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Diffstat (limited to 'mllib/src/main/scala/org/apache/spark/mllib/tree/impl/TreePoint.scala')
| -rw-r--r-- | mllib/src/main/scala/org/apache/spark/mllib/tree/impl/TreePoint.scala | 150 |
1 files changed, 0 insertions, 150 deletions
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 deleted file mode 100644 index 21919d69a3..0000000000 --- a/mllib/src/main/scala/org/apache/spark/mllib/tree/impl/TreePoint.scala +++ /dev/null @@ -1,150 +0,0 @@ -/* - * 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.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 binnedFeatures Binned feature values. - * Same length as LabeledPoint.features, but values are bin indices. - */ -private[spark] class TreePoint(val label: Double, val binnedFeatures: Array[Int]) - extends Serializable { -} - -private[spark] object TreePoint { - - /** - * Convert an input dataset into its TreePoint representation, - * binning feature values in preparation for DecisionTree training. - * @param input Input dataset. - * @param bins Bins for features, of size (numFeatures, numBins). - * @param metadata Learning and dataset metadata - * @return TreePoint dataset representation - */ - def convertToTreeRDD( - input: RDD[LabeledPoint], - bins: Array[Array[Bin]], - metadata: DecisionTreeMetadata): RDD[TreePoint] = { - // Construct arrays for featureArity for efficiency in the inner loop. - val featureArity: Array[Int] = new Array[Int](metadata.numFeatures) - var featureIndex = 0 - while (featureIndex < metadata.numFeatures) { - featureArity(featureIndex) = metadata.featureArity.getOrElse(featureIndex, 0) - featureIndex += 1 - } - input.map { x => - TreePoint.labeledPointToTreePoint(x, bins, featureArity) - } - } - - /** - * Convert one LabeledPoint into its TreePoint representation. - * @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. - */ - private def labeledPointToTreePoint( - labeledPoint: LabeledPoint, - bins: Array[Array[Bin]], - 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), - bins) - featureIndex += 1 - } - new TreePoint(labeledPoint.label, arr) - } - - /** - * Find bin for one (labeledPoint, feature). - * - * @param featureArity 0 for continuous features; number of categories for categorical features. - * @param bins Bins for features, of size (numFeatures, numBins). - */ - private def findBin( - featureIndex: Int, - labeledPoint: LabeledPoint, - featureArity: Int, - bins: Array[Array[Bin]]): Int = { - - /** - * Binary search helper method for continuous feature. - */ - def binarySearchForBins(): Int = { - val binForFeatures = bins(featureIndex) - val feature = labeledPoint.features(featureIndex) - var left = 0 - var right = binForFeatures.length - 1 - while (left <= right) { - val mid = left + (right - left) / 2 - val bin = binForFeatures(mid) - val lowThreshold = bin.lowSplit.threshold - val highThreshold = bin.highSplit.threshold - if ((lowThreshold < feature) && (highThreshold >= feature)) { - return mid - } else if (lowThreshold >= feature) { - right = mid - 1 - } else { - left = mid + 1 - } - } - -1 - } - - if (featureArity == 0) { - // Perform binary search for finding bin for continuous features. - val binIndex = binarySearchForBins() - if (binIndex == -1) { - throw new RuntimeException("No bin was found for continuous feature." + - " This error can occur when given invalid data values (such as NaN)." + - s" Feature index: $featureIndex. Feature value: ${labeledPoint.features(featureIndex)}") - } - binIndex - } else { - // Categorical feature bins are indexed by feature values. - val featureValue = labeledPoint.features(featureIndex) - if (featureValue < 0 || featureValue >= featureArity) { - throw new IllegalArgumentException( - s"DecisionTree given invalid data:" + - s" Feature $featureIndex is categorical with values in" + - s" {0,...,${featureArity - 1}," + - s" but a data point gives it value $featureValue.\n" + - " Bad data point: " + labeledPoint.toString) - } - featureValue.toInt - } - } -} |