topicModelingTickets/main.py

# -*- coding: utf-8 -*-
import matplotlib
matplotlib.use('Agg')
import time
import init
from datetime import datetime
import corporization
import preprocessing
import topicModeling
import cleaning

from miscellaneous import *

# ssh madonna "nohup /usr/bin/python3 -u /home/jannis.grundmann/PycharmProjects/topicModelingTickets/main.py &> /home/jannis.grundmann/PycharmProjects/topicModelingTickets/log/printout_main.log &"
start = time.time()

# idee http://bigartm.org/
# idee http://wiki.languagetool.org/tips-and-tricks
# idee https://en.wikipedia.org/wiki/Noisy_text_analytics
# idee https://gate.ac.uk/family/




# idee häufige n-gramme raus (zB damen und herren)
# idee llda topics zusammenfassen
# idee lda so trainieren, dass zuordnung term <-> topic nicht zu schwach wird, aber möglichst viele topics
# frage welche mitarbeiter bearbeiteten welche Topics? idee topics mit mitarbeiternummern erstzen
# idee word vorher mit semantischen netz abgleichen: wenn zu weit entfernt, dann ignore
# idee lda2vec
# todo modelle testen




logprint("main.py started")


init.main()
logprint("")

raw_corpus = corporization.main()
logprint("")

cleaned_corpus = cleaning.main(raw_corpus)
logprint("")

doc_term_matrix, id2term_dict = preprocessing.main(cleaned_corpus)
logprint("")

topicModeling.textacyTopicModeling_v2(doc_term_matrix, id2term_dict)





"""
ticket_number = "INC40484"
raw=""
pre=""
clean=""
for r in raw_corpus.get(lambda doc: doc.metadata["TicketNumber"] == ticket_number):
    raw = r
for c in cleaned_corpus.get(lambda doc: doc.metadata["TicketNumber"] == ticket_number):
    clean = c
for p in  pre_corpus.get(lambda doc: doc.metadata["TicketNumber"] == ticket_number):
    pre = p

for tok1,tok2,tok3 in zip(raw,clean,pre):

        logprint(tok1.text,tok1.pos_)
        logprint(tok2.text,tok2.pos_)
        logprint(tok3.text,tok3.pos_)
"""


    #for i in range(5):
    #    printRandomDoc(cleaned_corpus)







"""



#topicModeling.main(algorithm="lsa")
logprint("")


#topicModeling.main(algorithm="nmf")
logprint("")

"""
#topicModeling.main(pre_corpus=pre_corpus,cleaned_corpus=cleaned_corpus,algorithm="llda")
logprint("")


#topicModeling.main(pre_corpus=pre_corpus,cleaned_corpus=cleaned_corpus,algorithm="lda")
logprint("")




end = time.time()
logprint("main.py finished at {}".format(datetime.now()))
logprint("Total Time Elapsed: {0} min".format((end - start) / 60))