In this repository:
We provide Python scripts to reproduce the experiments for conventional ML models, SL-GCN and SSL-GCN comparisons.
- Anaconda 4.10.1
- Python 3.7.3
- Scikit-learn 0.23.2
- Pytorch 1.7.0 with CUDA 10.0
- Scipy 1.6.2
- Pandas 1.2.3
- Numpy 1.19.2
- Openpyxl 3.0.7
- xgboost 1.3.3
- dgl 0.5.2
- dgllife 0.2.6
- joblib 1.0.1
- rdkit 2020.09.1
Models and data used for reproducing experiments are available at: [Data] [Model]
Unzip the downloaded data.7z
and model.7z
files, place the data
folder and model
folder in the same folder as the scripts.
The main script is local_run.py
. There are four input parameters for this script:
python local_run.py -d <data_folder_path> -m <model_folder_path> -mt <model_type> -o <output_folder_path>
-d
:The path to the data folder (with "/" or "\" at the end).
-m
:The path to the model folder (with "/" or "\" at the end).
-mt
:Define the type of model, cm
- conventional ML models, sl
- SL-GCN models, ssl
- SSL-GCN models.
-o
:The path to an empty output folder where the experiment results will be stored (with "/" or "\" at the end).
python local_run.py -d ./data/ -m ./model/ -mt cm -o ./cm_output_result/
Running time for SL-GCN models is approx 3 min
.
Running time for SSL-GCN models is approx 13 min
.
Running time for CM models is approx 32 min
.
After running, there should be two types of files in the output folder.
As the following figure shows, the result files of SL-GCN models.
File in the RED box contains the average test performance (average AUC scores) of SL-GCN models on the 12 prediction tasks in 5 repeated experiments.
Files in the GREEN box contain the detailed AUC scores of SL-GCN models during 5 repeated experiments on the 12 prediction tasks.