214 lines
6.5 KiB
Python
214 lines
6.5 KiB
Python
# -*- coding: utf-8 -*-
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import spacy
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import textacy
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from spacy.tokens import Doc
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# -*- coding: utf-8 -*-
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import re
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import spacy
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import functools
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import textacy
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class TextCleaner:
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def __init__(self, parser, thesaurus=None, customClass_symbols=None, customClass_words=None, keep4All=None):
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"""
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:param parser: spacy-parser
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:param thesaurus: [[syn1, syn2, ...],[syn1, syn2, ...], ...]
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:param customClass_symbols:[str]
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:param customClass_words:[str]
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:param customClassPOS:[str]
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:param keep4All: [str]
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"""
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if thesaurus is None:
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DATAPATH_thesaurus = "openthesaurus.csv"
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## !!!!!! list wichtig, da sonst nicht die gleichen Synonyme zurückgegeben werden, weil ein generator während der laufzeit pickt
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self.thesaurus = list(textacy.fileio.read_csv(DATAPATH_thesaurus, delimiter=";"))
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else:
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self.thesaurus = thesaurus
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self.parser = parser
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#self.whitespaceFinder = re.compile(r'(\r\n|\r|\n|(\s)+)', re.IGNORECASE)
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self.mentionFinder = re.compile(r"@[a-z0-9_]{1,15}", re.IGNORECASE)
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self.emailFinder = re.compile(r"\b[A-Z0-9._%+-]+@[A-Z0-9.-]+\.[A-Z]{2,}\b", re.IGNORECASE)
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self.urlFinder = re.compile(r"^(?:https?:\/\/)?(?:www\.)?[a-zA-Z0-9./]+$", re.IGNORECASE)
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# to keep
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self.entities2keep = ["WORK_OF_ART", "ORG", "PRODUCT", "LOC"] # ,"PERSON"]
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self.pos2keep = ["NOUN"] # , "NUM" ]#,"VERB","ADJ"] #fürs TopicModeling nur Nomen http://aclweb.org/anthology/U15-1013
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"""
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# to remove
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self.symbols = ["-----", "---", "...", "“", "”", ".", "-", "<", ">", ",", "?", "!", "..", "n’t", "n't", "|", "||",
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";", ":",
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"…", "’s", "'s", ".", "(", ")", "[", "]", "#"] + (customClass_symbols if customClass_symbols is not None else [])
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self.stop_words = list(__import__("spacy." + self.parser.lang, globals(), locals(), ['object']).STOP_WORDS)+ (customClass_words if customClass_words is not None else [])
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self.entities2keep = self.entities2keep + (keep4All if keep4All is not None else [])
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self.pos2keep = self.pos2keep + (keep4All if keep4All is not None else [])
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keep = (keep4All if hasattr(keep4All, '__iter__') else []) + self.pos2keep + self.entities2keep
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# modify those to remove with those to keep
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for sym in keep:
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try:
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self.symbols.remove(sym)
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except ValueError:
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pass
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for sym in keep:
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try:
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self.stop_words.remove(sym)
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except ValueError:
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pass
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"""
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def loadString(self,string):
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self.currentDoc = self.parser(string)
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def removeWhitespace(self, string):
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return " ".join([tok.text for tok in self.currentDoc if not tok.is_space])
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def removePunctuation(self, string, custom_symbols=None, keep=None):
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symbols = self.symbols + (custom_symbols if custom_symbols is not None else [])
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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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symbols.remove(k)
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except ValueError:
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pass
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return " ".join([tok.text for tok in self.currentDoc if not tok.is_punct and tok.text not in symbols])
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def cleanDoc(doc, toDelete=None, toKeep=None):
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"""
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:param doc: spacyDoc
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:param toDelete: [str] pos_ , ent_type_ or tag_
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:return: str tokenlist
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"""
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#keep
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tokenlist = []
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for tok in doc:
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if tok.pos_ in toKeep or tok.ent_type_ in toKeep or tok.tag_ in toKeep:
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tokenlist.append(tok.text)
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#delete
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tokenlist = [tok.text for tok in doc if tok.pos_ in toDelete and not tok.ent_type_ in toDelete and not tok.tag_ in toDelete]
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result = " ".join(tokenlist)
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return result #problem: kein doc und daher nicht komponierbar
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def keepinDoc(doc, toKeep=None):
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"""
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:param doc: spacyDoc
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:param toDelete: [str]
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:return: str tokenlist
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"""
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return " ".join([tok.text for tok in doc if tok.pos_ in toKeep or tok.ent_type_ in toKeep or tok.tag_ in toKeep])
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# https://mathieularose.com/function-composition-in-python/
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parser = spacy.load('de')
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cleaner = TextCleaner(parser)
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corpus_raw = textacy.Corpus(parser)
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corpus_clean = textacy.Corpus(parser)
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def foo(doc, toKeep=None):
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words = [tok.text for tok in doc if tok.pos_ in toKeep or tok.ent_type_ in toKeep or tok.tag_ in toKeep]
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spaces = [True] * len(words)
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return Doc(doc.vocab,words=words,spaces=spaces)
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def foo2(doc, toDelete=None):#, toKeep=None):
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"""
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:param doc: spacyDoc
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:param toDelete: [str] pos_ , ent_type_ or tag_
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:return: str tokenlist
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"""
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#keep
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#tokenlist = [tok.text for tok in doc if tok.pos_ in toKeep or tok.ent_type_ in toKeep or tok.tag_ in toKeep]
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#delete
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words = [tok.text for tok in doc if tok.pos_ in toDelete and not tok.ent_type_ in toDelete and not tok.tag_ in toDelete]
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spaces = [True] * len(words)
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return Doc(doc.vocab, words=words, spaces=spaces)
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"""
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def compose(self,*functions):
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return functools.reduce(lambda f, g: lambda x: f(g(x)), functions, lambda x: x)
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def composeo(*functions):
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return functools.reduce(lambda f, g: lambda x: f(g(x)), functions)
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"""
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def double(a):
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return a*2
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def add(a, b):
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return a+b
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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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#pipeline = compose(removeFromDoc, cleaner.removeWhitespace, cleaner.loadString)
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"""
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def pipe1(string):
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cleaner.loadString(string)
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string = cleaner.removeWhitespace(string)
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string = cleaner.removePunctuation(string)
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return string
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"""
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def cleaningPipe(spacy_pipe, composition):
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for doc in spacy_pipe:
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yield composition(doc)
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pipeline = compose(
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functools.partial(foo2, toDelete=["PUNCT", "SPACE"]),
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functools.partial(foo, toKeep=["NOUN"]))
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string = "Frau Hinrichs überdenkt die tu Situation und 545453 macht ' dann neue Anträge. \n Dieses Ticket wird geschlossen \n \n test"
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doc = parser(string)
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#print(removeFromDoc(doc,toDelete=["PUNCT"]))
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print(pipeline(doc.text))
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for txt in cleaningPipe(parser.pipe([string]),pipeline):
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print(txt)
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"""
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corpus_raw.add_text(string)
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for doc in parser.pipe([string]):
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doc.text = removeFromDoc(doc, toDelete=["PUNCT"])
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"""
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#corpus_clean.add_texts(cleaningPipe(parser.pipe([string]),pipeline))
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#print(corpus_raw[0].text)
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