53 lines
2.0 KiB
Python
53 lines
2.0 KiB
Python
# 13_pm-case-clusters.py
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#
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# content: (1) Load data and create event log
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# (2) Evaluation for clusters
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#
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# input: results/eventlogs_2019_case-clusters.csv
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# output: results/eval_case_clusters.csv
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#
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# last mod: 2024-04-02
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import pm4py
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import pandas as pd
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from python_helpers import eval_pm
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#--------------- (1) Load data and create event logs ---------------
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dat = pd.read_csv("results/eventlogs_2019_case-clusters.csv", sep = ";")
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event_log = pm4py.format_dataframe(dat,
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case_id = "case",
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activity_key = "item",
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timestamp_key = "date.start")
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#--------------- (2) Evaluation for clusters ---------------
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thresholds = [0.1, 0.2, 0.3, 0.4, 0.5]
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for nt in thresholds:
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net, im, fm = pm4py.discover_petri_net_inductive(event_log, noise_threshold = nt)
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eval = pd.DataFrame(eval_pm(event_log, net, im, fm)).T
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eval.columns = ["fitness", "generalization", "simplicity"]
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#eval.columns = ["fitness", "precision", "generalization", "simplicity"]
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# Merge clusters into data frame
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for cluster in [1, 2, 3, 4, 5]:
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log_clst = pm4py.filter_event_attribute_values(event_log, "cluster", [cluster])
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net_clst, im_clst, fm_clst = pm4py.discover_petri_net_inductive(log_clst, noise_threshold = nt)
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eval_clst = pd.DataFrame(eval_pm(log_clst, net_clst, im_clst, fm_clst)).T
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eval_clst.columns = ["fitness", "generalization", "simplicity"]
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#eval_clst.columns = ["fitness", "precision", "generalization", "simplicity"]
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eval = pd.concat([eval, eval_clst])
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# Export process maps
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bpmn = pm4py.convert.convert_to_bpmn(net_clst, im_clst, fm_clst)
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pm4py.vis.save_vis_bpmn(bpmn, "results/processmaps/bpmn_cluster" + str(cluster) +
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"_cases" + str(int(nt*10)).zfill(2) + ".png")
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eval.index = ["Complete", "Cluster 1", "Cluster 2", "Cluster 3", "Cluster 4", "Cluster 5"]
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eval.to_csv("results/eval_case_clusters_" + str(int(nt*10)).zfill(2) + ".csv", sep = ";")
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