Repository: spark Updated Branches: refs/heads/master c3a54843c -> d703372f8
[SPARK-10234] [MLLIB] update since version in mllib.clustering Same as #8421 but for `mllib.clustering`. cc feynmanliang yu-iskw Author: Xiangrui Meng <m...@databricks.com> Closes #8435 from mengxr/SPARK-10234. Project: http://git-wip-us.apache.org/repos/asf/spark/repo Commit: http://git-wip-us.apache.org/repos/asf/spark/commit/d703372f Tree: http://git-wip-us.apache.org/repos/asf/spark/tree/d703372f Diff: http://git-wip-us.apache.org/repos/asf/spark/diff/d703372f Branch: refs/heads/master Commit: d703372f86d6a59383ba8569fcd9d379849cffbf Parents: c3a5484 Author: Xiangrui Meng <m...@databricks.com> Authored: Tue Aug 25 22:33:48 2015 -0700 Committer: Xiangrui Meng <m...@databricks.com> Committed: Tue Aug 25 22:33:48 2015 -0700 ---------------------------------------------------------------------- .../mllib/clustering/GaussianMixture.scala | 1 + .../mllib/clustering/GaussianMixtureModel.scala | 8 +++--- .../apache/spark/mllib/clustering/KMeans.scala | 1 + .../spark/mllib/clustering/KMeansModel.scala | 4 +-- .../spark/mllib/clustering/LDAModel.scala | 28 +++++++++++++++----- .../clustering/PowerIterationClustering.scala | 10 ++++--- .../mllib/clustering/StreamingKMeans.scala | 15 ++++++----- 7 files changed, 44 insertions(+), 23 deletions(-) ---------------------------------------------------------------------- http://git-wip-us.apache.org/repos/asf/spark/blob/d703372f/mllib/src/main/scala/org/apache/spark/mllib/clustering/GaussianMixture.scala ---------------------------------------------------------------------- diff --git a/mllib/src/main/scala/org/apache/spark/mllib/clustering/GaussianMixture.scala b/mllib/src/main/scala/org/apache/spark/mllib/clustering/GaussianMixture.scala index daa947e..f82bd82 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/clustering/GaussianMixture.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/clustering/GaussianMixture.scala @@ -53,6 +53,7 @@ import org.apache.spark.util.Utils * @param maxIterations The maximum number of iterations to perform */ @Experimental +@Since("1.3.0") class GaussianMixture private ( private var k: Int, private var convergenceTol: Double, http://git-wip-us.apache.org/repos/asf/spark/blob/d703372f/mllib/src/main/scala/org/apache/spark/mllib/clustering/GaussianMixtureModel.scala ---------------------------------------------------------------------- diff --git a/mllib/src/main/scala/org/apache/spark/mllib/clustering/GaussianMixtureModel.scala b/mllib/src/main/scala/org/apache/spark/mllib/clustering/GaussianMixtureModel.scala index 1a10a8b..7f6163e 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/clustering/GaussianMixtureModel.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/clustering/GaussianMixtureModel.scala @@ -46,9 +46,9 @@ import org.apache.spark.sql.{SQLContext, Row} */ @Since("1.3.0") @Experimental -class GaussianMixtureModel( - val weights: Array[Double], - val gaussians: Array[MultivariateGaussian]) extends Serializable with Saveable { +class GaussianMixtureModel @Since("1.3.0") ( + @Since("1.3.0") val weights: Array[Double], + @Since("1.3.0") val gaussians: Array[MultivariateGaussian]) extends Serializable with Saveable { require(weights.length == gaussians.length, "Length of weight and Gaussian arrays must match") @@ -178,7 +178,7 @@ object GaussianMixtureModel extends Loader[GaussianMixtureModel] { (weight, new MultivariateGaussian(mu, sigma)) }.unzip - return new GaussianMixtureModel(weights.toArray, gaussians.toArray) + new GaussianMixtureModel(weights.toArray, gaussians.toArray) } } http://git-wip-us.apache.org/repos/asf/spark/blob/d703372f/mllib/src/main/scala/org/apache/spark/mllib/clustering/KMeans.scala ---------------------------------------------------------------------- diff --git a/mllib/src/main/scala/org/apache/spark/mllib/clustering/KMeans.scala b/mllib/src/main/scala/org/apache/spark/mllib/clustering/KMeans.scala index 3e9545a..46920ff 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/clustering/KMeans.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/clustering/KMeans.scala @@ -37,6 +37,7 @@ import org.apache.spark.util.random.XORShiftRandom * This is an iterative algorithm that will make multiple passes over the data, so any RDDs given * to it should be cached by the user. */ +@Since("0.8.0") class KMeans private ( private var k: Int, private var maxIterations: Int, http://git-wip-us.apache.org/repos/asf/spark/blob/d703372f/mllib/src/main/scala/org/apache/spark/mllib/clustering/KMeansModel.scala ---------------------------------------------------------------------- diff --git a/mllib/src/main/scala/org/apache/spark/mllib/clustering/KMeansModel.scala b/mllib/src/main/scala/org/apache/spark/mllib/clustering/KMeansModel.scala index e425ecd..a741584 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/clustering/KMeansModel.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/clustering/KMeansModel.scala @@ -37,8 +37,8 @@ import org.apache.spark.sql.Row * A clustering model for K-means. Each point belongs to the cluster with the closest center. */ @Since("0.8.0") -class KMeansModel ( - val clusterCenters: Array[Vector]) extends Saveable with Serializable with PMMLExportable { +class KMeansModel @Since("1.1.0") (@Since("1.0.0") val clusterCenters: Array[Vector]) + extends Saveable with Serializable with PMMLExportable { /** * A Java-friendly constructor that takes an Iterable of Vectors. http://git-wip-us.apache.org/repos/asf/spark/blob/d703372f/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAModel.scala ---------------------------------------------------------------------- diff --git a/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAModel.scala b/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAModel.scala index 432bbed..15129e0 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAModel.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAModel.scala @@ -43,12 +43,15 @@ import org.apache.spark.util.BoundedPriorityQueue * including local and distributed data structures. */ @Experimental +@Since("1.3.0") abstract class LDAModel private[clustering] extends Saveable { /** Number of topics */ + @Since("1.3.0") def k: Int /** Vocabulary size (number of terms or terms in the vocabulary) */ + @Since("1.3.0") def vocabSize: Int /** @@ -57,6 +60,7 @@ abstract class LDAModel private[clustering] extends Saveable { * * This is the parameter to a Dirichlet distribution. */ + @Since("1.5.0") def docConcentration: Vector /** @@ -68,6 +72,7 @@ abstract class LDAModel private[clustering] extends Saveable { * Note: The topics' distributions over terms are called "beta" in the original LDA paper * by Blei et al., but are called "phi" in many later papers such as Asuncion et al., 2009. */ + @Since("1.5.0") def topicConcentration: Double /** @@ -81,6 +86,7 @@ abstract class LDAModel private[clustering] extends Saveable { * This is a matrix of size vocabSize x k, where each column is a topic. * No guarantees are given about the ordering of the topics. */ + @Since("1.3.0") def topicsMatrix: Matrix /** @@ -91,6 +97,7 @@ abstract class LDAModel private[clustering] extends Saveable { * (term indices, term weights in topic). * Each topic's terms are sorted in order of decreasing weight. */ + @Since("1.3.0") def describeTopics(maxTermsPerTopic: Int): Array[(Array[Int], Array[Double])] /** @@ -102,6 +109,7 @@ abstract class LDAModel private[clustering] extends Saveable { * (term indices, term weights in topic). * Each topic's terms are sorted in order of decreasing weight. */ + @Since("1.3.0") def describeTopics(): Array[(Array[Int], Array[Double])] = describeTopics(vocabSize) /* TODO (once LDA can be trained with Strings or given a dictionary) @@ -185,10 +193,11 @@ abstract class LDAModel private[clustering] extends Saveable { * @param topics Inferred topics (vocabSize x k matrix). */ @Experimental +@Since("1.3.0") class LocalLDAModel private[clustering] ( - val topics: Matrix, - override val docConcentration: Vector, - override val topicConcentration: Double, + @Since("1.3.0") val topics: Matrix, + @Since("1.5.0") override val docConcentration: Vector, + @Since("1.5.0") override val topicConcentration: Double, override protected[clustering] val gammaShape: Double = 100) extends LDAModel with Serializable { @@ -376,6 +385,7 @@ class LocalLDAModel private[clustering] ( } @Experimental +@Since("1.5.0") object LocalLDAModel extends Loader[LocalLDAModel] { private object SaveLoadV1_0 { @@ -479,13 +489,14 @@ object LocalLDAModel extends Loader[LocalLDAModel] { * than the [[LocalLDAModel]]. */ @Experimental +@Since("1.3.0") class DistributedLDAModel private[clustering] ( private[clustering] val graph: Graph[LDA.TopicCounts, LDA.TokenCount], private[clustering] val globalTopicTotals: LDA.TopicCounts, - val k: Int, - val vocabSize: Int, - override val docConcentration: Vector, - override val topicConcentration: Double, + @Since("1.3.0") val k: Int, + @Since("1.3.0") val vocabSize: Int, + @Since("1.5.0") override val docConcentration: Vector, + @Since("1.5.0") override val topicConcentration: Double, private[spark] val iterationTimes: Array[Double], override protected[clustering] val gammaShape: Double = 100) extends LDAModel { @@ -603,6 +614,7 @@ class DistributedLDAModel private[clustering] ( * (term indices, topic indices). Note that terms will be omitted if not present in * the document. */ + @Since("1.5.0") lazy val topicAssignments: RDD[(Long, Array[Int], Array[Int])] = { // For reference, compare the below code with the core part of EMLDAOptimizer.next(). val eta = topicConcentration @@ -634,6 +646,7 @@ class DistributedLDAModel private[clustering] ( } /** Java-friendly version of [[topicAssignments]] */ + @Since("1.5.0") lazy val javaTopicAssignments: JavaRDD[(java.lang.Long, Array[Int], Array[Int])] = { topicAssignments.asInstanceOf[RDD[(java.lang.Long, Array[Int], Array[Int])]].toJavaRDD() } @@ -770,6 +783,7 @@ class DistributedLDAModel private[clustering] ( @Experimental +@Since("1.5.0") object DistributedLDAModel extends Loader[DistributedLDAModel] { private object SaveLoadV1_0 { http://git-wip-us.apache.org/repos/asf/spark/blob/d703372f/mllib/src/main/scala/org/apache/spark/mllib/clustering/PowerIterationClustering.scala ---------------------------------------------------------------------- diff --git a/mllib/src/main/scala/org/apache/spark/mllib/clustering/PowerIterationClustering.scala b/mllib/src/main/scala/org/apache/spark/mllib/clustering/PowerIterationClustering.scala index 396b36f..da234bd 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/clustering/PowerIterationClustering.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/clustering/PowerIterationClustering.scala @@ -42,9 +42,10 @@ import org.apache.spark.{Logging, SparkContext, SparkException} */ @Since("1.3.0") @Experimental -class PowerIterationClusteringModel( - val k: Int, - val assignments: RDD[PowerIterationClustering.Assignment]) extends Saveable with Serializable { +class PowerIterationClusteringModel @Since("1.3.0") ( + @Since("1.3.0") val k: Int, + @Since("1.3.0") val assignments: RDD[PowerIterationClustering.Assignment]) + extends Saveable with Serializable { @Since("1.4.0") override def save(sc: SparkContext, path: String): Unit = { @@ -56,6 +57,8 @@ class PowerIterationClusteringModel( @Since("1.4.0") object PowerIterationClusteringModel extends Loader[PowerIterationClusteringModel] { + + @Since("1.4.0") override def load(sc: SparkContext, path: String): PowerIterationClusteringModel = { PowerIterationClusteringModel.SaveLoadV1_0.load(sc, path) } @@ -120,6 +123,7 @@ object PowerIterationClusteringModel extends Loader[PowerIterationClusteringMode * @see [[http://en.wikipedia.org/wiki/Spectral_clustering Spectral clustering (Wikipedia)]] */ @Experimental +@Since("1.3.0") class PowerIterationClustering private[clustering] ( private var k: Int, private var maxIterations: Int, http://git-wip-us.apache.org/repos/asf/spark/blob/d703372f/mllib/src/main/scala/org/apache/spark/mllib/clustering/StreamingKMeans.scala ---------------------------------------------------------------------- diff --git a/mllib/src/main/scala/org/apache/spark/mllib/clustering/StreamingKMeans.scala b/mllib/src/main/scala/org/apache/spark/mllib/clustering/StreamingKMeans.scala index 41f2668..1d50ffe 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/clustering/StreamingKMeans.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/clustering/StreamingKMeans.scala @@ -66,9 +66,10 @@ import org.apache.spark.util.random.XORShiftRandom */ @Since("1.2.0") @Experimental -class StreamingKMeansModel( - override val clusterCenters: Array[Vector], - val clusterWeights: Array[Double]) extends KMeansModel(clusterCenters) with Logging { +class StreamingKMeansModel @Since("1.2.0") ( + @Since("1.2.0") override val clusterCenters: Array[Vector], + @Since("1.2.0") val clusterWeights: Array[Double]) + extends KMeansModel(clusterCenters) with Logging { /** * Perform a k-means update on a batch of data. @@ -168,10 +169,10 @@ class StreamingKMeansModel( */ @Since("1.2.0") @Experimental -class StreamingKMeans( - var k: Int, - var decayFactor: Double, - var timeUnit: String) extends Logging with Serializable { +class StreamingKMeans @Since("1.2.0") ( + @Since("1.2.0") var k: Int, + @Since("1.2.0") var decayFactor: Double, + @Since("1.2.0") var timeUnit: String) extends Logging with Serializable { @Since("1.2.0") def this() = this(2, 1.0, StreamingKMeans.BATCHES) --------------------------------------------------------------------- To unsubscribe, e-mail: commits-unsubscr...@spark.apache.org For additional commands, e-mail: commits-h...@spark.apache.org
