def apply(log, petri_net, initial_marking, final_marking, align_variant=alignments.DEFAULT_VARIANT, parameters=None):
"""
Evaluate fitness based on alignments
Parameters
----------------
log
Event log
petri_net
Petri net
initial_marking
Initial marking
final_marking
Final marking
align_variant
Variants of the alignments to apply
parameters
Parameters of the algorithm
Returns
---------------
dictionary
Containing two keys (percFitTraces and averageFitness)
"""
if align_variant == decomp_alignments.Variants.RECOMPOS_MAXIMAL.value:
alignment_result = decomp_alignments.apply(log, petri_net, initial_marking, final_marking,
variant=align_variant, parameters=parameters)
else:
alignment_result = alignments.apply(log, petri_net, initial_marking, final_marking, variant=align_variant,
parameters=parameters)
return evaluate(alignment_result)
def execute_script():
# import the a32f0n00 log
log = xes_importer.apply(
os.path.join("..", "tests", "compressed_input_data",
"09_a32f0n00.xes.gz"))
# discover a model using the inductive miner
net, im, fm = inductive_miner.apply(log)
# apply the alignments decomposition with a maximal number of border disagreements set to 5
aa = time.time()
aligned_traces = dec_align.apply(
log,
net,
im,
fm,
parameters={
dec_align.Variants.RECOMPOS_MAXIMAL.value.Parameters.PARAM_THRESHOLD_BORDER_AGREEMENT:
5
})
bb = time.time()
print(bb - aa)
# print(aligned_traces)
# calculate the fitness over the recomposed alignment (use the classical evaluation)
fitness = rep_fit.evaluate(aligned_traces,
variant=rep_fit.Variants.ALIGNMENT_BASED)
print(fitness)
def test_decomp_alignment(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.conformance.decomp_alignments import algorithm as decomp_align
aligned_traces = decomp_align.apply(
log, net, im, fm, variant=decomp_align.Variants.RECOMPOS_MAXIMAL)
