def test1(classifier,ls1 , ls2):
#classifier= naive_bayes_classifier()
#ls=pu.convs_list_exp()
for i in range(0,20):
ft = pu.get_words_featureset(ls1[i])
pb =classifier.prob_classify(ft)
print pb.prob('pos'), pb.prob('neg')
#ls2=pu.convs_list_nexp()
for i in range(0,20):
ft = pu.get_words_featureset(ls2[i])
pb=classifier.prob_classify(ft)
print pb.prob('pos'), pb.prob('neg')
def eval_set(classifier, pos_conv_id, neg_conv_id):
poscount=0
for i in range(0,len(pos_conv_id)):
ft=pu.get_words_featureset(pos_conv_id[i])
pb=classifier.classify(ft)
if pb == 'pos':
poscount+=1
negcount=0
for i in range(0,len(neg_conv_id)):
ft=pu.get_words_featureset(neg_conv_id[i])
pb= classifier.classify(ft)
if pb == 'neg':
negcount+=1
print poscount, len(pos_conv_id)
print negcount, len(neg_conv_id)
def result(classifier, conv_ids):
for i in range(0,len(conv_ids)):
ft= pu.get_words_featureset(conv_ids[i])
pb=classifier.classify(ft)
if pb == 'pos':
handle_conv(classifier, conv_ids[i])
