def learning(self, sample):
if self.method == "cpc":
learner = otagr.ContinuousPC(sample, self.parameters['binNumber'],
self.parameters['alpha'])
start = time.time()
ndag = learner.learnDAG()
end = time.time()
# TTest = otagr.ContinuousTTest(sample, self.parameters['alpha'])
# jointDistributions = []
# for i in range(ndag.getSize()):
# d = 1+ndag.getParents(i).getSize()
# if d == 1:
# bernsteinCopula = ot.Uniform(0.0, 1.0)
# else:
# K = TTest.GetK(len(sample), d)
# indices = [int(n) for n in ndag.getParents(i)]
# indices = [i] + indices
# bernsteinCopula = ot.EmpiricalBernsteinCopula(sample.getMarginal(indices), K, False)
# jointDistributions.append(bernsteinCopula)
elif self.method == "cbic":
#print(sample.getDescription())
max_parents = self.parameters['max_parents']
n_restart_hc = self.parameters['hc_restart']
cmode = self.parameters['cmode']
learner = otagr.TabuList(sample, max_parents, n_restart_hc, 5)
learner.setCMode(cmode)
start = time.time()
ndag = learner.learnDAG()
end = time.time()
#bn = dag_to_bn(dag, Tstruct.names())
elif self.method == "cmiic":
cmode = self.parameters['cmode']
kmode = self.parameters['kmode']
learner = otagr.ContinuousMIIC(sample)
learner.setCMode(cmode)
learner.setKMode(kmode)
learner.setAlpha(self.kalpha)
# learner.setBeta(self.kbeta)
start = time.time()
ndag = learner.learnDAG()
end = time.time()
# bn = gu.named_dag_to_bn(ndag)
elif self.method == "dmiic":
# learner.setBeta(self.kbeta)
ndag, start, end = dsc.learnDAG(sample)
# bn = gu.named_dag_to_bn(ndag)
elif self.method == "lgbn":
start = time.time()
end = time.time()
else:
print("Wrong entry for method argument !")
return ndag, end - start
def testAsiaDirichlet():
data = ot.Sample.ImportFromTextFile(
os.path.join(os.path.dirname(__file__), "asia_dirichlet_5000.csv"),
",")
learner = otagrum.TabuList(data)
learner.setVerbosity(True)
dag = learner.learnDAG()
print(dag.toDot())
def testSpecificInstance():
size = 1000
data = generateDataForSpecificInstance(size)
learner = otagrum.TabuList(data)
# skel = learner.learnSkeleton()
# print(skel.toDot())
dag = learner.learnDAG()
print(dag.toDot())
sys.stdout.flush()
def BIC_learning(data, max_parents=3, restart=1, tabu_list_size=2):
# Try an estimation of the coefficients distribution using
# univariate kernel smoothing for the marginals and PC to learn the structure
# of dependence parameterized by Bernstein copula
dimension = data.getDimension()
print("Build BIC coefficients distribution")
t0 = time()
print(" Learning structure")
t1 = time()
learner = otagrum.TabuList(data, max_parents, restart, tabu_list_size)
dag = learner.learnDAG()
with open(
"dags/new_dag_BIC_{}_{}_{}.dot".format(max_parents, restart,
tabu_list_size), "w") as f:
f.write(dag.toDot())
print(" t=", time() - t1, "s")
cbn = CBN_parameter_learning(data, dag)
print("t=", time() - t0, "s")
# distribution = ot.ComposedDistribution(marginals, cbn)
return cbn
size_draw = 1000
size_draw = min(size, size_draw)
data_draw = data_ref[0:size_draw] # Number of realizations taken in order to plot figures
print("Processing reference data")
f = figure_path.joinpath("pairs_ref_" + dataset_name + ".pdf")
pairs(data_draw, f)
#####################LEARNING CBN MODEL############################
# LEARNING STRUCTURE #
# CBIC Algorithm
learner = otagr.TabuList(data_ref, 2, 10, 2) # Using CPC algorithm
cbic_dag = learner.learnDAG() # Learning DAG
write_graph(cbic_dag, structure_path.joinpath("cbic_dag_" + dataset_name + ".dot"))
# CPC Algorithm
learner = otagr.ContinuousPC(data_ref, 4, 0.05) # Using CPC algorithm
cpc_dag = learner.learnDAG() # Learning DAG
write_graph(cpc_dag, structure_path.joinpath("cpc_dag_" + dataset_name + ".dot"))
# CMIIC ALGORITHM
alphas = np.arange(10, 501, 5)/1000
# alphas = [0.04, 0.05]
fig, ax = plt.subplots()
import pyAgrum as gum
import openturns as ot
import otagrum as otagr
print('Importing data')
data = ot.Sample.ImportFromTextFile(
'../data/Standard_coefficients_0100000.csv', ';')
data = data[0:20000]
data = data.getMarginal(range(0, 12))
print('Initializing the learners')
learners = {
'cbic': otagr.TabuList(data, 3, 1, 2),
'cpc': otagr.ContinuousPC(data, 4, 0.01),
'cmiic': otagr.ContinuousMIIC(data)
}
dags = {}
for (name, learner) in learners.items():
print('Learning with ', name)
dags[name] = learner.learnDAG()
for (name, dag) in dags.items():
dot = dag.toDot()
with open("dag_{}.dot".format(name), "w") as f:
f.write(dot)
import otagrum
import pyAgrum as gum
import pyAgrum.lib.notebook as gnb
import numpy as np
from pipeline import Pipeline
import graph_utils as gu
import elidan.hill_climbing as hc
data = ot.Sample.ImportFromTextFile("../data/samples/dirichlet/alarm/sample01.csv", ',')
# learner = cmiic.ContinuousMIIC(data,
# cmode=cmiic.CModeType.Bernstein,
# kmode=cmiic.KModeType.Naive)
learner = otagrum.TabuList(data, 4, 5, 5)
learner.setVerbosity(True)
dag = learner.learnDAG()
gu.write_graph(dag, "output_tabulist_gaussian.dot")
# learner = otagrum.TabuList(data, 4, 5, 5)
# learner.setCMode(otagrum.CorrectedMutualInformation.CModeTypes_Bernstein)
# learner.setVerbosity(True)
# dag = learner.learnDAG()
# gu.write_graph(dag, "output_tabulist_bernstein.dot")
t = hc.hill_climbing(data)
names = list(data.getDescription())
gu.write_graph(otagrum.NamedDAG(t[1], names), "output_hc.dot")
print("Final score hc: ", t[2])
# learner.use3off2()