shortlist / code /scripts /figures.py
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Add the can-it-be-gamed check, the maze forward test notebook, license, and wording fixes
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"""Every number that appears in the post, the Space and the README, computed from the recorded data.
python scripts/figures.py # writes hf/numbers.json and prints it
Nothing in the write-up is typed by hand: it is read from this file. Episodes our own harness failed on
(model-server timeouts, API connection drops) are excluded everywhere, as they are in the verdicts.
"""
from __future__ import annotations
import importlib.util
import json
import sys
from collections import Counter, defaultdict
from pathlib import Path
ROOT = Path(__file__).resolve().parent.parent
sys.path.insert(0, str(ROOT))
from shortlist.checks import check_suite, harness_error # noqa: E402
RUN = ROOT / "runs" / "day2" # the clean run with the fixed harness (and later passes into it)
PILOT_DIPG = ROOT / "runs" / "day1c" # the DIPG marker experiment
EXPERIMENTS = ("dipg_safety_env-plain", "dipg_safety_env-brackets") # reported inside the DIPG finding, not as suites
PLAYERS = ["null", "random", "small", "beam-low", "beam-medium", "beam-high", "beam-max"]
def load(run: Path) -> dict[str, list[dict]]:
out = {}
for path in sorted(run.glob("*.jsonl")):
out[path.stem] = [r for r in (json.loads(l) for l in path.read_text().splitlines() if l.strip()) if not harness_error(r)]
return out
def census() -> dict:
spec = importlib.util.spec_from_file_location("hub_sweep", ROOT / "scripts" / "hub_sweep.py")
hs = importlib.util.module_from_spec(spec)
spec.loader.exec_module(hs)
rows = hs.merged(ROOT / "runs" / "hub" / "sweep")
label = {"passes floor checks": "runs and passes the floor checks",
"suspicious floor": "scores above zero for doing nothing",
"no standard API": "running, but no standard OpenEnv API",
"broken": "broken (build or runtime error on the Hub)",
"paused by owner": "paused by its owner",
"asleep, didn't wake": "asleep, no answer within 10 minutes",
"doesn't start": "not yet re-checked (first pass: no answer within 150 s)",
"crashes": "crashes on reset or step",
"refuses a session": "refuses a session"}
counts = Counter(r["class"] for r in rows)
n = len(rows)
groups = [{"group": label.get(k, k), "count": v, "share": round(v / n, 3)} for k, v in counts.most_common()]
floors = []
for r in rows:
if r["class"] == "suspicious floor":
null = next((f["return"] for f in r.get("floor", []) if f["policy"] == "null"), None)
floors.append({"space": r["id"], "do_nothing_score": null})
floors.sort(key=lambda x: -(x["do_nothing_score"] or 0))
rechecked = (ROOT / "runs" / "hub" / "sweep" / "census2.jsonl")
return {"spaces": n, "groups": groups, "sum_of_shares": round(sum(g["share"] for g in groups), 3),
"do_nothing_highest": floors[:5],
"do_nothing_at_least_1": sum(1 for f in floors if (f["do_nothing_score"] or 0) >= 1),
"second_pass_done": len(rechecked.read_text().splitlines()) if rechecked.exists() else 0}
def adversarial_floor() -> dict | None:
"""The can-it-be-gamed check (shortlist/gaming.py) over the small model's families."""
path = ROOT / "hf" / "gaming.json"
if not path.exists():
return None
g = json.loads(path.read_text())
return {"families": len(g), "gameable": sum(x["gameable"] for x in g),
"flagged": [{"suite": x["suite"], "strategy": x["best"]["strategy"], "label": x["best"]["label"], "answer": x["best"]["answer"],
"score": x["best"]["score"], "learner_mean": x["learner_mean"]} for x in g if x["gameable"]]}
def main() -> None:
data = load(RUN)
verdicts = {s: check_suite(s, rows) for s, rows in data.items() if rows}
shown = {s: v for s, v in verdicts.items() if s not in EXPERIMENTS}
rows_shown = [r for s, rows in data.items() if s not in EXPERIMENTS for r in rows]
played = {p: sum(1 for s in shown if any(r["policy"] == p and not r.get("error") for r in data[s])) for p in PLAYERS}
by_group: dict[str, Counter] = defaultdict(Counter)
for v in shown.values():
key = v.get("judged_for") or "floor players"
by_group[v.get("group", "?")][key + (" (provisional)" if v.get("provisional") else "")] += 1
def wins(suite):
out = {}
for p in ("null", "random", "beam-low"):
rs = [r for r in data.get(suite, []) if r["policy"] == p and not r.get("error")]
if rs:
out[p] = {"won": sum(r["return"] >= 1 for r in rs), "games": len(rs)}
return out
def gate(rows):
rs = [r for r in rows if r["policy"].startswith("beam") and not r.get("error")]
return {"cleared_format_gate": sum(r["return"] > -10 for r in rs), "answers": len(rs),
"fully_correct": sum(r["return"] >= 10 for r in rs),
"mean_score": round(sum(r["return"] for r in rs) / max(1, len(rs)), 2)}
pilot = load(PILOT_DIPG) if PILOT_DIPG.exists() else {}
small_vs_beam = []
for s, v in sorted(shown.items()):
t = v["tiers"]
if "small" in t and "beam-low" in t:
small_vs_beam.append({"suite": s, "beam_low_solved": t["beam-low"].get("pass@1"),
"small_solved": t["small"].get("pass@1"), "small_mean": round(t["small"]["mean"], 3),
"small_grpo_signal": t["small"].get("grpo_signal"), "verdict": v["verdict"]})
effort = []
for s, v in sorted(shown.items()):
t = v["tiers"]
rungs = {p: {"solved": t[p].get("pass@1"), "mean": round(t[p]["mean"], 3), "episodes": t[p]["episodes"]}
for p in PLAYERS if p.startswith("beam") and p in t}
if len(rungs) > 1:
effort.append({"suite": s, "rungs": rungs})
# Low effort against the highest effort tried, by share solved on the first try.
pairs = [(e["rungs"]["beam-low"]["solved"], list(e["rungs"].values())[-1]["solved"]) for e in effort
if "beam-low" in e["rungs"] and e["rungs"]["beam-low"]["solved"] is not None]
effort_summary = {"suites": len(effort), "compared": len(pairs),
"higher_solved_more": sum(hi > lo + 1e-9 for lo, hi in pairs),
"same": sum(abs(hi - lo) <= 1e-9 for lo, hi in pairs),
"higher_solved_less": sum(hi < lo - 1e-9 for lo, hi in pairs)}
training = {}
tdir = ROOT / "training" / "kaggle" / "results"
if (tdir / "results.json").exists():
res = json.loads((tdir / "results.json").read_text())
training = {"families": len(res), "held": sum(r["held"] for r in res),
"rows": [{k: r[k] for k in ("family", "prediction", "shortlist_verdict", "improved", "held")} |
{"before": r["before"]["mean_score"], "after": r["after"]["mean_score"], "p": r["gain"]["p"]} for r in res]}
out = {
"environments": len({v["env"] for v in shown.values()}),
"task_suites": len(shown),
"community_environments": sum(1 for s in shown if s.startswith("hub-")),
"episodes": len(rows_shown),
"beam_episodes": sum(1 for r in rows_shown if r["policy"].startswith("beam")),
"small_episodes": sum(1 for r in rows_shown if r["policy"] == "small"),
"beam_output_tokens": sum(r["tokens"]["completion"] for r in rows_shown if r["policy"].startswith("beam")),
"suites_played_by": played,
"verdict_groups": {g: dict(c) for g, c in sorted(by_group.items())},
"connect4": {"as_shipped": wins("connect4_env"), "with_beams_fix": wins("connect4_env-patched")},
"dipg": {"default_markers": gate(data.get("dipg_safety_env", [])),
"square_brackets": gate(data.get("dipg_safety_env-brackets", [])),
"pilot": {k: gate(v) for k, v in pilot.items() if k.startswith("dipg")}},
"small_vs_beam": small_vs_beam,
"effort_ladder": effort,
"effort_summary": effort_summary,
"adversarial_floor": adversarial_floor(),
"census": census(),
"training": training,
}
(ROOT / "hf").mkdir(exist_ok=True)
(ROOT / "hf" / "numbers.json").write_text(json.dumps(out, indent=2))
print(json.dumps(out, indent=1))
if __name__ == "__main__":
main()