Does this library support Gaussian Process regression from PyTorch or TensorFlow

[7sharp9]

Im currently using models from skikit that have been exported to Onnx but I was curious if I had a model for GPR in either PyTorch or TensorFlow would it be usable from RTNeural?

I think the TensorFlow GPR is in the probability module: https://www.tensorflow.org/probability/examples/Gaussian_Process_Regression_In_TFP and also in GPFlow: https://www.gpflow.org

And in PyTorch its available in GPytorch https://docs.gpytorch.ai/en/stable/ and Pyro: https://pyro.ai/examples/gp.html

I was curious if any of these wouild work as I was planning on using RTNeural in the future and could drop the usage of onnx runtime if RTNeural could load GPR models.

[jatinchowdhury18]

Hello, thanks for the question!

At the moment, RTNeural does not support either Gaussian Processes.

It should be possible to implement a Gaussian Process in RTNeural, that could take an explicitly defined covariance function, with parameters as determined during the "training" process. There's a few problems that I'm seeing with regards to how that implementation would work:

1. Generating random numbers. RTNeural does not currently support any operations that require random numbers, so we haven't had to deal with this before. I would rather not use the random number generators available in the standard library (i.e. #include <random>), since my understanding is that they're generally not real-time safe. From there, I think it's a question of how fast we want the random number generator to be, relative to the quality of numbers being generated. For most of my applications, speed is far more important than the quality of the random numbers, but of course there are situations where it is important to have high-quality random numbers, and we wouldn't necessarily want to exclude those use cases from using RTNeural. So then maybe the best thing to do would be to set up RTNeural so that the user can "bring their own" random number generator, that works for their application, (although RTNeural could provide some kind of "default" implementation).

2. Covariance Kernels. My understanding is that when using GPR, the user can select their own covariance kernel. From what I've read in the past few minutes, it seems that the "Exponentiated Quadratic" is a relatively common choice, but maybe there are others that are somewhat widely used as well? Again, maybe the best choice would be for RTNeural to implement some commonly used kernels, but also let users implement their own.

3. Covariance Kernel Parameters. Since different covariance kernels could have different parameters, the code for loading a "trained" Gaussian Process Model would need to be slightly different for each type of covariance kernel.

4. Testing. Most of the tests in RTNeural work by comparing the output of RTNeural's inferencing to the output generated by a "reference implementation" of the same layer/model in TensorFlow or PyTorch. Since the Gaussian Process is inherently stochastic, we wouldn't be able to do a direct comparison between the outputs of two separate Gaussian Processes (at least not without copying the random number generation algorithm used by the reference implementation), so we would likely need to do some kind of statistics-based test to make sure that after running the Gaussian Process some number of times, we get roughly the same "distribution" of outputs as the reference implementation.

Anyway, these are just my initial thoughts. If it seems like there's some aspects of the Gaussian Process that I'm not quite grasping, or if there's more relevant information you can provide, that would be great!

Thanks, Jatin

[7sharp9]

Im using GPR with the RBF kernel which is squared exponential as well as a constant kernel and a white kernel.

When I export to onnx I can see the types of calculations performed as well as the weights etc inside, Im not aware of any random number generators amongst the operators, not in the inference part.

Heres a list of the operators used in the scikit GPR:

Operators used in the model: {'Mul', 'MatMul', 'OneHotEncoder', 'CDist', 'Div', 'Exp', 'Add', 'Shape', 'ConstantOfShape', 'Cast', 'Concat', 'ArrayFeatureExtractor', 'Scaler', 'Reshape', 'ReduceSum', 'Transpose'}

Im doing one hot encoding and standard scaling to thats the ArrayFeatureExtractor, OneHotEncoder, and Scaler. For the GPR part It looks like its using Div, CDist, Mul, Exp, MatMul, and Add.

I think it would be a nice addition to the library as simple and more advanced regression probelms could be included rather than having to include the onnx runtime statically which is quite a large dependency, ~70MB with the excell operators removed.

In terms of Pytorch, and Tensor flow Im not sure what their native models look like, I did try Tensorflow but the export to onnx was incomplete to the gPR model could not be output to compare.

It depends whether you want to support regression and gaussian regression here rather than more neural net oriented functions. Linear regression is quite simple in Eigen but the scaling pipeline and onehot encoding would be handy to have in a library to save having to program that part yourself. Ive been trying to consolidate to use just onnx as its cross platform but the perfmance is a lot slower than more specialised alternatives such as this.

scikit has a function to convert a pipeline to onnx, but Im sure extracting the weights would be possible by stuying the implementation, or alternativly extracting then from onnx. Onnx also has models for neural nets etc Im not sure if you support Onnx in any way?

[7sharp9]

I think the random number generator you mentional was probably in refrence to generating a set of samples from the predictive distribution, this was not sometihng I was thinking of, just pure inference using the trainded model parameters. So the implementation would be a set of coefficients from a trained kernel along with cdist and matrix multiplications etc