def main(source_file, destination_file, source_type=None, destination_type=None, undirected=False): if undirected: create_using = networkx.Graph else: create_using = networkx.DiGraph graph = read_graph(source_file, source_type, create_using) write_graph(graph, destination_file, destination_type)
def main(vertex_count, neighbors, distance, undirected, append_attributes,
output_file, output_type):
if undirected:
graph = generate_circle_graph(networkx.Graph(), vertex_count,
neighbors, distance)
else:
graph = generate_circle_graph(networkx.DiGraph(), vertex_count,
neighbors, distance)
if not output_file:
output_file = "/dev/stdout"
if not output_type:
output_type = "gml"
write_graph(graph, output_file, output_type)
def CBN_PC(data, result_structure_path):
print("CBN with PC")
skeleton_path = result_structure_path.joinpath("skeleton")
skeleton_path.mkdir(parents=True, exist_ok=True)
pdag_path = result_structure_path.joinpath("pdag")
pdag_path.mkdir(parents=True, exist_ok=True)
dag_path = result_structure_path.joinpath("dag")
dag_path.mkdir(parents=True, exist_ok=True)
skeleton_file_name = "skeleton_" + str(size).zfill(7) + ".dot"
skeleton_done = skeleton_path.joinpath(skeleton_file_name).exists()
pdag_file_name = "pdag_" + str(size).zfill(7) + ".dot"
pdag_done = pdag_path.joinpath(pdag_file_name).exists()
dag_file_name = "dag_" + str(size).zfill(7) + ".dot"
dag_done = dag_path.joinpath(dag_file_name).exists()
alpha = 0.01
conditioningSet = 4
learner = otagr.ContinuousPC(data, conditioningSet, alpha)
learner.setVerbosity(True)
if not skeleton_done:
skel = learner.learnSkeleton()
gu.write_graph(
skel,
skeleton_path.joinpath("skeleton_" + str(size).zfill(7) + ".dot"))
if not pdag_done:
pdag = learner.learnPDAG()
gu.write_graph(
pdag, pdag_path.joinpath("pdag_" + str(size).zfill(7) + ".dot"))
if not dag_done:
dag = learner.learnDAG()
gu.write_graph(dag,
dag_path.joinpath("dag_" + str(size).zfill(7) + ".dot"))
else:
dag, names = gu.read_graph(
dag_path.joinpath("dag_" + str(size).zfill(7) + ".dot"))
dag = otagr.NamedDAG(dag, names)
print("Learning parameters")
factories = [
ot.KernelSmoothing(ot.Epanechnikov()),
ot.BernsteinCopulaFactory()
]
ot.Log.SetFile("log")
ot.Log.Show(ot.Log.INFO)
model = otagr.ContinuousBayesianNetworkFactory(factories, dag, alpha,
conditioningSet,
False).build(data)
ot.Log.Show(ot.Log.INFO)
return model
def write_struct(self, file, ndag):
gu.write_graph(ndag, file + '.dot')
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()
# pdag = learner.learnMixedStructure()
# dag = learner.learnStructure()
# It would be nice to put this in the pipeline
import pyAgrum as gum
import otagrum as otagr
import graph_utils as gu
from pathlib import Path
step = 1
start_size = 7
end_size = 7
restart = 1
density = 1.2
# Setting directories location and files
directory = Path("../data/structures/generated/")
directory.mkdir(parents=True, exist_ok=True)
generator = gum.BNGenerator()
gum.initRandom(10)
for r in range(restart):
for i in range(start_size, end_size + 1, step):
print("Number of node :", i, flush=True)
file_name = "size_" + str(i).zfill(3) + "_" + str(r + 1).zfill(2)
n_arc = int(density * (i - 1))
bn = generator.generate(i, n_arc)
ndag = otagr.NamedDAG(bn.dag(), bn.names())
gu.write_graph(ndag, directory.joinpath(file_name + '.dot'))