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Updated PM for case clusters and rerun it with precision

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Nora Wickelmaier 2024-04-15 15:24:38 +02:00
parent 4174162dfe
commit d4f5aa427d
2 changed files with 13 additions and 12 deletions
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14
code/13_pm-case-clusters.py
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@ -24,7 +24,7 @@ event_log = pm4py.format_dataframe(dat,
#--------------- (2) Evaluation for clusters --------------- #--------------- (2) Evaluation for clusters ---------------
thresholds = [0.0, 0.1, 0.2, 0.3, 0.4] thresholds = [0.1, 0.2, 0.3, 0.4]
for nt in thresholds: for nt in thresholds:
@ -33,9 +33,9 @@ for nt in thresholds:
eval = eval_append(event_log, net, im, fm) eval = eval_append(event_log, net, im, fm)
# Export process maps # Export process maps
pm4py.save_vis_petri_net(net, im, fm, "results/processmaps/petrinet_complete_cases" + str(int(nt*10)).zfill(2) + ".png") # pm4py.save_vis_petri_net(net, im, fm, "results/processmaps/petrinet_complete_cases" + str(int(nt*10)).zfill(2) + ".png")
bpmn = pm4py.convert.convert_to_bpmn(net, im, fm) # bpmn = pm4py.convert.convert_to_bpmn(net, im, fm)
pm4py.vis.save_vis_bpmn(bpmn, "results/processmaps/bpmn_complete_cases" + str(int(nt*10)).zfill(2) + ".png") # pm4py.vis.save_vis_bpmn(bpmn, "results/processmaps/bpmn_complete_cases" + str(int(nt*10)).zfill(2) + ".png")
# Merge clusters into data frame # Merge clusters into data frame
for cluster in [1, 2, 3, 4, 5]: for cluster in [1, 2, 3, 4, 5]:
@ -44,9 +44,9 @@ for nt in thresholds:
eval_clst = eval_append(log_clst, net_clst, im_clst, fm_clst) eval_clst = eval_append(log_clst, net_clst, im_clst, fm_clst)
eval = pd.concat([eval, eval_clst]) eval = pd.concat([eval, eval_clst])
# Export process maps # Export process maps
pm4py.save_vis_petri_net(net_clst, im_clst, fm_clst, "results/processmaps/petrinet_cluster" + str(cluster) + "_cases" + str(int(nt*10)).zfill(2) + ".png") # pm4py.save_vis_petri_net(net_clst, im_clst, fm_clst, "results/processmaps/petrinet_cluster" + str(cluster) + "_cases" + str(int(nt*10)).zfill(2) + ".png")
bpmn = pm4py.convert.convert_to_bpmn(net_clst, im_clst, fm_clst) # bpmn = pm4py.convert.convert_to_bpmn(net_clst, im_clst, fm_clst)
pm4py.vis.save_vis_bpmn(bpmn, "results/processmaps/bpmn_cluster" + str(cluster) + "_cases" + str(int(nt*10)).zfill(2) + ".png") # pm4py.vis.save_vis_bpmn(bpmn, "results/processmaps/bpmn_cluster" + str(cluster) + "_cases" + str(int(nt*10)).zfill(2) + ".png")
eval.index = ["Complete", "Cluster 1", "Cluster 2", "Cluster 3", "Cluster 4", "Cluster 5"] eval.index = ["Complete", "Cluster 1", "Cluster 2", "Cluster 3", "Cluster 4", "Cluster 5"]
eval.to_csv("results/eval_case_clusters_" + str(int(nt*10)).zfill(2) + ".csv", sep = ";") eval.to_csv("results/eval_case_clusters_" + str(int(nt*10)).zfill(2) + ".csv", sep = ";")

11
code/python_helpers.py
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@ -7,15 +7,15 @@ def eval_pm(data, net, initial_marking, final_marking):
"""Caculate fitness, precision, generalization, and simplicity for petri net""" """Caculate fitness, precision, generalization, and simplicity for petri net"""
print("Fitness is calculated") print("Fitness is calculated")
fitness = pm4py.fitness_token_based_replay(data, net, initial_marking, final_marking) fitness = pm4py.fitness_token_based_replay(data, net, initial_marking, final_marking)
#print("Precision is calculated") print("Precision is calculated")
#precisison = pm4py.precision_token_based_replay(data, net, initial_marking, final_marking) precisison = pm4py.precision_token_based_replay(data, net, initial_marking, final_marking)
print("Generalizability is calculated") print("Generalizability is calculated")
generalization = pm4py.algo.evaluation.generalization.algorithm.apply(data, net, generalization = pm4py.algo.evaluation.generalization.algorithm.apply(data, net,
initial_marking, final_marking) initial_marking, final_marking)
print("Simplicity is calculated") print("Simplicity is calculated")
simplicity = pm4py.algo.evaluation.simplicity.algorithm.apply(net) simplicity = pm4py.algo.evaluation.simplicity.algorithm.apply(net)
#return [fitness['average_trace_fitness'], precisison, generalization, simplicity] return [fitness['average_trace_fitness'], precisison, generalization, simplicity]
return [fitness['average_trace_fitness'], generalization, simplicity] #return [fitness['average_trace_fitness'], generalization, simplicity]
def pn_infos(log, colname, filter): def pn_infos(log, colname, filter):
@ -63,7 +63,8 @@ def eval_append(log, net, im, fm):
eval.append({k: sorted_variants[k] for k in list(sorted_variants)[:1]}) eval.append({k: sorted_variants[k] for k in list(sorted_variants)[:1]})
eval = pd.DataFrame(eval).T eval = pd.DataFrame(eval).T
eval.columns = ["fitness", "generalization", "simplicity", "narcs", "ntrans", "nplaces", "nvariants", "mostfreq"] #eval.columns = ["fitness", "generalization", "simplicity", "narcs", "ntrans", "nplaces", "nvariants", "mostfreq"]
#eval.columns = ["fitness", "precision", "generalization", "simplicity", "sound", "narcs", "ntrans", "nplaces", "nvariants", "mostfreq"] #eval.columns = ["fitness", "precision", "generalization", "simplicity", "sound", "narcs", "ntrans", "nplaces", "nvariants", "mostfreq"]
eval.columns = ["fitness", "precision", "generalization", "simplicity", "narcs", "ntrans", "nplaces", "nvariants", "mostfreq"]
return eval return eval