PLS-regerssion-tensorflow

PLS regression implementation for TensorFlow 2.0

Development setup

Install last release candidate for TensorFlow 2.0 pip install tensorflow==2.0.0-rc0

TODOs

• Look over the structure and ensure we have a tensorflow approach
• Make the code follow the graph structure of tensorflow
• Replace the use of lists to tensorflow operations instead
• Implement logging of the traning and paramters in tensorboard
• Update performance test, today it is not complete. Should we look at GPU, tests aswell
• Update all functions to use tf.functions
• Investigate the need for implementing numeric solution as option. Can we build a gradient descent solution for PLS?
• Should we look at tensorflow serving with our model to test it out?
• Implement test(pytest?) for all functions
• Make sure that the PLS can be saved and loaded correctly, should we use tf.savedmodel or pickle?
• Add more regression algorithms

Release History

• 0.0.1
  • PLS basic object working. More functionality needs to be added

Meta

Distributed under the MIT license. See LICENSE for more information.

Contributors

https://github.com/NikeNano https://github.com/jiwidi

Contributing

1. Fork it (https://github.com/jiwidi/PLS-regerssion-tensorflow/fork)
2. Create your feature branch (git checkout -b feature/fooBar)
3. Commit your changes (git commit -am 'Add some fooBar')
4. Push to the branch (git push origin feature/fooBar)
5. Create a new Pull Request