Github user yanboliang commented on a diff in the pull request:

    https://github.com/apache/spark/pull/18538#discussion_r133175365
  
    --- Diff: 
mllib/src/main/scala/org/apache/spark/ml/evaluation/ClusteringEvaluator.scala 
---
    @@ -0,0 +1,240 @@
    +/*
    + * 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.evaluation
    +
    +import org.apache.spark.SparkContext
    +import org.apache.spark.annotation.Experimental
    +import org.apache.spark.broadcast.Broadcast
    +import org.apache.spark.ml.linalg.{BLAS, DenseVector, Vector, Vectors, 
VectorUDT}
    +import org.apache.spark.ml.param.{Param, ParamMap, ParamValidators}
    +import org.apache.spark.ml.param.shared.{HasFeaturesCol, HasPredictionCol}
    +import org.apache.spark.ml.util.{DefaultParamsReadable, 
DefaultParamsWritable, Identifiable, SchemaUtils}
    +import org.apache.spark.sql.{DataFrame, Dataset}
    +import org.apache.spark.sql.functions.{avg, col, udf}
    +import org.apache.spark.sql.types.IntegerType
    +
    +/**
    + * Evaluator for clustering results.
    + * At the moment, the supported metrics are:
    + *  squaredSilhouette: silhouette measure using the squared Euclidean 
distance;
    + *  cosineSilhouette: silhouette measure using the cosine distance.
    + *  The implementation follows the proposal explained
    + * <a 
href="https://drive.google.com/file/d/0B0Hyo%5f%5fbG%5f3fdkNvSVNYX2E3ZU0/view";>
    + *   in this document</a>.
    + */
    +@Experimental
    +class ClusteringEvaluator (val uid: String)
    +  extends Evaluator with HasPredictionCol with HasFeaturesCol with 
DefaultParamsWritable {
    +
    +  def this() = this(Identifiable.randomUID("SquaredEuclideanSilhouette"))
    +
    +  override def copy(pMap: ParamMap): ClusteringEvaluator = 
this.defaultCopy(pMap)
    +
    +  override def isLargerBetter: Boolean = true
    +
    +  /** @group setParam */
    +  def setPredictionCol(value: String): this.type = set(predictionCol, 
value)
    +
    +  /** @group setParam */
    +  def setFeaturesCol(value: String): this.type = set(featuresCol, value)
    +
    +  /**
    +   * param for metric name in evaluation
    +   * (supports `"squaredSilhouette"` (default))
    +   * @group param
    +   */
    +  val metricName: Param[String] = {
    +    val allowedParams = ParamValidators.inArray(Array("squaredSilhouette"))
    +    new Param(
    +      this,
    +      "metricName",
    +      "metric name in evaluation (squaredSilhouette)",
    +      allowedParams
    +    )
    +  }
    +
    +  /** @group getParam */
    +  def getMetricName: String = $(metricName)
    +
    +  /** @group setParam */
    +  def setMetricName(value: String): this.type = set(metricName, value)
    +
    +  setDefault(metricName -> "squaredSilhouette")
    +
    +  override def evaluate(dataset: Dataset[_]): Double = {
    +    SchemaUtils.checkColumnType(dataset.schema, $(featuresCol), new 
VectorUDT)
    +    SchemaUtils.checkColumnType(dataset.schema, $(predictionCol), 
IntegerType)
    +
    +    val metric: Double = $(metricName) match {
    +      case "squaredSilhouette" =>
    +        SquaredEuclideanSilhouette.computeSquaredSilhouette(
    +          dataset,
    +          $(predictionCol),
    +          $(featuresCol)
    +        )
    +    }
    +    metric
    +  }
    +
    +}
    +
    +
    +object ClusteringEvaluator
    +  extends DefaultParamsReadable[ClusteringEvaluator] {
    +
    +  override def load(path: String): ClusteringEvaluator = super.load(path)
    +
    +}
    +
    +private[evaluation] object SquaredEuclideanSilhouette {
    +
    +  private[this] var kryoRegistrationPerformed: Boolean = false
    +
    +  /**
    +   * This method registers the class
    +   * 
[[org.apache.spark.ml.evaluation.SquaredEuclideanSilhouette.ClusterStats]]
    +   * for kryo serialization.
    +   *
    +   * @param sc `SparkContext` to be used
    +   */
    +  def registerKryoClasses(sc: SparkContext): Unit = {
    +    if (! kryoRegistrationPerformed) {
    +      sc.getConf.registerKryoClasses(
    +        Array(
    +          classOf[SquaredEuclideanSilhouette.ClusterStats]
    +        )
    +      )
    +      kryoRegistrationPerformed = true
    +    }
    +  }
    +
    +  case class ClusterStats(featureSum: Vector, squaredNormSum: Double, 
numOfPoints: Long)
    +
    +  def computeClusterStats(
    +    df: DataFrame,
    +    predictionCol: String,
    +    featuresCol: String): Map[Int, ClusterStats] = {
    +    val numFeatures = 
df.select(col(featuresCol)).first().getAs[Vector](0).size
    +    val clustersStatsRDD = df.select(col(predictionCol), col(featuresCol), 
col("squaredNorm"))
    +      .rdd
    +      .map { row => (row.getInt(0), (row.getAs[Vector](1), 
row.getDouble(2))) }
    +      .aggregateByKey[(DenseVector, Double, 
Long)]((Vectors.zeros(numFeatures).toDense, 0.0, 0L))(
    +        seqOp = {
    +          case (
    +              (featureSum: DenseVector, squaredNormSum: Double, 
numOfPoints: Long),
    +              (features, squaredNorm)
    +            ) =>
    +            BLAS.axpy(1.0, features, featureSum)
    +            (featureSum, squaredNormSum + squaredNorm, numOfPoints + 1)
    +        },
    +        combOp = {
    +          case (
    +              (featureSum1, squaredNormSum1, numOfPoints1),
    +              (featureSum2, squaredNormSum2, numOfPoints2)
    +            ) =>
    +            BLAS.axpy(1.0, featureSum2, featureSum1)
    +            (featureSum1, squaredNormSum1 + squaredNormSum2, numOfPoints1 
+ numOfPoints2)
    +        }
    +      )
    +
    +    clustersStatsRDD
    +      .collectAsMap()
    +      .mapValues {
    +        case (featureSum: DenseVector, squaredNormSum: Double, 
numOfPoints: Long) =>
    +          SquaredEuclideanSilhouette.ClusterStats(featureSum, 
squaredNormSum, numOfPoints)
    +      }
    +      .toMap
    +  }
    +
    +  def computeSquaredSilhouetteCoefficient(
    +     broadcastedClustersMap: Broadcast[Map[Int, ClusterStats]],
    +     vector: Vector,
    +     clusterId: Int,
    +     squaredNorm: Double): Double = {
    +
    +    def compute(squaredNorm: Double, point: Vector, clusterStats: 
ClusterStats): Double = {
    +      val pointDotClusterFeaturesSum = BLAS.dot(point, 
clusterStats.featureSum)
    +
    +      squaredNorm +
    +        clusterStats.squaredNormSum / clusterStats.numOfPoints -
    +        2 * pointDotClusterFeaturesSum / clusterStats.numOfPoints
    +    }
    +
    +    var minOther = Double.MaxValue
    --- End diff --
    
    ```minOther``` -> ```nearestClusterDistance```?


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