diff --git a/code/13_pm-case-clusters.py b/code/13_pm-case-clusters.py index e1d3b81..c19f2b7 100644 --- a/code/13_pm-case-clusters.py +++ b/code/13_pm-case-clusters.py @@ -24,7 +24,7 @@ event_log = pm4py.format_dataframe(dat, #--------------- (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: @@ -33,9 +33,9 @@ for nt in thresholds: eval = eval_append(event_log, net, im, fm) # Export process maps - 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) - pm4py.vis.save_vis_bpmn(bpmn, "results/processmaps/bpmn_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) + # pm4py.vis.save_vis_bpmn(bpmn, "results/processmaps/bpmn_complete_cases" + str(int(nt*10)).zfill(2) + ".png") # Merge clusters into data frame 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 = pd.concat([eval, eval_clst]) # 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") - 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.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) + # 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.to_csv("results/eval_case_clusters_" + str(int(nt*10)).zfill(2) + ".csv", sep = ";") diff --git a/code/python_helpers.py b/code/python_helpers.py index d2962ca..8b595c7 100644 --- a/code/python_helpers.py +++ b/code/python_helpers.py @@ -7,15 +7,15 @@ def eval_pm(data, net, initial_marking, final_marking): """Caculate fitness, precision, generalization, and simplicity for petri net""" print("Fitness is calculated") fitness = pm4py.fitness_token_based_replay(data, net, initial_marking, final_marking) - #print("Precision is calculated") - #precisison = pm4py.precision_token_based_replay(data, net, initial_marking, final_marking) + print("Precision is calculated") + precisison = pm4py.precision_token_based_replay(data, net, initial_marking, final_marking) print("Generalizability is calculated") generalization = pm4py.algo.evaluation.generalization.algorithm.apply(data, net, initial_marking, final_marking) print("Simplicity is calculated") simplicity = pm4py.algo.evaluation.simplicity.algorithm.apply(net) - #return [fitness['average_trace_fitness'], precisison, generalization, simplicity] - return [fitness['average_trace_fitness'], generalization, simplicity] + return [fitness['average_trace_fitness'], precisison, generalization, simplicity] + #return [fitness['average_trace_fitness'], generalization, simplicity] 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 = 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", "narcs", "ntrans", "nplaces", "nvariants", "mostfreq"] return eval