apache
diff --git a/‎mllib/src/main/scala/org/apache/spark/mllib/tree/DecisionTree.scala‎
Lines changed: 450 additions & 891 deletions b/‎mllib/src/main/scala/org/apache/spark/mllib/tree/DecisionTree.scala‎
Lines changed: 450 additions & 891 deletions
diff --git a/‎mllib/src/main/scala/org/apache/spark/mllib/tree/impl/DTStatsAggregator.scala‎
Lines changed: 213 additions & 0 deletions b/‎mllib/src/main/scala/org/apache/spark/mllib/tree/impl/DTStatsAggregator.scala‎
Lines changed: 213 additions & 0 deletions
diff --git a/‎mllib/src/main/scala/org/apache/spark/mllib/tree/impl/DecisionTreeMetadata.scala‎
Lines changed: 57 additions & 16 deletions b/‎mllib/src/main/scala/org/apache/spark/mllib/tree/impl/DecisionTreeMetadata.scala‎
Lines changed: 57 additions & 16 deletions
@@ -0,0 +1,213 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.mllib.tree.impl
+
+import org.apache.spark.mllib.tree.impurity._
+
+/**
+ * DecisionTree statistics aggregator.
+ * This holds a flat array of statistics for a set of (nodes, features, bins)
+ * and helps with indexing.
+ */
+private[tree] class DTStatsAggregator(
+    val metadata: DecisionTreeMetadata,
+    val numNodes: Int) extends Serializable {
+
+  /**
+   * [[ImpurityAggregator]] instance specifying the impurity type.
+   */
+  val impurityAggregator: ImpurityAggregator = metadata.impurity match {
+    case Gini => new GiniAggregator(metadata.numClasses)
+    case Entropy => new EntropyAggregator(metadata.numClasses)
+    case Variance => new VarianceAggregator()
+    case _ => throw new IllegalArgumentException(s"Bad impurity parameter: ${metadata.impurity}")
+  }
+
+  /**
+   * Number of elements (Double values) used for the sufficient statistics of each bin.
+   */
+  val statsSize: Int = impurityAggregator.statsSize
+
+  val numFeatures: Int = metadata.numFeatures
+
+  /**
+   * Number of bins for each feature.  This is indexed by the feature index.
+   */
+  val numBins: Array[Int] = metadata.numBins
+
+  /**
+   * Number of splits for the given feature.
+   */
+  def numSplits(featureIndex: Int): Int = metadata.numSplits(featureIndex)
+
+  /**
+   * Indicator for each feature of whether that feature is an unordered feature.
+   * TODO: Is Array[Boolean] any faster?
+   */
+  def isUnordered(featureIndex: Int): Boolean = metadata.isUnordered(featureIndex)
+
+  /**
+   * Offset for each feature for calculating indices into the [[allStats]] array.
+   */
+  private val featureOffsets: Array[Int] = {
+    def featureOffsetsCalc(total: Int, featureIndex: Int): Int = {
+      if (isUnordered(featureIndex)) {
+        total + 2 * numBins(featureIndex)
+      } else {
+        total + numBins(featureIndex)
+      }
+    }
+    Range(0, numFeatures).scanLeft(0)(featureOffsetsCalc).map(statsSize * _).toArray
+  }
+
+  /**
+   * Number of elements for each node, corresponding to stride between nodes in [[allStats]].
+   */
+  private val nodeStride: Int = featureOffsets.last
+
+  /**
+   * Total number of elements stored in this aggregator.
+   */
+  val allStatsSize: Int = numNodes * nodeStride
+
+  /**
+   * Flat array of elements.
+   * Index for start of stats for a (node, feature, bin) is:
+   *   index = nodeIndex * nodeStride + featureOffsets(featureIndex) + binIndex * statsSize
+   * Note: For unordered features, the left child stats have binIndex in [0, numBins(featureIndex))
+   *       and the right child stats in [numBins(featureIndex), 2 * numBins(featureIndex))
+   */
+  val allStats: Array[Double] = new Array[Double](allStatsSize)
+
+  /**
+   * Get an [[ImpurityCalculator]] for a given (node, feature, bin).
+   * @param nodeFeatureOffset  For ordered features, this is a pre-computed (node, feature) offset
+   *                           from [[getNodeFeatureOffset]].
+   *                           For unordered features, this is a pre-computed
+   *                           (node, feature, left/right child) offset from
+   *                           [[getLeftRightNodeFeatureOffsets]].
+   */
+  def getImpurityCalculator(nodeFeatureOffset: Int, binIndex: Int): ImpurityCalculator = {
+    impurityAggregator.getCalculator(allStats, nodeFeatureOffset + binIndex * statsSize)
+  }
+
+  /**
+   * Update the stats for a given (node, feature, bin) for ordered features, using the given label.
+   */
+  def update(nodeIndex: Int, featureIndex: Int, binIndex: Int, label: Double): Unit = {
+    val i = nodeIndex * nodeStride + featureOffsets(featureIndex) + binIndex * statsSize
+    impurityAggregator.update(allStats, i, label)
+  }
+
+  /**
+   * Pre-compute node offset for use with [[nodeUpdate]].
+   */
+  def getNodeOffset(nodeIndex: Int): Int = nodeIndex * nodeStride
+
+  /**
+   * Faster version of [[update]].
+   * Update the stats for a given (node, feature, bin) for ordered features, using the given label.
+   * @param nodeOffset  Pre-computed node offset from [[getNodeOffset]].
+   */
+  def nodeUpdate(nodeOffset: Int, featureIndex: Int, binIndex: Int, label: Double): Unit = {
+    val i = nodeOffset + featureOffsets(featureIndex) + binIndex * statsSize
+    impurityAggregator.update(allStats, i, label)
+  }
+
+  /**
+   * Pre-compute (node, feature) offset for use with [[nodeFeatureUpdate]].
+   * For ordered features only.
+   */
+  def getNodeFeatureOffset(nodeIndex: Int, featureIndex: Int): Int = {
+    require(!isUnordered(featureIndex),
+      s"DTStatsAggregator.getNodeFeatureOffset is for ordered features only, but was called" +
+      s" for unordered feature $featureIndex.")
+    nodeIndex * nodeStride + featureOffsets(featureIndex)
+  }
+
+  /**
+   * Pre-compute (node, feature) offset for use with [[nodeFeatureUpdate]].
+   * For unordered features only.
+   */
+  def getLeftRightNodeFeatureOffsets(nodeIndex: Int, featureIndex: Int): (Int, Int) = {
+    require(isUnordered(featureIndex),
+      s"DTStatsAggregator.getLeftRightNodeFeatureOffsets is for unordered features only," +
+      s" but was called for ordered feature $featureIndex.")
+    val baseOffset = nodeIndex * nodeStride + featureOffsets(featureIndex)
+    (baseOffset, baseOffset + numBins(featureIndex) * statsSize)
+  }
+
+  /**
+   * Faster version of [[update]].
+   * Update the stats for a given (node, feature, bin), using the given label.
+   * @param nodeFeatureOffset  For ordered features, this is a pre-computed (node, feature) offset
+   *                           from [[getNodeFeatureOffset]].
+   *                           For unordered features, this is a pre-computed
+   *                           (node, feature, left/right child) offset from
+   *                           [[getLeftRightNodeFeatureOffsets]].
+   */
+  def nodeFeatureUpdate(nodeFeatureOffset: Int, binIndex: Int, label: Double): Unit = {
+    impurityAggregator.update(allStats, nodeFeatureOffset + binIndex * statsSize, label)
+  }
+
+  /**
+   * For a given (node, feature), merge the stats for two bins.
+   * @param nodeFeatureOffset  For ordered features, this is a pre-computed (node, feature) offset
+   *                           from [[getNodeFeatureOffset]].
+   *                           For unordered features, this is a pre-computed
+   *                           (node, feature, left/right child) offset from
+   *                           [[getLeftRightNodeFeatureOffsets]].
+   * @param binIndex  The other bin is merged into this bin.
+   * @param otherBinIndex  This bin is not modified.
+   */
+  def mergeForNodeFeature(nodeFeatureOffset: Int, binIndex: Int, otherBinIndex: Int): Unit = {
+    impurityAggregator.merge(allStats, nodeFeatureOffset + binIndex * statsSize,
+      nodeFeatureOffset + otherBinIndex * statsSize)
+  }
+
+  /**
+   * Merge this aggregator with another, and returns this aggregator.
+   * This method modifies this aggregator in-place.
+   */
+  def merge(other: DTStatsAggregator): DTStatsAggregator = {
+    require(allStatsSize == other.allStatsSize,
+      s"DTStatsAggregator.merge requires that both aggregators have the same length stats vectors."
+      + s" This aggregator is of length $allStatsSize, but the other is ${other.allStatsSize}.")
+    var i = 0
+    // TODO: Test BLAS.axpy
+    while (i < allStatsSize) {
+      allStats(i) += other.allStats(i)
+      i += 1
+    }
+    this
+  }
+
+}
+
+private[tree] object DTStatsAggregator extends Serializable {
+
+  /**
+   * Combines two aggregates (modifying the first) and returns the combination.
+   */
+  def binCombOp(
+      agg1: DTStatsAggregator,
+      agg2: DTStatsAggregator): DTStatsAggregator = {
+    agg1.merge(agg2)
+  }
+
+}
@@ -26,14 +26,15 @@ import org.apache.spark.mllib.tree.configuration.Strategy
 import org.apache.spark.mllib.tree.impurity.Impurity
 import org.apache.spark.rdd.RDD
 
-
 /**
  * Learning and dataset metadata for DecisionTree.
  *
  * @param numClasses    For classification: labels can take values {0, ..., numClasses - 1}.
  *                      For regression: fixed at 0 (no meaning).
+ * @param maxBins  Maximum number of bins, for all features.
  * @param featureArity  Map: categorical feature index --> arity.
  *                      I.e., the feature takes values in {0, ..., arity - 1}.
+ * @param numBins  Number of bins for each feature.
  */
 private[tree] class DecisionTreeMetadata(
     val numFeatures: Int,
@@ -42,6 +43,7 @@ private[tree] class DecisionTreeMetadata(
     val maxBins: Int,
     val featureArity: Map[Int, Int],
     val unorderedFeatures: Set[Int],
+    val numBins: Array[Int],
     val impurity: Impurity,
     val quantileStrategy: QuantileStrategy) extends Serializable {
 
@@ -57,10 +59,26 @@ private[tree] class DecisionTreeMetadata(
 
   def isContinuous(featureIndex: Int): Boolean = !featureArity.contains(featureIndex)
 
+  /**
+   * Number of splits for the given feature.
+   * For unordered features, there are 2 bins per split.
+   * For ordered features, there is 1 more bin than split.
+   */
+  def numSplits(featureIndex: Int): Int = if (isUnordered(featureIndex)) {
+    numBins(featureIndex) >> 1
+  } else {
+    numBins(featureIndex) - 1
+  }
+
 }
 
 private[tree] object DecisionTreeMetadata {
 
+  /**
+   * Construct a [[DecisionTreeMetadata]] instance for this dataset and parameters.
+   * This computes which categorical features will be ordered vs. unordered,
+   * as well as the number of splits and bins for each feature.
+   */
   def buildMetadata(input: RDD[LabeledPoint], strategy: Strategy): DecisionTreeMetadata = {
 
     val numFeatures = input.take(1)(0).features.size
@@ -70,32 +88,55 @@ private[tree] object DecisionTreeMetadata {
       case Regression => 0
     }
 
-    val maxBins = math.min(strategy.maxBins, numExamples).toInt
-    val log2MaxBinsp1 = math.log(maxBins + 1) / math.log(2.0)
+    val maxPossibleBins = math.min(strategy.maxBins, numExamples).toInt
+
+    // We check the number of bins here against maxPossibleBins.
+    // This needs to be checked here instead of in Strategy since maxPossibleBins can be modified
+    // based on the number of training examples.
+    if (strategy.categoricalFeaturesInfo.nonEmpty) {
+      val maxCategoriesPerFeature = strategy.categoricalFeaturesInfo.values.max
+      require(maxCategoriesPerFeature <= maxPossibleBins,
+        s"DecisionTree requires maxBins (= $maxPossibleBins) >= max categories " +
+          s"in categorical features (= $maxCategoriesPerFeature)")
+    }
 
     val unorderedFeatures = new mutable.HashSet[Int]()
+    val numBins = Array.fill[Int](numFeatures)(maxPossibleBins)
     if (numClasses > 2) {
-      strategy.categoricalFeaturesInfo.foreach { case (f, k) =>
-        if (k - 1 < log2MaxBinsp1) {
-          // Note: The above check is equivalent to checking:
-          //       numUnorderedBins = (1 << k - 1) - 1 < maxBins
-          unorderedFeatures.add(f)
+      // Multiclass classification
+      val maxCategoriesForUnorderedFeature =
+        ((math.log(maxPossibleBins / 2 + 1) / math.log(2.0)) + 1).floor.toInt
+      strategy.categoricalFeaturesInfo.foreach { case (featureIndex, numCategories) =>
+        // Decide if some categorical features should be treated as unordered features,
+        //  which require 2 * ((1 << numCategories - 1) - 1) bins.
+        // We do this check with log values to prevent overflows in case numCategories is large.
+        // The next check is equivalent to: 2 * ((1 << numCategories - 1) - 1) <= maxBins
+        if (numCategories <= maxCategoriesForUnorderedFeature) {
+          unorderedFeatures.add(featureIndex)
+          numBins(featureIndex) = numUnorderedBins(numCategories)
         } else {
-          // TODO: Allow this case, where we simply will know nothing about some categories?
-          require(k < maxBins, s"maxBins (= $maxBins) should be greater than max categories " +
-            s"in categorical features (>= $k)")
+          numBins(featureIndex) = numCategories
         }
       }
     } else {
-      strategy.categoricalFeaturesInfo.foreach { case (f, k) =>
-        require(k < maxBins, s"maxBins (= $maxBins) should be greater than max categories " +
-          s"in categorical features (>= $k)")
+      // Binary classification or regression
+      strategy.categoricalFeaturesInfo.foreach { case (featureIndex, numCategories) =>
+        numBins(featureIndex) = numCategories
       }
     }
 
-    new DecisionTreeMetadata(numFeatures, numExamples, numClasses, maxBins,
-      strategy.categoricalFeaturesInfo, unorderedFeatures.toSet,
+    new DecisionTreeMetadata(numFeatures, numExamples, numClasses, numBins.max,
+      strategy.categoricalFeaturesInfo, unorderedFeatures.toSet, numBins,
       strategy.impurity, strategy.quantileCalculationStrategy)
   }
 
+  /**
+   * Given the arity of a categorical feature (arity = number of categories),
+   * return the number of bins for the feature if it is to be treated as an unordered feature.
+   * There is 1 split for every partitioning of categories into 2 disjoint, non-empty sets;
+   * there are math.pow(2, arity - 1) - 1 such splits.
+   * Each split has 2 corresponding bins.
+   */
+  def numUnorderedBins(arity: Int): Int = 2 * ((1 << arity - 1) - 1)
+
 }