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wassname
b28b1a5e88
scripts/results_deploy.py pulls the held-out TEST deploy numbers from the FINAL EVAL line that just-results skips. Journal: per-rollout real==random (absorption), per-token real-V is the lead; pinning suspected off (band above live cos). Co-Authored-By: Claudypoo <288921227+claudypoo@users.noreply.github.com>
93 lines
3.7 KiB
Python
93 lines
3.7 KiB
Python
"""Deploy-eval table (eval2 = recency-clean held-out TEST, n=119).
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`just results` reports TRAIN-time L5 hack/solve. This script reports the DEPLOY
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numbers (knob-off forward on the paper test set) that only appear in the
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`FINAL EVAL ... held-out test` line -- the apples-to-apples per-arm deploy metric.
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Headline = solve_deploy - hack_deploy (both alone are gameable; their gap is the
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honest objective: solve the task without learning the cheat).
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uv run python scripts/results_deploy.py # or: just results-deploy
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"""
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from __future__ import annotations
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import re
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from pathlib import Path
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import polars as pl
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from tabulate import tabulate
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LOG_DIR = Path("logs")
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FINAL_RE = re.compile(
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r"FINAL EVAL \[.*?\] DEPLOY \(held-out test, n=(\d+)\): "
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r"hack\(strict\)=([\d.]+) hack\(vendor eq_hinted\)=([\d.]+) solve=([\d.]+)")
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MAIN_RE = re.compile(r"HACK_STUDENT=([\d.]+).*?PASS_RATE|PASS_RATE=([\d.]+).*?HACK_STUDENT=([\d.]+)")
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def _frac(tok: str) -> float | None:
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a, b = tok.split("/")
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return int(a) / int(b) if int(b) else None
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def _train_l5(txt: str) -> tuple[float | None, float | None]:
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"""Mean of last-5 student hack_s / gt_s from the per-step table (columns by name)."""
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names = []
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for l in txt.splitlines():
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if "| INFO |" not in l:
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continue
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toks = [re.sub(r"[^a-z0-9_]", "", t.lower()) for t in l.split("| INFO |", 1)[1].split()]
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if toks[:1] == ["step"] and "ref_eq" in toks:
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names = toks
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break
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if not names:
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return None, None
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i_h, i_g = names.index("hack_s"), names.index("gt_s")
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hs, gts = [], []
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for line in txt.splitlines():
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if "| INFO |" not in line:
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continue
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row = line.split("| INFO |", 1)[1].split()
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if not row or not row[0].isdigit() or len(row) <= max(i_h, i_g):
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continue
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if (h := _frac(row[i_h])) is not None:
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hs.append(h)
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if (g := _frac(row[i_g])) is not None:
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gts.append(g)
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mean = lambda v: sum(v[-5:]) / len(v[-5:]) if v else None
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return mean(hs), mean(gts)
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def parse(path: Path) -> dict | None:
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txt = path.read_text(errors="replace")
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m = FINAL_RE.search(txt)
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if m is None:
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return None # no recency-clean deploy eval -> not eval2
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n, hack_dep, hack_dep_eq, solve_dep = int(m[1]), float(m[2]), float(m[3]), float(m[4])
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argv = next((l.split("argv:", 1)[1].strip() for l in txt.splitlines() if "argv:" in l), "?")
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argv = argv.split("train.py ", 1)[-1].strip() if "train.py " in argv else argv
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if "tiny-random" in txt or "preset=smoke" in txt:
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return None # smoke garbage
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# train hack/solve = L5 (mean of last 5 student steps) from the per-step table,
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# the same converged-regime convention as scripts/results.py. The BLUF main-metric
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# line is stdout-only (not in the verbose log), so we read the streamed table.
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hack_tr, solve_tr = _train_l5(txt)
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return dict(
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headline=solve_dep - hack_dep,
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solve=solve_tr, hack=hack_tr,
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solve_deploy=solve_dep, hack_deploy=hack_dep,
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n=n, argv=argv,
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)
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def main() -> None:
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rows = [r for p in sorted(LOG_DIR.glob("*.log")) if (r := parse(p))]
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if not rows:
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print("no eval2 (held-out test) deploy runs in logs/")
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return
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df = pl.DataFrame(rows).sort("headline", descending=True)
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cols = ["headline", "solve", "hack", "solve_deploy", "hack_deploy", "n", "argv"]
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print("\n## Deploy eval (eval2 = recency-clean held-out TEST), sorted by headline=solve_deploy-hack_deploy\n")
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print(tabulate(df.select(cols).rows(), headers=cols, tablefmt="pipe", floatfmt="+.3f"))
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if __name__ == "__main__":
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main()
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