File size: 6,920 Bytes
a5b5502 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 | from __future__ import annotations
import gzip,pickle,json,re,csv,math,time,heapq,glob,os
from pathlib import Path
from collections import Counter,defaultdict
from multiprocessing import Pool, get_context
import numpy as np
ROOT=Path('/mnt/data'); WORK=ROOT/'msmarco_scale_work'; OUT=WORK/'baselines'; OUT.mkdir(exist_ok=True)
N=8_841_823; TOPK=1000
# Same token boundary as frozen system / sklearn token_pattern for query terms.
TOKEN_RE=re.compile(r'(?u)\b\w\w+\b')
# Load qids and query texts
qrels_rows=[]; qids=set()
with open(ROOT/'test.tsv',newline='') as f:
r=csv.DictReader(f,delimiter='\t')
for x in r:
q=str(x['query-id']); d=int(x['corpus-id']); rel=float(x['score']); qids.add(q); qrels_rows.append((q,d,rel))
qids=sorted(qids,key=lambda x:int(x)); qindex={q:i for i,q in enumerate(qids)}
texts={}
with open(ROOT/'queries.jsonl') as f:
for line in f:
o=json.loads(line); q=str(o['_id'])
if q in qids: texts[q]=o['text']
assert len(texts)==len(qids)==43
qtoks={q:TOKEN_RE.findall(texts[q].lower()) for q in qids}
allterms=sorted(set(t for z in qtoks.values() for t in z))
# full-corpus known df from the exact 50k vocabulary build
with gzip.open(WORK/'final_vocab_50k.pkl.gz','rb') as f:z=pickle.load(f)
terms50=z['terms'].tolist(); df50=np.asarray(z['df']); term2df={t:int(df50[i]) for i,t in enumerate(terms50)}
oov=sorted(t for t in allterms if t not in term2df)
print('queries',len(qids),'query terms',len(allterms),'oov',oov,flush=True)
# locate 36 shards in order
def shard_path(i):
hits=list(ROOT.glob(f'corpus_{i:04d}.jsonl*.gz'))
if i==35 and len(hits)==0: hits=list((ROOT/'restored').glob(f'corpus_{i:04d}.jsonl*.gz'))
assert len(hits)==1,(i,hits)
return str(hits[0])
SHARDS=[shard_path(i) for i in range(36)]
# Exact OOV df with one cheap corpus scan, only 10 rare strings.
OOV_PAT=re.compile(r'(?u)\b(?:'+'|'.join(re.escape(t) for t in sorted(oov,key=len,reverse=True))+r')\b') if oov else None
def count_oov_one(args):
sid,path=args; c=Counter(); n=0; t0=time.time()
with gzip.open(path,'rt',encoding='utf-8') as f:
for line in f:
o=json.loads(line); tx=((o.get('title') or '')+' '+(o.get('text') or '')).lower(); n+=1
if OOV_PAT:
m=set(OOV_PAT.findall(tx))
for t in m:c[t]+=1
return sid,n,dict(c),time.time()-t0
if oov:
t0=time.time();
with get_context('fork').Pool(processes=8) as p:
rr=p.map(count_oov_one,list(enumerate(SHARDS)))
oo=Counter();
for sid,n,c,sec in rr: oo.update(c)
for t in oov: term2df[t]=int(oo[t])
print('oov df',dict(oo),'sec',time.time()-t0,flush=True)
# Robertson BM25 idf; base parameters match common Anserini MS MARCO defaults.
def make_idf():
return {t:math.log(1.0+(N-term2df[t]+0.5)/(term2df[t]+0.5)) for t in allterms}
idf=make_idf()
# Query term multiplicity mapping term -> [(query index,multiplicity)]
t2q=defaultdict(list)
for q in qids:
c=Counter(qtoks[q])
for t,m in c.items(): t2q[t].append((qindex[q],m))
# compiled union matcher exact same word boundaries, avoids tokenizing irrelevant words
PAT=re.compile(r'(?u)\b(?:'+'|'.join(re.escape(t) for t in sorted(allterms,key=len,reverse=True))+r')\b')
avgdl=float(json.load(open(WORK/'full_index/meta.json'))['avg_doc_length'])
# existing exact tokenizer document lengths
DL_PATH=str(WORK/'full_index/doc_lengths.u16')
# Each shard returns top1000 per q. Global top1000 is exactly merge of shard top1000.
def score_shard(args):
sid,path,k1,b=args
dlarr=np.memmap(DL_PATH,dtype=np.uint16,mode='r',shape=(N,))
heaps=[[] for _ in qids]
n=0; matched=0; t0=time.time()
with gzip.open(path,'rt',encoding='utf-8') as f:
for line in f:
o=json.loads(line); d=int(o['_id']); tx=((o.get('title') or '')+' '+(o.get('text') or '')).lower(); n+=1
mm=PAT.findall(tx)
if not mm: continue
matched+=1; tf=Counter(mm); norm=k1*(1.0-b+b*(float(dlarr[d])/avgdl))
qs={}
for term,freq in tf.items():
w=idf[term]*((freq*(k1+1.0))/(freq+norm))
for qi,qm in t2q[term]: qs[qi]=qs.get(qi,0.0)+w*qm
for qi,sc in qs.items():
h=heaps[qi]; item=(float(sc),-d) # lower docid wins exact tie
if len(h)<TOPK: heapq.heappush(h,item)
elif item>h[0]: heapq.heapreplace(h,item)
return sid,heaps,n,matched,time.time()-t0
def retrieve(k1,b,label):
t0=time.time()
with get_context('fork').Pool(processes=8) as p:
rr=p.map(score_shard,[(i,SHARDS[i],k1,b) for i in range(36)])
globalh=[[] for _ in qids]
for sid,heaps,n,matched,sec in rr:
for qi,h in enumerate(heaps):
gh=globalh[qi]
for item in h:
if len(gh)<TOPK: heapq.heappush(gh,item)
elif item>gh[0]: heapq.heapreplace(gh,item)
run={}
for qi,q in enumerate(qids):
arr=sorted(globalh[qi],reverse=True); run[q]=[-negd for sc,negd in arr]
sec=time.time()-t0
print(label,'retrieval scan sec',sec,flush=True)
return run,sec
# TREC-compliant eval: passage level 1 = related/not relevant for binary metrics. nDCG remains graded.
qrels=defaultdict(dict)
for q,d,r in qrels_rows:qrels[q][d]=r
def evaluate(run):
vals=defaultdict(list)
for q in qids:
qr=qrels[q]; rank=run.get(q,[])
binary={d for d,r in qr.items() if r>=2.0}; den=max(1,len(binary))
h10=sum(d in binary for d in rank[:10]); h100=sum(d in binary for d in rank[:100])
vals['P@10'].append(h10/10); vals['R@10'].append(h10/den); vals['R@100'].append(h100/den); vals['Hit@10'].append(float(h10>0)); vals['Hit@100'].append(float(h100>0))
rr=0.0
for i,d in enumerate(rank[:10],1):
if d in binary: rr=1.0/i;break
vals['MRR@10'].append(rr)
obs=[qr.get(d,0.0) for d in rank[:10]]; ideal=sorted(qr.values(),reverse=True)[:10]
dcg=sum(r/math.log2(i+2) for i,r in enumerate(obs)); idcg=sum(r/math.log2(i+2) for i,r in enumerate(ideal)); vals['nDCG@10'].append(dcg/idcg if idcg else 0.0)
return {k:float(np.mean(v)) for k,v in vals.items()} | {'n_queries':len(qids),'n_binary_relevant':sum(r>=2 for _,_,r in qrels_rows)}
if __name__=='__main__':
# Common MS MARCO/Anserini-like BM25 and a conventional default variant for sensitivity.
out={'tokenizer':'lowercase regex (?u)\\b\\w\\w+\\b; no stemming','N':N,'avgdl':avgdl,'oov_df':{t:term2df[t] for t in oov}}
for k1,b,label in [(0.9,0.4,'bm25_k1_0.9_b_0.4'),(1.2,0.75,'bm25_k1_1.2_b_0.75')]:
run,sec=retrieve(k1,b,label); met=evaluate(run); out[label]={'k1':k1,'b':b,'metrics':met,'full_corpus_parallel_scan_seconds':sec,'run_top1000':run}; print(label,met,flush=True)
json.dump(out,open(OUT/'bm25_local_test.json','w'),indent=2)
print('SAVED',OUT/'bm25_local_test.json',flush=True)
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