Github user mgaido91 commented on a diff in the pull request:
https://github.com/apache/spark/pull/18538#discussion_r133385546
--- 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
+ *
+ * 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.{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 --
Sorry but I can't understand your point. Resources in the test scope are
not included in the compiled jars. The same approach is used in the `sql`
component for instance, where the test data is in the resources
(https://github.com/apache/spark/tree/master/sql/core/src/test/resources/test-data).
If I generate randomly test data, I have to first perform a clustering on
those points, while with this dataset I have the result of the clustering ready
too. I am not sure this is the best approach. But maybe I am missing something.
Can you please clarify this to me?
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