Github user wangmiao1981 commented on a diff in the pull request:
https://github.com/apache/spark/pull/21090#discussion_r182243819
--- Diff:
mllib/src/test/scala/org/apache/spark/ml/clustering/PowerIterationClusteringSuite.scala
---
@@ -0,0 +1,239 @@
+/*
+ * 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 scala.collection.mutable
+
+import org.apache.spark.ml.util.DefaultReadWriteTest
+import org.apache.spark.mllib.util.MLlibTestSparkContext
+import org.apache.spark.sql.functions.col
+import org.apache.spark.sql.types._
+import org.apache.spark.sql.{DataFrame, Dataset, Row, SparkSession}
+import org.apache.spark.{SparkException, SparkFunSuite}
+
+
+class PowerIterationClusteringSuite extends SparkFunSuite
+ with MLlibTestSparkContext with DefaultReadWriteTest {
+
+ @transient var data: Dataset[_] = _
+ final val r1 = 1.0
+ final val n1 = 10
+ final val r2 = 4.0
+ final val n2 = 40
+
+ override def beforeAll(): Unit = {
+ super.beforeAll()
+
+ data = PowerIterationClusteringSuite.generatePICData(spark, r1, r2,
n1, n2)
+ }
+
+ test("default parameters") {
+ val pic = new PowerIterationClustering()
+
+ assert(pic.getK === 2)
+ assert(pic.getMaxIter === 20)
+ assert(pic.getInitMode === "random")
+ assert(pic.getPredictionCol === "prediction")
+ assert(pic.getIdCol === "id")
+ assert(pic.getNeighborsCol === "neighbors")
+ assert(pic.getSimilaritiesCol === "similarities")
+ }
+
+ test("parameter validation") {
+ intercept[IllegalArgumentException] {
+ new PowerIterationClustering().setK(1)
+ }
+ intercept[IllegalArgumentException] {
+ new PowerIterationClustering().setInitMode("no_such_a_mode")
+ }
+ intercept[IllegalArgumentException] {
+ new PowerIterationClustering().setIdCol("")
+ }
+ intercept[IllegalArgumentException] {
+ new PowerIterationClustering().setNeighborsCol("")
+ }
+ intercept[IllegalArgumentException] {
+ new PowerIterationClustering().setSimilaritiesCol("")
+ }
+ }
+
+ test("power iteration clustering") {
+ val n = n1 + n2
+
+ val model = new PowerIterationClustering()
+ .setK(2)
+ .setMaxIter(40)
+ val result = model.transform(data)
+
+ val predictions = Array.fill(2)(mutable.Set.empty[Long])
+ result.select("id", "prediction").collect().foreach {
+ case Row(id: Long, cluster: Integer) => predictions(cluster) += id
+ }
+ assert(predictions.toSet == Set((1 until n1).toSet, (n1 until
n).toSet))
+
+ val result2 = new PowerIterationClustering()
+ .setK(2)
+ .setMaxIter(10)
+ .setInitMode("degree")
+ .transform(data)
+ val predictions2 = Array.fill(2)(mutable.Set.empty[Long])
+ result2.select("id", "prediction").collect().foreach {
+ case Row(id: Long, cluster: Integer) => predictions2(cluster) += id
+ }
+ assert(predictions2.toSet == Set((1 until n1).toSet, (n1 until
n).toSet))
+ }
+
+ test("supported input types") {
+ val model = new PowerIterationClustering()
+ .setK(2)
+ .setMaxIter(1)
+
+ def runTest(idType: DataType, neighborType: DataType, similarityType:
DataType): Unit = {
+ val typedData = data.select(
+ col("id").cast(idType).alias("id"),
+ col("neighbors").cast(ArrayType(neighborType, containsNull =
false)).alias("neighbors"),
+ col("similarities").cast(ArrayType(similarityType, containsNull =
false))
+ .alias("similarities")
+ )
+ model.transform(typedData).collect()
+ }
+
+ for (idType <- Seq(IntegerType, LongType)) {
+ runTest(idType, LongType, DoubleType)
+ }
+ for (neighborType <- Seq(IntegerType, LongType)) {
+ runTest(LongType, neighborType, DoubleType)
+ }
+ for (similarityType <- Seq(FloatType, DoubleType)) {
+ runTest(LongType, LongType, similarityType)
+ }
+ }
+
+ test("invalid input: wrong types") {
+ val model = new PowerIterationClustering()
+ .setK(2)
+ .setMaxIter(1)
+ intercept[IllegalArgumentException] {
+ val typedData = data.select(
+ col("id").cast(DoubleType).alias("id"),
+ col("neighbors"),
+ col("similarities")
+ )
+ model.transform(typedData)
+ }
+ intercept[IllegalArgumentException] {
+ val typedData = data.select(
+ col("id"),
+ col("neighbors").cast(ArrayType(DoubleType, containsNull =
false)).alias("neighbors"),
+ col("similarities")
+ )
+ model.transform(typedData)
+ }
+ intercept[IllegalArgumentException] {
+
--- End diff --
remove blank line or add blank line after line 139 for consistence?
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