This is an example implementation of the LRGCPND model.
Requirement | Version |
---|---|
python | 3.8 |
numpy | 1.20 |
pandas | 1.2 |
scipy | 1.6 |
scikit-learn | 0.24 |
PyTorch | 1.8 |
cudatoolkit | 10.2 |
Run python run.py -h/--help
for more detailed usage:
usage: run.py [-h] [--n_num N_NUM] [--d_num D_NUM] [-K K] [-S S] [-r REG] [-l LR] [-e EPOCHS] [-b BATCH] [-f FOLD] [-t TIME] [--save_models]
[--have_trained]
optional arguments:
--n_num N_NUM Number of ncRNAs.
--d_num D_NUM Number of drugs.
-K K Depth of layers.
-r REG, --reg REG Coefficient of L2 regularization.
-l LR, --lr LR Initial learning rate.
-e EPOCHS, --epochs EPOCHS
Number of epochs to train.
-b BATCH, --batch BATCH
Batch size to train.
-f FOLD, --fold FOLD Number of folds for cross validation.
-t TIME, --time TIME Timestamp in milliseconds for training.
--save_models Save trained models (true or false).
--have_trained Have trained models (true or false).
python split.py
The randomly generated triples will be saved in /data/samples
.
python run.py
or if you'd like to save the models:
python run.py --save_models
Be aware that we use timestamps to mark models and corresponding results.
python run.py --have_trained -t {str_time}
where {str_time}
is the timestamp of your trained model.