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query2box_results.sh
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query2box_results.sh
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# Evaluate box sizes
# python3 eval_q2b_box_sizes.py --do_test --checkpoint_path checkpoints_FB15K-237/checkpoint_orig_attr_kblrn_q2b --data_path data/scripts/generated/FB15K-237_dummy_kblrn --use_attributes --rank 400 --geo q2b --test_batch_size 100 --print_on_screen
# (litcqd) renzhong@litcqd:~/LitCQD$ ./query2box_results.sh
# id Box size Mean MAD MAE count
# 102 2425.04810 0.009945867869614907 0.009384399356686115 0.001754575344952599 26
# 95 25.49397 0.968500226019753607 0.035066834951511777 0.012004767475043455 35
# 67 8232.63770 0.002807417382245438 0.005109476441458810 0.002119988745126038 258
# 44 22.82104 0.800509173447539135 0.146324880393196438 0.082095356582270060 204
# 65 50.35764 0.758737134397224566 0.067603222514195510 0.038857924590402278 328
# 66 50.19565 0.391950301104341037 0.170324870300690129 0.106489373983895949 316
# 101 7504.41699 0.001909407559453922 0.003073877646567583 0.002164861922205476 204
# 75 89.64756 0.869215686274509847 0.054386774317744406 0.039568665985702030 30
# 63 21.73389 0.947918446086584043 0.037214237378787533 0.028279850150392322 87
# 105 1072.33191 0.049687480016010879 0.075025577246085579 0.057383300750900687 32
# 83 94.67579 0.974880328158290621 0.012499264639729793 0.009864764082013517 407
# 12 27.89830 0.713239247311827973 0.153791623309053188 0.122755544053183716 24
# 103 33.01576 0.714880373666432645 0.181040964478098171 0.147364145326799117 32
# 85 74.59190 0.332658272394535603 0.131174846866843026 0.107284354158975567 20
# 79 28.05922 0.870606806783493337 0.077302010369565868 0.063894204980095418 124
# 104 184.89937 0.080965701374114935 0.061024866187841705 0.051750044575285967 36
# 92 68.54289 0.697937308216904584 0.116846485162309374 0.104227298710138833 88
# 60 36.42636 0.330340579259903622 0.250966974068104642 0.228820175977454326 24
# 82 23.02396 0.977142676848678393 0.022298289208545333 0.020442916039038358 102
# 84 75.33191 0.497450722348949936 0.097893341517829219 0.093223308384027856 307
# 11 23.22722 0.667085714285714282 0.197992228571428536 0.204851033744357841 30
# 61 33.54718 0.479567207657095318 0.245938718577087351 0.266256252905067881 24
# 94 24.99562 0.969249438936926055 0.033033411444057421 0.037169108365826736 27
# 106 91.50652 0.114420803782505909 0.089090086011770098 0.109463619536314269 26
# 7 71.51011 0.862255238213472830 0.067037708057993320 0.085798367160574249 48
# Average attribute box size: 254.33885770051376
# Average relation box size: 214.76249259168452
# Max relation box size: 402.7469177246094
# Min relation box size: 71.48856353759766
# Eval performance on complex queries
python3 main.py --cuda --do_test --checkpoint_path checkpoints_FB15K-237/checkpoint_orig_attr_literale_q2b --data_path data/FB15k-237-q2b --rank 400 --geo q2b --test_batch_size 10 --print_on_screen
python3 main.py --cuda --do_test --checkpoint_path checkpoints_FB15K-237/checkpoint_orig_attr_transea_q2b --data_path data/FB15k-237-q2b --rank 400 --geo q2b --test_batch_size 10 --print_on_screen
python3 main.py --cuda --do_test --checkpoint_path checkpoints_FB15K-237/checkpoint_orig_attr_kblrn_q2b --data_path data/FB15k-237-q2b --rank 400 --geo q2b --test_batch_size 10 --print_on_screen
# Eval performance on complex attribute quries
python3 main.py --cuda --do_test --checkpoint_path checkpoints_FB15K-237/checkpoint_orig_attr_kblrn_q2b --data_path data/scripts/generated/FB15K-237_dummy_kblrn --use_attributes --rank 400 --geo q2b --test_batch_size 10 --print_on_screen
# (litcqd) renzhong@litcqd:~/LitCQD$ ./query2box_results.sh
# logging to Experiments
# Loading queries for the training...
# 2023-03-08 20:06:58,596 INFO train: 1p: 149689
# Loading queries for the valid...
# 2023-03-08 20:07:02,442 INFO valid: 1p: 20101
# 2023-03-08 20:07:06,540 INFO valid: 1p: 20101
# 2023-03-08 20:07:06,541 INFO valid: 1dp: 0
# 2023-03-08 20:07:06,541 INFO valid: di: 0
# 2023-03-08 20:07:06,551 INFO Training starts...
# Traceback (most recent call last):
# File "main.py", line 654, in <module>
# main(parse_args())
# File "main.py", line 618, in main
# new_train(train_config,
# File "main.py", line 489, in new_train
# model = get_model(train_config, params, cqd_params, nentity, nrelation, nattribute)
# File "/home/renzhong/LitCQD/util_models.py", line 174, in get_model
# model = model.cuda()
# File "/home/renzhong/.conda/envs/litcqd/lib/python3.8/site-packages/torch/nn/modules/module.py", line 637, in cuda
# return self._apply(lambda t: t.cuda(device))
# File "/home/renzhong/.conda/envs/litcqd/lib/python3.8/site-packages/torch/nn/modules/module.py", line 530, in _apply
# module._apply(fn)
# File "/home/renzhong/.conda/envs/litcqd/lib/python3.8/site-packages/torch/nn/modules/module.py", line 552, in _apply
# param_applied = fn(param)
# File "/home/renzhong/.conda/envs/litcqd/lib/python3.8/site-packages/torch/nn/modules/module.py", line 637, in <lambda>
# return self._apply(lambda t: t.cuda(device))
# File "/home/renzhong/.conda/envs/litcqd/lib/python3.8/site-packages/torch/cuda/__init__.py", line 172, in _lazy_init
# torch._C._cuda_init()
# RuntimeError: No HIP GPUs are available
# logging to Experiments
# Loading queries for the training...
# 2023-03-08 20:07:40,640 INFO train: 1p: 149689
# Loading queries for the valid...
# 2023-03-08 20:07:44,540 INFO valid: 1p: 20101
# 2023-03-08 20:07:48,685 INFO valid: 1p: 20101
# 2023-03-08 20:07:48,686 INFO valid: 1dp: 0
# 2023-03-08 20:07:48,686 INFO valid: di: 0
# 2023-03-08 20:07:48,697 INFO Training starts...
# Traceback (most recent call last):
# File "main.py", line 654, in <module>
# main(parse_args())
# File "main.py", line 618, in main
# new_train(train_config,
# File "main.py", line 489, in new_train
# model = get_model(train_config, params, cqd_params, nentity, nrelation, nattribute)
# File "/home/renzhong/LitCQD/util_models.py", line 174, in get_model
# model = model.cuda()
# File "/home/renzhong/.conda/envs/litcqd/lib/python3.8/site-packages/torch/nn/modules/module.py", line 637, in cuda
# return self._apply(lambda t: t.cuda(device))
# File "/home/renzhong/.conda/envs/litcqd/lib/python3.8/site-packages/torch/nn/modules/module.py", line 530, in _apply
# module._apply(fn)
# File "/home/renzhong/.conda/envs/litcqd/lib/python3.8/site-packages/torch/nn/modules/module.py", line 552, in _apply
# param_applied = fn(param)
# File "/home/renzhong/.conda/envs/litcqd/lib/python3.8/site-packages/torch/nn/modules/module.py", line 637, in <lambda>
# return self._apply(lambda t: t.cuda(device))
# File "/home/renzhong/.conda/envs/litcqd/lib/python3.8/site-packages/torch/cuda/__init__.py", line 172, in _lazy_init
# torch._C._cuda_init()
# RuntimeError: No HIP GPUs are available
# logging to Experiments
# Loading queries for the training...
# 2023-03-08 20:08:23,027 INFO train: 1p: 149689
# Loading queries for the valid...
# 2023-03-08 20:08:26,976 INFO valid: 1p: 20101
# 2023-03-08 20:08:31,195 INFO valid: 1p: 20101
# 2023-03-08 20:08:31,196 INFO valid: 1dp: 0
# 2023-03-08 20:08:31,196 INFO valid: di: 0
# 2023-03-08 20:08:31,207 INFO Training starts...
# Traceback (most recent call last):
# File "main.py", line 654, in <module>
# main(parse_args())
# File "main.py", line 618, in main
# new_train(train_config,
# File "main.py", line 489, in new_train
# model = get_model(train_config, params, cqd_params, nentity, nrelation, nattribute)
# File "/home/renzhong/LitCQD/util_models.py", line 174, in get_model
# model = model.cuda()
# File "/home/renzhong/.conda/envs/litcqd/lib/python3.8/site-packages/torch/nn/modules/module.py", line 637, in cuda
# return self._apply(lambda t: t.cuda(device))
# File "/home/renzhong/.conda/envs/litcqd/lib/python3.8/site-packages/torch/nn/modules/module.py", line 530, in _apply
# module._apply(fn)
# File "/home/renzhong/.conda/envs/litcqd/lib/python3.8/site-packages/torch/nn/modules/module.py", line 552, in _apply
# param_applied = fn(param)
# File "/home/renzhong/.conda/envs/litcqd/lib/python3.8/site-packages/torch/nn/modules/module.py", line 637, in <lambda>
# return self._apply(lambda t: t.cuda(device))
# File "/home/renzhong/.conda/envs/litcqd/lib/python3.8/site-packages/torch/cuda/__init__.py", line 172, in _lazy_init
# torch._C._cuda_init()
# RuntimeError: No HIP GPUs are available
# logging to Experiments
# Loading queries for the training...
# 2023-03-08 20:09:33,229 INFO train: 1p: 173033
# 2023-03-08 20:09:33,229 INFO train: 1ap: 23229
# Loading queries for the valid...
# 2023-03-08 20:09:43,821 INFO valid: 1p: 20101
# 2023-03-08 20:09:43,822 INFO valid: 1ap: 3000
# 2023-03-08 20:09:47,649 INFO valid: 1p: 20101
# 2023-03-08 20:09:47,649 INFO valid: 1ap: 3000
# 2023-03-08 20:09:47,650 WARNING valid: 1dp: not in pkl file
# 2023-03-08 20:09:47,650 WARNING valid: di: not in pkl file
# 2023-03-08 20:09:47,667 INFO Training starts...
# 2023-03-08 20:09:48,130 INFO attribute batch size: 2
# Traceback (most recent call last):
# File "main.py", line 654, in <module>
# main(parse_args())
# File "main.py", line 618, in main
# new_train(train_config,
# File "main.py", line 489, in new_train
# model = get_model(train_config, params, cqd_params, nentity, nrelation, nattribute)
# File "/home/renzhong/LitCQD/util_models.py", line 174, in get_model
# model = model.cuda()
# File "/home/renzhong/.conda/envs/litcqd/lib/python3.8/site-packages/torch/nn/modules/module.py", line 637, in cuda
# return self._apply(lambda t: t.cuda(device))
# File "/home/renzhong/.conda/envs/litcqd/lib/python3.8/site-packages/torch/nn/modules/module.py", line 530, in _apply
# module._apply(fn)
# File "/home/renzhong/.conda/envs/litcqd/lib/python3.8/site-packages/torch/nn/modules/module.py", line 552, in _apply
# param_applied = fn(param)
# File "/home/renzhong/.conda/envs/litcqd/lib/python3.8/site-packages/torch/nn/modules/module.py", line 637, in <lambda>
# return self._apply(lambda t: t.cuda(device))
# File "/home/renzhong/.conda/envs/litcqd/lib/python3.8/site-packages/torch/cuda/__init__.py", line 172, in _lazy_init
# torch._C._cuda_init()
# RuntimeError: No HIP GPUs are available
# python3 main.py --cuda --do_test --checkpoint_path checkpoints_FB15K-237/checkpoint_orig_no_attr_q2b/ --data_path data/scripts/generated/FB15K-237_dummy_kblrn --use_attributes --rank 400 --geo q2b --test_batch_size 10 --print_on_screen
python3 main.py --cuda --do_test --checkpoint_path checkpoints_FB15K-237/checkpoint_orig_attr_kblrn_q2b/ --data_path data/scripts/generated/FB15K-237_dummy_kblrn --use_attributes --rank 400 --geo q2b --test_batch_size 10 --print_on_screen