[SPARK-24852][ML] Update spark.ml to use Instrumentation.instrumented.

mllib/src/main/scala/org/apache/spark/ml/classification/DecisionTreeClassifier.scala

@@ -29,6 +29,7 @@ import org.apache.spark.ml.tree._
 import org.apache.spark.ml.tree.DecisionTreeModelReadWrite._
 import org.apache.spark.ml.tree.impl.RandomForest
 import org.apache.spark.ml.util._
+import org.apache.spark.ml.util.Instrumentation.instrumented
 import org.apache.spark.mllib.tree.configuration.{Algo => OldAlgo, Strategy => OldStrategy}
 import org.apache.spark.mllib.tree.model.{DecisionTreeModel => OldDecisionTreeModel}
 import org.apache.spark.rdd.RDD
@@ -96,8 +97,10 @@ class DecisionTreeClassifier @Since("1.4.0") (
   @Since("1.6.0")
   override def setSeed(value: Long): this.type = set(seed, value)
 
-  override protected def train(dataset: Dataset[_]): DecisionTreeClassificationModel = {
-    val instr = Instrumentation.create(this, dataset)
+  override protected def train(
+      dataset: Dataset[_]): DecisionTreeClassificationModel = instrumented { instr =>
+    instr.logPipelineStage(this)
+    instr.logDataset(dataset)
     val categoricalFeatures: Map[Int, Int] =
       MetadataUtils.getCategoricalFeatures(dataset.schema($(featuresCol)))
     val numClasses: Int = getNumClasses(dataset)
@@ -112,30 +115,27 @@ class DecisionTreeClassifier @Since("1.4.0") (
     val oldDataset: RDD[LabeledPoint] = extractLabeledPoints(dataset, numClasses)
     val strategy = getOldStrategy(categoricalFeatures, numClasses)
 
-    instr.logParams(maxDepth, maxBins, minInstancesPerNode, minInfoGain, maxMemoryInMB,
+    instr.logParams(this, maxDepth, maxBins, minInstancesPerNode, minInfoGain, maxMemoryInMB,
       cacheNodeIds, checkpointInterval, impurity, seed)
 
     val trees = RandomForest.run(oldDataset, strategy, numTrees = 1, featureSubsetStrategy = "all",
       seed = $(seed), instr = Some(instr), parentUID = Some(uid))
 
-    val m = trees.head.asInstanceOf[DecisionTreeClassificationModel]
-    instr.logSuccess(m)
-    m
+    trees.head.asInstanceOf[DecisionTreeClassificationModel]
   }
 
   /** (private[ml]) Train a decision tree on an RDD */
   private[ml] def train(data: RDD[LabeledPoint],
-      oldStrategy: OldStrategy): DecisionTreeClassificationModel = {
-    val instr = Instrumentation.create(this, data)
-    instr.logParams(maxDepth, maxBins, minInstancesPerNode, minInfoGain, maxMemoryInMB,
+      oldStrategy: OldStrategy): DecisionTreeClassificationModel = instrumented { instr =>
+    instr.logPipelineStage(this)
+    instr.logDataset(data)
+    instr.logParams(this, maxDepth, maxBins, minInstancesPerNode, minInfoGain, maxMemoryInMB,
       cacheNodeIds, checkpointInterval, impurity, seed)
 
     val trees = RandomForest.run(data, oldStrategy, numTrees = 1, featureSubsetStrategy = "all",
       seed = 0L, instr = Some(instr), parentUID = Some(uid))
 
-    val m = trees.head.asInstanceOf[DecisionTreeClassificationModel]
-    instr.logSuccess(m)
-    m
+    trees.head.asInstanceOf[DecisionTreeClassificationModel]
   }
 
   /** (private[ml]) Create a Strategy instance to use with the old API. */

mllib/src/main/scala/org/apache/spark/ml/classification/GBTClassifier.scala

@@ -31,9 +31,9 @@ import org.apache.spark.ml.tree._
 import org.apache.spark.ml.tree.impl.GradientBoostedTrees
 import org.apache.spark.ml.util._
 import org.apache.spark.ml.util.DefaultParamsReader.Metadata
+import org.apache.spark.ml.util.Instrumentation.instrumented
 import org.apache.spark.mllib.tree.configuration.{Algo => OldAlgo}
 import org.apache.spark.mllib.tree.model.{GradientBoostedTreesModel => OldGBTModel}
-import org.apache.spark.rdd.RDD
 import org.apache.spark.sql.{DataFrame, Dataset, Row}
 import org.apache.spark.sql.functions._
 
@@ -152,7 +152,8 @@ class GBTClassifier @Since("1.4.0") (
     set(validationIndicatorCol, value)
   }
 
-  override protected def train(dataset: Dataset[_]): GBTClassificationModel = {
+  override protected def train(
+      dataset: Dataset[_]): GBTClassificationModel = instrumented { instr =>
     val categoricalFeatures: Map[Int, Int] =
       MetadataUtils.getCategoricalFeatures(dataset.schema($(featuresCol)))
 
@@ -189,8 +190,9 @@ class GBTClassifier @Since("1.4.0") (
         s" numClasses=$numClasses, but thresholds has length ${$(thresholds).length}")
     }
 
-    val instr = Instrumentation.create(this, dataset)
-    instr.logParams(labelCol, featuresCol, predictionCol, impurity, lossType,
+    instr.logPipelineStage(this)
+    instr.logDataset(dataset)
+    instr.logParams(this, labelCol, featuresCol, predictionCol, impurity, lossType,
       maxDepth, maxBins, maxIter, maxMemoryInMB, minInfoGain, minInstancesPerNode,
       seed, stepSize, subsamplingRate, cacheNodeIds, checkpointInterval, featureSubsetStrategy,
       validationIndicatorCol)
@@ -204,9 +206,7 @@ class GBTClassifier @Since("1.4.0") (
       GradientBoostedTrees.run(trainDataset, boostingStrategy, $(seed), $(featureSubsetStrategy))
     }
 
-    val m = new GBTClassificationModel(uid, baseLearners, learnerWeights, numFeatures)
-    instr.logSuccess(m)
-    m
+    new GBTClassificationModel(uid, baseLearners, learnerWeights, numFeatures)
   }
 
   @Since("1.4.1")

mllib/src/main/scala/org/apache/spark/ml/classification/LinearSVC.scala

@@ -33,6 +33,7 @@ import org.apache.spark.ml.optim.loss.{L2Regularization, RDDLossFunction}
 import org.apache.spark.ml.param._
 import org.apache.spark.ml.param.shared._
 import org.apache.spark.ml.util._
+import org.apache.spark.ml.util.Instrumentation.instrumented
 import org.apache.spark.mllib.linalg.VectorImplicits._
 import org.apache.spark.mllib.stat.MultivariateOnlineSummarizer
 import org.apache.spark.rdd.RDD
@@ -162,16 +163,17 @@ class LinearSVC @Since("2.2.0") (
   @Since("2.2.0")
   override def copy(extra: ParamMap): LinearSVC = defaultCopy(extra)
 
-  override protected def train(dataset: Dataset[_]): LinearSVCModel = {
+  override protected def train(dataset: Dataset[_]): LinearSVCModel = instrumented { instr =>
     val w = if (!isDefined(weightCol) || $(weightCol).isEmpty) lit(1.0) else col($(weightCol))
     val instances: RDD[Instance] =
       dataset.select(col($(labelCol)), w, col($(featuresCol))).rdd.map {
         case Row(label: Double, weight: Double, features: Vector) =>
           Instance(label, weight, features)
       }
 
-    val instr = Instrumentation.create(this, dataset)
-    instr.logParams(regParam, maxIter, fitIntercept, tol, standardization, threshold,
+    instr.logPipelineStage(this)
+    instr.logDataset(dataset)
+    instr.logParams(this, regParam, maxIter, fitIntercept, tol, standardization, threshold,
       aggregationDepth)
 
     val (summarizer, labelSummarizer) = {
@@ -276,9 +278,7 @@ class LinearSVC @Since("2.2.0") (
       (Vectors.dense(coefficientArray), intercept, scaledObjectiveHistory.result())
     }
 
-    val model = copyValues(new LinearSVCModel(uid, coefficientVector, interceptVector))
-    instr.logSuccess(model)
-    model
+    copyValues(new LinearSVCModel(uid, coefficientVector, interceptVector))
   }
 }
 

mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala

@@ -503,7 +503,7 @@ class LogisticRegression @Since("1.2.0") (
 
     instr.logPipelineStage(this)
     instr.logDataset(dataset)
-    instr.logParams(regParam, elasticNetParam, standardization, threshold,
+    instr.logParams(this, regParam, elasticNetParam, standardization, threshold,
       maxIter, tol, fitIntercept)
 
     val (summarizer, labelSummarizer) = {

mllib/src/main/scala/org/apache/spark/ml/classification/MultilayerPerceptronClassifier.scala

@@ -28,6 +28,7 @@ import org.apache.spark.ml.linalg.{Vector, Vectors}
 import org.apache.spark.ml.param._
 import org.apache.spark.ml.param.shared._
 import org.apache.spark.ml.util._
+import org.apache.spark.ml.util.Instrumentation.instrumented
 import org.apache.spark.sql.Dataset
 
 /** Params for Multilayer Perceptron. */
@@ -230,9 +231,11 @@ class MultilayerPerceptronClassifier @Since("1.5.0") (
    * @param dataset Training dataset
    * @return Fitted model
    */
-  override protected def train(dataset: Dataset[_]): MultilayerPerceptronClassificationModel = {
-    val instr = Instrumentation.create(this, dataset)
-    instr.logParams(labelCol, featuresCol, predictionCol, layers, maxIter, tol,
+  override protected def train(
+      dataset: Dataset[_]): MultilayerPerceptronClassificationModel = instrumented { instr =>
+    instr.logPipelineStage(this)
+    instr.logDataset(dataset)
+    instr.logParams(this, labelCol, featuresCol, predictionCol, layers, maxIter, tol,
       blockSize, solver, stepSize, seed)
 
     val myLayers = $(layers)
@@ -264,10 +267,7 @@ class MultilayerPerceptronClassifier @Since("1.5.0") (
     }
     trainer.setStackSize($(blockSize))
     val mlpModel = trainer.train(data)
-    val model = new MultilayerPerceptronClassificationModel(uid, myLayers, mlpModel.weights)
-
-    instr.logSuccess(model)
-    model
+    new MultilayerPerceptronClassificationModel(uid, myLayers, mlpModel.weights)
   }
 }
 

mllib/src/main/scala/org/apache/spark/ml/classification/NaiveBayes.scala

@@ -25,6 +25,7 @@ import org.apache.spark.ml.linalg._
 import org.apache.spark.ml.param.{DoubleParam, Param, ParamMap, ParamValidators}
 import org.apache.spark.ml.param.shared.HasWeightCol
 import org.apache.spark.ml.util._
+import org.apache.spark.ml.util.Instrumentation.instrumented
 import org.apache.spark.mllib.util.MLUtils
 import org.apache.spark.sql.{Dataset, Row}
 import org.apache.spark.sql.functions.{col, lit}
@@ -125,8 +126,9 @@ class NaiveBayes @Since("1.5.0") (
    */
   private[spark] def trainWithLabelCheck(
       dataset: Dataset[_],
-      positiveLabel: Boolean): NaiveBayesModel = {
-    val instr = Instrumentation.create(this, dataset)
+      positiveLabel: Boolean): NaiveBayesModel = instrumented { instr =>
+    instr.logPipelineStage(this)
+    instr.logDataset(dataset)
     if (positiveLabel && isDefined(thresholds)) {
       val numClasses = getNumClasses(dataset)
       instr.logNumClasses(numClasses)
@@ -148,7 +150,7 @@ class NaiveBayes @Since("1.5.0") (
       }
     }
 
-    instr.logParams(labelCol, featuresCol, weightCol, predictionCol, rawPredictionCol,
+    instr.logParams(this, labelCol, featuresCol, weightCol, predictionCol, rawPredictionCol,
       probabilityCol, modelType, smoothing, thresholds)
 
     val numFeatures = dataset.select(col($(featuresCol))).head().getAs[Vector](0).size
@@ -204,9 +206,7 @@ class NaiveBayes @Since("1.5.0") (
 
     val pi = Vectors.dense(piArray)
     val theta = new DenseMatrix(numLabels, numFeatures, thetaArray, true)
-    val model = new NaiveBayesModel(uid, pi, theta).setOldLabels(labelArray)
-    instr.logSuccess(model)
-    model
+    new NaiveBayesModel(uid, pi, theta).setOldLabels(labelArray)
   }
 
   @Since("1.5.0")

mllib/src/main/scala/org/apache/spark/ml/classification/OneVsRest.scala

@@ -36,6 +36,7 @@ import org.apache.spark.ml.linalg.{Vector, Vectors}
 import org.apache.spark.ml.param.{Param, ParamMap, ParamPair, Params}
 import org.apache.spark.ml.param.shared.{HasParallelism, HasWeightCol}
 import org.apache.spark.ml.util._
+import org.apache.spark.ml.util.Instrumentation.instrumented
 import org.apache.spark.sql.{DataFrame, Dataset, Row}
 import org.apache.spark.sql.functions._
 import org.apache.spark.sql.types._
@@ -362,11 +363,12 @@ final class OneVsRest @Since("1.4.0") (
   }
 
   @Since("2.0.0")
-  override def fit(dataset: Dataset[_]): OneVsRestModel = {
+  override def fit(dataset: Dataset[_]): OneVsRestModel = instrumented { instr =>
     transformSchema(dataset.schema)
 
-    val instr = Instrumentation.create(this, dataset)
-    instr.logParams(labelCol, featuresCol, predictionCol, parallelism, rawPredictionCol)
+    instr.logPipelineStage(this)
+    instr.logDataset(dataset)
+    instr.logParams(this, labelCol, featuresCol, predictionCol, parallelism, rawPredictionCol)
     instr.logNamedValue("classifier", $(classifier).getClass.getCanonicalName)
 
     // determine number of classes either from metadata if provided, or via computation.
@@ -440,7 +442,6 @@ final class OneVsRest @Since("1.4.0") (
       case attr: Attribute => attr
     }
     val model = new OneVsRestModel(uid, labelAttribute.toMetadata(), models).setParent(this)
-    instr.logSuccess(model)
     copyValues(model)
   }
 

mllib/src/main/scala/org/apache/spark/ml/classification/RandomForestClassifier.scala

@@ -28,6 +28,7 @@ import org.apache.spark.ml.tree._
 import org.apache.spark.ml.tree.impl.RandomForest
 import org.apache.spark.ml.util._
 import org.apache.spark.ml.util.DefaultParamsReader.Metadata
+import org.apache.spark.ml.util.Instrumentation.instrumented
 import org.apache.spark.mllib.tree.configuration.{Algo => OldAlgo}
 import org.apache.spark.mllib.tree.model.{RandomForestModel => OldRandomForestModel}
 import org.apache.spark.rdd.RDD
@@ -115,8 +116,10 @@ class RandomForestClassifier @Since("1.4.0") (
   override def setFeatureSubsetStrategy(value: String): this.type =
     set(featureSubsetStrategy, value)
 
-  override protected def train(dataset: Dataset[_]): RandomForestClassificationModel = {
-    val instr = Instrumentation.create(this, dataset)
+  override protected def train(
+      dataset: Dataset[_]): RandomForestClassificationModel = instrumented { instr =>
+    instr.logPipelineStage(this)
+    instr.logDataset(dataset)
     val categoricalFeatures: Map[Int, Int] =
       MetadataUtils.getCategoricalFeatures(dataset.schema($(featuresCol)))
     val numClasses: Int = getNumClasses(dataset)
@@ -131,7 +134,7 @@ class RandomForestClassifier @Since("1.4.0") (
     val strategy =
       super.getOldStrategy(categoricalFeatures, numClasses, OldAlgo.Classification, getOldImpurity)
 
-    instr.logParams(labelCol, featuresCol, predictionCol, probabilityCol, rawPredictionCol,
+    instr.logParams(this, labelCol, featuresCol, predictionCol, probabilityCol, rawPredictionCol,
       impurity, numTrees, featureSubsetStrategy, maxDepth, maxBins, maxMemoryInMB, minInfoGain,
       minInstancesPerNode, seed, subsamplingRate, thresholds, cacheNodeIds, checkpointInterval)
 
@@ -140,11 +143,9 @@ class RandomForestClassifier @Since("1.4.0") (
       .map(_.asInstanceOf[DecisionTreeClassificationModel])
 
     val numFeatures = oldDataset.first().features.size
-    val m = new RandomForestClassificationModel(uid, trees, numFeatures, numClasses)
     instr.logNumClasses(numClasses)
     instr.logNumFeatures(numFeatures)
-    instr.logSuccess(m)
-    m
+    new RandomForestClassificationModel(uid, trees, numFeatures, numClasses)
   }
 
   @Since("1.4.1")

mllib/src/main/scala/org/apache/spark/ml/clustering/BisectingKMeans.scala

@@ -26,6 +26,7 @@ import org.apache.spark.ml.linalg.Vector
 import org.apache.spark.ml.param._
 import org.apache.spark.ml.param.shared._
 import org.apache.spark.ml.util._
+import org.apache.spark.ml.util.Instrumentation.instrumented
 import org.apache.spark.mllib.clustering.{BisectingKMeans => MLlibBisectingKMeans,
   BisectingKMeansModel => MLlibBisectingKMeansModel}
 import org.apache.spark.mllib.linalg.VectorImplicits._
@@ -257,12 +258,13 @@ class BisectingKMeans @Since("2.0.0") (
   def setDistanceMeasure(value: String): this.type = set(distanceMeasure, value)
 
   @Since("2.0.0")
-  override def fit(dataset: Dataset[_]): BisectingKMeansModel = {
+  override def fit(dataset: Dataset[_]): BisectingKMeansModel = instrumented { instr =>
     transformSchema(dataset.schema, logging = true)
     val rdd = DatasetUtils.columnToOldVector(dataset, getFeaturesCol)
 
-    val instr = Instrumentation.create(this, dataset)
-    instr.logParams(featuresCol, predictionCol, k, maxIter, seed,
+    instr.logPipelineStage(this)
+    instr.logDataset(dataset)
+    instr.logParams(this, featuresCol, predictionCol, k, maxIter, seed,
       minDivisibleClusterSize, distanceMeasure)
 
     val bkm = new MLlibBisectingKMeans()
@@ -275,10 +277,8 @@ class BisectingKMeans @Since("2.0.0") (
     val model = copyValues(new BisectingKMeansModel(uid, parentModel).setParent(this))
     val summary = new BisectingKMeansSummary(
       model.transform(dataset), $(predictionCol), $(featuresCol), $(k), $(maxIter))
-    model.setSummary(Some(summary))
     instr.logNamedValue("clusterSizes", summary.clusterSizes)
-    instr.logSuccess(model)
-    model
+    model.setSummary(Some(summary))
   }
 
   @Since("2.0.0")

mllib/src/main/scala/org/apache/spark/ml/clustering/GaussianMixture.scala

@@ -29,6 +29,7 @@ import org.apache.spark.ml.param._
 import org.apache.spark.ml.param.shared._
 import org.apache.spark.ml.stat.distribution.MultivariateGaussian
 import org.apache.spark.ml.util._
+import org.apache.spark.ml.util.Instrumentation.instrumented
 import org.apache.spark.mllib.linalg.{Matrices => OldMatrices, Matrix => OldMatrix,
   Vector => OldVector, Vectors => OldVectors}
 import org.apache.spark.rdd.RDD
@@ -335,7 +336,7 @@ class GaussianMixture @Since("2.0.0") (
   private val numSamples = 5
 
   @Since("2.0.0")
-  override def fit(dataset: Dataset[_]): GaussianMixtureModel = {
+  override def fit(dataset: Dataset[_]): GaussianMixtureModel = instrumented { instr =>
     transformSchema(dataset.schema, logging = true)
 
     val sc = dataset.sparkSession.sparkContext
@@ -352,8 +353,9 @@ 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, dataset)
-    instr.logParams(featuresCol, predictionCol, probabilityCol, k, maxIter, seed, tol)
+    instr.logPipelineStage(this)
+    instr.logDataset(dataset)
+    instr.logParams(this, featuresCol, predictionCol, probabilityCol, k, maxIter, seed, tol)
     instr.logNumFeatures(numFeatures)
 
     val shouldDistributeGaussians = GaussianMixture.shouldDistributeGaussians(
@@ -425,11 +427,9 @@ 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, iter)
-    model.setSummary(Some(summary))
     instr.logNamedValue("logLikelihood", logLikelihood)
     instr.logNamedValue("clusterSizes", summary.clusterSizes)
-    instr.logSuccess(model)
-    model
+    model.setSummary(Some(summary))
   }
 
   @Since("2.0.0")