| """One-off cross-check of mcr's self-implemented metrics against pytrec_eval. |
| |
| Run once to gain confidence, then we never depend on pytrec_eval again: |
| |
| pip install pytrec_eval |
| PYTHONPATH=src python scripts/verify_metrics.py |
| |
| Generates random runs/qrels and asserts nDCG@k / MAP@k / Recall@k / P@k agree |
| with pytrec_eval within a tolerance. |
| """ |
|
|
| from __future__ import annotations |
|
|
| import random |
| import sys |
|
|
| from mcr.eval.metrics import compute_metrics |
|
|
| K_VALUES = [1, 5, 10, 20, 100] |
| TOL = 1e-6 |
|
|
|
|
| def random_case(n_queries=50, n_docs=200, seed=0): |
| rng = random.Random(seed) |
| qrels, run = {}, {} |
| doc_ids = [f"d{i}" for i in range(n_docs)] |
| for q in range(n_queries): |
| qid = f"q{q}" |
| n_rel = rng.randint(1, 5) |
| rels = rng.sample(doc_ids, n_rel) |
| qrels[qid] = {d: 1 for d in rels} |
| |
| retrieved = rng.sample(doc_ids, rng.randint(50, n_docs)) |
| run[qid] = {d: rng.random() for d in retrieved} |
| return run, qrels |
|
|
|
|
| def pytrec_metrics(run, qrels, k_values): |
| import pytrec_eval |
|
|
| measures = set() |
| for k in k_values: |
| measures |= {f"ndcg_cut_{k}", f"map_cut_{k}", f"recall_{k}", f"P_{k}"} |
| evaluator = pytrec_eval.RelevanceEvaluator(qrels, measures) |
| per_q = evaluator.evaluate(run) |
| agg = {} |
| for k in k_values: |
| for src, dst in [ |
| (f"ndcg_cut_{k}", f"ndcg@{k}"), |
| (f"map_cut_{k}", f"map@{k}"), |
| (f"recall_{k}", f"recall@{k}"), |
| (f"P_{k}", f"precision@{k}"), |
| ]: |
| agg[dst] = sum(v[src] for v in per_q.values()) / len(per_q) |
| return agg |
|
|
|
|
| def main() -> int: |
| try: |
| import pytrec_eval |
| except ImportError: |
| print("pytrec_eval not installed: pip install pytrec_eval", file=sys.stderr) |
| return 2 |
|
|
| ok = True |
| for seed in range(5): |
| run, qrels = random_case(seed=seed) |
| mine = compute_metrics(run, qrels, K_VALUES) |
| theirs = pytrec_metrics(run, qrels, K_VALUES) |
| for key, ref in theirs.items(): |
| diff = abs(mine[key] - ref) |
| if diff > TOL: |
| ok = False |
| print(f"[seed {seed}] MISMATCH {key}: mine={mine[key]:.6f} pytrec={ref:.6f} (Δ={diff:.2e})") |
| print("ALL METRICS MATCH ✓" if ok else "MISMATCHES FOUND ✗") |
| return 0 if ok else 1 |
|
|
|
|
| if __name__ == "__main__": |
| raise SystemExit(main()) |
|
|
