topicModelingTickets/preprocessing.py

# -*- coding: utf-8 -*-
import csv
import functools
import re
import spacy
import sys
import textacy
import xml.etree.ElementTree as ET
import io
csv.field_size_limit(sys.maxsize)



# Load the configuration file
import configparser as ConfigParser
config = ConfigParser.ConfigParser()
with open("config.ini") as f:
    config.read_file(f)


PARSER = spacy.load(config.get("default","language"))
corpus = textacy.Corpus(PARSER)

thesauruspath = config.get("default","thesauruspath")
THESAURUS = list(textacy.fileio.read_csv(thesauruspath, delimiter=";"))



def compose(*functions):
    def compose2(f, g):
        return lambda x: f(g(x))
    return functools.reduce(compose2, functions, lambda x: x)


################ generate Content and Metadata ########################

def generateMainTextfromTicketXML(path2xml, main_textfield='Beschreibung'):
    """
    generates strings from XML
    :param path2xml:
    :param main_textfield:
    :param cleaning_function:
    :yields strings
    """
    tree = ET.parse(path2xml, ET.XMLParser(encoding="utf-8"))
    root = tree.getroot()

    for ticket in root:
        for field in ticket:
            if field.tag == main_textfield:
                    yield field.text

def generateMetadatafromTicketXML(path2xml, leave_out=['Beschreibung']):
    tree = ET.parse(path2xml, ET.XMLParser(encoding="utf-8"))
    root = tree.getroot()

    for ticket in root:
        metadata = {}
        for field in ticket:
            if field.tag not in leave_out:

                    metadata[field.tag] = field.text

        yield metadata

def printRandomDoc(textacyCorpus):
    import random
    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()

################ Preprocess#########################

def processDictstream(dictstream, funcdict, parser=PARSER):
    for dic in dictstream:
        result = {}
        for key, value in dic.items():
            if key in funcdict:
                result[key] = funcdict[key](parser(value))
            else:
                result[key] = key
        yield result

def processTextstream(textstream, func, parser=PARSER):
   # input str-stream output str-stream
    pipe = parser.pipe(textstream)

    for doc in pipe:
        yield func(doc)




def keepOnlyPOS(pos_list, parser=PARSER):
    return lambda doc : parser(" ".join([tok.text for tok in doc if tok.pos_ in pos_list]))

def removeAllPOS(pos_list, parser=PARSER):
    return lambda doc:  parser(" ".join([tok.text for tok in doc if tok.pos_ not in pos_list]))

def keepOnlyENT(ent_list,parser=PARSER):
    return lambda doc:  parser(" ".join([tok.text for tok in doc if tok.ent_type_ in ent_list]))

def removeAllENT(ent_list, parser=PARSER):
    return lambda doc:  parser(" ".join([tok.text for tok in doc if tok.ent_type_ not in ent_list]))




doc2Set = lambda doc: str(set([tok.text for tok in doc]))
doc2String = lambda doc : doc.text


mentionFinder = re.compile(r"@[a-z0-9_]{1,15}", re.IGNORECASE)
emailFinder = re.compile(r"\b[A-Z0-9._%+-]+@[A-Z0-9.-]+\.[A-Z]{2,}\b", re.IGNORECASE)
urlFinder = re.compile(r"^(?:https?:\/\/)?(?:www\.)?[a-zA-Z0-9./]+$", re.IGNORECASE)

def replaceURLs(replace_with="URL",parser=PARSER):
    #return lambda doc: parser(textacy.preprocess.replace_urls(doc.text,replace_with=replace_with))
    return lambda doc: parser(urlFinder.sub(replace_with,doc.text))

def replaceEmails(replace_with="EMAIL",parser=PARSER):
    #return lambda doc: parser(textacy.preprocess.replace_emails(doc.text,replace_with=replace_with))
    return lambda doc : parser(emailFinder.sub(replace_with, doc.text))

def replaceTwitterMentions(replace_with="TWITTER_MENTION",parser=PARSER):
    return lambda doc : parser(mentionFinder.sub(replace_with, doc.text))

def replaceNumbers(replace_with="NUMBER",parser=PARSER):
    return lambda doc: parser(textacy.preprocess.replace_numbers(doc.text, replace_with=replace_with))

def replacePhonenumbers(replace_with="PHONE",parser=PARSER):
    return lambda doc: parser(textacy.preprocess.replace_phone_numbers(doc.text, replace_with=replace_with))






def resolveAbbreviations(parser=PARSER):
    pass #todo


def removeWords(words, keep=None,parser=PARSER):
    if hasattr(keep, '__iter__'):
        for k in keep:
            try:
                words.remove(k)
            except ValueError:
                pass
    return lambda doc : parser(" ".join([tok.text for tok in doc if tok.lower_ not in words]))




def normalizeSynonyms(default_return_first_Syn=False, parser=PARSER):
    #return lambda doc : parser(" ".join([tok.lower_ for tok in doc]))
    return lambda doc : parser(" ".join([getFirstSynonym(tok.lower_, THESAURUS, default_return_first_Syn=default_return_first_Syn) for tok in doc]))

def getFirstSynonym(word, thesaurus, default_return_first_Syn=False):
    if not isinstance(word, str):
        return str(word)

    word = word.lower()

    # durch den thesaurrus iterieren
    for syn_block in thesaurus:  # 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 str(getHauptform(syn_block, word, default_return_first_Syn=default_return_first_Syn))
            else:  # falls es ein satz ist
                if word in syn:
                    return str(getHauptform(syn_block, word, default_return_first_Syn=default_return_first_Syn))
    return str(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#todo gibts macnmal?? klammern aus
            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



stop_words=list(__import__("spacy." + PARSER.lang, globals(), locals(), ['object']).STOP_WORDS) + config.get("preprocessing","custom_words").split(",")

path2xml = config.get("default","path2xml")



content_generator = generateMainTextfromTicketXML(path2xml)
metadata_generator = generateMetadatafromTicketXML(path2xml)



ents = config.get("preprocessing","ents").split(",")



clean_in_content=compose(

    doc2String,
    #normalizeSynonyms(default_return_first_Syn=config.get("preprocessing","default_return_first_Syn")),
    replaceEmails(),
    replaceURLs(),
    replaceTwitterMentions(),
    removeWords(stop_words),
    #removeAllPOS(["SPACE","PUNCT"]),
    #removeAllENT(ents),
    keepOnlyPOS(['NOUN'])
)

clean_in_meta = {
    "Loesung":removeAllPOS(["SPACE"]),
    "Zusammenfassung":removeAllPOS(["SPACE","PUNCT"])
}


contentStream = processTextstream(content_generator, func=clean_in_content)
metaStream = processDictstream(metadata_generator, funcdict=clean_in_meta)


corpus.add_texts(contentStream,metaStream)
print(corpus[0].text)
printRandomDoc(corpus)