Mostly updated file paths
This commit is contained in:
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@ -8,8 +8,8 @@
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# ../data/metadata/feiertage.csv
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# ../data/metadata/schulferien_2016-2018_NI.csv
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# ../data/metadata/schulferien_2019-2025_NI.csv
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# output: raw_logfiles_<timestamp>.csv
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# event_logfiles_<timestamp>.csv
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# output: results/raw_logfiles_<timestamp>.csv
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# results/event_logfiles_<timestamp>.csv
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#
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# last mod: 2024-02-23, NW
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@ -29,12 +29,12 @@ folders <- dir(path)
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datraw <- parse_logfiles(folders, path)
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# 91 corrupt lines have been found and removed from the data set
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# datraw <- read.table("results/haum/raw_logfiles_2023-10-25_16-20-45.csv",
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# datraw <- read.table("results/raw_logfiles_2023-10-25_16-20-45.csv",
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# sep = ";", header = TRUE)
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## Export data
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write.table(datraw, paste0("results/haum/raw_logfiles_", now, ".csv"),
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write.table(datraw, paste0("results/raw_logfiles_", now, ".csv"),
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sep = ";", row.names = FALSE)
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#--------------- (2) Create event logs ---------------
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@ -131,6 +131,6 @@ dat2 <- dat2[order(dat2$fileId.start, dat2$date.start, dat2$timeMs.start), ]
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## Export data
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write.table(dat2, paste0("results/haum/event_logfiles_", now, ".csv"),
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write.table(dat2, paste0("results/event_logfiles_", now, ".csv"),
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sep = ";", row.names = FALSE)
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@ -9,8 +9,8 @@
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# (3.4) Artwork sequences
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# (3.5) Topics
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#
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# input: results/haum/event_logfiles_2024-02-21_16-07-33.csv
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# results/haum/raw_logfiles_2024-02-21_16-07-33.csv
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# input: results/event_logfiles_2024-02-21_16-07-33.csv
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# results/raw_logfiles_2024-02-21_16-07-33.csv
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# output: results/figures/counts_item.pdf
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# results/figures/counts_item_firsttouch.pdf
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# results/figures/duration.pdf
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@ -41,7 +41,7 @@
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#--------------- (1) Read data ---------------
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datlogs <- read.table("results/haum/event_logfiles_2024-02-21_16-07-33.csv",
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datlogs <- read.table("results/event_logfiles_2024-02-21_16-07-33.csv",
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colClasses = c("character", "character", "POSIXct",
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"POSIXct", "character", "integer",
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"numeric", "character", "character",
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@ -54,7 +54,7 @@ datlogs$event <- factor(datlogs$event, levels = c("move", "flipCard",
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"openTopic",
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"openPopup"))
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datraw <- read.table("results/haum/raw_logfiles_2024-02-21_16-07-33.csv",
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datraw <- read.table("results/raw_logfiles_2024-02-21_16-07-33.csv",
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sep = ";", header = TRUE)
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# Add weekdays to data frame
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@ -1,25 +1,24 @@
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# 04_conformance-checking.py
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#
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# content: (1) Load data and create event log
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# (2) Infos for items
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# (2) Check against normative Petri Net
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#
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# input: results/haum/event_logfiles_2024-02-21_16-07-33.csv
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# results/haum/conformative_petrinet_con.pnml
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# output: results/processmaps/dfg_complete_python.png
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# results/eval_all-miners_complete.csv
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# input: results/event_logfiles_2024-02-21_16-07-33.csv
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# results/normative_petrinet.pnml
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# output: results/eval_all-miners_complete.csv
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# results/eval_all-miners_clean.csv
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# results/processmaps/petrinet_conformative.png
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# results/processmaps/petrinet_heuristics_clean.png
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# results/processmaps/petrinet_alpha_clean.png
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# results/processmaps/petrinet_inductive_clean.png
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# results/processmaps/petrinet_ilp_clean.png
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# results/processmaps/bpmn_conformative.png
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# results/processmaps/bpmn_inductive_clean.png
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# results/processmaps/bpmn_ilp_clean.png
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# results/processmaps/bpmn_alpha_clean.png
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# results/processmaps/bpmn_heuristics_clean.png
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# ../../thesis/figures/petrinet_normative.png
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# ../../thesis/figures/petrinet_heuristics_clean.png
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# ../../thesis/figures/petrinet_alpha_clean.png
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# ../../thesis/figures/petrinet_inductive_clean.png
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# ../../thesis/figures/petrinet_ilp_clean.png
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# ../../thesis/figures/bpmn_normative.png
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# ../../thesis/figures/bpmn_inductive_clean.png
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# ../../thesis/figures/bpmn_ilp_clean.png
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# ../../thesis/figures/bpmn_alpha_clean.png
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# ../../thesis/figures/bpmn_heuristics_clean.png
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#
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# last mod: 2024-03-06
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# last mod: 2024-03-22
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import pm4py
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import pandas as pd
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@ -29,13 +28,13 @@ from python_helpers import eval_pm, pn_infos_miner
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#--------------- (1) Load data and create event logs ---------------
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dat = pd.read_csv("results/haum/event_logfiles_2024-02-21_16-07-33.csv", sep = ";")
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dat = pd.read_csv("results/event_logfiles_2024-02-21_16-07-33.csv", sep = ";")
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event_log = pm4py.format_dataframe(dat, case_id = "path",
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activity_key = "event",
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timestamp_key = "date.start")
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###### Descriptives of log data ######
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## Descriptives of log data
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# Distribution of events
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event_log.event.value_counts()
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@ -57,9 +56,9 @@ len(variants_no_move)
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sorted_variants_no_move = dict(sorted(variants_no_move.items(), key=lambda item: item[1], reverse = True))
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{k: sorted_variants_no_move[k] for k in list(sorted_variants_no_move)[:20]}
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###### Check against "conformative" Petri Net ######
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#--------------- (2) Check against normative Petri Net ---------------
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basenet, initial_marking, final_marking = pm4py.read_pnml("results/haum/conformative_petrinet_con.pnml")
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basenet, initial_marking, final_marking = pm4py.read_pnml("results/normative_petrinet.pnml")
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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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@ -93,23 +92,13 @@ event_log[event_log["@@case_index"] == index_broken[0]].item.unique().tolist()
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event_log[event_log["@@case_index"] == index_broken[0]]["fileId.start"].unique().tolist()
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# --> logging error in raw file
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## Footprints
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from pm4py.algo.discovery.footprints import algorithm as footprints_discovery
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from pm4py.visualization.footprints import visualizer as fp_visualizer
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fp_log = footprints_discovery.apply(event_log, variant=footprints_discovery.Variants.ENTIRE_EVENT_LOG)
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fp_net = footprints_discovery.apply(basenet, initial_marking, final_marking)
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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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efg_graph = pm4py.discover_eventually_follows_graph(event_log)
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## Fitting different miners
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eval = 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 miner in ["conformative", "alpha", "heuristics", "inductive", "ilp"]:
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for miner in ["normative", "alpha", "heuristics", "inductive", "ilp"]:
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eval = pd.concat([eval, pn_infos_miner(event_log, miner)])
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eval.to_csv("results/eval_all-miners_complete.csv", sep = ";")
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@ -121,7 +110,7 @@ eval_clean = 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 miner in ["conformative", "alpha", "heuristics", "inductive", "ilp"]:
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for miner in ["normative", "alpha", "heuristics", "inductive", "ilp"]:
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eval_clean = pd.concat([eval_clean, pn_infos_miner(event_log_clean, miner)])
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eval_clean.to_csv("results/eval_all-miners_clean.csv", sep = ";")
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@ -129,28 +118,27 @@ eval_clean.to_csv("results/eval_all-miners_clean.csv", sep = ";")
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## Directly-follows graph
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dfg, start_activities, end_activities = pm4py.discover_dfg(event_log_clean)
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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, "results/processmaps/dfg_complete_python.png")
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## Export petri nets
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pm4py.vis.save_vis_petri_net(basenet, initial_marking, final_marking, "results/processmaps/petrinet_conformative.png")
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pm4py.vis.save_vis_petri_net(basenet, initial_marking, final_marking,
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"../../thesis/figures/petrinet_normative.png")
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h_net, h_im, h_fm = pm4py.discover_petri_net_heuristics(event_log_clean)
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pm4py.vis.save_vis_petri_net(h_net, h_im, h_fm, "results/processmaps/petrinet_heuristics_clean.png")
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pm4py.vis.save_vis_petri_net(h_net, h_im, h_fm, "../../thesis/figures/petrinet_heuristics_clean.png")
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a_net, a_im, a_fm = pm4py.discover_petri_net_alpha(event_log_clean)
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pm4py.vis.save_vis_petri_net(a_net, a_im, a_fm, "results/processmaps/petrinet_alpha_clean.png")
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pm4py.vis.save_vis_petri_net(a_net, a_im, a_fm, "../../thesis/figures/petrinet_alpha_clean.png")
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i_net, i_im, i_fm = pm4py.discover_petri_net_inductive(event_log_clean)
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pm4py.vis.save_vis_petri_net(i_net, i_im, i_fm, "results/processmaps/petrinet_inductive_clean.png")
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pm4py.vis.save_vis_petri_net(i_net, i_im, i_fm, "../../thesis/figures/petrinet_inductive_clean.png")
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ilp_net, ilp_im, ilp_fm = pm4py.discover_petri_net_ilp(event_log_clean)
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pm4py.vis.save_vis_petri_net(ilp_net, ilp_im, ilp_fm, "results/processmaps/petrinet_ilp_clean.png")
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pm4py.vis.save_vis_petri_net(ilp_net, ilp_im, ilp_fm, "../../thesis/figures/petrinet_ilp_clean.png")
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# convert to BPMN
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base_bpmn = pm4py.convert.convert_to_bpmn(basenet, initial_marking, final_marking)
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pm4py.vis.save_vis_bpmn(base_bpmn, "results/processmaps/bpmn_conformative.png")
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pm4py.vis.save_vis_bpmn(base_bpmn, "../../thesis/figures/bpmn_normative.png")
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i_bpmn = pm4py.convert.convert_to_bpmn(i_net, i_im, i_fm)
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pm4py.vis.save_vis_bpmn(i_bpmn, "results/processmaps/bpmn_inductive_clean.png")
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pm4py.vis.save_vis_bpmn(i_bpmn, "../../thesis/figures/bpmn_inductive_clean.png")
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ilp_bpmn = pm4py.convert.convert_to_bpmn(ilp_net, ilp_im, ilp_fm)
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pm4py.vis.save_vis_bpmn(ilp_bpmn, "results/processmaps/bpmn_ilp_clean.png")
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pm4py.vis.save_vis_bpmn(ilp_bpmn, "../../thesis/figures/bpmn_ilp_clean.png")
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a_bpmn = pm4py.convert.convert_to_bpmn(a_net, a_im, a_fm)
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pm4py.vis.save_vis_bpmn(a_bpmn, "results/processmaps/bpmn_alpha_clean.png")
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pm4py.vis.save_vis_bpmn(a_bpmn, "../../thesis/figures/bpmn_alpha_clean.png")
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h_bpmn = pm4py.convert.convert_to_bpmn(h_net, h_im, h_fm)
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pm4py.vis.save_vis_bpmn(h_bpmn, "results/processmaps/bpmn_heuristics_clean.png")
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pm4py.vis.save_vis_bpmn(h_bpmn, "../../thesis/figures/bpmn_heuristics_clean.png")
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@ -5,22 +5,23 @@
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# (3) DFG for complete data
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# (4) Export data frame for analyses
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#
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# input: results/haum/event_logfiles_2024-02-21_16-07-33.csv
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# results/haum/raw_logfiles_2024-02-21_16-07-33.csv
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# output: results/haum/eventlogs_pre-corona_cleaned.RData
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# results/haum/eventlogs_pre-corona_cleaned.csv
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# input: results/event_logfiles_2024-02-21_16-07-33.csv
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# results/raw_logfiles_2024-02-21_16-07-33.csv
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# output: results/eventlogs_pre-corona_cleaned.RData
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# results/eventlogs_pre-corona_cleaned.csv
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# ../../thesis/figures/dfg_complete_WFnet_R.pdf
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#
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# last mod: 2024-03-06
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# last mod: 2024-03-23
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# setwd("C:/Users/nwickelmaier/Nextcloud/Documents/MDS/2023ss/60100_master_thesis/analysis/code")
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#--------------- (1) Look at broken trace ---------------
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datraw <- read.table("results/haum/raw_logfiles_2024-02-21_16-07-33.csv",
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datraw <- read.table("results/raw_logfiles_2024-02-21_16-07-33.csv",
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header = TRUE, sep = ";")
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datlogs <- read.table("results/haum/event_logfiles_2024-02-21_16-07-33.csv",
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datlogs <- read.table("results/event_logfiles_2024-02-21_16-07-33.csv",
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colClasses = c("character", "character", "POSIXct",
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"POSIXct", "character", "integer",
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"numeric", "character", "character",
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@ -84,7 +85,7 @@ dfg <- processmapR::process_map(alog,
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render = FALSE)
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processmapR::export_map(dfg,
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file_name = paste0("results/processmaps/dfg_complete_WFnet_R.pdf"),
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file_name = paste0("../../thesis/figures/dfg_complete_WFnet_R.pdf"),
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file_type = "pdf")
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rm(tmp)
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@ -109,10 +110,10 @@ dat <- datlogs[as.Date(datlogs$date.start) < "2020-03-13", ]
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# Remove corrupt trace
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dat <- dat[dat$path != 106098, ]
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save(dat, file = "results/haum/eventlogs_pre-corona_cleaned.RData")
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save(dat, file = "results/eventlogs_pre-corona_cleaned.RData")
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write.table(dat,
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file = "results/haum/eventlogs_pre-corona_cleaned.csv",
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file = "results/eventlogs_pre-corona_cleaned.csv",
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sep = ";",
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quote = FALSE,
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row.names = FALSE)
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# content: (1) Load data and create event log
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# (2) Infos for items
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#
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# input: results/haum/eventlogs_pre-corona_cleaned.csv
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# output: results/haum/pn_infos_items.csv
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# input: results/eventlogs_pre-corona_cleaned.csv
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# output: results/pn_infos_items.csv
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#
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# last mod: 2024-03-06
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# last mod: 2024-03-22
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import pm4py
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import pandas as pd
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@ -16,7 +16,7 @@ from python_helpers import eval_pm, pn_infos
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#--------------- (1) Load data and create event logs ---------------
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dat = pd.read_csv("results/haum/eventlogs_pre-corona_cleaned", sep = ";")
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dat = pd.read_csv("results/eventlogs_pre-corona_cleaned", 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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@ -33,5 +33,5 @@ for item in log_path.item.unique().tolist():
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eval = eval.sort_index()
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# Export
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eval.to_csv("results/haum/pn_infos_items.csv", sep = ";")
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eval.to_csv("results/pn_infos_items.csv", sep = ";")
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@ -7,12 +7,12 @@
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# (2) Clustering
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# (3) Visualization with pictures
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#
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# input: results/haum/eventlogs_pre-corona_cleaned.RData
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# results/haum/pn_infos_items.csv
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# output: results/haum/eventlogs_pre-corona_item-clusters.csv
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# input: results/eventlogs_pre-corona_cleaned.RData
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# results/pn_infos_items.csv
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# output: results/eventlogs_pre-corona_item-clusters.csv
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# ../../thesis/figures/data/clustering_items.RData"
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#
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# last mod: 2024-03-21
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# last mod: 2024-03-22
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# setwd("C:/Users/nwickelmaier/Nextcloud/Documents/MDS/2023ss/60100_master_thesis/analysis/code")
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@ -22,11 +22,11 @@ source("R_helpers.R")
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#--------------- (1.1) Read log event data ---------------
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load("results/haum/eventlogs_pre-corona_cleaned.RData")
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load("results/eventlogs_pre-corona_cleaned.RData")
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#--------------- (1.2) Read infos for PM for items ---------------
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datitem <- read.table("results/haum/pn_infos_items.csv", header = TRUE,
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datitem <- read.table("results/pn_infos_items.csv", header = TRUE,
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sep = ";", row.names = 1)
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#--------------- (1.3) Extract additional infos for clustering ---------------
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@ -126,6 +126,28 @@ item <- sprintf("%03d", as.numeric(gsub("item_([0-9]{3})", "\\1",
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res <- merge(dat, data.frame(item, cluster), by = "item", all.x = TRUE)
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res <- res[order(res$fileId.start, res$date.start, res$timeMs.start), ]
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# DFGs for clusters
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res$start <- res$date.start
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res$complete <- res$date.stop
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for (clst in sort(unique(res$cluster))) {
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alog <- bupaR::activitylog(res[res$cluster == clst, ],
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case_id = "path",
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activity_id = "event",
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resource_id = "item",
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timestamps = c("start", "complete"))
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processmapR::process_map(alog,
|
||||
type_nodes = processmapR::frequency("relative", color_scale = "Greys"),
|
||||
sec_nodes = processmapR::frequency("absolute"),
|
||||
type_edges = processmapR::frequency("relative", color_edges = "#FF6900"),
|
||||
sec_edges = processmapR::frequency("absolute"),
|
||||
rankdir = "LR")
|
||||
}
|
||||
|
||||
|
||||
# Look at clusters
|
||||
par(mfrow = c(2,2))
|
||||
vioplot::vioplot(duration ~ cluster, res)
|
||||
@ -134,7 +156,7 @@ vioplot::vioplot(scaleSize ~ cluster, res)
|
||||
vioplot::vioplot(rotationDegree ~ cluster, res)
|
||||
|
||||
write.table(res,
|
||||
file = "results/haum/eventlogs_pre-corona_item-clusters.csv",
|
||||
file = "results/eventlogs_pre-corona_item-clusters.csv",
|
||||
sep = ";",
|
||||
quote = FALSE,
|
||||
row.names = FALSE)
|
||||
|
||||
@ -36,8 +36,8 @@ def pn_infos_miner(log, miner):
|
||||
net, im, fm = pm4py.discover_petri_net_ilp(log)
|
||||
elif miner == "inductive":
|
||||
net, im, fm = pm4py.discover_petri_net_inductive(log)
|
||||
elif miner == "conformative":
|
||||
net, im, fm = pm4py.read_pnml("results/haum/conformative_petrinet_con.pnml")
|
||||
elif miner == "normative":
|
||||
net, im, fm = pm4py.read_pnml("results/normative_petrinet.pnml")
|
||||
|
||||
eval = eval_append(log, net, im, fm)
|
||||
eval.index = [miner]
|
||||
|
||||
Loading…
Reference in New Issue
Block a user