threshold)
or abs(owner) < lo_threshold):
return 0 # dame or own stones
if player is None and abs(owner) >= hi_threshold:
return round(owner) # surrounded empty intersection
if (player == "B"
and owner < -hi_threshold) or (player == "W"
and owner > hi_threshold):
return 2 * round(owner) # captured stone
return math.nan # unknown!
scored_squares = [
japanese_score_square((x, y), ownership_grid[y][x])
for y in range(board_size_y) for x in range(board_size_x)
]
num_sq = {
t: sum([s == t for s in scored_squares])
for t in [-2, -1, 0, 1, 2]
}
num_unkn = sum(math.isnan(s) for s in scored_squares)
prisoners = self.prisoner_count
score = sum([t * n for t, n in num_sq.items()
]) + prisoners["W"] - prisoners["B"] - self.komi
self.katrain.log(
f"Manual Scoring: {num_sq} score by square with {num_unkn} unknown, {prisoners} captures, and {self.komi} komi -> score = {score}",
OUTPUT_DEBUG,
)
if num_unkn > max_unknown or (num_sq[0] - len(stones)) > max_dame:
return None
return self.current_node.format_score(score)
def __repr__(self):
return ("\n".join("".join(self.chains[c][0].player if c >= 0 else "-"
for c in line) for line in self.board) +
f"\ncaptures: {self.prisoner_count}")
def write_sgf(
self,
path: str,
trainer_config: Optional[Dict] = None,
):
if trainer_config is None:
trainer_config = self.katrain.config("trainer")
save_feedback = trainer_config["save_feedback"]
eval_thresholds = trainer_config["eval_thresholds"]
def player_name(player_info):
return f"{i18n._(player_info.player_type)} ({i18n._(player_info.player_subtype)})"
root_properties = self.root.properties
x_properties = {}
if "KaTrain" in self.root.get_property("AP", ""):
for bw in "BW":
x_properties["P" + bw] = player_name(
self.katrain.players_info[bw])
player_info = self.katrain.players_info[bw]
if player_info.player_type == PLAYER_AI:
x_properties[bw + "R"] = rank_label(
player_info.calculated_rank)
if "+" in str(self.end_result):
x_properties["RE"] = self.end_result
self.root.properties = {
**root_properties,
**{k: [v]
for k, v in x_properties.items()}
}
player_names = {
bw: re.sub(r"['<>:\"/\\|?*]", "",
self.root.get_property("P" + bw, bw))
for bw in "BW"
}
game_name = f"katrain_{player_names['B']} vs {player_names['W']} {self.game_id}"
file_name = os.path.abspath(os.path.join(path, f"{game_name}.sgf"))
os.makedirs(os.path.dirname(file_name), exist_ok=True)
show_dots_for = {
bw: trainer_config.get("eval_show_ai", True)
or self.katrain.players_info[bw].human
for bw in "BW"
}
sgf = self.root.sgf(save_comments_player=show_dots_for,
save_comments_class=save_feedback,
eval_thresholds=eval_thresholds)
with open(file_name, "w", encoding="utf-8") as f:
f.write(sgf)
self.root.properties = root_properties
return i18n._("sgf written").format(file_name=file_name)
def analyze_extra(self, mode, **kwargs):
stones = {s.coords for s in self.stones}
cn = self.current_node
engine = self.engines[cn.next_player]
Clock.schedule_once(self.katrain.analysis_controls.hints.activate, 0)
if mode == "extra":
if kwargs.get("continuous", False):
visits = min(
1_000_000_000,
max(engine.config["max_visits"],
math.ceil(cn.analysis_visits_requested * 1.25)))
else:
visits = cn.analysis_visits_requested + engine.config[
"max_visits"]
self.katrain.controls.set_status(
i18n._("extra analysis").format(visits=visits),
STATUS_ANALYSIS)
cn.analyze(engine,
visits=visits,
priority=-1_000,
time_limit=False)
return
if mode == "game":
nodes = self.root.nodes_in_tree
if "visits" in kwargs:
visits = kwargs["visits"]
else:
min_visits = min(node.analysis_visits_requested
for node in nodes)
visits = min_visits + engine.config["max_visits"]
for node in nodes:
node.analyze(engine,
visits=visits,
priority=-1_000_000,
time_limit=False)
self.katrain.controls.set_status(
i18n._("game re-analysis").format(visits=visits),
STATUS_ANALYSIS)
return
elif mode == "sweep":
board_size_x, board_size_y = self.board_size
if cn.analysis_ready:
policy_grid = (var_to_grid(self.current_node.policy,
size=(board_size_x, board_size_y))
if self.current_node.policy else None)
analyze_moves = sorted(
[
Move(coords=(x, y), player=cn.next_player)
for x in range(board_size_x)
for y in range(board_size_y)
if (policy_grid is None and
(x, y) not in stones) or policy_grid[y][x] >= 0
],
key=lambda mv: -policy_grid[mv.coords[1]][mv.coords[0]],
)
else:
analyze_moves = [
Move(coords=(x, y), player=cn.next_player)
for x in range(board_size_x) for y in range(board_size_y)
if (x, y) not in stones
]
visits = engine.config["fast_visits"]
self.katrain.controls.set_status(
i18n._("sweep analysis").format(visits=visits),
STATUS_ANALYSIS)
priority = -1_000_000_000
elif mode in ["equalize", "alternative"]:
if not cn.analysis_ready:
self.katrain.controls.set_status(
i18n._("wait-before-equalize"), STATUS_INFO,
self.current_node)
return
analyze_moves = [
Move.from_gtp(gtp, player=cn.next_player)
for gtp, _ in cn.analysis["moves"].items()
]
priority = -1_000
if mode == "alternative": # also do a quick update on current candidates so it doesn't look too weird
self.katrain.controls.set_status(
i18n._("alternative analysis"), STATUS_ANALYSIS)
cn.analyze(engine,
priority=-500,
time_limit=False,
find_alternatives=True)
visits = engine.config["fast_visits"]
else:
visits = max(d["visits"]
for d in cn.analysis["moves"].values())
self.katrain.controls.set_status(
i18n._("equalizing analysis").format(visits=visits),
STATUS_ANALYSIS)
else:
raise ValueError("Invalid analysis mode")
for move in analyze_moves:
if cn.analysis["moves"].get(move.gtp(),
{"visits": 0})["visits"] < visits:
cn.analyze(
engine,
priority,
visits=visits,
refine_move=move,
time_limit=False
) # explicitly requested so take as long as you need
def analyze_undo(self, node):
train_config = self.katrain.config("trainer")
move = node.move
if node != self.current_node or node.auto_undo is not None or not node.analysis_ready or not move:
return
points_lost = node.points_lost
thresholds = train_config["eval_thresholds"]
num_undo_prompts = train_config["num_undo_prompts"]
i = 0
while i < len(thresholds) and points_lost < thresholds[i]:
i += 1
num_undos = num_undo_prompts[i] if i < len(num_undo_prompts) else 0
if num_undos == 0:
undo = False
elif num_undos < 1: # probability
undo = int(node.undo_threshold < num_undos) and len(
node.parent.children) == 1
else:
undo = len(node.parent.children) <= num_undos
node.auto_undo = undo
if undo:
self.undo(1)
self.katrain.controls.set_status(
i18n._("teaching undo message").format(
move=move.gtp(), points_lost=points_lost), STATUS_TEACHING)
self.katrain.update_state()