405 lines
11 KiB
Python
405 lines
11 KiB
Python
# -*- coding: utf-8 -*-
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import csv
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import functools
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import os.path
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import re
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import subprocess
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import time
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import xml.etree.ElementTree as ET
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import sys
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import spacy
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import textacy
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from scipy import *
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from textacy import Vectorizer
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csv.field_size_limit(sys.maxsize)
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# Load the configuration file
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import configparser as ConfigParser
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config = ConfigParser.ConfigParser()
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with open("config.ini") as f:
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config.read_file(f)
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#todo print&log
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path2xml = config.get("default","path2xml")
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PARSER = spacy.load(config.get("default","language"))
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corpus = textacy.Corpus(PARSER)
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thesauruspath = config.get("default","thesauruspath")
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THESAURUS = list(textacy.fileio.read_csv(thesauruspath, delimiter=";"))
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stop_words=list(__import__("spacy." + PARSER.lang, globals(), locals(), ['object']).STOP_WORDS) + config.get("preprocessing","custom_words").split(",")
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def compose(*functions):
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def compose2(f, g):
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return lambda x: f(g(x))
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return functools.reduce(compose2, functions, lambda x: x)
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def generateMainTextfromTicketXML(path2xml, main_textfield='Beschreibung'):
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"""
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generates strings from XML
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:param path2xml:
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:param main_textfield:
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:param cleaning_function:
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:yields strings
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"""
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tree = ET.parse(path2xml, ET.XMLParser(encoding="utf-8"))
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root = tree.getroot()
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for ticket in root:
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for field in ticket:
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if field.tag == main_textfield:
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yield field.text
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def generateMetadatafromTicketXML(path2xml, leave_out=['Beschreibung']):
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tree = ET.parse(path2xml, ET.XMLParser(encoding="utf-8"))
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root = tree.getroot()
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for ticket in root:
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metadata = {}
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for field in ticket:
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if field.tag not in leave_out:
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metadata[field.tag] = field.text
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yield metadata
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def printRandomDoc(textacyCorpus):
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import random
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print()
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print("len(textacyCorpus) = %i" % len(textacyCorpus))
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randIndex = int((len(textacyCorpus) - 1) * random.random())
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print("Index: {0} ; Text: {1} ; Metadata: {2}".format(randIndex, textacyCorpus[randIndex].text, textacyCorpus[randIndex].metadata))
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print()
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def processDictstream(dictstream, funcdict, parser=PARSER):
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for dic in dictstream:
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result = {}
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for key, value in dic.items():
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if key in funcdict:
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result[key] = funcdict[key](parser(value))
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else:
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result[key] = value
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yield result
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def processTextstream(textstream, func, parser=PARSER):
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# input str-stream output str-stream
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pipe = parser.pipe(textstream)
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for doc in pipe:
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yield func(doc)
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def keepOnlyPOS(pos_list, parser=PARSER):
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return lambda doc : parser(" ".join([tok.text for tok in doc if tok.pos_ in pos_list]))
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def removeAllPOS(pos_list, parser=PARSER):
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return lambda doc: parser(" ".join([tok.text for tok in doc if tok.pos_ not in pos_list]))
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def keepOnlyENT(ent_list,parser=PARSER):
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return lambda doc: parser(" ".join([tok.text for tok in doc if tok.ent_type_ in ent_list]))
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def removeAllENT(ent_list, parser=PARSER):
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return lambda doc: parser(" ".join([tok.text for tok in doc if tok.ent_type_ not in ent_list]))
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def keepUniqueTokens(parser=PARSER):
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return lambda doc: parser(" ".join(set([tok.text for tok in doc])))
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def lemmatize(parser=PARSER):
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return lambda doc: parser(" ".join([tok.lemma_ for tok in doc]))
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doc2String = lambda doc : doc.text
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mentionFinder = re.compile(r"@[a-z0-9_]{1,15}", re.IGNORECASE)
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emailFinder = re.compile(r"\b[A-Z0-9._%+-]+@[A-Z0-9.-]+\.[A-Z]{2,}\b", re.IGNORECASE)
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urlFinder = re.compile(r"^(?:https?:\/\/)?(?:www\.)?[a-zA-Z0-9./]+$", re.IGNORECASE)
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def replaceURLs(replace_with="URL",parser=PARSER):
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#return lambda doc: parser(textacy.preprocess.replace_urls(doc.text,replace_with=replace_with))
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return lambda doc: parser(urlFinder.sub(replace_with,doc.text))
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def replaceEmails(replace_with="EMAIL",parser=PARSER):
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#return lambda doc: parser(textacy.preprocess.replace_emails(doc.text,replace_with=replace_with))
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return lambda doc : parser(emailFinder.sub(replace_with, doc.text))
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def replaceTwitterMentions(replace_with="TWITTER_MENTION",parser=PARSER):
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return lambda doc : parser(mentionFinder.sub(replace_with, doc.text))
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def replaceNumbers(replace_with="NUMBER",parser=PARSER):
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return lambda doc: parser(textacy.preprocess.replace_numbers(doc.text, replace_with=replace_with))
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def replacePhonenumbers(replace_with="PHONE",parser=PARSER):
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return lambda doc: parser(textacy.preprocess.replace_phone_numbers(doc.text, replace_with=replace_with))
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def resolveAbbreviations(parser=PARSER):
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pass #todo
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def removeWords(words, keep=None,parser=PARSER):
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if hasattr(keep, '__iter__'):
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for k in keep:
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try:
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words.remove(k)
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except ValueError:
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pass
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return lambda doc : parser(" ".join([tok.text for tok in doc if tok.lower_ not in words]))
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def normalizeSynonyms(default_return_first_Syn=False, parser=PARSER):
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#return lambda doc : parser(" ".join([tok.lower_ for tok in doc]))
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return lambda doc : parser(" ".join([getFirstSynonym(tok.lower_, THESAURUS, default_return_first_Syn=default_return_first_Syn) for tok in doc]))
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def getFirstSynonym(word, thesaurus, default_return_first_Syn=False):
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if not isinstance(word, str):
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return str(word)
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word = word.lower()
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# durch den thesaurrus iterieren
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for syn_block in thesaurus: # syn_block ist eine liste mit Synonymen
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for syn in syn_block:
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syn = syn.lower()
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if re.match(r'\A[\w-]+\Z', syn): # falls syn einzelwort ist
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if word == syn:
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return str(getHauptform(syn_block, word, default_return_first_Syn=default_return_first_Syn))
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else: # falls es ein satz ist
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if word in syn:
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return str(getHauptform(syn_block, word, default_return_first_Syn=default_return_first_Syn))
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return str(word) # zur Not, das ursrpüngliche Wort zurückgeben
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def getHauptform(syn_block, word, default_return_first_Syn=False):
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for syn in syn_block:
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syn = syn.lower()
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if "hauptform" in syn and len(syn.split(" ")) <= 2:
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# nicht ausgeben, falls es in Klammern steht#todo gibts macnmal?? klammern aus
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for w in syn.split(" "):
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if not re.match(r'\([^)]+\)', w):
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return w
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if default_return_first_Syn:
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# falls keine hauptform enthalten ist, das erste Synonym zurückgeben, was kein satz ist und nicht in klammern steht
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for w in syn_block:
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if not re.match(r'\([^)]+\)', w):
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return w
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return word # zur Not, das ursrpüngliche Wort zurückgeben
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def label2ID(label):
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return {
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'Neuanschluss' : 0,
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'LSF' : 1,
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'Video' : 2,
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}.get(label,3)
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def generate_labled_lines(textacyCorpus):
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for doc in textacyCorpus:
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# generate [topic1, topic2....] tok1 tok2 tok3 out of corpus
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yield "[" + str(label2ID(doc.metadata["Kategorie"])) + "] " + doc.text
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####################'####################'####################'####################'####################'##############
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ents = config.get("preprocessing","ents2keep").split(",")
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clean_in_content=compose( #anmrk.: unterste-funktion iwrd zuerst ausgeführt
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doc2String,
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keepUniqueTokens(),
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#normalizeSynonyms(default_return_first_Syn=False),
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lemmatize(),
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replaceEmails(),
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replaceURLs(),
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replaceTwitterMentions(),
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#removeAllENT(ents),
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keepOnlyPOS(['NOUN'])
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)
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clean_in_meta = {
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"Loesung":removeAllPOS(["SPACE"]),
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"Zusammenfassung":removeAllPOS(["SPACE","PUNCT"])
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}
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## add files to textacy-corpus,
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print("add texts to textacy-corpus...")
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corpus.add_texts(
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processTextstream(generateMainTextfromTicketXML(path2xml), func=clean_in_content),
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processDictstream(generateMetadatafromTicketXML(path2xml), funcdict=clean_in_meta)
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)
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printRandomDoc(corpus)
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#idee 3 versch. Corpi
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####################'####################'
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####################'####################' todo alles in config
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ngrams = (1,2)
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min_df = 0
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max_df = 1.0
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no_below = 20
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no_above = 0.5
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topicModel = 'lda'
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# http://textacy.readthedocs.io/en/latest/api_reference.html#textacy.tm.topic_model.TopicModel.get_doc_topic_matrix
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weighting = ('tf' if topicModel == 'lda' else 'tfidf')
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top_topic_words = 5
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top_document_labels_per_topic = 5
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n_topics = len(set(corpus[0].metadata.keys()))+1 #+1 wegen einem default-topic
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####################'####################
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print("vectorize corpus...")
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vectorizer = Vectorizer(weighting=weighting, min_df=min_df, max_df=max_df)
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terms_list = (doc.to_terms_list(ngrams=ngrams, named_entities=False, as_strings=True) for doc in corpus)
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doc_term_matrix = vectorizer.fit_transform(terms_list)
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id2term = vectorizer.__getattribute__("id_to_term")
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"""
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##################### LSA, LDA, NMF Topic Modeling via Textacy ##############################################
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# Initialize and train a topic model
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print("Initialize and train a topic model")
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model = textacy.tm.TopicModel(topicModel, n_topics=n_topics)
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model.fit(doc_term_matrix)
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#Transform the corpus and interpret our model:
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print("Transform the corpus and interpret our model")
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doc_topic_matrix = model.transform(doc_term_matrix)
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print()
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for topic_idx, top_terms in model.top_topic_terms(vectorizer.id_to_term, top_n=top_topic_words):
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print('topic', topic_idx, ':', ' '.join(top_terms))
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print()
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for topic_idx, top_docs in model.top_topic_docs(doc_topic_matrix, top_n=top_document_labels_per_topic):
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print(topic_idx)
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for j in top_docs:
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print(corpus[j].metadata['Kategorie'])
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#####################################################################################################################
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print()
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print()
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"""
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##################### LLDA Topic Modeling via JGibbsLabledLDA ##############################################
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jgibbsLLDA_root = "java_LabledLDA/"
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filepath = "{0}models/tickets/tickets.gz".format(jgibbsLLDA_root)
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#create file
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textacy.fileio.write_file_lines(generate_labled_lines(corpus),filepath=filepath)
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# wait for file to exist
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while not os.path.exists(filepath):
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time.sleep(1)
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print("start LLDA..")
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#run JGibsslda file
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FNULL = open(os.devnull, 'w') # supress output
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subprocess.call(["java",
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"-cp", "{0}lib/trove-3.0.3.jar:{0}lib/args4j-2.0.6.jar:{0}out/production/LabledLDA/".format(jgibbsLLDA_root),
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"jgibblda.LDA",
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"-est",
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"-dir", "{0}models/tickets".format(jgibbsLLDA_root),
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"-dfile","tickets.gz",
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"-ntopics", str(n_topics)], stdout = FNULL)
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# ANMERKUNG: Dateien sind versteckt. zu finden in models/
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#print twords
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subprocess.call(["gzip",
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"-dc",
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"{0}/models/tickets/.twords.gz".format(jgibbsLLDA_root)])
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#####################################################################################################################
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print()
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print()
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