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])