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

    https://github.com/apache/spark/pull/18538#discussion_r133178531
  
    --- Diff: 
mllib/src/test/scala/org/apache/spark/ml/evaluation/ClusteringEvaluatorSuite.scala
 ---
    @@ -0,0 +1,225 @@
    +/*
    + * 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
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    + * 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.SparkFunSuite
    +import org.apache.spark.ml.linalg.{Vectors, VectorUDT}
    +import org.apache.spark.ml.param.ParamsSuite
    +import org.apache.spark.ml.util.DefaultReadWriteTest
    +import org.apache.spark.mllib.util.MLlibTestSparkContext
    +import org.apache.spark.sql.Row
    +import org.apache.spark.sql.types.{IntegerType, StructField, StructType}
    +
    +
    +class ClusteringEvaluatorSuite
    +  extends SparkFunSuite with MLlibTestSparkContext with 
DefaultReadWriteTest {
    +
    +  import testImplicits._
    +
    +  val dataset = Seq(Row(Vectors.dense(5.1, 3.5, 1.4, 0.2), 0),
    --- End diff --
    
    It's good to have this to verify the correctness of your implementation, 
but usually we don't hard code so much data for test. Could you try to find 
existing data in ```KMeansSuite``` or ```GaussianMixtureSuite``` for testing? 
If the hard code is necessary, please try to use small dataset.


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