279 lines
12 KiB
Python
279 lines
12 KiB
Python
import copy
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import random
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from typing import Dict, List, Optional, Tuple
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from katrain.core.utils import evaluation_class, var_to_grid
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from katrain.core.lang import i18n
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from katrain.core.sgf_parser import Move, SGFNode
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from katrain.gui.style import INFO_PV_COLOR
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class GameNode(SGFNode):
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"""Represents a single game node, with one or more moves and placements."""
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def __init__(self, parent=None, properties=None, move=None):
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super().__init__(parent=parent, properties=properties, move=move)
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self.analysis = {"moves": {}, "root": None}
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self.ownership = None
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self.policy = None
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self.auto_undo = None # None = not analyzed. False: not undone (good move). True: undone (bad move)
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self.ai_thoughts = ""
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self.note = ""
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self.move_number = 0
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self.time_used = 0
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self.analysis_visits_requested = 0
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self.undo_threshold = (
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random.random()
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) # for fractional undos, store the random threshold in the move itself for consistency
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self._favourite_child = None
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def sgf_properties(self, save_comments_player=None, save_comments_class=None, eval_thresholds=None):
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properties = copy.copy(super().sgf_properties())
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if self.points_lost and save_comments_class is not None and eval_thresholds is not None:
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show_class = save_comments_class[evaluation_class(self.points_lost, eval_thresholds)]
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else:
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show_class = False
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if (
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(save_comments_player or {}).get(self.player, False)
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and show_class
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and self.analysis_ready
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and self.parent
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and self.parent.analysis_ready
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):
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candidate_moves = self.parent.candidate_moves
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top_x = Move.from_gtp(candidate_moves[0]["move"]).sgf(self.board_size)
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best_sq = [
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Move.from_gtp(d["move"]).sgf(self.board_size) for d in candidate_moves[1:] if d["pointsLost"] <= 0.5
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]
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if best_sq and "SQ" not in properties:
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properties["SQ"] = best_sq
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if top_x and "MA" not in properties:
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properties["MA"] = [top_x]
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comment = self.comment(sgf=True, interactive=False)
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if comment:
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properties["C"] = ["\n".join(properties.get("C", "")) + comment]
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if self.is_root:
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properties["C"] = [
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i18n._("SGF start message")
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+ "\n"
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+ "\n".join(properties.get("C", ""))
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+ "\nSGF with review generated by KaTrain."
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]
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if self.note.strip():
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properties["C"] = ["\n".join(properties.get("C", "")) + f"\nNote: {self.note}"]
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return properties
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@staticmethod
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def order_children(children):
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return sorted(
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children, key=lambda c: 0.5 if c.auto_undo is None else int(c.auto_undo)
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) # analyzed/not undone main, non-teach second, undone last
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def set_favourite_child(self, c):
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self._favourite_child = c
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@property
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def favourite_child(self) -> Optional["GameNode"]:
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if self._favourite_child:
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return self._favourite_child
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elif self.children:
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return self.children[0]
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# various analysis functions
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def analyze(self, engine, priority=0, visits=None, time_limit=True, refine_move=None, analyze_fast=False):
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if visits and not refine_move:
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self.analysis_visits_requested = max(visits, engine.config["max_visits"])
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engine.request_analysis(
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self,
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lambda result: self.set_analysis(result, refine_move),
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priority=priority,
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visits=visits,
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analyze_fast=analyze_fast,
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time_limit=time_limit,
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next_move=refine_move,
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)
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def update_move_analysis(self, move_analysis, move_gtp):
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cur = self.analysis["moves"].get(move_gtp)
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if cur is None:
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self.analysis["moves"][move_gtp] = {
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"move": move_gtp,
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"order": 999,
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**move_analysis,
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} # some default values for keys missing in rootInfo
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elif cur["visits"] < move_analysis["visits"]:
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cur.update(move_analysis)
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def set_analysis(self, analysis_json, refine_move):
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if refine_move:
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pvtail = analysis_json["moveInfos"][0]["pv"] if analysis_json["moveInfos"] else []
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self.update_move_analysis(
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{"pv": [refine_move.gtp()] + pvtail, **analysis_json["rootInfo"]}, refine_move.gtp()
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)
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else:
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for move_analysis in analysis_json["moveInfos"]:
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self.update_move_analysis(move_analysis, move_analysis["move"])
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self.ownership = analysis_json.get("ownership")
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self.policy = analysis_json.get("policy")
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self.analysis["root"] = analysis_json["rootInfo"]
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if self.parent and self.move:
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analysis_json["rootInfo"]["pv"] = [self.move.gtp()] + (
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analysis_json["moveInfos"][0]["pv"] if analysis_json["moveInfos"] else []
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)
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self.parent.update_move_analysis(
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analysis_json["rootInfo"], self.move.gtp()
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) # update analysis in parent for consistency
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@property
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def analysis_ready(self):
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return self.analysis["root"] is not None
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@property
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def score(self) -> Optional[float]:
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if self.analysis_ready:
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return self.analysis["root"].get("scoreLead")
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def format_score(self, score=None):
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score = score or self.score
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if score is not None:
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return f"{'B' if score >= 0 else 'W'}+{abs(score):.1f}"
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@property
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def winrate(self) -> Optional[float]:
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if self.analysis_ready:
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return self.analysis["root"].get("winrate")
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def format_winrate(self, win_rate=None):
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win_rate = win_rate or self.winrate
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if win_rate is not None:
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return f"{'B' if win_rate > 0.5 else 'W'} {max(win_rate,1-win_rate):.1%}"
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def make_pv(self, player, pv, interactive):
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pvtext = f"{player}{' '.join(pv)}"
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if interactive:
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pvtext = f"[u][ref={pvtext}][color={INFO_PV_COLOR}]{pvtext}[/color][/ref][/u]"
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return pvtext
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def comment(self, sgf=False, teach=False, hints=False, interactive=True):
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single_move = self.move
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if not self.parent or not single_move: # root
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return ""
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text = i18n._("move").format(number=self.depth) + f": {single_move.player} {single_move.gtp()}\n"
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if self.analysis_ready:
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score = self.score
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if sgf:
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text += i18n._("Info:score").format(score=self.format_score(score)) + "\n"
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text += i18n._("Info:winrate").format(winrate=self.format_winrate()) + "\n"
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if self.parent and self.parent.analysis_ready:
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previous_top_move = self.parent.candidate_moves[0]
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if sgf or hints:
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if previous_top_move["move"] != single_move.gtp():
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points_lost = self.points_lost
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if sgf and points_lost > 0.5:
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text += i18n._("Info:point loss").format(points_lost=points_lost) + "\n"
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text += (
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i18n._("Info:top move").format(
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top_move=previous_top_move["move"],
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score=self.format_score(previous_top_move["scoreLead"]),
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)
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+ "\n"
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)
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else:
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text += i18n._("Info:best move") + "\n"
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if previous_top_move.get("pv") and (sgf or hints):
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text += (
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i18n._("Info:PV").format(
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pv=self.make_pv(single_move.player, previous_top_move["pv"], interactive)
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)
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+ "\n"
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)
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if sgf or hints or teach:
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policy_ranking = self.parent.policy_ranking
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currmove_policy_with_ix = [
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(ix + 1, p)
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for (p, m), ix in zip(policy_ranking, range(len(policy_ranking)))
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if m == single_move
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]
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if currmove_policy_with_ix:
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text += (
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i18n._("Info:policy rank").format(
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rank=currmove_policy_with_ix[0][0], probability=currmove_policy_with_ix[0][1]
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)
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+ "\n"
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)
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if not currmove_policy_with_ix or currmove_policy_with_ix[0] != 1 and (sgf or hints):
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text += (
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i18n._("Info:policy best").format(
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move=policy_ranking[0][1].gtp(), probability=policy_ranking[0][0]
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)
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+ "\n"
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)
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if self.auto_undo and sgf:
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text += i18n._("Info:teaching undo") + "\n"
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top_pv = self.analysis_ready and self.candidate_moves[0].get("pv")
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if top_pv:
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text += i18n._("Info:undo predicted PV").format(pv=f"{self.next_player}{' '.join(top_pv)}") + "\n"
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if self.ai_thoughts and (sgf or hints):
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text += "\n" + i18n._("Info:AI thoughts").format(thoughts=self.ai_thoughts)
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else:
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text = i18n._("No analysis available") if sgf else i18n._("Analyzing move...")
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return text
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@property
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def points_lost(self) -> Optional[float]:
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single_move = self.move
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if single_move and self.parent and self.analysis_ready and self.parent.analysis_ready:
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parent_score = self.parent.score
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score = self.score
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return self.player_sign(single_move.player) * (parent_score - score)
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@property
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def parent_realized_points_lost(self) -> Optional[float]:
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single_move = self.move
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if (
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single_move
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and self.parent
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and self.parent.parent
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and self.analysis_ready
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and self.parent.parent.analysis_ready
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):
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parent_parent_score = self.parent.parent.score
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score = self.score
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return self.player_sign(single_move.player) * (score - parent_parent_score)
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@staticmethod
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def player_sign(player):
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return {"B": 1, "W": -1, None: 0}[player]
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@property
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def candidate_moves(self) -> List[Dict]:
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if not self.analysis_ready:
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return []
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if not self.analysis["moves"]:
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polmoves = self.policy_ranking
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top_polmove = polmoves[0][1] if polmoves else Move(None) # if no info at all, pass
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return [
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{**self.analysis["root"], "pointsLost": 0, "order": 0, "move": top_polmove.gtp()}
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] # single visit -> go by policy/root
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root_score = self.analysis["root"]["scoreLead"]
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move_dicts = list(self.analysis["moves"].values()) # prevent incoming analysis from causing crash
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return sorted(
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[
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{"pointsLost": self.player_sign(self.next_player) * (root_score - d["scoreLead"]), **d}
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for d in move_dicts
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],
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key=lambda d: (d["order"], d["pointsLost"]),
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)
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@property
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def policy_ranking(self) -> Optional[List[Tuple[float, Move]]]: # return moves from highest policy value to lowest
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if self.policy:
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szx, szy = self.board_size
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policy_grid = var_to_grid(self.policy, size=(szx, szy))
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moves = [(policy_grid[y][x], Move((x, y), player=self.next_player)) for x in range(szx) for y in range(szy)]
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moves.append((self.policy[-1], Move(None, player=self.next_player)))
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return sorted(moves, key=lambda mp: -mp[0])
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