topicModelingTickets/textCleaning.py

# -*- coding: utf-8 -*-
import re
import spacy
import functools

import textacy


class TextCleaner:

    def __init__(self, parser, thesaurus=None, customClass_symbols=None, customClass_words=None, keep4All=None):
        """
        :param parser: spacy-parser
        :param thesaurus: [[syn1, syn2, ...],[syn1, syn2, ...], ...]
        :param customClass_symbols:[str]
        :param customClass_words:[str]
        :param customClassPOS:[str]
        :param keep4All: [str]
        """
        if thesaurus is None:
            DATAPATH_thesaurus = "openthesaurus.csv"

            ## !!!!!! list wichtig, da sonst nicht die gleichen Synonyme zurückgegeben werden, weil ein generator während der laufzeit pickt
            self.thesaurus = list(textacy.fileio.read_csv(DATAPATH_thesaurus, delimiter=";"))
        else:
            self.thesaurus = thesaurus

        self.parser = parser



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



        # to remove
        self.symbols = ["-----", "---", "...", "“", "”", ".", "-", "<", ">", ",", "?", "!", "..", "n’t", "n't", "|", "||",
                   ";", ":",
                   "…", "’s", "'s", ".", "(", ")", "[", "]", "#"] + (customClass_symbols if customClass_symbols is not None else [])
        self.stop_words = list(__import__("spacy." + self.parser.lang, globals(), locals(), ['object']).STOP_WORDS)+ (customClass_words if customClass_words is not None else [])



        # to keep
        self.entities2keep = ["WORK_OF_ART", "ORG", "PRODUCT", "LOC"]  # ,"PERSON"]
        self.pos2keep = ["NOUN"]  # , "NUM" ]#,"VERB","ADJ"] #fürs TopicModeling nur Nomen  http://aclweb.org/anthology/U15-1013

        self.entities2keep = self.entities2keep + (keep4All if keep4All is not None else [])
        self.pos2keep = self.pos2keep + (keep4All if keep4All is not None else [])


        keep = (keep4All if hasattr(keep4All, '__iter__') else []) + self.pos2keep + self.entities2keep


        # modify those to remove with those to keep
        for sym in keep:
            try:
                self.symbols.remove(sym)
            except ValueError:
                pass
        for sym in keep:
            try:
                self.stop_words.remove(sym)
            except ValueError:
                pass


        # idee self.currentDoc = spacy.Doc für jeden String aber nicht füpr jede methode
    def loadString(self,string):
        self.currentDoc = self.parser(string)

    """   
    def removeWhitespace(self, string):
        string = self.whitespaceFinder.sub(" ", string)
        return string
    """
    def removeWhitespace(self, string):
        return string

        #self.whitespaceFinder = re.compile(r'(\r\n|\r|\n|(\s)+)', re.IGNORECASE)

    def removePunctuation(self, string, custom_symbols=None, keep=None):


        symbols = self.symbols + (custom_symbols if custom_symbols is not None else [])

        if hasattr(keep, '__iter__'):
            for k in keep:
                try:
                    symbols.remove(k)
                except ValueError:
                    pass


        # parse with spaCy
        doc = self.parser(string)
        tokens = []

        # append Tokens to a list
        for tok in doc:
                if not tok.is_punct and not tok.is_space and tok.text not in symbols:
                    tokens.append(tok.text)

        return " ".join(tokens)

    def resolveAbbreviations(self,string):
        return string    #todo


    def keepPOSandENT(self, string, customPOS=None, customEnt=None, remove=None):

        pos2keep = self.pos2keep + (customPOS if customPOS is not None else [])
        ent = self.entities2keep + (customEnt if customEnt is not None else [])

        if hasattr(remove, '__iter__'):
            for k in remove:
                try:
                    ent.remove(k)
                except ValueError:
                    try:
                        pos2keep.remove(k)
                    except ValueError:
                        pass

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

        # append Tokens to a list
        for tok in spacy_doc:

            if tok.pos_ in pos2keep:
                tokens.append(tok.text)

            if tok.ent_type_ in ent:
                tokens.append(tok.text)

        return " ".join(set(tokens))



    def removeWords(self,string, custom_words=None, keep=None, lemmatize=False):

        wordlist = self.stop_words + (custom_words if custom_words is not None else [])
        if hasattr(keep, '__iter__'):
            for k in keep:
                try:
                    wordlist.remove(k)
                except ValueError:
                    pass



        string = self.urlFinder.sub("URL", string)
        string = self.emailFinder.sub("EMAIL", string)
        string = self.mentionFinder.sub("MENTION", string)
        string = string.replace("&amp;", "and").replace("&gt;", ">").replace("&lt;", "<")


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

        # append Tokens to a list
        for tok in spacy_doc:

                #do not include stopwords/customwords and single chars
                if tok.text not in wordlist and len(tok)>1:
                    if lemmatize:
                            tokens.append(tok.lemma_)
                    else:
                            tokens.append(tok.lower_)
        return " ".join(set(tokens))







    def normalizeSynonyms(self, string, default_return_first_Syn=False):
        # parse with spaCy
        spacy_doc = self.parser(string)
        tokens = []

        tokens = [str(self.getFirstSynonym(tok, self.thesaurus, default_return_first_Syn=default_return_first_Syn)) for tok in spacy_doc]

        return " ".join(set(tokens))



    def getFirstSynonym(self,word, thesaurus, default_return_first_Syn=False):
        if not isinstance(word, str):
            return 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 self.getHauptform(syn_block, word, default_return_first_Syn=default_return_first_Syn)
                else:  # falls es ein satz ist
                    if word in syn:
                        return self.getHauptform(syn_block, word, default_return_first_Syn=default_return_first_Syn)
        return word  # zur Not, das ursrpüngliche Wort zurückgeben

    def getHauptform(self,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




"""
#################################################################################################################

#todo funzt irgendwie nich wie's soll: https://mathieularose.com/function-composition-in-python/
def compose(self,*functions):
    return functools.reduce(lambda f, g: lambda x: f(g(x)), functions, lambda x: x)

pipeline = compose(functools.partial(cleaner.keepPOSandENT,lemmatize=True))#, cleaner.normalizeSynonyms)

#################################################################################################################
"""