| from pathlib import Path |
| import sys,json,numpy as np |
| R=Path(__file__).resolve().parent;sys.path.insert(0,str(R)) |
| from inference import run,PRODUCT,PIN |
| from humanization_research.parameters import ParameterPack,predict,predict_proba,transform,isolation_score |
| from humanization_research.predictors import MRIPredictor |
| p=ParameterPack(R/'parameters',PIN);checks=[] |
| with np.load(R/'parameters/reference_fixtures.npz',allow_pickle=False) as f: |
| if PRODUCT=='ncl': |
| x=f['inputs'][:1] |
| for key,model in p.data['models'].items(): |
| np.testing.assert_allclose(predict(model,x),f[key][:1],rtol=0,atol=2e-12) if key!='progression_agent' else np.testing.assert_array_equal(predict(model,x),f[key][:1]);checks.append(key) |
| np.testing.assert_allclose(predict_proba(p.data['models']['progression_agent'],x),f['progression_agent_proba'][:1],rtol=0,atol=2e-12);checks.append('probabilities') |
| elif PRODUCT=='mri': |
| for space in ['baseline','huret']: |
| values=MRIPredictor(p).scores(space,f[space+'_inputs'][:1]) |
| for value,key in zip(values,['raw','pct','cluster']):np.testing.assert_allclose(value,f[space+'_'+key][:1],rtol=0,atol=2e-10);checks.append(space+'_'+key) |
| else: |
| x=f['inputs'][:1];z=transform(p.data['scaler'],x);np.testing.assert_array_equal(z,f['scaled'][:1]);checks.append('scaler') |
| np.testing.assert_allclose(predict(p.data['state_encoder'],z),f['state'][:1],rtol=0,atol=2e-6);checks.append('state') |
| np.testing.assert_allclose(isolation_score(p.data['ood_model'],z),f['ood'][:1],rtol=0,atol=2e-12);checks.append('ood') |
| for name in ['fog_guard','balance_guard']: |
| g=p.data[name];np.testing.assert_allclose(predict_proba(g['model'],transform(g['scaler'],x)),f[name][:1],rtol=0,atol=2e-12);checks.append(name) |
| xx=f['response_input'][:1];np.testing.assert_allclose(predict(p.data['response_full_model'],xx),f['response_full'][:1],rtol=0,atol=2e-6);checks.append('response_full') |
| for i,m in enumerate(p.data['response_ensemble']):np.testing.assert_allclose(predict(m,xx),f['response_'+str(i)][:1],rtol=0,atol=2e-6);checks.append('response_'+str(i)) |
| request=json.loads((R/'example.json').read_text());answer=run(request);assert answer['clinical_claim'] is False;checks.append('clinical_disabled') |
| bad=dict(request);bad['synthetic']=False |
| try:run(bad) |
| except (ValueError,TypeError):checks.append('patient_input_rejected') |
| else:raise AssertionError('Patient input accepted') |
| if PRODUCT=='rhythm': |
| bad=dict(request);bad.pop('acknowledge_export_version_mismatch') |
| try:run(bad) |
| except (ValueError,TypeError):checks.append('version_mismatch_default_rejection') |
| else:raise AssertionError('Version warning bypassed') |
| print(json.dumps({'status':'PASS','checks':checks,'n_checks':len(checks),'fixture_rows_per_space':1,'scope':'First saved synthetic export fixture; no new clinical or training evaluation'},indent=2)) |
|
|
