code/11_investigate-variants.R

@@ -60,7 +60,7 @@ pdf("../../thesis/figures/freq-traces_powerlaw.pdf", height = 3.375,
     width = 3.375, pointsize = 10)
 par(mai = c(.6,.6,.1,.1), mgp = c(2.4, 1, 0))
 
-plot(x, y, log = "xy", xlab = "Process variants sorted by frequency",
+plot(x, y, log = "xy", xlab = "Absolute Frequency of Traces",
      ylab = "Frequency", pch = 16, col = rgb(0.262, 0.309, 0.309, 0.5))
 lines(x, pre, col = "#434F4F")
 legend("topright", paste0("Proportion of traces only occurring once: ",
@@ -73,7 +73,7 @@ pdf("../../thesis/figures/freq-traces_powerlaw_bw.pdf", height = 3.375,
     width = 3.375, pointsize = 10)
 par(mai = c(.6,.6,.1,.1), mgp = c(2.4, 1, 0))
 
-plot(x, y, log = "xy", xlab = "Process variants sorted by frequency",
+plot(x, y, log = "xy", xlab = "Absolute Frequency of Traces",
      ylab = "Frequency", pch = 16, col = rgb(0.3, 0.3, 0.3, 0.5))
 lines(x, pre, col = "#434F4F")
 legend("topright", paste0("Proportion of traces only occurring once: ",
@@ -129,7 +129,7 @@ pdf("../../thesis/figures/freq-traces_powerlaw_2019.pdf", height = 3.375,
     width = 3.375, pointsize = 10)
 par(mai = c(.6,.6,.1,.1), mgp = c(2.4, 1, 0))
 
-plot(x, y, log = "xy", xlab = "Process variants sorted by frequency",
+plot(x, y, log = "xy", xlab = "Absolute Frequency of Traces",
      ylab = "Frequency", pch = 16, col = rgb(0.262, 0.309, 0.309, 0.5))
 lines(x, pre, col = "#434F4F")
 legend("topright", paste0("Proportion of traces only occurring once: ",
@@ -142,7 +142,7 @@ pdf("../../thesis/figures/freq-traces_powerlaw_2019_bw.pdf", height = 3.375,
     width = 3.375, pointsize = 10)
 par(mai = c(.6,.6,.1,.1), mgp = c(2.4, 1, 0))
 
-plot(x, y, log = "xy", xlab = "Process variants sorted by frequency",
+plot(x, y, log = "xy", xlab = "Absolute Frequency of Traces",
      ylab = "Frequency", pch = 16, col = rgb(0.3, 0.3, 0.3, 0.5))
 lines(x, pre, col = "#434F4F")
 legend("topright", paste0("Proportion of traces only occurring once: ",

code/13_pm-case-clusters.py

@@ -1,52 +0,0 @@
-# 13_pm-case-clusters.py
-#
-# content: (1) Load data and create event log
-#          (2) Evaluation for clusters
-#
-# input:  results/eventlogs_2019_case-clusters.csv
-# output: results/eval_case_clusters.csv
-#
-# last mod: 2024-04-02
-
-import pm4py
-import pandas as pd
-
-from python_helpers import eval_pm
-
-#--------------- (1) Load data and create event logs ---------------
-
-dat = pd.read_csv("results/eventlogs_2019_case-clusters.csv", sep = ";")
-
-event_log = pm4py.format_dataframe(dat,
-                                   case_id = "case",
-                                   activity_key = "item",
-                                   timestamp_key = "date.start")
-
-#--------------- (2) Evaluation for clusters ---------------
-
-thresholds = [0.1, 0.2, 0.3, 0.4, 0.5]
-
-for nt in thresholds:
-
-  net, im, fm = pm4py.discover_petri_net_inductive(event_log, noise_threshold = nt)
-  
-  eval = pd.DataFrame(eval_pm(event_log, net, im, fm)).T
-  eval.columns = ["fitness", "generalization", "simplicity"]
-  #eval.columns = ["fitness", "precision", "generalization", "simplicity"]
-  
-  # Merge clusters into data frame
-  for cluster in [1, 2, 3, 4, 5]:
-      log_clst = pm4py.filter_event_attribute_values(event_log, "cluster", [cluster])
-      net_clst, im_clst, fm_clst = pm4py.discover_petri_net_inductive(log_clst, noise_threshold = nt)
-      eval_clst = pd.DataFrame(eval_pm(log_clst, net_clst, im_clst, fm_clst)).T
-      eval_clst.columns = ["fitness", "generalization", "simplicity"]
-      #eval_clst.columns = ["fitness", "precision", "generalization", "simplicity"]
-      eval = pd.concat([eval, eval_clst])
-      # Export process maps
-      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 = ";")
-

code/python_helpers.py

@@ -5,17 +5,12 @@ import pandas as pd
 
 def eval_pm(data, net, initial_marking, final_marking):
     """Caculate fitness, precision, generalizability, 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)
-    #precisison       = pm4py.precision_token_based_replay(data, net, initial_marking, final_marking)
-    print("Generalizability is calculated")
     generalizability = 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, generalizability, simplicity]
+    return [fitness['average_trace_fitness'], precisison, generalizability, simplicity]
-    return [fitness['average_trace_fitness'], generalizability, simplicity]
 
 
 def pn_infos(log, colname, filter):