File size: 2,108 Bytes
f7b6133 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 | from __future__ import annotations
import math
import numpy as np
def _dcg(rels: list[float], exp_gain: bool = True) -> float:
total = 0.0
for rank, rel in enumerate(rels, start=1):
gain = (2.0 ** rel - 1.0) if exp_gain else rel
total += gain / math.log2(rank + 1.0)
return total
def evaluate_run(
run: dict[str, list[str]],
qrels: dict[str, dict[str, float]],
ks: tuple[int, ...] = (10, 100),
ndcg_k: int = 10,
mrr_k: int = 10,
exp_gain: bool = True,
) -> dict[str, float]:
"""Binary top-K metrics plus graded nDCG.
'Accuracy' for retrieval is reported as Hit@K rather than ordinary
classification accuracy, which is meaningless with millions of negatives.
"""
qids = [q for q in qrels if q in run]
if not qids:
raise ValueError("No query IDs overlap between run and qrels")
vals: dict[str, list[float]] = {}
for k in ks:
vals[f"P@{k}"] = []
vals[f"R@{k}"] = []
vals[f"Hit@{k}"] = []
vals[f"MRR@{mrr_k}"] = []
vals[f"nDCG@{ndcg_k}"] = []
for qid in qids:
ranked = run[qid]
qr = qrels[qid]
positive = {d for d, r in qr.items() if r > 0}
npos = max(1, len(positive))
for k in ks:
top = ranked[:k]
hits = sum(1 for d in top if d in positive)
vals[f"P@{k}"].append(hits / float(k))
vals[f"R@{k}"].append(hits / float(npos))
vals[f"Hit@{k}"].append(float(hits > 0))
rr = 0.0
for rank, d in enumerate(ranked[:mrr_k], start=1):
if d in positive:
rr = 1.0 / rank
break
vals[f"MRR@{mrr_k}"].append(rr)
observed = [float(qr.get(d, 0.0)) for d in ranked[:ndcg_k]]
ideal = sorted((float(r) for r in qr.values()), reverse=True)[:ndcg_k]
idcg = _dcg(ideal, exp_gain=exp_gain)
vals[f"nDCG@{ndcg_k}"].append(_dcg(observed, exp_gain=exp_gain) / idcg if idcg > 0 else 0.0)
out = {k: float(np.mean(v)) for k, v in vals.items()}
out["n_queries"] = float(len(qids))
return out
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