This is the implementation of manuscript “***”.
- Python 3.7+
- PtTorch 1.8.0+cu111
- PySR 0.12.0
We provide in this repository the verification of SR-RFND on two known formulas, as follows:
$$E=\frac{q_1}{4\pi\epsilon r^2},$$ $$E=\frac{1}{2}m(v^2 + u^2 + w^2)$$
The demos for these two examples are stored under ./examples_of_known_formulas-1/
and examples_of_known_formulas-2
respectively.
Users can use our datasets, stored under ./{example folder}/datasets/
, or re-generate them by running ./{example folder}/dataset_made.py
.
- Run
./{example folder}/directly_SR/SR.py
.
- Run
./{example folder}/pipeline_on_noise_data/train_baseline.py
to train the baseline network for performing multivariate regression task. - Run
./{example folder}/pipeline_on_noise_data/RFE_feature_selection.py
to perform the feature filtering, the results will be reported inFeature importance.log
under the same folder. - Run
./{example folder}/pipeline_on_noise_data/SR.py
to perform symbolic regression and evaluation.
- Run
./{example folder}/train_baseline_ncr/train_baseline.py
to train the baseline network with neighborhood consistency regularization. Then move the trained model file to./{example folder}/pipeline_on_selected_data/checkpoints/
. - Run
./{example folder}/pipeline_on_selected_data/data_selection.py
to perform sample filtering. - Run
./{example folder}/pipeline_on_noise_data/train_baseline.py
to train the baseline network for performing multivariate regression task. - Run
./{example folder}/pipeline_on_noise_data/RFE_feature_selection.py
to perform the feature filtering, the results will be reported inFeature importance.log
under the same folder. - Run
./{example folder}/pipeline_on_noise_data/SR.py
to perform symbolic regression and evaluation.