Github user jkbradley commented on a diff in the pull request:
https://github.com/apache/spark/pull/11419#discussion_r57983018
--- Diff:
mllib/src/test/scala/org/apache/spark/ml/clustering/GaussianMixtureSuite.scala
---
@@ -0,0 +1,121 @@
+/*
+ * 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 org.apache.spark.SparkFunSuite
+import org.apache.spark.ml.util.DefaultReadWriteTest
+import org.apache.spark.mllib.linalg.{Vector, Vectors}
+import org.apache.spark.mllib.util.MLlibTestSparkContext
+import org.apache.spark.sql.{DataFrame, SQLContext}
+
+class GaussianMixtureSuite extends SparkFunSuite with MLlibTestSparkContext
+ with DefaultReadWriteTest {
+
+ final val k = 5
+ @transient var dataset: DataFrame = _
+
+ override def beforeAll(): Unit = {
+ super.beforeAll()
+
+ dataset = GaussianMixtureSuite.generateKMeansData(sqlContext, 50, 3, k)
+ }
+
+ test("default parameters") {
+ val gm = new GaussianMixture()
+
+ assert(gm.getK === 2)
+ assert(gm.getFeaturesCol === "features")
+ assert(gm.getPredictionCol === "prediction")
+ assert(gm.getMaxIter === 100)
+ assert(gm.getTol === 0.01)
+ }
+
+ test("set parameters") {
+ val gm = new GaussianMixture()
+ .setK(9)
+ .setFeaturesCol("test_feature")
+ .setPredictionCol("test_prediction")
+ .setProbabilityCol("test_probability")
+ .setMaxIter(33)
+ .setSeed(123)
+ .setTol(1e-3)
+
+ assert(gm.getK === 9)
+ assert(gm.getFeaturesCol === "test_feature")
+ assert(gm.getPredictionCol === "test_prediction")
+ assert(gm.getProbabilityCol === "test_probability")
+ assert(gm.getMaxIter === 33)
+ assert(gm.getSeed === 123)
+ assert(gm.getTol === 1e-3)
+ }
+
+ test("parameters validation") {
+ intercept[IllegalArgumentException] {
+ new GaussianMixture().setK(1)
+ }
+ }
+
+ test("fit & transform") {
+ val predictionColName = "gm_prediction"
+ val probabilityColName = "gm_probability"
+ val gm = new
GaussianMixture().setK(k).setMaxIter(2).setPredictionCol(predictionColName)
+ .setProbabilityCol(probabilityColName).setSeed(1)
+ val model = gm.fit(dataset)
+ assert(model.hasParent)
+ assert(model.weights.length === k)
+ assert(model.gaussians.length === k)
+
+ val transformed = model.transform(dataset)
+ val expectedColumns = Array("features", predictionColName,
probabilityColName)
+ expectedColumns.foreach { column =>
+ assert(transformed.columns.contains(column))
+ }
+ }
+
+ test("read/write") {
+ def checkModelData(model: GaussianMixtureModel, model2:
GaussianMixtureModel): Unit = {
+ assert(model.weights === model2.weights)
+ assert(model.gaussians === model2.gaussians)
+ }
+ val gm = new GaussianMixture()
+ testEstimatorAndModelReadWrite(gm, dataset,
+ GaussianMixtureSuite.allParamSettings, checkModelData)
+ }
+}
+
+object GaussianMixtureSuite {
+ def generateKMeansData(sql: SQLContext, rows: Int, dim: Int, k: Int):
DataFrame = {
--- End diff --
No need to copy this method; just call the KMeans one.
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