def xxxtest_check_consanguinity(self):
ped = Pedigree()
ped.load('../cplex/pedigree_50_4_8_2_G3.ped')
print(ped.all_consanguineous_ped(3))
print(ped.is_consanguineous('1','7',2))
print(ped.old_gen('1',4).intersection(ped.old_gen('7',4)))
print(ped.old_gen('7', 4))
print('---------------------------')
nb_ped = 50
nb_people = [10,20,50,100,200,300,500,1000,1500,2000,2200,2400]
nb_Gen_Max = [3,3,4,4,4,4,4,5,5,6,6,6]
# nb_people = [50]
# nb_Gen_Max = [4]
nb_Gen_Min = [math.ceil(x/2) for x in nb_Gen_Max]
cl = 3
cpt = 0
for p, g_max, g_min in zip(nb_people, nb_Gen_Max, nb_Gen_Min):
for nb in range(nb_ped):
nbChild = random.randint(4, 8)
g = random.randint(g_min, g_max)
ped = Pedigree()
ped.gen_ped(nb, p, g, nbChild, cl)
if len(ped.all_consanguineous_ped(cl)) != 0:
cpt+=1
print('ICI IL Y A UN PB',ped.all_consanguineous_ped(3))
print('le pedigree en question',ped)
pview.save(ped, f'../pedigree_{p}_{g}_{nbChild}_{cl - 1}_G{nb}')
pview.graph(ped, f'../generate_graph_{p}_{g}_{nbChild}_{cl - 1}_G{nb}', False)
print(cpt)
self.assertEqual(cpt,0)
def test_new_gen(self): #OK
nb_ped = 1
nb_people = [50]
nb_Gen_Max = [4]
nb_Gen_Min = [math.ceil(x/2) for x in nb_Gen_Max]
cl = 3
ped1 = Pedigree()
ped1.load("../data/ped/senegal2013.ped")
bn_sen = pview.ped_to_bn_compact(ped1,0.05)
print('clique sen',pview.max_clique_size(bn_sen))
print('uho', ped1.all_consanguineous_ped(cl))
for p, g_max, g_min in zip(nb_people, nb_Gen_Max, nb_Gen_Min):
for nb in range(nb_ped):
nbChild = random.randint(4, 8)
g = random.randint(g_min, g_max)
ped = Pedigree()
ped.gen_ped(nb, p, g, nbChild, cl)
# print('-------------------------')
# print(ped)
print('-------------------------')
print('Consanguin',ped.all_consanguineous_ped(cl))
bn = pview.ped_to_bn_compact(ped,0.05)
print('clique',pview.max_clique_size(bn))
print('-------------------------')
pview.save(ped, f'../pedigree_{p}_{g}_{nbChild}_{cl-1}_G{nb}')
pview.graph(ped, f'../generate_graph_{p}_{g}_{nbChild}_{cl-1}_G{nb}', False)
ie.setEpsilon(5e-4)
t1 = process_time()
ie.makeInference()
t2 = process_time()
time_sen_compact = t2 - t1
for p, g_max, g_min in zip(nb_people, nb_Gen_Max, nb_Gen_Min):
tab_lis = np.zeros(nb_ped)
tab_diff = np.zeros(nb_ped)
for nb in range(nb_ped):
nbChild = random.randint(6, 12)
g = random.randint(g_min, g_max)
ped = Pedigree()
ped.gen_ped(nb, p, g, nbChild, cl, 0.03)
bn_compact = pview.ped_to_bn_compact(ped, f)
pview.save(
ped, f'../cplex/samples/IS/pedigree_{p}_{g}_{nbChild}_{cl}_G{nb}')
pview.save_bn(
bn_compact,
f'../cplex/bn/IS/bn_compact_{p}_{g}_{nbChild}_{cl}_G{nb}')
t1 = process_time()
ie1 = imp.lisPosterior(
f'../cplex/bn/IS/bn_compact_{p}_{g}_{nbChild}_{cl}_G{nb}.bif')
t2 = process_time()
t2 = t2 - t1
tab_lis[nb] = t2
ie2 = laz.lazyPosterior(
f'../cplex/bn/IS/bn_compact_{p}_{g}_{nbChild}_{cl}_G{nb}.bif')
error_lbp_laz = []
for i in ped.get_pedigree().keys():
for p, g_max, g_min in zip(nb_people, nb_Gen_Max, nb_Gen_Min):
for nb in range(nb_ped):
nbChild = random.randint(6, 12)
g = random.randint(g_min, g_max)
ped = Pedigree()
ped.gen_ped(nb, p, g, nbChild, cl, 0.03)
for i in range(gene_depart, gene + 1):
#bn = pview.bn_multi_pb(ped, f, i, distance[:i-1])
bn = pview.bn_multi_morgans(ped, f, i, centimorgans[:i])
pview.save(ped, f'./samples/pedigree_{p}_{g}_{nbChild}_{cl}_G{nb}')
pview.save_bn(bn, f'./bn/bn_{p}_{g}_{nbChild}_{cl}_G{nb}')
ie1 = laz.lazyPosterior(
f"./bn/bn_{p}_{g}_{nbChild}_{cl}_G{nb}.bif", evidence)
ie2 = lbp.lbpPosterior(f"./bn/bn_{p}_{g}_{nbChild}_{cl}_G{nb}.bif",
evidence)
for j in ped.get_pedigree().keys():
#for g in range(1, 3):
for g in range(1, gene + 1):
p1 = ie1.posterior(f'X{j}_{g}')
p2 = ie2.posterior(f'X{j}_{g}')
x = [
abs(p1[0] - p2[0]),
abs(p1[1] - p2[1]),
def test_save(self):#OK
ped = Pedigree()
ped.load("../data/ped/fam9.ped")
pview.save(ped,"../data/ped/tanto.ped")
self.assertEqual(os.path.exists("../data/ped/tanto.ped"),1)
