diff --git a/mllib/src/main/scala/org/apache/spark/ml/clustering/ClusteringSummary.scala b/mllib/src/main/scala/org/apache/spark/ml/clustering/ClusteringSummary.scala new file mode 100644 index 0000000000000..8b5f525194f28 --- /dev/null +++ b/mllib/src/main/scala/org/apache/spark/ml/clustering/ClusteringSummary.scala @@ -0,0 +1,54 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.ml.clustering + +import org.apache.spark.annotation.Experimental +import org.apache.spark.sql.{DataFrame, Row} + +/** + * :: Experimental :: + * Summary of clustering algorithms. + * + * @param predictions [[DataFrame]] produced by model.transform(). + * @param predictionCol Name for column of predicted clusters in `predictions`. + * @param featuresCol Name for column of features in `predictions`. + * @param k Number of clusters. + */ +@Experimental +class ClusteringSummary private[clustering] ( + @transient val predictions: DataFrame, + val predictionCol: String, + val featuresCol: String, + val k: Int) extends Serializable { + + /** + * Cluster centers of the transformed data. + */ + @transient lazy val cluster: DataFrame = predictions.select(predictionCol) + + /** + * Size of (number of data points in) each cluster. + */ + lazy val clusterSizes: Array[Long] = { + val sizes = Array.fill[Long](k)(0) + cluster.groupBy(predictionCol).count().select(predictionCol, "count").collect().foreach { + case Row(cluster: Int, count: Long) => sizes(cluster) = count + } + sizes + } +} diff --git a/mllib/src/main/scala/org/apache/spark/ml/clustering/GaussianMixture.scala b/mllib/src/main/scala/org/apache/spark/ml/clustering/GaussianMixture.scala index 31cda5b9b6e7e..e3cb92f4f144d 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/clustering/GaussianMixture.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/clustering/GaussianMixture.scala @@ -368,7 +368,7 @@ object GaussianMixture extends DefaultParamsReadable[GaussianMixture] { class GaussianMixtureSummary private[clustering] ( predictions: DataFrame, predictionCol: String, - val probabilityCol: String, + @Since("2.0.0") val probabilityCol: String, featuresCol: String, k: Int) extends ClusteringSummary(predictions, predictionCol, featuresCol, k) { 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 52970c187e698..fee322d0c6a75 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 @@ -358,37 +358,3 @@ class KMeansSummary private[clustering] ( predictionCol: String, featuresCol: String, k: Int) extends ClusteringSummary(predictions, predictionCol, featuresCol, k) - -/** - * :: Experimental :: - * Summary of clustering algorithms. - * - * @param predictions [[DataFrame]] produced by model.transform(). - * @param predictionCol Name for column of predicted clusters in `predictions`. - * @param featuresCol Name for column of features in `predictions`. - * @param k Number of clusters. - */ -@Experimental -class ClusteringSummary private[clustering] ( - @transient val predictions: DataFrame, - val predictionCol: String, - val featuresCol: String, - val k: Int) extends Serializable { - - /** - * Cluster centers of the transformed data. - */ - @transient lazy val cluster: DataFrame = predictions.select(predictionCol) - - /** - * Size of (number of data points in) each cluster. - */ - lazy val clusterSizes: Array[Long] = { - val sizes = Array.fill[Long](k)(0) - cluster.groupBy(predictionCol).count().select(predictionCol, "count").collect().foreach { - case Row(cluster: Int, count: Long) => sizes(cluster) = count - } - sizes - } - -}