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fe31b61 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 | """Locked independent-day retest of unchanged full-hypothesis models.
The preceding dynamic-mixture study failed its source-clock requirement.
It stays failed. This separately registered study reports every day and control.
"""
from __future__ import annotations
import argparse,hashlib,io,json,math,re,time,urllib.request,zipfile
from pathlib import Path
import numpy as np
import torch
from unionmind.continuous_decision import source,factorial
from unionmind.continuous_decision.factor_study import MODELS,fit_one,predict,metrics
from unionmind.continuous_decision.factor_analysis import STRONG
from unionmind.continuous_decision.factor_acquisition import episode
ROOT=Path('artifacts/day_transfer_01');PROTOCOL=Path('configs/day_transfer_protocol_01.json')
def write(name,value):
path=ROOT/name;path.parent.mkdir(parents=True,exist_ok=True);path.write_text(json.dumps(value,indent=2)+'\n')
def verify():
lock=json.loads((ROOT/'lock.json').read_text())
assert all(source.sha(p)==h for p,h in lock['files'].items())
def lock():
assert not ROOT.exists();ROOT.mkdir()
files=[Path(__file__),PROTOCOL,*Path('unionmind/continuous_decision').glob('*.py'),Path('tests/test_continuous_decision.py')]
write('lock.json',dict(files={str(p):source.sha(p) for p in files},before_archive_or_numerical_access=True,
timestamp_utc=time.strftime('%Y-%m-%dT%H:%M:%SZ',time.gmtime()),
previous_strict_clock_study_not_amended=True,unchanged_algorithms_and_hyperparameters=True))
print('INDEPENDENT DAY STUDY LOCKED',flush=True)
def text_array(payload):
text=payload.decode('utf-8-sig');lines=text.strip().splitlines();header=[]
number=re.compile(r'^[-+]?(?:\d+(?:\.\d*)?|\.\d+)(?:[eE][-+]?\d+)?$')
while lines and not number.match(re.split(r'[\s,;]+',lines[0].strip())[0]):header.append(lines.pop(0))
body=re.sub('[,;]',' ','\n'.join(lines));array=np.fromstring(body,sep=' ',dtype=np.float64)
assert array.size==len(lines)*20,(array.size,len(lines))
return array.reshape(-1,20),header
def prepare(device='cuda:1'):
verify();config=json.loads(PROTOCOL.read_text());torch.cuda.set_device(device);start=time.perf_counter()
path=ROOT/'source.zip';assert not path.exists();urllib.request.urlretrieve(config['source']['url'],path)
write('source.json',dict(**config['source'],sha256=source.sha(path),bytes=path.stat().st_size,download_seconds=time.perf_counter()-start))
sources=[];outer=zipfile.ZipFile(path)
for entry in outer.infolist():
if entry.filename.endswith('.zip'):
inner=zipfile.ZipFile(io.BytesIO(outer.read(entry)));sources.append(inner)
if not sources:sources=[outer]
members=sorted([(z,a) for z in sources for a in z.infolist() if re.search(r'\d{8}_\d{6}',a.filename) and not a.is_dir()],key=lambda item:item[1].filename)
assert len(members)==13,[(a.filename,a.file_size) for z,a in members]
rows=[];labels=[];days=[];records=[];audits=[];excluded=[];headers={}
for day,(archive,entry) in enumerate(members):
values,header=text_array(archive.read(entry));headers[entry.filename]=header
raw=torch.as_tensor(values,device=device,dtype=torch.float64)
valid=bool(torch.isfinite(raw).all()) and bool((raw[1:,0]>=raw[:-1,0]).all())
audit=dict(day=day,file=entry.filename,raw_rows=len(raw),finite=bool(torch.isfinite(raw).all()),
duplicate_timestamps=int((raw[1:,0]==raw[:-1,0]).sum()),nondecreasing_clock=bool((raw[1:,0]>=raw[:-1,0]).all()))
if not valid:audits.append({**audit,'valid':False});continue
time_zero=raw[0,0];blocks=((raw[:,0]-time_zero)/900).floor().long()
for block in blocks.unique(sorted=True).tolist():
ids=torch.where(blocks==block)[0];a=int(ids[0]);b=int(ids[-1])+1
duration=float(raw[b-1,0]-raw[a,0])
if duration<850:
excluded.append(dict(day=day,file=entry.filename,block=block,duration=duration,reason='less_than_850_seconds'));continue
quarter=((raw[a:b,0]-time_zero-block*900)/225).floor().long().clamp(0,3)
means=torch.stack([raw[a:b,6:][quarter==q].mean(0) for q in range(4)],-1)
last=quarter==3;co=raw[a:b,1][last].median();rh=raw[a:b,2][last].mean()
h0=(co*9/20).round().long().clamp(0,9);h1=(rh>rh.new_tensor([35.,55.])).sum()
rows.append(means.flatten());labels.append(torch.stack((h0,h1)));days.append(day)
records.append(dict(id=f'{entry.filename}:block{block}',day=day,file=entry.filename,block=block,
first_row=a,last_row_exclusive=b,start_seconds=float(raw[a,0]),end_seconds=float(raw[b-1,0]),
reference_co_ppm=float(co),reference_humidity_percent=float(rh)))
audit.update(valid=True,episodes=sum(d==day for d in days));audits.append(audit)
print(json.dumps(audit),flush=True);del raw,values
x=torch.stack(rows);h=torch.stack(labels);day_tensor=torch.tensor(days,device=device)
grid=factorial.hypotheses([10,3],device)
ordering=sorted(grid.tolist(),key=lambda row:hashlib.sha256(('20821:'+','.join(map(str,row))).encode()).hexdigest())
held_bindings=ordering[:6];unseen=torch.zeros(len(x),device=device,dtype=torch.bool)
for binding in held_bindings:unseen|=(h==h.new_tensor(binding)).all(-1)
source_mask=(day_tensor<9)&~unseen;target=day_tensor>=9
eligibility=len(audits)==13 and all(a.get('valid') and a.get('episodes',0)>=90 for a in audits)
eligibility &= int(source_mask.sum())>=128 and all(int((target&unseen&(day_tensor==d)).sum())>=8 for d in range(9,13))
torch.save(dict(x=x,h=h,days=day_tensor,source=source_mask,target=target,unseen=unseen),ROOT/'prepared.pt')
torch.cuda.synchronize();write('preparation.json',dict(eligible=eligibility,records=records,audits=audits,headers=headers,
held_bindings=held_bindings,source_records=int(source_mask.sum()),held_records=int(target.sum()),
unseen_held_records=int((target&unseen).sum()),exclusions=excluded,seconds=time.perf_counter()-start,
measurements_are_episodes_not_independent_raw_rows=True,reference_columns_are_never_features=True))
verify();print(json.dumps(dict(eligible=eligibility,source_records=int(source_mask.sum()),held_records=int(target.sum()))),flush=True)
def run(device='cuda:1'):
verify();torch.cuda.set_device(device);torch.backends.cuda.matmul.allow_tf32=False
config=json.loads(PROTOCOL.read_text());start=time.perf_counter();prep=json.loads((ROOT/'preparation.json').read_text())
if not prep['eligible']:write('decision.json',dict(promotion=False,reason='locked_cohort_eligibility_failure'));return
p=torch.load(ROOT/'prepared.pt',map_location=device,weights_only=True);pool=torch.where(p['source'])[0];held=torch.where(p['target'])[0]
sizes=[10,3];grid=factorial.hypotheses(sizes,device);true=factorial.joint_index(p['h'][held],sizes);results=[]
for n in config['sample_budgets']:
for seed in config['seeds']:
rng=torch.Generator(device=device).manual_seed(seed);ids=pool[torch.randperm(len(pool),device=device,generator=rng)[:n]]
t=time.perf_counter();mean=p['x'][ids].mean(0);scale=p['x'][ids].std(0).clamp_min(1e-6)
x=(p['x'][ids]-mean)/scale;target=(p['x'][held]-mean)/scale;torch.cuda.synchronize();normalization=time.perf_counter()-t
supported=torch.ones(len(held),device=device,dtype=torch.bool)
for f,k in enumerate(sizes):supported &= torch.bincount(p['h'][ids,f],minlength=k)[p['h'][held,f]]>0
directory=ROOT/'models'/f'n{n}_s{seed}';directory.mkdir(parents=True,exist_ok=True)
for name in MODELS:
torch.cuda.synchronize();t=time.perf_counter();model=fit_one(x,p['h'][ids],sizes,name,seed)
torch.cuda.synchronize();fit_seconds=time.perf_counter()-t;t=time.perf_counter()
with torch.no_grad():prob=predict(model,target,range(x.shape[-1]));v=metrics(prob,true,grid,p['h'][held])
torch.cuda.synchronize();prediction=time.perf_counter()-t
checkpoint=directory/f'{name}.pt';torch.save(dict(model=model,mean=mean,scale=scale,fit_ids=ids),checkpoint)
row=dict(samples=n,seed=seed,model=name,held_rows=held,days=p['days'][held],unseen=p['unseen'][held],
supported_levels=supported,metrics=v,normalization_seconds=normalization,fit_seconds=fit_seconds,
prediction_seconds=prediction,neural_parameters=model.get('neural_parameters',0),
checkpoint_bytes=checkpoint.stat().st_size,adaptation_fits=model.get('adaptation_fits',1))
results.append(row)
print(json.dumps(dict(samples=n,seed=seed,model=name,top3_unseen=float(v['top3'][row['unseen']].mean()))),flush=True)
torch.save(dict(results=results),ROOT/'results.pt');write('execution.json',dict(seconds=time.perf_counter()-start,
peak_gpu_bytes=torch.cuda.max_memory_allocated(device),device=device,cells=len(results),backbone_executions=0,
timings=[{k:r[k] for k in ('samples','seed','model','normalization_seconds','fit_seconds','prediction_seconds','neural_parameters','checkpoint_bytes','adaptation_fits')} for r in results]))
verify()
@torch.no_grad()
def analyze(device='cuda:2'):
verify();torch.cuda.set_device(device);config=json.loads(PROTOCOL.read_text());start=time.perf_counter()
results=torch.load(ROOT/'results.pt',map_location=device,weights_only=True)['results'];rng=torch.Generator(device=device).manual_seed(20891)
summary=[];comparisons=[];gates=[]
for n in config['sample_budgets']:
records=[r for r in results if r['samples']==n];template=records[0];mask=template['unseen'];day=template['days'][mask]
matrix=torch.stack([torch.stack([r['metrics']['top3'] for r in records if r['model']==name]).mean(0)[mask] for name in MODELS],-1)
points=[];boot=[]
for d in range(9,13):
a=matrix[day==d];points.append(a.mean(0));sample=torch.randint(len(a),(20000,len(a)),device=device,generator=rng);boot.append(a[sample].mean(1))
day_points=torch.stack(points);point=day_points.mean(0);conditional_boot=torch.stack(boot).mean(0)
sampled_days=torch.randint(4,(20000,4),device=device,generator=rng)
day_boot=day_points[sampled_days].mean(1)
for name in MODELS:
rows=[r for r in records if r['model']==name];assert len(rows)==3
for stratum,include in (('unseen',mask),('seen',~mask)):
for d in (None,9,10,11,12):
m=include if d is None else include&(template['days']==d)
summary.append(dict(samples=n,model=name,stratum=stratum,day=d,records=int(m.sum()),
**{f:float(torch.stack([r['metrics'][f][m].mean() for r in rows]).mean()) for f in ('top1','top3','nll','confidence','top3_mass')}))
for support in (False,True):
count=correct=0
for r in rows:
m=r['unseen']&(r['supported_levels']==support);count+=int(m.sum());correct+=float(r['metrics']['top3'][m].sum())
summary.append(dict(samples=n,model=name,stratum='supported' if support else 'novel_level',records_across_seeds=count,top3=correct/count if count else None))
for threshold in (.5,.7,.9):
accepted=correct=0
for r in rows:
m=r['unseen']&(r['metrics']['confidence']>=threshold);accepted+=int(m.sum());correct+=float(r['metrics']['top1'][m].sum())
summary.append(dict(samples=n,model=name,stratum='selective_unseen',threshold=threshold,
coverage=accepted/(3*int(mask.sum())),accuracy=correct/accepted if accepted else None))
for candidate in config['candidates']:
ci=MODELS.index(candidate);rows=[];alpha=.05/(2*40)
for ri,reference in enumerate(MODELS):
if ci==ri:continue
diff=conditional_boot[:,ci]-conditional_boot[:,ri];lo,hi=torch.quantile(diff,diff.new_tensor([alpha,1-alpha]))
dd=day_boot[:,ci]-day_boot[:,ri];dlo,dhi=torch.quantile(dd,dd.new_tensor([alpha,1-alpha]))
row=dict(samples=n,candidate=candidate,control=reference,gain=float(point[ci]-point[ri]),
fixed_day_simultaneous_interval=[float(lo),float(hi)],day_cluster_simultaneous_interval=[float(dlo),float(dhi)],
day_gains=(day_points[:,ci]-day_points[:,ri]).tolist())
rows.append(row);comparisons.append(row)
strong=[r for r in rows if r['control'] in STRONG]
substantial=all(r['gain']>=.10 and r['fixed_day_simultaneous_interval'][0]>0 and r['day_cluster_simultaneous_interval'][0]>0 and min(r['day_gains'])>0 for r in strong)
best=all(r['fixed_day_simultaneous_interval'][0]>0 and r['day_cluster_simultaneous_interval'][0]>0 for r in rows)
learned=next((r['fixed_day_simultaneous_interval'][0]>0 and r['day_cluster_simultaneous_interval'][0]>0 for r in rows if r['control']=='additive_full'),False) if candidate=='factorized_neural' else None
gates.append(dict(samples=n,candidate=candidate,top3=float(point[ci]),substantial_gain=substantial,strongest_available=best,
learning_specific=learned,promotion=n==64 and substantial and best,
strongest_observed_model=MODELS[int(point.argmax())],strongest_top3=float(point.max())))
write('summary.json',dict(rows=summary,comparisons=comparisons,gates=gates,all_thirteen_days_retained=True,
confirmation_days=[9,10,11,12],one_physical_platform=True,draws=20000,comparison_family=40,
original_dynamic_mixture_source_gate_remains_failed=True))
write('analysis_execution.json',dict(seconds=time.perf_counter()-start,device=device));verify();print(json.dumps(gates),flush=True)
def acquisition(device='cuda:1'):
verify();torch.cuda.set_device(device);torch.backends.cuda.matmul.allow_tf32=False
config=json.loads(PROTOCOL.read_text());p=torch.load(ROOT/'prepared.pt',map_location=device,weights_only=True);prep=json.loads((ROOT/'preparation.json').read_text())
rows=[]
for day in range(9,13):
eligible=torch.where(p['target']&p['unseen']&(p['days']==day))[0].tolist()
rows+=sorted(eligible,key=lambda i:hashlib.sha256(('20901:'+prep['records'][i]['id']).encode()).hexdigest())[:8]
ids=torch.tensor(rows,device=device);true=factorial.joint_index(p['h'][ids],[10,3]);results=[];start=time.perf_counter()
for name in config['candidates']:
checkpoint=torch.load(ROOT/'models'/f'n64_s{config["seeds"][0]}'/f'{name}.pt',map_location=device,weights_only=True)
x=(p['x'][ids]-checkpoint['mean'])/checkpoint['scale'];model=checkpoint['model']
for policy in config['acquisition']['policies']:
snapshots,trace=episode(model,x,policy,20911,(2,4,8),.01)
for budget,out in snapshots.items():
credit=factorial.topk_credit(out['probability'],true,3)
results.append(dict(model=name,policy=policy,budget=budget,rows=ids,days=p['days'][ids],top3=credit,queries=out['queries'],trace=trace))
print(json.dumps(dict(model=name,policy=policy,seconds=trace['seconds'])),flush=True)
torch.save(dict(results=results),ROOT/'acquisition.pt');write('acquisition_execution.json',dict(seconds=time.perf_counter()-start,
peak_gpu_bytes=torch.cuda.max_memory_allocated(device),secondary_only=True));verify()
if __name__=='__main__':
parser=argparse.ArgumentParser();parser.add_argument('command',choices=['lock','prepare','run','analyze','acquisition']);a=parser.parse_args()
{'lock':lock,'prepare':prepare,'run':run,'analyze':analyze,'acquisition':acquisition}[a.command]()
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