- Install nsgp-torch package
pip install git+https://github.com/patel-zeel/nsgp-torch- Install other dependencies
pip install -r requirements.txt- Run the experiments from individual folders.
A - ARD enabled
A_bar - ARD disabled
N - Non-stationary kernel
N_bar - Stationary kernel
C - Using categorical kernel for categorical features without one-hot-encoding
C_bar - Using RBF/Matern kernel for categorical features with one-hot-encoding
L - Using Local periodic kernel for time feature
L_bar - Using RBF/Matern kernel for time feature
AN_barCL_bar - GP with ARD enabled stationary kernel with categorical kernel for categorical features and RBF/Matern kernel for time feature
| Folder | Description |
|---|---|
| data | data for each baseline and main approach |
| preprocessing | preprocessing pipeline applied to data |
| stat_gp_cat | Stationary GP with categorical kernel (C fixed, L variable) |
| stat_gp_no_cat | Stationary GP without categorical kernel (C_bar fixed, L variable) |
| nonstat_gp_cat | Non-stationary GP with categorical kernel (C fixed, L variable) |
Baseline implementation of paper "A Neural Attention Model for Urban Air Quality Inference: Learning the Weights of Monitoring Stations" (ADAIN) is available in this file.