regex zum weiteren cleaning hinzugefügt

config.ini

@@ -1,7 +1,19 @@
-[default]
+[filepath]
+
+thesauruspath = /home/jannis.grundmann/PycharmProjects/topicModelingTickets/openthesaurus.csv
+
+path2xml = /home/jannis.grundmann/PycharmProjects/topicModelingTickets/ticketSamples.xml
+
+path2csv = /home/jannis.grundmann/PycharmProjects/topicModelingTickets/M42-Export/Tickets_2017-09-13.csv
+
+small = /home/jannis.grundmann/PycharmProjects/topicModelingTickets/M42-Export/Tickets_small.csv
+
+
+logfile = /home/jannis.grundmann/PycharmProjects/topicModelingTickets/topicModelTickets.log
+
+lemmas = /home/jannis.grundmann/PycharmProjects/topicModelingTickets/lemmatization-de.txt
+
 
-thesauruspath = openthesaurus.csv
-path2xml = ticketSamples.xml
 
 language = de
 
@@ -9,7 +21,7 @@ language = de
 
 ents2keep = WORK_OF_ART,ORG,PRODUCT,LOC
 
-custom_words = grüßen,fragen
+custom_words = grüßen,fragen,damen,probleme,herren,dank
 
 #lemmatize = True
 

preprocessing.py

@@ -22,7 +22,7 @@ config = ConfigParser.ConfigParser()
 with open("config.ini") as f:
     config.read_file(f)
 
-
+#todo print&log
 
 path2xml = config.get("default","path2xml")
 
@@ -281,7 +281,7 @@ topicModel = 'lda'
 weighting = ('tf' if topicModel == 'lda' else 'tfidf')
 
 top_topic_words = 5
-top_document_labels_per_topic = 2
+top_document_labels_per_topic = 5
 
 n_topics = len(set(corpus[0].metadata.keys()))+1 #+1 wegen einem default-topic
 
@@ -311,7 +311,7 @@ id2term = vectorizer.__getattribute__("id_to_term")
 
 
 
-
+"""
 #####################     LSA, LDA, NMF         Topic Modeling via Textacy         ##############################################
 
 # Initialize and train a topic model
@@ -339,7 +339,7 @@ for topic_idx, top_docs in model.top_topic_docs(doc_topic_matrix, top_n=top_docu
 print()
 print()
 
-
+"""
 
 
 

test.py

@@ -1,7 +1,11 @@
 # -*- coding: utf-8 -*-
+
+
+
 import time
 start = time.time()
 
+import logging
 
 import csv
 import functools
@@ -16,45 +20,55 @@ import textacy
 from scipy import *
 from textacy import Vectorizer
 import warnings
-csv.field_size_limit(sys.maxsize)
+import configparser as ConfigParser
-
 import sys
 
 
-old_stdout = sys.stdout
+csv.field_size_limit(sys.maxsize)
-
-log_file = open("printout.log","w")
-
-sys.stdout = log_file
 
 
 
 
 # Load the configuration file
-import configparser as ConfigParser
+config_ini = "/home/jannis.grundmann/PycharmProjects/topicModelingTickets/config.ini"
+
 config = ConfigParser.ConfigParser()
-with open("config.ini") as f:
+with open(config_ini) as f:
     config.read_file(f)
 
 
 
 
+# config logging
+logging.basicConfig(filename=config.get("filepath","logfile"), level=logging.INFO)
 
-path2csv = "M42-Export/Tickets_small.csv"
-#path2csv = "M42-Export/Tickets_2017-09-13.csv" # 21167
 
-path2xml = config.get("default","path2xml")
-thesauruspath = config.get("default","thesauruspath")
-
-DE_PARSER = spacy.load("de")
-de_stop_words=list(__import__("spacy." + DE_PARSER.lang, globals(), locals(), ['object']).STOP_WORDS)
 
+thesauruspath = config.get("filepath","thesauruspath")
 THESAURUS = list(textacy.fileio.read_csv(thesauruspath, delimiter=";"))
 
 
+DE_PARSER = spacy.load("de") #todo spacherkennung idee: verschiedene Corpi für verschiedene Sprachen
+de_stop_words=list(__import__("spacy." + DE_PARSER.lang, globals(), locals(), ['object']).STOP_WORDS)
+
+
+LEMMAS=config.get("filepath","lemmas")
+
 
 
 ############# misc
+
+def printlog(string, level="INFO"):
+    """log and prints"""
+    print(string)
+    if level=="INFO":
+        logging.info(string)
+    elif level=="DEBUG":
+        logging.debug(string)
+    elif level == "WARNING":
+        logging.warning(string)
+printlog("Load functions")
+
 def compose(*functions):
     def compose2(f, g):
         return lambda x: f(g(x))
@@ -84,17 +98,17 @@ def get_calling_function():
                 return func
     raise AttributeError("func not found")
 
+
 def printRandomDoc(textacyCorpus):
     import random
     print()
 
-    print("len(textacyCorpus) = %i" % len(textacyCorpus))
+    printlog("len(textacyCorpus) = %i" % len(textacyCorpus))
     randIndex = int((len(textacyCorpus) - 1) * random.random())
-    print("Index: {0} ; Text: {1} ; Metadata: {2}".format(randIndex, textacyCorpus[randIndex].text, textacyCorpus[randIndex].metadata))
+    printlog("Index: {0} ; Text: {1} ; Metadata: {2}".format(randIndex, textacyCorpus[randIndex].text, textacyCorpus[randIndex].metadata))
 
     print()
 
-
 #############  load xml
 def generateMainTextfromTicketXML(path2xml, main_textfield='Description'):
     """
@@ -111,6 +125,7 @@ def generateMainTextfromTicketXML(path2xml, main_textfield='Description'):
         for field in ticket:
             if field.tag == main_textfield:
                     yield field.text
+
 def generateMetadatafromTicketXML(path2xml, leave_out=['Description']):
     tree = ET.parse(path2xml, ET.XMLParser(encoding="utf-8"))
     root = tree.getroot()
@@ -144,6 +159,7 @@ def csv_to_contentStream(path2csv: str, content_collumn_name: str):
                     content_collumn = j
         else:
             yield lst[content_collumn]
+
 def csv_to_metaStream(path2csv: str, metalist: [str]):
     """
     :param path2csv: string
@@ -171,9 +187,8 @@ def csv_to_metaStream(path2csv: str, metalist: [str]):
 
 
 
+############################################    Preprocessing   ##############################################
 
-print("############################################    Preprocessing   ##############################################")
-print()
 
 #############  on str-gen
 
@@ -211,7 +226,11 @@ def processTextstream(textstream, funclist, parser=DE_PARSER):
     pipe = parser.pipe(textstream)
 
     for doc in pipe:
-        tokens = [tok for tok in doc]
+
+        tokens = []
+        for tok in doc:
+            tokens.append(tok)
+
         tokens = processTokens(tokens,funclist,parser)
         yield " ".join([tok.lower_ for tok in tokens])
 
@@ -264,7 +283,6 @@ def removePOS(pos_list)-> bool:
     return ret
 
 def removeWords(words, keep=None)-> bool:
-    #todo in:str oder str-list
     if hasattr(keep, '__iter__'):
         for k in keep:
             try:
@@ -289,13 +307,37 @@ def removeENT(ent_list) -> bool:
     ret.__annotations__ = get_calling_function().__annotations__
     return ret
 
-def lemmatize() -> str:
+def remove_words_containing_Numbers() -> bool:
-    ret = lambda tok:  tok.lemma_
+    ret = lambda tok: not bool(re.search('\d', tok.lower_))
 
     ret.__annotations__ = get_calling_function().__annotations__
     return ret
 
 
+def remove_words_containing_specialCharacters() -> bool:
+    ret = lambda tok: not bool(re.search(r'[`\-=~!@#$%^&*()_+\[\]{};\'\\:"|<,./<>?]', tok.lower_))
+
+    ret.__annotations__ = get_calling_function().__annotations__
+    return ret
+
+def lemmatizeWord(word,filepath=LEMMAS):
+    """http://www.lexiconista.com/datasets/lemmatization/"""
+    for line in list(textacy.fileio.read_file_lines(filepath=filepath)):
+        if word.lower() == line.split()[1].strip().lower():
+            return line.split()[0].strip().lower()
+    return word.lower() # falls nix gefunden wurde
+
+def lemmatize() -> str:
+    ret = lambda tok: lemmatizeWord(tok.lower_)
+
+    ret.__annotations__ = get_calling_function().__annotations__
+    return ret
+
+
+
+
+
+
 def normalizeSynonyms(default_return_first_Syn=False) -> str:
     ret = lambda tok : getFirstSynonym(tok.lower_, default_return_first_Syn=default_return_first_Syn)
 
@@ -377,6 +419,15 @@ def replacePhonenumbers(replace_with="PHONENUMBER") -> str:
     return ret
 
 
+
+
+def fixUnicode() -> str:
+    ret = lambda tok: textacy.preprocess.fix_bad_unicode(tok.lower_, normalization=u'NFC')
+
+    ret.__annotations__ = get_calling_function().__annotations__
+    return ret
+
+
 def resolveAbbreviations():
     pass #todo
 
@@ -396,10 +447,44 @@ def lower() -> spacy.tokens.Doc:
     return ret
 
 
+################################################################################################################
+
+
+path2xml = config.get("filepath","path2xml")
+
+
+
+path2csv = config.get("filepath","path2csv")
+path2csv = "M42-Export/Tickets_med.csv"
+printlog("CSV: {0}".format(path2csv))
+
 
 ticketcorpus = textacy.Corpus(DE_PARSER)
 
 
+#idee ß zu ss ändern? prinzipiell?
+
+"""
+vllt kategorien in unterkategorien aufteilen 
+
+allg: 
+utf-korregieren, bei sonderzeichen wörter trennen
+namen raus
+
+emails, urls, nummern raus 
+vllt sogar alles, was ebend jenes enthält (oder auf .toplvldomain bzw. sonderzeichen enthält oder alles was ein @ enthält
+
+sinnvoller wörter von müll trennen: 8203;verfügung
+
+abkürzungen raus: m.a, o.ä.     
+
+
+sinnlose bsp: nr54065467  455a33c5   tvt?=      ------problem--------
+
+"""
+
+
+
 
 metaliste = [
     "Subject",
@@ -409,45 +494,55 @@ metaliste = [
 
 clean_in_meta = {
     "Solution":[removePOS(["SPACE"]),lower()],
-    "Subject":[removePOS(["SPACE","PUNCT"]),lower()]
+    "Subject":[removePOS(["SPACE","PUNCT"]),lower()],
+    "categoryName": [removePOS(["SPACE", "PUNCT"]), lower()]
+
 }
 
 
-
+printlog("Start Preprocessing")
 
 clean_in_content=[
-    #removePOS(["SPACE","PUNCT","NUM"]),
+
-    keepPOS(["NOUN"]),
     removePOS(["SPACE","PUNCT","NUM"]),
+    remove_words_containing_Numbers(),
+    remove_words_containing_specialCharacters(),
+
     #replaceURLs(),
     #replaceEmails(),
-    lemmatize(),
+    #fixUnicode(),
-    #removeWords(de_stop_words),
+
-    keepUniqeTokens(),
+    #lemmatize(),
-    removePOS(["PUNCT"]),
+    #removeWords(de_stop_words + config.get("preprocessing","custom_words").split(",")),
+
+    #removeENT("PERSON"),
+    #keepPOS(["NOUN"]),
+    #keepUniqeTokens(),
+    #keepENT(config.get("preprocessing","ents2keep"))
 
 ]
 
 
 
 ## add files to textacy-corpus,
-print("add texts to textacy-corpus...")
+printlog("add texts to textacy-corpus")
 ticketcorpus.add_texts(
     processTextstream(csv_to_contentStream(path2csv,"Description"), clean_in_content),
     processDictstream(csv_to_metaStream(path2csv,metaliste),clean_in_meta)
 )
 
-printRandomDoc(ticketcorpus)
+for i in range(10):
+    printRandomDoc(ticketcorpus)
 
 
 end = time.time()
-print("\n\nTime Elapsed Preprocessing:{0}\n\n".format(end - start))
+printlog("Time Elapsed Preprocessing:{0} min".format((end - start)/60))
 
 
 
 
 
-print("############################################    Topic Modeling   #############################################")
+############################################    Topic Modeling   #############################################
 print("\n\n")
 start = time.time()
 
@@ -482,7 +577,7 @@ def generate_labled_lines(textacyCorpus):
 
 ####################'####################'    todo alles in config
 
-ngrams = (1)
+ngrams = 1
 
 min_df = 0
 max_df = 1.0
@@ -508,7 +603,7 @@ n_topics = len(LABELDICT)#len(set(ticketcorpus[0].metadata.keys()))+1 #+1 wegen
 
 
 
-print("\nvectorize corpus...")
+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 ticketcorpus)
@@ -528,12 +623,12 @@ id2term = vectorizer.__getattribute__("id_to_term")
 #####################     LSA, LDA, NMF         Topic Modeling via Textacy         ##############################################
 
 # Initialize and train a topic model
-print("\nInitialize 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:
-print("Transform the corpus and interpret our model..")
+printlog("Transform the corpus and interpret our model..")
 doc_topic_matrix = model.transform(doc_term_matrix)
 print()
 
@@ -544,15 +639,15 @@ for topic_idx, top_terms in model.top_topic_terms(vectorizer.id_to_term, top_n=t
 
 print()
 for topic_idx, top_docs in model.top_topic_docs(doc_topic_matrix, top_n=top_document_labels_per_topic):
-     print(topic_idx)
+    print(topic_idx)
-     for j in top_docs:
+    for j in top_docs:
-        print(ticketcorpus[j].metadata['categoryName'])
+         print(ticketcorpus[j].metadata['categoryName'])
 
 #####################################################################################################################
 print()
 print()
 
-
+"""
 
 
 
@@ -560,19 +655,20 @@ print()
 
 
 
-jgibbsLLDA_root = "java_LabledLDA/"
+jgibbsLLDA_root = "/home/jannis.grundmann/PycharmProjects/topicModelingTickets/java_LabledLDA/"
-filepath = "{0}models/tickets/tickets.gz".format(jgibbsLLDA_root)
+LLDA_filepath = "{0}models/tickets/tickets.gz".format(jgibbsLLDA_root)
 
 
 #create file
-textacy.fileio.write_file_lines(generate_labled_lines(ticketcorpus),filepath=filepath)
+textacy.fileio.write_file_lines(generate_labled_lines(ticketcorpus), filepath=LLDA_filepath)
 
 
 # wait for file to exist
-while not os.path.exists(filepath):
+while not os.path.exists(LLDA_filepath):
     time.sleep(1)
 
-print("\nstart LLDA:\n")
+print("\n\n")
+printlog("start LLDA:")
 #run JGibsslda file
 FNULL = open(os.devnull, 'w') # supress output
 subprocess.call(["java",
@@ -586,7 +682,7 @@ subprocess.call(["java",
 
 # ANMERKUNG: Dateien sind versteckt. zu finden in models/
 
-#print twords
+#twords
 subprocess.call(["gzip",
                  "-dc",
                  "{0}/models/tickets/.twords.gz".format(jgibbsLLDA_root)])
@@ -594,7 +690,7 @@ subprocess.call(["gzip",
 print()
 print()
 
-
+"""
 
 
 
@@ -602,10 +698,4 @@ print()
 
 
 end = time.time()
-print("\n\n\nTime Elapsed Topic Modeling:{0}\n\n".format(end - start))
+printlog("\n\n\nTime Elapsed Topic Modeling:{0}\n\n".format(end - start))
-
-
-
-sys.stdout = old_stdout
-
-log_file.close()