[SPARK-19985][ML] Fixed copy method for some ML Models

## What changes were proposed in this pull request?
Some ML Models were using `defaultCopy` which expects a default constructor, and others were not setting the parent estimator.  This change fixes these by creating a new instance of the model and explicitly setting values and parent.

## How was this patch tested?
Added `MLTestingUtils.checkCopy` to the offending models to tests to verify the copy is made and parent is set.

Author: Bryan Cutler <cutlerb@gmail.com>

Closes #17326 from BryanCutler/ml-model-copy-error-SPARK-19985.

mllib/src/main/scala/org/apache/spark/ml/classification/MultilayerPerceptronClassifier.scala

@@ -329,7 +329,8 @@ class MultilayerPerceptronClassificationModel private[ml] (
 
   @Since("1.5.0")
   override def copy(extra: ParamMap): MultilayerPerceptronClassificationModel = {
-    copyValues(new MultilayerPerceptronClassificationModel(uid, layers, weights), extra)
+    val copied = new MultilayerPerceptronClassificationModel(uid, layers, weights).setParent(parent)
+    copyValues(copied, extra)
   }
 
   @Since("2.0.0")

mllib/src/main/scala/org/apache/spark/ml/feature/BucketedRandomProjectionLSH.scala

@@ -96,7 +96,10 @@ class BucketedRandomProjectionLSHModel private[ml](
   }
 
   @Since("2.1.0")
-  override def copy(extra: ParamMap): this.type = defaultCopy(extra)
+  override def copy(extra: ParamMap): BucketedRandomProjectionLSHModel = {
+    val copied = new BucketedRandomProjectionLSHModel(uid, randUnitVectors).setParent(parent)
+    copyValues(copied, extra)
+  }
 
   @Since("2.1.0")
   override def write: MLWriter = {

mllib/src/main/scala/org/apache/spark/ml/feature/MinHashLSH.scala

@@ -86,7 +86,10 @@ class MinHashLSHModel private[ml](
   }
 
   @Since("2.1.0")
-  override def copy(extra: ParamMap): this.type = defaultCopy(extra)
+  override def copy(extra: ParamMap): MinHashLSHModel = {
+    val copied = new MinHashLSHModel(uid, randCoefficients).setParent(parent)
+    copyValues(copied, extra)
+  }
 
   @Since("2.1.0")
   override def write: MLWriter = new MinHashLSHModel.MinHashLSHModelWriter(this)

mllib/src/main/scala/org/apache/spark/ml/feature/RFormula.scala

@@ -268,8 +268,10 @@ class RFormulaModel private[feature](
   }
 
   @Since("1.5.0")
-  override def copy(extra: ParamMap): RFormulaModel = copyValues(
-    new RFormulaModel(uid, resolvedFormula, pipelineModel))
+  override def copy(extra: ParamMap): RFormulaModel = {
+    val copied = new RFormulaModel(uid, resolvedFormula, pipelineModel).setParent(parent)
+    copyValues(copied, extra)
+  }
 
   @Since("2.0.0")
   override def toString: String = s"RFormulaModel($resolvedFormula) (uid=$uid)"

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

@@ -74,6 +74,7 @@ class MultilayerPerceptronClassifierSuite
       .setMaxIter(100)
       .setSolver("l-bfgs")
     val model = trainer.fit(dataset)
+    MLTestingUtils.checkCopy(model)
     val result = model.transform(dataset)
     val predictionAndLabels = result.select("prediction", "label").collect()
     predictionAndLabels.foreach { case Row(p: Double, l: Double) =>

mllib/src/test/scala/org/apache/spark/ml/feature/BucketedRandomProjectionLSHSuite.scala

@@ -23,7 +23,7 @@ import breeze.numerics.constants.Pi
 import org.apache.spark.SparkFunSuite
 import org.apache.spark.ml.linalg.{Vector, Vectors}
 import org.apache.spark.ml.param.ParamsSuite
-import org.apache.spark.ml.util.DefaultReadWriteTest
+import org.apache.spark.ml.util.{DefaultReadWriteTest, MLTestingUtils}
 import org.apache.spark.ml.util.TestingUtils._
 import org.apache.spark.mllib.util.MLlibTestSparkContext
 import org.apache.spark.sql.Dataset
@@ -89,10 +89,12 @@ class BucketedRandomProjectionLSHSuite
       .setOutputCol("values")
       .setBucketLength(1.0)
       .setSeed(12345)
-    val unitVectors = brp.fit(dataset).randUnitVectors
+    val brpModel = brp.fit(dataset)
+    val unitVectors = brpModel.randUnitVectors
     unitVectors.foreach { v: Vector =>
       assert(Vectors.norm(v, 2.0) ~== 1.0 absTol 1e-14)
     }
+    MLTestingUtils.checkCopy(brpModel)
   }
 
   test("BucketedRandomProjectionLSH: test of LSH property") {

mllib/src/test/scala/org/apache/spark/ml/feature/MinHashLSHSuite.scala

@@ -20,7 +20,7 @@ package org.apache.spark.ml.feature
 import org.apache.spark.SparkFunSuite
 import org.apache.spark.ml.linalg.{Vector, Vectors}
 import org.apache.spark.ml.param.ParamsSuite
-import org.apache.spark.ml.util.DefaultReadWriteTest
+import org.apache.spark.ml.util.{DefaultReadWriteTest, MLTestingUtils}
 import org.apache.spark.mllib.util.MLlibTestSparkContext
 import org.apache.spark.sql.Dataset
 
@@ -57,6 +57,15 @@ class MinHashLSHSuite extends SparkFunSuite with MLlibTestSparkContext with Defa
     testEstimatorAndModelReadWrite(mh, dataset, settings, settings, checkModelData)
   }
 
+  test("Model copy and uid checks") {
+    val mh = new MinHashLSH()
+      .setInputCol("keys")
+      .setOutputCol("values")
+    val model = mh.fit(dataset)
+    assert(mh.uid === model.uid)
+    MLTestingUtils.checkCopy(model)
+  }
+
   test("hashFunction") {
     val model = new MinHashLSHModel("mh", randCoefficients = Array((0, 1), (1, 2), (3, 0)))
     val res = model.hashFunction(Vectors.sparse(10, Seq((2, 1.0), (3, 1.0), (5, 1.0), (7, 1.0))))

mllib/src/test/scala/org/apache/spark/ml/feature/RFormulaSuite.scala

@@ -37,6 +37,7 @@ class RFormulaSuite extends SparkFunSuite with MLlibTestSparkContext with Defaul
     val formula = new RFormula().setFormula("id ~ v1 + v2")
     val original = Seq((0, 1.0, 3.0), (2, 2.0, 5.0)).toDF("id", "v1", "v2")
     val model = formula.fit(original)
+    MLTestingUtils.checkCopy(model)
     val result = model.transform(original)
     val resultSchema = model.transformSchema(original.schema)
     val expected = Seq(