File size: 5,134 Bytes
44084af | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 | #!/usr/bin/env python3
"""Print the README leaderboard table for one tier from space/data/runs.json (written by scripts/export_runs.py).
python scripts/leaderboard.py --tier expert # markdown table on stdout
python scripts/leaderboard.py --tier expert --readme README.md # rewrite the <!-- LEADERBOARD:expert --> block
One row per agent and model (provider prefix dropped), over model runs only: scripted reference policies
(oracle, nop, partial, OpenEnv traces) and runs flagged `invalid` are left out. The expert tier counts the
leaderboard tasks only (`tasks[...].leaderboard`). Re-graded runs count with their re-graded reward. Columns:
runs, mean reward, solved/total and, for the expert tier, the leaderboard tasks the row covers and the mean
`diagnosis` and `routine` scores (rows that cover different tasks are not directly comparable).
"""
from __future__ import annotations
import argparse
import json
import os
import re
import sys
ROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
def collect(data: dict, tier: str) -> tuple[list[dict], int, int]:
"""(rows, number of tasks with a counted run, number of invalid runs left out)."""
tasks, groups, tids, invalid = data.get("tasks", {}), {}, set(), 0
for r in data.get("runs", []):
t = tasks.get(r["task_id"], {})
own = r.get("tier") if r.get("tier") in ("core", "expert") else "expert" if r["task_id"].startswith("afx-") \
else "core" # runs.json v1 used `tier` for the difficulty
if own != tier:
continue
if r.get("model_family") == "reference" or r.get("source") != "harbor" or not r.get("model"):
continue
if tier == "expert" and t.get("leaderboard") is False:
continue
if r.get("invalid"):
invalid += 1
continue
tids.add(r["task_id"])
groups.setdefault((r["agent"].split(": ", 1)[-1], r["model"].rsplit("/", 1)[-1]), []).append(r)
rows = []
for (agent, model), rs in groups.items():
mean = lambda xs: sum(xs) / len(xs) # noqa: E731
rows.append({"agent": agent, "model": model, "runs": len(rs), "tasks": len({r["task_id"] for r in rs}),
"reward": mean([r["reward"] for r in rs]),
"solved": sum(1 for r in rs if (r.get("keys") or {}).get("solved", 0) >= 1),
"diagnosis": mean([float((r.get("keys") or {}).get("diagnosis") or 0) for r in rs]),
"routine": mean([float((r.get("keys") or {}).get("routine") or 0) for r in rs])})
rows.sort(key=lambda g: (-g["reward"], g["agent"], g["model"]))
return rows, len(tids), invalid
def table(rows: list[dict], tier: str, n_lb: int = 0) -> str:
expert = tier == "expert"
head = ["Agent", "Model", "Runs"] + (["Tasks"] if expert else []) + ["Mean reward", "Solved"] + \
(["Diagnosis", "Routine"] if expert else [])
lines = ["| " + " | ".join(head) + " |", "|" + "---|" * len(head)]
for g in rows:
cells = [g["agent"], g["model"], str(g["runs"])] + ([f"{g['tasks']}/{n_lb}"] if expert else []) + \
[f"{g['reward']:.3f}", f"{g['solved']}/{g['runs']}"]
if expert:
cells += [f"{g['diagnosis']:.3f}", f"{g['routine']:.3f}"]
lines.append("| " + " | ".join(cells) + " |")
return "\n".join(lines)
def fill(readme: str, tier: str, block: str) -> str:
rx = re.compile(rf"(<!-- LEADERBOARD:{re.escape(tier)} -->)(.*?)(<!-- /LEADERBOARD -->)", re.S)
if not rx.search(readme):
raise SystemExit(f"no <!-- LEADERBOARD:{tier} --> ... <!-- /LEADERBOARD --> block in the README")
return rx.sub(lambda m: f"{m.group(1)}\n{block}\n{m.group(3)}", readme, count=1)
def main() -> int:
ap = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter)
ap.add_argument("--tier", choices=["core", "expert"], default="expert")
ap.add_argument("--runs", default=os.path.join(ROOT, "space", "data", "runs.json"))
ap.add_argument("--readme", help="rewrite this README's leaderboard block in place instead of printing")
a = ap.parse_args()
with open(a.runs) as fh:
data = json.load(fh)
rows, n_tasks, invalid = collect(data, a.tier)
if not rows:
print(f"no valid model runs for tier {a.tier} in {a.runs}", file=sys.stderr)
return 1
n_lb = sum(1 for t in data.get("tasks", {}).values() if t.get("leaderboard")) or n_tasks
note = f"{sum(g['runs'] for g in rows)} runs on {n_tasks} tasks"
if invalid:
many = invalid != 1
note += f"; {invalid} run{'s' if many else ''} flagged invalid {'are' if many else 'is'} not counted"
block = f"{table(rows, a.tier, n_lb)}\n\n{note[0].upper()}{note[1:]}."
if not a.readme:
print(block)
return 0
with open(a.readme) as fh:
text = fh.read()
with open(a.readme, "w") as fh:
fh.write(fill(text, a.tier, block))
print(f"updated the {a.tier} leaderboard in {a.readme}: {note}")
return 0
if __name__ == "__main__":
sys.exit(main())
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