Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/1950#discussion_r16279832
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/tree/impl/TreePoint.scala ---
@@ -0,0 +1,202 @@
+/*
+ * 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.regression.LabeledPoint
+import org.apache.spark.mllib.tree.configuration.Strategy
+import org.apache.spark.mllib.tree.model.Bin
+import org.apache.spark.rdd.RDD
+
+
+/**
+ * Internal representation of LabeledPoint for DecisionTree.
+ * This bins feature values based on a subsampled of data as follows:
+ * (a) Continuous features are binned into ranges.
+ * (b) Unordered categorical features are binned based on subsets of
feature values.
+ * "Unordered categorical features" are categorical features with low
arity used in
+ * multiclass classification.
+ * (c) Ordered categorical features are binned based on feature values.
+ * "Ordered categorical features" are categorical features with high
arity,
+ * or any categorical feature used in regression or binary
classification.
+ *
+ * @param label Label from LabeledPoint
+ * @param features Binned feature values.
+ * Same length as LabeledPoint.features, but values are
bin indices.
+ */
+private[tree] class TreePoint(val label: Double, val features: Array[Int])
extends Serializable {
+}
+
+
+private[tree] object TreePoint {
+
+ /**
+ * Convert an input dataset into its TreePoint representation,
+ * binning feature values in preparation for DecisionTree training.
+ * @param input Input dataset.
+ * @param strategy DecisionTree training info, used for dataset
metadata.
+ * @param bins Bins for features, of size (numFeatures, numBins).
+ * @return TreePoint dataset representation
+ */
+ def convertToTreeRDD(
+ input: RDD[LabeledPoint],
+ strategy: Strategy,
+ bins: Array[Array[Bin]]): RDD[TreePoint] = {
+ input.map { x =>
+ TreePoint.labeledPointToTreePoint(x,
strategy.isMulticlassClassification, bins,
+ strategy.categoricalFeaturesInfo)
+ }
+ }
+
+ /**
+ * Convert one LabeledPoint into its TreePoint representation.
+ * @param bins Bins for features, of size (numFeatures, numBins).
+ * @param categoricalFeaturesInfo Map over categorical features:
feature index --> feature arity
+ */
+ private def labeledPointToTreePoint(
+ labeledPoint: LabeledPoint,
+ isMulticlassClassification: Boolean,
+ bins: Array[Array[Bin]],
+ categoricalFeaturesInfo: Map[Int, Int]): TreePoint = {
+
+ val numFeatures = labeledPoint.features.size
+ val numBins = bins(0).size
+ val arr = new Array[Int](numFeatures)
+ var featureIndex = 0 // offset by 1 for label
--- End diff --
Where does `offset by 1` happen?
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