print 'dataset:', dataset_list[dataset] print 'eta:', eta print 'lambda_r:', lambda_r print 'K0:', K0 print 'vali_test:', ['validation', 'test'][vali_test] print 'sample_rate:', sample_rate print 'batch_size:', batch_size print 'epoch:', epoch print 'top_k:', top_k print '''**************************main_function***************************''' '''**************************main_function***************************''' # load the data [train_data, train_data_aux, M, N] = readdata(path_train) validation_data = readdata(path_validation)[1] test_data = readdata(path_test)[1] # choose validation or test set if vali_test == 0: Test = validation_data else: Test = test_data data = [["Model", [Model]], ["dataset", [dataset_list[dataset]]], ["eta", [eta]], ["lambda_r", [lambda_r]], ["K0", [K0]], ["vali_test", [['validation', 'test'][vali_test]]], ["sample_rate", [sample_rate]], ["batch_size", [batch_size]], ['epoch', [epoch]], ["top_k", top_k]] path_excel = 'experiment_result\\' + dataset_list[ dataset] + '_' + Model + '_' + str(int(time.time())) + str(
print 'lambda_c', lambda_c print 'lambda_r', lambda_r print 'vali_test', vali_test print 'feat', feat print 'feature_length', feature_length print 'epoch', epoch print '''*************************main function****************************''' '''*************************main function****************************''' for i in range(1): # datasets dataset_list = ['', '_Women', '_Men', '_CLothes', '_Shoes', '_Jewelry'] # load data [train_data, train_data_aux, validate_data, test_data, P, Q] = readdata(dataset_list[dataset]) # load data for tensor factorization [train_record_aux, train_time_aux, R] = readdata_time(dataset_list[dataset]) # load features F = get_feature(dataset_list[dataset]) K = len(F[0]) # select test set or validation set if vali_test == 0: Test = validate_data else: Test = test_data for j in range(1): path_excel = 'E:\\experiment_result\\' + dataset_list[dataset] + '_DCFA_' + str(int(time.time())) + str(int(random.uniform(100,900))) + '.xls' save_parameter() print_parameter()