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author | Yanbo Liang <ybliang8@gmail.com> | 2016-05-19 10:27:17 -0700 |
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committer | Joseph K. Bradley <joseph@databricks.com> | 2016-05-19 10:27:17 -0700 |
commit | 8ecf7f77b2be0a178a8d94d60477876d4ab7517a (patch) | |
tree | 8a94965b565f6f6b52e8901f1da1a145da24d10c /mllib | |
parent | 1052d3644d7eb0e784eb883293ce63a352a3b123 (diff) | |
download | spark-8ecf7f77b2be0a178a8d94d60477876d4ab7517a.tar.gz spark-8ecf7f77b2be0a178a8d94d60477876d4ab7517a.tar.bz2 spark-8ecf7f77b2be0a178a8d94d60477876d4ab7517a.zip |
[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.
Diffstat (limited to 'mllib')
2 files changed, 91 insertions, 35 deletions
diff --git a/mllib/src/main/scala/org/apache/spark/ml/classification/MultilayerPerceptronClassifier.scala b/mllib/src/main/scala/org/apache/spark/ml/classification/MultilayerPerceptronClassifier.scala index 683ae4aaf4..c4e882240f 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/classification/MultilayerPerceptronClassifier.scala +++ b/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. */ @@ -145,15 +152,33 @@ 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. diff --git a/mllib/src/test/scala/org/apache/spark/ml/classification/MultilayerPerceptronClassifierSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/classification/MultilayerPerceptronClassifierSuite.scala index 85f325f076..e809dd4092 100644 --- a/mllib/src/test/scala/org/apache/spark/ml/classification/MultilayerPerceptronClassifierSuite.scala +++ b/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") |