def compareMeasureNG(edges,sims,ratio,names,wName=None,ciss=None,link_func=maxSim):
'''
NG means this experiment is not using the grouping
of cis-element
'''
precs=dict.fromkeys(names)
recalls=dict.fromkeys(names)
ss=dict.fromkeys(names)
ee=flatenDictList(edges)#ee is true edges
e=mdf.global_remove(edges,ratio)
for name in names:
ss[name]=flatenDict(netAlignInfer\
(link_func,e,sims[name]))
for name in names:
precs[name]=[]
recalls[name]=[]
for thresh in np.arange(0,1.0,0.1):
(p,r)=prEval(ee,ss[name],thresh)
precs[name].append(p)
recalls[name].append(r)
if not wName==None:
(precs[wName],recalls[wName])=doEntropyWeights(ee,e,ciss,\
link_func=link_func)
return (precs,recalls,ss)
def compareMeasureNG2(edges,sims,ratio,names,proms,wName=None,ciss=None,link_func=maxSim):
precs=dict.fromkeys(names)
recalls=dict.fromkeys(names)
ss=dict.fromkeys(names)
ee=flatenDictList(edges)#ee is true edges
e=mdf.global_remove(edges,ratio)
for name in names:
ss[name]=flatenDict(netAlignInfer\
(link_func,e,sims[name]))
tss=getTS2(edges,ciss,proms,ss[name],0.1)
factors=getAssoc2(*tss,min_c=0.9)
for name in names:
precs[name]=[]
recalls[name]=[]
for thresh in np.arange(0,1.0,0.1):
for key in ss[name].keys():
if key[1] in factors.keys():
if key[0]+str(ciss[key[1]].slen) == tss[1][key[1]]:
#print 'BEFORE',ss[name][key]
ss[name][key]=1
#print 'AFTER',ss[name][key]
(p,r)=prEval(ee,ss[name],thresh)
precs[name].append(p)
recalls[name].append(r)
if not wName==None:
(precs[wName],recalls[wName])=doEntropyWeights(ee,e,ciss,\
link_func=link_func)
return (precs,recalls,ss)
(edges,ciss,proms)=getPickle(netp)
nwSims=getPickle(pp1)
nwSimsL=getPickle(pp2)
dmSimsL=getPickle(pp3)
'''
# Repeats of experiments
N=5
ratio=0.3
x=[]
y=[]
x2=[]
y2=[]
x3=[]
y3=[]
ee=flatenDictList(edges)
for exp_i in range(N):
e=mdf.global_remove(edges,0.3)
scores1=netAlignInfer(maxSim,e,nwSims)
scores2=netAlignInfer(maxSim,e,nwSimsL)
scores3=netAlignInfer(maxSim,e,dmSimsL)
ss1=flatenDict(scores1)
ss2=flatenDict(scores2)
ss3=flatenDict(scores3)
for thresh in np.arange(0,1,0.1):
result=prEval(ee,ss1,thresh)
result2=prEval(ee,ss2,thresh)
result3=prEval(ee,ss3,thresh)
x.append(result[0])
y.append(result[1])
return len(com)/float(len(filt))
# Repeats of experiment
N=1
threshs=np.arange(0,1,0.3)
precs=[]
recalls=[]
for j in range(3):
precs.append({key:0 for key in range(len(threshs))})
recalls.append({key:0 for key in range(len(threshs))})
ee=flatenDictList(edges)
# Create the filter list of the hidden edges
# so that the evaluation only focus on these edges
for exp_i in range(N):
e=mdf.global_remove(edges,0.3)
# hid_e is a list of real edges hidden
entire_keys=edges.keys()
subset_keys=e.keys()
tmp_e=flatenDictList(e)
hid_e=set(ee.keys())-set(tmp_e.keys())
M=len(hid_e) # number of the hidden edges
print "start inferring"
scores1=netAlignInfer(maxSim,e,nwSims)
scores2=netAlignInfer(maxSim,e,nwSimsL)
scores3=netAlignInfer(maxSim,e,dmSimsL)