SPARK-4022 [CORE] [MLLIB] Replace colt dependency (LGPL) with commons-math
This change replaces usages of colt with commons-math3 equivalents, and makes some minor necessary adjustments to related code and tests to match. Author: Sean Owen <sowen@cloudera.com> Closes #2928 from srowen/SPARK-4022 and squashes the following commits: 61a232f [Sean Owen] Fix failure due to different sampling in JavaAPISuite.sample() 16d66b8 [Sean Owen] Simplify seeding with call to reseedRandomGenerator a1a78e0 [Sean Owen] Use Well19937c 31c7641 [Sean Owen] Fix Python Poisson test by choosing a different seed; about 88% of seeds should work but 1 didn't, it seems 5c9c67f [Sean Owen] Additional test fixes from review d8f88e0 [Sean Owen] Replace colt with commons-math3. Some tests do not pass yet.
This commit is contained in:
Sean Owen
Xiangrui Meng
parent
1d7bcc8840
commit
bfa614b127
12
LICENSE
12
LICENSE
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@ -712,18 +712,6 @@ THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
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NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE,
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EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
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========================================================================
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For colt:
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========================================================================
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Copyright (c) 1999 CERN - European Organization for Nuclear Research.
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Permission to use, copy, modify, distribute and sell this software and its documentation for any purpose is hereby granted without fee, provided that the above copyright notice appear in all copies and that both that copyright notice and this permission notice appear in supporting documentation. CERN makes no representations about the suitability of this software for any purpose. It is provided "as is" without expressed or implied warranty.
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Packages hep.aida.*
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Written by Pavel Binko, Dino Ferrero Merlino, Wolfgang Hoschek, Tony Johnson, Andreas Pfeiffer, and others. Check the FreeHEP home page for more info. Permission to use and/or redistribute this work is granted under the terms of the LGPL License, with the exception that any usage related to military applications is expressly forbidden. The software and documentation made available under the terms of this license are provided with no warranty.
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========================================================================
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For SnapTree:
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========================================================================
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@ -146,6 +146,10 @@
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<exclude>com/google/common/base/Present*</exclude>
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</excludes>
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</relocation>
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<relocation>
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<pattern>org.apache.commons.math3</pattern>
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<shadedPattern>org.spark-project.commons.math3</shadedPattern>
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</relocation>
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</relocations>
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<transformers>
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<transformer implementation="org.apache.maven.plugins.shade.resource.ServicesResourceTransformer" />
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@ -85,8 +85,6 @@
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<dependency>
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<groupId>org.apache.commons</groupId>
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<artifactId>commons-math3</artifactId>
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<version>3.3</version>
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<scope>test</scope>
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</dependency>
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<dependency>
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<groupId>com.google.code.findbugs</groupId>
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@ -162,10 +160,6 @@
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<artifactId>json4s-jackson_${scala.binary.version}</artifactId>
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<version>3.2.10</version>
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</dependency>
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<dependency>
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<groupId>colt</groupId>
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<artifactId>colt</artifactId>
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</dependency>
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<dependency>
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<groupId>org.apache.mesos</groupId>
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<artifactId>mesos</artifactId>
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@ -17,7 +17,7 @@
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package org.apache.spark.partial
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import cern.jet.stat.Probability
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import org.apache.commons.math3.distribution.NormalDistribution
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/**
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* An ApproximateEvaluator for counts.
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@ -46,7 +46,8 @@ private[spark] class CountEvaluator(totalOutputs: Int, confidence: Double)
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val mean = (sum + 1 - p) / p
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val variance = (sum + 1) * (1 - p) / (p * p)
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val stdev = math.sqrt(variance)
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val confFactor = Probability.normalInverse(1 - (1 - confidence) / 2)
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val confFactor = new NormalDistribution().
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inverseCumulativeProbability(1 - (1 - confidence) / 2)
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val low = mean - confFactor * stdev
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val high = mean + confFactor * stdev
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new BoundedDouble(mean, confidence, low, high)
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@ -24,7 +24,7 @@ import scala.collection.Map
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import scala.collection.mutable.HashMap
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import scala.reflect.ClassTag
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import cern.jet.stat.Probability
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import org.apache.commons.math3.distribution.NormalDistribution
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import org.apache.spark.util.collection.OpenHashMap
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@ -55,7 +55,8 @@ private[spark] class GroupedCountEvaluator[T : ClassTag](totalOutputs: Int, conf
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new HashMap[T, BoundedDouble]
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} else {
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val p = outputsMerged.toDouble / totalOutputs
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val confFactor = Probability.normalInverse(1 - (1 - confidence) / 2)
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val confFactor = new NormalDistribution().
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inverseCumulativeProbability(1 - (1 - confidence) / 2)
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val result = new JHashMap[T, BoundedDouble](sums.size)
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sums.foreach { case (key, sum) =>
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val mean = (sum + 1 - p) / p
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@ -17,7 +17,7 @@
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package org.apache.spark.partial
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import cern.jet.stat.Probability
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import org.apache.commons.math3.distribution.{NormalDistribution, TDistribution}
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import org.apache.spark.util.StatCounter
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@ -45,9 +45,10 @@ private[spark] class MeanEvaluator(totalOutputs: Int, confidence: Double)
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val stdev = math.sqrt(counter.sampleVariance / counter.count)
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val confFactor = {
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if (counter.count > 100) {
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Probability.normalInverse(1 - (1 - confidence) / 2)
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new NormalDistribution().inverseCumulativeProbability(1 - (1 - confidence) / 2)
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} else {
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Probability.studentTInverse(1 - confidence, (counter.count - 1).toInt)
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val degreesOfFreedom = (counter.count - 1).toInt
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new TDistribution(degreesOfFreedom).inverseCumulativeProbability(1 - (1 - confidence) / 2)
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}
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}
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val low = mean - confFactor * stdev
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@ -17,7 +17,7 @@
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package org.apache.spark.partial
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import cern.jet.stat.Probability
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import org.apache.commons.math3.distribution.{TDistribution, NormalDistribution}
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/**
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* A utility class for caching Student's T distribution values for a given confidence level
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@ -25,8 +25,10 @@ import cern.jet.stat.Probability
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* confidence intervals for many keys.
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*/
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private[spark] class StudentTCacher(confidence: Double) {
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val NORMAL_APPROX_SAMPLE_SIZE = 100 // For samples bigger than this, use Gaussian approximation
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val normalApprox = Probability.normalInverse(1 - (1 - confidence) / 2)
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val normalApprox = new NormalDistribution().inverseCumulativeProbability(1 - (1 - confidence) / 2)
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val cache = Array.fill[Double](NORMAL_APPROX_SAMPLE_SIZE)(-1.0)
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def get(sampleSize: Long): Double = {
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@ -35,7 +37,8 @@ private[spark] class StudentTCacher(confidence: Double) {
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} else {
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val size = sampleSize.toInt
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if (cache(size) < 0) {
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cache(size) = Probability.studentTInverse(1 - confidence, size - 1)
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val tDist = new TDistribution(size - 1)
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cache(size) = tDist.inverseCumulativeProbability(1 - (1 - confidence) / 2)
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}
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cache(size)
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}
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@ -17,7 +17,7 @@
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package org.apache.spark.partial
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import cern.jet.stat.Probability
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import org.apache.commons.math3.distribution.{TDistribution, NormalDistribution}
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import org.apache.spark.util.StatCounter
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@ -55,9 +55,10 @@ private[spark] class SumEvaluator(totalOutputs: Int, confidence: Double)
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val sumStdev = math.sqrt(sumVar)
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val confFactor = {
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if (counter.count > 100) {
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Probability.normalInverse(1 - (1 - confidence) / 2)
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new NormalDistribution().inverseCumulativeProbability(1 - (1 - confidence) / 2)
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} else {
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Probability.studentTInverse(1 - confidence, (counter.count - 1).toInt)
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val degreesOfFreedom = (counter.count - 1).toInt
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new TDistribution(degreesOfFreedom).inverseCumulativeProbability(1 - (1 - confidence) / 2)
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}
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}
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val low = sumEstimate - confFactor * sumStdev
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@ -21,8 +21,7 @@ import java.util.Random
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import scala.reflect.ClassTag
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import cern.jet.random.Poisson
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import cern.jet.random.engine.DRand
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import org.apache.commons.math3.distribution.PoissonDistribution
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import org.apache.spark.{Partition, TaskContext}
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@ -53,9 +52,11 @@ private[spark] class SampledRDD[T: ClassTag](
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if (withReplacement) {
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// For large datasets, the expected number of occurrences of each element in a sample with
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// replacement is Poisson(frac). We use that to get a count for each element.
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val poisson = new Poisson(frac, new DRand(split.seed))
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val poisson = new PoissonDistribution(frac)
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poisson.reseedRandomGenerator(split.seed)
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firstParent[T].iterator(split.prev, context).flatMap { element =>
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val count = poisson.nextInt()
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val count = poisson.sample()
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if (count == 0) {
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Iterator.empty // Avoid object allocation when we return 0 items, which is quite often
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} else {
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@ -19,8 +19,7 @@ package org.apache.spark.util.random
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import java.util.Random
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import cern.jet.random.Poisson
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import cern.jet.random.engine.DRand
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import org.apache.commons.math3.distribution.PoissonDistribution
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import org.apache.spark.annotation.DeveloperApi
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@ -87,15 +86,16 @@ class BernoulliSampler[T](lb: Double, ub: Double, complement: Boolean = false)
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@DeveloperApi
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class PoissonSampler[T](mean: Double) extends RandomSampler[T, T] {
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private[random] var rng = new Poisson(mean, new DRand)
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private[random] var rng = new PoissonDistribution(mean)
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override def setSeed(seed: Long) {
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rng = new Poisson(mean, new DRand(seed.toInt))
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rng = new PoissonDistribution(mean)
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rng.reseedRandomGenerator(seed)
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}
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override def sample(items: Iterator[T]): Iterator[T] = {
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items.flatMap { item =>
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val count = rng.nextInt()
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val count = rng.sample()
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if (count == 0) {
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Iterator.empty
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} else {
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@ -22,8 +22,7 @@ import scala.collection.mutable
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import scala.collection.mutable.ArrayBuffer
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import scala.reflect.ClassTag
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import cern.jet.random.Poisson
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import cern.jet.random.engine.DRand
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import org.apache.commons.math3.distribution.PoissonDistribution
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import org.apache.spark.Logging
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import org.apache.spark.SparkContext._
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@ -209,7 +208,7 @@ private[spark] object StratifiedSamplingUtils extends Logging {
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samplingRateByKey = computeThresholdByKey(finalResult, fractions)
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}
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(idx: Int, iter: Iterator[(K, V)]) => {
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val rng = new RandomDataGenerator
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val rng = new RandomDataGenerator()
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rng.reSeed(seed + idx)
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// Must use the same invoke pattern on the rng as in getSeqOp for without replacement
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// in order to generate the same sequence of random numbers when creating the sample
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@ -245,9 +244,9 @@ private[spark] object StratifiedSamplingUtils extends Logging {
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// Must use the same invoke pattern on the rng as in getSeqOp for with replacement
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// in order to generate the same sequence of random numbers when creating the sample
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val copiesAccepted = if (acceptBound == 0) 0L else rng.nextPoisson(acceptBound)
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val copiesWailisted = rng.nextPoisson(finalResult(key).waitListBound)
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val copiesWaitlisted = rng.nextPoisson(finalResult(key).waitListBound)
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val copiesInSample = copiesAccepted +
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(0 until copiesWailisted).count(i => rng.nextUniform() < thresholdByKey(key))
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(0 until copiesWaitlisted).count(i => rng.nextUniform() < thresholdByKey(key))
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if (copiesInSample > 0) {
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Iterator.fill(copiesInSample.toInt)(item)
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} else {
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@ -261,10 +260,10 @@ private[spark] object StratifiedSamplingUtils extends Logging {
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rng.reSeed(seed + idx)
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iter.flatMap { item =>
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val count = rng.nextPoisson(fractions(item._1))
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if (count > 0) {
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Iterator.fill(count)(item)
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} else {
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if (count == 0) {
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Iterator.empty
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} else {
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Iterator.fill(count)(item)
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}
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}
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}
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@ -274,15 +273,24 @@ private[spark] object StratifiedSamplingUtils extends Logging {
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/** A random data generator that generates both uniform values and Poisson values. */
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private class RandomDataGenerator {
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val uniform = new XORShiftRandom()
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var poisson = new Poisson(1.0, new DRand)
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// commons-math3 doesn't have a method to generate Poisson from an arbitrary mean;
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// maintain a cache of Poisson(m) distributions for various m
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val poissonCache = mutable.Map[Double, PoissonDistribution]()
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var poissonSeed = 0L
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def reSeed(seed: Long) {
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def reSeed(seed: Long): Unit = {
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uniform.setSeed(seed)
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poisson = new Poisson(1.0, new DRand(seed.toInt))
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poissonSeed = seed
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poissonCache.clear()
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}
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def nextPoisson(mean: Double): Int = {
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poisson.nextInt(mean)
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val poisson = poissonCache.getOrElseUpdate(mean, {
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val newPoisson = new PoissonDistribution(mean)
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newPoisson.reseedRandomGenerator(poissonSeed)
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newPoisson
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})
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poisson.sample()
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}
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def nextUniform(): Double = {
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@ -142,7 +142,7 @@ public class JavaAPISuite implements Serializable {
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JavaRDD<Integer> rdd = sc.parallelize(ints);
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JavaRDD<Integer> sample20 = rdd.sample(true, 0.2, 11);
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// expected 2 but of course result varies randomly a bit
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Assert.assertEquals(3, sample20.count());
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Assert.assertEquals(1, sample20.count());
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JavaRDD<Integer> sample20NoReplacement = rdd.sample(false, 0.2, 11);
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Assert.assertEquals(2, sample20NoReplacement.count());
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}
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@ -19,7 +19,8 @@ package org.apache.spark.util.random
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import java.util.Random
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import cern.jet.random.Poisson
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import org.apache.commons.math3.distribution.PoissonDistribution
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import org.scalatest.{BeforeAndAfter, FunSuite}
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import org.scalatest.mock.EasyMockSugar
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@ -28,11 +29,11 @@ class RandomSamplerSuite extends FunSuite with BeforeAndAfter with EasyMockSugar
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val a = List(1, 2, 3, 4, 5, 6, 7, 8, 9)
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var random: Random = _
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var poisson: Poisson = _
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var poisson: PoissonDistribution = _
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before {
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random = mock[Random]
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poisson = mock[Poisson]
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poisson = mock[PoissonDistribution]
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}
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test("BernoulliSamplerWithRange") {
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@ -101,7 +102,7 @@ class RandomSamplerSuite extends FunSuite with BeforeAndAfter with EasyMockSugar
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test("PoissonSampler") {
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expecting {
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for(x <- Seq(0, 1, 2, 0, 1, 1, 0, 0, 0)) {
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poisson.nextInt().andReturn(x)
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poisson.sample().andReturn(x)
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}
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}
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whenExecuting(poisson) {
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@ -156,6 +156,10 @@
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<artifactId>algebird-core_${scala.binary.version}</artifactId>
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<version>0.1.11</version>
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</dependency>
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<dependency>
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<groupId>org.apache.commons</groupId>
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<artifactId>commons-math3</artifactId>
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</dependency>
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<dependency>
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<groupId>org.scalatest</groupId>
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<artifactId>scalatest_${scala.binary.version}</artifactId>
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|
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@ -268,6 +272,10 @@
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<exclude>com.google.common.base.Optional**</exclude>
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</excludes>
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</relocation>
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<relocation>
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<pattern>org.apache.commons.math3</pattern>
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<shadedPattern>org.spark-project.commons.math3</shadedPattern>
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</relocation>
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</relocations>
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<transformers>
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<transformer implementation="org.apache.maven.plugins.shade.resource.ServicesResourceTransformer" />
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|
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@ -17,11 +17,7 @@
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package org.apache.spark.examples
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import scala.math.sqrt
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import cern.colt.matrix._
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import cern.colt.matrix.linalg._
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import cern.jet.math._
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import org.apache.commons.math3.linear._
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|
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/**
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* Alternating least squares matrix factorization.
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@ -30,84 +26,70 @@ import cern.jet.math._
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* please refer to org.apache.spark.mllib.recommendation.ALS
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*/
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object LocalALS {
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|
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// Parameters set through command line arguments
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var M = 0 // Number of movies
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var U = 0 // Number of users
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var F = 0 // Number of features
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var ITERATIONS = 0
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|
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val LAMBDA = 0.01 // Regularization coefficient
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|
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// Some COLT objects
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val factory2D = DoubleFactory2D.dense
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val factory1D = DoubleFactory1D.dense
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val algebra = Algebra.DEFAULT
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val blas = SeqBlas.seqBlas
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|
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def generateR(): DoubleMatrix2D = {
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val mh = factory2D.random(M, F)
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val uh = factory2D.random(U, F)
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algebra.mult(mh, algebra.transpose(uh))
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def generateR(): RealMatrix = {
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val mh = randomMatrix(M, F)
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val uh = randomMatrix(U, F)
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mh.multiply(uh.transpose())
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}
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|
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def rmse(targetR: DoubleMatrix2D, ms: Array[DoubleMatrix1D],
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us: Array[DoubleMatrix1D]): Double =
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{
|
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val r = factory2D.make(M, U)
|
||||
def rmse(targetR: RealMatrix, ms: Array[RealVector], us: Array[RealVector]): Double = {
|
||||
val r = new Array2DRowRealMatrix(M, U)
|
||||
for (i <- 0 until M; j <- 0 until U) {
|
||||
r.set(i, j, blas.ddot(ms(i), us(j)))
|
||||
r.setEntry(i, j, ms(i).dotProduct(us(j)))
|
||||
}
|
||||
blas.daxpy(-1, targetR, r)
|
||||
val sumSqs = r.aggregate(Functions.plus, Functions.square)
|
||||
sqrt(sumSqs / (M * U))
|
||||
val diffs = r.subtract(targetR)
|
||||
var sumSqs = 0.0
|
||||
for (i <- 0 until M; j <- 0 until U) {
|
||||
val diff = diffs.getEntry(i, j)
|
||||
sumSqs += diff * diff
|
||||
}
|
||||
math.sqrt(sumSqs / (M.toDouble * U.toDouble))
|
||||
}
|
||||
|
||||
def updateMovie(i: Int, m: DoubleMatrix1D, us: Array[DoubleMatrix1D],
|
||||
R: DoubleMatrix2D) : DoubleMatrix1D =
|
||||
{
|
||||
val XtX = factory2D.make(F, F)
|
||||
val Xty = factory1D.make(F)
|
||||
def updateMovie(i: Int, m: RealVector, us: Array[RealVector], R: RealMatrix) : RealVector = {
|
||||
var XtX: RealMatrix = new Array2DRowRealMatrix(F, F)
|
||||
var Xty: RealVector = new ArrayRealVector(F)
|
||||
// For each user that rated the movie
|
||||
for (j <- 0 until U) {
|
||||
val u = us(j)
|
||||
// Add u * u^t to XtX
|
||||
blas.dger(1, u, u, XtX)
|
||||
XtX = XtX.add(u.outerProduct(u))
|
||||
// Add u * rating to Xty
|
||||
blas.daxpy(R.get(i, j), u, Xty)
|
||||
Xty = Xty.add(u.mapMultiply(R.getEntry(i, j)))
|
||||
}
|
||||
// Add regularization coefs to diagonal terms
|
||||
// Add regularization coefficients to diagonal terms
|
||||
for (d <- 0 until F) {
|
||||
XtX.set(d, d, XtX.get(d, d) + LAMBDA * U)
|
||||
XtX.addToEntry(d, d, LAMBDA * U)
|
||||
}
|
||||
// Solve it with Cholesky
|
||||
val ch = new CholeskyDecomposition(XtX)
|
||||
val Xty2D = factory2D.make(Xty.toArray, F)
|
||||
val solved2D = ch.solve(Xty2D)
|
||||
solved2D.viewColumn(0)
|
||||
new CholeskyDecomposition(XtX).getSolver.solve(Xty)
|
||||
}
|
||||
|
||||
def updateUser(j: Int, u: DoubleMatrix1D, ms: Array[DoubleMatrix1D],
|
||||
R: DoubleMatrix2D) : DoubleMatrix1D =
|
||||
{
|
||||
val XtX = factory2D.make(F, F)
|
||||
val Xty = factory1D.make(F)
|
||||
def updateUser(j: Int, u: RealVector, ms: Array[RealVector], R: RealMatrix) : RealVector = {
|
||||
var XtX: RealMatrix = new Array2DRowRealMatrix(F, F)
|
||||
var Xty: RealVector = new ArrayRealVector(F)
|
||||
// For each movie that the user rated
|
||||
for (i <- 0 until M) {
|
||||
val m = ms(i)
|
||||
// Add m * m^t to XtX
|
||||
blas.dger(1, m, m, XtX)
|
||||
XtX = XtX.add(m.outerProduct(m))
|
||||
// Add m * rating to Xty
|
||||
blas.daxpy(R.get(i, j), m, Xty)
|
||||
Xty = Xty.add(m.mapMultiply(R.getEntry(i, j)))
|
||||
}
|
||||
// Add regularization coefs to diagonal terms
|
||||
// Add regularization coefficients to diagonal terms
|
||||
for (d <- 0 until F) {
|
||||
XtX.set(d, d, XtX.get(d, d) + LAMBDA * M)
|
||||
XtX.addToEntry(d, d, LAMBDA * M)
|
||||
}
|
||||
// Solve it with Cholesky
|
||||
val ch = new CholeskyDecomposition(XtX)
|
||||
val Xty2D = factory2D.make(Xty.toArray, F)
|
||||
val solved2D = ch.solve(Xty2D)
|
||||
solved2D.viewColumn(0)
|
||||
new CholeskyDecomposition(XtX).getSolver.solve(Xty)
|
||||
}
|
||||
|
||||
def showWarning() {
|
||||
|
|
@ -135,21 +117,28 @@ object LocalALS {
|
|||
|
||||
showWarning()
|
||||
|
||||
printf("Running with M=%d, U=%d, F=%d, iters=%d\n", M, U, F, ITERATIONS)
|
||||
println(s"Running with M=$M, U=$U, F=$F, iters=$ITERATIONS")
|
||||
|
||||
val R = generateR()
|
||||
|
||||
// Initialize m and u randomly
|
||||
var ms = Array.fill(M)(factory1D.random(F))
|
||||
var us = Array.fill(U)(factory1D.random(F))
|
||||
var ms = Array.fill(M)(randomVector(F))
|
||||
var us = Array.fill(U)(randomVector(F))
|
||||
|
||||
// Iteratively update movies then users
|
||||
for (iter <- 1 to ITERATIONS) {
|
||||
println("Iteration " + iter + ":")
|
||||
println(s"Iteration $iter:")
|
||||
ms = (0 until M).map(i => updateMovie(i, ms(i), us, R)).toArray
|
||||
us = (0 until U).map(j => updateUser(j, us(j), ms, R)).toArray
|
||||
println("RMSE = " + rmse(R, ms, us))
|
||||
println()
|
||||
}
|
||||
}
|
||||
|
||||
private def randomVector(n: Int): RealVector =
|
||||
new ArrayRealVector(Array.fill(n)(math.random))
|
||||
|
||||
private def randomMatrix(rows: Int, cols: Int): RealMatrix =
|
||||
new Array2DRowRealMatrix(Array.fill(rows, cols)(math.random))
|
||||
|
||||
}
|
||||
|
|
|
|||
|
|
@ -17,11 +17,7 @@
|
|||
|
||||
package org.apache.spark.examples
|
||||
|
||||
import scala.math.sqrt
|
||||
|
||||
import cern.colt.matrix._
|
||||
import cern.colt.matrix.linalg._
|
||||
import cern.jet.math._
|
||||
import org.apache.commons.math3.linear._
|
||||
|
||||
import org.apache.spark._
|
||||
|
||||
|
|
@ -32,62 +28,53 @@ import org.apache.spark._
|
|||
* please refer to org.apache.spark.mllib.recommendation.ALS
|
||||
*/
|
||||
object SparkALS {
|
||||
|
||||
// Parameters set through command line arguments
|
||||
var M = 0 // Number of movies
|
||||
var U = 0 // Number of users
|
||||
var F = 0 // Number of features
|
||||
var ITERATIONS = 0
|
||||
|
||||
val LAMBDA = 0.01 // Regularization coefficient
|
||||
|
||||
// Some COLT objects
|
||||
val factory2D = DoubleFactory2D.dense
|
||||
val factory1D = DoubleFactory1D.dense
|
||||
val algebra = Algebra.DEFAULT
|
||||
val blas = SeqBlas.seqBlas
|
||||
|
||||
def generateR(): DoubleMatrix2D = {
|
||||
val mh = factory2D.random(M, F)
|
||||
val uh = factory2D.random(U, F)
|
||||
algebra.mult(mh, algebra.transpose(uh))
|
||||
def generateR(): RealMatrix = {
|
||||
val mh = randomMatrix(M, F)
|
||||
val uh = randomMatrix(U, F)
|
||||
mh.multiply(uh.transpose())
|
||||
}
|
||||
|
||||
def rmse(targetR: DoubleMatrix2D, ms: Array[DoubleMatrix1D],
|
||||
us: Array[DoubleMatrix1D]): Double =
|
||||
{
|
||||
val r = factory2D.make(M, U)
|
||||
def rmse(targetR: RealMatrix, ms: Array[RealVector], us: Array[RealVector]): Double = {
|
||||
val r = new Array2DRowRealMatrix(M, U)
|
||||
for (i <- 0 until M; j <- 0 until U) {
|
||||
r.set(i, j, blas.ddot(ms(i), us(j)))
|
||||
r.setEntry(i, j, ms(i).dotProduct(us(j)))
|
||||
}
|
||||
blas.daxpy(-1, targetR, r)
|
||||
val sumSqs = r.aggregate(Functions.plus, Functions.square)
|
||||
sqrt(sumSqs / (M * U))
|
||||
val diffs = r.subtract(targetR)
|
||||
var sumSqs = 0.0
|
||||
for (i <- 0 until M; j <- 0 until U) {
|
||||
val diff = diffs.getEntry(i, j)
|
||||
sumSqs += diff * diff
|
||||
}
|
||||
math.sqrt(sumSqs / (M.toDouble * U.toDouble))
|
||||
}
|
||||
|
||||
def update(i: Int, m: DoubleMatrix1D, us: Array[DoubleMatrix1D],
|
||||
R: DoubleMatrix2D) : DoubleMatrix1D =
|
||||
{
|
||||
def update(i: Int, m: RealVector, us: Array[RealVector], R: RealMatrix) : RealVector = {
|
||||
val U = us.size
|
||||
val F = us(0).size
|
||||
val XtX = factory2D.make(F, F)
|
||||
val Xty = factory1D.make(F)
|
||||
val F = us(0).getDimension
|
||||
var XtX: RealMatrix = new Array2DRowRealMatrix(F, F)
|
||||
var Xty: RealVector = new ArrayRealVector(F)
|
||||
// For each user that rated the movie
|
||||
for (j <- 0 until U) {
|
||||
val u = us(j)
|
||||
// Add u * u^t to XtX
|
||||
blas.dger(1, u, u, XtX)
|
||||
XtX = XtX.add(u.outerProduct(u))
|
||||
// Add u * rating to Xty
|
||||
blas.daxpy(R.get(i, j), u, Xty)
|
||||
Xty = Xty.add(u.mapMultiply(R.getEntry(i, j)))
|
||||
}
|
||||
// Add regularization coefs to diagonal terms
|
||||
for (d <- 0 until F) {
|
||||
XtX.set(d, d, XtX.get(d, d) + LAMBDA * U)
|
||||
XtX.addToEntry(d, d, LAMBDA * U)
|
||||
}
|
||||
// Solve it with Cholesky
|
||||
val ch = new CholeskyDecomposition(XtX)
|
||||
val Xty2D = factory2D.make(Xty.toArray, F)
|
||||
val solved2D = ch.solve(Xty2D)
|
||||
solved2D.viewColumn(0)
|
||||
new CholeskyDecomposition(XtX).getSolver.solve(Xty)
|
||||
}
|
||||
|
||||
def showWarning() {
|
||||
|
|
@ -118,7 +105,7 @@ object SparkALS {
|
|||
|
||||
showWarning()
|
||||
|
||||
printf("Running with M=%d, U=%d, F=%d, iters=%d\n", M, U, F, ITERATIONS)
|
||||
println(s"Running with M=$M, U=$U, F=$F, iters=$ITERATIONS")
|
||||
|
||||
val sparkConf = new SparkConf().setAppName("SparkALS")
|
||||
val sc = new SparkContext(sparkConf)
|
||||
|
|
@ -126,21 +113,21 @@ object SparkALS {
|
|||
val R = generateR()
|
||||
|
||||
// Initialize m and u randomly
|
||||
var ms = Array.fill(M)(factory1D.random(F))
|
||||
var us = Array.fill(U)(factory1D.random(F))
|
||||
var ms = Array.fill(M)(randomVector(F))
|
||||
var us = Array.fill(U)(randomVector(F))
|
||||
|
||||
// Iteratively update movies then users
|
||||
val Rc = sc.broadcast(R)
|
||||
var msb = sc.broadcast(ms)
|
||||
var usb = sc.broadcast(us)
|
||||
for (iter <- 1 to ITERATIONS) {
|
||||
println("Iteration " + iter + ":")
|
||||
println(s"Iteration $iter:")
|
||||
ms = sc.parallelize(0 until M, slices)
|
||||
.map(i => update(i, msb.value(i), usb.value, Rc.value))
|
||||
.collect()
|
||||
msb = sc.broadcast(ms) // Re-broadcast ms because it was updated
|
||||
us = sc.parallelize(0 until U, slices)
|
||||
.map(i => update(i, usb.value(i), msb.value, algebra.transpose(Rc.value)))
|
||||
.map(i => update(i, usb.value(i), msb.value, Rc.value.transpose()))
|
||||
.collect()
|
||||
usb = sc.broadcast(us) // Re-broadcast us because it was updated
|
||||
println("RMSE = " + rmse(R, ms, us))
|
||||
|
|
@ -149,4 +136,11 @@ object SparkALS {
|
|||
|
||||
sc.stop()
|
||||
}
|
||||
|
||||
private def randomVector(n: Int): RealVector =
|
||||
new ArrayRealVector(Array.fill(n)(math.random))
|
||||
|
||||
private def randomMatrix(rows: Int, cols: Int): RealMatrix =
|
||||
new Array2DRowRealMatrix(Array.fill(rows, cols)(math.random))
|
||||
|
||||
}
|
||||
|
|
|
|||
|
|
@ -65,11 +65,11 @@
|
|||
<groupId>junit</groupId>
|
||||
<artifactId>junit</artifactId>
|
||||
</exclusion>
|
||||
<exclusion>
|
||||
</exclusions>
|
||||
</dependency>
|
||||
<dependency>
|
||||
<groupId>org.apache.commons</groupId>
|
||||
<artifactId>commons-math3</artifactId>
|
||||
</exclusion>
|
||||
</exclusions>
|
||||
</dependency>
|
||||
<dependency>
|
||||
<groupId>org.scalatest</groupId>
|
||||
|
|
|
|||
|
|
@ -17,8 +17,7 @@
|
|||
|
||||
package org.apache.spark.mllib.random
|
||||
|
||||
import cern.jet.random.Poisson
|
||||
import cern.jet.random.engine.DRand
|
||||
import org.apache.commons.math3.distribution.PoissonDistribution
|
||||
|
||||
import org.apache.spark.annotation.DeveloperApi
|
||||
import org.apache.spark.util.random.{XORShiftRandom, Pseudorandom}
|
||||
|
|
@ -89,12 +88,13 @@ class StandardNormalGenerator extends RandomDataGenerator[Double] {
|
|||
@DeveloperApi
|
||||
class PoissonGenerator(val mean: Double) extends RandomDataGenerator[Double] {
|
||||
|
||||
private var rng = new Poisson(mean, new DRand)
|
||||
private var rng = new PoissonDistribution(mean)
|
||||
|
||||
override def nextValue(): Double = rng.nextDouble()
|
||||
override def nextValue(): Double = rng.sample()
|
||||
|
||||
override def setSeed(seed: Long) {
|
||||
rng = new Poisson(mean, new DRand(seed.toInt))
|
||||
rng = new PoissonDistribution(mean)
|
||||
rng.reseedRandomGenerator(seed)
|
||||
}
|
||||
|
||||
override def copy(): PoissonGenerator = new PoissonGenerator(mean)
|
||||
|
|
|
|||
|
|
@ -18,7 +18,7 @@
|
|||
package org.apache.spark.mllib.stat.test
|
||||
|
||||
import breeze.linalg.{DenseMatrix => BDM}
|
||||
import cern.jet.stat.Probability.chiSquareComplemented
|
||||
import org.apache.commons.math3.distribution.ChiSquaredDistribution
|
||||
|
||||
import org.apache.spark.{SparkException, Logging}
|
||||
import org.apache.spark.mllib.linalg.{Matrices, Matrix, Vector, Vectors}
|
||||
|
|
@ -33,7 +33,7 @@ import scala.collection.mutable
|
|||
* on an input of type `Matrix` in which independence between columns is assessed.
|
||||
* We also provide a method for computing the chi-squared statistic between each feature and the
|
||||
* label for an input `RDD[LabeledPoint]`, return an `Array[ChiSquaredTestResult]` of size =
|
||||
* number of features in the inpuy RDD.
|
||||
* number of features in the input RDD.
|
||||
*
|
||||
* Supported methods for goodness of fit: `pearson` (default)
|
||||
* Supported methods for independence: `pearson` (default)
|
||||
|
|
@ -139,7 +139,7 @@ private[stat] object ChiSqTest extends Logging {
|
|||
}
|
||||
|
||||
/*
|
||||
* Pearon's goodness of fit test on the input observed and expected counts/relative frequencies.
|
||||
* Pearson's goodness of fit test on the input observed and expected counts/relative frequencies.
|
||||
* Uniform distribution is assumed when `expected` is not passed in.
|
||||
*/
|
||||
def chiSquared(observed: Vector,
|
||||
|
|
@ -188,12 +188,12 @@ private[stat] object ChiSqTest extends Logging {
|
|||
}
|
||||
}
|
||||
val df = size - 1
|
||||
val pValue = chiSquareComplemented(df, statistic)
|
||||
val pValue = 1.0 - new ChiSquaredDistribution(df).cumulativeProbability(statistic)
|
||||
new ChiSqTestResult(pValue, df, statistic, PEARSON.name, NullHypothesis.goodnessOfFit.toString)
|
||||
}
|
||||
|
||||
/*
|
||||
* Pearon's independence test on the input contingency matrix.
|
||||
* Pearson's independence test on the input contingency matrix.
|
||||
* TODO: optimize for SparseMatrix when it becomes supported.
|
||||
*/
|
||||
def chiSquaredMatrix(counts: Matrix, methodName:String = PEARSON.name): ChiSqTestResult = {
|
||||
|
|
@ -238,7 +238,13 @@ private[stat] object ChiSqTest extends Logging {
|
|||
j += 1
|
||||
}
|
||||
val df = (numCols - 1) * (numRows - 1)
|
||||
val pValue = chiSquareComplemented(df, statistic)
|
||||
if (df == 0) {
|
||||
// 1 column or 1 row. Constant distribution is independent of anything.
|
||||
// pValue = 1.0 and statistic = 0.0 in this case.
|
||||
new ChiSqTestResult(1.0, 0, 0.0, methodName, NullHypothesis.independence.toString)
|
||||
} else {
|
||||
val pValue = 1.0 - new ChiSquaredDistribution(df).cumulativeProbability(statistic)
|
||||
new ChiSqTestResult(pValue, df, statistic, methodName, NullHypothesis.independence.toString)
|
||||
}
|
||||
}
|
||||
}
|
||||
|
|
|
|||
|
|
@ -17,8 +17,7 @@
|
|||
|
||||
package org.apache.spark.mllib.tree.impl
|
||||
|
||||
import cern.jet.random.Poisson
|
||||
import cern.jet.random.engine.DRand
|
||||
import org.apache.commons.math3.distribution.PoissonDistribution
|
||||
|
||||
import org.apache.spark.rdd.RDD
|
||||
import org.apache.spark.util.Utils
|
||||
|
|
@ -60,12 +59,13 @@ private[tree] object BaggedPoint {
|
|||
input.mapPartitionsWithIndex { (partitionIndex, instances) =>
|
||||
// TODO: Support different sampling rates, and sampling without replacement.
|
||||
// Use random seed = seed + partitionIndex + 1 to make generation reproducible.
|
||||
val poisson = new Poisson(1.0, new DRand(seed + partitionIndex + 1))
|
||||
val poisson = new PoissonDistribution(1.0)
|
||||
poisson.reseedRandomGenerator(seed + partitionIndex + 1)
|
||||
instances.map { instance =>
|
||||
val subsampleWeights = new Array[Double](numSubsamples)
|
||||
var subsampleIndex = 0
|
||||
while (subsampleIndex < numSubsamples) {
|
||||
subsampleWeights(subsampleIndex) = poisson.nextInt()
|
||||
subsampleWeights(subsampleIndex) = poisson.sample()
|
||||
subsampleIndex += 1
|
||||
}
|
||||
new BaggedPoint(instance, subsampleWeights)
|
||||
|
|
|
|||
|
|
@ -187,7 +187,7 @@ class RandomForestSuite extends FunSuite with LocalSparkContext {
|
|||
numClassesForClassification = 3, categoricalFeaturesInfo = categoricalFeaturesInfo)
|
||||
val model = RandomForest.trainClassifier(input, strategy, numTrees = 2,
|
||||
featureSubsetStrategy = "sqrt", seed = 12345)
|
||||
RandomForestSuite.validateClassifier(model, arr, 1.0)
|
||||
RandomForestSuite.validateClassifier(model, arr, 0.0)
|
||||
}
|
||||
|
||||
}
|
||||
|
|
|
|||
6
pom.xml
6
pom.xml
|
|
@ -305,7 +305,6 @@
|
|||
<groupId>org.apache.commons</groupId>
|
||||
<artifactId>commons-math3</artifactId>
|
||||
<version>3.3</version>
|
||||
<scope>test</scope>
|
||||
</dependency>
|
||||
<dependency>
|
||||
<groupId>com.google.code.findbugs</groupId>
|
||||
|
|
@ -431,11 +430,6 @@
|
|||
<artifactId>akka-testkit_${scala.binary.version}</artifactId>
|
||||
<version>${akka.version}</version>
|
||||
</dependency>
|
||||
<dependency>
|
||||
<groupId>colt</groupId>
|
||||
<artifactId>colt</artifactId>
|
||||
<version>1.2.0</version>
|
||||
</dependency>
|
||||
<dependency>
|
||||
<groupId>org.apache.mesos</groupId>
|
||||
<artifactId>mesos</artifactId>
|
||||
|
|
|
|||
|
|
@ -107,7 +107,7 @@ class RandomRDDs(object):
|
|||
distribution with the input mean.
|
||||
|
||||
>>> mean = 100.0
|
||||
>>> x = RandomRDDs.poissonRDD(sc, mean, 1000, seed=1L)
|
||||
>>> x = RandomRDDs.poissonRDD(sc, mean, 1000, seed=2L)
|
||||
>>> stats = x.stats()
|
||||
>>> stats.count()
|
||||
1000L
|
||||
|
|
|
|||
Loading…
Reference in a new issue