Repository: spark Updated Branches: refs/heads/master 2f99c3727 -> 4eeda8d47
[SPARK-10260] [ML] Add @Since annotation to ml.clustering ### JIRA [[SPARK-10260] Add Since annotation to ml.clustering - ASF JIRA](https://issues.apache.org/jira/browse/SPARK-10260) Author: Yu ISHIKAWA <yuu.ishik...@gmail.com> Closes #8455 from yu-iskw/SPARK-10260. Project: http://git-wip-us.apache.org/repos/asf/spark/repo Commit: http://git-wip-us.apache.org/repos/asf/spark/commit/4eeda8d4 Tree: http://git-wip-us.apache.org/repos/asf/spark/tree/4eeda8d4 Diff: http://git-wip-us.apache.org/repos/asf/spark/diff/4eeda8d4 Branch: refs/heads/master Commit: 4eeda8d472498acd40ef54723d1be9924a273d76 Parents: 2f99c37 Author: Yu ISHIKAWA <yuu.ishik...@gmail.com> Authored: Fri Aug 28 00:50:26 2015 -0700 Committer: Xiangrui Meng <m...@databricks.com> Committed: Fri Aug 28 00:50:26 2015 -0700 ---------------------------------------------------------------------- .../org/apache/spark/ml/clustering/KMeans.scala | 32 ++++++++++++++++++-- 1 file changed, 29 insertions(+), 3 deletions(-) ---------------------------------------------------------------------- http://git-wip-us.apache.org/repos/asf/spark/blob/4eeda8d4/mllib/src/main/scala/org/apache/spark/ml/clustering/KMeans.scala ---------------------------------------------------------------------- diff --git a/mllib/src/main/scala/org/apache/spark/ml/clustering/KMeans.scala b/mllib/src/main/scala/org/apache/spark/ml/clustering/KMeans.scala index 47a18cd..f40ab71 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/clustering/KMeans.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/clustering/KMeans.scala @@ -17,7 +17,7 @@ package org.apache.spark.ml.clustering -import org.apache.spark.annotation.Experimental +import org.apache.spark.annotation.{Since, Experimental} import org.apache.spark.ml.param.{Param, Params, IntParam, ParamMap} import org.apache.spark.ml.param.shared._ import org.apache.spark.ml.util.{Identifiable, SchemaUtils} @@ -39,9 +39,11 @@ private[clustering] trait KMeansParams extends Params with HasMaxIter with HasFe * Set the number of clusters to create (k). Must be > 1. Default: 2. * @group param */ + @Since("1.5.0") final val k = new IntParam(this, "k", "number of clusters to create", (x: Int) => x > 1) /** @group getParam */ + @Since("1.5.0") def getK: Int = $(k) /** @@ -50,10 +52,12 @@ private[clustering] trait KMeansParams extends Params with HasMaxIter with HasFe * (Bahmani et al., Scalable K-Means++, VLDB 2012). Default: k-means||. * @group expertParam */ + @Since("1.5.0") final val initMode = new Param[String](this, "initMode", "initialization algorithm", (value: String) => MLlibKMeans.validateInitMode(value)) /** @group expertGetParam */ + @Since("1.5.0") def getInitMode: String = $(initMode) /** @@ -61,10 +65,12 @@ private[clustering] trait KMeansParams extends Params with HasMaxIter with HasFe * setting -- the default of 5 is almost always enough. Must be > 0. Default: 5. * @group expertParam */ + @Since("1.5.0") final val initSteps = new IntParam(this, "initSteps", "number of steps for k-means||", (value: Int) => value > 0) /** @group expertGetParam */ + @Since("1.5.0") def getInitSteps: Int = $(initSteps) /** @@ -84,27 +90,32 @@ private[clustering] trait KMeansParams extends Params with HasMaxIter with HasFe * * @param parentModel a model trained by spark.mllib.clustering.KMeans. */ +@Since("1.5.0") @Experimental class KMeansModel private[ml] ( - override val uid: String, + @Since("1.5.0") override val uid: String, private val parentModel: MLlibKMeansModel) extends Model[KMeansModel] with KMeansParams { + @Since("1.5.0") override def copy(extra: ParamMap): KMeansModel = { val copied = new KMeansModel(uid, parentModel) copyValues(copied, extra) } + @Since("1.5.0") override def transform(dataset: DataFrame): DataFrame = { val predictUDF = udf((vector: Vector) => predict(vector)) dataset.withColumn($(predictionCol), predictUDF(col($(featuresCol)))) } + @Since("1.5.0") override def transformSchema(schema: StructType): StructType = { validateAndTransformSchema(schema) } private[clustering] def predict(features: Vector): Int = parentModel.predict(features) + @Since("1.5.0") def clusterCenters: Array[Vector] = parentModel.clusterCenters } @@ -114,8 +125,11 @@ class KMeansModel private[ml] ( * * @see [[http://dx.doi.org/10.14778/2180912.2180915 Bahmani et al., Scalable k-means++.]] */ +@Since("1.5.0") @Experimental -class KMeans(override val uid: String) extends Estimator[KMeansModel] with KMeansParams { +class KMeans @Since("1.5.0") ( + @Since("1.5.0") override val uid: String) + extends Estimator[KMeansModel] with KMeansParams { setDefault( k -> 2, @@ -124,34 +138,45 @@ class KMeans(override val uid: String) extends Estimator[KMeansModel] with KMean initSteps -> 5, tol -> 1e-4) + @Since("1.5.0") override def copy(extra: ParamMap): KMeans = defaultCopy(extra) + @Since("1.5.0") def this() = this(Identifiable.randomUID("kmeans")) /** @group setParam */ + @Since("1.5.0") def setFeaturesCol(value: String): this.type = set(featuresCol, value) /** @group setParam */ + @Since("1.5.0") def setPredictionCol(value: String): this.type = set(predictionCol, value) /** @group setParam */ + @Since("1.5.0") def setK(value: Int): this.type = set(k, value) /** @group expertSetParam */ + @Since("1.5.0") def setInitMode(value: String): this.type = set(initMode, value) /** @group expertSetParam */ + @Since("1.5.0") def setInitSteps(value: Int): this.type = set(initSteps, value) /** @group setParam */ + @Since("1.5.0") def setMaxIter(value: Int): this.type = set(maxIter, value) /** @group setParam */ + @Since("1.5.0") def setTol(value: Double): this.type = set(tol, value) /** @group setParam */ + @Since("1.5.0") def setSeed(value: Long): this.type = set(seed, value) + @Since("1.5.0") override def fit(dataset: DataFrame): KMeansModel = { val rdd = dataset.select(col($(featuresCol))).map { case Row(point: Vector) => point } @@ -167,6 +192,7 @@ class KMeans(override val uid: String) extends Estimator[KMeansModel] with KMean copyValues(model) } + @Since("1.5.0") override def transformSchema(schema: StructType): StructType = { validateAndTransformSchema(schema) } --------------------------------------------------------------------- To unsubscribe, e-mail: commits-unsubscr...@spark.apache.org For additional commands, e-mail: commits-h...@spark.apache.org
