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import pm4py
from pm4py.algo.filtering.dfg import dfg_filtering
from pm4py.algo.simulation.playout.dfg.variants import classic as dfg_playout
from examples import examples_conf
import importlib.util
import os
def execute_script():
log = pm4py.read_xes(os.path.join("..", "tests", "input_data", "receipt.xes"))
activities = pm4py.get_event_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
if importlib.util.find_spec("graphviz"):
pm4py.view_dfg(dfg, sa, ea, log=log, format=examples_conf.TARGET_IMG_FORMAT)
new_dfg, new_sa, new_ea = pm4py.discover_dfg(simulated_log)
if importlib.util.find_spec("graphviz"):
pm4py.view_dfg(new_dfg, new_sa, new_ea, log=simulated_log, format=examples_conf.TARGET_IMG_FORMAT)
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_as_tuples(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)
if __name__ == "__main__":
execute_script()
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