def test_score_titanic(self):
scorer = BicScore(self.titanic_data2)
titanic = BayesianModel([("Sex", "Survived"), ("Pclass", "Survived")])
self.assertAlmostEqual(scorer.score(titanic), -1896.7250012840179)
titanic2 = BayesianModel([("Pclass", "Sex")])
titanic2.add_nodes_from(["Sex", "Survived", "Pclass"])
self.assertLess(scorer.score(titanic2), scorer.score(titanic))
def model_change(dag, data):
bay_model = []
data = pd.DataFrame(data)
print(data)
for i in range(len(dag)):
for j in range(np.size(data, 1)):
if dag[i][j] != 0:
bay_model.append((str(i), str(j)))
best_model = BayesianModel(bay_model)
bic = BicScore(data)
return bic.score(best_model)
#nodes=['A','S', 'T','L' ,'B', 'E', 'X', 'D']
nodes = ['Pollution', 'Smoker', 'Cancer', 'Xray', 'Dyspnoea']
nao_dag = []
bic_score = BicScore(data)
ind_size = round((len(nodes) * len(nodes) - len(nodes)) / 2)
gen_max = 50
gen = 0
melhor_fit = []
#populacao inicial
while len(ind) < pop_size:
aux = [random.randint(min_valor, max_valor) for i in range(ind_size)]
if aux not in nao_dag:
G = vetor_Rede(aux, nodes)
if G:
ind.append(aux)
fitness.append(abs(bic_score.score(G)))
else:
nao_dag.append(aux)
while gen < gen_max:
print(gen)
filhos = []
filhos_fitness = []
while len(filhos) < pop_size:
[fitness_sel1, ind_sel1] = torneio(fitness, ind)
[fitness_sel2, ind_sel2] = torneio(fitness, ind)
r = random.random()
if r <= p_cruzamento:
[filho1, filho2] = cruzamento_binario(ind_sel1, ind_sel2, ind_size)
if filho1 not in nao_dag:
G = vetor_Rede(filho1, nodes)
if G: