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)
def test_bn_compact(self):
ped = Pedigree()
ped.load('../cplex/samples/pedigree_50_7_4_4_G8.ped')
bn_no = pview.ped_to_bn(ped, 0.05)
bn_compact = pview.ped_to_bn_compact()
ie_no = pview.gum.LazyPropagation(bn_no)
ie_compact = pview.gum.LazyPropagation(bn_compact)
def test_clique_csg(self):
nb_ped = 50
nb_people = [1900]
nb_Gen_Max = [5]
nb_Gen_Min = [5]
cl = 3
min_clique = 100
max_clique = 0
mean_clique = 0
min_csg = 100
max_csg = 0
mean_csg = 0
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('csg', len(ped1.all_consanguineous_ped(5)))
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)
bn = pview.ped_to_bn_compact(ped, 0.05)
clique = pview.max_clique_size(bn)
csg = len(ped.all_consanguineous_ped(g))
if clique > max_clique:
max_clique = clique
if clique < min_clique:
min_clique = clique
mean_clique += clique
if csg > max_csg:
max_csg = csg
if csg < min_csg:
min_csg = csg
mean_csg += csg
print(max_clique, min_clique, mean_clique / nb_ped)
print(max_csg, min_csg, mean_csg / nb_ped)
mean_gen_is = []
errorValues_is = []
max_is = []
min_is = []
mean_gen_diff = []
errorValues_diff = []
max_diff = []
min_diff = []
w = 0
sen = Pedigree()
sen.load('../data/ped/senegal2013.ped')
len_sen = len(sen)
bn_sen = pview.ped_to_bn_compact(sen, f)
ie = pview.gum.ImportanceSampling(bn_sen)
ie.setMaxTime(200)
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()
w = 0
file = open('ttbn_1e4', 'w')
for p, g_max, g_min in zip(nb_people, nb_Gen_Max, nb_Gen_Min):
tab_diff = np.zeros(nb_ped)
for nb in range(nb_ped):
y = 0
nbChild = random.randint(6, 12)
g = random.randint(g_min, g_max)
ped = Pedigree()
ped.gen_ped(nb, p, g, nbChild, cl, 0.03)
pview.save(
ped,
f'../cplex/samples/TTBN/pedigree_{p}_{g}_{nbChild}_{cl}_G{nb}')
bn_compact = pview.ped_to_bn_compact(ped, f)
pview.save_bn(
bn_compact,
f'../cplex/bn/TTBN/bn_compact_{p}_{g}_{nbChild}_{cl}_G{nb}')
# bn1 = pview.gum.BayesNet()
# bn1.loadBIF(f'../cplex/bn/TTBN/bn_compact_{p}_{g}_{nbChild}_{cl}_G{nb}.bif')
# ie1 = ttgum.ShaferShenoyTensorTrain(bn1, precision=1e-2, info=False)
# marginalesSSTT, tempsSSTT, nb_param_SSTT = ie1.makeInference()
marginalesSSTT = ttbn.ttbn_posterior(
f'../cplex/bn/TTBN/bn_compact_{p}_{g}_{nbChild}_{cl}_G{nb}.bif')[0]
# bn2 = pview.gum.BayesNet()
# bn2.loadBIF(f'../cplex/bn/TTBN/bn_compact_{p}_{g}_{nbChild}_{cl}_G{nb}.bif')
# ie2 = pview.gum.LazyPropagation(bn2)
ie2 = laz.lazyPosterior(
def main(args=None):
"""
[--ped pedfile: format famid, id, patid (0 for founder), matid (0 for founder), gender (1 male, 2 female)
[--ev evidencefile]: format famid, id, ev00, ev10 (1 paternal), ev01 (1 maternal), ev11
[--famID Input the famID]
[--freq minorallelefreq (default: freq=0.01)]
[--targets list_of_ind (default: all)]
[--size] size of the rendered graph
[--bndot dotfile] export the BN into a dot file
[--peddot dotfile] export the pedigree into a dot file
[--audit textfile] introspect pedigree into a text file
[--verbose] display error/warning messages in stderr
[--mode] decide which version between normal, compact and multi version of a BN
[--thetha] format [probability, probability, ...]
[--centimorgans] format [distance, distance, ...]
[--inference ] Decide which inference use, LazyPropagation or LoobyBeliefPropagation
[--complete Bool] Decice which version between complete and compact for the audit file
[--out outfile] export probabilities into a out file
[--name_gen] format [str ,str ,str ...]
"""
parser = OptionParser(version="%prog 0.1",
usage="usage: %prog [options] pedfile")
parser.add_option("",
"--ev",
dest="evfile",
help="Specification of the profile",
metavar="FILE")
parser.add_option("",
"--freq",
dest="f",
help="minor allel freq (gamma)",
type="float",
default=0.01)
parser.add_option("", "--famID", dest='famID', help="Input the famID")
parser.add_option(
"",
"--targets",
dest="targets",
help="Specification of a list of targets (comma separated)")
parser.add_option("",
"--bndot",
dest="bndotfile",
help="Export the BN into a dot file",
metavar="FILE")
parser.add_option("",
"--peddot",
dest="peddotfile",
help="Export the pedigree into a dot file",
metavar="FILE")
parser.add_option("",
"--audit",
dest="auditfile",
help="Export some stats on pedigree in a text file",
metavar="FILE")
parser.add_option("",
"--verbose",
dest="verbose",
help="messages while processing",
default=False,
action="store_true")
parser.add_option("",
"--bn",
dest="bnfile",
help="Export the BN into a BIF file",
metavar="FILE")
parser.add_option(
"",
"--mode",
dest="mode",
help="Choose the type of the generate bn, compact, no compact or multi",
default='compact')
parser.add_option(
"",
"--theta",
dest="theta",
type="string",
help="Input a probability's distribution, shape: float;float; ...")
parser.add_option(
"",
"--centimorgans",
dest="centimorgans",
type="string",
help=
"Input the distance between genes in centimorgans, shape: float;float; ..."
)
parser.add_option("",
"--inference",
dest="inference",
help="Choose between LP and LBP",
default='LP')
parser.add_option(
"",
"--complete",
dest="complete",
help="Decide if the audit is a complete version or a compact version")
parser.add_option("",
"--out",
dest="out",
help="Export the evidence into a out file",
metavar="FILE")
parser.add_option("",
"--size",
dest="size",
help="Choose the size of the rendered graph",
type="int",
default=100)
parser.add_option("",
"--name_gen",
dest="name_gen",
type="string",
help="Input the gene's name, shape: str;str; ...")
if args is None:
(options, arguments) = parser.parse_args()
else:
(options, arguments) = parser.parse_args(args)
if len(arguments) != 2: # si on n'a pas mis de pedfile ou on en a mis trop
parser.parse_args(["--help"])
else:
# on retrouve toutes les valeurs dans options (principalement)
# options.peddotfile contient le nom du fichier
# options.verbose contient True or False
# etc.
print("=" * 40)
print(f"arguments: {arguments}")
print("=" * 40)
print(f"options: {options}")
print("=" * 40)
print("\n\n")
pedfile = arguments[1]
print(f"working on file : {pedfile}")
current_ped = ped.Pedigree()
current_ped.load(str(pedfile))
if options.famID:
famID = options.famID
else:
list_famID = current_ped.get_domain()
if len(list_famID) == 1:
famID = list_famID.pop()
else:
return "missing famID"
name_gen = None
if options.name_gen:
name_gen = options.name_gen.split(';')
print(name_gen)
if options.mode == 'compact':
bn = pview.ped_to_bn_compact(current_ped, options.f)
if options.verbose:
pview.gnb.showBN(bn, options.size)
elif options.mode == 'no_compact':
bn = pview.ped_to_bn(current_ped, options.f)
if options.verbose:
pview.gnb.showBN(bn, options.size)
elif options.mode == 'multi':
if options.theta:
distance = options.theta.split(';')
nb_gen = len(distance) + 1
for i in range(len(distance)):
distance[i] = float(distance[i])
bn = pview.bn_multi_pb(current_ped, options.f, nb_gen,
distance, name_gen)
if options.verbose:
pview.gnb.showBN(bn, options.size)
elif options.centimorgans:
centimorgans = options.centimorgans.split(';')
nb_gen = len(centimorgans)
for i in range(len(centimorgans)):
centimorgans[i] = float(centimorgans[i])
bn = pview.bn_multi_morgans(current_ped, options.f, nb_gen,
centimorgans, name_gen)
if options.verbose:
pview.gnb.showBN(bn, options.size)
else:
return "missing theta or centimorgan argument, cannot use the multi mode"
if options.bnfile:
bn.saveBIF(options.bndotfile)
pview.gum.availableBNExts()
pview.gum.saveBN(bn, options.bndotfile)
if options.verbose:
print('bndotfile saved in ' + options.bndotfile)
if options.bndotfile:
pview.save_dot(bn, options.bndotfile)
if options.verbose:
print('bn saved in ' + options.bndotfile)
if options.peddotfile:
if (len(current_ped)) > 1000:
pview.graph(current_ped, options.peddotfile, True)
else:
pview.graph(current_ped, options.peddotfile, False)
if options.verbose:
print("graph save in " + options.peddotfile)
if options.auditfile:
if options.complete == str(False):
print('false')
current_ped.pedigree_overview_file(options.auditfile, False)
else:
current_ped.pedigree_overview_file(options.auditfile, True)
if options.verbose:
print('audit file ' + options.auditfile + ' created')
if options.targets:
if options.evfile:
if options.mode == 'multi':
evidence = pview.load_evidence_multi(options.evfile, famID)
else:
evidence = pview.load_evidence(options.evfile, famID)
if options.verbose:
print(f"{options.evfile} loaded")
if options.inference == 'LBP':
ie = pview.gum.LoopyBeliefPropagation(bn)
else:
ie = pview.gum.LazyPropagation(bn)
ie.setEvidence(evidence)
ie.makeInference()
for i in current_ped.get_pedigree().keys():
if i in options.targets:
print(ie.posterior(f"X{i}"))
pview.create_out(options.out, current_ped, ie)
else:
if options.inference == 'LBP':
ie = pview.gum.LoopyBeliefPropagation(bn)
else:
ie = pview.gum.LazyPropagation(bn)
ie.makeInference()
for i in current_ped.get_pedigree().keys():
if i in options.targets:
print(ie.posterior(f"X{i}"))
pview.create_out(options.out, current_ped, ie)
else:
if options.evfile:
if options.mode == 'multi':
evidence = pview.load_evidence_multi(options.evfile, famID)
else:
evidence = pview.load_evidence(options.evfile, famID)
if options.verbose:
print(f"{options.evfile} loaded")
if options.inference == 'LBP':
ie = pview.gum.LoopyBeliefPropagation(bn)
else:
ie = pview.gum.LazyPropagation(bn)
ie.setEvidence(evidence)
ie.makeInference()
if options.mode == 'multi':
pview.create_out_multi(options.out, current_ped, ie,
nb_gen, name_gen)
else:
pview.create_out(options.out, current_ped, ie)
else:
if options.inference == 'LBP':
ie = pview.gum.LoopyBeliefPropagation(bn)
else:
ie = pview.gum.LazyPropagation(bn)
ie.makeInference()
if options.mode == 'multi':
pview.create_out_multi(options.out, current_ped, ie,
nb_gen, name_gen)
else:
pview.create_out(options.out, current_ped, ie)