topicModelingTickets/preprocessing.py

# -*- coding: utf-8 -*-
import matplotlib
matplotlib.use('Agg')
from datetime import datetime
import csv
import sys

from textacy import Vectorizer

import draw1
from miscellaneous import *
from datetime import datetime
import time
import textacy
from scipy import *
from scipy.stats import threshold
import draw
from spacy.tokens.token import Token as SpacyToken
from spacy.tokens.span import Span as SpacySpan
from topicModeling import jgibbsLLDAv2
import os
from ressources.iir.lda.llda import *

csv.field_size_limit(sys.maxsize)
FILEPATH = os.path.dirname(os.path.realpath(__file__)) + "/"



# load config
config_ini = FILEPATH + "config.ini"
ressources_path = FILEPATH +  "ressources/"

config = ConfigParser.ConfigParser()
with open(config_ini) as f:
    config.read_file(f)
"""
def init_glabal_vars():
    global THESAURUS, WORDS, LEMMAS, NOUNS, VORNAMEN, DE_STOP_WORDS, EN_STOP_WORDS, WHITELIST, FOOTER_FLAG, CURRENT_TICKET

THESAURUS = {}
WORDS= {}
LEMMAS= {}
NOUNS= {}
VORNAMEN= {}
DE_STOP_WORDS= {}
EN_STOP_WORDS= {}
WHITELIST= {}
CURRENT_TICKET = ""
    
    

REGEX_SPECIALCHAR = r'[`\-=~%^&*()_+\[\]{};\'\\:"|</>]' #+r',.'
REGEX_TOPLVL = r'\.[a-z]{2,3}(\.[a-z]{2,3})?'
"""



"""
THESAURUS = {}
WORDS= {}
LEMMAS= {}
NOUNS= {}
VORNAMEN= {}
DE_STOP_WORDS= {}
EN_STOP_WORDS= {}



#############  filter tokens


def filterTokens(tokens, funclist):
    # in:tokenlist, funclist
    # out: tokenlist
    for f in funclist:
        tokens = list(filter(f, tokens))

    for tok in tokens:
        if tok.pos_ =="NOUN":
            x=0

    return tokens


def keepPOS(pos_list):
    return lambda tok: tok.pos_ in pos_list


def keepNouns(noun_list=NOUNS):
    #return lambda tok: tok.lower_ in noun_list
    return lambda tok: tok.lower_ in noun_list or tok.pos_ == "NOUN"



def removePOS(pos_list):
    return lambda tok: tok.pos_ not in pos_list


def removeWords(words, keep=None):
    if hasattr(keep, '__iter__'):
        for k in keep:
            try:
                words.remove(k)
            except ValueError:
                pass

    return lambda tok: tok.lower_ not in words


def keepENT(ent_list):
    return lambda tok: tok.ent_type_ in ent_list


def removeENT(ent_list):
    return lambda tok: tok.ent_type_ not in ent_list


def remove_words_containing_Numbers():
    return lambda tok: not bool(re.search('\d', tok.lower_))


def remove_words_containing_topLVL():
    return lambda tok: not bool(re.search(REGEX_TOPLVL, tok.lower_))


def remove_words_containing_specialCharacters():
    return lambda tok: not bool(re.search(REGEX_SPECIALCHAR, tok.lower_))


def remove_long_words():
    return lambda tok: not len(tok.lower_) < 2


def remove_short_words():
    return lambda tok: not len(tok.lower_) > 35


def remove_first_names():
    return lambda tok: tok.lower_ not in [name.lower() for name in VORNAMEN]


############# strings
"""
def remove_addresses(string):
    pass  # todo remove_addresses  idee postal.parser und zu metadaten hinzufügen

def lemmatizeWord(word,lemma_dict,n=5):
    for i in range(n):
        try:
            word = lemma_dict[word.lower()] if word.lower() in lemma_dict.keys() else word.lower()
        except:
            print(word)
    return word


def getFirstSynonym(word, thesaurus, n=3):

    for i in range(n):

        try:
            if word in thesaurus.keys():
                return thesaurus[word]

            elif word.title() in thesaurus.keys():
                return thesaurus[word.title()]

            elif word.lower() in thesaurus.keys():
                return thesaurus[word.lower()]

            else:
                return word



        except:
            print("THESAURUSFEHLER BEI: {}".format(word))
            return word

"""
############# stringcleaning


def processContentstream(textstream, parser, token_filterlist=None):

    #pre parse
    textstream = preparse(textstream)

    pipe = parser.pipe(textstream)

    for doc in pipe:

        tokens = [tok for tok in doc]

        # in parse
        if token_filterlist is not None:
            tokens = filterTokens(tokens, token_filterlist)

        # post parse
        #todo STELLSCHRAUBE tokens = [postparse(tok) for tok in tokens]   #todo: informationsverlust von pos,tag etc.!
        tokens =  [tok.lower_ for tok in tokens]
        yield " ".join(tokens)

def preparse(stringstream):

    for string in stringstream:

        # cut_after
        words = ["gruss", "grusse","gruesse","gruessen","grusses"]

        for gr in words:
            if gr in string:
                string = string.rpartition(gr)[0]
                break

        yield string

def postparse(toktext):

    toktext = toktext.lower_

    # remove_words_containing_topLVL
    toktext = toktext if not re.search(REGEX_TOPLVL, toktext) else ""

    # lemmatize
    toktext = lemmatizeWord(toktext)

    # synonyme normalisieren
    toktext = getFirstSynonym(toktext)

    # autocorrect
    toktext = autocorrectWord(toktext)

    return toktext


def processDictstream(dictstream, funcdict, parser):
    
    for dic in dictstream:
        result = {}
        for key, value in dic.items():

            if key in funcdict:

                doc = parser(value)
                tokens = [tok for tok in doc]
                funclist = funcdict[key]

                tokens = filterTokens(tokens, funclist)

                result[key] = " ".join([tok.lower_ for tok in tokens])


            else:
                result[key] = value
        yield result

"""
##################################################################################################



"""
def extract_from_corpus(corpus):
"""
"""
    Extract from each doc from a corpus a string containing token_texts


    :param corpus: textacy.Corpus
    :return: string-gen
"""
"""

    weighting = 'tf' #'tfidf'
    ngrams = 1
    min_df = 1
    max_df = 0.9


    ######   vectorize corpi

    vectorizer = Vectorizer(weighting=weighting, min_df=min_df, max_df=max_df)

    terms_list = (doc.to_terms_list(ngrams=ngrams, named_entities=False, as_strings=True) for doc in corpus)
    doc_term_matrix = vectorizer.fit_transform(terms_list)
    id2term = vectorizer.id_to_term #__getattribute__("id_to_term")




    # WHITELIST erstellen. Enthält zumindest die evtuellen Topics

    WHITELIST = ["boss", "sap", "firefox"] #todo autogenerierung relv. techn. begriffe

    kb_cats = ['eldorado', 'cws_confluence', 'wsus', 'mail groupware', 'd.3 dms', 'serviceportal', 'softwarelizenzen', 'sophos', 'webserver', 'sap', 'ftp server', 'dhcp', 'tonerboerse', 'mailalias', 'arbeitsplatzsupport', 'mediendienste', 'mailverteiler', 'uni mail', 'basis app', 'videoschnitt', 'DEFAULT', 'verwaltung', 'matrix42_hilfe', 'hoersaaluebertragung', 'redmine', 'uniflow', 'keine rueckantwort', 'pools', 'leitung', 'netze', 'konteneinsicht', 'kennwort aenderung', 'datanet', 'neuanschluss', 'semesterticket', 'asknet', 'veranstaltungen', 'housing', 'fk 16', 'fiona', 'betrieb', 'vorlagenerstellung', 'studierendensekretariat', 'pvp', 'mobilfunkvertraege', 'ausleihe', 'web', 'spam phishing', 'sap urlaub', 'evaexam', 'vorlesungsaufzeichnung', 'firewall betreuung', 'ub basis it', 'virtuelle desktops citrix', 'fk15', 'virtuelle server', 'lizenzserver', 'elektronisches telefonbuch', 'joomla itmc website', 'weiterentwicklung', 'serversupport', 'wlan', 'kurse', 'technik', 'raumkalender', 'backup tsm', 'haustechnik', 'voicemail box', 'facility', 'unicard ausgabe', 'mdm mobile device management', 'entwicklung', 'webgestaltung', 'unicard sperrung', 'forensic', 'basis applikationen', 'overhead projektor', 'plagiatserkennung', 'uniaccount zugangsdaten', 'zentrale webserver', 'webmailer', 'fk12 webauftritt', 'plotter', 'campus management', 'ub_stoerungen', 'rundmail', 'telefon', 'raumbuchung', 'fk12 migration', 'dienstreise', 'hardware', 'it sicherheit sic', 'hochleistungsrechnen', 'unicard', 'sos', 'benutzerverwaltung_probleme', 'confluence', 'vpn', 'zhb', 'campus app', 'itmc_aufgaben', 'sicherheit', 'schulungsraum verwaltung', 'unicard produktion', 'schulung', 'video', 'dokoll support', 'sd', 'servicedesk', 'v2 campus app feedback', 'lido', 'app feedback', 'ibz raumbuchung', 'hcm stammdaten', 'itmc_stoerungen', 'boss service desk', 'exchange nutzung', 'office', 'rektorat -buero', 'bestellung', 'moodle', 'fk raumplanung 09', 'aenderung', 'neuausstattung', 'benutzerverwaltung', 'rechnerraeume', 'designentwicklung', 'fk 12', 'werkstoffe lehrstuhl bauwesen', 'server storage', 'beantragung', 'visitenkartenproduktion', 'gastaufenthalt', 'telefonkonferenzen', 'raumbuchungssysteme', 'fk14_test', 'e mail dienste', 'grafik', 'ews', 'itmc schulungsraeume', 'tsm', 'softwareverteilung', 'beamer', 'lizenzmanagement', 'fileserver einrichtung', 'redmine projektverwaltung', 'service desk itmc', 'pruefungsmanagement', 'prozess- und projektmanagement', 'formulare antraege', 'namensaenderung', 'verkauf', 'software', 'itmc medienraeume ef50', 'zugangsdaten', 'medientechnik', 'lan', 'veeam', 'unicard redaktionsteam', 'changes', 'service portal', 'limesurvey', 'dns', 'dokoll pvp', 'uhren', 'nrw ticket', 'itmc_als', 'linux bs', 'werkvertraege', 'blogs wikis foren', 'test', 'abmeldung', 'desktop & basisdienste', 'telefonzentrale', 'siport zugangskontrolle', 'antrag auf rechnungserstellung', 'verschiedene aufgaben', 'kundenserver', 'medienraeume ef50', 'videokonferenzen', 'benutzungsverwaltung', 'mailverteiler exchange', 'lsf', 'telefonabrechnung', 'werkstaette', 'uniaccount', 'outlook_einrichtung', 'itmc webauftritt', 'zertifikate server dfn', 'allgemein', 'umzug', 'service portal redaktion', 'pos', 'beschaffung', 'boss', 'hacker angriff', 'software entwicklung', 'cd dvd produktion', 'sam spider', 'viren', 'kursplanung', 'itmc pools', 'kms', 'e learning']
    kb_keys = ['zugriff_onlinedienste_rueckmeldung', 'uniaccount', 'freischaltung', 'asknet', 'eduroam', 'donnerstagsmail namensaenderung', 'asiexception', 'lsf', 'kundenantwort', 'chip', 'unitymedia', 'citavi', 'fehler', 'windows beziehen', 'wlan', 'ipv6', 'freischaltung verzoegert', 'betrag', '"defekte karte"', 'risse', 'laden', 'sap portal anderer modus', 'goeke', 'informationen des itmc zum einsatz', 'transport wurde durchgefuehrt.', 'wi-fi', 'unicard_auszahlung', 'ausleihe', 'unimail', 'uni-account', 'unicard','beantragung', 'nrw-ticket', 'printservice', 'dms', 'ip6', 'transport und beschreibung zum transportauftrag !', 'wlan passwort', 'dokumentenmanagementsystem', 'webmailer', 'vpn', 'repository', 'unicard', 'projekte', 'eingeschrieben', 'unicard abholung oeffnungszeiten', 'd3', 'beantragung', 'app tu-dortmund feedback', 'semester ticket', 'redmine', 'git', 'geldkarte', 'outlook_exchange', 'spam standardmeldung phishing', 'automatische aktualisierung der selbst angelegten kontakte in outlook', '"beschaedigte unicard"', 'elektronische telefonbuch', 'boss', 'wwrite', 'DEFAULT', 'anyconnect', 'wifi']
    kb_subjs =['sd_office 365 plus support', 'citavi_lizenzschluessel_nicht bekommen', 'uni card', 'sd_office 356 plus bestellung', 'sd_gastaufenthalter', 'sd_outlook kontakte automatische aktualisierung', 'benutzer zum redmine hinzufuegen', 'sd_matlab lizenzdatei pc-pools', 'sd_tu-app feedback standard', 'vpn_ipsec_stoerung', 'vpn verbindung fuer unitymedia kunden', 'ub_prod_abholung_ abholfristen_benachrichtigungen', 'einrichtung des eduroam netzwerks', 'sd_webmailer_threadanzeige und weiterleitung', 'sd_wlan passwort setzen', 'ub_prod_namenskorrektur_student', 'sd_unimail imap_pop3', 'sd_outlook_in_exchange_einbinden', 'sd_keine rueckantwort kunde', 'sd_asknet_und_dreamspark', 'sd_heirat_namensaenderung_student', 'bd_unicard_nicht_eingeschrieben', 'wlan', 'sd_telefonbuch_prof_eintragung', 'change produktiv nehmen chn00146 - transport e01k909284', 'ungueltiges ticket siehe journal', 'apps_dms_d.3 client installation/login d.3 funktioniert nicht', 'd.3 client installation', 'unicard_restbetrag_auszahlung', 'cm_asiexception', 'sd_origin_workaround', 'sd_vpn_aktualisierung', 'problem mit der beantragung von der unicard', 'sd_unicard fehlerhafte geldbuchung', 'sd_login tu portals english', 'sd_gmx_web.de', 'studierendenausweis', 'sd_citavi', 'sd_fk9 test', 'sd_webmailer_thread-anzeige', 'bd_unicard_geldkarte_laden', 'ub_unicard_unicard mit vollmacht abholen', 'sd_stellenausschreibung schwarzes brett', 'freischaltung uniaccount', 'sd_asknet_mitarbeiter_softwarebestellung', 'how to setup eduroam', 'sd_citavi bestellung', 'unicard vergessen abzuholen und nicht mehr da', 'sd_unimail zu exchange', 'sd_diensthandy beschaffung', 'sd_sap konteneinsicht antrag', 'sd_unicard_defekt', 'sd_webmailer einrichtung weiterleitung', 'sd_kurs-angebote anmeldung', 'm42_dokumentationen_zu_neuen_ous', 'sd_sap_initialkennwort', 'sd_sap_freischaltung ohne passwortaenderung', 'sd_telefonbuch-eintrag_aenderung', 'sd_pruefungsamt', 'sd_phishing', 'apps_dms-passwort d.3', 'sd_goeke drucker', 'sd_sap_dienstreise', 'unicard nochmal beantragen', 'sd_outlook anmeldung gestoert', 'sd_citavi_support', 'DEFAULT', 'sd_geraeteausleihe', 'sd_account_abmelden', 'sd_uniaccount freischaltung verzoegert englisch', 'ub_beschaedigte unicard', 'sd_gleitzeitanlage_dez3_stoerung', 'transportdurchfuehung', 'sd_sap_initialkennwort_englisch', 'sd_antwort_phishingmail', 'sd_namensaenderung mitarbeiter', 're: elektroarbeiten fuer leitsystem 2. und 3. obergeschoss', 'lsf freischaltung als mitarbeiter/in', 'ub_unicard_spaetere abholung moeglich?', 'sd_antrag funktionale mailadresse', 'sd_apple-on-campus', 'sd_office365_asknet', 'sd_sophos download', 'sd_freischaltung uniaccount verzoegert', 'ub_unicard_zusendung der karte moeglich?', 'ohne betreff', 'sd_immatrikulationsbescheinigung_portal', 'sd_studisek_buchung_semesterbeitrag', 'sd_studisek_englisch', 'probleme mit der namensaenderung/ neue unicard', 'sd_telefonbuch, neues system', 'fehlender eintrag im elektronischen telefonbuch', 'sd_boss_notenverbuchung', 'sd_laufzeit unimail account', 'sd_semesterticket', 'sd_kontakt_asknet', 'windows 10', 'sd_login_tu_portale', 'ub_geldchip-problem bei uc', 'sd_zugriff_onlinedienste_rueckmeldung', 'sd_wlan-gastkonto', 'sd_tu_app_keine internetverbindung', 'sd_uniaccount_ehemalige_passwortaenderung', 'sd_verlust/antrag unicard', 'sd_sap_konteneinsicht_ workaround', 'apps_redmine_repository', 'sd_itmc kurse anmeldebestaetigung', 'sd_mail_als_anhang', 'bd_unicard_chip_defekt', 'probleme mit unicard', 'ub_unicard_abholungszeiten', 'sd_falsche_personendaten', 'sd_uniaccount_ehemalige_studierende', 'sd_vpn anleitungen', 'sd_kurs-angebote itmc', 'sd_studisek', 'sd_login tu portale', 'sd_immatrikulationsbescheigung_druckfehler', 'ub_drucker kopierer', 'sd_vpn_temporaerer fehler ub', 'sd_spss_online_bestellung', 'sd_dreamspark', 'sd_unicard_gesperrte unicard entsperre', 'sd_boss-bescheinigung', 'bd_goeke_allgemein', 'sd_uniaccount_passwortaenderung', 'sd_namensaenderung_englisch', 'sd_email_namensaenderung', 'bd_unicard_freigabe_beantragung', 'spam ohne tu bezug', 'sd_internationaloffice', 'sd

    WHITELIST = WHITELIST + kb_cats + kb_keys + kb_subjs


    THESAURUS = load_obj(path2thesaurus_dict)
    #WORDS = load_obj(path2wordsdict)
    LEMMAS = load_obj(path2lemmadict)
    DE_STOP_WORDS = load_obj(path2DEstopwordlist)
    #EN_STOP_WORDS = load_obj(path2ENstopwordlist)
    VORNAMEN = load_obj(path2firstnameslist)

    ents_boss = []
    ents_sap = []

    for doc in corpus:
        result = []

        #if doc.metadata["TicketNumber"] == "INC40506":
        #    breakpoint()


        for tok in doc:


"""
#if tok.lower_ =="boss":
#    ents_boss.append(tok.ent_type_)

#if tok.lower_ =="sap":
#    ents_sap.append(tok.ent_type_)
"""

            # wenn in whitelist, direkt übernehmen
            if tok.lower_ in WHITELIST:
                result.append(tok.lower_)


            # ignore header, urls , emails, stop, vornamen
            lemmatized_word = lemmatizeWord(tok.text,lemma_dict=LEMMAS)
            if lemmatized_word.lower() in ["sehr", "geehrt", "herr" ,"herrn", "herren", "dame" , "damen", "liebe","lieben", "hallo", "guten", "tag","ehre","hi"] \
            or tok.like_url \
            or tok.like_email \
            or tok.is_stop \
            or tok.is_punct \
            or tok.lower_ in DE_STOP_WORDS \
            or tok.lower_ in VORNAMEN:
                continue



            # cut after footer
            if replaceRockDots(tok.lower_) in ["gruss", "grusse", "gruesse", "gruessen", "grusses"]:    # fehler schneidet bei zB INC40506 das meiste weg
                break



            if tok.pos_ in ["NOUN","PROPN"] \
            or tok.ent_type_ in ["NORP","FACILITY","ORG","PRODUCT","WORK_OF_ART","LOC"]:
            #or tok.dep_ == "ROOT":
            # or tok.lower_ in NOUNS \ #,"PERSON"] \
                toktext = tok.lower_


                toktext = lemmatized_word

                use_thesaurus = False
                if use_thesaurus:
                    # hauptsynonym bilden idee zwar das Huaptsyn bilden und zählen aber die originalen wörter in den llda algo geben
                    #fehler ergibt nonsens frage werden die gesamtzahl an termen signifikant reduziert?
                    first_synonym = getFirstSynonym(toktext, thesaurus=THESAURUS)
                    if first_synonym is not None or first_synonym != '':
                        toktext = first_synonym if len(first_synonym.split()) == 1 else toktext



                result.append(toktext)



        yield " ".join(result)

    #return doc_term_matrix



    """


#print(list(set(ents_sap)))
#    ['', 'ORG', 'PERSON', 'LOC']

#print(list(set(ents_boss)))
#    ['', 'ORG', 'PERSON', 'LOC']





# LOAD FROM CONFIG

path2thesaurus_dict = ressources_path + config.get("thesaurus","pickle_file")
path2wordsdict = ressources_path + config.get("spellchecking", "pickle_file")
path2lemmadict = ressources_path + config.get("lemmatization","pickle_file")
path2firstnameslist = ressources_path + config.get("firstnames","pickle_file")
path2DEstopwordlist = ressources_path + config.get("de_stopwords", "pickle_file")
path2ENstopwordlist = ressources_path + config.get("en_stopwords", "pickle_file")
custom_words = get_list_from_config("preprocessing", "custom_words")

corpus_de_path = FILEPATH + config.get("de_corpus", "path")
de_plainpath = FILEPATH + config.get("de_corpus", "path") + "pre_labled_lines.txt"

corpus_en_path = FILEPATH + config.get("en_corpus", "path")







def norma(token_or_span):

    if isinstance(token_or_span, SpacyToken):
        return normalize_token(token_or_span)

    elif isinstance(token_or_span, SpacySpan):
        result= ' '.join(normalize_token(subtok) for subtok in token_or_span)
        return textacy.preprocess.normalize_whitespace(result)

    else:
        msg = 'Input must be a spacy Token or Span, not {}.'.format(type(token_or_span))
        raise TypeError(msg)


def normalize_token(tok):
    global CURRENT_TICKET, FOOTER_FLAG



    # check if CURRENT_TICKET is assigned
    try:
        CURRENT_TICKET
        FOOTER_FLAG
    except NameError:
        CURRENT_TICKET = tok.doc
        FOOTER_FLAG = False



    #aktuell verarbeitendes Doc merken. wenn neues Ticket, dann Footer_flag auf False setzen
    if tok.doc != CURRENT_TICKET:
        FOOTER_FLAG = False
        CURRENT_TICKET = tok.doc

    # wenn in whitelist, direkt übernehmen
    if tok.lower_ in WHITELIST:
        return tok.lower_


    # ignore header, urls , emails, stop, vornamen, blacklisted
    lemmatized_word = lemmatizeWord(tok.text,lemma_dict=LEMMAS)
    if lemmatized_word.lower() in ["sehr", "geehrt", "herr" ,"herrn", "herren", "dame" , "damen", "liebe","lieben", "hallo", "guten", "tag","ehre","hi"] \
    or tok.like_url \
    or tok.like_email \
    or tok.is_stop \
    or tok.is_punct \
    or tok.lower_ in DE_STOP_WORDS \
    or tok.lower_ in VORNAMEN\
    or tok.lower_ in BLACKLIST:
        return "" #todo bla das alles hier kommt zwischen to_terms_list und fitransform



    # cut after footer

    if replaceRockDots(tok.lower_) in ["gruss", "grusse", "gruesse", "gruessen", "grusses"]:    # fehler schneidet bei zB INC40506 das meiste weg
        FOOTER_FLAG = True



    if ( tok.pos_ in ["NOUN","PROPN"] or tok.ent_type_ in ["NORP","FACILITY","ORG","PRODUCT","WORK_OF_ART","LOC"] ) and not FOOTER_FLAG:

    #or tok.dep_ == "ROOT":
    # or tok.lower_ in NOUNS \ #,"PERSON"] \
        toktext = tok.lower_


        toktext = lemmatized_word

        use_thesaurus = False
        if use_thesaurus:
            # hauptsynonym bilden idee zwar das Huaptsyn bilden und zählen aber die originalen wörter in den llda algo geben
            #fehler ergibt nonsens frage werden die gesamtzahl an termen signifikant reduziert?
            first_synonym = getFirstSynonym(toktext, thesaurus=THESAURUS)
            if first_synonym is not None or first_synonym != '':
                toktext = first_synonym if len(first_synonym.split()) == 1 else toktext



        return toktext
    else:
        return ""




def preprocessCorpus(cleaned_corpus):
    logprint("Preprocess {0}_corpus at {1}".format(cleaned_corpus.lang, datetime.now()))
    global THESAURUS, WORDS, LEMMAS, NOUNS, VORNAMEN, DE_STOP_WORDS, EN_STOP_WORDS, WHITELIST, BLACKLIST






    weighting = 'tf' #'tfidf'
    ngrams = (1,2)
    min_df = 1
    max_df = 0.3
    min_label_freq = 1







    WHITELIST = ["boss", "sap", "firefox"]  # todo autogenerierung von whitelist
    kb_cats = ['eldorado', 'cws_confluence', 'wsus', 'mail groupware', 'd.3 dms', 'serviceportal', 'softwarelizenzen',
               'sophos', 'webserver', 'sap', 'ftp server', 'dhcp', 'tonerboerse', 'mailalias', 'arbeitsplatzsupport',
               'mediendienste', 'mailverteiler', 'uni mail', 'basis app', 'videoschnitt', 'DEFAULT', 'verwaltung',
               'matrix42_hilfe', 'hoersaaluebertragung', 'redmine', 'uniflow', 'keine rueckantwort', 'pools', 'leitung',
               'netze', 'konteneinsicht', 'kennwort aenderung', 'datanet', 'neuanschluss', 'semesterticket', 'asknet',
               'veranstaltungen', 'housing', 'fk 16', 'fiona', 'betrieb', 'vorlagenerstellung',
               'studierendensekretariat', 'pvp', 'mobilfunkvertraege', 'ausleihe', 'web', 'spam phishing', 'sap urlaub',
               'evaexam', 'vorlesungsaufzeichnung', 'firewall betreuung', 'ub basis it', 'virtuelle desktops citrix',
               'fk15', 'virtuelle server', 'lizenzserver', 'elektronisches telefonbuch', 'joomla itmc website',
               'weiterentwicklung', 'serversupport', 'wlan', 'kurse', 'technik', 'raumkalender', 'backup tsm',
               'haustechnik', 'voicemail box', 'facility', 'unicard ausgabe', 'mdm mobile device management',
               'entwicklung', 'webgestaltung', 'unicard sperrung', 'forensic', 'basis applikationen',
               'overhead projektor', 'plagiatserkennung', 'uniaccount zugangsdaten', 'zentrale webserver', 'webmailer',
               'fk12 webauftritt', 'plotter', 'campus management', 'ub_stoerungen', 'rundmail', 'telefon',
               'raumbuchung', 'fk12 migration', 'dienstreise', 'hardware', 'it sicherheit sic', 'hochleistungsrechnen',
               'unicard', 'sos', 'benutzerverwaltung_probleme', 'confluence', 'vpn', 'zhb', 'campus app',
               'itmc_aufgaben', 'sicherheit', 'schulungsraum verwaltung', 'unicard produktion', 'schulung', 'video',
               'dokoll support', 'sd', 'servicedesk', 'v2 campus app feedback', 'lido', 'app feedback',
               'ibz raumbuchung', 'hcm stammdaten', 'itmc_stoerungen', 'boss service desk', 'exchange nutzung',
               'office', 'rektorat -buero', 'bestellung', 'moodle', 'fk raumplanung 09', 'aenderung', 'neuausstattung',
               'benutzerverwaltung', 'rechnerraeume', 'designentwicklung', 'fk 12', 'werkstoffe lehrstuhl bauwesen',
               'server storage', 'beantragung', 'visitenkartenproduktion', 'gastaufenthalt', 'telefonkonferenzen',
               'raumbuchungssysteme', 'fk14_test', 'e mail dienste', 'grafik', 'ews', 'itmc schulungsraeume', 'tsm',
               'softwareverteilung', 'beamer', 'lizenzmanagement', 'fileserver einrichtung',
               'redmine projektverwaltung', 'service desk itmc', 'pruefungsmanagement',
               'prozess- und projektmanagement', 'formulare antraege', 'namensaenderung', 'verkauf', 'software',
               'itmc medienraeume ef50', 'zugangsdaten', 'medientechnik', 'lan', 'veeam', 'unicard redaktionsteam',
               'changes', 'service portal', 'limesurvey', 'dns', 'dokoll pvp', 'uhren', 'nrw ticket', 'itmc_als',
               'linux bs', 'werkvertraege', 'blogs wikis foren', 'test', 'abmeldung', 'desktop & basisdienste',
               'telefonzentrale', 'siport zugangskontrolle', 'antrag auf rechnungserstellung', 'verschiedene aufgaben',
               'kundenserver', 'medienraeume ef50', 'videokonferenzen', 'benutzungsverwaltung',
               'mailverteiler exchange', 'lsf', 'telefonabrechnung', 'werkstaette', 'uniaccount', 'outlook_einrichtung',
               'itmc webauftritt', 'zertifikate server dfn', 'allgemein', 'umzug', 'service portal redaktion', 'pos',
               'beschaffung', 'boss', 'hacker angriff', 'software entwicklung', 'cd dvd produktion', 'sam spider',
               'viren', 'kursplanung', 'itmc pools', 'kms', 'e learning']
    kb_keys = ['zugriff_onlinedienste_rueckmeldung', 'uniaccount', 'freischaltung', 'asknet', 'eduroam',
               'donnerstagsmail namensaenderung', 'asiexception', 'lsf', 'kundenantwort', 'chip', 'unitymedia',
               'citavi', 'fehler', 'windows beziehen', 'wlan', 'ipv6', 'freischaltung verzoegert', 'betrag',
               '"defekte karte"', 'risse', 'laden', 'sap portal anderer modus', 'goeke',
               'informationen des itmc zum einsatz', 'transport wurde durchgefuehrt.', 'wi-fi', 'unicard_auszahlung',
               'ausleihe', 'unimail', 'uni-account', 'unicard', 'beantragung', 'nrw-ticket', 'printservice', 'dms',
               'ip6', 'transport und beschreibung zum transportauftrag !', 'wlan passwort',
               'dokumentenmanagementsystem', 'webmailer', 'vpn', 'repository', 'unicard', 'projekte', 'eingeschrieben',
               'unicard abholung oeffnungszeiten', 'd3', 'beantragung', 'app tu-dortmund feedback', 'semester ticket',
               'redmine', 'git', 'geldkarte', 'outlook_exchange', 'spam standardmeldung phishing',
               'automatische aktualisierung der selbst angelegten kontakte in outlook', '"beschaedigte unicard"',
               'elektronische telefonbuch', 'boss', 'wwrite', 'DEFAULT', 'anyconnect', 'wifi']
    kb_subjs = ['sd_office 365 plus support', 'citavi_lizenzschluessel_nicht bekommen', 'uni card',
                'sd_office 356 plus bestellung', 'sd_gastaufenthalter',
                'sd_outlook kontakte automatische aktualisierung', 'benutzer zum redmine hinzufuegen',
                'sd_matlab lizenzdatei pc-pools', 'sd_tu-app feedback standard', 'vpn_ipsec_stoerung',
                'vpn verbindung fuer unitymedia kunden', 'ub_prod_abholung_ abholfristen_benachrichtigungen',
                'einrichtung des eduroam netzwerks', 'sd_webmailer_threadanzeige und weiterleitung',
                'sd_wlan passwort setzen', 'ub_prod_namenskorrektur_student', 'sd_unimail imap_pop3',
                'sd_outlook_in_exchange_einbinden', 'sd_keine rueckantwort kunde', 'sd_asknet_und_dreamspark',
                'sd_heirat_namensaenderung_student', 'bd_unicard_nicht_eingeschrieben', 'wlan',
                'sd_telefonbuch_prof_eintragung', 'change produktiv nehmen chn00146 - transport e01k909284',
                'ungueltiges ticket siehe journal', 'apps_dms_d.3 client installation/login d.3 funktioniert nicht',
                'd.3 client installation', 'unicard_restbetrag_auszahlung', 'cm_asiexception', 'sd_origin_workaround',
                'sd_vpn_aktualisierung', 'problem mit der beantragung von der unicard',
                'sd_unicard fehlerhafte geldbuchung', 'sd_login tu portals english', 'sd_gmx_web.de',
                'studierendenausweis', 'sd_citavi', 'sd_fk9 test', 'sd_webmailer_thread-anzeige',
                'bd_unicard_geldkarte_laden', 'ub_unicard_unicard mit vollmacht abholen',
                'sd_stellenausschreibung schwarzes brett', 'freischaltung uniaccount',
                'sd_asknet_mitarbeiter_softwarebestellung', 'how to setup eduroam', 'sd_citavi bestellung',
                'unicard vergessen abzuholen und nicht mehr da', 'sd_unimail zu exchange', 'sd_diensthandy beschaffung',
                'sd_sap konteneinsicht antrag', 'sd_unicard_defekt', 'sd_webmailer einrichtung weiterleitung',
                'sd_kurs-angebote anmeldung', 'm42_dokumentationen_zu_neuen_ous', 'sd_sap_initialkennwort',
                'sd_sap_freischaltung ohne passwortaenderung', 'sd_telefonbuch-eintrag_aenderung', 'sd_pruefungsamt',
                'sd_phishing', 'apps_dms-passwort d.3', 'sd_goeke drucker', 'sd_sap_dienstreise',
                'unicard nochmal beantragen', 'sd_outlook anmeldung gestoert', 'sd_citavi_support', 'DEFAULT',
                'sd_geraeteausleihe', 'sd_account_abmelden', 'sd_uniaccount freischaltung verzoegert englisch',
                'ub_beschaedigte unicard', 'sd_gleitzeitanlage_dez3_stoerung', 'transportdurchfuehung',
                'sd_sap_initialkennwort_englisch', 'sd_antwort_phishingmail', 'sd_namensaenderung mitarbeiter',
                're: elektroarbeiten fuer leitsystem 2. und 3. obergeschoss', 'lsf freischaltung als mitarbeiter/in',
                'ub_unicard_spaetere abholung moeglich?', 'sd_antrag funktionale mailadresse', 'sd_apple-on-campus',
                'sd_office365_asknet', 'sd_sophos download', 'sd_freischaltung uniaccount verzoegert',
                'ub_unicard_zusendung der karte moeglich?', 'ohne betreff', 'sd_immatrikulationsbescheinigung_portal',
                'sd_studisek_buchung_semesterbeitrag', 'sd_studisek_englisch',
                'probleme mit der namensaenderung/ neue unicard', 'sd_telefonbuch, neues system',
                'fehlender eintrag im elektronischen telefonbuch', 'sd_boss_notenverbuchung',
                'sd_laufzeit unimail account', 'sd_semesterticket', 'sd_kontakt_asknet', 'windows 10',
                'sd_login_tu_portale', 'ub_geldchip-problem bei uc', 'sd_zugriff_onlinedienste_rueckmeldung',
                'sd_wlan-gastkonto', 'sd_tu_app_keine internetverbindung', 'sd_uniaccount_ehemalige_passwortaenderung',
                'sd_verlust/antrag unicard', 'sd_sap_konteneinsicht_ workaround', 'apps_redmine_repository',
                'sd_itmc kurse anmeldebestaetigung', 'sd_mail_als_anhang', 'bd_unicard_chip_defekt',
                'probleme mit unicard', 'ub_unicard_abholungszeiten', 'sd_falsche_personendaten',
                'sd_uniaccount_ehemalige_studierende', 'sd_vpn anleitungen', 'sd_kurs-angebote itmc', 'sd_studisek',
                'sd_login tu portale', 'sd_immatrikulationsbescheigung_druckfehler', 'ub_drucker kopierer',
                'sd_vpn_temporaerer fehler ub', 'sd_spss_online_bestellung', 'sd_dreamspark',
                'sd_unicard_gesperrte unicard entsperre', 'sd_boss-bescheinigung', 'bd_goeke_allgemein',
                'sd_uniaccount_passwortaenderung', 'sd_namensaenderung_englisch', 'sd_email_namensaenderung',
                'bd_unicard_freigabe_beantragung', 'spam ohne tu bezug', 'sd_internationaloffice',
                'sd_tu-app feedback_englisch', 'cm_lsf-boss_freischaltung', 'sd-e-mail_adresse_funktional_beantragen',
                'sd_vpn_webvpn', 'sd_vpn_probleme_mit_unitymedia', 'sd_plotauftrag_zv', 'sd_beantragung_unicard',
                'sd_antworten_korrekt', 'ub_prod_neue unicard bei beschaedigung',
                'sd_telefonantrag_aenderung_neuantrag', 'sd_wlan passwort englisch', 'sd_aktivierung uniaccount',
                'sd_spam e-mail bekannt meldung', 'sd_wlan_beratung', 'ub_namensaenderung',
                'sd_telefon (antrag: neuanschluss, umzug, aenderung erledigt)', 'sd_unicard_abholung',
                'sd_uniaccount_dauer freischaltung', 'sd_uniaccount activation englisch', 'sd_unicard_max_laufzeit',
                'sd_unicard_workaround_bestellung', 'sd_sap_firefox_esr', 'sap portal "im anderen modus geoeffnet"',
                'sd_origin nur noch eine seriennummer', 'sd_login_unibib ub-it']

    BLACKLIST = get_list_from_config("preprocessing", "custom_words")
    WHITELIST = WHITELIST + kb_cats + kb_keys + kb_subjs
    DE_STOP_WORDS = load_obj(path2DEstopwordlist)
    VORNAMEN = load_obj(path2firstnameslist)

    LEMMAS = load_obj(path2lemmadict)
    THESAURUS = load_obj(path2thesaurus_dict)




    ######   vectorize corpus
    logprint("vectorize corpus")

    vectorizer = Vectorizer(weighting=weighting, min_df=min_df, max_df=max_df)  #fehler norma() kickt tokens raus, also bezieht sich min/max_df nicht mehr auf cleaned

    terms_list_gen = (doc.to_terms_list(ngrams=ngrams, as_strings=True, normalize=norma) for doc in cleaned_corpus)

    doc_term_matrix = vectorizer.fit_transform(terms_list_gen)
    id2term = vectorizer.id_to_term
    term2id = vectorizer.vocabulary

    logprint("corpus vectorized")






    # write labeled_lines.txt
    line_gen = gen_lines(doc_term_matrix,term2id,cleaned_corpus)
    lines_txt = "/home/jannis.grundmann/PycharmProjects/topicModelingTickets/corpi/labled_lines.txt"
    textacy.fileio.write_file_lines(line_gen,
                                    filepath=lines_txt)
    logprint("labled_lines.txt written")






    ####    ticket2label_cat

    # get all categrories
    """
    labelist_cat = []
    for doc in cleaned_corpus:
        category = normalize_str(doc.metadata["categoryName"])
        labelist_cat.append(category)
    """
    labelist_cat = [ normalize_str(doc.metadata["categoryName"]) for doc in cleaned_corpus]


    #create label_dict
    labeldict_cat = create_labeldict(labelist_cat, min_label_freq = 1, add_default_label=True)

    # map tickets to labels
    """
    ticket2label_cat = {}
    for doc in cleaned_corpus:
        ticketID = doc.metadata["TicketNumber"]
        category_name_ = doc.metadata["categoryName"]
        label_num = labeldict_cat.get(category_name_,labeldict_cat['DEFAULT'])

        ticket2label_cat.update({ticketID : label_num})
    """
    ticket2label_cat = { doc.metadata["TicketNumber"] : labeldict_cat.get(doc.metadata["categoryName"],labeldict_cat['DEFAULT'])  for doc in cleaned_corpus  }

    ##################################################################################################

    kb2keywords_dict, kb2subjects_dict, ticket2kbs_dict, ticket2keywords_dict, ticket2subjects_dict = create_ticket2sth_dicts()


    ####    ticket2label_keys
    ticket2label_keys = create_ticket2label_dict(ticket2keywords_dict,cleaned_corpus)

    """
    labelist_keys = ticket2keywords_dict.values()
    labelist_keys = flatten(labelist_keys)


    labeldict_keys = create_labeldict(labelist_keys, min_label_freq = 1, add_default_label=True)

    ticket2label_keys = {}
    for doc in cleaned_corpus:
        ticketID = doc.metadata["TicketNumber"]


        keywords = ticket2keywords_dict.get(ticketID, ['DEFAULT'])


        label = ""
        for kw in keywords:
            label = label + str(labeldict_keys.get(normalize_str(str(kw)), labeldict_keys['DEFAULT'])) + " "


        ticket2label_keys.update({ticketID : label})

    """
    ##################################################################################################



    ####  ticket2label_subjs
    ticket2label_subjs = create_ticket2label_dict(ticket2subjects_dict,cleaned_corpus)

    """
    labelist_subjs = ticket2subjects_dict.values()
    labelist_subjs = flatten(labelist_subjs)


    labeldict_subjs = create_labeldict(labelist_subjs, min_label_freq = 1, add_default_label=True)

    ticket2label_subjs = {}
    for doc in cleaned_corpus:
        ticketID = doc.metadata["TicketNumber"]


        keywords = ticket2subjects_dict.get(ticketID, ['DEFAULT'])


        label = ""
        for kw in keywords:
            label = label + str(labeldict_subjs.get(normalize_str(str(kw)), labeldict_subjs['DEFAULT'])) + " "

        ticket2label_subjs.update({ticketID : label})
    """


    ####  ticket2label_kb
    ticket2label_kb = create_ticket2label_dict(ticket2kbs_dict,cleaned_corpus)

    """
    labelist_kbs = ticket2kbs_dict.values()
    labelist_kbs = flatten(labelist_kbs)

    labeldict_kbs = create_labeldict(labelist_kbs, min_label_freq = 1, add_default_label=True)

    ticket2label_kb = {}
    for doc in cleaned_corpus:
        ticketID = doc.metadata["TicketNumber"]


        keywords = ticket2kbs_dict.get(ticketID, ['DEFAULT'])


        label = ""
        for kw in keywords:
            label = label + str(labeldict_kbs.get(normalize_str(str(kw)), labeldict_kbs['DEFAULT'])) + " "

            ticket2label_kb.update({ticketID : label})
    """

    def relabele_lines(file,ticket2label_dict):

        line_gen = textacy.fileio.read_file_lines(file)

        for line in line_gen:
            label = re.findall(r'\[(.*?)\]', line)

            new_label = "[ "
            for lbl in label:
                new_label = new_label + str(ticket2label_dict.get(str(lbl), "")).strip() + " "

            new_label = new_label + "] "
            result = new_label + str(line.rpartition("]")[2])

            #            new_label = str([ticket2label_dict.get(str(lbl),"") for lbl in label])

            #            result = "[ " + new_label + " ] " + line.rpartition("]")[2]
            # print(result)

            yield result

    lines_sub = "/home/jannis.grundmann/PycharmProjects/topicModelingTickets/corpi/labled_lines_sub.txt"
    generator = relabele_lines(lines_txt, ticket2subjects_dict)
    textacy.fileio.write_file_lines(generator, lines_sub)


    labelset, corpus, labels = load_corp(lines_sub)

    K = 20 #Number of topics, ist egal, wird in implementierung nicht verwertet
    alpha = 0.001
    beta = 0.001
    number_of_iterations = 10


    llda = LLDA(K, alpha, beta)
    llda.set_corpus(labelset, corpus, labels)

    for i in range(number_of_iterations):
        llda.inference()

    phi = llda.phi()
    # print(llda.vocas)

    # for v, voca in enumerate(llda.vocas):
    # print ','.join([voca]+[str(x) for x in llda.n_z_t[:,v]])
    # print(','.join([voca] + [str(x) for x in phi[:, v]]))




    #################   termite plot    ######################################
    topic_labels = list(labelset)
    term_labels = list(llda.vocas)

    term_topic_weights = phi.transpose()

    threshmin = 0.05


    thresholded = threshold(term_topic_weights, threshmin=threshmin)

    draw.draw_termite(thresholded, topic_labels, term_labels, save="test.png")
    #jgibbsLLDAv2("corpi/labled_lines.txt",ticket2kbs_dict,cleaned_corpus,"results")




    return doc_term_matrix, id2term



def create_ticket2label_dict(ticket2chunk_dict,corpus):
    """
    Creates a dictionary to map a TicketNumber to a label
    :param ticket2chunk_dict: e.g. { TicketNumber :  KB_entries }
    :return: {TicketNumber : label }
    """

    labelist = ticket2chunk_dict.values()
    labelist = flatten(labelist)

    labeldict = create_labeldict(labelist, min_label_freq=1, add_default_label=True)

    ticket2label = {}
    for doc in corpus:
        ticketID = doc.metadata["TicketNumber"]

        keywords = ticket2chunk_dict.get(ticketID, ['DEFAULT'])

        label = ""
        for kw in keywords:
            label = label + str(labeldict.get(normalize_str(str(kw)), labeldict['DEFAULT'])) + " "

            ticket2label.update({ticketID: label})

    return ticket2label



def create_labeldict(labelist, min_label_freq=1, add_default_label=True):

    # nur die x häufigsten labels benutzen
    labelist = [l for l in labelist if labelist.count(l) >= min_label_freq]

    in_labelist_ = {k: labelist.count(k) for k in labelist}  # { label1 : 3 , label2 : 5, label3 : 1   }
    labelist = sort_dictionary(in_labelist_)  # [ (label3, 1), (label1, 3), (label2, 5) ]
    labelist.reverse()  # [ (label2, 5), (label1, 3), (label3, 1) ]
    labeldict = {elem[0]: i for i, elem in enumerate(labelist)}  # { label2  : 0, label1 : 1 , label3 : 2  }
    if add_default_label:
        if 'DEFAULT' not in labeldict.keys():
            labeldict.update({'DEFAULT': len(labelist)})  # { label2  : 0, label1 : 1 , label3 : 2 , DEFAULT : 3 }
    return labeldict



def create_ticket2sth_dicts():
    """
    Return: {str : [str] }

     kb2keywords_dict {'KBA10230': ['DEFAULT'], 'KBA10129': ['DEFAULT'], 'KBA10287': ['sd_ansys_informationen'], } len = 260
     kb2subjects_dict {'KBA10230': ['unicard nochmal beantragen'], 'KBA10129': ['sd_entsperrung unicard nach verlust/wiederfinden'], } len = 260
     ticket2kbs_dict  {'INC44526': ['KBA10056'], 'INC67205': ['KBA10056'], } len = 4832
     ticket2keywords_dict {'INC44526': ['DEFAULT'], 'INC67205': ['DEFAULT'], 'INC71863': ['DEFAULT'], 'INC44392': ['asknet'] } len=4832
     ticket2subjects_dict {'INC44526': ['sd_telefon (antrag: neuanschluss, umzug, aenderung erledigt)'],  len=4832

    """

    # kb2keywords_dict / kb2subjects_dict  --> {str : [str]}

    kb2keywords_dict = {}
    kb2subjects_dict = {}

    kb_gen = textacy.fileio.read_csv(FILEPATH + "M42-Export/KB_2017-09-13.csv", delimiter=";")
    next(kb_gen, None)  # skip first line     "ArticleID";"Subject";"Keywords";...

    for line in kb_gen:

        kb_id = line[0]

        subject = normalize_str(line[1])

        keywords = [normalize_str(x) for x in str(line[2]).split(",")]

        if kb_id not in kb2keywords_dict.keys():
            kb2keywords_dict[kb_id] = keywords if keywords != [''] else ["DEFAULT"]
        else:
            kb2keywords_dict[kb_id] = kb2keywords_dict[kb_id] + keywords

        if kb_id not in kb2subjects_dict.keys():
            kb2subjects_dict[kb_id] = [normalize_str(subject) if subject != [''] else "DEFAULT"]
        else:
            kb2subjects_dict[kb_id].append(normalize_str(subject))

    # ticket2kbs_dict  -->  {str : [str]}
    ticket2kbs_dict = {}
    kb2ticket_gen = textacy.fileio.read_csv(FILEPATH + "M42-Export/KB2Ticket_2017-09-13.csv", delimiter=";")
    next(kb2ticket_gen, None)  # skip first line"TicketNumber";"ArticleID"

    for line in kb2ticket_gen:
        ticket_id = line[0]
        kb_id = line[1]

        if ticket_id not in ticket2kbs_dict.keys():
            ticket2kbs_dict[ticket_id] = [kb_id]
        else:
            ticket2kbs_dict[ticket_id].append(kb_id)

    # ticket2keywords --> {str:[str]}
    ticket2keywords_dict = {}

    for ticket_id, kb_ids in ticket2kbs_dict.items():

        if ticket_id not in ticket2keywords_dict.keys():
            ticket2keywords_dict[ticket_id] = []

        for kb_id in kb_ids:
            ticket2keywords_dict[ticket_id].append(kb2keywords_dict[kb_id])

        ticket2keywords_dict[ticket_id] = flatten(ticket2keywords_dict[ticket_id])

    # ticket2subjects --> {str:[str]}
    ticket2subjects_dict = {}

    for ticket_id, kb_ids in ticket2kbs_dict.items():

        if ticket_id not in ticket2subjects_dict.keys():
            ticket2subjects_dict[ticket_id] = []

        for kb_id in kb_ids:
            ticket2subjects_dict[ticket_id].append(kb2subjects_dict[kb_id])

        ticket2subjects_dict[ticket_id] = flatten(ticket2subjects_dict[ticket_id])


    """
    count_dict = {}
    for v in ticket2kbs_dict.values():
        for kb in v:
            if kb in count_dict.keys():
                count_dict[kb] +=1
            else:
                count_dict[kb] = 1

    sorted_dict = sorted(count_dict.items(), key=operator.itemgetter(1))

    for k,v in sorted_dict:
        subs = kb2subjects_dict[k]
        keys = kb2keywords_dict[k]
        print(subs, keys , v) # frage wieviele tickets pro topic?

    print("kb_entrys used: {}".format(len(sorted_dict)))     # frage wie viele kb_entry's insg genutzt?: 155
    """



    return kb2keywords_dict, kb2subjects_dict, ticket2kbs_dict, ticket2keywords_dict, ticket2subjects_dict
    #labelist = ticket2keywords_dict.values()

    #labelist = flatten(labelist)
    #labelist = list(set(labelist))
    #labeldict = {k: v for v, k in enumerate(labelist)}

    ##############################################################################################



def gen_lines(doc_term_matrix,term2id,corpus,label="TicketNumber"):

    for i,doc in enumerate(corpus):
        line = "[" + doc.metadata[label] + "]"

        for term, id_ in term2id.items():
            if doc_term_matrix[i, id_] != 0:
                term = term if len(term.split()) == 1 else "_".join(term.split())

                line = line + " " + term
        if len(line) != 0:
            yield line
        else:
            continue








def main(cleaned_corpus):
    start = time.time()





    """
    filter_tokens = [

        keepNouns(NOUNS),

        removeWords(DE_STOP_WORDS + custom_words + VORNAMEN),

        removePOS(["PUNCT", "SPACE", "NUM"]),

        #todo STELLSCHRAUBE remove_words_containing_Numbers(),
        #todo STELLSCHRAUBE remove_long_words(),
        #todo STELLSCHRAUBE remove_short_words()

    ]
    
    clean_in_meta = {
        "Solution": [removePOS(["SPACE"])],
        "Subject": [removePOS(["SPACE", "PUNCT"])],
        "categoryName": [removePOS(["SPACE", "PUNCT"])]
    }
    """





    doc_term_matrix, id2term_dict = preprocessCorpus(cleaned_corpus)




    end = time.time()
    logprint("Time Elapsed Preprocessing:{0} min".format((end - start) / 60))
    return doc_term_matrix, id2term_dict







if __name__ == "__main__":
    logprint("Load Corpus...")

    corpus_name = "de_clean_small" # _small
    cleaned_corpus, parser = load_corpus(corpus_path="/home/jannis.grundmann/PycharmProjects/topicModelingTickets/corpi/", corpus_name=corpus_name)

    logprint("... Done")

    main(cleaned_corpus)







"""
pipe=[

    ##String

    fixUnicode(),
    replaceHardS(),
    resolveAbbrivations(),

    remove_words_containing_topLVL(),

    replaceSpecialChars(" "), (mit Leerzeichen erstzen, dadruch werden Terme wie 8203;verfügung getrennt

    remove_words_containing_Numbers(),



    ##spacyParse

    removeENT("PERSON"),
    keepPOS(["NOUN"]),

    #ODER

    lemmatize(),
    removeWords(de_stop_words + config.get("preprocessing","custom_words").split(",")),


    # evtl.
    spellCorrection(),
    keepUniqeTokens(),

]




"""

"""
filter_tokens=[
#removeENT(["PERSON"]),
#idee addressen enfernen #bisher mit cut_after("gruss") --> postal.parser
#idee rechtschreibkorrektur --> PyEnchant
#idee thesaurus --> WordNet, eigener

remove_words_containing_Numbers(),

removePOS(["PUNCT","SPACE","NUM"]),

removeWords(de_stop_words+custom_words),

remove_long_words(),
remove_short_words(),
remove_first_names(),

keepPOS(["NOUN"]),

]
"""