Script cleaning; data are now exported better
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@ -11,7 +11,7 @@
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# output: raw_logfiles_<timestamp>.csv
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# event_logfiles_<timestamp>.csv
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#
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# last mod: 2024-01-18, NW
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# last mod: 2024-02-23, NW
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# setwd("C:/Users/nwickelmaier/Nextcloud/Documents/MDS/2023ss/60100_master_thesis/analysis/code")
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@ -1,7 +1,25 @@
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# 03_create-petrinet.py
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#
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# content: (1) Create places and transitions
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# (2) Sequential net
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# (3) Concurrent net
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#
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# input: --
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# output: results/haum/conformative_petrinet_con.pnml
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# results/processmaps/conformative_petrinet_con.png
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# results/processmaps/conformative_bpmn_con.png
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# results/haum/conformative_petrinet_seq.pnml
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# results/processmaps/conformative_petrinet_seq.png
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# results/processmaps/conformative_bpmn_seq.png
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#
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# last mod: 2024-03-06
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import pm4py
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from pm4py.objects.petri_net.obj import PetriNet, Marking
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from pm4py.objects.petri_net.utils import petri_utils
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#--------------- (1) Create places and transitions ---------------
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# Create places
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source = PetriNet.Place("source")
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sink = PetriNet.Place("sink")
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@ -44,7 +62,8 @@ t_16 = PetriNet.Transition("t_16")
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t_17 = PetriNet.Transition("t_17")
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t_18 = PetriNet.Transition("t_18")
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## Sequential net
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#--------------- (2) Sequential net ---------------
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net_seq = PetriNet("new_petri_net")
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# Add places
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@ -149,7 +168,8 @@ pm4py.view_bpmn(bpmn)
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pm4py.vis.save_vis_bpmn(bpmn, "results/processmaps/conformative_bpmn_seq.png")
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## Concurrent net
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#--------------- (3) Concurrent net ---------------
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net_con = PetriNet("new_petri_net")
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# Add places
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@ -1,9 +1,33 @@
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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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#
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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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# 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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#
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# last mod: 2024-03-06
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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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from python_helpers import eval_pm, pn_infos_miner
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###### Load data and create event logs ######
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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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@ -129,3 +153,4 @@ 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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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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@ -1,3 +1,16 @@
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# 05_check-traces.R
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#
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# content: (1) Look at broken trace
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# (2) Function to find broken traces
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# (3) 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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#
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# last mod: 2024-03-06
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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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@ -49,3 +62,31 @@ check <- check_traces(tmp)
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check[check$check, ]
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#--------------- (3) Export data frame for analyses ---------------
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datlogs$event <- factor(datlogs$event, levels = c("move", "flipCard",
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"openTopic",
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"openPopup"))
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datlogs$topic <- factor(datlogs$topic)
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datlogs$weekdays <- factor(weekdays(datlogs$date.start),
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levels = c("Montag", "Dienstag", "Mittwoch",
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"Donnerstag", "Freitag", "Samstag",
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"Sonntag"),
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labels = c("Monday", "Tuesday", "Wednesday",
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"Thursday", "Friday", "Saturday",
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"Sunday"))
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# Select data pre Corona
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dat <- datlogs[as.Date(datlogs$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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write.table(dat,
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file = "results/haum/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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@ -1,28 +1,37 @@
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# 06_infos-items.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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#
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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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#
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# last mod: 2024-03-06
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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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from python_helpers import eval_pm, pn_infos
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###### Load data and create event logs ######
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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 = dat[dat["date.start"] < "2020-03-13"]
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# --> only pre corona (before artworks were updated)
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dat = dat[dat["path"] != 106098]
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# exclude broken trace
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dat = pd.read_csv("results/haum/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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timestamp_key = "date.start")
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###### Infos for items ######
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#--------------- (2) Infos for items ---------------
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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 item in log_path.item.unique().tolist():
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eval = pd.concat([eval, pn_infos(log_path, "item", item)])
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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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@ -7,12 +7,11 @@
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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/event_logfiles_2024-02-21_16-07-33.csv
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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/event_logfiles_pre-corona_with-clusters.csv
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# output: results/haum/eventlogs_pre-corona_item-clusters.csv
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#
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# last mod: 2024-02-23
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# last mod: 2024-03-06
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# setwd("C:/Users/nwickelmaier/Nextcloud/Documents/MDS/2023ss/60100_master_thesis/analysis/code")
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@ -23,34 +22,16 @@ library(factoextra)
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#--------------- (1.1) Read log event data ---------------
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dat0 <- read.table("results/haum/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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rep("numeric", 3), "character",
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"character", rep("numeric", 11),
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"character", "character"),
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sep = ";", header = TRUE)
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dat0$event <- factor(dat0$event, levels = c("move", "flipCard", "openTopic",
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"openPopup"))
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# TODO: Maybe look at this with complete data?
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# Select data pre Corona
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dat <- dat0[as.Date(dat0$date.start) < "2020-03-13", ]
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dat <- dat[dat$path != 106098, ]
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load("results/haum/eventlogs_pre-corona_cleaned.RData")
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#--------------- (1.2) Read infos for PM for items ---------------
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datitem <- read.table("results/haum/pn_infos_items.csv", header = TRUE,
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sep = ";", row.names = 1)
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#--------------- (1.3) Extract additional infos for clustering ---------------
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dat_split <- split(dat, ~ path)
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time_minmax <- function(subdata) {
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time_minmax_ms <- function(subdata) {
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subdata$min_time <- min(subdata$timeMs.start)
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if (all(is.na(subdata$timeMs.stop))) {
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subdata$max_time <- NA
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@ -59,18 +40,18 @@ time_minmax <- function(subdata) {
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}
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subdata
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}
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# TODO: Move to helper file
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dat_list <- pbapply::pblapply(dat_split, time_minmax)
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# Get average duration per path
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dat_split <- split(dat, ~ path)
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dat_list <- pbapply::pblapply(dat_split, time_minmax_ms)
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dat_minmax <- dplyr::bind_rows(dat_list)
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datpath <- aggregate(duration ~ item + path, dat, mean, na.action = NULL)
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datpath$min_time <- aggregate(min_time ~ path, dat_minmax, unique, na.action = NULL)$min_time
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datpath$max_time <- aggregate(max_time ~ path, dat_minmax, unique, na.action = NULL)$max_time
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datpath$duration <- datpath$max_time - datpath$min_time
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datpath$duration_path <- datpath$max_time - datpath$min_time
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# average duration per path
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datitem$duration <- aggregate(duration ~ item, datpath, mean)$duration
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datitem$distance <- aggregate(distance ~ item, dat, mean)$distance
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datitem$scaleSize <- aggregate(scaleSize ~ item, dat, mean)$scaleSize
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@ -89,66 +70,39 @@ df <- datitem[, c("precision", "generalizability", "nvariants", "duration",
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"ncases", "nmoves", "nopenTopic", "nopenPopup")] |>
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scale()
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mat <- dist(df)
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dist_mat <- dist(df)
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heatmap(as.matrix(mat))
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heatmap(as.matrix(dist_mat))
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# Choosing best linkage method
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h1 <- hclust(mat, method = "average")
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h2 <- hclust(mat, method = "complete")
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h3 <- hclust(mat, method = "ward.D")
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h4 <- hclust(mat, method = "ward.D2")
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h5 <- hclust(mat, method = "single")
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method <- c(average = "average", single = "single", complete = "complete",
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ward = "ward")
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# Cophenetic Distances, for each linkage
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c1 <- cophenetic(h1)
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c2 <- cophenetic(h2)
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c3 <- cophenetic(h3)
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c4 <- cophenetic(h4)
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c5 <- cophenetic(h5)
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# Correlations
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cor(mat, c1)
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cor(mat, c2)
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cor(mat, c3)
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cor(mat, c4)
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cor(mat, c5)
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# https://en.wikipedia.org/wiki/Cophenetic_correlation
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# https://stats.stackexchange.com/questions/195446/choosing-the-right-linkage-method-for-hierarchical-clustering
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hcs <- lapply(method, function(x) cluster::agnes(dist_mat, method = x))
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acs <- sapply(hcs, function(x) x$ac)
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# Dendograms
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par(mfrow=c(3,2))
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plot(h1, main = "Average Linkage")
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plot(h2, main = "Complete Linkage")
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plot(h3, main = "Ward Linkage")
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plot(h4, main = "Ward 2 Linkage")
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plot(h5, main = "Single Linkage")
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par(mfrow=c(4,2))
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for (hc in hcs) plot(hc, main = "")
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hc <- h1
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# Note that ‘agnes(*, method="ward")’ corresponds to ‘hclust(*, "ward.D2")’
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hc <- hcs$ward
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k <- 4 # number of clusters
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mycols <- c("#78004B", "#FF6900", "#3CB4DC", "#91C86E")
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grp <- cutree(hc, k = k)
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datitem$grp <- grp
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fviz_dend(hc, k = k,
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cex = 0.5,
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k_colors = c("#78004B", "#FF6900", "#3CB4DC", "#91C86E",
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"#000000", "gold", "#434F4F"),
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k_colors = mycols,
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#type = "phylogenic",
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rect = TRUE
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)
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plot(hc)
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rect.hclust(hc, k=8, border="red")
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rect.hclust(hc, k=7, border="blue")
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rect.hclust(hc, k=6, border="green")
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p <- fviz_cluster(list(data = df, cluster = grp),
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palette = c("#78004B", "#FF6900", "#3CB4DC", "#91C86E",
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"#000000", "#434F4F", "gold"),
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palette = mycols,
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ellipse.type = "convex",
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repel = TRUE,
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show.clust.cent = FALSE, ggtheme = theme_bw())
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@ -156,14 +110,16 @@ p
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aggregate(cbind(duration, distance, scaleSize , rotationDegree, npaths,
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ncases, nmoves, nflipCard, nopenTopic, nopenPopup) ~ grp,
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datitem, median)
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datitem, mean)
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aggregate(cbind(duration, distance, scaleSize , rotationDegree, npaths,
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ncases, nmoves, nflipCard, nopenTopic, nopenPopup) ~ grp,
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datitem, max)
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# Something like a scree plot (??)
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plot(rev(seq_along(hc$height)), hc$height, type = "l")
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points(rev(seq_along(hc$height)), hc$height, pch = 16, cex = .5)
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plot(rev(hc$height), type = "b", pch = 16, cex = .5)
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datitem$item <- sprintf("%03d",
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as.numeric(gsub("item_([0-9]{3})", "\\1", row.names(datitem))))
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@ -179,7 +135,7 @@ vioplot::vioplot(scaleSize ~ grp, res)
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vioplot::vioplot(rotationDegree ~ grp, res)
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write.table(res,
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file = "results/haum/event_logfiles_pre-corona_with-clusters.csv",
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file = "results/haum/eventlogs_pre-corona_item-clusters.csv",
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sep = ";",
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quote = FALSE,
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row.names = FALSE)
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@ -207,8 +163,6 @@ for (cluster in sort(unique(res$grp))) {
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file_name = paste0("results/processmaps/dfg_cluster", cluster, "_R.pdf"),
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file_type = "pdf",
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title = paste("DFG Cluster", cluster))
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}
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#--------------- (3) Visualization with pictures ---------------
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@ -217,8 +171,6 @@ library(png)
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library(jpeg)
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library(grid)
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colors <- c("#78004B", "#FF6900", "#3CB4DC", "#91C86E")
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pdf("results/figures/clustering_artworks.pdf", height = 8, width = 8, pointsize = 10)
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#png("results/figures/clustering_artworks.png", units = "in", height = 8, width = 8, pointsize = 10, res = 300)
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@ -244,7 +196,7 @@ for (item in sprintf("%03d", as.numeric(rownames(p$data)))) {
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y <- p$data$y[sprintf("%03d", as.numeric(rownames(p$data))) == item]
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points(x, y,
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col = colors[p$data$cluster[sprintf("%03d", as.numeric(rownames(p$data))) == item]],
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col = mycols[p$data$cluster[sprintf("%03d", as.numeric(rownames(p$data))) == item]],
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cex = 9,
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pch = 15)
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@ -255,7 +207,7 @@ for (item in sprintf("%03d", as.numeric(rownames(p$data)))) {
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ytop = y + .2)
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}
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legend("topright", paste("Cluster", 1:k), col = colors, pch = 15, bty = "n")
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legend("topright", paste("Cluster", 1:k), col = mycols, pch = 15, bty = "n")
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dev.off()
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# 08_infos-clusters.py
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#
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# content: (1) Load data and create event log
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# (2) Infos for clusters
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# (3) Process maps for clusters
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#
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# input: results/haum/eventlogs_pre-corona_item-clusters.csv
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# output: results/haum/pn_infos_clusters.csv
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||||
#
|
||||
# last mod: 2024-03-06
|
||||
|
||||
import pm4py
|
||||
import pandas as pd
|
||||
|
||||
from python_helpers import eval_pm, pn_infos
|
||||
|
||||
###### Load data and create event logs ######
|
||||
#--------------- (1) Load data and create event logs ---------------
|
||||
|
||||
dat = pd.read_csv("results/haum/event_logfiles_pre-corona_with-clusters.csv", sep = ";")
|
||||
dat = pd.read_csv("results/haum/eventlogs_pre-corona_item-clusters.csv", sep = ";")
|
||||
|
||||
log_path = pm4py.format_dataframe(dat, case_id = "path", activity_key = "event",
|
||||
timestamp_key = "date.start")
|
||||
timestamp_key = "date.start")
|
||||
|
||||
###### Infos for clusters ######
|
||||
#--------------- (2) Infos for clusters ---------------
|
||||
|
||||
# Merge clusters into data frame
|
||||
eval = pd.DataFrame(columns = ["fitness", "precision", "generalizability",
|
||||
@ -22,12 +33,13 @@ eval = eval.sort_index()
|
||||
|
||||
eval.to_csv("results/haum/pn_infos_clusters.csv", sep = ";")
|
||||
|
||||
###### Process maps for clusters ######
|
||||
#--------------- (3) Process maps for clusters ---------------
|
||||
|
||||
for cluster in log_path.grp.unique().tolist():
|
||||
subdata = log_path[log_path.grp == cluster]
|
||||
subnet, subim, subfm = pm4py.discover_petri_net_inductive(subdata)
|
||||
subnet, subim, subfm = pm4py.discover_petri_net_inductive(subdata, noise_threshold=0.5)
|
||||
pm4py.save_vis_petri_net(subnet, subim, subfm,
|
||||
"results/processmaps/petrinet_cluster" + str(cluster).zfill(3) + ".png")
|
||||
"results/processmaps/petrinet_cluster" + str(cluster).zfill(3) + ".png")
|
||||
bpmn = pm4py.convert.convert_to_bpmn(subnet, subim, subfm)
|
||||
pm4py.vis.save_vis_bpmn(bpmn, "results/processmaps/bpmn_cluster_" + str(cluster).zfill(3) + ".png")
|
||||
pm4py.vis.save_vis_bpmn(bpmn, "results/processmaps/bpmn_cluster_" +
|
||||
str(cluster).zfill(3) + ".png")
|
||||
|
||||
Loading…
Reference in New Issue
Block a user