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SPARK-3278 added initial version of Isotonic regression algorithm including proposed API
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‎mllib/src/main/scala/org/apache/spark/mllib/regression/IsotonicRegression.scala‎

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/*
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* Licensed to the Apache Software Foundation (ASF) under one or more
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* contributor license agreements. See the NOTICE file distributed with
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* this work for additional information regarding copyright ownership.
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* The ASF licenses this file to You under the Apache License, Version 2.0
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* (the "License"); you may not use this file except in compliance with
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* the License. You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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package org.apache.spark.mllib.regression
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import org.apache.spark.mllib.linalg.Vector
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import org.apache.spark.rdd.RDD
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sealed trait MonotonicityConstraint {
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def holds(current: LabeledPoint, next: LabeledPoint): Boolean
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}
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case object Isotonic extends MonotonicityConstraint {
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override def holds(current: LabeledPoint, next: LabeledPoint): Boolean = {
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current.label <= next.label
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}
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}
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case object Antitonic extends MonotonicityConstraint {
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override def holds(current: LabeledPoint, next: LabeledPoint): Boolean = {
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current.label >= next.label
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}
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}
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/**
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* Regression model for Isotonic regression
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*
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* @param predictions Weights computed for every feature.
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*/
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class IsotonicRegressionModel(
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val predictions: Seq[LabeledPoint],
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val monotonicityConstraint: MonotonicityConstraint)
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extends RegressionModel {
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override def predict(testData: RDD[Vector]): RDD[Double] =
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testData.map(predict)
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//take the highest of elements smaller than our feature or weight with lowest feature
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override def predict(testData: Vector): Double =
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(predictions.head +: predictions.filter(y => y.features.toArray.head <= testData.toArray.head)).last.label
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}
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trait IsotonicRegressionAlgorithm
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extends Serializable {
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protected def createModel(weights: Seq[LabeledPoint], monotonicityConstraint: MonotonicityConstraint): IsotonicRegressionModel
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def run(input: RDD[LabeledPoint], monotonicityConstraint: MonotonicityConstraint): IsotonicRegressionModel
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def run(input: RDD[LabeledPoint], initialWeights: Vector, monotonicityConstraint: MonotonicityConstraint): IsotonicRegressionModel
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}
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class PoolAdjacentViolators extends IsotonicRegressionAlgorithm {
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override def run(input: RDD[LabeledPoint], monotonicityConstraint: MonotonicityConstraint): IsotonicRegressionModel =
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createModel(parallelPoolAdjacentViolators(input, monotonicityConstraint), monotonicityConstraint)
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override def run(input: RDD[LabeledPoint], initialWeights: Vector, monotonicityConstraint: MonotonicityConstraint): IsotonicRegressionModel = ???
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override protected def createModel(weights: Seq[LabeledPoint], monotonicityConstraint: MonotonicityConstraint): IsotonicRegressionModel =
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new IsotonicRegressionModel(weights, monotonicityConstraint)
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//Performs a pool adjacent violators algorithm (PAVA)
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//Uses approach with single processing of data where violators in previously processed
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//data created by pooling are fixed immediatelly.
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//Uses optimization of discovering monotonicity violating sequences
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//Method in situ mutates input array
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private def poolAdjacentViolators(in: Array[LabeledPoint], monotonicityConstraint: MonotonicityConstraint): Array[LabeledPoint] = {
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//Pools sub array within given bounds assigning weighted average value to all elements
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def pool(in: Array[LabeledPoint], start: Int, end: Int): Unit = {
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val poolSubArray = in.slice(start, end + 1)
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val weightedSum = poolSubArray.map(_.label).sum
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val weight = poolSubArray.length
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for(i <- start to end) {
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in(i) = LabeledPoint(weightedSum / weight, in(i).features)
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}
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}
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var i = 0
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while(i < in.length) {
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var j = i
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//find monotonicity violating sequence, if any
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while(j < in.length - 1 && !monotonicityConstraint.holds(in(j), in(j + 1))) {
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j = j + 1
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}
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//if monotonicity was not violated, move to next data point
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if(i == j) {
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i = i + 1
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} else {
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//otherwise pool the violating sequence
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//and check if pooling caused monotonicity violation in previously processed points
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while (i >= 0 && !monotonicityConstraint.holds(in(i), in(i + 1))) {
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pool(in, i, j)
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i = i - 1
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}
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i = j
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}
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}
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in
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}
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private def parallelPoolAdjacentViolators(testData: RDD[LabeledPoint], monotonicityConstraint: MonotonicityConstraint): Seq[LabeledPoint] = {
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poolAdjacentViolators(
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testData
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.sortBy(_.features.toArray.head)
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.mapPartitions(it => poolAdjacentViolators(it.toArray, monotonicityConstraint).toIterator)
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.collect(), monotonicityConstraint)
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}
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}
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/**
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* Top-level methods for calling IsotonicRegression.
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*/
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object IsotonicRegression {
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/**
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* Train a Linear Regression model given an RDD of (label, features) pairs. We run a fixed number
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* of iterations of gradient descent using the specified step size. Each iteration uses
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* `miniBatchFraction` fraction of the data to calculate a stochastic gradient. The weights used
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* in gradient descent are initialized using the initial weights provided.
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*
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* @param input RDD of (label, array of features) pairs. Each pair describes a row of the data
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* matrix A as well as the corresponding right hand side label y
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* @param initialWeights Initial set of weights to be used. Array should be equal in size to
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* the number of features in the data.
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*/
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def train(
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input: RDD[LabeledPoint],
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initialWeights: Vector,
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monotonicityConstraint: MonotonicityConstraint): IsotonicRegressionModel = {
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new PoolAdjacentViolators().run(input, initialWeights, monotonicityConstraint)
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}
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/**
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* Train a LinearRegression model given an RDD of (label, features) pairs. We run a fixed number
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* of iterations of gradient descent using the specified step size. Each iteration uses
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* `miniBatchFraction` fraction of the data to calculate a stochastic gradient.
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*
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* @param input RDD of (label, array of features) pairs. Each pair describes a row of the data
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* matrix A as well as the corresponding right hand side label y
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*/
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def train(
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input: RDD[LabeledPoint],
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monotonicityConstraint: MonotonicityConstraint): IsotonicRegressionModel = {
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new PoolAdjacentViolators().run(input, monotonicityConstraint)
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}
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}
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/*def functionalOption(in: Array[LabeledPoint], monotonicityConstraint: MonotonicityConstraint): Array[LabeledPoint] = {
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def pool2(in: Array[LabeledPoint]): Array[LabeledPoint] =
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in.map(p => LabeledPoint(in.map(_.label).sum / in.length, p.features))
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def iterate(checked: Array[LabeledPoint], remaining: Array[LabeledPoint], monotonicityConstraint: MonotonicityConstraint): Array[LabeledPoint] = {
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if(remaining.size < 2) {
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checked ++ remaining
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} else {
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val newRemaining = if(remaining.size == 2) Array[LabeledPoint]() else remaining.slice(2, remaining.length)
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if(!monotonicityConstraint.holds(remaining.head, remaining.tail.head)) {
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iterate(checked ++ pool2(remaining.slice(0, 2)), newRemaining, monotonicityConstraint)
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} else {
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iterate(checked ++ remaining.slice(0, 2), newRemaining, monotonicityConstraint)
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}
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}
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}
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iterate(Array(), in, monotonicityConstraint)
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}
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functionalOption(in, monotonicityConstraint)*/
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/*def option1(in: Array[LabeledPoint], monotonicityConstraint: MonotonicityConstraint) = {
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def findMonotonicityViolators(in: Array[LabeledPoint], start: Int, monotonicityConstraint: MonotonicityConstraint): Unit = {
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var j = start
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while (j >= 1 && !monotonicityConstraint.holds(in(j - 1), in(j))) {
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pool(in, j - 1, start + 1)
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j = j - 1
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}
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}
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for (i <- 0 to in.length - 1) {
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findMonotonicityViolators(in, i, monotonicityConstraint)
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}
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in
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}*/
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/*
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def pool(in: Array[LabeledPoint], start: Int, end: Int): Unit = {
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val subArraySum = in.slice(start, end).map(_.label).sum
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val subArrayLength = math.abs(start - end)
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for(i <- start to end - 1) {
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in(i) = LabeledPoint(subArraySum / subArrayLength, in(i).features)
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}
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}*/
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/*
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OPTION 2
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def pool(in: Array[LabeledPoint], range: Range): Unit = {
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val subArray = in.slice(range.start, range.end + 1)
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val subArraySum = subArray.map(_.label).sum
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val subArrayLength = subArray.length
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for(i <- range.start to range.end) {
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in(i) = LabeledPoint(subArraySum / subArrayLength, in(i).features)
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}
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}
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def poolExtendedViolators(in: Array[LabeledPoint], range: Range, monotonicityConstraint: MonotonicityConstraint): Unit = {
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var extendedRange = Range(range.start, range.end)
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while (extendedRange.start >= 0 && !monotonicityConstraint.holds(in(extendedRange.start), in(extendedRange.start + 1))) {
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pool(in, Range(extendedRange.start, extendedRange.end))
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extendedRange = Range(extendedRange.start - 1, extendedRange.end)
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}
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}
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def findViolatingSequence(in: Array[LabeledPoint], start: Int, monotonicityConstraint: MonotonicityConstraint): Option[Range] = {
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var j = start
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while(j < in.length - 1 && !monotonicityConstraint.holds(in(start), in(j + 1))) {
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j = j + 1
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}
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if(j == start) {
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None
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} else {
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Some(Range(start, j))
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}
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}
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var i = 0;
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while(i < in.length) {
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findViolatingSequence(in, i, monotonicityConstraint).fold[Unit]({
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i = i + 1
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})(r => {
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poolExtendedViolators(in, r, monotonicityConstraint)
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i = r.end
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})
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}
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in
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*/

‎mllib/src/test/scala/org/apache/spark/mllib/regression/IsotonicRegressionSuite.scala‎

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/*
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* Licensed to the Apache Software Foundation (ASF) under one or more
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* contributor license agreements. See the NOTICE file distributed with
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* this work for additional information regarding copyright ownership.
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* The ASF licenses this file to You under the Apache License, Version 2.0
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* (the "License"); you may not use this file except in compliance with
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* the License. You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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package org.apache.spark.mllib.regression
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import org.apache.spark.mllib.linalg.Vectors
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import org.apache.spark.mllib.util.{LocalClusterSparkContext, MLlibTestSparkContext}
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import org.scalatest.{Matchers, FunSuite}
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import scala.util.Random
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class IsotonicRegressionSuite
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extends FunSuite
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with MLlibTestSparkContext
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with Matchers {
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def generateDataPoints(labels: Double*): Seq[LabeledPoint] =
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labels.zip((1 to labels.size)).map(point => LabeledPoint(point._1, Vectors.dense(point._2)))
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test("increasing isotonic regression") {
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val testRDD = sc.parallelize(generateDataPoints(1, 2, 3, 3, 1, 6, 7, 8, 11, 9, 10, 12, 14, 15, 17, 16, 17, 18, 19, 20)).cache()
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val alg = new PoolAdjacentViolators
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val model = alg.run(testRDD, Isotonic)
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model.predictions should be(generateDataPoints(1, 2, 7d/3, 7d/3, 7d/3, 6, 7, 8, 10, 10, 10, 12, 14, 15, 16.5, 16.5, 17, 18, 19, 20))
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}
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test("isotonic regression with size 0") {
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val testRDD = sc.parallelize(List[LabeledPoint]()).cache()
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val alg = new PoolAdjacentViolators
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val model = alg.run(testRDD, Isotonic)
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model.predictions should be(List())
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}
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test("isotonic regression with size 1") {
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val testRDD = sc.parallelize(generateDataPoints(1)).cache()
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val alg = new PoolAdjacentViolators
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val model = alg.run(testRDD, Isotonic)
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model.predictions should be(generateDataPoints(1))
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}
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test("isotonic regression strictly increasing sequence") {
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val testRDD = sc.parallelize(generateDataPoints(1, 2, 3, 4, 5)).cache()
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val alg = new PoolAdjacentViolators
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val model = alg.run(testRDD, Isotonic)
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model.predictions should be(generateDataPoints(1, 2, 3, 4, 5))
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}
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test("isotonic regression strictly decreasing sequence") {
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val testRDD = sc.parallelize(generateDataPoints(5, 4, 3, 2, 1)).cache()
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val alg = new PoolAdjacentViolators
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val model = alg.run(testRDD, Isotonic)
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model.predictions should be(generateDataPoints(3, 3, 3, 3, 3))
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}
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test("isotonic regression prediction") {
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val testRDD = sc.parallelize(generateDataPoints(1, 2, 7, 1, 2)).cache()
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val alg = new PoolAdjacentViolators
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val model = alg.run(testRDD, Isotonic)
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model.predict(Vectors.dense(0)) should be(1)
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model.predict(Vectors.dense(2)) should be(2)
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model.predict(Vectors.dense(3)) should be(10d/3)
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model.predict(Vectors.dense(10)) should be(10d/3)
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}
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test("antitonic regression prediction") {
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val testRDD = sc.parallelize(generateDataPoints(7, 5, 3, 5, 1)).cache()
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val alg = new PoolAdjacentViolators
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val model = alg.run(testRDD, Antitonic)
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model.predict(Vectors.dense(0)) should be(7)
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model.predict(Vectors.dense(2)) should be(5)
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model.predict(Vectors.dense(3)) should be(4)
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model.predict(Vectors.dense(10)) should be(1)
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}
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}
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class IsotonicRegressionClusterSuite extends FunSuite with LocalClusterSparkContext {
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test("task size should be small in both training and prediction") {
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val m = 4
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val n = 200000
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val points = sc.parallelize(0 until m, 2).mapPartitionsWithIndex { (idx, iter) =>
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val random = new Random(idx)
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iter.map(i => LabeledPoint(1.0, Vectors.dense(Array.fill(n)(random.nextDouble()))))
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}.cache()
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// If we serialize data directly in the task closure, the size of the serialized task would be
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// greater than 1MB and hence Spark would throw an error.
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val model = IsotonicRegression.train(points, Isotonic)
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val predictions = model.predict(points.map(_.features))
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}
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}

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