def execute_script():
log = xes_importer.apply(
os.path.join("..", "tests", "input_data", "running-example.xes"))
tree = inductive_miner.apply_tree(log)
new_log_1 = tree_playout.apply(tree)
print(len(new_log_1))
new_tree_1 = inductive_miner.apply_tree(new_log_1)
print(new_tree_1)
new_log_2 = tree_playout.apply(tree,
variant=tree_playout.Variants.EXTENSIVE)
print(len(new_log_2))
new_tree_2 = inductive_miner.apply_tree(new_log_2)
print(new_tree_2)
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 test_playout_tree_basic(self):
log = xes_importer.apply(
os.path.join("input_data", "running-example.xes"))
from pm4py.algo.discovery.inductive import algorithm as inductive_miner
tree = inductive_miner.apply_tree(log)
from pm4py.algo.simulation.tree_playout import algorithm as tree_playout
new_log = tree_playout.apply(tree)
def execute_script():
# read an event log
log = pm4py.read_xes("../tests/compressed_input_data/02_teleclaims.xes.gz")
# log = pm4py.read_xes("../tests/input_data/receipt.xes")
print("number of variants of the original log ->",
len(pm4py.get_variants(log)))
# discover a process model
tree = pm4py.discover_tree_inductive(log)
# simulate a log out of the model (to have another log that is similar to the original)
aa = time.time()
min_trace_length = bottomup_discovery.get_min_trace_length(tree)
simulated_log = tree_playout.apply(
tree,
variant=tree_playout.Variants.EXTENSIVE,
parameters={"max_trace_length": min_trace_length + 2})
print("number of variants of the simulated log -> ", len(simulated_log))
# apply the alignments between this log and the model
bb = time.time()
aligned_traces = logs_alignment.apply(log, simulated_log)
cc = time.time()
print(aligned_traces[0])
print("playout time", bb - aa)
print("alignments time", cc - bb)
print("TOTAL", cc - aa)
print(alignment_based.evaluate(aligned_traces))
# apply the anti alignments between this log and the model
dd = time.time()
anti_aligned_traces = logs_alignment.apply(
log,
simulated_log,
parameters={
logs_alignment.Variants.EDIT_DISTANCE.value.Parameters.PERFORM_ANTI_ALIGNMENT:
True
})
ee = time.time()
print(anti_aligned_traces[0])
print("anti alignments time", ee - dd)
print(alignment_based.evaluate(anti_aligned_traces))