topicModelingTickets/test.py

# -*- 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

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-corpus,
printlog("add texts to textacy-corpus")
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 corpus
        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 corpus...")
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 corpus and interpret our model:
printlog("Transform the corpus 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))