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from pathlib import Path
import argparse,csv,gzip,hashlib,json,sys,time
PROCESS_START=time.perf_counter()
import numpy as np
import torch
from reproduce import ROOT,load,predict,verify
from unionmind.decision_sufficiency import core
from unionmind.decision_sufficiency.analysis import paired_interval
METHODS=['identity','query_joint','posterior_clusters','posterior_bins',
'future_marginal','raw_information','raw_entropy','hash_code','action_values']
def sync():
torch.cuda.synchronize()
def action_profiles(fitted):
"""63 finite action values, computed exclusively from the estimated law."""
mass=fitted.sum((2,3))
conditional=fitted/mass[...,None,None].clamp_min(1e-300)
masks,children=core.context_tables(fitted.device)
masks=masks.to(fitted.dtype)
utility=fitted.new_tensor([[1.,-2.,0.],[-2.,1.,0.]])
terminal=torch.einsum('wrhy,ty,ha->wrta',conditional,masks,utility)
context_mass=torch.einsum('wrhy,ty->wrt',conditional,masks)
raw_plan=core.plan(conditional)
profile=[terminal.flatten(2)]
w,r=fitted.shape[:2]
costs=fitted.new_tensor([0.,.01,.05])
for budget in [1,2]:
previous=raw_plan['values'][budget-1].reshape(w,r,3,9)
for q in [0,1]:
for t,child in enumerate(children):
if child[q] is not None:
profile.append(previous[...,child[q]].sum(-1)-context_mass[...,t,None]*costs)
result=torch.cat(profile,-1)
assert result.shape==(w,r,63) and bool(torch.isfinite(result).all())
return result
def encode(fitted,name):
if name=='action_values':
return core.weighted_clusters(action_profiles(fitted),fitted.sum((2,3))),8
return core.encode(fitted,name)
def csv_write(path,records):
with gzip.open(path,'wt',newline='') as f:
writer=csv.DictWriter(f,fieldnames=list(records[0]))
writer.writeheader();writer.writerows(records)
@torch.no_grad()
def controlled(out,device):
archive=load('data/controlled/worlds',device)
manifest=json.loads((ROOT/'data/controlled/manifest.json').read_text())['worlds']
families=np.array([r['family'] for r in manifest])
worlds=[r['id'] for r in manifest]
rows=[];frontiers=[];timings=[]
for available in [6,3]:
truth=archive['joint'] if available==6 else core.project(archive['joint'],3)
for n in [64,256,1024,4096]:
for estimator in ['joint_jeffreys','naive_bayes','support_mixture']:
group={}
for stream in range(3):
sync();start=time.perf_counter()
fitted=core.estimate(core.count_samples(archive['samples'][:,stream,:n],available),estimator)
sync();estimation=time.perf_counter()-start
for method in METHODS:
sync();start=time.perf_counter()
code,k=encode(fitted,method);compressed=core.push(fitted,code,k)
policy=core.plan(compressed)
sync();construction=time.perf_counter()-start
gold=core.push(truth,code,k)
score=core.realized(gold,policy)['utility']
if available==6 and n==1024 and estimator=='support_mixture':
oracle=core.plan(gold)['expected'][:,1:,:].mean((1,2))
else:oracle=torch.full_like(score[:,0,0],float('nan'))
mean=score[:,1:,:].mean((1,2)).tolist()
oracle_values=oracle.tolist()
for i,value in enumerate(mean):
rows.append(dict(available=available,labels=n,estimator=estimator,
stream=stream,method=method,world=worlds[i],family=families[i],
utility=value,encoded_oracle=oracle_values[i]))
group.setdefault(method,[]).append(score.mean(0))
timings.append(dict(available=available,labels=n,estimator=estimator,
stream=stream,method=method,estimation_seconds=estimation,
encoding_push_planning_seconds=construction))
for method,records in group.items():
scores=torch.stack(records).mean(0)
for b in range(3):
for c,cost in enumerate([0.,.01,.05]):
frontiers.append(dict(available=available,labels=n,estimator=estimator,
method=method,budget=b,cost=cost,utility=float(scores[b,c])))
csv_write(out/'controlled_worlds.csv.gz',rows)
csv_write(out/'controlled_frontiers.csv.gz',frontiers)
primary=[r for r in rows if r['available']==6 and r['labels']==1024
and r['estimator']=='support_mixture']
per={}
for method in METHODS:
per[method]=np.array([[r['utility'] for r in primary if r['method']==method
and r['stream']==s] for s in range(3)]).mean(0)
old=json.loads((ROOT/'results/controlled/decision.json').read_text())
for reference in old['primary_metrics']:
assert abs(float(per[reference['method']].mean())-reference['utility'])<1e-10
delta=per['query_joint']-per['action_values']
comparisons=dict(joint_minus_action_values=float(delta.mean()),
descriptive_paired_95=paired_interval(delta,families,18701),
mean_utility={name:float(a.mean()) for name,a in per.items()},
status='Post-confirmation sensitivity on opened worlds; no new confirmation or promotion')
# Every declared encoder is timed, with construction and planner work charged.
bench=[];warmup=[]
for repetition in range(-1,11):
names=METHODS if repetition%2==0 else list(reversed(METHODS))
for method in names:
sync();start=time.perf_counter()
fitted=core.estimate(core.count_samples(archive['samples'][:,0,:1024],6),'support_mixture')
code,k=encode(fitted,method);compressed=core.push(fitted,code,k);policy=core.plan(compressed)
sync();elapsed=time.perf_counter()-start
row=dict(repetition=repetition,method=method,complete_construction_seconds=elapsed,
retained_encoder_and_law_bytes=code.numel()*code.element_size()+compressed.numel()*compressed.element_size())
(warmup if repetition<0 else bench).append(row)
comparisons['warm_batched_construction_medians']={
name:float(np.median([r['complete_construction_seconds'] for r in bench if r['method']==name]))
for name in METHODS}
(out/'controlled_timing.json').write_text(json.dumps(dict(
scope='192-world batch; estimation, profiles, clustering, pushforward and exact plans included',
warmup=warmup,repetitions=bench,grid_timings=timings),indent=2)+'\n')
(out/'controlled_summary.json').write_text(json.dumps(comparisons,indent=2)+'\n')
return comparisons
@torch.no_grad()
def sensors(out,device):
accounting={};normalization=[];hydraulic=None
for cohort in ['hydraulic','temperature']:
p=load(f'data/{cohort}/prepared',device)
if cohort=='hydraulic':p.update(p['masks']);hydraulic=p
if cohort=='temperature':
excluded=(p['days']<9)&p['unseen']
assert int(excluded.sum())==128
assert bool((p['source']|p['target']|excluded).all())
assert not bool((p['source']&p['target']).any())
accounting[cohort]=dict(retained=len(p['x']),source=int(p['source'].sum()),
target=int(p['target'].sum()),excluded_source_bindings=int(excluded.sum()),
per_day=[dict(day=int(d),retained=int((p['days']==d).sum()),
source=int(((p['days']==d)&p['source']).sum()),
excluded_source_bindings=int(((p['days']==d)&excluded).sum()),
target=int(((p['days']==d)&p['target']).sum())) for d in p['days'].unique()])
for n in [64,128]:
for directory in sorted((ROOT/'models'/cohort).glob(f'n{n}_s*')):
common=None
for path in sorted(directory.glob('*.json')):
c=load(str(path.relative_to(ROOT).with_suffix('')),device)
ids=c['fit_ids']
assert len(ids)==n and bool(p['source'][ids].all()) and not bool(p['unseen'][ids].any())
if common is None:common=ids
else:assert torch.equal(ids,common)
dm=float((c['mean']-p['x'][ids].mean(0)).abs().max())
ds=float((c['scale']-p['x'][ids].std(0).clamp_min(1e-6)).abs().max())
assert dm<1e-10 and ds<1e-10,(cohort,n,directory.name,path.name,dm,ds)
normalization.append(dict(cohort=cohort,prefix=n,fit=directory.name,
model=path.stem,maximum_mean_error=dm,maximum_scale_error=ds,
normalization_records=n,additional_unlabeled_records=0))
p=hydraulic;sizes=[3,4,3,4];truth=load('results/hydraulic/predictions',device)['results'][0]
held=truth['held_rows'][truth['unseen']]
true=core.bits(1,device) # no fitted module is instantiated or trained
from unionmind.continuous_decision import factorial
true=factorial.joint_index(p['h'][held],sizes)
fits=[];cross=[];bindings=[]
for seed in [20751,20752,20753]:
ck={n:load(f'models/hydraulic/n{n}_s{seed}/factorized_neural',device) for n in [64,128]}
assert torch.equal(ck[64]['fit_ids'],ck[128]['fit_ids'][:64])
for n,c in ck.items():
ids=c['fit_ids'];m=c['model'];x=(p['x'][ids]-c['mean'])/c['scale']
hx=factorial.joint_index(p['h'][ids],sizes)
evalx=(p['x'][held]-c['mean'])/c['scale']
eig=torch.linalg.eigvalsh(m['cov'])
fits.append(dict(seed=seed,labels=n,nested_prefix=True,
observed_bindings=len(hx.unique()),
factor_level_counts=[torch.bincount(p['h'][ids,f],minlength=k).tolist() for f,k in enumerate(sizes)],
training_mean_squared_error=float((x-m['mean'][hx]).square().mean()),
unseen_true_mean_squared_error=float((evalx-m['mean'][true]).square().mean()),
covariance_trace=float(eig.sum()),covariance_minimum_eigenvalue=float(eig.min()),
covariance_maximum_eigenvalue=float(eig.max()),covariance_condition_number=float(eig.max()/eig.min())))
for mean_n in [64,128]:
for cov_n in [64,128]:
c=ck[mean_n];model=dict(c['model'])
ratio=ck[cov_n]['scale']/c['scale']
model['cov']=ck[cov_n]['model']['cov']*ratio[:,None]*ratio[None,:]
x=(p['x'][held]-c['mean'])/c['scale']
prob=factorial.posterior(model,x,range(56));score=factorial.topk_credit(prob,true,3)
cross.append(dict(seed=seed,mean_labels=mean_n,covariance_labels=cov_n,
top3=float(score.mean()),hypothesis_log_loss=float(-prob.gather(1,true[:,None]).clamp_min(1e-300).log().mean())))
for h in true.unique():
mask=true==h
bindings.append(dict(seed=seed,mean_labels=mean_n,covariance_labels=cov_n,
binding=int(h),records=int(mask.sum()),top3=float(score[mask].mean())))
result=dict(status='Descriptive frozen-checkpoint diagnostics, no refitting',
normalization_checkpoints=len(normalization),normalization=normalization,
episode_accounting=accounting,frozen_hydraulic_fits=fits,crossed_mean_covariance=cross)
(out/'sensor_diagnostics.json').write_text(json.dumps(result,indent=2)+'\n')
csv_write(out/'hydraulic_bindings.csv.gz',bindings)
return result
def main():
parser=argparse.ArgumentParser(description=__doc__)
parser.add_argument('--device',default='cuda:0');parser.add_argument('--output',required=True)
a=parser.parse_args()
assert a.device.startswith('cuda') and torch.cuda.is_available()
out=Path(a.output).resolve();assert not out.exists(),'Use a new output directory.'
protocol=json.loads((ROOT/'protocols/postconfirmation_analysis_01.json').read_text())
assert protocol['status']=='Post-confirmation analysis of opened data'
assert hashlib.sha256(Path(__file__).read_bytes()).hexdigest()==protocol['code_sha256']
out.mkdir(parents=True);torch.cuda.set_device(a.device)
torch.backends.cuda.matmul.allow_tf32=False
sync();start=time.perf_counter();initialization=start-PROCESS_START
with torch.no_grad():
controlled_summary=controlled(out,a.device);sensor_summary=sensors(out,a.device)
sync()
ledger=dict(opened_data=True,new_neural_modules=0,refitted_models=0,backbone_executions=0,
source_files_modified=False,initialization_seconds=initialization,
complete_analysis_seconds=time.perf_counter()-start,
complete_process_since_framework_import_seconds=time.perf_counter()-PROCESS_START,
peak_cuda_allocated_bytes=torch.cuda.max_memory_allocated(),
gpu=torch.cuda.get_device_name(),device=a.device,protocol_code_sha256=protocol['code_sha256'])
(out/'compute.json').write_text(json.dumps(ledger,indent=2)+'\n')
print(json.dumps(dict(controlled=controlled_summary,
normalization_checkpoints=sensor_summary['normalization_checkpoints'],
episode_accounting=sensor_summary['episode_accounting'],compute=ledger),indent=2))
if __name__=='__main__':main()
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