from __future__ import annotations import sys,time,json import numpy as np,pandas as pd sys.path.insert(0,'/mnt/data') import msmarco_best_tail_core as b import msmarco_full_search_uniform1m as m ROOT=m.ROOT; WORK=m.WORK; idx=b.idx LLEX=np.float32(4.0); LSEM=np.float32(0.1) def rank(p,k=100): if p is None:return [] docs=p['cand_docs'][:b.P]; ts=p['cand_tail'][:b.P]; lx=p['lex'][:b.P]; sm=p['sem'][:b.P] fin=m.zscore(ts)+LLEX*m.zscore(lx)+LSEM*m.zscore(sm); oo=np.argsort(fin)[::-1][:k] return [int(x) for x in docs[oo]] df=pd.read_csv(ROOT/'dev.tsv',sep='\t',usecols=['query-id']); ids=[str(x) for x in np.unique(df['query-id'].to_numpy())]; del df texts=m.load_query_texts(ids); qrels=m.qrels_from_tsv(ROOT/'dev.tsv',ids,positive_only=True) _=b.prepare(texts[ids[0]]) run={}; times=[]; routehit=0; poolhit=0; den=0; cands=[] for z,qid in enumerate(ids): t=time.perf_counter(); p=b.prepare(texts[qid]); run[qid]=rank(p); times.append((time.perf_counter()-t)*1000) rels=[int(d) for d,r in qrels[qid].items() if r>0]; den+=len(rels) if p: cands.append(p['candidate_docs']); ud=p['ud']; pool=p['cand_docs'][:b.P] for d in rels: kk=np.searchsorted(ud,d); routehit+=int(kk