[ML] Refactor DFA custom processor to cross validation splitter #53915
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While `CustomProcessor` is generic and allows for flexibility, there are new requirements that make cross validation a concept it's hard to abstract behind custom processor. In particular, we would like to add data_counts to the DFA jobs stats. Counting training VS. test docs would be a useful statistic. We would also want to add a different cross validation strategy for multiclass classification. This commit renames custom processors to cross validation splitters which allows for those enhancements without cryptically doing things as a side effect of the abstract custom processing.
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Pinging @elastic/ml-core (:ml) |
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run elasticsearch-ci/packaging-sample-matrix-unix |
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run elasticsearch-ci/2 |
dimitris-athanasiou
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Mar 23, 2020
…53915) (#53956) While `CustomProcessor` is generic and allows for flexibility, there are new requirements that make cross validation a concept it's hard to abstract behind custom processor. In particular, we would like to add data_counts to the DFA jobs stats. Counting training VS. test docs would be a useful statistic. We would also want to add a different cross validation strategy for multiclass classification. This commit renames custom processors to cross validation splitters which allows for those enhancements without cryptically doing things as a side effect of the abstract custom processing. Backport of #53915
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While
CustomProcessoris generic and allows for flexibility, thereare new requirements that make cross validation a concept it's hard
to abstract behind custom processor. In particular, we would like to
add data_counts to the DFA jobs stats. Counting training VS. test
docs would be a useful statistic. We would also want to add a
different cross validation strategy for multiclass classification.
This commit renames custom processors to cross validation splitters
which allows for those enhancements without cryptically doing
things as a side effect of the abstract custom processing.