39 lines
1.4 KiB
Python
39 lines
1.4 KiB
Python
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import pm4py
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import pandas as pd
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import numpy as np
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import matplotlib.pyplot as plt
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from python_helpers import eval_pm, pn_infos
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###### Load data and create event logs ######
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dat = pd.read_csv("results/haum/event_logfiles_pre-corona_with-clusters.csv", sep = ";")
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log_path = pm4py.format_dataframe(dat, case_id = "path", activity_key = "event",
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timestamp_key = "date.start")
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###### Infos for clusters ######
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# Merge clusters into data frame
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mdc = pd.DataFrame(columns = ["fitness", "precision", "generalizability",
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"simplicity", "sound", "narcs", "ntrans",
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"nplaces", "nvariants", "mostfreq"])
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for cluster in log_path.grp.unique().tolist():
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mdc = pd.concat([mdc, pn_infos(log_path, "grp", cluster)])
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mdc = mdc.sort_index()
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# Export
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mdc.to_csv("results/haum/pn_infos_clusters.csv", sep = ";")
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###### Process maps for clusters ######
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for cluster in log_path.grp.unique().tolist():
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subdata = log_path[log_path.grp == cluster]
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subnet, subim, subfm = pm4py.discover_petri_net_inductive(subdata)
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pm4py.save_vis_petri_net(subnet, subim, subfm,
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"results/processmaps/petrinet_cluster" + str(cluster).zfill(3) + ".png")
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bpmn = pm4py.convert.convert_to_bpmn(subnet, subim, subfm)
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pm4py.vis.save_vis_bpmn(bpmn, "results/processmaps/bpmn_cluster_" + str(cluster).zfill(3) + ".png")
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