[Minor][ML] Refactor clustering summary.

mllib/src/main/scala/org/apache/spark/ml/clustering/BisectingKMeans.scala

@@ -289,35 +289,15 @@ object BisectingKMeans extends DefaultParamsReadable[BisectingKMeans] {
  * :: Experimental ::
  * Summary of BisectingKMeans.
  *
- * @param predictions  [[DataFrame]] produced by [[BisectingKMeansModel.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
+ * @param predictions  [[DataFrame]] produced by [[BisectingKMeansModel.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.
  */
 @Since("2.1.0")
 @Experimental
 class BisectingKMeansSummary private[clustering] (
-    @Since("2.1.0") @transient val predictions: DataFrame,
-    @Since("2.1.0") val predictionCol: String,
-    @Since("2.1.0") val featuresCol: String,
-    @Since("2.1.0") val k: Int) extends Serializable {
-
-  /**
-   * Cluster centers of the transformed data.
-   */
-  @Since("2.1.0")
-  @transient lazy val cluster: DataFrame = predictions.select(predictionCol)
-
-  /**
-   * Size of (number of data points in) each cluster.
-   */
-  @Since("2.1.0")
-  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
-  }
-
-}
+    predictions: DataFrame,
+    predictionCol: String,
+    featuresCol: String,
+    k: Int) extends ClusteringSummary(predictions, predictionCol, featuresCol, k)

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
+  }
+}

mllib/src/main/scala/org/apache/spark/ml/clustering/GaussianMixture.scala

@@ -356,42 +356,25 @@ object GaussianMixture extends DefaultParamsReadable[GaussianMixture] {
  * :: Experimental ::
  * Summary of GaussianMixture.
  *
- * @param predictions  [[DataFrame]] produced by [[GaussianMixtureModel.transform()]]
- * @param predictionCol  Name for column of predicted clusters in `predictions`
- * @param probabilityCol  Name for column of predicted probability of each cluster in `predictions`
- * @param featuresCol  Name for column of features in `predictions`
- * @param k  Number of clusters
+ * @param predictions  [[DataFrame]] produced by [[GaussianMixtureModel.transform()]].
+ * @param predictionCol  Name for column of predicted clusters in `predictions`.
+ * @param probabilityCol  Name for column of predicted probability of each cluster
+ *                        in `predictions`.
+ * @param featuresCol  Name for column of features in `predictions`.
+ * @param k  Number of clusters.
  */
 @Since("2.0.0")
 @Experimental
 class GaussianMixtureSummary private[clustering] (
-    @Since("2.0.0") @transient val predictions: DataFrame,
-    @Since("2.0.0") val predictionCol: String,
+    predictions: DataFrame,
+    predictionCol: String,
     @Since("2.0.0") val probabilityCol: String,
-    @Since("2.0.0") val featuresCol: String,
-    @Since("2.0.0") val k: Int) extends Serializable {
-
-  /**
-   * Cluster centers of the transformed data.
-   */
-  @Since("2.0.0")
-  @transient lazy val cluster: DataFrame = predictions.select(predictionCol)
+    featuresCol: String,
+    k: Int) extends ClusteringSummary(predictions, predictionCol, featuresCol, k) {
 
   /**
    * Probability of each cluster.
    */
   @Since("2.0.0")
   @transient lazy val probability: DataFrame = predictions.select(probabilityCol)
-
-  /**
-   * Size of (number of data points in) each cluster.
-   */
-  @Since("2.0.0")
-  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
-  }
 }

mllib/src/main/scala/org/apache/spark/ml/clustering/KMeans.scala

@@ -346,35 +346,15 @@ object KMeans extends DefaultParamsReadable[KMeans] {
  * :: Experimental ::
  * Summary of KMeans.
  *
- * @param predictions  [[DataFrame]] produced by [[KMeansModel.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
+ * @param predictions  [[DataFrame]] produced by [[KMeansModel.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.
  */
 @Since("2.0.0")
 @Experimental
 class KMeansSummary private[clustering] (
-    @Since("2.0.0") @transient val predictions: DataFrame,
-    @Since("2.0.0") val predictionCol: String,
-    @Since("2.0.0") val featuresCol: String,
-    @Since("2.0.0") val k: Int) extends Serializable {
-
-  /**
-   * Cluster centers of the transformed data.
-   */
-  @Since("2.0.0")
-  @transient lazy val cluster: DataFrame = predictions.select(predictionCol)
-
-  /**
-   * Size of (number of data points in) each cluster.
-   */
-  @Since("2.0.0")
-  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
-  }
-
-}
+    predictions: DataFrame,
+    predictionCol: String,
+    featuresCol: String,
+    k: Int) extends ClusteringSummary(predictions, predictionCol, featuresCol, k)