def test_emd_1(self):
from pm4py.algo.evaluation.earth_mover_distance import algorithm as earth_mover_distance
M = {
("a", "b", "d", "e"): 0.49,
("a", "d", "b", "e"): 0.49,
("a", "c", "d", "e"): 0.01,
("a", "d", "c", "e"): 0.01
}
L1 = {
("a", "b", "d", "e"): 0.49,
("a", "d", "b", "e"): 0.49,
("a", "c", "d", "e"): 0.01,
("a", "d", "c", "e"): 0.01
}
earth_mover_distance.apply(M, L1)
def test_emd_2(self):
from pm4py.algo.evaluation.earth_mover_distance import algorithm as earth_mover_distance
log = xes_importer.apply(
os.path.join("input_data", "running-example.xes"))
lang_log = variants_get.get_language(log)
net1, im1, fm1 = inductive_miner.apply(log)
lang_model1 = variants_get.get_language(
algorithm.apply(
net1,
im1,
fm1,
variant=algorithm.Variants.STOCHASTIC_PLAYOUT,
parameters={
algorithm.Variants.STOCHASTIC_PLAYOUT.value.Parameters.LOG:
log
}))
emd = earth_mover_distance.apply(lang_model1, lang_log)
def execute_script():
M = {
("a", "b", "d", "e"): 0.49,
("a", "d", "b", "e"): 0.49,
("a", "c", "d", "e"): 0.01,
("a", "d", "c", "e"): 0.01
}
L1 = {
("a", "b", "d", "e"): 0.49,
("a", "d", "b", "e"): 0.49,
("a", "c", "d", "e"): 0.01,
("a", "d", "c", "e"): 0.01
}
# the distance between M and L1, that we expect to be zero, is zero according to the EMD
emd = earth_mover_distance.apply(M, L1)
print("M L1 emd distance:", emd)
L2 = {
("a", "b", "e"): 0.5,
("a", "b", "d", "e"): 0.245,
("a", "d", "b", "e"): 0.245,
("a", "c", "d", "e"): 0.005,
("a", "d", "c", "e"): 0.005
}
# the distance between M and L2 according to the EMD is 0.1275 (paper value 0.125)
emd = earth_mover_distance.apply(M, L2)
print("M L2 emd distance:", emd)
L3 = {
("a", "b", "d", "e"): 0.489,
("a", "d", "b", "e"): 0.489,
("a", "c", "d", "e"): 0.01,
("a", "d", "c", "e"): 0.01,
("a", "b", "e"): 0.002
}
# the distance between M and L3 according to the EMD is 0.0005 (paper value 0.0005), perfect!
emd = earth_mover_distance.apply(M, L3)
print("M L3 emd distance:", emd)
L4 = {("a", "b", "d", "e"): 0.5, ("a", "d", "b", "e"): 0.5}
# the distance between M and L4 according to the EMD is 0.005 (paper value 0.005), perfect!
emd = earth_mover_distance.apply(M, L4)
print("M L4 emd distance:", emd)
L5 = {("a", "c", "d", "e"): 0.5, ("a", "d", "c", "e"): 0.5}
# the distance between M and L5 according to the EMD is 0.245 (paper value 0.245), perfect!
emd = earth_mover_distance.apply(M, L5)
print("M L5 emd distance:", emd)
log = xes_importer.apply(
os.path.join("..", "tests", "input_data", "running-example.xes"))
lang_log = variants_get.get_language(log)
net0, im0, fm0 = alpha_miner.apply(log)
net1, im1, fm1 = inductive_miner.apply(log)
lang_model0 = variants_get.get_language(
algorithm.apply(
net0,
im0,
fm0,
variant=algorithm.Variants.STOCHASTIC_PLAYOUT,
parameters={
algorithm.Variants.STOCHASTIC_PLAYOUT.value.Parameters.LOG: log
}))
lang_model1 = variants_get.get_language(
algorithm.apply(
net1,
im1,
fm1,
variant=algorithm.Variants.STOCHASTIC_PLAYOUT,
parameters={
algorithm.Variants.STOCHASTIC_PLAYOUT.value.Parameters.LOG: log
}))
emd = earth_mover_distance.apply(lang_model0, lang_log)
print("running-example alpha emd distance: ", emd)
emd = earth_mover_distance.apply(lang_model1, lang_log)
print("running-example inductive emd distance: ", emd)