Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/79#discussion_r10442815
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/tree/DecisionTree.scala ---
@@ -0,0 +1,1055 @@
+/*
+ * 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
+
+import org.apache.spark.SparkContext._
+import org.apache.spark.rdd.RDD
+import org.apache.spark.mllib.tree.model._
+import org.apache.spark.{SparkContext, Logging}
+import org.apache.spark.mllib.regression.LabeledPoint
+import org.apache.spark.mllib.tree.model.Split
+import org.apache.spark.mllib.tree.configuration.Strategy
+import org.apache.spark.mllib.tree.configuration.QuantileStrategy._
+import org.apache.spark.mllib.tree.configuration.FeatureType._
+import org.apache.spark.mllib.tree.configuration.Algo._
+import org.apache.spark.mllib.tree.impurity.{Variance, Entropy, Gini,
Impurity}
+import scala.util.control.Breaks._
+
+/**
+ * A class that implements a decision tree algorithm for classification
and regression. It
+ * supports both continuous and categorical features.
+ * @param strategy The configuration parameters for the tree algorithm
which specify the type
+ * of algorithm (classification, regression, etc.),
feature type (continuous,
+ * categorical),
+ * depth of the tree, quantile calculation strategy, etc.
+ */
+class DecisionTree private(val strategy: Strategy) extends Serializable
with Logging {
+
+ /**
+ * Method to train a decision tree model over an RDD
+ * @param input RDD of
[[org.apache.spark.mllib.regression.LabeledPoint]] used as training data
+ * for DecisionTree
+ * @return a DecisionTreeModel that can be used for prediction
+ */
+ def train(input: RDD[LabeledPoint]): DecisionTreeModel = {
+
+ //Cache input RDD for speedup during multiple passes
+ input.cache()
+ logDebug("algo = " + strategy.algo)
+
+ //Finding the splits and the corresponding bins (interval between the
splits) using a sample
+ // of the input data.
+ val (splits, bins) = DecisionTree.findSplitsBins(input, strategy)
+ logDebug("numSplits = " + bins(0).length)
+
+ //Noting numBins for the input data
+ strategy.numBins = bins(0).length
+
+ //The depth of the decision tree
+ val maxDepth = strategy.maxDepth
+ //The max number of nodes possible given the depth of the tree
+ val maxNumNodes = scala.math.pow(2, maxDepth).toInt - 1
+ //Initalizing an array to hold filters applied to points for each node
+ val filters = new Array[List[Filter]](maxNumNodes)
+ //The filter at the top node is an empty list
+ filters(0) = List()
+ //Initializing an array to hold parent impurity calculations for each
node
+ val parentImpurities = new Array[Double](maxNumNodes)
+ //Dummy value for top node (updated during first split calculation)
+ val nodes = new Array[Node](maxNumNodes)
+
+ //The main-idea here is to perform level-wise training of the decision
tree nodes thus
+ // reducing the passes over the data from l to log2(l) where l is the
total number of nodes.
+ // Each data sample is checked for validity w.r.t to each node at a
given level -- i.e.,
+ // the sample is only used for the split calculation at the node if
the sampled would have
+ // still survived the filters of the parent nodes.
+ breakable {
+ for (level <- 0 until maxDepth) {
+
+ logDebug("#####################################")
+ logDebug("level = " + level)
+ logDebug("#####################################")
+
+ //Find best split for all nodes at a level
+ val splitsStatsForLevel = DecisionTree.findBestSplits(input,
parentImpurities, strategy,
+ level, filters, splits, bins)
+
+ for ((nodeSplitStats, index) <-
splitsStatsForLevel.view.zipWithIndex) {
+ //Extract info for nodes at the current level
+ extractNodeInfo(nodeSplitStats, level, index, nodes)
+ //Extract info for nodes at the next lower level
+ extractInfoForLowerLevels(level, index, maxDepth,
nodeSplitStats, parentImpurities,
+ filters)
+ logDebug("final best split = " + nodeSplitStats._1)
+
+ }
+ require(scala.math.pow(2, level) == splitsStatsForLevel.length)
+ //Check whether all the nodes at the current level at leaves
+ val allLeaf = splitsStatsForLevel.forall(_._2.gain <= 0)
+ logDebug("all leaf = " + allLeaf)
+ if (allLeaf) break //no more tree construction
+
+ }
+ }
+
+ //Initialize the top or root node of the tree
+ val topNode = nodes(0)
+ //Build the full tree using the node info calculated in the level-wise
best split calculations
+ topNode.build(nodes)
+
+ //Return a decision tree model
+ return new DecisionTreeModel(topNode, strategy.algo)
+ }
+
+ /**
+ * Extract the decision tree node information for th given tree level
and node index
+ */
+ private def extractNodeInfo(
+ nodeSplitStats: (Split, InformationGainStats),
+ level: Int,
+ index: Int,
+ nodes: Array[Node])
+ : Unit = {
+
+ val split = nodeSplitStats._1
+ val stats = nodeSplitStats._2
+ val nodeIndex = scala.math.pow(2, level).toInt - 1 + index
+ val isLeaf = (stats.gain <= 0) || (level == strategy.maxDepth - 1)
+ val node = new Node(nodeIndex, stats.predict, isLeaf, Some(split),
None, None, Some(stats))
+ logDebug("Node = " + node)
+ nodes(nodeIndex) = node
+ }
+
+ /**
+ * Extract the decision tree node information for the children of the
node
+ */
+ private def extractInfoForLowerLevels(
+ level: Int,
+ index: Int,
+ maxDepth: Int,
+ nodeSplitStats: (Split, InformationGainStats),
+ parentImpurities: Array[Double],
+ filters: Array[List[Filter]])
+ : Unit = {
+
+ // 0 corresponds to the left child node and 1 corresponds to the right
child node.
+ for (i <- 0 to 1) {
+ //Calculating the index of the node from the node level and the index
at the current level
+ val nodeIndex = scala.math.pow(2, level + 1).toInt - 1 + 2 * index +
i
+ if (level < maxDepth - 1) {
+ val impurity = if (i == 0) {
+ nodeSplitStats._2.leftImpurity
+ } else {
+ nodeSplitStats._2.rightImpurity
+ }
+ logDebug("nodeIndex = " + nodeIndex + ", impurity = " + impurity)
+ //noting the parent impurities
+ parentImpurities(nodeIndex) = impurity
+ //noting the parents filters for the child nodes
+ val childFilter = new Filter(nodeSplitStats._1, if (i == 0) -1
else 1)
+ filters(nodeIndex) = childFilter :: filters((nodeIndex - 1) / 2)
+ for (filter <- filters(nodeIndex)) {
+ logDebug("Filter = " + filter)
+ }
+ }
+ }
+ }
+}
+
+object DecisionTree extends Serializable with Logging {
+
+ /**
+ * Method to train a decision tree model over an RDD
+ * @param input RDD of
[[org.apache.spark.mllib.regression.LabeledPoint]] used as training data
+ * for DecisionTree
+ * @param strategy The configuration parameters for the tree algorithm
which specify the type
+ * of algoritm (classification, regression, etc.),
feature type (continuous,
+ * categorical), depth of the tree, quantile calculation
strategy, etc.
+ * @return a DecisionTreeModel that can be used for prediction
+ */
+ def train(input: RDD[LabeledPoint], strategy: Strategy):
DecisionTreeModel = {
+ new DecisionTree(strategy).train(input: RDD[LabeledPoint])
+ }
+
+ /**
+ * Method to train a decision tree model over an RDD
+ * @param input input RDD of
[[org.apache.spark.mllib.regression.LabeledPoint]] used as
+ * training data
+ * @param algo algo classification or regression
+ * @param impurity impurity criterion used for information gain
calculation
+ * @param maxDepth maxDepth maximum depth of the tree
+ * @return a DecisionTreeModel that can be used for prediction
+ */
+ def train(
+ input: RDD[LabeledPoint],
+ algo: Algo,
+ impurity: Impurity,
+ maxDepth: Int)
+ : DecisionTreeModel = {
+ val strategy = new Strategy(algo,impurity,maxDepth)
+ new DecisionTree(strategy).train(input: RDD[LabeledPoint])
+ }
+
+
+ /**
+ * Method to train a decision tree model over an RDD
+ * @param input input RDD of
[[org.apache.spark.mllib.regression.LabeledPoint]] used as
+ * training data for DecisionTree
+ * @param algo classification or regression
+ * @param impurity criterion used for information gain calculation
+ * @param maxDepth maximum depth of the tree
+ * @param maxBins maximum number of bins used for splitting features
+ * @param quantileCalculationStrategy algorithm for calculating
quantiles
+ * @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.
+ * @return a DecisionTreeModel that can be used for prediction
+ */
+ def train(
+ input: RDD[LabeledPoint],
+ algo: Algo,
+ impurity: Impurity,
+ maxDepth: Int,
+ maxBins: Int,
+ quantileCalculationStrategy: QuantileStrategy,
+ categoricalFeaturesInfo: Map[Int,Int])
+ : DecisionTreeModel = {
+ val strategy = new Strategy(algo, impurity, maxDepth, maxBins,
quantileCalculationStrategy,
+ categoricalFeaturesInfo)
+ new DecisionTree(strategy).train(input: RDD[LabeledPoint])
+ }
+
+ /**
+ * Returns an array of optimal splits for all nodes at a given level
+ * @param input RDD of
[[org.apache.spark.mllib.regression.LabeledPoint]] used as training data
+ * for DecisionTree
+ * @param parentImpurities Impurities for all parent nodes for the
current level
+ * @param strategy
[[org.apache.spark.mllib.tree.configuration.Strategy]] instance containing
+ * parameters for construction the DecisionTree
+ * @param level Level of the tree
+ * @param filters Filters for all nodes at a given level
+ * @param splits possible splits for all features
+ * @param bins possible bins for all features
+ * @return array of splits with best splits for all nodes at a given
level.
+ */
+ def findBestSplits(
+ input: RDD[LabeledPoint],
+ parentImpurities: Array[Double],
+ strategy: Strategy,
+ level: Int,
+ filters: Array[List[Filter]],
+ splits: Array[Array[Split]],
+ bins: Array[Array[Bin]])
+ : Array[(Split, InformationGainStats)] = {
+
+ //Common calculations for multiple nested methods
+ val numNodes = scala.math.pow(2, level).toInt
+ logDebug("numNodes = " + numNodes)
+ //Find the number of features by looking at the first sample
+ val numFeatures = input.take(1)(0).features.length
+ logDebug("numFeatures = " + numFeatures)
+ val numBins = strategy.numBins
+ logDebug("numBins = " + numBins)
+
+ /*Find the filters used before reaching the current code*/
+ def findParentFilters(nodeIndex: Int): List[Filter] = {
+ if (level == 0) {
+ List[Filter]()
+ } else {
+ val nodeFilterIndex = scala.math.pow(2, level).toInt - 1 +
nodeIndex
+ filters(nodeFilterIndex)
+ }
+ }
+
+ /**
+ * Find whether the sample is valid input for the current node. In
other words,
+ * does it pass through all the filters for the current node.
+ */
+ def isSampleValid(parentFilters: List[Filter], labeledPoint:
LabeledPoint): Boolean = {
+
+ //Leaf
+ if ((level > 0) & (parentFilters.length == 0) ){
+ return false
+ }
+
+ for (filter <- parentFilters) {
+ val features = labeledPoint.features
+ val featureIndex = filter.split.feature
+ val threshold = filter.split.threshold
+ val comparison = filter.comparison
+ val categories = filter.split.categories
+ val isFeatureContinuous = filter.split.featureType == Continuous
+ val feature = features(featureIndex)
+ if (isFeatureContinuous){
+ comparison match {
+ case(-1) => if (feature > threshold) return false
+ case(1) => if (feature <= threshold) return false
+ }
+ } else {
+ val containsFeature = categories.contains(feature)
+ comparison match {
+ case(-1) => if (!containsFeature) return false
+ case(1) => if (containsFeature) return false
+ }
+
+ }
+ }
+ true
+ }
+
+ /**
+ * Finds the right bin for the given feature
+ */
+ def findBin(
+ featureIndex: Int,
+ labeledPoint: LabeledPoint,
+ isFeatureContinuous: Boolean)
+ : Int = {
+
+ if (isFeatureContinuous){
+ for (binIndex <- 0 until strategy.numBins) {
--- End diff --
Because this is called for many times, we should use binary search or at
least a while loop instead of for.
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