| |
| from __future__ import annotations |
| import argparse, csv, math, sys |
| from pathlib import Path |
|
|
| ROOT = Path(__file__).resolve().parents[1] |
| if str(ROOT) not in sys.path: |
| sys.path.insert(0, str(ROOT)) |
|
|
| import matplotlib.pyplot as plt |
|
|
|
|
| def read_csv(path: Path) -> list[dict]: |
| with path.open('r', encoding='utf-8', newline='') as f: |
| return list(csv.DictReader(f)) |
|
|
|
|
| def group_by(rows: list[dict], key: str) -> dict[str, list[dict]]: |
| out: dict[str, list[dict]] = {} |
| for row in rows: |
| out.setdefault(row[key], []).append(row) |
| return out |
|
|
|
|
| def verdict_band(ax): |
| ax.axhline(0.65, linestyle='--', linewidth=1) |
| ax.axhline(0.35, linestyle='--', linewidth=1) |
| ax.axhline(0.50, linestyle=':', linewidth=1) |
| ax.set_ylim(0, 1) |
| ax.set_ylabel('p_forward') |
| ax.set_xlabel('CoT / evidence step') |
|
|
|
|
| def plot_gold_trajectories(gold_rows: list[dict], out_dir: Path) -> None: |
| grouped = group_by(gold_rows, 'scenario_id') |
| fig, ax = plt.subplots(figsize=(11, 6)) |
| for sid, rows in sorted(grouped.items()): |
| rows = sorted(rows, key=lambda r: int(r['step'])) |
| xs = [int(r['step']) for r in rows] |
| ys = [float(r['p_forward']) for r in rows] |
| ax.plot(xs, ys, marker='o', linewidth=1.6, label=sid) |
| verdict_band(ax) |
| ax.set_title('Gold-label belief trajectories') |
| ax.legend(ncol=2, fontsize=8) |
| fig.tight_layout() |
| fig.savefig(out_dir / 'gold_trajectories.png', dpi=180) |
| plt.close(fig) |
|
|
|
|
| def plot_final_p(summary_rows: list[dict], out_dir: Path) -> None: |
| rows = sorted(summary_rows, key=lambda r: r['scenario_id']) |
| labels = [r['scenario_id'] for r in rows] |
| vals = [float(r['final_p']) for r in rows] |
| fig, ax = plt.subplots(figsize=(10, 5)) |
| ax.bar(labels, vals) |
| ax.axhline(0.65, linestyle='--', linewidth=1) |
| ax.axhline(0.35, linestyle='--', linewidth=1) |
| ax.axhline(0.50, linestyle=':', linewidth=1) |
| ax.set_ylim(0, 1) |
| ax.set_ylabel('Final p_forward') |
| ax.set_title('Final belief by scenario') |
| fig.tight_layout() |
| fig.savefig(out_dir / 'final_p_by_scenario.png', dpi=180) |
| plt.close(fig) |
|
|
|
|
| def plot_converter_accuracy(eval_rows: list[dict], out_dir: Path) -> None: |
| grouped = group_by(eval_rows, 'scenario_id') |
| labels, vals = [], [] |
| for sid, rows in sorted(grouped.items()): |
| usable = [r for r in rows if r.get('auto_supports_forward') not in ('', 'None', None)] |
| if not usable: |
| acc = 0.0 |
| else: |
| acc = sum(1 for r in usable if r.get('direction_correct') == 'True') / len(usable) |
| labels.append(sid) |
| vals.append(acc) |
| fig, ax = plt.subplots(figsize=(10, 5)) |
| ax.bar(labels, vals) |
| ax.set_ylim(0, 1) |
| ax.set_ylabel('Direction accuracy') |
| ax.set_title('Baseline converter direction accuracy') |
| fig.tight_layout() |
| fig.savefig(out_dir / 'converter_direction_accuracy.png', dpi=180) |
| plt.close(fig) |
|
|
|
|
| def plot_gold_vs_auto(gold_rows: list[dict], auto_rows: list[dict], out_dir: Path) -> None: |
| gold = group_by(gold_rows, 'scenario_id') |
| auto = group_by(auto_rows, 'scenario_id') |
| scenario_dir = out_dir / 'scenario_plots' |
| scenario_dir.mkdir(parents=True, exist_ok=True) |
| for sid in sorted(gold.keys()): |
| g_rows = sorted(gold[sid], key=lambda r: int(r['step'])) |
| a_rows = sorted(auto.get(sid, []), key=lambda r: int(r['step'])) |
| fig, ax = plt.subplots(figsize=(8, 4.5)) |
| ax.plot([int(r['step']) for r in g_rows], [float(r['p_forward']) for r in g_rows], marker='o', label='gold') |
| if a_rows: |
| ax.plot([int(r['step']) for r in a_rows], [float(r['p_forward']) for r in a_rows], marker='x', label='auto') |
| verdict_band(ax) |
| ax.set_title(f'{sid}: gold vs auto trajectory') |
| ax.legend() |
| fig.tight_layout() |
| fig.savefig(scenario_dir / f'{sid.lower()}_gold_vs_auto.png', dpi=180) |
| plt.close(fig) |
|
|
|
|
| def main() -> None: |
| parser = argparse.ArgumentParser(description='Visualize TBG-CoT-Bench result CSV files.') |
| parser.add_argument('--results', default='results') |
| parser.add_argument('--figures', default='figures') |
| args = parser.parse_args() |
|
|
| results = Path(args.results) |
| figures = Path(args.figures) |
| figures.mkdir(parents=True, exist_ok=True) |
|
|
| gold_rows = read_csv(results / 'trajectories_gold.csv') |
| summary_rows = read_csv(results / 'scenario_summary.csv') |
| auto_rows = read_csv(results / 'trajectories_auto.csv') if (results / 'trajectories_auto.csv').exists() else [] |
| eval_rows = read_csv(results / 'converter_eval.csv') if (results / 'converter_eval.csv').exists() else [] |
|
|
| plot_gold_trajectories(gold_rows, figures) |
| plot_final_p(summary_rows, figures) |
| if eval_rows: |
| plot_converter_accuracy(eval_rows, figures) |
| if auto_rows: |
| plot_gold_vs_auto(gold_rows, auto_rows, figures) |
|
|
| print(f'Wrote figures to {figures}') |
|
|
|
|
| if __name__ == '__main__': |
| main() |
|
|
