Repository: spark Updated Branches: refs/heads/master ab431f8a9 -> c3a54843c
[SPARK-10240] [SPARK-10242] [MLLIB] update since versions in mlilb.random and mllib.stat The same as #8241 but for `mllib.stat` and `mllib.random`. cc feynmanliang Author: Xiangrui Meng <m...@databricks.com> Closes #8439 from mengxr/SPARK-10242. Project: http://git-wip-us.apache.org/repos/asf/spark/repo Commit: http://git-wip-us.apache.org/repos/asf/spark/commit/c3a54843 Tree: http://git-wip-us.apache.org/repos/asf/spark/tree/c3a54843 Diff: http://git-wip-us.apache.org/repos/asf/spark/diff/c3a54843 Branch: refs/heads/master Commit: c3a54843c0c8a14059da4e6716c1ad45c69bbe6c Parents: ab431f8 Author: Xiangrui Meng <m...@databricks.com> Authored: Tue Aug 25 22:31:23 2015 -0700 Committer: Xiangrui Meng <m...@databricks.com> Committed: Tue Aug 25 22:31:23 2015 -0700 ---------------------------------------------------------------------- .../mllib/random/RandomDataGenerator.scala | 43 ++++++++++-- .../apache/spark/mllib/random/RandomRDDs.scala | 69 +++++++++++++++++--- .../distribution/MultivariateGaussian.scala | 6 +- .../spark/mllib/stat/test/TestResult.scala | 24 ++++--- 4 files changed, 117 insertions(+), 25 deletions(-) ---------------------------------------------------------------------- http://git-wip-us.apache.org/repos/asf/spark/blob/c3a54843/mllib/src/main/scala/org/apache/spark/mllib/random/RandomDataGenerator.scala ---------------------------------------------------------------------- diff --git a/mllib/src/main/scala/org/apache/spark/mllib/random/RandomDataGenerator.scala b/mllib/src/main/scala/org/apache/spark/mllib/random/RandomDataGenerator.scala index 9349eca..a2d85a6 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/random/RandomDataGenerator.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/random/RandomDataGenerator.scala @@ -20,7 +20,7 @@ package org.apache.spark.mllib.random import org.apache.commons.math3.distribution.{ExponentialDistribution, GammaDistribution, LogNormalDistribution, PoissonDistribution} -import org.apache.spark.annotation.DeveloperApi +import org.apache.spark.annotation.{Since, DeveloperApi} import org.apache.spark.util.random.{XORShiftRandom, Pseudorandom} /** @@ -28,17 +28,20 @@ import org.apache.spark.util.random.{XORShiftRandom, Pseudorandom} * Trait for random data generators that generate i.i.d. data. */ @DeveloperApi +@Since("1.1.0") trait RandomDataGenerator[T] extends Pseudorandom with Serializable { /** * Returns an i.i.d. sample as a generic type from an underlying distribution. */ + @Since("1.1.0") def nextValue(): T /** * Returns a copy of the RandomDataGenerator with a new instance of the rng object used in the * class when applicable for non-locking concurrent usage. */ + @Since("1.1.0") def copy(): RandomDataGenerator[T] } @@ -47,17 +50,21 @@ trait RandomDataGenerator[T] extends Pseudorandom with Serializable { * Generates i.i.d. samples from U[0.0, 1.0] */ @DeveloperApi +@Since("1.1.0") class UniformGenerator extends RandomDataGenerator[Double] { // XORShiftRandom for better performance. Thread safety isn't necessary here. private val random = new XORShiftRandom() + @Since("1.1.0") override def nextValue(): Double = { random.nextDouble() } + @Since("1.1.0") override def setSeed(seed: Long): Unit = random.setSeed(seed) + @Since("1.1.0") override def copy(): UniformGenerator = new UniformGenerator() } @@ -66,17 +73,21 @@ class UniformGenerator extends RandomDataGenerator[Double] { * Generates i.i.d. samples from the standard normal distribution. */ @DeveloperApi +@Since("1.1.0") class StandardNormalGenerator extends RandomDataGenerator[Double] { // XORShiftRandom for better performance. Thread safety isn't necessary here. private val random = new XORShiftRandom() + @Since("1.1.0") override def nextValue(): Double = { random.nextGaussian() } + @Since("1.1.0") override def setSeed(seed: Long): Unit = random.setSeed(seed) + @Since("1.1.0") override def copy(): StandardNormalGenerator = new StandardNormalGenerator() } @@ -87,16 +98,21 @@ class StandardNormalGenerator extends RandomDataGenerator[Double] { * @param mean mean for the Poisson distribution. */ @DeveloperApi -class PoissonGenerator(val mean: Double) extends RandomDataGenerator[Double] { +@Since("1.1.0") +class PoissonGenerator @Since("1.1.0") ( + @Since("1.1.0") val mean: Double) extends RandomDataGenerator[Double] { private val rng = new PoissonDistribution(mean) + @Since("1.1.0") override def nextValue(): Double = rng.sample() + @Since("1.1.0") override def setSeed(seed: Long) { rng.reseedRandomGenerator(seed) } + @Since("1.1.0") override def copy(): PoissonGenerator = new PoissonGenerator(mean) } @@ -107,16 +123,21 @@ class PoissonGenerator(val mean: Double) extends RandomDataGenerator[Double] { * @param mean mean for the exponential distribution. */ @DeveloperApi -class ExponentialGenerator(val mean: Double) extends RandomDataGenerator[Double] { +@Since("1.3.0") +class ExponentialGenerator @Since("1.3.0") ( + @Since("1.3.0") val mean: Double) extends RandomDataGenerator[Double] { private val rng = new ExponentialDistribution(mean) + @Since("1.3.0") override def nextValue(): Double = rng.sample() + @Since("1.3.0") override def setSeed(seed: Long) { rng.reseedRandomGenerator(seed) } + @Since("1.3.0") override def copy(): ExponentialGenerator = new ExponentialGenerator(mean) } @@ -128,16 +149,22 @@ class ExponentialGenerator(val mean: Double) extends RandomDataGenerator[Double] * @param scale scale for the gamma distribution */ @DeveloperApi -class GammaGenerator(val shape: Double, val scale: Double) extends RandomDataGenerator[Double] { +@Since("1.3.0") +class GammaGenerator @Since("1.3.0") ( + @Since("1.3.0") val shape: Double, + @Since("1.3.0") val scale: Double) extends RandomDataGenerator[Double] { private val rng = new GammaDistribution(shape, scale) + @Since("1.3.0") override def nextValue(): Double = rng.sample() + @Since("1.3.0") override def setSeed(seed: Long) { rng.reseedRandomGenerator(seed) } + @Since("1.3.0") override def copy(): GammaGenerator = new GammaGenerator(shape, scale) } @@ -150,15 +177,21 @@ class GammaGenerator(val shape: Double, val scale: Double) extends RandomDataGen * @param std standard deviation for the log normal distribution */ @DeveloperApi -class LogNormalGenerator(val mean: Double, val std: Double) extends RandomDataGenerator[Double] { +@Since("1.3.0") +class LogNormalGenerator @Since("1.3.0") ( + @Since("1.3.0") val mean: Double, + @Since("1.3.0") val std: Double) extends RandomDataGenerator[Double] { private val rng = new LogNormalDistribution(mean, std) + @Since("1.3.0") override def nextValue(): Double = rng.sample() + @Since("1.3.0") override def setSeed(seed: Long) { rng.reseedRandomGenerator(seed) } + @Since("1.3.0") override def copy(): LogNormalGenerator = new LogNormalGenerator(mean, std) } http://git-wip-us.apache.org/repos/asf/spark/blob/c3a54843/mllib/src/main/scala/org/apache/spark/mllib/random/RandomRDDs.scala ---------------------------------------------------------------------- diff --git a/mllib/src/main/scala/org/apache/spark/mllib/random/RandomRDDs.scala b/mllib/src/main/scala/org/apache/spark/mllib/random/RandomRDDs.scala index 174d5e0..4dd5ea2 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/random/RandomRDDs.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/random/RandomRDDs.scala @@ -20,7 +20,7 @@ package org.apache.spark.mllib.random import scala.reflect.ClassTag import org.apache.spark.SparkContext -import org.apache.spark.annotation.{DeveloperApi, Experimental} +import org.apache.spark.annotation.{DeveloperApi, Experimental, Since} import org.apache.spark.api.java.{JavaDoubleRDD, JavaRDD, JavaSparkContext} import org.apache.spark.mllib.linalg.Vector import org.apache.spark.mllib.rdd.{RandomRDD, RandomVectorRDD} @@ -32,6 +32,7 @@ import org.apache.spark.util.Utils * Generator methods for creating RDDs comprised of `i.i.d.` samples from some distribution. */ @Experimental +@Since("1.1.0") object RandomRDDs { /** @@ -46,6 +47,7 @@ object RandomRDDs { * @param seed Random seed (default: a random long integer). * @return RDD[Double] comprised of `i.i.d.` samples ~ `U(0.0, 1.0)`. */ + @Since("1.1.0") def uniformRDD( sc: SparkContext, size: Long, @@ -58,6 +60,7 @@ object RandomRDDs { /** * Java-friendly version of [[RandomRDDs#uniformRDD]]. */ + @Since("1.1.0") def uniformJavaRDD( jsc: JavaSparkContext, size: Long, @@ -69,6 +72,7 @@ object RandomRDDs { /** * [[RandomRDDs#uniformJavaRDD]] with the default seed. */ + @Since("1.1.0") def uniformJavaRDD(jsc: JavaSparkContext, size: Long, numPartitions: Int): JavaDoubleRDD = { JavaDoubleRDD.fromRDD(uniformRDD(jsc.sc, size, numPartitions)) } @@ -76,6 +80,7 @@ object RandomRDDs { /** * [[RandomRDDs#uniformJavaRDD]] with the default number of partitions and the default seed. */ + @Since("1.1.0") def uniformJavaRDD(jsc: JavaSparkContext, size: Long): JavaDoubleRDD = { JavaDoubleRDD.fromRDD(uniformRDD(jsc.sc, size)) } @@ -92,6 +97,7 @@ object RandomRDDs { * @param seed Random seed (default: a random long integer). * @return RDD[Double] comprised of `i.i.d.` samples ~ N(0.0, 1.0). */ + @Since("1.1.0") def normalRDD( sc: SparkContext, size: Long, @@ -104,6 +110,7 @@ object RandomRDDs { /** * Java-friendly version of [[RandomRDDs#normalRDD]]. */ + @Since("1.1.0") def normalJavaRDD( jsc: JavaSparkContext, size: Long, @@ -115,6 +122,7 @@ object RandomRDDs { /** * [[RandomRDDs#normalJavaRDD]] with the default seed. */ + @Since("1.1.0") def normalJavaRDD(jsc: JavaSparkContext, size: Long, numPartitions: Int): JavaDoubleRDD = { JavaDoubleRDD.fromRDD(normalRDD(jsc.sc, size, numPartitions)) } @@ -122,6 +130,7 @@ object RandomRDDs { /** * [[RandomRDDs#normalJavaRDD]] with the default number of partitions and the default seed. */ + @Since("1.1.0") def normalJavaRDD(jsc: JavaSparkContext, size: Long): JavaDoubleRDD = { JavaDoubleRDD.fromRDD(normalRDD(jsc.sc, size)) } @@ -137,6 +146,7 @@ object RandomRDDs { * @param seed Random seed (default: a random long integer). * @return RDD[Double] comprised of `i.i.d.` samples ~ Pois(mean). */ + @Since("1.1.0") def poissonRDD( sc: SparkContext, mean: Double, @@ -150,6 +160,7 @@ object RandomRDDs { /** * Java-friendly version of [[RandomRDDs#poissonRDD]]. */ + @Since("1.1.0") def poissonJavaRDD( jsc: JavaSparkContext, mean: Double, @@ -162,6 +173,7 @@ object RandomRDDs { /** * [[RandomRDDs#poissonJavaRDD]] with the default seed. */ + @Since("1.1.0") def poissonJavaRDD( jsc: JavaSparkContext, mean: Double, @@ -173,6 +185,7 @@ object RandomRDDs { /** * [[RandomRDDs#poissonJavaRDD]] with the default number of partitions and the default seed. */ + @Since("1.1.0") def poissonJavaRDD(jsc: JavaSparkContext, mean: Double, size: Long): JavaDoubleRDD = { JavaDoubleRDD.fromRDD(poissonRDD(jsc.sc, mean, size)) } @@ -188,6 +201,7 @@ object RandomRDDs { * @param seed Random seed (default: a random long integer). * @return RDD[Double] comprised of `i.i.d.` samples ~ Pois(mean). */ + @Since("1.3.0") def exponentialRDD( sc: SparkContext, mean: Double, @@ -201,6 +215,7 @@ object RandomRDDs { /** * Java-friendly version of [[RandomRDDs#exponentialRDD]]. */ + @Since("1.3.0") def exponentialJavaRDD( jsc: JavaSparkContext, mean: Double, @@ -213,6 +228,7 @@ object RandomRDDs { /** * [[RandomRDDs#exponentialJavaRDD]] with the default seed. */ + @Since("1.3.0") def exponentialJavaRDD( jsc: JavaSparkContext, mean: Double, @@ -224,6 +240,7 @@ object RandomRDDs { /** * [[RandomRDDs#exponentialJavaRDD]] with the default number of partitions and the default seed. */ + @Since("1.3.0") def exponentialJavaRDD(jsc: JavaSparkContext, mean: Double, size: Long): JavaDoubleRDD = { JavaDoubleRDD.fromRDD(exponentialRDD(jsc.sc, mean, size)) } @@ -240,6 +257,7 @@ object RandomRDDs { * @param seed Random seed (default: a random long integer). * @return RDD[Double] comprised of `i.i.d.` samples ~ Pois(mean). */ + @Since("1.3.0") def gammaRDD( sc: SparkContext, shape: Double, @@ -254,6 +272,7 @@ object RandomRDDs { /** * Java-friendly version of [[RandomRDDs#gammaRDD]]. */ + @Since("1.3.0") def gammaJavaRDD( jsc: JavaSparkContext, shape: Double, @@ -267,6 +286,7 @@ object RandomRDDs { /** * [[RandomRDDs#gammaJavaRDD]] with the default seed. */ + @Since("1.3.0") def gammaJavaRDD( jsc: JavaSparkContext, shape: Double, @@ -279,11 +299,12 @@ object RandomRDDs { /** * [[RandomRDDs#gammaJavaRDD]] with the default number of partitions and the default seed. */ + @Since("1.3.0") def gammaJavaRDD( - jsc: JavaSparkContext, - shape: Double, - scale: Double, - size: Long): JavaDoubleRDD = { + jsc: JavaSparkContext, + shape: Double, + scale: Double, + size: Long): JavaDoubleRDD = { JavaDoubleRDD.fromRDD(gammaRDD(jsc.sc, shape, scale, size)) } @@ -299,6 +320,7 @@ object RandomRDDs { * @param seed Random seed (default: a random long integer). * @return RDD[Double] comprised of `i.i.d.` samples ~ Pois(mean). */ + @Since("1.3.0") def logNormalRDD( sc: SparkContext, mean: Double, @@ -313,6 +335,7 @@ object RandomRDDs { /** * Java-friendly version of [[RandomRDDs#logNormalRDD]]. */ + @Since("1.3.0") def logNormalJavaRDD( jsc: JavaSparkContext, mean: Double, @@ -326,6 +349,7 @@ object RandomRDDs { /** * [[RandomRDDs#logNormalJavaRDD]] with the default seed. */ + @Since("1.3.0") def logNormalJavaRDD( jsc: JavaSparkContext, mean: Double, @@ -338,11 +362,12 @@ object RandomRDDs { /** * [[RandomRDDs#logNormalJavaRDD]] with the default number of partitions and the default seed. */ + @Since("1.3.0") def logNormalJavaRDD( - jsc: JavaSparkContext, - mean: Double, - std: Double, - size: Long): JavaDoubleRDD = { + jsc: JavaSparkContext, + mean: Double, + std: Double, + size: Long): JavaDoubleRDD = { JavaDoubleRDD.fromRDD(logNormalRDD(jsc.sc, mean, std, size)) } @@ -359,6 +384,7 @@ object RandomRDDs { * @return RDD[Double] comprised of `i.i.d.` samples produced by generator. */ @DeveloperApi + @Since("1.1.0") def randomRDD[T: ClassTag]( sc: SparkContext, generator: RandomDataGenerator[T], @@ -381,6 +407,7 @@ object RandomRDDs { * @param seed Seed for the RNG that generates the seed for the generator in each partition. * @return RDD[Vector] with vectors containing i.i.d samples ~ `U(0.0, 1.0)`. */ + @Since("1.1.0") def uniformVectorRDD( sc: SparkContext, numRows: Long, @@ -394,6 +421,7 @@ object RandomRDDs { /** * Java-friendly version of [[RandomRDDs#uniformVectorRDD]]. */ + @Since("1.1.0") def uniformJavaVectorRDD( jsc: JavaSparkContext, numRows: Long, @@ -406,6 +434,7 @@ object RandomRDDs { /** * [[RandomRDDs#uniformJavaVectorRDD]] with the default seed. */ + @Since("1.1.0") def uniformJavaVectorRDD( jsc: JavaSparkContext, numRows: Long, @@ -417,6 +446,7 @@ object RandomRDDs { /** * [[RandomRDDs#uniformJavaVectorRDD]] with the default number of partitions and the default seed. */ + @Since("1.1.0") def uniformJavaVectorRDD( jsc: JavaSparkContext, numRows: Long, @@ -435,6 +465,7 @@ object RandomRDDs { * @param seed Random seed (default: a random long integer). * @return RDD[Vector] with vectors containing `i.i.d.` samples ~ `N(0.0, 1.0)`. */ + @Since("1.1.0") def normalVectorRDD( sc: SparkContext, numRows: Long, @@ -448,6 +479,7 @@ object RandomRDDs { /** * Java-friendly version of [[RandomRDDs#normalVectorRDD]]. */ + @Since("1.1.0") def normalJavaVectorRDD( jsc: JavaSparkContext, numRows: Long, @@ -460,6 +492,7 @@ object RandomRDDs { /** * [[RandomRDDs#normalJavaVectorRDD]] with the default seed. */ + @Since("1.1.0") def normalJavaVectorRDD( jsc: JavaSparkContext, numRows: Long, @@ -471,6 +504,7 @@ object RandomRDDs { /** * [[RandomRDDs#normalJavaVectorRDD]] with the default number of partitions and the default seed. */ + @Since("1.1.0") def normalJavaVectorRDD( jsc: JavaSparkContext, numRows: Long, @@ -491,6 +525,7 @@ object RandomRDDs { * @param seed Random seed (default: a random long integer). * @return RDD[Vector] with vectors containing `i.i.d.` samples. */ + @Since("1.3.0") def logNormalVectorRDD( sc: SparkContext, mean: Double, @@ -507,6 +542,7 @@ object RandomRDDs { /** * Java-friendly version of [[RandomRDDs#logNormalVectorRDD]]. */ + @Since("1.3.0") def logNormalJavaVectorRDD( jsc: JavaSparkContext, mean: Double, @@ -521,6 +557,7 @@ object RandomRDDs { /** * [[RandomRDDs#logNormalJavaVectorRDD]] with the default seed. */ + @Since("1.3.0") def logNormalJavaVectorRDD( jsc: JavaSparkContext, mean: Double, @@ -535,6 +572,7 @@ object RandomRDDs { * [[RandomRDDs#logNormalJavaVectorRDD]] with the default number of partitions and * the default seed. */ + @Since("1.3.0") def logNormalJavaVectorRDD( jsc: JavaSparkContext, mean: Double, @@ -556,6 +594,7 @@ object RandomRDDs { * @param seed Random seed (default: a random long integer). * @return RDD[Vector] with vectors containing `i.i.d.` samples ~ Pois(mean). */ + @Since("1.1.0") def poissonVectorRDD( sc: SparkContext, mean: Double, @@ -570,6 +609,7 @@ object RandomRDDs { /** * Java-friendly version of [[RandomRDDs#poissonVectorRDD]]. */ + @Since("1.1.0") def poissonJavaVectorRDD( jsc: JavaSparkContext, mean: Double, @@ -583,6 +623,7 @@ object RandomRDDs { /** * [[RandomRDDs#poissonJavaVectorRDD]] with the default seed. */ + @Since("1.1.0") def poissonJavaVectorRDD( jsc: JavaSparkContext, mean: Double, @@ -595,6 +636,7 @@ object RandomRDDs { /** * [[RandomRDDs#poissonJavaVectorRDD]] with the default number of partitions and the default seed. */ + @Since("1.1.0") def poissonJavaVectorRDD( jsc: JavaSparkContext, mean: Double, @@ -615,6 +657,7 @@ object RandomRDDs { * @param seed Random seed (default: a random long integer). * @return RDD[Vector] with vectors containing `i.i.d.` samples ~ Exp(mean). */ + @Since("1.3.0") def exponentialVectorRDD( sc: SparkContext, mean: Double, @@ -630,6 +673,7 @@ object RandomRDDs { /** * Java-friendly version of [[RandomRDDs#exponentialVectorRDD]]. */ + @Since("1.3.0") def exponentialJavaVectorRDD( jsc: JavaSparkContext, mean: Double, @@ -643,6 +687,7 @@ object RandomRDDs { /** * [[RandomRDDs#exponentialJavaVectorRDD]] with the default seed. */ + @Since("1.3.0") def exponentialJavaVectorRDD( jsc: JavaSparkContext, mean: Double, @@ -656,6 +701,7 @@ object RandomRDDs { * [[RandomRDDs#exponentialJavaVectorRDD]] with the default number of partitions * and the default seed. */ + @Since("1.3.0") def exponentialJavaVectorRDD( jsc: JavaSparkContext, mean: Double, @@ -678,6 +724,7 @@ object RandomRDDs { * @param seed Random seed (default: a random long integer). * @return RDD[Vector] with vectors containing `i.i.d.` samples ~ Exp(mean). */ + @Since("1.3.0") def gammaVectorRDD( sc: SparkContext, shape: Double, @@ -693,6 +740,7 @@ object RandomRDDs { /** * Java-friendly version of [[RandomRDDs#gammaVectorRDD]]. */ + @Since("1.3.0") def gammaJavaVectorRDD( jsc: JavaSparkContext, shape: Double, @@ -707,6 +755,7 @@ object RandomRDDs { /** * [[RandomRDDs#gammaJavaVectorRDD]] with the default seed. */ + @Since("1.3.0") def gammaJavaVectorRDD( jsc: JavaSparkContext, shape: Double, @@ -720,6 +769,7 @@ object RandomRDDs { /** * [[RandomRDDs#gammaJavaVectorRDD]] with the default number of partitions and the default seed. */ + @Since("1.3.0") def gammaJavaVectorRDD( jsc: JavaSparkContext, shape: Double, @@ -744,6 +794,7 @@ object RandomRDDs { * @return RDD[Vector] with vectors containing `i.i.d.` samples produced by generator. */ @DeveloperApi + @Since("1.1.0") def randomVectorRDD(sc: SparkContext, generator: RandomDataGenerator[Double], numRows: Long, http://git-wip-us.apache.org/repos/asf/spark/blob/c3a54843/mllib/src/main/scala/org/apache/spark/mllib/stat/distribution/MultivariateGaussian.scala ---------------------------------------------------------------------- diff --git a/mllib/src/main/scala/org/apache/spark/mllib/stat/distribution/MultivariateGaussian.scala b/mllib/src/main/scala/org/apache/spark/mllib/stat/distribution/MultivariateGaussian.scala index bd4d813..92a5af7 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/stat/distribution/MultivariateGaussian.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/stat/distribution/MultivariateGaussian.scala @@ -35,9 +35,9 @@ import org.apache.spark.mllib.util.MLUtils */ @Since("1.3.0") @DeveloperApi -class MultivariateGaussian ( - val mu: Vector, - val sigma: Matrix) extends Serializable { +class MultivariateGaussian @Since("1.3.0") ( + @Since("1.3.0") val mu: Vector, + @Since("1.3.0") val sigma: Matrix) extends Serializable { require(sigma.numCols == sigma.numRows, "Covariance matrix must be square") require(mu.size == sigma.numCols, "Mean vector length must match covariance matrix size") http://git-wip-us.apache.org/repos/asf/spark/blob/c3a54843/mllib/src/main/scala/org/apache/spark/mllib/stat/test/TestResult.scala ---------------------------------------------------------------------- diff --git a/mllib/src/main/scala/org/apache/spark/mllib/stat/test/TestResult.scala b/mllib/src/main/scala/org/apache/spark/mllib/stat/test/TestResult.scala index f44be13..d01b370 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/stat/test/TestResult.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/stat/test/TestResult.scala @@ -17,7 +17,7 @@ package org.apache.spark.mllib.stat.test -import org.apache.spark.annotation.Experimental +import org.apache.spark.annotation.{Experimental, Since} /** * :: Experimental :: @@ -25,28 +25,33 @@ import org.apache.spark.annotation.Experimental * @tparam DF Return type of `degreesOfFreedom`. */ @Experimental +@Since("1.1.0") trait TestResult[DF] { /** * The probability of obtaining a test statistic result at least as extreme as the one that was * actually observed, assuming that the null hypothesis is true. */ + @Since("1.1.0") def pValue: Double /** * Returns the degree(s) of freedom of the hypothesis test. * Return type should be Number(e.g. Int, Double) or tuples of Numbers for toString compatibility. */ + @Since("1.1.0") def degreesOfFreedom: DF /** * Test statistic. */ + @Since("1.1.0") def statistic: Double /** * Null hypothesis of the test. */ + @Since("1.1.0") def nullHypothesis: String /** @@ -78,11 +83,12 @@ trait TestResult[DF] { * Object containing the test results for the chi-squared hypothesis test. */ @Experimental +@Since("1.1.0") class ChiSqTestResult private[stat] (override val pValue: Double, - override val degreesOfFreedom: Int, - override val statistic: Double, - val method: String, - override val nullHypothesis: String) extends TestResult[Int] { + @Since("1.1.0") override val degreesOfFreedom: Int, + @Since("1.1.0") override val statistic: Double, + @Since("1.1.0") val method: String, + @Since("1.1.0") override val nullHypothesis: String) extends TestResult[Int] { override def toString: String = { "Chi squared test summary:\n" + @@ -96,11 +102,13 @@ class ChiSqTestResult private[stat] (override val pValue: Double, * Object containing the test results for the Kolmogorov-Smirnov test. */ @Experimental +@Since("1.5.0") class KolmogorovSmirnovTestResult private[stat] ( - override val pValue: Double, - override val statistic: Double, - override val nullHypothesis: String) extends TestResult[Int] { + @Since("1.5.0") override val pValue: Double, + @Since("1.5.0") override val statistic: Double, + @Since("1.5.0") override val nullHypothesis: String) extends TestResult[Int] { + @Since("1.5.0") override val degreesOfFreedom = 0 override def toString: String = { --------------------------------------------------------------------- To unsubscribe, e-mail: commits-unsubscr...@spark.apache.org For additional commands, e-mail: commits-h...@spark.apache.org
