topicModelingTickets/testra.py

# -*- coding: utf-8 -*-

import re
import time
import json

#import spacy
#import textacy
from functools import reduce

import textacy

start = time.time()

import enchant

from datetime import datetime
import os
import xml.etree.ElementTree as ET
FILEPATH = os.path.dirname(os.path.realpath(__file__)) + "/"
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"

config = ConfigParser.ConfigParser()
with open(config_ini) as f:
    config.read_file(f)


PARSER=spacy.load("de")


corpi = textacy.Corpus(PARSER)

testcontetn = [
    "fdsfdsfsd",
    "juzdtjlkö",
    "gfadojplk"
]

testmetda = [
    {"categoryName":"zhb","Solution":"","Subject":"schulungstest"},
    {"categoryName":"neuanschluss","Solution":"subject","Subject":"telephone contract"},
    {"categoryName":"zhb","Solution":"","Subject":"setuji"}
]


def makecontent(testcontetn):
    for content in testcontetn:
        yield content


def makemeta( testmetda):
    for  metdata in  testmetda:
        yield  metdata


def corpus2Text(corpus):
    for doc in corpus:
        yield doc.text

corpi.add_texts(
    makecontent(testcontetn),
    makemeta(testmetda)
)
corpus_de_path = "/home/jannis.grundmann/PycharmProjects/topicModelingTickets/test/"
rawCorpus_name =  "de_test_ticket"
print(corpi)

#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")


dict = {"unicard redaktionsteam": 189, "kms": 131, "itmc_st\u00f6rungen": 17, "benutzerverwaltung_probleme": 168, "mailverteiler exchange": 130, "beamer": 70, "cws_confluence": 190, "benutzerverwaltung": 26, "sos": 166, "virtuelle server": 116, "sap": 7, "wlan": 21, "lsf": 6, "gastaufenthalt": 8, "umzug": 5, "firewall betreuung": 129, "ausleihe": 39, "fiona": 10, "kursplanung": 195, "schulungsraum verwaltung": 200, "plagiatserkennung": 32, "designentwicklung": 100, "ub basis it": 184, "tsm": 51, "backup tsm": 110, "raumkalender": 174, "veeam": 149, "linux bs": 42, "hochleistungsrechnen": 90, "e learning": 37, "h\u00f6rsaal\u00fcbertragung": 52, "sophos": 88, "service portal redaktion": 182, "verkauf": 93, "fk 16": 30, "campus app": 54, "dns": 71, "kurse": 196, "itmc schulungsr\u00e4ume": 96, "leitung": 91, "telefon": 14, "housing": 135, "softwarelizenzen": 35, "hcm stammdaten": 68, "semesterticket": 197, "exchange nutzung": 33, "mediendienste": 167, "sam spider": 172, "pvp": 27, "webserver": 29, "werkvertr\u00e4ge": 158, "ibz raumbuchung": 177, "webmailer": 126, "unicard sperrung": 64, "cd dvd produktion": 114, "lizenzserver": 92, "pr\u00fcfungsmanagement": 38, "blogs wikis foren": 87, "unicard ausgabe": 161, "pools": 157, "desktop & basisdienste": 144, "antrag auf rechnungserstellung": 193, "mailalias": 121, "evaexam": 133, "neuanschluss": 0, "mobilfunkvertr\u00e4ge": 69, "ftp server": 191, "haustechnik": 77, "raumbuchungssysteme": 186, "confluence": 181, "uniaccount zugangsdaten": 47, "itmc medienr\u00e4ume ef50": 171, "dokoll support": 128, "elektronisches telefonbuch": 3, "softwareverteilung": 153, "overhead projektor": 104, "sicherheit": 145, "itmc_als": 48, "itmc pools": 160, "zhb": 60, "serversupport": 101, "veranstaltungen": 61, "fk12 webauftritt": 138, "hardware": 142, "unicard produktion": 156, "telefonkonferenzen": 170, "dhcp": 188, "zertifikate server dfn": 139, "lan": 1, "datanet": 49, "neuausstattung": 173, "moodle": 16, "abmeldung": 13, "uni mail": 15, "medienr\u00e4ume ef50": 117, "verschiedene aufgaben": 40, "zentrale webserver": 75, "vorlesungsaufzeichnung": 152, "grafik": 132, "campus management": 72, "hacker angriff": 46, "pos": 23, "zugangsdaten": 41, "serviceportal": 63, "ews": 24, "voicemail box": 150, "service desk itmc": 74, "test": 180, "beschaffung": 57, "bestellung": 185, "vpn": 55, "app feedback": 66, "allgemein": 134, "rundmail": 105, "telefonabrechnung": 199, "limesurvey": 31, "unicard": 28, "eldorado": 140, "uniaccount": 12, "plotter": 125, "mdm mobile device management": 120, "namens\u00e4nderung": 43, "sd": 84, "basis applikationen": 103, "\u00e4nderung": 194, "fileserver einrichtung": 187, "fk14_test": 154, "werkst\u00e4tte": 179, "itmc_aufgaben": 45, "formulare antr\u00e4ge": 81, "facility": 192, "web": 169, "asknet": 136, "server storage": 113, "mail groupware": 20, "rektorat -b\u00fcro": 178, "office": 50, "werkstoffe lehrstuhl bauwesen": 59, "telefonzentrale": 115, "verwaltung": 4, "netze": 22, "beantragung": 82, "d.3 dms": 148, "redmine projektverwaltung": 141, "wsus": 106, "lido": 118, "rechnerr\u00e4ume": 143, "matrix42_hilfe": 18, "boss service desk": 44, "konteneinsicht": 62, "spam phishing": 53, "forensic": 164, "fk 12": 11, "benutzungsverwaltung": 198, "redmine": 79, "basis app": 85, "viren": 95, "fk12 migration": 155, "raumbuchung": 109, "virtuelle desktops citrix": 176, "outlook_einrichtung": 123, "kundenserver": 137, "nrw ticket": 80, "weiterentwicklung": 127, "siport zugangskontrolle": 98, "e mail dienste": 99, "vorlagenerstellung": 36, "video": 19, "studierendensekretariat": 111, "it sicherheit sic": 86, "boss": 25, "technik": 58, "dokoll pvp": 112, "betrieb": 2, "v2 campus app feedback": 151, "mailverteiler": 108, "videoschnitt": 119, "fk raumplanung 09": 9, "sap urlaub": 73, "keine r\u00fcckantwort": 124, "prozess- und projektmanagement": 67, "dienstreise": 34, "webgestaltung": 78, "schulung": 175, "software": 89, "medientechnik": 76, "servicedesk": 107, "service portal": 94, "software entwicklung": 165, "uniflow": 159, "ub_st\u00f6rungen": 162, "fk15": 183, "uhren": 

list = [(key,value) for key,value in dict.items()]

list.sort(key=lambda tup : tup[1])
"""
"""
from spacy.tokens.doc import Doc as SpacyDoc

filepath = "/home/jannis.grundmann/PycharmProjects/topicModelingTickets/corpi/de_clean_ticket_content.bin"

# load parser
parser = spacy.load("de")

corpus_path = "/home/jannis.grundmann/PycharmProjects/topicModelingTickets/corpi/"

stringstorepath = corpus_path + 'de_parser/vocab/strings.json'
with open(stringstorepath) as file:
    parser.vocab.strings.load(file)

vocabpath = Path(corpus_path + 'de_parser/vocab/lexemes.bin')
parser.vocab.load_lexemes(vocabpath)

spacy_vocab = parser.vocab

def readCorpus(filepath):
    with open_sesame(filepath, mode='rb') as f:
        for bytes_string in SpacyDoc.read_bytes(f):
            yield SpacyDoc(spacy_vocab).from_bytes(bytes_string).text


textacy.fileio.write_file_lines(readCorpus(filepath),"/home/jannis.grundmann/PycharmProjects/topicModelingTickets/result.txt")
"""



# load raw corpus and create new one
#raw_corpus, parser = load_corpusV2(corpus_name=rawCorpus_name, corpus_path=corpus_de_path)

#printRandomDoc(raw_corpus)


"""
spacy_doc = PARSER("test")
save_obj(spacy_doc, "/home/jannis.grundmann/PycharmProjects/topicModelingTickets/doc.pkl")

spacy_doc2 = load_obj("/home/jannis.grundmann/PycharmProjects/topicModelingTickets/doc.pkl")

print("Doc: {0}".format(spacy_doc2))



jgibbsLLDA_root = "/home/jannis.grundmann/PycharmProjects/topicModelingTickets/"

LLDA_filepath = "{0}labeldict.txt".format(jgibbsLLDA_root)
laveldict = {'fiona': 10, 'vorlagenerstellung': 36, 'webserver': 29, 'matrix42_hilfe': 18, 'sap': 7, 'pos': 23, 'verwaltung': 4, 'lan': 1}
with open(LLDA_filepath, 'w') as file:
    file.write(json.dumps(laveldict))
"""
"""
def load_corpus(corpus_path, corpus_name, lang="de"):
    from pathlib import Path

    # load parser
    parser = spacy.load(lang)

    stringstorepath = corpus_path + str(lang) + '_parser'+'/vocab/strings.json'
    with open(stringstorepath) as file:
        parser.vocab.strings.load(file)

    vocabpath = Path(corpus_path + str(lang) + '_parser'+'/vocab/lexemes.bin')
    parser.vocab.load_lexemes(vocabpath)

    corpus = textacy.Corpus(parser)


    contentpath = corpus_path + corpus_name + "_content.bin"
    metapath = corpus_path + corpus_name + "_meta.json"



    metadata_stream = textacy.fileio.read_json_lines(metapath)
    spacy_docs = textacy.fileio.read_spacy_docs(corpus.spacy_vocab, contentpath)
    for spacy_doc, metadata in zip(spacy_docs, metadata_stream):
        corpus.add_doc(
            textacy.Doc(spacy_doc, lang=corpus.spacy_lang, metadata=metadata))
    return corpus
"""


"""
# THESAURUS
lexicalentries = "/home/jannis.grundmann/PycharmProjects/topicModelingTickets/lexicalentries_small.xml"
lexicalentries = "/home/jannis.grundmann/PycharmProjects/topicModelingTickets/lexicalentries.xml"
synsets = "/home/jannis.grundmann/PycharmProjects/topicModelingTickets/synsets.xml"

def build_thesaurus(path2lexicalentries):#, path2synsets):
    lextree = ET.parse(path2lexicalentries, ET.XMLParser(encoding="utf-8"))
    #syntree = ET.parse(path2synsets, ET.XMLParser(encoding="utf-8"))

    lexroot = lextree.getroot()
    #synroot = syntree.getroot()


    word2synsets = {}
    template = {"w1": ["s1", "s2"]}

    for ro in lexroot:
        for elem in ro:
            if elem.tag == "LexicalEntry":
                lex_dictlist = [subentry.attrib for subentry in elem]



                synlist = []
                string = "WORD"

                for lex_dict in lex_dictlist:
                    if "synset" in lex_dict.keys():

                            synset = lex_dict["synset"]
                            synlist.append(synset)

                    if 'writtenForm' in lex_dict.keys():
                            string = (lex_dict["writtenForm"])

                            # replaceRockDots
                            string = re.sub(r'[ß]', "ss", string)
                            string = re.sub(r'[ö]', "oe", string)
                            string = re.sub(r'[ü]', "ue", string)
                            string = re.sub(r'[ä]', "ae", string)

                            # alle punkte raus
                            string = re.sub(r'[.]', "", string)

                            # alles in klammern raus
                            string = re.sub(r"\((.*)\)", " ", string)

                            # längeres leerzeichen normalisieren
                            string = textacy.preprocess.normalize_whitespace(string)

                            string = string.lower().strip()

                word2synsets[string] = synlist

    synset2Words = {}
    template = {"s1": ["w1","w2"]}

    for word,synset in word2synsets.items():
        for syn in synset:
            if syn not in synset2Words.keys():
                synset2Words[syn] = [word]
            else:
                synset2Words[syn].append(word)

    # nach anzhal der wörter in den strings sortieren
    for synset in word2synsets.values():
        synset.sort(key=lambda x: len(x.split()))

    thesaurus = {}
    thesaurus_template = {"w1" : "mainsyn"}

    for word,synset in word2synsets.items():
        try:
            thesaurus[word] = synset2Words[synset[0]][0]    #Ann.: erstes synonym ist das Hauptsynonym
        except:
            pass
    return thesaurus

   
    for r in synroot:
        for element in r:

            if element.tag == "Synset":
                synset = []
                attrib = element.attrib
                id = attrib["id"]

                if id not in synset2Words.keys():
                    synset2Words[id] = "WORD"

"""

"""
from postal.parser import parse_address


address = "Nicolas Rauner LS Biomaterialien und Polymerwissenschaften Fakultät Bio- und Chemieingenieurwesen TU Dortmund D-44227 Dortmund Tel: + 49-(0)231 / 755 - 3015 Fax: + 49-(0)231 / 755 - 2480"
print(parse_address(address))


address = "Technische Universität Dortmund Maschinenbau/Lehrstuhl für Förder- und Lagerwesen LogistikCampus Joseph-von-Fraunhofer-Str. 2-4 D-44227 Dortmund "
print(parse_address(address))
"""

"""

corpus_path = "/home/jannis.grundmann/PycharmProjects/topicModelingTickets/corpi/"
corpus_name = "testcorpus"


#corpi.save(corpus_path, name=corpus_name, compression=corpus_compression)
#corpi =  textacy.Corpus.load(corpus_path, name=corpus_name, compression=corpus_compression)



import pathlib

strings_path = pathlib.Path(corpus_path + 'strings.json')
path_lexemes_bin_ = pathlib.Path(corpus_path + 'lexemes.bin')

PARSER.vocab.dump(path_lexemes_bin_)
nlp.vocab.load_lexemes(path_lexemes_bin_)


def save_corpus(corpus_path,corpus_name):

    # save stringstore
    stringstore_path = corpus_path + corpus_name + '_strings.json'
    with open(stringstore_path, "w") as file:
        PARSER.vocab.strings.dump(file)


    #save content
    contentpath = corpus_path + corpus_name+ "_content.bin"
    textacy.fileio.write_spacy_docs((doc.spacy_doc for doc in corpi),contentpath)


    #save meta
    metapath = corpus_path + corpus_name +"_meta.json"
    textacy.fileio.write_json_lines((doc.metadata for doc in corpi), metapath)



def load_corpus(corpus_path,corpus_name):
    # load new lang
    nlp = spacy.load("de")

    #load stringstore
    stringstore_path = corpus_path + corpus_name + '_strings.json'
    with open(stringstore_path,"r") as file:
        nlp.vocab.strings.load(file)

    # define corpi
    corpi = textacy.Corpus(nlp)

    # load meta
    metapath = corpus_path + corpus_name +"_meta.json"
    metadata_stream = textacy.fileio.read_json_lines(metapath)

    #load content
    contentpath = corpus_path + corpus_name+ "_content.bin"
    spacy_docs = textacy.fileio.read_spacy_docs(corpi.spacy_vocab, contentpath)

    for spacy_doc, metadata in zip(spacy_docs, metadata_stream):
        corpi.add_doc(
            textacy.Doc(spacy_doc, lang=corpi.spacy_lang, metadata=metadata))

    return corpi


save_corpus(corpus_path,corpus_name)

print(load_corpus(corpus_path,corpus_name))

"""

"""
def normalizeSynonyms(default_return_first_Syn=False, parser=PARSER):
    #return lambda doc : parser(" ".join([tok.lower_ for tok in doc]))
    return lambda doc : parser(" ".join([getFirstSynonym(tok.lower_, THESAURUS, default_return_first_Syn=default_return_first_Syn) for tok in doc]))

def getFirstSynonym(word, thesaurus, default_return_first_Syn=False):
    if not isinstance(word, str):
        return str(word)

    word = word.lower()

    # durch den thesaurrus iterieren
    for syn_block in thesaurus:  # syn_block ist eine liste mit Synonymen

        for syn in syn_block:
            syn = syn.lower()
            if re.match(r'\A[\w-]+\Z', syn):  # falls syn einzelwort ist
                if word == syn:
                    return str(getHauptform(syn_block, word, default_return_first_Syn=default_return_first_Syn))
            else:  # falls es ein satz ist
                if word in syn:
                    return str(getHauptform(syn_block, word, default_return_first_Syn=default_return_first_Syn))
    return str(word)  # zur Not, das ursrpüngliche Wort zurückgeben

def getHauptform(syn_block, word, default_return_first_Syn=False):
    for syn in syn_block:
        syn = syn.lower()

        if "hauptform" in syn and len(syn.split(" ")) <= 2:
            # nicht ausgeben, falls es in Klammern steht#todo gibts macnmal?? klammern aus
            for w in syn.split(" "):
                if not re.match(r'\([^)]+\)', w):
                    return w

    if default_return_first_Syn:
        # falls keine hauptform enthalten ist, das erste Synonym zurückgeben, was kein satz ist und nicht in klammern steht
        for w in syn_block:
            if not re.match(r'\([^)]+\)', w):
                return w
    return word  # zur Not, das ursrpüngliche Wort zurückgeben
"""

"""
path2xml="/home/jannis.grundmann/PycharmProjects/topicModelingTickets/deWordNet.xml"

tree = ET.parse(path2xml, ET.XMLParser(encoding="utf-8"))
root = tree.getroot()

for r in root:
    for element in r:

            if element.tag == "Synset":
                attrib = element.attrib
                for i,subentry in enumerate(element):
                    if subentry.tag == "Lemma" and subentry.attrib["partOfSpeech"] == "n":
                            string = (subentry.attrib["writtenForm"])
                            # replaceRockDots
                            string = re.sub(r'[ß]', "ss", string)
                            string = re.sub(r'[ö]', "oe", string)
                            string = re.sub(r'[ü]', "ue", string)
                            string = re.sub(r'[ä]', "ae", string)

                            # seperate_words_on_regex:
                            string = " ".join(re.compile(regex_specialChars).split(string))
                            string_list=string.split()
                            if len(string_list) == 1:
                                nomen.append(string.lower().strip())
"""

"""
import re
from collections import Counter

def words(text): return re.findall(r'\w+', text.lower())

WORDS = Counter(words(open('/home/jannis.grundmann/PycharmProjects/topicModelingTickets/deu_news_2015_1M-sentences.txt').read()))

def P(word, N=sum(WORDS.values())):
    "Probability of `word`."
    return WORDS[word] / N

def correction(word):
    "Most probable spelling correction for word."
    return max(candidates(word), key=P)

def candidates(word):
    "Generate possible spelling corrections for word."
    return (known([word]) or known(edits1(word)) or known(edits2(word)) or [word])

def known(words):
    "The subset of `words` that appear in the dictionary of WORDS."
    return set(w for w in words if w in WORDS)

def edits1(word):
    "All edits that are one edit away from `word`."
    letters    = 'abcdefghijklmnopqrstuvwxyz'
    splits     = [(word[:i], word[i:])    for i in range(len(word) + 1)]
    deletes    = [L + R[1:]               for L, R in splits if R]
    transposes = [L + R[1] + R[0] + R[2:] for L, R in splits if len(R)>1]
    replaces   = [L + c + R[1:]           for L, R in splits if R for c in letters]
    inserts    = [L + c + R               for L, R in splits for c in letters]
    return set(deletes + transposes + replaces + inserts)

def edits2(word):
    "All edits that are two edits away from `word`."
    return (e2 for e1 in edits1(word) for e2 in edits1(e1))

"""

"""
### extract from derewo

#http://www1.ids-mannheim.de/kl/projekte/methoden/derewo.html


raw = textacy.fileio.read_file_lines("DeReKo-2014-II-MainArchive-STT.100000.freq")

for line in raw:
    line_list=line.split()
    if line_list[2] == "NN":
        string = line_list[1].lower()

        # replaceRockDots
        string = re.sub(r'[ß]', "ss", string)
        string = re.sub(r'[ö]', "oe", string)
        string = re.sub(r'[ü]', "ue", string)
        string = re.sub(r'[ä]', "ae", string)


        nomen.append(string.lower().strip())


textacy.fileio.write_file_lines(nomen,"nomen2.txt")
"""

"""
stream = textacy.fileio.read_csv("/home/jannis.grundmann/PycharmProjects/topicModelingTickets/M42-Export/Tickets_2017-09-13.csv", delimiter=";")
content_collumn_name = "Description"
content_collumn = 9  # standardvalue

de_tickets=[]
en_tickets=[]
misc_tickets=[]

error_count = 0
for i, lst in enumerate(stream):
    if i == 0:
        de_tickets.append(lst)
        en_tickets.append(lst)
        misc_tickets.append(lst)
    else:
        try:
            content_collumn_ = lst[content_collumn]
            if detect(content_collumn_) == "de":
                de_tickets.append(lst)
            elif detect(content_collumn_) == "en":
                en_tickets.append(lst)
            else:
                misc_tickets.append(lst)

        except:
            misc_tickets.append(lst)
            error_count += 1

print(error_count)

textacy.fileio.write_csv(de_tickets,"M42-Export/de_tickets.csv", delimiter=";")
textacy.fileio.write_csv(en_tickets,"M42-Export/en_tickets.csv", delimiter=";")
textacy.fileio.write_csv(misc_tickets,"M42-Export/misc_tickets.csv", delimiter=";")


"""

"""
regex_specialChars = r'[`\-=~!#@,.$%^&*()_+\[\]{};\'\\:"|</>?]'


def stringcleaning(stringstream, funclist):
    for string in stringstream:
        for f in funclist:
            string = f(string)
        yield string


def seperate_words_on_regex(regex=regex_specialChars):
    return lambda string: " ".join(re.compile(regex).split(string))


words = [
    "uniaccount",
    "nr54065467",
    "nr54065467",
    "455a33c5,"
    "tvt?=",
    "tanja.saborowski@tu-dortmund.de",
    "-",
    "m-sw1-vl4053.itmc.tu-dortmund.de",
    "------problem--------"
]



topLVLFinder = re.compile(r'\.[a-z]{2,3}(\.[a-z]{2,3})?', re.IGNORECASE)
specialFinder = re.compile(r'[`\-=~!@#$%^&*()_+\[\]{};\'\\:"|<,./>?]', re.IGNORECASE)

for s in stringcleaning((w for w in words),[seperate_words_on_regex()]):
    print(s.strip())

    #print(stringcleaning(w,string_comp))
    #print(bool(re.search(r'\.[a-z]{2,3}(\.[a-z]{2,3})?',w)))
    #print(bool(re.search(r'[`\-=~!@#$%^&*()_+\[\]{};\'\\:"|<,./>?]',w)))
    #result = specialFinder.sub(" ", w)
    #print(re.sub(r'[`\-=~!@#$%^&*()_+\[\]{};\'\\:"|<,./>?]'," ",w))

    #print(re.sub(r'\.[a-z]{2,3}(\.[a-z]{2,3})?', " ", w))
"""

"""
def replaceRockDots():
    return lambda string: re.sub(r'[ß]', "ss", (re.sub(r'[ö]', "oe", (re.sub(r'[ü]', "ue", (re.sub(r'[ä]', "ae", string.lower())))))))



de_stop_words = list(textacy.fileio.read_file_lines(filepath="german_stopwords_full.txt"))


#blob = Text(str(textacy.fileio.read_file("teststring.txt")))#,parser=PatternParser(pprint=True, lemmata=True))

#print(blob.entities)

de_stop_words = list(map(replaceRockDots(),de_stop_words))
#LEMMAS = list(map(replaceRockDots(),LEMMAS))
#VORNAMEN = list(map(replaceRockDots(),VORNAMEN))

de_stop_words = list(map(textacy.preprocess.normalize_whitespace,de_stop_words))
#LEMMAS = list(map(textacy.preprocess.normalize_whitespace,LEMMAS))
#VORNAMEN = list(map(textacy.preprocess.normalize_whitespace,VORNAMEN))




#textacy.fileio.write_file_lines(LEMMAS,"lemmas.txt")
#textacy.fileio.write_file_lines(VORNAMEN,"firstnames.txt")
textacy.fileio.write_file_lines(de_stop_words,"german_stopwords.txt")

"""

end = time.time()
print("\n\n\nTime Elapsed Topic:{0}\n\n".format(end - start))