topicModelingTickets/old/preprocessing.py

# -*- coding: utf-8 -*-
import csv
import random
import sys

import spacy
import textacy

"""
import keras
import numpy as np
from keras.layers import Dense, SimpleRNN, LSTM, TimeDistributed, Dropout
from keras.models import Sequential
import keras.backend as K
"""
csv.field_size_limit(sys.maxsize)

"""
def getFirstSynonym(word, thesaurus_gen):

        word = word.lower()
        # TODO word cleaning https://stackoverflow.com/questions/3939361/remove-specific-characters-from-a-string-in-python


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

            # durch den synonymblock iterieren
            for syn in syn_block:
                syn = syn.lower().split(" ") if not re.match(r'\A[\w-]+\Z', syn) else syn # aus synonym mach liste (um evtl. sätze zu identifieziren)

                # falls das wort in dem synonym enthalten ist (also == einem Wort in der liste ist)
                if word in syn:

                    # Hauptform suchen
                    if "auptform" in syn:
                        # nicht ausgeben, falls es in Klammern steht
                        for w in syn:
                            if not re.match(r'\([^)]+\)', w) and w is not None:
                                return w

                    # falls keine hauptform enthalten ist, das erste Synonym zurückgeben, was kein satz ist und nicht in klammern steht
                    if len(syn) == 1:
                        w = syn[0]
                        if not re.match(r'\([^)]+\)', w) and w is not None:
                            return w

        return word # zur Not die eingabe ausgeben


"""
"""
def cleanText(string,custom_stopwords=None, custom_symbols=None, custom_words=None, customPreprocessing=None, lemmatize=False, normalize_synonyms=False):

    # use preprocessing
    if customPreprocessing is not None:
        string = customPreprocessing(string)



    if custom_stopwords is not None:
        custom_stopwords = custom_stopwords
    else:
        custom_stopwords = []

    if custom_words is not None:
        custom_words = custom_words
    else:
        custom_words = []

    if custom_symbols is not None:
        custom_symbols = custom_symbols
    else:
        custom_symbols = []


    # custom stoplist
    # https://stackoverflow.com/questions/9806963/how-to-use-pythons-import-function-properly-import
    stop_words = __import__("spacy." + PARSER.lang, globals(), locals(), ['object']).STOP_WORDS

    stoplist =list(stop_words) + custom_stopwords
    # List of symbols we don't care about either
    symbols = ["-----","---","...","“","”",".","-","<",">",",","?","!","..","n’t","n't","|","||",";",":","…","’s","'s",".","(",")","[","]","#"] + custom_symbols



    # get rid of newlines
    string = string.strip().replace("\n", " ").replace("\r", " ")

    # replace twitter
    mentionFinder = re.compile(r"@[a-z0-9_]{1,15}", re.IGNORECASE)
    string = mentionFinder.sub("MENTION", string)

    # replace emails
    emailFinder = re.compile(r"\b[A-Z0-9._%+-]+@[A-Z0-9.-]+\.[A-Z]{2,}\b", re.IGNORECASE)
    string = emailFinder.sub("EMAIL", string)

    # replace urls
    urlFinder = re.compile(r"^(?:https?:\/\/)?(?:www\.)?[a-zA-Z0-9./]+$", re.IGNORECASE)
    string = urlFinder.sub("URL", string)

    # replace HTML symbols
    string = string.replace("&amp;", "and").replace("&gt;", ">").replace("&lt;", "<")




    # parse with spaCy
    spacy_doc = PARSER(string)
    tokens = []

    added_entities = ["WORK_OF_ART","ORG","PRODUCT", "LOC"]#,"PERSON"]
    added_POS = ["NOUN"]#, "NUM" ]#,"VERB","ADJ"] #IDEE NUM mit in den Corpus aufnehmen, aber fürs TopicModeling nur Nomen  http://aclweb.org/anthology/U15-1013

    # append Tokens to a list
    for tok in spacy_doc:
            if tok.pos_ in added_POS:
                if lemmatize:
                    tokens.append(tok.lemma_.lower().strip())
                else:
                    tokens.append(tok.text.lower().strip())

            # add entities
            if tok.ent_type_ in added_entities:
                tokens.append(tok.text.lower())



    # remove stopwords
    tokens = [tok for tok in tokens if tok not in stoplist]

    # remove symbols
    tokens = [tok for tok in tokens if tok not in symbols]

    # remove custom_words
    tokens = [tok for tok in tokens if tok not in custom_words]

    # remove single characters
    tokens = [tok for tok in tokens if len(tok)>1]

    # remove large strings of whitespace
    remove_large_strings_of_whitespace(" ".join(tokens))


    #idee abkürzungen auflösen (v.a. TU -> Technische Universität)

    if normalize_synonyms:
        tokens = [str(getFirstSynonym(tok,THESAURUS_list)) for tok in tokens]

    return " ".join(tokens)


def remove_large_strings_of_whitespace(sentence):

    whitespaceFinder = re.compile(r'(\r\n|\r|\n)', re.IGNORECASE)
    sentence = whitespaceFinder.sub(" ", sentence)

    tokenlist = sentence.split(" ")

    while "" in tokenlist:
        tokenlist.remove("")
    while " " in tokenlist:
        tokenlist.remove(" ")

    return " ".join(tokenlist)
"""
"""
def generateFromXML(path2xml,  textfield='Beschreibung', clean=False, normalize_Synonyms=False,lemmatize=False):
    import xml.etree.ElementTree as ET

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

    for ticket in root:
        metadata = {}
        text = "ERROR"
        for field in ticket:
            if field.tag == textfield:
                if clean:
                    text = cleanText_words(field.text,PARSER,normalize_synonyms=normalize_Synonyms,lemmatize=lemmatize)
                else:
                    text = field.text
            else:
                #idee hier auch cleanen?
                metadata[field.tag] = field.text
        yield text, metadata
"""


LANGUAGE = 'de'
#PARSER = de_core_news_md.load()
PARSER = spacy.load(LANGUAGE)

from old.textCleaning import TextCleaner

cleaner = TextCleaner(parser=PARSER)


def generateTextfromTicketXML(path2xml, textfield='Beschreibung', clean=False, normalize_Synonyms=False, lemmatize=False):
    import xml.etree.ElementTree as ET

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


    for ticket in root:
        text = "ERROR"
        for field in ticket:
            if field.tag == textfield:
                if clean:
                    text = cleaner.normalizeSynonyms(cleaner.removeWords(cleaner.keepPOSandENT(field.text))) #,normalize_synonyms=normalize_Synonyms,lemmatize=lemmatize)
                else:
                    text = field.text
        yield text

def generateMetadatafromTicketXML(path2xml, textfield='Beschreibung'):#,keys_to_clean=["Loesung","Zusammenfassung"]):
    import xml.etree.ElementTree as ET

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

    root = tree.getroot()

    for ticket in root:
        metadata = {}
        for field in ticket:
            if field.tag != textfield:
                if field.tag == "Zusammenfassung":
                    metadata[field.tag] = cleaner.removePunctuation(field.text)
                elif field.tag == "Loesung":
                    metadata[field.tag] = cleaner.removeWhitespace(field.text)
                else:
                    metadata[field.tag] = field.text

        yield metadata




"""
def cleanText_symbols(string, parser=PARSER, custom_symbols=None, keep=None):

    if custom_symbols is not None:
        custom_symbols = custom_symbols
    else:
        custom_symbols = []

    if keep is not None:
        keep = keep
    else:
        keep = []

    # List of symbols we don't care about
    symbols = ["-----","---","...","“","”",".","-","<",">",",","?","!","..","n’t","n't","|","||",";",":","…","’s","'s",".","(",")","[","]","#"] + custom_symbols

    # parse with spaCy
    spacy_doc = parser(string)
    tokens = []

    pos = ["NUM", "SPACE", "PUNCT"]
    for p in keep:
        pos.remove(p)


    # append Tokens to a list
    for tok in spacy_doc:
            if tok.pos_ not in pos and tok.text not in symbols:
                tokens.append(tok.text)

    return " ".join(tokens)

def cleanText_words(string,parser=PARSER, custom_stopwords=None, custom_words=None, customPreprocessing=cleanText_symbols, lemmatize=False, normalize_synonyms=False):

    # use preprocessing
    if customPreprocessing is not None:
        string = customPreprocessing(string)

    if custom_stopwords is not None:
        custom_stopwords = custom_stopwords
    else:
        custom_stopwords = []

    if custom_words is not None:
        custom_words = custom_words
    else:
        custom_words = []


    # custom stoplist
    # https://stackoverflow.com/questions/9806963/how-to-use-pythons-import-function-properly-import
    stop_words = __import__("spacy." + parser.lang, globals(), locals(), ['object']).STOP_WORDS

    stoplist =list(stop_words) + custom_stopwords

    # replace twitter
    mentionFinder = re.compile(r"@[a-z0-9_]{1,15}", re.IGNORECASE)
    string = mentionFinder.sub("MENTION", string)

    # replace emails
    emailFinder = re.compile(r"\b[A-Z0-9._%+-]+@[A-Z0-9.-]+\.[A-Z]{2,}\b", re.IGNORECASE)
    string = emailFinder.sub("EMAIL", string)

    # replace urls
    urlFinder = re.compile(r"^(?:https?:\/\/)?(?:www\.)?[a-zA-Z0-9./]+$", re.IGNORECASE)
    string = urlFinder.sub("URL", string)

    # replace HTML symbols
    string = string.replace("&amp;", "and").replace("&gt;", ">").replace("&lt;", "<")



    # parse with spaCy
    spacy_doc = parser(string)
    tokens = []

    added_entities = ["WORK_OF_ART","ORG","PRODUCT", "LOC"]#,"PERSON"]
    added_POS = ["NOUN"]#, "NUM" ]#,"VERB","ADJ"] #fürs TopicModeling nur Nomen  http://aclweb.org/anthology/U15-1013

    # append Tokens to a list
    for tok in spacy_doc:
            if tok.pos_ in added_POS:
                if lemmatize:
                    tokens.append(tok.lemma_.lower().strip())
                else:
                    tokens.append(tok.text.lower().strip())

            # add entities
            if tok.ent_type_ in added_entities:
                tokens.append(tok.text.lower())



    # remove stopwords
    tokens = [tok for tok in tokens if tok not in stoplist]

    # remove custom_words
    tokens = [tok for tok in tokens if tok not in custom_words]

    # remove single characters
    tokens = [tok for tok in tokens if len(tok)>1]

    # remove large strings of whitespace
    #remove_whitespace(" ".join(tokens))


    #idee abkürzungen auflösen (v.a. TU -> Technische Universität): abkürzungsverezeichnis

    if normalize_synonyms:
        tokens = [str(getFirstSynonym(tok,THESAURUS_list)) for tok in tokens]

    return " ".join(set(tokens))

def cleanText_removeWhitespace(sentence):
    whitespaceFinder = re.compile(r'(\r\n|\r|\n|(\s)+)', re.IGNORECASE)
    sentence = whitespaceFinder.sub(" ", sentence)
    return sentence

#todo: preprocess pipe: removewhitespace, removePUNCT, resolveAbk, keepPOS, keepEnt,  removeWords, normalizeSynonyms


def getFirstSynonym(word, thesaurus_gen):

    word = word.lower()


    # durch den thesaurrus iterieren
    for syn_block in thesaurus_gen:  # 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 getHauptform(syn_block, word)
            else:  # falls es ein satz ist
                if word in syn:
                    return getHauptform(syn_block, word)
    return 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
            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
"""

def printRandomDoc(textacyCorpus):
    print()

    print("len(textacyCorpus) = %i" % len(textacyCorpus))
    randIndex = int((len(textacyCorpus) - 1) * random.random())
    print("Index: {0} ; Text: {1} ; Metadata: {2}".format(randIndex, textacyCorpus[randIndex].text, textacyCorpus[randIndex].metadata))

    print()

####################'####################'####################'####################'####################'##############
# todo config-file

DATAPATH = "ticketSamples.xml"
DATAPATH_thesaurus = "openthesaurus.csv"



normalize_Synonyms = True
clean = True
lemmatize = True

custom_words = ["grüßen", "fragen"]

####################'####################'####################'####################'####################'##############


## files to textacy-corpi
textacyCorpus = textacy.Corpus(PARSER)

print("add texts to textacy-corpi...")
textacyCorpus.add_texts(texts=generateTextfromTicketXML(DATAPATH, normalize_Synonyms=normalize_Synonyms, clean=clean, lemmatize=lemmatize), metadatas=generateMetadatafromTicketXML(DATAPATH))


#for txt, dic in generateFromXML(DATAPATH, normalize_Synonyms=normalize_Synonyms, clean=clean, lemmatize=lemmatize):
#    textacyCorpus.add_text(txt,dic)



for doc in textacyCorpus:
    print(doc.metadata)
    print(doc.text)

#print(textacyCorpus[2].text)
#printRandomDoc(textacyCorpus)
#print(textacyCorpus[len(textacyCorpus)-1].text)


print()
print()