76 lines
1.5 KiB
Python
76 lines
1.5 KiB
Python
# -*- coding: utf-8 -*-
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import matplotlib
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matplotlib.use('Agg')
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import time
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import init
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from datetime import datetime
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import corporization
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import preprocessing
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import topicModeling
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import cleaning
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from miscellaneous import *
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# ssh madonna "nohup /usr/bin/python3 -u /home/jannis.grundmann/PycharmProjects/topicModelingTickets/main.py &> /home/jannis.grundmann/PycharmProjects/topicModelingTickets/log/printout_main.log &"
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start = time.time()
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# idee http://bigartm.org/
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# idee http://wiki.languagetool.org/tips-and-tricks
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# idee https://en.wikipedia.org/wiki/Noisy_text_analytics
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# idee https://gate.ac.uk/family/
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# idee häufige n-gramme raus (zB damen und herren)
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# idee llda topics zusammenfassen
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# idee lda so trainieren, dass zuordnung term <-> topic nicht zu schwach wird, aber möglichst viele topics
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# frage welche mitarbeiter bearbeiteten welche Topics? idee topics mit mitarbeiternummern erstzen
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# idee word vorher mit semantischen netz abgleichen: wenn zu weit entfernt, dann ignore
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# todo modelle testen
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logprint("main.py started at {}".format(datetime.now()))
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"""
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init.main()
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logprint("")
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corporization.main()
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logprint("")
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cleaning.main()
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logprint("")
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preprocessing.main()
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logprint("")
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"""
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#topicModeling.main(algorithm="lsa")
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logprint("")
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#topicModeling.main(algorithm="nmf")
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logprint("")
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#topicModeling.main(algorithm="llda")
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logprint("")
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topicModeling.main(algorithm="lda")
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logprint("")
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end = time.time()
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logprint("main.py finished at {}".format(datetime.now()))
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logprint("Total Time Elapsed: {0} min".format((end - start) / 60))
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