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[SPARK-14661] [MLlib] trim PCAModel by required explained variance #12419

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[SPARK-14661] [MLlib] trim PCAModel by required explained variance #12419

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ac1b9fe
SPARK-14661: PCA trimmed by variance
Apr 15, 2016
e0b2b76
SPARK-14661: changes after code review (implemented also for 'ml' and…
Apr 15, 2016
c759e4c
SPARK-14661: changes after code review (modifying RowMatrix.computePr…
Apr 26, 2016
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SPARK-14661: PCA trimmed by variance
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Piotr Suszyński committed Apr 15, 2016
commit ac1b9fe9c920171b6baf1a3bb12a4c7ea7d79ec1
19 changes: 19 additions & 0 deletions mllib/src/main/scala/org/apache/spark/mllib/feature/PCA.scala
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Expand Up @@ -17,6 +17,8 @@

package org.apache.spark.mllib.feature

import java.util.Arrays

import org.apache.spark.annotation.Since
import org.apache.spark.api.java.JavaRDD
import org.apache.spark.mllib.linalg._
Expand Down Expand Up @@ -109,4 +111,21 @@ class PCAModel private[spark] (
s"SparseVector or DenseVector. Instead got: ${vector.getClass}")
}
}

def minimalByVarianceExplained(requiredVarianceRetained: Double): PCAModel = {
val minFeaturesNum = explainedVariance
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@srowen srowen Apr 15, 2016 •
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How about explainedVariance.values.scanLeft(0.0)(_ + _).indexWhere(_ >= requiredVarianceRetained) + 1. Eh, OK to make that robust you'd have to handle the case where that returns 0 (means you need to keep all the PCs, so, could just return this), and also arg-check the required variance to be in [0,1].

.values.zipWithIndex
.foldLeft((0.0, 0)) { case ((varianceSum, bestIndex), (variance, index)) =>
if (varianceSum >= requiredVarianceRetained) {
(varianceSum, bestIndex)
} else {
(varianceSum + variance, index)
}
}._2 + 1
val trimmedPc = Arrays.copyOfRange(pc.values, 0, pc.numRows * minFeaturesNum)
val trimmedExplainedVariance = Arrays.copyOfRange(explainedVariance.values, 0, minFeaturesNum)
new PCAModel(minFeaturesNum,
Matrices.dense(pc.numRows, minFeaturesNum, trimmedPc).asInstanceOf[DenseMatrix],
Vectors.dense(trimmedExplainedVariance).asInstanceOf[DenseVector])
}
}
14 changes: 14 additions & 0 deletions mllib/src/test/scala/org/apache/spark/mllib/feature/PCASuite.scala
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Expand Up @@ -45,4 +45,18 @@ class PCASuite extends SparkFunSuite with MLlibTestSparkContext {
assert(pca_transform.toSet === mat_multiply.toSet)
assert(pca.explainedVariance === explainedVariance)
}

test("should return model with minimal number of features that retain given level of variance") {
// given
val pca = new PCA(4).fit(dataRDD)

// when
val trimmed = pca.minimalByVarianceExplained(0.90)

// then
val pcaWithExpectedK = new PCA(2).fit(dataRDD)
assert(trimmed.k === pcaWithExpectedK.k)
assert(trimmed.explainedVariance === pcaWithExpectedK.explainedVariance)
assert(trimmed.pc === pcaWithExpectedK.pc)
}
}