From 312cbd394956575736d06e0244143bf65559d8b9 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=EC=A0=95=EC=8B=9C=EC=9B=90?= Date: Thu, 7 May 2026 01:03:09 +0900 Subject: [PATCH] =?UTF-8?q?Deep=20CFR=20evaluation=20=EC=A7=84=EB=8B=A8=20?= =?UTF-8?q?metric=20=EC=B6=94=EA=B0=80?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- pyproject.toml | 1 + .../games/classic/deep_cfr/analyze.py | 136 ++++++ .../games/classic/deep_cfr/cli.py | 14 + .../games/classic/deep_cfr/evaluate.py | 442 ++++++++++++++++-- tests/games/classic/test_deep_cfr_trainer.py | 9 + uv.lock | 429 +++++++++++++++++ 6 files changed, 1004 insertions(+), 27 deletions(-) create mode 100644 src/coolrl_lost_cities/games/classic/deep_cfr/analyze.py diff --git a/pyproject.toml b/pyproject.toml index 0ca2190..4f910ab 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -9,6 +9,7 @@ authors = [ requires-python = ">=3.11" dependencies = [ "numpy>=1.26.0", + "matplotlib>=3.8", "pydantic>=2.7", "PyYAML>=6.0", "torch>=2.3.0", diff --git a/src/coolrl_lost_cities/games/classic/deep_cfr/analyze.py b/src/coolrl_lost_cities/games/classic/deep_cfr/analyze.py new file mode 100644 index 0000000..f04b606 --- /dev/null +++ b/src/coolrl_lost_cities/games/classic/deep_cfr/analyze.py @@ -0,0 +1,136 @@ +from __future__ import annotations + +import argparse +import json +from pathlib import Path +from typing import Any + +PLOT_GROUPS: dict[str, list[str]] = { + "Action Distribution": [ + "play_action_rate", + "discard_action_rate", + "draw_deck_rate", + "draw_pile_rate", + ], + "Game Flow": [ + "avg_opened_colors", + "5_color_open_count", + "avg_expedition_cards", + ], + "Open Quality": [ + "bad_open_rate", + "weak_open_rate", + "good_open_rate", + "opening_recoverable_score_mean", + ], + "Expedition Outcomes": [ + "positive_expedition_rate", + "negative_expedition_rate", + "bonus_expedition_rate", + "final_expedition_score_p25", + "final_expedition_score_median", + "final_expedition_score_p75", + "final_expedition_score_p90", + ], + "Calibration": [ + "first_open_recoverable_score_mean_for_positive_final", + "first_open_recoverable_score_mean_for_negative_final", + ], +} + + +def load_metrics(path: Path) -> list[dict[str, Any]]: + rows: list[dict[str, Any]] = [] + with path.open("r", encoding="utf-8") as handle: + for line in handle: + line = line.strip() + if line: + rows.append(json.loads(line)) + return rows + + +def opponent_names(rows: list[dict[str, Any]]) -> list[str]: + names: set[str] = set() + for row in rows: + for key in row: + if not key.startswith("eval_"): + continue + rest = key[len("eval_") :] + for metric in _all_group_metrics(): + suffix = f"_{metric}" + if rest.endswith(suffix): + names.add(rest[: -len(suffix)]) + return sorted(names) + + +def plot_group( + rows: list[dict[str, Any]], + *, + opponent: str, + title: str, + metrics: list[str], + output: Path, +) -> bool: + import matplotlib.pyplot as plt + + x = [int(row["iteration"]) for row in rows if "iteration" in row] + if not x: + return False + plotted = False + fig, ax = plt.subplots(figsize=(10, 5)) + for metric in metrics: + key = f"eval_{opponent}_{metric}" + values = [row.get(key) for row in rows] + if all(value is None for value in values): + continue + y = [float("nan") if value is None else float(value) for value in values] + ax.plot(x, y, marker="o", linewidth=1.5, label=metric) + plotted = True + if not plotted: + plt.close(fig) + return False + ax.set_title(f"{title} - {opponent}") + ax.set_xlabel("iteration") + ax.grid(True, alpha=0.3) + ax.legend(loc="best", fontsize="small") + fig.tight_layout() + fig.savefig(output) + plt.close(fig) + return True + + +def analyze_run(run_dir: Path, output_dir: Path | None = None) -> list[Path]: + metrics_path = run_dir / "metrics.jsonl" + rows = load_metrics(metrics_path) + output_dir = output_dir or run_dir + output_dir.mkdir(parents=True, exist_ok=True) + written: list[Path] = [] + for opponent in opponent_names(rows): + for title, metrics in PLOT_GROUPS.items(): + filename = f"analysis_{_slug(opponent)}_{_slug(title)}.png" + path = output_dir / filename + if plot_group(rows, opponent=opponent, title=title, metrics=metrics, output=path): + written.append(path) + return written + + +def _all_group_metrics() -> set[str]: + return {metric for metrics in PLOT_GROUPS.values() for metric in metrics} + + +def _slug(value: str) -> str: + return value.lower().replace(" ", "_").replace("-", "_") + + +def main(argv: list[str] | None = None) -> None: + parser = argparse.ArgumentParser(description="Plot Lost Cities Deep CFR evaluation metrics.") + parser.add_argument("--run", required=True, type=Path) + parser.add_argument("--output-dir", type=Path) + args = parser.parse_args(argv) + written = analyze_run(args.run, args.output_dir) + for path in written: + print(path) + + +if __name__ == "__main__": + main() diff --git a/src/coolrl_lost_cities/games/classic/deep_cfr/cli.py b/src/coolrl_lost_cities/games/classic/deep_cfr/cli.py index 008e0b4..b38c670 100644 --- a/src/coolrl_lost_cities/games/classic/deep_cfr/cli.py +++ b/src/coolrl_lost_cities/games/classic/deep_cfr/cli.py @@ -2,8 +2,10 @@ from __future__ import annotations import argparse import json +from pathlib import Path from typing import Any +from coolrl_lost_cities.games.classic.deep_cfr.analyze import analyze_run from coolrl_lost_cities.games.classic.deep_cfr.benchmark import ( benchmark_traversal, benchmark_traversal_modes, @@ -85,6 +87,7 @@ def eval_command(args: argparse.Namespace) -> None: opponent=args.opponent, device=args.device, max_steps=args.max_steps, + encoding=policy.encoding, ) print(json.dumps(result, indent=2, sort_keys=True)) @@ -149,6 +152,12 @@ def policy_gradient_command(args: argparse.Namespace) -> None: print(json.dumps(metrics.__dict__, sort_keys=True)) +def analyze_command(args: argparse.Namespace) -> None: + written = analyze_run(args.run, args.output_dir) + for path in written: + print(path) + + def main(argv: list[str] | None = None) -> None: parser = argparse.ArgumentParser(description="Lost Cities classic Deep CFR tools.") subparsers = parser.add_subparsers(dest="command", required=True) @@ -202,6 +211,11 @@ def main(argv: list[str] | None = None) -> None: pg.add_argument("--output") pg.set_defaults(func=policy_gradient_command) + analyze = subparsers.add_parser("analyze") + analyze.add_argument("--run", required=True, type=Path) + analyze.add_argument("--output-dir", type=Path) + analyze.set_defaults(func=analyze_command) + args = parser.parse_args(argv) args.func(args) diff --git a/src/coolrl_lost_cities/games/classic/deep_cfr/evaluate.py b/src/coolrl_lost_cities/games/classic/deep_cfr/evaluate.py index 8753a5d..b2e91ec 100644 --- a/src/coolrl_lost_cities/games/classic/deep_cfr/evaluate.py +++ b/src/coolrl_lost_cities/games/classic/deep_cfr/evaluate.py @@ -1,5 +1,7 @@ from __future__ import annotations +import time +from dataclasses import dataclass, field from pathlib import Path import numpy as np @@ -9,11 +11,108 @@ from coolrl_lost_cities.games.classic.bots import build_bot from coolrl_lost_cities.games.classic.deep_cfr.config import EncodingConfig, config_from_dict from coolrl_lost_cities.games.classic.deep_cfr.encoding import encode_info_state from coolrl_lost_cities.games.classic.deep_cfr.networks import DeepCFRMLP -from coolrl_lost_cities.games.classic.evaluation import evaluate_policy from coolrl_lost_cities.games.classic.game import GameState, LostCitiesConfig from coolrl_lost_cities.games.classic.policy import LostCitiesPolicy, PolicyInput +@dataclass +class PolicyEvalDiagnostics: + games: int = 0 + wins: int = 0 + losses: int = 0 + draws: int = 0 + score: list[int] = field(default_factory=list) + opponent_score: list[int] = field(default_factory=list) + diff: list[int] = field(default_factory=list) + lengths: list[int] = field(default_factory=list) + timeouts: int = 0 + policy_actions: int = 0 + play_actions: int = 0 + discard_actions: int = 0 + draw_deck_actions: int = 0 + draw_pile_actions: int = 0 + entropies: list[float] = field(default_factory=list) + opened_colors: list[int] = field(default_factory=list) + five_color_open_count: int = 0 + expedition_cards: list[int] = field(default_factory=list) + opening_counts: list[int] = field(default_factory=list) + bad_open_counts: list[int] = field(default_factory=list) + weak_open_counts: list[int] = field(default_factory=list) + good_open_counts: list[int] = field(default_factory=list) + opening_recoverable_scores: list[float] = field(default_factory=list) + score_per_opened_color: list[float] = field(default_factory=list) + positive_expeditions: list[int] = field(default_factory=list) + negative_expeditions: list[int] = field(default_factory=list) + breakeven_expeditions: list[int] = field(default_factory=list) + bonus_expeditions: list[int] = field(default_factory=list) + below_minus_20_expeditions: list[int] = field(default_factory=list) + final_expedition_scores: list[int] = field(default_factory=list) + positive_expedition_scores: list[int] = field(default_factory=list) + negative_expedition_scores: list[int] = field(default_factory=list) + first_open_positive_recoverable_scores: list[float] = field(default_factory=list) + first_open_negative_recoverable_scores: list[float] = field(default_factory=list) + + def to_dict(self, elapsed_seconds: float) -> dict[str, float | int]: + games = max(1, self.games) + total_steps = sum(self.lengths) + opened_expeditions = len(self.final_expedition_scores) + total_policy_actions = max(1, self.policy_actions) + return { + "games": self.games, + "wins0": self.wins, + "wins1": self.losses, + "draws": self.draws, + "win_rate0": self.wins / games, + "win_rate1": self.losses / games, + "avg_score0": _mean(self.score), + "avg_score1": _mean(self.opponent_score), + "avg_score_diff0": _mean(self.diff), + "avg_game_length": _mean(self.lengths), + "max_step_timeouts": self.timeouts, + "elapsed_seconds": elapsed_seconds, + "games_per_second": self.games / max(elapsed_seconds, 1.0e-12), + "steps_per_second": total_steps / max(elapsed_seconds, 1.0e-12), + "play_action_rate": self.play_actions / total_policy_actions, + "discard_action_rate": self.discard_actions / total_policy_actions, + "draw_deck_rate": self.draw_deck_actions / total_policy_actions, + "draw_pile_rate": self.draw_pile_actions / total_policy_actions, + "policy_entropy": _mean(self.entropies), + "avg_opened_colors": _mean(self.opened_colors), + "5_color_open_count": self.five_color_open_count, + "avg_expedition_cards": _mean(self.expedition_cards), + "opening_play_actions": _mean(self.opening_counts), + "bad_open_actions": _mean(self.bad_open_counts), + "weak_open_actions": _mean(self.weak_open_counts), + "good_open_actions": _mean(self.good_open_counts), + "bad_open_rate": sum(self.bad_open_counts) / max(1, sum(self.opening_counts)), + "weak_open_rate": sum(self.weak_open_counts) / max(1, sum(self.opening_counts)), + "good_open_rate": sum(self.good_open_counts) / max(1, sum(self.opening_counts)), + "opening_recoverable_score_mean": _mean(self.opening_recoverable_scores), + "score_per_opened_color": _mean(self.score_per_opened_color), + "per_game_positive_expeditions": _mean(self.positive_expeditions), + "per_game_negative_expeditions": _mean(self.negative_expeditions), + "per_game_breakeven_expeditions": _mean(self.breakeven_expeditions), + "per_game_bonus_expeditions": _mean(self.bonus_expeditions), + "per_game_below_minus_20_expeditions": _mean(self.below_minus_20_expeditions), + "positive_expedition_rate": sum(self.positive_expeditions) / max(1, opened_expeditions), + "negative_expedition_rate": sum(self.negative_expeditions) / max(1, opened_expeditions), + "bonus_expedition_rate": sum(self.bonus_expeditions) / max(1, opened_expeditions), + "avg_final_score_per_opened_expedition": _mean(self.final_expedition_scores), + "final_expedition_score_p25": _percentile(self.final_expedition_scores, 25), + "final_expedition_score_median": _percentile(self.final_expedition_scores, 50), + "final_expedition_score_p75": _percentile(self.final_expedition_scores, 75), + "final_expedition_score_p90": _percentile(self.final_expedition_scores, 90), + "positive_expedition_score_mean": _mean(self.positive_expedition_scores), + "negative_expedition_score_mean": _mean(self.negative_expedition_scores), + "first_open_recoverable_score_mean_for_positive_final": _mean( + self.first_open_positive_recoverable_scores + ), + "first_open_recoverable_score_mean_for_negative_final": _mean( + self.first_open_negative_recoverable_scores + ), + } + + class StrategyNetPolicy(LostCitiesPolicy): def __init__( self, @@ -30,14 +129,7 @@ class StrategyNetPolicy(LostCitiesPolicy): self.rng = np.random.default_rng(seed) self.encoding = encoding - def act(self, obs_or_state: PolicyInput) -> int: - if not isinstance(obs_or_state, GameState): - legal = np.asarray(obs_or_state["legal_mask"], dtype=bool) - legal_actions = np.flatnonzero(legal) - if len(legal_actions) == 0: - raise RuntimeError("no legal action available") - return int(legal_actions[0]) - state = obs_or_state + def action_distribution(self, state: GameState) -> tuple[np.ndarray, np.ndarray]: legal = np.asarray(state.unified_legal_mask(), dtype=bool) legal_actions = np.flatnonzero(legal) if len(legal_actions) == 0: @@ -47,14 +139,32 @@ class StrategyNetPolicy(LostCitiesPolicy): x = torch.as_tensor(info, dtype=torch.float32, device=self.device).unsqueeze(0) logits = self.strategy_network(x).squeeze(0).detach().cpu().numpy() masked = np.where(legal, logits, -np.inf) + stable = masked[legal_actions] - np.max(masked[legal_actions]) + probs = np.exp(stable) + probs = probs / probs.sum() + distribution = np.zeros_like(masked, dtype=np.float32) + distribution[legal_actions] = probs.astype(np.float32) + return legal_actions, distribution + + def select_action(self, state: GameState) -> tuple[int, float]: + legal_actions, distribution = self.action_distribution(state) + probs = distribution[legal_actions] + entropy = _entropy(probs) if self.sample: - stable = masked[legal_actions] - np.max(masked[legal_actions]) - probs = np.exp(stable) - probs = probs / probs.sum() unified = int(self.rng.choice(legal_actions, p=probs)) else: - unified = int(np.argmax(masked)) - return state.from_unified_action(unified) + unified = int(legal_actions[int(np.argmax(probs))]) + return state.from_unified_action(unified), entropy + + def act(self, obs_or_state: PolicyInput) -> int: + if not isinstance(obs_or_state, GameState): + legal = np.asarray(obs_or_state["legal_mask"], dtype=bool) + legal_actions = np.flatnonzero(legal) + if len(legal_actions) == 0: + raise RuntimeError("no legal action available") + return int(legal_actions[0]) + action, _entropy_value = self.select_action(obs_or_state) + return action def evaluate_strategy_network( @@ -69,24 +179,302 @@ def evaluate_strategy_network( encoding: EncodingConfig | None = None, ) -> dict[str, float | int]: strategy_network.eval() - - def make_strategy(seed_value: int | None = None) -> StrategyNetPolicy: - return StrategyNetPolicy( - strategy_network, device=device, seed=seed_value, encoding=encoding - ) - - def opponent_factory(seed_value=None): - return build_bot(opponent, seed=seed_value) - - result = evaluate_policy( - make_strategy, - opponent_factory, + return _evaluate_strategy_network_with_diagnostics( + strategy_network, config, games=games, seed=seed, + opponent=opponent, + device=device, max_steps=max_steps, + encoding=encoding, ) - return result.to_dict() + + +def _evaluate_strategy_network_with_diagnostics( + strategy_network: torch.nn.Module, + config: LostCitiesConfig, + *, + games: int, + seed: int, + opponent: str, + device: torch.device | str, + max_steps: int, + encoding: EncodingConfig | None, +) -> dict[str, float | int]: + if games <= 0: + raise ValueError(f"games must be positive, got {games}") + diagnostics = PolicyEvalDiagnostics() + started = time.perf_counter() + for index in range(games): + game_seed = seed + index + swap = index % 2 == 1 + policy_player = 1 if swap else 0 + policy = StrategyNetPolicy( + strategy_network, + device=device, + seed=game_seed * 2 + policy_player, + encoding=encoding, + ) + opponent_policy = build_bot(opponent, seed=game_seed * 2 + (1 - policy_player)) + policies = [opponent_policy, policy] if swap else [policy, opponent_policy] + game_diag = _evaluate_one_game( + policies, + policy_player, + config, + seed=game_seed, + max_steps=max_steps, + ) + _accumulate_game_diagnostics(diagnostics, game_diag) + return diagnostics.to_dict(time.perf_counter() - started) + + +def _evaluate_one_game( + policies: list[LostCitiesPolicy], + policy_player: int, + config: LostCitiesConfig, + *, + seed: int, + max_steps: int, +) -> PolicyEvalDiagnostics: + state = GameState.new_game(config, seed=seed) + diagnostics = PolicyEvalDiagnostics(games=1) + first_open_recoverable_by_color: dict[int, float] = {} + steps = 0 + for _ in range(max_steps): + if state.terminal: + break + current_player = state.current_player + policy = policies[current_player] + if current_player == policy_player and isinstance(policy, StrategyNetPolicy): + action, entropy = policy.select_action(state) + diagnostics.entropies.append(entropy) + _record_policy_action(diagnostics, state, action, first_open_recoverable_by_color) + else: + action = policy.act(state) + state.apply_action(action) + steps += 1 + + timed_out = not state.terminal + if timed_out: + steps = max_steps + _record_final_game_state( + diagnostics, + state, + policy_player, + steps, + timed_out, + first_open_recoverable_by_color, + ) + return diagnostics + + +def _record_policy_action( + diagnostics: PolicyEvalDiagnostics, + state: GameState, + action: int, + first_open_recoverable_by_color: dict[int, float], +) -> None: + diagnostics.policy_actions += 1 + if state.phase == "draw": + if action == 0: + diagnostics.draw_deck_actions += 1 + else: + diagnostics.draw_pile_actions += 1 + return + + slot = action // 2 + play = action % 2 == 0 + if play: + diagnostics.play_actions += 1 + else: + diagnostics.discard_actions += 1 + return + + hand = state.hand_slots(state.current_player) + if slot >= len(hand) or hand[slot] is None: + return + card = hand[slot] + color = int(card.color) + if state.expeditions[state.current_player][color]: + return + + summary = _visible_recoverable_summary(state, state.current_player, color) + recoverable_score = float(summary["recoverable_score"]) + has_bonus_path = bool(summary["has_bonus_path"]) + diagnostics.opening_recoverable_scores.append(recoverable_score) + if color not in first_open_recoverable_by_color: + first_open_recoverable_by_color[color] = recoverable_score + if recoverable_score >= 0: + diagnostics.good_open_counts.append(1) + diagnostics.bad_open_counts.append(0) + diagnostics.weak_open_counts.append(0) + elif has_bonus_path: + diagnostics.good_open_counts.append(0) + diagnostics.bad_open_counts.append(0) + diagnostics.weak_open_counts.append(1) + else: + diagnostics.good_open_counts.append(0) + diagnostics.bad_open_counts.append(1) + diagnostics.weak_open_counts.append(0) + diagnostics.opening_counts.append(1) + + +def _record_final_game_state( + diagnostics: PolicyEvalDiagnostics, + state: GameState, + policy_player: int, + steps: int, + timed_out: bool, + first_open_recoverable_by_color: dict[int, float], +) -> None: + policy_score = state.total_score(policy_player) + opponent_score = state.total_score(1 - policy_player) + diff = policy_score - opponent_score + diagnostics.score.append(policy_score) + diagnostics.opponent_score.append(opponent_score) + diagnostics.diff.append(diff) + diagnostics.lengths.append(steps) + diagnostics.timeouts += int(timed_out) + if diff > 0: + diagnostics.wins += 1 + elif diff < 0: + diagnostics.losses += 1 + else: + diagnostics.draws += 1 + + opened = 0 + expedition_cards = 0 + positive = negative = breakeven = bonus = below_minus_20 = 0 + for color, expedition in enumerate(state.expeditions[policy_player]): + if not expedition: + continue + opened += 1 + expedition_cards += len(expedition) + score = state.expedition_score(policy_player, color) + diagnostics.final_expedition_scores.append(score) + if score > 0: + positive += 1 + diagnostics.positive_expedition_scores.append(score) + if color in first_open_recoverable_by_color: + diagnostics.first_open_positive_recoverable_scores.append( + first_open_recoverable_by_color[color] + ) + elif score < 0: + negative += 1 + diagnostics.negative_expedition_scores.append(score) + if color in first_open_recoverable_by_color: + diagnostics.first_open_negative_recoverable_scores.append( + first_open_recoverable_by_color[color] + ) + else: + breakeven += 1 + if len(expedition) >= state.config.bonus_threshold: + bonus += 1 + if score < -20: + below_minus_20 += 1 + + diagnostics.opened_colors.append(opened) + diagnostics.five_color_open_count += int(opened == state.config.n_colors) + diagnostics.expedition_cards.append(expedition_cards) + diagnostics.score_per_opened_color.append(policy_score / max(1, opened)) + diagnostics.positive_expeditions.append(positive) + diagnostics.negative_expeditions.append(negative) + diagnostics.breakeven_expeditions.append(breakeven) + diagnostics.bonus_expeditions.append(bonus) + diagnostics.below_minus_20_expeditions.append(below_minus_20) + + +def _accumulate_game_diagnostics( + target: PolicyEvalDiagnostics, + source: PolicyEvalDiagnostics, +) -> None: + target.games += source.games + target.wins += source.wins + target.losses += source.losses + target.draws += source.draws + target.score.extend(source.score) + target.opponent_score.extend(source.opponent_score) + target.diff.extend(source.diff) + target.lengths.extend(source.lengths) + target.timeouts += source.timeouts + target.policy_actions += source.policy_actions + target.play_actions += source.play_actions + target.discard_actions += source.discard_actions + target.draw_deck_actions += source.draw_deck_actions + target.draw_pile_actions += source.draw_pile_actions + target.entropies.extend(source.entropies) + target.opened_colors.extend(source.opened_colors) + target.five_color_open_count += source.five_color_open_count + target.expedition_cards.extend(source.expedition_cards) + target.opening_counts.append(sum(source.opening_counts)) + target.bad_open_counts.append(sum(source.bad_open_counts)) + target.weak_open_counts.append(sum(source.weak_open_counts)) + target.good_open_counts.append(sum(source.good_open_counts)) + target.opening_recoverable_scores.extend(source.opening_recoverable_scores) + target.score_per_opened_color.extend(source.score_per_opened_color) + target.positive_expeditions.extend(source.positive_expeditions) + target.negative_expeditions.extend(source.negative_expeditions) + target.breakeven_expeditions.extend(source.breakeven_expeditions) + target.bonus_expeditions.extend(source.bonus_expeditions) + target.below_minus_20_expeditions.extend(source.below_minus_20_expeditions) + target.final_expedition_scores.extend(source.final_expedition_scores) + target.positive_expedition_scores.extend(source.positive_expedition_scores) + target.negative_expedition_scores.extend(source.negative_expedition_scores) + target.first_open_positive_recoverable_scores.extend( + source.first_open_positive_recoverable_scores + ) + target.first_open_negative_recoverable_scores.extend( + source.first_open_negative_recoverable_scores + ) + + +def _visible_recoverable_summary( + state: GameState, + player: int, + color: int, +) -> dict[str, float | bool]: + expedition = state.expeditions[player][color] + hand_cards = [ + card for card in state.hand_slots(player) if card is not None and card.color == color + ] + last_numeric = state.last_numeric_rank(player, color) + current_sum = sum(_numeric_value(card, state.config.min_rank) for card in expedition) + current_wagers = sum(1 for card in expedition if card.rank == 0) + playable_numeric = [card for card in hand_cards if card.rank > 0 and card.rank > last_numeric] + playable_wagers = [card for card in hand_cards if card.rank == 0 and last_numeric == 0] + projected_sum = current_sum + sum( + _numeric_value(card, state.config.min_rank) for card in playable_numeric + ) + projected_wagers = current_wagers + len(playable_wagers) + projected_len = len(expedition) + len(playable_numeric) + len(playable_wagers) + margin = projected_sum + state.config.expedition_penalty + recoverable_score = margin * (projected_wagers + 1) + return { + "recoverable_score": float(recoverable_score), + "has_bonus_path": projected_len >= state.config.bonus_threshold, + } + + +def _numeric_value(card, min_rank: int) -> int: + if card.rank == 0: + return 0 + return min_rank + card.rank - 1 + + +def _entropy(probs: np.ndarray) -> float: + probs = probs[probs > 0.0] + if len(probs) == 0: + return 0.0 + return float(-(probs * np.log(probs)).sum()) + + +def _mean(values: list[float] | list[int]) -> float: + return float(np.mean(values)) if values else 0.0 + + +def _percentile(values: list[int], percentile: float) -> float: + return float(np.percentile(values, percentile)) if values else 0.0 def load_strategy_policy_from_checkpoint( diff --git a/tests/games/classic/test_deep_cfr_trainer.py b/tests/games/classic/test_deep_cfr_trainer.py index 0ed24c9..54810f4 100644 --- a/tests/games/classic/test_deep_cfr_trainer.py +++ b/tests/games/classic/test_deep_cfr_trainer.py @@ -300,6 +300,15 @@ def test_deep_cfr_trainer_saves_loads_and_evaluates_checkpoint(tmp_path) -> None assert (checkpoint_dir / "train.log").exists() assert restored.iteration == 1 assert "eval_random_games" in metrics[0].eval_metrics + assert "eval_random_play_action_rate" in metrics[0].eval_metrics + assert "eval_random_policy_entropy" in metrics[0].eval_metrics + 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