def median_approach(llimit,ulimit,isphrase,pathname): posmedlist=[] negmedlist=[] medians=[] lpcount=0 totalcount=ulimit-llimit cnt_var=0 print '\nNo of +ve reviews trained : ' for fid in movie_reviews.fileids(categories=['pos'])[llimit:ulimit]: testmed=proximity_tagger.medianlist(movie_reviews.abspath(fid),isphrase,cnt_var,0,pathname) posmedlist.append(testmed) lpcount=lpcount+1 cnt_var+=1 print 'Training +ve review ',lpcount,'.'*10,(float(lpcount)*100/float(totalcount)),'%' lpcount=0 cnt_var=0 print '\nNo of -ve reviews trained : ' for fid in movie_reviews.fileids(categories=['neg'])[llimit:ulimit]: testmed=proximity_tagger.medianlist(movie_reviews.abspath(fid),isphrase,cnt_var,1,pathname) negmedlist.append(testmed) lpcount=lpcount+1 cnt_var+=1 print 'Training -ve review ',lpcount,'.'*10,(float(lpcount)*100/float(totalcount)),'%' medians.append([numpy.median(x) for x in itertools.izip(*posmedlist)]) medians.append([numpy.median(x) for x in itertools.izip(*negmedlist)]) f = open('train_result\proximity_median_train_result_'+str(isphrase),'w') json.dump(medians,f) f.close()
def median_result(file_to_test,isphrase): median_testset=[] f = open('train_result/proximity_median_train_result_'+str(isphrase),'r') median_testset=json.load(f) f.close() median_test = proximity_tagger.medianlist(file_to_test,isphrase) med_pos_val = median_testset[0][0]-median_test[0] med_neg_val = median_testset[1][1]-median_test[1] if(med_pos_val<med_neg_val): return 1 return -1
def median_result(file_to_test,isphrase): alpha=0.4 beta =0.4 gamma=0.2 median_testset=[] f = open('train_result/proximity_median_train_result_'+str(isphrase),'r') median_testset=json.load(f) #print '\nMedian Test Set : ', median_testset f.close() median_test = proximity_tagger.medianlist(file_to_test,isphrase) #print '\nMedian Test : ', median_test med_pos_val = median_testset[0][0]-median_test[0] med_neg_val = median_testset[1][1]-median_test[1] #print 'Values : ', med_pos_val, med_neg_val if(med_pos_val<med_neg_val): return 1 return -1