| |
| """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() |
|
|
