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[SPARK-17704][ML][MLlib] ChiSqSelector performance improvement. #15299

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[SPARK-17704][ML][MLlib] ChiSqSelector performance improvement. #15299

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c0c0174
Partial revert of #15277 to instead sort and store input to model rat…
srowen Sep 29, 2016
02a2b40
Fix selectedFeatures doc
srowen Sep 29, 2016
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2 changes: 1 addition & 1 deletion mllib/src/main/scala/org/apache/spark/ml/feature/ChiSqSelector.scala
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Expand Up @@ -193,7 +193,7 @@ final class ChiSqSelectorModel private[ml] (

import ChiSqSelectorModel._

/** list of indices to select (filter). Must be ordered asc */
/** list of indices to select (filter). */
@Since("1.6.0")
val selectedFeatures: Array[Int] = chiSqSelector.selectedFeatures

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22 changes: 11 additions & 11 deletions mllib/src/main/scala/org/apache/spark/mllib/feature/ChiSqSelector.scala
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Expand Up @@ -35,14 +35,15 @@ import org.apache.spark.sql.{Row, SparkSession}
/**
* Chi Squared selector model.
*
* @param selectedFeatures list of indices to select (filter). Must be ordered asc
* @param selectedFeatures list of indices to select (filter).
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I wonder if we should say "since the model requires sorted indices, selectedFeatures will be sorted" or something - just to make it clear the model does have this requirement, but takes care of that itself?

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I don't mind that, though, my original theory behind this little change was that the sorting is wholly an implementation detail that callers don't need to promise or be promised about these features. It's very small, but, do we need to even promise these are sorted here?

*/
@Since("1.3.0")
class ChiSqSelectorModel @Since("1.3.0") (
@Since("1.3.0") val selectedFeatures: Array[Int]) extends VectorTransformer with Saveable {

require(isSorted(selectedFeatures), "Array has to be sorted asc")
private val filterIndices = selectedFeatures.sorted

@deprecated("not intended for subclasses to use", "2.1.0")
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Yeah, I also fail to see why this needs to be exposed. +1 on deprecation.

protected def isSorted(array: Array[Int]): Boolean = {
var i = 1
val len = array.length
Expand All @@ -61,17 +62,16 @@ class ChiSqSelectorModel @Since("1.3.0") (
*/
@Since("1.3.0")
override def transform(vector: Vector): Vector = {
compress(vector, selectedFeatures)
compress(vector)
}

/**
* Returns a vector with features filtered.
* Preserves the order of filtered features the same as their indices are stored.
* Might be moved to Vector as .slice
* @param features vector
* @param filterIndices indices of features to filter, must be ordered asc
*/
private def compress(features: Vector, filterIndices: Array[Int]): Vector = {
private def compress(features: Vector): Vector = {
features match {
case SparseVector(size, indices, values) =>
val newSize = filterIndices.length
Expand Down Expand Up @@ -230,23 +230,23 @@ class ChiSqSelector @Since("2.1.0") () extends Serializable {
*/
@Since("1.3.0")
def fit(data: RDD[LabeledPoint]): ChiSqSelectorModel = {
val chiSqTestResult = Statistics.chiSqTest(data)
val chiSqTestResult = Statistics.chiSqTest(data).zipWithIndex
val features = selectorType match {
case ChiSqSelector.KBest =>
chiSqTestResult.zipWithIndex
chiSqTestResult
.sortBy { case (res, _) => -res.statistic }
.take(numTopFeatures)
case ChiSqSelector.Percentile =>
chiSqTestResult.zipWithIndex
chiSqTestResult
.sortBy { case (res, _) => -res.statistic }
.take((chiSqTestResult.length * percentile).toInt)
case ChiSqSelector.FPR =>
chiSqTestResult.zipWithIndex
.filter{ case (res, _) => res.pValue < alpha }
chiSqTestResult
.filter { case (res, _) => res.pValue < alpha }
case errorType =>
throw new IllegalStateException(s"Unknown ChiSqSelector Type: $errorType")
}
val indices = features.map { case (_, indices) => indices }.sorted
val indices = features.map { case (_, index) => index }
new ChiSqSelectorModel(indices)
}
}
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2 changes: 1 addition & 1 deletion python/pyspark/ml/feature.py
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Expand Up @@ -2705,7 +2705,7 @@ class ChiSqSelectorModel(JavaModel, JavaMLReadable, JavaMLWritable):
@since("2.0.0")
def selectedFeatures(self):
"""
List of indices to select (filter). Must be ordered asc.
List of indices to select (filter).
"""
return self._call_java("selectedFeatures")

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