473 lines
14 KiB
Python
473 lines
14 KiB
Python
# -*- coding: utf-8 -*-
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from datetime import datetime
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import time
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import numpy as np
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import csv
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import sys
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import json
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import os.path
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import subprocess
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from textacy import Vectorizer, viz
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from miscellaneous import *
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import textacy
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from scipy import *
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import os
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csv.field_size_limit(sys.maxsize)
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FILEPATH = os.path.dirname(os.path.realpath(__file__)) + "/"
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# load config
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config_ini = FILEPATH + "config.ini"
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config = ConfigParser.ConfigParser()
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with open(config_ini) as f:
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config.read_file(f)
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def label2ID(label, labeldict):
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return labeldict.get(label, len(labeldict))
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def generate_lablelID_lines(textacyCorpus, labeldict):
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for doc in textacyCorpus:
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# generate [topic1, topic2....] tok1 tok2 tok3 out of corpi
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yield "[" + str(label2ID(doc.metadata["categoryName"], labeldict)) + "] " + doc.text
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"""
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def printvecotorization(de_corpus, ngrams=1, min_df=1, max_df=1.0, weighting='tf', named_entities=True):
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logprint(str("ngrams: {0}".format(ngrams)))
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logprint(str("min_df: {0}".format(min_df)))
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logprint(str("max_df: {0}".format(max_df)))
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logprint(str("named_entities: {0}".format(named_entities)))
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# printlog("vectorize corpi...")
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vectorizer = Vectorizer(weighting=weighting, min_df=min_df, max_df=max_df)
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terms_list = (doc.to_terms_list(ngrams=ngrams, named_entities=named_entities, as_strings=True) for doc in de_corpus)
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doc_term_matrix = vectorizer.fit_transform(terms_list)
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id2term = vectorizer.__getattribute__("id_to_term")
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for t in terms_list:
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print(t)
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logprint("doc_term_matrix: {0}".format(doc_term_matrix))
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logprint("id2term: {0}".format(id2term))
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"""
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def textacyTopicModeling(corpus,
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n_topics = 15, top_topic_words = 7, top_document_labels_per_topic = 5,
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ngrams = 1, min_df=1, max_df=1.0,
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topicModel='lda'):
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n_terms = int(n_topics * top_topic_words)
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sort_terms_by = 'seriation' # 'seriation', 'weight', 'index', 'alphabetical'
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rank_terms_by = 'corpus' # 'corpus', 'topic'
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logprint(
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"############### Topic Modeling {0} ###########################".format(
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topicModel))
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logprint(str("ngrams: {0}".format(ngrams)))
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logprint(str("min_df: {0}".format(min_df)))
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logprint(str("max_df: {0}".format(max_df)))
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logprint(str("n_topics: {0}".format(n_topics)))
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logprint("\n")
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start = time.time()
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# http://textacy.readthedocs.io/en/latest/api_reference.html#textacy.tm.topic_model.TopicModel.get_doc_topic_matrix
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weighting = ('tf' if topicModel == 'lda' else 'tfidf')
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#################### vectorize corpi ####################
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vectorizer = Vectorizer(weighting=weighting, min_df=min_df, max_df=max_df)
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terms_list = (doc.to_terms_list(ngrams=ngrams, named_entities=False, as_strings=True) for doc in corpus)
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doc_term_matrix = vectorizer.fit_transform(terms_list)
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id2term = vectorizer.__getattribute__("id_to_term")
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# printlog("terms_list: {0}".format(list(terms_list)))
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# printlog("doc_term_matrix: {0}".format(doc_term_matrix))
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##################### Initialize and train a topic model ##############################################
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model = textacy.tm.TopicModel(topicModel, n_topics=n_topics)
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model.fit(doc_term_matrix)
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doc_topic_matrix = model.transform(doc_term_matrix)
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for topic_idx, top_terms in model.top_topic_terms(vectorizer.id_to_term, top_n=top_topic_words):
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logprint('topic {0}: {1}'.format(topic_idx, " ".join(top_terms)))
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for topic_idx, top_docs in model.top_topic_docs(doc_topic_matrix, top_n=top_document_labels_per_topic):
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logprint(topic_idx)
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for j in top_docs:
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logprint(corpus[j].metadata['categoryName'])
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####################### termite plot ###################################################################
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grams_label = "uni" if ngrams == 1 else "bi"
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model.termite_plot(doc_term_matrix, id2term,
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n_terms=n_terms,
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sort_terms_by=sort_terms_by,
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rank_terms_by=rank_terms_by+'_weight',
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save= FILEPATH + "results/{}_{}_{}_{}_{}_{}.png".format(grams_label,topicModel,n_topics,n_terms,sort_terms_by,rank_terms_by))
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end = time.time()
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logprint("\n\n\nTime Elapsed Topic Modeling with {1}:{0} min\n\n".format((end - start) / 60, topicModel))
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def jgibbsLLDA(corpus, path2save_results, top_topic_words=7):
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start = time.time()
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jgibbsLLDA_root = FILEPATH + "java_LabledLDA/"
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LLDA_filepath = "{0}models/tickets/tickets.gz".format(jgibbsLLDA_root)
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# build dictionary of ticketcategories
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labelist = []
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for texdoc in corpus.get(lambda texdoc: texdoc.metadata["categoryName"] not in labelist):
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labelist.append(texdoc.metadata["categoryName"])
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labeldict = {k: v for v, k in enumerate(labelist)}
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reverse_labeldict = {v: k for k, v in labeldict.items()}
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#and save
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labeldict_path = FILEPATH + "results/labeldict.txt"
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with open(labeldict_path, 'w') as file:
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file.write(json.dumps(labeldict))
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n_topics = len(labeldict) #+1 #default-topic
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# create file with label_IDs (input for llda)
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textacy.fileio.write_file_lines(generate_lablelID_lines(corpus, labeldict), filepath=LLDA_filepath)
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# wait for file to exist
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while not os.path.exists(LLDA_filepath):
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time.sleep(1)
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logprint("")
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logprint("start LLDA:")
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# run JGibbsLLDA file
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FNULL = open(os.devnull, 'w') # supress output
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cmd_jgibbs_java = ["java", "-cp",
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"{0}lib/trove-3.0.3.jar:{0}lib/args4j-2.0.6.jar:{0}out/production/LabledLDA/".format(
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jgibbsLLDA_root),
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"jgibblda.LDA", "-est", "-dir", "{0}models/tickets".format(jgibbsLLDA_root), "-dfile",
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"tickets.gz",
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"-twords", str(top_topic_words), "-ntopics", str(n_topics)]
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subprocess.call(cmd_jgibbs_java, stdout=FNULL)
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# ANMERKUNG: Dateien sind versteckt. zu finden in models/
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cmd_gzip = ["gzip", "-dc", "{0}/models/tickets/.twords.gz".format(jgibbsLLDA_root)]
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output = subprocess.check_output(cmd_gzip).decode("utf-8")
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topic_regex = re.compile(r'Topic [0-9]*')
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#####################################
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# todo save results in file aufgrund von results
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result = []
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for line in output.splitlines():
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findall = topic_regex.findall(line)
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if len(findall) != 0:
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try:
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index = int(findall[0].split()[1])
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result.append("Topic {} {}:".format(index, reverse_labeldict[index]))
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except:
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result.append(line)
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else:
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result.append(line)
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textacy.fileio.write_file_lines(result, path2save_results+".txt")
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#####################################
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results = []
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res_dict = {}
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count =0
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for line in output.splitlines():
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findall = topic_regex.findall(line)
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if len(findall) != 0:
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if len(res_dict) != 0:
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results.append(res_dict) #vorheriges an die liste ran (ist ja dann fertig)
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index = int(findall[0].split()[1])
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res_dict = {index : str(reverse_labeldict[index]) }
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else:
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splitted = line.split()
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res_dict[splitted[0]] = float(splitted[1])
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### print terms that are topics
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for s in list(res_dict.values()):
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if isinstance(s,str) and splitted[0] in s:
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vals = list(res_dict.values())
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keys = list(res_dict.keys())
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for v in vals:
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if not isinstance(v,float):
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print("{}".format(v))
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print("{}".format(splitted[0]))
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count +=1
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print()
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###
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if len(res_dict) != 0:
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results.append(res_dict) # letzes an die liste ran
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print(count)
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print(float(count)/float(len(labelist)))
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# {0: 'betrieb', 'service': 0.24162679425837305, 'support': 0.24162679425837305, 'browser': 0.24162679425837305, 'unicard': 0.24162679425837305, 'telefon': 0.0023923444976076593}
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# every term in the resulsts to a list
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terms=[]
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for res in results:
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for key,value in res.items():
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if not isinstance(key, int) and not key in terms:
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terms.append(key)
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term2id = {t:i for i,t in enumerate(terms)} #and to dict
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################# termite plot #####################################################################
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#term_topic_weights.shape = (len(term_ids),len(topic_ids)
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#topic_labels = tuple(labelist)
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topic_labels = list(range(len(labelist)))
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term_labels = list(range(len(term2id))) #tuple([key for key in term2id.keys()])
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term_topic_weights = np.zeros((len(term2id),len(topic_labels)))
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for i,res in enumerate(results):
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for key,value in res.items():
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if not isinstance(key, int):
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term_topic_weights[term2id[key]][i] = value
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term_labels[term2id[key]] = key
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else:
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topic_labels[i] = reverse_labeldict[key]
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viz.draw_termite_plot(
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term_topic_weights, topic_labels, term_labels, save=path2save_results+".png")
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end = time.time()
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logprint("Time Elapsed Topic Modeling JGibbsLLDA:{0} min\n".format((end - start) / 60))
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def main(use_cleaned=False, algorithm="llda"):
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# idee http://bigartm.org/
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# idee http://wiki.languagetool.org/tips-and-tricks
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# idee https://en.wikipedia.org/wiki/Noisy_text_analytics
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# idee https://gate.ac.uk/family/
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logprint("Topic Modeling: {0}".format(datetime.now()))
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corpus_de_path = FILEPATH + config.get("de_corpus", "path")
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corpus_en_path = FILEPATH + config.get("en_corpus", "path")
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if use_cleaned:
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preCorpus_name = "de" + "_clean_ticket"
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resultspath = FILEPATH + "results/clean"
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else:
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preCorpus_name = "de" + "_pre_ticket"
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resultspath = FILEPATH + "results/pre"
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# load cleand corpus
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de_corpus, parser = load_corpus(corpus_name=preCorpus_name, corpus_path=corpus_de_path)
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logprint("Corpus loaded: {0}".format(de_corpus.lang))
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# todo llda topics zusammenfassen
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# idee lda so trainieren, dass zuordnung term <-> topic nicht zu schwach wird, aber möglichst viele topics
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# frage wieviele tickets pro topic?
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"""
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ngrams = 1
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min_df = 1
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max_df = 1.0
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weighting = 'tf'
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# weighting ='tfidf'
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named_entities = False
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printvecotorization(ngrams=1, min_df=1, max_df=1.0, weighting=weighting)
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printvecotorization(ngrams=1, min_df=1, max_df=0.5, weighting=weighting)
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printvecotorization(ngrams=1, min_df=1, max_df=0.8, weighting=weighting)
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printvecotorization(ngrams=(1, 2), min_df=1, max_df=1.0, weighting=weighting)
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printvecotorization(ngrams=(1, 2), min_df=1, max_df=0.5, weighting=weighting)
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printvecotorization(ngrams=(1, 2), min_df=1, max_df=0.8, weighting=weighting)
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"""
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if algorithm == "llda":
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top_topic_words = 5
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path2save_results = resultspath + "_{}_{}".format(algorithm,"top"+str(top_topic_words))
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jgibbsLLDA(de_corpus, path2save_results=path2save_results, top_topic_words=top_topic_words)
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"""
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top_topic_words = 10
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path2save_results = resultspath + "_{}_{}".format(algorithm,"top"+str(top_topic_words))
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jgibbsLLDA(de_corpus, path2save_results=path2save_results, top_topic_words=top_topic_words)
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top_topic_words = 15
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path2save_results = resultspath + "_{}_{}".format(algorithm, "top" + str(top_topic_words))
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jgibbsLLDA(de_corpus, path2save_results=path2save_results, top_topic_words=top_topic_words)
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top_topic_words = 20
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path2save_results = resultspath + "_{}_{}".format(algorithm, "top" + str(top_topic_words))
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jgibbsLLDA(de_corpus, path2save_results=path2save_results, top_topic_words=top_topic_words)
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"""
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else:
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textacyTopicModeling(ngrams = 1,
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min_df = 1,
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max_df = 0.9,
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topicModel = algorithm,
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n_topics =15,
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corpus=de_corpus)
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"""
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textacyTopicModeling(ngrams=1,
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min_df=1,
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max_df=0.9,
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topicModel=algorithm,
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n_topics=20,
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corpus=de_corpus)
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textacyTopicModeling(ngrams=1,
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min_df=1,
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max_df=0.9,
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topicModel=algorithm,
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n_topics=25,
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corpus=de_corpus)
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textacyTopicModeling(ngrams=1,
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min_df=1,
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max_df=0.9,
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topicModel=algorithm,
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n_topics=30,
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corpus=de_corpus)
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"""
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textacyTopicModeling(ngrams=(1, 2),
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min_df=1,
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max_df=0.9,
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topicModel=algorithm,
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n_topics=15,
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corpus=de_corpus)
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"""
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textacyTopicModeling(ngrams = (1,2),
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min_df = 1,
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max_df = 0.9,
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topicModel = algorithm,
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n_topics =20,
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corpus=de_corpus)
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textacyTopicModeling(ngrams = (1,2),
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min_df = 1,
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max_df = 0.9,
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topicModel = algorithm,
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n_topics =25,
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corpus=de_corpus)
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textacyTopicModeling(ngrams = (1,2),
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min_df = 1,
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max_df = 0.9,
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topicModel = algorithm,
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n_topics =30,
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corpus=de_corpus)
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"""
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if __name__ == "__main__":
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main()
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