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https://issues.apache.org/jira/browse/SPARK-18755?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16247910#comment-16247910
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Ganesh Sivalingam commented on SPARK-18755:
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[~yuhaoyan] No problem, I do have some things to add:
If you have a look in the scikit-learn code base, the {{RandomizedSearchCV}}
and {{GridSearchCV}} functions are exactly the same, except in the way they
handle the incoming parameter distributions.
{{GridSearchCV }} does the same thing as {{ParamGridBuilder.build()}} and
{{RandomizedSearchCV}} does the equivalent of what
{{RandomParamGridBuilder.build()}} (which I just submitted) does.
Once the parameter sets have been created they both use {{BaseSearchCV}} for
everything else, and this is does the same as the current Spark
{{CrossValidator}} class.
I could create a {{RandomSearchCrossValidator}} class using the logic in
{{RandomParamGridBuilder}} if you like? I will also be available for doing
benchmarking.
> Add Randomized Grid Search to Spark ML
> --------------------------------------
>
> Key: SPARK-18755
> URL: https://issues.apache.org/jira/browse/SPARK-18755
> Project: Spark
> Issue Type: Improvement
> Components: ML
> Reporter: yuhao yang
>
> Randomized Grid Search implements a randomized search over parameters, where
> each setting is sampled from a distribution over possible parameter values.
> This has two main benefits over an exhaustive search:
> 1. A budget can be chosen independent of the number of parameters and
> possible values.
> 2. Adding parameters that do not influence the performance does not decrease
> efficiency.
> Randomized Grid search usually gives similar result as exhaustive search,
> while the run time for randomized search is drastically lower.
> For more background, please refer to:
> sklearn: http://scikit-learn.org/stable/modules/grid_search.html
> http://blog.kaggle.com/2015/07/16/scikit-learn-video-8-efficiently-searching-for-optimal-tuning-parameters/
> http://www.jmlr.org/papers/volume13/bergstra12a/bergstra12a.pdf
> https://www.r-bloggers.com/hyperparameter-optimization-in-h2o-grid-search-random-search-and-the-future/.
> There're two ways to implement this in Spark as I see:
> 1. Add searchRatio to ParamGridBuilder and conduct sampling directly during
> build. Only 1 new public function is required.
> 2. Add trait RadomizedSearch and create new class RandomizedCrossValidator
> and RandomizedTrainValiationSplit, which can be complicated since we need to
> deal with the models.
> I'd prefer option 1 as it's much simpler and straightforward. We can support
> Randomized grid search via some smallest change.
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