Worked on analysis

This commit is contained in:
Nora Wickelmaier 2024-01-16 09:59:23 +01:00
parent a276d84cd6
commit 07b1f5adc4
7 changed files with 281 additions and 152 deletions

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@ -41,7 +41,7 @@ write.table(datraw, paste0("results/haum/raw_logfiles_", now, ".csv"),
datlogs <- create_eventlogs(datraw, datlogs <- create_eventlogs(datraw,
#xmlpath = "../data/haum/ContentEyevisit/eyevisit_cards_light/", #xmlpath = "../data/haum/ContentEyevisit/eyevisit_cards_light/",
glossar = FALSE) glossar = FALSE, save = TRUE)
# 2,136,694 no change moves removed # 2,136,694 no change moves removed
# OLD: # OLD:

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@ -2,78 +2,148 @@
# Read data # Read data
dat0 <- read.table("results/haum/event_logfiles_2023-10-25_17-29-52.csv", dat <- read.table("results/haum/event_logfiles_2024-01-02_19-44-50.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) sep = ";", header = TRUE)
dat0$date.start <- as.POSIXct(dat0$date.start)
dat0$date.stop <- as.POSIXct(dat0$date.stop)
dat0$artwork <- sprintf("%03d", dat0$artwork)
table(dat0[!duplicated(dat0$trace), "event"]) dat$event <- factor(dat$event, levels = c("move", "flipCard", "openTopic",
"openPopup"))
proportions(table(dat0[!duplicated(dat0$trace), "event"])) dat$weekdays <- factor(weekdays(dat$date.start),
levels = c("Montag", "Dienstag", "Mittwoch",
"Donnerstag", "Freitag", "Samstag",
"Sonntag"),
labels = c("Monday", "Tuesday", "Wednesday",
"Thursday", "Friday", "Saturday",
"Sunday"))
tmp <- dat0[!duplicated(dat0$trace) & dat0$event %in% c("openTopic", # Select data pre Corona
"openPopup"), ] dat <- dat[as.Date(dat$date.start) < "2020-03-13", ]
dat <- dat[dat["path"] != 81621, ]
dat <- dat0 table(dat$event)
i <- 1 proportions(table(dat$event))
stop <- 1
while (stop > 0) { # Investigate paths (will separate items and give clusters of artworks!)
stop <- sum(!duplicated(dat$trace) & dat$event %in% c("openTopic", "openPopup")) length(unique(dat$path))
dat <- dat[!(!duplicated(dat$trace) &
dat$event %in% c("openTopic", "openPopup")), ] datpath <- aggregate(cbind(duration, distance, scaleSize, rotationDegree) ~
print(i) path, dat, function(x) mean(x, na.rm = TRUE), na.action = NULL)
i <- i + 1
print(table(dat[!duplicated(dat$trace), "event"])) datpath$length <- aggregate(item ~ path, dat, length)$item
datpath$nitems <- aggregate(item ~ path, dat, function(x)
length(unique(x)), na.action = NULL)$item
datpath$ntopics <- aggregate(topic ~ path, dat,
function(x) ifelse(all(is.na(x)), NA, length(unique(na.omit(x)))),
na.action = NULL)$topic
datpath$vacation <- aggregate(vacation ~ path, dat,
function(x) ifelse(all(is.na(x)), 0, 1),
na.action = NULL)$vacation
datpath$holiday <- aggregate(holiday ~ path, dat,
function(x) ifelse(all(is.na(x)), 0, 1),
na.action = NULL)$holiday
datpath$weekend <- aggregate(weekdays ~ path, dat,
function(x) ifelse(any(x %in% c("Saturday", "Sunday")), 1, 0),
na.action = NULL)$weekdays
datpath$morning <- aggregate(date.start ~ path, dat,
function(x) ifelse(lubridate::hour(x[1]) > 13, 0, 1),
na.action = NULL)$date.start
# Investigate cases (= interactions per time intervall)
length(unique(dat$case))
datcase <- aggregate(cbind(duration, distance, scaleSize, rotationDegree) ~
case, dat, function(x) mean(x, na.rm = TRUE), na.action = NULL)
datcase$length <- aggregate(item ~ case, dat, length)$item
datcase$nitems <- aggregate(item ~ case, dat, function(x)
length(unique(x)), na.action = NULL)$item
datcase$ntopics <- aggregate(topic ~ case, dat,
function(x) ifelse(all(is.na(x)), NA, length(unique(na.omit(x)))),
na.action = NULL)$topic
datcase$vacation <- aggregate(vacation ~ case, dat,
function(x) ifelse(all(is.na(x)), 0, 1),
na.action = NULL)$vacation
datcase$holiday <- aggregate(holiday ~ case, dat,
function(x) ifelse(all(is.na(x)), 0, 1),
na.action = NULL)$holiday
datcase$weekend <- aggregate(weekdays ~ case, dat,
function(x) ifelse(any(x %in% c("Saturday", "Sunday")), 1, 0),
na.action = NULL)$weekdays
datcase$morning <- aggregate(date.start ~ case, dat,
function(x) ifelse(lubridate::hour(x[1]) > 13, 0, 1),
na.action = NULL)$date.start
# Paths with more than one case associated
tmp <- aggregate(case ~ path, dat, function(x) length(unique(x)))
sum(tmp$case > 1)
table(tmp$case)
dat$date <- as.Date(dat$date.start)
tmp <- aggregate(date ~ path, dat, function(x) length(unique(x)))
sum(tmp$date > 1)
table(tmp$date)
tmp[tmp$date > 1, ]
for (p in tmp$path[tmp$date > 1]) {
print(dat[dat$path == p, 3:9])
cat("\n\n")
} }
dat[dat$date == "2017-02-28" & dat$item == "503", ]
# Creating event logs # Creating event logs
library(bupaverse) library(bupaverse)
names(dat)[names(dat) %in% c("date.start", "date.stop")] <- c("start", dat$start <- dat$date.start
"complete") dat$complete <- dat$date.stop
table(table(dat$start)) table(table(dat$start))
# --> hmm... # --> hmm...
summary(aggregate(duration ~ trace, dat, mean)) summary(aggregate(duration ~ path, dat, mean))
# TODO: Find trace that has flipCard --> openPopup --> openTopic
dato <- dat[dat$event != "move", ]
dato_split <- split(dato, ~ trace)
tmp <- lapply(dato_split, function(x) unique(x$event))
#tmp <- lapply(unique(dato$trace), function(x) unique(dato[dato$trace == x, "event"]))
ids <- sapply(tmp, length) == 3
tmp2 <- as.data.frame(do.call(rbind, tmp[ids]))
names(tmp2) <- c("flipCard", "openTopic", "openPopup")
table(tmp2$flipCard)
table(tmp2$openTopic)
table(tmp2$openPopup)
frag_ids <- which(tmp2$openTopic == "openPopup")
tmp3 <- dat[dat$trace %in% rownames(tmp2)[frag_ids], ]
tmp4 <- tmp3[!tmp3$glossar == 1, ]
dat6 <- rbind(dat[!dat$trace %in% rownames(tmp2)[frag_ids], ], tmp4)
alog <- activitylog(dat, alog <- activitylog(dat,
case_id = "trace", case_id = "path",
activity_id = "event", activity_id = "event",
#resource_id = "case", resource_id = "item",
resource_id = "artwork",
timestamps = c("start", "complete")) timestamps = c("start", "complete"))
process_map(alog) process_map(alog,
type_nodes = frequency("absolute"),
sec_nodes = frequency("relative"),
type_edges = frequency("absolute"),
sec_edges = frequency("relative"),
rankdir = "LR")
alog2 <- activitylog(dat,
case_id = "case",
activity_id = "event",
resource_id = "item",
timestamps = c("start", "complete"))
process_map(alog2,
type_nodes = frequency("absolute"),
sec_nodes = frequency("relative"),
type_edges = frequency("absolute"),
sec_edges = frequency("relative"),
rankdir = "LR")
process_map(alog, frequency("relative"))
process_map(alog, frequency("relative_consequent"))
library(processanimateR) library(processanimateR)
@ -112,30 +182,4 @@ animate_process(elog[elog$artwork %in% c("080", "054"), ],
mapping = token_aes(color = token_scale("artwork", mapping = token_aes(color = token_scale("artwork",
scale = "ordinal", scale = "ordinal",
range = c("black", "gray")))) range = c("black", "gray"))))
# --> not sure, yet, how to interpret this...
alog080 <- activitylog(dat[dat$artwork %in% "080", ],
#case_id = "case",
case_id = "trace",
activity_id = "event",
#resource_id = "trace",
resource_id = "case",
timestamps = c("start", "complete"))
process_map(alog080, frequency("relative"))
alog054 <- activitylog(dat[dat$artwork %in% "054", ],
#case_id = "case",
case_id = "trace",
activity_id = "event",
#resource_id = "trace",
resource_id = "case",
timestamps = c("start", "complete"))
process_map(alog054, frequency("relative"))

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@ -10,24 +10,7 @@ net_con.places
net_con.transitions net_con.transitions
net_con.arcs net_con.arcs
help(pm4py.objects.petri_net.obj.Marking) final_marking = Marking()
# Places
source = PetriNet.Place("source")
sink = PetriNet.Place("sink")
p_1 = PetriNet.Place("p_1")
p_2 = PetriNet.Place("p_2")
p_3 = PetriNet.Place("p_3")
p_4 = PetriNet.Place("p_4")
p_5 = PetriNet.Place("p_5")
p_6 = PetriNet.Place("p_6")
p_7 = PetriNet.Place("p_7")
p_8 = PetriNet.Place("p_8")
p_9 = PetriNet.Place("p_9")
p_10 = PetriNet.Place("p_10")
p_11 = PetriNet.Place("p_11")
p_12 = PetriNet.Place("p_12")
# Add tokens for traces # Add tokens for traces
# ('flipCard', 'openTopic', 'openPopup', 'openTopic', 'move'): 14 # ('flipCard', 'openTopic', 'openPopup', 'openTopic', 'move'): 14
@ -75,7 +58,8 @@ pm4py.vis.save_vis_petri_net(net_con, marking, final_marking, file_path="../figu
marking = pm4py.generate_marking(net_con, {'p_5': 1, 'p_12' : 1}) marking = pm4py.generate_marking(net_con, {'p_5': 1, 'p_12' : 1})
pm4py.vis.save_vis_petri_net(net_con, marking, final_marking, file_path="../figures/processmaps/conformative_net_con_markings_1_15.png") pm4py.vis.save_vis_petri_net(net_con, marking, final_marking, file_path="../figures/processmaps/conformative_net_con_markings_1_15.png")
#pm4py.view_petri_net(net_con, marking) #pm4py.view_petri_net(net_con, marking)
pm4py.vis.save_vis_petri_net(net_con, final_marking, final_marking, file_path="../figures/processmaps/conformative_net_con_markings_1_16.png") marking = pm4py.generate_marking(net_con, {'sink': 1})
pm4py.vis.save_vis_petri_net(net_con, marking, final_marking, file_path="../figures/processmaps/conformative_net_con_markings_1_16.png")
#pm4py.view_petri_net(net_con, final_marking) #pm4py.view_petri_net(net_con, final_marking)
# ('move', 'move', 'flipCard', 'move', 'openTopic', 'openPopup'): 14 # ('move', 'move', 'flipCard', 'move', 'openTopic', 'openPopup'): 14
@ -110,5 +94,6 @@ marking = pm4py.generate_marking(net_con, {'p_4': 1, 'p_12' : 1})
pm4py.vis.save_vis_petri_net(net_con, marking, final_marking, file_path="../figures/processmaps/conformative_net_con_markings_2_15.png") pm4py.vis.save_vis_petri_net(net_con, marking, final_marking, file_path="../figures/processmaps/conformative_net_con_markings_2_15.png")
marking = pm4py.generate_marking(net_con, {'p_5': 1, 'p_12' : 1}) marking = pm4py.generate_marking(net_con, {'p_5': 1, 'p_12' : 1})
pm4py.vis.save_vis_petri_net(net_con, marking, final_marking, file_path="../figures/processmaps/conformative_net_con_markings_2_16.png") pm4py.vis.save_vis_petri_net(net_con, marking, final_marking, file_path="../figures/processmaps/conformative_net_con_markings_2_16.png")
pm4py.vis.save_vis_petri_net(net_con, final_marking, final_marking, file_path="../figures/processmaps/conformative_net_con_markings_2_17.png") marking = pm4py.generate_marking(net_con, {'sink': 1})
pm4py.vis.save_vis_petri_net(net_con, marking, final_marking, file_path="../figures/processmaps/conformative_net_con_markings_2_17.png")

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@ -15,7 +15,7 @@ dat = dat[dat["date.start"] < "2020-03-13"]
event_log = pm4py.format_dataframe(dat, case_id='path', activity_key='event', event_log = pm4py.format_dataframe(dat, case_id='path', activity_key='event',
timestamp_key='date.start') timestamp_key='date.start')
event_log = event_log.rename(columns={'artwork': 'case:artwork'}) event_log = event_log.rename(columns={'item': 'case:item'})
###### Descrptives of log data ###### ###### Descrptives of log data ######
@ -34,10 +34,10 @@ sorted_variants = dict(sorted(variants.items(), key=lambda item: item[1], revers
{k: sorted_variants[k] for k in list(sorted_variants)[:20]} {k: sorted_variants[k] for k in list(sorted_variants)[:20]}
filtered_log = event_log[event_log["event"] != "move"] filtered_log = event_log[event_log["event"] != "move"]
variants = pm4py.get_variants(filtered_log) variants_no_move = pm4py.get_variants(filtered_log)
len(variants) len(variants_no_move)
sorted_variants = dict(sorted(variants.items(), key=lambda item: item[1], reverse = True)) sorted_variants_no_move = dict(sorted(variants_no_move.items(), key=lambda item: item[1], reverse = True))
{k: sorted_variants[k] for k in list(sorted_variants)[:20]} {k: sorted_variants_no_move[k] for k in list(sorted_variants_no_move)[:20]}
# Path length # Path length
event_log.path.value_counts() event_log.path.value_counts()
@ -94,8 +94,11 @@ l4[index_broken]
replayed_traces[index_broken] replayed_traces[index_broken]
# 216295 # --> broken trace! Must be in artwork 176!!!!! event_log[event_log['@@case_index'] == index_broken].event
event_log[event_log['@@case_index'] == index_broken].path
event_log[event_log['@@case_index'] == index_broken].item
event_log[event_log['@@case_index'] == index_broken]["fileId.start"]
# --> logging error in file!
from pm4py.algo.conformance.tokenreplay import algorithm as token_based_replay from pm4py.algo.conformance.tokenreplay import algorithm as token_based_replay
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} 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}
@ -156,9 +159,9 @@ pm4py.save_vis_dfg(dfg, start_activities, end_activities, '../figures/processmap
## Heuristics Miner ## Heuristics Miner
h_net, im, fm = pm4py.discover_petri_net_heuristics(event_log) h_net, im, fm = pm4py.discover_petri_net_heuristics(event_log)
h_eval = eval_pm(event_log, h_net, im, fm)
pm4py.vis.view_petri_net(h_net, im, fm) pm4py.vis.view_petri_net(h_net, im, fm)
pm4py.vis.save_vis_petri_net(h_net, im, fm, "../figures/processmaps/pn_heuristics_complete.png") pm4py.vis.save_vis_petri_net(h_net, im, fm, "../figures/processmaps/petrinet_heuristics_complete.png")
h_eval = eval_pm(event_log, h_net, im, fm)
is_sound = pm4py.check_soundness(h_net, im, fm) is_sound = pm4py.check_soundness(h_net, im, fm)
is_sound[0] is_sound[0]
@ -172,7 +175,7 @@ len(h_net.places)
from pm4py.visualization.petri_net import visualizer as pn_visualizer from pm4py.visualization.petri_net import visualizer as pn_visualizer
parameters = {pn_visualizer.Variants.FREQUENCY.value.Parameters.FORMAT: "png"} parameters = {pn_visualizer.Variants.FREQUENCY.value.Parameters.FORMAT: "png"}
gviz = pn_visualizer.apply(h_net, im, fm, parameters=parameters, variant=pn_visualizer.Variants.FREQUENCY, log=event_log) gviz = pn_visualizer.apply(h_net, im, fm, parameters=parameters, variant=pn_visualizer.Variants.FREQUENCY, log=event_log)
pn_visualizer.save(gviz, "../figures/processmaps/pn_heuristics_complete_decorated.png") pn_visualizer.save(gviz, "../figures/processmaps/petrinet_heuristics_complete_decorated.png")
# convert to BPMN # convert to BPMN
bpmn = pm4py.convert.convert_to_bpmn(h_net, im, fm) bpmn = pm4py.convert.convert_to_bpmn(h_net, im, fm)
@ -180,9 +183,9 @@ pm4py.vis.view_bpmn(bpmn)
## Alpha Miner ## Alpha Miner
a_net, im, fm = pm4py.discover_petri_net_alpha(event_log) a_net, im, fm = pm4py.discover_petri_net_alpha(event_log)
a_eval = eval_pm(event_log, a_net, im, fm)
pm4py.vis.view_petri_net(a_net, im, fm) pm4py.vis.view_petri_net(a_net, im, fm)
pm4py.vis.save_vis_petri_net(a_net, im, fm, "../figures/processmaps/pn_alpha_complete.png") pm4py.vis.save_vis_petri_net(a_net, im, fm, "../figures/processmaps/petrinet_alpha_complete.png")
a_eval = eval_pm(event_log, a_net, im, fm)
is_sound = pm4py.check_soundness(a_net, im, fm) is_sound = pm4py.check_soundness(a_net, im, fm)
is_sound[0] is_sound[0]
@ -193,9 +196,9 @@ len(a_net.places)
## Inductive Miner ## Inductive Miner
i_net, im, fm = pm4py.discover_petri_net_inductive(event_log) i_net, im, fm = pm4py.discover_petri_net_inductive(event_log)
i_eval = eval_pm(event_log, i_net, im, fm)
pm4py.vis.view_petri_net(i_net, im, fm) pm4py.vis.view_petri_net(i_net, im, fm)
pm4py.vis.save_vis_petri_net(i_net, im, fm, "../figures/processmaps/pn_induction_complete.png") pm4py.vis.save_vis_petri_net(i_net, im, fm, "../figures/processmaps/petrinet_induction_complete.png")
i_eval = eval_pm(event_log, i_net, im, fm)
# as process tree (does not work for heuristics miner!) # as process tree (does not work for heuristics miner!)
pt = pm4py.discover_process_tree_inductive(event_log) pt = pm4py.discover_process_tree_inductive(event_log)
@ -217,9 +220,9 @@ pm4py.view_bpmn(bpmn)
from pm4py.algo.conformance.tokenreplay import algorithm as token_based_replay from pm4py.algo.conformance.tokenreplay import algorithm as token_based_replay
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} 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}
replayed_traces, place_fitness, trans_fitness, unwanted_activities = token_based_replay.apply(event_log, i_net, replayed_traces, place_fitness, trans_fitness, unwanted_activities = token_based_replay.apply(event_log, i_net,
im, im,
fm, fm,
parameters=parameters_tbr) parameters=parameters_tbr)
l1 = list() l1 = list()
l2 = list() l2 = list()
@ -232,15 +235,29 @@ for i in range(len(replayed_traces)):
l4.append(replayed_traces[i]["transitions_with_problems"]) l4.append(replayed_traces[i]["transitions_with_problems"])
np.mean(l1) np.mean(l1)
set(l1)
index_broken = l1.index(1)
np.mean(l2) np.mean(l2)
set(l2)
l2.index(1)
set(l3) set(l3)
l4.count([]) l4.count([])
l3[index_broken]
l4[index_broken]
replayed_traces[index_broken]
event_log[event_log['@@case_index'] == index_broken].event
event_log[event_log['@@case_index'] == index_broken].path
event_log[event_log['@@case_index'] == index_broken].item
event_log[event_log['@@case_index'] == index_broken]["fileId.start"]
## ILP Miner ## ILP Miner
ilp_net, im, fm = pm4py.discover_petri_net_ilp(event_log) ilp_net, im, fm = pm4py.discover_petri_net_ilp(event_log)
ilp_eval = eval_pm(event_log, ilp_net, im, fm)
pm4py.vis.view_petri_net(ilp_net, im, fm) pm4py.vis.view_petri_net(ilp_net, im, fm)
pm4py.vis.save_vis_petri_net(ilp_net, im, fm, "../figures/processmaps/pn_ilp_complete.png") pm4py.vis.save_vis_petri_net(ilp_net, im, fm, "../figures/processmaps/petrinet_ilp_complete.png")
ilp_eval = eval_pm(event_log, ilp_net, im, fm)
is_sound = pm4py.check_soundness(ilp_net, im, fm) is_sound = pm4py.check_soundness(ilp_net, im, fm)
is_sound[0] is_sound[0]
@ -257,6 +274,27 @@ eval
eval.to_csv("results/eval_all-miners_complete.csv", sep=";") eval.to_csv("results/eval_all-miners_complete.csv", sep=";")
## Without broken trace
event_log_clean = event_log[event_log['@@case_index'] != index_broken]
h_net, a_im, h_fm = pm4py.discover_petri_net_heuristics(event_log_clean)
a_net, h_im, a_fm = pm4py.discover_petri_net_alpha(event_log_clean)
i_net, i_im, i_fm = pm4py.discover_petri_net_inductive(event_log_clean)
ilp_net, ilp_im, ilp_fm = pm4py.discover_petri_net_ilp(event_log_clean)
baseline_eval = eval_pm(event_log_clean, basenet, initial_marking, final_marking)
h_eval = eval_pm(event_log_clean, h_net, h_im, h_fm)
a_eval = eval_pm(event_log_clean, a_net, a_im, a_fm)
i_eval = eval_pm(event_log_clean, i_net, i_im, i_fm)
ilp_eval = eval_pm(event_log_clean, ilp_net, ilp_im, ilp_fm)
eval = pd.DataFrame(np.row_stack([baseline_eval, h_eval, a_eval, i_eval, ilp_eval]))
eval.columns = ["fitness", "precision", "generalizability", "simplicity"]
eval.index = ["conformative", "heuristics", "alpha", "inductive", "ilp"]
eval
eval.to_csv("results/eval_all-miners_clean.csv", sep=";")
###### Process Mining - individual artworks ###### ###### Process Mining - individual artworks ######
def pm_artworks(miner): def pm_artworks(miner):
@ -308,40 +346,4 @@ for miner in ["heuristics", "inductive", "alpha", "ilp"]:
eval_art = pm_artworks(miner = "inductive") eval_art = pm_artworks(miner = "inductive")
##### Clustering ######
## KMeans
#eval_artworks = eval_art[eval_art.nettype == "alldata"].iloc[:,range(1,5)]
eval_artworks = eval_art[eval_art.nettype == "subdata"].iloc[:,range(1,5)]
kmeans = KMeans(n_clusters=4, max_iter=1000).fit(eval_artworks)
#from sklearn.manifold import MDS
#coord = pd.DataFrame(MDS(normalized_stress='auto').fit_transform(eval_artworks))
coord = eval_artworks
coord["clusters"] = kmeans.labels_
for i in coord.clusters.unique():
#plt.scatter(coord[coord.clusters == i].iloc[:,0], coord[coord.clusters == i].iloc[:,1],
plt.scatter(coord[coord.clusters == i].iloc[:,1], coord[coord.clusters == i].iloc[:,2],
#plt.scatter(coord[coord.clusters == i].iloc[:,2], coord[coord.clusters == i].iloc[:,4],
label = i)
plt.legend()
plt.show()
### Scree plot
sse = {}
for k in range(1, 10):
kmeans = KMeans(n_clusters=k, max_iter=1000).fit(eval_artworks[["precision", "generalizability"]])
#data["clusters"] = kmeans.labels_
#print(data["clusters"])
sse[k] = kmeans.inertia_ # Inertia: Sum of distances of samples to their closest cluster center
plt.figure()
plt.plot(list(sse.keys()), list(sse.values()))
plt.xlabel("Number of clusters")
plt.ylabel("SSE")
plt.show()

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%reset
import pm4py
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from pm4py.visualization.petri_net import visualizer as pn_visualizer
parameters = {pn_visualizer.Variants.FREQUENCY.value.Parameters.FORMAT: "png"}
###### Load data and create event logs ######
dat = pd.read_csv("results/haum/event_logfiles_2024-01-02_19-44-50.csv", sep = ";")
dat = dat[dat["date.start"] < "2020-03-13"]
dat = dat[dat["path"] != 81621] # exclude broken trace
# --> only pre corona (before artworks were updated)
event_log = pm4py.format_dataframe(dat, case_id='case', activity_key='event',
timestamp_key='date.start')
event_log.event.value_counts()
event_log.event.value_counts(normalize=True)
dfg, start_activities, end_activities = pm4py.discover_dfg(event_log)
pm4py.view_dfg(dfg, start_activities, end_activities)
#filtered_log = pm4py.filter_event_attribute_values(event_log, 'item', [80])
i_net, im, fm = pm4py.discover_petri_net_inductive(event_log)
pm4py.vis.view_petri_net(i_net, im, fm)
gviz = pn_visualizer.apply(i_net, im, fm, parameters=parameters,
variant=pn_visualizer.Variants.FREQUENCY,
log=event_log)
pn_visualizer.view(gviz)
len(i_net.places)
len(i_net.transitions)
len(i_net.arcs)
a_net, im, fm = pm4py.discover_petri_net_alpha(event_log)
pm4py.vis.view_petri_net(a_net, im, fm)
gviz = pn_visualizer.apply(a_net, im, fm, parameters=parameters,
variant=pn_visualizer.Variants.FREQUENCY,
log=event_log)
pn_visualizer.view(gviz)
len(a_net.places)
len(a_net.transitions)
len(a_net.arcs)
h_net, im, fm = pm4py.discover_petri_net_heuristics(filtered_log)
pm4py.vis.view_petri_net(h_net, im, fm)
len(h_net.places)
len(h_net.transitions)
len(h_net.arcs)

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code/trace-clustering.py Normal file
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from sklearn.cluster import KMeans
import matplotlib.pyplot as plt
##### Clustering ######
## KMeans
#eval_artworks = eval_art[eval_art.nettype == "alldata"].iloc[:,range(1,5)]
eval_artworks = eval_art[eval_art.nettype == "subdata"].iloc[:,range(1,5)]
kmeans = KMeans(n_clusters=4, max_iter=1000).fit(eval_artworks)
#from sklearn.manifold import MDS
#coord = pd.DataFrame(MDS(normalized_stress='auto').fit_transform(eval_artworks))
coord = eval_artworks
coord["clusters"] = kmeans.labels_
for i in coord.clusters.unique():
#plt.scatter(coord[coord.clusters == i].iloc[:,0], coord[coord.clusters == i].iloc[:,1],
plt.scatter(coord[coord.clusters == i].iloc[:,1], coord[coord.clusters == i].iloc[:,2],
#plt.scatter(coord[coord.clusters == i].iloc[:,2], coord[coord.clusters == i].iloc[:,4],
label = i)
plt.legend()
plt.show()
### Scree plot
sse = {}
for k in range(1, 10):
kmeans = KMeans(n_clusters=k, max_iter=1000).fit(eval_artworks[["precision", "generalizability"]])
#data["clusters"] = kmeans.labels_
#print(data["clusters"])
sse[k] = kmeans.inertia_ # Inertia: Sum of distances of samples to their closest cluster center
plt.figure()
plt.plot(list(sse.keys()), list(sse.values()))
plt.xlabel("Number of clusters")
plt.ylabel("SSE")
plt.show()