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Updated python script(s) for pm on case clusters

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This commit is contained in:
Nora Wickelmaier 2024-04-09 12:08:58 +02:00
parent 76aa35da3f
commit b1d2c5ec99
2 changed files with 33 additions and 32 deletions
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23
code/13_pm-case-clusters.py
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@ -6,12 +6,12 @@
# input: results/eventlogs_2019_case-clusters.csv # input: results/eventlogs_2019_case-clusters.csv
# output: results/eval_case_clusters.csv # output: results/eval_case_clusters.csv
# #
# last mod: 2024-04-02 # last mod: 2024-04-04
import pm4py import pm4py
import pandas as pd import pandas as pd
from python_helpers import eval_pm from python_helpers import eval_pm, eval_append
#--------------- (1) Load data and create event logs --------------- #--------------- (1) Load data and create event logs ---------------
@ -24,28 +24,29 @@ event_log = pm4py.format_dataframe(dat,
#--------------- (2) Evaluation for clusters --------------- #--------------- (2) Evaluation for clusters ---------------
thresholds = [0.1, 0.2, 0.3, 0.4, 0.5] thresholds = [0.0, 0.1, 0.2, 0.3, 0.4]
for nt in thresholds: for nt in thresholds:
net, im, fm = pm4py.discover_petri_net_inductive(event_log, noise_threshold = nt) 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 = eval_append(event_log, net, im, fm)
eval.columns = ["fitness", "generalization", "simplicity"]
#eval.columns = ["fitness", "precision", "generalization", "simplicity"] # 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")
# 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]:
log_clst = pm4py.filter_event_attribute_values(event_log, "cluster", [cluster]) 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) 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 = eval_append(log_clst, net_clst, im_clst, fm_clst)
eval_clst.columns = ["fitness", "generalization", "simplicity"]
#eval_clst.columns = ["fitness", "precision", "generalization", "simplicity"]
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")
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) + pm4py.vis.save_vis_bpmn(bpmn, "results/processmaps/bpmn_cluster" + str(cluster) + "_cases" + str(int(nt*10)).zfill(2) + ".png")
"_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 = ";")

16
code/python_helpers.py
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@ -4,18 +4,18 @@ import pandas as pd
###### Extract metadata for petri nets on filtered logs ###### ###### Extract metadata for petri nets on filtered logs ######
def eval_pm(data, net, initial_marking, final_marking): def eval_pm(data, net, initial_marking, final_marking):
"""Caculate fitness, precision, generalizability, 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")
generalizability = 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, generalizability, simplicity] #return [fitness['average_trace_fitness'], precisison, generalization, simplicity]
return [fitness['average_trace_fitness'], generalizability, simplicity] return [fitness['average_trace_fitness'], generalization, simplicity]
def pn_infos(log, colname, filter): def pn_infos(log, colname, filter):
@ -51,8 +51,8 @@ def pn_infos_miner(log, miner):
def eval_append(log, net, im, fm): def eval_append(log, net, im, fm):
eval = eval_pm(log, net, im, fm) eval = eval_pm(log, net, im, fm)
is_sound = pm4py.check_soundness(net, im, fm) #is_sound = pm4py.check_soundness(net, im, fm)
eval.append(is_sound[0]) #eval.append(is_sound[0])
eval.append(len(net.arcs)) eval.append(len(net.arcs))
eval.append(len(net.transitions)) eval.append(len(net.transitions))
eval.append(len(net.places)) eval.append(len(net.places))
@ -63,7 +63,7 @@ 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", "precision", "generalizability", "simplicity", eval.columns = ["fitness", "generalization", "simplicity", "narcs", "ntrans", "nplaces", "nvariants", "mostfreq"]
"sound", "narcs", "ntrans", "nplaces", "nvariants", "mostfreq"] #eval.columns = ["fitness", "precision", "generalization", "simplicity", "sound", "narcs", "ntrans", "nplaces", "nvariants", "mostfreq"]
return eval return eval