nwickelmaier/mtt_haum
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This commit is contained in:
Nora Wickelmaier 2024-01-09 14:34:15 +01:00
parent 5925a420b3
commit ea002bb7c3
2 changed files with 43 additions and 17 deletions
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25
code/00_current-analysis.R
View File

@ -9,7 +9,7 @@
# (3.4) Artwork sequences
# (3.5) Topics
#
# input: results/haum/event_logfiles_glossar_2023-10-29_10-26-42.csv
# input: results/haum/event_logfiles_glossar_2023-12-28_09-49-43.csv
# output:
#
# last mod: 2023-11-15, NW
@ -27,16 +27,25 @@ library(bupaverse)
#--------------- (1) Read data ---------------
dat <- read.table("results/haum/event_logfiles_glossar_2023-11-03_17-46-28.csv",
sep = ";", header = TRUE)
dat$date <- as.POSIXct(dat$date)
dat$date.start <- as.POSIXct(dat$date.start)
dat$date.stop <- as.POSIXct(dat$date.stop)
dat$artwork <- sprintf("%03d", dat$artwork)
dat <- read.table("results/haum/event_logfiles_glossar_2023-12-28_09-49-43.csv",
sep = ";", header = TRUE,
colClasses = c("POSIXct", "character", "integer",
"integer", "numeric", "integer",
"character", "character", "character",
"character", "POSIXct", "POSIXct",
"numeric", "numeric", "numeric",
"integer", "character",
rep("numeric", 11), "integer",
"character", "character", "logical",
"logical", "logical", "character",
"character"))
dat$date <- NULL
# TODO: Remove, after rerunning preprocessing
dat$event <- factor(dat$event, levels = c("move", "flipCard", "openTopic", "openPopup"))
# Add weekdays to data frame
dat$weekdays <- factor(weekdays(dat$date.start),
levels = c("Montag", "Dienstag", "Mittwoch",
"Donnerstag", "Freitag", "Samstag",

35
code/00_pm.py
View File

@ -18,6 +18,8 @@ dat = dat[dat.date < "2020-03-13"]
event_log = pm4py.format_dataframe(dat, case_id='trace', activity_key='event',
timestamp_key='date.start')
# event_log = pm4py.format_dataframe(dat, case_id='trace', activity_key='event',
# timestamp_key='date.stop', start_timestamp_key='date.start')
event_log = event_log.rename(columns={'artwork': 'case:artwork'})
#event_log = pm4py.convert_to_event_log(dat_log) # deprecated
@ -48,6 +50,15 @@ pm4py.vis.save_vis_petri_net(net, im, fm, "../figures/processmaps/pn_heuristics_
is_sound = pm4py.check_soundness(net, im, fm)
is_sound[0]
len(is_sound[1]["s_c_net"].arcs)
# 46
len(is_sound[1]["s_c_net"].transitions)
# 23
len(is_sound[1]["s_c_net"].places)
# 10
# decorated petri net
from pm4py.visualization.petri_net import visualizer as pn_visualizer
parameters = {pn_visualizer.Variants.FREQUENCY.value.Parameters.FORMAT: "png"}
@ -67,6 +78,10 @@ pm4py.vis.save_vis_petri_net(net, im, fm, "../figures/processmaps/pn_alpha_compl
is_sound = pm4py.check_soundness(net, im, fm)
is_sound[0]
len(is_sound[1]["s_c_net"].arcs)
len(is_sound[1]["s_c_net"].transitions)
len(is_sound[1]["s_c_net"].places)
## Inductive Miner
net, im, fm = pm4py.discover_petri_net_inductive(event_log)
i_eval = eval_pm(event_log, net, im, fm)
@ -99,9 +114,6 @@ eval.to_csv("results/eval_all-miners_complete.csv", sep=";")
###### Process Mining - individual artworks ######
net, im, fm = pm4py.discover_petri_net_heuristics(event_log)
#net, im, fm = pm4py.discover_petri_net_inductive(event_log)
def pm_artworks(miner):
retval1 = np.empty((len(event_log["case:artwork"].unique()), 4))
@ -129,8 +141,8 @@ def pm_artworks(miner):
subnet, subim, subfm = pm4py.discover_petri_net_alpha(subdata)
elif miner == "ilp":
subnet, subim, subfm = pm4py.discover_petri_net_ilp(subdata)
pm4py.save_vis_petri_net(subnet, subim, subfm,
"../figures/processmaps/artworks/petrinet_" + miner + "_" + str(artwork).zfill(3) + ".png")
#pm4py.save_vis_petri_net(subnet, subim, subfm,
# "../figures/processmaps/artworks/petrinet_" + miner + "_" + str(artwork).zfill(3) + ".png")
retval1[i] = eval_pm(subdata, net, im, fm)
retval2[i] = eval_pm(subdata, subnet, subim, subfm)
@ -149,17 +161,21 @@ for miner in ["heuristics", "inductive", "alpha", "ilp"]:
eval_art = pm_artworks(miner = miner)
eval_art.to_csv("results/eval_artworks_" + miner + ".csv", sep=";")
eval_art = pm_artworks(miner = "inductive")
##### Clustering ######
## KMeans
kmeans = KMeans(n_clusters=4, max_iter=1000).fit(eval_art)
#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_art))
#coord = pd.DataFrame(MDS(normalized_stress='auto').fit_transform(eval_artworks))
coord = eval_art
coord = eval_artworks
coord["clusters"] = kmeans.labels_
for i in coord.clusters.unique():
@ -174,7 +190,7 @@ plt.show()
sse = {}
for k in range(1, 10):
kmeans = KMeans(n_clusters=k, max_iter=1000).fit(eval_art[["precision", "generalizability"]])
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
@ -183,3 +199,4 @@ plt.plot(list(sse.keys()), list(sse.values()))
plt.xlabel("Number of clusters")
plt.ylabel("SSE")
plt.show()