File size: 8,248 Bytes
00f7555 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 | from __future__ import annotations
import json, math, time
from pathlib import Path
import numpy as np, pandas as pd
from numba import njit,set_num_threads
ROOT=Path('/mnt/data'); WORK=ROOT/'msmarco_scale_work'; C=WORK/'finish_train_cache'; P=2000; NQ=5000
meta=np.load(C/'meta.npz',allow_pickle=False); qids=meta['qids'].astype(str); fold_id=meta['fold_id']; nvalid=meta['nvalid'].astype(np.int32); qlen=meta['qlen'].astype(np.int32)
docs=np.memmap(C/'docs.u32',np.uint32,'r',shape=(NQ,P)); tail=np.memmap(C/'tail.f32',np.float32,'r',shape=(NQ,P)); lex2=np.memmap(C/'lex2raw.f32',np.float32,'r',shape=(NQ,P)); sem=np.memmap(C/'sem.f32',np.float32,'r',shape=(NQ,P)); cnt=np.memmap(C/'cnt.u8',np.uint8,'r',shape=(NQ,P)); rm=np.memmap(C/'raremask.u8',np.uint8,'r',shape=(NQ,P))
IDX=WORK/'full_index_uniform1m'; N=8841823; dl=np.memmap(IDX/'doc_lengths.u16',np.uint16,'r',shape=(N,)); avgdl=float(json.load(open(IDX/'meta.json'))['avg_doc_length'])
# relevance padded (TRAIN is binary, max 7 positives/query)
df=pd.read_csv(ROOT/'train.tsv',sep='\t'); wanted={q:i for i,q in enumerate(qids)}; rel_lists=[[] for _ in range(NQ)]
for q,d,s in zip(df['query-id'].astype(str),df['corpus-id'],df['score']):
i=wanted.get(q)
if i is not None and float(s)>0: rel_lists[i].append(int(d))
maxr=max(map(len,rel_lists)); rel=np.full((NQ,maxr),np.uint32(0xffffffff),np.uint32); nr=np.zeros(NQ,np.int32)
for i,r in enumerate(rel_lists): nr[i]=len(r); rel[i,:len(r)]=r
set_num_threads(5)
@njit(cache=False)
def popcnt5(x):
c=0
for b in range(5): c += (x>>b)&1
return c
@njit(cache=False)
def eval_params(docs,tail,lex2,sem,cnt,rm,nvalid,qlen,dl,avgdl,rel,nr,fold_id,b,alpha,wlex,wsem,rarek,wrare):
# sums: ndcg,mrr,p10,r10,r100,hit10,hit100,count per fold
sums=np.zeros((5,8),np.float64)
for qi in range(docs.shape[0]):
n=int(nvalid[qi]); f=int(fold_id[qi]); ql=max(1,int(qlen[qi])); nrel=max(1,int(nr[qi]))
if n<=0:
sums[f,7]+=1.0
continue
# component means
mt=0.; ml=0.; ms=0.; mr=0.
# scratch raw lexical and rare values
lv=np.empty(n,np.float32); rv=np.empty(n,np.float32)
masklim=(1<<rarek)-1 if rarek>0 else 0
denomrare=max(1,min(rarek,ql)) if rarek>0 else 1
for j in range(n):
d=int(docs[qi,j]); den=(1.0-b)+b*(float(dl[d])/avgdl)
if den<=0: den=1.0
cov=float(cnt[qi,j])/ql
if cov<1e-6: cov=1e-6
l=float(lex2[qi,j])/den*(cov**alpha)
if rarek>0:
rr=popcnt5(int(rm[qi,j]) & masklim)/denomrare
else: rr=0.0
lv[j]=l; rv[j]=rr; mt+=float(tail[qi,j]); ml+=l; ms+=float(sem[qi,j]); mr+=rr
mt/=n; ml/=n; ms/=n; mr/=n
vt=0.; vl=0.; vs=0.; vr=0.
for j in range(n):
x=float(tail[qi,j])-mt; vt+=x*x
x=float(lv[j])-ml; vl+=x*x
x=float(sem[qi,j])-ms; vs+=x*x
x=float(rv[j])-mr; vr+=x*x
st=math.sqrt(vt/n)+1e-8; sl=math.sqrt(vl/n)+1e-8; ss=math.sqrt(vs/n)+1e-8; sr=math.sqrt(vr/n)+1e-8
sc=np.empty(n,np.float32)
for j in range(n):
zt=(float(tail[qi,j])-mt)/st; zl=(float(lv[j])-ml)/sl; zs=(float(sem[qi,j])-ms)/ss
zr=0.0 if rarek<=0 or sr<1e-7 else (float(rv[j])-mr)/sr
sc[j]=zt+wlex*zl+wsem*zs+wrare*zr
order=np.argsort(sc)[::-1]
h10=0; h100=0; rrmetric=0.; dc=0.
for rnk in range(min(100,n)):
d=int(docs[qi,order[rnk]]); hit=False
for k in range(int(nr[qi])):
if d==int(rel[qi,k]): hit=True; break
if hit:
h100+=1
if rnk<10:
h10+=1; dc += 1.0/math.log2(rnk+2.0)
if rrmetric==0.: rrmetric=1.0/(rnk+1.0)
ideal=0.
for rnk in range(min(10,int(nr[qi]))): ideal += 1.0/math.log2(rnk+2.0)
ndcg=dc/ideal if ideal>0 else 0.
sums[f,0]+=ndcg; sums[f,1]+=rrmetric; sums[f,2]+=h10/10.; sums[f,3]+=h10/nrel; sums[f,4]+=h100/nrel; sums[f,5]+=1.0 if h10>0 else 0.; sums[f,6]+=1.0 if h100>0 else 0.; sums[f,7]+=1.
return sums
def metrics(s):
out=[]
for f in range(5):
n=s[f,7]; out.append({'fold':f,'nDCG@10':float(s[f,0]/n),'MRR@10':float(s[f,1]/n),'P@10':float(s[f,2]/n),'R@10':float(s[f,3]/n),'R@100':float(s[f,4]/n),'Hit@10':float(s[f,5]/n),'Hit@100':float(s[f,6]/n)})
return out
def run_grid(name,params,baseline_key=None):
rows=[]; start=time.time()
for z,p in enumerate(params):
s=eval_params(docs,tail,lex2,sem,cnt,rm,nvalid,qlen,dl,avgdl,rel,nr,fold_id,*p['args']); fm=metrics(s); nd=np.array([x['nDCG@10'] for x in fm]); mr=np.array([x['MRR@10'] for x in fm]); r100=np.array([x['R@100'] for x in fm]); row={**p['meta'],'fold_metrics':fm,'mean_nDCG@10':float(nd.mean()),'mean_MRR@10':float(mr.mean()),'mean_R@100':float(r100.mean())}; rows.append(row); print(name,z+1,'/',len(params),p['meta'],'mean',row['mean_nDCG@10'],flush=True)
# deltas vs designated current baseline
if baseline_key is not None:
base=next(r for r in rows if all(r.get(k)==v for k,v in baseline_key.items())); bnd=np.array([x['nDCG@10'] for x in base['fold_metrics']])
for r in rows:
d=np.array([x['nDCG@10'] for x in r['fold_metrics']])-bnd; r['mean_delta_vs_baseline']=float(d.mean()); r['min_delta_vs_baseline']=float(d.min()); r['positive_folds_vs_baseline']=int((d>0).sum()); r['fold_deltas_vs_baseline']=d.tolist()
path=WORK/f'finish_{name}.json'; json.dump({'name':name,'rows':rows,'seconds':time.time()-start},open(path,'w'),indent=2); print('SAVED',path,flush=True)
return rows
# JIT
_=eval_params(docs[:1],tail[:1],lex2[:1],sem[:1],cnt[:1],rm[:1],nvalid[:1],qlen[:1],dl,avgdl,rel[:1],nr[:1],fold_id[:1],.1,.25,4.,.3,3,1.)
# A: length/coordination robustness, current rest fixed.
A=[]
for bb in [0.0,0.05,0.1,0.15,0.2]:
for aa in [0.0,0.125,0.25,0.375,0.5]: A.append({'args':(bb,aa,4.,.3,3,1.),'meta':{'b':bb,'alpha':aa}})
ra=run_grid('length_coord_multifold',A,{'b':0.1,'alpha':0.25})
# choose by all-fold robustness first; otherwise max mean.
cand=[r for r in ra if r.get('positive_folds_vs_baseline',0)==5 and r.get('min_delta_vs_baseline',-1)>0]
if cand: besta=max(cand,key=lambda r:(r['mean_nDCG@10'],r['min_delta_vs_baseline']))
else: besta=max(ra,key=lambda r:r['mean_nDCG@10'])
bb=float(besta['b']); aa=float(besta['alpha']); print('SELECT_A',bb,aa,besta['mean_nDCG@10'],flush=True)
# B: final weights around current.
B=[]
for wl in [3.,4.,5.,6.]:
for ws in [0.,0.1,0.2,0.3,0.4,0.5]: B.append({'args':(bb,aa,wl,ws,3,1.),'meta':{'wlex':wl,'wsem':ws}})
rb=run_grid('final_weights_multifold',B,{'wlex':4.0,'wsem':0.3})
cand=[r for r in rb if r.get('positive_folds_vs_baseline',0)==5 and r.get('min_delta_vs_baseline',-1)>0]
if cand: bestb=max(cand,key=lambda r:(r['mean_nDCG@10'],r['min_delta_vs_baseline']))
else: bestb=max(rb,key=lambda r:r['mean_nDCG@10'])
wl=float(bestb['wlex']); ws=float(bestb['wsem']); print('SELECT_B',wl,ws,bestb['mean_nDCG@10'],flush=True)
# C: rare conjunction depth/weight, include current.
C=[]
for k in [2,3,4,5]:
for wr in [0.5,0.75,1.0,1.25,1.5]: C.append({'args':(bb,aa,wl,ws,k,wr),'meta':{'rarek':k,'wrare':wr}})
rc=run_grid('rare_final_multifold',C,{'rarek':3,'wrare':1.0})
cand=[r for r in rc if r.get('positive_folds_vs_baseline',0)==5 and r.get('min_delta_vs_baseline',-1)>0]
if cand: bestc=max(cand,key=lambda r:(r['mean_nDCG@10'],r['min_delta_vs_baseline']))
else: bestc=max(rc,key=lambda r:r['mean_nDCG@10'])
k=int(bestc['rarek']); wr=float(bestc['wrare']); print('SELECT_C',k,wr,bestc['mean_nDCG@10'],flush=True)
final={'protocol':'finish remaining TRAIN-only development on five fixed disjoint 1000-query folds; no DEV/test selection','selected':{'final_length_b':bb,'coordination_alpha':aa,'lambda_lex':wl,'lambda_sem':ws,'rare_topk':k,'rare_weight':wr,'final_idf_power':2.0,'preselection_eta':1.0,'preselection_idf_power':1.0,'P':2000,'gamma_tail':0.25,'lambda_M':0.125,'h':0,'S':16},'stageA_selected':besta,'stageB_selected':bestb,'stageC_selected':bestc}
json.dump(final,open(WORK/'finish_train_selected.json','w'),indent=2); print('FINAL_SELECTED',json.dumps(final['selected'],indent=2),flush=True)
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