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topicModelingTickets
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topicModelingTickets/test.py

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# -*- coding: utf-8 -*-
import time
start = time.time()
import logging
import csv
import functools
import os.path
import re
import subprocess
import time
import xml.etree.ElementTree as ET
import sys
import spacy
import textacy
from scipy import *
from textacy import Vectorizer
import warnings
import configparser as ConfigParser
import sys
csv.field_size_limit(sys.maxsize)
# Load the configuration file
config_ini = "/home/jannis.grundmann/PycharmProjects/topicModelingTickets/config.ini"
config = ConfigParser.ConfigParser()
with open(config_ini) as f:
config.read_file(f)
# config logging
logging.basicConfig(filename=config.get("filepath","logfile"), level=logging.INFO)
thesauruspath = config.get("filepath","thesauruspath")
THESAURUS = list(textacy.fileio.read_csv(thesauruspath, delimiter=";"))
DE_PARSER = spacy.load("de") #todo spacherkennung idee: verschiedene Corpi für verschiedene Sprachen
de_stop_words=list(__import__("spacy." + DE_PARSER.lang, globals(), locals(), ['object']).STOP_WORDS)
LEMMAS=config.get("filepath","lemmas")
############# misc
def printlog(string, level="INFO"):
"""log and prints"""
print(string)
if level=="INFO":
logging.info(string)
elif level=="DEBUG":
logging.debug(string)
elif level == "WARNING":
logging.warning(string)
printlog("Load functions")
def compose(*functions):
def compose2(f, g):
return lambda x: f(g(x))
return functools.reduce(compose2, functions, lambda x: x)
def get_calling_function():
"""finds the calling function in many decent cases.
https://stackoverflow.com/questions/39078467/python-how-to-get-the-calling-function-not-just-its-name
"""
fr = sys._getframe(1) # inspect.stack()[1][0]
co = fr.f_code
for get in (
lambda:fr.f_globals[co.co_name],
lambda:getattr(fr.f_locals['self'], co.co_name),
lambda:getattr(fr.f_locals['cls'], co.co_name),
lambda:fr.f_back.f_locals[co.co_name], # nested
lambda:fr.f_back.f_locals['func'], # decorators
lambda:fr.f_back.f_locals['meth'],
lambda:fr.f_back.f_locals['f'],
):
try:
func = get()
except (KeyError, AttributeError):
pass
else:
if func.__code__ == co:
return func
raise AttributeError("func not found")
def printRandomDoc(textacyCorpus):
import random
print()
printlog("len(textacyCorpus) = %i" % len(textacyCorpus))
randIndex = int((len(textacyCorpus) - 1) * random.random())
printlog("Index: {0} ; Text: {1} ; Metadata: {2}".format(randIndex, textacyCorpus[randIndex].text, textacyCorpus[randIndex].metadata))
print()
############# load xml
def generateMainTextfromTicketXML(path2xml, main_textfield='Description'):
"""
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=['Description']):
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
############# load csv
def csv_to_contentStream(path2csv: str, content_collumn_name: str):
"""
:param path2csv: string
:param content_collumn_name: string
:return: string-generator
"""
stream = textacy.fileio.read_csv(path2csv, delimiter=";") # ,encoding='utf8')
content_collumn = 0 # standardvalue
for i,lst in enumerate(stream):
if i == 0:
# look for desired column
for j,col in enumerate(lst):
if col == content_collumn_name:
content_collumn = j
else:
yield lst[content_collumn]
def csv_to_metaStream(path2csv: str, metalist: [str]):
"""
:param path2csv: string
:param metalist: list of strings
:return: dict-generator
"""
stream = textacy.fileio.read_csv(path2csv, delimiter=";") # ,encoding='utf8')
content_collumn = 0 # standardvalue
metaindices = []
metadata_temp = {}
for i,lst in enumerate(stream):
if i == 0:
for j,col in enumerate(lst): # geht bestimmt effizienter... egal, weil passiert nur einmal
for key in metalist:
if key == col:
metaindices.append(j)
metadata_temp = dict(zip(metalist,metaindices)) # zB {'Subject' : 1, 'categoryName' : 3, 'Solution' : 10}
else:
metadata = metadata_temp.copy()
for key,value in metadata.items():
metadata[key] = lst[value]
yield metadata
############################################ Preprocessing ##############################################
############# on str-gen
def processTokens(tokens, funclist, parser):
# in:tokenlist, funclist
# out: tokenlist
for f in funclist:
# idee: funclist sortieren,s.d. erst alle string-methoden ausgeführt werden, dann wird geparesed, dann wird auf tokens gearbeitet, dann evtl. auf dem ganzen Doc
if 'bool' in str(f.__annotations__):
tokens = list(filter(f, tokens))
elif 'str' in str(f.__annotations__):
tokens = list(map(f, tokens)) # purer text
doc = parser(" ".join(tokens)) # neu parsen
tokens = [tok for tok in doc] # nur tokens
elif 'spacy.tokens.doc.Doc' in str(f.__annotations__):
#todo wirkt gefrickelt
doc = parser(" ".join(tok.lower_ for tok in tokens)) # geparsed
tokens = f(doc)
doc = parser(" ".join(tokens)) # geparsed
tokens = [tok for tok in doc] # nur tokens
else:
warnings.warn("Unknown Annotation while preprocessing. Function: {0}".format(str(f)))
return tokens
def processTextstream(textstream, funclist, parser=DE_PARSER):
"""
:param textstream: string-gen
:param funclist: [func]
:param parser: spacy-parser
:return: string-gen
"""
# input:str-stream output:str-stream
pipe = parser.pipe(textstream)
for doc in pipe:
tokens = []
for tok in doc:
tokens.append(tok)
tokens = processTokens(tokens,funclist,parser)
yield " ".join([tok.lower_ for tok in tokens])
def processDictstream(dictstream, funcdict, parser=DE_PARSER):
"""
:param dictstream: dict-gen
:param funcdict:
clean_in_meta = {
"Solution":funclist,
...
}
:param parser: spacy-parser
:return: dict-gen
"""
for dic in dictstream:
result = {}
for key, value in dic.items():
if key in funcdict:
doc = parser(value)
tokens = [tok for tok in doc]
funclist = funcdict[key]
tokens = processTokens(tokens,funclist,parser)
result[key] = " ".join([tok.lower_ for tok in tokens])
else:
result[key] = value
yield result
############# return bool
def keepPOS(pos_list) -> bool:
ret = lambda tok : tok.pos_ in pos_list
ret.__annotations__ = get_calling_function().__annotations__
return ret
def removePOS(pos_list)-> bool:
ret = lambda tok : tok.pos_ not in pos_list
ret.__annotations__ = get_calling_function().__annotations__
return ret
def removeWords(words, keep=None)-> bool:
if hasattr(keep, '__iter__'):
for k in keep:
try:
words.remove(k)
except ValueError:
pass
ret = lambda tok : tok.lower_ not in words
ret.__annotations__ = get_calling_function().__annotations__
return ret
def keepENT(ent_list) -> bool:
ret = lambda tok : tok.ent_type_ in ent_list
ret.__annotations__ = get_calling_function().__annotations__
return ret
def removeENT(ent_list) -> bool:
ret = lambda tok: tok.ent_type_ not in ent_list
ret.__annotations__ = get_calling_function().__annotations__
return ret
def remove_words_containing_Numbers() -> bool:
ret = lambda tok: not bool(re.search('\d', tok.lower_))
ret.__annotations__ = get_calling_function().__annotations__
return ret
def remove_words_containing_specialCharacters() -> bool:
ret = lambda tok: not bool(re.search(r'[`\-=~!@#$%^&*()_+\[\]{};\'\\:"|<,./<>?]', tok.lower_))
ret.__annotations__ = get_calling_function().__annotations__
return ret
def remove_words_containing_topLVL() -> bool:
ret = lambda tok: not bool(re.search(r'\.[a-z]{2,3}(\.[a-z]{2,3})?', tok.lower_))
ret.__annotations__ = get_calling_function().__annotations__
return ret
def lemmatizeWord(word,filepath=LEMMAS):
"""http://www.lexiconista.com/datasets/lemmatization/"""
for line in list(textacy.fileio.read_file_lines(filepath=filepath)):
if word.lower() == line.split()[1].strip().lower():
return line.split()[0].strip().lower()
return word.lower() # falls nix gefunden wurde
def lemmatize() -> str:
ret = lambda tok: lemmatizeWord(tok.lower_)
ret.__annotations__ = get_calling_function().__annotations__
return ret
############# return strings
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./]+$", re.IGNORECASE)
topLVLFinder = re.compile(r'\.[a-z]{2,3}(\.[a-z]{2,3})?', re.IGNORECASE)
specialFinder = re.compile(r'[`\-=~!@#$%^&*()_+\[\]{};\'\\:"|<,./>?]', re.IGNORECASE)
hardSFinder = re.compile(r'[ß]', re.IGNORECASE)
def replaceEmails(replace_with="EMAIL") -> str:
ret = lambda tok : emailFinder.sub(replace_with, tok.lower_)
ret.__annotations__ = get_calling_function().__annotations__
return ret
def replaceURLs(replace_with="URL") -> str:
ret = lambda tok: textacy.preprocess.replace_urls(tok.lower_,replace_with=replace_with)
#ret = lambda tok: urlFinder.sub(replace_with,tok.lower_)
ret.__annotations__ = get_calling_function().__annotations__
return ret
def replaceSpecialChars(replace_with=" ") -> str:
ret = lambda tok: specialFinder.sub(replace_with,tok.lower_)
ret.__annotations__ = get_calling_function().__annotations__
return ret
def replaceTwitterMentions(replace_with="TWITTER_MENTION") -> str:
ret = lambda tok : mentionFinder.sub(replace_with,tok.lower_)
ret.__annotations__ = get_calling_function().__annotations__
return ret
def replaceNumbers(replace_with="NUMBER") -> str:
ret = lambda tok: textacy.preprocess.replace_numbers(tok.lower_, replace_with=replace_with)
ret.__annotations__ = get_calling_function().__annotations__
return ret
def replacePhonenumbers(replace_with="PHONENUMBER") -> str:
ret = lambda tok: textacy.preprocess.replace_phone_numbers(tok.lower_, replace_with=replace_with)
ret.__annotations__ = get_calling_function().__annotations__
return ret
def replaceHardS(replace_with="ss") -> str:
ret = lambda tok: hardSFinder.sub(replace_with,tok.lower_)
ret.__annotations__ = get_calling_function().__annotations__
return ret
def fixUnicode() -> str:
ret = lambda tok: textacy.preprocess.fix_bad_unicode(tok.lower_, normalization=u'NFC')
ret.__annotations__ = get_calling_function().__annotations__
return ret
def resolveAbbreviations():
pass #todo
#todo wörter mit len < 2 entfernen( vorher abkürzungen (v.a. tu und fh) auflösen) und > 35 oder 50 ("Reiserücktrittskostenversicherung)
############# return docs
def keepUniqeTokens() -> spacy.tokens.Doc:
ret = lambda doc: (set([tok.lower_ for tok in doc]))
ret.__annotations__ = get_calling_function().__annotations__
return ret
def lower() -> spacy.tokens.Doc:
ret = lambda doc: ([tok.lower_ for tok in doc])
ret.__annotations__ = get_calling_function().__annotations__
return ret
################################################################################################################
path2xml = config.get("filepath","path2xml")
path2csv = config.get("filepath","path2csv")
path2csv = "M42-Export/Tickets_med.csv"
printlog("CSV: {0}".format(path2csv))
ticketcorpus = textacy.Corpus(DE_PARSER)
"""
vllt kategorien in unterkategorien aufteilen
allg:
utf-korregieren, bei sonderzeichen wörter trennen
namen raus, addressen nach grüßen
emails, urls, nummern raus
vllt sogar alles, was ebend jenes enthält (oder auf .toplvldomain bzw. sonderzeichen enthält oder alles was ein @ enthält
sinnvoller wörter von müll trennen: 8203;verfügung
abkürzungen raus: m.a, o.ä.
sinnlose bsp: nr54065467 455a33c5 tvt?= ------problem--------
"""
metaliste = [
"Subject",
"categoryName",
"Solution"
]
clean_in_meta = {
"Solution":[removePOS(["SPACE"]),lower()],
"Subject":[removePOS(["SPACE","PUNCT"]),lower()],
"categoryName": [removePOS(["SPACE", "PUNCT"]), lower()]
}
printlog("Start Preprocessing")
clean_in_content=[
replaceHardS(),
replaceSpecialChars(),
remove_words_containing_topLVL(),
remove_words_containing_Numbers(),
remove_words_containing_specialCharacters(),
#removePOS(["SPACE","PUNCT","NUM"]),
#removeENT("PERSON"),
#keepPOS(["NOUN"]),
#replaceURLs(),
#replaceEmails(),
#fixUnicode(),
lemmatize(),
removeWords(de_stop_words + config.get("preprocessing","custom_words").split(",")),
#keepUniqeTokens(),
#keepENT(config.get("preprocessing","ents2keep"))
]
## add files to textacy-corpi,
printlog("add texts to textacy-corpi")
ticketcorpus.add_texts(
processTextstream(csv_to_contentStream(path2csv,"Description"), clean_in_content),
processDictstream(csv_to_metaStream(path2csv,metaliste),clean_in_meta)
)
for i in range(10):
printRandomDoc(ticketcorpus)
end = time.time()
printlog("Time Elapsed Preprocessing:{0} min".format((end - start)/60))
############################################ Topic Modeling #############################################
print("\n\n")
start = time.time()
# build citionary of ticketcategories
labelist = []
for texdoc in ticketcorpus.get(lambda texdoc : texdoc.metadata["categoryName"] not in labelist):
labelist.append(texdoc.metadata["categoryName"])
LABELDICT = {k: v for v, k in enumerate(labelist)}
print(LABELDICT)
def label2ID(label,labeldict=LABELDICT):
return labeldict.get(label,len(labeldict))
def generate_labled_lines(textacyCorpus):
for doc in textacyCorpus:
# generate [topic1, topic2....] tok1 tok2 tok3 out of corpi
yield "[" + str(label2ID(doc.metadata["categoryName"])) + "] " + doc.text
####################'####################' todo alles in config
ngrams = 1
min_df = 0
max_df = 1.0
no_below = 20
no_above = 0.5
topicModel = 'lda'
# http://textacy.readthedocs.io/en/latest/api_reference.html#textacy.tm.topic_model.TopicModel.get_doc_topic_matrix
weighting = ('tf' if topicModel == 'lda' else 'tfidf')
top_topic_words = 7
top_document_labels_per_topic = 2
n_topics = len(LABELDICT)#len(set(ticketcorpus[0].metadata.keys()))+1 #+1 wegen einem default-topic
####################'####################
printlog("vectorize corpi...")
vectorizer = Vectorizer(weighting=weighting, min_df=min_df, max_df=max_df)
terms_list = (doc.to_terms_list(ngrams=ngrams, named_entities=False, as_strings=True) for doc in ticketcorpus)
doc_term_matrix = vectorizer.fit_transform(terms_list)
id2term = vectorizer.__getattribute__("id_to_term")
##################### LSA, LDA, NMF Topic Modeling via Textacy ##############################################
# Initialize and train a topic model
printlog("Initialize and train a topic model..")
model = textacy.tm.TopicModel(topicModel, n_topics=n_topics)
model.fit(doc_term_matrix)
#Transform the corpi and interpret our model:
printlog("Transform the corpi and interpret our model..")
doc_topic_matrix = model.transform(doc_term_matrix)
print()
for topic_idx, top_terms in model.top_topic_terms(vectorizer.id_to_term, top_n=top_topic_words):
print('topic', topic_idx, ':', ' '.join(top_terms))
print()
for topic_idx, top_docs in model.top_topic_docs(doc_topic_matrix, top_n=top_document_labels_per_topic):
print(topic_idx)
for j in top_docs:
print(ticketcorpus[j].metadata['categoryName'])
#####################################################################################################################
print()
print()
"""
##################### LLDA Topic Modeling via JGibbsLabledLDA ##############################################
jgibbsLLDA_root = "/home/jannis.grundmann/PycharmProjects/topicModelingTickets/java_LabledLDA/"
LLDA_filepath = "{0}models/tickets/tickets.gz".format(jgibbsLLDA_root)
#create file
textacy.fileio.write_file_lines(generate_labled_lines(ticketcorpus), filepath=LLDA_filepath)
# wait for file to exist
while not os.path.exists(LLDA_filepath):
time.sleep(1)
print("\n\n")
printlog("start LLDA:")
#run JGibsslda file
FNULL = open(os.devnull, 'w') # supress output
subprocess.call(["java",
"-cp", "{0}lib/trove-3.0.3.jar:{0}lib/args4j-2.0.6.jar:{0}out/production/LabledLDA/".format(jgibbsLLDA_root),
"jgibblda.LDA",
"-est",
"-dir", "{0}models/tickets".format(jgibbsLLDA_root),
"-dfile","tickets.gz",
"-twords",str(top_topic_words),
"-ntopics", str(n_topics)], stdout = FNULL)
# ANMERKUNG: Dateien sind versteckt. zu finden in models/
#twords
subprocess.call(["gzip",
"-dc",
"{0}/models/tickets/.twords.gz".format(jgibbsLLDA_root)])
#####################################################################################################################
print()
print()
"""
end = time.time()
printlog("\n\n\nTime Elapsed Topic Modeling:{0}\n\n".format(end - start))
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