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[SPARK-23528][ML] Add numIter to ClusteringSummary #20701

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[SPARK-23528][ML] Add numIter to ClusteringSummary #20701

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e3a217c
[SPARK-23528][ML] Add numIter to ClusteringSummary
mgaido91 Feb 28, 2018
d5c8af7
add MiMa excludes
mgaido91 Mar 1, 2018
b3d0523
avoid setting maxIter to 2 in BisectingKMeansSuite
mgaido91 Mar 10, 2018
8b16af6
Merge branch 'master' into SPARK-23528
mgaido91 Mar 16, 2018
f6ee4a2
add comments
mgaido91 Mar 18, 2018
41f0371
make KMeansModel constructor private
mgaido91 Apr 2, 2018
59fef4e
Merge branch 'master' into SPARK-23528
mgaido91 Apr 23, 2018
e2f68ac
Merge branch 'master' into SPARK-23528
mgaido91 May 29, 2018
4a6bd2d
Merge branch 'master' into SPARK-23528
mgaido91 Jul 12, 2018
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5 changes: 3 additions & 2 deletions mllib/src/main/scala/org/apache/spark/ml/clustering/BisectingKMeans.scala
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Expand Up @@ -276,7 +276,7 @@ class BisectingKMeans @Since("2.0.0") (
val parentModel = bkm.run(rdd)
val model = copyValues(new BisectingKMeansModel(uid, parentModel).setParent(this))
val summary = new BisectingKMeansSummary(
model.transform(dataset), $(predictionCol), $(featuresCol), $(k))
model.transform(dataset), $(predictionCol), $(featuresCol), $(k), $(maxIter))
model.setSummary(Some(summary))
instr.logSuccess(model)
model
Expand Down Expand Up @@ -312,4 +312,5 @@ class BisectingKMeansSummary private[clustering] (
predictions: DataFrame,
predictionCol: String,
featuresCol: String,
k: Int) extends ClusteringSummary(predictions, predictionCol, featuresCol, k)
k: Int,
numIter: Int) extends ClusteringSummary(predictions, predictionCol, featuresCol, k, numIter)
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Here (and in the others), we should add this as param in the comment above as done with the other params

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thanks for pointing this out, I completely missed it. Thank you, I am adding them.

5 changes: 3 additions & 2 deletions mllib/src/main/scala/org/apache/spark/ml/clustering/ClusteringSummary.scala
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Expand Up @@ -17,7 +17,7 @@

package org.apache.spark.ml.clustering

import org.apache.spark.annotation.Experimental
import org.apache.spark.annotation.{Experimental, Since}
import org.apache.spark.sql.{DataFrame, Row}

/**
Expand All @@ -34,7 +34,8 @@ class ClusteringSummary private[clustering] (
@transient val predictions: DataFrame,
val predictionCol: String,
val featuresCol: String,
val k: Int) extends Serializable {
val k: Int,
@Since("2.4.0") val numIter: Int) extends Serializable {
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Please add this param in the comment above.


/**
* Cluster centers of the transformed data.
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7 changes: 4 additions & 3 deletions mllib/src/main/scala/org/apache/spark/ml/clustering/GaussianMixture.scala
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Expand Up @@ -421,7 +421,7 @@ class GaussianMixture @Since("2.0.0") (

val model = copyValues(new GaussianMixtureModel(uid, weights, gaussianDists)).setParent(this)
val summary = new GaussianMixtureSummary(model.transform(dataset),
$(predictionCol), $(probabilityCol), $(featuresCol), $(k), logLikelihood)
$(predictionCol), $(probabilityCol), $(featuresCol), $(k), logLikelihood, iter)
model.setSummary(Some(summary))
instr.logSuccess(model)
model
Expand Down Expand Up @@ -692,8 +692,9 @@ class GaussianMixtureSummary private[clustering] (
@Since("2.0.0") val probabilityCol: String,
featuresCol: String,
k: Int,
@Since("2.2.0") val logLikelihood: Double)
extends ClusteringSummary(predictions, predictionCol, featuresCol, k) {
@Since("2.2.0") val logLikelihood: Double,
numIter: Int)
extends ClusteringSummary(predictions, predictionCol, featuresCol, k, numIter) {

/**
* Probability of each cluster.
Expand Down
5 changes: 3 additions & 2 deletions mllib/src/main/scala/org/apache/spark/ml/clustering/KMeans.scala
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Expand Up @@ -332,7 +332,7 @@ class KMeans @Since("1.5.0") (
val parentModel = algo.run(instances, Option(instr))
val model = copyValues(new KMeansModel(uid, parentModel).setParent(this))
val summary = new KMeansSummary(
model.transform(dataset), $(predictionCol), $(featuresCol), $(k))
model.transform(dataset), $(predictionCol), $(featuresCol), $(k), parentModel.numIter)

model.setSummary(Some(summary))
instr.logSuccess(model)
Expand Down Expand Up @@ -370,4 +370,5 @@ class KMeansSummary private[clustering] (
predictions: DataFrame,
predictionCol: String,
featuresCol: String,
k: Int) extends ClusteringSummary(predictions, predictionCol, featuresCol, k)
k: Int,
numIter: Int) extends ClusteringSummary(predictions, predictionCol, featuresCol, k, numIter)
2 changes: 1 addition & 1 deletion mllib/src/main/scala/org/apache/spark/mllib/clustering/KMeans.scala
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Expand Up @@ -348,7 +348,7 @@ class KMeans private (

logInfo(s"The cost is $cost.")

new KMeansModel(centers.map(_.vector), distanceMeasure)
new KMeansModel(centers.map(_.vector), distanceMeasure, iteration)
}

/**
Expand Down
9 changes: 7 additions & 2 deletions mllib/src/main/scala/org/apache/spark/mllib/clustering/KMeansModel.scala
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Expand Up @@ -36,8 +36,9 @@ import org.apache.spark.sql.{Row, SparkSession}
* A clustering model for K-means. Each point belongs to the cluster with the closest center.
*/
@Since("0.8.0")
class KMeansModel @Since("2.4.0") (@Since("1.0.0") val clusterCenters: Array[Vector],
@Since("2.4.0") val distanceMeasure: String)
class KMeansModel private[spark] (@Since("1.0.0") val clusterCenters: Array[Vector],
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So previously the main constructor was not private, any particular reason we are making in private? if someone else is implementing something which extends the kmeans model this might be a little frustrating.

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I just didn't want the user to be able to create a KMeansModel setting the number of iterations. I moved the other constructor which is still available. I don't have strong reasons against making this public, so I am removing the private clause if you think we best let it to be public.

@Since("2.4.0") val distanceMeasure: String,
private[spark] val numIter: Int)
extends Saveable with Serializable with PMMLExportable {

private val distanceMeasureInstance: DistanceMeasure =
Expand All @@ -46,6 +47,10 @@ class KMeansModel @Since("2.4.0") (@Since("1.0.0") val clusterCenters: Array[Vec
private val clusterCentersWithNorm =
if (clusterCenters == null) null else clusterCenters.map(new VectorWithNorm(_))

@Since("2.4.0")
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So I think the correct since annotation here would be 0.8.0 since this is just a move of the previous constructor right?

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I think this is the right one. 0.8.0 is the annotation for the KMeansModel class, while the previous main constructor was added (by me) is a previous PR for 2.4.0 in order to add the distanceMeasure variable.

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Why does this constructor need to be public?

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yes, I will make it private, thanks.

def this(clusterCenters: Array[Vector], distanceMeasure: String) =
this(clusterCenters: Array[Vector], distanceMeasure, -1)
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So were using -1 to indicate we don't have the numIter information

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yes, this can happen for instance when reloading a persisted model. Moreover this is only for the mllib model, which as far as I know is suggested not to be used anymore in favor of the new ml api. Any concern/suggestion about this?

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Sounds reasonable, I personally don't enjoy -1 to indicate lack of information but it seems to be what we have generally used in the past for mllib summary info into ml so my personal feelings aren't important :)


@Since("1.1.0")
def this(clusterCenters: Array[Vector]) =
this(clusterCenters: Array[Vector], DistanceMeasure.EUCLIDEAN)
Expand Down
2 changes: 2 additions & 0 deletions mllib/src/test/scala/org/apache/spark/ml/clustering/BisectingKMeansSuite.scala
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Expand Up @@ -99,6 +99,7 @@ class BisectingKMeansSuite
test("fit, transform and summary") {
val predictionColName = "bisecting_kmeans_prediction"
val bkm = new BisectingKMeans().setK(k).setPredictionCol(predictionColName).setSeed(1)

val model = bkm.fit(dataset)
assert(model.clusterCenters.length === k)

Expand Down Expand Up @@ -129,6 +130,7 @@ class BisectingKMeansSuite
assert(clusterSizes.length === k)
assert(clusterSizes.sum === numRows)
assert(clusterSizes.forall(_ >= 0))
assert(summary.numIter == 20)

model.setSummary(None)
assert(!model.hasSummary)
Expand Down
1 change: 1 addition & 0 deletions mllib/src/test/scala/org/apache/spark/ml/clustering/GaussianMixtureSuite.scala
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Expand Up @@ -150,6 +150,7 @@ class GaussianMixtureSuite extends SparkFunSuite with MLlibTestSparkContext
assert(clusterSizes.length === k)
assert(clusterSizes.sum === numRows)
assert(clusterSizes.forall(_ >= 0))
assert(summary.numIter == 2)

model.setSummary(None)
assert(!model.hasSummary)
Expand Down
1 change: 1 addition & 0 deletions mllib/src/test/scala/org/apache/spark/ml/clustering/KMeansSuite.scala
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Expand Up @@ -130,6 +130,7 @@ class KMeansSuite extends SparkFunSuite with MLlibTestSparkContext with DefaultR
assert(clusterSizes.length === k)
assert(clusterSizes.sum === numRows)
assert(clusterSizes.forall(_ >= 0))
assert(summary.numIter == 1)

model.setSummary(None)
assert(!model.hasSummary)
Expand Down
5 changes: 5 additions & 0 deletions project/MimaExcludes.scala
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Expand Up @@ -36,6 +36,11 @@ object MimaExcludes {

// Exclude rules for 2.4.x
lazy val v24excludes = v23excludes ++ Seq(
// [SPARK-23528] Add numIter to ClusteringSummary
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Just a note for other reviewers/myself these are all private spark constructors

ProblemFilters.exclude[DirectMissingMethodProblem]("org.apache.spark.ml.clustering.ClusteringSummary.this"),
ProblemFilters.exclude[DirectMissingMethodProblem]("org.apache.spark.ml.clustering.KMeansSummary.this"),
ProblemFilters.exclude[DirectMissingMethodProblem]("org.apache.spark.ml.clustering.BisectingKMeansSummary.this"),

// [SPARK-23412][ML] Add cosine distance measure to BisectingKmeans
ProblemFilters.exclude[InheritedNewAbstractMethodProblem]("org.apache.spark.ml.param.shared.HasDistanceMeasure.org$apache$spark$ml$param$shared$HasDistanceMeasure$_setter_$distanceMeasure_="),
ProblemFilters.exclude[InheritedNewAbstractMethodProblem]("org.apache.spark.ml.param.shared.HasDistanceMeasure.getDistanceMeasure"),
Expand Down