#!/usr/bin/env python3 """Choose which tasks ship, so the corpus hits a target resolved rate. Difficulty is set by composition, not by any one task. Given a measured resolved rate per tier, this picks how many of each tier to include so the aggregate lands in the target band, while keeping the easy tier non-empty -- a corpus that scores a model at zero discriminates no better than one that scores it at 100%. Rates come from measurement (see CALIBRATION.md); they are inputs here, never assumptions baked into the selection. """ import argparse, collections, json, sys from pathlib import Path HERE = Path(__file__).parent HAND_COMPOUND = { "incident-triage", "scheduler-regressions", "retry-subsystem-broken", "api-review-findings", "routing-regressions", "negotiation-and-headers", "audit-findings", } def tier_of(task_id: str) -> str: if any(k in task_id for k in ("triage3", "review3", "audit3")): return "d3" if any(k in task_id for k in ("triage2", "review2", "audit2")): return "d2" if task_id.split("-", 1)[1] in HAND_COMPOUND: return "d3" return "single" def load(path: Path): return [json.loads(line) for line in path.read_text().splitlines() if line.strip()] def choose(rows, rates, target, total, floor_single, easy_repo="ledger"): """Pick counts per tier whose weighted rate is closest to `target`.""" pools = collections.defaultdict(list) for row in rows: tier = tier_of(row["task_id"]) # A compound inherits the difficulty of the repo it sits in. The small # repo can be read end to end, which removes the orientation cost that # makes compounds hard, so its tasks count as easy however many defects # they carry. if tier != "single" and row.get("repo") == easy_repo: tier = "single" pools[tier].append(row) for tasks in pools.values(): tasks.sort(key=lambda r: r["task_id"]) best = None for n_single in range(floor_single, min(len(pools["single"]), total) + 1): for n_d2 in range(0, min(len(pools["d2"]), total - n_single) + 1): n_d3 = total - n_single - n_d2 if n_d3 < 0 or n_d3 > len(pools["d3"]): continue solved = (n_single * rates["single"] + n_d2 * rates["d2"] + n_d3 * rates["d3"]) rate = solved / total score = abs(rate - target) if best is None or score < best[0]: best = (score, rate, n_single, n_d2, n_d3) return best, pools def main(): ap = argparse.ArgumentParser() ap.add_argument("--corpus", default=str(HERE / "agentic-corpus.jsonl")) ap.add_argument("--out", default=str(HERE / "agentic-corpus-selected.jsonl")) ap.add_argument("--total", type=int, default=60) ap.add_argument("--target", type=float, default=0.175, help="midpoint of the wanted resolved band") ap.add_argument("--floor-single", type=int, default=4, help="minimum easy-tier tasks, so the floor is not zero") ap.add_argument("--rate-single", type=float, required=True) ap.add_argument("--rate-d2", type=float, required=True) ap.add_argument("--rate-d3", type=float, required=True) ap.add_argument("--easy-repo", default="ledger", help="repo whose tasks are treated as the easy tier at every " "defect count, and excluded from the hard tiers") args = ap.parse_args() rows = load(Path(args.corpus)) rates = {"single": args.rate_single, "d2": args.rate_d2, "d3": args.rate_d3} best, pools = choose(rows, rates, args.target, args.total, args.floor_single, args.easy_repo) if best is None: print("no selection satisfies the constraints") return 1 _, rate, n_single, n_d2, n_d3 = best picked = (pools["single"][:n_single] + pools["d2"][:n_d2] + pools["d3"][:n_d3]) print(f"measured rates: single={rates['single']:.0%} " f"d2={rates['d2']:.0%} d3={rates['d3']:.0%}") print(f"selection ({args.total} tasks): " f"{n_single} single + {n_d2} two-defect + {n_d3} three-defect") print(f"projected resolved rate: {rate:.1%}") langs = collections.Counter(r["lang"] for r in picked) repos = collections.Counter(r["repo"] for r in picked) print(f"languages: {dict(langs)}") print(f"repos: {dict(repos)}") Path(args.out).write_text("".join(json.dumps(r) + "\n" for r in picked)) print(f"\nwrote {len(picked)} tasks -> {args.out}") return 0 if __name__ == "__main__": sys.exit(main())