CML/Params.py at main · anonymousCML/CML · GitHub

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import argparse


def parse_args():
	parser = argparse.ArgumentParser(description='Model Params')


#---------Tmall--------------------------------------------------------------------------------------------------------------
	# #for this model
	# parser.add_argument('--hidden_dim', default=16, type=int, help='embedding size')
	# parser.add_argument('--gnn_layer', default="[16,16,16]", type=str, help='gnn layers: number + dim')
	# parser.add_argument('--dataset', default='Tmall', type=str, help='name of dataset')
	# parser.add_argument('--point', default='for_meta_hidden_dim', type=str, help='')
	# parser.add_argument('--title', default='dim__8', type=str, help='title of model')
	# parser.add_argument('--sampNum', default=40, type=int, help='batch size for sampling')

	# #for train
	# parser.add_argument('--lr', default=3e-4, type=float, help='learning rate')
	# parser.add_argument('--opt_base_lr', default=1e-3, type=float, help='learning rate')
	# parser.add_argument('--opt_max_lr', default=5e-3, type=float, help='learning rate')
	# parser.add_argument('--opt_weight_decay', default=1e-4, type=float, help='weight decay regularizer')
	# parser.add_argument('--meta_opt_base_lr', default=1e-4, type=float, help='learning rate')
	# parser.add_argument('--meta_opt_max_lr', default=2e-3, type=float, help='learning rate')
	# parser.add_argument('--meta_opt_weight_decay', default=1e-4, type=float, help='weight decay regularizer')
	# parser.add_argument('--meta_lr', default=1e-3, type=float, help='_meta_learning rate')

	# parser.add_argument('--batch', default=8192, type=int, help='batch size')
	# parser.add_argument('--meta_batch', default=128, type=int, help='batch size')
	# parser.add_argument('--SSL_batch', default=18, type=int, help='batch size')
	# parser.add_argument('--reg', default=1e-3, type=float, help='weight decay regularizer')
	# parser.add_argument('--beta', default=0.005, type=float, help='scale of infoNCELoss')
	# parser.add_argument('--epoch', default=300, type=int, help='number of epochs')
	# # parser.add_argument('--decay', default=0.96, type=float, help='weight decay rate')
	# parser.add_argument('--shoot', default=10, type=int, help='K of top k')
	# parser.add_argument('--inner_product_mult', default=1, type=float, help='multiplier for the result')
	# parser.add_argument('--drop_rate', default=0.8, type=float, help='drop_rate')
	# parser.add_argument('--drop_rate1', default=0.5, type=float, help='drop_rate')
	# parser.add_argument('--seed', type=int, default=6)
	# parser.add_argument('--slope', type=float, default=0.1)
	# parser.add_argument('--patience', type=int, default=100)
	# #for save and read
	# parser.add_argument('--path', default='/home/ww/Code/DATASET/work3_dataset/', type=str, help='data path')
	# parser.add_argument('--save_path', default='tem', help='file name to save model and training record')
	# parser.add_argument('--load_model', default=None, help='model name to load')
	# parser.add_argument('--target', default='buy', type=str, help='target behavior to predict on')
	# parser.add_argument('--isload', default=False , type=bool, help='whether load model')
	# parser.add_argument('--isJustTest', default=False , type=bool, help='whether load model')
	# parser.add_argument('--loadModelPath', default='/home/ww/Code/work3/BSTRec/Model/Tmall/for_meta_hidden_dim_dim__8_Tmall_2021_07_08__01_35_54_lr_0.0003_reg_0.001_batch_size_4096_gnn_layer_[16,16,16].pth', type=str, help='loadModelPath')
	# #Tmall: # loadPath_SSL_meta = "/home/ww/Code/work3/BSTRec/Model/Tmall/for_meta_hidden_dim_dim__8_Tmall_2021_07_08__01_35_54_lr_0.0003_reg_0.001_batch_size_4096_gnn_layer_[16,16,16].pth"
	# #IJCAI_15: # loadPath_SSL_meta = "/home/ww/Code/work3/BSTRec/Model/IJCAI_15/for_meta_hidden_dim_dim__8_IJCAI_15_2021_07_10__14_11_55_lr_0.0003_reg_0.001_batch_size_4096_gnn_layer_[16,16,16].pth"
	# #retailrocket: # loadPath_SSL_meta = "/home/ww/Code/work3/BSTRec/Model/retailrocket/for_meta_hidden_dim_dim__8_retailrocket_2021_07_10__18_35_32_lr_0.0003_reg_0.01_batch_size_1024_gnn_layer_[16,16,16].pth"


	# #use less
	# # parser.add_argument('--memosize', default=2, type=int, help='memory size')
	# parser.add_argument('--head_num', default=4, type=int, help='head_num_of_multihead_attention')
	# parser.add_argument('--beta_multi_behavior', default=0.005, type=float, help='scale of infoNCELoss')
	# parser.add_argument('--sampNum_slot', default=30, type=int, help='SSL_step')
	# parser.add_argument('--SSL_slot', default=1, type=int, help='SSL_step')
	# parser.add_argument('--k', default=2, type=float, help='MFB')
	# parser.add_argument('--meta_time_rate', default=0.8, type=float, help='gating rate')
	# parser.add_argument('--meta_behavior_rate', default=0.8, type=float, help='gating rate')
	# parser.add_argument('--meta_slot', default=2, type=int, help='epoch number for each SSL')
	# parser.add_argument('--time_slot', default=60*60*24*360, type=float, help='length of time slots')
	# parser.add_argument('--hidden_dim_meta', default=16, type=int, help='embedding size')
	# # parser.add_argument('--att_head', default=2, type=int, help='number of attention heads')
	# # parser.add_argument('--gnn_layer', default=2, type=int, help='number of gnn layers')
	# # parser.add_argument('--trnNum', default=10000, type=int, help='number of training instances per epoch')
	# # parser.add_argument('--deep_layer', default=0, type=int, help='number of deep layers to make the final prediction')
	# # parser.add_argument('--iiweight', default=0.3, type=float, help='weight for ii')
	# # parser.add_argument('--graphSampleN', default=10000, type=int, help='use 25000 for training and 200000 for testing, empirically')
	# # parser.add_argument('--divSize', default=1000, type=int, help='div size for smallTestEpoch')
	# # parser.add_argument('--tstEpoch', default=1, type=int, help='number of epoch to test while training')
	# # parser.add_argument('--subUsrSize', default=10, type=int, help='number of item for each sub-user')
	# # parser.add_argument('--subUsrDcy', default=0.9, type=float, help='decay factor for sub-users over time')
	# # parser.add_argument('--slot', default=0.5, type=float, help='length of time slots')
# ---------Tmall--------------------------------------------------------------------------------------------------------------


# # # #---------IJCAI--------------------------------------------------------------------------------------------------------------
	# #for this model
	# parser.add_argument('--hidden_dim', default=16, type=int, help='embedding size')
	# parser.add_argument('--gnn_layer', default="[16,16,16]", type=str, help='gnn layers: number + dim')
	# parser.add_argument('--dataset', default='IJCAI_15', type=str, help='name of dataset')
	# parser.add_argument('--point', default='for_meta_hidden_dim', type=str, help='')
	# parser.add_argument('--title', default='dim__8', type=str, help='title of model')
	# parser.add_argument('--sampNum', default=10, type=int, help='batch size for sampling')

	# #for train
	# parser.add_argument('--lr', default=3e-4, type=float, help='learning rate')
	# parser.add_argument('--opt_base_lr', default=1e-3, type=float, help='learning rate')
	# parser.add_argument('--opt_max_lr', default=2e-3, type=float, help='learning rate')
	# parser.add_argument('--opt_weight_decay', default=1e-4, type=float, help='weight decay regularizer')
	# parser.add_argument('--meta_opt_base_lr', default=1e-4, type=float, help='learning rate')
	# parser.add_argument('--meta_opt_max_lr', default=1e-3, type=float, help='learning rate')
	# parser.add_argument('--meta_opt_weight_decay', default=1e-4, type=float, help='weight decay regularizer')
	# parser.add_argument('--meta_lr', default=1e-3, type=float, help='_meta_learning rate')

	# parser.add_argument('--batch', default=8192, type=int, help='batch size')
	# parser.add_argument('--meta_batch', default=128, type=int, help='batch size')
	# parser.add_argument('--SSL_batch', default=30, type=int, help='batch size')
	# parser.add_argument('--reg', default=1e-3, type=float, help='weight decay regularizer')
	# parser.add_argument('--beta', default=0.005, type=float, help='scale of infoNCELoss')
	# parser.add_argument('--epoch', default=300, type=int, help='number of epochs')
	# # parser.add_argument('--decay', default=0.96, type=float, help='weight decay rate')
	# parser.add_argument('--shoot', default=10, type=int, help='K of top k')
	# parser.add_argument('--inner_product_mult', default=1, type=float, help='multiplier for the result')
	# parser.add_argument('--drop_rate', default=0.8, type=float, help='drop_rate')
	# parser.add_argument('--drop_rate1', default=0.5, type=float, help='drop_rate')
	# parser.add_argument('--seed', type=int, default=6)
	# parser.add_argument('--slope', type=float, default=0.1)
	# parser.add_argument('--patience', type=int, default=100)
	# #for save and read
	# parser.add_argument('--path', default='/home/ww/Code/DATASET/work3_dataset/', type=str, help='data path')
	# parser.add_argument('--save_path', default='tem', help='file name to save model and training record')
	# parser.add_argument('--load_model', default=None, help='model name to load')
	# parser.add_argument('--target', default='buy', type=str, help='target behavior to predict on')
	# parser.add_argument('--isload', default=False , type=bool, help='whether load model')
	# parser.add_argument('--isJustTest', default=False , type=bool, help='whether load model')
	# parser.add_argument('--loadModelPath', default='/home/ww/Code/work3/BSTRec/Model/IJCAI_15/for_meta_hidden_dim_dim__8_IJCAI_15_2021_07_10__14_11_55_lr_0.0003_reg_0.001_batch_size_4096_gnn_layer_[16,16,16].pth', type=str, help='loadModelPath')
	# # #Tmall: # loadPath_SSL_meta = "/home/ww/Code/work3/BSTRec/Model/Tmall/for_meta_hidden_dim_dim__8_Tmall_2021_07_08__01_35_54_lr_0.0003_reg_0.001_batch_size_4096_gnn_layer_[16,16,16].pth"
	# # #IJCAI_15: # loadPath_SSL_meta = "/home/ww/Code/work3/BSTRec/Model/IJCAI_15/for_meta_hidden_dim_dim__8_IJCAI_15_2021_07_10__14_11_55_lr_0.0003_reg_0.001_batch_size_4096_gnn_layer_[16,16,16].pth"
	# # #retailrocket: # loadPath_SSL_meta = "/home/ww/Code/work3/BSTRec/Model/retailrocket/for_meta_hidden_dim_dim__8_retailrocket_2021_07_10__18_35_32_lr_0.0003_reg_0.01_batch_size_1024_gnn_layer_[16,16,16].pth"


	# #use less
	# # parser.add_argument('--memosize', default=2, type=int, help='memory size')
	# parser.add_argument('--head_num', default=4, type=int, help='head_num_of_multihead_attention')
	# parser.add_argument('--beta_multi_behavior', default=0.005, type=float, help='scale of infoNCELoss')
	# parser.add_argument('--sampNum_slot', default=30, type=int, help='SSL_step')
	# parser.add_argument('--SSL_slot', default=1, type=int, help='SSL_step')
	# parser.add_argument('--k', default=2, type=float, help='MFB')
	# parser.add_argument('--meta_time_rate', default=0.8, type=float, help='gating rate')
	# parser.add_argument('--meta_behavior_rate', default=0.8, type=float, help='gating rate')
	# parser.add_argument('--meta_slot', default=2, type=int, help='epoch number for each SSL')
	# parser.add_argument('--time_slot', default=60*60*24*360, type=float, help='length of time slots')
	# parser.add_argument('--hidden_dim_meta', default=16, type=int, help='embedding size')
	# # parser.add_argument('--att_head', default=2, type=int, help='number of attention heads')
	# # parser.add_argument('--gnn_layer', default=2, type=int, help='number of gnn layers')
	# # parser.add_argument('--trnNum', default=10000, type=int, help='number of training instances per epoch')
	# # parser.add_argument('--deep_layer', default=0, type=int, help='number of deep layers to make the final prediction')
	# # parser.add_argument('--iiweight', default=0.3, type=float, help='weight for ii')
	# # parser.add_argument('--graphSampleN', default=10000, type=int, help='use 25000 for training and 200000 for testing, empirically')
	# # parser.add_argument('--divSize', default=1000, type=int, help='div size for smallTestEpoch')
	# # parser.add_argument('--tstEpoch', default=1, type=int, help='number of epoch to test while training')
	# # parser.add_argument('--subUsrSize', default=10, type=int, help='number of item for each sub-user')
	# # parser.add_argument('--subUsrDcy', default=0.9, type=float, help='decay factor for sub-users over time')
	# # parser.add_argument('--slot', default=0.5, type=float, help='length of time slots')
# #---------IJCAI--------------------------------------------------------------------------------------------------------------


# # ---------retail--------------------------------------------------------------------------------------------------------------
	#for this model
	parser.add_argument('--hidden_dim', default=16, type=int, help='embedding size')
	parser.add_argument('--gnn_layer', default="[16,16,16]", type=str, help='gnn layers: number + dim')
	parser.add_argument('--dataset', default='retailrocket', type=str, help='name of dataset')
	parser.add_argument('--point', default='for_meta_hidden_dim', type=str, help='')
	parser.add_argument('--title', default='dim__8', type=str, help='title of model')
	parser.add_argument('--sampNum', default=40, type=int, help='batch size for sampling')

	#for train
	parser.add_argument('--lr', default=3e-4, type=float, help='learning rate')
	parser.add_argument('--opt_base_lr', default=1e-4, type=float, help='learning rate')
	parser.add_argument('--opt_max_lr', default=1e-3, type=float, help='learning rate')
	parser.add_argument('--opt_weight_decay', default=1e-4, type=float, help='weight decay regularizer')
	parser.add_argument('--meta_opt_base_lr', default=1e-4, type=float, help='learning rate')
	parser.add_argument('--meta_opt_max_lr', default=1e-3, type=float, help='learning rate')
	parser.add_argument('--meta_opt_weight_decay', default=1e-3, type=float, help='weight decay regularizer')
	parser.add_argument('--meta_lr', default=1e-3, type=float, help='_meta_learning rate')

	parser.add_argument('--batch', default=2048, type=int, help='batch size')
	parser.add_argument('--meta_batch', default=128, type=int, help='batch size')
	parser.add_argument('--SSL_batch', default=15, type=int, help='batch size')
	parser.add_argument('--reg', default=1e-2, type=float, help='weight decay regularizer')
	parser.add_argument('--beta', default=0.005, type=float, help='scale of infoNCELoss')
	parser.add_argument('--epoch', default=200, type=int, help='number of epochs')
	# parser.add_argument('--decay', default=0.96, type=float, help='weight decay rate')
	parser.add_argument('--shoot', default=10, type=int, help='K of top k')
	parser.add_argument('--inner_product_mult', default=1, type=float, help='multiplier for the result')
	parser.add_argument('--drop_rate', default=0.8, type=float, help='drop_rate')
	parser.add_argument('--drop_rate1', default=0.5, type=float, help='drop_rate')
	parser.add_argument('--seed', type=int, default=6)
	parser.add_argument('--slope', type=float, default=0.1)
	parser.add_argument('--patience', type=int, default=100)
	#for save and read
	parser.add_argument('--path', default='/home/ww/Code/DATASET/work3_dataset/', type=str, help='data path')
	parser.add_argument('--save_path', default='tem', help='file name to save model and training record')
	parser.add_argument('--load_model', default=None, help='model name to load')
	parser.add_argument('--target', default='buy', type=str, help='target behavior to predict on')
	parser.add_argument('--isload', default=False , type=bool, help='whether load model')
	parser.add_argument('--isJustTest', default=False , type=bool, help='whether load model')
	parser.add_argument('--loadModelPath', default='/home/ww/Code/work3/BSTRec/Model/retailrocket/for_meta_hidden_dim_dim__8_retailrocket_2021_07_10__18_35_32_lr_0.0003_reg_0.01_batch_size_1024_gnn_layer_[16,16,16].pth', type=str, help='loadModelPath')


	#use less
	# parser.add_argument('--memosize', default=2, type=int, help='memory size')
	parser.add_argument('--head_num', default=4, type=int, help='head_num_of_multihead_attention')
	parser.add_argument('--beta_multi_behavior', default=0.005, type=float, help='scale of infoNCELoss')
	parser.add_argument('--sampNum_slot', default=30, type=int, help='SSL_step')
	parser.add_argument('--SSL_slot', default=1, type=int, help='SSL_step')
	parser.add_argument('--k', default=2, type=float, help='MFB')
	parser.add_argument('--meta_time_rate', default=0.8, type=float, help='gating rate')
	parser.add_argument('--meta_behavior_rate', default=0.8, type=float, help='gating rate')
	parser.add_argument('--meta_slot', default=2, type=int, help='epoch number for each SSL')
	parser.add_argument('--time_slot', default=60*60*24*360, type=float, help='length of time slots')
	parser.add_argument('--hidden_dim_meta', default=16, type=int, help='embedding size')
	# parser.add_argument('--att_head', default=2, type=int, help='number of attention heads')
	# parser.add_argument('--gnn_layer', default=2, type=int, help='number of gnn layers')
	# parser.add_argument('--trnNum', default=10000, type=int, help='number of training instances per epoch')
	# parser.add_argument('--deep_layer', default=0, type=int, help='number of deep layers to make the final prediction')
	# parser.add_argument('--iiweight', default=0.3, type=float, help='weight for ii')
	# parser.add_argument('--graphSampleN', default=10000, type=int, help='use 25000 for training and 200000 for testing, empirically')
	# parser.add_argument('--divSize', default=1000, type=int, help='div size for smallTestEpoch')
	# parser.add_argument('--tstEpoch', default=1, type=int, help='number of epoch to test while training')
	# parser.add_argument('--subUsrSize', default=10, type=int, help='number of item for each sub-user')
	# parser.add_argument('--subUsrDcy', default=0.9, type=float, help='decay factor for sub-users over time')
	# parser.add_argument('--slot', default=0.5, type=float, help='length of time slots')
#---------retail--------------------------------------------------------------------------------------------------------------


	return parser.parse_args()
args = parse_args()

# TODO: 这几句被注释掉了, 后面用到了. 问题是: args还可以按照后面的这个直接加???
# args.user = 805506#147894
# args.item = 584050#99037
# ML10M
# args.user = 67788
# args.item = 8704
# yelp
# args.user = 19800
# args.item = 22734

# swap user and item
# tem = args.user
# args.user = args.item
# args.item = tem

# args.decay_step = args.trn_num
# args.decay_step = args.item
# args.decay_step = args.trnNum