def test_collide(self, new_game):
b = Game(MockKaTrain(force_package_config=True), MockEngine(), move_tree=new_game)
b.play(Move.from_gtp("B9", player="B"))
with pytest.raises(IllegalMoveException):
b.play(Move.from_gtp("B9", player="W"))
assert 1 == len(self.nonempty_chains(b))
assert 1 == len(b.stones)
assert 0 == len(b.prisoners)
def test_collide(self):
b = Game(MockKaTrain(), MockEngine())
b.play(Move.from_gtp("B9", player="B"))
with pytest.raises(IllegalMoveException):
b.play(Move.from_gtp("B9", player="W"))
assert 1 == len(self.nonempty_chains(b))
assert 1 == len(b.stones)
assert 0 == len(b.prisoners)
def test_merge(self, new_game):
b = Game(MockKaTrain(force_package_config=True), MockEngine(), move_tree=new_game)
b.play(Move.from_gtp("B9", player="B"))
b.play(Move.from_gtp("A3", player="B"))
b.play(Move.from_gtp("A9", player="B"))
assert 2 == len(self.nonempty_chains(b))
assert 3 == len(b.stones)
assert 0 == len(b.prisoners)
def test_merge(self):
b = Game(MockKaTrain(), MockEngine())
b.play(Move.from_gtp("B9", player="B"))
b.play(Move.from_gtp("A3", player="B"))
b.play(Move.from_gtp("A9", player="B"))
assert 2 == len(self.nonempty_chains(b))
assert 3 == len(b.stones)
assert 0 == len(b.prisoners)
def test_capture(self, new_game):
b = Game(MockKaTrain(force_package_config=True), MockEngine(), move_tree=new_game)
b.play(Move.from_gtp("A2", player="B"))
b.play(Move.from_gtp("B1", player="W"))
b.play(Move.from_gtp("A1", player="W"))
b.play(Move.from_gtp("C1", player="B"))
assert 3 == len(self.nonempty_chains(b))
assert 4 == len(b.stones)
assert 0 == len(b.prisoners)
b.play(Move.from_gtp("B2", player="B"))
assert 2 == len(self.nonempty_chains(b))
assert 3 == len(b.stones)
assert 2 == len(b.prisoners)
b.play(Move.from_gtp("B1", player="B"))
with pytest.raises(IllegalMoveException, match="Single stone suicide"):
b.play(Move.from_gtp("A1", player="W"))
assert 1 == len(self.nonempty_chains(b))
assert 4 == len(b.stones)
assert 2 == len(b.prisoners)
def test_capture(self):
b = Game(MockKaTrain(), MockEngine())
b.play(Move.from_gtp("A2", player="B"))
b.play(Move.from_gtp("B1", player="W"))
b.play(Move.from_gtp("A1", player="W"))
b.play(Move.from_gtp("C1", player="B"))
assert 3 == len(self.nonempty_chains(b))
assert 4 == len(b.stones)
assert 0 == len(b.prisoners)
b.play(Move.from_gtp("B2", player="B"))
assert 2 == len(self.nonempty_chains(b))
assert 3 == len(b.stones)
assert 2 == len(b.prisoners)
b.play(Move.from_gtp("B1", player="B"))
with pytest.raises(IllegalMoveException) as exc:
b.play(Move.from_gtp("A1", player="W"))
assert "Suicide" in str(exc.value)
assert 1 == len(self.nonempty_chains(b))
assert 4 == len(b.stones)
assert 2 == len(b.prisoners)
def test_snapback(self, new_game):
b = Game(MockKaTrain(force_package_config=True), MockEngine(), move_tree=new_game)
for move in ["C1", "D1", "E1", "C2", "D3", "E4", "F2", "F3", "F4"]:
b.play(Move.from_gtp(move, player="B"))
for move in ["D2", "E2", "C3", "D4", "C4"]:
b.play(Move.from_gtp(move, player="W"))
assert 5 == len(self.nonempty_chains(b))
assert 14 == len(b.stones)
assert 0 == len(b.prisoners)
b.play(Move.from_gtp("E3", player="W"))
assert 4 == len(self.nonempty_chains(b))
assert 14 == len(b.stones)
assert 1 == len(b.prisoners)
b.play(Move.from_gtp("D3", player="B"))
assert 4 == len(self.nonempty_chains(b))
assert 12 == len(b.stones)
assert 4 == len(b.prisoners)
def test_suicide(self):
rulesets_to_test = BaseEngine.RULESETS_ABBR + [('{"suicide":true}', ""), ('{"suicide":false}', "")]
for shortrule, _ in rulesets_to_test:
new_game = GameNode(properties={"SZ": 19, "RU": shortrule})
b = Game(MockKaTrain(force_package_config=True), MockEngine(), move_tree=new_game)
b.play(Move.from_gtp("A18", player="B"))
b.play(Move.from_gtp("B18", player="B"))
b.play(Move.from_gtp("C19", player="B"))
b.play(Move.from_gtp("A19", player="W"))
assert 4 == len(b.stones)
assert 0 == len(b.prisoners)
if shortrule in ["tt", "nz", '{"suicide":true}']:
b.play(Move.from_gtp("B19", player="W"))
assert 3 == len(b.stones)
assert 2 == len(b.prisoners)
else:
with pytest.raises(IllegalMoveException, match="Suicide"):
b.play(Move.from_gtp("B19", player="W"))
assert 4 == len(b.stones)
assert 0 == len(b.prisoners)
class KaTrainGui(Screen, KaTrainBase):
"""Top level class responsible for tying everything together"""
zen = NumericProperty(0)
controls = ObjectProperty(None)
def __init__(self, **kwargs):
super().__init__(**kwargs)
self.engine = None
self.new_game_popup = None
self.fileselect_popup = None
self.config_popup = None
self.ai_settings_popup = None
self.teacher_settings_popup = None
self.timer_settings_popup = None
self.idle_analysis = False
self.message_queue = Queue()
self._keyboard = Window.request_keyboard(None, self, "")
self._keyboard.bind(on_key_down=self._on_keyboard_down)
Clock.schedule_interval(self.animate_pondering, 0.1)
def log(self, message, level=OUTPUT_INFO):
super().log(message, level)
if level == OUTPUT_KATAGO_STDERR and "ERROR" not in self.controls.status.text:
if "starting" in message.lower():
self.controls.set_status(f"KataGo engine starting...", STATUS_INFO)
if message.startswith("Tuning"):
self.controls.set_status(
f"KataGo is tuning settings for first startup, please wait." + message, STATUS_INFO
)
return
if "ready" in message.lower():
self.controls.set_status(f"KataGo engine ready.", STATUS_INFO)
if (
level == OUTPUT_ERROR
or (level == OUTPUT_KATAGO_STDERR and "error" in message.lower() and "tuning" not in message.lower())
) and getattr(self, "controls", None):
self.controls.set_status(f"ERROR: {message}", STATUS_ERROR)
def animate_pondering(self, *_args):
if not self.idle_analysis:
self.board_controls.engine_status_pondering = -1
else:
self.board_controls.engine_status_pondering += 5
@property
def play_analyze_mode(self):
return self.play_mode.mode
def toggle_continuous_analysis(self):
self.idle_analysis = not self.idle_analysis
if not self.idle_analysis:
self.controls.set_status("", STATUS_INFO)
self.update_state()
def start(self):
if self.engine:
return
self.board_gui.trainer_config = self.config("trainer")
self.engine = KataGoEngine(self, self.config("engine"))
threading.Thread(target=self._message_loop_thread, daemon=True).start()
self._do_new_game()
def update_gui(self, cn, redraw_board=False):
# Handle prisoners and next player display
prisoners = self.game.prisoner_count
top, bot = [w.__self__ for w in self.board_controls.circles] # no weakref
if self.next_player_info.player == "W":
top, bot = bot, top
self.controls.players["W"].active = True
self.controls.players["B"].active = False
else:
self.controls.players["W"].active = False
self.controls.players["B"].active = True
self.board_controls.mid_circles_container.clear_widgets()
self.board_controls.mid_circles_container.add_widget(bot)
self.board_controls.mid_circles_container.add_widget(top)
self.board_controls.branch.disabled = not cn.parent or len(cn.parent.children) <= 1
self.controls.players["W"].captures = prisoners["W"]
self.controls.players["B"].captures = prisoners["B"]
# update engine status dot
if not self.engine or not self.engine.katago_process or self.engine.katago_process.poll() is not None:
self.board_controls.engine_status_col = ENGINE_DOWN_COL
elif len(self.engine.queries) == 0:
self.board_controls.engine_status_col = ENGINE_READY_COL
else:
self.board_controls.engine_status_col = ENGINE_BUSY_COL
# redraw board/stones
if redraw_board:
self.board_gui.draw_board()
self.board_gui.redraw_board_contents_trigger()
self.controls.update_evaluation()
self.controls.update_timer(1)
# update move tree
self.controls.move_tree.current_node = self.game.current_node
def update_state(self, redraw_board=False): # redirect to message queue thread
self("update_state", redraw_board=redraw_board)
def _do_update_state(
self, redraw_board=False
): # is called after every message and on receiving analyses and config changes
# AI and Trainer/auto-undo handlers
if not self.game or not self.game.current_node:
return
cn = self.game.current_node
last_player, next_player = self.players_info[cn.player], self.players_info[cn.next_player]
if self.play_analyze_mode == MODE_PLAY and self.nav_drawer.state != "open" and self.popup_open is None:
teaching_undo = cn.player and last_player.being_taught and cn.parent
if (
teaching_undo
and cn.analysis_ready
and cn.parent.analysis_ready
and not cn.children
and not self.game.end_result
):
self.game.analyze_undo(cn) # not via message loop
if (
cn.analysis_ready
and next_player.ai
and not cn.children
and not self.game.end_result
and not (teaching_undo and cn.auto_undo is None)
): # cn mismatch stops this if undo fired. avoid message loop here or fires repeatedly.
self._do_ai_move(cn)
Clock.schedule_once(self.board_gui.play_stone_sound, 0)
if len(self.engine.queries) == 0 and self.idle_analysis:
self("analyze-extra", "extra", continuous=True)
Clock.schedule_once(lambda _dt: self.update_gui(cn, redraw_board=redraw_board), -1) # trigger?
def update_player(self, bw, **kwargs):
super().update_player(bw, **kwargs)
if self.controls:
self.controls.update_players()
self.update_state()
for player_setup_block in PlayerSetupBlock.INSTANCES:
player_setup_block.update_players(bw, self.players_info[bw])
def set_note(self, note):
self.game.current_node.note = note
def _message_loop_thread(self):
while True:
game, msg, args, kwargs = self.message_queue.get()
try:
self.log(f"Message Loop Received {msg}: {args} for Game {game}", OUTPUT_EXTRA_DEBUG)
if game != self.game.game_id:
self.log(
f"Message skipped as it is outdated (current game is {self.game.game_id}", OUTPUT_EXTRA_DEBUG
)
continue
fn = getattr(self, f"_do_{msg.replace('-','_')}")
fn(*args, **kwargs)
if msg != "update_state":
self._do_update_state()
except Exception as exc:
self.log(f"Exception in processing message {msg} {args}: {exc}", OUTPUT_ERROR)
traceback.print_exc()
def __call__(self, message, *args, **kwargs):
if self.game:
if message.endswith("popup"): # gui code needs to run in main kivy thread.
fn = getattr(self, f"_do_{message.replace('-', '_')}")
Clock.schedule_once(lambda _dt: fn(*args, **kwargs), -1)
else: # game related actions
self.message_queue.put([self.game.game_id, message, args, kwargs])
def _do_new_game(self, move_tree=None, analyze_fast=False):
self.idle_analysis = False
mode = self.play_analyze_mode
if (move_tree is not None and mode == MODE_PLAY) or (move_tree is None and mode == MODE_ANALYZE):
self.play_mode.switch_ui_mode() # for new game, go to play, for loaded, analyze
self.board_gui.animating_pv = None
self.engine.on_new_game() # clear queries
self.game = Game(self, self.engine, move_tree=move_tree, analyze_fast=analyze_fast)
if move_tree:
for bw, player_info in self.players_info.items():
player_info.player_type = PLAYER_HUMAN
player_info.player_subtype = PLAYING_NORMAL
player_info.sgf_rank = move_tree.root.get_property(bw + "R")
player_info.calculated_rank = None
player_info.name = move_tree.root.get_property("P" + bw)
self.update_player(bw)
self.controls.graph.initialize_from_game(self.game.root)
# self.controls.rank_graph.initialize_from_game(self.game.root)
self.update_state(redraw_board=True)
def _do_ai_move(self, node=None):
if node is None or self.game.current_node == node:
mode = self.next_player_info.strategy
settings = self.config(f"ai/{mode}")
if settings is not None:
generate_ai_move(self.game, mode, settings)
else:
self.log(f"AI Mode {mode} not found!", OUTPUT_ERROR)
def _do_undo(self, n_times=1):
if n_times == "smart":
n_times = 1
if self.play_analyze_mode == MODE_PLAY and self.last_player_info.ai and self.next_player_info.human:
n_times = 2
self.board_gui.animating_pv = None
self.game.undo(n_times)
def _do_resign(self):
self.game.current_node.end_state = f"{self.game.current_node.player}+R"
def _do_redo(self, n_times=1):
self.board_gui.animating_pv = None
self.game.redo(n_times)
def _do_cycle_children(self, *args):
self.board_gui.animating_pv = None
self.game.cycle_children(*args)
def _do_switch_branch(self, *args):
self.board_gui.animating_pv = None
self.controls.move_tree.switch_branch(*args)
def _do_play(self, coords):
self.board_gui.animating_pv = None
try:
self.game.play(Move(coords, player=self.next_player_info.player))
except IllegalMoveException as e:
self.controls.set_status(f"Illegal Move: {str(e)}", STATUS_ERROR)
def _do_analyze_extra(self, mode, **kwargs):
self.game.analyze_extra(mode, **kwargs)
def _do_new_game_popup(self):
self.controls.timer.paused = True
if not self.new_game_popup:
self.new_game_popup = I18NPopup(
title_key="New Game title", size=[dp(800), dp(800)], content=NewGamePopup(self)
).__self__
self.new_game_popup.content.popup = self.new_game_popup
self.new_game_popup.open()
def _do_timer_popup(self):
self.controls.timer.paused = True
if not self.timer_settings_popup:
self.timer_settings_popup = I18NPopup(
title_key="timer settings", size=[dp(350), dp(400)], content=ConfigTimerPopup(self)
).__self__
self.timer_settings_popup.content.popup = self.timer_settings_popup
self.timer_settings_popup.open()
def _do_teacher_popup(self):
self.controls.timer.paused = True
if not self.teacher_settings_popup:
self.teacher_settings_popup = I18NPopup(
title_key="teacher settings", size=[dp(800), dp(750)], content=ConfigTeacherPopup(self)
).__self__
self.teacher_settings_popup.content.popup = self.teacher_settings_popup
self.teacher_settings_popup.open()
def _do_config_popup(self):
self.controls.timer.paused = True
if not self.config_popup:
self.config_popup = I18NPopup(
title_key="general settings title", size=[dp(1200), dp(950)], content=ConfigPopup(self)
).__self__
self.config_popup.content.popup = self.config_popup
self.config_popup.open()
def _do_ai_popup(self):
self.controls.timer.paused = True
if not self.ai_settings_popup:
self.ai_settings_popup = I18NPopup(
title_key="ai settings", size=[dp(600), dp(650)], content=ConfigAIPopup(self)
).__self__
self.ai_settings_popup.content.popup = self.ai_settings_popup
self.ai_settings_popup.open()
def load_sgf_file(self, file, fast=False, rewind=False):
try:
move_tree = KaTrainSGF.parse_file(file)
except ParseError as e:
self.log(i18n._("Failed to load SGF").format(error=e), OUTPUT_ERROR)
return
self._do_new_game(move_tree=move_tree, analyze_fast=fast)
if not rewind:
self.game.redo(999)
def _do_analyze_sgf_popup(self):
if not self.fileselect_popup:
popup_contents = LoadSGFPopup()
popup_contents.filesel.path = os.path.abspath(os.path.expanduser(self.config("general/sgf_load", ".")))
self.fileselect_popup = I18NPopup(
title_key="load sgf title", size=[dp(1200), dp(800)], content=popup_contents
).__self__
def readfile(*_args):
filename = popup_contents.filesel.filename
self.fileselect_popup.dismiss()
path, file = os.path.split(filename)
settings_path = self.config("general/sgf_load")
if path != settings_path:
self.log(f"Updating sgf load path default to {path}", OUTPUT_INFO)
self._config["general"]["sgf_load"] = path
self.save_config("general")
self.load_sgf_file(filename, popup_contents.fast.active, popup_contents.rewind.active)
popup_contents.filesel.on_success = readfile
popup_contents.filesel.on_submit = readfile
self.fileselect_popup.open()
def _do_output_sgf(self):
msg = self.game.write_sgf(self.config("general/sgf_save"))
self.log(msg, OUTPUT_INFO)
self.controls.set_status(msg, STATUS_INFO)
def load_sgf_from_clipboard(self):
clipboard = Clipboard.paste()
if not clipboard:
self.controls.set_status(f"Ctrl-V pressed but clipboard is empty.", STATUS_INFO)
return
try:
move_tree = KaTrainSGF.parse_sgf(clipboard)
except Exception as exc:
self.controls.set_status(
i18n._("Failed to import from clipboard").format(error=exc, contents=clipboard[:50]), STATUS_INFO
)
return
move_tree.nodes_in_tree[-1].analyze(
self.engine, analyze_fast=False
) # speed up result for looking at end of game
self._do_new_game(move_tree=move_tree, analyze_fast=True)
self("redo", 999)
self.log("Imported game from clipboard.", OUTPUT_INFO)
def on_touch_up(self, touch):
if (
self.board_gui.collide_point(*touch.pos)
or self.board_controls.collide_point(*touch.pos)
or self.controls.move_tree.collide_point(*touch.pos)
):
if touch.is_mouse_scrolling:
if touch.button == "scrollup":
self("redo")
elif touch.button == "scrolldown":
self("undo")
return super().on_touch_up(touch)
@property
def shortcuts(self):
return {
"q": self.analysis_controls.show_children,
"w": self.analysis_controls.eval,
"e": self.analysis_controls.hints,
"t": self.analysis_controls.ownership,
"r": self.analysis_controls.policy,
"enter": ("ai-move",),
"numpadenter": ("ai-move",),
"a": ("analyze-extra", "extra"),
"s": ("analyze-extra", "equalize"),
"d": ("analyze-extra", "sweep"),
"f": ("analyze-extra", "alternative"),
"p": ("play", None),
"down": ("switch-branch", 1),
"up": ("switch-branch", -1),
"f5": ("timer-popup",),
"f6": ("teacher-popup",),
"f7": ("ai-popup",),
"f8": ("config-popup",),
}
@property
def popup_open(self) -> Popup:
app = App.get_running_app()
first_child = app.root_window.children[0]
return first_child if isinstance(first_child, Popup) else None
def _on_keyboard_down(self, _keyboard, keycode, _text, modifiers):
if self.controls.note.focus:
return # when making notes, don't allow keyboard shortcuts
popup = self.popup_open
if popup:
if keycode[1] in ["f5", "f6", "f7", "f8"]: # switch between popups
popup.dismiss()
return
else:
return
shortcuts = self.shortcuts
if keycode[1] == "tab":
self.play_mode.switch_ui_mode()
elif keycode[1] == "shift":
self.nav_drawer.set_state("toggle")
elif keycode[1] == "spacebar":
self.toggle_continuous_analysis()
elif keycode[1] == "b" and "ctrl" not in modifiers:
self.controls.timer.paused = not self.controls.timer.paused
elif keycode[1] in ["`", "~", "m"] and "ctrl" not in modifiers:
self.zen = (self.zen + 1) % 3
elif keycode[1] in ["left", "z"]:
self("undo", 1 + ("alt" in modifiers) * 9 + ("ctrl" in modifiers) * 999)
elif keycode[1] in ["right", "x"]:
self("redo", 1 + ("alt" in modifiers) * 9 + ("ctrl" in modifiers) * 999)
elif keycode[1] == "n" and "ctrl" in modifiers:
self("new-game-popup")
elif keycode[1] == "l" and "ctrl" in modifiers:
self("analyze-sgf-popup")
elif keycode[1] == "s" and "ctrl" in modifiers:
self("output-sgf")
elif keycode[1] == "c" and "ctrl" in modifiers:
Clipboard.copy(self.game.root.sgf())
self.controls.set_status(i18n._("Copied SGF to clipboard."), STATUS_INFO)
elif keycode[1] == "v" and "ctrl" in modifiers:
self.load_sgf_from_clipboard()
elif keycode[1] in shortcuts.keys() and "ctrl" not in modifiers:
shortcut = shortcuts[keycode[1]]
if isinstance(shortcut, Widget):
shortcut.trigger_action(duration=0)
else:
self(*shortcut)
return True
def test_ko(self, new_game):
b = Game(MockKaTrain(force_package_config=True),
MockEngine(),
move_tree=new_game)
for move in ["A2", "B1"]:
b.play(Move.from_gtp(move, player="B"))
for move in ["B2", "C1"]:
b.play(Move.from_gtp(move, player="W"))
b.play(Move.from_gtp("A1", player="W"))
assert 4 == len(self.nonempty_chains(b))
assert 4 == len(b.stones)
assert 1 == len(b.prisoners)
with pytest.raises(IllegalMoveException) as exc:
b.play(Move.from_gtp("B1", player="B"))
assert "Ko" in str(exc.value)
b.play(Move.from_gtp("B1", player="B"), ignore_ko=True)
assert 2 == len(b.prisoners)
with pytest.raises(IllegalMoveException) as exc:
b.play(Move.from_gtp("A1", player="W"))
b.play(Move.from_gtp("F1", player="W"))
b.play(Move(coords=None, player="B"))
b.play(Move.from_gtp("A1", player="W"))
assert 3 == len(b.prisoners)
def generate_ai_move(game: Game, ai_mode: str,
ai_settings: Dict) -> Tuple[Move, GameNode]:
cn = game.current_node
while not cn.analysis_ready:
time.sleep(0.01)
game.engines[cn.next_player].check_alive(exception_if_dead=True)
ai_thoughts = ""
if (ai_mode
in AI_STRATEGIES_POLICY) and cn.policy: # pure policy based move
policy_moves = cn.policy_ranking
pass_policy = cn.policy[-1]
# dont make it jump around for the last few sensible non pass moves
top_5_pass = any([polmove[1].is_pass for polmove in policy_moves[:5]])
size = game.board_size
policy_grid = var_to_grid(cn.policy, size) # type: List[List[float]]
top_policy_move = policy_moves[0][1]
ai_thoughts += f"Using policy based strategy, base top 5 moves are {fmt_moves(policy_moves[:5])}. "
if (ai_mode == AI_POLICY and cn.depth <= ai_settings["opening_moves"]
) or (ai_mode in [AI_LOCAL, AI_TENUKI]
and not (cn.move and cn.move.coords)):
ai_mode = AI_WEIGHTED
ai_thoughts += f"Strategy override, using policy-weighted strategy instead. "
ai_settings = {
"pick_override": 0.9,
"weaken_fac": 1,
"lower_bound": 0.02
}
if top_5_pass:
aimove = top_policy_move
ai_thoughts += "Playing top one because one of them is pass."
elif ai_mode == AI_POLICY:
aimove = top_policy_move
ai_thoughts += f"Playing top policy move {aimove.gtp()}."
else: # weighted or pick-based
legal_policy_moves = [(pol, mv) for pol, mv in policy_moves
if not mv.is_pass and pol > 0]
board_squares = size[0] * size[1]
if ai_mode == AI_RANK: # calibrated, override from 0.8 at start to ~0.4 at full board
override = 0.8 * (
1 - 0.5 *
(board_squares - len(legal_policy_moves)) / board_squares)
else:
override = ai_settings["pick_override"]
if policy_moves[0][0] > override:
aimove = top_policy_move
ai_thoughts += f"Top policy move has weight > {override:.1%}, so overriding other strategies."
elif ai_mode == AI_WEIGHTED:
aimove, ai_thoughts = policy_weighted_move(
policy_moves, ai_settings["lower_bound"],
ai_settings["weaken_fac"])
elif ai_mode in AI_STRATEGIES_PICK:
if ai_mode != AI_RANK:
n_moves = int(ai_settings["pick_frac"] *
len(legal_policy_moves) +
ai_settings["pick_n"])
else:
n_moves = int(
round(
board_squares / 361 *
10**(-0.05737 * ai_settings["kyu_rank"] + 1.9482)))
if ai_mode in [
AI_INFLUENCE, AI_TERRITORY, AI_LOCAL, AI_TENUKI
]:
if cn.depth > ai_settings["endgame"] * board_squares:
weighted_coords = [(pol, 1, *mv.coords)
for pol, mv in legal_policy_moves]
x_ai_thoughts = (
f"Generated equal weights as move number >= {ai_settings['endgame'] * size[0] * size[1]}. "
)
n_moves = int(
max(n_moves, 0.5 * len(legal_policy_moves)))
elif ai_mode in [AI_INFLUENCE, AI_TERRITORY]:
weighted_coords, x_ai_thoughts = generate_influence_territory_weights(
ai_mode, ai_settings, policy_grid, size)
else: # ai_mode in [AI_LOCAL, AI_TENUKI]
weighted_coords, x_ai_thoughts = generate_local_tenuki_weights(
ai_mode, ai_settings, policy_grid, cn, size)
ai_thoughts += x_ai_thoughts
else: # ai_mode in [AI_PICK, AI_RANK]:
weighted_coords = [(policy_grid[y][x], 1, x, y)
for x in range(size[0])
for y in range(size[1])
if policy_grid[y][x] > 0]
pick_moves = weighted_selection_without_replacement(
weighted_coords, n_moves)
ai_thoughts += f"Picked {min(n_moves,len(weighted_coords))} random moves according to weights. "
if pick_moves:
new_top = [
(p, Move((x, y), player=cn.next_player))
for p, wt, x, y in heapq.nlargest(5, pick_moves)
]
aimove = new_top[0][1]
ai_thoughts += f"Top 5 among these were {fmt_moves(new_top)} and picked top {aimove.gtp()}. "
if new_top[0][0] < pass_policy:
ai_thoughts += f"But found pass ({pass_policy:.2%} to be higher rated than {aimove.gtp()} ({new_top[0][0]:.2%}) so will play top policy move instead."
aimove = top_policy_move
else:
aimove = top_policy_move
ai_thoughts += f"Pick policy strategy {ai_mode} failed to find legal moves, so is playing top policy move {aimove.gtp()}."
else:
raise ValueError(f"Unknown Policy-based AI mode {ai_mode}")
else: # Engine based move
candidate_ai_moves = cn.candidate_moves
top_cand = Move.from_gtp(candidate_ai_moves[0]["move"],
player=cn.next_player)
if top_cand.is_pass: # don't play suicidal to balance score - pass when it's best
aimove = top_cand
ai_thoughts += f"Top move is pass, so passing regardless of strategy."
else:
if ai_mode == AI_JIGO:
sign = cn.player_sign(cn.next_player)
jigo_move = min(
candidate_ai_moves,
key=lambda move: abs(sign * move["scoreLead"] -
ai_settings["target_score"]))
aimove = Move.from_gtp(jigo_move["move"],
player=cn.next_player)
ai_thoughts += f"Jigo strategy found {len(candidate_ai_moves)} candidate moves (best {top_cand.gtp()}) and chose {aimove.gtp()} as closest to 0.5 point win"
elif ai_mode == AI_SCORELOSS:
c = ai_settings["strength"]
moves = [(
d["pointsLost"],
math.exp(min(200, -c * max(0, d["pointsLost"]))),
Move.from_gtp(d["move"], player=cn.next_player),
) for d in candidate_ai_moves]
topmove = weighted_selection_without_replacement(moves, 1)[0]
aimove = topmove[2]
ai_thoughts += f"ScoreLoss strategy found {len(candidate_ai_moves)} candidate moves (best {top_cand.gtp()}) and chose {aimove.gtp()} (weight {topmove[1]:.3f}, point loss {topmove[0]:.1f}) based on score weights."
else:
if ai_mode != AI_DEFAULT:
game.katrain.log(
f"Unknown AI mode {ai_mode} or policy missing, using default.",
OUTPUT_INFO)
ai_thoughts += f"Strategy {ai_mode} not found or unexpected fallback."
aimove = top_cand
ai_thoughts += f"Default strategy found {len(candidate_ai_moves)} moves returned from the engine and chose {aimove.gtp()} as top move"
game.katrain.log(f"AI thoughts: {ai_thoughts}", OUTPUT_DEBUG)
played_node = game.play(aimove)
played_node.ai_thoughts = ai_thoughts
return aimove, played_node
def generate_ai_move(game: Game, ai_mode: str,
ai_settings: Dict) -> Tuple[Move, GameNode]:
cn = game.current_node
if ai_mode == AI_HANDICAP:
pda = ai_settings["pda"]
if ai_settings["automatic"]:
n_handicaps = len(game.root.get_list_property("AB", []))
MOVE_VALUE = 14 # could be rules dependent
b_stones_advantage = max(
n_handicaps - 1, 0) - (cn.komi - MOVE_VALUE / 2) / MOVE_VALUE
pda = min(3, max(
-3, -b_stones_advantage *
(3 /
8))) # max PDA at 8 stone adv, normal 9 stone game is 8.46
handicap_analysis = request_ai_analysis(
game, cn, {
"playoutDoublingAdvantage": pda,
"playoutDoublingAdvantagePla": "BLACK"
})
if not handicap_analysis:
game.katrain.log(f"Error getting handicap-based move",
OUTPUT_ERROR)
ai_mode = AI_DEFAULT
while not cn.analysis_ready:
time.sleep(0.01)
game.engines[cn.next_player].check_alive(exception_if_dead=True)
ai_thoughts = ""
if (ai_mode
in AI_STRATEGIES_POLICY) and cn.policy: # pure policy based move
policy_moves = cn.policy_ranking
pass_policy = cn.policy[-1]
# dont make it jump around for the last few sensible non pass moves
top_5_pass = any([polmove[1].is_pass for polmove in policy_moves[:5]])
size = game.board_size
policy_grid = var_to_grid(cn.policy, size) # type: List[List[float]]
top_policy_move = policy_moves[0][1]
ai_thoughts += f"Using policy based strategy, base top 5 moves are {fmt_moves(policy_moves[:5])}. "
if (ai_mode == AI_POLICY and cn.depth <= ai_settings["opening_moves"]
) or (ai_mode in [AI_LOCAL, AI_TENUKI]
and not (cn.move and cn.move.coords)):
ai_mode = AI_WEIGHTED
ai_thoughts += f"Strategy override, using policy-weighted strategy instead. "
ai_settings = {
"pick_override": 0.9,
"weaken_fac": 1,
"lower_bound": 0.02
}
if top_5_pass:
aimove = top_policy_move
ai_thoughts += "Playing top one because one of them is pass."
elif ai_mode == AI_POLICY:
aimove = top_policy_move
ai_thoughts += f"Playing top policy move {aimove.gtp()}."
else: # weighted or pick-based
legal_policy_moves = [(pol, mv) for pol, mv in policy_moves
if not mv.is_pass and pol > 0]
board_squares = size[0] * size[1]
if ai_mode == AI_RANK: # calibrated, override from 0.8 at start to ~0.4 at full board
override = 0.8 * (
1 - 0.5 *
(board_squares - len(legal_policy_moves)) / board_squares)
overridetwo = 0.85 + max(0, 0.02 *
(ai_settings["kyu_rank"] - 8))
else:
override = ai_settings["pick_override"]
overridetwo = 1.0
if policy_moves[0][0] > override:
aimove = top_policy_move
ai_thoughts += f"Top policy move has weight > {override:.1%}, so overriding other strategies."
elif policy_moves[0][0] + policy_moves[1][0] > overridetwo:
aimove = top_policy_move
ai_thoughts += (
f"Top two policy moves have cumulative weight > {overridetwo:.1%}, so overriding other strategies."
)
elif ai_mode == AI_WEIGHTED:
aimove, ai_thoughts = policy_weighted_move(
policy_moves, ai_settings["lower_bound"],
ai_settings["weaken_fac"])
elif ai_mode in AI_STRATEGIES_PICK:
if ai_mode != AI_RANK:
n_moves = max(
1,
int(ai_settings["pick_frac"] *
len(legal_policy_moves) + ai_settings["pick_n"]))
else:
orig_calib_avemodrank = 0.063015 + 0.7624 * board_squares / (
10**(-0.05737 * ai_settings["kyu_rank"] + 1.9482))
norm_leg_moves = len(legal_policy_moves) / board_squares
modified_calib_avemodrank = (
0.3931 + 0.6559 * norm_leg_moves * math.exp(
-1 *
(3.002 * norm_leg_moves * norm_leg_moves -
norm_leg_moves - 0.034889 *
ai_settings["kyu_rank"] - 0.5097)**2) - 0.01093 *
ai_settings["kyu_rank"]) * orig_calib_avemodrank
n_moves = board_squares * norm_leg_moves / (
1.31165 * (modified_calib_avemodrank + 1) - 0.082653)
n_moves = max(1, round(n_moves))
if ai_mode in [
AI_INFLUENCE, AI_TERRITORY, AI_LOCAL, AI_TENUKI
]:
if cn.depth > ai_settings["endgame"] * board_squares:
weighted_coords = [(pol, 1, *mv.coords)
for pol, mv in legal_policy_moves]
x_ai_thoughts = (
f"Generated equal weights as move number >= {ai_settings['endgame'] * size[0] * size[1]}. "
)
n_moves = int(
max(n_moves,
len(legal_policy_moves) // 2))
elif ai_mode in [AI_INFLUENCE, AI_TERRITORY]:
weighted_coords, x_ai_thoughts = generate_influence_territory_weights(
ai_mode, ai_settings, policy_grid, size)
else: # ai_mode in [AI_LOCAL, AI_TENUKI]
weighted_coords, x_ai_thoughts = generate_local_tenuki_weights(
ai_mode, ai_settings, policy_grid, cn, size)
ai_thoughts += x_ai_thoughts
else: # ai_mode in [AI_PICK, AI_RANK]:
weighted_coords = [(policy_grid[y][x], 1, x, y)
for x in range(size[0])
for y in range(size[1])
if policy_grid[y][x] > 0]
pick_moves = weighted_selection_without_replacement(
weighted_coords, n_moves)
ai_thoughts += f"Picked {min(n_moves,len(weighted_coords))} random moves according to weights. "
if pick_moves:
new_top = [
(p, Move((x, y), player=cn.next_player))
for p, wt, x, y in heapq.nlargest(5, pick_moves)
]
aimove = new_top[0][1]
ai_thoughts += f"Top 5 among these were {fmt_moves(new_top)} and picked top {aimove.gtp()}. "
if new_top[0][0] < pass_policy:
ai_thoughts += f"But found pass ({pass_policy:.2%} to be higher rated than {aimove.gtp()} ({new_top[0][0]:.2%}) so will play top policy move instead."
aimove = top_policy_move
else:
aimove = top_policy_move
ai_thoughts += f"Pick policy strategy {ai_mode} failed to find legal moves, so is playing top policy move {aimove.gtp()}."
else:
raise ValueError(f"Unknown Policy-based AI mode {ai_mode}")
else: # Engine based move
candidate_ai_moves = cn.candidate_moves
if ai_mode == AI_HANDICAP:
candidate_ai_moves = handicap_analysis["moveInfos"]
top_cand = Move.from_gtp(candidate_ai_moves[0]["move"],
player=cn.next_player)
if top_cand.is_pass and ai_mode not in [
AI_DEFAULT, AI_HANDICAP
]: # don't play suicidal to balance score
aimove = top_cand
ai_thoughts += f"Top move is pass, so passing regardless of strategy. "
else:
if ai_mode == AI_JIGO:
sign = cn.player_sign(cn.next_player)
jigo_move = min(
candidate_ai_moves,
key=lambda move: abs(sign * move["scoreLead"] -
ai_settings["target_score"]))
aimove = Move.from_gtp(jigo_move["move"],
player=cn.next_player)
ai_thoughts += f"Jigo strategy found {len(candidate_ai_moves)} candidate moves (best {top_cand.gtp()}) and chose {aimove.gtp()} as closest to 0.5 point win"
elif ai_mode == AI_SCORELOSS:
c = ai_settings["strength"]
moves = [(
d["pointsLost"],
math.exp(min(200, -c * max(0, d["pointsLost"]))),
Move.from_gtp(d["move"], player=cn.next_player),
) for d in candidate_ai_moves]
topmove = weighted_selection_without_replacement(moves, 1)[0]
aimove = topmove[2]
ai_thoughts += f"ScoreLoss strategy found {len(candidate_ai_moves)} candidate moves (best {top_cand.gtp()}) and chose {aimove.gtp()} (weight {topmove[1]:.3f}, point loss {topmove[0]:.1f}) based on score weights."
else:
if ai_mode not in [AI_DEFAULT, AI_HANDICAP]:
game.katrain.log(
f"Unknown AI mode {ai_mode} or policy missing, using default.",
OUTPUT_INFO)
ai_thoughts += f"Strategy {ai_mode} not found or unexpected fallback."
aimove = top_cand
if ai_mode == AI_HANDICAP:
ai_thoughts += f"Handicap strategy found {len(candidate_ai_moves)} moves returned from the engine and chose {aimove.gtp()} as top move. PDA based score {cn.format_score(handicap_analysis['rootInfo']['scoreLead'])} and win rate {cn.format_winrate(handicap_analysis['rootInfo']['winrate'])}"
else:
ai_thoughts += f"Default strategy found {len(candidate_ai_moves)} moves returned from the engine and chose {aimove.gtp()} as top move"
game.katrain.log(f"AI thoughts: {ai_thoughts}", OUTPUT_DEBUG)
played_node = game.play(aimove)
played_node.ai_thoughts = ai_thoughts
return aimove, played_node
def generate_ai_move(game: Game, ai_mode: str,
ai_settings: Dict) -> Tuple[Move, GameNode]:
cn = game.current_node
if ai_mode == AI_HANDICAP:
pda = ai_settings["pda"]
if ai_settings["automatic"]:
n_handicaps = len(game.root.get_list_property("AB", []))
MOVE_VALUE = 14 # could be rules dependent
b_stones_advantage = max(
n_handicaps - 1, 0) - (cn.komi - MOVE_VALUE / 2) / MOVE_VALUE
pda = min(3, max(
-3, -b_stones_advantage *
(3 /
8))) # max PDA at 8 stone adv, normal 9 stone game is 8.46
handicap_analysis = request_ai_analysis(
game, cn, {
"playoutDoublingAdvantage": pda,
"playoutDoublingAdvantagePla": "BLACK"
})
if not handicap_analysis:
game.katrain.log(f"Error getting handicap-based move",
OUTPUT_ERROR)
ai_mode = AI_DEFAULT
while not cn.analysis_complete:
time.sleep(0.01)
game.engines[cn.next_player].check_alive(exception_if_dead=True)
ai_thoughts = ""
if (ai_mode
in AI_STRATEGIES_POLICY) and cn.policy: # pure policy based move
policy_moves = cn.policy_ranking
pass_policy = cn.policy[-1]
# dont make it jump around for the last few sensible non pass moves
top_5_pass = any([polmove[1].is_pass for polmove in policy_moves[:5]])
size = game.board_size
policy_grid = var_to_grid(cn.policy, size) # type: List[List[float]]
top_policy_move = policy_moves[0][1]
ai_thoughts += f"Using policy based strategy, base top 5 moves are {fmt_moves(policy_moves[:5])}. "
if (ai_mode == AI_POLICY and cn.depth <= ai_settings["opening_moves"]
) or (ai_mode in [AI_LOCAL, AI_TENUKI]
and not (cn.move and cn.move.coords)):
ai_mode = AI_WEIGHTED
ai_thoughts += f"Strategy override, using policy-weighted strategy instead. "
ai_settings = {
"pick_override": 0.9,
"weaken_fac": 1,
"lower_bound": 0.02
}
if top_5_pass:
aimove = top_policy_move
ai_thoughts += "Playing top one because one of them is pass."
elif ai_mode == AI_POLICY:
aimove = top_policy_move
ai_thoughts += f"Playing top policy move {aimove.gtp()}."
else: # weighted or pick-based
legal_policy_moves = [(pol, mv) for pol, mv in policy_moves
if not mv.is_pass and pol > 0]
board_squares = size[0] * size[1]
if ai_mode == AI_RANK: # calibrated, override from 0.8 at start to ~0.4 at full board
override = 0.8 * (
1 - 0.5 *
(board_squares - len(legal_policy_moves)) / board_squares)
overridetwo = 0.85 + max(0, 0.02 *
(ai_settings["kyu_rank"] - 8))
else:
override = ai_settings["pick_override"]
overridetwo = 1.0
if policy_moves[0][0] > override:
aimove = top_policy_move
ai_thoughts += f"Top policy move has weight > {override:.1%}, so overriding other strategies."
elif policy_moves[0][0] + policy_moves[1][0] > overridetwo:
aimove = top_policy_move
ai_thoughts += (
f"Top two policy moves have cumulative weight > {overridetwo:.1%}, so overriding other strategies."
)
elif ai_mode == AI_WEIGHTED:
aimove, ai_thoughts = policy_weighted_move(
policy_moves, ai_settings["lower_bound"],
ai_settings["weaken_fac"])
elif ai_mode in AI_STRATEGIES_PICK:
if ai_mode != AI_RANK:
n_moves = max(
1,
int(ai_settings["pick_frac"] *
len(legal_policy_moves) + ai_settings["pick_n"]))
else:
orig_calib_avemodrank = 0.063015 + 0.7624 * board_squares / (
10**(-0.05737 * ai_settings["kyu_rank"] + 1.9482))
norm_leg_moves = len(legal_policy_moves) / board_squares
modified_calib_avemodrank = (
0.3931 + 0.6559 * norm_leg_moves * math.exp(
-1 *
(3.002 * norm_leg_moves * norm_leg_moves -
norm_leg_moves - 0.034889 *
ai_settings["kyu_rank"] - 0.5097)**2) - 0.01093 *
ai_settings["kyu_rank"]) * orig_calib_avemodrank
n_moves = board_squares * norm_leg_moves / (
1.31165 * (modified_calib_avemodrank + 1) - 0.082653)
n_moves = max(1, round(n_moves))
if ai_mode in [
AI_INFLUENCE, AI_TERRITORY, AI_LOCAL, AI_TENUKI
]:
if cn.depth > ai_settings["endgame"] * board_squares:
weighted_coords = [(pol, 1, *mv.coords)
for pol, mv in legal_policy_moves]
x_ai_thoughts = (
f"Generated equal weights as move number >= {ai_settings['endgame'] * size[0] * size[1]}. "
)
n_moves = int(
max(n_moves,
len(legal_policy_moves) // 2))
elif ai_mode in [AI_INFLUENCE, AI_TERRITORY]:
weighted_coords, x_ai_thoughts = generate_influence_territory_weights(
ai_mode, ai_settings, policy_grid, size)
else: # ai_mode in [AI_LOCAL, AI_TENUKI]
weighted_coords, x_ai_thoughts = generate_local_tenuki_weights(
ai_mode, ai_settings, policy_grid, cn, size)
ai_thoughts += x_ai_thoughts
else: # ai_mode in [AI_PICK, AI_RANK]:
weighted_coords = [(policy_grid[y][x], 1, x, y)
for x in range(size[0])
for y in range(size[1])
if policy_grid[y][x] > 0]
pick_moves = weighted_selection_without_replacement(
weighted_coords, n_moves)
ai_thoughts += f"Picked {min(n_moves,len(weighted_coords))} random moves according to weights. "
if pick_moves:
new_top = [
(p, Move((x, y), player=cn.next_player))
for p, wt, x, y in heapq.nlargest(5, pick_moves)
]
aimove = new_top[0][1]
ai_thoughts += f"Top 5 among these were {fmt_moves(new_top)} and picked top {aimove.gtp()}. "
if new_top[0][0] < pass_policy:
ai_thoughts += f"But found pass ({pass_policy:.2%} to be higher rated than {aimove.gtp()} ({new_top[0][0]:.2%}) so will play top policy move instead."
aimove = top_policy_move
else:
aimove = top_policy_move
ai_thoughts += f"Pick policy strategy {ai_mode} failed to find legal moves, so is playing top policy move {aimove.gtp()}."
else:
raise ValueError(f"Unknown Policy-based AI mode {ai_mode}")
else: # Engine based move
candidate_ai_moves = cn.candidate_moves
if ai_mode == AI_HANDICAP:
candidate_ai_moves = handicap_analysis["moveInfos"]
top_cand = Move.from_gtp(candidate_ai_moves[0]["move"],
player=cn.next_player)
if top_cand.is_pass and ai_mode not in [
AI_DEFAULT,
AI_HANDICAP,
]: # don't play suicidal to balance score
aimove = top_cand
ai_thoughts += f"Top move is pass, so passing regardless of strategy. "
else:
if ai_mode == AI_JIGO:
sign = cn.player_sign(cn.next_player)
jigo_move = min(
candidate_ai_moves,
key=lambda move: abs(sign * move["scoreLead"] -
ai_settings["target_score"]))
aimove = Move.from_gtp(jigo_move["move"],
player=cn.next_player)
ai_thoughts += f"Jigo strategy found {len(candidate_ai_moves)} candidate moves (best {top_cand.gtp()}) and chose {aimove.gtp()} as closest to 0.5 point win"
elif ai_mode == AI_SCORELOSS:
c = ai_settings["strength"]
moves = [(
d["pointsLost"],
math.exp(min(200, -c * max(0, d["pointsLost"]))),
Move.from_gtp(d["move"], player=cn.next_player),
) for d in candidate_ai_moves]
topmove = weighted_selection_without_replacement(moves, 1)[0]
aimove = topmove[2]
ai_thoughts += f"ScoreLoss strategy found {len(candidate_ai_moves)} candidate moves (best {top_cand.gtp()}) and chose {aimove.gtp()} (weight {topmove[1]:.3f}, point loss {topmove[0]:.1f}) based on score weights."
elif ai_mode in [AI_SIMPLE_OWNERSHIP, AI_SETTLE_STONES]:
stones_with_player = {(*s.coords, s.player)
for s in game.stones}
next_player_sign = cn.player_sign(cn.next_player)
if ai_mode == AI_SIMPLE_OWNERSHIP:
def settledness(d, player_sign, player):
return sum([
abs(o) for o in d["ownership"]
if player_sign * o > 0
])
else:
board_size_x, board_size_y = game.board_size
def settledness(d, player_sign, player):
ownership_grid = var_to_grid(
d["ownership"], (board_size_x, board_size_y))
return sum([
abs(ownership_grid[s.coords[0]][s.coords[1]])
for s in game.stones if s.player == player
])
def is_attachment(move):
if move.is_pass:
return False
attach_opponent_stones = sum(
(move.coords[0] + dx, move.coords[1] + dy,
cn.player) in stones_with_player for dx in [-1, 0, 1]
for dy in [-1, 0, 1] if abs(dx) + abs(dy) == 1)
nearby_own_stones = sum(
(move.coords[0] + dx, move.coords[1] + dy,
cn.next_player) in stones_with_player
for dx in [-2, 0, 1, 2] for dy in [-2 - 1, 0, 1, 2]
if abs(dx) + abs(dy) <= 2 # allows clamps/jumps
)
return attach_opponent_stones >= 1 and nearby_own_stones == 0
def is_tenuki(d):
return not d.is_pass and not any(
not node or not node.move or node.move.is_pass or max(
abs(last_c - cand_c) for last_c, cand_c in zip(
node.move.coords, d.coords)) < 5
for node in [cn, cn.parent])
moves_with_settledness = sorted(
[(
move,
settledness(d, next_player_sign, cn.next_player),
settledness(d, -next_player_sign, cn.player),
is_attachment(move),
is_tenuki(move),
d,
) for d in candidate_ai_moves
if d["pointsLost"] < ai_settings["max_points_lost"]
and "ownership" in d and (
d["order"] <= 1
or d["visits"] >= ai_settings.get("min_visits", 1))
for move in
[Move.from_gtp(d["move"], player=cn.next_player)]
if not (move.is_pass and d["pointsLost"] > 0.75)],
key=lambda t: t[5]["pointsLost"] + ai_settings[
"attach_penalty"] * t[3] + ai_settings[
"tenuki_penalty"] * t[4] - ai_settings[
"settled_weight"] *
(t[1] + ai_settings["opponent_fac"] * t[2]),
)
if moves_with_settledness:
cands = [
f"{move.gtp()} ({d['pointsLost']:.1f} pt lost, {d['visits']} visits, {settled:.1f} settledness, {oppsettled:.1f} opponent settledness{', attachment' if isattach else ''}{', tenuki' if istenuki else ''})"
for move, settled, oppsettled, isattach, istenuki, d in
moves_with_settledness[:5]
]
ai_thoughts += f"{ai_mode} strategy. Top 5 Candidates {', '.join(cands)} "
aimove = moves_with_settledness[0][0]
else:
raise (Exception(
"No moves found - are you using an older KataGo with no per-move ownership info?"
))
else:
if ai_mode not in [AI_DEFAULT, AI_HANDICAP]:
game.katrain.log(
f"Unknown AI mode {ai_mode} or policy missing, using default.",
OUTPUT_INFO)
ai_thoughts += f"Strategy {ai_mode} not found or unexpected fallback."
aimove = top_cand
if ai_mode == AI_HANDICAP:
ai_thoughts += f"Handicap strategy found {len(candidate_ai_moves)} moves returned from the engine and chose {aimove.gtp()} as top move. PDA based score {cn.format_score(handicap_analysis['rootInfo']['scoreLead'])} and win rate {cn.format_winrate(handicap_analysis['rootInfo']['winrate'])}"
else:
ai_thoughts += f"Default strategy found {len(candidate_ai_moves)} moves returned from the engine and chose {aimove.gtp()} as top move"
game.katrain.log(f"AI thoughts: {ai_thoughts}", OUTPUT_DEBUG)
played_node = game.play(aimove)
played_node.ai_thoughts = ai_thoughts
return aimove, played_node
class KaTrainGui(Screen, KaTrainBase):
"""Top level class responsible for tying everything together"""
zen = NumericProperty(0)
controls = ObjectProperty(None)
def __init__(self, **kwargs):
super().__init__(**kwargs)
self.engine = None
self.new_game_popup = None
self.fileselect_popup = None
self.config_popup = None
self.ai_settings_popup = None
self.teacher_settings_popup = None
self.timer_settings_popup = None
self.idle_analysis = False
self.message_queue = Queue()
self.last_key_down = None
self.last_focus_event = 0
def log(self, message, level=OUTPUT_INFO):
super().log(message, level)
if level == OUTPUT_KATAGO_STDERR and "ERROR" not in self.controls.status.text:
if "starting" in message.lower():
self.controls.set_status("KataGo engine starting...", STATUS_INFO)
if message.startswith("Tuning"):
self.controls.set_status(
"KataGo is tuning settings for first startup, please wait." + message, STATUS_INFO
)
return
if "ready" in message.lower():
self.controls.set_status("KataGo engine ready.", STATUS_INFO)
if (
level == OUTPUT_ERROR
or (level == OUTPUT_KATAGO_STDERR and "error" in message.lower() and "tuning" not in message.lower())
) and getattr(self, "controls", None):
self.controls.set_status(f"ERROR: {message}", STATUS_ERROR)
def animate_pondering(self, *_args):
if not self.idle_analysis:
self.board_controls.engine_status_pondering = -1
else:
self.board_controls.engine_status_pondering += 5
@property
def play_analyze_mode(self):
return self.play_mode.mode
def toggle_continuous_analysis(self):
if self.idle_analysis:
self.controls.set_status("", STATUS_INFO)
self.idle_analysis = not self.idle_analysis
self.update_state()
def start(self):
if self.engine:
return
self.board_gui.trainer_config = self.config("trainer")
self.engine = KataGoEngine(self, self.config("engine"))
threading.Thread(target=self._message_loop_thread, daemon=True).start()
sgf_args = [
f
for f in sys.argv[1:]
if os.path.isfile(f) and any(f.lower().endswith(ext) for ext in ["sgf", "ngf", "gib"])
]
if sgf_args:
self.load_sgf_file(sgf_args[0], fast=True, rewind=True)
else:
self._do_new_game()
Clock.schedule_interval(self.animate_pondering, 0.1)
Window.request_keyboard(None, self, "").bind(on_key_down=self._on_keyboard_down, on_key_up=self._on_keyboard_up)
def set_focus_event(*args):
self.last_focus_event = time.time()
MDApp.get_running_app().root_window.bind(focus=set_focus_event)
def update_gui(self, cn, redraw_board=False):
# Handle prisoners and next player display
prisoners = self.game.prisoner_count
top, bot = [w.__self__ for w in self.board_controls.circles] # no weakref
if self.next_player_info.player == "W":
top, bot = bot, top
self.controls.players["W"].active = True
self.controls.players["B"].active = False
else:
self.controls.players["W"].active = False
self.controls.players["B"].active = True
self.board_controls.mid_circles_container.clear_widgets()
self.board_controls.mid_circles_container.add_widget(bot)
self.board_controls.mid_circles_container.add_widget(top)
self.controls.players["W"].captures = prisoners["W"]
self.controls.players["B"].captures = prisoners["B"]
# update engine status dot
if not self.engine or not self.engine.katago_process or self.engine.katago_process.poll() is not None:
self.board_controls.engine_status_col = Theme.ENGINE_DOWN_COLOR
elif self.engine.is_idle():
self.board_controls.engine_status_col = Theme.ENGINE_READY_COLOR
else:
self.board_controls.engine_status_col = Theme.ENGINE_BUSY_COLOR
self.board_controls.queries_remaining = self.engine.queries_remaining()
# redraw board/stones
if redraw_board:
self.board_gui.draw_board()
self.board_gui.redraw_board_contents_trigger()
self.controls.update_evaluation()
self.controls.update_timer(1)
# update move tree
self.controls.move_tree.current_node = self.game.current_node
def update_state(self, redraw_board=False): # redirect to message queue thread
self("update_state", redraw_board=redraw_board)
def _do_update_state(
self, redraw_board=False
): # is called after every message and on receiving analyses and config changes
# AI and Trainer/auto-undo handlers
if not self.game or not self.game.current_node:
return
cn = self.game.current_node
last_player, next_player = self.players_info[cn.player], self.players_info[cn.next_player]
if self.play_analyze_mode == MODE_PLAY and self.nav_drawer.state != "open" and self.popup_open is None:
teaching_undo = cn.player and last_player.being_taught and cn.parent
if (
teaching_undo
and cn.analysis_complete
and cn.parent.analysis_complete
and not cn.children
and not self.game.end_result
):
self.game.analyze_undo(cn) # not via message loop
if (
cn.analysis_complete
and next_player.ai
and not cn.children
and not self.game.end_result
and not (teaching_undo and cn.auto_undo is None)
): # cn mismatch stops this if undo fired. avoid message loop here or fires repeatedly.
self._do_ai_move(cn)
Clock.schedule_once(self.board_gui.play_stone_sound, 0.25)
if self.engine.is_idle() and self.idle_analysis:
self("analyze-extra", "extra", continuous=True)
Clock.schedule_once(lambda _dt: self.update_gui(cn, redraw_board=redraw_board), -1) # trigger?
def update_player(self, bw, **kwargs):
super().update_player(bw, **kwargs)
if self.game:
sgf_name = self.game.root.get_property("P" + bw)
self.players_info[bw].name = None if not sgf_name or SGF_INTERNAL_COMMENTS_MARKER in sgf_name else sgf_name
if self.controls:
self.controls.update_players()
self.update_state()
for player_setup_block in PlayerSetupBlock.INSTANCES:
player_setup_block.update_player_info(bw, self.players_info[bw])
def set_note(self, note):
self.game.current_node.note = note
# The message loop is here to make sure moves happen in the right order, and slow operations don't hang the GUI
def _message_loop_thread(self):
while True:
game, msg, args, kwargs = self.message_queue.get()
try:
self.log(f"Message Loop Received {msg}: {args} for Game {game}", OUTPUT_EXTRA_DEBUG)
if game != self.game.game_id:
self.log(
f"Message skipped as it is outdated (current game is {self.game.game_id}", OUTPUT_EXTRA_DEBUG
)
continue
fn = getattr(self, f"_do_{msg.replace('-','_')}")
fn(*args, **kwargs)
if msg != "update_state":
self._do_update_state()
except Exception as exc:
self.log(f"Exception in processing message {msg} {args}: {exc}", OUTPUT_ERROR)
traceback.print_exc()
def __call__(self, message, *args, **kwargs):
if self.game:
if message.endswith("popup"): # gui code needs to run in main kivy thread.
fn = getattr(self, f"_do_{message.replace('-', '_')}")
Clock.schedule_once(lambda _dt: fn(*args, **kwargs), -1)
else: # game related actions
self.message_queue.put([self.game.game_id, message, args, kwargs])
def _do_new_game(self, move_tree=None, analyze_fast=False, sgf_filename=None):
self.idle_analysis = False
mode = self.play_analyze_mode
if (move_tree is not None and mode == MODE_PLAY) or (move_tree is None and mode == MODE_ANALYZE):
self.play_mode.switch_ui_mode() # for new game, go to play, for loaded, analyze
self.board_gui.animating_pv = None
self.engine.on_new_game() # clear queries
self.game = Game(
self,
self.engine,
move_tree=move_tree,
analyze_fast=analyze_fast or not move_tree,
sgf_filename=sgf_filename,
)
if move_tree:
for bw, player_info in self.players_info.items():
player_info.player_type = PLAYER_HUMAN
player_info.player_subtype = PLAYING_NORMAL
player_info.sgf_rank = move_tree.root.get_property(bw + "R")
player_info.calculated_rank = None
self.update_player(bw)
self.controls.graph.initialize_from_game(self.game.root)
self.update_state(redraw_board=True)
def _do_insert_mode(self, mode="toggle"):
self.game.set_insert_mode(mode)
if self.play_analyze_mode != MODE_ANALYZE:
self.play_mode.switch_ui_mode()
def _do_ai_move(self, node=None):
if node is None or self.game.current_node == node:
mode = self.next_player_info.strategy
settings = self.config(f"ai/{mode}")
if settings is not None:
generate_ai_move(self.game, mode, settings)
else:
self.log(f"AI Mode {mode} not found!", OUTPUT_ERROR)
def _do_undo(self, n_times=1):
if n_times == "smart":
n_times = 1
if self.play_analyze_mode == MODE_PLAY and self.last_player_info.ai and self.next_player_info.human:
n_times = 2
self.board_gui.animating_pv = None
self.game.undo(n_times)
def _do_resign(self):
self.game.current_node.end_state = f"{self.game.current_node.player}+R"
def _do_redo(self, n_times=1):
self.board_gui.animating_pv = None
self.game.redo(n_times)
def _do_find_mistake(self, fn="redo"):
self.board_gui.animating_pv = None
getattr(self.game, fn)(9999, stop_on_mistake=self.config("trainer/eval_thresholds")[-4])
def _do_cycle_children(self, *args):
self.board_gui.animating_pv = None
self.game.cycle_children(*args)
def _do_switch_branch(self, *args):
self.board_gui.animating_pv = None
self.controls.move_tree.switch_branch(*args)
def _do_play(self, coords):
self.board_gui.animating_pv = None
try:
self.game.play(Move(coords, player=self.next_player_info.player))
except IllegalMoveException as e:
self.controls.set_status(f"Illegal Move: {str(e)}", STATUS_ERROR)
def _do_analyze_extra(self, mode, **kwargs):
self.game.analyze_extra(mode, **kwargs)
def _do_play_to_end(self):
self.game.play_to_end()
def _do_select_box(self):
self.controls.set_status(i18n._("analysis:region:start"), STATUS_INFO)
self.board_gui.selecting_region_of_interest = True
def _do_new_game_popup(self):
self.controls.timer.paused = True
if not self.new_game_popup:
self.new_game_popup = I18NPopup(
title_key="New Game title", size=[dp(800), dp(800)], content=NewGamePopup(self)
).__self__
self.new_game_popup.content.popup = self.new_game_popup
self.new_game_popup.open()
self.new_game_popup.content.update_from_current_game()
def _do_timer_popup(self):
self.controls.timer.paused = True
if not self.timer_settings_popup:
self.timer_settings_popup = I18NPopup(
title_key="timer settings", size=[dp(600), dp(500)], content=ConfigTimerPopup(self)
).__self__
self.timer_settings_popup.content.popup = self.timer_settings_popup
self.timer_settings_popup.open()
def _do_teacher_popup(self):
self.controls.timer.paused = True
if not self.teacher_settings_popup:
self.teacher_settings_popup = I18NPopup(
title_key="teacher settings", size=[dp(800), dp(800)], content=ConfigTeacherPopup(self)
).__self__
self.teacher_settings_popup.content.popup = self.teacher_settings_popup
self.teacher_settings_popup.open()
def _do_config_popup(self):
self.controls.timer.paused = True
if not self.config_popup:
self.config_popup = I18NPopup(
title_key="general settings title", size=[dp(1200), dp(950)], content=ConfigPopup(self)
).__self__
self.config_popup.content.popup = self.config_popup
self.config_popup.open()
def _do_ai_popup(self):
self.controls.timer.paused = True
if not self.ai_settings_popup:
self.ai_settings_popup = I18NPopup(
title_key="ai settings", size=[dp(750), dp(750)], content=ConfigAIPopup(self)
).__self__
self.ai_settings_popup.content.popup = self.ai_settings_popup
self.ai_settings_popup.open()
def load_sgf_file(self, file, fast=False, rewind=True):
try:
move_tree = KaTrainSGF.parse_file(file)
except (ParseError, FileNotFoundError) as e:
self.log(i18n._("Failed to load SGF").format(error=e), OUTPUT_ERROR)
return
self._do_new_game(move_tree=move_tree, analyze_fast=fast, sgf_filename=file)
if not rewind:
self.game.redo(999)
def _do_analyze_sgf_popup(self):
if not self.fileselect_popup:
popup_contents = LoadSGFPopup()
popup_contents.filesel.path = os.path.abspath(os.path.expanduser(self.config("general/sgf_load", ".")))
self.fileselect_popup = I18NPopup(
title_key="load sgf title", size=[dp(1200), dp(800)], content=popup_contents
).__self__
def readfile(*_args):
filename = popup_contents.filesel.filename
self.fileselect_popup.dismiss()
path, file = os.path.split(filename)
if path != self.config("general/sgf_load"):
self.log(f"Updating sgf load path default to {path}", OUTPUT_DEBUG)
self._config["general"]["sgf_load"] = path
self.save_config("general")
self.load_sgf_file(filename, popup_contents.fast.active, popup_contents.rewind.active)
popup_contents.filesel.on_success = readfile
popup_contents.filesel.on_submit = readfile
self.fileselect_popup.open()
self.fileselect_popup.content.filesel.ids.list_view._trigger_update()
def _do_save_game(self, filename=None):
filename = filename or self.game.sgf_filename
if not filename:
return self("save-game-as-popup")
try:
msg = self.game.write_sgf(filename)
self.log(msg, OUTPUT_INFO)
self.controls.set_status(msg, STATUS_INFO, check_level=False)
except Exception as e:
self.log(f"Failed to save SGF to {filename}: {e}", OUTPUT_ERROR)
def _do_save_game_as_popup(self):
popup_contents = SaveSGFPopup(suggested_filename=self.game.generate_filename())
save_game_popup = I18NPopup(
title_key="save sgf title", size=[dp(1200), dp(800)], content=popup_contents
).__self__
def readfile(*_args):
filename = popup_contents.filesel.filename
if not filename.lower().endswith(".sgf"):
filename += ".sgf"
save_game_popup.dismiss()
path, file = os.path.split(filename.strip())
if not path:
path = popup_contents.filesel.path # whatever dir is shown
if path != self.config("general/sgf_save"):
self.log(f"Updating sgf save path default to {path}", OUTPUT_DEBUG)
self._config["general"]["sgf_save"] = path
self.save_config("general")
self._do_save_game(os.path.join(path, file))
popup_contents.filesel.on_success = readfile
popup_contents.filesel.on_submit = readfile
save_game_popup.open()
def load_sgf_from_clipboard(self):
clipboard = Clipboard.paste()
if not clipboard:
self.controls.set_status("Ctrl-V pressed but clipboard is empty.", STATUS_INFO)
return
url_match = re.match(r"(?P<url>https?://[^\s]+)", clipboard)
if url_match:
self.log("Recognized url: " + url_match.group(), OUTPUT_INFO)
http = urllib3.PoolManager()
response = http.request("GET", url_match.group())
clipboard = response.data.decode("utf-8")
try:
move_tree = KaTrainSGF.parse_sgf(clipboard)
except Exception as exc:
self.controls.set_status(
i18n._("Failed to import from clipboard").format(error=exc, contents=clipboard[:50]), STATUS_INFO
)
return
move_tree.nodes_in_tree[-1].analyze(
self.engine, analyze_fast=False
) # speed up result for looking at end of game
self._do_new_game(move_tree=move_tree, analyze_fast=True)
self("redo", 9999)
self.log("Imported game from clipboard.", OUTPUT_INFO)
def on_touch_up(self, touch):
if (
self.board_gui.collide_point(*touch.pos)
or self.board_controls.collide_point(*touch.pos)
or self.controls.move_tree.collide_point(*touch.pos)
):
if touch.is_mouse_scrolling:
if touch.button == "scrollup":
self("redo")
elif touch.button == "scrolldown":
self("undo")
return super().on_touch_up(touch)
@property
def shortcuts(self):
return {
"q": self.analysis_controls.show_children,
"w": self.analysis_controls.eval,
"e": self.analysis_controls.hints,
"t": self.analysis_controls.ownership,
"r": self.analysis_controls.policy,
"enter": ("ai-move",),
"numpadenter": ("ai-move",),
"a": ("analyze-extra", "extra"),
"s": ("analyze-extra", "equalize"),
"d": ("analyze-extra", "sweep"),
"f": ("analyze-extra", "alternative"),
"g": ("select-box",),
"i": ("insert-mode",),
"p": ("play", None),
"l": ("play-to-end",),
"b": ("undo", "branch"),
"down": ("switch-branch", 1),
"up": ("switch-branch", -1),
"f5": ("timer-popup",),
"f6": ("teacher-popup",),
"f7": ("ai-popup",),
"f8": ("config-popup",),
}
@property
def popup_open(self) -> Popup:
app = App.get_running_app()
if app:
first_child = app.root_window.children[0]
return first_child if isinstance(first_child, Popup) else None
def _on_keyboard_down(self, _keyboard, keycode, _text, modifiers):
self.last_key_down = keycode
ctrl_pressed = "ctrl" in modifiers
if self.controls.note.focus:
return # when making notes, don't allow keyboard shortcuts
popup = self.popup_open
if popup:
if keycode[1] in ["f5", "f6", "f7", "f8"]: # switch between popups
popup.dismiss()
return
elif keycode[1] in ["enter", "numpadenter"]:
fn = getattr(popup.content, "on_submit", None)
if fn:
fn()
return
else:
return
shift_pressed = "shift" in modifiers
shortcuts = self.shortcuts
if keycode[1] == "spacebar":
self.toggle_continuous_analysis()
elif keycode[1] == "k":
self.board_gui.toggle_coordinates()
elif keycode[1] in ["pause", "break", "f15"] and not ctrl_pressed:
self.controls.timer.paused = not self.controls.timer.paused
elif keycode[1] in ["`", "~", "f12"]:
self.zen = (self.zen + 1) % 3
elif keycode[1] in ["left", "z"]:
self("undo", 1 + shift_pressed * 9 + ctrl_pressed * 9999)
elif keycode[1] in ["right", "x"]:
self("redo", 1 + shift_pressed * 9 + ctrl_pressed * 9999)
elif keycode[1] == "home":
self("undo", 9999)
elif keycode[1] == "end":
self("redo", 9999)
elif keycode[1] == "pageup":
self.controls.move_tree.make_selected_node_main_branch()
elif keycode[1] == "n" and not ctrl_pressed:
self("find-mistake", "undo" if shift_pressed else "redo")
elif keycode[1] == "delete" and ctrl_pressed:
self.controls.move_tree.delete_selected_node()
elif keycode[1] == "c" and not ctrl_pressed:
self.controls.move_tree.toggle_selected_node_collapse()
elif keycode[1] == "n" and ctrl_pressed:
self("new-game-popup")
elif keycode[1] == "l" and ctrl_pressed:
self("analyze-sgf-popup")
elif keycode[1] == "s" and ctrl_pressed:
self("save-game")
elif keycode[1] == "d" and ctrl_pressed:
self("save-game-as-popup")
elif keycode[1] == "c" and ctrl_pressed:
Clipboard.copy(self.game.root.sgf())
self.controls.set_status(i18n._("Copied SGF to clipboard."), STATUS_INFO)
elif keycode[1] == "v" and ctrl_pressed:
self.load_sgf_from_clipboard()
elif keycode[1] == "b" and shift_pressed:
self("undo", "main-branch")
elif keycode[1] in shortcuts.keys() and not ctrl_pressed:
shortcut = shortcuts[keycode[1]]
if isinstance(shortcut, Widget):
shortcut.trigger_action(duration=0)
else:
self(*shortcut)
elif keycode[1] == "f9" and self.debug_level >= OUTPUT_EXTRA_DEBUG:
import yappi
yappi.set_clock_type("cpu")
yappi.start()
self.log("starting profiler", OUTPUT_ERROR)
elif keycode[1] == "f10" and self.debug_level >= OUTPUT_EXTRA_DEBUG:
import time
import yappi
stats = yappi.get_func_stats()
filename = f"callgrind.{int(time.time())}.prof"
stats.save(filename, type="callgrind")
self.log(f"wrote profiling results to {filename}", OUTPUT_ERROR)
def _on_keyboard_up(self, _keyboard, keycode):
if keycode[1] in ["alt", "tab"]:
Clock.schedule_once(lambda *_args: self._single_key_action(keycode), 0.05)
def _single_key_action(self, keycode):
if (
self.controls.note.focus
or self.popup_open
or keycode != self.last_key_down
or time.time() - self.last_focus_event < 0.2 # this is here to prevent alt-tab from firing alt or tab
):
return
if keycode[1] == "alt":
self.nav_drawer.set_state("toggle")
elif keycode[1] == "tab":
self.play_mode.switch_ui_mode()
num_passes = sum([
int(n.is_pass or False)
for n in game.current_node.nodes_from_root[::-1][0:2 * MAX_PASS:2]
])
bx, by = game.board_size
if num_passes >= MAX_PASS and game.current_node.depth - 2 * MAX_PASS >= bx + by:
logger.log(f"Forced pass as opponent is passing {MAX_PASS} times",
OUTPUT_ERROR)
pol = game.current_node.policy
if not pol:
pol = ["??"]
print(
f"DISCUSSION:OK, since you passed {MAX_PASS} times after the {bx+by}th move, I will pass as well [policy {pol[-1]:.3%}].",
file=sys.stderr,
)
move = game.play(Move(None,
player=game.current_node.next_player)).move
else:
move, node = generate_ai_move(game, ai_strategy, ai_settings)
logger.log(f"Generated move {move}", OUTPUT_ERROR)
print(f"= {move.gtp()}\n")
sys.stdout.flush()
malkovich_analysis(game.current_node)
continue
elif line.startswith("play"):
_, player, move = line.split(" ")
node = game.play(Move.from_gtp(move.upper(), player=player[0].upper()),
analyze=False)
logger.log(f"played {player} {move}", OUTPUT_ERROR)
elif line.startswith("final_score"):
logger.log("line=" + line, OUTPUT_ERROR)
if "{" in line: