[SPARK-3724][ML] RandomForest: More options for feature subset size.

mllib/src/main/scala/org/apache/spark/ml/tree/impl/DecisionTreeMetadata.scala

@@ -183,11 +183,16 @@ private[spark] object DecisionTreeMetadata extends Logging {
         }
       case _ => featureSubsetStrategy
     }
+
+    val isIntRegex = "^([1-9]\\d*)$".r
+    val isFractionRegex = "^(0?\\.\\d*[1-9]\\d*|1\\.0+)$".r
     val numFeaturesPerNode: Int = _featureSubsetStrategy match {
       case "all" => numFeatures
       case "sqrt" => math.sqrt(numFeatures).ceil.toInt
       case "log2" => math.max(1, (math.log(numFeatures) / math.log(2)).ceil.toInt)
       case "onethird" => (numFeatures / 3.0).ceil.toInt
+      case isIntRegex(number) => if (BigInt(number) > numFeatures) numFeatures else number.toInt
+      case isFractionRegex(fraction) => (fraction.toDouble * numFeatures).ceil.toInt
     }
 
     new DecisionTreeMetadata(numFeatures, numExamples, numClasses, numBins.max,

mllib/src/main/scala/org/apache/spark/ml/tree/treeParams.scala

@@ -329,6 +329,8 @@ private[ml] trait HasFeatureSubsetStrategy extends Params {
    *  - "onethird": use 1/3 of the features
    *  - "sqrt": use sqrt(number of features)
    *  - "log2": use log2(number of features)
+   *  - "(0.0-1.0]": use the specified fraction of features
+   *  - "n": use n features, for integer 0 < n <= (number of features)
    * (default = "auto")
    *
    * These various settings are based on the following references:
@@ -346,7 +348,8 @@ private[ml] trait HasFeatureSubsetStrategy extends Params {
     "The number of features to consider for splits at each tree node." +
       s" Supported options: ${RandomForestParams.supportedFeatureSubsetStrategies.mkString(", ")}",
     (value: String) =>
-      RandomForestParams.supportedFeatureSubsetStrategies.contains(value.toLowerCase))
+      RandomForestParams.supportedFeatureSubsetStrategies.contains(value.toLowerCase)
+      || value.matches(RandomForestParams.supportedFeatureSubsetStrategiesRegex))
 
   setDefault(featureSubsetStrategy -> "auto")
 
@@ -393,6 +396,9 @@ private[spark] object RandomForestParams {
   // These options should be lowercase.
   final val supportedFeatureSubsetStrategies: Array[String] =
     Array("auto", "all", "onethird", "sqrt", "log2").map(_.toLowerCase)
+
+  // The regex to capture "(0.0-1.0]", and "n" for integer 0 < n <= (number of features)
+  final val supportedFeatureSubsetStrategiesRegex = "^(?:[1-9]\\d*|0?\\.\\d*[1-9]\\d*|1\\.0+)$"
 }
 
 private[ml] trait RandomForestClassifierParams

mllib/src/main/scala/org/apache/spark/mllib/tree/RandomForest.scala

@@ -55,10 +55,16 @@ import org.apache.spark.util.Utils
  * @param numTrees If 1, then no bootstrapping is used.  If > 1, then bootstrapping is done.
  * @param featureSubsetStrategy Number of features to consider for splits at each node.
  *                              Supported values: "auto", "all", "sqrt", "log2", "onethird".
+ *                              Supported numerical values: "(0.0-1.0]", "[1-n]".
  *                              If "auto" is set, this parameter is set based on numTrees:
  *                                if numTrees == 1, set to "all";
  *                                if numTrees > 1 (forest) set to "sqrt" for classification and
  *                                  to "onethird" for regression.
+ *                              If a real value "(0.0-1.0]" is set, this parameter specifies
+ *                                the fraction of features in each subset.
+ *                              If an integer value "n" is set, this parameter specifies
+ *                                the number of features used in each subset,
+ *                                for integer 0 < n <= (number of features).
  * @param seed Random seed for bootstrapping and choosing feature subsets.
  */
 private class RandomForest (
@@ -70,9 +76,11 @@ private class RandomForest (
 
   strategy.assertValid()
   require(numTrees > 0, s"RandomForest requires numTrees > 0, but was given numTrees = $numTrees.")
-  require(RandomForest.supportedFeatureSubsetStrategies.contains(featureSubsetStrategy),
+  require(RandomForest.supportedFeatureSubsetStrategies.contains(featureSubsetStrategy)
+    || featureSubsetStrategy.matches(NewRFParams.supportedFeatureSubsetStrategiesRegex),
     s"RandomForest given invalid featureSubsetStrategy: $featureSubsetStrategy." +
-    s" Supported values: ${RandomForest.supportedFeatureSubsetStrategies.mkString(", ")}.")
+    s" Supported values: ${RandomForest.supportedFeatureSubsetStrategies.mkString(", ")}," +
+    s" (0.0-1.0], [1-n].")
 
   /**
    * Method to train a decision tree model over an RDD

mllib/src/test/java/org/apache/spark/ml/classification/JavaRandomForestClassifierSuite.java

@@ -80,6 +80,15 @@ public void runDT() {
     for (String featureSubsetStrategy: RandomForestClassifier.supportedFeatureSubsetStrategies()) {
       rf.setFeatureSubsetStrategy(featureSubsetStrategy);
     }
+    rf.setFeatureSubsetStrategy(".1");
+    rf.setFeatureSubsetStrategy(".10");
+    rf.setFeatureSubsetStrategy("0.10");
+    rf.setFeatureSubsetStrategy("0.1");
+    rf.setFeatureSubsetStrategy("0.9");
+    rf.setFeatureSubsetStrategy("1.0");
+    rf.setFeatureSubsetStrategy("1");
+    rf.setFeatureSubsetStrategy("100");
+    rf.setFeatureSubsetStrategy("1000");
     RandomForestClassificationModel model = rf.fit(dataFrame);
 
     model.transform(dataFrame);

mllib/src/test/java/org/apache/spark/ml/regression/JavaRandomForestRegressorSuite.java

@@ -80,6 +80,15 @@ public void runDT() {
     for (String featureSubsetStrategy: RandomForestRegressor.supportedFeatureSubsetStrategies()) {
       rf.setFeatureSubsetStrategy(featureSubsetStrategy);
     }
+    rf.setFeatureSubsetStrategy(".1");
+    rf.setFeatureSubsetStrategy(".10");
+    rf.setFeatureSubsetStrategy("0.10");
+    rf.setFeatureSubsetStrategy("0.1");
+    rf.setFeatureSubsetStrategy("0.9");
+    rf.setFeatureSubsetStrategy("1.0");
+    rf.setFeatureSubsetStrategy("1");
+    rf.setFeatureSubsetStrategy("100");
+    rf.setFeatureSubsetStrategy("1000");
     RandomForestRegressionModel model = rf.fit(dataFrame);
 
     model.transform(dataFrame);

mllib/src/test/scala/org/apache/spark/ml/tree/impl/RandomForestSuite.scala

@@ -27,6 +27,7 @@ import org.apache.spark.mllib.regression.LabeledPoint
 import org.apache.spark.mllib.tree.{DecisionTreeSuite => OldDTSuite, EnsembleTestHelper}
 import org.apache.spark.mllib.tree.configuration.{Algo => OldAlgo, QuantileStrategy, Strategy => OldStrategy}
 import org.apache.spark.mllib.tree.impurity.{Entropy, Gini, GiniCalculator}
+import org.apache.spark.mllib.tree.model.RandomForestModel
 import org.apache.spark.mllib.util.MLlibTestSparkContext
 import org.apache.spark.mllib.util.TestingUtils._
 import org.apache.spark.util.collection.OpenHashMap
@@ -422,13 +423,27 @@ class RandomForestSuite extends SparkFunSuite with MLlibTestSparkContext {
     checkFeatureSubsetStrategy(numTrees = 1, "log2",
       (math.log(numFeatures) / math.log(2)).ceil.toInt)
     checkFeatureSubsetStrategy(numTrees = 1, "onethird", (numFeatures / 3.0).ceil.toInt)
+    checkFeatureSubsetStrategy(numTrees = 1, "0.1", (0.1 * numFeatures).ceil.toInt)
+    checkFeatureSubsetStrategy(numTrees = 1, "0.5", (0.5 * numFeatures).ceil.toInt)
+    checkFeatureSubsetStrategy(numTrees = 1, "1.0", (1.0 * numFeatures).ceil.toInt)
+    checkFeatureSubsetStrategy(numTrees = 1, "1", 1)
+    checkFeatureSubsetStrategy(numTrees = 1, "2", 2)
+    checkFeatureSubsetStrategy(numTrees = 1, numFeatures.toString, numFeatures)
+    checkFeatureSubsetStrategy(numTrees = 1, (numFeatures * 2).toString, numFeatures)
 
     checkFeatureSubsetStrategy(numTrees = 2, "all", numFeatures)
     checkFeatureSubsetStrategy(numTrees = 2, "auto", math.sqrt(numFeatures).ceil.toInt)
     checkFeatureSubsetStrategy(numTrees = 2, "sqrt", math.sqrt(numFeatures).ceil.toInt)
     checkFeatureSubsetStrategy(numTrees = 2, "log2",
       (math.log(numFeatures) / math.log(2)).ceil.toInt)
     checkFeatureSubsetStrategy(numTrees = 2, "onethird", (numFeatures / 3.0).ceil.toInt)
+    checkFeatureSubsetStrategy(numTrees = 2, "0.1", (0.1 * numFeatures).ceil.toInt)
+    checkFeatureSubsetStrategy(numTrees = 2, "0.5", (0.5 * numFeatures).ceil.toInt)
+    checkFeatureSubsetStrategy(numTrees = 2, "1.0", (1.0 * numFeatures).ceil.toInt)
+    checkFeatureSubsetStrategy(numTrees = 2, "1", 1)
+    checkFeatureSubsetStrategy(numTrees = 2, "2", 2)
+    checkFeatureSubsetStrategy(numTrees = 2, numFeatures.toString, numFeatures)
+    checkFeatureSubsetStrategy(numTrees = 2, (numFeatures * 2).toString, numFeatures)
   }
 
   test("Binary classification with continuous features: subsampling features") {