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https://issues.apache.org/jira/browse/SPARK-20502?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15989317#comment-15989317
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yuhao yang commented on SPARK-20502:
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Check here https://issues.apache.org/jira/browse/SPARK-18319 for previous
discussion. I updated the list according to the change we made last release. So
far I don't think we need to make any change about the sealed and experimental
API. But I listed some final class we have in ml which may be ready to be
unmarked.
sealed:
org.apache.spark.ml.attribute.Attribute
org.apache.spark.ml.attribute.AttributeType
org.apache.spark.ml.classification.LogisticRegressionTrainingSummary
org.apache.spark.ml.classification.LogisticRegressionSummary
org.apache.spark.ml.feature.Term
org.apache.spark.ml.feature.InteractableTerm
org.apache.spark.ml.optim.WeightedLeastSquares.Solver
org.apache.spark.ml.optim.NormalEquationSolver
org.apache.spark.ml.tree.Node
org.apache.spark.ml.tree.Split
org.apache.spark.ml.util.BaseReadWrite
org.apache.spark.ml.linalg.Matrix
org.apache.spark.ml.linalg.Vector
org.apache.spark.mllib.stat.test.StreamingTestMethod
org.apache.spark.mllib.tree.model.TreeEnsembleModel
Experimental:
org.apache.spark.ml.classification.LinearSVC
org.apache.spark.ml.classification.LinearSVCModel
org.apache.spark.ml.classification.BinaryLogisticRegressionTrainingSummary
org.apache.spark.ml.classification.BinaryLogisticRegressionSummary
org.apache.spark.ml.clustering.ClusteringSummary
org.apache.spark.ml.clustering.BisectingKMeansSummary
org.apache.spark.ml.clustering.GaussianMixtureSummary
org.apache.spark.ml.clustering.KMeansSummary
org.apache.spark.ml.evaluation.BinaryClassificationEvaluator
org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator
org.apache.spark.ml.evaluation.RegressionEvaluator
org.apache.spark.ml.feature.BucketedRandomProjectionLSH(Model)
org.apache.spark.ml.feature.Imputer(Model)
org.apache.spark.ml.feature.MinHash(Model)
org.apache.spark.ml.feature.RFormula(Model)
org.apache.spark.ml.fpm.FPGrowth(Model)
org.apache.spark.ml.regression.AFTSurvivalRegression(Model)
org.apache.spark.ml.regression.GeneralizedLinearRegression(Model) and summary
org.apache.spark.ml.regression.LinearRegressionTrainingSummary
org.apache.spark.ml.stat.ChiSquareTest
org.apache.spark.ml.stat.ChiSquareTest
Developer API
Most developer API are the basic components for ML pipeline, such like
Transformer, Estimator, PipelineStage, Params and Attributes, which I don't see
necessary to change.
final class:
org.apache.spark.ml.classification.OneVsRest
org.apache.spark.ml.evaluation.RegressionEvaluator
org.apache.spark.ml.feature.Binarizer
org.apache.spark.ml.feature.Bucketizer
org.apache.spark.ml.feature.ChiSqSelector
org.apache.spark.ml.feature.IDF
org.apache.spark.ml.feature.QuantileDiscretizer
org.apache.spark.ml.feature.VectorSlicer
org.apache.spark.ml.feature.Word2Vec
org.apache.spark.ml.param.ParamMap
Most of the final class here should be ready to be unmarked. I also checked
final method and fields (most params) which can be kept the same for now.
> ML, Graph 2.2 QA: API: Experimental, DeveloperApi, final, sealed audit
> ----------------------------------------------------------------------
>
> Key: SPARK-20502
> URL: https://issues.apache.org/jira/browse/SPARK-20502
> Project: Spark
> Issue Type: Sub-task
> Components: Documentation, GraphX, ML, MLlib
> Reporter: Joseph K. Bradley
> Priority: Blocker
>
> We should make a pass through the items marked as Experimental or
> DeveloperApi and see if any are stable enough to be unmarked.
> We should also check for items marked final or sealed to see if they are
> stable enough to be opened up as APIs.
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