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4b8d886 | 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 | #!/usr/bin/env python3
"""Evaluate MDU-RiskBench participant-level prediction JSONL."""
from __future__ import annotations
import argparse
import gzip
import json
import math
from collections import Counter
from pathlib import Path
ORDER = {
"no_observed_risk": 0,
"mild_risk": 1,
"moderate_risk": 2,
"high_risk": 3,
}
ABSTAIN = "insufficient_evidence"
def read_jsonl(path: Path):
opener = gzip.open if path.suffix == ".gz" else open
with opener(path, "rt", encoding="utf-8") as handle:
for line in handle:
if line.strip():
yield json.loads(line)
def main() -> None:
parser = argparse.ArgumentParser()
parser.add_argument("--gold", type=Path, required=True)
parser.add_argument("--predictions", type=Path, required=True)
parser.add_argument("--output", type=Path)
args = parser.parse_args()
gold = {
row["sample_id"]: row["target_label"]["risk_level"]
for row in read_jsonl(args.gold)
}
pred = {row["sample_id"]: row["risk_level"] for row in read_jsonl(args.predictions)}
unknown = sorted(set(pred) - set(gold))
if unknown:
raise SystemExit(f"unknown prediction IDs: {unknown[:5]}")
labels = list(ORDER)
total = len(gold)
covered = [(gold[sid], pred.get(sid)) for sid in gold if pred.get(sid) in ORDER]
abstained = total - len(covered)
exact = sum(actual == predicted for actual, predicted in covered)
distances = [abs(ORDER[actual] - ORDER[predicted]) for actual, predicted in covered]
squared = [value * value for value in distances]
f1_values = []
for label in labels:
tp = sum(actual == label and predicted == label for actual, predicted in covered)
fp = sum(actual != label and predicted == label for actual, predicted in covered)
fn = sum(actual == label and predicted != label for actual, predicted in covered)
precision = tp / (tp + fp) if tp + fp else 0.0
recall = tp / (tp + fn) if tp + fn else 0.0
f1_values.append(
2 * precision * recall / (precision + recall) if precision + recall else 0.0
)
n = len(covered)
report = {
"gold_records": total,
"prediction_records": len(pred),
"covered_records": n,
"abstained_or_missing_records": abstained,
"coverage": n / total if total else 0.0,
"selective_accuracy": exact / n if n else None,
"selective_macro_f1": sum(f1_values) / len(f1_values) if n else None,
"ordinal_mae": sum(distances) / n if n else None,
"ordinal_rmse": math.sqrt(sum(squared) / n) if n else None,
"prediction_distribution": dict(sorted(Counter(pred.values()).items())),
}
rendered = json.dumps(report, indent=2) + "\n"
if args.output:
args.output.write_text(rendered, encoding="utf-8")
print(rendered, end="")
if __name__ == "__main__":
main()
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