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[SPARK-24155][ML] Instrumentation improvements for clustering #21218

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[SPARK-24155][ML] Instrumentation improvements for clustering #21218

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1810dbe
instrumentation improvement for clustering
lu-wang-dl May 2, 2018
07c20a4
add more info for instrument
lu-wang-dl May 2, 2018
9053281
change the bug for logging clustersizes
lu-wang-dl May 3, 2018
4e2cb81
change the mkString and add TODO for logging Array "ClusterSizes"
lu-wang-dl May 11, 2018
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6 changes: 4 additions & 2 deletions mllib/src/main/scala/org/apache/spark/ml/clustering/BisectingKMeans.scala
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Original file line number Diff line number Diff line change
Expand Up @@ -264,8 +264,9 @@ class BisectingKMeans @Since("2.0.0") (
case Row(point: Vector) => OldVectors.fromML(point)
}

val instr = Instrumentation.create(this, rdd)
instr.logParams(featuresCol, predictionCol, k, maxIter, seed, minDivisibleClusterSize)
val instr = Instrumentation.create(this, dataset)
instr.logParams(featuresCol, predictionCol, k, maxIter, seed,
minDivisibleClusterSize, distanceMeasure)

val bkm = new MLlibBisectingKMeans()
.setK($(k))
Expand All @@ -278,6 +279,7 @@ class BisectingKMeans @Since("2.0.0") (
val summary = new BisectingKMeansSummary(
model.transform(dataset), $(predictionCol), $(featuresCol), $(k))
model.setSummary(Some(summary))
instr.logNamedValue("clusterSizes", summary.clusterSizes.toString)
instr.logSuccess(model)
model
}
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4 changes: 3 additions & 1 deletion mllib/src/main/scala/org/apache/spark/ml/clustering/GaussianMixture.scala
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Expand Up @@ -350,7 +350,7 @@ class GaussianMixture @Since("2.0.0") (
s"than ${GaussianMixture.MAX_NUM_FEATURES} features because the size of the covariance" +
s" matrix is quadratic in the number of features.")

val instr = Instrumentation.create(this, instances)
val instr = Instrumentation.create(this, dataset)
instr.logParams(featuresCol, predictionCol, probabilityCol, k, maxIter, seed, tol)
instr.logNumFeatures(numFeatures)

Expand Down Expand Up @@ -423,6 +423,8 @@ class GaussianMixture @Since("2.0.0") (
val summary = new GaussianMixtureSummary(model.transform(dataset),
$(predictionCol), $(probabilityCol), $(featuresCol), $(k), logLikelihood)
model.setSummary(Some(summary))
instr.logNamedValue("logLikelihood", logLikelihood)
instr.logNamedValue("clusterSizes", summary.clusterSizes.toString)
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The clusterSizes.toString will get an unreadable object address string.
We need to print the content of the array. I suggest add a method like:
def logNamedArray[T](array: Array[T]): Unit

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@WeichenXu123 The function clusterSizes.mkString(", ") could change the array to a string, separating each String in the array with comma. What do you think?

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also ok I think.

instr.logSuccess(model)
model
}
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3 changes: 2 additions & 1 deletion mllib/src/main/scala/org/apache/spark/ml/clustering/KMeans.scala
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Expand Up @@ -361,7 +361,7 @@ class KMeans @Since("1.5.0") (
instances.persist(StorageLevel.MEMORY_AND_DISK)
}

val instr = Instrumentation.create(this, instances)
val instr = Instrumentation.create(this, dataset)
instr.logParams(featuresCol, predictionCol, k, initMode, initSteps, distanceMeasure,
maxIter, seed, tol)
val algo = new MLlibKMeans()
Expand All @@ -378,6 +378,7 @@ class KMeans @Since("1.5.0") (
model.transform(dataset), $(predictionCol), $(featuresCol), $(k))

model.setSummary(Some(summary))
instr.logNamedValue("clusterSizes", summary.clusterSizes.toString)
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ditto.

instr.logSuccess(model)
if (handlePersistence) {
instances.unpersist()
Expand Down