def pfaget(f):
if not 'sig' in f: raise ValueError("feaget without sig for: " + f['fn'])
fea = ifea.fft(f['sig'],
crate=icfg.get('pfa.crate'),
wlen=icfg.get('pfa.wlen')).db()
ifea.cavg(fea, rat=4, inplace=True)
fea.sel(axis=len(fea.shape) - 1, len=icfg.get('pfa.dim'), inplace=True)
return fea
def hmmtrn(ftrn,ftst,fea,s,kwargs={}):
print('hmm start '+fea+'_'+s)
chmm=ihmm.trn(flst=ftrn,fea=fea,its=[0],states=3,**kwargs)
nldtrn=ihmm.evlp(chmm,flst=ftrn,fea=fea)
nldtst=ihmm.evlp(chmm,flst=ftst,fea=fea)
if icfg.get('exp')=='triclass' or icfg.get('trn.regression')==True:
print('hmm finish '+fea+'_'+s)
else:
res=np.array(chmm['cls'])[nldtst.argmin(axis=1)]
icls.cmp(ftst,res,'hmm finish '+fea+'_'+s)
return (chmm,nldtrn,nldtst)
def dnntrn(ftrn,ftst,fea,s,kwargs={}):
print('dnn start '+fea+'_'+s)
cdnn=idnn.trn(ftrn,ftst,fea=fea,dmod=dmod,**kwargs)
if cdnn is None:
print('dnn failed '+fea+'_'+s)
return (None,None,[])
else:
restrn=idnn.evlp(cdnn,ftrn,fea=fea)
restst=idnn.evlp(cdnn,ftst,fea=fea)
if icfg.get('exp')=='triclass' or icfg.get('trn.regression')==True:
print('dnn finish '+fea+'_'+s)
else:
res=np.array(cdnn['cls'])[prob.argmax(axis=1)]
icls.cmp(ftst,res,'dnn finish '+fea+'_'+s)
return (cdnn,restrn,restst)
def run_sen(s):
print("flst [%s]"%(s))
fdb=ifdb.Fdb(s)
ftrns=ftrn.expandsensor(s,fdb).maplab(labmap)
ftsts=ftst.expandsensor(s,fdb).maplab(labmap)
for typ in ['sig','pfa']: fdb.analyse(typ,eval(typ+'get'))
sfaget(ftrns,ftsts,fdb)
fdb.save()
if not regression: ftrns=ftrns.equalcls()
if '-n' in sys.argv: return
for cls in clsuse:
for fea in feause:
if cls=='hmm' and ftrns[0][fea].shape[-1]>40: continue
resfn=os.path.join(dlog,'res_'+cls+'_'+fea+'_'+s+'.npy')
if os.path.exists(resfn): continue
print('####################',fea,cls,s,'####################')
kwargs=icfg.get('trnargs.%s.%s'%(cls,fea))
if kwargs is None:
if cls=='dnn' or cls[:3]=='cnn': continue
kwargs={}
else:
print('trnargs = '+kwargs)
kwargs=eval(kwargs)
kwargs['regression']=regression
fnctrn=eval(cls[:3]+'trn')
for i in range(3):
(mod,restrn,restst)=fnctrn(ftrns,ftsts,fea,s,kwargs)
if len(restst)>0:
np.save(resfn,restst)
np.save(resfn[:-4]+'_trn.npy',restrn)
eval('i'+cls[:3]+'.save')(mod,resfn[:-4]+'.model')
break
def dnntest(s,fea='sfa',**kwargs):
fdb=ifdb.load(s)
ftrns=ftrn.expandsensor(s,fdb).maplab(labmap)
ftsts=ftst.expandsensor(s,fdb).maplab(labmap)
if icfg.get('trn.regression')!=True: ftrns=ftrns.equalcls()
cdnn=idnn.trn(ftrns,ftsts,fea=fea,dmod=dmod,verbose=True,**kwargs)
if not cdnn is None: cdnn['idim']=ftrns[0][fea].shape
return cdnn
def plot_roc(ftst, nld):
okpat = 'Z00'
if icfg.get('db') == 'izfp/cfk': okpat = 'Z0[0-2]'
detect = (
nld[:,
np.array([not re.match(okpat, f['lab']) is None
for f in ftst])].mean(axis=0),
nld[:, np.array([re.match(okpat, f['lab']) is None
for f in ftst])].mean(axis=0),
)
dres = []
for v in sorted([*detect[0], *detect[1]]):
dres.append({
'x': v,
'tp': np.sum(detect[0] > v),
'fp': np.sum(detect[1] > v),
'fn': np.sum(detect[0] <= v),
'tn': np.sum(detect[1] <= v),
})
for r in dres:
r['facc'] = r['fp'] / (r['fp'] + r['tn'])
r['frej'] = r['fn'] / (r['fn'] + r['tp'])
dpl = sorted([[r['facc'], r['frej']] for r in dres],
key=lambda a: a[0] * 100 + (1 - a[1]))
def sigget(f):
dsig = icfg.getdir('sig')
sigext = '.' + icfg.get('sig.ext', 'wav')
sig = isig.load(os.path.join(dsig, f['fn'] + sigext)).rmaxis()
sig.inc = [1 / icfg.get('sig.srate')]
return sig
if len(sys.argv) < 2:
raise ValueError("Usage: " + sys.argv[0] + " (CFG prob_*.npy | csv)")
if sys.argv[1] == 'csv':
for cfgi in range(1, 4):
cfg = '../cfk/X%i/info/default.cfg' % cfgi
os.system(str.join(' ', ['python3', sys.argv[0], cfg, 'csv']))
raise SystemExit()
icfg.Cfg(sys.argv[1])
ocsv = len(sys.argv) > 2 and sys.argv[2] == 'csv'
if ocsv: sys.argv.remove('csv')
ftst = icfg.readflst('test')
ftrn = icfg.readflst('train')
dlog = icfg.getdir('log')
sen = getsensors()
regression = icfg.get('trn.regression') == True
labmap = {}
if icfg.get('db') == 'izfp/cfk': labmap = {'Z0[0-2]': 'Z00'}
ftst.maplab(labmap)
lcls = np.array(icls.getcls(ftst))
if regression:
icls.labf(ftst)
lab = np.array([f['labf'] for f in ftst])
lfcls = [i[-1] for i in rle(sorted(lab))]
resh = {}
for fn in argv2resfns('res_', sys.argv[2:]):
if fn.find('_trn.npy') >= 0 or fn.find('.model') >= 0: continue
cls, fea, s = os.path.basename(fn)[4:-4].split('_')
np.save(dlog+'/dnnrnd/dnnrnd_'+str(int(time.time()))+'_par.npy',xpar)
if len(stat)==3: np.save(dlog+'/dnnrnd/dnnrnd_'+str(int(time.time()))+'_stat.npy',stat)
def dnnrndloop():
while not os.path.exists('stop'): dnnrnd()
if len(sys.argv)<2: raise ValueError("Usage: "+sys.argv[0]+" CFG [-n]")
icfg.Cfg(*sys.argv[1:])
ftrn=icfg.readflst('train')
ftst=icfg.readflst('test')
dmod=icfg.getdir('model')
dlog=icfg.getdir('log')
sen=getsensors()
regression=icfg.get('trn.regression')==True
senuse=sen
feause=['pfa','sfa','sig']
clsuse=['hmm','svm','cnnb','cnn','dnn']
if not icfg.get('senuse') is None: senuse=icfg.get('senuse').split(',')
if not icfg.get('feause') is None: feause=icfg.get('feause').split(',')
if not icfg.get('clsuse') is None: clsuse=icfg.get('clsuse').split(',')
labmap={}
if icfg.get('db')=='izfp/cfk': labmap={'Z0[0-2]':'Z00'}
maxjob=18
if '-nn' in sys.argv: raise SystemExit()
def prtres(prob, flst):
eer, cm = icls.eer(
prob,
flst=flst,
okpat='Z0[0-2]' if icfg.get('db') == 'izfp/cfk' else 'Z00')
return 'EER: %6.2f%% CM: %6.2f%%' % (eer * 100, cm * 100)
if len(sys.argv) < 2: raise ValueError("Usage: " + sys.argv[0] + " CFG [-n]")
icfg.Cfg(*sys.argv[1:])
if '-nn' in sys.argv: raise SystemExit()
print("flst")
ftrn = icfg.readflst('train')
ftst = icfg.readflst('test')
#fdev=[] if icfg.get('flist.dev') is None else icfg.readflst('dev')
dmod = icfg.getdir('model')
dlog = icfg.getdir('log')
dsig = icfg.getdir('sig')
sigext = '.' + icfg.get('sig.ext', 'wav')
sen = getsensors()
senuse = sen
feause = ['pfa', 'sfa', 'sig']
clsuse = ['hmm', 'svm']
if not icfg.get('senuse') is None: senuse = icfg.get('senuse').split(',')
if not icfg.get('feause') is None: feause = icfg.get('feause').split(',')
if not icfg.get('clsuse') is None: clsuse = icfg.get('clsuse').split(',')
maxjobs = 16
# Generate ftrns & ftsts for universal background model
ftrns = {}
for strn in senuse:
ftrns[strn] = []
dlog = icfg.getdir('log')
fns = argv2resfns('prob_', fns)
out = []
for fn in sorted(fns):
prob = np.load(fn)
msg = ''
if fn[-8:] == '_old.npy': continue
if fn.find('_hmm_') >= 0 and prob.shape[-1] == 3:
if np.sum(prob.take(2, -1) != prob.take(2, -1)[0]) == 0:
msg = ' ERR in hmm[1]'
prob = hmmprob(prob.take([1, 2], -1))
#prob=prob.take(0,-1)
okpat = 'Z0[0-2]' if icfg.get('db') == 'izfp/cfk' else 'Z00'
#okpat='Z0[01]'
eer, cm = icls.eer(prob, flst=ftst, okpat=okpat)
#ref=[not re.match(okpat,f['lab']) is None for f in ftst]
#msg+=' [%.2f<=>%.2f]'%(np.mean(prob.mean(axis=0)[ref]),np.mean(prob.mean(axis=0)[np.array(ref)==False]))
if np.max(prob.mean(0)) < 0.4:
msg += ' ERR low max %.2f' % (np.max(prob.mean(0)))
if np.min(prob.mean(0)) > 0.6:
msg += ' ERR high min %.2f' % (np.min(prob.mean(0)))
if ocsv:
(cls, fea) = os.path.basename(fn)[5:-4].split('_')
out.append(
'"%s" "%s" "%s" %s %s "%s"' %
(icfg.get('db').split('/')[-1] + '/' + icfg.get('exp'), fea, cls,