termiteplot für lda
This commit is contained in:
jannis.grundmann
parent
3137dc6e54
commit
6ea03b2f65
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@ -29,7 +29,7 @@ global REGEX_SPECIALCHAR
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global WORDS
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REGEX_SPECIALCHAR = r'[`\-=~%^&*()_+\[\]{};\'\\:"|</>]' #+r',.'
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REGEX_SPECIALCHAR = r'[`\=~%^&*()_+\[\]{};\'"|</>]' #+r',.-\\:' #+r',.?!'
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WORDS= {}
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@ -99,7 +99,7 @@ def clean(stringstream,autocorrect=False):
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string = textacy.preprocess.fix_bad_unicode(string.lower(), normalization=u'NFC')
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# seperate_words_on_regex:
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string = " ".join(re.compile(REGEX_SPECIALCHAR).split(string)) #frage ,.?!
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string = " ".join(re.compile(REGEX_SPECIALCHAR).split(string))
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#normalize whitespace
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string = textacy.preprocess.normalize_whitespace(string)
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@ -41,8 +41,8 @@ filename=topicModelTickets.log
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[de_corpus]
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input=M42-Export/Tickets_small.csv
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#input=M42-Export/de_tickets.csv
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#input=M42-Export/Tickets_small.csv
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input=M42-Export/de_tickets.csv
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path=corpi/
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@ -62,12 +62,11 @@ metaliste=TicketNumber,Subject,CreatedDate,categoryName,Impact,Urgency,BenutzerI
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[preprocessing]
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ents2keep=WORK_OF_ART,ORG,PRODUCT,LOC
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#ents2keep=WORK_OF_ART,ORG,PRODUCT,LOC
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custom_words=geehrt,dame,herr,hilfe,problem,lauten,bedanken,voraus,hallo,gerne,freundlich,fragen,fehler,bitten,ehre,lieb,helfen,versuchen,unbestimmt,woche,tadelos,klappen,mittlerweile,bekommen,erreichbar,gruss,auffahren,vorgang,hinweis,institut,universitaet,name,gruss,id,erfolg,mail,folge,nummer,team,fakultaet,email,absender,tu,versenden,vorname,message,service,strasse,prozess,portal,raum,personal,moeglichkeit,fremd,wende,rueckfrage,stehen,verfuegung,funktionieren,kollege,pruefen,hoffen
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custom_words=geehrt,dr,not,frage,betreff,gerne,dame,herr,frau,hilfe,moeglichkeit,beste,freuen,voraus,problem,lauten,bedanken,voraus,hallo,gerne,freundlich,fragen,fehler,bitten,ehre,lieb,liebe,gruesse,helfen,versuchen,unbestimmt,woche,tadelos,klappen,mittlerweile,bekommen,erreichbar,gruss,auffahren,vorgang,hinweis,name,gruss,id,erfolg,folge,team,absender,versenden,vorname,strasse,prozess,portal,moeglichkeit,fremd,wende,rueckfrage,stehen,verfuegung,funktionieren,pruefen,hoffen,ok
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#lemmatize=True
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[topicmodeling]
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File diff suppressed because it is too large
Load Diff
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@ -0,0 +1,622 @@
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a
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ab
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aber
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ach
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acht
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achte
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trotz
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achten
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achter
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achtes
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ag
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alle
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allein
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allem
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allen
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aller
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allerdings
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alles
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allgemeinen
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als
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also
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am
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an
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ander
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andere
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anderem
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anderen
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anderer
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anderes
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anderm
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andern
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anderr
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anders
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au
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auch
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auf
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aus
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ausser
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ausserdem
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außer
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außerdem
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b
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bald
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bei
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beide
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beiden
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beim
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beispiel
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bekannt
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bereits
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besonders
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besser
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besten
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bin
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bis
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bisher
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bist
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c
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d
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d.h
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da
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dabei
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dadurch
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dafür
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dagegen
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daher
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dahin
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dahinter
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damals
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damit
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danach
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daneben
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dank
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dann
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daran
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darauf
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daraus
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darf
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darfst
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darin
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darum
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darunter
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darüber
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das
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dasein
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daselbst
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dass
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dasselbe
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davon
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davor
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dazu
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dazwischen
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daß
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dein
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deine
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deinem
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deinen
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deiner
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deines
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dem
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dementsprechend
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demgegenüber
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demgemäss
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demgemäß
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demselben
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demzufolge
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den
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denen
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denn
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denselben
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der
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deren
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derer
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derjenige
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derjenigen
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dermassen
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dermaßen
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derselbe
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derselben
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des
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deshalb
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desselben
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dessen
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deswegen
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dich
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die
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diejenige
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diejenigen
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dies
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diese
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dieselbe
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dieselben
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diesem
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diesen
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dieser
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dieses
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dir
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doch
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dort
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drei
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drin
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dritte
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dritten
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dritter
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drittes
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du
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durch
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durchaus
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durfte
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durften
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dürfen
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dürft
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e
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eben
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ebenso
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ehrlich
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ei
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ei,
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eigen
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eigene
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eigenen
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eigener
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eigenes
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ein
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einander
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eine
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einem
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einen
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einer
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eines
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einig
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einige
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einigem
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einigen
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einiger
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einiges
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einmal
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eins
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elf
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en
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ende
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endlich
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entweder
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er
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ernst
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erst
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erste
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ersten
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erster
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erstes
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es
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etwa
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etwas
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euch
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euer
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||||
eure
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eurem
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euren
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eurer
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eures
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f
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folgende
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früher
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fünf
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fünfte
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fünften
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fünfter
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fünftes
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für
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g
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gab
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ganz
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ganze
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ganzen
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ganzer
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ganzes
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gar
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gedurft
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gegen
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gegenüber
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gehabt
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gehen
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geht
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gekannt
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gekonnt
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gemacht
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gemocht
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||||
gemusst
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genug
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gerade
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gern
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gesagt
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geschweige
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gewesen
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gewollt
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geworden
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gibt
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ging
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gleich
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gott
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gross
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grosse
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grossen
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grosser
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grosses
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groß
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große
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großen
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großer
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großes
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gut
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gute
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guter
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gutes
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h
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hab
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habe
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haben
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habt
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hast
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hat
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hatte
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hatten
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hattest
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hattet
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heisst
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her
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heute
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hier
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hin
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hinter
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hoch
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hätte
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hätten
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i
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ich
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ihm
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ihn
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ihnen
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ihr
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ihre
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ihrem
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ihren
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ihrer
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ihres
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im
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immer
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in
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indem
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infolgedessen
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ins
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irgend
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ist
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j
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||||
ja
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jahr
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jahre
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||||
jahren
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je
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jede
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jedem
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jeden
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jeder
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jedermann
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jedermanns
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jedes
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jedoch
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||||
jemand
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||||
jemandem
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jemanden
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jene
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jenem
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jenen
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jener
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||||
jenes
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jetzt
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k
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||||
kam
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kann
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||||
kannst
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kaum
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kein
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keine
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||||
keinem
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keinen
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keiner
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keines
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kleine
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kleinen
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kleiner
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kleines
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kommen
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kommt
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konnte
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konnten
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kurz
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können
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könnt
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könnte
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l
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lang
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lange
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leicht
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leide
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lieber
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los
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m
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machen
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macht
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machte
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mag
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magst
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mahn
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mal
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man
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manche
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manchem
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manchen
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mancher
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manches
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mann
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mehr
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mein
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meine
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meinem
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meinen
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meiner
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meines
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mensch
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menschen
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mich
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mir
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mit
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mittel
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mochte
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||||
mochten
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morgen
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muss
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musst
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musste
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mussten
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muß
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mußt
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möchte
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mögen
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möglich
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mögt
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müssen
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müsst
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müßt
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n
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na
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nach
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nachdem
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nahm
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natürlich
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neben
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nein
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neue
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neuen
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neun
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neunte
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neunten
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neunter
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neuntes
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nicht
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nichts
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nie
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niemand
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niemandem
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niemanden
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noch
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nun
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nur
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o
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ob
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oben
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oder
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offen
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oft
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ohne
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ordnung
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p
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q
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r
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recht
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rechte
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rechten
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rechter
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rechtes
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richtig
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rund
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s
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sa
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sache
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sagt
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||||
sagte
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sah
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||||
satt
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schlecht
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schluss
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schon
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sechs
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||||
sechste
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sechsten
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sechster
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sechstes
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sehr
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sei
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seid
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||||
seien
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sein
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||||
seine
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||||
seinem
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||||
seinen
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||||
seiner
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||||
seines
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||||
seit
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||||
seitdem
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selbst
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sich
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sie
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||||
sieben
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||||
siebente
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||||
siebenten
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||||
siebenter
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||||
siebentes
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sind
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so
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solang
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solche
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solchem
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||||
solchen
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||||
solcher
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||||
solches
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soll
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||||
sollen
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||||
sollst
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||||
sollt
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||||
sollte
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sollten
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sondern
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sonst
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soweit
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||||
sowie
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später
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startseite
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||||
statt
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steht
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||||
suche
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t
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tag
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tage
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tagen
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tat
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teil
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tel
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tritt
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trotzdem
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tun
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u
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uhr
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um
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und
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||||
und?
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uns
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unse
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||||
unsem
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||||
unsen
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unser
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||||
unsere
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||||
unserer
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||||
unses
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unter
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v
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vergangenen
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viel
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viele
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vielem
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vielen
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vielleicht
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vier
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||||
vierte
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||||
vierten
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||||
vierter
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||||
viertes
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vom
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von
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vor
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w
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wahr?
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wann
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||||
war
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||||
waren
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||||
warst
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||||
wart
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||||
warum
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||||
was
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weg
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||||
wegen
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||||
weil
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||||
weit
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||||
weiter
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||||
weitere
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||||
weiteren
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||||
weiteres
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welche
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welchem
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||||
welchen
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||||
welcher
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||||
welches
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||||
wem
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||||
wen
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||||
wenig
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||||
wenige
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||||
weniger
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||||
weniges
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||||
wenigstens
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||||
wenn
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wer
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werde
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||||
werden
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werdet
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weshalb
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wessen
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wie
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||||
wieder
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wieso
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will
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willst
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wir
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||||
wird
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wirklich
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||||
wirst
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||||
wissen
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wo
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woher
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||||
wohin
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||||
wohl
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||||
wollen
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||||
wollt
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||||
wollte
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||||
wollten
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||||
worden
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wurde
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||||
wurden
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||||
während
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||||
währenddem
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||||
währenddessen
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||||
wäre
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würde
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||||
würden
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x
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||||
y
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||||
z
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||||
z.b
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zehn
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||||
zehnte
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||||
zehnten
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||||
zehnter
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||||
zehntes
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||||
zeit
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||||
zu
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zuerst
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||||
zugleich
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||||
zum
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||||
zunächst
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||||
zur
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zurück
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zusammen
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zwanzig
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zwar
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||||
zwei
|
||||
zweite
|
||||
zweiten
|
||||
zweiter
|
||||
zweites
|
||||
zwischen
|
||||
zwölf
|
||||
über
|
||||
überhaupt
|
||||
übrigens
|
||||
File diff suppressed because it is too large
Load Diff
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14
main.py
14
main.py
|
|
@ -1,5 +1,6 @@
|
|||
# -*- coding: utf-8 -*-
|
||||
|
||||
import matplotlib
|
||||
matplotlib.use('Agg')
|
||||
import time
|
||||
import corporization
|
||||
import preprocessing
|
||||
|
|
@ -21,12 +22,17 @@ logprint("")
|
|||
cleaning.main()
|
||||
logprint("")
|
||||
|
||||
preprocessing.main()
|
||||
preprocessing.main() # ~5h
|
||||
logprint("")
|
||||
|
||||
topicModeling.main(use_raw=False)
|
||||
#topicModeling.main(use_raw=False,algorithm="llda")
|
||||
logprint("")
|
||||
topicModeling.main(use_raw=True)
|
||||
|
||||
#topicModeling.main(use_raw=True)
|
||||
|
||||
topicModeling.main(use_raw=False,algorithm="lda")
|
||||
logprint("")
|
||||
|
||||
logprint("")
|
||||
|
||||
end = time.time()
|
||||
|
|
|
|||
|
|
@ -201,10 +201,16 @@ def save_corpus(corpus, corpus_path, corpus_name):
|
|||
|
||||
# save meta
|
||||
metapath = corpus_path + corpus_name + "_meta.json"
|
||||
textacy.fileio.write_json_lines((doc.metadata for doc in corpus), metapath)
|
||||
|
||||
#meta_gen = (doc.metadata.update({"index": doc.corpus_index}) for doc in corpus)
|
||||
meta_gen = gen_meta(corpus)
|
||||
textacy.fileio.write_json_lines(meta_gen, metapath)
|
||||
|
||||
|
||||
def gen_meta(corpus):
|
||||
for doc in corpus:
|
||||
meta = doc.metadata
|
||||
meta.update({"index": doc.corpus_index})
|
||||
yield meta
|
||||
|
||||
|
||||
|
||||
|
|
@ -242,7 +248,7 @@ def load_corpus(corpus_path, corpus_name, lang="de"):
|
|||
contentpath = corpus_path + corpus_name + "_content.bin"
|
||||
plainpath = corpus_path + corpus_name + "_content.json"
|
||||
metapath = corpus_path + corpus_name + "_meta.json"
|
||||
|
||||
"""
|
||||
try:
|
||||
spacy_docs = textacy.fileio.read_spacy_docs(corpus.spacy_vocab, contentpath)
|
||||
metadata_stream = textacy.fileio.read_json_lines(metapath)
|
||||
|
|
@ -251,15 +257,18 @@ def load_corpus(corpus_path, corpus_name, lang="de"):
|
|||
corpus.add_doc(
|
||||
textacy.Doc(spacy_doc, lang=corpus.spacy_lang, metadata=metadata))
|
||||
except:
|
||||
"""
|
||||
# neu init!!
|
||||
corpus = textacy.Corpus(parser)
|
||||
#corpus = textacy.Corpus(parser)
|
||||
|
||||
plain_stream = textacy.fileio.read_json_lines(plainpath) # yields {int : str}
|
||||
metadata_stream = textacy.fileio.read_json_lines(metapath)
|
||||
|
||||
for plain, metadata in zip(plain_stream, metadata_stream):
|
||||
corpus.add_doc(
|
||||
textacy.Doc(plain["content"], lang=corpus.spacy_lang, metadata=metadata))
|
||||
if plain["index"] == metadata["index"]:
|
||||
corpus.add_doc(textacy.Doc(plain["content"], lang=corpus.spacy_lang, metadata=metadata))
|
||||
else:
|
||||
raise IndexError
|
||||
|
||||
|
||||
return corpus, corpus.spacy_lang
|
||||
|
|
|
|||
|
|
@ -247,7 +247,7 @@ def processContentstream2(textstream, parser, token_filterlist=None):
|
|||
tokens = filterTokens(tokens, token_filterlist)
|
||||
|
||||
# post parse
|
||||
tokens = [postparse(tok) for tok in tokens] #todo informationsverlust!
|
||||
tokens = [postparse(tok) for tok in tokens] #todo informationsverlust von pos,tag etc.!
|
||||
|
||||
yield " ".join(tokens)
|
||||
|
||||
|
|
@ -415,7 +415,7 @@ def preprocessCorpus(corpus_path, filter_tokens, clean_in_meta, lang="de", print
|
|||
|
||||
|
||||
#save corpus as labled, plain text
|
||||
plainpath = FILEPATH + config.get("de_corpus", "path") + "labled_lines.txt"
|
||||
plainpath = FILEPATH + config.get("de_corpus", "path") + "pre_labled_lines.txt"
|
||||
textacy.fileio.write_file_lines(labledCorpiLines(corpus),filepath=plainpath )
|
||||
|
||||
return corpus
|
||||
|
|
@ -438,6 +438,8 @@ def main():
|
|||
NOUNS = load_obj(path2nouns_list)
|
||||
VORNAMEN = load_obj(path2firstnameslist)
|
||||
|
||||
custom_words = config.get("preprocessing","custom_words").split(",")
|
||||
|
||||
filter_tokens = [
|
||||
# removeENT(["PERSON"]),
|
||||
|
||||
|
|
@ -447,8 +449,8 @@ def main():
|
|||
|
||||
removePOS(["PUNCT", "SPACE", "NUM"]),
|
||||
|
||||
# removeWords(de_stop_words + custom_words),
|
||||
removeWords(DE_STOP_WORDS),
|
||||
removeWords(DE_STOP_WORDS + custom_words),
|
||||
#removeWords(DE_STOP_WORDS),
|
||||
|
||||
remove_long_words(),
|
||||
remove_short_words(),
|
||||
|
|
|
|||
23
testra.py
23
testra.py
|
|
@ -22,11 +22,7 @@ from miscellaneous import *
|
|||
|
||||
# ssh madonna "nohup /usr/bin/python3 -u /home/jannis.grundmann/PycharmProjects/topicModelingTickets/topicModeling.py &> /home/jannis.grundmann/PycharmProjects/topicModelingTickets/printout_topicModeling.log &"
|
||||
|
||||
parser = spacy.load("de")
|
||||
|
||||
|
||||
|
||||
"""
|
||||
# load config
|
||||
config_ini = FILEPATH + "config.ini"
|
||||
|
||||
|
|
@ -71,10 +67,23 @@ corpi.add_texts(
|
|||
makecontent(testcontetn),
|
||||
makemeta(testmetda)
|
||||
)
|
||||
corpus_de_path = "/home/jannis.grundmann/PycharmProjects/topicModelingTickets/test/"
|
||||
rawCorpus_name = "de_test_ticket"
|
||||
print(corpi)
|
||||
|
||||
|
||||
save_corpus(corpi,corpus_path="/home/jannis.grundmann/PycharmProjects/topicModelingTickets/test",corpus_name="test")
|
||||
|
||||
bla = "uni mail account adresse woche falsch laufen schicken gerne januar betreff herr nachricht gruesse dezernat liebe datum freitag anfrage dienstag unicard karte abholen defekt bibliothek abholung dezember beantragung status gerne portal email nummer service id vorname prozess dez schauen eg rechner mitarbeiterin benutzerkonto oktober wissenschaftliche projekt fr download hilfskraft verantwortliche link dringend antrag schnelle arbeitsplatz november admin rahmen stand geschickt server outlook ordner bild konto postfach campus hi ueberpruefung sued beste daten freuen semester login benutzer gerne erstellen stelle frage system boss moeglichkeit student schoen spam alias geld vertrag juni ansprechpartner telefon raum einrichtung gebaeude telefonbuch abteilung element eintrag nutzer raum pc gerne lehrstuhl voraus fakultaet verfuegung herzliche drucker erreichen tlaptop kabel problem klaerung url adapter feedback koeln grundsaetzlich kaufmann problem fehler verbindung anhang meldung client netz netzwerk wenden funktionieren liebe mitarbeiter unterstuetzung aktuell herr benoetigt raumplanung gb weber vorab ueckmeldung software lizenz programm kurze urlaub gerne installation dankbar informieren team service problem loesung bestellung verlaengern verteiler alte aendern februar oeffnen update pdf browser notwendig fenster schulung beginn wege nord tkurs frage studierende personen teilnehmer standort gerne herunterladen voraus zusenden ews veranstaltung datei iso text umstellung absender message date html arbeit kaiser erfolgreich thema ablauf art at einfuehrung umfrage cloud zugang zugreifen montag probleme kollegin profil server handy web file ticket drucker einrichten senden nr mittwoch card mitteilen nrw kontakt mail fax universitaet it institut hardware hinweis fakultaet not strasse loeschen liste funktion auftrag zeitraum verwaltung angebot vorgehen entfernen moeglichkeit gefunden benutzername informatik gruppe eingabe nachname chemie dame b. angepasst name schoene abt post zukommen verlaengerung sommersemester fehlen namensaenderung auskunft tu dr prof pruefung herr namen fakultaet bereich lehrstuhl installieren buero ok anschluss maerz theologie notebook herr berechtigung master vorbeikommen passwort anmelden account hilfe helfen uniaccount anmeldung kennwort problem boss zugriff referat screenshot support laufwerk bildschirm super tastatur button auswaehlen"
|
||||
bla = bla.split()
|
||||
print(len(bla))
|
||||
print(len(set(bla)))
|
||||
print()
|
||||
|
||||
x = {'a':1, 'b': 2}
|
||||
y = {'b':10, 'c': 11}
|
||||
z = x.update(y)
|
||||
|
||||
print(x)
|
||||
|
||||
"""
|
||||
#save_corpusV2(corpi,corpus_path=corpus_de_path,corpus_name=rawCorpus_name)
|
||||
|
||||
#textacy.fileio.write_file_lines(corpus2Text(corpi), filepath=corpus_de_path+"plain.txt")
|
||||
|
|
|
|||
138
topicModeling.py
138
topicModeling.py
|
|
@ -73,12 +73,15 @@ def textacyTopicModeling(ngrams, min_df, max_df, corpus, n_topics, topicModel='l
|
|||
|
||||
start = time.time()
|
||||
|
||||
top_topic_words = 10
|
||||
top_topic_words = 7
|
||||
top_document_labels_per_topic = 5
|
||||
|
||||
# http://textacy.readthedocs.io/en/latest/api_reference.html#textacy.tm.topic_model.TopicModel.get_doc_topic_matrix
|
||||
weighting = ('tf' if topicModel == 'lda' else 'tfidf')
|
||||
|
||||
|
||||
|
||||
|
||||
####################'####################
|
||||
|
||||
|
||||
|
|
@ -120,16 +123,31 @@ def textacyTopicModeling(ngrams, min_df, max_df, corpus, n_topics, topicModel='l
|
|||
print()
|
||||
print()
|
||||
|
||||
|
||||
# termite plot
|
||||
n_terms = int(n_topics*top_topic_words)
|
||||
sort_terms_by = 'seriation' #'seriation', 'weight', 'index', 'alphabetical'
|
||||
rank_terms_by = 'corpus' # 'corpus', 'topic'
|
||||
model.termite_plot(doc_term_matrix, id2term,
|
||||
|
||||
n_terms=n_terms,
|
||||
sort_terms_by=sort_terms_by,
|
||||
rank_terms_by=rank_terms_by+'_weight',
|
||||
|
||||
save="/home/jannis.grundmann/PycharmProjects/topicModelingTickets/results/{}_{}_{}_{}_{}.png".format(topicModel,n_topics,n_terms,sort_terms_by,rank_terms_by))
|
||||
|
||||
|
||||
|
||||
end = time.time()
|
||||
logprint("\n\n\nTime Elapsed Topic Modeling with {1}:{0} min\n\n".format((end - start) / 60, topicModel))
|
||||
|
||||
|
||||
def jgibbsLLDA(corpus, path2save_results, top_topic_words=15, add_default_topic=False):
|
||||
def jgibbsLLDA(corpus, path2save_results, top_topic_words=7, add_default_topic=False):
|
||||
##################### LLDA Topic Modeling via JGibbsLabledLDA ##############################################
|
||||
|
||||
start = time.time()
|
||||
|
||||
# build citionary of ticketcategories
|
||||
# build dictionary of ticketcategories
|
||||
labelist = []
|
||||
|
||||
for texdoc in corpus.get(lambda texdoc: texdoc.metadata["categoryName"] not in labelist):
|
||||
|
|
@ -161,7 +179,7 @@ def jgibbsLLDA(corpus, path2save_results, top_topic_words=15, add_default_topic=
|
|||
# wait for file to exist
|
||||
while not os.path.exists(LLDA_filepath):
|
||||
time.sleep(1)
|
||||
|
||||
#top_topic_words=1
|
||||
logprint("")
|
||||
logprint("start LLDA:")
|
||||
# run JGibsslda file
|
||||
|
|
@ -208,7 +226,7 @@ def jgibbsLLDA(corpus, path2save_results, top_topic_words=15, add_default_topic=
|
|||
|
||||
reverse_labeldict = {v: k for k, v in labeldict.items()}
|
||||
result = []
|
||||
regex = re.compile(r'Topic [0-9]')
|
||||
regex = re.compile(r'Topic [0-9]*')
|
||||
for line in output.splitlines():
|
||||
|
||||
findall = regex.findall(line)
|
||||
|
|
@ -231,16 +249,22 @@ def jgibbsLLDA(corpus, path2save_results, top_topic_words=15, add_default_topic=
|
|||
logprint("\n\n\nTime Elapsed Topic Modeling JGibbsLLDA:{0} min\n\n".format((end - start) / 60))
|
||||
|
||||
|
||||
def main(use_raw=False):
|
||||
def main(use_raw=False, algorithm="llda"):
|
||||
logprint("Topic Modeling: {0}".format(datetime.now()))
|
||||
|
||||
corpus_de_path = FILEPATH + config.get("de_corpus", "path")
|
||||
|
||||
corpus_en_path = FILEPATH + config.get("en_corpus", "path")
|
||||
|
||||
|
||||
if use_raw:
|
||||
# fehler Unknown document label ( X ) for document 352.
|
||||
preCorpus_name = "de" + "_raw_ticket"
|
||||
resultspath = FILEPATH + "results/raw"
|
||||
|
||||
else:
|
||||
preCorpus_name = "de" + "_pre_ticket"
|
||||
resultspath = FILEPATH + "results/pre"
|
||||
|
||||
# load raw corpus and create new one
|
||||
de_corpus, parser = load_corpus(corpus_name=preCorpus_name, corpus_path=corpus_de_path)
|
||||
|
|
@ -252,11 +276,10 @@ def main(use_raw=False):
|
|||
# idee https://gate.ac.uk/family/
|
||||
|
||||
# todo gescheites tf(-idf) maß finden
|
||||
# todo pro model: gelabelten corpus, ergebnisse und labeldict speichern
|
||||
# todo topics zusammenfassen
|
||||
# frage wieviele tickets pro topic?
|
||||
|
||||
|
||||
"""
|
||||
ngrams = 1
|
||||
min_df = 1
|
||||
max_df = 1.0
|
||||
|
|
@ -264,7 +287,7 @@ def main(use_raw=False):
|
|||
# weighting ='tfidf'
|
||||
named_entities = False
|
||||
|
||||
"""
|
||||
|
||||
printvecotorization(ngrams=1, min_df=1, max_df=1.0, weighting=weighting)
|
||||
printvecotorization(ngrams=1, min_df=1, max_df=0.5, weighting=weighting)
|
||||
printvecotorization(ngrams=1, min_df=1, max_df=0.8, weighting=weighting)
|
||||
|
|
@ -274,32 +297,28 @@ def main(use_raw=False):
|
|||
printvecotorization(ngrams=(1, 2), min_df=1, max_df=0.8, weighting=weighting)
|
||||
"""
|
||||
|
||||
if use_raw:
|
||||
resultspath = FILEPATH + "results/raw"
|
||||
else:
|
||||
resultspath = FILEPATH + "results/pre"
|
||||
|
||||
if algorithm == "llda":
|
||||
top_topic_words = 5
|
||||
add_default_topic = False
|
||||
path2save_results = resultspath + "{}_{}.txt".format(top_topic_words, add_default_topic)
|
||||
path2save_results = resultspath + "_{}_{}.txt".format("top"+str(top_topic_words), "wdefault" if add_default_topic else "")
|
||||
jgibbsLLDA(de_corpus, path2save_results=path2save_results, top_topic_words=top_topic_words,
|
||||
add_default_topic=add_default_topic)
|
||||
|
||||
top_topic_words = 5
|
||||
add_default_topic = True
|
||||
path2save_results = resultspath + "{}_{}.txt".format(top_topic_words, add_default_topic)
|
||||
path2save_results = resultspath + "_{}_{}.txt".format("top"+str(top_topic_words), "wdefault" if add_default_topic else "")
|
||||
jgibbsLLDA(de_corpus, path2save_results=path2save_results, top_topic_words=top_topic_words,
|
||||
add_default_topic=add_default_topic)
|
||||
|
||||
top_topic_words = 10
|
||||
add_default_topic = False
|
||||
path2save_results = resultspath + "{}_{}.txt".format(top_topic_words, add_default_topic)
|
||||
path2save_results = resultspath + "_{}_{}.txt".format("top"+str(top_topic_words), "wdefault" if add_default_topic else "")
|
||||
jgibbsLLDA(de_corpus, path2save_results=path2save_results, top_topic_words=top_topic_words,
|
||||
add_default_topic=add_default_topic)
|
||||
|
||||
top_topic_words = 10
|
||||
add_default_topic = True
|
||||
path2save_results = resultspath + "{}_{}.txt".format(top_topic_words, add_default_topic)
|
||||
path2save_results = resultspath + "_{}_{}.txt".format("top"+str(top_topic_words), "wdefault" if add_default_topic else "")
|
||||
jgibbsLLDA(de_corpus, path2save_results=path2save_results, top_topic_words=top_topic_words,
|
||||
add_default_topic=add_default_topic)
|
||||
|
||||
|
|
@ -310,52 +329,79 @@ def main(use_raw=False):
|
|||
# n_topics = len(LABELDICT)#len(set(ticketcorpus[0].metadata.keys()))+1 #+1 wegen einem default-topic
|
||||
|
||||
|
||||
else:
|
||||
|
||||
# build dictionary of ticketcategories
|
||||
labelist = []
|
||||
|
||||
for texdoc in de_corpus.get(lambda texdoc: texdoc.metadata["categoryName"] not in labelist):
|
||||
labelist.append(texdoc.metadata["categoryName"])
|
||||
|
||||
labeldict = {k: v for v, k in enumerate(labelist)}
|
||||
|
||||
n_topics = 15
|
||||
|
||||
|
||||
textacyTopicModeling(ngrams = 1,
|
||||
min_df = 1,
|
||||
max_df = 0.8,
|
||||
topicModel = algorithm,
|
||||
n_topics =n_topics,
|
||||
corpus=de_corpus)
|
||||
"""
|
||||
textacyTopicModeling(ngrams = (1,2),
|
||||
min_df = 1,
|
||||
max_df = 0.8,
|
||||
topicModel = algorithm,
|
||||
n_topics =n_topics,
|
||||
corpus=de_corpus)
|
||||
|
||||
"""
|
||||
topicModeling(ngrams = 1,
|
||||
min_df = 1,
|
||||
max_df = 1.0,
|
||||
topicModel = 'lda',
|
||||
n_topics = len(LABELDICT),
|
||||
corpi=de_corpus)
|
||||
|
||||
topicModeling(ngrams = 1,
|
||||
"""
|
||||
textacyTopicModeling(ngrams = 1,
|
||||
min_df = 0.1,
|
||||
max_df = 0.6,
|
||||
topicModel = 'lda',
|
||||
n_topics = len(LABELDICT),
|
||||
corpi=de_corpus)
|
||||
topicModel = algorithm,
|
||||
n_topics =n_topics,
|
||||
corpus=de_corpus)
|
||||
|
||||
topicModeling(ngrams = (1,2),
|
||||
|
||||
|
||||
|
||||
textacyTopicModeling(ngrams = (1,2),
|
||||
min_df = 1,
|
||||
max_df = 1.0,
|
||||
topicModel = 'lda',
|
||||
n_topics = len(LABELDICT),
|
||||
corpi=de_corpus)
|
||||
topicModel = algorithm,
|
||||
n_topics =n_topics,
|
||||
corpus=de_corpus)
|
||||
|
||||
topicModeling(ngrams = (1,2),
|
||||
textacyTopicModeling(ngrams = (1,2),
|
||||
min_df = 0.1,
|
||||
max_df = 0.6,
|
||||
topicModel = 'lda',
|
||||
n_topics = len(LABELDICT),
|
||||
corpi=de_corpus)
|
||||
topicModel = algorithm,
|
||||
n_topics =n_topics,
|
||||
corpus=de_corpus)
|
||||
|
||||
topicModeling(ngrams = (1,2),
|
||||
|
||||
|
||||
|
||||
textacyTopicModeling(ngrams = (1,2),
|
||||
min_df = 0.2,
|
||||
max_df = 0.8,
|
||||
topicModel = 'lda',
|
||||
topicModel = algorithm,
|
||||
n_topics = 20,
|
||||
corpi=de_corpus)
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
corpus=de_corpus)
|
||||
"""
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
|
||||
|
|
|
|||
Loading…
Reference in New Issue