import argparse
import random
import sys

parser = argparse.ArgumentParser()
parser.add_argument('-p', type=str, default='train', help='train | test')
parser.add_argument('--dataset', type=str, default='book', help='book | taobao')
parser.add_argument('--random_seed', type=int, default=19)
parser.add_argument('--embedding_dim', type=int, default=64)
parser.add_argument('--hidden_size', type=int, default=64)
parser.add_argument('--num_interest', type=int, default=4)
parser.add_argument('--model_type', type=str, default='none', help='DNN | GRU4REC | ..')
parser.add_argument('--learning_rate', type=float, default=0.001, help='')
parser.add_argument('--max_iter', type=int, default=1000, help='(k)')
parser.add_argument('--patience', type=int, default=50)
parser.add_argument('--coef', default=None)
parser.add_argument('--topN', type=int, default=50)

if __name__ == '__main__':
    print(sys.argv)
    args = parser.parse_args()
    SEED = args.random_seed

    random.seed(SEED)

    train_name = 'train'
    valid_name = 'valid'
    test_name = 'test'

    if args.dataset == 'taobao':
        path = './data/taobao_data/'
        item_count = 1708531
        batch_size = 256
        maxlen = 50
        test_iter = 500
    elif args.dataset == 'book':
        path = './data/book_data/'
        item_count = 367983
        batch_size = 128
        maxlen = 20
        test_iter = 1000

    train_file = path + args.dataset + '_train.txt'
    valid_file = path + args.dataset + '_valid.txt'
    test_file = path + args.dataset + '_test.txt'
    cate_file = path + args.dataset + '_item_cate.txt'
    dataset = args.dataset
    print('train_file: ', train_file)
    print('valid_file: ', valid_file)
    print('test_file: ', test_file)
    print('cate_file: ', cate_file)
    print('dataset: ', dataset)