Deep CFR 분석 대시보드 정리
This commit is contained in:
@@ -5,37 +5,74 @@ import json
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from pathlib import Path
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from typing import Any
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PLOT_GROUPS: dict[str, list[str]] = {
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"Action Distribution": [
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"play_action_rate",
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"discard_action_rate",
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"draw_deck_rate",
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"draw_pile_rate",
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],
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"Game Flow": [
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"avg_opened_colors",
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"5_color_open_count",
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"avg_expedition_cards",
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],
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"Open Quality": [
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"bad_open_rate",
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"weak_open_rate",
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"good_open_rate",
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"opening_recoverable_score_mean",
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],
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"Expedition Outcomes": [
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"positive_expedition_rate",
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"negative_expedition_rate",
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"bonus_expedition_rate",
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"final_expedition_score_p25",
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"final_expedition_score_median",
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"final_expedition_score_p75",
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"final_expedition_score_p90",
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],
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"Calibration": [
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"first_open_recoverable_score_mean_for_positive_final",
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"first_open_recoverable_score_mean_for_negative_final",
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TRAINING_PLOTS: list[tuple[str, list[str], str]] = [
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("Advantage Loss", ["advantage_loss"], "loss"),
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("Strategy Loss", ["strategy_loss"], "loss"),
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("Iteration Time", ["iteration_seconds"], "seconds"),
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("Throughput", ["nodes_per_second"], "nodes / second"),
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("Samples", ["advantage_samples", "strategy_samples"], "samples"),
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("Memory Size", ["advantage_memory_size", "strategy_memory_size"], "samples"),
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(
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"Traversal Depth",
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["traversal_avg_endpoint_depth", "traversal_max_depth_reached"],
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"depth",
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),
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(
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"Traversal Endpoints",
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[
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"traversal_terminals",
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"traversal_node_limit_cutoffs",
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"traversal_depth_cutoffs",
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],
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"count",
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),
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]
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EVAL_PLOTS: list[tuple[str, str, str, float]] = [
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("Win Rate", "win_rate0", "rate (%)", 100.0),
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("Avg Score Diff", "avg_score_diff0", "score diff", 1.0),
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("Avg Score", "avg_score0", "score", 1.0),
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("Policy Entropy", "policy_entropy", "entropy", 1.0),
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("Play Action Rate", "play_action_rate", "rate (%)", 100.0),
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("Discard Action Rate", "discard_action_rate", "rate (%)", 100.0),
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("Draw Deck Rate", "draw_deck_rate", "rate (%)", 100.0),
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("Draw Pile Rate", "draw_pile_rate", "rate (%)", 100.0),
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("Opened Colors", "avg_opened_colors", "colors", 1.0),
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("5-Color Open Count", "5_color_open_count", "games / eval", 1.0),
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("Expedition Cards", "avg_expedition_cards", "cards", 1.0),
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("Bad Open Rate", "bad_open_rate", "rate (%)", 100.0),
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("Good Open Rate", "good_open_rate", "rate (%)", 100.0),
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("Opening Recoverable Score", "opening_recoverable_score_mean", "score", 1.0),
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("Score per Opened Color", "score_per_opened_color", "score / color", 1.0),
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("Negative Expedition Rate", "negative_expedition_rate", "rate (%)", 100.0),
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("Positive Expedition Rate", "positive_expedition_rate", "rate (%)", 100.0),
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(
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"Final Score per Expedition",
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"avg_final_score_per_opened_expedition",
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"score",
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1.0,
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),
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("Max Step Timeouts", "max_step_timeouts", "timeouts", 1.0),
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]
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SUMMARY_EVAL_METRICS: list[tuple[str, str, float]] = [
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("win_rate0", "win rate (%)", 100.0),
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("avg_score_diff0", "avg score diff", 1.0),
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("avg_score0", "avg score", 1.0),
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("play_action_rate", "play rate (%)", 100.0),
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("avg_opened_colors", "opened colors", 1.0),
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("bad_open_rate", "bad open (%)", 100.0),
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("good_open_rate", "good open (%)", 100.0),
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("score_per_opened_color", "score / opened color", 1.0),
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]
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OPPONENT_COLORS: dict[str, str] = {
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"noisy_safe": "tab:blue",
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"passive_discard": "tab:orange",
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"random": "tab:green",
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"safe_heuristic": "tab:red",
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"safe_heuristic_loose": "tab:purple",
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"safe_heuristic_strict": "tab:brown",
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}
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@@ -56,45 +93,110 @@ def opponent_names(rows: list[dict[str, Any]]) -> list[str]:
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if not key.startswith("eval_"):
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continue
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rest = key[len("eval_") :]
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for metric in _all_group_metrics():
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for metric in _all_eval_metrics():
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suffix = f"_{metric}"
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if rest.endswith(suffix):
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names.add(rest[: -len(suffix)])
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return sorted(names)
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def plot_group(
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rows: list[dict[str, Any]],
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*,
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opponent: str,
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title: str,
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metrics: list[str],
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output: Path,
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) -> bool:
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def plot_training_dashboard(rows: list[dict[str, Any]], output: Path) -> bool:
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import matplotlib.pyplot as plt
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x = [int(row["iteration"]) for row in rows if "iteration" in row]
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if not x:
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return False
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plotted = False
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fig, ax = plt.subplots(figsize=(10, 5))
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for metric in metrics:
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key = f"eval_{opponent}_{metric}"
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values = [row.get(key) for row in rows]
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if all(value is None for value in values):
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continue
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y = [float("nan") if value is None else float(value) for value in values]
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ax.plot(x, y, marker="o", linewidth=1.5, label=metric)
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plotted = True
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if not plotted:
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fig, axes = plt.subplots(3, 3, figsize=(16, 12))
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axes_flat = list(axes.flat)
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plotted_any = False
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for ax, (title, metrics, ylabel) in zip(axes_flat, TRAINING_PLOTS, strict=False):
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plotted = _plot_row_metrics(ax, rows, x, metrics)
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_finish_axis(ax, title, ylabel=ylabel, plotted=plotted)
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plotted_any = plotted_any or plotted
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_plot_latest_depth_buckets(axes_flat[len(TRAINING_PLOTS)], rows)
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plotted_any = plotted_any or bool(_latest_depth_buckets(rows))
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fig.suptitle("Lost Cities Deep CFR training metrics", fontsize=14, fontweight="bold")
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fig.tight_layout(rect=(0, 0, 1, 0.97))
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if not plotted_any:
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plt.close(fig)
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return False
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ax.set_title(f"{title} - {opponent}")
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ax.set_xlabel("iteration")
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ax.grid(True, alpha=0.3)
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ax.legend(loc="best", fontsize="small")
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fig.savefig(output, dpi=150)
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plt.close(fig)
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return True
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def plot_eval_dashboard(rows: list[dict[str, Any]], output: Path) -> bool:
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import matplotlib.pyplot as plt
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opponents = opponent_names(rows)
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if not opponents:
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return False
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fig, axes = plt.subplots(5, 4, figsize=(20, 18))
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axes_flat = list(axes.flat)
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plotted_any = False
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for ax, (title, metric, ylabel, scale) in zip(axes_flat, EVAL_PLOTS, strict=False):
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plotted = _plot_eval_metric(ax, rows, opponents, metric, scale)
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_finish_axis(ax, title, ylabel=ylabel, plotted=plotted)
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plotted_any = plotted_any or plotted
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for ax in axes_flat[len(EVAL_PLOTS) :]:
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ax.axis("off")
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handles, labels = _legend_items(axes_flat)
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if handles:
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fig.legend(handles, labels, loc="upper center", ncols=min(len(labels), 6), fontsize="small")
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fig.suptitle("Lost Cities Deep CFR eval metrics", fontsize=14, fontweight="bold")
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fig.tight_layout(rect=(0, 0, 1, 0.96))
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if not plotted_any:
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plt.close(fig)
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return False
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fig.savefig(output, dpi=150)
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plt.close(fig)
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return True
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def plot_final_eval_summary(rows: list[dict[str, Any]], output: Path) -> bool:
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import matplotlib.pyplot as plt
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opponents = opponent_names(rows)
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latest = _latest_eval_row(rows)
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if not opponents or latest is None:
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return False
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fig, axes = plt.subplots(2, 4, figsize=(18, 8))
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plotted_any = False
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for ax, (metric, title, scale) in zip(axes.flat, SUMMARY_EVAL_METRICS, strict=False):
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labels: list[str] = []
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values: list[float] = []
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colors: list[str] = []
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for opponent in opponents:
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value = latest.get(f"eval_{opponent}_{metric}")
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if value is None:
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continue
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labels.append(opponent)
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values.append(float(value) * scale)
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colors.append(_opponent_color(opponent))
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if values:
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ax.bar(labels, values, color=colors)
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plotted_any = True
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ax.tick_params(axis="x", labelrotation=35, labelsize="x-small")
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_finish_axis(ax, title, xlabel="", plotted=bool(values))
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iteration = latest.get("iteration", "latest")
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fig.suptitle(
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f"Lost Cities Deep CFR final eval summary: iteration {iteration}",
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fontsize=14,
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fontweight="bold",
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)
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fig.tight_layout()
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fig.savefig(output)
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if not plotted_any:
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plt.close(fig)
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return False
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fig.savefig(output, dpi=150)
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plt.close(fig)
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return True
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@@ -105,17 +207,160 @@ def analyze_run(run_dir: Path, output_dir: Path | None = None) -> list[Path]:
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output_dir = output_dir or run_dir
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output_dir.mkdir(parents=True, exist_ok=True)
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written: list[Path] = []
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for opponent in opponent_names(rows):
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for title, metrics in PLOT_GROUPS.items():
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filename = f"analysis_{_slug(opponent)}_{_slug(title)}.png"
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path = output_dir / filename
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if plot_group(rows, opponent=opponent, title=title, metrics=metrics, output=path):
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written.append(path)
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training_path = output_dir / "analysis_training_dashboard.png"
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if plot_training_dashboard(rows, training_path):
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written.append(training_path)
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eval_path = output_dir / "analysis_eval_dashboard.png"
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if _has_eval_history(rows) and plot_eval_dashboard(rows, eval_path):
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written.append(eval_path)
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final_eval_path = output_dir / "analysis_final_eval_summary.png"
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if plot_final_eval_summary(rows, final_eval_path):
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written.append(final_eval_path)
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return written
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def _all_group_metrics() -> set[str]:
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return {metric for metrics in PLOT_GROUPS.values() for metric in metrics}
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def _all_eval_metrics() -> set[str]:
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metrics = {metric for _, metric, _, _ in EVAL_PLOTS}
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metrics.update(metric for metric, _, _ in SUMMARY_EVAL_METRICS)
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return metrics
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def _plot_row_metrics(
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ax: Any, rows: list[dict[str, Any]], x: list[int], metrics: list[str]
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) -> bool:
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plotted = False
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for metric in metrics:
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values = [row.get(metric) for row in rows]
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if all(value is None for value in values):
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continue
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y = [float("nan") if value is None else float(value) for value in values]
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ax.plot(x, y, marker="o", linewidth=1.5, markersize=3, label=_label(metric))
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plotted = True
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return plotted
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def _plot_eval_metric(
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ax: Any,
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rows: list[dict[str, Any]],
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opponents: list[str],
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metric: str,
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scale: float,
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) -> bool:
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plotted = False
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for opponent in opponents:
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pairs = [
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(int(row["iteration"]), float(row[f"eval_{opponent}_{metric}"]) * scale)
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for row in rows
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if "iteration" in row and f"eval_{opponent}_{metric}" in row
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]
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if not pairs:
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continue
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x, y = zip(*pairs, strict=True)
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ax.plot(
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x,
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y,
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marker="o",
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linewidth=1.5,
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markersize=3,
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label=opponent,
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color=_opponent_color(opponent),
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)
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plotted = True
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return plotted
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def _plot_latest_depth_buckets(ax: Any, rows: list[dict[str, Any]]) -> None:
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buckets = _latest_depth_buckets(rows)
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if not buckets:
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_finish_axis(ax, "Latest Endpoint Depth Buckets", xlabel="endpoint depth", plotted=False)
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return
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labels = [label for label, _ in buckets]
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values = [value for _, value in buckets]
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ax.bar(labels, values, color="tab:blue")
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ax.tick_params(axis="x", labelrotation=45, labelsize="x-small")
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_finish_axis(
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ax,
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"Latest Endpoint Depth Buckets",
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xlabel="endpoint depth",
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ylabel="traversals",
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plotted=True,
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)
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def _latest_depth_buckets(rows: list[dict[str, Any]]) -> list[tuple[str, float]]:
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for row in reversed(rows):
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buckets = [
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(
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key.removeprefix("traversal_endpoint_depth_bucket_"),
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float(value),
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)
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for key, value in row.items()
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if key.startswith("traversal_endpoint_depth_bucket_")
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]
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if buckets:
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return sorted(buckets, key=lambda item: item[0])
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return []
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def _latest_eval_row(rows: list[dict[str, Any]]) -> dict[str, Any] | None:
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for row in reversed(rows):
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if any(key.startswith("eval_") for key in row):
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return row
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return None
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def _has_eval_history(rows: list[dict[str, Any]]) -> bool:
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eval_iterations = {
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int(row["iteration"])
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for row in rows
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if "iteration" in row and any(key.startswith("eval_") for key in row)
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}
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return len(eval_iterations) >= 2
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def _finish_axis(
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ax: Any,
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title: str,
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*,
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xlabel: str = "iteration",
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ylabel: str | None = None,
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plotted: bool,
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) -> None:
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ax.set_title(title, fontsize=10, fontweight="bold")
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if xlabel:
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ax.set_xlabel(xlabel)
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if ylabel:
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ax.set_ylabel(ylabel)
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ax.grid(True, alpha=0.3)
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if plotted:
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handles, _ = ax.get_legend_handles_labels()
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if handles:
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ax.legend(loc="best", fontsize="x-small")
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else:
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ax.text(0.5, 0.5, "No data", ha="center", va="center", transform=ax.transAxes)
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def _legend_items(axes: list[Any]) -> tuple[list[Any], list[str]]:
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handles_by_label: dict[str, Any] = {}
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for ax in axes:
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handles, labels = ax.get_legend_handles_labels()
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for handle, label in zip(handles, labels, strict=True):
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handles_by_label.setdefault(label, handle)
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if ax.get_legend() is not None:
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ax.get_legend().remove()
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labels = list(handles_by_label)
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return [handles_by_label[label] for label in labels], labels
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def _opponent_color(opponent: str) -> str:
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return OPPONENT_COLORS.get(opponent, "tab:gray")
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def _label(metric: str) -> str:
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return metric.replace("_", " ")
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def _slug(value: str) -> str:
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