Deep_match_rank_emb/match_recall/LightGCN_match/utility/parser.py

'''
Created on Oct 10, 2018
Tensorflow Implementation of Neural Graph Collaborative Filtering (NGCF) model in:
Wang Xiang et al. Neural Graph Collaborative Filtering. In SIGIR 2019.

@author: Xiang Wang (xiangwang@u.nus.edu)
'''
import argparse

def parse_args():
    parser = argparse.ArgumentParser(description="Run NGCF.")
    parser.add_argument('--weights_path', nargs='?', default='',
                        help='Store model path.')
    parser.add_argument('--data_path', nargs='?', default='Data/',
                        help='Input data path.')
    parser.add_argument('--proj_path', nargs='?', default='',
                        help='Project path.')

    parser.add_argument('--dataset', nargs='?', default='gowalla',
                        help='Choose a dataset from {gowalla, yelp2018, amazon-book}')
    parser.add_argument('--pretrain', type=int, default=0,
                        help='0: No pretrain, -1: Pretrain with the learned embeddings, 1:Pretrain with stored models.')
    parser.add_argument('--verbose', type=int, default=1,
                        help='Interval of evaluation.')
    parser.add_argument('--is_norm', type=int, default=1,
                    help='Interval of evaluation.')
    parser.add_argument('--epoch', type=int, default=1000,
                        help='Number of epoch.')

    parser.add_argument('--embed_size', type=int, default=64,
                        help='Embedding size.')
    parser.add_argument('--layer_size', nargs='?', default='[64, 64, 64, 64]',
                        help='Output sizes of every layer')
    parser.add_argument('--batch_size', type=int, default=1024,
                        help='Batch size.')

    parser.add_argument('--regs', nargs='?', default='[1e-5,1e-5,1e-2]',
                        help='Regularizations.')
    parser.add_argument('--lr', type=float, default=0.01,
                        help='Learning rate.')

    parser.add_argument('--model_type', nargs='?', default='lightgcn',
                        help='Specify the name of model (lightgcn).')
    parser.add_argument('--adj_type', nargs='?', default='pre',
                        help='Specify the type of the adjacency (laplacian) matrix from {plain, norm, mean}.')
    parser.add_argument('--alg_type', nargs='?', default='lightgcn',
                        help='Specify the type of the graph convolutional layer from {ngcf, gcn, gcmc}.')

    #parser.add_argument('--gpu_id', type=int, default=0,
    #                    help='0 for NAIS_prod, 1 for NAIS_concat')

    parser.add_argument('--node_dropout_flag', type=int, default=0,
                        help='0: Disable node dropout, 1: Activate node dropout')
    parser.add_argument('--node_dropout', nargs='?', default='[0.1]',
                        help='Keep probability w.r.t. node dropout (i.e., 1-dropout_ratio) for each deep layer. 1: no dropout.')
    parser.add_argument('--mess_dropout', nargs='?', default='[0.1]',
                        help='Keep probability w.r.t. message dropout (i.e., 1-dropout_ratio) for each deep layer. 1: no dropout.')

    parser.add_argument('--Ks', nargs='?', default='[20]',
                        help='Top k(s) recommend')

    parser.add_argument('--save_flag', type=int, default=0,
                        help='0: Disable model saver, 1: Activate model saver')

    parser.add_argument('--test_flag', nargs='?', default='part',
                        help='Specify the test type from {part, full}, indicating whether the reference is done in mini-batch')

    parser.add_argument('--report', type=int, default=0,
                        help='0: Disable performance report w.r.t. sparsity levels, 1: Show performance report w.r.t. sparsity levels')

    return parser.parse_args()