| """Evaluate a complete set of finite numeric predictions against rational references. |
| |
| Copyright 2026 Calxo AI. MIT license; see LICENSE. |
| """ |
| import argparse |
| import json |
| import math |
| from fractions import Fraction |
| from pathlib import Path |
|
|
| def load_lines(path): |
| return [json.loads(line) for line in Path(path).read_text(encoding="utf-8").splitlines() if line.strip()] |
|
|
| def evaluate(reference, predictions): |
| expected_by_id = {row["case_id"]: row for row in reference} |
| if len(expected_by_id) != len(reference): |
| raise ValueError("Reference data contains duplicate IDs.") |
| actual_by_id = {} |
| for row in predictions: |
| case_id, value = row["case_id"], row["prediction"] |
| if case_id in actual_by_id: |
| raise ValueError(f"Duplicate prediction: {case_id}") |
| if isinstance(value, bool) or not isinstance(value, (int, float)) or not math.isfinite(value): |
| raise ValueError(f"Prediction must be a finite JSON number: {case_id}") |
| actual_by_id[case_id] = value |
| missing = set(expected_by_id) - set(actual_by_id) |
| extra = set(actual_by_id) - set(expected_by_id) |
| if missing or extra: |
| raise ValueError(f"Prediction coverage mismatch: {len(missing)} missing, {len(extra)} unknown.") |
| passed = 0 |
| failures = [] |
| for case_id, row in expected_by_id.items(): |
| expected = float(Fraction(int(row["expected_numerator"]), int(row["expected_denominator"]))) |
| tolerance = max(row["absolute_tolerance"], row["relative_tolerance"] * abs(expected)) |
| if abs(actual_by_id[case_id] - expected) <= tolerance: |
| passed += 1 |
| else: |
| failures.append(case_id) |
| return {"total": len(reference), "predictions": len(predictions), "passed": passed, "failed": len(failures), "accuracy": passed / len(reference) if reference else None, "failed_case_ids": failures} |
|
|
| if __name__ == "__main__": |
| parser = argparse.ArgumentParser(description=__doc__) |
| parser.add_argument("predictions", type=Path) |
| parser.add_argument("--reference", type=Path, default=Path(__file__).with_name("conversion-reference.jsonl")) |
| args = parser.parse_args() |
| try: |
| report = evaluate(load_lines(args.reference), load_lines(args.predictions)) |
| except (ValueError, KeyError, TypeError, OverflowError) as error: |
| parser.error(str(error)) |
| print(json.dumps(report, indent=2)) |
|
|
