start auswertung
This commit is contained in:
jannis.grundmann
parent
7214911606
commit
873e9ff7d2
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@ -90,6 +90,7 @@ def autocorrectWord(word):
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def clean(stringstream,autocorrect=False):
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for string in stringstream:
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@ -165,6 +166,8 @@ autocorrect = config.getboolean("preprocessing", "autocorrect")
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def cleanCorpus(corpus_path, clean_in_meta, lang="de", printrandom=10,autocorrect=False):
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autocorrect = False #todo STELLSCHRAUBE
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logprint("Clean {0}_corpus at {1}".format(lang, datetime.now()))
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rawCorpus_name = lang + "_raw_ticket"
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26
main.py
26
main.py
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@ -3,7 +3,7 @@ import matplotlib
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matplotlib.use('Agg')
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import time
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import init
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from datetime import datetime
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import corporization
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import preprocessing
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import topicModeling
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@ -21,6 +21,8 @@ start = time.time()
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# idee häufige n-gramme raus (zB damen und herren)
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# idee 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 welche mitarbeiter bearbeiteten welche Topics? idee topics mit mitarbeiternummern erstzen
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@ -29,9 +31,10 @@ start = time.time()
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# todo modelle testen
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logprint("main.py started at {}".format(datetime.now()))
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"""
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init.main()
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logprint("")
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@ -41,13 +44,13 @@ logprint("")
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cleaning.main()
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logprint("")
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preprocessing.main() # ~5h
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preprocessing.main()
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logprint("")
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"""
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#topicModeling.main(algorithm="lsa")
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logprint("")
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@ -56,16 +59,17 @@ logprint("")
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logprint("")
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topicModeling.main(algorithm="llda")
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logprint("")
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#topicModeling.main(algorithm="llda")
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logprint("")
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topicModeling.main(algorithm="lda")
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logprint("")
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end = time.time()
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logprint("main.py finished at {}".format(datetime.now()))
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logprint("Total Time Elapsed: {0} min".format((end - start) / 60))
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#800*400
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@ -49,6 +49,10 @@ def filterTokens(tokens, funclist):
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for f in funclist:
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tokens = list(filter(f, tokens))
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for tok in tokens:
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if tok.pos_ =="NOUN":
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x=0
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return tokens
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@ -57,7 +61,9 @@ def keepPOS(pos_list):
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def keepNouns(noun_list=NOUNS):
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return lambda tok: tok.lower_ in noun_list
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#return lambda tok: tok.lower_ in noun_list
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return lambda tok: tok.lower_ in noun_list or tok.pos_ == "NOUN"
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def removePOS(pos_list):
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@ -204,8 +210,8 @@ def processContentstream(textstream, parser, token_filterlist=None):
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tokens = filterTokens(tokens, token_filterlist)
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# post parse
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tokens = [postparse(tok) for tok in tokens] #todo: informationsverlust von pos,tag etc.!
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#todo STELLSCHRAUBE tokens = [postparse(tok) for tok in tokens] #todo: informationsverlust von pos,tag etc.!
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tokens = [tok.lower_ for tok in tokens]
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yield " ".join(tokens)
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def preparse(stringstream):
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@ -360,16 +366,13 @@ def main():
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keepNouns(NOUNS),
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remove_words_containing_Numbers(),
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removeWords(DE_STOP_WORDS + custom_words + VORNAMEN),
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removePOS(["PUNCT", "SPACE", "NUM"]),
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removeWords(DE_STOP_WORDS + custom_words + VORNAMEN),
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#removeWords(DE_STOP_WORDS),
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remove_long_words(),
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remove_short_words(),
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remove_first_names()
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#todo STELLSCHRAUBE remove_words_containing_Numbers(),
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#todo STELLSCHRAUBE remove_long_words(),
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#todo STELLSCHRAUBE remove_short_words()
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]
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200
test.py
200
test.py
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@ -27,16 +27,189 @@ corpus_de_path = FILEPATH + config.get("de_corpus", "path")
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preCorpus_name = "de" + "_pre_ticket"
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corpus, parser = load_corpus(corpus_name=preCorpus_name, corpus_path=corpus_de_path)
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logprint("Corpus loaded: {0}".format(corpus.lang))
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#
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#todo randomize
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split_index = int(float(len(corpus)) * 0.8)
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split = 0.8
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weighting = "tf"
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min_df = 0
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max_df = 1
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ngrams = 1
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n_topics = 3
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top_n = 7
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split_index = int(float(len(corpus)) * split)
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corpus_train = corpus[0:split_index]
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corpus_test = corpus[split_index:len(corpus)-1]
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###### Initialize and train a topic model
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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_train)
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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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model = textacy.tm.TopicModel("lda", n_topics=n_topics)
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model.fit(doc_term_matrix)
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######
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compenents = model.model.components_
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"""
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components_ : array, [n_components, n_features]
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Variational parameters for topic word distribution.
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Since the complete conditional for topic word distribution is a Dirichlet,
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components_[i, j] can be viewed as pseudocount that represents
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the number of times word j was assigned to topic i.
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It can also be viewed as distribution over the words for each topic after normalization:
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model.components_ / model.components_.sum(axis=1)[:, np.newaxis].
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"""
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test_doc = corpus_test[0]
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end = time.time()
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print("\n\n\nTime Elapsed Test:{0}\n\n".format(end - start))
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"""
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# frage wieviele tickets pro topic?
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ticket_gen = textacy.fileio.read_csv(FILEPATH + "M42-Export/de_tickets.csv", delimiter=";")
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cat_dict = {}
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cat2id_dict = {}
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for line in ticket_gen:
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tick_id = line[0]
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cat = normalize(line[3])
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cat2id_dict[cat] = tick_id
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if cat not in cat_dict.keys():
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cat_dict[cat] = 1
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else:
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cat_dict[cat] += 1
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import operator
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sorted_dict = sorted(cat_dict.items(), key=operator.itemgetter(1))
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for k, v in sorted_dict:
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if k == "sd":
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print(cat2id_dict[k])
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print(k, v)
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print(len(sorted_dict))
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kb2ticket_gen = textacy.fileio.read_csv(FILEPATH + "M42-Export/KB2Ticket_2017-09-13.csv", delimiter=";")
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ticket2kb_dict = {}
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@ -118,14 +291,7 @@ for k,v in kb2keywords_dict.items(): #str,list
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import operator
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"""
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sorted_dict = sorted(count_dict.items(), key=operator.itemgetter(1))
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for k,v in sorted_dict:
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print(k,v)
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print(len(sorted_dict))
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"""
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@ -152,6 +318,7 @@ for kb_entry in kb2keywords_gen:
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else:
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count_dict[entry_] += 1
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import operator
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sorted_dict = sorted(count_dict.items(), key=operator.itemgetter(1))
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@ -159,20 +326,7 @@ sorted_dict = sorted(count_dict.items(), key=operator.itemgetter(1))
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# print(k,v)
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#print(len(sorted_dict))
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end = time.time()
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print("\n\n\nTime Elapsed Test:{0}\n\n".format(end - start))
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"""
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100
topicModeling.py
100
topicModeling.py
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@ -5,6 +5,7 @@ import draw
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import draw1
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import time
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import numpy as np
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import operator
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import csv
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import sys
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@ -80,9 +81,8 @@ def textacyTopicModeling(corpus,
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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('{0}: {1}'.format(topic_idx, " ".join(top_terms)))
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for topic_idx, top_terms in model.top_topic_terms(vectorizer.id_to_term, top_n=top_topic_words, weights=True):
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logprint('{0}: {1}'.format(topic_idx, str(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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@ -96,7 +96,7 @@ def textacyTopicModeling(corpus,
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grams_label = "uni" if ngrams == 1 else "bi"
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draw1.termite_plot(model,doc_term_matrix, id2term,
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draw1.termite_plot(model,doc_term_matrix, vectorizer.id_to_term,
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n_terms=n_terms,
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sort_terms_by=sort_terms_by,
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@ -117,8 +117,6 @@ def jgibbsLLDA(labeldict,line_gen,path2save_results, top_topic_words=7):
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LLDA_filepath = "{0}models/tickets/tickets.gz".format(jgibbsLLDA_root)
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textacy.fileio.write_file_lines(line_gen, filepath=LLDA_filepath)
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@ -241,24 +239,31 @@ def jgibbsLLDA_category(corpus, path2save_results, top_topic_words=7):
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logprint("start Category-LLDA:")
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# build dictionary of ticketcategories
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labelist = []
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for doc in corpus:
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labelist.append(normalize(doc.metadata["categoryName"]))
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category = normalize(doc.metadata["categoryName"])
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labelist.append(category)
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labelist = list(set(labelist))
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print("len(labelist): {}".format(len(labelist)))
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#print("len(labelist): {}".format(len(labelist)))
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labeldict = {k: v for v, k in enumerate(labelist)}
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labeldict.update({'DEFAULT' : len(labeldict)})
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def gen_cat_lines(textacyCorpus, labeldict):
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""" generates [topic1, topic2....] tok1 tok2 tok3 out of corpi"""
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for doc in textacyCorpus:
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yield "[" + str(labeldict.get(doc.metadata["categoryName"], len(labeldict))) + "] " + doc.text
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label = labeldict.get(normalize(doc.metadata["categoryName"]), labeldict['DEFAULT'])
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# frage nur die x häufigsten labels benutzen, rest raus?
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yield "[ " + str(label) + " ] " + doc.text
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line_gen = gen_cat_lines(corpus, labeldict)
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@ -274,6 +279,7 @@ def jgibbsLLDA_category(corpus, path2save_results, top_topic_words=7):
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logprint("\n\n\nTime Elapsed Category-LLDA :{0} min\n\n".format((end - start) / 60))
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@deprecated
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def jgibbsLLDA_KB(corpus, path2save_results, top_topic_words = 7, kb_keywords=False):
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"""ticket_ID -> KB_ID -> keywords / subject -> llda"""
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@ -420,7 +426,7 @@ def jgibbsLLDA_KB_v2(corpus, path2save_results, top_topic_words = 7):
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# kb2keywords_dict / kb2subj_dict {str : [str]}
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# kb2keywords_dict / kb2subjects_dict --> {str : [str]}
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kb2keywords_dict = {}
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kb2subjects_dict = {}
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@ -458,7 +464,7 @@ def jgibbsLLDA_KB_v2(corpus, path2save_results, top_topic_words = 7):
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# ticket2kbs_dict
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# ticket2kbs_dict --> {str : [str]}
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ticket2kbs_dict = {}
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kb2ticket_gen = textacy.fileio.read_csv(FILEPATH + "M42-Export/KB2Ticket_2017-09-13.csv", delimiter=";")
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next(kb2ticket_gen, None) # skip first line"TicketNumber";"ArticleID"
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@ -479,8 +485,8 @@ def jgibbsLLDA_KB_v2(corpus, path2save_results, top_topic_words = 7):
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# ticket2keywords
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ticket2keywords_dict = {} # {str:[str]}
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# ticket2keywords --> {str:[str]}
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ticket2keywords_dict = {}
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for ticket_id, kb_ids in ticket2kbs_dict.items():
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@ -496,8 +502,8 @@ def jgibbsLLDA_KB_v2(corpus, path2save_results, top_topic_words = 7):
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# ticket2subjects
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ticket2subjects_dict = {} # {str:[str]}
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# ticket2subjects --> {str:[str]}
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ticket2subjects_dict = {}
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for ticket_id, kb_ids in ticket2kbs_dict.items():
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@ -513,13 +519,12 @@ def jgibbsLLDA_KB_v2(corpus, path2save_results, top_topic_words = 7):
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# kb2keywords_dict {'KBA10230': ['DEFAULT'], 'KBA10129': ['DEFAULT'], 'KBA10287': ['sd_ansys_informationen'], } len = 260
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#kb2subjects_dict {'KBA10230': ['unicard nochmal beantragen'], 'KBA10129': ['sd_entsperrung unicard nach verlust/wiederfinden'], } len = 260
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# kb2subjects_dict {'KBA10230': ['unicard nochmal beantragen'], 'KBA10129': ['sd_entsperrung unicard nach verlust/wiederfinden'], } len = 260
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# ticket2kbs_dict {'INC44526': ['KBA10056'], 'INC67205': ['KBA10056'], } len = 4832
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# ticket2keywords_dict {'INC44526': ['DEFAULT'], 'INC67205': ['DEFAULT'], 'INC71863': ['DEFAULT'], 'INC44392': ['asknet'] } len=4832
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#ticket2subjects_dioct {'INC44526': ['sd_telefon (antrag: neuanschluss, umzug, aenderung erledigt)'], len=4832
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# ticket2subjects_dict {'INC44526': ['sd_telefon (antrag: neuanschluss, umzug, aenderung erledigt)'], len=4832
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# frage wieviele tickets pro topic?
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count_dict = {}
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for v in ticket2kbs_dict.values():
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for kb in v:
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@ -527,18 +532,17 @@ def jgibbsLLDA_KB_v2(corpus, path2save_results, top_topic_words = 7):
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count_dict[kb] +=1
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else:
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count_dict[kb] = 1
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import operator
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sorted_dict = sorted(count_dict.items(), key=operator.itemgetter(1))
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print("kb_entrys used: {}".format(len(sorted_dict)))
|
||||
|
||||
for k,v in sorted_dict:
|
||||
print(k,kb2subjects_dict[k],v) #todo das selbe mit keywords
|
||||
subs = kb2subjects_dict[k]
|
||||
keys = kb2keywords_dict[k]
|
||||
print(subs, keys , v) # frage wieviele tickets pro topic?
|
||||
|
||||
print("kb_entrys used: {}".format(len(sorted_dict))) # frage wie viele kb_entry's insg genutzt?: 155
|
||||
|
||||
|
||||
#todo hier weiter
|
||||
|
||||
|
||||
# todo frage wie viele kb_entry's insg genutzt?
|
||||
|
||||
labelist = ticket2keywords_dict.values()
|
||||
labelist = flatten(labelist)
|
||||
|
|
@ -564,6 +568,7 @@ def jgibbsLLDA_KB_v2(corpus, path2save_results, top_topic_words = 7):
|
|||
|
||||
yield "[ " + label + "] " + doc.text
|
||||
|
||||
|
||||
keys_line_gen = gen_key_lines(corpus, labeldict, ticket2keywords_dict)
|
||||
|
||||
path2save_keys_results = path2save_results + "_kb_keys_llda_{}".format("top" + str(top_topic_words))
|
||||
|
|
@ -574,28 +579,13 @@ def jgibbsLLDA_KB_v2(corpus, path2save_results, top_topic_words = 7):
|
|||
|
||||
|
||||
|
||||
"""
|
||||
def gen_subj_lines(textacyCorpus, labeldict, ticket2subjects_dict):
|
||||
|
||||
for doc in corpus:
|
||||
|
||||
ticket_number = doc.metadata["TicketNumber"]
|
||||
|
||||
keywords = ticket2subjects_dict.get(ticket_number, ['DEFAULT'])
|
||||
|
||||
if keywords != ['DEFAULT']:
|
||||
|
||||
label = ""
|
||||
for kw in keywords:
|
||||
label = label + str(labeldict.get(normalize(str(kw)), len(labeldict))) + " "
|
||||
|
||||
yield "[ " + label + "] " + doc.text
|
||||
"""
|
||||
|
||||
labelist = ticket2subjects_dict.values()
|
||||
labelist = flatten(labelist)
|
||||
labelist = list(set(labelist))
|
||||
labeldict = {k: v for v, k in enumerate(labelist)}
|
||||
|
||||
labeldict.update({'DEFAULT' : len(labeldict)})
|
||||
|
||||
subj_line_gen = gen_key_lines(corpus, labeldict, ticket2subjects_dict)
|
||||
|
|
@ -616,19 +606,13 @@ def jgibbsLLDA_KB_v2(corpus, path2save_results, top_topic_words = 7):
|
|||
|
||||
|
||||
def main( 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")
|
||||
|
||||
|
||||
preCorpus_name = "de" + "_pre_ticket_old"
|
||||
preCorpus_name = "de" + "_pre_ticket"
|
||||
|
||||
resultspath = FILEPATH + "results/pre"
|
||||
|
||||
|
||||
# load corpus
|
||||
de_corpus, parser = load_corpus(corpus_name=preCorpus_name, corpus_path=corpus_de_path)
|
||||
logprint("Corpus loaded: {0}".format(de_corpus.lang))
|
||||
|
|
@ -643,15 +627,15 @@ def main( algorithm="llda"):
|
|||
|
||||
jgibbsLLDA_KB_v2(de_corpus, path2save_results=resultspath, top_topic_words=top_topic_words)
|
||||
|
||||
kb_keywords = False
|
||||
#jgibbsLLDA_KB(de_corpus, path2save_results=resultspath, top_topic_words=top_topic_words, kb_keywords=kb_keywords)
|
||||
|
||||
kb_keywords = True
|
||||
#jgibbsLLDA_KB(de_corpus, path2save_results=resultspath, top_topic_words=top_topic_words, kb_keywords=kb_keywords)
|
||||
|
||||
|
||||
|
||||
"""
|
||||
kb_keywords = False
|
||||
jgibbsLLDA_KB(de_corpus, path2save_results=resultspath, top_topic_words=top_topic_words, kb_keywords=kb_keywords)
|
||||
|
||||
kb_keywords = True
|
||||
jgibbsLLDA_KB(de_corpus, path2save_results=resultspath, top_topic_words=top_topic_words, kb_keywords=kb_keywords)
|
||||
|
||||
|
||||
top_topic_words = 10
|
||||
path2save_results = resultspath + "_{}_{}".format(algorithm,"top"+str(top_topic_words))
|
||||
jgibbsLLDA(de_corpus, path2save_results=path2save_results, top_topic_words=top_topic_words)
|
||||
|
|
|
|||
Loading…
Reference in New Issue