File size: 5,031 Bytes
e1e980e | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 | #!/usr/bin/env python3
"""Score System One answers on CLI Command Risk decisions with the metrics of cli-command-risk/baseline.py.
Reads an answers JSONL ({"id", "answers"}) and cli-data/decisions/<split>.parquet. Reports risk-level macro F1
and accuracy (argmax of the score question), category macro F1 at 0.5, F1 for `risky`, `reversible` and
`requires_root`, and, when --val answers are given, recall on high and critical rows at the cutoff that keeps
1% of validation safe rows flagged. Intervals are 95% bootstrap intervals over 500 resamples of families.
python score_cli_risk.py runs/cli-d1-3b/finetuned.test.jsonl --val runs/cli-d1-3b/finetuned.validation.jsonl
"""
import argparse
import json
import os
import random
from pathlib import Path
import pyarrow.parquet as pq
HERE = Path(__file__).resolve().parent
DATA = Path(os.environ.get("DECISIONS_DATA", HERE / "cli-data"))
LEVELS = ["safe", "low", "moderate", "high", "critical"]
FLAGS = ("risky", "reversible", "requires_root")
RESAMPLES = 500
def load(split, answers_path):
rows = pq.read_table(DATA / "decisions" / f"{split}.parquet", columns=["id", "task_slug", "questions", "gold"]).to_pylist()
answers = {}
for line in Path(answers_path).read_text().splitlines():
record = json.loads(line)
answers[record["id"]] = record["answers"]
missing = [r["id"] for r in rows if r["id"] not in answers]
if missing:
raise SystemExit(f"{len(missing)} {split} cases have no answer, first {missing[0]}")
questions = json.loads(rows[0]["questions"])
cases = []
for r in rows:
g, a = json.loads(r["gold"]), answers[r["id"]]
probs = [a["risk_level"]["probabilities"][str(i)] for i in range(len(LEVELS))]
cases.append({"family": r["task_slug"], "level": int(g["risk_level"]["label"]),
"level_pred": max(range(len(LEVELS)), key=probs.__getitem__),
"p_risky_level": 1 - probs[0],
"nouls": {n: (g[n]["label"] == "true", a[n]["noul"]) for n, q in questions.items() if q["type"] == "noul"}})
cats = [n for n in questions if questions[n]["type"] == "noul" and n not in FLAGS]
return cases, cats
def f1(pairs):
tp = sum(1 for gold, pred in pairs if gold and pred)
fp = sum(1 for gold, pred in pairs if not gold and pred)
fn = sum(1 for gold, pred in pairs if gold and not pred)
return 2 * tp / (2 * tp + fp + fn) if tp else 0.0
def level_f1(cases):
return sum(f1([(c["level"] == k, c["level_pred"] == k) for c in cases]) for k in range(len(LEVELS))) / len(LEVELS)
def noul_f1(cases, name):
return f1([(c["nouls"][name][0], c["nouls"][name][1] >= 0.5) for c in cases])
def cat_f1(cases, cats):
return sum(noul_f1(cases, c) for c in cats) / len(cats)
def interval(cases, metric):
groups = {}
for c in cases:
groups.setdefault(c["family"], []).append(c)
keys = sorted(groups)
rng = random.Random(0)
values = sorted(metric([c for _ in keys for c in groups[rng.choice(keys)]]) for _ in range(RESAMPLES))
return [round(values[int(0.025 * RESAMPLES)], 3), round(values[int(0.975 * RESAMPLES) - 1], 3)]
def main():
ap = argparse.ArgumentParser()
ap.add_argument("answers")
ap.add_argument("--split", default="test", choices=("validation", "test"))
ap.add_argument("--val", help="validation answers, for the 1%% false-positive cutoff")
ap.add_argument("--json")
args = ap.parse_args()
cases, cats = load(args.split, args.answers)
result = {
"cases": len(cases), "families": len({c["family"] for c in cases}),
"level_macro_f1": round(level_f1(cases), 3), "level_interval": interval(cases, level_f1),
"level_accuracy": round(sum(c["level"] == c["level_pred"] for c in cases) / len(cases), 3),
"f1_by_level": {lv: round(f1([(c["level"] == k, c["level_pred"] == k) for c in cases]), 3) for k, lv in enumerate(LEVELS)},
"category_macro_f1": round(cat_f1(cases, cats), 3),
"category_interval": interval(cases, lambda cs: cat_f1(cs, cats)),
"f1_by_category": {c: round(noul_f1(cases, c), 3) for c in cats},
**{f"{n}_f1": round(noul_f1(cases, n), 3) for n in FLAGS},
}
if args.val:
val, _ = load("validation", args.val)
safe = sorted(c["p_risky_level"] for c in val if c["level"] == 0)
cut = safe[min(len(safe) - 1, int(0.99 * len(safe)))]
severe = [c for c in cases if c["level"] >= 3]
result["cutoff_at_1pct_validation_fpr"] = round(cut, 4)
result["test_safe_fpr"] = round(sum(c["p_risky_level"] > cut for c in cases if c["level"] == 0)
/ max(1, sum(c["level"] == 0 for c in cases)), 4)
result["recall_high_critical"] = round(sum(c["p_risky_level"] > cut for c in severe) / max(1, len(severe)), 3)
print(json.dumps(result, indent=1))
if args.json:
Path(args.json).write_text(json.dumps(result, indent=1) + "\n")
if __name__ == "__main__":
main()
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