Github user yanboliang commented on a diff in the pull request:
https://github.com/apache/spark/pull/18538#discussion_r137278367
--- Diff:
mllib/src/test/scala/org/apache/spark/ml/evaluation/ClusteringEvaluatorSuite.scala
---
@@ -0,0 +1,89 @@
+/*
+ * 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.SparkFunSuite
+import org.apache.spark.ml.linalg.{Vector, Vectors}
+import org.apache.spark.ml.param.ParamsSuite
+import org.apache.spark.ml.util.DefaultReadWriteTest
+import org.apache.spark.ml.util.TestingUtils._
+import org.apache.spark.mllib.util.MLlibTestSparkContext
+import org.apache.spark.sql.{DataFrame, SparkSession}
+
+
+private[ml] case class ClusteringEvaluationTestData(features: Vector,
label: Int)
+
+class ClusteringEvaluatorSuite
+ extends SparkFunSuite with MLlibTestSparkContext with
DefaultReadWriteTest {
+
+ import testImplicits._
+
+ test("params") {
+ ParamsSuite.checkParams(new ClusteringEvaluator)
+ }
+
+ test("read/write") {
+ val evaluator = new ClusteringEvaluator()
+ .setPredictionCol("myPrediction")
+ .setFeaturesCol("myLabel")
+ testDefaultReadWrite(evaluator)
+ }
+
+ /*
+ Use the following python code to load the data and evaluate it using
scikit-learn package.
+
+ from sklearn import datasets
+ from sklearn.metrics import silhouette_score
+ iris = datasets.load_iris()
+ round(silhouette_score(iris.data, iris.target, metric='sqeuclidean'),
10)
+
+ 0.6564679231
+ */
+ test("squared euclidean Silhouette") {
+ val iris = ClusteringEvaluatorSuite.irisDataset(spark)
+ val evaluator = new ClusteringEvaluator()
+ .setFeaturesCol("features")
+ .setPredictionCol("label")
+
+ assert(evaluator.evaluate(iris) ~== 0.6564679231 relTol 1e-10)
+ }
+
--- End diff --
Yeah, I support to keep consistent result. Otherwise, any real value is a
confused result. What do you think of it? Thanks.
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