def showInference(model, evs=None, size=None):
try:
# fails outside notebook
import pyAgrum.lib.notebook as gnb
gnb.showInference(model, evs=evs, size=size)
except ImportError:
pass
bn.cpt('relationship')[0:] = [ 1, 1, 1,10,10,10]
bn.cpt('relationship')[1:] = [ 10, 10, 10,1,1,1]
bn.cpt('relationship').normalizeAsCPT()
bn.cpt('relationship')
bn.cpt('marital_status')[0:] = [ 1, 1, 1,1,10,10,10]
bn.cpt('marital_status')[1:] = [ 10, 10, 10,10,1,1,1]
bn.cpt('marital_status').normalizeAsCPT()
bn.cpt('marital_status')
bn.cpt('occupation')[0:] = [ 1, 1, 1,1,1,1,1,10,10,10,10,10,10,10]
bn.cpt('occupation')[1:] = [ 10, 10,10,10,10,10,10,1,1,1,1,1,1,1]
bn.cpt('occupation').normalizeAsCPT()
bn.cpt('occupation')
gnb.showInference(bn,size="10")
gnb.showInference(bn,size="10", evs={'target':'<=50K'})
gnb.showInference(bn,size="10", evs={'target':'>50K'})
gnb.showInference(bn,size="10", evs={'target':'<=50K', 'sex':' Male'})
male_str=[" ?"]
for (row, col) in train_df.iterrows():
if str.lower(col.occupation) in male_str:
train_df['occupation'].replace(to_replace=col.occupation, value=' Sales', inplace=True)
if str.lower(col.workclass) in male_str:
train_df['workclass'].replace(to_replace=col.workclass, value=' Private', inplace=True)
ie=gum.LazyPropagation(bn)
bn.cpt(s)[{'c': 1}] = [0.9,0.1]
bn.cpt(s)
bn.cpt(w)[{'r': 0, 's': 0}] = [1, 0]
bn.cpt(w)[{'r': 0, 's': 1}] = [0.1, 0.9]
bn.cpt(w)[{'r': 1, 's': 0}] = [0.1, 0.9]
bn.cpt(w)[{'r': 1, 's': 1}] = [0.01, 0.99]
bn.cpt(w)
bn.cpt(r)[{'c':0}]=[0.8,0.2]
bn.cpt(r)[{'c':1}]=[0.2,0.8]
bn.cpt(r)
"""Qual é a probabilidade de a grama estar molhada em um caso geral?"""
gnb.showInference(bn,evs={})
"""Vemos que a probabilidade da grama estar molhada no geral é de 64,7%
Supondo que queremos inferir a partir das seguintes condições a priori:
Não está nublado e o aspersor está ligado. Qual a probabilidade de a grama estar molhada?
"""
gnb.showInference(bn,evs={'s':1,'c':0})
"""Vemos que a probabilidade é de 91,8%.
## Agora é sua vez!
Imagine que está nublado e está chovendo. Qual a probabilidade da grama estar molhada?
"""
s = bn.cpt("L").var_names
s.reverse()
p.reorganize(s)
bn.cpt("L").fillWith(p)
p = gum.Potential().add(bn.variable("J")).add(bn.variable("T")).add(
bn.variable("V"))
p.fillWith(list(VibrationKnowingTorqueAndJoint))
s = bn.cpt("V").var_names
s.reverse()
p.reorganize(s)
bn.cpt("V").fillWith(p).normalizeAsCPT()
showInformation(bn)
# %%
showInference(bn, size="20")
# %%
showInference(bn, evs={"L": True}, size="20")
# %%
showInference(bn, evs={"L": False, "A": "0.2"}, size="20")
# %%
ie = gum.LazyPropagation(bn)
ie.addJointTarget(set(["T", "J"]))
ie.setEvidence({"L": True})
ie.makeInference()
# %%
distrib = otagrum.Utils.FromPotential(ie.jointPosterior({"T", "J"}))
def main():
bn = gum.BayesNet('nuc_inf')
#add variables to the network
va = gum.LabelizedVariable('nuc', 'a labelized variable', 2)
va.addLabel('-1')
nuc = bn.add(va)
A = bn.add('A', 6)
R, N = [bn.add(name, 7) for name in ['R', 'N']]
D, Q = [bn.add(name, 2) for name in ['D', 'Q']]
partition("protein")
learner = gum.BNLearner("protein_train.csv", bn)
#These arcs can be added or deleted
#learner.addMandatoryArc('A','nuc')
#learner.addMandatoryArc('R','nuc')
#learner.addMandatoryArc('Q','nuc')
#learner.addMandatoryArc('N','nuc')
#learner.addMandatoryArc('D','nuc')
learner.useLocalSearchWithTabuList()
bn0 = learner.learnBN()
gnb.showBN(bn0)
learner.useGreedyHillClimbing()
bn1 = learner.learnBN()
gnb.showBN(bn1)
learner.useK2([5, 4, 3, 2, 1, 0])
bn2 = learner.learnBN()
gnb.showBN(bn2)
#We have 2 different BN structures according to the previous parts. Now, we do parameter learning
learner = gum.BNLearner("protein_train.csv", bn)
learner.setInitialDAG(bn0.dag())
learner.useAprioriSmoothing(1)
bn01 = learner.learnParameters() #first
gnb.showBN(bn01)
learner = gum.BNLearner("protein_train.csv", bn)
learner.setInitialDAG(bn2.dag())
learner.useAprioriSmoothing(1)
bn11 = learner.learnParameters() #second
gnb.showBN(bn11)
#first
ie1 = gum.LazyPropagation(bn01)
ie1.makeInference()
gnb.showInference(bn01, evs={})
#second
ie2 = gum.LazyPropagation(bn11)
ie2.makeInference()
gnb.showInference(bn11, evs={})
with open('protein_test.csv', 'r', encoding="utf-8") as csvfile:
reader = csv.reader(csvfile)
count1 = 1
count2 = 1
acc1 = 0
acc2 = 0
for line in list(reader)[1:]:
vnuc, vA, vR, vN, vD, vQ = [
int(line[0]),
int(line[1]),
int(line[2]),
int(line[3]),
int(line[4]),
int(line[5])
]
#print(vnuc,vA,vR,vN,vD,vQ)
ie2.eraseAllEvidence()
ie1.eraseAllEvidence()
ie1.setEvidence({'A': vA, 'R': vR, 'N': vN, 'D': vD, 'Q': vQ})
ie2.setEvidence({'A': vA, 'R': vR, 'N': vN, 'D': vD, 'Q': vQ})
ie1.makeInference()
ie2.makeInference()
ie2.addTarget(nuc)
ie1.addTarget(nuc)
if len(ie2.posterior(nuc).argmax()
) == 1: #if we have one determined value of prob
#print(ie2.posterior(nuc))
#print(ie2.posterior(nuc).argmax()[0]['nuc'])
if ie2.posterior(nuc).argmax()[0]['nuc'] == 2: #nuc=-1
if vnuc == -1:
acc2 = acc2 + 1
if ie2.posterior(nuc).argmax()[0]['nuc'] == vnuc:
acc2 = acc2 + 1
count2 = count2 + 1
if len(ie1.posterior(nuc).argmax()) == 1:
#print(ie1.posterior(nuc))
#print(ie1.posterior(nuc).argmax()[0]['nuc'])
if ie1.posterior(nuc).argmax()[0]['nuc'] == 2:
if vnuc == -1:
acc1 = acc1 + 1
if ie1.posterior(nuc).argmax()[0]['nuc'] == vnuc:
acc1 = acc1 + 1
count1 = count1 + 1
acc2 = acc2 / count2
acc1 = acc1 / count1
print(acc2, acc1)