467 lines
14 KiB
Python
467 lines
14 KiB
Python
# -*- coding: utf-8 -*-
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import csv
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import random
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import sys
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import spacy
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import textacy
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"""
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import keras
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import numpy as np
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from keras.layers import Dense, SimpleRNN, LSTM, TimeDistributed, Dropout
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from keras.models import Sequential
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import keras.backend as K
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"""
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csv.field_size_limit(sys.maxsize)
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"""
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def getFirstSynonym(word, thesaurus_gen):
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word = word.lower()
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# TODO word cleaning https://stackoverflow.com/questions/3939361/remove-specific-characters-from-a-string-in-python
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# durch den thesaurrus iterieren
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for syn_block in thesaurus_gen: # syn_block ist eine liste mit Synonymen
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# durch den synonymblock iterieren
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for syn in syn_block:
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syn = syn.lower().split(" ") if not re.match(r'\A[\w-]+\Z', syn) else syn # aus synonym mach liste (um evtl. sätze zu identifieziren)
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# falls das wort in dem synonym enthalten ist (also == einem Wort in der liste ist)
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if word in syn:
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# Hauptform suchen
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if "auptform" in syn:
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# nicht ausgeben, falls es in Klammern steht
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for w in syn:
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if not re.match(r'\([^)]+\)', w) and w is not None:
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return w
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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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if len(syn) == 1:
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w = syn[0]
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if not re.match(r'\([^)]+\)', w) and w is not None:
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return w
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return word # zur Not die eingabe ausgeben
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"""
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"""
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def cleanText(string,custom_stopwords=None, custom_symbols=None, custom_words=None, customPreprocessing=None, lemmatize=False, normalize_synonyms=False):
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# use preprocessing
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if customPreprocessing is not None:
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string = customPreprocessing(string)
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if custom_stopwords is not None:
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custom_stopwords = custom_stopwords
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else:
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custom_stopwords = []
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if custom_words is not None:
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custom_words = custom_words
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else:
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custom_words = []
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if custom_symbols is not None:
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custom_symbols = custom_symbols
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else:
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custom_symbols = []
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# custom stoplist
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# https://stackoverflow.com/questions/9806963/how-to-use-pythons-import-function-properly-import
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stop_words = __import__("spacy." + PARSER.lang, globals(), locals(), ['object']).STOP_WORDS
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stoplist =list(stop_words) + custom_stopwords
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# List of symbols we don't care about either
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symbols = ["-----","---","...","“","”",".","-","<",">",",","?","!","..","n’t","n't","|","||",";",":","…","’s","'s",".","(",")","[","]","#"] + custom_symbols
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# get rid of newlines
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string = string.strip().replace("\n", " ").replace("\r", " ")
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# replace twitter
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mentionFinder = re.compile(r"@[a-z0-9_]{1,15}", re.IGNORECASE)
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string = mentionFinder.sub("MENTION", string)
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# replace emails
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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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string = emailFinder.sub("EMAIL", string)
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# replace urls
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urlFinder = re.compile(r"^(?:https?:\/\/)?(?:www\.)?[a-zA-Z0-9./]+$", re.IGNORECASE)
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string = urlFinder.sub("URL", string)
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# replace HTML symbols
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string = string.replace("&", "and").replace(">", ">").replace("<", "<")
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# parse with spaCy
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spacy_doc = PARSER(string)
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tokens = []
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added_entities = ["WORK_OF_ART","ORG","PRODUCT", "LOC"]#,"PERSON"]
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added_POS = ["NOUN"]#, "NUM" ]#,"VERB","ADJ"] #IDEE NUM mit in den Corpus aufnehmen, aber fürs TopicModeling nur Nomen http://aclweb.org/anthology/U15-1013
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# append Tokens to a list
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for tok in spacy_doc:
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if tok.pos_ in added_POS:
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if lemmatize:
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tokens.append(tok.lemma_.lower().strip())
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else:
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tokens.append(tok.text.lower().strip())
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# add entities
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if tok.ent_type_ in added_entities:
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tokens.append(tok.text.lower())
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# remove stopwords
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tokens = [tok for tok in tokens if tok not in stoplist]
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# remove symbols
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tokens = [tok for tok in tokens if tok not in symbols]
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# remove custom_words
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tokens = [tok for tok in tokens if tok not in custom_words]
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# remove single characters
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tokens = [tok for tok in tokens if len(tok)>1]
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# remove large strings of whitespace
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remove_large_strings_of_whitespace(" ".join(tokens))
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#idee abkürzungen auflösen (v.a. TU -> Technische Universität)
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if normalize_synonyms:
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tokens = [str(getFirstSynonym(tok,THESAURUS_list)) for tok in tokens]
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return " ".join(tokens)
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def remove_large_strings_of_whitespace(sentence):
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whitespaceFinder = re.compile(r'(\r\n|\r|\n)', re.IGNORECASE)
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sentence = whitespaceFinder.sub(" ", sentence)
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tokenlist = sentence.split(" ")
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while "" in tokenlist:
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tokenlist.remove("")
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while " " in tokenlist:
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tokenlist.remove(" ")
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return " ".join(tokenlist)
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"""
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"""
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def generateFromXML(path2xml, textfield='Beschreibung', clean=False, normalize_Synonyms=False,lemmatize=False):
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import xml.etree.ElementTree as ET
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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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text = "ERROR"
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for field in ticket:
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if field.tag == textfield:
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if clean:
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text = cleanText_words(field.text,PARSER,normalize_synonyms=normalize_Synonyms,lemmatize=lemmatize)
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else:
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text = field.text
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else:
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#idee hier auch cleanen?
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metadata[field.tag] = field.text
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yield text, metadata
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"""
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LANGUAGE = 'de'
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#PARSER = de_core_news_md.load()
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PARSER = spacy.load(LANGUAGE)
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from old.textCleaning import TextCleaner
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cleaner = TextCleaner(parser=PARSER)
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def generateTextfromTicketXML(path2xml, textfield='Beschreibung', clean=False, normalize_Synonyms=False, lemmatize=False):
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import xml.etree.ElementTree as ET
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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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text = "ERROR"
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for field in ticket:
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if field.tag == textfield:
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if clean:
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text = cleaner.normalizeSynonyms(cleaner.removeWords(cleaner.keepPOSandENT(field.text))) #,normalize_synonyms=normalize_Synonyms,lemmatize=lemmatize)
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else:
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text = field.text
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yield text
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def generateMetadatafromTicketXML(path2xml, textfield='Beschreibung'):#,keys_to_clean=["Loesung","Zusammenfassung"]):
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import xml.etree.ElementTree as ET
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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 != textfield:
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if field.tag == "Zusammenfassung":
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metadata[field.tag] = cleaner.removePunctuation(field.text)
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elif field.tag == "Loesung":
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metadata[field.tag] = cleaner.removeWhitespace(field.text)
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else:
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metadata[field.tag] = field.text
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yield metadata
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"""
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def cleanText_symbols(string, parser=PARSER, custom_symbols=None, keep=None):
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if custom_symbols is not None:
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custom_symbols = custom_symbols
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else:
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custom_symbols = []
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if keep is not None:
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keep = keep
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else:
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keep = []
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# List of symbols we don't care about
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symbols = ["-----","---","...","“","”",".","-","<",">",",","?","!","..","n’t","n't","|","||",";",":","…","’s","'s",".","(",")","[","]","#"] + custom_symbols
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# parse with spaCy
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spacy_doc = parser(string)
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tokens = []
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pos = ["NUM", "SPACE", "PUNCT"]
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for p in keep:
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pos.remove(p)
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# append Tokens to a list
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for tok in spacy_doc:
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if tok.pos_ not in pos and tok.text not in symbols:
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tokens.append(tok.text)
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return " ".join(tokens)
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def cleanText_words(string,parser=PARSER, custom_stopwords=None, custom_words=None, customPreprocessing=cleanText_symbols, lemmatize=False, normalize_synonyms=False):
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# use preprocessing
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if customPreprocessing is not None:
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string = customPreprocessing(string)
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if custom_stopwords is not None:
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custom_stopwords = custom_stopwords
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else:
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custom_stopwords = []
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if custom_words is not None:
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custom_words = custom_words
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else:
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custom_words = []
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# custom stoplist
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# https://stackoverflow.com/questions/9806963/how-to-use-pythons-import-function-properly-import
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stop_words = __import__("spacy." + parser.lang, globals(), locals(), ['object']).STOP_WORDS
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stoplist =list(stop_words) + custom_stopwords
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# replace twitter
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mentionFinder = re.compile(r"@[a-z0-9_]{1,15}", re.IGNORECASE)
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string = mentionFinder.sub("MENTION", string)
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# replace emails
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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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string = emailFinder.sub("EMAIL", string)
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# replace urls
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urlFinder = re.compile(r"^(?:https?:\/\/)?(?:www\.)?[a-zA-Z0-9./]+$", re.IGNORECASE)
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string = urlFinder.sub("URL", string)
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# replace HTML symbols
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string = string.replace("&", "and").replace(">", ">").replace("<", "<")
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# parse with spaCy
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spacy_doc = parser(string)
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tokens = []
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added_entities = ["WORK_OF_ART","ORG","PRODUCT", "LOC"]#,"PERSON"]
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added_POS = ["NOUN"]#, "NUM" ]#,"VERB","ADJ"] #fürs TopicModeling nur Nomen http://aclweb.org/anthology/U15-1013
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# append Tokens to a list
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for tok in spacy_doc:
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if tok.pos_ in added_POS:
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if lemmatize:
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tokens.append(tok.lemma_.lower().strip())
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else:
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tokens.append(tok.text.lower().strip())
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# add entities
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if tok.ent_type_ in added_entities:
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tokens.append(tok.text.lower())
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# remove stopwords
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tokens = [tok for tok in tokens if tok not in stoplist]
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# remove custom_words
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tokens = [tok for tok in tokens if tok not in custom_words]
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# remove single characters
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tokens = [tok for tok in tokens if len(tok)>1]
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# remove large strings of whitespace
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#remove_whitespace(" ".join(tokens))
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#idee abkürzungen auflösen (v.a. TU -> Technische Universität): abkürzungsverezeichnis
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if normalize_synonyms:
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tokens = [str(getFirstSynonym(tok,THESAURUS_list)) for tok in tokens]
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return " ".join(set(tokens))
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def cleanText_removeWhitespace(sentence):
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whitespaceFinder = re.compile(r'(\r\n|\r|\n|(\s)+)', re.IGNORECASE)
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sentence = whitespaceFinder.sub(" ", sentence)
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return sentence
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#todo: preprocess pipe: removewhitespace, removePUNCT, resolveAbk, keepPOS, keepEnt, removeWords, normalizeSynonyms
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def getFirstSynonym(word, thesaurus_gen):
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word = word.lower()
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# durch den thesaurrus iterieren
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for syn_block in thesaurus_gen: # 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 getHauptform(syn_block, word)
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else: # falls es ein satz ist
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if word in syn:
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return getHauptform(syn_block, word)
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return 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
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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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"""
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def printRandomDoc(textacyCorpus):
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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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####################'####################'####################'####################'####################'##############
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# todo config-file
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DATAPATH = "ticketSamples.xml"
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DATAPATH_thesaurus = "openthesaurus.csv"
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normalize_Synonyms = True
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clean = True
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lemmatize = True
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custom_words = ["grüßen", "fragen"]
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####################'####################'####################'####################'####################'##############
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## files to textacy-corpi
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textacyCorpus = textacy.Corpus(PARSER)
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print("add texts to textacy-corpi...")
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textacyCorpus.add_texts(texts=generateTextfromTicketXML(DATAPATH, normalize_Synonyms=normalize_Synonyms, clean=clean, lemmatize=lemmatize), metadatas=generateMetadatafromTicketXML(DATAPATH))
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#for txt, dic in generateFromXML(DATAPATH, normalize_Synonyms=normalize_Synonyms, clean=clean, lemmatize=lemmatize):
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# textacyCorpus.add_text(txt,dic)
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for doc in textacyCorpus:
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print(doc.metadata)
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print(doc.text)
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#print(textacyCorpus[2].text)
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#printRandomDoc(textacyCorpus)
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#print(textacyCorpus[len(textacyCorpus)-1].text)
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print()
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print()
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