mtt_haum/code/08_infos-clusters.py

# 08_infos-clusters.py
#
# content: (1) Load data and create event log
#          (2) Infos for clusters
#          (3) Process maps for clusters
#
# input:  results/haum/eventlogs_pre-corona_item-clusters.csv
# output: results/haum/pn_infos_clusters.csv
#
# last mod: 2024-03-06

import pm4py
import pandas as pd

from python_helpers import eval_pm, pn_infos

#--------------- (1) Load data and create event logs ---------------

dat = pd.read_csv("results/haum/eventlogs_pre-corona_item-clusters.csv", sep = ";")

log_path = pm4py.format_dataframe(dat, case_id = "path", activity_key = "event",
                                  timestamp_key = "date.start")

#--------------- (2) Infos for clusters ---------------

# Merge clusters into data frame
eval = pd.DataFrame(columns = ["fitness", "precision", "generalizability",
                               "simplicity", "sound", "narcs", "ntrans",
                               "nplaces", "nvariants", "mostfreq"])
for cluster in log_path.grp.unique().tolist():
    eval = pd.concat([eval, pn_infos(log_path, "grp", cluster)])
eval = eval.sort_index()

eval.to_csv("results/haum/pn_infos_clusters.csv", sep = ";")

#--------------- (3) Process maps for clusters ---------------

for cluster in log_path.grp.unique().tolist():
    subdata = log_path[log_path.grp == cluster]
    subnet, subim, subfm = pm4py.discover_petri_net_inductive(subdata, noise_threshold=0.5)
    pm4py.save_vis_petri_net(subnet, subim, subfm,
       "results/processmaps/petrinet_cluster" + str(cluster).zfill(3) + ".png")
    bpmn = pm4py.convert.convert_to_bpmn(subnet, subim, subfm)
    pm4py.vis.save_vis_bpmn(bpmn, "results/processmaps/bpmn_cluster_" +
            str(cluster).zfill(3) + ".png")