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params.py
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params.py
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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="[16,16,16,16]", type=str, help='gnn layers: number + dim') #
# parser.add_argument('--time_slot', default=864000, type=float, help='length of time slots') #
# parser.add_argument('--head_num', default=4, type=int, help='head_num_of_multihead_attention') #
# parser.add_argument('--gate_rate', default=0.8, type=float, help='gating rate') #
# parser.add_argument('--point', default='ICDE_CIKM_WOCL', type=str, help='')
# parser.add_argument('--title', default='self_attention_behavior', type=str, help='title of model')
# #for train
# parser.add_argument('--lr', default=1e-3, 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('--batch', default=4096, type=int, help='batch size') #
# parser.add_argument('--reg', default=1.45e-2, type=float, help='weight decay regularizer') #
# parser.add_argument('--epoch', default=1000, 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('--mult', default=1, type=float, help='multiplier for the result') #
# parser.add_argument('--drop_rate', default=0.1, type=float, help='drop_rate') #
# parser.add_argument('--seed', type=int, default=19) #
# parser.add_argument('--slope', type=float, default=0.1) #
# parser.add_argument('--patience', type=int, default=300)
# parser.add_argument('--cl_long_rate', default=0.013, type=float, help='cl_rate')
# parser.add_argument('--cl_short_rate', default=0.0005, type=float, help='cl_rate')
# #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('--dataset', default='Tmall', type=str, help='name of dataset') #
# 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/work1/master_behavior_attention/Model/IJCAI_15/ICDE_CIKM_WOCL_self_attention_behavior_IJCAI_15_2022_04_26__11_30_42_lr_0.001_reg_0.0145_batch_size_4096_time_slot_31104000.0_gnn_layer_[16,16].pth', type=str, help='loadModelPath')
# #use less
# # parser.add_argument('--memosize', default=2, type=int, help='memory size')
# parser.add_argument('--sampNum', default=10, type=int, help='batch size for sampling') #
# # 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') #
# parser.add_argument('--tau', default=0.5, type=float, help='') #
# parser.add_argument('--positional_rate', default=0.0001, type=float, help='') #
# #Tmall------------------------------------------------------------------------------------------------------------------------------
#IJCAI_15------------------------------------------------------------------------------------------------------------------------------
#for this model
parser.add_argument('--hidden_dim', default=16, type=int, help='embedding size') #
parser.add_argument('--gnn_layer', default="[16,16]", type=str, help='gnn layers: number + dim') #
parser.add_argument('--time_slot', default=31104000, type=float, help='length of time slots') #
parser.add_argument('--head_num', default=4, type=int, help='head_num_of_multihead_attention') #
parser.add_argument('--gate_rate', default=0.8, type=float, help='gating rate') #
parser.add_argument('--point', default='ICDE_CIKM_WOCL', type=str, help='')
parser.add_argument('--title', default='self_attention_behavior', type=str, help='title of model')
#for train
parser.add_argument('--lr', default=1e-3, 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('--batch', default=4096, type=int, help='batch size') #
parser.add_argument('--reg', default=1.45e-2, type=float, help='weight decay regularizer') #
parser.add_argument('--epoch', default=1000, 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('--mult', default=1, type=float, help='multiplier for the result') #
parser.add_argument('--drop_rate', default=0.1, type=float, help='drop_rate') #
parser.add_argument('--seed', type=int, default=19) #
parser.add_argument('--slope', type=float, default=0.1) #
parser.add_argument('--patience', type=int, default=300)
parser.add_argument('--cl_long_rate', default=0.013, type=float, help='cl_rate')
parser.add_argument('--cl_short_rate', default=0.00005, type=float, help='cl_rate')
#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('--dataset', default='IJCAI_15', type=str, help='name of dataset') #
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/work1/master_behavior_attention/Model/IJCAI_15/ICDE_CIKM_WOCL_self_attention_behavior_IJCAI_15_2022_04_26__11_30_42_lr_0.001_reg_0.0145_batch_size_4096_time_slot_31104000.0_gnn_layer_[16,16].pth', type=str, help='loadModelPath')
#use less
# parser.add_argument('--memosize', default=2, type=int, help='memory size')
parser.add_argument('--sampNum', default=10, type=int, help='batch size for sampling') #
# 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') #
parser.add_argument('--tau', default=0.035, type=float, help='') #
parser.add_argument('--positional_rate', default=0.0001, type=float, help='') #
#IJCAI_15------------------------------------------------------------------------------------------------------------------------------
# #JD------------------------------------------------------------------------------------------------------------------------------
# #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('--time_slot', default=7776000, type=float, help='length of time slots') #
# parser.add_argument('--head_num', default=4, type=int, help='head_num_of_multihead_attention') #
# parser.add_argument('--gate_rate', default=0.8, type=float, help='gating rate') #
# parser.add_argument('--point', default='ICDE_CIKM_WOCL', type=str, help='')
# parser.add_argument('--title', default='self_attention_behavior', type=str, help='title of model')
# #for train
# parser.add_argument('--lr', default=1e-3, 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('--batch', default=4096, type=int, help='batch size') #
# parser.add_argument('--reg', default=1.45e-2, type=float, help='weight decay regularizer') #
# parser.add_argument('--epoch', default=1000, 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('--mult', default=1, type=float, help='multiplier for the result') #
# parser.add_argument('--drop_rate', default=0.1, type=float, help='drop_rate') #
# parser.add_argument('--seed', type=int, default=19) #
# parser.add_argument('--slope', type=float, default=0.1) #
# parser.add_argument('--patience', type=int, default=300)
# parser.add_argument('--cl_long_rate', default=0.013, type=float, help='cl_rate')
# parser.add_argument('--cl_short_rate', default=0.0005, type=float, help='cl_rate')
# #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('--dataset', default='JD', type=str, help='name of dataset') #
# 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/work1/master_behavior_attention/Model/IJCAI_15/ICDE_CIKM_WOCL_self_attention_behavior_IJCAI_15_2022_04_26__11_30_42_lr_0.001_reg_0.0145_batch_size_4096_time_slot_31104000.0_gnn_layer_[16,16].pth', type=str, help='loadModelPath')
# #use less
# # parser.add_argument('--memosize', default=2, type=int, help='memory size')
# parser.add_argument('--sampNum', default=10, type=int, help='batch size for sampling') #
# # 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') #
# parser.add_argument('--tau', default=0.1, type=float, help='') #
# parser.add_argument('--positional_rate', default=0.0001, type=float, help='') #
# #JD------------------------------------------------------------------------------------------------------------------------------
return parser.parse_args()
args = parse_args()
# 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.batch
args.decay_step = args.trnNum//args.batch
# #----IJCAI_15-------------------------------------------------------------------------------------------------------------------------------------
# python ./main_ssl_dynamic.py --dataset=IJCAI_15 --cl_long_rate=0.013 --cl_short_rate=0.00005 --tau=0.1 --time_slot=31104000 --gnn_layer=[16,16] --tau=0.035 --head_num=4
# [01:36:30] /opt/dgl/src/runtime/tensordispatch.cc:43: TensorDispatcher: dlopen failed: /home/ww/anaconda3/lib/python3.8/site-packages/dgl/tensoradapter/pytorch/libtensoradapter_pytorch_1.10.0.so: cannot open shared object file: No such file or directory
# Using backend: pytorch
# Namespace(batch=4096, cl_long_rate=0.015, cl_short_rate=5e-05, dataset='IJCAI_15', decay=0.96, decay_step=2, deep_layer=0, divSize=1000, drop_rate=0.1, epoch=1000, gate_rate=0.8, gnn_layer='[16,16]', graphSampleN=10000, head_num=4, hidden_dim=16, iiweight=0.3, isJustTest=False, isload=False, loadModelPath='/home/ww/Code/work1/master_behavior_attention/Model/IJCAI_15/topk_20_self_attention_behavior_IJCAI_15_2021_07_29__17_13_23_lr_0.001_reg_0.0145_batch_size_4096_time_slot_31104000_gnn_layer_[16,16].pth', load_model=None, lr=0.001, mult=1, opt_base_lr=0.0001, opt_max_lr=0.001, opt_weight_decay=0.0001, path='/home/ww/Code/MultiBehavior_BASELINE/MB-GCN/Datasets/', patience=300, point='topk_20', positional_rate=0.0001, reg=0.0145, sampNum=10, save_path='tem', seed=19, shoot=10, slope=0.1, slot=0.5, subUsrDcy=0.9, subUsrSize=10, target='buy', tau=0.1, time_slot=7776000.0, title='self_attention_behavior', trnNum=10000, tstEpoch=1)
# #----IJCAI_15-------------------------------------------------------------------------------------------------------------------------------------
# # #----Tmall-------------------------------------------------------------------------------------------------------------------------------------
# python ./main_ssl_dynamic.py --dataset=Tmall --cl_long_rate=0.013 --cl_short_rate=0.0005 --tau=0.1 --time_slot=864000 --hidden_dim=16 --tau=0.035 --gnn_layer=[16,16,16,16]
# [09:08:13] /opt/dgl/src/runtime/tensordispatch.cc:43: TensorDispatcher: dlopen failed: /home/ww/anaconda3/lib/python3.8/site-packages/dgl/tensoradapter/pytorch/libtensoradapter_pytorch_1.10.0.so: cannot open shared object file: No such file or directory
# Using backend: pytorch
# Namespace(batch=4096, cl_long_rate=0.013, cl_short_rate=0.0005, dataset='Tmall', decay=0.96, decay_step=2, deep_layer=0, divSize=1000, drop_rate=0.1, epoch=1000, gate_rate=0.8, gnn_layer='[16,16,16,16]', graphSampleN=10000, head_num=4, hidden_dim=16, iiweight=0.3, isJustTest=False, isload=False, loadModelPath='/home/ww/Code/work1/master_behavior_attention/Model/IJCAI_15/topk_20_self_attention_behavior_IJCAI_15_2021_07_29__17_13_23_lr_0.001_reg_0.0145_batch_size_4096_time_slot_31104000_gnn_layer_[16,16].pth', load_model=None, lr=0.001, mult=1, opt_base_lr=0.0001, opt_max_lr=0.001, opt_weight_decay=0.0001, path='/home/ww/Code/MultiBehavior_BASELINE/MB-GCN/Datasets/', patience=300, point='topk_20', positional_rate=0.0001, reg=0.0145, sampNum=10, save_path='tem', seed=19, shoot=10, slope=0.1, slot=0.5, subUsrDcy=0.9, subUsrSize=10, target='buy', tau=0.035, time_slot=864000.0, title='self_attention_behavior', trnNum=10000, tstEpoch=1)
# # #----Tmall-------------------------------------------------------------------------------------------------------------------------------------
# python ./main_ssl_dynamic.py --cl_long_rate=0.013 --cl_short_rate=0.0005 --dataset=Tmall --decay=0.96 --deep_layer=0 --divSize=1000 --drop_rate=0.1 --epoch=1000 --gate_rate=0.8 --gnn_layer=[16,16,16,16] --graphSampleN=10000 --head_num=4 --hidden_dim=16 --iiweight=0.3 --lr=0.001 --mult=1 --opt_base_lr=0.0001 --opt_max_lr=0.001 --opt_weight_decay=0.0001 --patience=300 --positional_rate=0.0001 --reg=0.0145 --sampNum=10 --seed=19 --shoot=10 --slope=0.1 --slot=0.5 --subUsrDcy=0.9 --subUsrSize=10 --tau=0.035 --time_slot=864000 --trnNum=10000 --tstEpoch=1
# # #----JD-------------------------------------------------------------------------------------------------------------------------------------
# python ./main_ssl_dynamic.py --dataset=JD --cl_long_rate=0.013 --cl_short_rate=0.0005 --tau=0.1 --time_slot=7776000 --gnn_layer=[16,16,16]
# [00:12:41] /opt/dgl/src/runtime/tensordispatch.cc:43: TensorDispatcher: dlopen failed: /home/ww/anaconda3/lib/python3.8/site-packages/dgl/tensoradapter/pytorch/libtensoradapter_pytorch_1.10.0.so: cannot open shared object file: No such file or directory
# Using backend: pytorch
# Namespace(batch=4096, cl_long_rate=0.013, cl_short_rate=0.0005, dataset='JD', decay=0.96, decay_step=2, deep_layer=0, divSize=1000, drop_rate=0.1, epoch=1000, gate_rate=0.8, gnn_layer='[16,16,16]', graphSampleN=10000, head_num=4, hidden_dim=16, iiweight=0.3, isJustTest=False, isload=False, loadModelPath='/home/ww/Code/work1/master_behavior_attention/Model/IJCAI_15/topk_20_self_attention_behavior_IJCAI_15_2021_07_29__17_13_23_lr_0.001_reg_0.0145_batch_size_4096_time_slot_31104000_gnn_layer_[16,16].pth', load_model=None, lr=0.001, mult=1, opt_base_lr=0.0001, opt_max_lr=0.001, opt_weight_decay=0.0001, path='/home/ww/Code/MultiBehavior_BASELINE/MB-GCN/Datasets/', patience=300, point='topk_20', positional_rate=0.0001, reg=0.0145, sampNum=10, save_path='tem', seed=19, shoot=10, slope=0.1, slot=0.5, subUsrDcy=0.9, subUsrSize=10, target='buy', tau=0.1, time_slot=7776000.0, title='self_attention_behavior', trnNum=10000, tstEpoch=1)
# # #----JD-------------------------------------------------------------------------------------------------------------------------------------