Merge branch 'master' of www.github.com:sanderland/katrain

Author: Sander Land

.gitignore

@@ -13,6 +13,8 @@ sgf_ogs
 log*
 tmp.pickle
 my
+logs
+callgrind.*
 
 # debug
 outdated_log.txt

bots/ai2gtp.py

@@ -2,6 +2,7 @@
 import json
 import sys
 import time
+import random
 
 from core.ai import ai_move
 from core.common import OUTPUT_ERROR, OUTPUT_INFO
@@ -85,7 +86,6 @@ def malkovich_analysis(cn):
 
 
 while True:
-    p = game.current_node.next_player
     line = input()
     logger.log(f"GOT INPUT {line}", OUTPUT_ERROR)
     if "boardsize" in line:
@@ -103,8 +103,14 @@ def malkovich_analysis(cn):
         logger.log(f"Setting komi {game.root.properties}", OUTPUT_ERROR)
     elif "place_free_handicap" in line:
         _, n = line.split(" ")
-        game.place_handicap_stones(int(n))
-        gtp = [Move.from_sgf(m, game.board_size, "B").gtp() for m in game.root.get_list_property("AB")]
+        n = int(n)
+        game.place_handicap_stones(n)
+        handicaps = set(game.root.get_list_property("AB"))
+        bx, by = game.board_size
+        while len(handicaps) < min(n, bx * by):  # really obscure cases
+            handicaps.add(Move((random.randint(0, bx - 1), random.randint(0, by - 1)), player="B").sgf(board_size=game.board_size))
+        game.root.set_property("AB", list(handicaps))
+        gtp = [Move.from_sgf(m, game.board_size, "B").gtp() for m in handicaps]
         logger.log(f"Chose handicap placements as {gtp}", OUTPUT_ERROR)
         print(f"= {' '.join(gtp)}\n")
         sys.stdout.flush()
@@ -115,6 +121,12 @@ def malkovich_analysis(cn):
         game.root.set_property("AB", [Move.from_gtp(move.upper()).sgf(game.board_size) for move in stones])
         logger.log(f"Set handicap placements to {game.root.get_list_property('AB')}", OUTPUT_ERROR)
     elif "genmove" in line:
+        _, player = line.strip().split(" ")
+        if player[0].upper() != game.next_player:
+            logger.log(f"ERROR generating move: UNEXPECTED PLAYER {player} != {game.next_player}.", OUTPUT_ERROR)
+            print(f"= ??\n")
+            sys.stdout.flush()
+            continue
         logger.log(f"{ai_strategy} generating move", OUTPUT_ERROR)
         game.current_node.analyze(engine)
         malkovich_analysis(game.current_node)
@@ -130,7 +142,12 @@ def malkovich_analysis(cn):
             move = game.play(Move(None, player=game.next_player)).single_move
         else:
             move, node = ai_move(game, ai_strategy, ai_settings)
-            logger.log(f"Generated move {move}", OUTPUT_ERROR)
+            if node is None:
+                while node is None:
+                    logger.log(f"ERROR generating move, backing up with weighted.", OUTPUT_ERROR)
+                    move, node = ai_move(game, "p:weighted", {"pick_override": 1.0, "lower_bound": 0.001, "weaken_fac": 1})
+            else:
+                logger.log(f"Generated move {move}", OUTPUT_ERROR)
         print(f"= {move.gtp()}\n")
         sys.stdout.flush()
         malkovich_analysis(game.current_node)

core/ai.py

@@ -34,8 +34,7 @@ def ai_move(game: Game, ai_mode: str, ai_settings: Dict) -> Tuple[Move, GameNode
             raise EngineDiedException(f"Engine for {cn.next_player} ({engine.config}) died")
     ai_mode = ai_mode.lower()
     ai_thoughts = ""
-    candidate_ai_moves = cn.candidate_moves
-    if ("policy" in ai_mode or "p:" in ai_mode) and cn.policy:
+    if ("policy" in ai_mode or "p:" in ai_mode) and cn.policy:  # pure policy based move
         policy_moves = cn.policy_ranking
         pass_policy = cn.policy[-1]
         top_5_pass = any([polmove[1].is_pass for polmove in policy_moves[:5]])  # dont make it jump around for the last few sensible non pass moves
@@ -131,32 +130,35 @@ def ai_move(game: Game, ai_mode: str, ai_settings: Dict) -> Tuple[Move, GameNode
                 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 AI mode {ai_mode}")
-    elif "balance" in ai_mode and candidate_ai_moves[0]["move"] != "pass":  # don't play suicidal to balance score - pass when it's best
-        sign = cn.player_sign(cn.next_player)
-        sel_moves = [  # top move, or anything not too bad, or anything that makes you still ahead
-            move
-            for i, move in enumerate(candidate_ai_moves)
-            if i == 0
-            or move["visits"] >= ai_settings["min_visits"]
-            and (move["pointsLost"] < ai_settings["random_loss"] or move["pointsLost"] < ai_settings["max_loss"] and sign * move["scoreLead"] > ai_settings["target_score"])
-        ]
-        aimove = Move.from_gtp(random.choice(sel_moves)["move"], player=cn.next_player)
-        ai_thoughts += f"Balance strategy selected moves {sel_moves} based on target score and max points lost, and randomly chose {aimove.gtp()}."
-    elif "jigo" in ai_mode and candidate_ai_moves[0]["move"] != "pass":
-        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 candidate moves {candidate_ai_moves} moves and chose {aimove.gtp()} as closest to 0.5 point win"
-    else:
-        if "default" not in ai_mode and "katago" not in ai_mode:
-            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 = Move.from_gtp(candidate_ai_moves[0]["move"], player=cn.next_player)
-        ai_thoughts += f"Default strategy found {len(candidate_ai_moves)} moves returned from the engine and chose {aimove.gtp()} as top move"
+    else:  # Engine based move
+        candidate_ai_moves = cn.candidate_moves
+        if "balance" in ai_mode and candidate_ai_moves[0]["move"] != "pass":  # don't play suicidal to balance score - pass when it's best
+            sign = cn.player_sign(cn.next_player)
+            sel_moves = [  # top move, or anything not too bad, or anything that makes you still ahead
+                move
+                for i, move in enumerate(candidate_ai_moves)
+                if i == 0
+                or move["visits"] >= ai_settings["min_visits"]
+                and (move["pointsLost"] < ai_settings["random_loss"] or move["pointsLost"] < ai_settings["max_loss"] and sign * move["scoreLead"] > ai_settings["target_score"])
+            ]
+            aimove = Move.from_gtp(random.choice(sel_moves)["move"], player=cn.next_player)
+            ai_thoughts += f"Balance strategy selected moves {sel_moves} based on target score and max points lost, and randomly chose {aimove.gtp()}."
+        elif "jigo" in ai_mode and candidate_ai_moves[0]["move"] != "pass":
+            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 candidate moves {candidate_ai_moves} moves and chose {aimove.gtp()} as closest to 0.5 point win"
+        else:
+            if "default" not in ai_mode and "katago" not in ai_mode:
+                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 = Move.from_gtp(candidate_ai_moves[0]["move"], player=cn.next_player)
+            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)
     try:
         played_node = game.play(aimove)
         played_node.ai_thoughts = ai_thoughts
         return aimove, played_node
     except IllegalMoveException as e:
         game.katrain.log(f"AI Strategy {ai_mode} generated illegal move {aimove.gtp()}:  {e}", OUTPUT_ERROR)
+        return None, None

core/engine.py

@@ -113,7 +113,7 @@ def send_query(self, query, callback, error_callback, next_move=None):
                 query["id"] = f"QUERY:{str(self.query_counter)}"
             self.queries[query["id"]] = (callback, error_callback, time.time(), next_move)
         if self.katago_process:
-            self.katrain.log(f"Sending query {query['id']}: {str(query)}", OUTPUT_DEBUG)
+            self.katrain.log(f"Sending query {query['id']}: {json.dumps(query)}", OUTPUT_DEBUG)
             try:
                 self.katago_process.stdin.write((json.dumps(query) + "\n").encode())
                 self.katago_process.stdin.flush()

core/game.py

@@ -154,10 +154,8 @@ def switch_branch(self, direction):
 
     def place_handicap_stones(self, n_handicaps):
         board_size_x, board_size_y = self.board_size
-        near_x = 3 if board_size_x >= 13 else 2
-        near_y = 3 if board_size_y >= 13 else 2
-        if board_size_x < 3 or board_size_y < 3:
-            return
+        near_x = 3 if board_size_x >= 13 else min(2, board_size_x - 1)
+        near_y = 3 if board_size_y >= 13 else min(2, board_size_y - 1)
         far_x = board_size_x - 1 - near_x
         far_y = board_size_y - 1 - near_y
         middle_x = board_size_x // 2  # what for even sizes?
@@ -176,7 +174,7 @@ def place_handicap_stones(self, n_handicaps):
             if n_handicaps % 2 == 1:
                 stones.append((middle_x, middle_y))
             stones += [(near_x, middle_y), (far_x, middle_y), (middle_x, near_y), (middle_x, far_y)]
-        self.root.set_property("AB", [Move(stone).sgf(board_size=(board_size_x, board_size_y)) for stone in stones[:n_handicaps]])
+        self.root.set_property("AB", list({Move(stone).sgf(board_size=(board_size_x, board_size_y)) for stone in stones[:n_handicaps]}))
 
     @property
     def board_size(self):

core/sgf_parser.py

@@ -9,7 +9,7 @@ class ParseError(Exception):
 
 
 class Move:
-    GTP_COORD = list("ABCDEFGHJKLMNOPQRSTUVWXYZ") + ["A" + c for c in "ABCDEFGHJKLMNOPQRSTUVWXYZ"]  # kata board size 29 support
+    GTP_COORD = list("ABCDEFGHJKLMNOPQRSTUVWXYZ") + [xa + c for xa in "AB" for c in "ABCDEFGHJKLMNOPQRSTUVWXYZ"]  # kata board size 29 support
     PLAYERS = "BW"
     SGF_COORD = list("ABCDEFGHIJKLMNOPQRSTUVWXYZ".lower()) + list("ABCDEFGHIJKLMNOPQRSTUVWXYZ")
 
@@ -79,10 +79,11 @@ def order_children(children):
 
     def sgf(self, **xargs) -> str:
         """Generates an SGF, calling sgf_properties on each node with the given xargs, so it can filter relevant properties if needed."""
-        import sys
+        if self.is_root:
+            import sys
 
-        bszx, bszy = self.board_size
-        sys.setrecursionlimit(max(sys.getrecursionlimit(), 3 * bszx * bszy))  # thanks to lightvector for causing stack overflows ;)
+            bszx, bszy = self.board_size
+            sys.setrecursionlimit(max(sys.getrecursionlimit(), 4 * bszx * bszy))
         sgf_str = "".join([prop + "".join(f"[{v}]" for v in values) for prop, values in self.sgf_properties(**xargs).items() if values])
         if self.children:
             children = [c.sgf(**xargs) for c in self.order_children(self.children)]
@@ -211,15 +212,17 @@ def play(self, move) -> "SGFNode":
 
     @property
     def next_player(self):
-        if "B" in self.properties or "AB" in self.properties:
+        if "B" in self.properties or "AB" in self.properties: # root or black moved
             return "W"
-        return "B"
+        else:
+            return "B"
 
     @property
     def player(self):
-        if "W" in self.properties:
-            return "W"
-        return "B"
+        if "B" in self.properties or "AB" in self.properties:
+            return "B"
+        else:
+            return "W" # nb root is considered white played if no handicap stones are placed
 
 
 class SGF:

gui/popups.py

@@ -173,13 +173,12 @@ def restart_engine(_dt):
                 old_proc = old_engine.katago_process
                 if old_proc:
                     old_engine.shutdown(finish=True)
-
                 new_engine = KataGoEngine(self.katrain, self.config["engine"])
                 self.katrain.engine = new_engine
                 self.katrain.game.engines = {"B": new_engine, "W": new_engine}
                 if not old_proc:
                     self.katrain.game.analyze_all_nodes()  # old engine was broken, so make sure we redo any failures
-
+                self.katrain.update_state()
             Clock.schedule_once(restart_engine, 0)
 
         self.katrain.debug_level = self.config["debug"]["level"]