def main():
with warnings.catch_warnings():
warnings.simplefilter("ignore")
warnings.filterwarnings("ignore", category=DeprecationWarning)
preTime = time.time()
LogFile = 'logging_Miner.txt'
logging.basicConfig(filename = LogFile, level = logging.DEBUG,
filemode= 'w', format = ('%(filename)s: ''%(levelname)s: ''%(funcName)s(): ''%(lineno)d:\t''%(message)s'))
logging.info('Start Miner')
readPath = ''
writePath = ''
#remember two spaces for yaml file and no tabs
with open("ET_MinerConfig.yml",'r') as ymlFile:
config = yaml.load(ymlFile)
readPath = config['folder']['readpath']
writePath = config['folder']['writepath']
logging.info('reading from path: ' + readPath)
#nltk.download()
#'C:\\Users\\fhokhold\\Documents\\Projects\\Vulcan\\vulcan-data\\hotel_info.csv'
# data = pd.read_csv(Miner.find_most_recent_file(readPath),sep='\t',usecols=['Ad','Description line 1','Description line 2',
# 'Impressions','Cost','Conversions',
# 'top_sq_tokens', 'lp_text'])
data = pd.read_csv(readPath)
colnames = list(data.columns.values)
#data['ad_text'] = data['Ad'] + data['Description line 1'] + data['Description line 2']
#data = data.sort(['lp_text'],ascending=False)
data.columns = Miner.MyFlattten([['hotel_id'],colnames[1:]])
#data[25:11400].to_csv('C:\\Users\\fhokhold\\Documents\\Projects\\Vulcan\\ET\\data_text.csv',index=False)
#sys.exit()
data = Miner.ReIndex(data)
#reviewData = pd.read_csv('C:\\Users\\fhokhold\\Documents\\Projects\\Vulcan\\vulcan-data\\hotel_review.csv')
##############################Freq Dist##################################################################
# reviewText = getDocuments(data,'lp_text',True, False)
#
# totalText = ''
# for k in xrange(int(len(reviewText)/20)):
# totalText += str(reviewText[k])
#
# reviewText0 = nltk.word_tokenize(totalText)
# #reviewText0 = Miner.tokenStemmer(reviewText0) #stemmer is not working very well
#
#
# tempTokens = [word for word in reviewText0 if not Miner.punctuationNumbers.search(word)]
# reviewText = [i.lower() for i in tempTokens if i.lower() not in Miner.stopWords]
#
# #Miner.stopWords.difference(set(['hotel','hotels','near'])) # this will effect the Miner file
#
# freqDistText = Miner.getFreqDist(reviewText, True) #pickle freqDist
#
# #print('frequency plot')
# #freqDistText[0].plot(30,cumulative=True)
#
# logging.info('Top words: ' )
# logging.info(freqDistText[1][3:10])
#
# #logging.info('cfDist predictions: ')
#
# #############################################word predictions######################################################
#
# print('top words')
# print([i for i,j in freqDistText[1][3:10]])
#
# topWords = [i for i,j in freqDistText[1][3:100]]
# wordsPred = [i for i,j in freqDistText[1][3:10]]
#
# print('topWords')
# print(topWords)
# print('wordsPred')
# print(wordsPred)
#
# wordsPredictions = Miner.getConditionalDist(reviewText, topWords, wordsPred)
#
# logging.info(wordsPredictions)
#
# Ngrams = Miner.getNgram(reviewText, zip(wordsPred,wordsPred[::-1]), True)
#
# logging.info('Ngrams')
# logging.info(' ')
# logging.info(Ngrams[1])
#
#combineData = pd.merge(data.ix[:,['hotel_id','tags']], reviewData.ix[:,['hotel_id','title','body']], on=['hotel_id'],how='inner')
#combineData.to_csv('C:\\Users\\fhokhold\\Documents\\Projects\\Vulcan\\vulcan-data\\combineData_text.csv')
###############################Topic Modeling######################################################################
topicData = (data[:5000].ix[133,:] , 'lp_text')
print('topicData')
print(topicData[0]['lp_text'])
lda = Miner.getTopicsLDAandLSA(topicData[0],topicData[1],'lda',True)
logging.info('lda topics')
logging.info(lda[0].print_topics(10))
logging.info('LDA perplexity: ' + str(lda[1]))
lsa = Miner.getTopicsLDAandLSA(topicData[0],topicData[1],'lsa',True)
logging.info('lsa topics')
logging.info(lsa.print_topics(10))
textBlockFlag = True
dataText = Miner.getDocuments(topicData[0],topicData[1],True, textBlockFlag)
print('dataText')
print(dataText)
tfidf_searchTerms, modelTfIdf = Miner.tf_idf(dataText)
print('tfidf_searchTerms.T.toarray()')
print(tfidf_searchTerms.T.toarray()) #word by doc, before transpose doc by word (row by col format)
logging.info('tfidf_searchTerms transposed')
logging.info(tfidf_searchTerms.T.toarray())
tfidf_review, reviewModelTfIdf = Miner.tf_idf(Miner.getDocuments(data[:5000].ix[133,:],'lp_text',True, textBlockFlag))
topicsNMF = Miner.getTopicNMF(tfidf_searchTerms, modelTfIdf)
logging.info('NMF topics')
#################Similiarity testing###################################################################################
colNames = ['ad_text','lp_text']
topicData = (data[:5000], 'lp_text')
#
# logging.info('Pairwise similiarity')
# #logging.info(Miner.similiarity(np.array([1,2,0,1]), np.array([0,2,2,1]), None, None))
# logging.info(' ')
#
combinePhraseDoc = Miner.CombineDocumentPhrase(data, colNames, True) #takes a long time to compute
tfidf_review, combineModelTfIdf = Miner.tf_idf(combinePhraseDoc)
dimReview = tfidf_review.toarray().shape
tfidf_review_matrix = tfidf_review.toarray()
for i in xrange(0,int(dimReview[0]/10),2): #loop through by twos
try:
#logging.info('phrase: ' + str(combinePhraseDoc[i]))
#logging.info('document: ' + str(combinePhraseDoc[i+1]))
#logging.info('phrase vector: ' )
#logging.info(tfidf_review_matrix[i,:])
#logging.info('doc vector: ' )
#logging.info(tfidf_review_matrix[i+1,:])
logging.info('similiarity: ' + str(Miner.similiarity(tfidf_review_matrix[i,:], tfidf_review_matrix[i+1,:], None, None)))
except Exception, e:
logging.warn('Error: ' + str(e))
