65 lines
2.5 KiB
Python
65 lines
2.5 KiB
Python
import pm4py
|
|
import pandas as pd
|
|
|
|
###### Extract metadata for petri nets on filtered logs ######
|
|
|
|
def eval_pm(data, net, initial_marking, final_marking):
|
|
"""Caculate fitness, precision, generalizability, and simplicity for petri net"""
|
|
fitness = pm4py.fitness_token_based_replay(data, net, initial_marking, final_marking)
|
|
precisison = pm4py.precision_token_based_replay(data, net, initial_marking, final_marking)
|
|
generalizability = pm4py.algo.evaluation.generalization.algorithm.apply(data, net,
|
|
initial_marking, final_marking)
|
|
simplicity = pm4py.algo.evaluation.simplicity.algorithm.apply(net)
|
|
return [fitness['average_trace_fitness'], precisison, generalizability, simplicity]
|
|
|
|
|
|
def pn_infos(log, colname, filter):
|
|
"""Create data frame with relevant infos for petri nets on filtered logs"""
|
|
filtered_log = pm4py.filter_event_attribute_values(log, colname, [filter])
|
|
|
|
net, im, fm = pm4py.discover_petri_net_inductive(filtered_log)
|
|
|
|
eval = eval_append(filtered_log, net, im, fm)
|
|
eval.index = [str(filter).zfill(3)]
|
|
return eval
|
|
|
|
|
|
def pn_infos_miner(log, miner):
|
|
"""Create data frame with relevant infos for petri nets created with
|
|
different miners"""
|
|
|
|
if miner == "alpha":
|
|
net, im, fm = pm4py.discover_petri_net_alpha(log)
|
|
elif miner == "heuristics":
|
|
net, im, fm = pm4py.discover_petri_net_heuristics(log)
|
|
elif miner == "ilp":
|
|
net, im, fm = pm4py.discover_petri_net_ilp(log)
|
|
elif miner == "inductive":
|
|
net, im, fm = pm4py.discover_petri_net_inductive(log)
|
|
elif miner == "normative":
|
|
net, im, fm = pm4py.read_pnml("results/normative_petrinet.pnml")
|
|
|
|
eval = eval_append(log, net, im, fm)
|
|
eval.index = [miner]
|
|
return eval
|
|
|
|
def eval_append(log, net, im, fm):
|
|
|
|
eval = eval_pm(log, net, im, fm)
|
|
is_sound = pm4py.check_soundness(net, im, fm)
|
|
eval.append(is_sound[0])
|
|
eval.append(len(net.arcs))
|
|
eval.append(len(net.transitions))
|
|
eval.append(len(net.places))
|
|
variants = pm4py.get_variants(log)
|
|
eval.append(len(variants))
|
|
|
|
sorted_variants = dict(sorted(variants.items(), key=lambda item: item[1], reverse = True))
|
|
eval.append({k: sorted_variants[k] for k in list(sorted_variants)[:1]})
|
|
|
|
eval = pd.DataFrame(eval).T
|
|
eval.columns = ["fitness", "precision", "generalizability", "simplicity",
|
|
"sound", "narcs", "ntrans", "nplaces", "nvariants", "mostfreq"]
|
|
return eval
|
|
|