def parse_model_args(parser, model_name='NLR'):
parser.add_argument('--v_vector_size',
type=int,
default=64,
help='Size of feature vectors.')
parser.add_argument('--r_logic',
type=float,
default=0.1,
help='Weight of logic regularizer loss')
parser.add_argument('--r_length',
type=float,
default=0.001,
help='Weight of vector length regularizer loss')
parser.add_argument(
'--sim_scale',
type=int,
default=10,
help='Expand the raw similarity *sim_scale before sigmoid.')
parser.add_argument(
'--sim_alpha',
type=float,
default=0,
help='Similarity function divide (square sum then sim_alpha)')
parser.add_argument('--layers',
type=int,
default=1,
help='Number of or/and/not hidden layers.')
return BaseModel.parse_model_args(parser, model_name)
def parse_model_args(parser, model_name='RecModel'):
parser.add_argument('--u_vector_size',
type=int,
default=64,
help='Size of user vectors.')
parser.add_argument('--i_vector_size',
type=int,
default=64,
help='Size of item vectors.')
return BaseModel.parse_model_args(parser, model_name)
def parse_model_args(parser, model_name='CFKG'):
parser.add_argument('--emb_size',
type=int,
default=64,
help='Size of embedding vectors.')
parser.add_argument('--margin',
type=float,
default=0,
help='Margin in hinge loss.')
return BaseModel.parse_model_args(parser, model_name)
def parse_model_args(parser):
parser.add_argument('--emb_size',
type=int,
default=64,
help='Size of embedding vectors.')
parser.add_argument('--time_scalar',
type=int,
default=60 * 60 * 24 * 100,
help='Time scalar for time intervals.')
return BaseModel.parse_model_args(parser)
def parse_model_args(parser, model_name='GRU4Rec'):
parser.add_argument('--emb_size',
type=int,
default=64,
help='Size of embedding vectors.')
parser.add_argument('--hidden_size',
type=int,
default=200,
help='Size of hidden vectors in GRU.')
return BaseModel.parse_model_args(parser, model_name)
def parse_model_args(parser, model_name='DeepModel'):
parser.add_argument('--f_vector_size',
type=int,
default=64,
help='Size of feature vectors.')
parser.add_argument('--layers',
type=str,
default='[64]',
help="Size of each layer.")
return BaseModel.parse_model_args(parser, model_name)
def parse_model_args(parser, model_name='HawkesKT'):
parser.add_argument('--emb_size',
type=int,
default=64,
help='Size of embedding vectors.')
parser.add_argument('--time_log',
type=float,
default=np.e,
help='Log base of time intervals.')
return BaseModel.parse_model_args(parser, model_name)
def parse_model_args(parser, model_name='NCR'):
parser.add_argument('--u_vector_size', type=int, default=64,
help='Size of user vectors.')
parser.add_argument('--i_vector_size', type=int, default=64,
help='Size of item vectors.')
parser.add_argument('--r_weight', type=float, default=10,
help='Weight of logic regularizer loss')
parser.add_argument('--ppl_weight', type=float, default=0,
help='Weight of uv interaction prediction loss')
parser.add_argument('--pos_weight', type=float, default=0,
help='Weight of positive purchase loss')
return BaseModel.parse_model_args(parser, model_name)
def parse_model_args(parser, model_name='DKT'):
parser.add_argument('--emb_size',
type=int,
default=64,
help='Size of embedding vectors.')
parser.add_argument('--hidden_size',
type=int,
default=64,
help='Size of hidden vectors in LSTM.')
parser.add_argument('--num_layer',
type=int,
default=1,
help='Number of GRU layers.')
return BaseModel.parse_model_args(parser, model_name)
def parse_model_args(parser, model_name='AKT'):
parser.add_argument('--emb_size',
type=int,
default=64,
help='Size of embedding vectors.')
parser.add_argument('--num_layer',
type=int,
default=1,
help='Self-attention layers.')
parser.add_argument('--num_head',
type=int,
default=4,
help='Self-attention heads.')
return BaseModel.parse_model_args(parser, model_name)
def parse_model_args(parser, model_name='KTM'):
parser.add_argument('--emb_size',
type=int,
default=64,
help='Size of embedding vectors.')
return BaseModel.parse_model_args(parser, model_name)