| from __future__ import annotations |
| import sys,time,json |
| from pathlib import Path |
| import numpy as np |
| from numba import njit,prange,set_num_threads |
| sys.path.insert(0,'/mnt/data') |
| import msmarco_full_search_uniform1m as m |
| import msmarco_best_tail_core as b |
| ROOT=m.ROOT; WORK=m.WORK; OUT=WORK/'structural_fusion'; OUT.mkdir(exist_ok=True); idx=b.idx; set_num_threads(5); M=m.M; S=m.S |
| z=np.load(WORK/'amplitude_diag'/'fixed_eta1_pools.npz',allow_pickle=False); qids=[str(x) for x in z['qids'].tolist()]; valid=z['valid'].astype(np.int32); docs=z['docs']; texts=m.load_query_texts(qids) |
| @njit(cache=False) |
| def find_doc(pd,a,bb,d): |
| lo=np.int64(a); hi=np.int64(bb) |
| while lo<hi: |
| md=(lo+hi)//2; x=int(pd[md]) |
| if x<d: lo=md+1 |
| else: hi=md |
| if lo<bb and int(pd[lo])==d:return lo |
| return -1 |
| @njit(parallel=True,cache=False) |
| def pool_features(dd,rterms,rd,offs,pd,pm,pr,ps,qd,ct,cv,rp,ri,rv): |
| n=len(dd); geom=np.zeros(n,np.float32); cons=np.zeros(n,np.float32); bc=np.zeros(n,np.float32); gabs=np.zeros(n,np.float32); gmax=np.zeros(n,np.float32); cmax=np.zeros(n,np.float32); pos=np.zeros(n,np.float32); neg=np.zeros(n,np.float32) |
| for zz in prange(n): |
| d=int(dd[zz]); gs=0.; cs=0.; cnt=0.; ab=0.; mx=-1e30; cm=0.; pp=0.; nn=0. |
| for jj in range(len(rterms)): |
| j=int(rterms[jj]); a=int(offs[j]); bb=int(offs[j+1]); p=find_doc(pd,a,bb,d) |
| if p<0: continue |
| cnt+=1.; c=float(pm[p])*float(rd[j]); local=0.; sig=0.; bits=int(ps[p]) |
| for r in range(S): |
| t=int(pr[p,r]) |
| if t==65535: continue |
| qv=float(qd[t]); cen=float(m.lookup_center(ct[j],cv[j],t)); rel=float(m.lookup_rel(rp,ri,rv,j,t)); sgn=1. if ((bits>>r)&1) else -1. |
| local += rel*(qv-cen)*sgn; sig += qv*qv |
| g=c*local*(sig**0.25 if sig>0 else 0.) |
| gs+=g; cs+=c; ab+=abs(g); mx=max(mx,g); cm=max(cm,c); pp+=1. if g>0 else 0.; nn+=1. if g<0 else 0. |
| geom[zz]=gs; cons[zz]=cs; bc[zz]=cnt; gabs[zz]=ab; gmax[zz]=0 if mx<-1e20 else mx; cmax[zz]=cm; pos[zz]=pp; neg[zz]=nn |
| return geom,cons,bc,gabs,gmax,cmax,pos,neg |
| |
| q=idx.query_vec(texts[qids[0]]); qd=np.zeros(M,np.float32); qd[q.indices]=q.data; rt,rd=idx.route(q); _=pool_features(docs[0,:1],rt,rd,idx.offs,idx.pd,idx.pm,idx.pr,idx.ps,qd,idx.ct,idx.cv,idx.rp,idx.ri,idx.rv) |
| shape=docs.shape; names=['geom','cons','branch_count','geom_abs','geom_max','cons_max','pos_count','neg_count']; arr={n:np.zeros(shape,np.float32) for n in names}; times=[]; errs=[] |
| for i,qid in enumerate(qids): |
| k=int(valid[i]); |
| if not k:continue |
| q=idx.query_vec(texts[qid]); qd=np.zeros(M,np.float32); qd[q.indices]=q.data; rt,rd=idx.route(q); t=time.perf_counter(); vals=pool_features(docs[i,:k],rt,rd,idx.offs,idx.pd,idx.pm,idx.pr,idx.ps,qd,idx.ct,idx.cv,idx.rp,idx.ri,idx.rv); times.append((time.perf_counter()-t)*1000) |
| for nm,v in zip(names,vals): arr[nm][i,:k]=v |
| |
| recon=vals[0]+.125*vals[1]; errs.append(float(np.max(np.abs(recon-z['tail'][i,:k])))) |
| if (i+1)%200==0:print(i+1,'median_ms',float(np.median(times)),'max_tail_err',max(errs),flush=True) |
| np.savez_compressed(OUT/'branch_features.npz',qids=np.asarray(qids),valid=valid,**arr) |
| meta={'protocol':'fixed eta=1 P=2000 validation pools; branch-level features recovered from current sorted branch postings only','features':names,'median_ms':float(np.median(times)),'p95_ms':float(np.percentile(times,95)),'max_tail_reconstruction_error':max(errs)}; json.dump(meta,open(OUT/'branch_feature_meta.json','w'),indent=2); print('DONE',meta,flush=True) |
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