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Neuraxio / Neuraxle

The world's cleanest AutoML library ✨ - Do hyperparameter tuning with the right pipeline abstractions to write clean deep learning production pipelines. Let your pipeline steps have hyperparameter spaces. Design steps in your pipeline like components. Compatible with Scikit-Learn, TensorFlow, and most other libraries, frameworks and MLOps environments.

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Bug: Some discrete distributions may not have a linear dependence between its points (e.g.: Choice v.s. RandInt)

guillaume-chevalier opened this issue · comments

commented

Describe the bug
In the Orthogonal (SVD) TPE (see #464), we need discrete distributions to be made continuous. This is ok for some distributions (e.g.: quantized distributions, such as RandInt), however for some other distributions, there is no linear (growing) dependency between the values

To Reproduce
E.g.: in a Choice, there is no transitive relation between the item 0, 1, and 2 of the Choice. This doesn't happend in a RandInt.

Expected behavior
The Choice hyperparam to be encoded as a one-hot. And its rounding (as per #466) after undoing the SVD would need to pick the argmax of this one-hot.

Additional context
#464

commented

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