topicModelingTickets/topicModeling.py

# -*- coding: utf-8 -*-

from datetime import datetime

import time

import csv
import sys
import json
import os.path
import subprocess
from textacy import Vectorizer

from miscellaneous import *
import textacy
from scipy import *

import os

csv.field_size_limit(sys.maxsize)
FILEPATH = os.path.dirname(os.path.realpath(__file__)) + "/"

# ssh madonna "nohup /usr/bin/python3 -u /home/jannis.grundmann/PycharmProjects/topicModelingTickets/topicModeling.py &> /home/jannis.grundmann/PycharmProjects/topicModelingTickets/printout_topicModeling.log &"


# load config
config_ini = FILEPATH + "config.ini"

config = ConfigParser.ConfigParser()
with open(config_ini) as f:
    config.read_file(f)


def label2ID(label, labeldict):
    return labeldict.get(label, len(labeldict))


def generate_labled_lines(textacyCorpus, labeldict):
    for doc in textacyCorpus:
        # generate [topic1, topic2....] tok1 tok2 tok3 out of corpi
        yield "[" + str(label2ID(doc.metadata["categoryName"], labeldict)) + "] " + doc.text


def printvecotorization(de_corpus, ngrams=1, min_df=1, max_df=1.0, weighting='tf', named_entities=True):
    logprint(str("ngrams: {0}".format(ngrams)))
    logprint(str("min_df: {0}".format(min_df)))
    logprint(str("max_df: {0}".format(max_df)))
    logprint(str("named_entities: {0}".format(named_entities)))

    # 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=named_entities, as_strings=True) for doc in de_corpus)
    doc_term_matrix = vectorizer.fit_transform(terms_list)
    id2term = vectorizer.__getattribute__("id_to_term")

    for t in terms_list:
        print(t)
    logprint("doc_term_matrix: {0}".format(doc_term_matrix))
    logprint("id2term: {0}".format(id2term))


def textacyTopicModeling(ngrams, min_df, max_df, corpus, n_topics, topicModel='lda', named_entities=False):
    logprint(
        "############################################ Topic Modeling {0}   #############################################".format(
            topicModel))
    print("\n\n")
    logprint(str("ngrams: {0}".format(ngrams)))
    logprint(str("min_df: {0}".format(min_df)))
    logprint(str("max_df: {0}".format(max_df)))
    logprint(str("n_topics: {0}".format(n_topics)))
    logprint(str("named_entities: {0}".format(named_entities)))

    start = time.time()

    top_topic_words = 7
    top_document_labels_per_topic = 5

    # 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')




    ####################'####################


    # 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=named_entities, as_strings=True) for doc in corpus)
    doc_term_matrix = vectorizer.fit_transform(terms_list)
    id2term = vectorizer.__getattribute__("id_to_term")

    # printlog("terms_list: {0}".format(list(terms_list)))
    # printlog("doc_term_matrix: {0}".format(doc_term_matrix))



    #####################     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):
        logprint('topic {0}: {1}'.format(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):
        logprint(topic_idx)
        for j in top_docs:
            logprint(corpus[j].metadata['categoryName'])
        print()

    #####################################################################################################################
    print()
    print()


    # termite plot
    n_terms = int(n_topics*top_topic_words)
    sort_terms_by = 'seriation' #'seriation', 'weight', 'index', 'alphabetical'
    rank_terms_by = 'corpus'  # 'corpus', 'topic'
    model.termite_plot(doc_term_matrix, id2term,

                       n_terms=n_terms,
                       sort_terms_by=sort_terms_by,
                       rank_terms_by=rank_terms_by+'_weight',

    save="/home/jannis.grundmann/PycharmProjects/topicModelingTickets/results/{}_{}_{}_{}_{}.png".format(topicModel,n_topics,n_terms,sort_terms_by,rank_terms_by))



    end = time.time()
    logprint("\n\n\nTime Elapsed Topic Modeling with {1}:{0} min\n\n".format((end - start) / 60, topicModel))


def jgibbsLLDA(corpus, path2save_results, top_topic_words=7, add_default_topic=False):
    #####################   LLDA           Topic Modeling via JGibbsLabledLDA     ##############################################

    start = time.time()

    # build dictionary of ticketcategories
    labelist = []

    for texdoc in corpus.get(lambda texdoc: texdoc.metadata["categoryName"] not in labelist):
        labelist.append(texdoc.metadata["categoryName"])

    labeldict = {k: v for v, k in enumerate(labelist)}

    if add_default_topic:
        n_topics = len(labeldict) + 1  # len(set(ticketcorpus[0].metadata.keys()))+1 #+1 wegen einem default-topic
    else:
        n_topics = len(labeldict)  # + 1  # len(set(ticketcorpus[0].metadata.keys()))+1 #+1 wegen einem default-topic

    jgibbsLLDA_root = FILEPATH + "/java_LabledLDA/"

    LLDA_filepath = "{0}models/tickets/tickets.gz".format(jgibbsLLDA_root)
    dict_path = "{0}models/tickets/labeldict.txt".format(jgibbsLLDA_root)

    # printlog(str("LABELDICT: {0}".format(labeldict)))
    #logprint(str("LABELDICT-length: {0}".format(len(labeldict))))
    with open(dict_path, 'w') as file:
        file.write(json.dumps(labeldict))

    # for line in generate_labled_lines(de_corpus,labeldict):
    #    print(line)

    # create file
    textacy.fileio.write_file_lines(generate_labled_lines(corpus, labeldict), filepath=LLDA_filepath)

    # wait for file to exist
    while not os.path.exists(LLDA_filepath):
        time.sleep(1)
    #top_topic_words=1
    logprint("")
    logprint("start LLDA:")
    # run JGibsslda file
    FNULL = open(os.devnull, 'w')  # supress output
    cmd_jgibbs_java = ["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)]
    subprocess.call(cmd_jgibbs_java, stdout=FNULL)

    # ANMERKUNG: Dateien sind versteckt. zu finden in models/

    # twords
    """
    subprocess.call(["gzip",
                     "-dc",
                     "{0}/models/tickets/.twords.gz".format(jgibbsLLDA_root)])
    """

    cmd_gzip = ["gzip", "-dc", "{0}/models/tickets/.twords.gz".format(jgibbsLLDA_root)]
    """
    proc = subprocess.Popen(cmd_gzip, stdout=subprocess.PIPE)

    process = subprocess.Popen(cmd_gzip, shell=True,
                               stdout=subprocess.PIPE,
                               stderr=subprocess.PIPE)

    # wait for the process to terminate
    out, err = process.communicate()
    errcode = process.returncode

    result = subprocess.check_output(cmd_gzip)

    #result = proc.stdout.read()
    result = proc.communicate()
    out=[]
    for line in result:
        out.append(line)
    """

    output = subprocess.check_output(cmd_gzip).decode("utf-8")

    reverse_labeldict = {v: k for k, v in labeldict.items()}
    result = []
    regex = re.compile(r'Topic [0-9]*')
    for line in output.splitlines():

        findall = regex.findall(line)
        if len(findall) != 0:
            try:
                index = int(findall[0].split()[1])
                result.append("Topic {} {}:".format(index, reverse_labeldict[index]))

            except:
                result.append(line)

        else:
            result.append(line)

    textacy.fileio.write_file_lines(result, path2save_results)
    #####################################################################################################################
    logprint("")

    end = time.time()
    logprint("\n\n\nTime Elapsed Topic Modeling JGibbsLLDA:{0} min\n\n".format((end - start) / 60))


def main(use_raw=False, algorithm="llda"):
    logprint("Topic Modeling: {0}".format(datetime.now()))

    corpus_de_path = FILEPATH + config.get("de_corpus", "path")

    corpus_en_path = FILEPATH + config.get("en_corpus", "path")


    if use_raw:
        # fehler Unknown document label ( X ) for document 352.
        preCorpus_name = "de" + "_raw_ticket"
        resultspath = FILEPATH + "results/raw"

    else:
        preCorpus_name = "de" + "_pre_ticket"
        resultspath = FILEPATH + "results/pre"

    # load raw corpus and create new one
    de_corpus, parser = load_corpus(corpus_name=preCorpus_name, corpus_path=corpus_de_path)
    logprint("Corpus loaded: {0}".format(de_corpus.lang))

    # idee http://bigartm.org/
    # idee http://wiki.languagetool.org/tips-and-tricks
    # idee https://en.wikipedia.org/wiki/Noisy_text_analytics
    # idee https://gate.ac.uk/family/

    # todo gescheites tf(-idf) maß finden
    # todo topics zusammenfassen
    # frage wieviele tickets pro topic?

    """
    ngrams = 1
    min_df = 1
    max_df = 1.0
    weighting = 'tf'
    # weighting ='tfidf'
    named_entities = False

  
    printvecotorization(ngrams=1, min_df=1, max_df=1.0, weighting=weighting)
    printvecotorization(ngrams=1, min_df=1, max_df=0.5, weighting=weighting)
    printvecotorization(ngrams=1, min_df=1, max_df=0.8, weighting=weighting)

    printvecotorization(ngrams=(1, 2), min_df=1, max_df=1.0, weighting=weighting)
    printvecotorization(ngrams=(1, 2), min_df=1, max_df=0.5, weighting=weighting)
    printvecotorization(ngrams=(1, 2), min_df=1, max_df=0.8, weighting=weighting)
    """

    if algorithm == "llda":
        top_topic_words = 5
        add_default_topic = False
        path2save_results = resultspath +  "_{}_{}.txt".format("top"+str(top_topic_words), "wdefault" if add_default_topic else "")
        jgibbsLLDA(de_corpus, path2save_results=path2save_results, top_topic_words=top_topic_words,
                   add_default_topic=add_default_topic)

        top_topic_words = 5
        add_default_topic = True
        path2save_results = resultspath +  "_{}_{}.txt".format("top"+str(top_topic_words), "wdefault" if add_default_topic else "")
        jgibbsLLDA(de_corpus, path2save_results=path2save_results, top_topic_words=top_topic_words,
                   add_default_topic=add_default_topic)

        top_topic_words = 10
        add_default_topic = False
        path2save_results = resultspath +  "_{}_{}.txt".format("top"+str(top_topic_words), "wdefault" if add_default_topic else "")
        jgibbsLLDA(de_corpus, path2save_results=path2save_results, top_topic_words=top_topic_words,
                   add_default_topic=add_default_topic)

        top_topic_words = 10
        add_default_topic = True
        path2save_results = resultspath +  "_{}_{}.txt".format("top"+str(top_topic_words), "wdefault" if add_default_topic else "")
        jgibbsLLDA(de_corpus, path2save_results=path2save_results, top_topic_words=top_topic_words,
                   add_default_topic=add_default_topic)

    # no_below = 20
    # no_above = 0.5


    # n_topics = len(LABELDICT)#len(set(ticketcorpus[0].metadata.keys()))+1 #+1 wegen einem default-topic


    else:

        # build dictionary of ticketcategories
        labelist = []

        for texdoc in de_corpus.get(lambda texdoc: texdoc.metadata["categoryName"] not in labelist):
            labelist.append(texdoc.metadata["categoryName"])

        labeldict = {k: v for v, k in enumerate(labelist)}

        n_topics = 15


        textacyTopicModeling(ngrams = 1,
                             min_df = 1,
                             max_df = 0.8,
                             topicModel = algorithm,
                             n_topics =n_topics,
                             corpus=de_corpus)
        """
        textacyTopicModeling(ngrams = (1,2),
                             min_df = 1,
                             max_df = 0.8,
                             topicModel = algorithm,
                             n_topics =n_topics,
                             corpus=de_corpus)

        """

        """
        textacyTopicModeling(ngrams = 1,
                             min_df = 0.1,
                             max_df = 0.6,
                             topicModel = algorithm,
                             n_topics =n_topics,
                             corpus=de_corpus)




        textacyTopicModeling(ngrams = (1,2),
                             min_df = 1,
                             max_df = 1.0,
                             topicModel = algorithm,
                             n_topics =n_topics,
                             corpus=de_corpus)

        textacyTopicModeling(ngrams = (1,2),
                             min_df = 0.1,
                             max_df = 0.6,
                             topicModel = algorithm,
                             n_topics =n_topics,
                             corpus=de_corpus)




        textacyTopicModeling(ngrams = (1,2),
                      min_df = 0.2,
                      max_df = 0.8,
                      topicModel = algorithm,
                      n_topics = 20,
                      corpus=de_corpus)
        """









if __name__ == "__main__":
    main()