def test_playout(self):
log = xes_importer.apply(
os.path.join("input_data", "running-example.xes"))
from pm4py.algo.discovery.alpha import algorithm as alpha_miner
net, im, fm = alpha_miner.apply(log)
from pm4py.algo.simulation.playout import simulator
log2 = simulator.apply(net, im, fm)
def play_out(*args: Union[Tuple[PetriNet, Marking, Marking], dict, Counter, ProcessTree], **kwargs) -> EventLog:
"""
Performs the playout of the provided model,
i.e., gets a set of traces from the model.
The function either takes a petri net, initial and final marking, or, a process tree as an input.
Parameters
---------------
args
Model (Petri net, initial, final marking) or ProcessTree
kwargs
Parameters of the playout
Returns
--------------
log
Simulated event log
"""
if len(args) == 3:
from pm4py.objects.petri.petrinet import PetriNet
if type(args[0]) is PetriNet:
from pm4py.algo.simulation.playout import simulator
return simulator.apply(args[0], args[1], final_marking=args[2], **kwargs)
elif type(args[0]) is dict or type(args[0]) is Counter:
from pm4py.objects.dfg.utils import dfg_playout
return dfg_playout.apply(args[0], args[1], args[2], **kwargs)
elif len(args) == 1:
from pm4py.objects.process_tree.process_tree import ProcessTree
if type(args[0]) is ProcessTree:
from pm4py.algo.simulation.tree_playout import algorithm
return algorithm.apply(args[0], **kwargs)
raise Exception("unsupported model for playout")
def generate_process_tree(**kwargs) -> ProcessTree:
"""
Generates a process tree
Parameters
-------------
kwargs
Parameters of the process tree generator algorithm
Returns
-------------
model
process tree
"""
from pm4py.algo.simulation.tree_generator import simulator
return simulator.apply(**kwargs)
def test_emd_2(self):
from pm4py.algo.evaluation.earth_mover_distance import evaluator 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(
simulator.apply(
net1,
im1,
fm1,
variant=simulator.Variants.STOCHASTIC_PLAYOUT,
parameters={
simulator.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(
simulator.apply(
net0,
im0,
fm0,
variant=simulator.Variants.STOCHASTIC_PLAYOUT,
parameters={
simulator.Variants.STOCHASTIC_PLAYOUT.value.Parameters.LOG: log
}))
lang_model1 = variants_get.get_language(
simulator.apply(
net1,
im1,
fm1,
variant=simulator.Variants.STOCHASTIC_PLAYOUT,
parameters={
simulator.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)