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()
