[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.
This commit is contained in:
Yanbo Liang
Joseph K. Bradley
parent
1052d3644d
commit
8ecf7f77b2
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@ -32,22 +32,22 @@ import org.apache.spark.ml.util._
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import org.apache.spark.sql.Dataset
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/** Params for Multilayer Perceptron. */
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private[ml] trait MultilayerPerceptronParams extends PredictorParams
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private[classification] trait MultilayerPerceptronParams extends PredictorParams
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with HasSeed with HasMaxIter with HasTol with HasStepSize {
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/**
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* Layer sizes including input size and output size.
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* Default: Array(1, 1)
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*
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* @group param
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*/
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@Since("1.5.0")
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final val layers: IntArrayParam = new IntArrayParam(this, "layers",
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"Sizes of layers from input layer to output layer" +
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" E.g., Array(780, 100, 10) means 780 inputs, " +
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"Sizes of layers from input layer to output layer. " +
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"E.g., Array(780, 100, 10) means 780 inputs, " +
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"one hidden layer with 100 neurons and output layer of 10 neurons.",
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(t: Array[Int]) => t.forall(ParamValidators.gt(0)) && t.length > 1
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)
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(t: Array[Int]) => t.forall(ParamValidators.gt(0)) && t.length > 1)
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/** @group getParam */
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@Since("1.5.0")
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final def getLayers: Array[Int] = $(layers)
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/**
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@ -59,42 +59,49 @@ private[ml] trait MultilayerPerceptronParams extends PredictorParams
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*
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* @group expertParam
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*/
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@Since("1.5.0")
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final val blockSize: IntParam = new IntParam(this, "blockSize",
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"Block size for stacking input data in matrices. Data is stacked within partitions." +
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" If block size is more than remaining data in a partition then " +
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"it is adjusted to the size of this data. Recommended size is between 10 and 1000",
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ParamValidators.gt(0))
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/** @group getParam */
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/** @group expertGetParam */
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@Since("1.5.0")
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final def getBlockSize: Int = $(blockSize)
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/**
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* Allows setting the solver: minibatch gradient descent (gd) or l-bfgs.
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* l-bfgs is the default one.
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* The solver algorithm for optimization.
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* Supported options: "gd" (minibatch gradient descent) or "l-bfgs".
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* Default: "l-bfgs"
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*
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* @group expertParam
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*/
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@Since("2.0.0")
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final val solver: Param[String] = new Param[String](this, "solver",
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" Allows setting the solver: minibatch gradient descent (gd) or l-bfgs. " +
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" l-bfgs is the default one.",
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ParamValidators.inArray[String](Array("gd", "l-bfgs")))
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"The solver algorithm for optimization. Supported options: " +
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s"${MultilayerPerceptronClassifier.supportedSolvers.mkString(", ")}. (Default l-bfgs)",
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ParamValidators.inArray[String](MultilayerPerceptronClassifier.supportedSolvers))
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/** @group getParam */
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final def getOptimizer: String = $(solver)
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/** @group expertGetParam */
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@Since("2.0.0")
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final def getSolver: String = $(solver)
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/**
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* Model weights. Can be returned either after training or after explicit setting
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* The initial weights of the model.
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*
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* @group expertParam
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*/
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final val weights: Param[Vector] = new Param[Vector](this, "weights",
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" Sets the weights of the model ")
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@Since("2.0.0")
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final val initialWeights: Param[Vector] = new Param[Vector](this, "initialWeights",
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"The initial weights of the model")
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/** @group getParam */
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final def getWeights: Vector = $(weights)
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/** @group expertGetParam */
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@Since("2.0.0")
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final def getInitialWeights: Vector = $(initialWeights)
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setDefault(maxIter -> 100, tol -> 1e-4, blockSize -> 128, solver -> "l-bfgs", stepSize -> 0.03)
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setDefault(maxIter -> 100, tol -> 1e-4, blockSize -> 128,
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solver -> MultilayerPerceptronClassifier.LBFGS, stepSize -> 0.03)
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}
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/** Label to vector converter. */
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@ -145,14 +152,32 @@ class MultilayerPerceptronClassifier @Since("1.5.0") (
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@Since("1.5.0")
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def this() = this(Identifiable.randomUID("mlpc"))
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/** @group setParam */
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/**
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* Sets the value of param [[layers]].
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*
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* @group setParam
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*/
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@Since("1.5.0")
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def setLayers(value: Array[Int]): this.type = set(layers, value)
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/** @group setParam */
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/**
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* Sets the value of param [[blockSize]].
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* Default is 128.
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*
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* @group expertSetParam
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*/
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@Since("1.5.0")
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def setBlockSize(value: Int): this.type = set(blockSize, value)
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/**
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* Sets the value of param [[solver]].
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* Default is "l-bfgs".
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*
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* @group expertSetParam
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*/
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@Since("2.0.0")
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def setSolver(value: String): this.type = set(solver, value)
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/**
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* Set the maximum number of iterations.
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* Default is 100.
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@ -181,12 +206,21 @@ class MultilayerPerceptronClassifier @Since("1.5.0") (
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def setSeed(value: Long): this.type = set(seed, value)
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/**
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* Sets the model weights.
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* Sets the value of param [[initialWeights]].
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*
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* @group expertParam
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* @group expertSetParam
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*/
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@Since("2.0.0")
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def setWeights(value: Vector): this.type = set(weights, value)
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def setInitialWeights(value: Vector): this.type = set(initialWeights, value)
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/**
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* Sets the value of param [[stepSize]] (applicable only for solver "gd").
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* Default is 0.03.
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*
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* @group setParam
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*/
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@Since("2.0.0")
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def setStepSize(value: Double): this.type = set(stepSize, value)
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@Since("1.5.0")
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override def copy(extra: ParamMap): MultilayerPerceptronClassifier = defaultCopy(extra)
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@ -204,16 +238,26 @@ class MultilayerPerceptronClassifier @Since("1.5.0") (
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val labels = myLayers.last
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val lpData = extractLabeledPoints(dataset)
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val data = lpData.map(lp => LabelConverter.encodeLabeledPoint(lp, labels))
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val topology = FeedForwardTopology.multiLayerPerceptron(myLayers, true)
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val topology = FeedForwardTopology.multiLayerPerceptron(myLayers, softmaxOnTop = true)
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val trainer = new FeedForwardTrainer(topology, myLayers(0), myLayers.last)
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if (isDefined(weights)) {
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trainer.setWeights($(weights))
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if (isDefined(initialWeights)) {
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trainer.setWeights($(initialWeights))
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} else {
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trainer.setSeed($(seed))
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}
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trainer.LBFGSOptimizer
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.setConvergenceTol($(tol))
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.setNumIterations($(maxIter))
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if ($(solver) == MultilayerPerceptronClassifier.LBFGS) {
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trainer.LBFGSOptimizer
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.setConvergenceTol($(tol))
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.setNumIterations($(maxIter))
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} else if ($(solver) == MultilayerPerceptronClassifier.GD) {
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trainer.SGDOptimizer
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.setNumIterations($(maxIter))
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.setConvergenceTol($(tol))
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.setStepSize($(stepSize))
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} else {
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throw new IllegalArgumentException(
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s"The solver $solver is not supported by MultilayerPerceptronClassifier.")
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}
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trainer.setStackSize($(blockSize))
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val mlpModel = trainer.train(data)
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new MultilayerPerceptronClassificationModel(uid, myLayers, mlpModel.weights)
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@ -224,6 +268,15 @@ class MultilayerPerceptronClassifier @Since("1.5.0") (
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object MultilayerPerceptronClassifier
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extends DefaultParamsReadable[MultilayerPerceptronClassifier] {
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/** String name for "l-bfgs" solver. */
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private[classification] val LBFGS = "l-bfgs"
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/** String name for "gd" (minibatch gradient descent) solver. */
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private[classification] val GD = "gd"
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/** Set of solvers that MultilayerPerceptronClassifier supports. */
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private[classification] val supportedSolvers = Array(LBFGS, GD)
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@Since("2.0.0")
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override def load(path: String): MultilayerPerceptronClassifier = super.load(path)
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}
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@ -250,7 +303,9 @@ class MultilayerPerceptronClassificationModel private[ml] (
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@Since("1.6.0")
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override val numFeatures: Int = layers.head
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private val mlpModel = FeedForwardTopology.multiLayerPerceptron(layers, true).model(weights)
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private val mlpModel = FeedForwardTopology
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.multiLayerPerceptron(layers, softmaxOnTop = true)
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.model(weights)
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/**
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* Returns layers in a Java List.
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@ -70,6 +70,7 @@ class MultilayerPerceptronClassifierSuite
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.setBlockSize(1)
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.setSeed(123L)
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.setMaxIter(100)
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.setSolver("l-bfgs")
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val model = trainer.fit(dataset)
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val result = model.transform(dataset)
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val predictionAndLabels = result.select("prediction", "label").collect()
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@ -93,9 +94,9 @@ class MultilayerPerceptronClassifierSuite
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.setMaxIter(1)
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.setTol(1e-6)
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val initialWeights = trainer.fit(dataFrame).weights
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trainer.setWeights(initialWeights.copy)
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trainer.setInitialWeights(initialWeights.copy)
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val weights1 = trainer.fit(dataFrame).weights
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trainer.setWeights(initialWeights.copy)
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trainer.setInitialWeights(initialWeights.copy)
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val weights2 = trainer.fit(dataFrame).weights
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assert(weights1 ~== weights2 absTol 10e-5,
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"Training should produce the same weights given equal initial weights and number of steps")
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