This repository contains code for the reprsentation proposed in the ICLR2021 paper titled "Explainable Subgraph Reasoning for Forecasting on Temporal Knowledge Graphs".
- clone repository
- install virtualenv
pip install virtualenv
- create virtual environment
virtualenv -p python3 venv
- activate virtualenv
source venv/bin/activate
- install packages
pip install -r requirements.txt
- specify directory to save Checkpoint
cd tKGR
vim local_config.py
For example if you want to save checkpoints where local_config.py is
from pathlib import Path
save_dir = Path(__file__).parent.absolute()Training:
python train.py --warm_start_time 48 --emb_dim 256 128 64 32 --batch_size 128 --lr 0.0002 --dataset ICEWS14_forecasting --epoch 10 --sampling 3 --device 0 --DP_steps 3 --DP_num_edges 15 --max_attended_edges 40 --node_score_aggregation sum --ent_score_aggregation sum
python train.py --warm_start_time 48 --emb_dim 256 128 64 32 --batch_size 128 --lr 0.0002 --dataset ICEWS0515_forecasting --epoch 10 --sampling 3 --device 0 --DP_steps 3 --DP_num_edges 15 --max_attended_edges 40 --node_score_aggregation sum --ent_score_aggregation sum
python train.py --warm_start_time 48 --emb_dim 256 128 64 32 --batch_size 128 --lr 0.0002 --dataset ICEWS18_forecasting --epoch 10 --sampling 3 --device 0 --DP_steps 3 --DP_num_edges 15 --max_attended_edges 60 --node_score_aggregation sum --ent_score_aggregation sum --ratio_update 0.75
python train.py --warm_start_time 48 --emb_dim 256 128 64 32 --batch_size 128 --lr 0.0002 --dataset YAGO1830 --epoch 10 --sampling 3 --device 0 --DP_steps 3 --DP_num_edges 15 --max_attended_edges 60 --node_score_aggregation sum --ent_score_aggregation sum --ratio_update 0.75
Evaluation:
python eval.py --load_checkpoint checkpoints_dir_name/checkpoint_name.pt --device 0
different num_neighbors can be specified by adding
--DP_num_neighbors 30 --max_attended_edges 40
--mongo can store configurations and results into mongodb, it's not necessary.