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This repository was archived by the owner on Sep 18, 2026. It is now read-only.
This repository was archived by the owner on Sep 18, 2026. It is now read-only.

Ensure diversity sampling instead of random (proportionate) sampling #205

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@mlopatka

donor_df = clusters_df.sampleBy("prediction",

The diversity of the donor pool is only ensured by the assumption that higher level clustering is substantially diverse. This could be improved by verification of cross-cluster diversity in the addons space.

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  1. mlopatka commented on Jan 28, 2019

    @mlopatka
    ContributorAuthor

    This also comes back when we specify a proportionate sampling strategy here:

    donor_df = clusters_df.sampleBy(

    A solution here would be to specify weights that emphasize specific (niche) cluster representation in the final sample without compromising the non-addon diversity of "large" cluster sampling.

    Even an inverse of the current strategy could be evaluated.

  2. mlopatka commented on Jan 28, 2019

    @mlopatka
    ContributorAuthor

    @Dexterp37 can you assign this issue to me please? I have insufficient privileges to grab it :|

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