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from pm4py.objects.log.importer.xes import importer as xes_importer
from pm4py.algo.discovery.alpha import algorithm as alpha_miner
from pm4py.algo.discovery.inductive import algorithm as inductive_miner
from pm4py.algo.evaluation.earth_mover_distance import algorithm as earth_mover_distance
from pm4py.statistics.variants.log import get as variants_get
from pm4py.algo.simulation.playout.petri_net import algorithm
from pm4py.objects.conversion.process_tree import converter as process_tree_converter
import os
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)
process_tree = inductive_miner.apply(log)
net1, im1, fm1 = process_tree_converter.apply(process_tree)
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)
#log = xes_importer.apply(os.path.join("..", "tests", "input_data", "receipt.xes"))
#net, im, fm = inductive_miner.apply(log)
#lang_model = variants_get.get_language(simulator.apply(net, im, fm, variant=simulator.Variants.STOCHASTIC_PLAYOUT,
# parameters={
# simulator.Variants.STOCHASTIC_PLAYOUT.value.Parameters.LOG: log}))
#emd = earth_mover_distance.apply(lang_model, lang_log)
#print("receipt inductive emd distance: ", emd)
if __name__ == "__main__":
execute_script()
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