add tests for copy summary

mllib/src/main/scala/org/apache/spark/ml/clustering/BisectingKMeans.scala

@@ -94,8 +94,9 @@ class BisectingKMeansModel private[ml] (
 
   @Since("2.0.0")
   override def copy(extra: ParamMap): BisectingKMeansModel = {
-    val copied = new BisectingKMeansModel(uid, parentModel)
-    copyValues(copied, extra)
+    val copied = copyValues(new BisectingKMeansModel(uid, parentModel), extra)
+    if (trainingSummary.isDefined) copied.setSummary(trainingSummary.get)
+    copied.setParent(this.parent)
   }
 
   @Since("2.0.0")

mllib/src/main/scala/org/apache/spark/ml/clustering/GaussianMixture.scala

@@ -89,8 +89,9 @@ class GaussianMixtureModel private[ml] (
 
   @Since("2.0.0")
   override def copy(extra: ParamMap): GaussianMixtureModel = {
-    val copied = new GaussianMixtureModel(uid, weights, gaussians)
-    copyValues(copied, extra).setParent(this.parent)
+    val copied = copyValues(new GaussianMixtureModel(uid, weights, gaussians), extra)
+    if (trainingSummary.isDefined) copied.setSummary(trainingSummary.get)
+    copied.setParent(this.parent)
   }
 
   @Since("2.0.0")
@@ -169,6 +170,13 @@ class GaussianMixtureModel private[ml] (
     throw new RuntimeException(
       s"No training summary available for the ${this.getClass.getSimpleName}")
   }
+
+//  @Since("2.1.0")
+//  override def copy(extra: ParamMap): GaussianMixtureModel = {
+//    val newModel = copyValues(new GaussianMixtureModel(uid, weights, gaussians), extra)
+//    if (trainingSummary.isDefined) newModel.setSummary(trainingSummary.get)
+//    newModel.setParent(parent)
+//  }
 }
 
 @Since("2.0.0")

mllib/src/main/scala/org/apache/spark/ml/clustering/KMeans.scala

@@ -108,8 +108,9 @@ class KMeansModel private[ml] (
 
   @Since("1.5.0")
   override def copy(extra: ParamMap): KMeansModel = {
-    val copied = new KMeansModel(uid, parentModel)
-    copyValues(copied, extra)
+    val copied = copyValues(new KMeansModel(uid, parentModel), extra)
+    if (trainingSummary.isDefined) copied.setSummary(trainingSummary.get)
+    copied.setParent(this.parent)
   }
 
   /** @group setParam */

mllib/src/main/scala/org/apache/spark/ml/regression/GeneralizedLinearRegression.scala

@@ -776,8 +776,10 @@ class GeneralizedLinearRegressionModel private[ml] (
 
   @Since("2.0.0")
   override def copy(extra: ParamMap): GeneralizedLinearRegressionModel = {
-    copyValues(new GeneralizedLinearRegressionModel(uid, coefficients, intercept), extra)
-      .setParent(parent)
+    val copied = copyValues(new GeneralizedLinearRegressionModel(uid, coefficients, intercept),
+      extra)
+    if (trainingSummary.isDefined) copied.setSummary(trainingSummary.get)
+    copied.setParent(parent)
   }
 
   /**

mllib/src/test/scala/org/apache/spark/ml/classification/LogisticRegressionSuite.scala

@@ -27,7 +27,7 @@ import org.apache.spark.ml.attribute.NominalAttribute
 import org.apache.spark.ml.classification.LogisticRegressionSuite._
 import org.apache.spark.ml.feature.{Instance, LabeledPoint}
 import org.apache.spark.ml.linalg.{DenseMatrix, Matrices, SparseMatrix, SparseVector, Vector, Vectors}
-import org.apache.spark.ml.param.ParamsSuite
+import org.apache.spark.ml.param.{ParamMap, ParamsSuite}
 import org.apache.spark.ml.util.{DefaultReadWriteTest, MLTestingUtils}
 import org.apache.spark.ml.util.TestingUtils._
 import org.apache.spark.mllib.util.MLlibTestSparkContext
@@ -141,6 +141,10 @@ class LogisticRegressionSuite
     assert(model.getProbabilityCol === "probability")
     assert(model.intercept !== 0.0)
     assert(model.hasParent)
+
+    assert(model.hasSummary)
+    val copiedModel = model.copy(ParamMap.empty)
+    assert(copiedModel.hasSummary)
   }
 
   test("empty probabilityCol") {

mllib/src/test/scala/org/apache/spark/ml/clustering/BisectingKMeansSuite.scala

@@ -18,7 +18,8 @@
 package org.apache.spark.ml.clustering
 
 import org.apache.spark.SparkFunSuite
-import org.apache.spark.ml.util.DefaultReadWriteTest
+import org.apache.spark.ml.param.ParamMap
+import org.apache.spark.ml.util.{MLTestingUtils, DefaultReadWriteTest}
 import org.apache.spark.mllib.util.MLlibTestSparkContext
 import org.apache.spark.sql.Dataset
 
@@ -41,6 +42,12 @@ class BisectingKMeansSuite
     assert(bkm.getPredictionCol === "prediction")
     assert(bkm.getMaxIter === 20)
     assert(bkm.getMinDivisibleClusterSize === 1.0)
+
+    val model = bkm.setMaxIter(1).fit(dataset)
+    assert(model.hasSummary)
+    val copiedModel = model.copy(ParamMap.empty)
+    assert(copiedModel.hasSummary)
+    MLTestingUtils.checkCopy(model)
   }
 
   test("setter/getter") {

mllib/src/test/scala/org/apache/spark/ml/clustering/GaussianMixtureSuite.scala

@@ -18,8 +18,10 @@
 package org.apache.spark.ml.clustering
 
 import org.apache.spark.SparkFunSuite
-import org.apache.spark.ml.util.DefaultReadWriteTest
-import org.apache.spark.mllib.util.MLlibTestSparkContext
+import org.apache.spark.ml.linalg.Vectors
+import org.apache.spark.ml.param.ParamMap
+import org.apache.spark.ml.util.{MLTestingUtils, DefaultReadWriteTest}
+import org.apache.spark.mllib.util.{MLUtils, MLlibTestSparkContext}
 import org.apache.spark.sql.Dataset
 
 
@@ -43,6 +45,12 @@ class GaussianMixtureSuite extends SparkFunSuite with MLlibTestSparkContext
     assert(gm.getPredictionCol === "prediction")
     assert(gm.getMaxIter === 100)
     assert(gm.getTol === 0.01)
+
+    val model = gm.setMaxIter(1).fit(dataset)
+    assert(model.hasSummary)
+    val copiedModel = model.copy(ParamMap.empty)
+    assert(copiedModel.hasSummary)
+    MLTestingUtils.checkCopy(model)
   }
 
   test("set parameters") {

mllib/src/test/scala/org/apache/spark/ml/clustering/KMeansSuite.scala

@@ -19,7 +19,8 @@ package org.apache.spark.ml.clustering
 
 import org.apache.spark.SparkFunSuite
 import org.apache.spark.ml.linalg.{Vector, Vectors}
-import org.apache.spark.ml.util.DefaultReadWriteTest
+import org.apache.spark.ml.param.ParamMap
+import org.apache.spark.ml.util.{MLTestingUtils, DefaultReadWriteTest}
 import org.apache.spark.mllib.clustering.{KMeans => MLlibKMeans}
 import org.apache.spark.mllib.util.MLlibTestSparkContext
 import org.apache.spark.sql.{DataFrame, Dataset, SparkSession}
@@ -47,6 +48,12 @@ class KMeansSuite extends SparkFunSuite with MLlibTestSparkContext with DefaultR
     assert(kmeans.getInitMode === MLlibKMeans.K_MEANS_PARALLEL)
     assert(kmeans.getInitSteps === 2)
     assert(kmeans.getTol === 1e-4)
+
+    val model = kmeans.setMaxIter(1).fit(dataset)
+    assert(model.hasSummary)
+    val copiedModel = model.copy(ParamMap.empty)
+    assert(copiedModel.hasSummary)
+    MLTestingUtils.checkCopy(model)
   }
 
   test("set parameters") {

mllib/src/test/scala/org/apache/spark/ml/regression/GeneralizedLinearRegressionSuite.scala

@@ -24,7 +24,7 @@ import org.apache.spark.ml.classification.LogisticRegressionSuite._
 import org.apache.spark.ml.feature.Instance
 import org.apache.spark.ml.feature.LabeledPoint
 import org.apache.spark.ml.linalg.{BLAS, DenseVector, Vector, Vectors}
-import org.apache.spark.ml.param.ParamsSuite
+import org.apache.spark.ml.param.{ParamMap, ParamsSuite}
 import org.apache.spark.ml.util.{DefaultReadWriteTest, MLTestingUtils}
 import org.apache.spark.ml.util.TestingUtils._
 import org.apache.spark.mllib.random._
@@ -183,6 +183,9 @@ class GeneralizedLinearRegressionSuite
 
     // copied model must have the same parent.
     MLTestingUtils.checkCopy(model)
+    assert(model.hasSummary)
+    val copiedModel = model.copy(ParamMap.empty)
+    assert(copiedModel.hasSummary)
 
     assert(model.getFeaturesCol === "features")
     assert(model.getPredictionCol === "prediction")

mllib/src/test/scala/org/apache/spark/ml/regression/LinearRegressionSuite.scala

@@ -23,7 +23,7 @@ import org.apache.spark.SparkFunSuite
 import org.apache.spark.ml.feature.Instance
 import org.apache.spark.ml.feature.LabeledPoint
 import org.apache.spark.ml.linalg.{DenseVector, Vector, Vectors}
-import org.apache.spark.ml.param.ParamsSuite
+import org.apache.spark.ml.param.{ParamMap, ParamsSuite}
 import org.apache.spark.ml.util.{DefaultReadWriteTest, MLTestingUtils}
 import org.apache.spark.ml.util.TestingUtils._
 import org.apache.spark.mllib.util.{LinearDataGenerator, MLlibTestSparkContext}
@@ -140,9 +140,13 @@ class LinearRegressionSuite
     assert(lir.getStandardization)
     assert(lir.getSolver == "auto")
     val model = lir.fit(datasetWithDenseFeature)
+    assert(model.hasSummary)
 
     // copied model must have the same parent.
     MLTestingUtils.checkCopy(model)
+    val copiedModel = model.copy(ParamMap.empty)
+    assert(copiedModel.hasSummary)
+
 
     model.transform(datasetWithDenseFeature)
       .select("label", "prediction")