def clean_steam():
documents = Document.objects.all()
goal = 0
current = 0
leng = len(documents)
for document in documents:
goal, current = avance(current, leng, goal)
if document.steamed_content:
text_to_clean = document.steamed_content
aux = unicode(text_to_clean)
#Quito <>,[], <!-- --> y saltos de linea
aux = strip_tags(aux)
#quito espacios en bordes, llevo a lowercase y saco tildes
aux = ' '+remove_non_unicode(aux.strip().lower())+' '
#quito Numeros y Caracteres
aux = remove_non_alphanumeric(aux)
#quito espacios
aux = remove_spaces(aux)
document.steamed_content = aux
document.save()
def clean_content(request):
"""Limpia el contenido de un documento filtrando stop words y caracteres que no son letras"""
documents = Document.objects.filter(Q(cleaned_content='') | Q(cleaned_content=None)| Q(steamed_content='') | Q(steamed_content=None))
goal = 0
current = 0
leng = len(documents)
print " -> Removing Stop Words and weird chars..."
sw = Stopword.objects.all()
stopwords = '|'.join([" "+str(x)+" " for x in sw])
print " -> Cleaning Documents"
for d in documents:
goal, current = avance(current, leng, goal)
if not d.cleaned_content:
d.clean_content(stopwords)
if d.cleaned_content:
#d.stemmed_content = freeling_stemming(d.cleaned_content)
d.save()
else:
d.delete()
print " Documents cleaned!"
def remove_common_words(ldamodel):
""" This method removes words which have similar frequency among diffrent datasets """
print "Checking and Removing common words"
remove_list = []
n_of_datasets = DataSetLdaModel.objects.filter(ldamodel=ldamodel).count()
datasets = DataSet.objects.filter(datasetldamodel__ldamodel = ldamodel)
if n_of_datasets < 2: return
lda_words = WordLdaModel.objects.filter(ldamodel=ldamodel)
goal = 0
current = 0
leng = len(lda_words)
for this_word in lda_words:
goal, current = avance(current, leng, goal)
freq_table = n_of_datasets*[0]
#print freq_table
wsf_involved = WordDataSetFrequency.objects.filter(word = this_word, dataset__in = datasets)
#print wsf_involved
for i in range(0,len(wsf_involved)):
freq_table[i] = wsf_involved[i].frequency
freq_tot = sum(freq_table)
freq_avg = float(freq_tot)/n_of_datasets
# Promedio deltas
delta_avg = 0
for i in range(0,n_of_datasets-1):
for j in range(i+1,n_of_datasets):
delta_avg += abs(freq_table[i]-freq_table[j])
delta_avg = float(delta_avg)*2/((n_of_datasets-1)*n_of_datasets)
# Remove
if delta_avg < freq_avg:
remove_list.append(str(this_word.id))
if remove_list:
sql = "DELETE FROM application_wordldamodel WHERE id IN (%s)" % ",".join(remove_list)
cursor = connection.cursor()
cursor.execute(sql)
cursor.execute("COMMIT")
connection.close()
print " -> %s Words removed" % len(remove_list)
else:
print " -> No words removed"
def load_words(request, ldamodel = None):
cursor = connection.cursor()
"""Carga el diccionario a partir de las palabras previamente limpiadas"""
print "Reading words we already have..."
historic_words = []
for w in Word.objects.all():
historic_words.append(w.name)
print "We have %s words..." % len(historic_words)
dictionary = {}
print "Loading words..."
documents = Document.objects.filter(loaded_words = 0)
i = 0
print " -> %s documents loaded..." % len(documents)
for d in documents:
for a in d.cleaned_content.split(" "): dictionary[a] = 1
goal = 0
current = 0
leng = len(dictionary)
for d in dictionary:
goal, current = avance(current, leng, goal)
if d not in historic_words:
w = Word(name = d)
try:
w.save()
except Exception as e:
#print e[0]
if not int(e[0]) == 1062:
print "Fallo "+w.name
print " Words Loaded!"
print " Updating documents..."
cursor.execute("UPDATE application_document SET loaded_words = 1 WHERE loaded_words = 0;")
cursor.execute("COMMIT")
print " Documents Updated!"
connection.close()
def get_freq(request, ldamodel = None):
"""calculos de la tabla frequency"""
#LIMPIO LA TABLA
cursor = connection.cursor()
print "Getting Frequencies..."
documents = Document.objects.filter(frec_calculated = 0)
#Caluclo LAS FRECUENCIAS
wf = {}
words = {}
for w in Word.objects.all():
words[w.name] = w
print " Getting simple frequency calculated..."
goal = 0
current = 0
leng = len(documents)
i = 0
for d in documents:
goal, current = avance(current, leng, goal)
wf.clear()
#print "Counting Words..."
if ldamodel and ldamodel.stemming and d.steamed_content:
if i == 0: print "using stemming"
i += 1
content = d.steamed_content.split(' ')
else:
content = d.cleaned_content.split(' ')
for s in content:
wf[s] = wf.get(s,0) + 1
#print "Saving..."
freqs = []
for kw in wf:
try:
freqs.append("("+str(d.id)+","+str(words[kw].id)+","+str(wf[kw])+")")
except Exception as e:
print "En documento %s error:" % d.id
print e
query = "INSERT INTO application_frequency (document_id, word_id, frequency) VALUES %s;" % ",".join(freqs)
#print query
cursor.execute(query)
cursor.execute("COMMIT")
print " Simple frequency calculated!"
print " Getting max frequency..."
cursor.execute("""UPDATE application_document D SET max_word_frec=(SELECT MAX(F.frequency) FROM application_frequency F WHERE F.document_id=D.id AND max_word_frec is NULL)""")
print " Max frequency calculated!"
print " Updating documents..."
cursor.execute("UPDATE application_document SET frec_calculated = 1 WHERE frec_calculated = 0;")
print " Documents Updated!"
cursor.execute("COMMIT")
connection.close()
def lda(documents_dist,topic_local_to_universal,alpha,beta):
""" Runs LDA over a set of documents, saving results over a set of predefined topics """
cursor = connection.cursor()
n_topics = len(topic_local_to_universal)
word_local_to_universal = {}
word_universal_to_local = {}
document_local_to_universal = {}
print "Getting document matrix..."
dic = [word_mapper(map(lambda x: int(str(x),16),document_dist.distribution[:-1].split(',')),word_local_to_universal,word_universal_to_local) for document_dist in documents_dist]
document_local_to_universal = dict(enumerate([document_dist.document.id for document_dist in documents_dist]))
n_documents = str(len(dic))
n_words = len(word_local_to_universal)
print "Numero de documentos: "+str(n_documents)
print "Numero de palabras: "+str(n_words)
if int(n_documents) == 0:
raise Exception('LDAmodel has no documents assigned or the documents had only irrelevant words. No document matrix founded.')
f_label = 1
numsamp = 50
randseed = 194582
alpha_vector = alpha * ones((f_label,n_topics))
beta_vector = beta * ones((n_topics,n_words))
print "Calculating LDA using..."
print " beta: "+str(beta)
print " alpha: "+str(alpha)
print " ntopics: "+str(n_topics)
(phi,theta,sample) = deltaLDA(dic,alpha_vector,beta_vector,numsamp,randseed)
print "Saving Results..."
########################
# document_topic
########################
print "Saving Document and topic correlation..."
document_local_id = 0
goal = 0
current = 0
theta_len = len(theta)
for d in theta:
st = "INSERT INTO application_documenttopic (document_id, topic_id, value) VALUES "
goal, current = avance(current, theta_len, goal)
topic_local_id = 0
for document_weight in d:
st = st + "("+str(document_local_to_universal[document_local_id])+","+str(topic_local_to_universal[topic_local_id])+","+str(document_weight)+"),"
topic_local_id += 1
st = st[:-1]+";"
cursor.execute(st)
cursor.execute("COMMIT")
document_local_id += 1
#####################
# topic_word
#####################
print "Saving topics and word correlation to file"
topic_local_id = 0
goal = 0
current = 0
phi_len = len(phi)
nbest = int(n_words*0.5)
os.system("touch /tmp/application_topicword.txt")
os.system("chmod 777 /tmp/application_topicword.txt")
FILE = '/tmp/application_topicword.txt'
print 'Opening %s' % FILE
fw = open (FILE,'w')
for t in phi:
goal, current = avance(current, phi_len, goal)
word_local_id = 0
for word_weight in t:
fw.write(str(topic_local_to_universal[topic_local_id])+';'+str(word_local_to_universal[word_local_id])+';'+str(word_weight)+'\n')
word_local_id += 1
topic_local_id += 1
fw.close()
load_data_in_file()
return True
