Solution for skew/drift detection in distribution of numerical feature

[wrapper228]

I opened issue #101 about dealing with numerical features due to the need for ML data quality control in my company. I have made small workaround suitable to our pipeline, which is proprietary, sadly.
Now I would like to recreate this temporary adapter to deal with numerical feature distribution skew/drift between training and serving data until complete solution is developed. This adapter simply maps training numerical features to categorical by .qcut( ) -ing them and saving resulting bounds for cross-session interaction. These bounds are also used for cutting serving numerical features. I will consider any feedback, thanks!

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[wrapper228]

@googlebot I signed it!

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[wrapper228]

@googlebot I fixed it.

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