Repository: spark Updated Branches: refs/heads/master 439c69511 -> d4a0895c6
[SPARK-22884][ML] ML tests for StructuredStreaming: spark.ml.clustering ## What changes were proposed in this pull request? Converting clustering tests to also check code with structured streaming, using the ML testing infrastructure implemented in SPARK-22882. This PR is a new version of https://github.com/apache/spark/pull/20319 Author: Sandor Murakozi <smurak...@gmail.com> Author: Joseph K. Bradley <jos...@databricks.com> Closes #21358 from jkbradley/smurakozi-SPARK-22884. Project: http://git-wip-us.apache.org/repos/asf/spark/repo Commit: http://git-wip-us.apache.org/repos/asf/spark/commit/d4a0895c Tree: http://git-wip-us.apache.org/repos/asf/spark/tree/d4a0895c Diff: http://git-wip-us.apache.org/repos/asf/spark/diff/d4a0895c Branch: refs/heads/master Commit: d4a0895c628ca854895c3c35c46ed990af36ec61 Parents: 439c695 Author: Sandor Murakozi <smurak...@gmail.com> Authored: Thu May 17 16:33:06 2018 -0700 Committer: Xiangrui Meng <m...@databricks.com> Committed: Thu May 17 16:33:06 2018 -0700 ---------------------------------------------------------------------- .../ml/clustering/BisectingKMeansSuite.scala | 41 ++++++++++---------- .../ml/clustering/GaussianMixtureSuite.scala | 22 ++++------- .../spark/ml/clustering/KMeansSuite.scala | 31 +++++++-------- .../apache/spark/ml/clustering/LDASuite.scala | 21 ++++------ 4 files changed, 50 insertions(+), 65 deletions(-) ---------------------------------------------------------------------- http://git-wip-us.apache.org/repos/asf/spark/blob/d4a0895c/mllib/src/test/scala/org/apache/spark/ml/clustering/BisectingKMeansSuite.scala ---------------------------------------------------------------------- diff --git a/mllib/src/test/scala/org/apache/spark/ml/clustering/BisectingKMeansSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/clustering/BisectingKMeansSuite.scala index f3ff2af..81842af 100644 --- a/mllib/src/test/scala/org/apache/spark/ml/clustering/BisectingKMeansSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/ml/clustering/BisectingKMeansSuite.scala @@ -19,17 +19,18 @@ package org.apache.spark.ml.clustering import scala.language.existentials -import org.apache.spark.{SparkException, SparkFunSuite} +import org.apache.spark.SparkException import org.apache.spark.ml.linalg.{Vector, Vectors} import org.apache.spark.ml.param.ParamMap -import org.apache.spark.ml.util.{DefaultReadWriteTest, MLTestingUtils} +import org.apache.spark.ml.util.{DefaultReadWriteTest, MLTest, MLTestingUtils} import org.apache.spark.ml.util.TestingUtils._ import org.apache.spark.mllib.clustering.DistanceMeasure -import org.apache.spark.mllib.util.MLlibTestSparkContext -import org.apache.spark.sql.{DataFrame, Dataset} +import org.apache.spark.sql.Dataset -class BisectingKMeansSuite - extends SparkFunSuite with MLlibTestSparkContext with DefaultReadWriteTest { + +class BisectingKMeansSuite extends MLTest with DefaultReadWriteTest { + + import testImplicits._ final val k = 5 @transient var dataset: Dataset[_] = _ @@ -68,10 +69,13 @@ class BisectingKMeansSuite // Verify fit does not fail on very sparse data val model = bkm.fit(sparseDataset) - val result = model.transform(sparseDataset) - val numClusters = result.select("prediction").distinct().collect().length - // Verify we hit the edge case - assert(numClusters < k && numClusters > 1) + + testTransformerByGlobalCheckFunc[Tuple1[Vector]](sparseDataset.toDF(), model, "prediction") { + rows => + val numClusters = rows.distinct.length + // Verify we hit the edge case + assert(numClusters < k && numClusters > 1) + } } test("setter/getter") { @@ -104,19 +108,16 @@ class BisectingKMeansSuite val bkm = new BisectingKMeans().setK(k).setPredictionCol(predictionColName).setSeed(1) val model = bkm.fit(dataset) assert(model.clusterCenters.length === k) - - val transformed = model.transform(dataset) - val expectedColumns = Array("features", predictionColName) - expectedColumns.foreach { column => - assert(transformed.columns.contains(column)) - } - val clusters = - transformed.select(predictionColName).rdd.map(_.getInt(0)).distinct().collect().toSet - assert(clusters.size === k) - assert(clusters === Set(0, 1, 2, 3, 4)) assert(model.computeCost(dataset) < 0.1) assert(model.hasParent) + testTransformerByGlobalCheckFunc[Tuple1[Vector]](dataset.toDF(), model, + "features", predictionColName) { rows => + val clusters = rows.map(_.getAs[Int](predictionColName)).toSet + assert(clusters.size === k) + assert(clusters === Set(0, 1, 2, 3, 4)) + } + // Check validity of model summary val numRows = dataset.count() assert(model.hasSummary) http://git-wip-us.apache.org/repos/asf/spark/blob/d4a0895c/mllib/src/test/scala/org/apache/spark/ml/clustering/GaussianMixtureSuite.scala ---------------------------------------------------------------------- diff --git a/mllib/src/test/scala/org/apache/spark/ml/clustering/GaussianMixtureSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/clustering/GaussianMixtureSuite.scala index d0d461a..0b91f50 100644 --- a/mllib/src/test/scala/org/apache/spark/ml/clustering/GaussianMixtureSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/ml/clustering/GaussianMixtureSuite.scala @@ -23,16 +23,15 @@ import org.apache.spark.SparkFunSuite import org.apache.spark.ml.linalg.{DenseMatrix, Matrices, Vector, Vectors} import org.apache.spark.ml.param.ParamMap import org.apache.spark.ml.stat.distribution.MultivariateGaussian -import org.apache.spark.ml.util.{DefaultReadWriteTest, MLTestingUtils} +import org.apache.spark.ml.util.{DefaultReadWriteTest, MLTest, MLTestingUtils} import org.apache.spark.ml.util.TestingUtils._ -import org.apache.spark.mllib.util.MLlibTestSparkContext -import org.apache.spark.sql.{DataFrame, Dataset, Row} +import org.apache.spark.sql.{Dataset, Row} -class GaussianMixtureSuite extends SparkFunSuite with MLlibTestSparkContext - with DefaultReadWriteTest { - import testImplicits._ +class GaussianMixtureSuite extends MLTest with DefaultReadWriteTest { + import GaussianMixtureSuite._ + import testImplicits._ final val k = 5 private val seed = 538009335 @@ -119,15 +118,10 @@ class GaussianMixtureSuite extends SparkFunSuite with MLlibTestSparkContext 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)) - } - // Check prediction matches the highest probability, and probabilities sum to one. - transformed.select(predictionColName, probabilityColName).collect().foreach { - case Row(pred: Int, prob: Vector) => + testTransformer[Tuple1[Vector]](dataset.toDF(), model, + "features", predictionColName, probabilityColName) { + case Row(_, pred: Int, prob: Vector) => val probArray = prob.toArray val predFromProb = probArray.zipWithIndex.maxBy(_._1)._2 assert(pred === predFromProb) http://git-wip-us.apache.org/repos/asf/spark/blob/d4a0895c/mllib/src/test/scala/org/apache/spark/ml/clustering/KMeansSuite.scala ---------------------------------------------------------------------- diff --git a/mllib/src/test/scala/org/apache/spark/ml/clustering/KMeansSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/clustering/KMeansSuite.scala index 680a7c2..2569e7a 100644 --- a/mllib/src/test/scala/org/apache/spark/ml/clustering/KMeansSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/ml/clustering/KMeansSuite.scala @@ -22,20 +22,21 @@ import scala.util.Random import org.dmg.pmml.{ClusteringModel, PMML} -import org.apache.spark.{SparkException, SparkFunSuite} +import org.apache.spark.SparkException import org.apache.spark.ml.linalg.{Vector, Vectors} import org.apache.spark.ml.param.ParamMap -import org.apache.spark.ml.util._ +import org.apache.spark.ml.util.{DefaultReadWriteTest, MLTest, MLTestingUtils, PMMLReadWriteTest} import org.apache.spark.ml.util.TestingUtils._ -import org.apache.spark.mllib.clustering.{DistanceMeasure, KMeans => MLlibKMeans, KMeansModel => MLlibKMeansModel} +import org.apache.spark.mllib.clustering.{DistanceMeasure, KMeans => MLlibKMeans, + KMeansModel => MLlibKMeansModel} import org.apache.spark.mllib.linalg.{Vectors => MLlibVectors} -import org.apache.spark.mllib.util.MLlibTestSparkContext import org.apache.spark.sql.{DataFrame, Dataset, SparkSession} private[clustering] case class TestRow(features: Vector) -class KMeansSuite extends SparkFunSuite with MLlibTestSparkContext with DefaultReadWriteTest - with PMMLReadWriteTest { +class KMeansSuite extends MLTest with DefaultReadWriteTest with PMMLReadWriteTest { + + import testImplicits._ final val k = 5 @transient var dataset: Dataset[_] = _ @@ -109,15 +110,13 @@ class KMeansSuite extends SparkFunSuite with MLlibTestSparkContext with DefaultR val model = kmeans.fit(dataset) assert(model.clusterCenters.length === k) - val transformed = model.transform(dataset) - val expectedColumns = Array("features", predictionColName) - expectedColumns.foreach { column => - assert(transformed.columns.contains(column)) + testTransformerByGlobalCheckFunc[Tuple1[Vector]](dataset.toDF(), model, + "features", predictionColName) { rows => + val clusters = rows.map(_.getAs[Int](predictionColName)).toSet + assert(clusters.size === k) + assert(clusters === Set(0, 1, 2, 3, 4)) } - val clusters = - transformed.select(predictionColName).rdd.map(_.getInt(0)).distinct().collect().toSet - assert(clusters.size === k) - assert(clusters === Set(0, 1, 2, 3, 4)) + assert(model.computeCost(dataset) < 0.1) assert(model.hasParent) @@ -149,9 +148,7 @@ class KMeansSuite extends SparkFunSuite with MLlibTestSparkContext with DefaultR model.setFeaturesCol(featuresColName).setPredictionCol(predictionColName) val transformed = model.transform(dataset.withColumnRenamed("features", featuresColName)) - Seq(featuresColName, predictionColName).foreach { column => - assert(transformed.columns.contains(column)) - } + assert(transformed.schema.fieldNames.toSet === Set(featuresColName, predictionColName)) assert(model.getFeaturesCol == featuresColName) assert(model.getPredictionCol == predictionColName) } http://git-wip-us.apache.org/repos/asf/spark/blob/d4a0895c/mllib/src/test/scala/org/apache/spark/ml/clustering/LDASuite.scala ---------------------------------------------------------------------- diff --git a/mllib/src/test/scala/org/apache/spark/ml/clustering/LDASuite.scala b/mllib/src/test/scala/org/apache/spark/ml/clustering/LDASuite.scala index 4d84820..096b541 100644 --- a/mllib/src/test/scala/org/apache/spark/ml/clustering/LDASuite.scala +++ b/mllib/src/test/scala/org/apache/spark/ml/clustering/LDASuite.scala @@ -21,11 +21,9 @@ import scala.language.existentials import org.apache.hadoop.fs.Path -import org.apache.spark.SparkFunSuite import org.apache.spark.ml.linalg.{Vector, Vectors} -import org.apache.spark.ml.util.{DefaultReadWriteTest, MLTestingUtils} +import org.apache.spark.ml.util.{DefaultReadWriteTest, MLTest, MLTestingUtils} import org.apache.spark.ml.util.TestingUtils._ -import org.apache.spark.mllib.util.MLlibTestSparkContext import org.apache.spark.sql._ object LDASuite { @@ -61,7 +59,7 @@ object LDASuite { } -class LDASuite extends SparkFunSuite with MLlibTestSparkContext with DefaultReadWriteTest { +class LDASuite extends MLTest with DefaultReadWriteTest { import testImplicits._ @@ -186,16 +184,11 @@ class LDASuite extends SparkFunSuite with MLlibTestSparkContext with DefaultRead assert(model.topicsMatrix.numCols === k) assert(!model.isDistributed) - // transform() - val transformed = model.transform(dataset) - val expectedColumns = Array("features", lda.getTopicDistributionCol) - expectedColumns.foreach { column => - assert(transformed.columns.contains(column)) - } - transformed.select(lda.getTopicDistributionCol).collect().foreach { r => - val topicDistribution = r.getAs[Vector](0) - assert(topicDistribution.size === k) - assert(topicDistribution.toArray.forall(w => w >= 0.0 && w <= 1.0)) + testTransformer[Tuple1[Vector]](dataset.toDF(), model, + "features", lda.getTopicDistributionCol) { + case Row(_, topicDistribution: Vector) => + assert(topicDistribution.size === k) + assert(topicDistribution.toArray.forall(w => w >= 0.0 && w <= 1.0)) } // logLikelihood, logPerplexity --------------------------------------------------------------------- To unsubscribe, e-mail: commits-unsubscr...@spark.apache.org For additional commands, e-mail: commits-h...@spark.apache.org
