[SPARK-3443][MLLIB] update default values of tree:

docs/mllib-decision-tree.md

@@ -80,7 +80,7 @@ The ordered splits create "bins" and the maximum number of such
 bins can be specified using the `maxBins` parameter.
 
 Note that the number of bins cannot be greater than the number of instances `$N$` (a rare scenario
-since the default `maxBins` value is 100). The tree algorithm automatically reduces the number of
+since the default `maxBins` value is 32). The tree algorithm automatically reduces the number of
 bins if the condition is not satisfied.
 
 **Categorical features**
@@ -117,7 +117,7 @@ all nodes at each level of the tree. This could lead to high memory requirements
 of the tree, potentially leading to memory overflow errors. To alleviate this problem, a `maxMemoryInMB`
 training parameter specifies the maximum amount of memory at the workers (twice as much at the
 master) to be allocated to the histogram computation. The default value is conservatively chosen to
-be 128 MB to allow the decision algorithm to work in most scenarios. Once the memory requirements
+be 256 MB to allow the decision algorithm to work in most scenarios. Once the memory requirements
 for a level-wise computation cross the `maxMemoryInMB` threshold, the node training tasks at each
 subsequent level are split into smaller tasks.
 
@@ -167,7 +167,7 @@ val numClasses = 2
 val categoricalFeaturesInfo = Map[Int, Int]()
 val impurity = "gini"
 val maxDepth = 5
-val maxBins = 100
+val maxBins = 32
 
 val model = DecisionTree.trainClassifier(data, numClasses, categoricalFeaturesInfo, impurity,
   maxDepth, maxBins)
@@ -213,7 +213,7 @@ Integer numClasses = 2;
 HashMap<Integer, Integer> categoricalFeaturesInfo = new HashMap<Integer, Integer>();
 String impurity = "gini";
 Integer maxDepth = 5;
-Integer maxBins = 100;
+Integer maxBins = 32;
 
 // Train a DecisionTree model for classification.
 final DecisionTreeModel model = DecisionTree.trainClassifier(data, numClasses,
@@ -250,7 +250,7 @@ data = MLUtils.loadLibSVMFile(sc, 'data/mllib/sample_libsvm_data.txt').cache()
 # Train a DecisionTree model.
 #  Empty categoricalFeaturesInfo indicates all features are continuous.
 model = DecisionTree.trainClassifier(data, numClasses=2, categoricalFeaturesInfo={},
-                                     impurity='gini', maxDepth=5, maxBins=100)
+                                     impurity='gini', maxDepth=5, maxBins=32)
 
 # Evaluate model on training instances and compute training error
 predictions = model.predict(data.map(lambda x: x.features))
@@ -293,7 +293,7 @@ val data = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt").cache
 val categoricalFeaturesInfo = Map[Int, Int]()
 val impurity = "variance"
 val maxDepth = 5
-val maxBins = 100
+val maxBins = 32
 
 val model = DecisionTree.trainRegressor(data, categoricalFeaturesInfo, impurity,
   maxDepth, maxBins)
@@ -338,7 +338,7 @@ JavaSparkContext sc = new JavaSparkContext(sparkConf);
 HashMap<Integer, Integer> categoricalFeaturesInfo = new HashMap<Integer, Integer>();
 String impurity = "variance";
 Integer maxDepth = 5;
-Integer maxBins = 100;
+Integer maxBins = 32;
 
 // Train a DecisionTree model.
 final DecisionTreeModel model = DecisionTree.trainRegressor(data,
@@ -380,7 +380,7 @@ data = MLUtils.loadLibSVMFile(sc, 'data/mllib/sample_libsvm_data.txt').cache()
 # Train a DecisionTree model.
 #  Empty categoricalFeaturesInfo indicates all features are continuous.
 model = DecisionTree.trainRegressor(data, categoricalFeaturesInfo={},
-                                    impurity='variance', maxDepth=5, maxBins=100)
+                                    impurity='variance', maxDepth=5, maxBins=32)
 
 # Evaluate model on training instances and compute training error
 predictions = model.predict(data.map(lambda x: x.features))

examples/src/main/java/org/apache/spark/examples/mllib/JavaDecisionTree.java

@@ -63,7 +63,7 @@ public static void main(String[] args) {
     HashMap<Integer, Integer> categoricalFeaturesInfo = new HashMap<Integer, Integer>();
     String impurity = "gini";
     Integer maxDepth = 5;
-    Integer maxBins = 100;
+    Integer maxBins = 32;
 
     // Train a DecisionTree model for classification.
     final DecisionTreeModel model = DecisionTree.trainClassifier(data, numClasses,

examples/src/main/scala/org/apache/spark/examples/mllib/DecisionTreeRunner.scala

@@ -52,9 +52,9 @@ object DecisionTreeRunner {
       input: String = null,
       dataFormat: String = "libsvm",
       algo: Algo = Classification,
-      maxDepth: Int = 4,
+      maxDepth: Int = 5,
       impurity: ImpurityType = Gini,
-      maxBins: Int = 100,
+      maxBins: Int = 32,
       fracTest: Double = 0.2)
 
   def main(args: Array[String]) {

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

@@ -330,9 +330,9 @@ object DecisionTree extends Serializable with Logging {
    *                 Supported values: "gini" (recommended) or "entropy".
    * @param maxDepth Maximum depth of the tree.
    *                 E.g., depth 0 means 1 leaf node; depth 1 means 1 internal node + 2 leaf nodes.
-   *                  (suggested value: 4)
+   *                  (suggested value: 5)
    * @param maxBins maximum number of bins used for splitting features
-   *                 (suggested value: 100)
+   *                 (suggested value: 32)
    * @return DecisionTreeModel that can be used for prediction
    */
   def trainClassifier(
@@ -374,9 +374,9 @@ object DecisionTree extends Serializable with Logging {
    *                 Supported values: "variance".
    * @param maxDepth Maximum depth of the tree.
    *                 E.g., depth 0 means 1 leaf node; depth 1 means 1 internal node + 2 leaf nodes.
-   *                  (suggested value: 4)
+   *                  (suggested value: 5)
    * @param maxBins maximum number of bins used for splitting features
-   *                 (suggested value: 100)
+   *                 (suggested value: 32)
    * @return DecisionTreeModel that can be used for prediction
    */
   def trainRegressor(

mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/Strategy.scala

@@ -50,18 +50,18 @@ import org.apache.spark.mllib.tree.configuration.QuantileStrategy._
  *                                1, 2, ... , k-1. It's important to note that features are
  *                                zero-indexed.
  * @param maxMemoryInMB Maximum memory in MB allocated to histogram aggregation. Default value is
- *                      128 MB.
+ *                      256 MB.
  */
 @Experimental
 class Strategy (
     val algo: Algo,
     val impurity: Impurity,
     val maxDepth: Int,
     val numClassesForClassification: Int = 2,
-    val maxBins: Int = 100,
+    val maxBins: Int = 32,
     val quantileCalculationStrategy: QuantileStrategy = Sort,
     val categoricalFeaturesInfo: Map[Int, Int] = Map[Int, Int](),
-    val maxMemoryInMB: Int = 128) extends Serializable {
+    val maxMemoryInMB: Int = 256) extends Serializable {
 
   if (algo == Classification) {
     require(numClassesForClassification >= 2)

mllib/src/test/scala/org/apache/spark/mllib/tree/DecisionTreeSuite.scala

@@ -31,7 +31,6 @@ import org.apache.spark.mllib.tree.impurity.{Entropy, Gini, Variance}
 import org.apache.spark.mllib.tree.model.{DecisionTreeModel, Node}
 import org.apache.spark.mllib.util.LocalSparkContext
 
-
 class DecisionTreeSuite extends FunSuite with LocalSparkContext {
 
   def validateClassifier(
@@ -353,8 +352,6 @@ class DecisionTreeSuite extends FunSuite with LocalSparkContext {
     assert(splits(0).length === 99)
     assert(bins.length === 2)
     assert(bins(0).length === 100)
-    assert(splits(0).length === 99)
-    assert(bins(0).length === 100)
 
     val treeInput = TreePoint.convertToTreeRDD(rdd, bins, metadata)
     val bestSplits = DecisionTree.findBestSplits(treeInput, new Array(8), metadata, 0,
@@ -381,8 +378,6 @@ class DecisionTreeSuite extends FunSuite with LocalSparkContext {
     assert(splits(0).length === 99)
     assert(bins.length === 2)
     assert(bins(0).length === 100)
-    assert(splits(0).length === 99)
-    assert(bins(0).length === 100)
 
     val treeInput = TreePoint.convertToTreeRDD(rdd, bins, metadata)
     val bestSplits = DecisionTree.findBestSplits(treeInput, new Array(2), metadata, 0,
@@ -410,8 +405,6 @@ class DecisionTreeSuite extends FunSuite with LocalSparkContext {
     assert(splits(0).length === 99)
     assert(bins.length === 2)
     assert(bins(0).length === 100)
-    assert(splits(0).length === 99)
-    assert(bins(0).length === 100)
 
     val treeInput = TreePoint.convertToTreeRDD(rdd, bins, metadata)
     val bestSplits = DecisionTree.findBestSplits(treeInput, new Array(2), metadata, 0,
@@ -439,8 +432,6 @@ class DecisionTreeSuite extends FunSuite with LocalSparkContext {
     assert(splits(0).length === 99)
     assert(bins.length === 2)
     assert(bins(0).length === 100)
-    assert(splits(0).length === 99)
-    assert(bins(0).length === 100)
 
     val treeInput = TreePoint.convertToTreeRDD(rdd, bins, metadata)
     val bestSplits = DecisionTree.findBestSplits(treeInput, new Array(2), metadata, 0,
@@ -464,8 +455,6 @@ class DecisionTreeSuite extends FunSuite with LocalSparkContext {
     assert(splits(0).length === 99)
     assert(bins.length === 2)
     assert(bins(0).length === 100)
-    assert(splits(0).length === 99)
-    assert(bins(0).length === 100)
 
     // Train a 1-node model
     val strategyOneNode = new Strategy(Classification, Entropy, 1, 2, 100)
@@ -600,7 +589,7 @@ class DecisionTreeSuite extends FunSuite with LocalSparkContext {
     val arr = DecisionTreeSuite.generateContinuousDataPointsForMulticlass()
     val rdd = sc.parallelize(arr)
     val strategy = new Strategy(algo = Classification, impurity = Gini, maxDepth = 4,
-      numClassesForClassification = 3)
+      numClassesForClassification = 3, maxBins = 100)
     assert(strategy.isMulticlassClassification)
     val metadata = DecisionTreeMetadata.buildMetadata(rdd, strategy)
 
@@ -626,7 +615,7 @@ class DecisionTreeSuite extends FunSuite with LocalSparkContext {
     val arr = DecisionTreeSuite.generateContinuousDataPointsForMulticlass()
     val rdd = sc.parallelize(arr)
     val strategy = new Strategy(algo = Classification, impurity = Gini, maxDepth = 4,
-      numClassesForClassification = 3, categoricalFeaturesInfo = Map(0 -> 3))
+      numClassesForClassification = 3, maxBins = 100, categoricalFeaturesInfo = Map(0 -> 3))
     assert(strategy.isMulticlassClassification)
     val metadata = DecisionTreeMetadata.buildMetadata(rdd, strategy)
     assert(metadata.isUnordered(featureIndex = 0))
@@ -652,7 +641,8 @@ class DecisionTreeSuite extends FunSuite with LocalSparkContext {
     val arr = DecisionTreeSuite.generateCategoricalDataPointsForMulticlassForOrderedFeatures()
     val rdd = sc.parallelize(arr)
     val strategy = new Strategy(algo = Classification, impurity = Gini, maxDepth = 4,
-      numClassesForClassification = 3, categoricalFeaturesInfo = Map(0 -> 10, 1 -> 10))
+      numClassesForClassification = 3, maxBins = 100,
+      categoricalFeaturesInfo = Map(0 -> 10, 1 -> 10))
     assert(strategy.isMulticlassClassification)
     val metadata = DecisionTreeMetadata.buildMetadata(rdd, strategy)
     assert(!metadata.isUnordered(featureIndex = 0))

python/pyspark/mllib/tree.py

@@ -138,7 +138,7 @@ class DecisionTree(object):
 
     @staticmethod
     def trainClassifier(data, numClasses, categoricalFeaturesInfo,
-                        impurity="gini", maxDepth=4, maxBins=100):
+                        impurity="gini", maxDepth=5, maxBins=32):
         """
         Train a DecisionTreeModel for classification.
 
@@ -170,7 +170,7 @@ def trainClassifier(data, numClasses, categoricalFeaturesInfo,
 
     @staticmethod
     def trainRegressor(data, categoricalFeaturesInfo,
-                       impurity="variance", maxDepth=4, maxBins=100):
+                       impurity="variance", maxDepth=5, maxBins=32):
         """
         Train a DecisionTreeModel for regression.