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