This code is a PyTorch 1.10.x cuda 11.3 implementation of the MGP method described in the paper entitled "Input Dependent Sparse Gaussian Processes". For comparison, we also implemented the following methods from:
DGP, VSGP and MGP
Install the dependencies using the following command:
pip install -r requirements.txtPut your data in "datasets" folder and run your experiments using the following command with optional arguments.
python run_experiment.py -h
-h, --help show this help message and exit
--dataset_name DATASET_NAME
name of the data set (should have subfolders with the
name s0, s1, s2, etc.) (default: None)
--scaling SCALING scaling method [MeanStd|MinMax|MaxAbs|Robust|None]
(default: MeanStd)
--split_number split_number
data set split number [0|1|2|etc] (default: 0)
--name svgp svgp
--nGPU NGPU GPU number (for cpu use -1) [-1|0|1|2] (default: -1)
--minibatch_size BATCHSIZE
Batch size (default: 100) (default: 100)
--M NIP number of inducing points (default: 100) (default:
1024)
--M2 NIP2 number of inducing points (default: 100) (default:
1024)
--imputation mean mean|median|knn|mice|None
--kernel Matern|RBF (defaults:matern)
--likelihood_var variance noise gaussian likelihood (0.01)
--lrate learning rate (0.01)
--missing consider missing (should be on for MGP, otherwise return normal SVGP)
--nGPU GPU number
--n_epoch number of training epochs
--n_samples number of MC samples
--nolayers number of layers
--numThreads number of threads
--var_noise variance noise
--consider_miss consider missing for DGP and VSGP
You can run experiments using UCI data set with the above options. To replicate results from the paper:
python run_experiment.py --dataset_name parkinson_10 --lrate 0.01 --split_number 0 --name svgp --n_samples 20 --M 100 --M2 100 --no_iterations 10000 --nolayers 1 --nGPU 0 --minibatch_size 100 --fitting --imputation mean --missingJafrasteh, B., Hernández-Lobato, D., Lubián-López, S. P., & Benavente-Fernández, I. (2023). Gaussian processes for missing value imputation. Knowledge-Based Systems, 273, 110603. Missing GPs
This project is licensed under the MIT License.