[SPARK-13761] [ML] Deprecate validateParams #11620
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Test build #52781 has finished for PR 11620 at commit
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Test build #52822 has finished for PR 11620 at commit
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Test build #52833 has finished for PR 11620 at commit
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| */ | ||
| @deprecated("All the checks should be merged into transformSchema", "2.0.0") |
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Also say this method will be removed in 2.1.0
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Only a few small comments |
| @@ -61,7 +63,8 @@ private[regression] trait GeneralizedLinearRegressionBase extends PredictorParam | |||
| * Param for the name of link function which provides the relationship | |||
| * between the linear predictor and the mean of the distribution function. | |||
| * Supported options: "identity", "log", "inverse", "logit", "probit", "cloglog" and "sqrt". | |||
| * @group param | |||
| * | |||
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Thanks for the updates. Still some indentation issues, but that's it. |
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Test build #53347 has finished for PR 11620 at commit
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@jkbradley Sorry for those unintentional changes and thanks for the patience. |
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Test build #53375 has finished for PR 11620 at commit
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No problem. Thanks for the PR! |
| */ | ||
| @deprecated("Will be removed in 2.1.0. Checks should be merged into transformSchema.", "2.0.0") |
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It looks like this now causes a number of deprecation warnings in the Spark code, which we're trying to get rid of. Can most of the remaining usages be transformed to not use this method?
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Apologies! I should have checked the Jenkins logs. I'll send a clean-up PR.
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Hi @jkbradley have you already started on this? Sorry for the troubling. I didn't remove the ones in CrossValidator and TrainValidationSplit because I think it can be handy if we can run some validation before submitting the paramMap. Let me know if I can help in any way.
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No problem; I just sent a PR for it.
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## What changes were proposed in this pull request? Cleanups from [#11620]: remove remaining uses of validateParams, and put functionality into transformSchema ## How was this patch tested? Existing unit tests, modified to check using transformSchema instead of validateParams Author: Joseph K. Bradley <joseph@databricks.com> Closes #11790 from jkbradley/SPARK-13761-cleanup.
roygao94
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## What changes were proposed in this pull request? Deprecate validateParams() method here: https://github.com/apache/spark/blob/035d3acdf3c1be5b309a861d5c5beb803b946b5e/mllib/src/main/scala/org/apache/spark/ml/param/params.scala#L553 Move all functionality in overridden methods to transformSchema(). Check docs to make sure they indicate complex Param interaction checks should be done in transformSchema. ## How was this patch tested? unit tests Author: Yuhao Yang <hhbyyh@gmail.com> Closes apache#11620 from hhbyyh/depreValid.
roygao94
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## What changes were proposed in this pull request? Cleanups from [apache#11620]: remove remaining uses of validateParams, and put functionality into transformSchema ## How was this patch tested? Existing unit tests, modified to check using transformSchema instead of validateParams Author: Joseph K. Bradley <joseph@databricks.com> Closes apache#11790 from jkbradley/SPARK-13761-cleanup.
ghost
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…hon API. ## What changes were proposed in this pull request? apache#14597 modified ```ChiSqSelector``` to support ```fpr``` type selector, however, it left some issue need to be addressed: * We should allow users to set selector type explicitly rather than switching them by using different setting function, since the setting order will involves some unexpected issue. For example, if users both set ```numTopFeatures``` and ```percentile```, it will train ```kbest``` or ```percentile``` model based on the order of setting (the latter setting one will be trained). This make users confused, and we should allow users to set selector type explicitly. We handle similar issues at other place of ML code base such as ```GeneralizedLinearRegression``` and ```LogisticRegression```. * Meanwhile, if there are more than one parameter except ```alpha``` can be set for ```fpr``` model, we can not handle it elegantly in the existing framework. And similar issues for ```kbest``` and ```percentile``` model. Setting selector type explicitly can solve this issue also. * If setting selector type explicitly by users is allowed, we should handle param interaction such as if users set ```selectorType = percentile``` and ```alpha = 0.1```, we should notify users the parameter ```alpha``` will take no effect. We should handle complex parameter interaction checks at ```transformSchema```. (FYI apache#11620) * We should use lower case of the selector type names to follow MLlib convention. * Add ML Python API. ## How was this patch tested? Unit test. Author: Yanbo Liang <ybliang8@gmail.com> Closes apache#15214 from yanboliang/spark-17017.
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What changes were proposed in this pull request?
Deprecate validateParams() method here:
spark/mllib/src/main/scala/org/apache/spark/ml/param/params.scala
Line 553 in 035d3ac
Move all functionality in overridden methods to transformSchema().
Check docs to make sure they indicate complex Param interaction checks should be done in transformSchema.
How was this patch tested?
unit tests