[SPARK-15292][ML] ML 2.0 QA: Scala APIs audit for classification

## What changes were proposed in this pull request?
Audit Scala API for classification, almost all issues were related ```MultilayerPerceptronClassifier``` in this section.
* Fix one wrong param getter function: ```getOptimizer``` -> ```getSolver```
* Add missing setter function for ```solver``` and ```stepSize```.
* Make ```GD``` solver take effect.
* Update docs, annotations and fix other minor issues.

## How was this patch tested?
Existing unit tests.

Author: Yanbo Liang <ybliang8@gmail.com>

Closes #13076 from yanboliang/spark-15292.

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

@@ -32,22 +32,22 @@ import org.apache.spark.ml.util._
 import org.apache.spark.sql.Dataset
 
 /** Params for Multilayer Perceptron. */
-private[ml] trait MultilayerPerceptronParams extends PredictorParams
+private[classification] trait MultilayerPerceptronParams extends PredictorParams
   with HasSeed with HasMaxIter with HasTol with HasStepSize {
   /**
    * Layer sizes including input size and output size.
-   * Default: Array(1, 1)
    *
    * @group param
    */
+  @Since("1.5.0")
   final val layers: IntArrayParam = new IntArrayParam(this, "layers",
-    "Sizes of layers from input layer to output layer" +
-      " E.g., Array(780, 100, 10) means 780 inputs, " +
+    "Sizes of layers from input layer to output layer. " +
+      "E.g., Array(780, 100, 10) means 780 inputs, " +
       "one hidden layer with 100 neurons and output layer of 10 neurons.",
-    (t: Array[Int]) => t.forall(ParamValidators.gt(0)) && t.length > 1
-  )
+    (t: Array[Int]) => t.forall(ParamValidators.gt(0)) && t.length > 1)
 
   /** @group getParam */
+  @Since("1.5.0")
   final def getLayers: Array[Int] = $(layers)
 
   /**
@@ -59,42 +59,49 @@ private[ml] trait MultilayerPerceptronParams extends PredictorParams
    *
    * @group expertParam
    */
+  @Since("1.5.0")
   final val blockSize: IntParam = new IntParam(this, "blockSize",
     "Block size for stacking input data in matrices. Data is stacked within partitions." +
       " If block size is more than remaining data in a partition then " +
       "it is adjusted to the size of this data. Recommended size is between 10 and 1000",
     ParamValidators.gt(0))
 
-  /** @group getParam */
+  /** @group expertGetParam */
+  @Since("1.5.0")
   final def getBlockSize: Int = $(blockSize)
 
   /**
-   * Allows setting the solver: minibatch gradient descent (gd) or l-bfgs.
-   * l-bfgs is the default one.
+   * The solver algorithm for optimization.
+   * Supported options: "gd" (minibatch gradient descent) or "l-bfgs".
+   * Default: "l-bfgs"
    *
    * @group expertParam
    */
+  @Since("2.0.0")
   final val solver: Param[String] = new Param[String](this, "solver",
-    " Allows setting the solver: minibatch gradient descent (gd) or l-bfgs. " +
-      " l-bfgs is the default one.",
-    ParamValidators.inArray[String](Array("gd", "l-bfgs")))
+    "The solver algorithm for optimization. Supported options: " +
+      s"${MultilayerPerceptronClassifier.supportedSolvers.mkString(", ")}. (Default l-bfgs)",
+    ParamValidators.inArray[String](MultilayerPerceptronClassifier.supportedSolvers))
 
-  /** @group getParam */
-  final def getOptimizer: String = $(solver)
+  /** @group expertGetParam */
+  @Since("2.0.0")
+  final def getSolver: String = $(solver)
 
   /**
-   * Model weights. Can be returned either after training or after explicit setting
+   * The initial weights of the model.
    *
    * @group expertParam
    */
-  final val weights: Param[Vector] = new Param[Vector](this, "weights",
-    " Sets the weights of the model ")
-
-  /** @group getParam */
-  final def getWeights: Vector = $(weights)
+  @Since("2.0.0")
+  final val initialWeights: Param[Vector] = new Param[Vector](this, "initialWeights",
+    "The initial weights of the model")
 
+  /** @group expertGetParam */
+  @Since("2.0.0")
+  final def getInitialWeights: Vector = $(initialWeights)
 
-  setDefault(maxIter -> 100, tol -> 1e-4, blockSize -> 128, solver -> "l-bfgs", stepSize -> 0.03)
+  setDefault(maxIter -> 100, tol -> 1e-4, blockSize -> 128,
+    solver -> MultilayerPerceptronClassifier.LBFGS, stepSize -> 0.03)
 }
 
 /** Label to vector converter. */
@@ -145,14 +152,32 @@ class MultilayerPerceptronClassifier @Since("1.5.0") (
   @Since("1.5.0")
   def this() = this(Identifiable.randomUID("mlpc"))
 
-  /** @group setParam */
+  /**
+   * Sets the value of param [[layers]].
+   *
+   * @group setParam
+   */
   @Since("1.5.0")
   def setLayers(value: Array[Int]): this.type = set(layers, value)
 
-  /** @group setParam */
+  /**
+   * Sets the value of param [[blockSize]].
+   * Default is 128.
+   *
+   * @group expertSetParam
+   */
   @Since("1.5.0")
   def setBlockSize(value: Int): this.type = set(blockSize, value)
 
+  /**
+   * Sets the value of param [[solver]].
+   * Default is "l-bfgs".
+   *
+   * @group expertSetParam
+   */
+  @Since("2.0.0")
+  def setSolver(value: String): this.type = set(solver, value)
+
   /**
    * Set the maximum number of iterations.
    * Default is 100.
@@ -181,12 +206,21 @@ class MultilayerPerceptronClassifier @Since("1.5.0") (
   def setSeed(value: Long): this.type = set(seed, value)
 
   /**
-   * Sets the model weights.
+   * Sets the value of param [[initialWeights]].
    *
-   * @group expertParam
+   * @group expertSetParam
+   */
+  @Since("2.0.0")
+  def setInitialWeights(value: Vector): this.type = set(initialWeights, value)
+
+  /**
+   * Sets the value of param [[stepSize]] (applicable only for solver "gd").
+   * Default is 0.03.
+   *
+   * @group setParam
    */
   @Since("2.0.0")
-  def setWeights(value: Vector): this.type = set(weights, value)
+  def setStepSize(value: Double): this.type = set(stepSize, value)
 
   @Since("1.5.0")
   override def copy(extra: ParamMap): MultilayerPerceptronClassifier = defaultCopy(extra)
@@ -204,16 +238,26 @@ class MultilayerPerceptronClassifier @Since("1.5.0") (
     val labels = myLayers.last
     val lpData = extractLabeledPoints(dataset)
     val data = lpData.map(lp => LabelConverter.encodeLabeledPoint(lp, labels))
-    val topology = FeedForwardTopology.multiLayerPerceptron(myLayers, true)
+    val topology = FeedForwardTopology.multiLayerPerceptron(myLayers, softmaxOnTop = true)
     val trainer = new FeedForwardTrainer(topology, myLayers(0), myLayers.last)
-    if (isDefined(weights)) {
-      trainer.setWeights($(weights))
+    if (isDefined(initialWeights)) {
+      trainer.setWeights($(initialWeights))
     } else {
       trainer.setSeed($(seed))
     }
-    trainer.LBFGSOptimizer
-      .setConvergenceTol($(tol))
-      .setNumIterations($(maxIter))
+    if ($(solver) == MultilayerPerceptronClassifier.LBFGS) {
+      trainer.LBFGSOptimizer
+        .setConvergenceTol($(tol))
+        .setNumIterations($(maxIter))
+    } else if ($(solver) == MultilayerPerceptronClassifier.GD) {
+      trainer.SGDOptimizer
+        .setNumIterations($(maxIter))
+        .setConvergenceTol($(tol))
+        .setStepSize($(stepSize))
+    } else {
+      throw new IllegalArgumentException(
+        s"The solver $solver is not supported by MultilayerPerceptronClassifier.")
+    }
     trainer.setStackSize($(blockSize))
     val mlpModel = trainer.train(data)
     new MultilayerPerceptronClassificationModel(uid, myLayers, mlpModel.weights)
@@ -224,6 +268,15 @@ class MultilayerPerceptronClassifier @Since("1.5.0") (
 object MultilayerPerceptronClassifier
   extends DefaultParamsReadable[MultilayerPerceptronClassifier] {
 
+  /** String name for "l-bfgs" solver. */
+  private[classification] val LBFGS = "l-bfgs"
+
+  /** String name for "gd" (minibatch gradient descent) solver. */
+  private[classification] val GD = "gd"
+
+  /** Set of solvers that MultilayerPerceptronClassifier supports. */
+  private[classification] val supportedSolvers = Array(LBFGS, GD)
+
   @Since("2.0.0")
   override def load(path: String): MultilayerPerceptronClassifier = super.load(path)
 }
@@ -250,7 +303,9 @@ class MultilayerPerceptronClassificationModel private[ml] (
   @Since("1.6.0")
   override val numFeatures: Int = layers.head
 
-  private val mlpModel = FeedForwardTopology.multiLayerPerceptron(layers, true).model(weights)
+  private val mlpModel = FeedForwardTopology
+    .multiLayerPerceptron(layers, softmaxOnTop = true)
+    .model(weights)
 
   /**
    * Returns layers in a Java List.

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

@@ -70,6 +70,7 @@ class MultilayerPerceptronClassifierSuite
       .setBlockSize(1)
       .setSeed(123L)
       .setMaxIter(100)
+      .setSolver("l-bfgs")
     val model = trainer.fit(dataset)
     val result = model.transform(dataset)
     val predictionAndLabels = result.select("prediction", "label").collect()
@@ -93,9 +94,9 @@ class MultilayerPerceptronClassifierSuite
       .setMaxIter(1)
       .setTol(1e-6)
     val initialWeights = trainer.fit(dataFrame).weights
-    trainer.setWeights(initialWeights.copy)
+    trainer.setInitialWeights(initialWeights.copy)
     val weights1 = trainer.fit(dataFrame).weights
-    trainer.setWeights(initialWeights.copy)
+    trainer.setInitialWeights(initialWeights.copy)
     val weights2 = trainer.fit(dataFrame).weights
     assert(weights1 ~== weights2 absTol 10e-5,
       "Training should produce the same weights given equal initial weights and number of steps")