measure: claim-24 unfloored semantic leg vs bm25lex baseline - net +0, NOT-SHOWN
gains q14,q25 (gold at global cosine rank 1, previously discarded by the 0.60 floor); losses q15,q28 (the semantic leg was EMPTY on those queries under the floor, so filling it turns a 2-leg rotation into a 3-leg one and halves the associative leg's share of the top 5). Guards held: exact_rare 6/6, phrase 7/7, nonsense 2/3, superseded 2/3 outranks. recall@10 61.8->65.4pp, latency 0.99x. Baseline reproduced from source (results-bm25base-rerun.json is byte-identical to the committed results-bm25lex.json), candidate deterministic across 2 runs.
This commit is contained in:
Tim Lingo
@@ -0,0 +1,150 @@
|
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{
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"baseline": "bm25lex",
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"candidate": "claim24",
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"n_shared_queries": 38,
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"fixed_by_candidate": [
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"q14",
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"q25"
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],
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"broken_by_candidate": [
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"q15",
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"q28"
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],
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"discordant": 4,
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"net_queries": 0,
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"mcnemar_exact_p": 1.0,
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"min_detectable_swing_queries": 6,
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"observed_run_to_run_drift_queries": 0,
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"noise_floor_queries": 6,
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"verdict": "no measurable difference",
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"baseline_aggregate": {
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"n_queries": 38,
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"n_scored": 35,
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"hit@5": 0.7428571428571429,
|
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"recall@5": 0.5536485340056769,
|
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"recall@10": 0.6175677497106068,
|
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"precision@5": 0.20000000000000007,
|
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"mrr@10": 0.5021428571428571,
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"nonsense_clean": "2/3",
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"superseded_outranks": "2/3",
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"latency_ms_p50": 1184.4,
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"latency_ms_p95": 1620.0,
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"latency_ms_max": 1655.4,
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"errors": 0,
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"by_category": {
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"associative": {
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"n": 6,
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"hit@5": 0.6666666666666666,
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"recall@5": 0.08857808857808858,
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"recall@10": 0.23310023310023312,
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"mrr@10": 0.25
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},
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"exact_rare": {
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"n": 6,
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"hit@5": 1.0,
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"recall@5": 1.0,
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"recall@10": 1.0,
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"mrr@10": 1.0
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},
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"nonsense": {
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"n": 3,
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"clean": 2,
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"avg_false_positives": 3.3333333333333335
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},
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"paraphrase": {
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"n": 13,
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"hit@5": 0.6153846153846154,
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"recall@5": 0.6153846153846154,
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"recall@10": 0.6153846153846154,
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"mrr@10": 0.2846153846153846
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},
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"phrase": {
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"n": 7,
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"hit@5": 1.0,
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"recall@5": 0.5494614512471656,
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"recall@10": 0.6023242630385487,
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"mrr@10": 0.8214285714285714
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},
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"superseded": {
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"n": 3,
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"hit@5": 0.3333333333333333,
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"recall@5": 0.3333333333333333,
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"recall@10": 0.6666666666666666,
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"mrr@10": 0.20833333333333334,
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"outranks": 2
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}
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}
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},
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"candidate_aggregate": {
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"n_queries": 38,
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"n_scored": 35,
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"hit@5": 0.7428571428571429,
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"recall@5": 0.5768475572047,
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"recall@10": 0.6537440733869305,
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"precision@5": 0.19428571428571437,
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"mrr@10": 0.5021428571428572,
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"nonsense_clean": "2/3",
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"superseded_outranks": "2/3",
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"latency_ms_p50": 1173.7,
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"latency_ms_p95": 1623.0,
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"latency_ms_max": 1647.9,
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"errors": 0,
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"by_category": {
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"associative": {
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"n": 6,
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"hit@5": 0.5,
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"recall@5": 0.07575757575757576,
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"recall@10": 0.12121212121212122,
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"mrr@10": 0.22916666666666666
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},
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"exact_rare": {
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"n": 6,
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"hit@5": 1.0,
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"recall@5": 1.0,
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"recall@10": 1.0,
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"mrr@10": 1.0
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},
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"nonsense": {
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"n": 3,
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"clean": 2,
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"avg_false_positives": 3.3333333333333335
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},
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"paraphrase": {
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"n": 13,
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"hit@5": 0.6923076923076923,
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"recall@5": 0.6923076923076923,
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"recall@10": 0.7692307692307693,
|
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"mrr@10": 0.29423076923076924
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},
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"phrase": {
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"n": 7,
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"hit@5": 1.0,
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"recall@5": 0.5335884353741497,
|
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"recall@10": 0.5933956916099773,
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"mrr@10": 0.8214285714285714
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},
|
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"superseded": {
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"n": 3,
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"hit@5": 0.3333333333333333,
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"recall@5": 0.3333333333333333,
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"recall@10": 0.6666666666666666,
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"mrr@10": 0.20833333333333334,
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"outranks": 2
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}
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}
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},
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"repeat_variance": {
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"baseline": {
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"runs": 3,
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"hit@5_min": 0.7428571428571429,
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"hit@5_max": 0.7428571428571429,
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"spread_queries": 0
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},
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"candidate": {
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"runs": 2,
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"hit@5_min": 0.7428571428571429,
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"hit@5_max": 0.7428571428571429,
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"spread_queries": 0
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}
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}
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}
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@@ -0,0 +1,110 @@
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import numpy as np, json, urllib.request, collections, math, re, sys, time, pickle, os
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SP="/private/tmp/claude-501/-Users-timlingo/82369039-a20e-4b5a-8a5e-28234a57b996/scratchpad"
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EV="/Users/timlingo/Development/neuron-technologies/_wt-bm25lex/tools/retrieval-eval/"
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np.seterr(all='ignore')
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t0=time.time()
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M=np.load(SP+'/emb.npy'); eids=open(SP+'/ids.txt',encoding='utf-8',errors='surrogateescape').read().split('\n')
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eidx={k:i for i,k in enumerate(eids)}
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d=json.load(open('/Users/timlingo/neuron-memory-backups/snapshot-pre-repair-20260806.json',encoding='utf-8',errors='surrogateescape'))
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STRUCT={"identity","contains","superseded_by","references","embodies","demonstrated_by","canonical-self","depends_on","currently_holds","activates"}
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adj=collections.defaultdict(list)
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for e in d['edges']:
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if e.get('relation') not in STRUCT: continue
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w=float(e.get('weight') or 0.0)
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adj[e['from_id']].append((e['to_id'],w)); adj[e['to_id']].append((e['from_id'],w))
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nodes=d['nodes']
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N={n['id']:n for n in nodes}
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PRINT=re.compile(r'^[\x20-\x7e]+$')
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ids=[]; hay=[]; dl=[]; sal=[]; addressable=[]
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for n in nodes:
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i=n.get('id') or ''
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h=((n.get('content') or '')+'\x00'+(n.get('label') or '')+'\x00'+(n.get('tags') or '')).lower()
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ids.append(i); hay.append(h); dl.append(len(h)); sal.append(float(n.get('salience') or 0.0))
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addressable.append(bool(PRINT.match(i)))
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del d
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NN=len(ids); avgdl=sum(dl)/NN
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print("nodes=%d avgdl=%.0f %.1fs"%(NN,avgdl,time.time()-t0),file=sys.stderr)
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gold={q['id']:q for q in json.load(open(EV+"gold_set.json"),)['queries']}
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CACHE={}
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def emb(t):
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if t in CACHE: return CACHE[t]
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b=json.dumps({"model":"nomic-embed-text","prompt":t}).encode()
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r=urllib.request.Request("http://127.0.0.1:11434/api/embeddings",data=b,headers={"Content-Type":"application/json"})
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v=np.array(json.load(urllib.request.urlopen(r,timeout=60))["embedding"],dtype=np.float32)
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v=v/(np.linalg.norm(v)+1e-9); CACHE[t]=v; return v
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K1,B=1.2,0.75
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def lexleg(query, lim=10):
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toks=[]
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for w in query.split():
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wl=w.lower()
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if wl not in toks: toks.append(wl)
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nt=len(toks)
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masks=[]; df=[0]*nt
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for i in range(NN):
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if not addressable[i]: continue
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h=hay[i]; m=0; sc=0
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for t in range(nt):
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if toks[t] in h: m|=(1<<t); sc+=1; df[t]+=1
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if sc: masks.append((i,m,sc))
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idf=[math.log(1.0+(NN-df[t]+0.5)/(df[t]+0.5)) for t in range(nt)]
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scored=[]
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for i,m,sc in masks:
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norm=1.0-B+B*dl[i]/avgdl
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s=0.0
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for t in range(nt):
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if m&(1<<t): s+=idf[t]*(K1+1.0)/(1.0+K1*norm)
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scored.append((s,i))
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scored.sort(key=lambda x:(-x[0], -sal[x[1]]))
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return [ids[i] for s,i in scored[:lim]], len(masks), sum(df)
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FIRE=0.02; DECAY=0.7; DEPTH=2; SEED_MIN=0.60; ASSOC_MAX=64
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def assoc(seeds, s):
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act={x:1.0 for x in seeds}; seen={x:2 for x in seeds}
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Q=[(x,0) for x in seeds]; h=0
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while h<len(Q):
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cur,hop=Q[h]; h+=1
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if hop>=DEPTH: continue
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p=act[cur]
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for oid,w in adj.get(cur,()):
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n=N.get(oid)
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if not n or n.get('node_type') in ('Tag','InternalStateEvent'): continue
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na=p*w*DECAY*float(n.get('salience') or 0.0)
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if na<FIRE: continue
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if oid in seen and na<=act.get(oid,0): continue
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act[oid]=na
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if oid not in seen: seen[oid]=1
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Q.append((oid,hop+1))
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out=[]
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for k,v in seen.items():
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if v!=1 or k not in eidx: continue
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c=float(s[eidx[k]])
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if c<=0: continue
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out.append((c,k))
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out.sort(reverse=True)
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return [k for c,k in out[:ASSOC_MAX]]
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LEGS={}
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for qid,q in gold.items():
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v=emb(q['query']); s=M@v; s[~np.isfinite(s)]=-1
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L,nmatch,dfsum=lexleg(q['query'])
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ordr=np.argsort(-s)
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Sall=[eids[j] for j in ordr[:40] if PRINT.match(eids[j] or '')]
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seeds=[x for x in L[:3] if x in N]
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seeds=seeds+[eids[j] for j in ordr[:8] if eids[j] in N and eids[j] not in seeds and PRINT.match(eids[j] or '')]
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A=assoc(seeds,s) if seeds else []
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A=[x for x in A if PRINT.match(x or '')]
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LEGS[qid]=dict(L=L,Sall=Sall,A=A,scos={x:float(s[eidx[x]]) for x in set(Sall[:20]+A[:20]+list(q.get('relevant') or [])) if x in eidx},nmatch=nmatch)
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pickle.dump(LEGS,open(SP+'/legs6.pkl','wb'))
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FOCUS=['q14','q17','q23','q24','q25','q30','q32','q33','q34','q35','q36','q37','q38']
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for qid in FOCUS:
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q=gold[qid]; g=LEGS[qid]; rel=set(q.get('relevant') or [])
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def rk(lst):
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for i,x in enumerate(lst):
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if x in rel: return i+1
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return None
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print("%s %-12s nmatch=%-6d Lrank=%s Srank=%s Arank=%s |A|=%d"%(
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qid,q['category'],g['nmatch'],rk(g['L']),rk(g['Sall']),rk(g['A']),len(g['A'])))
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for r in list(rel)[:2]:
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print(" rel cos=%.3f"%(g['scos'].get(r,-9)))
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print(" topS cos:", ["%.3f"%g['scos'].get(x,-9) for x in g['Sall'][:3]])
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print("elapsed %.1fs"%(time.time()-t0),file=sys.stderr)
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@@ -0,0 +1,959 @@
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{
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"label": "bm25base-rerun",
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"soul_binary": "/private/tmp/claude-501/-Users-timlingo/82369039-a20e-4b5a-8a5e-28234a57b996/scratchpad/soul-bm25base",
|
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"soul_md5": "36c8dfa09c073b85fe7e00b02904d0ed",
|
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"corpus": "/Users/timlingo/neuron-eval-corpora/snapshot-pre-repair-20260806-embedded.json",
|
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"corpus_nodes": 78768,
|
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"corpus_edges": 14214,
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"gold_set": "/Users/timlingo/Development/neuron-technologies/_wt-bm25lex/tools/retrieval-eval/gold_set.json",
|
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"limit": 10,
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||||
"port": 7894,
|
||||
"wall_clock_s": 50.6,
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"child_pid": 93451,
|
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"child_confirmed_dead": true,
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||||
"aggregate": {
|
||||
"n_queries": 38,
|
||||
"n_scored": 35,
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||||
"hit@5": 0.7428571428571429,
|
||||
"recall@5": 0.5536485340056769,
|
||||
"recall@10": 0.6175677497106068,
|
||||
"precision@5": 0.20000000000000007,
|
||||
"mrr@10": 0.5021428571428571,
|
||||
"nonsense_clean": "2/3",
|
||||
"superseded_outranks": "2/3",
|
||||
"latency_ms_p50": 1186.1,
|
||||
"latency_ms_p95": 1632.5,
|
||||
"latency_ms_max": 1669.8,
|
||||
"errors": 0,
|
||||
"by_category": {
|
||||
"associative": {
|
||||
"n": 6,
|
||||
"hit@5": 0.6666666666666666,
|
||||
"recall@5": 0.08857808857808858,
|
||||
"recall@10": 0.23310023310023312,
|
||||
"mrr@10": 0.25
|
||||
},
|
||||
"exact_rare": {
|
||||
"n": 6,
|
||||
"hit@5": 1.0,
|
||||
"recall@5": 1.0,
|
||||
"recall@10": 1.0,
|
||||
"mrr@10": 1.0
|
||||
},
|
||||
"nonsense": {
|
||||
"n": 3,
|
||||
"clean": 2,
|
||||
"avg_false_positives": 3.3333333333333335
|
||||
},
|
||||
"paraphrase": {
|
||||
"n": 13,
|
||||
"hit@5": 0.6153846153846154,
|
||||
"recall@5": 0.6153846153846154,
|
||||
"recall@10": 0.6153846153846154,
|
||||
"mrr@10": 0.2846153846153846
|
||||
},
|
||||
"phrase": {
|
||||
"n": 7,
|
||||
"hit@5": 1.0,
|
||||
"recall@5": 0.5494614512471656,
|
||||
"recall@10": 0.6023242630385487,
|
||||
"mrr@10": 0.8214285714285714
|
||||
},
|
||||
"superseded": {
|
||||
"n": 3,
|
||||
"hit@5": 0.3333333333333333,
|
||||
"recall@5": 0.3333333333333333,
|
||||
"recall@10": 0.6666666666666666,
|
||||
"mrr@10": 0.20833333333333334,
|
||||
"outranks": 2
|
||||
}
|
||||
}
|
||||
},
|
||||
"rows": [
|
||||
{
|
||||
"id": "q01",
|
||||
"category": "exact_rare",
|
||||
"query": "unjailbreakable",
|
||||
"returned": [
|
||||
"mem-7f61beb4-271c-4feb-9f6e-1c9c837a6226"
|
||||
],
|
||||
"n_returned": 1,
|
||||
"latency_ms": 302.9,
|
||||
"error": null,
|
||||
"hit@5": 1.0,
|
||||
"recall@5": 1.0,
|
||||
"recall@10": 1.0,
|
||||
"precision@5": 0.2,
|
||||
"mrr@10": 1.0
|
||||
},
|
||||
{
|
||||
"id": "q02",
|
||||
"category": "exact_rare",
|
||||
"query": "engram-migrate",
|
||||
"returned": [
|
||||
"mem-6fdf6545-5e1a-43a9-8bdc-d2cd248146a5",
|
||||
"bl-ba764d70-e9d7-4f62-848f-719cb665f45e",
|
||||
"mem-22fe5ec8-ae0d-4583-a05c-d1ef50353257",
|
||||
"bl-b28d7256-6f74-4567-bd90-40d0ef2a6d78",
|
||||
"project-engram",
|
||||
"ctx-45bc",
|
||||
"project-engram-lang",
|
||||
"ctx-175f",
|
||||
"mem-60778715-758c-4677-933d-fc39b8f94152",
|
||||
"ctx-74ed"
|
||||
],
|
||||
"n_returned": 10,
|
||||
"latency_ms": 336.7,
|
||||
"error": null,
|
||||
"hit@5": 1.0,
|
||||
"recall@5": 1.0,
|
||||
"recall@10": 1.0,
|
||||
"precision@5": 0.2,
|
||||
"mrr@10": 1.0
|
||||
},
|
||||
{
|
||||
"id": "q03",
|
||||
"category": "exact_rare",
|
||||
"query": "cartabandonedevent",
|
||||
"returned": [
|
||||
"mem-1ba7c67d-85b9-4c2e-9fe2-39f8b0477091"
|
||||
],
|
||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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|
||||
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|
||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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|
||||
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|
||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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|
||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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||||
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||||
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|
||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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||||
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|
||||
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||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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|
||||
{
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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||||
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|
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||||
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||||
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|
||||
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|
||||
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||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
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|
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||||
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|
||||
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|
||||
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|
||||
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|
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|
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|
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|
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|
||||
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|
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|
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|
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|
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},
|
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{
|
||||
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|
||||
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|
||||
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|
||||
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|
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|
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|
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|
||||
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|
||||
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|
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|
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|
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{
|
||||
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|
||||
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|
||||
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|
||||
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|
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|
||||
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|
||||
"?Z?<S???K ?",
|
||||
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|
||||
"'?T?a\"B~-?8",
|
||||
"kn-6061318f-046b-4935-907d-8eafdce14930"
|
||||
],
|
||||
"n_returned": 10,
|
||||
"latency_ms": 1424.8,
|
||||
"error": null,
|
||||
"hit@5": 0.0,
|
||||
"recall@5": 0.0,
|
||||
"recall@10": 0.0,
|
||||
"precision@5": 0.0,
|
||||
"mrr@10": 0.0
|
||||
},
|
||||
{
|
||||
"id": "q33",
|
||||
"category": "nonsense",
|
||||
"query": "zqxjvw plimforth grebulon",
|
||||
"returned": [],
|
||||
"n_returned": 0,
|
||||
"latency_ms": 705.7,
|
||||
"error": null,
|
||||
"clean": true,
|
||||
"false_positives": 0
|
||||
},
|
||||
{
|
||||
"id": "q34",
|
||||
"category": "nonsense",
|
||||
"query": "flarnbistle quommetry",
|
||||
"returned": [],
|
||||
"n_returned": 0,
|
||||
"latency_ms": 484.4,
|
||||
"error": null,
|
||||
"clean": true,
|
||||
"false_positives": 0
|
||||
},
|
||||
{
|
||||
"id": "q35",
|
||||
"category": "nonsense",
|
||||
"query": "xxqzzt vurblenacht throom",
|
||||
"returned": [
|
||||
"bl-4476e856-c567-4b49-8ff7-d7dca3e5715e",
|
||||
"?V?",
|
||||
"knw-6b48dce2-f21c-452a-9db5-4e6aa61c87ca",
|
||||
"knw-920c891f-bb8c-48c4-9afc-018ef12dcdc4",
|
||||
"?m?\\}Q??6??",
|
||||
"8cbb60c5-4999-4ec1-8682-2592aedc4249",
|
||||
"kn-66a21179-2adc-4b19-a109-880cf4674d7d",
|
||||
"?m?\\}Q??6??",
|
||||
"knw-723551f5-1950-42a3-8b89-b6a06913cef0",
|
||||
"kn-333542cb-6dab-4662-9725-bf7440d28bf7"
|
||||
],
|
||||
"n_returned": 10,
|
||||
"latency_ms": 740.2,
|
||||
"error": null,
|
||||
"clean": false,
|
||||
"false_positives": 10
|
||||
},
|
||||
{
|
||||
"id": "q36",
|
||||
"category": "superseded",
|
||||
"query": "is the self-improvement architecture called DARMA or DHARMA",
|
||||
"returned": [
|
||||
"mem-f3b37427-b7d1-4f7e-b32c-0241a20ce8da",
|
||||
"mem-80d7416b-20e9-48a0-b176-b215527e2f56",
|
||||
"tag-__darma____cgi____patents____self-improvement____character-preservation____autonomous____architecture__",
|
||||
"kn-b7e98d63-8b83-4911-b4d0-990602a7f575",
|
||||
"tag-__darma____cgi____patents____self-improvement____character-preservation____autonomous____kotlin____architecture__",
|
||||
"knw-e047bb42-dc5b-4383-9e88-e508dc03abe3",
|
||||
"mem-e6327f52-2bda-4ce7-9471-2fffd1e172de",
|
||||
"bl-5b17bd3b-0c41-46cb-a710-6fa4429692ff",
|
||||
"mem-82b93b21-a865-410f-9ec1-fc54121d9bb5",
|
||||
"bl-145a0985-2382-400f-a7c5-c335c5e30a72"
|
||||
],
|
||||
"n_returned": 10,
|
||||
"latency_ms": 1307.7,
|
||||
"error": null,
|
||||
"hit@5": 1.0,
|
||||
"recall@5": 1.0,
|
||||
"recall@10": 1.0,
|
||||
"precision@5": 0.2,
|
||||
"mrr@10": 0.5,
|
||||
"outranks": true,
|
||||
"rank_correct": 2,
|
||||
"rank_stale": 8
|
||||
},
|
||||
{
|
||||
"id": "q37",
|
||||
"category": "superseded",
|
||||
"query": "how many provisional patents does Will actually have",
|
||||
"returned": [
|
||||
"mem-6f0b2b45-90c1-4356-ac01-3daac05b09c8",
|
||||
"12082f7e-e320-438b-bd65-083d8259748f",
|
||||
"015644f5-8194-4af0-800d-dd4a0cd71396",
|
||||
"13705072-4515-4124-963d-083af490494f",
|
||||
"527ecb25-2587-47eb-8269-73be2431abd4",
|
||||
"6de314bf-5c4c-4cfc-871f-fa2e422d45e6",
|
||||
"a1000001-0000-0000-0000-000000000002",
|
||||
"3cf706a1-3825-45d8-b0a9-06cae6cdf5b8",
|
||||
"kn-eb1b9e18-3dc6-4b9b-9cc6-86e0ae6b6be8",
|
||||
"7ac62daa-2eac-4c7a-a97e-e4203fc1b57b"
|
||||
],
|
||||
"n_returned": 10,
|
||||
"latency_ms": 1669.8,
|
||||
"error": null,
|
||||
"hit@5": 0.0,
|
||||
"recall@5": 0.0,
|
||||
"recall@10": 1.0,
|
||||
"precision@5": 0.0,
|
||||
"mrr@10": 0.125,
|
||||
"outranks": true,
|
||||
"rank_correct": 8,
|
||||
"rank_stale": null
|
||||
},
|
||||
{
|
||||
"id": "q38",
|
||||
"category": "superseded",
|
||||
"query": "is MCP still the live integration layer",
|
||||
"returned": [
|
||||
"5fcba804-eb5b-48ec-82da-146b1c6bb50d",
|
||||
"bl-7328cbe3-0200-43c2-88e7-0a164e15fca4",
|
||||
"bl-c8c19362-430b-4817-9cf4-9e85e0099c64",
|
||||
"bl-c5c6571e-118f-47c7-8cbb-3ed0ebf64a51",
|
||||
"mem-101e81b4-8097-4749-8d8d-7bb66de34517",
|
||||
"ctx-3a55",
|
||||
"86228228-7adf-41fb-b4c4-9ceea87953ae",
|
||||
"4509ed62-9fb2-48b8-9038-ac569fca9604",
|
||||
"bl-4f7b651b-6b33-449c-8a3b-cfce12ce984b",
|
||||
"mem-3a2cf162-d93b-4f29-86f2-5066fb7fe1f5"
|
||||
],
|
||||
"n_returned": 10,
|
||||
"latency_ms": 1018.2,
|
||||
"error": null,
|
||||
"hit@5": 0.0,
|
||||
"recall@5": 0.0,
|
||||
"recall@10": 0.0,
|
||||
"precision@5": 0.0,
|
||||
"mrr@10": 0.0,
|
||||
"outranks": false,
|
||||
"rank_correct": null,
|
||||
"rank_stale": 5
|
||||
}
|
||||
]
|
||||
}
|
||||
File diff suppressed because it is too large
Load Diff
File diff suppressed because it is too large
Load Diff
@@ -0,0 +1,135 @@
|
||||
import numpy as np, json, urllib.request, collections, math, re, sys, time
|
||||
SP="/private/tmp/claude-501/-Users-timlingo/82369039-a20e-4b5a-8a5e-28234a57b996/scratchpad"
|
||||
EV="/Users/timlingo/Development/neuron-technologies/_wt-bm25lex/tools/retrieval-eval/"
|
||||
np.seterr(all='ignore'); t0=time.time()
|
||||
M=np.load(SP+'/emb.npy'); eids=open(SP+'/ids.txt',encoding='utf-8',errors='surrogateescape').read().split('\n')
|
||||
eidx={k:i for i,k in enumerate(eids)}
|
||||
d=json.load(open('/Users/timlingo/neuron-memory-backups/snapshot-pre-repair-20260806.json',encoding='utf-8',errors='surrogateescape'))
|
||||
STRUCT={"identity","contains","superseded_by","references","embodies","demonstrated_by","canonical-self","depends_on","currently_holds","activates"}
|
||||
adj=collections.defaultdict(list)
|
||||
for e in d['edges']:
|
||||
if e.get('relation') not in STRUCT: continue
|
||||
w=float(e.get('weight') or 0.0)
|
||||
adj[e['from_id']].append((e['to_id'],w)); adj[e['to_id']].append((e['from_id'],w))
|
||||
nodes=d['nodes']; N={n['id']:n for n in nodes}
|
||||
PRINT=re.compile(r'^[\x20-\x7e]+$')
|
||||
ids=[];hay=[];dl=[];sal=[];addr=[]
|
||||
for n in nodes:
|
||||
i=n.get('id') or ''
|
||||
h=((n.get('content') or '')+'\x00'+(n.get('label') or '')+'\x00'+(n.get('tags') or '')).lower()
|
||||
ids.append(i);hay.append(h);dl.append(len(h));sal.append(float(n.get('salience') or 0.0));addr.append(bool(PRINT.match(i)))
|
||||
del d
|
||||
NN=len(ids); avgdl=sum(dl)/NN
|
||||
gold={q['id']:q for q in json.load(open(EV+"gold_set.json"))['queries']}
|
||||
CACHE={}
|
||||
def emb(t):
|
||||
if t in CACHE: return CACHE[t]
|
||||
b=json.dumps({"model":"nomic-embed-text","prompt":t}).encode()
|
||||
r=urllib.request.Request("http://127.0.0.1:11434/api/embeddings",data=b,headers={"Content-Type":"application/json"})
|
||||
v=np.array(json.load(urllib.request.urlopen(r,timeout=60))["embedding"],dtype=np.float32)
|
||||
v=v/(np.linalg.norm(v)+1e-9); CACHE[t]=v; return v
|
||||
K1,B=1.2,0.75
|
||||
LEXC={}
|
||||
def lexleg(qid,query,lim=10):
|
||||
if qid in LEXC: return LEXC[qid]
|
||||
toks=[]
|
||||
for w in query.split():
|
||||
wl=w.lower()
|
||||
if wl not in toks: toks.append(wl)
|
||||
nt=len(toks); masks=[]; df=[0]*nt
|
||||
for i in range(NN):
|
||||
if not addr[i]: continue
|
||||
h=hay[i]; m=0; sc=0
|
||||
for t in range(nt):
|
||||
if toks[t] in h: m|=(1<<t); sc+=1; df[t]+=1
|
||||
if sc: masks.append((i,m))
|
||||
idf=[math.log(1.0+(NN-df[t]+0.5)/(df[t]+0.5)) for t in range(nt)]
|
||||
scored=[]
|
||||
for i,m in masks:
|
||||
norm=1.0-B+B*dl[i]/avgdl; s=0.0
|
||||
for t in range(nt):
|
||||
if m&(1<<t): s+=idf[t]*(K1+1.0)/(1.0+K1*norm)
|
||||
scored.append((s,i))
|
||||
scored.sort(key=lambda x:(-x[0],-sal[x[1]]))
|
||||
LEXC[qid]=([ids[i] for s,i in scored[:lim]], len(masks))
|
||||
return LEXC[qid]
|
||||
FIRE=0.02; DECAY=0.7; DEPTH=2; SEED_MIN=0.60; ASSOC_MAX=64
|
||||
def assoc(seeds, s, use_cos, order):
|
||||
act={x:1.0 for x in seeds}; seen={x:2 for x in seeds}
|
||||
Q=[(x,0) for x in seeds]; h=0
|
||||
while h<len(Q):
|
||||
cur,hop=Q[h]; h+=1
|
||||
if hop>=DEPTH: continue
|
||||
p=act[cur]
|
||||
for oid,w in adj.get(cur,()):
|
||||
n=N.get(oid)
|
||||
if not n or n.get('node_type') in ('Tag','InternalStateEvent'): continue
|
||||
c=1.0
|
||||
if use_cos:
|
||||
j=eidx.get(oid)
|
||||
c=max(0.0,float(s[j])) if j is not None else 0.0
|
||||
na=p*w*DECAY*float(n.get('salience') or 0.0)*c
|
||||
if na<FIRE: continue
|
||||
if oid in seen and na<=act.get(oid,0): continue
|
||||
act[oid]=na
|
||||
if oid not in seen: seen[oid]=1
|
||||
Q.append((oid,hop+1))
|
||||
out=[]
|
||||
for k,v in seen.items():
|
||||
if v!=1 or k not in eidx: continue
|
||||
c=float(s[eidx[k]])
|
||||
if c<=0: continue
|
||||
out.append((act[k] if order=='act' else c,k))
|
||||
out.sort(reverse=True)
|
||||
return [k for c,k in out[:ASSOC_MAX] if PRINT.match(k or '')]
|
||||
def inter(legs,lim=10):
|
||||
out=[];idx=[0]*len(legs)
|
||||
while len(out)<lim and any(idx[i]<len(legs[i]) for i in range(len(legs))):
|
||||
for i in range(len(legs)):
|
||||
if idx[i]<len(legs[i]):
|
||||
if legs[i][idx[i]] not in out: out.append(legs[i][idx[i]])
|
||||
idx[i]+=1
|
||||
if len(out)>=lim: break
|
||||
return out
|
||||
def run(floor, vocabgate, use_cos, order):
|
||||
res={}; legs={}
|
||||
for qid,q in gold.items():
|
||||
v=emb(q['query']); s=M@v; s[~np.isfinite(s)]=-1
|
||||
L,nmatch=lexleg(qid,q['query'])
|
||||
if vocabgate and nmatch==0:
|
||||
res[qid]=[]; legs[qid]=([],[],[]); continue
|
||||
ordr=np.argsort(-s)
|
||||
S=[eids[j] for j in ordr[:10] if PRINT.match(eids[j] or '') and (not floor or s[j]>SEED_MIN)]
|
||||
seeds=[x for x in L[:3] if x in N]
|
||||
seeds=seeds+[eids[j] for j in ordr[:8] if eids[j] in N and eids[j] not in seeds and PRINT.match(eids[j] or '')]
|
||||
A=assoc(seeds,s,use_cos,order) if seeds else []
|
||||
res[qid]=inter([L,S,A]); legs[qid]=(L,S,A)
|
||||
return res,legs
|
||||
def score(res,label,base=None):
|
||||
det={}
|
||||
for qid,q in gold.items():
|
||||
out=res[qid][:5]
|
||||
if q['category']=='nonsense': ok=(len(res[qid])==0)
|
||||
elif q['category']=='superseded':
|
||||
must=q.get('must_outrank') or {}; ok=False
|
||||
for good,bad in (must.items() if isinstance(must,dict) else []):
|
||||
ok = good in res[qid] and (bad not in res[qid] or res[qid].index(good)<res[qid].index(bad))
|
||||
if not must: ok=any(r in out for r in q['relevant'])
|
||||
else: ok=any(r in out for r in q['relevant'])
|
||||
det[qid]=ok
|
||||
line="%-34s true=%d/38"%(label,sum(det.values()))
|
||||
if base is not None:
|
||||
dd=[q for q in sorted(gold) if det[q]!=base[q]]
|
||||
line+=" moved=%d gains=%s losses=%s"%(len(dd),[q for q in dd if det[q]],[q for q in dd if not det[q]])
|
||||
print(line, flush=True)
|
||||
return det
|
||||
if __name__=="__main__":
|
||||
b,_=run(True,False,False,'cos'); base=score(b,'BASE bm25lex replica')
|
||||
for lab,args in [
|
||||
("A floor-off+vocabgate", (False,True,False,'cos')),
|
||||
("B A+cos-in-traversal", (False,True,True ,'cos')),
|
||||
("C A+cos-trav+act-order", (False,True,True ,'act')),
|
||||
("D floor-off NO gate", (False,False,False,'cos')),
|
||||
]:
|
||||
r,_=run(*args); score(r,lab,base)
|
||||
print("elapsed %.1fs"%(time.time()-t0),file=sys.stderr)
|
||||
@@ -0,0 +1,32 @@
|
||||
exec(open('sim6.py').read().split('if __name__')[0])
|
||||
HASSTRUCT=set(adj.keys())
|
||||
print("nodes with >=1 structural edge:",len(HASSTRUCT),file=sys.stderr)
|
||||
def run2(sfilter, seedout, lim=10):
|
||||
res={}
|
||||
for qid,q in gold.items():
|
||||
v=emb(q['query']); s=M@v; s[~np.isfinite(s)]=-1
|
||||
L,nmatch=lexleg(qid,q['query'])
|
||||
if nmatch==0: res[qid]=[]; continue
|
||||
ordr=np.argsort(-s)
|
||||
cand=[eids[j] for j in ordr[:200] if PRINT.match(eids[j] or '')]
|
||||
S=[x for x in cand if (not sfilter or x in HASSTRUCT)][:10]
|
||||
seeds=[x for x in L[:3] if x in N]
|
||||
semseeds=[eids[j] for j in ordr[:8] if eids[j] in N and eids[j] not in seeds and PRINT.match(eids[j] or '')]
|
||||
seeds=seeds+semseeds
|
||||
A=assoc(seeds,s,False,'cos') if seeds else []
|
||||
if seedout:
|
||||
extra=[(float(s[eidx[x]]),x) for x in semseeds if x in HASSTRUCT and x in eidx]
|
||||
merged=[(float(s[eidx[x]]),x) for x in A if x in eidx]+extra
|
||||
merged.sort(reverse=True)
|
||||
seen=set(); A=[]
|
||||
for c,x in merged:
|
||||
if x in seen: continue
|
||||
seen.add(x); A.append(x)
|
||||
A=A[:ASSOC_MAX]
|
||||
res[qid]=inter([L,S,A])
|
||||
return res
|
||||
b,_=run(True,False,False,'cos'); base=score(b,'BASE bm25lex replica')
|
||||
a,_=run(False,True,False,'cos'); score(a,'A floor-off+vocabgate',base)
|
||||
score(run2(False,True),'E A+struct-seeds-in-graphleg',base)
|
||||
score(run2(True,False),'F A+S-restricted-to-graph',base)
|
||||
score(run2(True,True),'G E+F',base)
|
||||
@@ -0,0 +1,30 @@
|
||||
exec(open('sim6.py').read().split('if __name__')[0])
|
||||
HASSTRUCT=set(adj.keys())
|
||||
import json as _j
|
||||
d2=_j.load(open('/Users/timlingo/neuron-memory-backups/snapshot-pre-repair-20260806.json',encoding='utf-8',errors='surrogateescape'))
|
||||
ANYEDGE=set()
|
||||
for e in d2['edges']: ANYEDGE.add(e['from_id']); ANYEDGE.add(e['to_id'])
|
||||
del d2
|
||||
print("struct=%d anyedge=%d"%(len(HASSTRUCT),len(ANYEDGE)),file=sys.stderr)
|
||||
def run4(pool, nlegs, lim=10):
|
||||
P = HASSTRUCT if pool=='struct' else ANYEDGE
|
||||
res={}
|
||||
for qid,q in gold.items():
|
||||
v=emb(q['query']); s=M@v; s[~np.isfinite(s)]=-1
|
||||
L,nmatch=lexleg(qid,q['query'])
|
||||
if nmatch==0: res[qid]=[]; continue
|
||||
ordr=np.argsort(-s)
|
||||
cand=[eids[j] for j in ordr[:3000] if PRINT.match(eids[j] or '')]
|
||||
S=cand[:10]
|
||||
G=[x for x in cand if x in P][:10]
|
||||
seeds=[x for x in L[:3] if x in N]
|
||||
seeds=seeds+[eids[j] for j in ordr[:8] if eids[j] in N and eids[j] not in seeds and PRINT.match(eids[j] or '')]
|
||||
A=assoc(seeds,s,False,'cos') if seeds else []
|
||||
legs=[L,S,G,A] if nlegs==4 else [L,G,A]
|
||||
res[qid]=inter(legs)
|
||||
return res
|
||||
b,_=run(True,False,False,'cos'); base=score(b,'BASE bm25lex replica')
|
||||
score(run4('struct',4),'I 4leg L,S,G(struct),A',base)
|
||||
score(run4('any',4), 'J 4leg L,S,G(anyedge),A',base)
|
||||
score(run4('struct',3),'K 3leg L,G(struct),A',base)
|
||||
score(run4('any',3), 'L 3leg L,G(anyedge),A',base)
|
||||
Reference in New Issue
Block a user