mtt_haum/code/13_pm-case-clusters.py

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# 12_pm-case-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
#
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# last mod: 2024-03-10
import pm4py
import pandas as pd
from python_helpers import eval_pm, pn_infos
#--------------- (1) Load data and create event logs ---------------
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dat = pd.read_csv("results/haum/eventlogs_2019_case-clusters_new.csv", sep = ";")
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event_log = pm4py.format_dataframe(dat, case_id = "case", activity_key = "event_new",
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 event_log.cluster.unique().tolist():
eval = pd.concat([eval, pn_infos(event_log, "cluster", cluster)])
eval = eval.sort_index()
eval.to_csv("results/haum/pn_infos_clusters.csv", sep = ";")
#--------------- (3) Process maps for clusters ---------------
for cluster in event_log.cluster.unique().tolist():
subdata = event_log[event_log.cluster == cluster]
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subnet, subim, subfm = pm4py.discover_petri_net_inductive(subdata, noise_threshold = .7)
pm4py.save_vis_petri_net(subnet, subim, subfm,
"results/processmaps/petrinet_cluster" + str(cluster) + "_cases.png")
bpmn = pm4py.convert.convert_to_bpmn(subnet, subim, subfm)
pm4py.vis.save_vis_bpmn(bpmn, "results/processmaps/bpmn_cluster" +
str(cluster) + "_cases.png")
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