173 lines
8.4 KiB
Python
173 lines
8.4 KiB
Python
import copy
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import math
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import random
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from typing import Dict, List, Optional, Tuple
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from common import evaluation_class, var_to_grid
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from sgf_parser import Move, SGFNode
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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.move_number = 0
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self.undo_threshold = random.random() # for fractional undos, store the random threshold in the move itself for consistency
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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 (save_comments_player or {}).get(self.player, False) and show_class and self.analysis_ready:
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candidate_moves = self.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 = [Move.from_gtp(d["move"]).sgf(self.board_size) for d in candidate_moves[1:] if d["pointsLost"] <= 0.5]
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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)
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if comment:
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properties["C"] = [properties.get("C", "") + comment]
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if self.is_root:
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properties["C"] = [
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"Moves marked 'X' indicate the top move according to KataGo, those with a square are all moves which lose at most 0.5 points.\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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return properties
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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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engine.request_analysis(
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self, lambda result: self.set_analysis(result, refine_move), priority=priority, visits=visits, analyze_fast=analyze_fast, time_limit=time_limit, 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] = {"move": move_gtp, "order": 999, **move_analysis} # 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({"pv": [refine_move.gtp()] + pvtail, **analysis_json["rootInfo"]}, refine_move.gtp())
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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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@property
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def analysis_ready(self):
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return self.analysis["root"] is not None
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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:
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return f"{'B' if score >= 0 else 'W'}+{abs(score):.1f}"
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def format_win_rate(self, win_rate=None):
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win_rate = win_rate or self.analysis["root"].get("winrate")
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if win_rate:
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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 comment(self, sgf=False, teach=False, hints=False):
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single_move = self.single_move
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if not self.parent or not single_move: # root
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return ""
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text = f"Move {self.depth}: {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 += f"Score: {self.format_score(score)}\n"
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text += f"Win Rate: {self.format_win_rate()}\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 += f"Estimated point loss: {points_lost:.1f}\n"
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text += f"Predicted top move was {previous_top_move['move']} ({self.format_score(previous_top_move['scoreLead'])}).\n"
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else:
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text += f"Move was predicted best move.\n"
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if sgf:
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if previous_top_move.get("pv") and (sgf or hints):
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text += f"PV: {single_move.player}{' '.join(previous_top_move['pv'])}\n"
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if sgf or hints or teach:
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policy_ranking = self.parent.policy_ranking
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policy_ix = [ix + 1 for (p, m), ix in zip(policy_ranking, range(len(policy_ranking))) if m == single_move]
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if policy_ix:
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text += f"Move was #{policy_ix[0]} according to policy.\n"
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if not policy_ix or policy_ix[0] != 1 and (sgf or hints):
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text += f"Top policy move was {policy_ranking[0][1].gtp()} ({policy_ranking[0][0]:.1%}).\n"
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if self.auto_undo and sgf:
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text += "Move was automatically undone in teaching mode."
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if self.ai_thoughts and (sgf or hints):
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text += f"\nAI thought process: {self.ai_thoughts}"
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else:
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text = "No analysis available" if sgf else "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.single_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.single_move
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if single_move and self.parent and self.parent.parent and self.analysis_ready and self.parent.parent.analysis_ready:
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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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@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"]["scoreLead"]
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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 [{**self.analysis["root"], "pointsLost": 0, "order": 0, "move": top_polmove.gtp()}] # single visit -> go by policy/root
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return sorted(
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[{"pointsLost": self.player_sign(self.next_player) * (self.analysis["root"]["scoreLead"] - d["scoreLead"]), **d} for d in self.analysis["moves"].values()],
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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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