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author | Joseph K. Bradley <joseph.kurata.bradley@gmail.com> | 2014-08-03 10:36:52 -0700 |
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committer | Xiangrui Meng <meng@databricks.com> | 2014-08-03 10:36:52 -0700 |
commit | 2998e38a942351974da36cb619e863c6f0316e7a (patch) | |
tree | bba84386898fa3fd49cbc4acfc3b38457f0c4882 /mllib/src/main | |
parent | a0bcbc159e89be868ccc96175dbf1439461557e1 (diff) | |
download | spark-2998e38a942351974da36cb619e863c6f0316e7a.tar.gz spark-2998e38a942351974da36cb619e863c6f0316e7a.tar.bz2 spark-2998e38a942351974da36cb619e863c6f0316e7a.zip |
[SPARK-2197] [mllib] Java DecisionTree bug fix and easy-of-use
Bug fix: Before, when an RDD was created in Java and passed to DecisionTree.train(), the fake class tag caused problems.
* Fix: DecisionTree: Used new RDD.retag() method to allow passing RDDs from Java.
Other improvements to Decision Trees for easy-of-use with Java:
* impurity classes: Added instance() methods to help with Java interface.
* Strategy: Added Java-friendly constructor
--> Note: I removed quantileCalculationStrategy from the Java-friendly constructor since (a) it is a special class and (b) there is only 1 option currently. I suspect we will redo the API before the other options are included.
CC: mengxr
Author: Joseph K. Bradley <joseph.kurata.bradley@gmail.com>
Closes #1740 from jkbradley/dt-java-new and squashes the following commits:
0805dc6 [Joseph K. Bradley] Changed Strategy to use JavaConverters instead of JavaConversions
519b1b7 [Joseph K. Bradley] * Organized imports in JavaDecisionTreeSuite.java * Using JavaConverters instead of JavaConversions in DecisionTreeSuite.scala
f7b5ca1 [Joseph K. Bradley] Improvements to make it easier to run DecisionTree from Java. * DecisionTree: Used new RDD.retag() method to allow passing RDDs from Java. * impurity classes: Added instance() methods to help with Java interface. * Strategy: Added Java-friendly constructor ** Note: I removed quantileCalculationStrategy from the Java-friendly constructor since (a) it is a special class and (b) there is only 1 option currently. I suspect we will redo the API before the other options are included.
d78ada6 [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into dt-java
320853f [Joseph K. Bradley] Added JavaDecisionTreeSuite, partly written
13a585e [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into dt-java
f1a8283 [Joseph K. Bradley] Added old JavaDecisionTreeSuite, to be updated later
225822f [Joseph K. Bradley] Bug: In DecisionTree, the method sequentialBinSearchForOrderedCategoricalFeatureInClassification() indexed bins from 0 to (math.pow(2, featureCategories.toInt - 1) - 1). This upper bound is the bound for unordered categorical features, not ordered ones. The upper bound should be the arity (i.e., max value) of the feature.
Diffstat (limited to 'mllib/src/main')
5 files changed, 54 insertions, 4 deletions
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 382e76a9b7..1d03e6e3b3 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 @@ -48,12 +48,12 @@ class DecisionTree (private val strategy: Strategy) extends Serializable with Lo def train(input: RDD[LabeledPoint]): DecisionTreeModel = { // Cache input RDD for speedup during multiple passes. - input.cache() + val retaggedInput = input.retag(classOf[LabeledPoint]).cache() logDebug("algo = " + strategy.algo) // Find the splits and the corresponding bins (interval between the splits) using a sample // of the input data. - val (splits, bins) = DecisionTree.findSplitsBins(input, strategy) + val (splits, bins) = DecisionTree.findSplitsBins(retaggedInput, strategy) val numBins = bins(0).length logDebug("numBins = " + numBins) @@ -70,7 +70,7 @@ class DecisionTree (private val strategy: Strategy) extends Serializable with Lo // dummy value for top node (updated during first split calculation) val nodes = new Array[Node](maxNumNodes) // num features - val numFeatures = input.take(1)(0).features.size + val numFeatures = retaggedInput.take(1)(0).features.size // Calculate level for single group construction @@ -107,7 +107,7 @@ class DecisionTree (private val strategy: Strategy) extends Serializable with Lo logDebug("#####################################") // Find best split for all nodes at a level. - val splitsStatsForLevel = DecisionTree.findBestSplits(input, parentImpurities, + val splitsStatsForLevel = DecisionTree.findBestSplits(retaggedInput, parentImpurities, strategy, level, filters, splits, bins, maxLevelForSingleGroup) for ((nodeSplitStats, index) <- splitsStatsForLevel.view.zipWithIndex) { 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 fdad4f029a..4ee4bcd0bc 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 @@ -17,6 +17,8 @@ package org.apache.spark.mllib.tree.configuration +import scala.collection.JavaConverters._ + import org.apache.spark.annotation.Experimental import org.apache.spark.mllib.tree.impurity.Impurity import org.apache.spark.mllib.tree.configuration.Algo._ @@ -61,4 +63,31 @@ class Strategy ( val isMulticlassWithCategoricalFeatures = isMulticlassClassification && (categoricalFeaturesInfo.size > 0) + /** + * Java-friendly constructor. + * + * @param algo classification or regression + * @param impurity criterion used for information gain calculation + * @param maxDepth Maximum depth of the tree. + * E.g., depth 0 means 1 leaf node; depth 1 means 1 internal node + 2 leaf nodes. + * @param numClassesForClassification number of classes for classification. Default value is 2 + * leads to binary classification + * @param maxBins maximum number of bins used for splitting features + * @param categoricalFeaturesInfo A map storing information about the categorical variables and + * the number of discrete values they take. For example, an entry + * (n -> k) implies the feature n is categorical with k categories + * 0, 1, 2, ... , k-1. It's important to note that features are + * zero-indexed. + */ + def this( + algo: Algo, + impurity: Impurity, + maxDepth: Int, + numClassesForClassification: Int, + maxBins: Int, + categoricalFeaturesInfo: java.util.Map[java.lang.Integer, java.lang.Integer]) { + this(algo, impurity, maxDepth, numClassesForClassification, maxBins, Sort, + categoricalFeaturesInfo.asInstanceOf[java.util.Map[Int, Int]].asScala.toMap) + } + } diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/impurity/Entropy.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/impurity/Entropy.scala index 9297c20596..96d2471e1f 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/tree/impurity/Entropy.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/impurity/Entropy.scala @@ -66,4 +66,11 @@ object Entropy extends Impurity { @DeveloperApi override def calculate(count: Double, sum: Double, sumSquares: Double): Double = throw new UnsupportedOperationException("Entropy.calculate") + + /** + * Get this impurity instance. + * This is useful for passing impurity parameters to a Strategy in Java. + */ + def instance = this + } diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/impurity/Gini.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/impurity/Gini.scala index 2874bcf496..d586f44904 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/tree/impurity/Gini.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/impurity/Gini.scala @@ -62,4 +62,11 @@ object Gini extends Impurity { @DeveloperApi override def calculate(count: Double, sum: Double, sumSquares: Double): Double = throw new UnsupportedOperationException("Gini.calculate") + + /** + * Get this impurity instance. + * This is useful for passing impurity parameters to a Strategy in Java. + */ + def instance = this + } diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/impurity/Variance.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/impurity/Variance.scala index 698a1a2a8e..f7d99a40eb 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/tree/impurity/Variance.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/impurity/Variance.scala @@ -53,4 +53,11 @@ object Variance extends Impurity { val squaredLoss = sumSquares - (sum * sum) / count squaredLoss / count } + + /** + * Get this impurity instance. + * This is useful for passing impurity parameters to a Strategy in Java. + */ + def instance = this + } |