diff --git a/Params.py b/Params.py
index e210f67..7c12a2a 100644
--- a/Params.py
+++ b/Params.py
@@ -5,256 +5,81 @@ 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 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-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--------------------------------------------------------------------------------------------------------------
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+# 	#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/MultiBehavior_BASELINE/MB-GCN/Datasets/', 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')  
 
 	
 	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
diff --git a/README.md b/README.md
index 5c3c025..955237d 100644
--- a/README.md
+++ b/README.md
@@ -1,26 +1,90 @@
 # CML
-The implementation of Contrastive Meta Learning with Behavior Multiplicity for Recommendation
+ 
 
-mkdir /History
 
-mkdir /Model
+This repository contains PyTorch codes and datasets for the paper:
 
-### Requirements
-numpy==1.20.3
+> Wei, Wei and Huang, Chao and Xia, Lianghao and Xu, Yong and Zhao, Jiashu and Yin, Dawei. Contrastive Meta Learning with Behavior Multiplicity forRecommendation. <a href='https://arxiv.org/pdf/2202.08523.pdf'>Paper in arXiv</a>.
 
-scipy==1.6.2
 
-torch==1.8.1+cu111
+## Introduction
+Contrastive Meta Learning (CML) leverages multi-behavior learning paradigm to model diverse and multiplex user-item relationships, as well as tackling the label scarcity problem for target behaviors. The designed multi-behavior contrastive task is to capture the transferable user-item relationships from multi-typed user behavior data heterogeneity. And the proposed meta contrastive encoding scheme allows CML to preserve the personalized multi-behavior characteristics, so as to be reflective of the diverse behavior-aware user preference under a customized self-supervised framework.
 
-tqdm==4.61.2
 
+## Citation
+```
+@inproceedings{wei2022contrastive,
+  title={Contrastive meta learning with behavior multiplicity for recommendation},
+  author={Wei, Wei and Huang, Chao and Xia, Lianghao and Xu, Yong and Zhao, Jiashu and Yin, Dawei},
+  booktitle={Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining},
+  pages={1120--1128},
+  year={2022}
+}
+```
+
+
+## Environment
+
+The codes of CML are implemented and tested under the following development environment:
+
+- Python 3.6
+- torch==1.8.1+cu111
+- scipy==1.6.2
+- tqdm==4.61.2
+
+
+
+## Datasets
+
+#### Raw data：
+- IJCAI contest:  https://tianchi.aliyun.com/dataset/dataDetail?dataId=47
+- Retail Rocket: https://www.kaggle.com/retailrocket/ecommerce-dataset
+- Tmall:  https://tianchi.aliyun.com/dataset/dataDetail?dataId=649 
+#### Processed data：
+- The processed IJCAI are under the /datasets folder.
+
+
+## Usage
+
+The command to train CML on the Tmall/IJCAI/Retailrocket datasets are as follows. The commands specify the hyperparameter settings that generate the reported results in the paper.
+
+* Tmall
+```
+python main.py --path=./datasets/ --dataset=Tmall --opt_base_lr=1e-3 --opt_max_lr=5e-3 --opt_weight_decay=1e-4 --meta_opt_base_lr=1e-4 --meta_opt_max_lr=2e-3 --meta_opt_weight_decay=1e-4 --meta_lr=1e-3 --batch=8192 --meta_batch=128 --SSL_batch=18
+```
+* IJCAI
+```
+python main.py --path=./datasets/ --dataset=IJCAI_15 --sampNum=10 --opt_base_lr=1e-3 --opt_max_lr=2e-3 --opt_weight_decay=1e-4 --meta_opt_base_lr=1e-4 --meta_opt_max_lr=1e-3 --meta_opt_weight_decay=1e-4 --meta_lr=1e-3 --batch=8192 --meta_batch=128 --SSL_batch=30 
+```
+* Retailrocket
+```
+python main.py --path=./datasets/ --dataset='retailrocket' --sampNum=40 --lr=3e-4 --opt_base_lr=1e-4 --opt_max_lr=1e-3 --opt_weight_decay=1e-4 --opt_weight_decay=1e-4 --meta_opt_base_lr=1e-4 --meta_opt_max_lr=1e-3 --meta_opt_weight_decay=1e-3 --meta_lr=1e-3 --batch=2048 --meta_batch=128 --SSL_batch=15
+```         
+        
+
+
+<!-- Important arguments:
+* `reg`: It is the weight for weight-decay regularization. We tune this hyperparameter from the set `{1e-2, 1e-3, 1e-4, 1e-5}`.
+* `ssl_reg`: This is the weight for the hypergraph-graph contrastive learning loss. The value is tuned from `1e-2` to `1e-8`.
+* `temp`: This is the temperature factor in the InfoNCE loss in our contrastive learning. The value is selected from `{10, 3, 1, 0.3, 0.1}`.
+* `keepRate`: It denotes the rate to keep edges in the graph dropout, which is tuned from `{0.25, 0.5, 0.75, 1.0}`.
+ -->
+
+
+
+
+
+<!-- ## Acknowledgement
+ -->
+
+
+
+
+
+It will be released again in few days in the optimized code version.
 
-### Datasets
-IJCAI contest:  https://tianchi.aliyun.com/dataset/dataDetail?dataId=47
 
-Retail Rocket: https://www.kaggle.com/retailrocket/ecommerce-dataset
 
-Tmall:  https://tianchi.aliyun.com/dataset/dataDetail?dataId=649 
 
 
 
diff --git a/Utils/README.md b/Utils/README.md
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@@ -0,0 +1 @@
+.
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+.
diff --git a/main.py b/main.py
index b596baf..5ec6051 100644
--- a/main.py
+++ b/main.py
@@ -486,7 +486,8 @@ def trainEpoch(self):
         time = datetime.datetime.now()
         print("start_ng_samp:  ", time)
         train_loader.dataset.ng_sample()
-        time = datetime.datetime.now()
+
+
         print("end_ng_samp:  ", time)
         
         epoch_loss = 0