eisterman
23ae7a2572
Report how often a Tecnica is played and how often Kiai decides a Kumite or an Exam fight. The tie rule moves to RulesConfig.attacker_wins, shared by the engine and the metric; games are unchanged. RULES.md drops the Kiai +1 and catch-up variants from the rules under test. Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
124 lines
5.7 KiB
Python
124 lines
5.7 KiB
Python
"""Simulation output: a plain-text summary per scenario, and CSV files with one row per game
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or one row per scenario."""
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import csv
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import dataclasses
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from collections.abc import Iterable
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from pathlib import Path
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from dojo.metrics import CONTENTION_BELTS, GameMetrics, SeatSummary, Spread, Summary
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def format_summary(summary: Summary) -> str:
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bots = ", ".join(f"{bot} {rate:.0%}" for bot, rate in summary.win_rate_by_bot.items())
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income = ", ".join(
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f"{source} +{gain:.2f}/{loss:.2f}"
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for source, (gain, loss) in summary.income_per_turn.items()
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)
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lines = [
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f"== {summary.scenario} ({summary.games} games, {summary.finished:.0%} finished)",
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f"Rounds {_spread(summary.rounds)}",
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f"Minutes {_spread(summary.minutes)}",
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f"Kumite {summary.kumite_per_round:.2f}/round; upsets {summary.upset_rate:.0%}, "
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f"higher beats lower (no stripes change) {summary.expected_rate:.0%}",
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f"Longest chain {_spread(summary.max_chain)} (Kumite in one turn, per game)",
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f"Exam {summary.exam_attempts_per_game:.2f} attempts/game, "
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f"{summary.exam_success:.0%} passed, {summary.exam_challengers:.2f} challengers/game",
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f"Tecnica played in {summary.tecnica_rate:.0%} of Kumite; Kiai decided "
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f"{summary.kiai_decided_rate:.0%} of Kumite, {summary.exam_kiai_decided_rate:.0%} of "
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"Exam fights",
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f"Income/turn {income}",
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f"Square income home {summary.square_income_home_per_turn:+.2f}/turn, "
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f"away {summary.square_income_away_per_turn:+.2f}/turn (net)",
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f"Winner pace {_spread(summary.winner_turns_to_brown)} (own turns to reach Brown)",
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f"Lead changes {_spread(summary.lead_changes)}",
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f"Last change {_shares(summary.last_lead_change)} (point of the game)",
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f"Gap 1st-2nd {_spread(summary.lead_gap)} (per-game mean, stripes)",
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f"Winner margin {_spread(summary.winner_margin)} (over best rival at the end)",
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f"Winner led {summary.winner_lead_share:.0%} of turns; halfway leader won "
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f"{summary.halfway_leader_wins:.0%} of {summary.halfway_games} games",
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f"Contention {summary.contenders_at_end:.2f} rivals within one belt at the end; "
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f"{summary.out_of_contention:.0%} of player-turns out (>{CONTENTION_BELTS} belts behind)",
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f"Worst player {_shares(summary.worst_out_of_contention)} (share of turns out)",
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f"Rank swaps {_spread(summary.rank_swaps)}",
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f"Leader beaten {summary.leader_kumite_losses:.2f} Kumite lost by the leader per game",
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f"Final spread {_spread(summary.final_spread)}",
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f"Move options {summary.end_squares_per_move:.1f} end squares/move; can end on "
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f"Allenamento {summary.allenamento_reachable:.0%}, Fatica {summary.fatica_reachable:.0%}",
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*(f"Seat {seat:<11}{_seat(stats)}" for seat, stats in enumerate(summary.seats)),
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f"Wins by bot {bots}",
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]
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return "\n".join(lines)
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def write_games_csv(path: Path, games: Iterable[GameMetrics]) -> None:
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fields = [field.name for field in dataclasses.fields(GameMetrics)]
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with path.open("w", encoding="utf-8", newline="") as file:
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writer = csv.DictWriter(file, fieldnames=fields)
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writer.writeheader()
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for game in games:
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writer.writerow(
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{
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name: ",".join(map(str, value)) if isinstance(value, tuple) else value
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for name, value in dataclasses.asdict(game).items()
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}
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)
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def write_summary_csv(path: Path, summaries: Iterable[Summary]) -> None:
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"""One row per scenario; a column missing from a scenario (fewer seats, other bots) is empty."""
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rows = [_summary_row(summary) for summary in summaries]
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fields = list(dict.fromkeys(name for row in rows for name in row))
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with path.open("w", encoding="utf-8", newline="") as file:
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writer = csv.DictWriter(file, fieldnames=fields, restval="")
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writer.writeheader()
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writer.writerows(rows)
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def _summary_row(summary: Summary) -> dict[str, object]:
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row: dict[str, object] = {}
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for field in dataclasses.fields(Summary):
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name, value = field.name, getattr(summary, field.name)
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match value:
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case Spread():
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row |= {f"{name}_{stat}": round(v, 3) for stat, v in _numbers(value).items()}
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case float():
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row[name] = round(value, 4)
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case str() | int():
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row[name] = value
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case _:
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pass # Income, seats and bots are flattened below.
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for source, (gain, loss) in summary.income_per_turn.items():
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row[f"{source}_gain_per_turn"] = round(gain, 4)
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row[f"{source}_loss_per_turn"] = round(loss, 4)
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for seat, stats in enumerate(summary.seats):
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row |= {f"seat{seat}_{stat}": round(v, 4) for stat, v in _numbers(stats).items()}
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row |= {f"win_rate_{bot}": round(v, 4) for bot, v in summary.win_rate_by_bot.items()}
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return row
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def _numbers(record: Spread | SeatSummary) -> dict[str, float]:
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return dataclasses.asdict(record)
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def _seat(stats: SeatSummary) -> str:
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return (
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f"won {stats.win_rate:.0%}, final track {stats.final_track:.1f}, "
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f"Exam {stats.exam_attempts_per_game:.2f} attempts/game, "
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f"defended in {stats.exam_defended:.0%} of games"
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)
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def _spread(spread: Spread) -> str:
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return (
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f"mean {spread.mean:.1f} p10 {spread.p10:.1f} median {spread.median:.1f} "
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f"p90 {spread.p90:.1f}"
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)
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def _shares(spread: Spread) -> str:
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return (
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f"mean {spread.mean:.0%} p10 {spread.p10:.0%} median {spread.median:.0%} "
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f"p90 {spread.p90:.0%}"
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)
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