Worked on analysis
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@ -41,7 +41,7 @@ write.table(datraw, paste0("results/haum/raw_logfiles_", now, ".csv"),
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datlogs <- create_eventlogs(datraw,
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#xmlpath = "../data/haum/ContentEyevisit/eyevisit_cards_light/",
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glossar = FALSE)
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glossar = FALSE, save = TRUE)
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# 2,136,694 no change moves removed
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# OLD:
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@ -2,78 +2,148 @@
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# Read data
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dat0 <- read.table("results/haum/event_logfiles_2023-10-25_17-29-52.csv",
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dat <- read.table("results/haum/event_logfiles_2024-01-02_19-44-50.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$date.start <- as.POSIXct(dat0$date.start)
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dat0$date.stop <- as.POSIXct(dat0$date.stop)
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dat0$artwork <- sprintf("%03d", dat0$artwork)
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table(dat0[!duplicated(dat0$trace), "event"])
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dat$event <- factor(dat$event, levels = c("move", "flipCard", "openTopic",
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"openPopup"))
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proportions(table(dat0[!duplicated(dat0$trace), "event"]))
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dat$weekdays <- factor(weekdays(dat$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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tmp <- dat0[!duplicated(dat0$trace) & dat0$event %in% c("openTopic",
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"openPopup"), ]
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# Select data pre Corona
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dat <- dat[as.Date(dat$date.start) < "2020-03-13", ]
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dat <- dat[dat["path"] != 81621, ]
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dat <- dat0
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i <- 1
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stop <- 1
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table(dat$event)
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proportions(table(dat$event))
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while (stop > 0) {
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stop <- sum(!duplicated(dat$trace) & dat$event %in% c("openTopic", "openPopup"))
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dat <- dat[!(!duplicated(dat$trace) &
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dat$event %in% c("openTopic", "openPopup")), ]
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print(i)
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i <- i + 1
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print(table(dat[!duplicated(dat$trace), "event"]))
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# Investigate paths (will separate items and give clusters of artworks!)
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length(unique(dat$path))
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datpath <- aggregate(cbind(duration, distance, scaleSize, rotationDegree) ~
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path, dat, function(x) mean(x, na.rm = TRUE), na.action = NULL)
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datpath$length <- aggregate(item ~ path, dat, length)$item
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datpath$nitems <- aggregate(item ~ path, dat, function(x)
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length(unique(x)), na.action = NULL)$item
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datpath$ntopics <- aggregate(topic ~ path, dat,
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function(x) ifelse(all(is.na(x)), NA, length(unique(na.omit(x)))),
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na.action = NULL)$topic
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datpath$vacation <- aggregate(vacation ~ path, dat,
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function(x) ifelse(all(is.na(x)), 0, 1),
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na.action = NULL)$vacation
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datpath$holiday <- aggregate(holiday ~ path, dat,
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function(x) ifelse(all(is.na(x)), 0, 1),
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na.action = NULL)$holiday
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datpath$weekend <- aggregate(weekdays ~ path, dat,
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function(x) ifelse(any(x %in% c("Saturday", "Sunday")), 1, 0),
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na.action = NULL)$weekdays
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datpath$morning <- aggregate(date.start ~ path, dat,
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function(x) ifelse(lubridate::hour(x[1]) > 13, 0, 1),
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na.action = NULL)$date.start
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# Investigate cases (= interactions per time intervall)
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length(unique(dat$case))
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datcase <- aggregate(cbind(duration, distance, scaleSize, rotationDegree) ~
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case, dat, function(x) mean(x, na.rm = TRUE), na.action = NULL)
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datcase$length <- aggregate(item ~ case, dat, length)$item
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datcase$nitems <- aggregate(item ~ case, dat, function(x)
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length(unique(x)), na.action = NULL)$item
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datcase$ntopics <- aggregate(topic ~ case, dat,
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function(x) ifelse(all(is.na(x)), NA, length(unique(na.omit(x)))),
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na.action = NULL)$topic
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datcase$vacation <- aggregate(vacation ~ case, dat,
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function(x) ifelse(all(is.na(x)), 0, 1),
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na.action = NULL)$vacation
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datcase$holiday <- aggregate(holiday ~ case, dat,
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function(x) ifelse(all(is.na(x)), 0, 1),
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na.action = NULL)$holiday
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datcase$weekend <- aggregate(weekdays ~ case, dat,
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function(x) ifelse(any(x %in% c("Saturday", "Sunday")), 1, 0),
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na.action = NULL)$weekdays
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datcase$morning <- aggregate(date.start ~ case, dat,
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function(x) ifelse(lubridate::hour(x[1]) > 13, 0, 1),
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na.action = NULL)$date.start
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# Paths with more than one case associated
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tmp <- aggregate(case ~ path, dat, function(x) length(unique(x)))
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sum(tmp$case > 1)
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table(tmp$case)
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dat$date <- as.Date(dat$date.start)
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tmp <- aggregate(date ~ path, dat, function(x) length(unique(x)))
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sum(tmp$date > 1)
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table(tmp$date)
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tmp[tmp$date > 1, ]
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for (p in tmp$path[tmp$date > 1]) {
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print(dat[dat$path == p, 3:9])
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cat("\n\n")
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}
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dat[dat$date == "2017-02-28" & dat$item == "503", ]
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# Creating event logs
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library(bupaverse)
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names(dat)[names(dat) %in% c("date.start", "date.stop")] <- c("start",
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"complete")
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dat$start <- dat$date.start
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dat$complete <- dat$date.stop
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table(table(dat$start))
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# --> hmm...
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summary(aggregate(duration ~ trace, dat, mean))
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# TODO: Find trace that has flipCard --> openPopup --> openTopic
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dato <- dat[dat$event != "move", ]
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dato_split <- split(dato, ~ trace)
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tmp <- lapply(dato_split, function(x) unique(x$event))
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#tmp <- lapply(unique(dato$trace), function(x) unique(dato[dato$trace == x, "event"]))
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ids <- sapply(tmp, length) == 3
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tmp2 <- as.data.frame(do.call(rbind, tmp[ids]))
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names(tmp2) <- c("flipCard", "openTopic", "openPopup")
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table(tmp2$flipCard)
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table(tmp2$openTopic)
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table(tmp2$openPopup)
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frag_ids <- which(tmp2$openTopic == "openPopup")
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tmp3 <- dat[dat$trace %in% rownames(tmp2)[frag_ids], ]
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tmp4 <- tmp3[!tmp3$glossar == 1, ]
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dat6 <- rbind(dat[!dat$trace %in% rownames(tmp2)[frag_ids], ], tmp4)
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summary(aggregate(duration ~ path, dat, mean))
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alog <- activitylog(dat,
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case_id = "trace",
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case_id = "path",
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activity_id = "event",
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#resource_id = "case",
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resource_id = "artwork",
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resource_id = "item",
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timestamps = c("start", "complete"))
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process_map(alog)
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process_map(alog,
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type_nodes = frequency("absolute"),
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sec_nodes = frequency("relative"),
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type_edges = frequency("absolute"),
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sec_edges = frequency("relative"),
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rankdir = "LR")
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alog2 <- activitylog(dat,
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case_id = "case",
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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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process_map(alog2,
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type_nodes = frequency("absolute"),
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sec_nodes = frequency("relative"),
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type_edges = frequency("absolute"),
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sec_edges = frequency("relative"),
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rankdir = "LR")
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process_map(alog, frequency("relative"))
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process_map(alog, frequency("relative_consequent"))
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library(processanimateR)
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@ -112,30 +182,4 @@ animate_process(elog[elog$artwork %in% c("080", "054"), ],
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mapping = token_aes(color = token_scale("artwork",
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scale = "ordinal",
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range = c("black", "gray"))))
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# --> not sure, yet, how to interpret this...
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alog080 <- activitylog(dat[dat$artwork %in% "080", ],
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#case_id = "case",
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case_id = "trace",
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activity_id = "event",
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#resource_id = "trace",
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resource_id = "case",
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timestamps = c("start", "complete"))
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process_map(alog080, frequency("relative"))
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alog054 <- activitylog(dat[dat$artwork %in% "054", ],
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#case_id = "case",
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case_id = "trace",
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activity_id = "event",
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#resource_id = "trace",
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resource_id = "case",
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timestamps = c("start", "complete"))
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process_map(alog054, frequency("relative"))
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@ -10,24 +10,7 @@ net_con.places
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net_con.transitions
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net_con.arcs
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help(pm4py.objects.petri_net.obj.Marking)
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# Places
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source = PetriNet.Place("source")
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sink = PetriNet.Place("sink")
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p_1 = PetriNet.Place("p_1")
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p_2 = PetriNet.Place("p_2")
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p_3 = PetriNet.Place("p_3")
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p_4 = PetriNet.Place("p_4")
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p_5 = PetriNet.Place("p_5")
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p_6 = PetriNet.Place("p_6")
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p_7 = PetriNet.Place("p_7")
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p_8 = PetriNet.Place("p_8")
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p_9 = PetriNet.Place("p_9")
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p_10 = PetriNet.Place("p_10")
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p_11 = PetriNet.Place("p_11")
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p_12 = PetriNet.Place("p_12")
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final_marking = Marking()
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# Add tokens for traces
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# ('flipCard', 'openTopic', 'openPopup', 'openTopic', 'move'): 14
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@ -75,7 +58,8 @@ pm4py.vis.save_vis_petri_net(net_con, marking, final_marking, file_path="../figu
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marking = pm4py.generate_marking(net_con, {'p_5': 1, 'p_12' : 1})
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pm4py.vis.save_vis_petri_net(net_con, marking, final_marking, file_path="../figures/processmaps/conformative_net_con_markings_1_15.png")
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#pm4py.view_petri_net(net_con, marking)
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pm4py.vis.save_vis_petri_net(net_con, final_marking, final_marking, file_path="../figures/processmaps/conformative_net_con_markings_1_16.png")
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marking = pm4py.generate_marking(net_con, {'sink': 1})
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pm4py.vis.save_vis_petri_net(net_con, marking, final_marking, file_path="../figures/processmaps/conformative_net_con_markings_1_16.png")
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#pm4py.view_petri_net(net_con, final_marking)
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# ('move', 'move', 'flipCard', 'move', 'openTopic', 'openPopup'): 14
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@ -110,5 +94,6 @@ marking = pm4py.generate_marking(net_con, {'p_4': 1, 'p_12' : 1})
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pm4py.vis.save_vis_petri_net(net_con, marking, final_marking, file_path="../figures/processmaps/conformative_net_con_markings_2_15.png")
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marking = pm4py.generate_marking(net_con, {'p_5': 1, 'p_12' : 1})
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pm4py.vis.save_vis_petri_net(net_con, marking, final_marking, file_path="../figures/processmaps/conformative_net_con_markings_2_16.png")
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pm4py.vis.save_vis_petri_net(net_con, final_marking, final_marking, file_path="../figures/processmaps/conformative_net_con_markings_2_17.png")
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marking = pm4py.generate_marking(net_con, {'sink': 1})
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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"]
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event_log = pm4py.format_dataframe(dat, case_id='path', activity_key='event',
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timestamp_key='date.start')
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event_log = event_log.rename(columns={'artwork': 'case:artwork'})
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event_log = event_log.rename(columns={'item': 'case:item'})
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###### Descrptives of log data ######
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@ -34,10 +34,10 @@ sorted_variants = dict(sorted(variants.items(), key=lambda item: item[1], revers
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{k: sorted_variants[k] for k in list(sorted_variants)[:20]}
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filtered_log = event_log[event_log["event"] != "move"]
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variants = pm4py.get_variants(filtered_log)
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len(variants)
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sorted_variants = dict(sorted(variants.items(), key=lambda item: item[1], reverse = True))
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{k: sorted_variants[k] for k in list(sorted_variants)[:20]}
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variants_no_move = pm4py.get_variants(filtered_log)
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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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# Path length
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event_log.path.value_counts()
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@ -94,8 +94,11 @@ l4[index_broken]
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replayed_traces[index_broken]
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# 216295 # --> broken trace! Must be in artwork 176!!!!!
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event_log[event_log['@@case_index'] == index_broken].event
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event_log[event_log['@@case_index'] == index_broken].path
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event_log[event_log['@@case_index'] == index_broken].item
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event_log[event_log['@@case_index'] == index_broken]["fileId.start"]
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# --> logging error in file!
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from pm4py.algo.conformance.tokenreplay import algorithm as token_based_replay
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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}
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@ -156,9 +159,9 @@ pm4py.save_vis_dfg(dfg, start_activities, end_activities, '../figures/processmap
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## Heuristics Miner
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h_net, im, fm = pm4py.discover_petri_net_heuristics(event_log)
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h_eval = eval_pm(event_log, h_net, im, fm)
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pm4py.vis.view_petri_net(h_net, im, fm)
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pm4py.vis.save_vis_petri_net(h_net, im, fm, "../figures/processmaps/pn_heuristics_complete.png")
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pm4py.vis.save_vis_petri_net(h_net, im, fm, "../figures/processmaps/petrinet_heuristics_complete.png")
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h_eval = eval_pm(event_log, h_net, im, fm)
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is_sound = pm4py.check_soundness(h_net, im, fm)
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is_sound[0]
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@ -172,7 +175,7 @@ len(h_net.places)
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from pm4py.visualization.petri_net import visualizer as pn_visualizer
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parameters = {pn_visualizer.Variants.FREQUENCY.value.Parameters.FORMAT: "png"}
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gviz = pn_visualizer.apply(h_net, im, fm, parameters=parameters, variant=pn_visualizer.Variants.FREQUENCY, log=event_log)
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pn_visualizer.save(gviz, "../figures/processmaps/pn_heuristics_complete_decorated.png")
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pn_visualizer.save(gviz, "../figures/processmaps/petrinet_heuristics_complete_decorated.png")
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# convert to BPMN
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bpmn = pm4py.convert.convert_to_bpmn(h_net, im, fm)
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@ -180,9 +183,9 @@ pm4py.vis.view_bpmn(bpmn)
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## Alpha Miner
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a_net, im, fm = pm4py.discover_petri_net_alpha(event_log)
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a_eval = eval_pm(event_log, a_net, im, fm)
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pm4py.vis.view_petri_net(a_net, im, fm)
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pm4py.vis.save_vis_petri_net(a_net, im, fm, "../figures/processmaps/pn_alpha_complete.png")
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pm4py.vis.save_vis_petri_net(a_net, im, fm, "../figures/processmaps/petrinet_alpha_complete.png")
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a_eval = eval_pm(event_log, a_net, im, fm)
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is_sound = pm4py.check_soundness(a_net, im, fm)
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is_sound[0]
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@ -193,9 +196,9 @@ len(a_net.places)
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## Inductive Miner
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i_net, im, fm = pm4py.discover_petri_net_inductive(event_log)
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i_eval = eval_pm(event_log, i_net, im, fm)
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pm4py.vis.view_petri_net(i_net, im, fm)
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pm4py.vis.save_vis_petri_net(i_net, im, fm, "../figures/processmaps/pn_induction_complete.png")
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pm4py.vis.save_vis_petri_net(i_net, im, fm, "../figures/processmaps/petrinet_induction_complete.png")
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i_eval = eval_pm(event_log, i_net, im, fm)
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# as process tree (does not work for heuristics miner!)
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pt = pm4py.discover_process_tree_inductive(event_log)
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@ -232,15 +235,29 @@ for i in range(len(replayed_traces)):
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l4.append(replayed_traces[i]["transitions_with_problems"])
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np.mean(l1)
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set(l1)
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index_broken = l1.index(1)
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np.mean(l2)
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set(l2)
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l2.index(1)
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set(l3)
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l4.count([])
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l3[index_broken]
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l4[index_broken]
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replayed_traces[index_broken]
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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_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.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[0]
|
||||
@ -257,6 +274,27 @@ eval
|
||||
|
||||
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 ######
|
||||
|
||||
def pm_artworks(miner):
|
||||
@ -308,40 +346,4 @@ for miner in ["heuristics", "inductive", "alpha", "ilp"]:
|
||||
|
||||
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()
|
||||
|
||||
57
code/pm_navigation-behavior.py
Normal file
57
code/pm_navigation-behavior.py
Normal file
@ -0,0 +1,57 @@
|
||||
%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)
|
||||
41
code/trace-clustering.py
Normal file
41
code/trace-clustering.py
Normal file
@ -0,0 +1,41 @@
|
||||
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()
|
||||
Loading…
Reference in New Issue
Block a user