File size: 3,608 Bytes
d992912 | 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 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 | import logging
import time
from dataclasses import dataclass
from typing import Dict, List, Set
import numpy as np
from tqdm.auto import tqdm
from backend.app.engine.search_engine import ASOSSearchEngine
logger = logging.getLogger("asos_search")
__all__ = ["EvalResult", "SearchEvaluator"]
@dataclass
class EvalResult:
query: str
recall_at_k: Dict[int, float]
precision_at_k: Dict[int, float]
mrr: float
latency_ms: float
class SearchEvaluator:
def __init__(self, engine: ASOSSearchEngine):
self.engine = engine
def evaluate_single(
self, query: str, relevant_skus: Set[str], k_values: List[int] = [5, 10, 20]
) -> EvalResult:
max_k = max(k_values)
t0 = time.time()
results = self.engine.search(query, top_n=max_k)
latency = (time.time() - t0) * 1000
retrieved = results["sku"].astype(str).tolist()
relevant = set(str(s) for s in relevant_skus)
recall_at, precision_at = {}, {}
for k in k_values:
top_k = retrieved[:k]
found = len(set(top_k) & relevant)
recall_at[k] = found / len(relevant) if relevant else 0.0
precision_at[k] = found / k if k > 0 else 0.0
mrr = 0.0
for rank, sku in enumerate(retrieved, 1):
if sku in relevant:
mrr = 1.0 / rank
break
return EvalResult(
query=query,
recall_at_k=recall_at,
precision_at_k=precision_at,
mrr=mrr,
latency_ms=latency,
)
def evaluate(
self, test_queries: List[Dict], k_values: List[int] = [5, 10, 20]
) -> Dict:
results = []
for tq in tqdm(test_queries, desc="Evaluating"):
try:
res = self.evaluate_single(
tq["query"],
set(str(s) for s in tq["relevant_skus"]),
k_values,
)
results.append(res)
except Exception as e:
logger.warning(f"Eval failed for '{tq['query']}': {e}")
if not results:
return {"error": "No successful evaluations"}
agg = {
"n_queries": len(results),
"avg_latency_ms": float(np.mean([r.latency_ms for r in results])),
"median_latency_ms": float(np.median([r.latency_ms for r in results])),
"mean_mrr": float(np.mean([r.mrr for r in results])),
}
for k in k_values:
agg[f"mean_recall@{k}"] = float(
np.mean([r.recall_at_k.get(k, 0) for r in results])
)
agg[f"mean_precision@{k}"] = float(
np.mean([r.precision_at_k.get(k, 0) for r in results])
)
return {"aggregate": agg, "per_query": [
{"query": r.query, "mrr": r.mrr, "latency_ms": r.latency_ms,
"recall_at_k": r.recall_at_k, "precision_at_k": r.precision_at_k}
for r in results
]}
@staticmethod
def print_report(report: Dict):
agg = report.get("aggregate", {})
print("\n" + "=" * 65)
print(" SEARCH ENGINE EVALUATION REPORT")
print("=" * 65)
print(f" Queries evaluated: {agg.get('n_queries', 0)}")
print(f" Avg latency: {agg.get('avg_latency_ms', 0):.1f} ms")
print(f" Mean MRR: {agg.get('mean_mrr', 0):.4f}")
for key, val in sorted(agg.items()):
if "recall" in key or "precision" in key:
print(f" {key:25s} {val:.4f}")
print("=" * 65)
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