update

Author: zhengruifeng

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

@@ -892,7 +892,7 @@ class LDA @Since("1.6.0") (
     val instr = Instrumentation.create(this, dataset)
     instr.logParams(featuresCol, topicDistributionCol, k, maxIter, subsamplingRate,
       checkpointInterval, keepLastCheckpoint, optimizeDocConcentration, topicConcentration,
-      learningDecay, optimizer, learningOffset, seed)
+      docConcentration, learningDecay, optimizer, learningOffset, seed)
 
     val oldLDA = new OldLDA()
       .setK($(k))
@@ -912,6 +912,7 @@ class LDA @Since("1.6.0") (
         new DistributedLDAModel(uid, m.vocabSize, m, dataset.sparkSession, None)
     }
 
+    instr.logNumFeatures(newModel.vocabSize)
     val model = copyValues(newModel).setParent(this)
     instr.logSuccess(model)
     model

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

@@ -229,7 +229,7 @@ class AFTSurvivalRegression @Since("1.6.0") (@Since("1.6.0") override val uid: S
 
     val instr = Instrumentation.create(this, dataset)
     instr.logParams(labelCol, featuresCol, censorCol, predictionCol, quantilesCol,
-      fitIntercept, maxIter, tol, aggregationDepth)
+      quantileProbabilities, fitIntercept, maxIter, tol, aggregationDepth)
     instr.logNumFeatures(numFeatures)
 
     if (!$(fitIntercept) && (0 until numFeatures).exists { i =>

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

@@ -230,8 +230,10 @@ class LinearRegression @Since("1.3.0") (@Since("1.3.0") override val uid: String
         summaryModel,
         model.diagInvAtWA.toArray,
         model.objectiveHistory)
+
+      lrModel.setSummary(Some(trainingSummary))
       instr.logSuccess(lrModel)
-      return lrModel.setSummary(Some(trainingSummary))
+      return lrModel
     }
 
     val handlePersistence = dataset.rdd.getStorageLevel == StorageLevel.NONE
@@ -284,8 +286,10 @@ class LinearRegression @Since("1.3.0") (@Since("1.3.0") override val uid: String
           model,
           Array(0D),
           Array(0D))
+
+        model.setSummary(Some(trainingSummary))
         instr.logSuccess(model)
-        return model.setSummary(Some(trainingSummary))
+        return model
       } else {
         require($(regParam) == 0.0, "The standard deviation of the label is zero. " +
           "Model cannot be regularized.")
@@ -407,8 +411,10 @@ class LinearRegression @Since("1.3.0") (@Since("1.3.0") override val uid: String
       model,
       Array(0D),
       objectiveHistory)
-    instr.logSuccess(model)
+
     model.setSummary(Some(trainingSummary))
+    instr.logSuccess(model)
+    model
   }
 
   @Since("1.4.0")