coolguyandClaude Opus 4.7
004b913a7b
Three coordinated hygiene changes; none target the diagnosed
selection-bias bottleneck. They make the codebase honestly reflect the
pure-self-play stance and reduce dashboard noise.
Bot rename (drop the unhelpful safe_ prefix; suffixes describe behaviour):
- safe_heuristic_loose -> heuristic_aggressive
- safe_heuristic -> heuristic_balanced
- safe_heuristic_strict -> heuristic_cautious
- noisy_safe -> heuristic_noisy
- passive_discard -> discard_only
Class renames in bots/: SafeHeuristicBot -> HeuristicBot,
SafeHeuristicParams -> HeuristicParams, PassiveDiscardBot -> DiscardOnlyBot,
plus loose/strict parameter constants. Backwards compatibility was dropped
intentionally per user instruction; no aliases. Active configs, docs,
scripts, tests updated. Archive directories (configs/archive,
docs/archive, runs/archive) left intact and may still reference old
names per their read-only policy. The src/.../bots/passive.py module was
renamed to discard_only.py via git mv.
Analyze plot curation (deep_cfr/analyze.py):
- Added analysis_00_core.png as the canonical daily dashboard with 10
heuristic-free metrics (loss/{advantage,strategy}; vs heuristic_cautious:
avg_score_diff0, win_rate0, avg_opened_colors, positive_expedition_rate,
bonus_expedition_rate, score_per_opened_color, policy_entropy; vs random:
win_rate0).
- Removed analysis_05_open_quality.png (bad/weak/good open rates,
recoverable score) and analysis_07_calibration.png (calibration gap,
recoverable mean) - both relied on the heuristic recoverable_score
classifier already dropped from inputs.
- Removed SELECTIVITY_PLOTS and plot_selectivity (heuristic-laden).
- SUMMARY_EVAL_METRICS no longer includes bad_open_rate or
calibration_gap.
- PlotSpec gained an opponents allowlist so the new core section can pin
a specific opponent per panel without restructuring plot_section.
Tiered evaluation cadence (EvaluationConfig):
- Added extended_opponents and extended_eval_every (default 0 = disabled).
- opponents_for_iteration(iteration) returns the core list every
eval_every and appends extended_opponents (de-duplicated) when
iteration is also a multiple of extended_eval_every.
- default.yaml now uses 3 core opponents (random, discard_only,
heuristic_cautious) every 5 iterations and 3 extended opponents
(heuristic_balanced, heuristic_aggressive, heuristic_noisy) every 50
iterations. random is the floor sanity. discard_only is the
zero-pit detector / absolute-score reference (its score is always 0,
so eval/discard_only/avg_score_diff0 directly equals the model's raw
average score). heuristic_cautious is the ceiling and the
archive-comparable benchmark used in the prior diagnostic sections.
Net eval cost reduction: roughly 50% (3 opponents x every 5 iter, plus
6 opponents x every 50 iter, vs the prior 6 x every 5).
Documented in docs/plans/deep-cfr-selectivity.md section 9.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
57 lines
1.8 KiB
Python
57 lines
1.8 KiB
Python
from __future__ import annotations
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import pytest
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from coolrl_lost_cities.games.classic.game import GameState, LostCitiesConfig
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from coolrl_lost_cities.games.classic.bots.heuristic import HeuristicBot
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from coolrl_lost_cities.games.classic.bots.heuristic_py import (
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HeuristicBot as PythonHeuristicBot,
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)
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from coolrl_lost_cities.games.classic.bots.registry import (
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AGGRESSIVE_HEURISTIC_PARAMS,
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CAUTIOUS_HEURISTIC_PARAMS,
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)
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VARIANTS = (
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("default", None),
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("loose", AGGRESSIVE_HEURISTIC_PARAMS),
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("strict", CAUTIOUS_HEURISTIC_PARAMS),
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)
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CONFIGS = (
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LostCitiesConfig(),
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LostCitiesConfig(n_colors=2, n_ranks=8, hand_size=3),
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LostCitiesConfig(n_colors=3, n_ranks=5, n_handshakes=0, hand_size=5),
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)
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@pytest.mark.parametrize(("variant_name", "params"), VARIANTS)
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@pytest.mark.parametrize("config", CONFIGS)
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@pytest.mark.parametrize("seed", range(2))
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def test_cython_heuristic_matches_python_action_sequence(
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variant_name: str,
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params,
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config: LostCitiesConfig,
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seed: int,
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) -> None:
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py_bot = PythonHeuristicBot(params)
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cy_bot = HeuristicBot(params)
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py_state = GameState.new_game(config, seed=seed)
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cy_state = GameState.new_game(config, seed=seed)
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turn = 0
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while not py_state.terminal:
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assert turn < 500, f"variant={variant_name} seed={seed} did not terminate"
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py_action = py_bot.act(py_state)
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cy_action = cy_bot.act(cy_state)
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assert cy_action == py_action, (
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f"variant={variant_name} seed={seed} turn={turn} "
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f"phase={py_state.phase} player={py_state.current_player} "
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f"python={py_action} cython={cy_action}"
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)
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py_state.apply_action(py_action)
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cy_state.apply_action(cy_action)
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turn += 1
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assert cy_state.terminal is True
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