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author | Xiangrui Meng <meng@databricks.com> | 2015-11-17 10:17:16 -0800 |
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committer | Joseph K. Bradley <joseph@databricks.com> | 2015-11-17 10:17:16 -0800 |
commit | 21fac5434174389e8b83a2f11341fa7c9e360bfd (patch) | |
tree | 3e34fff4c100b7bbf273b95f87bdd6a456028937 /mllib/src | |
parent | cc567b6634c3142125526f4875795c1b1e862838 (diff) | |
download | spark-21fac5434174389e8b83a2f11341fa7c9e360bfd.tar.gz spark-21fac5434174389e8b83a2f11341fa7c9e360bfd.tar.bz2 spark-21fac5434174389e8b83a2f11341fa7c9e360bfd.zip |
[SPARK-11766][MLLIB] add toJson/fromJson to Vector/Vectors
This is to support JSON serialization of Param[Vector] in the pipeline API. It could be used for other purposes too. The schema is the same as `VectorUDT`. jkbradley
Author: Xiangrui Meng <meng@databricks.com>
Closes #9751 from mengxr/SPARK-11766.
Diffstat (limited to 'mllib/src')
-rw-r--r-- | mllib/src/main/scala/org/apache/spark/mllib/linalg/Vectors.scala | 45 | ||||
-rw-r--r-- | mllib/src/test/scala/org/apache/spark/mllib/linalg/VectorsSuite.scala | 17 |
2 files changed, 62 insertions, 0 deletions
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/linalg/Vectors.scala b/mllib/src/main/scala/org/apache/spark/mllib/linalg/Vectors.scala index bd9badc03c..4dcf351df4 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/linalg/Vectors.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/linalg/Vectors.scala @@ -24,6 +24,9 @@ import scala.annotation.varargs import scala.collection.JavaConverters._ import breeze.linalg.{DenseVector => BDV, SparseVector => BSV, Vector => BV} +import org.json4s.DefaultFormats +import org.json4s.JsonDSL._ +import org.json4s.jackson.JsonMethods.{compact, render, parse => parseJson} import org.apache.spark.SparkException import org.apache.spark.annotation.{AlphaComponent, Since} @@ -171,6 +174,12 @@ sealed trait Vector extends Serializable { */ @Since("1.5.0") def argmax: Int + + /** + * Converts the vector to a JSON string. + */ + @Since("1.6.0") + def toJson: String } /** @@ -339,6 +348,27 @@ object Vectors { parseNumeric(NumericParser.parse(s)) } + /** + * Parses the JSON representation of a vector into a [[Vector]]. + */ + @Since("1.6.0") + def fromJson(json: String): Vector = { + implicit val formats = DefaultFormats + val jValue = parseJson(json) + (jValue \ "type").extract[Int] match { + case 0 => // sparse + val size = (jValue \ "size").extract[Int] + val indices = (jValue \ "indices").extract[Seq[Int]].toArray + val values = (jValue \ "values").extract[Seq[Double]].toArray + sparse(size, indices, values) + case 1 => // dense + val values = (jValue \ "values").extract[Seq[Double]].toArray + dense(values) + case _ => + throw new IllegalArgumentException(s"Cannot parse $json into a vector.") + } + } + private[mllib] def parseNumeric(any: Any): Vector = { any match { case values: Array[Double] => @@ -650,6 +680,12 @@ class DenseVector @Since("1.0.0") ( maxIdx } } + + @Since("1.6.0") + override def toJson: String = { + val jValue = ("type" -> 1) ~ ("values" -> values.toSeq) + compact(render(jValue)) + } } @Since("1.3.0") @@ -837,6 +873,15 @@ class SparseVector @Since("1.0.0") ( }.unzip new SparseVector(selectedIndices.length, sliceInds.toArray, sliceVals.toArray) } + + @Since("1.6.0") + override def toJson: String = { + val jValue = ("type" -> 0) ~ + ("size" -> size) ~ + ("indices" -> indices.toSeq) ~ + ("values" -> values.toSeq) + compact(render(jValue)) + } } @Since("1.3.0") diff --git a/mllib/src/test/scala/org/apache/spark/mllib/linalg/VectorsSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/linalg/VectorsSuite.scala index 6508ddeba4..f895e2a8e4 100644 --- a/mllib/src/test/scala/org/apache/spark/mllib/linalg/VectorsSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/mllib/linalg/VectorsSuite.scala @@ -20,6 +20,7 @@ package org.apache.spark.mllib.linalg import scala.util.Random import breeze.linalg.{DenseMatrix => BDM, squaredDistance => breezeSquaredDistance} +import org.json4s.jackson.JsonMethods.{parse => parseJson} import org.apache.spark.{Logging, SparkException, SparkFunSuite} import org.apache.spark.mllib.util.TestingUtils._ @@ -374,4 +375,20 @@ class VectorsSuite extends SparkFunSuite with Logging { assert(v.slice(Array(2, 0)) === new SparseVector(2, Array(0), Array(2.2))) assert(v.slice(Array(2, 0, 3, 4)) === new SparseVector(4, Array(0, 3), Array(2.2, 4.4))) } + + test("toJson/fromJson") { + val sv0 = Vectors.sparse(0, Array.empty, Array.empty) + val sv1 = Vectors.sparse(1, Array.empty, Array.empty) + val sv2 = Vectors.sparse(2, Array(1), Array(2.0)) + val dv0 = Vectors.dense(Array.empty[Double]) + val dv1 = Vectors.dense(1.0) + val dv2 = Vectors.dense(0.0, 2.0) + for (v <- Seq(sv0, sv1, sv2, dv0, dv1, dv2)) { + val json = v.toJson + parseJson(json) // `json` should be a valid JSON string + val u = Vectors.fromJson(json) + assert(u.getClass === v.getClass, "toJson/fromJson should preserve vector types.") + assert(u === v, "toJson/fromJson should preserve vector values.") + } + } } |