Upgrade to Spark 2.2 Fix copying the params to the model (#92)

* Upgrade to Spark 2.2

* Fix copying the params to the model

* Fix long line

* Comment

* Simplify

* Add a test to verify we are copy the params correctly

* test copy while we are there

* And the testing lib too

* Fix some tests/copy methods

* Push to 2.2.0 0.7.2 for windows build too

* Fix copy

build.sbt

@@ -15,7 +15,7 @@ crossScalaVersions := Seq("2.11.6")
 javacOptions ++= Seq("-source", "1.8", "-target", "1.8")
 
 //tag::sparkVersion[]
-sparkVersion := "2.1.0"
+sparkVersion := "2.2.0"
 //end::sparkVersion[]
 
 //tag::sparkComponents[]
@@ -40,7 +40,7 @@ libraryDependencies ++= Seq(
   "org.scalacheck" %% "scalacheck" % "1.13.4",
   "junit" % "junit" % "4.12",
   "junit" % "junit" % "4.11",
-  "com.holdenkarau" %% "spark-testing-base" % "2.1.0_0.6.0",
+  "com.holdenkarau" %% "spark-testing-base" % "2.2.0_0.7.2",
   "com.novocode" % "junit-interface" % "0.11" % "test->default",
   //tag::scalaLogging[]
   "com.typesafe.scala-logging" %% "scala-logging" % "3.5.0",

build_windows.sbt

@@ -15,7 +15,7 @@ crossScalaVersions := Seq("2.11.6")
 javacOptions ++= Seq("-source", "1.8", "-target", "1.8")
 
 //tag::sparkVersion[]
-sparkVersion := "2.1.0"
+sparkVersion := "2.2.0"
 //end::sparkVersion[]
 
 //tag::sparkComponents[]
@@ -40,7 +40,7 @@ libraryDependencies ++= Seq(
   "org.scalacheck" %% "scalacheck" % "1.13.4",
   "junit" % "junit" % "4.12",
   "junit" % "junit" % "4.11",
-  "com.holdenkarau" %% "spark-testing-base" % "2.1.0_0.6.0",
+  "com.holdenkarau" %% "spark-testing-base" % "2.2.0_0.7.2",
   "com.novocode" % "junit-interface" % "0.11" % "test->default",
   //tag::sacalLogging[]
   "com.typesafe.scala-logging" %% "scala-logging" % "3.5.0",

src/main/scala/com/high-performance-spark-examples/ml/CustomPipeline.scala

@@ -122,9 +122,10 @@ class SimpleIndexer(override val uid: String)
     import dataset.sparkSession.implicits._
     val words = dataset.select(dataset($(inputCol)).as[String]).distinct
       .collect()
+    // Construct the model
     val model = new SimpleIndexerModel(uid, words)
-    this.copyValues(model)
-    model
+    // Copy the parameters to the model
+    copyValues(model)
   }
 }
 

src/main/scala/com/high-performance-spark-examples/ml/SimpleNaiveBayes.scala

@@ -82,11 +82,14 @@ class SimpleNaiveBayes(val uid: String)
     // Unpersist now that we are done computing everything
     ds.unpersist()
     // Construct a model
-    new SimpleNaiveBayesModel(uid, numClasses, numFeatures, Vectors.dense(pi),
+    val model = new SimpleNaiveBayesModel(
+      uid, numClasses, numFeatures, Vectors.dense(pi),
       new DenseMatrix(numClasses, theta(0).length, theta.flatten, true))
+    // Copy the params values to the model
+    copyValues(model)
   }
 
-  override def copy(extra: ParamMap) = {
+  override def copy(extra: ParamMap): SimpleNaiveBayes = {
     defaultCopy(extra)
   }
 }
@@ -100,8 +103,9 @@ case class SimpleNaiveBayesModel(
   val theta: DenseMatrix) extends
     ClassificationModel[Vector, SimpleNaiveBayesModel] {
 
-  override def copy(extra: ParamMap) = {
-    defaultCopy(extra)
+  override def copy(extra: ParamMap): SimpleNaiveBayesModel = {
+    val copied = new SimpleNaiveBayesModel(uid, numClasses, numFeatures, pi, theta)
+    copyValues(copied, extra).setParent(parent)
   }
 
   // We have to do some tricks here because we are using Spark's

src/test/scala/com/high-performance-spark-examples/ml/SimpleNaiveBayes.scala

@@ -9,6 +9,7 @@ import com.holdenkarau.spark.testing._
 
 import org.apache.spark.ml._
 import org.apache.spark.ml.feature._
+import org.apache.spark.ml.param._
 import org.apache.spark.sql.types._
 import org.apache.spark.sql.{DataFrame, Dataset, Row, SQLContext}
 import org.scalatest.Matchers._
@@ -30,14 +31,17 @@ class SimpleNaiveBayesSuite extends FunSuite with DataFrameSuiteBase {
     val ds: Dataset[MiniPanda] = session.createDataset(miniPandasList)
     val assembler = new VectorAssembler()
     assembler.setInputCols(Array("fuzzy", "old"))
-    assembler.setOutputCol("features")
+    assembler.setOutputCol("magical_features")
     val snb = new SimpleNaiveBayes()
     snb.setLabelCol("happy")
-    snb.setFeaturesCol("features")
+    snb.setFeaturesCol("magical_features")
     val pipeline = new Pipeline().setStages(Array(assembler, snb))
     val model = pipeline.fit(ds)
     val test = ds.select("fuzzy", "old")
     val predicted = model.transform(test)
-    println(predicted.collect())
+    assert(predicted.count() === miniPandasList.size)
+    val nbModel = model.stages(1).asInstanceOf[SimpleNaiveBayesModel]
+    assert(nbModel.getFeaturesCol === "magical_features")
+    assert(nbModel.copy(ParamMap.empty).getFeaturesCol === "magical_features")
   }
 }