[jira] [Updated] (SPARK-32271) Add option for k-fold cross-validation to CrossValidator

["Austin Jordan (Jira)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/SPARK-32271?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Austin Jordan updated SPARK-32271:
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    Summary: Add option for k-fold cross-validation to CrossValidator  (was: Update CrossValidator to parallelize fit method across folds)

> Add option for k-fold cross-validation to CrossValidator
> --------------------------------------------------------
>
>                 Key: SPARK-32271
>                 URL: https://issues.apache.org/jira/browse/SPARK-32271
>             Project: Spark
>          Issue Type: Improvement
>          Components: ML
>    Affects Versions: 3.1.0
>            Reporter: Austin Jordan
>            Priority: Minor
>
> *What changes were proposed in this pull request?*
> I have added a `method` parameter to `CrossValidator.scala` to allow the user to choose between repeated random sub-sampling cross-validation (current behavior) and _k_-fold cross-validation (optional new behavior). The default method is random sub-sampling cross-validation.
> If _k_-fold cross-validation is chosen, the new behavior is as follows:
>  # Instead of splitting the input dataset into _k_ training and validation sets, I split them into _k_ folds; for each fold of training, one of the _k_ splits is selected for validation, and the others are unioned together for training.
>  # Instead of caching each training and validation set _k_ times, I cache each of the folds once.
>  # Instead of waiting for every model to finish training on fold _n_ before moving on to fold _n+1_, new fold/model combinations will be trained as soon as resources are available.
>  # Instead of creating one `Future` per model for each fold in series, all `Future`s for each fold & parameter grid pair are created and trained in parallel.
>  # A new `Int` parameter is added to the `Future` (now `Future[Int, Double]` instead of `Future[Double]`) in order to keep track of which `Future` belongs to which parameter grid.
> *Why are the changes needed?*
> These changes allow the user to choose between repeated random sub-sampling cross-validation (current behavior) and _k_-fold cross-validation (optional new behavior). These changes:
>  1. allow the user to choose between two types of cross-validation.
>  2. (If _k_-fold is chosen) only require caching the entire dataset once (instead of _k_ times in repeated random sub-sampling cross-validation, as it does now).
>  3. (if _k_-fold is chosen) free resources to train new model/fold combinations as soon as the previous one finishes. Currently, a model can only train one fold at a time. If _k_-fold is chosen, the added functionality will allow the `fit` to train multiple folds at once for the same model, and, in the case of a grid search, allow it to train multiple model/fold combinations at once, without needing to wait for the slowest model to fit the first fold before moving onto the second.
> *Does this PR introduce _any_ user-facing change?*
> Yes. This PR introduces the `setMethod` method to `CrossValidator`. If the `method` parameter is not set, the behavior will be the same as it has always been.
> *How was this patch tested?*
> Unit tests will be added.



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