348 lines
13 KiB
Python
348 lines
13 KiB
Python
#%% # needed for shortcuts to run properly in VSCode *eyeroll*
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%reset
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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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###### Load data and create event logs ######
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dat = pd.read_csv("results/haum/event_logfiles_2024-01-02_19-44-50.csv", sep = ";")
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dat = dat[dat["date.start"] < "2020-03-13"]
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# --> only pre corona (before artworks were updated)
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event_log = pm4py.format_dataframe(dat, case_id='path', activity_key='event',
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timestamp_key='date.start')
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event_log = event_log.rename(columns={'artwork': 'case:artwork'})
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###### Descrptives of log data ######
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# Distribution of events
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event_log.event.value_counts()
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event_log.event.value_counts(normalize=True)
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# Number of paths
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len(event_log.path.unique())
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# Number of variants
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variants = pm4py.get_variants(event_log)
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len(variants)
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sorted_variants = dict(sorted(variants.items(), key=lambda item: item[1], reverse = True))
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{k: sorted_variants[k] for k in list(sorted_variants)[:20]}
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filtered_log = event_log[event_log["event"] != "move"]
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variants = pm4py.get_variants(filtered_log)
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len(variants)
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sorted_variants = dict(sorted(variants.items(), key=lambda item: item[1], reverse = True))
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{k: sorted_variants[k] for k in list(sorted_variants)[:20]}
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# Path length
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event_log.path.value_counts()
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event_log.path.value_counts().mean()
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event_log.path.value_counts().median()
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event_log.path.value_counts().min()
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event_log.path.value_counts().max()
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plt.hist(event_log.path.value_counts(), bins=200)
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plt.show()
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# TODO: Do it again in R -- much smoother and more info, better plots
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###### Read "conformative" Petri Net ######
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basenet, initial_marking, final_marking = pm4py.read_pnml("results/conformative_petrinet_con.pnml")
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def eval_pm(data, net, initial_marking, final_marking):
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"""Caculate fitness, precision, generalizability, and simplicity for petri net"""
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fitness = pm4py.fitness_token_based_replay(data, net, initial_marking, final_marking)
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precisison = pm4py.precision_token_based_replay(data, net,
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initial_marking, final_marking)
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generalizability = pm4py.algo.evaluation.generalization.algorithm.apply(data, net,
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initial_marking, final_marking)
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simplicity = pm4py.algo.evaluation.simplicity.algorithm.apply(net)
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return [fitness['average_trace_fitness'], precisison, generalizability, simplicity]
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baseline_eval = eval_pm(event_log, basenet, initial_marking, final_marking)
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# TBR
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replayed_traces = pm4py.conformance_diagnostics_token_based_replay(event_log, basenet, initial_marking, final_marking)
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l1 = list()
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l2 = list()
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l3 = list()
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l4 = list()
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for i in range(len(replayed_traces)):
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l1.append(replayed_traces[i]["remaining_tokens"])
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l2.append(replayed_traces[i]["missing_tokens"])
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l3.append(replayed_traces[i]["reached_marking"])
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l4.append(replayed_traces[i]["transitions_with_problems"])
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np.mean(l1)
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set(l1)
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index_broken = l1.index(1)
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np.mean(l2)
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set(l2)
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l2.index(1)
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set(l3)
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l4.count([])
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l3[index_broken]
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l4[index_broken]
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replayed_traces[index_broken]
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# 216295 # --> broken trace! Must be in artwork 176!!!!!
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from pm4py.algo.conformance.tokenreplay import algorithm as token_based_replay
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parameters_tbr = {token_based_replay.Variants.TOKEN_REPLAY.value.Parameters.DISABLE_VARIANTS: True, token_based_replay.Variants.TOKEN_REPLAY.value.Parameters.ENABLE_PLTR_FITNESS: True}
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replayed_traces, place_fitness, trans_fitness, unwanted_activities = token_based_replay.apply(event_log, basenet,
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initial_marking,
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final_marking,
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parameters=parameters_tbr)
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from pm4py.algo.conformance.tokenreplay.diagnostics import duration_diagnostics
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trans_diagnostics = duration_diagnostics.diagnose_from_trans_fitness(event_log, trans_fitness)
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for trans in trans_diagnostics:
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print(trans, trans_diagnostics[trans])
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# Footprints
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from pm4py.algo.discovery.footprints import algorithm as footprints_discovery
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fp_log = footprints_discovery.apply(event_log, variant=footprints_discovery.Variants.ENTIRE_EVENT_LOG)
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fp_trace_by_trace = footprints_discovery.apply(event_log, variant=footprints_discovery.Variants.TRACE_BY_TRACE)
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fp_net = footprints_discovery.apply(basenet, initial_marking, final_marking)
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from pm4py.visualization.footprints import visualizer as fp_visualizer
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gviz = fp_visualizer.apply(fp_net, parameters={fp_visualizer.Variants.SINGLE.value.Parameters.FORMAT: "svg"})
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fp_visualizer.view(gviz)
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gviz = fp_visualizer.apply(fp_log, fp_net, parameters={fp_visualizer.Variants.COMPARISON.value.Parameters.FORMAT: "svg"})
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fp_visualizer.view(gviz)
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conf_fp = pm4py.conformance_diagnostics_footprints(fp_trace_by_trace, fp_net)
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from pm4py.algo.conformance.footprints import algorithm as fp_conformance
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conf_result = fp_conformance.apply(fp_log, fp_net, variant=fp_conformance.Variants.LOG_EXTENSIVE)
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from pm4py.algo.conformance.footprints.util import evaluation
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fitness = evaluation.fp_fitness(fp_log, fp_net, conf_result)
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precision = evaluation.fp_precision(fp_log, fp_net)
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# Skeleton
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from pm4py.algo.discovery.log_skeleton import algorithm as lsk_discovery
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skeleton = lsk_discovery.apply(event_log, parameters={lsk_discovery.Variants.CLASSIC.value.Parameters.NOISE_THRESHOLD: 0.0})
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from pm4py.algo.conformance.log_skeleton import algorithm as lsk_conformance
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conf_result = lsk_conformance.apply(event_log, skeleton)
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pm4py.vis.view_petri_net(basenet, initial_marking, final_marking)
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is_sound = pm4py.check_soundness(basenet, initial_marking, final_marking)
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is_sound[0]
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len(basenet.arcs)
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len(basenet.transitions)
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len(basenet.places)
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efg_graph = pm4py.discover_eventually_follows_graph(event_log)
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## Directly-follows graph
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dfg, start_activities, end_activities = pm4py.discover_dfg(event_log)
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pm4py.view_dfg(dfg, start_activities, end_activities)
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pm4py.save_vis_dfg(dfg, start_activities, end_activities, '../figures/processmaps/dfg_complete.png')
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## Heuristics Miner
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h_net, im, fm = pm4py.discover_petri_net_heuristics(event_log)
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h_eval = eval_pm(event_log, h_net, im, fm)
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pm4py.vis.view_petri_net(h_net, im, fm)
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pm4py.vis.save_vis_petri_net(h_net, im, fm, "../figures/processmaps/pn_heuristics_complete.png")
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is_sound = pm4py.check_soundness(h_net, im, fm)
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is_sound[0]
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len(h_net.arcs)
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len(h_net.transitions)
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len(h_net.places)
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# decorated petri net
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from pm4py.visualization.petri_net import visualizer as pn_visualizer
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parameters = {pn_visualizer.Variants.FREQUENCY.value.Parameters.FORMAT: "png"}
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gviz = pn_visualizer.apply(h_net, im, fm, parameters=parameters, variant=pn_visualizer.Variants.FREQUENCY, log=event_log)
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pn_visualizer.save(gviz, "../figures/processmaps/pn_heuristics_complete_decorated.png")
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# convert to BPMN
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bpmn = pm4py.convert.convert_to_bpmn(h_net, im, fm)
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pm4py.vis.view_bpmn(bpmn)
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## Alpha Miner
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a_net, im, fm = pm4py.discover_petri_net_alpha(event_log)
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a_eval = eval_pm(event_log, a_net, im, fm)
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pm4py.vis.view_petri_net(a_net, im, fm)
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pm4py.vis.save_vis_petri_net(a_net, im, fm, "../figures/processmaps/pn_alpha_complete.png")
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is_sound = pm4py.check_soundness(a_net, im, fm)
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is_sound[0]
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len(a_net.arcs)
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len(a_net.transitions)
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len(a_net.places)
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## Inductive Miner
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i_net, im, fm = pm4py.discover_petri_net_inductive(event_log)
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i_eval = eval_pm(event_log, i_net, im, fm)
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pm4py.vis.view_petri_net(i_net, im, fm)
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pm4py.vis.save_vis_petri_net(i_net, im, fm, "../figures/processmaps/pn_induction_complete.png")
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# as process tree (does not work for heuristics miner!)
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pt = pm4py.discover_process_tree_inductive(event_log)
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pm4py.vis.view_process_tree(pt)
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is_sound = pm4py.check_soundness(i_net, im, fm)
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is_sound[0]
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# TODO: Can I show that this simpler net does not include all traces? (Probably not,
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# since fitness is 1, but WHY?)
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len(i_net.arcs)
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len(i_net.transitions)
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len(i_net.places)
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bpmn = pm4py.convert.convert_to_bpmn(i_net, im, fm)
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pm4py.view_bpmn(bpmn)
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from pm4py.algo.conformance.tokenreplay import algorithm as token_based_replay
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parameters_tbr = {token_based_replay.Variants.TOKEN_REPLAY.value.Parameters.DISABLE_VARIANTS: True, token_based_replay.Variants.TOKEN_REPLAY.value.Parameters.ENABLE_PLTR_FITNESS: True}
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replayed_traces, place_fitness, trans_fitness, unwanted_activities = token_based_replay.apply(event_log, i_net,
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im,
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fm,
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parameters=parameters_tbr)
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l1 = list()
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l2 = list()
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l3 = list()
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l4 = list()
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for i in range(len(replayed_traces)):
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l1.append(replayed_traces[i]["remaining_tokens"])
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l2.append(replayed_traces[i]["missing_tokens"])
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l3.append(replayed_traces[i]["reached_marking"])
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l4.append(replayed_traces[i]["transitions_with_problems"])
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np.mean(l1)
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np.mean(l2)
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set(l3)
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l4.count([])
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## ILP Miner
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ilp_net, im, fm = pm4py.discover_petri_net_ilp(event_log)
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ilp_eval = eval_pm(event_log, ilp_net, im, fm)
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pm4py.vis.view_petri_net(ilp_net, im, fm)
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pm4py.vis.save_vis_petri_net(ilp_net, im, fm, "../figures/processmaps/pn_ilp_complete.png")
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is_sound = pm4py.check_soundness(ilp_net, im, fm)
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is_sound[0]
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len(ilp_net.arcs)
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len(ilp_net.transitions)
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len(ilp_net.places)
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## Export for all miners
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eval = pd.DataFrame(np.row_stack([baseline_eval, h_eval, a_eval, i_eval, ilp_eval]))
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eval.columns = ["fitness", "precision", "generalizability", "simplicity"]
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eval.index = ["conformative", "heuristics", "alpha", "inductive", "ilp"]
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eval
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eval.to_csv("results/eval_all-miners_complete.csv", sep=";")
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###### Process Mining - individual artworks ######
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def pm_artworks(miner):
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retval1 = np.empty((len(event_log["case:artwork"].unique()), 4))
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retval2 = np.empty((len(event_log["case:artwork"].unique()), 4))
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if miner == "heuristics":
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net, im, fm = pm4py.discover_petri_net_heuristics(event_log)
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elif miner == "inductive":
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net, im, fm = pm4py.discover_petri_net_inductive(event_log)
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elif miner == "alpha":
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net, im, fm = pm4py.discover_petri_net_alpha(event_log)
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elif miner == "ilp":
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net, im, fm = pm4py.discover_petri_net_ilp(event_log)
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for i in range(len(event_log["case:artwork"].unique())):
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artwork = event_log["case:artwork"].unique()[i]
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subdata = pm4py.filter_event_attribute_values(event_log, "case:artwork",
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[artwork],
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level="case", retain=True)
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if miner == "heuristics":
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subnet, subim, subfm = pm4py.discover_petri_net_heuristics(subdata)
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elif miner == "inductive":
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subnet, subim, subfm = pm4py.discover_petri_net_inductive(subdata)
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elif miner == "alpha":
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subnet, subim, subfm = pm4py.discover_petri_net_alpha(subdata)
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elif miner == "ilp":
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subnet, subim, subfm = pm4py.discover_petri_net_ilp(subdata)
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#pm4py.save_vis_petri_net(subnet, subim, subfm,
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# "../figures/processmaps/artworks/petrinet_" + miner + "_" + str(artwork).zfill(3) + ".png")
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retval1[i] = eval_pm(subdata, net, im, fm)
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retval2[i] = eval_pm(subdata, subnet, subim, subfm)
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retval1 = pd.DataFrame(retval1)
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retval1.columns = ["fitness", "precision", "generalizability", "simplicity"]
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retval1.index = event_log["case:artwork"].unique()
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retval1.insert(0, "nettype", "alldata")
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retval2 = pd.DataFrame(retval2)
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retval2.columns = ["fitness", "precision", "generalizability", "simplicity"]
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retval2.index = event_log["case:artwork"].unique()
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retval2.insert(0, "nettype", "subdata")
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return pd.concat([retval1, retval2])
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for miner in ["heuristics", "inductive", "alpha", "ilp"]:
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eval_art = pm_artworks(miner = miner)
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eval_art.to_csv("results/eval_artworks_" + miner + ".csv", sep=";")
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eval_art = pm_artworks(miner = "inductive")
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##### Clustering ######
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## KMeans
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#eval_artworks = eval_art[eval_art.nettype == "alldata"].iloc[:,range(1,5)]
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eval_artworks = eval_art[eval_art.nettype == "subdata"].iloc[:,range(1,5)]
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kmeans = KMeans(n_clusters=4, max_iter=1000).fit(eval_artworks)
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#from sklearn.manifold import MDS
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#coord = pd.DataFrame(MDS(normalized_stress='auto').fit_transform(eval_artworks))
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coord = eval_artworks
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coord["clusters"] = kmeans.labels_
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for i in coord.clusters.unique():
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#plt.scatter(coord[coord.clusters == i].iloc[:,0], coord[coord.clusters == i].iloc[:,1],
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plt.scatter(coord[coord.clusters == i].iloc[:,1], coord[coord.clusters == i].iloc[:,2],
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#plt.scatter(coord[coord.clusters == i].iloc[:,2], coord[coord.clusters == i].iloc[:,4],
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label = i)
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plt.legend()
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plt.show()
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### Scree plot
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sse = {}
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for k in range(1, 10):
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kmeans = KMeans(n_clusters=k, max_iter=1000).fit(eval_artworks[["precision", "generalizability"]])
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#data["clusters"] = kmeans.labels_
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#print(data["clusters"])
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sse[k] = kmeans.inertia_ # Inertia: Sum of distances of samples to their closest cluster center
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plt.figure()
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plt.plot(list(sse.keys()), list(sse.values()))
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plt.xlabel("Number of clusters")
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plt.ylabel("SSE")
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plt.show()
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