Went over clustering and helper scripts
This commit is contained in:
parent
6ade6444ac
commit
76af291686
@ -1,9 +1,6 @@
|
|||||||
#%reset
|
|
||||||
|
|
||||||
import pm4py
|
import pm4py
|
||||||
import pandas as pd
|
import pandas as pd
|
||||||
import numpy as np
|
import numpy as np
|
||||||
import matplotlib.pyplot as plt
|
|
||||||
|
|
||||||
from python_helpers import eval_pm, pn_infos
|
from python_helpers import eval_pm, pn_infos
|
||||||
|
|
||||||
@ -11,43 +8,21 @@ from python_helpers import eval_pm, pn_infos
|
|||||||
|
|
||||||
dat = pd.read_csv("results/haum/event_logfiles_2024-01-18_09-58-52.csv", sep = ";")
|
dat = pd.read_csv("results/haum/event_logfiles_2024-01-18_09-58-52.csv", sep = ";")
|
||||||
dat = dat[dat["date.start"] < "2020-03-13"]
|
dat = dat[dat["date.start"] < "2020-03-13"]
|
||||||
dat = dat[dat["path"] != 106098] # exclude broken trace
|
|
||||||
# --> only pre corona (before artworks were updated)
|
# --> only pre corona (before artworks were updated)
|
||||||
|
dat = dat[dat["path"] != 106098]
|
||||||
|
# exclude broken trace
|
||||||
|
|
||||||
log_path = pm4py.format_dataframe(dat, case_id = "path", activity_key = "event",
|
log_path = pm4py.format_dataframe(dat, case_id = "path", activity_key = "event",
|
||||||
timestamp_key = "date.start")
|
timestamp_key = "date.start")
|
||||||
|
|
||||||
###### Infos for items ######
|
###### Infos for items ######
|
||||||
|
|
||||||
mdi = pd.DataFrame(columns = ["fitness", "precision", "generalizability",
|
eval = pd.DataFrame(columns = ["fitness", "precision", "generalizability",
|
||||||
"simplicity", "sound", "narcs", "ntrans",
|
"simplicity", "sound", "narcs", "ntrans",
|
||||||
"nplaces", "nvariants", "mostfreq"])
|
"nplaces", "nvariants", "mostfreq"])
|
||||||
for item in log_path.item.unique().tolist():
|
for item in log_path.item.unique().tolist():
|
||||||
mdi = pd.concat([mdi, pn_infos(log_path, "item", item)])
|
eval = pd.concat([eval, pn_infos(log_path, "item", item)])
|
||||||
mdi = mdi.sort_index()
|
eval = eval.sort_index()
|
||||||
|
|
||||||
# Export
|
# Export
|
||||||
mdi.to_csv("results/haum/pn_infos_items.csv", sep = ";")
|
eval.to_csv("results/haum/pn_infos_items.csv", sep = ";")
|
||||||
|
|
||||||
# datitem = dat.groupby("item")[["duration", "distance",
|
|
||||||
# "scaleSize", "rotationDegree"]].mean()
|
|
||||||
#
|
|
||||||
# def length_path(data):
|
|
||||||
# x = data.path
|
|
||||||
# return len(x.unique())
|
|
||||||
# def length_case(data):
|
|
||||||
# x = data.case
|
|
||||||
# return len(x.unique())
|
|
||||||
# def length_topic(data):
|
|
||||||
# x = data.topic.dropna()
|
|
||||||
# return len(x.unique())
|
|
||||||
#
|
|
||||||
# datitem["npaths"] = dat.groupby(["item"]).apply(length_path)
|
|
||||||
# datitem["ncases"] = dat.groupby(["item"]).apply(length_case)
|
|
||||||
# datitem["ntopics"] = dat.groupby(["item"]).apply(length_topic)
|
|
||||||
#
|
|
||||||
# datitem.index = datitem.index.astype(str).str.rjust(3, "0")
|
|
||||||
# datitem = datitem.sort_index()
|
|
||||||
# datitem.index = mdi.index
|
|
||||||
#
|
|
||||||
# datitem = pd.concat([mdi, datitem], yaxis = 1)
|
|
||||||
|
|||||||
@ -1,5 +1,24 @@
|
|||||||
|
# 05_item-clustering.R
|
||||||
|
#
|
||||||
|
# content: (1) Read data
|
||||||
|
# (1.1) Read log event data
|
||||||
|
# (1.2) Read infos for PM for infos
|
||||||
|
# (1.3) Extract additional infos for clustering
|
||||||
|
# (2) Clustering
|
||||||
|
# (3) Visualization with pictures
|
||||||
|
#
|
||||||
|
# input: results/haum/event_logfiles_2024-01-18_09-58-52.csv
|
||||||
|
# results/haum/pn_infos_items.csv
|
||||||
|
# output: results/haum/event_logfiles_pre-corona_with-clusters.csv
|
||||||
|
#
|
||||||
|
# last mod: 2024-01-30
|
||||||
|
|
||||||
|
|
||||||
# setwd("C:/Users/nwickelmaier/Nextcloud/Documents/MDS/2023ss/60100_master_thesis/analysis/code")
|
# setwd("C:/Users/nwickelmaier/Nextcloud/Documents/MDS/2023ss/60100_master_thesis/analysis/code")
|
||||||
|
|
||||||
|
library(bupaverse)
|
||||||
|
library(factoextra)
|
||||||
|
|
||||||
#--------------- (1) Read data ---------------
|
#--------------- (1) Read data ---------------
|
||||||
|
|
||||||
#--------------- (1.1) Read log event data ---------------
|
#--------------- (1.1) Read log event data ---------------
|
||||||
@ -46,10 +65,8 @@ mat <- dist(df)
|
|||||||
|
|
||||||
hc <- hclust(mat, method = "ward.D2")
|
hc <- hclust(mat, method = "ward.D2")
|
||||||
|
|
||||||
library(factoextra)
|
grp <- cutree(hc, k = 6)
|
||||||
fviz_dend(hc, cex = 0.5)
|
datitem$grp <- grp
|
||||||
|
|
||||||
datitem$grp <- cutree(hc, k = 6)
|
|
||||||
|
|
||||||
fviz_dend(hc, k = 6,
|
fviz_dend(hc, k = 6,
|
||||||
cex = 0.5,
|
cex = 0.5,
|
||||||
@ -70,19 +87,25 @@ p
|
|||||||
aggregate(cbind(duration, distance, scaleSize , rotationDegree, npaths,
|
aggregate(cbind(duration, distance, scaleSize , rotationDegree, npaths,
|
||||||
ncases, ntopics) ~ grp, datitem, mean)
|
ncases, ntopics) ~ grp, datitem, mean)
|
||||||
|
|
||||||
datitem$item <- gsub("item_([0-9]{3})", "\\1", row.names(datitem))
|
datitem$item <- sprintf("%03d",
|
||||||
|
as.numeric(gsub("item_([0-9]{3})", "\\1", row.names(datitem))))
|
||||||
|
|
||||||
res <- merge(dat, datitem[, c("item", "grp")], by = "item", all.x = TRUE)
|
res <- merge(dat, datitem[, c("item", "grp")], by = "item", all.x = TRUE)
|
||||||
res <- res[order(res$fileId.start, res$date.start, res$timeMs.start), ]
|
res <- res[order(res$fileId.start, res$date.start, res$timeMs.start), ]
|
||||||
|
|
||||||
|
# Look at clusters
|
||||||
|
vioplot::vioplot(duration ~ grp, res)
|
||||||
|
vioplot::vioplot(distance ~ grp, res)
|
||||||
|
vioplot::vioplot(scaleSize ~ grp, res)
|
||||||
|
vioplot::vioplot(rotationDegree ~ grp, res)
|
||||||
|
|
||||||
write.table(res,
|
write.table(res,
|
||||||
file = "results/haum/event_logfiles_pre-corona_with-clusters.csv",
|
file = "results/haum/event_logfiles_pre-corona_with-clusters.csv",
|
||||||
sep = ";",
|
sep = ";",
|
||||||
quote = FALSE,
|
quote = FALSE,
|
||||||
row.names = FALSE)
|
row.names = FALSE)
|
||||||
|
|
||||||
library(bupaverse)
|
# DFGs for clusters
|
||||||
|
|
||||||
res$start <- res$date.start
|
res$start <- res$date.start
|
||||||
res$complete <- res$date.stop
|
res$complete <- res$date.stop
|
||||||
|
|
||||||
@ -95,9 +118,9 @@ for (cluster in sort(unique(res$grp))) {
|
|||||||
timestamps = c("start", "complete"))
|
timestamps = c("start", "complete"))
|
||||||
|
|
||||||
dfg <- process_map(alog,
|
dfg <- process_map(alog,
|
||||||
type_nodes = frequency("relative"),
|
type_nodes = frequency("relative", color_scale = "Greys"),
|
||||||
sec_nodes = frequency("absolute"),
|
sec_nodes = frequency("absolute"),
|
||||||
type_edges = frequency("relative"),
|
type_edges = frequency("relative", color_edges = "#FF6900"),
|
||||||
sec_edges = frequency("absolute"),
|
sec_edges = frequency("absolute"),
|
||||||
rankdir = "LR",
|
rankdir = "LR",
|
||||||
render = FALSE)
|
render = FALSE)
|
||||||
|
|||||||
@ -1,9 +1,5 @@
|
|||||||
%reset
|
|
||||||
|
|
||||||
import pm4py
|
import pm4py
|
||||||
import pandas as pd
|
import pandas as pd
|
||||||
import numpy as np
|
|
||||||
import matplotlib.pyplot as plt
|
|
||||||
|
|
||||||
from python_helpers import eval_pm, pn_infos
|
from python_helpers import eval_pm, pn_infos
|
||||||
|
|
||||||
@ -17,15 +13,14 @@ log_path = pm4py.format_dataframe(dat, case_id = "path", activity_key = "event",
|
|||||||
###### Infos for clusters ######
|
###### Infos for clusters ######
|
||||||
|
|
||||||
# Merge clusters into data frame
|
# Merge clusters into data frame
|
||||||
mdc = pd.DataFrame(columns = ["fitness", "precision", "generalizability",
|
eval = pd.DataFrame(columns = ["fitness", "precision", "generalizability",
|
||||||
"simplicity", "sound", "narcs", "ntrans",
|
"simplicity", "sound", "narcs", "ntrans",
|
||||||
"nplaces", "nvariants", "mostfreq"])
|
"nplaces", "nvariants", "mostfreq"])
|
||||||
for cluster in log_path.grp.unique().tolist():
|
for cluster in log_path.grp.unique().tolist():
|
||||||
mdc = pd.concat([mdc, pn_infos(log_path, "grp", cluster)])
|
eval = pd.concat([eval, pn_infos(log_path, "grp", cluster)])
|
||||||
mdc = mdc.sort_index()
|
eval = eval.sort_index()
|
||||||
|
|
||||||
# Export
|
eval.to_csv("results/haum/pn_infos_clusters.csv", sep = ";")
|
||||||
mdc.to_csv("results/haum/pn_infos_clusters.csv", sep = ";")
|
|
||||||
|
|
||||||
###### Process maps for clusters ######
|
###### Process maps for clusters ######
|
||||||
|
|
||||||
|
|||||||
@ -2,153 +2,32 @@
|
|||||||
|
|
||||||
# setwd("C:/Users/nwickelmaier/Nextcloud/Documents/MDS/2023ss/60100_master_thesis/analysis/code")
|
# setwd("C:/Users/nwickelmaier/Nextcloud/Documents/MDS/2023ss/60100_master_thesis/analysis/code")
|
||||||
|
|
||||||
library(bupaverse)
|
#--------------- (1) Look at broken trace ---------------
|
||||||
|
|
||||||
# Read data
|
datraw <- read.table("results/haum/raw_logfiles_2024-01-18_09-58-52.csv",
|
||||||
|
header = TRUE, sep = ";")
|
||||||
dat0 <- read.table("results/haum/event_logfiles_2024-01-18_09-58-52.csv",
|
|
||||||
colClasses = c("character", "character", "POSIXct",
|
|
||||||
"POSIXct", "character", "integer",
|
|
||||||
"numeric", "character", "character",
|
|
||||||
rep("numeric", 3), "character",
|
|
||||||
"character", rep("numeric", 11),
|
|
||||||
"character", "character"),
|
|
||||||
sep = ";", header = TRUE)
|
|
||||||
|
|
||||||
dat0$event <- factor(dat0$event, levels = c("move", "flipCard", "openTopic",
|
|
||||||
"openPopup"))
|
|
||||||
|
|
||||||
dat0$weekdays <- factor(weekdays(dat0$date.start),
|
|
||||||
levels = c("Montag", "Dienstag", "Mittwoch",
|
|
||||||
"Donnerstag", "Freitag", "Samstag",
|
|
||||||
"Sonntag"),
|
|
||||||
labels = c("Monday", "Tuesday", "Wednesday",
|
|
||||||
"Thursday", "Friday", "Saturday",
|
|
||||||
"Sunday"))
|
|
||||||
|
|
||||||
# Select data pre Corona
|
|
||||||
dat <- dat0[as.Date(dat0$date.start) < "2020-03-13", ]
|
|
||||||
dat <- dat[dat$path != 106098, ]
|
|
||||||
|
|
||||||
|
|
||||||
table(table(dat$start))
|
datlogs <- read.table("results/haum/event_logfiles_2024-01-18_09-58-52.csv",
|
||||||
|
colClasses = c("character", "character", "POSIXct",
|
||||||
|
"POSIXct", "character", "integer",
|
||||||
|
"numeric", "character", "character",
|
||||||
|
rep("numeric", 3), "character",
|
||||||
|
"character", rep("numeric", 11),
|
||||||
|
"character", "character"),
|
||||||
|
sep = ";", header = TRUE)
|
||||||
|
|
||||||
table(dat$event)
|
artwork <- "176"
|
||||||
proportions(table(dat$event))
|
fileId <- c('2017_06_16-13_49_00.log', '2017_06_16-13_59_00.log')
|
||||||
|
path <- 106098
|
||||||
|
|
||||||
dat_dur <- aggregate(duration ~ item, dat, mean)
|
datraw[datraw$item == artwork & datraw$fileId %in% fileId, ]
|
||||||
barplot(duration - mean(dat_dur$duration) ~ item, dat_dur, col = "#434F4F",
|
|
||||||
las = 3)
|
|
||||||
|
|
||||||
|
datlogs[datlogs$path == path, ]
|
||||||
|
|
||||||
# Investigate paths (will separate items and give clusters of artworks!)
|
#--------------- (2) Function to find broken traces ---------------
|
||||||
length(unique(dat$path))
|
|
||||||
# DFGs per Cluster
|
|
||||||
dat$start <- dat$date.start
|
|
||||||
dat$complete <- dat$date.stop
|
|
||||||
|
|
||||||
summary(aggregate(duration ~ path, dat, mean))
|
tmp <- datlogs[datlogs$event != "move", ]
|
||||||
|
|
||||||
alog <- activitylog(dat,
|
|
||||||
case_id = "path",
|
|
||||||
activity_id = "event",
|
|
||||||
resource_id = "item",
|
|
||||||
timestamps = c("start", "complete"))
|
|
||||||
|
|
||||||
process_map(alog,
|
|
||||||
type_nodes = frequency("absolute"),
|
|
||||||
sec_nodes = frequency("relative"),
|
|
||||||
type_edges = frequency("absolute"),
|
|
||||||
sec_edges = frequency("relative"),
|
|
||||||
rankdir = "LR")
|
|
||||||
|
|
||||||
### Separate for items
|
|
||||||
|
|
||||||
datitem <- aggregate(cbind(duration, distance, scaleSize, rotationDegree) ~
|
|
||||||
item, dat, function(x) mean(x, na.rm = TRUE), na.action = NULL)
|
|
||||||
datitem$npaths <- aggregate(path ~ item, dat,
|
|
||||||
function(x) length(unique(x)),
|
|
||||||
na.action = NULL)$path
|
|
||||||
datitem$ncases <- aggregate(case ~ item, dat,
|
|
||||||
function(x) length(unique(x)),
|
|
||||||
na.action = NULL)$case
|
|
||||||
datitem$ntopics <- aggregate(topic ~ item, dat,
|
|
||||||
function(x) ifelse(all(is.na(x)), NA, length(unique(na.omit(x)))),
|
|
||||||
na.action = NULL)$topic
|
|
||||||
|
|
||||||
|
|
||||||
set.seed(1211)
|
|
||||||
|
|
||||||
nclusters <- 6
|
|
||||||
k1 <- kmeans(datitem[, -1], nclusters)
|
|
||||||
|
|
||||||
#colors <- c("#3CB4DC", "#78004B", "#91C86E", "#FF6900")
|
|
||||||
|
|
||||||
colors <- palette.colors(palette = "Okabe-Ito")
|
|
||||||
|
|
||||||
xy <- cmdscale(dist(datitem[, -1]))
|
|
||||||
|
|
||||||
plot(xy, type = "n")
|
|
||||||
text(xy[,1], xy[,2], datitem$item, col = colors[k1$cluster])
|
|
||||||
legend("topright", paste("Cluster", 1:nclusters), col = colors, lty = 1)
|
|
||||||
|
|
||||||
## Scree plot
|
|
||||||
|
|
||||||
ks <- 1:10
|
|
||||||
|
|
||||||
sse <- NULL
|
|
||||||
for (k in ks) sse <- c(sse, kmeans(datitem[, -1], k)$tot.withinss)
|
|
||||||
|
|
||||||
plot(sse ~ ks, type = "l")
|
|
||||||
|
|
||||||
|
|
||||||
datitem$cluster <- k1$cluster
|
|
||||||
|
|
||||||
datitem_agg <- aggregate(. ~ cluster, datitem[, -1], mean)
|
|
||||||
|
|
||||||
|
|
||||||
dat_cl <- merge(dat, datitem[, c("item", "cluster")], by = "item", all.x = TRUE)
|
|
||||||
dat_cl <- dat_cl[order(dat_cl$fileId.start, dat_cl$date.start, dat_cl$timeMs.start), ]
|
|
||||||
|
|
||||||
write.table(dat_cl, "results/haum/event_logfiles_with-clusters_kmeans.csv",
|
|
||||||
sep = ";", row.names = FALSE)
|
|
||||||
|
|
||||||
vioplot::vioplot(datitem$duration)
|
|
||||||
|
|
||||||
vioplot::vioplot(duration ~ item, dat, las = 3)
|
|
||||||
|
|
||||||
vioplot::vioplot(duration ~ cluster, dat_cl)
|
|
||||||
vioplot::vioplot(distance ~ cluster, dat_cl)
|
|
||||||
vioplot::vioplot(scaleSize ~ cluster, dat_cl)
|
|
||||||
vioplot::vioplot(rotationDegree ~ cluster, dat_cl)
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
for (cluster in sort(unique(dat_cl$cluster))) {
|
|
||||||
|
|
||||||
alog <- activitylog(dat_cl[dat_cl$cluster == cluster, ],
|
|
||||||
case_id = "path",
|
|
||||||
activity_id = "event",
|
|
||||||
resource_id = "item",
|
|
||||||
timestamps = c("start", "complete"))
|
|
||||||
|
|
||||||
dfg <- process_map(alog,
|
|
||||||
type_nodes = frequency("relative"),
|
|
||||||
sec_nodes = frequency("absolute"),
|
|
||||||
type_edges = frequency("relative"),
|
|
||||||
sec_edges = frequency("absolute"),
|
|
||||||
rankdir = "LR",
|
|
||||||
render = FALSE)
|
|
||||||
export_map(dfg,
|
|
||||||
file_name = paste0("results/processmaps/dfg_cluster", cluster, "_R.pdf"),
|
|
||||||
file_type = "pdf",
|
|
||||||
title = paste("DFG Cluster", cluster))
|
|
||||||
|
|
||||||
|
|
||||||
}
|
|
||||||
|
|
||||||
|
|
||||||
tmp <- dat[dat$event != "move", ]
|
|
||||||
|
|
||||||
check_traces <- function(data) {
|
check_traces <- function(data) {
|
||||||
|
|
||||||
@ -170,127 +49,5 @@ check_traces <- function(data) {
|
|||||||
|
|
||||||
check <- check_traces(tmp)
|
check <- check_traces(tmp)
|
||||||
|
|
||||||
sum(check$check)
|
check[check$check, ]
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
alog <- activitylog(dat,
|
|
||||||
case_id = "case",
|
|
||||||
activity_id = "item",
|
|
||||||
resource_id = "path",
|
|
||||||
timestamps = c("start", "complete"))
|
|
||||||
|
|
||||||
process_map(alog,
|
|
||||||
type_nodes = frequency("absolute"),
|
|
||||||
sec_nodes = frequency("relative"),
|
|
||||||
type_edges = frequency("absolute"),
|
|
||||||
sec_edges = frequency("relative"),
|
|
||||||
rankdir = "LR")
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
datcase <- dat[!duplicated(dat[, c("case", "path", "item")]),
|
|
||||||
c("case", "path", "event", "item")]
|
|
||||||
datcase$duration <- aggregate(duration ~ path, dat,
|
|
||||||
function(x) mean(x, na.rm = TRUE), na.action = NULL)$duration
|
|
||||||
datcase$distance <- aggregate(distance ~ path, dat,
|
|
||||||
function(x) mean(x, na.rm = TRUE), na.action = NULL)$distance
|
|
||||||
datcase$scaleSize <- aggregate(scaleSize ~ path, dat,
|
|
||||||
function(x) mean(x, na.rm = TRUE), na.action = NULL)$scaleSize
|
|
||||||
datcase$rotationDegree <- aggregate(rotationDegree ~ path, dat,
|
|
||||||
function(x) mean(x, na.rm = TRUE), na.action = NULL)$rotationDegree
|
|
||||||
# datcase$ntopics <- aggregate(topic ~ path, dat,
|
|
||||||
# function(x) ifelse(all(is.na(x)), NA, length(unique(na.omit(x)))),
|
|
||||||
# na.action = NULL)$topic
|
|
||||||
datcase$move <- ifelse(datcase$event == "move", 1, 0)
|
|
||||||
# paths that start with move
|
|
||||||
|
|
||||||
for (item in sort(unique(datcase$item))) {
|
|
||||||
datcase[paste0("item_", item)] <- ifelse(datcase$item == item, 1, 0)
|
|
||||||
}
|
|
||||||
|
|
||||||
mat <- na.omit(datcase[, -c(1:4)])
|
|
||||||
|
|
||||||
|
|
||||||
set.seed(1610)
|
|
||||||
|
|
||||||
nclusters <- 6
|
|
||||||
k1 <- kmeans(mat, nclusters)
|
|
||||||
|
|
||||||
#colors <- c("#3CB4DC", "#78004B", "#91C86E", "#FF6900")
|
|
||||||
|
|
||||||
colors <- palette.colors(palette = "Okabe-Ito")[1:nclusters]
|
|
||||||
|
|
||||||
library(distances)
|
|
||||||
mat_dist <- distances(mat)
|
|
||||||
|
|
||||||
xy <- cmdscale(mat_dist)
|
|
||||||
|
|
||||||
plot(xy, type = "n")
|
|
||||||
text(xy[,1], xy[,2], datcase$path, col = colors[k1$cluster])
|
|
||||||
legend("topright", paste("Cluster", 1:nclusters), col = colors, lty = 1)
|
|
||||||
|
|
||||||
## Scree plot
|
|
||||||
|
|
||||||
ks <- 1:10
|
|
||||||
|
|
||||||
sse <- NULL
|
|
||||||
for (k in ks) sse <- c(sse, kmeans(datitem[, -1], k)$tot.withinss)
|
|
||||||
|
|
||||||
plot(sse ~ ks, type = "l")
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
alog <- activitylog(datcase,
|
|
||||||
case_id = "case",
|
|
||||||
activity_id = "item",
|
|
||||||
resource_id = "path",
|
|
||||||
timestamps = c("start", "complete"))
|
|
||||||
|
|
||||||
process_map(alog,
|
|
||||||
type_nodes = frequency("relative"),
|
|
||||||
sec_nodes = frequency("absolute"),
|
|
||||||
type_edges = frequency("relative"),
|
|
||||||
sec_edges = frequency("absolute"),
|
|
||||||
rankdir = "LR")
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
# setwd("C:/Users/nwickelmaier/Nextcloud/Documents/MDS/2023ss/60100_master_thesis/analysis/code")
|
|
||||||
|
|
||||||
datraw <- read.table("results/haum/raw_logfiles_2024-01-18_09-58-52.csv",
|
|
||||||
header = TRUE, sep = ";")
|
|
||||||
|
|
||||||
|
|
||||||
# Read data
|
|
||||||
|
|
||||||
datlogs <- read.table("results/haum/event_logfiles_2024-01-18_09-58-52.csv",
|
|
||||||
colClasses = c("character", "character", "POSIXct",
|
|
||||||
"POSIXct", "character", "integer",
|
|
||||||
"numeric", "character", "character",
|
|
||||||
rep("numeric", 3), "character",
|
|
||||||
"character", rep("numeric", 11),
|
|
||||||
"character", "character"),
|
|
||||||
sep = ";", header = TRUE)
|
|
||||||
|
|
||||||
datlogs <- datlogs[order(datlogs$fileId.start, datlogs$date.start, datlogs$timeMs.start), ]
|
|
||||||
|
|
||||||
artwork <- "176"
|
|
||||||
fileId <- c('2017_06_16-13_49_00.log', '2017_06_16-13_59_00.log')
|
|
||||||
path <- 106098
|
|
||||||
|
|
||||||
datraw[datraw$item == artwork & datraw$fileId %in% fileId, ]
|
|
||||||
|
|
||||||
datlogs[datlogs$path == path, ]
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|||||||
@ -19,7 +19,7 @@ def pn_infos(log, colname, filter):
|
|||||||
|
|
||||||
net, im, fm = pm4py.discover_petri_net_inductive(filtered_log)
|
net, im, fm = pm4py.discover_petri_net_inductive(filtered_log)
|
||||||
|
|
||||||
eval = eval_append(log, net, im, fm)
|
eval = eval_append(filtered_log, net, im, fm)
|
||||||
eval.index = [str(filter).zfill(3)]
|
eval.index = [str(filter).zfill(3)]
|
||||||
return eval
|
return eval
|
||||||
|
|
||||||
|
|||||||
Loading…
Reference in New Issue
Block a user