pipe effizienter gemacht
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@ -199,6 +199,9 @@ def getHauptform(syn_block, word, default_return_first_Syn=False):
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return w
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return word # zur Not, das ursrpüngliche Wort zurückgeben
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def label2ID(label):
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return {
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'Neuanschluss' : 0,
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229
test.py
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test.py
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@ -1,17 +1,42 @@
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# -*- coding: utf-8 -*-
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import csv
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import functools
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import os.path
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import re
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import subprocess
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import time
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import xml.etree.ElementTree as ET
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import sys
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import spacy
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import textacy
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from scipy import *
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from textacy import Vectorizer
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path2xml = "ticketSamples.xml"
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csv.field_size_limit(sys.maxsize)
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path2xml = "ticket.xml"
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import de_core_news_md
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PARSER = de_core_news_md.load()
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corpus = textacy.Corpus(PARSER)
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thesauruspath = "openthesaurus.csv"
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THESAURUS = list(textacy.fileio.read_csv(thesauruspath, delimiter=";"))
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def printRandomDoc(textacyCorpus):
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import random
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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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@ -31,16 +56,19 @@ def generateMainTextfromTicketXML(path2xml, main_textfield='Beschreibung'):
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if field.tag == main_textfield:
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yield field.text
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def generateMetadatafromTicketXML(path2xml, leave_out=['Beschreibung']):
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tree = ET.parse(path2xml, ET.XMLParser(encoding="utf-8"))
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root = tree.getroot()
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def printRandomDoc(textacyCorpus):
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import random
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print()
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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 not in leave_out:
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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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metadata[field.tag] = field.text
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yield metadata
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print()
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@ -51,20 +79,48 @@ def processTextstream(textstream, funclist, parser=PARSER):
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for doc in pipe:
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tokens = [tok for tok in doc]
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for f in funclist:
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tokens = filter(f,tokens)
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#tokens = map(funclist,tokens)
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if 'bool' in str(f.__annotations__):
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tokens = list(filter(f,tokens))
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elif 'str' in str(f.__annotations__):
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x=0
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tokens = list(map(f, tokens))
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#tokens = [f(tok.lower_) for tok in tokens] #purer text
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doc = parser(" ".join(tokens)) #geparsed
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tokens = [tok for tok in doc] #nur tokens
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elif 'spacy.tokens.Doc' in str(f.__annotations__):
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tokens = [tok for tok in f(tokens)]
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yield " ".join([tok.lower_ for tok in tokens])
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def processDictstream(dictstream, funcdict, parser=PARSER): #todo das selbe wie mit textstream idee: processDoc(doc,funcs)
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for dic in dictstream:
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result = {}
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for key, value in dic.items():
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if key in funcdict:
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result[key] = funcdict[key](parser(value))
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else:
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result[key] = value
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yield result
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def keepPOS(pos_list):
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return lambda tok : tok.pos_ in pos_list
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def keepPOS(pos_list) -> bool:
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ret = lambda tok : tok.pos_ in pos_list
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def removePOS(pos_list):
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return lambda tok : tok.pos_ not in pos_list
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ret.__annotations__ = keepPOS.__annotations__
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return ret
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def removeWords(words, keep=None):
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def removePOS(pos_list)-> bool:
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ret = lambda tok : tok.pos_ not in pos_list
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ret.__annotations__ = removePOS.__annotations__
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return ret
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def removeWords(words, keep=None)-> bool:
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#todo in:str oder str-list
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if hasattr(keep, '__iter__'):
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for k in keep:
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@ -72,22 +128,143 @@ def removeWords(words, keep=None):
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words.remove(k)
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except ValueError:
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pass
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return lambda tok : tok.lower_ not in words
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ret = lambda tok : tok.lower_ not in words
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ret.__annotations__ = removeWords.__annotations__
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return ret
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def keepENT(ent_list) -> bool:
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ret = lambda tok : tok.ent_type_ in ent_list
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ret.__annotations__ = keepENT.__annotations__
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return ret
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def removeENT(ent_list) -> bool:
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ret = lambda tok: tok.ent_type_ not in ent_list
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ret.__annotations__ = removeENT.__annotations__
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return ret
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def keepUniqueTokens() -> spacy.tokens.Doc:
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ret = lambda doc: (set([tok.lower_ for tok in doc]))
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ret.__annotations__ = keepUniqueTokens.__annotations__
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return ret
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def lemmatize() -> str:
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ret = lambda tok: tok.lemma_
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ret.__annotations__ = lemmatize.__annotations__
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return ret
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mentionFinder = re.compile(r"@[a-z0-9_]{1,15}", re.IGNORECASE)
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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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urlFinder = re.compile(r"^(?:https?:\/\/)?(?:www\.)?[a-zA-Z0-9./]+$", re.IGNORECASE)
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def replaceEmails(replace_with="EMAIL") -> str:
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ret = lambda tok : emailFinder.sub(replace_with, tok.lower_)
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ret.__annotations__ = replaceEmails.__annotations__
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return ret
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def replaceURLs(replace_with="URL") -> str:
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ret = lambda tok: textacy.preprocess.replace_urls(tok.lower_,replace_with=replace_with)
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#ret = lambda tok: urlFinder.sub(replace_with,tok.lower_)
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ret.__annotations__ = replaceURLs.__annotations__
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return ret
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def replaceTwitterMentions(replace_with="TWITTER_MENTION") -> str:
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ret = lambda tok : mentionFinder.sub(replace_with,tok.lower_)
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ret.__annotations__ = replaceTwitterMentions.__annotations__
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return ret
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def replaceNumbers(replace_with="NUMBER") -> str:
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ret = lambda tok: textacy.preprocess.replace_numbers(tok.lower_, replace_with=replace_with)
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ret.__annotations__ = replaceNumbers.__annotations__
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return ret
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def replacePhonenumbers(replace_with="PHONENUMBER",parser=PARSER):
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ret = lambda tok: textacy.preprocess.replace_phone_numbers(tok.lower_, replace_with=replace_with)
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ret.__annotations__ = replacePhonenumbers.__annotations__
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return ret
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def replaceEmails(replace_with="EMAIL"):
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return lambda tok : emailFinder.sub(replace_with, tok.lower_)
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def resolveAbbreviations():
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pass #todo
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def normalizeSynonyms(default_return_first_Syn=False) -> str:
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ret = lambda tok : getFirstSynonym(tok.lower_, default_return_first_Syn=default_return_first_Syn)
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ret.__annotations__ = normalizeSynonyms.__annotations__
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return ret
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def getFirstSynonym(word, thesaurus=THESAURUS, default_return_first_Syn=False):
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if not isinstance(word, str):
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return str(word)
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word = word.lower()
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# durch den thesaurrus iterieren
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for syn_block in thesaurus: # 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 str(getHauptform(syn_block, word, default_return_first_Syn=default_return_first_Syn))
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else: # falls es ein satz ist
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if word in syn:
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return str(getHauptform(syn_block, word, default_return_first_Syn=default_return_first_Syn))
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return str(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#todo gibts macnmal?? klammern aus
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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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stop_words=list(__import__("spacy." + PARSER.lang, globals(), locals(), ['object']).STOP_WORDS)
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clean_in_content=[
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removePOS(["SPACE"]),
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removeWords(["dezernat"]),
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removePOS(["PUNCT"]),
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removeWords(stop_words,keep=["und"]),
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replaceEmails
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replaceURLs(),
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removePOS(["NUM"]),
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lemmatize(),
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removeWords(stop_words),
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keepUniqueTokens(),
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normalizeSynonyms()
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]
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printRandomDoc(corpus)
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#todo https://stackoverflow.com/questions/15200048/how-to-get-the-parameters-type-and-return-type-of-a-function
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