Github user jkbradley commented on a diff in the pull request:
https://github.com/apache/spark/pull/5626#discussion_r29001008
--- Diff:
mllib/src/main/scala/org/apache/spark/ml/impl/tree/treeParams.scala ---
@@ -298,3 +302,200 @@ private[ml] object TreeRegressorParams {
// These options should be lowercase.
val supportedImpurities: Array[String] =
Array("variance").map(_.toLowerCase)
}
+
+/**
+ * :: DeveloperApi ::
+ * Parameters for Decision Tree-based ensemble algorithms.
+ *
+ * Note: Marked as private and DeveloperApi since this may be made public
in the future.
+ */
+@DeveloperApi
+private[ml] trait TreeEnsembleParams extends DecisionTreeParams {
+
+ /**
+ * Fraction of the training data used for learning each decision tree.
+ * (default = 1.0)
+ * @group param
+ */
+ final val subsamplingRate: DoubleParam = new DoubleParam(this,
"subsamplingRate",
+ "Fraction of the training data used for learning each decision tree.")
+
+ /**
+ * Random seed for bootstrapping and choosing feature subsets.
+ * @group param
+ */
+ final val seed: LongParam = new LongParam(this, "seed",
+ "Random seed for bootstrapping and choosing feature subsets.")
+
+ setDefault(subsamplingRate -> 1.0, seed -> Utils.random.nextLong())
+
+ /** @group setParam */
+ def setSubsamplingRate(value: Double): this.type = {
+ require(value > 0.0 && value <= 1.0,
+ s"Subsampling rate must be in range (0,1]. Bad rate: $value")
+ set(subsamplingRate, value)
+ this
+ }
+
+ /** @group getParam */
+ def getSubsamplingRate: Double = getOrDefault(subsamplingRate)
+
+ /** @group setParam */
+ def setSeed(value: Long): this.type = {
+ set(seed, value)
+ this
+ }
+
+ /** @group getParam */
+ def getSeed: Long = getOrDefault(seed)
+
+ /**
+ * Create a Strategy instance to use with the old API.
+ * NOTE: The caller should set impurity and seed.
+ */
+ private[ml] def getOldStrategy(
+ categoricalFeatures: Map[Int, Int],
+ numClasses: Int,
+ oldAlgo: OldAlgo.Algo,
+ oldImpurity: OldImpurity): OldStrategy = {
+ super.getOldStrategy(categoricalFeatures, numClasses, oldAlgo,
oldImpurity, getSubsamplingRate)
+ }
+}
+
+/**
+ * :: DeveloperApi ::
+ * Parameters for Random Forest algorithms.
+ *
+ * Note: Marked as private and DeveloperApi since this may be made public
in the future.
+ */
+@DeveloperApi
+private[ml] trait RandomForestParams extends TreeEnsembleParams {
+
+ /**
+ * Number of trees to train (>= 1).
+ * If 1, then no bootstrapping is used. If > 1, then bootstrapping is
done.
+ * TODO: Change to always do bootstrapping (simpler).
+ * (default = 20)
+ * @group param
+ */
+ final val numTrees: IntParam = new IntParam(this, "numTrees", "Number of
trees to train (>= 1)")
+
+ /**
+ * The number of features to consider for splits at each tree node.
+ * Supported options:
+ * - "auto": Choose automatically for task:
+ * If numTrees == 1, set to "all."
+ * If numTrees > 1 (forest), set to "sqrt" for classification
and
+ * to "onethird" for regression.
+ * - "all": use all features
+ * - "onethird": use 1/3 of the features
+ * - "sqrt": use sqrt(number of features)
+ * - "log2": use log2(number of features)
+ * (default = "auto")
+ *
+ * These various settings are based on the following references:
+ * - log2: tested in Breiman (2001)
+ * - sqrt: recommended by Breiman manual for random forests
+ * - The defaults of sqrt (classification) and onethird (regression)
match the R randomForest
+ * package.
+ * @see [[http://www.stat.berkeley.edu/~breiman/randomforest2001.pdf
Breiman (2001)]]
+ * @see
[[http://www.stat.berkeley.edu/~breiman/Using_random_forests_V3.1.pdf Breiman
manual for
+ * random forests]]
+ *
+ * @group param
+ */
+ final val featuresPerNode: Param[String] = new Param[String](this,
"featuresPerNode",
+ "The number of features to consider for splits at each tree node." +
+ s" Supported options:
${RandomForestParams.supportedFeaturesPerNode.mkString(", ")}")
+
+ setDefault(numTrees -> 20, featuresPerNode -> "auto")
+
+ /** @group setParam */
+ def setNumTrees(value: Int): this.type = {
+ require(value >= 1, s"Random Forest numTrees parameter cannot be
$value; it must be >= 1.")
+ set(numTrees, value)
+ this
+ }
+
+ /** @group getParam */
+ def getNumTrees: Int = getOrDefault(numTrees)
+
+ /** @group setParam */
+ def setFeaturesPerNode(value: String): this.type = {
+ val featuresPerNodeStr = value.toLowerCase
+
require(RandomForestParams.supportedFeaturesPerNode.contains(featuresPerNodeStr),
+ s"RandomForestParams was given unrecognized featuresPerNode:
$value." +
+ s" Supported options:
${RandomForestParams.supportedFeaturesPerNode.mkString(", ")}")
+ set(featuresPerNode, value)
+ this
+ }
+
+ /** @group getParam */
+ def getFeaturesPerNodeStr: String = getOrDefault(featuresPerNode)
+}
+
+private[ml] object RandomForestParams {
+ // These options should be lowercase.
+ val supportedFeaturesPerNode: Array[String] = Array("auto", "all",
"onethird", "sqrt", "log2")
+}
+
+/**
+ * :: DeveloperApi ::
+ * Parameters for Gradient-Boosted Tree algorithms.
+ *
+ * Note: Marked as private and DeveloperApi since this may be made public
in the future.
+ */
+@DeveloperApi
+private[ml] trait GBTParams extends TreeEnsembleParams with HasMaxIter {
+
+ /**
+ * Learning rate in interval (0, 1] for shrinking the contribution of
each estimator.
+ * (default = 0.1)
+ * @group param
+ */
+ final val learningRate: DoubleParam = new DoubleParam(this,
"learningRate",
--- End diff --
I'm hesitating about putting it in sharedParams since the intended range
can differ between algorithms. For GBTs, it should be in (0, 1], but it could
be different for other algs.
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