Comments (6)
I'd really appreciate this. For example, on https://aws-dglke.readthedocs.io/en/latest/train_user_data.html It's not super clear what should be in --data_path
and --data_files
.
For example, --data_path
says "to specify the path to the knowledge graph dataset"; however, I presume this means "to specify the path to the folder containing the knowledge graph dataset".
Also, --data_files
says "to specify the triplets of a knowledge graph as well as node/relation ID mapping"; however, it's not immediately clear the order of these files. For example, I would presume this would follow the order of the files listed under udd_[h|r|t]
:
DGLBACKEND=pytorch dglke_train \
--data_path results_SXSW_2018 \
--data_files entities.tsv relations.tsv train.tsv valid.tsv test.tsv \
--format udd_hrt \
--model_name ComplEx \
--max_step 12000 --batch_size 1000 --neg_sample_size 200 --batch_size_eval 16 \
--hidden_dim 400 --gamma 19.9 --lr 0.25 --regularization_coef=1e-9 -adv \
--gpu 0 1 --async_update --force_sync_interval 1000 --log_interval 1000 \
--test
^^^ But the order isn't clear. It seems like entities.txt
and relations.tsv
should go at the end since if someone uses to raw_udd_[h|r|t]
option this would keep the first three elements consistently for training, validation, and testing files.
Perhaps there should be --data_tuple_files
and --data_mapping_files
options?
UPDATE:
When I ran the code above, it gave me this output with FB_15k in the checkpoints, which doesn't seem right...
(dglke) amruch@wit:~/graphika/kg$ DGLBACKEND=pytorch dglke_train --data_path results_SXSW_2018 --data_files entities.tsv relations.tsv train.tsv valid.tsv test.tsv--format udd_hrt --model_name ComplEx --max_step 12000 --batch_size 1000 --neg_sample_size 200 --batch_size_eval 16 --hidden_dim 400 --gamma 19.9 --lr 0.25 --regularization_coef=1e-9 -adv --gpu 0 1 --async_update --force_sync_interval 1000 --log_interval 1000 --test
Using backend: pytorch
Logs are being recorded at: ckpts/ComplEx_FB15k_0/train.log
Reading train triples....
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Thank you very much for your feedback. We'll prioritize it and provide documentation of the argument options.
If you find the explanation from --help
isn't clear, please post them here. We'll improve them. Thanks a lot for your help.
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from dgl-ke.
We need to clarify our documentation to address all of the questions in this issue: #84
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The docs for command line arguments was updated along with 0.1.1 release.
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Related Issues (20)
- Upgrade DGL dependency HOT 2
- Can DGL_KE models be implemented on dynamic knowledge graphs? HOT 1
- Force dtype to int64 to ensure that we don't index with non-long tensor
- IndexError: list index out of range when training on raw user defined knowledge graph HOT 4
- Support Adam or Adagrad HOT 8
- Can not install dgl 0.4.3 HOT 4
- DGLBACKEND s not recognized as an internal or external command HOT 1
- No module named 'ogb
- whether just assign vertexes but not the edges together with on graph partition when use METIS
- [BUG] Quick start example code does not work HOT 4
- dgl.__version__ >= 0.8 breaks on partition.py HOT 2
- RuntimeError: Cannot re-initialize CUDA in forked subprocess HOT 1
- Multi-gpu training is not effective on specific cases
- `graph.HeteroGraph` Error happened when running example HOT 1
- !DGLBACKEND=pytorch dglke_train Not Working HOT 1
- pytorch dglke_train Not Working, Expected type graph.Graph but get graph.HeteroGraph HOT 3
- can't train my KG ,it keeps telling me 'AssertionError: test set is not provided'
- Installation error, no corresponding version HOT 1
- DGL-KE TransR Predict Error
- 'dgl' has no attribute '_deprecate'
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