relational_pattern_benchmarking/hyperparams.txt at master · mlwin-de/relational_pattern_benchmarking · GitHub

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This file includes the training details and hyperparameters and command lines used to train each model.


CUDA_VISIBLE_DEVICES=2 python -u codes/run.py --do_train \
 --cuda \
 --do_valid \
 --do_test \
 --data_path data/FB15k \
 --model MDE \
 -n 128 -b 1024 -d 500 \
 -g 24.0 -a 1.0 -adv \
 -lr 0.0001 --max_steps 150000 \
 -save models/MDE_FB15k_0 --test_batch_size 16


Running OpenKE models from grail

Dataset FB15K
Transductive - Symm_People, inverse, Inference, AntiSymm
  CUDA_VISIBLE_DEVICES=2 python -u run.py --do_train \
 --cuda \
 --do_valid \
 --do_test \
 --data_path ../data/FB15K \
 --model DistMult \
 -n 128 -b 1024 -d 500 \
 -g 24.0 -a 1.0 -adv \
 -lr 0.0001 --max_steps 6000 \
 -save models/DistMult_FB15k_0 --test_batch_size 16


 CUDA_VISIBLE_DEVICES=2 python -u run.py --do_train \
 --cuda \
 --do_valid \
 --do_test \
 --data_path ../data/FB15K \
 --model TransE \
 -n 128 -b 1024 -d 500 \
 -g 24.0 -a 1.0 -adv \
 -lr 0.0001 --max_steps 6000 \
 -save models/TransE_FB15k_0 --test_batch_size 16


  CUDA_VISIBLE_DEVICES=2 python -u run.py --do_train \
 --cuda \
 --do_valid \
 --do_test \
 --data_path ../data/FB15K \
 --model RotatE \
 -n 128 -b 1024 -d 500 \
 -g 24.0 -a 1.0 -adv \
 -lr 0.0001 --max_steps 6000 \
 -save models/RotatE_FB15k_0 --test_batch_size 16 -de


 Dataset - SemiInductiveHeadOrTailBased - Symm_People
   CUDA_VISIBLE_DEVICES=0 python -u run.py --do_train \
 --cuda \
 --do_valid \
 --do_test \
 --data_path ../data/FB15K \
 --model RotatE \
 -n 128 -b 120 -d 500 \
 -g 24.0 -a 1.0 -adv \
 -lr 0.0001 --max_steps 6000 \
 -save models/RotatE_FB15k_0 --test_batch_size 2 -de

 -Inverse , Inference, AntiSymm
    CUDA_VISIBLE_DEVICES=1 python -u run.py --do_train \
 --cuda \
 --do_valid \
 --do_test \
 --data_path ../data/FB15K \
 --model RotatE \
 -n 128 -b 500 -d 500 \
 -g 24.0 -a 1.0 -adv \
 -lr 0.0001 --max_steps 6000 \
 -save models/RotatE_FB15k_0 --test_batch_size 16 -de


     CUDA_VISIBLE_DEVICES=1 python -u run.py --do_train \
 --cuda \
 --do_valid \
 --do_test \
 --data_path ../data/FB15K \
 --model MDE \
 -n 128 -b 500 -d 500 \
 -g 24.0 -a 1.0 -adv \
 -lr 0.0001 --max_steps 6000 \
 -save models/MDE_FB15k_0 --test_batch_size 16 -de

 Dataset - SemiInductiveCountBased - Symm_People
     CUDA_VISIBLE_DEVICES=2 python -u run.py --do_train \
 --cuda \
 --do_valid \
 --do_test \
 --data_path ../data/FB15K \
 --model RotatE \
 -n 128 -b 120 -d 500 \
 -g 24.0 -a 1.0 -adv \
 -lr 0.0001 --max_steps 6000 \
 -save models/RotatE_FB15k_0 --test_batch_size 2 -de

 inverse, antisymm, inference
     CUDA_VISIBLE_DEVICES=0 python -u run.py --do_train \
 --cuda \
 --do_valid \
 --do_test \
 --data_path ../data/FB15K \
 --model RotatE \
 -n 128 -b 500 -d 500 \
 -g 24.0 -a 1.0 -adv \
 -lr 0.0001 --max_steps 6000 \
 -save models/RotatE_FB15k_0 --test_batch_size 16 -de

Inductive - Symm_people
     CUDA_VISIBLE_DEVICES=0 python -u run.py --do_train \
 --cuda \
 --do_valid \
 --do_test \
 --data_path ../data/FB15K \
 --model RotatE \
 -n 128 -b 120 -d 500 \
 -g 24.0 -a 1.0 -adv \
 -lr 0.0001 --max_steps 6000 \
 -save models/RotatE_FB15k_0 --test_batch_size 2 -de

 inverse, antisymm, inference
CUDA_VISIBLE_DEVICES=0 python -u run.py --do_train \
 --cuda \
 --do_valid \
 --do_test \
 --data_path ../data/FB15K \
 --model RotatE \
 -n 128 -b 500 -d 500 \
 -g 24.0 -a 1.0 -adv \
 -lr 0.0001 --max_steps 6000 \
 -save models/RotatE_FB15k_0 --test_batch_size 16 -de

 CUDA_VISIBLE_DEVICES=3 python -u run.py --do_train  --cuda  --do_valid  --do_test  --data_path ../data/FB15K  --model TransE  -n 128 -b 500 -d 500  -g 24.0 -a 1.0 -adv  -lr 0.0001 --max_steps 6000  -save models/transE_FB15k_0 --test_batch_size 16

 MDE_graphical
Dataset :  Transductive
 1. python store_graph_properties.py
 2. CUDA_VISIBLE_DEVICES=2 python run.py --do_train --do_test -save experiments/kge_baselines_fb15k_t_symm --data_path data/FB15k --model MDE -n 624 -b 210 -d 300 -g 2.5 -a 2.5 -adv -lr .0005 --max_steps 250000 --test_batch_size 16 --valid_steps 10000 --log_steps 10000 --do_valid -node_feat_path data/FB15k/train_node_features --cuda -psi 14.0
CUDA_VISIBLE_DEVICES=1 python run.py --do_train --do_test -save experiments/kge_baselines_fb15k_SemiInductive_HOTailBased_inverse --data_path data/FB15k --model MDE -n 624 -b 210 -d 300 -g 2.5 -a 2.5 -adv -lr .0005 --max_steps 25000 --test_batch_size 16 --valid_steps 1000 --log_steps 1000 --do_valid -node_feat_path data/FB15k/train_node_features --cuda -psi 14.0


MDE from grail
CUDA_VISIBLE_DEVICES=0 python -u run.py --do_train \
 --cuda \
 --do_valid \
 --do_test \
 --data_path ../data/FB15K \
 --model MDE \
 -n 128 -b 120 -d 500 \
 -g 24.0 -a 1.0 -adv \
 -lr 0.0001 --max_steps 12000 \
 -save models/RotatE_FB15k_0 --test_batch_size 16

 Grail
 python train.py -d FB15K -e FB15K_t
 python test_auc.py -d FB15K -e FB15K_t
 python test_ranking.py -d FB15K -e FB15K_t