Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/79#discussion_r10360401
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/tree/DecisionTree.scala ---
@@ -0,0 +1,915 @@
+/*
+ * 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 scala.util.control.Breaks._
+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.Impurity
+
+/**
+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()
+
+ val (splits, bins) = DecisionTree.findSplitsBins(input, strategy)
+ logDebug("numSplits = " + bins(0).length)
+ strategy.numBins = bins(0).length
+
+ val maxDepth = strategy.maxDepth
+
+ val maxNumNodes = scala.math.pow(2,maxDepth).toInt - 1
+ val filters = new Array[List[Filter]](maxNumNodes)
+ filters(0) = List()
+ val parentImpurities = new Array[Double](maxNumNodes)
+ //Dummy value for top node (updated during first split calculation)
+ //parentImpurities(0) = Double.MinValue
+ val nodes = new Array[Node](maxNumNodes)
+
+ logDebug("algo = " + strategy.algo)
+
+ 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){
+
+ extractNodeInfo(nodeSplitStats, level, index, nodes)
+ extractInfoForLowerLevels(level, index, maxDepth,
nodeSplitStats, parentImpurities,
+ filters)
+ logDebug("final best split = " + nodeSplitStats._1)
+
+ }
+ require(scala.math.pow(2,level)==splitsStatsForLevel.length)
+
+ val allLeaf = splitsStatsForLevel.forall(_._2.gain <= 0 )
+ logDebug("all leaf = " + allLeaf)
+ if (allLeaf) break
+
+ }
+ }
+
+ val topNode = nodes(0)
+ topNode.build(nodes)
+
+ val decisionTreeModel = {
+ return new DecisionTreeModel(topNode, strategy.algo)
+ }
+
+ return decisionTreeModel
+ }
+
--- End diff --
Remove extra blank line.
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