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