thesaurus auf basis von deWornNet weitergemacht

testo.py

@@ -4,6 +4,14 @@ from datetime import datetime
 
 
 print(datetime.now())
+
+
+#path2csv = "/home/jannis.grundmann/PycharmProjects/topicModelingTickets/M42-Export/Tickets_med.csv"
+path2csv = "/home/jannis.grundmann/PycharmProjects/topicModelingTickets/M42-Export/Tickets_small.csv"
+path2csv = "/home/jannis.grundmann/PycharmProjects/topicModelingTickets/M42-Export/de_tickets.csv"
+
+path_csv_split = path2csv.split("/")
+print(path_csv_split[len(path_csv_split)-1])
 import time
 
 import enchant
@@ -528,7 +536,8 @@ def processContentstream(textstream, token_filterlist=None, parser=DE_PARSER):
 
 
 
-        yield " ".join([tok.lower_ for tok in tokens])
+        #yield " ".join([tok.lower_ for tok in tokens])
+        yield " ".join(list(set([tok.lower_ for tok in tokens])))
 
 
 
@@ -675,10 +684,6 @@ pipe=[
 
 
 
-#path2csv = "/home/jannis.grundmann/PycharmProjects/topicModelingTickets/M42-Export/Tickets_med.csv"
-path2csv = "/home/jannis.grundmann/PycharmProjects/topicModelingTickets/M42-Export/Tickets_small.csv"
-path2csv = "/home/jannis.grundmann/PycharmProjects/topicModelingTickets/M42-Export/de_tickets.csv"
-
 de_corpus = textacy.Corpus(DE_PARSER)
 #en_corpus = textacy.Corpus(EN_PARSER)
 
@@ -701,96 +706,194 @@ end = time.time()
 printlog("Time Elapsed Preprocessing:{0} min".format((end - start)/60))
 
 
+"""
+corpus_path = "/home/jannis.grundmann/PycharmProjects/topicModelingTickets/corpus/"
+corpus_name = "de_corpus"
+corpus_compression = 'gzip'
+de_corpus.save(corpus_path, name=corpus_name, compression=corpus_compression)
+de_corpus =  textacy.Corpus.load(corpus_path, name=corpus_name, compression=corpus_compression)
+"""
+
+# build citionary 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)}
+
+printlog(str("LABELDICT: {0}".format(LABELDICT)))
+
+
+def topicModeling(ngrams,min_df,max_df,topicModel = 'lda',n_topics = len(LABELDICT),named_entities=False,corpus=de_corpus):
+
+
+    printlog("############################################ Topic Modeling {0}   #############################################".format(topicModel))
+    print("\n\n")
+    printlog(str("ngrams: {0}".format(ngrams)))
+    printlog(str("min_df: {0}".format(min_df)))
+    printlog(str("max_df: {0}".format(max_df)))
+    printlog(str("n_topics: {0}".format(n_topics)))
+    printlog(str("named_entities: {0}".format(named_entities)))
+
+
+    start = time.time()
+
+    top_topic_words = 10
+    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 corpus...")
+    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         ##############################################
 
-############################################    Topic Modeling   #############################################
-print("\n\n")
-start = time.time()
+    # 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()
 
 
-ngrams = 1
-
-min_df = 0.1
-max_df = 0.6
-#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 = 15
-top_document_labels_per_topic = 7
-
-n_topics = 20 #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 de_corpus)
-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):
+    for topic_idx, top_terms in model.top_topic_terms(vectorizer.id_to_term, top_n=top_topic_words):
         printlog('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):
+    print()
+    for topic_idx, top_docs in model.top_topic_docs(doc_topic_matrix, top_n=top_document_labels_per_topic):
         printlog(topic_idx)
         for j in top_docs:
-        printlog(de_corpus[j].metadata['categoryName'])
+            printlog(corpus[j].metadata['categoryName'])
         print()
 
-#####################################################################################################################
-print()
-print()
+    #####################################################################################################################
+    print()
+    print()
+
+
+
+    end = time.time()
+    printlog("\n\n\nTime Elapsed Topic Modeling with {1}:{0} min\n\n".format((end - start)/60,topicModel))
+
+
+
+
+
+
+#no_below = 20
+#no_above = 0.5
+
+
+#n_topics = len(LABELDICT)#len(set(ticketcorpus[0].metadata.keys()))+1 #+1 wegen einem default-topic
+
+
+
+
+topicModeling(ngrams = 1,
+              min_df = 1,
+              max_df = 1.0,
+              topicModel = 'lda',
+              n_topics = len(LABELDICT),
+              corpus=de_corpus)
+
+topicModeling(ngrams = 1,
+              min_df = 0.1,
+              max_df = 0.6,
+              topicModel = 'lda',
+              n_topics = len(LABELDICT),
+              corpus=de_corpus)
+
+topicModeling(ngrams = (1,2),
+              min_df = 1,
+              max_df = 1.0,
+              topicModel = 'lda',
+              n_topics = len(LABELDICT),
+              corpus=de_corpus)
+
+topicModeling(ngrams = (1,2),
+              min_df = 0.1,
+              max_df = 0.6,
+              topicModel = 'lda',
+              n_topics = len(LABELDICT),
+              corpus=de_corpus)
+
+topicModeling(ngrams = (1,2),
+              min_df = 0.2,
+              max_df = 0.8,
+              topicModel = 'lda',
+              n_topics = 20,
+              corpus=de_corpus)
+
+
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+
 
 
 
-end = time.time()
-printlog("\n\n\nTime Elapsed Topic Modeling with {1}:{0} min\n\n".format((end - start)/60,topicModel))
 
 
 
 
 
 """
+
+
+
+
 #####################   LLDA           Topic Modeling via JGibbsLabledLDA     ##############################################
 
 print("\n\n")
@@ -862,8 +965,6 @@ end = time.time()
 printlog("\n\n\nTime Elapsed Topic Modeling JGibbsLLDA:{0} min\n\n".format((end - start)/60))
 
 
-
-
 """
 
 
@@ -873,3 +974,5 @@ printlog("\n\n\nTime Elapsed Topic Modeling JGibbsLLDA:{0} min\n\n".format((end
 
 
 
+
+

testra.py

@@ -2,6 +2,7 @@
 import re
 import time
 
+import spacy
 import textacy
 
 start = time.time()
@@ -14,27 +15,72 @@ import xml.etree.ElementTree as ET
 print(datetime.now())
 
 
+
+
+nomen=[]
+#PARSER=spacy.load("de")
+
+#todo: thesaurus....yay...
+
+"""
+def normalizeSynonyms(default_return_first_Syn=False, parser=PARSER):
+    #return lambda doc : parser(" ".join([tok.lower_ for tok in doc]))
+    return lambda doc : parser(" ".join([getFirstSynonym(tok.lower_, THESAURUS, default_return_first_Syn=default_return_first_Syn) for tok in doc]))
+
+def getFirstSynonym(word, thesaurus, default_return_first_Syn=False):
+    if not isinstance(word, str):
+        return str(word)
+
+    word = word.lower()
+
+    # durch den thesaurrus iterieren
+    for syn_block in thesaurus:  # syn_block ist eine liste mit Synonymen
+
+        for syn in syn_block:
+            syn = syn.lower()
+            if re.match(r'\A[\w-]+\Z', syn):  # falls syn einzelwort ist
+                if word == syn:
+                    return str(getHauptform(syn_block, word, default_return_first_Syn=default_return_first_Syn))
+            else:  # falls es ein satz ist
+                if word in syn:
+                    return str(getHauptform(syn_block, word, default_return_first_Syn=default_return_first_Syn))
+    return str(word)  # zur Not, das ursrpüngliche Wort zurückgeben
+
+def getHauptform(syn_block, word, default_return_first_Syn=False):
+    for syn in syn_block:
+        syn = syn.lower()
+
+        if "hauptform" in syn and len(syn.split(" ")) <= 2:
+            # nicht ausgeben, falls es in Klammern steht#todo gibts macnmal?? klammern aus
+            for w in syn.split(" "):
+                if not re.match(r'\([^)]+\)', w):
+                    return w
+
+    if default_return_first_Syn:
+        # falls keine hauptform enthalten ist, das erste Synonym zurückgeben, was kein satz ist und nicht in klammern steht
+        for w in syn_block:
+            if not re.match(r'\([^)]+\)', w):
+                return w
+    return word  # zur Not, das ursrpüngliche Wort zurückgeben
+"""
+
+### extract from deWordNet.xml
+
+#https://github.com/hdaSprachtechnologie/odenet
+#idee synsets bilden
+
+
+"""
 path2xml="/home/jannis.grundmann/PycharmProjects/topicModelingTickets/deWordNet.xml"
 
 tree = ET.parse(path2xml, ET.XMLParser(encoding="utf-8"))
 root = tree.getroot()
 
-regex_specialChars = r'[`\-=~!#@,.$%^&*()_+\[\]{};\'\\:"|</>?]'
-
-nomen=[]
-
-
-
-
-
-### extract from deWordNet.xml
-
-#https://github.com/hdaSprachtechnologie/odenet
-
 for r in root:
     for element in r:
 
-            if element.tag == "LexicalEntry":
+            if element.tag == "Synset":
+                attrib = element.attrib
                 for i,subentry in enumerate(element):
                     if subentry.tag == "Lemma" and subentry.attrib["partOfSpeech"] == "n":
                             string = (subentry.attrib["writtenForm"])
@@ -49,9 +95,75 @@ for r in root:
                             string_list=string.split()
                             if len(string_list) == 1:
                                 nomen.append(string.lower().strip())
+"""
+
+
+lexicalentries = "/home/jannis.grundmann/PycharmProjects/topicModelingTickets/lexicalentries.xml"
+synsets = "/home/jannis.grundmann/PycharmProjects/topicModelingTickets/synsets.xml"
+
+
+lextree = ET.parse(lexicalentries, ET.XMLParser(encoding="utf-8"))
+syntree = ET.parse(synsets, ET.XMLParser(encoding="utf-8"))
+
+lexroot = lextree.getroot()
+synroot = syntree.getroot()
+
+
+
+for r in synroot:
+    for element in r:
+
+            if element.tag == "Synset":
+                sysnet = []
+                attrib = element.attrib
+                id = attrib["id"]
+
+
+                for ro in lexroot:
+                    for elem in ro:
+                        if elem.tag == "LexicalEntry":
+                            subs_dicts = [subentry.attrib for subentry in elem]
+                            #<class 'list'>: [{'partOfSpeech': 'n', 'writtenForm': 'Kernspaltung'}, {'synset': 'de-1-n', 'id': 'w1_1-n'}]
+
+                            dic = {k:v for x in subs_dicts for k,v in x.items()}    # to one dict
+                            if "synset" in dic.keys():
+                                if dic["synset"] == id:
+
+                                    if id == "de-1004-n":
+                                        x = 0
+
+                                    string = (dic["writtenForm"])
+
+                                    # replaceRockDots
+                                    string = re.sub(r'[ß]', "ss", string)
+                                    string = re.sub(r'[ö]', "oe", string)
+                                    string = re.sub(r'[ü]', "ue", string)
+                                    string = re.sub(r'[ä]', "ae", string)
+
+
+
+                                    # alle punkte raus
+                                    string = re.sub(r'[.]', "", string)
+
+
+                                    # alles in klammern raus
+                                    stringlist = string.split()
+                                    strings=[]
+                                    for w in stringlist:
+                                        if not bool(re.match(r'/\(([^)]+)\)/', w)): #todo funzt nich wie's soll
+                                            strings.append(w)
+                                    string = " ".join(strings)
+
+                                    #re.sub(r'/\(([^)]+)\)/', " ", string)
+
+
+                                    sysnet.append(string.lower().strip())
+
+
+                print(id,sysnet)
+
 
 
-textacy.fileio.write_file_lines(nomen,"nomen.txt")