[SPARK-3841] [mllib] Pretty-print params for ML examples

Provide a parent class for the Params case classes used in many MLlib examples, where the parent class pretty-prints the case class fields:
Param1Name	Param1Value
Param2Name	Param2Value
...
Using this class will make it easier to print test settings to logs.

Also, updated DecisionTreeRunner to print a little more info.

CC: mengxr

Author: Joseph K. Bradley <joseph.kurata.bradley@gmail.com>

Closes #2700 from jkbradley/dtrunner-update and squashes the following commits:

cff873f [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into dtrunner-update
7a08ae4 [Joseph K. Bradley] code review comment updates
b4d2043 [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into dtrunner-update
d8228a7 [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into dtrunner-update
0fc9c64 [Joseph K. Bradley] Added abstract TestParams class for mllib example parameters
12b7798 [Joseph K. Bradley] Added abstract class TestParams for pretty-printing Params values
5f84f03 [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into dtrunner-update
f7441b6 [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into dtrunner-update
19eb6fc [Joseph K. Bradley] Updated DecisionTreeRunner to print training time.

examples/src/main/scala/org/apache/spark/examples/mllib/AbstractParams.scala

+/*
+ * 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.examples.mllib
+
+import scala.reflect.runtime.universe._
+
+/**
+ * Abstract class for parameter case classes.
+ * This overrides the [[toString]] method to print all case class fields by name and value.
+ * @tparam T  Concrete parameter class.
+ */
+abstract class AbstractParams[T: TypeTag] {
+
+  private def tag: TypeTag[T] = typeTag[T]
+
+  /**
+   * Finds all case class fields in concrete class instance, and outputs them in JSON-style format:
+   * {
+   *   [field name]:\t[field value]\n
+   *   [field name]:\t[field value]\n
+   *   ...
+   * }
+   */
+  override def toString: String = {
+    val tpe = tag.tpe
+    val allAccessors = tpe.declarations.collect {
+      case m: MethodSymbol if m.isCaseAccessor => m
+    }
+    val mirror = runtimeMirror(getClass.getClassLoader)
+    val instanceMirror = mirror.reflect(this)
+    allAccessors.map { f =>
+      val paramName = f.name.toString
+      val fieldMirror = instanceMirror.reflectField(f)
+      val paramValue = fieldMirror.get
+      s"  $paramName:\t$paramValue"
+    }.mkString("{\n", ",\n", "\n}")
+  }
+}

examples/src/main/scala/org/apache/spark/examples/mllib/BinaryClassification.scala

@@ -55,7 +55,7 @@ object BinaryClassification {
       stepSize: Double = 1.0,
       algorithm: Algorithm = LR,
       regType: RegType = L2,
-      regParam: Double = 0.1)
+      regParam: Double = 0.1) extends AbstractParams[Params]
 
   def main(args: Array[String]) {
     val defaultParams = Params()

examples/src/main/scala/org/apache/spark/examples/mllib/Correlations.scala

@@ -35,6 +35,7 @@ import org.apache.spark.{SparkConf, SparkContext}
 object Correlations {
 
   case class Params(input: String = "data/mllib/sample_linear_regression_data.txt")
+    extends AbstractParams[Params]
 
   def main(args: Array[String]) {
 

examples/src/main/scala/org/apache/spark/examples/mllib/CosineSimilarity.scala

@@ -43,6 +43,7 @@ import org.apache.spark.{SparkConf, SparkContext}
  */
 object CosineSimilarity {
   case class Params(inputFile: String = null, threshold: Double = 0.1)
+    extends AbstractParams[Params]
 
   def main(args: Array[String]) {
     val defaultParams = Params()

examples/src/main/scala/org/apache/spark/examples/mllib/DecisionTreeRunner.scala

@@ -62,7 +62,7 @@ object DecisionTreeRunner {
       minInfoGain: Double = 0.0,
       numTrees: Int = 1,
       featureSubsetStrategy: String = "auto",
-      fracTest: Double = 0.2)
+      fracTest: Double = 0.2) extends AbstractParams[Params]
 
   def main(args: Array[String]) {
     val defaultParams = Params()
@@ -138,9 +138,11 @@ object DecisionTreeRunner {
 
   def run(params: Params) {
 
-    val conf = new SparkConf().setAppName("DecisionTreeRunner")
+    val conf = new SparkConf().setAppName(s"DecisionTreeRunner with $params")
     val sc = new SparkContext(conf)
 
+    println(s"DecisionTreeRunner with parameters:\n$params")
+
     // Load training data and cache it.
     val origExamples = params.dataFormat match {
       case "dense" => MLUtils.loadLabeledPoints(sc, params.input).cache()
@@ -235,7 +237,10 @@ object DecisionTreeRunner {
           minInstancesPerNode = params.minInstancesPerNode,
           minInfoGain = params.minInfoGain)
     if (params.numTrees == 1) {
+      val startTime = System.nanoTime()
       val model = DecisionTree.train(training, strategy)
+      val elapsedTime = (System.nanoTime() - startTime) / 1e9
+      println(s"Training time: $elapsedTime seconds")
       if (model.numNodes < 20) {
         println(model.toDebugString) // Print full model.
       } else {
@@ -259,8 +264,11 @@ object DecisionTreeRunner {
     } else {
       val randomSeed = Utils.random.nextInt()
       if (params.algo == Classification) {
+        val startTime = System.nanoTime()
         val model = RandomForest.trainClassifier(training, strategy, params.numTrees,
           params.featureSubsetStrategy, randomSeed)
+        val elapsedTime = (System.nanoTime() - startTime) / 1e9
+        println(s"Training time: $elapsedTime seconds")
         if (model.totalNumNodes < 30) {
           println(model.toDebugString) // Print full model.
         } else {
@@ -275,8 +283,11 @@ object DecisionTreeRunner {
         println(s"Test accuracy = $testAccuracy")
       }
       if (params.algo == Regression) {
+        val startTime = System.nanoTime()
         val model = RandomForest.trainRegressor(training, strategy, params.numTrees,
           params.featureSubsetStrategy, randomSeed)
+        val elapsedTime = (System.nanoTime() - startTime) / 1e9
+        println(s"Training time: $elapsedTime seconds")
         if (model.totalNumNodes < 30) {
           println(model.toDebugString) // Print full model.
         } else {

examples/src/main/scala/org/apache/spark/examples/mllib/DenseKMeans.scala

@@ -44,7 +44,7 @@ object DenseKMeans {
       input: String = null,
       k: Int = -1,
       numIterations: Int = 10,
-      initializationMode: InitializationMode = Parallel)
+      initializationMode: InitializationMode = Parallel) extends AbstractParams[Params]
 
   def main(args: Array[String]) {
     val defaultParams = Params()

examples/src/main/scala/org/apache/spark/examples/mllib/LinearRegression.scala

@@ -47,7 +47,7 @@ object LinearRegression extends App {
       numIterations: Int = 100,
       stepSize: Double = 1.0,
       regType: RegType = L2,
-      regParam: Double = 0.1)
+      regParam: Double = 0.1) extends AbstractParams[Params]
 
   val defaultParams = Params()
 

examples/src/main/scala/org/apache/spark/examples/mllib/MovieLensALS.scala

@@ -55,7 +55,7 @@ object MovieLensALS {
       rank: Int = 10,
       numUserBlocks: Int = -1,
       numProductBlocks: Int = -1,
-      implicitPrefs: Boolean = false)
+      implicitPrefs: Boolean = false) extends AbstractParams[Params]
 
   def main(args: Array[String]) {
     val defaultParams = Params()

examples/src/main/scala/org/apache/spark/examples/mllib/MultivariateSummarizer.scala

@@ -36,6 +36,7 @@ import org.apache.spark.{SparkConf, SparkContext}
 object MultivariateSummarizer {
 
   case class Params(input: String = "data/mllib/sample_linear_regression_data.txt")
+    extends AbstractParams[Params]
 
   def main(args: Array[String]) {
 

examples/src/main/scala/org/apache/spark/examples/mllib/SampledRDDs.scala

@@ -33,6 +33,7 @@ import org.apache.spark.SparkContext._
 object SampledRDDs {
 
   case class Params(input: String = "data/mllib/sample_binary_classification_data.txt")
+    extends AbstractParams[Params]
 
   def main(args: Array[String]) {
     val defaultParams = Params()

examples/src/main/scala/org/apache/spark/examples/mllib/SparseNaiveBayes.scala

@@ -37,7 +37,7 @@ object SparseNaiveBayes {
       input: String = null,
       minPartitions: Int = 0,
       numFeatures: Int = -1,
-      lambda: Double = 1.0)
+      lambda: Double = 1.0) extends AbstractParams[Params]
 
   def main(args: Array[String]) {
     val defaultParams = Params()