| """ |
| eval_postprocess.py |
| ------------------- |
| Compute clip_rate_095, clip_rate_099, detach_rate, action_max_mean, and |
| palm_handle_dist_max_mean from one or more per-step metrics CSVs that |
| ``evaluate_ppo_baseline.py`` writes via ``--log_csv``. |
| |
| Usage: |
| python scripts/eval_postprocess.py CSV [CSV ...] |
| |
| Prints a short JSON-per-CSV line and a Markdown summary table on stdout. |
| """ |
| import argparse |
| import csv |
| import json |
| import sys |
| from collections import defaultdict |
| from pathlib import Path |
| from statistics import mean |
|
|
|
|
| def load_rows(path: Path): |
| with open(path, newline="") as f: |
| reader = csv.DictReader(f) |
| return [ |
| { |
| "episode": int(r["episode"]), |
| "step": int(r["step"]), |
| "reward": float(r["reward"]), |
| "done": int(r["done"]), |
| "action_abs_max": float(r["action_abs_max"]), |
| "palm_to_handle_dist": float(r["palm_to_handle_dist"]), |
| "success": int(r["success"]), |
| "normalized_progress": float(r["normalized_progress"]), |
| } |
| for r in reader |
| ] |
|
|
|
|
| def episode_stats(rows): |
| """Return per-episode dicts with terminal reward, success flag, max palm dist.""" |
| by_ep = defaultdict(list) |
| for r in rows: |
| by_ep[r["episode"]].append(r) |
| eps = [] |
| for ep, ep_rows in sorted(by_ep.items()): |
| ep_rows.sort(key=lambda r: r["step"]) |
| last = ep_rows[-1] |
| |
| |
| min_reward = min(r["reward"] for r in ep_rows) |
| any_success = any(r["success"] for r in ep_rows) |
| max_palm = max(r["palm_to_handle_dist"] for r in ep_rows) |
| eps.append({ |
| "episode": ep, |
| "steps": len(ep_rows), |
| "terminal_reward": last["reward"], |
| "min_reward": min_reward, |
| "any_success": any_success, |
| "max_palm_dist": max_palm, |
| }) |
| return eps |
|
|
|
|
| def summarize(path: Path): |
| rows = load_rows(path) |
| eps = episode_stats(rows) |
| n_steps = len(rows) |
| if n_steps == 0: |
| return {"path": str(path), "error": "empty"} |
| clip095 = sum(1 for r in rows if r["action_abs_max"] >= 0.95) / n_steps |
| clip099 = sum(1 for r in rows if r["action_abs_max"] >= 0.99) / n_steps |
| action_max_mean_step = mean(r["action_abs_max"] for r in rows) |
| n_eps = len(eps) |
| detach_rate = ( |
| sum(1 for e in eps if (not e["any_success"]) and e["min_reward"] <= -40.0) |
| / n_eps |
| ) |
| success_rate = sum(1 for e in eps if e["any_success"]) / n_eps |
| palm_max_mean = mean(e["max_palm_dist"] for e in eps) |
| length_mean = mean(e["steps"] for e in eps) |
| return { |
| "path": str(path), |
| "episodes": n_eps, |
| "n_steps": n_steps, |
| "success_rate": success_rate, |
| "length_mean": length_mean, |
| "action_max_mean_step": action_max_mean_step, |
| "clip_rate_095": clip095, |
| "clip_rate_099": clip099, |
| "detach_rate": detach_rate, |
| "palm_handle_dist_max_mean": palm_max_mean, |
| } |
|
|
|
|
| def main(): |
| p = argparse.ArgumentParser() |
| p.add_argument("csvs", nargs="+", type=Path) |
| p.add_argument("--summary_json", default=None, type=Path, |
| help="Optional: dump a list of summaries to this path.") |
| args = p.parse_args() |
|
|
| results = [] |
| for path in args.csvs: |
| if not path.exists(): |
| print(f"[skip] missing: {path}", file=sys.stderr) |
| continue |
| results.append(summarize(path)) |
|
|
| print("\n| csv | eps | success | length | act_max | clip095 | clip099 | detach | palm_max |") |
| print("|---|---:|---:|---:|---:|---:|---:|---:|---:|") |
| for r in results: |
| if "error" in r: |
| print(f"| {Path(r['path']).name} | ERROR ({r['error']}) | | | | | | | |") |
| continue |
| print( |
| f"| {Path(r['path']).name} | " |
| f"{r['episodes']} | " |
| f"{r['success_rate']:.2f} | " |
| f"{r['length_mean']:.1f} | " |
| f"{r['action_max_mean_step']:.3f} | " |
| f"{r['clip_rate_095']:.3f} | " |
| f"{r['clip_rate_099']:.3f} | " |
| f"{r['detach_rate']:.2f} | " |
| f"{r['palm_handle_dist_max_mean']:.3f} |" |
| ) |
|
|
| print("\nJSON:") |
| for r in results: |
| print(json.dumps(r)) |
|
|
| if args.summary_json: |
| args.summary_json.parent.mkdir(parents=True, exist_ok=True) |
| with open(args.summary_json, "w") as f: |
| json.dump(results, f, indent=2) |
|
|
|
|
| if __name__ == "__main__": |
| main() |
|
|
