From 50a4fa774a0e8a17d7743b33ce8941bf4041144d Mon Sep 17 00:00:00 2001
From: Xiangrui Meng <meng@databricks.com>
Date: Mon, 8 Sep 2014 18:59:57 -0700
Subject: [PATCH] [SPARK-3443][MLLIB] update default values of tree:
Adjust the default values of decision tree, based on the memory requirement discussed in https://github.com/apache/spark/pull/2125 :
1. maxMemoryInMB: 128 -> 256
2. maxBins: 100 -> 32
3. maxDepth: 4 -> 5 (in some example code)
jkbradley
Author: Xiangrui Meng <meng@databricks.com>
Closes #2322 from mengxr/tree-defaults and squashes the following commits:
cda453a [Xiangrui Meng] fix tests
5900445 [Xiangrui Meng] update comments
8c81831 [Xiangrui Meng] update default values of tree:
---
docs/mllib-decision-tree.md | 16 ++++++++--------
.../spark/examples/mllib/JavaDecisionTree.java | 2 +-
.../examples/mllib/DecisionTreeRunner.scala | 4 ++--
.../apache/spark/mllib/tree/DecisionTree.scala | 8 ++++----
.../mllib/tree/configuration/Strategy.scala | 6 +++---
.../spark/mllib/tree/DecisionTreeSuite.scala | 18 ++++--------------
python/pyspark/mllib/tree.py | 4 ++--
7 files changed, 24 insertions(+), 34 deletions(-)
diff --git a/docs/mllib-decision-tree.md b/docs/mllib-decision-tree.md
index 1166d9cd15..12a6afbeea 100644
--- a/docs/mllib-decision-tree.md
+++ b/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))
diff --git a/examples/src/main/java/org/apache/spark/examples/mllib/JavaDecisionTree.java b/examples/src/main/java/org/apache/spark/examples/mllib/JavaDecisionTree.java
index e4468e8bf1..1f82e3f4cb 100644
--- a/examples/src/main/java/org/apache/spark/examples/mllib/JavaDecisionTree.java
+++ b/examples/src/main/java/org/apache/spark/examples/mllib/JavaDecisionTree.java
@@ -63,7 +63,7 @@ public final class JavaDecisionTree {
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,
diff --git a/examples/src/main/scala/org/apache/spark/examples/mllib/DecisionTreeRunner.scala b/examples/src/main/scala/org/apache/spark/examples/mllib/DecisionTreeRunner.scala
index cf3d2cca81..72c3ab475b 100644
--- a/examples/src/main/scala/org/apache/spark/examples/mllib/DecisionTreeRunner.scala
+++ b/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]) {
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/DecisionTree.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/DecisionTree.scala
index dd766c12d2..d1309b2b20 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/DecisionTree.scala
+++ b/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(
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/Strategy.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/Strategy.scala
index cfc8192a85..23f74d5360 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/Strategy.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/Strategy.scala
@@ -50,7 +50,7 @@ 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 (
@@ -58,10 +58,10 @@ class Strategy (
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)
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/tree/DecisionTreeSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/tree/DecisionTreeSuite.scala
index 8e556c917b..69482f2acb 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/tree/DecisionTreeSuite.scala
+++ b/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))
diff --git a/python/pyspark/mllib/tree.py b/python/pyspark/mllib/tree.py
index a2fade61e9..ccc000ac70 100644
--- a/python/pyspark/mllib/tree.py
+++ b/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 @@ class DecisionTree(object):
@staticmethod
def trainRegressor(data, categoricalFeaturesInfo,
- impurity="variance", maxDepth=4, maxBins=100):
+ impurity="variance", maxDepth=5, maxBins=32):
"""
Train a DecisionTreeModel for regression.
--
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