From 3c513e51e13d610f730788375103c2afc62254ab Mon Sep 17 00:00:00 2001 From: notconfusing Date: Tue, 11 Jun 2013 13:20:48 -0700 Subject: [PATCH] first commit --- ItemsByLangPlot.R | 258 ++++++++++++++++++++++++++++++++++ alln.json | 1 + filter1.json | 1 + filter2.json | 1 + filter3.json | 1 + item-analysis.py | 344 ++++++++++++++++++++++++++++++++++++++++++++++ setmaker.py | 99 +++++++++++++ 7 files changed, 705 insertions(+) create mode 100644 ItemsByLangPlot.R create mode 100644 alln.json create mode 100644 filter1.json create mode 100644 filter2.json create mode 100644 filter3.json create mode 100755 item-analysis.py create mode 100644 setmaker.py diff --git a/ItemsByLangPlot.R b/ItemsByLangPlot.R new file mode 100644 index 0000000..1beacf3 --- /dev/null +++ b/ItemsByLangPlot.R @@ -0,0 +1,258 @@ +require(rjson) +require(reshape) +require(ggplot2) +library(scales) +library(nlstools) +library(stringr) +library(gridExtra) +setwd("~/workspace/venn-analysis") +filter1 <- fromJSON(file='filter1.json') +filter2 <- fromJSON(file='filter2.json') +filter3 <- fromJSON(file='filter3.json') +alln <- fromJSON(file='alln.json') +langtot <- fromJSON(file='langtotals_1.json') + +filter1m <- melt(filter1) +filter2m <- melt(filter2) +filter3m <- melt(filter3) +allnm <- melt(alln) + + +langtotm <-melt(langtot) +langtotm <- langtotm[order(langtotm$value),] + + +a <- data.frame(matrix(ncol = 0, nrow = length(filter1m$value))) +a["langs"] <- filter1m[c(order(filter1m$L1,filter1m$value)),"L1"] +a["uniqs"] <- filter1m[c(order(filter1m$L1,filter1m$value)),"value"] +a["tots"] <- langtotm[c(order(langtotm$L1,langtotm$value)),"value"] +b <- subset(a, tots > 1000) + +c <- b[-63,] +#linear scale +p1 <- ggplot(b, aes(x=tots, y=uniqs), geom="line")+ geom_point(stat="identity") + + geom_text(data=b, aes(x=tots,y=uniqs,label=langs,colour=factor(langs),alpha=.9), + position=position_dodge(width=0.8,height=0.8),vjust=0, angle = 0, size = 8, guide = FALSE) + + scale_x_continuous(labels=comma) + scale_y_continuous(labels=comma) + + geom_smooth(method="loess", se=FALSE) + + labs(x="Total Language Links ", y="Unique Language Links", title="Comparison of Languages in Wikidata, by Language Links \n Minimum 1,000 Items \n Linear Scale") + + theme(legend.position="none") +p1 + +#log-log plot +p2 <- ggplot(b, aes(x=tots, y=uniqs), geom="line")+ geom_point(stat="identity") + + geom_text(data=b, aes(x=tots,y=uniqs,label=langs,colour=factor(langs),alpha=.9), position=position_dodge(width=0.8,height=0.8),vjust=0, angle = 0, size = 5, guide = FALSE) + + scale_x_log10(labels=comma) + scale_y_log10(labels=comma) + + geom_smooth(method="auto", se=FALSE) + + labs(x="Total Language Links ", y="Unique Language Links", title="Comparison of Languages in Wikidata, by Language Links \n Minimum 1,000 Items \n Log-Log Scale") + + theme(legend.position="none") +p2 + + +#lets try this with a insstead of filter1m +q <- data.frame(matrix(ncol = 0, nrow = length(a$tots))) +q["langs"] <- a[c(order(a$tots,a$langs)),"langs"] +q["uniqs"] <- a[c(order(a$tots,a$langs)),"uniqs"] +q["tots"] <- a[c(order(a$tots,a$langs)),"tots"] +#q["resid"] <- a[c(order(a$uniqs,a$langs)),"resid"] +q["rat"] <- q[,"uniqs"] / q[,"tots"] + +q.1 <- subset(q, q[,"tots"]>=100000) +q.2 <- subset(q, q[,"tots"]>=10000 & q[,"tots"]<100000) + +totordplot1 <- ggplot(q.1, aes(x=langs, y=rat, fill=tots/max(tots)*100))+ + geom_bar(stat="identity", width = .9) + + #geom_bar(stat="identity", width = .5, alpha = .5, colour='red', aes(x=langs, y=resid), data=q.1)+ + scale_x_discrete(limits=q.1$langs) + + scale_y_continuous(labels=percent)+ + scale_fill_gradient(low=muted("darkgreen"), high=muted("purple"), + limits=c(0,40), "Total Language Links \n as % of English")+ + geom_text(aes(x=langs,y=0,label=paste(round(100*rat,2), '%', sep='')),hjust=0, angle = 90, colour='grey', data=q.1) + + labs(title="More than 100,000 Language Links")+ + theme_bw()+ + theme(axis.title.x=element_blank(), axis.title.y=element_blank()) +totordplot1 + +totordplot2 <- ggplot(q.2, aes(x=langs, y=rat, fill=tots/max(q.1$tots)*100))+ + geom_bar(stat="identity", width = .95) + + #geom_bar(stat="identity", width = .5, alpha = .5, colour='red', aes(x=langs, y=resid), data=q.1)+ + scale_x_discrete(limits=q.2$langs) + + scale_y_continuous(labels=percent)+ + scale_fill_gradient(low=muted("lightblue"), high=muted("lightgreen"), "Total Language Links \n as % of English")+ + geom_text(aes(x=langs,y=0, + label=paste(round(100*rat,1), '%', sep='')), + hjust=0, angle = 90, colour='grey')+ + labs(title="10,000 to 100,000 Language Links")+ + theme_bw()+ + theme(axis.title.x=element_blank(), axis.title.y=element_blank()) +totordplot2 + +grid.arrange(totordplot2, totordplot1, + left="Percentage of Unique Items", main="Wikipedia Unique Article Percentage by Wikipedia Size (size as % of English)" ) + +oq <- data.frame(matrix(ncol = 0, nrow = length(filter1m$value))) +oq["langs"] <- q[c(order(q$rat,q$langs)),"langs]" +oq["rat"] <- q[c(order(q$rat,q$langs)),"rat"] +oq["tots"] <- q[c(order(q$rat,q$langs)),"tots"] +#oq["resid"] <- q[c(order(q$rat,q$langs)),"resid"] + +oq.1 <- subset(oq, oq[,"tots"]>=100000) +oq.2 <- subset(oq, oq[,"tots"]>=10000 & oq[,"tots"]<100000) +p6 <- ggplot(oq.1, aes(x=langs, y=rat))+ + geom_bar(stat="identity", width = .75, alpha = .5, colour='blue') + + #geom_bar(stat="identity", width = .5, alpha = .5, colour='red', aes(x=langs, y=resid), data=oq.1)+ + scale_x_discrete(limits=oq.1$langs) + + scale_y_continuous(labels=comma)+ + geom_text(aes(x=langs,y=0,label=paste(round(100*rat,2), '%', sep='')),hjust=0, angle = 90, colour='black', data=oq.1) + + labs(y="Red difference from expected Unique articles. Blue percentage of articles Unique", title="Wikipedia article uniqueness percentage and difference in expected Uniqueness \n Minimum 100,000 articles") +p6 + + +p7 <- ggplot(oq.2, aes(x=langs, y=rat))+ + geom_bar(stat="identity", width = .75, alpha = oq.2$tots/max(oq.2$tots), colour='blue') + + scale_x_discrete(limits=oq.2$langs) + + scale_y_continuous(labels=comma)+ + geom_text(aes(x=langs,y=0,label=paste(round(100*rat,2), '%', sep='')),hjust=0, angle = 90, colour='black', data=oq.2) + + labs(y="Red difference from expected Unique articles. Blue percentage of articles Unique", title="Wikipedia article uniqueness percentage and difference in expected Uniqueness \n Maximum 100,000 articles") +p7 + + +#on to the pairs +#i'd like to make an upper triangular heatmap +filter2m.5k <- subset(filter2m, value > 5000) +pairs <- data.frame(matrix(ncol = 0, nrow = length(filter2m.5k$value))) +pairs["langs"] <- filter2m.5k[order(-filter2m.5k$value),"L1"] +pairs["tots"] <- filter2m.5k[order(-filter2m.5k$value),"value"] +pairs["hasEN"] <- grepl('en', pairs[,"langs"]) +pairs["isP"] <- rep(TRUE, nrow(pairs)) +#triples + +filter3m.1k <- subset(filter3m, value > 1000) +trips <- data.frame(matrix(ncol = 0, nrow = length(filter3m.1k$value))) +trips["langs"] <- filter3m.1k[order(-filter3m.1k$value),"L1"] + trips["tots"] <- filter3m.1k[order(-filter3m.1k$value),"value"] + trips["hasEN"] <- grepl('en', trips[,"langs"]) + trips["isP"] <- rep(FALSE, nrow(trips)) + +pairsplot <- ggplot(pairs[1:20,], aes(x=langs, y=tots, fill=factor(! hasEN)))+ + geom_bar(stat="identity", width=.75, size=3, alpha=1)+ + scale_x_discrete(limits=pairs[1:20,]$langs,labels=rep('',20))+ + scale_y_continuous(label=comma)+ + scale_fill_brewer("English in cluster?", labels=c('Yes', 'No'), palette="Set2")+ + geom_text(aes(x=langs,y=0, + label=paste(langs, ' ', prettyNum(tots, big.mark=","))), + hjust=0, angle = 90, colour=muted('pink'))+ + theme(axis.text.x = element_text(angle = 90, hjust = 1))+ + labs(x="Pair",y='') +pairsplot + + + +tripsplot <- ggplot(trips[1:20,], aes(x=langs, y=tots, fill=factor(! hasEN)))+ + geom_bar(stat="identity", width=.75, size=3, alpha=1)+ + scale_x_discrete(limits=trips[1:20,]$langs,labels=rep('',20))+ + scale_y_continuous(label=comma)+ + scale_fill_brewer("English in cluster?", labels=c('Yes', 'No'), palette="Set2")+ + geom_text(aes(x=langs,y=0, + label=paste(langs, ' ', prettyNum(tots, big.mark=","))), + hjust=0, angle = 90, colour=muted('pink'))+ + theme(axis.text.x = element_text(angle = 90, hjust = 1))+ + labs(x="Triple",y='') +tripsplot + + + +tmp <- ggplot_gtable(ggplot_build(tripsplot)) +leg <- which(sapply(tmp$grobs, function(x) x$name) == "guide-box") +legend <- tmp$grobs[[leg]] + +grid.arrange(arrangeGrob(pairsplot + theme(legend.position="none"), + tripsplot + theme(legend.position="none"), + main ="Comparison of Highest Co-occurring Languge Clusters \n Pairs and Triples", + left ="Number of Wikidata Items in Cluster"), legend, + widths=unit.c(unit(.85, "npc"), legend$width), nrow=1) + +tap <- rbind(pairs[1:20,], trips[1:20,]) +tapo <-data.frame(matrix(ncol = 0, nrow = length(tap$tots))) +tapo["ordr"] <- order(tap$tots) +tapo["tots"]<- tap[order(tap$tots),"tots"] +tapo["langs"]<- tap[order(tap$tots),"langs"] +tapo["isP"]<- tap[order(tap$tots),"isP"] +tapo["hasEN"]<- tap[order(tap$tots),"hasEN"] + +tapplot <- ggplot(tapo, aes(x=langs, y=tots, fill=factor(! isP), colour=factor(! hasEN)))+ +geom_bar(stat="identity", width=.5, size=3, alpha=.7)+ + scale_x_discrete(limits=tapo$langs)+ + scale_y_continuous(label=comma)+ + scale_colour_brewer("English in cluster?", labels=c('Yes', 'No'), palette="Set1")+ + scale_fill_brewer("Cluster type", labels=c('Pair', 'Triple'), palette="Set2")+ + #scale_fill_hue("Cluster type", labels=c('Pair', 'Triple'), l=50, h=c(315,135), c=80)+ + theme(axis.text.x = element_text(angle = 90, hjust = 1))+ + labs(y="Number of Wikidata Items in Cluster", x="Cluster", + title="Comparison of Highest Co-occurring Languges Clusters") + + + +tapplot + +#alln + + + +allnm["univgroup"] <- rep(1,length(allnm[,0])) +allnm["ninecol"] <- as.numeric(allnm[,"L1"]) %% 8 +allnm["rat"] <- allnm[,"value"] / sum(allnm[,"value"]) +allnm["sumsupto"] <- cumsum(allnm$rat) + + + +allnplot1 <- ggplot(allnm, aes(x=factor(univgroup), y=rat, fill=factor(ninecol))) + + scale_y_continuous(breaks=subset(allnm$sumsupto, allnm$sumsupto <0.971), + labels=paste(round(100*subset(allnm$sumsupto, allnm$sumsupto <0.971),2), '%', sep=''))+ + scale_x_discrete(breaks="none",label="none")+ + geom_bar(stat ="identity" ,position = "stack") + + geom_text(aes(y=sumsupto-(rat/2), + label=allnm$L1), + size=(1/as.numeric(allnm$L1)) *15 ) + + scale_fill_brewer(type = "seq", palette = 3,guide='none')+ + coord_cartesian(ylim=c(0, 1))+ + theme(title=element_blank()) + +allnplot2 <- ggplot(allnm, aes(x=factor(univgroup), y=rat, fill=factor(ninecol))) + + scale_y_continuous(breaks=subset(allnm$sumsupto, allnm$sumsupto <0.992), + labels=paste(round(100*subset(allnm$sumsupto, allnm$sumsupto <0.992),2), '%', sep=''))+ + scale_x_discrete(breaks="none",label="none")+ + geom_bar(stat ="identity" ,position = "stack") + + geom_text(aes(y=sumsupto-(rat/2), + label=allnm$L1), + size=(1/as.numeric(allnm$L1)) *60 ) + + scale_fill_brewer(type = "seq", palette = 3,guide='none')+ + coord_cartesian(ylim=c(0.9442275, 1))+ + theme(title=element_blank()) + +allnplot3 <- ggplot(allnm, aes(x=factor(univgroup), y=rat, fill=factor(ninecol))) + + scale_y_continuous(breaks=subset(allnm$sumsupto, allnm$sumsupto <0.9987), + labels=paste(round(100*subset(allnm$sumsupto, allnm$sumsupto <0.9987),2), '%', sep=''))+ + scale_x_discrete(breaks="none",label="none")+ + geom_bar(stat ="identity" ,position = "stack") + + geom_text(aes(y=sumsupto-(rat/2), + label=allnm$L1), + size=(1/as.numeric(allnm$L1)) *120 ) + + scale_fill_brewer(type = "seq", palette = 3,guide='none')+ + coord_cartesian(ylim=c(0.9951513, 1))+ + theme(title=element_blank()) + +allnplot4 <- ggplot(allnm, aes(x=factor(univgroup), y=rat, fill=factor(ninecol))) + + scale_y_continuous(breaks=subset(allnm$sumsupto, allnm$sumsupto <0.99995), + labels=paste(round(100*subset(allnm$sumsupto, allnm$sumsupto <0.99995),5), '%', sep=''))+ + scale_x_discrete(breaks="none",label="none")+ + geom_bar(stat ="identity" ,position = "stack", label="none") + + geom_text(aes(y=sumsupto-(rat/2), + label=allnm$L1), + size=(1/as.numeric(allnm$L1)) *360 ) + + scale_fill_brewer(type = "seq", palette = 3,guide='none')+ + coord_cartesian(ylim=c(0.9998727, 1))+ + theme(title=element_blank()) + +grid.arrange(allnplot1, allnplot2, allnplot3, allnplot4, ncol=4, + left="Composition of Wikidata Items", sub="Number of Language Links in Wikidata Item", main="Composition of Wikidata Items by Language Link Count") \ No newline at end of file diff --git a/alln.json b/alln.json new file mode 100644 index 0000000..1492e6f --- /dev/null +++ b/alln.json @@ -0,0 +1 @@ +{"1": 7972524, "2": 1578043, "3": 735573, "4": 410988, "5": 244478, "6": 147416, "7": 110289, "8": 87146, "9": 71751, "10": 60068, "11": 47381, "12": 39861, "13": 35213, "14": 29190, "15": 25439, "16": 22633, "17": 21041, "18": 19071, "19": 15768, "20": 14086, "21": 14027, "22": 14276, "23": 14195, "24": 13593, "25": 12421, "26": 11072, "27": 10455, "28": 8645, "29": 6976, "30": 5337, "31": 4352, "32": 3680, "33": 3209, "34": 2840, "35": 2567, "36": 2357, "37": 2224, "38": 2019, "39": 1862, "40": 1709, "41": 1594, "42": 1528, "43": 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"de*el*ksh": 1, "arc*en*he": 1, "en*jv*ms": 3, "es*id*ja": 2, "en*jv*no": 1, "en*mk*simple": 16, "en*ht*ka": 1, "bcl*ceb*en": 3, "be*mhr*ru": 4, "be*de*fr": 1, "be_x_old*de*fr": 1, "it*pl*scn": 23, "fi*gu*ne": 1, "en*hu*sk": 49, "en*hu*sh": 11, "it*ro*tr": 2, "en*hu*sl": 182, "en*hu*sr": 46, "en*hu*sq": 2, "en*hu*sw": 8, "en*hu*sv": 232, "de*nds*tr": 1, "en*mrj*rw": 6, "en*simple*yi": 4, "it*lt*tr": 1, "da*de*he": 1, "en*ka*ro": 7, "fi*lt*sv": 2, "en*fo*zh": 1, "bg*eo*es": 1, "en*ka*ru": 231, "fr*pms*pt": 5, "en*no*sco": 18, "da*de*hu": 4, "da*de*hr": 1, "en*ga*lt": 1, "br*cy*ru": 9, "it*ro*th": 1, "en*lt*sco": 1, "fr*lb*ru": 9, "en*nds_nl*zh": 1, "es*et*it": 6, "ka*nn*sv": 1, "lt*lv*pl": 8, "de*eo*uk": 5} \ No newline at end of file diff --git a/item-analysis.py b/item-analysis.py new file mode 100755 index 0000000..2b74726 --- /dev/null +++ b/item-analysis.py @@ -0,0 +1,344 @@ +#!/usr/bin/env python2 +import sys, bz2, time, gzip, os, urllib, re +import json +from collections import defaultdict +# This scripts creates the knowledge base and collects a few numbers by going through +# all dailies up to and the latest available dump. This usually runs a few hours. + +def log(txt) : + print txt + +log('Calculating Wikidata stats') + +start_time = time.time() + +# for dictionary creation +langs = [ 'en', 'de', 'hr', 'uz' ] + +# read the list of bots +log('Loading list of bots') +bots = [] +botsjson = urllib.urlopen('http://www.wikidata.org/w/api.php?action=query&list=allusers&augroup=bot&aulimit=500&format=json').read() +botsjson = eval(botsjson) +for bot in botsjson['query']['allusers'] : + bots.append(bot['name']) +log('List of bots: ' + str(bots)) + +linecount = 0 +charactercount = 0 + +# Items +itemcount = 0 +itemswithclaims = 0 +claimcount = 0 +claimsperitem = {} +claimswithrefs = 0 +refs = 0 +itemswithrefs = 0 +labelcount = 0 +descriptioncount = 0 +sitelinkcount = 0 +itemrevisioncount = 0 +botrevisioncount = 0 +revisionsperitem = {} +titleofmostclaims = '' +langlabels = {} +langdescriptions = {} +langsitelinks = {} + +# Properties +propertycount = 0 +propertylabelcount = 0 +propertydescriptioncount = 0 + +# General +pagecount = 0 +revisioncount = 0 +processedpages = set() +processedrevisions = set() + +#langsofitem +langsofitem = defaultdict(list) + +# if there is no data directory, create one +if not os.path.exists('data') : + os.makedirs('data') +os.chdir('data') + +# download the dumps directory file and figure out the date of the latest dump +log('Checking for the date of the last dump') +latestdump = '20121026' +for line in urllib.urlopen('http://dumps.wikimedia.org/wikidatawiki/') : + if not line.startswith('') : continue + date = line[27:35] + if not re.match('\d\d\d\d\d\d\d\d', date) : continue + log("Checking dump of " + date) + # check if dump is finished + finished = False + for md5 in urllib.urlopen('http://dumps.wikimedia.org/wikidatawiki/' + date + '/wikidatawiki-' + date + '-md5sums.txt') : + if md5.endswith('-pages-meta-history.xml.bz2' + "\n") : + finished = True + if finished : + latestdump = date +log('Latest dump has been on ' + latestdump) +#latestdump = '20130417' + +# download the latest stats if needed +if not os.path.exists('dump' + latestdump) : + os.makedirs('dump' + latestdump) +os.chdir('dump' + latestdump) +if not os.path.exists('site_stats.sql.gz') : + log('Downloading stats of the latest dump') + urllib.urlretrieve('http://dumps.wikimedia.org/wikidatawiki/' + latestdump + '/wikidatawiki-' + latestdump + '-site_stats.sql.gz', 'site_stats.sql.gz') + +# download the latest dump if needed +if not os.path.exists('pages-meta-history.xml.bz2') : + log('Downloading latest dump') + urllib.urlretrieve('http://dumps.wikimedia.org/wikidatawiki/' + latestdump + '/wikidatawiki-' + latestdump + '-pages-meta-history.xml.bz2', 'pages-meta-history.xml.bz2') + +# get the maxrevid of the latest dump +maxrevid = 0 +for line in gzip.open('site_stats.sql.gz'): + if not line.startswith('INSERT INTO') : continue + stats = eval(line[32:-2]) + maxrevid = int(stats[2]) +log('maxrevid of the latest dump: ' + str(maxrevid)) + +os.chdir('..') + +# check the dailies +dailies = [] +for line in urllib.urlopen('http://dumps.wikimedia.org/other/incr/wikidatawiki/') : + if not line.startswith('') : continue + date = line[27:35] + if not re.match('\d\d\d\d\d\d\d\d', date) : continue + dailies.append(date) + +# download the dailies in reversed order until the daily maxrevid is smaller than our maxrevid +stopdaily = '20121026' +lastdaily = 0 +for daily in reversed(dailies) : + log('Checking daily of ' + daily) + if not os.path.exists('daily' + daily) : + os.makedirs('daily' + daily) + os.chdir('daily' + daily) + if not os.path.exists('maxrevid.txt') : + urllib.urlretrieve('http://dumps.wikimedia.org/other/incr/wikidatawiki/' + daily + '/maxrevid.txt', 'maxrevid.txt') + dailymaxrevid = int(open('maxrevid.txt').read()) + if dailymaxrevid < maxrevid : + log('Daily ' + daily + ' is within latest dump') + stopdaily = daily + os.chdir('..') + break + if not os.path.exists('pages-meta-hist-incr.xml.bz2') : + log('Downloading daily ' + daily) + if urllib.urlopen('http://dumps.wikimedia.org/other/incr/wikidatawiki/' + daily + '/status.txt').read() == 'done' : + urllib.urlretrieve('http://dumps.wikimedia.org/other/incr/wikidatawiki/' + daily + '/wikidatawiki-' + daily + '-pages-meta-hist-incr.xml.bz2', 'pages-meta-hist-incr.xml.bz2') + log('Done downloading daily ' + daily) + if lastdaily == 0 : lastdaily = daily + else : + log('Daily not done yet - download aborted') + os.chdir('..') + +def processfile(file) : + global linecount + global charactercount + + # Items + global itemcount + global itemswithclaims + global claimcount + global claimsperitem + global claimswithrefs + global refs + global itemswithrefs + global mostclaims + global labelcount + global descriptioncount + global sitelinkcount + global itemrevisioncount + global botrevisioncount + global titleofmostclaims + global langlabels + global langdescriptions + global langsitelinks + + # Properties + global propertycount + global propertylabelcount + global propertydescriptioncount + + # General + global revisioncount + + #langsofitem + global langsofitem + + # local variables + title = '' + item = False + property = False + newrev = False + newtitle = False + val = {} + revid = 0 + + for line in file : + linecount += 1 + charactercount += len(line) + if linecount % 1000000 == 0 : log(str(linecount / 1000000)) + + # starts a new page + if line == ' \n' : + title = '' + item = False + property = False + newrev = False + newtitle = False + val = {} + content = '' + revid = 0 + + if line == ' \n' : + revid = 0 + + # title + if line.startswith(' ') : + title = line[11:-9] + item = title.startswith('Q') + property = title.startswith('Property:P') + if title not in processedpages : + newtitle = True + processedpages.add(title) + + + if line.startswith(' <id>') : + revid = line[10:-6] + if revid not in processedrevisions : + newrev = True + processedrevisions.add(revid) + + # finished a page + if line == ' </page>\n' : + if not newtitle : continue + + if item: + content = content.replace('"', '"') + val = eval(content) + + #langsofitem begin + try: + for langlink in val['links']: + langsofitem[title].append(langlink[:-4]) + except KeyError: + print val + ri = raw_input() +#print langsofitem + #ri = raw_input + + if line == ' </revision>\n' : + if not newrev : continue + revisioncount += 1 + if item: + itemrevisioncount += 1 + if line.startswith(' <username>') : + if not newrev : continue + username = line[18:-12] + if username in bots: + botrevisioncount += 1 + if line.startswith(' <timestamp>') : + timestamp = line[17:-23] + + # checks for anomalies + if line.startswith(' <text xml:space="preserve">') : + if item or property : + if not line.endswith('</text>\n') : + log(line) + else : + content = line[33:-8] + #if linecount >= 1000000 : break + +kb = open('kb.txt', 'w') +kb.write('# ' + str(lastdaily) + "\n") +dic = dict() +for lang in langs: + dic[lang] = open('dict-' + lang + '.txt', 'w') + +# process the dailies, starting with the newest +files = 0 +for daily in reversed(dailies) : + if daily == stopdaily : break + log('Analysing daily ' + daily) + os.chdir('daily' + daily) + if not os.path.exists('pages-meta-hist-incr.xml.bz2') : + log('No data available') + os.chdir('..') + continue + file = bz2.BZ2File('pages-meta-hist-incr.xml.bz2') + + processfile(file) + + os.chdir('..') + +# process the dump +log('Analysing dump ' + str(latestdump)) +os.chdir('dump' + latestdump) +file = bz2.BZ2File('pages-meta-history.xml.bz2') +processfile(file) +os.chdir('..') +langsofitemfile = open('langsofitem.json', 'w') +json.dump(langsofitem, langsofitemfile, indent=4) +langsofitemfile.close() + +kb.close() +for lang in langs: + dic[lang].close() + +os.chdir('..') + +# if there is no results directory, create one +if not os.path.exists('results') : + os.makedirs('results') + +output = open('results/daily.html', 'w') +output.write('<!doctype html>' + "\n") +output.write('<html>' + "\n") +output.write(' <head>' + "\n") +output.write(' <meta charset="utf-8">' + "\n") +output.write(' <title>Analysis results' + "\n") +output.write(' ' + "\n") +output.write(' ' + "\n") +output.write(' ' + "\n") +output.write('

Analysis results on the Wikidata dump

' + "\n") +output.write('

' + "\n") +output.write(' As of: ' + str(lastdaily) + '
' + "\n") +output.write(' Pages: ' + str(len(processedpages)) + '
' + "\n") +output.write(' Items: ' + str(itemcount) + '
' + "\n") +output.write(' Items with claims: ' + str(itemswithclaims) + '
' + "\n") +output.write(' Claims: ' + str(claimcount) + '
' + "\n") +output.write(' Claims per item: ' + str(claimsperitem) + '
' + "\n") +output.write(' References: ' + str(refs) + '
' + "\n") +output.write(' Claims with references: ' + str(claimswithrefs) + '
' + "\n") +output.write(' Items with references: ' + str(itemswithrefs) + '
' + "\n") +output.write(' Item with most claims: ' + titleofmostclaims + '
' + "\n") +output.write(' Properties: ' + str(propertycount) + '
' + "\n") +output.write(' Links: ' + str(sitelinkcount) + '
' + "\n") +output.write(' Links per language: ' + str(langsitelinks) + '
' + "\n") +output.write(' Labels: ' + str(labelcount) + '
' + "\n") +output.write(' Labels per language: ' + str(langlabels) + '
' + "\n") +output.write(' Labels of properties: ' + str(propertylabelcount) + '
' + "\n") +output.write(' Descriptions: ' + str(descriptioncount) + '
' + "\n") +output.write(' Descriptions per language: ' + str(langdescriptions) + '
' + "\n") +output.write(' Descriptions of properties: ' + str(propertydescriptioncount) + '
' + "\n") +output.write(' Revisions: ' + str(revisioncount) + '
' + "\n") +output.write(' Item revisions: ' + str(itemrevisioncount) + '
' + "\n") +output.write(' Bot revisions: ' + str(botrevisioncount) + '
' + "\n") +output.write(' Lines: ' + str(linecount) + '
' + "\n") +output.write(' Characters: ' + str(charactercount) + '
' + "\n") +output.write(' Time: ' + str(time.time() - start_time) + ' seconds
' + "\n") +output.write('

' + "\n") +output.write(' ' + "\n") +output.write('' + "\n") +output.close() + +log('Done.') diff --git a/setmaker.py b/setmaker.py new file mode 100644 index 0000000..38b74e4 --- /dev/null +++ b/setmaker.py @@ -0,0 +1,99 @@ +import json +from collections import defaultdict + +alllangs = set([u'gv', u'gu', u'scn', u'km', u'wuu', u'cdo', u'sco', u'kbd', u'gd', u'fiu_vro', u'ga', u'gn', u'gl', u'als', u'lg', u'pnb', u'lb', u'szl', u'vep', u'la', u'ln', u'frp', u'tt', u'tr', u'ts', u'li', u'lv', u'bat_smg', u'lt', u'vec', u'th', u'ti', u'tg', u'te', u'ksh', u'pcd', u'ta', u'yi', u'ceb', u'yo', u'de', u'da', u'bxr', u'dz', u'hif', u'rm', u'dv', u'be_x_old', u'nov', u'kaa', u'bar', u'ang', u'fur', u'kn', u'eml', u'bpy', u'crh', u'nds_nl', u'diq', u'ho', u'el', u'eo', u'en', u'zh', u'pms', u'ee', u'tpi', u'arz', u'rmy', u'mdf', u'za', u'mh', u'arc', u'uk', u'eu', u'et', u'tet', u'es', u'ru', u'rw', u'mus', u'kl', u'ha', u'ak', u'bm', u'new', u'rn', u'ro', u'dsb', u'bn', u'hsb', u'be', u'bg', u'myv', u'ba', u'wa', u'ast', u'wo', u'got', u'jv', u'bo', u'bh', u'bi', u'rue', u'hak', u'tum', u'br', u'bs', u'lez', u'ja', u'om', u'glk', u'ace', u'ng', u'ilo', u'ty', u'oc', u'ltg', u'st', u'lo', u'krc', u'nds', u'os', u'xmf', u'udm', u'xh', u'ch', u'co', u'nso', u'simple', u'bjn', u'ca', u'lmo', u'ce', u'cy', u'ab', u'cs', u'cr', u'cv', u'cu', u've', u'koi', u'ps', u'fj', u'or', u'srn', u'pt', u'to', u'sm', u'ext', u'tl', u'cho', u'frr', u'chr', u'pa', u'xal', u'chy', u'pi', u'war', u'pl', u'tk', u'hz', u'hy', u'nrm', u'hr', u'iu', u'pfl', u'ht', u'hu', u'gan', u'hi', u'vls', u'tw', u'gag', u'an', u'bug', u'kj', u'he', u'mg', u'roa_tara', u'uz', u'ml', u'mo', u'mn', u'mi', u'as', u'mk', u'ur', u'zea', u'mt', u'stq', u'ms', u'mr', u'zh_classical', u'ku', u'ug', u'mwl', u'my', u'ki', u'pih', u'mhr', u'aa', u'sah', u'ss', u'af', u'tn', u'vi', u'is', u'am', u'it', u'vo', u'ii', u'ay', u'ik', u'ar', u'lbe', u'zh_yue', u'io', u'av', u'ia', u'haw', u'az', u'ie', u'id', u'ig', u'pap', u'qu', u'nl', u'nn', u'no', u'na', u'nah', u'ne', u'lij', u'csb', u'ny', u'nap', u'zh_min_nan', u'cbk_zam', u'map_bms', u'pag', u'zu', u'so', u'pam', u'nv', u'kv', u'kab', u'fr', u'roa_rup', u'lad', u'fy', u'pnt', u'fa', u'ks', u'ff', u'fi', u'mzn', u'ky', u'fo', u'bcl', u'ka', u'kg', u'ckb', u'kk', u'sr', u'sq', u'min', u'ko', u'sv', u'su', u'jbo', u'sk', u'kr', u'si', u'sh', u'kw', u'sn', u'mrj', u'sl', u'sc', u'sa', u'sd', u'sg', u'sw', u'se', u'pdc']) +#to keep order on alllangs +alllangs = list(alllangs) +alllangs.sort() + + +langsofitemjson = open('langsofitem.json', 'r') +langsofitem = json.load(langsofitemjson) + +#our main datastructure +intersections = defaultdict(int) +numintersections = defaultdict(int) +langtotals = defaultdict(int) + +def makeKey(langlist): + returnkey = '' + for lang in alllangs: + if lang in langlist: + returnkey += '1' + else: + returnkey += '0' + return returnkey + +def numOfLangs(binkey): + #this is the digitsum + return sum(map(int, binkey)) + + +def reverseKey(langsofitem): + for item, langlist in langsofitem.iteritems(): + itemKey = makeKey(langlist) + intersections[itemKey] += 1 + #print intersections + #print len(intersections) + +def reverseKeyFilterN(langsofitem, n): + total, ngrams = 0, 0 + for item, langlist in langsofitem.iteritems(): + total += 1 + #update lang totals + for lang in langlist: + langtotals[lang] += 1 + #record the ones of lenght n + if len(langlist) == n: + ngrams += 1 + langlist.sort() + langlistkey = reduce(lambda x,y: x+'*'+y,langlist) + intersections[langlistkey] += 1 + print len(intersections) + + if n == 1: + for lang in alllangs: + if not lang in intersections: + print lang + intersections[lang] += 0 + + numintersections[n] = ngrams + print 'total', total, 'ngrams', ngrams + +def exportToJSON(intersections): + filternjson = open('filtern.json', 'w') + json.dump(obj=intersections, fp=filternjson) + #langtotalsjson = open('langtotals.json', 'w') + #json.dump(obj=langtotals, fp=langtotalsjson) + +def exportToR(intersections): + csvfile = open('langsofitemR.csv', 'w') + line = '' + #write headers + for lang in alllangs: + if line == '': + line += lang + else: + line += (', ' + lang) + line += '\n' + csvfile.write(line) + line = '' + for binaryRepresentation, count in intersections.iteritems(): + for binary in binaryRepresentation: + line += (binary + ',') + line += (count + '\n') + + + +#reverseKey(langsofitem) +#reverseKeyFilterN(langsofitem, 2) +''' +for i in range(1, len(alllangs)): + print i + reverseKeyFilterN(langsofitem, i) +exportToJSON(numintersections) +''' +reverseKeyFilterN(langsofitem, 3) +exportToJSON(intersections) +#exportToR(intersections) + \ No newline at end of file