Code for CCKS 2023 task2, inductive link prediction

Team Member:

• Tianyu Liu,
• Deqiang Huang,
• Guoqing Zhao (All of us are students of USTC.)

Preparation

1. All the data and pretrained model are included into data folder and exp/NBFNet/CCKS folder.
2. Preparing conda environment by running these commands:

# conda install
conda install pytorch=1.8.0 cudatoolkit=11.1 pyg -c pytorch -c pyg -c conda-forge
conda install ninja easydict pyyaml -c conda-forge

# pip install
pip install torch==1.8.0+cu111 -f https://download.pytorch.org/whl/torch_stable.html
pip install torch-scatter==2.0.8 torch-sparse==0.6.12 torch-geometric -f https://data.pyg.org/whl/torch-1.8.0+cu111.html
pip install ninja easydict pyyaml

Training

To train a model, just run this command:

python script/run.py -c config/inductive/ccks.yaml --gpus [0]

All the training hyper-parameters are stored in config/inductive/ccks.yaml. Feel free to change them to get different results.

Inference

To inference with the test data and generate submission files, just run this command:

python script/inference.py -c config/inductive/ccks.yaml --gpus [0]

This will generate a test.json file in the experiment folder (given by ccks.yaml).

A scores.pt file will be output in the experiment folder as well, which represents the prediction of a specific model.

Reproduce the best results

We perform grid search for best hyper-parameters. Each experiment has a unique config file (in config/inductive/grid_search). Then we perform stacking ensemble to get best results. To reproduce the best results, just run:

python script/ensemble.py

This will generate a test.json file in the current path.

Citation

We use the official codebase of NBFNet, thanks for their contribution.