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/botorch/models/utils/assorted.py:173: InputDataWarning:
Input data is not contained to the unit cube. Please consider min-max scaling the input data.
/botorch/models/utils/assorted.py:201: InputDataWarning:
Input data is not standardized. Please consider scaling the input to zero mean and unit variance.
Pitch
To improve these warnings,
Make it clear which of these is about train_X and which is about train_Y. (Should the y data be "outcome" data?)
Suggest using transforms to fix this. In this case, a better implementation would be
Definitely! The one thing that may be tricky here is that BoTorch requires 100% test coverage and requires all tests to pass; to achieve that, you'll need to update existing tests such as this one. We also suppress the input data warnings in unit tests here, so that may need to be updated too.
We also suppress the input data warnings in unit tests here, so that may need to be updated too.
These warnings can be locally brought back and checked for using something like
with warnings.catch_warnings(record=True) as ws:
warnings.simplefilter("always")
function_call_to_produce_the_warning(...)
self.assertTrue(any(expected_warning_msg in str(w) for w in ws))
I'd prefer a local solution like this to bringing them back for all of the test suite.
Motivation
It's common to see warnings like this:
Pitch
To improve these warnings,
train_X
and which is abouttrain_Y
. (Should the y data be "outcome" data?)Are you willing to open a pull request? (See CONTRIBUTING)
Yes, but this is pretty easy so it would make a good first task for a newcomer
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