Repository: spark Updated Branches: refs/heads/master b271c265b -> 9b746f380
[SPARK-3381] [MLlib] Eliminate bins for unordered features in DecisionTrees For unordered features, it is sufficient to use splits since the threshold of the split corresponds the threshold of the HighSplit of the bin and there is no use of the LowSplit. Author: MechCoder <manojkumarsivaraj...@gmail.com> Closes #4231 from MechCoder/spark-3381 and squashes the following commits: 58c19a5 [MechCoder] COSMIT c274b74 [MechCoder] Remove unordered feature calculation in labeledPointToTreePoint b2b9b89 [MechCoder] COSMIT d3ee042 [MechCoder] [SPARK-3381] [MLlib] Eliminate bins for unordered features Project: http://git-wip-us.apache.org/repos/asf/spark/repo Commit: http://git-wip-us.apache.org/repos/asf/spark/commit/9b746f38 Tree: http://git-wip-us.apache.org/repos/asf/spark/tree/9b746f38 Diff: http://git-wip-us.apache.org/repos/asf/spark/diff/9b746f38 Branch: refs/heads/master Commit: 9b746f380869b54d673e3758ca5e4475f76c864a Parents: b271c26 Author: MechCoder <manojkumarsivaraj...@gmail.com> Authored: Tue Feb 17 11:19:23 2015 -0800 Committer: Joseph K. Bradley <jos...@databricks.com> Committed: Tue Feb 17 11:19:23 2015 -0800 ---------------------------------------------------------------------- .../apache/spark/mllib/tree/DecisionTree.scala | 37 ++++++-------------- .../spark/mllib/tree/impl/TreePoint.scala | 14 +++----- .../spark/mllib/tree/DecisionTreeSuite.scala | 37 +------------------- 3 files changed, 15 insertions(+), 73 deletions(-) ---------------------------------------------------------------------- http://git-wip-us.apache.org/repos/asf/spark/blob/9b746f38/mllib/src/main/scala/org/apache/spark/mllib/tree/DecisionTree.scala ---------------------------------------------------------------------- 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 f1f8599..b9d0c56 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 @@ -327,14 +327,14 @@ object DecisionTree extends Serializable with Logging { * @param agg Array storing aggregate calculation, with a set of sufficient statistics for * each (feature, bin). * @param treePoint Data point being aggregated. - * @param bins possible bins for all features, indexed (numFeatures)(numBins) + * @param splits possible splits indexed (numFeatures)(numSplits) * @param unorderedFeatures Set of indices of unordered features. * @param instanceWeight Weight (importance) of instance in dataset. */ private def mixedBinSeqOp( agg: DTStatsAggregator, treePoint: TreePoint, - bins: Array[Array[Bin]], + splits: Array[Array[Split]], unorderedFeatures: Set[Int], instanceWeight: Double, featuresForNode: Option[Array[Int]]): Unit = { @@ -362,7 +362,7 @@ object DecisionTree extends Serializable with Logging { val numSplits = agg.metadata.numSplits(featureIndex) var splitIndex = 0 while (splitIndex < numSplits) { - if (bins(featureIndex)(splitIndex).highSplit.categories.contains(featureValue)) { + if (splits(featureIndex)(splitIndex).categories.contains(featureValue)) { agg.featureUpdate(leftNodeFeatureOffset, splitIndex, treePoint.label, instanceWeight) } else { @@ -506,8 +506,8 @@ object DecisionTree extends Serializable with Logging { if (metadata.unorderedFeatures.isEmpty) { orderedBinSeqOp(agg(aggNodeIndex), baggedPoint.datum, instanceWeight, featuresForNode) } else { - mixedBinSeqOp(agg(aggNodeIndex), baggedPoint.datum, bins, metadata.unorderedFeatures, - instanceWeight, featuresForNode) + mixedBinSeqOp(agg(aggNodeIndex), baggedPoint.datum, splits, + metadata.unorderedFeatures, instanceWeight, featuresForNode) } } } @@ -1024,35 +1024,15 @@ object DecisionTree extends Serializable with Logging { // Categorical feature val featureArity = metadata.featureArity(featureIndex) if (metadata.isUnordered(featureIndex)) { - // TODO: The second half of the bins are unused. Actually, we could just use - // splits and not build bins for unordered features. That should be part of - // a later PR since it will require changing other code (using splits instead - // of bins in a few places). // Unordered features - // 2^(maxFeatureValue - 1) - 1 combinations + // 2^(maxFeatureValue - 1) - 1 combinations splits(featureIndex) = new Array[Split](numSplits) - bins(featureIndex) = new Array[Bin](numBins) var splitIndex = 0 while (splitIndex < numSplits) { val categories: List[Double] = extractMultiClassCategories(splitIndex + 1, featureArity) splits(featureIndex)(splitIndex) = new Split(featureIndex, Double.MinValue, Categorical, categories) - bins(featureIndex)(splitIndex) = { - if (splitIndex == 0) { - new Bin( - new DummyCategoricalSplit(featureIndex, Categorical), - splits(featureIndex)(0), - Categorical, - Double.MinValue) - } else { - new Bin( - splits(featureIndex)(splitIndex - 1), - splits(featureIndex)(splitIndex), - Categorical, - Double.MinValue) - } - } splitIndex += 1 } } else { @@ -1060,8 +1040,11 @@ object DecisionTree extends Serializable with Logging { // Bins correspond to feature values, so we do not need to compute splits or bins // beforehand. Splits are constructed as needed during training. splits(featureIndex) = new Array[Split](0) - bins(featureIndex) = new Array[Bin](0) } + // For ordered features, bins correspond to feature values. + // For unordered categorical features, there is no need to construct the bins. + // since there is a one-to-one correspondence between the splits and the bins. + bins(featureIndex) = new Array[Bin](0) } featureIndex += 1 } http://git-wip-us.apache.org/repos/asf/spark/blob/9b746f38/mllib/src/main/scala/org/apache/spark/mllib/tree/impl/TreePoint.scala ---------------------------------------------------------------------- diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/impl/TreePoint.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/impl/TreePoint.scala index 35e361a..50b292e 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/tree/impl/TreePoint.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/impl/TreePoint.scala @@ -55,17 +55,15 @@ private[tree] object TreePoint { input: RDD[LabeledPoint], bins: Array[Array[Bin]], metadata: DecisionTreeMetadata): RDD[TreePoint] = { - // Construct arrays for featureArity and isUnordered for efficiency in the inner loop. + // Construct arrays for featureArity for efficiency in the inner loop. val featureArity: Array[Int] = new Array[Int](metadata.numFeatures) - val isUnordered: Array[Boolean] = new Array[Boolean](metadata.numFeatures) var featureIndex = 0 while (featureIndex < metadata.numFeatures) { featureArity(featureIndex) = metadata.featureArity.getOrElse(featureIndex, 0) - isUnordered(featureIndex) = metadata.isUnordered(featureIndex) featureIndex += 1 } input.map { x => - TreePoint.labeledPointToTreePoint(x, bins, featureArity, isUnordered) + TreePoint.labeledPointToTreePoint(x, bins, featureArity) } } @@ -74,19 +72,17 @@ private[tree] object TreePoint { * @param bins Bins for features, of size (numFeatures, numBins). * @param featureArity Array indexed by feature, with value 0 for continuous and numCategories * for categorical features. - * @param isUnordered Array index by feature, with value true for unordered categorical features. */ private def labeledPointToTreePoint( labeledPoint: LabeledPoint, bins: Array[Array[Bin]], - featureArity: Array[Int], - isUnordered: Array[Boolean]): TreePoint = { + featureArity: Array[Int]): TreePoint = { val numFeatures = labeledPoint.features.size val arr = new Array[Int](numFeatures) var featureIndex = 0 while (featureIndex < numFeatures) { arr(featureIndex) = findBin(featureIndex, labeledPoint, featureArity(featureIndex), - isUnordered(featureIndex), bins) + bins) featureIndex += 1 } new TreePoint(labeledPoint.label, arr) @@ -96,14 +92,12 @@ private[tree] object TreePoint { * Find bin for one (labeledPoint, feature). * * @param featureArity 0 for continuous features; number of categories for categorical features. - * @param isUnorderedFeature (only applies if feature is categorical) * @param bins Bins for features, of size (numFeatures, numBins). */ private def findBin( featureIndex: Int, labeledPoint: LabeledPoint, featureArity: Int, - isUnorderedFeature: Boolean, bins: Array[Array[Bin]]): Int = { /** http://git-wip-us.apache.org/repos/asf/spark/blob/9b746f38/mllib/src/test/scala/org/apache/spark/mllib/tree/DecisionTreeSuite.scala ---------------------------------------------------------------------- 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 7b1aed5..4c162df 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 @@ -190,7 +190,7 @@ class DecisionTreeSuite extends FunSuite with MLlibTestSparkContext { assert(splits.length === 2) assert(bins.length === 2) assert(splits(0).length === 3) - assert(bins(0).length === 6) + assert(bins(0).length === 0) // Expecting 2^2 - 1 = 3 bins/splits assert(splits(0)(0).feature === 0) @@ -228,41 +228,6 @@ class DecisionTreeSuite extends FunSuite with MLlibTestSparkContext { assert(splits(1)(2).categories.contains(0.0)) assert(splits(1)(2).categories.contains(1.0)) - // Check bins. - - assert(bins(0)(0).category === Double.MinValue) - assert(bins(0)(0).lowSplit.categories.length === 0) - assert(bins(0)(0).highSplit.categories.length === 1) - assert(bins(0)(0).highSplit.categories.contains(0.0)) - assert(bins(1)(0).category === Double.MinValue) - assert(bins(1)(0).lowSplit.categories.length === 0) - assert(bins(1)(0).highSplit.categories.length === 1) - assert(bins(1)(0).highSplit.categories.contains(0.0)) - - assert(bins(0)(1).category === Double.MinValue) - assert(bins(0)(1).lowSplit.categories.length === 1) - assert(bins(0)(1).lowSplit.categories.contains(0.0)) - assert(bins(0)(1).highSplit.categories.length === 1) - assert(bins(0)(1).highSplit.categories.contains(1.0)) - assert(bins(1)(1).category === Double.MinValue) - assert(bins(1)(1).lowSplit.categories.length === 1) - assert(bins(1)(1).lowSplit.categories.contains(0.0)) - assert(bins(1)(1).highSplit.categories.length === 1) - assert(bins(1)(1).highSplit.categories.contains(1.0)) - - assert(bins(0)(2).category === Double.MinValue) - assert(bins(0)(2).lowSplit.categories.length === 1) - assert(bins(0)(2).lowSplit.categories.contains(1.0)) - assert(bins(0)(2).highSplit.categories.length === 2) - assert(bins(0)(2).highSplit.categories.contains(1.0)) - assert(bins(0)(2).highSplit.categories.contains(0.0)) - assert(bins(1)(2).category === Double.MinValue) - assert(bins(1)(2).lowSplit.categories.length === 1) - assert(bins(1)(2).lowSplit.categories.contains(1.0)) - assert(bins(1)(2).highSplit.categories.length === 2) - assert(bins(1)(2).highSplit.categories.contains(1.0)) - assert(bins(1)(2).highSplit.categories.contains(0.0)) - } test("Multiclass classification with ordered categorical features: split and bin calculations") { --------------------------------------------------------------------- To unsubscribe, e-mail: commits-unsubscr...@spark.apache.org For additional commands, e-mail: commits-h...@spark.apache.org
