ipl-uv / jaxkern Goto Github PK

Some resources:

• RFF Kernel
• sklearn

Resources

• KRR Example
• GPU Example

I encountered two issues during the installation.

1. an URL link in the pip command is incorrect: pip install "git+https://github.com/IPL-UV/jaxkern.git" not git clone https://gihub.com/ipl-uv/jaxkern.git .
2. a Loader issue of pyyaml package during pip install command.

Here is the traceback of the second issue.

        File "/tmp/pip-req-build-38oewkkk/.eggs/setuptools_yaml-0.4-py3.8.egg/setuptools_yaml.py", line 22, in metadata_yaml
          metadata = yaml.load(yaml_file)
      TypeError: load() missing 1 required positional argument: 'Loader'
      [end of output]

The issue is typical when using pyyaml. It's better to use .safe_load() rather than .load() method.

I downgraded the pyyaml version to 5.4.1 from 6.0, then I avoided the issue.

A function that does HSIC with the RFF kernel. Would also be nice to show the derivatives.

• My old code

I would like to add some example notebooks of how we can use JAX with the kernel methods.

Some ideas:

Tutorials

• Kernels and Derivatives with autograd
• Estimating sigma (scott, silverman, median, mean, kth distance)
• Sensitivity Analysis
  • KRR, GPR
  • SVR
  • HSIC
• Uncertain Inputs for GPs

Kernels

• random fourier features (RFF)

Algorithms

• kernel ridge regression (exact, stochastic gradient descent)
• KRR w. RFF
• RV Coefficient (linear multidimensional Pearsons)
• Approximate SVR (RFF + Weights + SGD)
• Approximate GP (RFF + Variational Layer + SGD)
• Optimized / Kernel Entropy Components Analysis (O/KECA)

I like functional methods but with a didactic library, it would be nice to use OOP to have a bit of control over some things.