# trained on the tic-tac-toe data set with one GPU.
python3 experiment.py -d tic-tac-toe -bs 32 -s [email protected] -e401 -lrde 200 -lr 0.002 -ki 0 -mp 12481 -i 0 -wd 1e-6 &

tensorboard --logdir=log_folder/ --bind_all

log_folder/tic-tac-toe/tic-tac-toe_e401_bs32_lr0.002_lrdr0.75_lrde200_wd1[email protected]

[INFO] - On Test Set:
        Accuracy of RRL  Model: 1.0
        F1 Score of RRL  Model: 1.0

$ python3 experiment.py --help
usage: experiment.py [-h] [-d DATA_SET] [-i DEVICE_IDS] [-nr NR] [-e EPOCH]
                     [-bs BATCH_SIZE] [-lr LEARNING_RATE]
                     [-lrdr LR_DECAY_RATE] [-lrde LR_DECAY_EPOCH]
                     [-wd WEIGHT_DECAY] [-ki ITH_KFOLD] [-rc ROUND_COUNT]
                     [-ma MASTER_ADDRESS] [-mp MASTER_PORT] [-li LOG_ITER]
                     [--use_not] [--save_best] [--estimated_grad]
                     [-s STRUCTURE]

optional arguments:
  -h, --help            show this help message and exit
  -d DATA_SET, --data_set DATA_SET
                        Set the data set for training. All the data sets in
                        the dataset folder are available. (default: tic-tac-
                        toe)
  -i DEVICE_IDS, --device_ids DEVICE_IDS
                        Set the device (GPU ids). Split by @. E.g., [email protected]@3.
                        (default: None)
  -nr NR, --nr NR       ranking within the nodes (default: 0)
  -e EPOCH, --epoch EPOCH
                        Set the total epoch. (default: 41)
  -bs BATCH_SIZE, --batch_size BATCH_SIZE
                        Set the batch size. (default: 64)
  -lr LEARNING_RATE, --learning_rate LEARNING_RATE
                        Set the initial learning rate. (default: 0.01)
  -lrdr LR_DECAY_RATE, --lr_decay_rate LR_DECAY_RATE
                        Set the learning rate decay rate. (default: 0.75)
  -lrde LR_DECAY_EPOCH, --lr_decay_epoch LR_DECAY_EPOCH
                        Set the learning rate decay epoch. (default: 10)
  -wd WEIGHT_DECAY, --weight_decay WEIGHT_DECAY
                        Set the weight decay (L2 penalty). (default: 0.0)
  -ki ITH_KFOLD, --ith_kfold ITH_KFOLD
                        Do the i-th 5-fold validation, 0 <= ki < 5. (default:
                        0)
  -rc ROUND_COUNT, --round_count ROUND_COUNT
                        Count the round of experiments. (default: 0)
  -ma MASTER_ADDRESS, --master_address MASTER_ADDRESS
                        Set the master address. (default: 127.0.0.1)
  -mp MASTER_PORT, --master_port MASTER_PORT
                        Set the master port. (default: 12345)
  -li LOG_ITER, --log_iter LOG_ITER
                        The number of iterations (batches) to log once.
                        (default: 50)
  --use_not             Use the NOT (~) operator in logical rules. It will
                        enhance model capability but make the RRL more
                        complex. (default: False)
  --save_best           Save the model with best performance on the validation
                        set. (default: False)
  --estimated_grad      Use estimated gradient. (default: False)
  -s STRUCTURE, --structure STRUCTURE
                        Set the number of nodes in the binarization layer and
                        logical layers. E.g., [email protected], [email protected]@[email protected]. (default:
                        [email protected])

@article{wang2021scalable,
  title={Scalable Rule-Based Representation Learning for Interpretable Classification},
  author={Wang, Zhuo and Zhang, Wei and Liu, Ning and Wang, Jianyong},
  journal={arXiv preprint arXiv:2109.15103},
  year={2021}
}