413 lines
14 KiB
Python
413 lines
14 KiB
Python
# -*- 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()
|
|
|
|
|
|
|
|
|
|
|
|
|