def create_classifier_dump(): ls1 = pu.convs_list_exp() ls2 = pu.convs_list_nonexp() pos, neg = create_feature1(.01,.01,500,500) classifier = naive_bayes_classifier(pos,neg) class_file = open(pth.TEMP + '/classifier','w') pickle.dump(classifier, class_file) class_file.close()
def mk_nonexp_wordfreq():
#convs= pu.convs_list_train()
fl = open(pth.NonexpConvs,'r')
nexps= pu.convs_list_nonexp()
NonexpFreq = nltk.FreqDist()
#count = 0
for conv in nexps:
msgs= pu.get_msgs_in(conv)
tally=[]
for msg in msgs:
#print msg
#count +=1
#if count >3:
# break
if msg is not None:
words = msg.split()
for word in words:
lw= word.lower()
if lw not in tally:
tally.append(lw)
NonexpFreq.inc(lw)
fl = open(pth.NonexpDict,'w')
pickle.dump(NonexpFreq,fl)
