| """ |
| pusula_eval.py — Bilgi erisimi metrikleri ve dayaniklilik karsilastirmasi. |
| |
| Metrikler pytrec_eval (BEIR/MTEB'in kullandigi) ile birebir dogrulandi: |
| nDCG@k dogrusal kazanc (gain = ilgi skoru), Recall@k, MRR@k. |
| Bir run: {qid: {doc_id: skor}}. Skor esitliginde dokuman kimligine gore azalan siralama (trec_eval kurali). |
| """ |
| import math |
|
|
| import numpy as np |
|
|
|
|
| def _ranked(doc_scores, k): |
| return [d for d, _ in sorted(doc_scores.items(), key=lambda x: (x[1], x[0]), reverse=True)[:k]] |
|
|
|
|
| def ndcg_at_k(ranked, rels, k): |
| dcg = sum(rels.get(d, 0) / math.log2(i + 2) for i, d in enumerate(ranked[:k]) if rels.get(d, 0) > 0) |
| ideal = sorted((r for r in rels.values() if r > 0), reverse=True)[:k] |
| idcg = sum(r / math.log2(i + 2) for i, r in enumerate(ideal)) |
| return dcg / idcg if idcg > 0 else 0.0 |
|
|
|
|
| def recall_at_k(ranked, rels, k): |
| relevant = {d for d, r in rels.items() if r > 0} |
| return len(relevant & set(ranked[:k])) / len(relevant) if relevant else 0.0 |
|
|
|
|
| def mrr_at_k(ranked, rels, k): |
| for i, d in enumerate(ranked[:k]): |
| if rels.get(d, 0) > 0: |
| return 1.0 / (i + 1) |
| return 0.0 |
|
|
|
|
| METRICS = { |
| "ndcg@10": lambda r, q: ndcg_at_k(r, q, 10), |
| "recall@10": lambda r, q: recall_at_k(r, q, 10), |
| "recall@100": lambda r, q: recall_at_k(r, q, 100), |
| "mrr@10": lambda r, q: mrr_at_k(r, q, 10), |
| } |
|
|
|
|
| def evaluate(run, qrels, metrics=METRICS): |
| """Sorgu basina metrikler: {metrik: {qid: deger}}. Run'da olmayan sorgu 0 alir (sessizce dusmez).""" |
| out = {m: {} for m in metrics} |
| for qid, rels in qrels.items(): |
| ranked = _ranked(run.get(qid, {}), 100) |
| for m, fn in metrics.items(): |
| out[m][qid] = fn(ranked, rels) |
| return out |
|
|
|
|
| def paired_bootstrap(a, b, n_boot=2000, seed=0): |
| """Ayni sorgularda iki kosulun ortalama farki (b - a) ve %95 guven araligi.""" |
| a, b = np.asarray(a, float), np.asarray(b, float) |
| diff = b - a |
| if len(diff) == 0: |
| return float("nan"), (float("nan"), float("nan")) |
| rng = np.random.default_rng(seed) |
| idx = rng.integers(0, len(diff), size=(n_boot, len(diff))) |
| boots = diff[idx].mean(axis=1) |
| return float(diff.mean()), (float(np.percentile(boots, 2.5)), float(np.percentile(boots, 97.5))) |
|
|
|
|
| def robustness_row(clean_scores, pert_scores, changed_qids, metric="ndcg@10"): |
| """Bir bozulma icin: tum sorgularda ve SADECE degisen sorgularda koruma orani + fark CI.""" |
| qids = sorted(clean_scores[metric]) |
| c = np.array([clean_scores[metric][q] for q in qids]) |
| p = np.array([pert_scores[metric][q] for q in qids]) |
| mask = np.array([q in changed_qids for q in qids]) |
| d_all, ci_all = paired_bootstrap(c, p) |
| d_chg, ci_chg = paired_bootstrap(c[mask], p[mask]) |
| return { |
| "temiz": c.mean(), "bozuk": p.mean(), |
| "koruma": p.mean() / c.mean() if c.mean() > 0 else float("nan"), |
| "fark": d_all, "fark_ci": ci_all, |
| "değişen_oran": mask.mean(), |
| "koruma_değişen": p[mask].mean() / c[mask].mean() if mask.any() and c[mask].mean() > 0 else float("nan"), |
| "fark_değişen": d_chg, "fark_değişen_ci": ci_chg, |
| } |
|
|
