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_net.obj import PetriNet
if type(args[0]) is PetriNet:
from pm4py.algo.simulation.playout.petri_net import algorithm
return algorithm.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.obj import ProcessTree
if type(args[0]) is ProcessTree:
from pm4py.algo.simulation.playout.process_tree import algorithm
return algorithm.apply(args[0], **kwargs)
raise Exception("unsupported model for playout")
def execute_script():
log = pm4py.read_xes(
os.path.join("..", "tests", "input_data", "receipt.xes"))
activities = pm4py.get_attribute_values(log, "concept:name")
dfg, sa, ea = pm4py.discover_dfg(log)
# filters the DFG to make a simpler one
perc = 0.5
dfg, sa, ea, activities = dfg_filtering.filter_dfg_on_activities_percentage(
dfg, sa, ea, activities, perc)
dfg, sa, ea, activities = dfg_filtering.filter_dfg_on_paths_percentage(
dfg, sa, ea, activities, perc)
# creates the simulated log
simulated_log = dfg_playout.apply(dfg, sa, ea)
print(simulated_log)
print(len(simulated_log))
print(sum(x.attributes["probability"] for x in simulated_log))
# shows the two DFGs to show that they are identical
pm4py.view_dfg(dfg, sa, ea, log=log, format="svg")
new_dfg, new_sa, new_ea = pm4py.discover_dfg(simulated_log)
pm4py.view_dfg(new_dfg, new_sa, new_ea, log=simulated_log, format="svg")
for trace in simulated_log:
print(list(x["concept:name"] for x in trace))
print(trace.attributes["probability"],
dfg_playout.get_trace_probability(trace, dfg, sa, ea))
break
dfg, sa, ea = pm4py.discover_dfg(log)
variants = pm4py.get_variants(log)
sum_prob_log_variants = 0.0
for var in variants:
sum_prob_log_variants += dfg_playout.get_trace_probability(
variants[var][0], dfg, sa, ea)
print(
"percentage of behavior allowed from DFG that is in the log (from 0.0 to 1.0): ",
sum_prob_log_variants)
def test_dfg_playout(self):
import pm4py
from pm4py.objects.dfg.utils import dfg_playout
log = pm4py.read_xes(os.path.join("input_data", "running-example.xes"))
dfg, sa, ea = pm4py.discover_dfg(log)
dfg_playout.apply(dfg, sa, ea)