Datasets:
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9126e0d | 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 | """Image-paired summaries and calibration with explicit finite-sample limits.
The bound concerns ADDITIONAL false abstention from an already nonempty CACP
mask. It does not bound total false-empty errors and does not survive arbitrary
train-to-test distribution shift. All tested thresholds share a Bonferroni bound.
"""
from pathlib import Path
import argparse,json,collections,csv,gzip,hashlib
import numpy as np
from scipy.optimize import minimize
from scipy.special import expit
from scipy.stats import beta
ROOT=Path(__file__).resolve().parents[1];OUT=ROOT/'results/analysis'
SEEDS=[11,23,37];GRID=np.r_[np.arange(.025,1.001,.025),1.001]
FAMILY_SIZE=40*2*3*2 # thresholds x positive strata x fits x backbones
def fit_logistic(rows,seed):
x=np.array([r['features'] for r in rows]);y=np.array([r['target_count']==0 for r in rows],float)
rng=np.random.RandomState(seed);idx=np.concatenate([rng.choice(np.flatnonzero(y==v),int((y==v).sum()),replace=True) for v in [0,1]])
x=x[idx];y=y[idx];mu=x.mean(0);sd=x.std(0);sd[sd<1e-8]=1.;z=np.c_[(x-mu)/sd,np.ones(len(x))]
w=np.where(y==1,.5/max((y==1).mean(),1e-6),.5/max((y==0).mean(),1e-6))
def objective(b):
v=z@b;loss=np.mean(w*(np.logaddexp(0,v)-y*v))+.01*np.dot(b[:-1],b[:-1])
grad=z.T@(w*(expit(v)-y))/len(y);grad[:-1]+=.02*b[:-1]
return loss,grad
opt=minimize(objective,np.zeros(z.shape[1]),jac=True,method='L-BFGS-B',options={'maxiter':1000,'ftol':1e-12})
if not opt.success:raise RuntimeError('Logistic fit failed: '+opt.message)
return dict(mean=mu.tolist(),std=sd.tolist(),coef=opt.x.tolist(),n=len(y),seed=seed,objective=float(opt.fun))
def probability(rows,fit):
x=np.array([r['features'] for r in rows]);return expit(np.c_[(x-np.array(fit['mean']))/np.array(fit['std']),np.ones(len(x))]@np.array(fit['coef']))
def cp_upper(k,n,alpha):
if n==0:return 1.
return 1. if k==n else float(beta.ppf(1-alpha,k+1,n-k))
def operating_table(rows,fit):
iid_images=len({r['scene_id'] for r in rows})==len(rows)
p=probability(rows,fit);y=np.array([r['target_count']==0 for r in rows]);base=np.array([r['scores']['cacp']['empty'] for r in rows]);counts=np.array([r['target_count'] for r in rows]);out=[]
for t in GRID:
reject=p>=t;empty=base|reject;additional=reject&~base;groups={}
for name,sel in [('one',counts==1),('multi',counts>1)]:
n=int(sel.sum());k=int((additional&sel).sum())
groups[name]={'n':n,'k':k,'upper':(0. if t>1 else cp_upper(k,n,.05/FAMILY_SIZE)) if iid_images else None}
acc0=float(empty[y].mean()) if y.any() else 0.;far=float(empty[~y].mean()) if (~y).any() else 0.
out.append({'threshold':float(t),'no_target_accuracy':acc0,'false_empty':far,'additional_false_abstention':float(additional[~y].mean()),'balanced_accuracy':.5*(acc0+1-far),'groups':groups,'worst_upper':max(g['upper'] for g in groups.values()) if iid_images else None})
return out
def add_calibration(rows):
params={};curve=[]
for seed in SEEDS:
for domain in ['controlled','natural']:
train=[r for r in rows if r['domain']==domain and r['split']=='fit' and r['mode']!='action']
cal=[r for r in rows if r['domain']==domain and r['split']=='cal' and r['mode']!='action']
fit=fit_logistic(train,seed);table=operating_table(cal,fit)
uncon=max(table,key=lambda x:(x['balanced_accuracy'],x['threshold']))
variants={('source_unconstrained' if domain=='controlled' else 'target_unconstrained'):uncon}
if domain=='natural':
for eps in [.01,.025,.05,.10]:
allowed=[x for x in table if x['worst_upper']<=eps]
variants[f'constrained_{eps:g}']=max(allowed,key=lambda x:(x['no_target_accuracy'],-x['additional_false_abstention'],x['threshold']))
curve.extend(dict(seed=seed,**x) for x in table)
scores=probability(rows,fit)
for variant,point in variants.items():
name=f'{variant}_s{seed}';t=point['threshold'];params[name]={'fit':fit,'operating_point':point,'calibration_domain':domain}
for r,pr in zip(rows,scores):
base=r['scores']['cacp'];reject=bool(pr>=t)
# Explicit action semantics are already handled by CACP.
val=dict(base)
if reject:val.update(iou=1. if r['target_count']==0 else 0.,intersection=0,union=base['gt_pixels'],pred_pixels=0,empty=True)
val['additional_abstention']=reject and not base['empty'];r['scores'][name]=val
if domain=='natural' and seed==11:r['absence_probability']=float(pr)
return params,curve
def cluster_ci(values,keys,strata=None,reps=2000):
vals=np.asarray(values,float);groups=collections.defaultdict(list)
for i,k in enumerate(keys):groups[k].append(i)
ids=list(groups);arr=np.array([vals[groups[k]].mean(0) for k in ids])
if strata is None:labels=np.zeros(len(ids),int)
else:labels=np.array([str(strata[groups[k][0]]) for k in ids])
rng=np.random.RandomState(20260906);boot=np.zeros((reps,)+arr.shape[1:]);n=0
for label in sorted(set(labels)):
a=arr[labels==label];draw=rng.randint(0,len(a),size=(reps,len(a)));boot+=a[draw].sum(axis=1);n+=len(a)
boot/=n
return arr.mean(axis=0),np.quantile(boot,.025,axis=0),np.quantile(boot,.975,axis=0),len(arr)
def summarize(rows,methods):
summaries=[]
def emit(name,rr,metric,threshold=.5):
if not rr:return
if metric=='pc':
pairs=collections.defaultdict(list)
for r in rr:pairs[r['pair_id']].append(r)
assert all(len(x)==2 for x in pairs.values())
data=[];keys=[];strata=[]
for pp in pairs.values():
data.append([float(all(r['scores'][m]['iou']>=threshold for r in pp)) for m in methods]);keys.append(pp[0]['scene_id']);strata.append(pp[0]['seed'])
else:
data=[];keys=[];strata=[]
for r in rr:
data.append([float(r['scores'][m]['empty']) if metric in ['no_target_accuracy','false_empty'] else float(r['scores'][m].get('additional_abstention',False)) if metric=='additional_false_abstention' else r['scores'][m]['iou'] for m in methods]);keys.append(r['scene_id']);strata.append(r['seed'] if r['domain']=='controlled' else r['split']+'_'+r['mode'])
data=np.array(data);mean,lo,hi,n=cluster_ci(data,keys,strata)
delta,dlo,dhi,_=cluster_ci(data-data[:,[methods.index('frozen')]],keys,strata)
for i,m in enumerate(methods):summaries.append(dict(population=name,metric=('PC50' if threshold==.5 else 'PC70') if metric=='pc' else metric,method=m,estimate=float(mean[i]),lo=float(lo[i]),hi=float(hi[i]),gain=float(delta[i]),gain_lo=float(dlo[i]),gain_hi=float(dhi[i]),clusters=n,units=len(data)))
controlled=[r for r in rows if r['domain']=='controlled' and r['split']=='test']
clean=[r for r in controlled if r['corruption']=='clean']
primary=[r for r in clean if r['mode'] in ['attribute','relation','quantifier','absence']]
emit('controlled_primary',primary,'pc');emit('controlled_primary',primary,'pc',.7)
for family in ['attribute','relation','quantifier','action','absence','paraphrase']:
emit('family_'+family,[r for r in clean if r['mode']==family],'pc')
for template in ['seen','held']:emit('template_'+template,[r for r in primary if r['template']==template],'pc')
stress_ids={r['scene_id'] for r in controlled if r['corruption']!='clean'}
for corruption in ['clean','blur','noise','dim']:
rr=[r for r in controlled if r['corruption']==corruption and r['scene_id'] in stress_ids and r['mode'] in ['attribute','relation','quantifier','absence']]
emit('stress_'+corruption,rr,'pc')
for name,rr in [('controlled_clean',clean),('natural_test',[r for r in rows if r['domain']=='natural' and r['split'] in ['testA','testB']])]+[(s,[r for r in rows if r['domain']=='natural' and r['split']==s]) for s in ['val','testA','testB']]:
emit(name,rr,'gIoU');emit(name,[r for r in rr if r['target_count']>0],'positive_mIoU');emit(name,[r for r in rr if r['target_count']==0],'no_target_accuracy');emit(name,[r for r in rr if r['target_count']>0],'false_empty');emit(name,[r for r in rr if r['target_count']>0],'additional_false_abstention')
for mode in ['zero','one','multi']:
rr=[r for r in rows if r['domain']=='natural' and r['split'] in ['testA','testB'] and r['mode']==mode]
emit('natural_'+mode,rr,'gIoU')
return summaries
def main():
a=argparse.ArgumentParser();a.add_argument('--model',choices=['clipseg','groundedsam']);args=a.parse_args();OUT.mkdir(parents=True,exist_ok=True)
models=[args.model] if args.model else ['clipseg','groundedsam'];allsummary=[];allparam={};allcurves=[];metadata={}
main_methods=['frozen','action_only','largest','global_direction','target_only','anchor_nogate','anchor_gate','counterfactual_nogate','cacp','source_unconstrained_s11','target_unconstrained_s11','constrained_0.05_s11']
for model in models:
shards=2 if model=='clipseg' else 8
files=sorted((ROOT/'results').glob(model+f'_main_*of{shards}.jsonl'));metas=sorted((ROOT/'results').glob(model+f'_main_*of{shards}_meta.json'))
if len(files)!=shards or len(metas)!=shards:raise RuntimeError('Model runs are incomplete: '+model)
rows=[json.loads(l) for f in files for l in f.read_text().splitlines()];expected=json.loads((ROOT/'data/dataset_summary.json').read_text())['records']
assert len(rows)==expected and len({r['id'] for r in rows})==expected
assert all(json.loads(m.read_text())['status']=='COMPLETE' for m in metas)
params,curves=add_calibration(rows);allparam[model]=params;allcurves.extend(dict(model=model,**c) for c in curves)
ss=summarize(rows,main_methods);allsummary.extend(dict(model=model,**s) for s in ss)
# Seed/tolerance sensitivity is a separate table, not independent evidence.
sensitivity=[]
for seed in SEEDS:
for eps in [.01,.025,.05,.1]:
name=f'constrained_{eps:g}_s{seed}'
for pop,sel in [('natural_test',[r for r in rows if r['domain']=='natural' and r['split'] in ['testA','testB']]),('val',[r for r in rows if r['domain']=='natural' and r['split']=='val'])]:
pos=[r for r in sel if r['target_count']>0];neg=[r for r in sel if r['target_count']==0]
sensitivity.append(dict(model=model,seed=seed,tolerance=eps,population=pop,threshold=params[name]['operating_point']['threshold'],cal_upper=params[name]['operating_point']['worst_upper'],positive_iou=float(np.mean([r['scores'][name]['iou'] for r in pos])),no_target_accuracy=float(np.mean([r['scores'][name]['empty'] for r in neg])),false_empty=float(np.mean([r['scores'][name]['empty'] for r in pos])),additional_false_abstention=float(np.mean([r['scores'][name]['additional_abstention'] for r in pos]))))
(OUT/f'{model}_sensitivity.json').write_text(json.dumps(sensitivity,indent=2))
# Oracle anchor substitution: diagnostic, not a guaranteed upper bound.
oracle_rows=[r for r in rows if r['domain']=='controlled'];os=summarize(oracle_rows,['frozen','cacp','oracle_anchor'])
(OUT/f'{model}_oracle.json').write_text(json.dumps(os,indent=2))
reasons=collections.Counter(r['flags']['reason'] for r in rows if r['split'] in ['test','testA','testB','val']);bydomain={}
for domain in ['controlled','natural']:
rr=[r for r in rows if r['domain']==domain and r['split'] in ['test','testA','testB','val'] and r['corruption']=='clean']
bydomain[domain]={'n':len(rr),'supported':sum(r['flags']['supported'] for r in rr),'changed':sum(r['flags']['changed'] for r in rr),'harm_iou':sum(r['scores']['cacp']['iou']<r['scores']['frozen']['iou'] for r in rr),'help_iou':sum(r['scores']['cacp']['iou']>r['scores']['frozen']['iou'] for r in rr)}
metadata[model]={'runs':[json.loads(m.read_text()) for m in metas],'reason_counts':dict(reasons),'coverage':bydomain}
with gzip.open(OUT/f'{model}_evaluated.jsonl.gz','wt') as f:
for r in rows:f.write(json.dumps(r)+'\n')
# Test trade-off curves use the fixed calibration-trained score; no tuning.
test=[r for r in rows if r['domain']=='natural' and r['split'] in ['testA','testB']]
curve_test=operating_table(test,params['target_unconstrained_s11']['fit'])
(OUT/f'{model}_test_curve.json').write_text(json.dumps(curve_test,indent=2))
print('ANALYZED',model,flush=True)
(OUT/'summary.json').write_text(json.dumps(allsummary,indent=2))
with (OUT/'summary.csv').open('w') as f:
writer=csv.DictWriter(f,fieldnames=list(allsummary[0]));writer.writeheader();writer.writerows(allsummary)
(OUT/'calibration.json').write_text(json.dumps(allparam,indent=2));(OUT/'calibration_curves.json').write_text(json.dumps(allcurves,indent=2));(OUT/'run_summary.json').write_text(json.dumps(metadata,indent=2))
(OUT/'statistical_notes.json').write_text(json.dumps({'bootstrap_replicates':2000,'interval':'95% percentile, paired image/scene clusters, stratified by seed or official split and target stratum','multiplicity':'Confidence intervals are descriptive and unadjusted; ablations are not independent replications','calibration_family_size':FAMILY_SIZE,'calibration_alpha':.05,'calibration_scope':'Simultaneous stratum-wise Clopper-Pearson bounds for additional false abstention under independent, identically distributed Bernoulli sampling within each stratum; official train/test shifts are evaluated empirically, not guaranteed','minimum_n_zero_failures':{str(eps):int(np.ceil(np.log(.05/FAMILY_SIZE)/np.log(1-eps))) for eps in [.01,.025,.05,.1]}},indent=2))
if __name__=='__main__':main()
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