| """Score parsing and aggregation. |
| |
| The headline metric is IF_Score: ``product(hard_pass) x mean(soft_scores)``. A |
| single failed hard constraint zeroes the instance; soft constraints average into |
| a 0-1 multiplier. Instances with no soft constraints use a multiplier of 1.0, so |
| they score 1.0 or 0.0. |
| """ |
|
|
| from __future__ import annotations |
|
|
| import json |
| import re |
| from collections import Counter, defaultdict |
| from typing import Any |
|
|
| SCORES = (0.0, 0.5, 1.0) |
|
|
|
|
| def parse_quality_score(response: str) -> float | None: |
| """Parse a 0 / 0.5 / 1 quality verdict. Returns None if unparseable.""" |
| text = (response or "").strip() |
| if text.startswith("[") and text.endswith("]"): |
| text = text[1:-1].strip() |
| if not re.fullmatch(r"(?:0(?:\.0+)?|0\.50*|1(?:\.0+)?)", text): |
| return None |
| return float(text) |
|
|
|
|
| def parse_soft_constraint_scores( |
| response: str, constraint_ids: list[str] |
| ) -> dict[str, dict[str, Any]]: |
| """Parse the soft-constraint Judge's JSON verdict, keyed by constraint id.""" |
| unscored = {cid: {"score": None} for cid in constraint_ids} |
| if not response: |
| return unscored |
|
|
| data = None |
| try: |
| data = json.loads(response.strip()) |
| except json.JSONDecodeError: |
| |
| match = re.search(r"\{[\s\S]*\}", response) |
| if match: |
| try: |
| data = json.loads(match.group(0)) |
| except json.JSONDecodeError: |
| pass |
|
|
| if not isinstance(data, dict): |
| return unscored |
|
|
| results: dict[str, dict[str, Any]] = {} |
| for cid in constraint_ids: |
| entry = data.get(cid) |
| if isinstance(entry, dict): |
| try: |
| score = float(entry.get("score")) |
| except (TypeError, ValueError): |
| score = None |
| if score in (0, 0.5, 1): |
| results[cid] = {"score": score, "note": entry.get("note", "")} |
| continue |
| results[cid] = {"score": None} |
| return results |
|
|
|
|
| def compute_if_score( |
| hard_results: dict[str, Any], soft_results: dict[str, Any] |
| ) -> float: |
| hard_pass = 1.0 |
| for result in hard_results.values(): |
| if not result.get("is_valid", True): |
| hard_pass = 0.0 |
| break |
| soft_values = [r["score"] for r in soft_results.values() if r.get("score") is not None] |
| soft_mean = sum(soft_values) / len(soft_values) if soft_values else 1.0 |
| return hard_pass * soft_mean |
|
|
|
|
| def _score_key(score: float) -> str: |
| return "0" if score == 0 else "0.5" if score == 0.5 else "1" |
|
|
|
|
| def aggregate_group(rows: list[dict[str, Any]]) -> dict[str, Any]: |
| total = len(rows) |
| covered = sum(row["prediction_status"] == "present" for row in rows) |
| if_scores = [float(row["if_score"]) for row in rows] |
| quality = [row["quality_score"] for row in rows if row["quality_score"] is not None] |
| return { |
| "total": total, |
| "prediction_coverage": covered, |
| "prediction_coverage_rate": covered / total if total else 0.0, |
| "if_score": sum(if_scores) / total if total else 0.0, |
| "translation_quality": sum(quality) / len(quality) if quality else None, |
| "quality_scored": len(quality), |
| "quality_distribution": { |
| _score_key(s): sum(1 for q in quality if q == s) for s in SCORES |
| }, |
| } |
|
|
|
|
| def aggregate_constraints(rows: list[dict[str, Any]]) -> dict[str, Any]: |
| """Per-constraint pass rate (hard) or mean score (soft).""" |
| hard: dict[str, Counter] = defaultdict(Counter) |
| soft: dict[str, list[float]] = defaultdict(list) |
|
|
| for row in rows: |
| for cid, result in row["hard_constraint_results"].items(): |
| hard[cid]["total"] += 1 |
| if result.get("is_valid", True): |
| hard[cid]["pass"] += 1 |
| for cid, result in row["soft_constraint_results"].items(): |
| if result.get("score") is not None: |
| soft[cid].append(float(result["score"])) |
|
|
| out: dict[str, Any] = {} |
| for cid, counts in sorted(hard.items()): |
| total = counts["total"] |
| out[cid] = { |
| "type": "hard", |
| "total": total, |
| "pass": counts["pass"], |
| "pass_rate": counts["pass"] / total if total else None, |
| } |
| for cid, scores in sorted(soft.items()): |
| out[cid] = { |
| "type": "soft", |
| "total": len(scores), |
| "mean_score": sum(scores) / len(scores) if scores else None, |
| "score_distribution": { |
| _score_key(s): sum(1 for v in scores if v == s) for s in SCORES |
| }, |
| } |
| return out |
|
|
|
|
| def compute_summary(rows: list[dict[str, Any]]) -> dict[str, Any]: |
| summary = aggregate_group(rows) |
| summary["by_constraint"] = aggregate_constraints(rows) |
|
|
| grouped: dict[str, dict[str, list[dict[str, Any]]]] = { |
| "scenario": defaultdict(list), |
| "domain": defaultdict(list), |
| "language_pair": defaultdict(list), |
| } |
| for row in rows: |
| grouped["scenario"][row["scenario"]].append(row) |
| grouped["domain"][row["domain"]].append(row) |
| grouped["language_pair"][f"{row['source_lang']}-{row['target_lang']}"].append(row) |
|
|
| summary["breakdowns"] = { |
| dimension: {key: aggregate_group(group) for key, group in sorted(groups.items())} |
| for dimension, groups in grouped.items() |
| } |
| return summary |
|
|
