def process_OOD(file_name):
name = os.path.basename(file_name).split(".")[0]
print("### " + name)
print("```")
model = Model(file_name)
de = DE(model)
stat = de.run()
print("Time Taken : ", stat.runtime)
stat.tiles()
data_map = stat.median_spread()
objs = stat.spit_objectives()
headers = [obj.__name__.split("_")[-1] for obj in de.settings.obj_funcs]
cluster_input = [headers] + objs
print("")
g = KMeans(k=2)
clusters = g.run(cluster_input)
med_spread_plot(data_map, headers, fig_name="img/gen_bin_" + name)
plot_clusters(clusters,
fig_name="img/bin_" + name,
col_names=headers,
s=50,
edgecolors='none')
print("```")
print("" % name)
return stat.runtime
def test_pystar():
from pystar.models.CSServices import graph
from pystar.model import Model
#print(graph.nodes)
model = Model(graph)
de = DE(model)
stat = de.run()
stat.tiles()
lastgen = stat.generations[-1]
maxs = [-1] * len(lastgen[0].objectives)
bests = [None] * len(lastgen[0].objectives)
for point in lastgen:
for i in range(len(maxs)):
if point.objectives[i] > maxs[i]:
maxs[i] = point.objectives[i]
bests[i] = point
print(maxs)
columns = ["id", "type", "value", "is_random", "name"]
stat.tabulate(columns, bests[0])
def __init__(self, model, **settings):
O.__init__(self)
self.model = model
self.model.bases.extend(self.get_conflicts())
self.settings = default().update(**settings)
self.de = DE(model, gens=self.settings.k1)
