def computeScores(bn_name,csv_name,visible=False,transforme_label=None):
if isinstance(bn_name,str):
bn=gum.loadBN(bn_name)
else:
bn=bn_name
nbr_lines=lines_count(csv_name)-1
csvfile = open(csv_name, "rb")
dialect = csv.Sniffer().sniff(csvfile.read(1024))
csvfile.seek(0)
batchReader = csv.reader(open(csv_name,'rb'),dialect)
titre = batchReader.next()
fields = {}
for i,nom in enumerate(titre):
fields[nom]=i
positions=checkCompatibility(bn,fields,csv_name)
if positions is None:
sys.exit(1)
inst=gum.Instantiation()
bn.completeInstantiation(inst)
if visible:
prog = ProgressBar(csv_name+' : ',0, nbr_lines, 77, mode='static', char='#')
prog.display()
nbr_insignificant=0
num_ligne=0
likelihood=0.0
for data in batchReader:
num_ligne+=1
for i in range(inst.nbrDim()):
try:
inst.chgVal(i,getNumLabel(inst,i,data[positions[i]],transforme_label))
except gum.OutOfBounds:
print("out of bounds",i,positions[i],data[positions[i]],inst.variable(i))
p=bn.jointProbability(inst)
if p==0.0:
print(str(num_ligne)+":"+str(inst))
nbr_insignificant+=1
else:
likelihood+=math.log(p,2)
if visible:
prog.increment_amount()
prog.display()
if visible:
print
nbr_arcs=1.0*bn.sizeArcs()
dim=1.0*bn.dim()
aic=likelihood-dim
aicc=2*aic-2*dim*(dim+1)/(nbr_lines-dim+1) if (nbr_lines-dim+1>0) else "undefined"
bic=likelihood-dim*math.log(nbr_lines,2)
mdl=likelihood-nbr_arcs*math.log(nbr_lines,2)-32*dim #32=nbr bits for a params
return ((nbr_lines-nbr_insignificant)*100.0/nbr_lines,
{'likelihood':likelihood,'aic':aic,'aicc':aicc,'bic':bic,'mdl':mdl})
