[SPARK-18206][ML]Add instrumentation for MLP,NB,LDA,AFT,GLM,Isotonic,LiR

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

@@ -160,7 +160,9 @@ class GBTClassifier @Since("1.4.0") (
     val boostingStrategy = super.getOldBoostingStrategy(categoricalFeatures, OldAlgo.Classification)
 
     val instr = Instrumentation.create(this, oldDataset)
-    instr.logParams(params: _*)
+    instr.logParams(labelCol, featuresCol, predictionCol, impurity, lossType,
+      maxDepth, maxBins, maxIter, maxMemoryInMB, minInfoGain, minInstancesPerNode,
+      seed, stepSize, subsamplingRate, cacheNodeIds, checkpointInterval)
     instr.logNumFeatures(numFeatures)
     instr.logNumClasses(2)
 

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

@@ -232,8 +232,15 @@ class MultilayerPerceptronClassifier @Since("1.5.0") (
    * @return Fitted model
    */
   override protected def train(dataset: Dataset[_]): MultilayerPerceptronClassificationModel = {
+    val instr = Instrumentation.create(this, dataset)
+    instr.logParams(labelCol, featuresCol, predictionCol, layers, maxIter, tol,
+      blockSize, solver, stepSize, seed)
+
     val myLayers = $(layers)
     val labels = myLayers.last
+    instr.logNumClasses(labels)
+    instr.logNumFeatures(myLayers.head)
+
     val lpData = extractLabeledPoints(dataset)
     val data = lpData.map(lp => LabelConverter.encodeLabeledPoint(lp, labels))
     val topology = FeedForwardTopology.multiLayerPerceptron(myLayers, softmaxOnTop = true)
@@ -258,7 +265,10 @@ class MultilayerPerceptronClassifier @Since("1.5.0") (
     }
     trainer.setStackSize($(blockSize))
     val mlpModel = trainer.train(data)
-    new MultilayerPerceptronClassificationModel(uid, myLayers, mlpModel.weights)
+    val model = new MultilayerPerceptronClassificationModel(uid, myLayers, mlpModel.weights)
+
+    instr.logSuccess(model)
+    model
   }
 }
 

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

@@ -147,7 +147,12 @@ class NaiveBayes @Since("1.5.0") (
       }
     }
 
+    val instr = Instrumentation.create(this, dataset)
+    instr.logParams(labelCol, featuresCol, weightCol, predictionCol, rawPredictionCol,
+      probabilityCol, modelType, smoothing, thresholds)
+
     val numFeatures = dataset.select(col($(featuresCol))).head().getAs[Vector](0).size
+    instr.logNumFeatures(numFeatures)
     val w = if (!isDefined(weightCol) || $(weightCol).isEmpty) lit(1.0) else col($(weightCol))
 
     // Aggregates term frequencies per label.
@@ -169,6 +174,7 @@ class NaiveBayes @Since("1.5.0") (
       }).collect().sortBy(_._1)
 
     val numLabels = aggregated.length
+    instr.logNumClasses(numLabels)
     val numDocuments = aggregated.map(_._2._1).sum
 
     val labelArray = new Array[Double](numLabels)
@@ -198,7 +204,9 @@ class NaiveBayes @Since("1.5.0") (
 
     val pi = Vectors.dense(piArray)
     val theta = new DenseMatrix(numLabels, numFeatures, thetaArray, true)
-    new NaiveBayesModel(uid, pi, theta).setOldLabels(labelArray)
+    val model = new NaiveBayesModel(uid, pi, theta).setOldLabels(labelArray)
+    instr.logSuccess(model)
+    model
   }
 
   @Since("1.5.0")

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

@@ -131,7 +131,9 @@ class RandomForestClassifier @Since("1.4.0") (
       super.getOldStrategy(categoricalFeatures, numClasses, OldAlgo.Classification, getOldImpurity)
 
     val instr = Instrumentation.create(this, oldDataset)
-    instr.logParams(params: _*)
+    instr.logParams(labelCol, featuresCol, predictionCol, probabilityCol, rawPredictionCol,
+      impurity, numTrees, featureSubsetStrategy, maxDepth, maxBins, maxMemoryInMB, minInfoGain,
+      minInstancesPerNode, seed, subsamplingRate, thresholds, cacheNodeIds, checkpointInterval)
 
     val trees = RandomForest
       .run(oldDataset, strategy, getNumTrees, getFeatureSubsetStrategy, getSeed, Some(instr))

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

@@ -888,6 +888,12 @@ class LDA @Since("1.6.0") (
   @Since("2.0.0")
   override def fit(dataset: Dataset[_]): LDAModel = {
     transformSchema(dataset.schema, logging = true)
+
+    val instr = Instrumentation.create(this, dataset)
+    instr.logParams(featuresCol, topicDistributionCol, k, maxIter, subsamplingRate,
+      checkpointInterval, keepLastCheckpoint, optimizeDocConcentration, topicConcentration,
+      learningDecay, optimizer, learningOffset, seed)
+
     val oldLDA = new OldLDA()
       .setK($(k))
       .setDocConcentration(getOldDocConcentration)
@@ -905,7 +911,11 @@ class LDA @Since("1.6.0") (
       case m: OldDistributedLDAModel =>
         new DistributedLDAModel(uid, m.vocabSize, m, dataset.sparkSession, None)
     }
-    copyValues(newModel).setParent(this)
+
+    instr.logNumFeatures(newModel.vocabSize)
+    val model = copyValues(newModel).setParent(this)
+    instr.logSuccess(model)
+    model
   }
 
   @Since("1.6.0")

mllib/src/main/scala/org/apache/spark/ml/recommendation/ALS.scala

@@ -457,10 +457,12 @@ class ALS(@Since("1.4.0") override val uid: String) extends Estimator[ALSModel]
       .map { row =>
         Rating(row.getInt(0), row.getInt(1), row.getFloat(2))
       }
+
     val instr = Instrumentation.create(this, ratings)
-    instr.logParams(rank, numUserBlocks, numItemBlocks, implicitPrefs, alpha,
-                       userCol, itemCol, ratingCol, predictionCol, maxIter,
-                       regParam, nonnegative, checkpointInterval, seed)
+    instr.logParams(rank, numUserBlocks, numItemBlocks, implicitPrefs, alpha, userCol,
+      itemCol, ratingCol, predictionCol, maxIter, regParam, nonnegative, checkpointInterval,
+      seed, intermediateStorageLevel, finalStorageLevel)
+
     val (userFactors, itemFactors) = ALS.train(ratings, rank = $(rank),
       numUserBlocks = $(numUserBlocks), numItemBlocks = $(numItemBlocks),
       maxIter = $(maxIter), regParam = $(regParam), implicitPrefs = $(implicitPrefs),

mllib/src/main/scala/org/apache/spark/ml/regression/AFTSurvivalRegression.scala

@@ -227,6 +227,12 @@ class AFTSurvivalRegression @Since("1.6.0") (@Since("1.6.0") override val uid: S
     val featuresStd = featuresSummarizer.variance.toArray.map(math.sqrt)
     val numFeatures = featuresStd.size
 
+    val instr = Instrumentation.create(this, dataset)
+    instr.logParams(labelCol, featuresCol, censorCol, predictionCol, quantilesCol,
+      fitIntercept, maxIter, tol, aggregationDepth)
+    instr.logNamedValue("quantileProbabilities.size", $(quantileProbabilities).length)
+    instr.logNumFeatures(numFeatures)
+
     if (!$(fitIntercept) && (0 until numFeatures).exists { i =>
         featuresStd(i) == 0.0 && featuresSummarizer.mean(i) != 0.0 }) {
       logWarning("Fitting AFTSurvivalRegressionModel without intercept on dataset with " +
@@ -276,8 +282,10 @@ class AFTSurvivalRegression @Since("1.6.0") (@Since("1.6.0") override val uid: S
     val coefficients = Vectors.dense(rawCoefficients)
     val intercept = parameters(1)
     val scale = math.exp(parameters(0))
-    val model = new AFTSurvivalRegressionModel(uid, coefficients, intercept, scale)
-    copyValues(model.setParent(this))
+    val model = copyValues(new AFTSurvivalRegressionModel(uid, coefficients,
+      intercept, scale).setParent(this))
+    instr.logSuccess(model)
+    model
   }
 
   @Since("1.6.0")

mllib/src/main/scala/org/apache/spark/ml/regression/GBTRegressor.scala

@@ -148,7 +148,9 @@ class GBTRegressor @Since("1.4.0") (@Since("1.4.0") override val uid: String)
     val boostingStrategy = super.getOldBoostingStrategy(categoricalFeatures, OldAlgo.Regression)
 
     val instr = Instrumentation.create(this, oldDataset)
-    instr.logParams(params: _*)
+    instr.logParams(labelCol, featuresCol, predictionCol, impurity, lossType,
+      maxDepth, maxBins, maxIter, maxMemoryInMB, minInfoGain, minInstancesPerNode,
+      seed, stepSize, subsamplingRate, cacheNodeIds, checkpointInterval)
     instr.logNumFeatures(numFeatures)
 
     val (baseLearners, learnerWeights) = GradientBoostedTrees.run(oldDataset, boostingStrategy,

mllib/src/main/scala/org/apache/spark/ml/regression/GeneralizedLinearRegression.scala

@@ -251,6 +251,11 @@ class GeneralizedLinearRegression @Since("2.0.0") (@Since("2.0.0") override val
     val familyAndLink = new FamilyAndLink(familyObj, linkObj)
 
     val numFeatures = dataset.select(col($(featuresCol))).first().getAs[Vector](0).size
+    val instr = Instrumentation.create(this, dataset)
+    instr.logParams(labelCol, featuresCol, weightCol, predictionCol, linkPredictionCol,
+      family, solver, fitIntercept, link, maxIter, regParam, tol)
+    instr.logNumFeatures(numFeatures)
+
     if (numFeatures > WeightedLeastSquares.MAX_NUM_FEATURES) {
       val msg = "Currently, GeneralizedLinearRegression only supports number of features" +
         s" <= ${WeightedLeastSquares.MAX_NUM_FEATURES}. Found $numFeatures in the input dataset."
@@ -264,7 +269,7 @@ class GeneralizedLinearRegression @Since("2.0.0") (@Since("2.0.0") override val
           Instance(label, weight, features)
       }
 
-    if (familyObj == Gaussian && linkObj == Identity) {
+    val model = if (familyObj == Gaussian && linkObj == Identity) {
       // TODO: Make standardizeFeatures and standardizeLabel configurable.
       val optimizer = new WeightedLeastSquares($(fitIntercept), $(regParam), elasticNetParam = 0.0,
         standardizeFeatures = true, standardizeLabel = true)
@@ -274,21 +279,23 @@ class GeneralizedLinearRegression @Since("2.0.0") (@Since("2.0.0") override val
           .setParent(this))
       val trainingSummary = new GeneralizedLinearRegressionTrainingSummary(dataset, model,
         wlsModel.diagInvAtWA.toArray, 1, getSolver)
-      return model.setSummary(Some(trainingSummary))
+      model.setSummary(Some(trainingSummary))
+    } else {
+      // Fit Generalized Linear Model by iteratively reweighted least squares (IRLS).
+      val initialModel = familyAndLink.initialize(instances, $(fitIntercept), $(regParam))
+      val optimizer = new IterativelyReweightedLeastSquares(initialModel,
+        familyAndLink.reweightFunc, $(fitIntercept), $(regParam), $(maxIter), $(tol))
+      val irlsModel = optimizer.fit(instances)
+      val model = copyValues(
+        new GeneralizedLinearRegressionModel(uid, irlsModel.coefficients, irlsModel.intercept)
+          .setParent(this))
+      val trainingSummary = new GeneralizedLinearRegressionTrainingSummary(dataset, model,
+        irlsModel.diagInvAtWA.toArray, irlsModel.numIterations, getSolver)
+      model.setSummary(Some(trainingSummary))
     }
 
-    // Fit Generalized Linear Model by iteratively reweighted least squares (IRLS).
-    val initialModel = familyAndLink.initialize(instances, $(fitIntercept), $(regParam))
-    val optimizer = new IterativelyReweightedLeastSquares(initialModel, familyAndLink.reweightFunc,
-      $(fitIntercept), $(regParam), $(maxIter), $(tol))
-    val irlsModel = optimizer.fit(instances)
-
-    val model = copyValues(
-      new GeneralizedLinearRegressionModel(uid, irlsModel.coefficients, irlsModel.intercept)
-        .setParent(this))
-    val trainingSummary = new GeneralizedLinearRegressionTrainingSummary(dataset, model,
-      irlsModel.diagInvAtWA.toArray, irlsModel.numIterations, getSolver)
-    model.setSummary(Some(trainingSummary))
+    instr.logSuccess(model)
+    model
   }
 
   @Since("2.0.0")

mllib/src/main/scala/org/apache/spark/ml/regression/IsotonicRegression.scala

@@ -171,10 +171,16 @@ class IsotonicRegression @Since("1.5.0") (@Since("1.5.0") override val uid: Stri
     val handlePersistence = dataset.rdd.getStorageLevel == StorageLevel.NONE
     if (handlePersistence) instances.persist(StorageLevel.MEMORY_AND_DISK)
 
+    val instr = Instrumentation.create(this, dataset)
+    instr.logParams(labelCol, featuresCol, weightCol, predictionCol, featureIndex, isotonic)
+    instr.logNumFeatures(1)
+
     val isotonicRegression = new MLlibIsotonicRegression().setIsotonic($(isotonic))
     val oldModel = isotonicRegression.run(instances)
 
-    copyValues(new IsotonicRegressionModel(uid, oldModel).setParent(this))
+    val model = copyValues(new IsotonicRegressionModel(uid, oldModel).setParent(this))
+    instr.logSuccess(model)
+    model
   }
 
   @Since("1.5.0")

mllib/src/main/scala/org/apache/spark/ml/regression/LinearRegression.scala

@@ -91,7 +91,7 @@ class LinearRegression @Since("1.3.0") (@Since("1.3.0") override val uid: String
   setDefault(regParam -> 0.0)
 
   /**
-   * Set if we should fit the intercept
+   * Set if we should fit the intercept.
    * Default is true.
    *
    * @group setParam
@@ -204,6 +204,11 @@ class LinearRegression @Since("1.3.0") (@Since("1.3.0") override val uid: String
         Instance(label, weight, features)
     }
 
+    val instr = Instrumentation.create(this, dataset)
+    instr.logParams(labelCol, featuresCol, weightCol, predictionCol, solver, tol,
+      elasticNetParam, fitIntercept, maxIter, regParam, standardization, aggregationDepth)
+    instr.logNumFeatures(numFeatures)
+
     if (($(solver) == "auto" &&
       numFeatures <= WeightedLeastSquares.MAX_NUM_FEATURES) || $(solver) == "normal") {
       // For low dimensional data, WeightedLeastSquares is more efficient since the
@@ -226,7 +231,9 @@ class LinearRegression @Since("1.3.0") (@Since("1.3.0") override val uid: String
         model.diagInvAtWA.toArray,
         model.objectiveHistory)
 
-      return lrModel.setSummary(Some(trainingSummary))
+      lrModel.setSummary(Some(trainingSummary))
+      instr.logSuccess(lrModel)
+      return lrModel
     }
 
     val handlePersistence = dataset.rdd.getStorageLevel == StorageLevel.NONE
@@ -251,10 +258,10 @@ class LinearRegression @Since("1.3.0") (@Since("1.3.0") override val uid: String
     val rawYStd = math.sqrt(ySummarizer.variance(0))
     if (rawYStd == 0.0) {
       if ($(fitIntercept) || yMean == 0.0) {
-        // If the rawYStd is zero and fitIntercept=true, then the intercept is yMean with
+        // If the rawYStd==0 and fitIntercept==true, then the intercept is yMean with
         // zero coefficient; as a result, training is not needed.
         // Also, if yMean==0 and rawYStd==0, all the coefficients are zero regardless of
-        // the fitIntercept
+        // the fitIntercept.
         if (yMean == 0.0) {
           logWarning(s"Mean and standard deviation of the label are zero, so the coefficients " +
             s"and the intercept will all be zero; as a result, training is not needed.")
@@ -279,7 +286,10 @@ class LinearRegression @Since("1.3.0") (@Since("1.3.0") override val uid: String
           model,
           Array(0D),
           Array(0D))
-        return model.setSummary(Some(trainingSummary))
+
+        model.setSummary(Some(trainingSummary))
+        instr.logSuccess(model)
+        return model
       } else {
         require($(regParam) == 0.0, "The standard deviation of the label is zero. " +
           "Model cannot be regularized.")
@@ -401,7 +411,10 @@ class LinearRegression @Since("1.3.0") (@Since("1.3.0") override val uid: String
       model,
       Array(0D),
       objectiveHistory)
+
     model.setSummary(Some(trainingSummary))
+    instr.logSuccess(model)
+    model
   }
 
   @Since("1.4.0")

mllib/src/main/scala/org/apache/spark/ml/regression/RandomForestRegressor.scala

@@ -122,7 +122,9 @@ class RandomForestRegressor @Since("1.4.0") (@Since("1.4.0") override val uid: S
       super.getOldStrategy(categoricalFeatures, numClasses = 0, OldAlgo.Regression, getOldImpurity)
 
     val instr = Instrumentation.create(this, oldDataset)
-    instr.logParams(params: _*)
+    instr.logParams(labelCol, featuresCol, predictionCol, impurity, numTrees,
+      featureSubsetStrategy, maxDepth, maxBins, maxMemoryInMB, minInfoGain,
+      minInstancesPerNode, seed, subsamplingRate, cacheNodeIds, checkpointInterval)
 
     val trees = RandomForest
       .run(oldDataset, strategy, getNumTrees, getFeatureSubsetStrategy, getSeed, Some(instr))

mllib/src/main/scala/org/apache/spark/ml/util/Instrumentation.scala

@@ -96,7 +96,7 @@ private[spark] class Instrumentation[E <: Estimator[_]] private (
   }
 
   /**
-   * Logs the successful completion of the training session and the value of the learned model.
+   * Logs the successful completion of the training session.
    */
   def logSuccess(model: Model[_]): Unit = {
     log(s"training finished")