| from __future__ import annotations |
|
|
| import math |
| from typing import Any |
|
|
|
|
| def query_metrics(candidates: list[dict[str, Any]], gold_ids: set[str] | None, cutoffs: tuple[int, ...] = (5, 10, 30, 100)) -> dict[str, Any]: |
| if gold_ids is None: |
| return { |
| "has_gold": None, |
| **{f"gold_at_{k}": None for k in cutoffs}, |
| "mrr": None, |
| "ndcg_at_10": None, |
| } |
| has_gold = bool(gold_ids) |
| ranks = [idx + 1 for idx, row in enumerate(candidates) if row["candidate_id"] in gold_ids] |
| first_rank = min(ranks) if ranks else None |
| metrics = {"has_gold": has_gold} |
| for cutoff in cutoffs: |
| metrics[f"gold_at_{cutoff}"] = bool(first_rank is not None and first_rank <= cutoff) |
| metrics["mrr"] = 1.0 / first_rank if first_rank else 0.0 |
| dcg = 0.0 |
| for idx, row in enumerate(candidates[:10], start=1): |
| if row["candidate_id"] in gold_ids: |
| dcg += 1.0 / math.log2(idx + 1) |
| ideal_hits = min(len(gold_ids), 10) |
| idcg = sum(1.0 / math.log2(idx + 1) for idx in range(1, ideal_hits + 1)) |
| metrics["ndcg_at_10"] = dcg / idcg if idcg > 0 else 0.0 |
| return metrics |
|
|
|
|
| def aggregate_metrics(dataset: str, split: str, method: str, rows: list[dict[str, Any]], notes: str) -> dict[str, Any]: |
| evaluable = [row for row in rows if row["metrics"].get("has_gold") is not None] |
| if not evaluable: |
| return { |
| "Dataset": dataset, |
| "Split": split, |
| "Method": method, |
| "R@5": "N/A", |
| "R@10": "N/A", |
| "R@30": "N/A", |
| "R@100": "N/A", |
| "MRR": "N/A", |
| "nDCG@10": "N/A", |
| "candidate_count_avg": round(sum(len(row["candidates"]) for row in rows) / max(1, len(rows)), 4), |
| "gold_coverage": "N/A", |
| "Notes": notes, |
| } |
| with_gold = [row for row in evaluable if row["metrics"].get("has_gold")] |
| denom = max(1, len(with_gold)) |
|
|
| def avg_bool(key: str) -> float: |
| return sum(1 for row in with_gold if row["metrics"].get(key)) / denom |
|
|
| return { |
| "Dataset": dataset, |
| "Split": split, |
| "Method": method, |
| "R@5": round(avg_bool("gold_at_5"), 6), |
| "R@10": round(avg_bool("gold_at_10"), 6), |
| "R@30": round(avg_bool("gold_at_30"), 6), |
| "R@100": round(avg_bool("gold_at_100"), 6), |
| "MRR": round(sum(float(row["metrics"].get("mrr", 0.0)) for row in with_gold) / denom, 6), |
| "nDCG@10": round(sum(float(row["metrics"].get("ndcg_at_10", 0.0)) for row in with_gold) / denom, 6), |
| "candidate_count_avg": round(sum(len(row["candidates"]) for row in rows) / max(1, len(rows)), 4), |
| "gold_coverage": round(len(with_gold) / max(1, len(evaluable)), 6), |
| "Notes": notes, |
| } |
|
|
