[SPARK-14615][ML] Use the new ML Vector and Matrix in the ML pipeline based algorithms

## What changes were proposed in this pull request? Once SPARK-14487 and SPARK-14549 are merged, we will migrate to use the new vector and matrix type in the new ml pipeline based apis. ## How was this patch tested? Unit tests Author: DB Tsai <dbt@netflix.com> Author: Liang-Chi Hsieh <simonh@tw.ibm.com> Author: Xiangrui Meng <meng@databricks.com> Closes #12627 from dbtsai/SPARK-14615-NewML.
author: DB Tsai <dbt@netflix.com> 2016-05-17 12:51:07 -0700
committer: Xiangrui Meng <meng@databricks.com> 2016-05-17 12:51:07 -0700
commit: e2efe0529acd748f26dbaa41331d1733ed256237 (patch)
tree: fe1a5aeeadfbf220b5dbe1429e0235153db8117b /mllib
parent: 9f176dd3918129a72282a6b7a12e2899cbb6dac9 (diff)
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spark-e2efe0529acd748f26dbaa41331d1733ed256237.tar.bz2
spark-e2efe0529acd748f26dbaa41331d1733ed256237.zip
152 files changed, 1007 insertions, 345 deletions
diff --git a/mllib/src/main/scala/org/apache/spark/ml/Predictor.scala b/mllib/src/main/scala/org/apache/spark/ml/Predictor.scala
index 81140d1f7b..569a5fb993 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/Predictor.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/Predictor.scala
@@ -18,11 +18,11 @@
 package org.apache.spark.ml
 
 import org.apache.spark.annotation.{DeveloperApi, Since}
+import org.apache.spark.ml.feature.LabeledPoint
+import org.apache.spark.ml.linalg.{Vector, VectorUDT}
 import org.apache.spark.ml.param._
 import org.apache.spark.ml.param.shared._
 import org.apache.spark.ml.util.SchemaUtils
-import org.apache.spark.mllib.linalg.{Vector, VectorUDT}
-import org.apache.spark.mllib.regression.LabeledPoint
 import org.apache.spark.rdd.RDD
 import org.apache.spark.sql.{DataFrame, Dataset, Row}
 import org.apache.spark.sql.functions._
diff --git a/mllib/src/main/scala/org/apache/spark/ml/ann/Layer.scala b/mllib/src/main/scala/org/apache/spark/ml/ann/Layer.scala
index a27ee51874..0a569c4917 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/ann/Layer.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/ann/Layer.scala
@@ -21,7 +21,9 @@ import java.util.Random
 
 import breeze.linalg.{*, axpy => Baxpy, DenseMatrix => BDM, DenseVector => BDV, Vector => BV}
 
-import org.apache.spark.mllib.linalg.{Vector, Vectors}
+import org.apache.spark.ml.linalg.{Vector, Vectors}
+import org.apache.spark.mllib.linalg.{Vector => OldVector, Vectors => OldVectors}
+import org.apache.spark.mllib.linalg.VectorImplicits._
 import org.apache.spark.mllib.optimization._
 import org.apache.spark.rdd.RDD
 import org.apache.spark.util.random.XORShiftRandom
@@ -580,10 +582,10 @@ private[ann] object FeedForwardModel {
  */
 private[ann] class ANNGradient(topology: Topology, dataStacker: DataStacker) extends Gradient {
   override def compute(
-    data: Vector,
+    data: OldVector,
     label: Double,
-    weights: Vector,
-    cumGradient: Vector): Double = {
+    weights: OldVector,
+    cumGradient: OldVector): Double = {
     val (input, target, realBatchSize) = dataStacker.unstack(data)
     val model = topology.model(weights)
     model.computeGradient(input, target, cumGradient, realBatchSize)
@@ -657,15 +659,15 @@ private[ann] class DataStacker(stackSize: Int, inputSize: Int, outputSize: Int)
 private[ann] class ANNUpdater extends Updater {
 
   override def compute(
-    weightsOld: Vector,
-    gradient: Vector,
+    weightsOld: OldVector,
+    gradient: OldVector,
     stepSize: Double,
     iter: Int,
-    regParam: Double): (Vector, Double) = {
+    regParam: Double): (OldVector, Double) = {
     val thisIterStepSize = stepSize
     val brzWeights: BV[Double] = weightsOld.toBreeze.toDenseVector
     Baxpy(-thisIterStepSize, gradient.toBreeze, brzWeights)
-    (Vectors.fromBreeze(brzWeights), 0)
+    (OldVectors.fromBreeze(brzWeights), 0)
   }
 }
 
@@ -808,7 +810,9 @@ private[ml] class FeedForwardTrainer(
       getWeights
     }
     // TODO: deprecate standard optimizer because it needs Vector
-    val newWeights = optimizer.optimize(dataStacker.stack(data), w)
+    val newWeights = optimizer.optimize(dataStacker.stack(data).map { v =>
+      (v._1, OldVectors.fromML(v._2))
+    }, w)
     topology.model(newWeights)
   }
 
diff --git a/mllib/src/main/scala/org/apache/spark/ml/attribute/AttributeGroup.scala b/mllib/src/main/scala/org/apache/spark/ml/attribute/AttributeGroup.scala
index 2c29eeb01a..12b9732a4c 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/attribute/AttributeGroup.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/attribute/AttributeGroup.scala
@@ -20,7 +20,7 @@ package org.apache.spark.ml.attribute
 import scala.collection.mutable.ArrayBuffer
 
 import org.apache.spark.annotation.DeveloperApi
-import org.apache.spark.mllib.linalg.VectorUDT
+import org.apache.spark.ml.linalg.VectorUDT
 import org.apache.spark.sql.types.{Metadata, MetadataBuilder, StructField}
 
 /**
diff --git a/mllib/src/main/scala/org/apache/spark/ml/classification/Classifier.scala b/mllib/src/main/scala/org/apache/spark/ml/classification/Classifier.scala
index bc5fe35ad4..e35b04a1cf 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/classification/Classifier.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/classification/Classifier.scala
@@ -20,10 +20,10 @@ package org.apache.spark.ml.classification
 import org.apache.spark.SparkException
 import org.apache.spark.annotation.DeveloperApi
 import org.apache.spark.ml.{PredictionModel, Predictor, PredictorParams}
+import org.apache.spark.ml.feature.LabeledPoint
+import org.apache.spark.ml.linalg.{Vector, VectorUDT}
 import org.apache.spark.ml.param.shared.HasRawPredictionCol
 import org.apache.spark.ml.util.{MetadataUtils, SchemaUtils}
-import org.apache.spark.mllib.linalg.{Vector, VectorUDT}
-import org.apache.spark.mllib.regression.LabeledPoint
 import org.apache.spark.rdd.RDD
 import org.apache.spark.sql.{DataFrame, Dataset, Row}
 import org.apache.spark.sql.functions._
diff --git a/mllib/src/main/scala/org/apache/spark/ml/classification/DecisionTreeClassifier.scala b/mllib/src/main/scala/org/apache/spark/ml/classification/DecisionTreeClassifier.scala
index 31a69d49a0..881dcefb79 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/classification/DecisionTreeClassifier.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/classification/DecisionTreeClassifier.scala
@@ -22,13 +22,13 @@ import org.json4s.{DefaultFormats, JObject}
 import org.json4s.JsonDSL._
 
 import org.apache.spark.annotation.{Experimental, Since}
+import org.apache.spark.ml.feature.LabeledPoint
+import org.apache.spark.ml.linalg.{DenseVector, SparseVector, Vector, Vectors}
 import org.apache.spark.ml.param.ParamMap
 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.mllib.linalg.{DenseVector, SparseVector, Vector, Vectors}
-import org.apache.spark.mllib.regression.LabeledPoint
 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
diff --git a/mllib/src/main/scala/org/apache/spark/ml/classification/GBTClassifier.scala b/mllib/src/main/scala/org/apache/spark/ml/classification/GBTClassifier.scala
index acc04582b8..f843df449c 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/classification/GBTClassifier.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/classification/GBTClassifier.scala
@@ -24,14 +24,14 @@ import org.json4s.JsonDSL._
 import org.apache.spark.annotation.{Experimental, Since}
 import org.apache.spark.internal.Logging
 import org.apache.spark.ml.{PredictionModel, Predictor}
+import org.apache.spark.ml.feature.LabeledPoint
+import org.apache.spark.ml.linalg.Vector
 import org.apache.spark.ml.param.ParamMap
 import org.apache.spark.ml.regression.DecisionTreeRegressionModel
 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.mllib.linalg.Vector
-import org.apache.spark.mllib.regression.LabeledPoint
 import org.apache.spark.mllib.tree.configuration.{Algo => OldAlgo}
 import org.apache.spark.mllib.tree.model.{GradientBoostedTreesModel => OldGBTModel}
 import org.apache.spark.rdd.RDD
diff --git a/mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala b/mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala
index ffd03e55b5..ac2cd8726b 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala
@@ -27,12 +27,13 @@ import org.apache.spark.SparkException
 import org.apache.spark.annotation.{Experimental, Since}
 import org.apache.spark.internal.Logging
 import org.apache.spark.ml.feature.Instance
+import org.apache.spark.ml.linalg._
+import org.apache.spark.ml.linalg.BLAS._
 import org.apache.spark.ml.param._
 import org.apache.spark.ml.param.shared._
 import org.apache.spark.ml.util._
 import org.apache.spark.mllib.evaluation.BinaryClassificationMetrics
-import org.apache.spark.mllib.linalg._
-import org.apache.spark.mllib.linalg.BLAS._
+import org.apache.spark.mllib.linalg.VectorImplicits._
 import org.apache.spark.mllib.stat.MultivariateOnlineSummarizer
 import org.apache.spark.mllib.util.MLUtils
 import org.apache.spark.rdd.RDD
diff --git a/mllib/src/main/scala/org/apache/spark/ml/classification/MultilayerPerceptronClassifier.scala b/mllib/src/main/scala/org/apache/spark/ml/classification/MultilayerPerceptronClassifier.scala
index 72cf55f6bb..683ae4aaf4 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/classification/MultilayerPerceptronClassifier.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/classification/MultilayerPerceptronClassifier.scala
@@ -24,11 +24,11 @@ import org.apache.hadoop.fs.Path
 import org.apache.spark.annotation.{Experimental, Since}
 import org.apache.spark.ml.{PredictionModel, Predictor, PredictorParams}
 import org.apache.spark.ml.ann.{FeedForwardTopology, FeedForwardTrainer}
+import org.apache.spark.ml.feature.LabeledPoint
+import org.apache.spark.ml.linalg.{Vector, Vectors}
 import org.apache.spark.ml.param._
 import org.apache.spark.ml.param.shared.{HasMaxIter, HasSeed, HasStepSize, HasTol}
 import org.apache.spark.ml.util._
-import org.apache.spark.mllib.linalg.{Vector, Vectors}
-import org.apache.spark.mllib.regression.LabeledPoint
 import org.apache.spark.sql.Dataset
 
 /** Params for Multilayer Perceptron. */
diff --git a/mllib/src/main/scala/org/apache/spark/ml/classification/NaiveBayes.scala b/mllib/src/main/scala/org/apache/spark/ml/classification/NaiveBayes.scala
index 267d63b51e..a98bdeca6b 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/classification/NaiveBayes.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/classification/NaiveBayes.scala
@@ -22,14 +22,14 @@ import org.apache.hadoop.fs.Path
 import org.apache.spark.SparkException
 import org.apache.spark.annotation.{Experimental, Since}
 import org.apache.spark.ml.PredictorParams
+import org.apache.spark.ml.linalg._
 import org.apache.spark.ml.param.{DoubleParam, Param, ParamMap, ParamValidators}
 import org.apache.spark.ml.util._
 import org.apache.spark.mllib.classification.{NaiveBayes => OldNaiveBayes}
 import org.apache.spark.mllib.classification.{NaiveBayesModel => OldNaiveBayesModel}
-import org.apache.spark.mllib.linalg._
-import org.apache.spark.mllib.regression.LabeledPoint
+import org.apache.spark.mllib.regression.{LabeledPoint => OldLabeledPoint}
 import org.apache.spark.rdd.RDD
-import org.apache.spark.sql.{DataFrame, Dataset}
+import org.apache.spark.sql.Dataset
 
 /**
  * Params for Naive Bayes Classifiers.
@@ -102,7 +102,8 @@ class NaiveBayes @Since("1.5.0") (
   setDefault(modelType -> OldNaiveBayes.Multinomial)
 
   override protected def train(dataset: Dataset[_]): NaiveBayesModel = {
-    val oldDataset: RDD[LabeledPoint] = extractLabeledPoints(dataset)
+    val oldDataset: RDD[OldLabeledPoint] =
+      extractLabeledPoints(dataset).map(OldLabeledPoint.fromML)
     val oldModel = OldNaiveBayes.train(oldDataset, $(smoothing), $(modelType))
     NaiveBayesModel.fromOld(oldModel, this)
   }
diff --git a/mllib/src/main/scala/org/apache/spark/ml/classification/OneVsRest.scala b/mllib/src/main/scala/org/apache/spark/ml/classification/OneVsRest.scala
index f10c60a78d..047a378b79 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/classification/OneVsRest.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/classification/OneVsRest.scala
@@ -32,9 +32,9 @@ import org.apache.spark.SparkContext
 import org.apache.spark.annotation.{Experimental, Since}
 import org.apache.spark.ml._
 import org.apache.spark.ml.attribute._
+import org.apache.spark.ml.linalg.Vector
 import org.apache.spark.ml.param.{Param, ParamMap, ParamPair, Params}
 import org.apache.spark.ml.util._
-import org.apache.spark.mllib.linalg.Vector
 import org.apache.spark.sql.{DataFrame, Dataset, Row}
 import org.apache.spark.sql.functions._
 import org.apache.spark.sql.types._
diff --git a/mllib/src/main/scala/org/apache/spark/ml/classification/ProbabilisticClassifier.scala b/mllib/src/main/scala/org/apache/spark/ml/classification/ProbabilisticClassifier.scala
index d00fee12b0..59277d0f42 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/classification/ProbabilisticClassifier.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/classification/ProbabilisticClassifier.scala
@@ -18,9 +18,9 @@
 package org.apache.spark.ml.classification
 
 import org.apache.spark.annotation.DeveloperApi
+import org.apache.spark.ml.linalg.{DenseVector, Vector, Vectors, VectorUDT}
 import org.apache.spark.ml.param.shared._
 import org.apache.spark.ml.util.SchemaUtils
-import org.apache.spark.mllib.linalg.{DenseVector, Vector, Vectors, VectorUDT}
 import org.apache.spark.sql.{DataFrame, Dataset}
 import org.apache.spark.sql.functions._
 import org.apache.spark.sql.types.{DataType, StructType}
diff --git a/mllib/src/main/scala/org/apache/spark/ml/classification/RandomForestClassifier.scala b/mllib/src/main/scala/org/apache/spark/ml/classification/RandomForestClassifier.scala
index 1d33ae83c2..b3c074f839 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/classification/RandomForestClassifier.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/classification/RandomForestClassifier.scala
@@ -21,13 +21,13 @@ import org.json4s.{DefaultFormats, JObject}
 import org.json4s.JsonDSL._
 
 import org.apache.spark.annotation.{Experimental, Since}
+import org.apache.spark.ml.feature.LabeledPoint
+import org.apache.spark.ml.linalg.{DenseVector, SparseVector, Vector, Vectors}
 import org.apache.spark.ml.param.ParamMap
 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.mllib.linalg.{DenseVector, SparseVector, Vector, Vectors}
-import org.apache.spark.mllib.regression.LabeledPoint
 import org.apache.spark.mllib.tree.configuration.{Algo => OldAlgo}
 import org.apache.spark.mllib.tree.model.{RandomForestModel => OldRandomForestModel}
 import org.apache.spark.rdd.RDD
diff --git a/mllib/src/main/scala/org/apache/spark/ml/clustering/BisectingKMeans.scala b/mllib/src/main/scala/org/apache/spark/ml/clustering/BisectingKMeans.scala
index 6cc9117da3..138e059f94 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/clustering/BisectingKMeans.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/clustering/BisectingKMeans.scala
@@ -21,12 +21,14 @@ import org.apache.hadoop.fs.Path
 
 import org.apache.spark.annotation.{Experimental, Since}
 import org.apache.spark.ml.{Estimator, Model}
+import org.apache.spark.ml.linalg.{Vector, VectorUDT}
 import org.apache.spark.ml.param._
 import org.apache.spark.ml.param.shared._
 import org.apache.spark.ml.util._
-import org.apache.spark.mllib.clustering.
-  {BisectingKMeans => MLlibBisectingKMeans, BisectingKMeansModel => MLlibBisectingKMeansModel}
-import org.apache.spark.mllib.linalg.{Vector, VectorUDT}
+import org.apache.spark.mllib.clustering.{BisectingKMeans => MLlibBisectingKMeans, BisectingKMeansModel => MLlibBisectingKMeansModel}
+import org.apache.spark.mllib.linalg.{Vector => OldVector, Vectors => OldVectors}
+import org.apache.spark.mllib.linalg.VectorImplicits._
+import org.apache.spark.rdd.RDD
 import org.apache.spark.sql.{DataFrame, Dataset, Row}
 import org.apache.spark.sql.functions.{col, udf}
 import org.apache.spark.sql.types.{IntegerType, StructType}
@@ -105,7 +107,7 @@ class BisectingKMeansModel private[ml] (
   private[clustering] def predict(features: Vector): Int = parentModel.predict(features)
 
   @Since("2.0.0")
-  def clusterCenters: Array[Vector] = parentModel.clusterCenters
+  def clusterCenters: Array[Vector] = parentModel.clusterCenters.map(_.asML)
 
   /**
    * Computes the sum of squared distances between the input points and their corresponding cluster
@@ -115,7 +117,7 @@ class BisectingKMeansModel private[ml] (
   def computeCost(dataset: Dataset[_]): Double = {
     SchemaUtils.checkColumnType(dataset.schema, $(featuresCol), new VectorUDT)
     val data = dataset.select(col($(featuresCol))).rdd.map { case Row(point: Vector) => point }
-    parentModel.computeCost(data)
+    parentModel.computeCost(data.map(OldVectors.fromML))
   }
 
   @Since("2.0.0")
@@ -216,7 +218,9 @@ class BisectingKMeans @Since("2.0.0") (
 
   @Since("2.0.0")
   override def fit(dataset: Dataset[_]): BisectingKMeansModel = {
-    val rdd = dataset.select(col($(featuresCol))).rdd.map { case Row(point: Vector) => point }
+    val rdd: RDD[OldVector] = dataset.select(col($(featuresCol))).rdd.map {
+      case Row(point: Vector) => OldVectors.fromML(point)
+    }
 
     val bkm = new MLlibBisectingKMeans()
       .setK($(k))
diff --git a/mllib/src/main/scala/org/apache/spark/ml/clustering/GaussianMixture.scala b/mllib/src/main/scala/org/apache/spark/ml/clustering/GaussianMixture.scala
index ac86e4ce25..63ca812609 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/clustering/GaussianMixture.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/clustering/GaussianMixture.scala
@@ -32,6 +32,7 @@ import org.apache.spark.ml.util._
 import org.apache.spark.mllib.clustering.{GaussianMixture => MLlibGM}
 import org.apache.spark.mllib.linalg.{Matrices => OldMatrices, Matrix => OldMatrix,
   Vector => OldVector, Vectors => OldVectors, VectorUDT => OldVectorUDT}
+import org.apache.spark.rdd.RDD
 import org.apache.spark.sql.{DataFrame, Dataset, Row, SQLContext}
 import org.apache.spark.sql.functions.{col, udf}
 import org.apache.spark.sql.types.{IntegerType, StructType}
@@ -94,8 +95,8 @@ class GaussianMixtureModel private[ml] (
 
   @Since("2.0.0")
   override def transform(dataset: Dataset[_]): DataFrame = {
-    val predUDF = udf((vector: OldVector) => predict(vector.asML))
-    val probUDF = udf((vector: OldVector) => OldVectors.fromML(predictProbability(vector.asML)))
+    val predUDF = udf((vector: Vector) => predict(vector))
+    val probUDF = udf((vector: Vector) => predictProbability(vector))
     dataset.withColumn($(predictionCol), predUDF(col($(featuresCol))))
       .withColumn($(probabilityCol), probUDF(col($(featuresCol))))
   }
@@ -296,7 +297,9 @@ class GaussianMixture @Since("2.0.0") (
 
   @Since("2.0.0")
   override def fit(dataset: Dataset[_]): GaussianMixtureModel = {
-    val rdd = dataset.select(col($(featuresCol))).rdd.map { case Row(point: OldVector) => point }
+    val rdd: RDD[OldVector] = dataset.select(col($(featuresCol))).rdd.map {
+      case Row(point: Vector) => OldVectors.fromML(point)
+    }
 
     val algo = new MLlibGM()
       .setK($(k))
diff --git a/mllib/src/main/scala/org/apache/spark/ml/clustering/KMeans.scala b/mllib/src/main/scala/org/apache/spark/ml/clustering/KMeans.scala
index 42a25396ad..41c0aec0ec 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/clustering/KMeans.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/clustering/KMeans.scala
@@ -22,11 +22,14 @@ import org.apache.hadoop.fs.Path
 import org.apache.spark.SparkException
 import org.apache.spark.annotation.{Experimental, Since}
 import org.apache.spark.ml.{Estimator, Model}
+import org.apache.spark.ml.linalg.{Vector, VectorUDT}
 import org.apache.spark.ml.param.{IntParam, Param, ParamMap, Params}
 import org.apache.spark.ml.param.shared._
 import org.apache.spark.ml.util._
 import org.apache.spark.mllib.clustering.{KMeans => MLlibKMeans, KMeansModel => MLlibKMeansModel}
-import org.apache.spark.mllib.linalg.{Vector, VectorUDT}
+import org.apache.spark.mllib.linalg.{Vector => OldVector, Vectors => OldVectors}
+import org.apache.spark.mllib.linalg.VectorImplicits._
+import org.apache.spark.rdd.RDD
 import org.apache.spark.sql.{DataFrame, Dataset, Row}
 import org.apache.spark.sql.functions.{col, udf}
 import org.apache.spark.sql.types.{IntegerType, StructType}
@@ -127,7 +130,7 @@ class KMeansModel private[ml] (
   private[clustering] def predict(features: Vector): Int = parentModel.predict(features)
 
   @Since("1.5.0")
-  def clusterCenters: Array[Vector] = parentModel.clusterCenters
+  def clusterCenters: Array[Vector] = parentModel.clusterCenters.map(_.asML)
 
   /**
    * Return the K-means cost (sum of squared distances of points to their nearest center) for this
@@ -137,7 +140,9 @@ class KMeansModel private[ml] (
   @Since("2.0.0")
   def computeCost(dataset: Dataset[_]): Double = {
     SchemaUtils.checkColumnType(dataset.schema, $(featuresCol), new VectorUDT)
-    val data = dataset.select(col($(featuresCol))).rdd.map { case Row(point: Vector) => point }
+    val data: RDD[OldVector] = dataset.select(col($(featuresCol))).rdd.map {
+      case Row(point: Vector) => OldVectors.fromML(point)
+    }
     parentModel.computeCost(data)
   }
 
@@ -210,7 +215,8 @@ object KMeansModel extends MLReadable[KMeansModel] {
       val dataPath = new Path(path, "data").toString
       val data: Dataset[Data] = sqlContext.read.parquet(dataPath).as[Data]
       val clusterCenters = data.collect().sortBy(_.clusterIdx).map(_.clusterCenter)
-      val model = new KMeansModel(metadata.uid, new MLlibKMeansModel(clusterCenters))
+      val model = new KMeansModel(metadata.uid,
+        new MLlibKMeansModel(clusterCenters.map(OldVectors.fromML)))
 
       DefaultParamsReader.getAndSetParams(model, metadata)
       model
@@ -277,7 +283,9 @@ class KMeans @Since("1.5.0") (
 
   @Since("2.0.0")
   override def fit(dataset: Dataset[_]): KMeansModel = {
-    val rdd = dataset.select(col($(featuresCol))).rdd.map { case Row(point: Vector) => point }
+    val rdd: RDD[OldVector] = dataset.select(col($(featuresCol))).rdd.map {
+      case Row(point: Vector) => OldVectors.fromML(point)
+    }
 
     val instr = Instrumentation.create(this, rdd)
     instr.logParams(featuresCol, predictionCol, k, initMode, initSteps, maxIter, seed, tol)
diff --git a/mllib/src/main/scala/org/apache/spark/ml/clustering/LDA.scala b/mllib/src/main/scala/org/apache/spark/ml/clustering/LDA.scala
index 38ecc5a102..5a83b28700 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/clustering/LDA.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/clustering/LDA.scala
@@ -22,6 +22,7 @@ import org.apache.hadoop.fs.{FileSystem, Path}
 import org.apache.spark.annotation.{DeveloperApi, Experimental, Since}
 import org.apache.spark.internal.Logging
 import org.apache.spark.ml.{Estimator, Model}
+import org.apache.spark.ml.linalg.{Matrix, Vector, Vectors, VectorUDT}
 import org.apache.spark.ml.param._
 import org.apache.spark.ml.param.shared.{HasCheckpointInterval, HasFeaturesCol, HasMaxIter, HasSeed}
 import org.apache.spark.ml.util._
@@ -30,7 +31,10 @@ import org.apache.spark.mllib.clustering.{DistributedLDAModel => OldDistributedL
   LDAOptimizer => OldLDAOptimizer, LocalLDAModel => OldLocalLDAModel,
   OnlineLDAOptimizer => OldOnlineLDAOptimizer}
 import org.apache.spark.mllib.impl.PeriodicCheckpointer
-import org.apache.spark.mllib.linalg.{Matrix, Vector, Vectors, VectorUDT}
+import org.apache.spark.mllib.linalg.{Matrices => OldMatrices, Vector => OldVector,
+  Vectors => OldVectors}
+import org.apache.spark.mllib.linalg.MatrixImplicits._
+import org.apache.spark.mllib.linalg.VectorImplicits._
 import org.apache.spark.rdd.RDD
 import org.apache.spark.sql.{DataFrame, Dataset, Row, SparkSession}
 import org.apache.spark.sql.functions.{col, monotonicallyIncreasingId, udf}
@@ -405,7 +409,11 @@ sealed abstract class LDAModel private[ml] (
   @Since("2.0.0")
   override def transform(dataset: Dataset[_]): DataFrame = {
     if ($(topicDistributionCol).nonEmpty) {
-      val t = udf(oldLocalModel.getTopicDistributionMethod(sparkSession.sparkContext))
+
+      // TODO: Make the transformer natively in ml framework to avoid extra conversion.
+      val transformer = oldLocalModel.getTopicDistributionMethod(sparkSession.sparkContext)
+
+      val t = udf { (v: Vector) => transformer(OldVectors.fromML(v)).asML }
       dataset.withColumn($(topicDistributionCol), t(col($(featuresCol)))).toDF
     } else {
       logWarning("LDAModel.transform was called without any output columns. Set an output column" +
@@ -437,7 +445,7 @@ sealed abstract class LDAModel private[ml] (
    *          collecting a large amount of data to the driver (on the order of vocabSize x k).
    */
   @Since("1.6.0")
-  def topicsMatrix: Matrix = oldLocalModel.topicsMatrix
+  def topicsMatrix: Matrix = oldLocalModel.topicsMatrix.asML
 
   /** Indicates whether this instance is of type [[DistributedLDAModel]] */
   @Since("1.6.0")
@@ -872,13 +880,13 @@ class LDA @Since("1.6.0") (
 private[clustering] object LDA extends DefaultParamsReadable[LDA] {
 
   /** Get dataset for spark.mllib LDA */
-  def getOldDataset(dataset: Dataset[_], featuresCol: String): RDD[(Long, Vector)] = {
+  def getOldDataset(dataset: Dataset[_], featuresCol: String): RDD[(Long, OldVector)] = {
     dataset
       .withColumn("docId", monotonicallyIncreasingId())
       .select("docId", featuresCol)
       .rdd
       .map { case Row(docId: Long, features: Vector) =>
-        (docId, features)
+        (docId, OldVectors.fromML(features))
       }
   }
 
diff --git a/mllib/src/main/scala/org/apache/spark/ml/evaluation/BinaryClassificationEvaluator.scala b/mllib/src/main/scala/org/apache/spark/ml/evaluation/BinaryClassificationEvaluator.scala
index 0cbc391d96..bff72b20e1 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/evaluation/BinaryClassificationEvaluator.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/evaluation/BinaryClassificationEvaluator.scala
@@ -18,11 +18,11 @@
 package org.apache.spark.ml.evaluation
 
 import org.apache.spark.annotation.{Experimental, Since}
+import org.apache.spark.ml.linalg.{Vector, VectorUDT}
 import org.apache.spark.ml.param._
 import org.apache.spark.ml.param.shared._
 import org.apache.spark.ml.util.{DefaultParamsReadable, DefaultParamsWritable, Identifiable, SchemaUtils}
 import org.apache.spark.mllib.evaluation.BinaryClassificationMetrics
-import org.apache.spark.mllib.linalg.{Vector, VectorUDT}
 import org.apache.spark.sql.{Dataset, Row}
 import org.apache.spark.sql.functions._
 import org.apache.spark.sql.types.DoubleType
diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/Binarizer.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/Binarizer.scala
index 35bc46a5f3..318c8b8b2f 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/feature/Binarizer.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/feature/Binarizer.scala
@@ -22,10 +22,10 @@ import scala.collection.mutable.ArrayBuilder
 import org.apache.spark.annotation.{Experimental, Since}
 import org.apache.spark.ml.Transformer
 import org.apache.spark.ml.attribute.BinaryAttribute
+import org.apache.spark.ml.linalg._
 import org.apache.spark.ml.param._
 import org.apache.spark.ml.param.shared.{HasInputCol, HasOutputCol}
 import org.apache.spark.ml.util._
-import org.apache.spark.mllib.linalg._
 import org.apache.spark.sql._
 import org.apache.spark.sql.functions._
 import org.apache.spark.sql.types._
diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/ChiSqSelector.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/ChiSqSelector.scala
index 29f55a7f71..e73a8f5d66 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/feature/ChiSqSelector.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/feature/ChiSqSelector.scala
@@ -22,12 +22,14 @@ import org.apache.hadoop.fs.Path
 import org.apache.spark.annotation.{Experimental, Since}
 import org.apache.spark.ml._
 import org.apache.spark.ml.attribute.{AttributeGroup, _}
+import org.apache.spark.ml.linalg.{Vector, VectorUDT}
 import org.apache.spark.ml.param._
 import org.apache.spark.ml.param.shared._
 import org.apache.spark.ml.util._
 import org.apache.spark.mllib.feature
-import org.apache.spark.mllib.linalg.{Vector, VectorUDT}
-import org.apache.spark.mllib.regression.LabeledPoint
+import org.apache.spark.mllib.linalg.{Vectors => OldVectors}
+import org.apache.spark.mllib.regression.{LabeledPoint => OldLabeledPoint}
+import org.apache.spark.rdd.RDD
 import org.apache.spark.sql._
 import org.apache.spark.sql.functions._
 import org.apache.spark.sql.types.{DoubleType, StructField, StructType}
@@ -80,10 +82,11 @@ final class ChiSqSelector(override val uid: String)
   @Since("2.0.0")
   override def fit(dataset: Dataset[_]): ChiSqSelectorModel = {
     transformSchema(dataset.schema, logging = true)
-    val input = dataset.select(col($(labelCol)).cast(DoubleType), col($(featuresCol))).rdd.map {
-      case Row(label: Double, features: Vector) =>
-        LabeledPoint(label, features)
-    }
+    val input: RDD[OldLabeledPoint] =
+      dataset.select(col($(labelCol)).cast(DoubleType), col($(featuresCol))).rdd.map {
+        case Row(label: Double, features: Vector) =>
+          OldLabeledPoint(label, OldVectors.fromML(features))
+      }
     val chiSqSelector = new feature.ChiSqSelector($(numTopFeatures)).fit(input)
     copyValues(new ChiSqSelectorModel(uid, chiSqSelector).setParent(this))
   }
@@ -132,7 +135,11 @@ final class ChiSqSelectorModel private[ml] (
   override def transform(dataset: Dataset[_]): DataFrame = {
     val transformedSchema = transformSchema(dataset.schema, logging = true)
     val newField = transformedSchema.last
-    val selector = udf { chiSqSelector.transform _ }
+
+    // TODO: Make the transformer natively in ml framework to avoid extra conversion.
+    val transformer: Vector => Vector = v => chiSqSelector.transform(OldVectors.fromML(v)).asML
+
+    val selector = udf(transformer)
     dataset.withColumn($(outputCol), selector(col($(featuresCol))), newField.metadata)
   }
 
diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/CountVectorizer.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/CountVectorizer.scala
index 3fbfce9d48..fc4885bf4b 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/feature/CountVectorizer.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/feature/CountVectorizer.scala
@@ -21,10 +21,10 @@ import org.apache.hadoop.fs.Path
 import org.apache.spark.annotation.{Experimental, Since}
 import org.apache.spark.broadcast.Broadcast
 import org.apache.spark.ml.{Estimator, Model}
+import org.apache.spark.ml.linalg.{Vectors, VectorUDT}
 import org.apache.spark.ml.param._
 import org.apache.spark.ml.param.shared.{HasInputCol, HasOutputCol}
 import org.apache.spark.ml.util._
-import org.apache.spark.mllib.linalg.{Vectors, VectorUDT}
 import org.apache.spark.rdd.RDD
 import org.apache.spark.sql.{DataFrame, Dataset}
 import org.apache.spark.sql.functions._
diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/DCT.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/DCT.scala
index a6f878151d..301358ef12 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/feature/DCT.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/feature/DCT.scala
@@ -21,9 +21,9 @@ import edu.emory.mathcs.jtransforms.dct._
 
 import org.apache.spark.annotation.{Experimental, Since}
 import org.apache.spark.ml.UnaryTransformer
+import org.apache.spark.ml.linalg.{Vector, Vectors, VectorUDT}
 import org.apache.spark.ml.param.BooleanParam
 import org.apache.spark.ml.util._
-import org.apache.spark.mllib.linalg.{Vector, Vectors, VectorUDT}
 import org.apache.spark.sql.types.DataType
 
 /**
diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/ElementwiseProduct.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/ElementwiseProduct.scala
index 1b0a9a12e8..91989c3d2f 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/feature/ElementwiseProduct.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/feature/ElementwiseProduct.scala
@@ -19,10 +19,12 @@ package org.apache.spark.ml.feature
 
 import org.apache.spark.annotation.{Experimental, Since}
 import org.apache.spark.ml.UnaryTransformer
+import org.apache.spark.ml.linalg.{Vector, VectorUDT}
 import org.apache.spark.ml.param.Param
 import org.apache.spark.ml.util.{DefaultParamsReadable, DefaultParamsWritable, Identifiable}
 import org.apache.spark.mllib.feature
-import org.apache.spark.mllib.linalg.{Vector, VectorUDT}
+import org.apache.spark.mllib.linalg.{Vectors => OldVectors}
+import org.apache.spark.mllib.linalg.VectorImplicits._
 import org.apache.spark.sql.types.DataType
 
 /**
@@ -52,7 +54,7 @@ class ElementwiseProduct(override val uid: String)
   override protected def createTransformFunc: Vector => Vector = {
     require(params.contains(scalingVec), s"transformation requires a weight vector")
     val elemScaler = new feature.ElementwiseProduct($(scalingVec))
-    elemScaler.transform
+    v => elemScaler.transform(v)
   }
 
   override protected def outputDataType: DataType = new VectorUDT()
diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/HashingTF.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/HashingTF.scala
index 66ae91cfc0..94e1825ba6 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/feature/HashingTF.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/feature/HashingTF.scala
@@ -86,7 +86,8 @@ class HashingTF(override val uid: String)
   override def transform(dataset: Dataset[_]): DataFrame = {
     val outputSchema = transformSchema(dataset.schema)
     val hashingTF = new feature.HashingTF($(numFeatures)).setBinary($(binary))
-    val t = udf { terms: Seq[_] => hashingTF.transform(terms) }
+    // TODO: Make the hashingTF.transform natively in ml framework to avoid extra conversion.
+    val t = udf { terms: Seq[_] => hashingTF.transform(terms).asML }
     val metadata = outputSchema($(outputCol)).metadata
     dataset.select(col("*"), t(col($(inputCol))).as($(outputCol), metadata))
   }
diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/IDF.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/IDF.scala
index 5075b78c98..f85f4c65af 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/feature/IDF.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/feature/IDF.scala
@@ -21,11 +21,13 @@ import org.apache.hadoop.fs.Path
 
 import org.apache.spark.annotation.{Experimental, Since}
 import org.apache.spark.ml._
+import org.apache.spark.ml.linalg.{Vector, VectorUDT}
 import org.apache.spark.ml.param._
 import org.apache.spark.ml.param.shared._
 import org.apache.spark.ml.util._
 import org.apache.spark.mllib.feature
-import org.apache.spark.mllib.linalg.{Vector, VectorUDT}
+import org.apache.spark.mllib.linalg.{Vector => OldVector, Vectors => OldVectors}
+import org.apache.spark.rdd.RDD
 import org.apache.spark.sql._
 import org.apache.spark.sql.functions._
 import org.apache.spark.sql.types.StructType
@@ -79,7 +81,9 @@ final class IDF(override val uid: String) extends Estimator[IDFModel] with IDFBa
   @Since("2.0.0")
   override def fit(dataset: Dataset[_]): IDFModel = {
     transformSchema(dataset.schema, logging = true)
-    val input = dataset.select($(inputCol)).rdd.map { case Row(v: Vector) => v }
+    val input: RDD[OldVector] = dataset.select($(inputCol)).rdd.map {
+      case Row(v: Vector) => OldVectors.fromML(v)
+    }
     val idf = new feature.IDF($(minDocFreq)).fit(input)
     copyValues(new IDFModel(uid, idf).setParent(this))
   }
@@ -119,7 +123,8 @@ class IDFModel private[ml] (
   @Since("2.0.0")
   override def transform(dataset: Dataset[_]): DataFrame = {
     transformSchema(dataset.schema, logging = true)
-    val idf = udf { vec: Vector => idfModel.transform(vec) }
+    // TODO: Make the idfModel.transform natively in ml framework to avoid extra conversion.
+    val idf = udf { vec: Vector => idfModel.transform(OldVectors.fromML(vec)).asML }
     dataset.withColumn($(outputCol), idf(col($(inputCol))))
   }
 
@@ -134,7 +139,7 @@ class IDFModel private[ml] (
 
   /** Returns the IDF vector. */
   @Since("1.6.0")
-  def idf: Vector = idfModel.idf
+  def idf: Vector = idfModel.idf.asML
 
   @Since("1.6.0")
   override def write: MLWriter = new IDFModelWriter(this)
@@ -166,7 +171,7 @@ object IDFModel extends MLReadable[IDFModel] {
         .select("idf")
         .head()
       val idf = data.getAs[Vector](0)
-      val model = new IDFModel(metadata.uid, new feature.IDFModel(idf))
+      val model = new IDFModel(metadata.uid, new feature.IDFModel(OldVectors.fromML(idf)))
       DefaultParamsReader.getAndSetParams(model, metadata)
       model
     }
diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/Instance.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/Instance.scala
index 12176757ae..cce3ca45cc 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/feature/Instance.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/feature/Instance.scala
@@ -17,7 +17,7 @@
 
 package org.apache.spark.ml.feature
 
-import org.apache.spark.mllib.linalg.Vector
+import org.apache.spark.ml.linalg.Vector
 
 /**
  * Class that represents an instance of weighted data point with label and features.
diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/Interaction.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/Interaction.scala
index 9ca34e9ae2..fa65ff9879 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/feature/Interaction.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/feature/Interaction.scala
@@ -26,7 +26,7 @@ import org.apache.spark.ml.param._
 import org.apache.spark.ml.param.shared._
 import org.apache.spark.ml.util._
 import org.apache.spark.ml.Transformer
-import org.apache.spark.mllib.linalg.{Vector, Vectors, VectorUDT}
+import org.apache.spark.ml.linalg.{Vector, Vectors, VectorUDT}
 import org.apache.spark.sql.{DataFrame, Dataset, Row}
 import org.apache.spark.sql.functions._
 import org.apache.spark.sql.types._
diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/LabeledPoint.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/LabeledPoint.scala
new file mode 100644
index 0000000000..f7f1d42039
--- /dev/null
+++ b/mllib/src/main/scala/org/apache/spark/ml/feature/LabeledPoint.scala
@@ -0,0 +1,38 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.ml.feature
+
+import scala.beans.BeanInfo
+
+import org.apache.spark.annotation.{Experimental, Since}
+import org.apache.spark.ml.linalg.Vector
+
+/**
+ * Class that represents the features and labels of a data point.
+ *
+ * @param label Label for this data point.
+ * @param features List of features for this data point.
+ */
+@Since("2.0.0")
+@Experimental
+@BeanInfo
+case class LabeledPoint(@Since("2.0.0") label: Double, @Since("2.0.0") features: Vector) {
+  override def toString: String = {
+    s"($label,$features)"
+  }
+}
diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/MaxAbsScaler.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/MaxAbsScaler.scala
index e9df600c8a..0dffba93ac 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/feature/MaxAbsScaler.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/feature/MaxAbsScaler.scala
@@ -21,11 +21,13 @@ import org.apache.hadoop.fs.Path
 
 import org.apache.spark.annotation.{Experimental, Since}
 import org.apache.spark.ml.{Estimator, Model}
+import org.apache.spark.ml.linalg.{Vector, Vectors, VectorUDT}
 import org.apache.spark.ml.param.{ParamMap, Params}
 import org.apache.spark.ml.param.shared.{HasInputCol, HasOutputCol}
 import org.apache.spark.ml.util._
-import org.apache.spark.mllib.linalg.{Vector, Vectors, VectorUDT}
+import org.apache.spark.mllib.linalg.{Vector => OldVector, Vectors => OldVectors}
 import org.apache.spark.mllib.stat.Statistics
+import org.apache.spark.rdd.RDD
 import org.apache.spark.sql._
 import org.apache.spark.sql.functions._
 import org.apache.spark.sql.types.{StructField, StructType}
@@ -69,7 +71,9 @@ class MaxAbsScaler @Since("2.0.0") (override val uid: String)
   @Since("2.0.0")
   override def fit(dataset: Dataset[_]): MaxAbsScalerModel = {
     transformSchema(dataset.schema, logging = true)
-    val input = dataset.select($(inputCol)).rdd.map { case Row(v: Vector) => v }
+    val input: RDD[OldVector] = dataset.select($(inputCol)).rdd.map {
+      case Row(v: Vector) => OldVectors.fromML(v)
+    }
     val summary = Statistics.colStats(input)
     val minVals = summary.min.toArray
     val maxVals = summary.max.toArray
diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/MinMaxScaler.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/MinMaxScaler.scala
index 125becbb8a..c6ff639f29 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/feature/MinMaxScaler.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/feature/MinMaxScaler.scala
@@ -21,11 +21,14 @@ import org.apache.hadoop.fs.Path
 
 import org.apache.spark.annotation.{Experimental, Since}
 import org.apache.spark.ml.{Estimator, Model}
+import org.apache.spark.ml.linalg.{Vector, Vectors, VectorUDT}
 import org.apache.spark.ml.param.{DoubleParam, ParamMap, Params}
 import org.apache.spark.ml.param.shared.{HasInputCol, HasOutputCol}
 import org.apache.spark.ml.util._
-import org.apache.spark.mllib.linalg.{Vector, Vectors, VectorUDT}
+import org.apache.spark.mllib.linalg.{Vector => OldVector, Vectors => OldVectors}
+import org.apache.spark.mllib.linalg.VectorImplicits._
 import org.apache.spark.mllib.stat.Statistics
+import org.apache.spark.rdd.RDD
 import org.apache.spark.sql._
 import org.apache.spark.sql.functions._
 import org.apache.spark.sql.types.{StructField, StructType}
@@ -106,7 +109,9 @@ class MinMaxScaler(override val uid: String)
   @Since("2.0.0")
   override def fit(dataset: Dataset[_]): MinMaxScalerModel = {
     transformSchema(dataset.schema, logging = true)
-    val input = dataset.select($(inputCol)).rdd.map { case Row(v: Vector) => v }
+    val input: RDD[OldVector] = dataset.select($(inputCol)).rdd.map {
+      case Row(v: Vector) => OldVectors.fromML(v)
+    }
     val summary = Statistics.colStats(input)
     copyValues(new MinMaxScalerModel(uid, summary.min, summary.max).setParent(this))
   }
diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/Normalizer.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/Normalizer.scala
index a603b3f833..942ac7ebdb 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/feature/Normalizer.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/feature/Normalizer.scala
@@ -19,10 +19,11 @@ package org.apache.spark.ml.feature
 
 import org.apache.spark.annotation.{Experimental, Since}
 import org.apache.spark.ml.UnaryTransformer
+import org.apache.spark.ml.linalg.{Vector, VectorUDT}
 import org.apache.spark.ml.param.{DoubleParam, ParamValidators}
 import org.apache.spark.ml.util._
 import org.apache.spark.mllib.feature
-import org.apache.spark.mllib.linalg.{Vector, VectorUDT}
+import org.apache.spark.mllib.linalg.{Vectors => OldVectors}
 import org.apache.spark.sql.types.DataType
 
 /**
@@ -52,7 +53,7 @@ class Normalizer(override val uid: String)
 
   override protected def createTransformFunc: Vector => Vector = {
     val normalizer = new feature.Normalizer($(p))
-    normalizer.transform
+    vector => normalizer.transform(OldVectors.fromML(vector)).asML
   }
 
   override protected def outputDataType: DataType = new VectorUDT()
diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/OneHotEncoder.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/OneHotEncoder.scala
index 99357793db..3d1e6dd818 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/feature/OneHotEncoder.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/feature/OneHotEncoder.scala
@@ -20,10 +20,10 @@ package org.apache.spark.ml.feature
 import org.apache.spark.annotation.{Experimental, Since}
 import org.apache.spark.ml.Transformer
 import org.apache.spark.ml.attribute._
+import org.apache.spark.ml.linalg.Vectors
 import org.apache.spark.ml.param._
 import org.apache.spark.ml.param.shared.{HasInputCol, HasOutputCol}
 import org.apache.spark.ml.util._
-import org.apache.spark.mllib.linalg.Vectors
 import org.apache.spark.sql.{DataFrame, Dataset}
 import org.apache.spark.sql.functions.{col, udf}
 import org.apache.spark.sql.types.{DoubleType, NumericType, StructType}
diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/PCA.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/PCA.scala
index 9cf722e121..141d3b924b 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/feature/PCA.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/feature/PCA.scala
@@ -21,11 +21,16 @@ import org.apache.hadoop.fs.Path
 
 import org.apache.spark.annotation.{Experimental, Since}
 import org.apache.spark.ml._
+import org.apache.spark.ml.linalg._
 import org.apache.spark.ml.param._
 import org.apache.spark.ml.param.shared._
 import org.apache.spark.ml.util._
 import org.apache.spark.mllib.feature
-import org.apache.spark.mllib.linalg._
+import org.apache.spark.mllib.linalg.{DenseMatrix => OldDenseMatrix, DenseVector => OldDenseVector,
+  Matrices => OldMatrices, Vector => OldVector, Vectors => OldVectors}
+import org.apache.spark.mllib.linalg.MatrixImplicits._
+import org.apache.spark.mllib.linalg.VectorImplicits._
+import org.apache.spark.rdd.RDD
 import org.apache.spark.sql._
 import org.apache.spark.sql.functions._
 import org.apache.spark.sql.types.{StructField, StructType}
@@ -71,7 +76,9 @@ class PCA (override val uid: String) extends Estimator[PCAModel] with PCAParams
   @Since("2.0.0")
   override def fit(dataset: Dataset[_]): PCAModel = {
     transformSchema(dataset.schema, logging = true)
-    val input = dataset.select($(inputCol)).rdd.map { case Row(v: Vector) => v}
+    val input: RDD[OldVector] = dataset.select($(inputCol)).rdd.map {
+      case Row(v: Vector) => OldVectors.fromML(v)
+    }
     val pca = new feature.PCA(k = $(k))
     val pcaModel = pca.fit(input)
     copyValues(new PCAModel(uid, pcaModel.pc, pcaModel.explainedVariance).setParent(this))
@@ -128,8 +135,14 @@ class PCAModel private[ml] (
   @Since("2.0.0")
   override def transform(dataset: Dataset[_]): DataFrame = {
     transformSchema(dataset.schema, logging = true)
-    val pcaModel = new feature.PCAModel($(k), pc, explainedVariance)
-    val pcaOp = udf { pcaModel.transform _ }
+    val pcaModel = new feature.PCAModel($(k),
+      OldMatrices.fromML(pc).asInstanceOf[OldDenseMatrix],
+      OldVectors.fromML(explainedVariance).asInstanceOf[OldDenseVector])
+
+    // TODO: Make the transformer natively in ml framework to avoid extra conversion.
+    val transformer: Vector => Vector = v => pcaModel.transform(OldVectors.fromML(v)).asML
+
+    val pcaOp = udf(transformer)
     dataset.withColumn($(outputCol), pcaOp(col($(inputCol))))
   }
 
diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/PolynomialExpansion.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/PolynomialExpansion.scala
index 0a9b9719c1..a01867701b 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/feature/PolynomialExpansion.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/feature/PolynomialExpansion.scala
@@ -21,9 +21,9 @@ import scala.collection.mutable
 
 import org.apache.spark.annotation.{Experimental, Since}
 import org.apache.spark.ml.UnaryTransformer
+import org.apache.spark.ml.linalg._
 import org.apache.spark.ml.param.{IntParam, ParamMap, ParamValidators}
 import org.apache.spark.ml.util._
-import org.apache.spark.mllib.linalg._
 import org.apache.spark.sql.types.DataType
 
 /**
diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/RFormula.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/RFormula.scala
index a2f3d44132..c0feaa01fc 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/feature/RFormula.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/feature/RFormula.scala
@@ -25,10 +25,10 @@ import org.apache.hadoop.fs.Path
 import org.apache.spark.annotation.{Experimental, Since}
 import org.apache.spark.ml.{Estimator, Model, Pipeline, PipelineModel, PipelineStage, Transformer}
 import org.apache.spark.ml.attribute.AttributeGroup
+import org.apache.spark.ml.linalg.VectorUDT
 import org.apache.spark.ml.param.{Param, ParamMap}
 import org.apache.spark.ml.param.shared.{HasFeaturesCol, HasLabelCol}
 import org.apache.spark.ml.util._
-import org.apache.spark.mllib.linalg.VectorUDT
 import org.apache.spark.sql.{DataFrame, Dataset}
 import org.apache.spark.sql.types._
 
diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/RFormulaParser.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/RFormulaParser.scala
index cf52710ab8..19aecff038 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/feature/RFormulaParser.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/feature/RFormulaParser.scala
@@ -20,7 +20,7 @@ package org.apache.spark.ml.feature
 import scala.collection.mutable
 import scala.util.parsing.combinator.RegexParsers
 
-import org.apache.spark.mllib.linalg.VectorUDT
+import org.apache.spark.ml.linalg.VectorUDT
 import org.apache.spark.sql.types._
 
 /**
diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/StandardScaler.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/StandardScaler.scala
index 626e97efb4..9d084b520c 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/feature/StandardScaler.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/feature/StandardScaler.scala
@@ -21,11 +21,14 @@ import org.apache.hadoop.fs.Path
 
 import org.apache.spark.annotation.{Experimental, Since}
 import org.apache.spark.ml._
+import org.apache.spark.ml.linalg.{Vector, VectorUDT}
 import org.apache.spark.ml.param._
 import org.apache.spark.ml.param.shared._
 import org.apache.spark.ml.util._
 import org.apache.spark.mllib.feature
-import org.apache.spark.mllib.linalg.{Vector, VectorUDT}
+import org.apache.spark.mllib.linalg.{Vector => OldVector, Vectors => OldVectors}
+import org.apache.spark.mllib.linalg.VectorImplicits._
+import org.apache.spark.rdd.RDD
 import org.apache.spark.sql._
 import org.apache.spark.sql.functions._
 import org.apache.spark.sql.types.{StructField, StructType}
@@ -93,7 +96,9 @@ class StandardScaler(override val uid: String) extends Estimator[StandardScalerM
   @Since("2.0.0")
   override def fit(dataset: Dataset[_]): StandardScalerModel = {
     transformSchema(dataset.schema, logging = true)
-    val input = dataset.select($(inputCol)).rdd.map { case Row(v: Vector) => v }
+    val input: RDD[OldVector] = dataset.select($(inputCol)).rdd.map {
+      case Row(v: Vector) => OldVectors.fromML(v)
+    }
     val scaler = new feature.StandardScaler(withMean = $(withMean), withStd = $(withStd))
     val scalerModel = scaler.fit(input)
     copyValues(new StandardScalerModel(uid, scalerModel.std, scalerModel.mean).setParent(this))
@@ -145,7 +150,11 @@ class StandardScalerModel private[ml] (
   override def transform(dataset: Dataset[_]): DataFrame = {
     transformSchema(dataset.schema, logging = true)
     val scaler = new feature.StandardScalerModel(std, mean, $(withStd), $(withMean))
-    val scale = udf { scaler.transform _ }
+
+    // TODO: Make the transformer natively in ml framework to avoid extra conversion.
+    val transformer: Vector => Vector = v => scaler.transform(OldVectors.fromML(v)).asML
+
+    val scale = udf(transformer)
     dataset.withColumn($(outputCol), scale(col($(inputCol))))
   }
 
diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/VectorAssembler.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/VectorAssembler.scala
index 4d3e46e488..1bc24202b7 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/feature/VectorAssembler.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/feature/VectorAssembler.scala
@@ -23,10 +23,10 @@ import org.apache.spark.SparkException
 import org.apache.spark.annotation.{Experimental, Since}
 import org.apache.spark.ml.Transformer
 import org.apache.spark.ml.attribute.{Attribute, AttributeGroup, NumericAttribute, UnresolvedAttribute}
+import org.apache.spark.ml.linalg.{Vector, Vectors, VectorUDT}
 import org.apache.spark.ml.param.ParamMap
 import org.apache.spark.ml.param.shared._
 import org.apache.spark.ml.util._
-import org.apache.spark.mllib.linalg.{Vector, Vectors, VectorUDT}
 import org.apache.spark.sql.{DataFrame, Dataset, Row}
 import org.apache.spark.sql.functions._
 import org.apache.spark.sql.types._
diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/VectorIndexer.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/VectorIndexer.scala
index 68b699d569..2bc9d225ac 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/feature/VectorIndexer.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/feature/VectorIndexer.scala
@@ -27,10 +27,10 @@ import org.apache.hadoop.fs.Path
 import org.apache.spark.annotation.{Experimental, Since}
 import org.apache.spark.ml.{Estimator, Model}
 import org.apache.spark.ml.attribute._
+import org.apache.spark.ml.linalg.{DenseVector, SparseVector, Vector, VectorUDT}
 import org.apache.spark.ml.param._
 import org.apache.spark.ml.param.shared._
 import org.apache.spark.ml.util._
-import org.apache.spark.mllib.linalg.{DenseVector, SparseVector, Vector, VectorUDT}
 import org.apache.spark.sql.{DataFrame, Dataset, Row}
 import org.apache.spark.sql.functions.udf
 import org.apache.spark.sql.types.{StructField, StructType}
diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/VectorSlicer.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/VectorSlicer.scala
index 7a9468b87b..103738cd91 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/feature/VectorSlicer.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/feature/VectorSlicer.scala
@@ -20,10 +20,10 @@ package org.apache.spark.ml.feature
 import org.apache.spark.annotation.{Experimental, Since}
 import org.apache.spark.ml.Transformer
 import org.apache.spark.ml.attribute.{Attribute, AttributeGroup}
+import org.apache.spark.ml.linalg._
 import org.apache.spark.ml.param.{IntArrayParam, ParamMap, StringArrayParam}
 import org.apache.spark.ml.param.shared.{HasInputCol, HasOutputCol}
 import org.apache.spark.ml.util._
-import org.apache.spark.mllib.linalg._
 import org.apache.spark.sql.{DataFrame, Dataset}
 import org.apache.spark.sql.functions._
 import org.apache.spark.sql.types.StructType
diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/Word2Vec.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/Word2Vec.scala
index c49e263df0..1469bfd5e8 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/feature/Word2Vec.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/feature/Word2Vec.scala
@@ -22,11 +22,12 @@ import org.apache.hadoop.fs.Path
 import org.apache.spark.SparkContext
 import org.apache.spark.annotation.{Experimental, Since}
 import org.apache.spark.ml.{Estimator, Model}
+import org.apache.spark.ml.linalg.{BLAS, Vector, Vectors, VectorUDT}
 import org.apache.spark.ml.param._
 import org.apache.spark.ml.param.shared._
 import org.apache.spark.ml.util._
 import org.apache.spark.mllib.feature
-import org.apache.spark.mllib.linalg.{BLAS, Vector, Vectors, VectorUDT}
+import org.apache.spark.mllib.linalg.VectorImplicits._
 import org.apache.spark.sql.{DataFrame, Dataset, SQLContext}
 import org.apache.spark.sql.functions._
 import org.apache.spark.sql.types._
diff --git a/mllib/src/main/scala/org/apache/spark/ml/linalg/VectorUDT.scala b/mllib/src/main/scala/org/apache/spark/ml/linalg/VectorUDT.scala
index c29f7f86e9..0b9b2ff5c5 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/linalg/VectorUDT.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/linalg/VectorUDT.scala
@@ -25,7 +25,7 @@ import org.apache.spark.sql.types._
  * User-defined type for [[Vector]] in [[mllib-local]] which allows easy interaction with SQL
  * via [[org.apache.spark.sql.Dataset]].
  */
-private[ml] class VectorUDT extends UserDefinedType[Vector] {
+private[spark] class VectorUDT extends UserDefinedType[Vector] {
 
   override def sqlType: StructType = {
     // type: 0 = sparse, 1 = dense
diff --git a/mllib/src/main/scala/org/apache/spark/ml/optim/IterativelyReweightedLeastSquares.scala b/mllib/src/main/scala/org/apache/spark/ml/optim/IterativelyReweightedLeastSquares.scala
index a2b52835e1..6ed193cf57 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/optim/IterativelyReweightedLeastSquares.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/optim/IterativelyReweightedLeastSquares.scala
@@ -19,7 +19,7 @@ package org.apache.spark.ml.optim
 
 import org.apache.spark.internal.Logging
 import org.apache.spark.ml.feature.Instance
-import org.apache.spark.mllib.linalg._
+import org.apache.spark.ml.linalg._
 import org.apache.spark.rdd.RDD
 
 /**
diff --git a/mllib/src/main/scala/org/apache/spark/ml/optim/WeightedLeastSquares.scala b/mllib/src/main/scala/org/apache/spark/ml/optim/WeightedLeastSquares.scala
index 7d21302f96..8f5f4427e1 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/optim/WeightedLeastSquares.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/optim/WeightedLeastSquares.scala
@@ -19,7 +19,8 @@ package org.apache.spark.ml.optim
 
 import org.apache.spark.internal.Logging
 import org.apache.spark.ml.feature.Instance
-import org.apache.spark.mllib.linalg._
+import org.apache.spark.ml.linalg._
+import org.apache.spark.mllib.linalg.CholeskyDecomposition
 import org.apache.spark.rdd.RDD
 
 /**
diff --git a/mllib/src/main/scala/org/apache/spark/ml/param/params.scala b/mllib/src/main/scala/org/apache/spark/ml/param/params.scala
index c368aadd23..82f2de7ccd 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/param/params.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/param/params.scala
@@ -29,8 +29,9 @@ import org.json4s._
 import org.json4s.jackson.JsonMethods._
 
 import org.apache.spark.annotation.{DeveloperApi, Experimental, Since}
+import org.apache.spark.ml.linalg.{Vector, Vectors}
+import org.apache.spark.ml.linalg.JsonVectorConverter
 import org.apache.spark.ml.util.Identifiable
-import org.apache.spark.mllib.linalg.{Vector, Vectors}
 
 /**
  * :: DeveloperApi ::
@@ -92,7 +93,7 @@ class Param[T](val parent: String, val name: String, val doc: String, val isVali
       case x: String =>
         compact(render(JString(x)))
       case v: Vector =>
-        v.toJson
+        JsonVectorConverter.toJson(v)
       case _ =>
         throw new NotImplementedError(
           "The default jsonEncode only supports string and vector. " +
@@ -128,7 +129,7 @@ private[ml] object Param {
         val keys = v.map(_._1)
         assert(keys.contains("type") && keys.contains("values"),
           s"Expect a JSON serialized vector but cannot find fields 'type' and 'values' in $json.")
-        Vectors.fromJson(json).asInstanceOf[T]
+        JsonVectorConverter.fromJson(json).asInstanceOf[T]
       case _ =>
         throw new NotImplementedError(
           "The default jsonDecode only supports string and vector. " +
diff --git a/mllib/src/main/scala/org/apache/spark/ml/regression/AFTSurvivalRegression.scala b/mllib/src/main/scala/org/apache/spark/ml/regression/AFTSurvivalRegression.scala
index 89ba6ab5d2..cc16c2f038 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/regression/AFTSurvivalRegression.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/regression/AFTSurvivalRegression.scala
@@ -27,10 +27,11 @@ import org.apache.spark.SparkException
 import org.apache.spark.annotation.{Experimental, Since}
 import org.apache.spark.internal.Logging
 import org.apache.spark.ml.{Estimator, Model}
+import org.apache.spark.ml.linalg.{BLAS, Vector, Vectors, VectorUDT}
 import org.apache.spark.ml.param._
 import org.apache.spark.ml.param.shared._
 import org.apache.spark.ml.util._
-import org.apache.spark.mllib.linalg.{BLAS, Vector, Vectors, VectorUDT}
+import org.apache.spark.mllib.linalg.VectorImplicits._
 import org.apache.spark.mllib.stat.MultivariateOnlineSummarizer
 import org.apache.spark.rdd.RDD
 import org.apache.spark.sql.{DataFrame, Dataset, Row}
diff --git a/mllib/src/main/scala/org/apache/spark/ml/regression/DecisionTreeRegressor.scala b/mllib/src/main/scala/org/apache/spark/ml/regression/DecisionTreeRegressor.scala
index 874d2a81db..c4df9d1112 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/regression/DecisionTreeRegressor.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/regression/DecisionTreeRegressor.scala
@@ -23,13 +23,13 @@ import org.json4s.JsonDSL._
 
 import org.apache.spark.annotation.{Experimental, Since}
 import org.apache.spark.ml.{PredictionModel, Predictor}
+import org.apache.spark.ml.feature.LabeledPoint
+import org.apache.spark.ml.linalg.Vector
 import org.apache.spark.ml.param.ParamMap
 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.mllib.linalg.Vector
-import org.apache.spark.mllib.regression.LabeledPoint
 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
diff --git a/mllib/src/main/scala/org/apache/spark/ml/regression/GBTRegressor.scala b/mllib/src/main/scala/org/apache/spark/ml/regression/GBTRegressor.scala
index c41fb4b062..81f2139f0b 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/regression/GBTRegressor.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/regression/GBTRegressor.scala
@@ -24,13 +24,13 @@ import org.json4s.JsonDSL._
 import org.apache.spark.annotation.{Experimental, Since}
 import org.apache.spark.internal.Logging
 import org.apache.spark.ml.{PredictionModel, Predictor}
+import org.apache.spark.ml.feature.LabeledPoint
+import org.apache.spark.ml.linalg.Vector
 import org.apache.spark.ml.param.ParamMap
 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.mllib.linalg.Vector
-import org.apache.spark.mllib.regression.LabeledPoint
 import org.apache.spark.mllib.tree.configuration.{Algo => OldAlgo}
 import org.apache.spark.mllib.tree.model.{GradientBoostedTreesModel => OldGBTModel}
 import org.apache.spark.rdd.RDD
diff --git a/mllib/src/main/scala/org/apache/spark/ml/regression/GeneralizedLinearRegression.scala b/mllib/src/main/scala/org/apache/spark/ml/regression/GeneralizedLinearRegression.scala
index 05fffa0d97..4aa7c2cc0b 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/regression/GeneralizedLinearRegression.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/regression/GeneralizedLinearRegression.scala
@@ -25,11 +25,11 @@ import org.apache.spark.annotation.{Experimental, Since}
 import org.apache.spark.internal.Logging
 import org.apache.spark.ml.PredictorParams
 import org.apache.spark.ml.feature.Instance
+import org.apache.spark.ml.linalg.{BLAS, Vector}
 import org.apache.spark.ml.optim._
 import org.apache.spark.ml.param._
 import org.apache.spark.ml.param.shared._
 import org.apache.spark.ml.util._
-import org.apache.spark.mllib.linalg.{BLAS, Vector}
 import org.apache.spark.rdd.RDD
 import org.apache.spark.sql.{Column, DataFrame, Dataset, Row}
 import org.apache.spark.sql.functions._
diff --git a/mllib/src/main/scala/org/apache/spark/ml/regression/IsotonicRegression.scala b/mllib/src/main/scala/org/apache/spark/ml/regression/IsotonicRegression.scala
index 7a78ecbdf1..ba0f59e89b 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/regression/IsotonicRegression.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/regression/IsotonicRegression.scala
@@ -22,11 +22,11 @@ import org.apache.hadoop.fs.Path
 import org.apache.spark.annotation.{Experimental, Since}
 import org.apache.spark.internal.Logging
 import org.apache.spark.ml.{Estimator, Model}
+import org.apache.spark.ml.linalg.{Vector, Vectors, VectorUDT}
 import org.apache.spark.ml.param._
 import org.apache.spark.ml.param.shared._
 import org.apache.spark.ml.regression.IsotonicRegressionModel.IsotonicRegressionModelWriter
 import org.apache.spark.ml.util._
-import org.apache.spark.mllib.linalg.{Vector, Vectors, VectorUDT}
 import org.apache.spark.mllib.regression.{IsotonicRegression => MLlibIsotonicRegression}
 import org.apache.spark.mllib.regression.{IsotonicRegressionModel => MLlibIsotonicRegressionModel}
 import org.apache.spark.rdd.RDD
diff --git a/mllib/src/main/scala/org/apache/spark/ml/regression/LinearRegression.scala b/mllib/src/main/scala/org/apache/spark/ml/regression/LinearRegression.scala
index d13b15fd82..3e9a3f9db5 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/regression/LinearRegression.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/regression/LinearRegression.scala
@@ -28,14 +28,16 @@ import org.apache.spark.SparkException
 import org.apache.spark.annotation.{Experimental, Since}
 import org.apache.spark.internal.Logging
 import org.apache.spark.ml.feature.Instance
+import org.apache.spark.ml.linalg.{Vector, Vectors}
+import org.apache.spark.ml.linalg.BLAS._
 import org.apache.spark.ml.optim.WeightedLeastSquares
 import org.apache.spark.ml.PredictorParams
 import org.apache.spark.ml.param.ParamMap
 import org.apache.spark.ml.param.shared._
 import org.apache.spark.ml.util._
 import org.apache.spark.mllib.evaluation.RegressionMetrics
-import org.apache.spark.mllib.linalg.{Vector, Vectors}
-import org.apache.spark.mllib.linalg.BLAS._
+import org.apache.spark.mllib.linalg.{Vectors => OldVectors}
+import org.apache.spark.mllib.linalg.VectorImplicits._
 import org.apache.spark.mllib.stat.MultivariateOnlineSummarizer
 import org.apache.spark.rdd.RDD
 import org.apache.spark.sql.{DataFrame, Dataset, Row}
diff --git a/mllib/src/main/scala/org/apache/spark/ml/regression/RandomForestRegressor.scala b/mllib/src/main/scala/org/apache/spark/ml/regression/RandomForestRegressor.scala
index 9605de7202..a6dbf21d55 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/regression/RandomForestRegressor.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/regression/RandomForestRegressor.scala
@@ -22,13 +22,13 @@ import org.json4s.JsonDSL._
 
 import org.apache.spark.annotation.{Experimental, Since}
 import org.apache.spark.ml.{PredictionModel, Predictor}
+import org.apache.spark.ml.feature.LabeledPoint
+import org.apache.spark.ml.linalg.Vector
 import org.apache.spark.ml.param.ParamMap
 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.mllib.linalg.Vector
-import org.apache.spark.mllib.regression.LabeledPoint
 import org.apache.spark.mllib.tree.configuration.{Algo => OldAlgo}
 import org.apache.spark.mllib.tree.model.{RandomForestModel => OldRandomForestModel}
 import org.apache.spark.rdd.RDD
diff --git a/mllib/src/main/scala/org/apache/spark/ml/source/libsvm/LibSVMRelation.scala b/mllib/src/main/scala/org/apache/spark/ml/source/libsvm/LibSVMRelation.scala
index 39bdd1afad..5ba768d551 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/source/libsvm/LibSVMRelation.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/source/libsvm/LibSVMRelation.scala
@@ -26,8 +26,8 @@ import org.apache.hadoop.mapreduce.{Job, RecordWriter, TaskAttemptContext}
 import org.apache.hadoop.mapreduce.lib.output.TextOutputFormat
 
 import org.apache.spark.annotation.Since
-import org.apache.spark.mllib.linalg.{Vector, Vectors, VectorUDT}
-import org.apache.spark.mllib.regression.LabeledPoint
+import org.apache.spark.ml.feature.LabeledPoint
+import org.apache.spark.ml.linalg.{Vector, Vectors, VectorUDT}
 import org.apache.spark.mllib.util.MLUtils
 import org.apache.spark.sql.{DataFrame, DataFrameReader, Row, SparkSession}
 import org.apache.spark.sql.catalyst.InternalRow
diff --git a/mllib/src/main/scala/org/apache/spark/ml/tree/Node.scala b/mllib/src/main/scala/org/apache/spark/ml/tree/Node.scala
index f71d28cf59..d5e5c45460 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/tree/Node.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/tree/Node.scala
@@ -18,7 +18,7 @@
 package org.apache.spark.ml.tree
 
 import org.apache.spark.annotation.DeveloperApi
-import org.apache.spark.mllib.linalg.Vector
+import org.apache.spark.ml.linalg.Vector
 import org.apache.spark.mllib.tree.impurity.ImpurityCalculator
 import org.apache.spark.mllib.tree.model.{ImpurityStats,
   InformationGainStats => OldInformationGainStats, Node => OldNode, Predict => OldPredict}
diff --git a/mllib/src/main/scala/org/apache/spark/ml/tree/Split.scala b/mllib/src/main/scala/org/apache/spark/ml/tree/Split.scala
index a4287483d1..9704e15cd8 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/tree/Split.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/tree/Split.scala
@@ -20,7 +20,7 @@ package org.apache.spark.ml.tree
 import java.util.Objects
 
 import org.apache.spark.annotation.{DeveloperApi, Since}
-import org.apache.spark.mllib.linalg.Vector
+import org.apache.spark.ml.linalg.Vector
 import org.apache.spark.mllib.tree.configuration.{FeatureType => OldFeatureType}
 import org.apache.spark.mllib.tree.model.{Split => OldSplit}
 
diff --git a/mllib/src/main/scala/org/apache/spark/ml/tree/impl/DecisionTreeMetadata.scala b/mllib/src/main/scala/org/apache/spark/ml/tree/impl/DecisionTreeMetadata.scala
index 5f7c40f607..442f52bf02 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/tree/impl/DecisionTreeMetadata.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/tree/impl/DecisionTreeMetadata.scala
@@ -21,8 +21,8 @@ import scala.collection.mutable
 import scala.util.Try
 
 import org.apache.spark.internal.Logging
+import org.apache.spark.ml.feature.LabeledPoint
 import org.apache.spark.ml.tree.RandomForestParams
-import org.apache.spark.mllib.regression.LabeledPoint
 import org.apache.spark.mllib.tree.configuration.Algo._
 import org.apache.spark.mllib.tree.configuration.QuantileStrategy._
 import org.apache.spark.mllib.tree.configuration.Strategy
diff --git a/mllib/src/main/scala/org/apache/spark/ml/tree/impl/GradientBoostedTrees.scala b/mllib/src/main/scala/org/apache/spark/ml/tree/impl/GradientBoostedTrees.scala
index b6334762c7..a0faff236e 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/tree/impl/GradientBoostedTrees.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/tree/impl/GradientBoostedTrees.scala
@@ -18,10 +18,10 @@
 package org.apache.spark.ml.tree.impl
 
 import org.apache.spark.internal.Logging
+import org.apache.spark.ml.feature.LabeledPoint
+import org.apache.spark.ml.linalg.Vector
 import org.apache.spark.ml.regression.{DecisionTreeRegressionModel, DecisionTreeRegressor}
 import org.apache.spark.mllib.impl.PeriodicRDDCheckpointer
-import org.apache.spark.mllib.linalg.Vector
-import org.apache.spark.mllib.regression.LabeledPoint
 import org.apache.spark.mllib.tree.configuration.{Algo => OldAlgo}
 import org.apache.spark.mllib.tree.configuration.{BoostingStrategy => OldBoostingStrategy}
 import org.apache.spark.mllib.tree.impurity.{Variance => OldVariance}
diff --git a/mllib/src/main/scala/org/apache/spark/ml/tree/impl/RandomForest.scala b/mllib/src/main/scala/org/apache/spark/ml/tree/impl/RandomForest.scala
index 2038a6873d..be3792eb77 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/tree/impl/RandomForest.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/tree/impl/RandomForest.scala
@@ -24,10 +24,10 @@ import scala.util.Random
 
 import org.apache.spark.internal.Logging
 import org.apache.spark.ml.classification.DecisionTreeClassificationModel
+import org.apache.spark.ml.feature.LabeledPoint
 import org.apache.spark.ml.regression.DecisionTreeRegressionModel
 import org.apache.spark.ml.tree._
 import org.apache.spark.ml.util.Instrumentation
-import org.apache.spark.mllib.regression.LabeledPoint
 import org.apache.spark.mllib.tree.configuration.{Algo => OldAlgo, Strategy => OldStrategy}
 import org.apache.spark.mllib.tree.impurity.ImpurityCalculator
 import org.apache.spark.mllib.tree.model.ImpurityStats
diff --git a/mllib/src/main/scala/org/apache/spark/ml/tree/impl/TreePoint.scala b/mllib/src/main/scala/org/apache/spark/ml/tree/impl/TreePoint.scala
index 3a2bf3c725..a6ac64a046 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/tree/impl/TreePoint.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/tree/impl/TreePoint.scala
@@ -17,8 +17,8 @@
 
 package org.apache.spark.ml.tree.impl
 
+import org.apache.spark.ml.feature.LabeledPoint
 import org.apache.spark.ml.tree.{ContinuousSplit, Split}
-import org.apache.spark.mllib.regression.LabeledPoint
 import org.apache.spark.rdd.RDD
 
 
diff --git a/mllib/src/main/scala/org/apache/spark/ml/tree/treeModels.scala b/mllib/src/main/scala/org/apache/spark/ml/tree/treeModels.scala
index f38e1ec7c0..56c85c9b53 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/tree/treeModels.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/tree/treeModels.scala
@@ -23,11 +23,11 @@ import org.apache.hadoop.fs.Path
 import org.json4s._
 import org.json4s.jackson.JsonMethods._
 
+import org.apache.spark.ml.linalg.{Vector, Vectors}
 import org.apache.spark.ml.param.{Param, Params}
 import org.apache.spark.ml.tree.DecisionTreeModelReadWrite.NodeData
 import org.apache.spark.ml.util.{DefaultParamsReader, DefaultParamsWriter}
 import org.apache.spark.ml.util.DefaultParamsReader.Metadata
-import org.apache.spark.mllib.linalg.{Vector, Vectors}
 import org.apache.spark.mllib.tree.impurity.ImpurityCalculator
 import org.apache.spark.mllib.tree.model.{DecisionTreeModel => OldDecisionTreeModel}
 import org.apache.spark.rdd.RDD
diff --git a/mllib/src/main/scala/org/apache/spark/ml/util/MetadataUtils.scala b/mllib/src/main/scala/org/apache/spark/ml/util/MetadataUtils.scala
index 96a38a3bde..f34a8310dd 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/util/MetadataUtils.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/util/MetadataUtils.scala
@@ -20,7 +20,7 @@ package org.apache.spark.ml.util
 import scala.collection.immutable.HashMap
 
 import org.apache.spark.ml.attribute._
-import org.apache.spark.mllib.linalg.VectorUDT
+import org.apache.spark.ml.linalg.VectorUDT
 import org.apache.spark.sql.types.StructField
 
 
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala b/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala
index 8daee7b3aa..90d3827531 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala
@@ -30,6 +30,8 @@ import net.razorvine.pickle._
 
 import org.apache.spark.api.java.{JavaRDD, JavaSparkContext}
 import org.apache.spark.api.python.SerDeUtil
+import org.apache.spark.ml.feature.{LabeledPoint => MLLabeledPoint}
+import org.apache.spark.ml.linalg.{DenseMatrix => NewDenseMatrix, DenseVector => NewDenseVector, SparseMatrix => NewSparseMatrix, SparseVector => NewSparseVector, Vector => NewVector, Vectors => NewVectors}
 import org.apache.spark.mllib.classification._
 import org.apache.spark.mllib.clustering._
 import org.apache.spark.mllib.evaluation.RankingMetrics
@@ -1207,6 +1209,7 @@ private[python] class PythonMLLibAPI extends Serializable {
 private[spark] object SerDe extends Serializable {
 
   val PYSPARK_PACKAGE = "pyspark.mllib"
+  val PYSPARK_ML_PACKAGE = "pyspark.ml"
 
   /**
    * Base class used for pickle
@@ -1214,8 +1217,10 @@ private[spark] object SerDe extends Serializable {
   private[python] abstract class BasePickler[T: ClassTag]
     extends IObjectPickler with IObjectConstructor {
 
+    protected def packageName: String = PYSPARK_PACKAGE
+
     private val cls = implicitly[ClassTag[T]].runtimeClass
-    private val module = PYSPARK_PACKAGE + "." + cls.getName.split('.')(4)
+    private val module = packageName + "." + cls.getName.split('.')(4)
     private val name = cls.getSimpleName
 
     // register this to Pickler and Unpickler
@@ -1262,7 +1267,7 @@ private[spark] object SerDe extends Serializable {
     private[python] def saveState(obj: Object, out: OutputStream, pickler: Pickler)
   }
 
-  // Pickler for DenseVector
+  // Pickler for (mllib) DenseVector
   private[python] class DenseVectorPickler extends BasePickler[DenseVector] {
 
     def saveState(obj: Object, out: OutputStream, pickler: Pickler): Unit = {
@@ -1294,7 +1299,41 @@ private[spark] object SerDe extends Serializable {
     }
   }
 
-  // Pickler for DenseMatrix
+  // Pickler for (new) DenseVector
+  private[python] class NewDenseVectorPickler extends BasePickler[NewDenseVector] {
+
+    override protected def packageName = PYSPARK_ML_PACKAGE
+
+    def saveState(obj: Object, out: OutputStream, pickler: Pickler): Unit = {
+      val vector: NewDenseVector = obj.asInstanceOf[NewDenseVector]
+      val bytes = new Array[Byte](8 * vector.size)
+      val bb = ByteBuffer.wrap(bytes)
+      bb.order(ByteOrder.nativeOrder())
+      val db = bb.asDoubleBuffer()
+      db.put(vector.values)
+
+      out.write(Opcodes.BINSTRING)
+      out.write(PickleUtils.integer_to_bytes(bytes.length))
+      out.write(bytes)
+      out.write(Opcodes.TUPLE1)
+    }
+
+    def construct(args: Array[Object]): Object = {
+      require(args.length == 1)
+      if (args.length != 1) {
+        throw new PickleException("should be 1")
+      }
+      val bytes = getBytes(args(0))
+      val bb = ByteBuffer.wrap(bytes, 0, bytes.length)
+      bb.order(ByteOrder.nativeOrder())
+      val db = bb.asDoubleBuffer()
+      val ans = new Array[Double](bytes.length / 8)
+      db.get(ans)
+      NewVectors.dense(ans)
+    }
+  }
+
+  // Pickler for (mllib) DenseMatrix
   private[python] class DenseMatrixPickler extends BasePickler[DenseMatrix] {
 
     def saveState(obj: Object, out: OutputStream, pickler: Pickler): Unit = {
@@ -1331,7 +1370,46 @@ private[spark] object SerDe extends Serializable {
     }
   }
 
-  // Pickler for SparseMatrix
+  // Pickler for (new) DenseMatrix
+  private[python] class NewDenseMatrixPickler extends BasePickler[NewDenseMatrix] {
+
+    override protected def packageName = PYSPARK_ML_PACKAGE
+
+    def saveState(obj: Object, out: OutputStream, pickler: Pickler): Unit = {
+      val m: NewDenseMatrix = obj.asInstanceOf[NewDenseMatrix]
+      val bytes = new Array[Byte](8 * m.values.length)
+      val order = ByteOrder.nativeOrder()
+      val isTransposed = if (m.isTransposed) 1 else 0
+      ByteBuffer.wrap(bytes).order(order).asDoubleBuffer().put(m.values)
+
+      out.write(Opcodes.MARK)
+      out.write(Opcodes.BININT)
+      out.write(PickleUtils.integer_to_bytes(m.numRows))
+      out.write(Opcodes.BININT)
+      out.write(PickleUtils.integer_to_bytes(m.numCols))
+      out.write(Opcodes.BINSTRING)
+      out.write(PickleUtils.integer_to_bytes(bytes.length))
+      out.write(bytes)
+      out.write(Opcodes.BININT)
+      out.write(PickleUtils.integer_to_bytes(isTransposed))
+      out.write(Opcodes.TUPLE)
+    }
+
+    def construct(args: Array[Object]): Object = {
+      if (args.length != 4) {
+        throw new PickleException("should be 4")
+      }
+      val bytes = getBytes(args(2))
+      val n = bytes.length / 8
+      val values = new Array[Double](n)
+      val order = ByteOrder.nativeOrder()
+      ByteBuffer.wrap(bytes).order(order).asDoubleBuffer().get(values)
+      val isTransposed = args(3).asInstanceOf[Int] == 1
+      new NewDenseMatrix(args(0).asInstanceOf[Int], args(1).asInstanceOf[Int], values, isTransposed)
+    }
+  }
+
+  // Pickler for (mllib) SparseMatrix
   private[python] class SparseMatrixPickler extends BasePickler[SparseMatrix] {
 
     def saveState(obj: Object, out: OutputStream, pickler: Pickler): Unit = {
@@ -1386,7 +1464,64 @@ private[spark] object SerDe extends Serializable {
     }
   }
 
-  // Pickler for SparseVector
+  // Pickler for (new) SparseMatrix
+  private[python] class NewSparseMatrixPickler extends BasePickler[NewSparseMatrix] {
+
+    override protected def packageName = PYSPARK_ML_PACKAGE
+
+    def saveState(obj: Object, out: OutputStream, pickler: Pickler): Unit = {
+      val s = obj.asInstanceOf[NewSparseMatrix]
+      val order = ByteOrder.nativeOrder()
+
+      val colPtrsBytes = new Array[Byte](4 * s.colPtrs.length)
+      val indicesBytes = new Array[Byte](4 * s.rowIndices.length)
+      val valuesBytes = new Array[Byte](8 * s.values.length)
+      val isTransposed = if (s.isTransposed) 1 else 0
+      ByteBuffer.wrap(colPtrsBytes).order(order).asIntBuffer().put(s.colPtrs)
+      ByteBuffer.wrap(indicesBytes).order(order).asIntBuffer().put(s.rowIndices)
+      ByteBuffer.wrap(valuesBytes).order(order).asDoubleBuffer().put(s.values)
+
+      out.write(Opcodes.MARK)
+      out.write(Opcodes.BININT)
+      out.write(PickleUtils.integer_to_bytes(s.numRows))
+      out.write(Opcodes.BININT)
+      out.write(PickleUtils.integer_to_bytes(s.numCols))
+      out.write(Opcodes.BINSTRING)
+      out.write(PickleUtils.integer_to_bytes(colPtrsBytes.length))
+      out.write(colPtrsBytes)
+      out.write(Opcodes.BINSTRING)
+      out.write(PickleUtils.integer_to_bytes(indicesBytes.length))
+      out.write(indicesBytes)
+      out.write(Opcodes.BINSTRING)
+      out.write(PickleUtils.integer_to_bytes(valuesBytes.length))
+      out.write(valuesBytes)
+      out.write(Opcodes.BININT)
+      out.write(PickleUtils.integer_to_bytes(isTransposed))
+      out.write(Opcodes.TUPLE)
+    }
+
+    def construct(args: Array[Object]): Object = {
+      if (args.length != 6) {
+        throw new PickleException("should be 6")
+      }
+      val order = ByteOrder.nativeOrder()
+      val colPtrsBytes = getBytes(args(2))
+      val indicesBytes = getBytes(args(3))
+      val valuesBytes = getBytes(args(4))
+      val colPtrs = new Array[Int](colPtrsBytes.length / 4)
+      val rowIndices = new Array[Int](indicesBytes.length / 4)
+      val values = new Array[Double](valuesBytes.length / 8)
+      ByteBuffer.wrap(colPtrsBytes).order(order).asIntBuffer().get(colPtrs)
+      ByteBuffer.wrap(indicesBytes).order(order).asIntBuffer().get(rowIndices)
+      ByteBuffer.wrap(valuesBytes).order(order).asDoubleBuffer().get(values)
+      val isTransposed = args(5).asInstanceOf[Int] == 1
+      new NewSparseMatrix(
+        args(0).asInstanceOf[Int], args(1).asInstanceOf[Int], colPtrs, rowIndices, values,
+        isTransposed)
+    }
+  }
+
+  // Pickler for (mllib) SparseVector
   private[python] class SparseVectorPickler extends BasePickler[SparseVector] {
 
     def saveState(obj: Object, out: OutputStream, pickler: Pickler): Unit = {
@@ -1428,7 +1563,51 @@ private[spark] object SerDe extends Serializable {
     }
   }
 
-  // Pickler for LabeledPoint
+  // Pickler for (new) SparseVector
+  private[python] class NewSparseVectorPickler extends BasePickler[NewSparseVector] {
+
+    override protected def packageName = PYSPARK_ML_PACKAGE
+
+    def saveState(obj: Object, out: OutputStream, pickler: Pickler): Unit = {
+      val v: NewSparseVector = obj.asInstanceOf[NewSparseVector]
+      val n = v.indices.length
+      val indiceBytes = new Array[Byte](4 * n)
+      val order = ByteOrder.nativeOrder()
+      ByteBuffer.wrap(indiceBytes).order(order).asIntBuffer().put(v.indices)
+      val valueBytes = new Array[Byte](8 * n)
+      ByteBuffer.wrap(valueBytes).order(order).asDoubleBuffer().put(v.values)
+
+      out.write(Opcodes.BININT)
+      out.write(PickleUtils.integer_to_bytes(v.size))
+      out.write(Opcodes.BINSTRING)
+      out.write(PickleUtils.integer_to_bytes(indiceBytes.length))
+      out.write(indiceBytes)
+      out.write(Opcodes.BINSTRING)
+      out.write(PickleUtils.integer_to_bytes(valueBytes.length))
+      out.write(valueBytes)
+      out.write(Opcodes.TUPLE3)
+    }
+
+    def construct(args: Array[Object]): Object = {
+      if (args.length != 3) {
+        throw new PickleException("should be 3")
+      }
+      val size = args(0).asInstanceOf[Int]
+      val indiceBytes = getBytes(args(1))
+      val valueBytes = getBytes(args(2))
+      val n = indiceBytes.length / 4
+      val indices = new Array[Int](n)
+      val values = new Array[Double](n)
+      if (n > 0) {
+        val order = ByteOrder.nativeOrder()
+        ByteBuffer.wrap(indiceBytes).order(order).asIntBuffer().get(indices)
+        ByteBuffer.wrap(valueBytes).order(order).asDoubleBuffer().get(values)
+      }
+      new NewSparseVector(size, indices, values)
+    }
+  }
+
+  // Pickler for MLlib LabeledPoint
   private[python] class LabeledPointPickler extends BasePickler[LabeledPoint] {
 
     def saveState(obj: Object, out: OutputStream, pickler: Pickler): Unit = {
@@ -1482,6 +1661,10 @@ private[spark] object SerDe extends Serializable {
         new DenseMatrixPickler().register()
         new SparseMatrixPickler().register()
         new SparseVectorPickler().register()
+        new NewDenseVectorPickler().register()
+        new NewDenseMatrixPickler().register()
+        new NewSparseMatrixPickler().register()
+        new NewSparseVectorPickler().register()
         new LabeledPointPickler().register()
         new RatingPickler().register()
         initialized = true
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/classification/LogisticRegression.scala b/mllib/src/main/scala/org/apache/spark/mllib/classification/LogisticRegression.scala
index 1d25a58e0f..f3c52f61a3 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/classification/LogisticRegression.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/classification/LogisticRegression.scala
@@ -431,7 +431,7 @@ class LogisticRegressionWithLBFGS
         if (userSuppliedWeights) {
           val uid = Identifiable.randomUID("logreg-static")
           lr.setInitialModel(new org.apache.spark.ml.classification.LogisticRegressionModel(
-            uid, initialWeights, 1.0))
+            uid, initialWeights.asML, 1.0))
         }
         lr.setFitIntercept(addIntercept)
         lr.setMaxIter(optimizer.getNumIterations())
@@ -439,7 +439,7 @@ class LogisticRegressionWithLBFGS
         // Convert our input into a DataFrame
         val sqlContext = new SQLContext(input.context)
         import sqlContext.implicits._
-        val df = input.toDF()
+        val df = input.map(_.asML).toDF()
         // Determine if we should cache the DF
         val handlePersistence = input.getStorageLevel == StorageLevel.NONE
         // Train our model
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/linalg/Matrices.scala b/mllib/src/main/scala/org/apache/spark/mllib/linalg/Matrices.scala
index 076cca6016..5c9a112ca6 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/linalg/Matrices.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/linalg/Matrices.scala
@@ -20,6 +20,7 @@ package org.apache.spark.mllib.linalg
 import java.util.{Arrays, Random}
 
 import scala.collection.mutable.{ArrayBuffer, ArrayBuilder => MArrayBuilder, HashSet => MHashSet}
+import scala.language.implicitConversions
 
 import breeze.linalg.{CSCMatrix => BSM, DenseMatrix => BDM, Matrix => BM}
 import com.github.fommil.netlib.BLAS.{getInstance => blas}
@@ -1212,3 +1213,24 @@ object Matrices {
       SparseMatrix.fromML(sm)
   }
 }
+
+/**
+ * Implicit methods available in Scala for converting [[org.apache.spark.mllib.linalg.Matrix]] to
+ * [[org.apache.spark.ml.linalg.Matrix]] and vice versa.
+ */
+private[spark] object MatrixImplicits {
+
+  implicit def mllibMatrixToMLMatrix(m: Matrix): newlinalg.Matrix = m.asML
+
+  implicit def mllibDenseMatrixToMLDenseMatrix(m: DenseMatrix): newlinalg.DenseMatrix = m.asML
+
+  implicit def mllibSparseMatrixToMLSparseMatrix(m: SparseMatrix): newlinalg.SparseMatrix = m.asML
+
+  implicit def mlMatrixToMLlibMatrix(m: newlinalg.Matrix): Matrix = Matrices.fromML(m)
+
+  implicit def mlDenseMatrixToMLlibDenseMatrix(m: newlinalg.DenseMatrix): DenseMatrix =
+    Matrices.fromML(m).asInstanceOf[DenseMatrix]
+
+  implicit def mlSparseMatrixToMLlibSparseMatrix(m: newlinalg.SparseMatrix): SparseMatrix =
+    Matrices.fromML(m).asInstanceOf[SparseMatrix]
+}
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/linalg/Vectors.scala b/mllib/src/main/scala/org/apache/spark/mllib/linalg/Vectors.scala
index 132e54a8c3..1f1cfa0cb2 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/linalg/Vectors.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/linalg/Vectors.scala
@@ -22,6 +22,7 @@ import java.util
 
 import scala.annotation.varargs
 import scala.collection.JavaConverters._
+import scala.language.implicitConversions
 
 import breeze.linalg.{DenseVector => BDV, SparseVector => BSV, Vector => BV}
 import org.json4s.DefaultFormats
@@ -926,3 +927,24 @@ object SparseVector {
     new SparseVector(v.size, v.indices, v.values)
   }
 }
+
+/**
+ * Implicit methods available in Scala for converting [[org.apache.spark.mllib.linalg.Vector]] to
+ * [[org.apache.spark.ml.linalg.Vector]] and vice versa.
+ */
+private[spark] object VectorImplicits {
+
+  implicit def mllibVectorToMLVector(v: Vector): newlinalg.Vector = v.asML
+
+  implicit def mllibDenseVectorToMLDenseVector(v: DenseVector): newlinalg.DenseVector = v.asML
+
+  implicit def mllibSparseVectorToMLSparseVector(v: SparseVector): newlinalg.SparseVector = v.asML
+
+  implicit def mlVectorToMLlibVector(v: newlinalg.Vector): Vector = Vectors.fromML(v)
+
+  implicit def mlDenseVectorToMLlibDenseVector(v: newlinalg.DenseVector): DenseVector =
+    Vectors.fromML(v).asInstanceOf[DenseVector]
+
+  implicit def mlSparseVectorToMLlibSparseVector(v: newlinalg.SparseVector): SparseVector =
+    Vectors.fromML(v).asInstanceOf[SparseVector]
+}
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/regression/LabeledPoint.scala b/mllib/src/main/scala/org/apache/spark/mllib/regression/LabeledPoint.scala
index 45540f0c5c..f082b16b95 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/regression/LabeledPoint.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/regression/LabeledPoint.scala
@@ -20,6 +20,7 @@ package org.apache.spark.mllib.regression
 import scala.beans.BeanInfo
 
 import org.apache.spark.annotation.Since
+import org.apache.spark.ml.feature.{LabeledPoint => NewLabeledPoint}
 import org.apache.spark.mllib.linalg.{Vector, Vectors}
 import org.apache.spark.mllib.util.NumericParser
 import org.apache.spark.SparkException
@@ -38,6 +39,10 @@ case class LabeledPoint @Since("1.0.0") (
   override def toString: String = {
     s"($label,$features)"
   }
+
+  private[spark] def asML: NewLabeledPoint = {
+    NewLabeledPoint(label, features.asML)
+  }
 }
 
 /**
@@ -67,4 +72,8 @@ object LabeledPoint {
       LabeledPoint(label, features)
     }
   }
+
+  private[spark] def fromML(point: NewLabeledPoint): LabeledPoint = {
+    LabeledPoint(point.label, Vectors.fromML(point.features))
+  }
 }
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/GradientBoostedTrees.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/GradientBoostedTrees.scala
index 7fe60e2d99..ece1e41d98 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/GradientBoostedTrees.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/GradientBoostedTrees.scala
@@ -20,6 +20,7 @@ package org.apache.spark.mllib.tree
 import org.apache.spark.annotation.Since
 import org.apache.spark.api.java.JavaRDD
 import org.apache.spark.internal.Logging
+import org.apache.spark.ml.feature.{LabeledPoint => NewLabeledPoint}
 import org.apache.spark.ml.tree.impl.{GradientBoostedTrees => NewGBT}
 import org.apache.spark.mllib.regression.LabeledPoint
 import org.apache.spark.mllib.tree.configuration.BoostingStrategy
@@ -66,7 +67,9 @@ class GradientBoostedTrees private[spark] (
   @Since("1.2.0")
   def run(input: RDD[LabeledPoint]): GradientBoostedTreesModel = {
     val algo = boostingStrategy.treeStrategy.algo
-    val (trees, treeWeights) = NewGBT.run(input, boostingStrategy, seed.toLong)
+    val (trees, treeWeights) = NewGBT.run(input.map { point =>
+      NewLabeledPoint(point.label, point.features.asML)
+    }, boostingStrategy, seed.toLong)
     new GradientBoostedTreesModel(algo, trees.map(_.toOld), treeWeights)
   }
 
@@ -94,8 +97,11 @@ class GradientBoostedTrees private[spark] (
       input: RDD[LabeledPoint],
       validationInput: RDD[LabeledPoint]): GradientBoostedTreesModel = {
     val algo = boostingStrategy.treeStrategy.algo
-    val (trees, treeWeights) = NewGBT.runWithValidation(input, validationInput, boostingStrategy,
-      seed.toLong)
+    val (trees, treeWeights) = NewGBT.runWithValidation(input.map { point =>
+      NewLabeledPoint(point.label, point.features.asML)
+    }, validationInput.map { point =>
+      NewLabeledPoint(point.label, point.features.asML)
+    }, boostingStrategy, seed.toLong)
     new GradientBoostedTreesModel(algo, trees.map(_.toOld), treeWeights)
   }
 
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/RandomForest.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/RandomForest.scala
index c31ed9c1ce..14f11ce51b 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/RandomForest.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/RandomForest.scala
@@ -90,8 +90,8 @@ private class RandomForest (
    * @return RandomForestModel that can be used for prediction.
    */
   def run(input: RDD[LabeledPoint]): RandomForestModel = {
-    val trees: Array[NewDTModel] =
-      NewRandomForest.run(input, strategy, numTrees, featureSubsetStrategy, seed.toLong, None)
+    val trees: Array[NewDTModel] = NewRandomForest.run(input.map(_.asML), strategy, numTrees,
+      featureSubsetStrategy, seed.toLong, None)
     new RandomForestModel(strategy.algo, trees.map(_.toOld))
   }
 
diff --git a/mllib/src/test/java/org/apache/spark/ml/JavaPipelineSuite.java b/mllib/src/test/java/org/apache/spark/ml/JavaPipelineSuite.java
index e0c4363597..46c26e8b92 100644
--- a/mllib/src/test/java/org/apache/spark/ml/JavaPipelineSuite.java
+++ b/mllib/src/test/java/org/apache/spark/ml/JavaPipelineSuite.java
@@ -24,12 +24,12 @@ import org.junit.Test;
 import org.apache.spark.api.java.JavaRDD;
 import org.apache.spark.api.java.JavaSparkContext;
 import org.apache.spark.ml.classification.LogisticRegression;
+import static org.apache.spark.ml.classification.LogisticRegressionSuite.generateLogisticInputAsList;
+import org.apache.spark.ml.feature.LabeledPoint;
 import org.apache.spark.ml.feature.StandardScaler;
-import org.apache.spark.mllib.regression.LabeledPoint;
 import org.apache.spark.sql.Dataset;
 import org.apache.spark.sql.Row;
 import org.apache.spark.sql.SparkSession;
-import static org.apache.spark.mllib.classification.LogisticRegressionSuite.generateLogisticInputAsList;
 
 /**
  * Test Pipeline construction and fitting in Java.
diff --git a/mllib/src/test/java/org/apache/spark/ml/classification/JavaDecisionTreeClassifierSuite.java b/mllib/src/test/java/org/apache/spark/ml/classification/JavaDecisionTreeClassifierSuite.java
index 8b89991327..c76a1947c6 100644
--- a/mllib/src/test/java/org/apache/spark/ml/classification/JavaDecisionTreeClassifierSuite.java
+++ b/mllib/src/test/java/org/apache/spark/ml/classification/JavaDecisionTreeClassifierSuite.java
@@ -27,9 +27,9 @@ import org.junit.Test;
 
 import org.apache.spark.api.java.JavaRDD;
 import org.apache.spark.api.java.JavaSparkContext;
+import org.apache.spark.ml.classification.LogisticRegressionSuite;
+import org.apache.spark.ml.feature.LabeledPoint;
 import org.apache.spark.ml.tree.impl.TreeTests;
-import org.apache.spark.mllib.classification.LogisticRegressionSuite;
-import org.apache.spark.mllib.regression.LabeledPoint;
 import org.apache.spark.sql.Dataset;
 import org.apache.spark.sql.Row;
 import org.apache.spark.sql.SparkSession;
diff --git a/mllib/src/test/java/org/apache/spark/ml/classification/JavaGBTClassifierSuite.java b/mllib/src/test/java/org/apache/spark/ml/classification/JavaGBTClassifierSuite.java
index 682371eb9e..4648926c34 100644
--- a/mllib/src/test/java/org/apache/spark/ml/classification/JavaGBTClassifierSuite.java
+++ b/mllib/src/test/java/org/apache/spark/ml/classification/JavaGBTClassifierSuite.java
@@ -27,9 +27,9 @@ import org.junit.Test;
 
 import org.apache.spark.api.java.JavaRDD;
 import org.apache.spark.api.java.JavaSparkContext;
+import org.apache.spark.ml.classification.LogisticRegressionSuite;
+import org.apache.spark.ml.feature.LabeledPoint;
 import org.apache.spark.ml.tree.impl.TreeTests;
-import org.apache.spark.mllib.classification.LogisticRegressionSuite;
-import org.apache.spark.mllib.regression.LabeledPoint;
 import org.apache.spark.sql.Dataset;
 import org.apache.spark.sql.Row;
 import org.apache.spark.sql.SparkSession;
diff --git a/mllib/src/test/java/org/apache/spark/ml/classification/JavaLogisticRegressionSuite.java b/mllib/src/test/java/org/apache/spark/ml/classification/JavaLogisticRegressionSuite.java
index e3ff68364e..98abca221c 100644
--- a/mllib/src/test/java/org/apache/spark/ml/classification/JavaLogisticRegressionSuite.java
+++ b/mllib/src/test/java/org/apache/spark/ml/classification/JavaLogisticRegressionSuite.java
@@ -27,12 +27,12 @@ import org.junit.Test;
 
 import org.apache.spark.api.java.JavaRDD;
 import org.apache.spark.api.java.JavaSparkContext;
-import org.apache.spark.mllib.linalg.Vector;
-import org.apache.spark.mllib.regression.LabeledPoint;
+import static org.apache.spark.ml.classification.LogisticRegressionSuite.generateLogisticInputAsList;
+import org.apache.spark.ml.feature.LabeledPoint;
+import org.apache.spark.ml.linalg.Vector;
 import org.apache.spark.sql.Dataset;
 import org.apache.spark.sql.Row;
 import org.apache.spark.sql.SparkSession;
-import static org.apache.spark.mllib.classification.LogisticRegressionSuite.generateLogisticInputAsList;
 
 public class JavaLogisticRegressionSuite implements Serializable {
 
diff --git a/mllib/src/test/java/org/apache/spark/ml/classification/JavaMultilayerPerceptronClassifierSuite.java b/mllib/src/test/java/org/apache/spark/ml/classification/JavaMultilayerPerceptronClassifierSuite.java
index b0624cea3e..48edbc838c 100644
--- a/mllib/src/test/java/org/apache/spark/ml/classification/JavaMultilayerPerceptronClassifierSuite.java
+++ b/mllib/src/test/java/org/apache/spark/ml/classification/JavaMultilayerPerceptronClassifierSuite.java
@@ -26,8 +26,8 @@ import org.junit.Assert;
 import org.junit.Before;
 import org.junit.Test;
 
-import org.apache.spark.mllib.linalg.Vectors;
-import org.apache.spark.mllib.regression.LabeledPoint;
+import org.apache.spark.ml.feature.LabeledPoint;
+import org.apache.spark.ml.linalg.Vectors;
 import org.apache.spark.sql.Dataset;
 import org.apache.spark.sql.Row;
 import org.apache.spark.sql.SparkSession;
diff --git a/mllib/src/test/java/org/apache/spark/ml/classification/JavaNaiveBayesSuite.java b/mllib/src/test/java/org/apache/spark/ml/classification/JavaNaiveBayesSuite.java
index 3fc3648627..787909821b 100644
--- a/mllib/src/test/java/org/apache/spark/ml/classification/JavaNaiveBayesSuite.java
+++ b/mllib/src/test/java/org/apache/spark/ml/classification/JavaNaiveBayesSuite.java
@@ -26,8 +26,8 @@ import org.junit.Before;
 import org.junit.Test;
 import static org.junit.Assert.assertEquals;
 
-import org.apache.spark.mllib.linalg.VectorUDT;
-import org.apache.spark.mllib.linalg.Vectors;
+import org.apache.spark.ml.linalg.VectorUDT;
+import org.apache.spark.ml.linalg.Vectors;
 import org.apache.spark.sql.Dataset;
 import org.apache.spark.sql.Row;
 import org.apache.spark.sql.RowFactory;
diff --git a/mllib/src/test/java/org/apache/spark/ml/classification/JavaOneVsRestSuite.java b/mllib/src/test/java/org/apache/spark/ml/classification/JavaOneVsRestSuite.java
index 486fbbd58c..58bc5a448a 100644
--- a/mllib/src/test/java/org/apache/spark/ml/classification/JavaOneVsRestSuite.java
+++ b/mllib/src/test/java/org/apache/spark/ml/classification/JavaOneVsRestSuite.java
@@ -29,11 +29,11 @@ import org.junit.Test;
 
 import org.apache.spark.api.java.JavaRDD;
 import org.apache.spark.api.java.JavaSparkContext;
-import org.apache.spark.mllib.regression.LabeledPoint;
+import static org.apache.spark.ml.classification.LogisticRegressionSuite.generateMultinomialLogisticInput;
+import org.apache.spark.ml.feature.LabeledPoint;
 import org.apache.spark.sql.Dataset;
 import org.apache.spark.sql.Row;
 import org.apache.spark.sql.SparkSession;
-import static org.apache.spark.mllib.classification.LogisticRegressionSuite.generateMultinomialLogisticInput;
 
 public class JavaOneVsRestSuite implements Serializable {
 
diff --git a/mllib/src/test/java/org/apache/spark/ml/classification/JavaRandomForestClassifierSuite.java b/mllib/src/test/java/org/apache/spark/ml/classification/JavaRandomForestClassifierSuite.java
index e3855662fb..1ed20b1bfa 100644
--- a/mllib/src/test/java/org/apache/spark/ml/classification/JavaRandomForestClassifierSuite.java
+++ b/mllib/src/test/java/org/apache/spark/ml/classification/JavaRandomForestClassifierSuite.java
@@ -28,10 +28,10 @@ import org.junit.Test;
 
 import org.apache.spark.api.java.JavaRDD;
 import org.apache.spark.api.java.JavaSparkContext;
+import org.apache.spark.ml.classification.LogisticRegressionSuite;
+import org.apache.spark.ml.feature.LabeledPoint;
+import org.apache.spark.ml.linalg.Vector;
 import org.apache.spark.ml.tree.impl.TreeTests;
-import org.apache.spark.mllib.classification.LogisticRegressionSuite;
-import org.apache.spark.mllib.linalg.Vector;
-import org.apache.spark.mllib.regression.LabeledPoint;
 import org.apache.spark.sql.Dataset;
 import org.apache.spark.sql.Row;
 import org.apache.spark.sql.SparkSession;
diff --git a/mllib/src/test/java/org/apache/spark/ml/clustering/JavaKMeansSuite.java b/mllib/src/test/java/org/apache/spark/ml/clustering/JavaKMeansSuite.java
index 3ab09ac27d..9d07170fa1 100644
--- a/mllib/src/test/java/org/apache/spark/ml/clustering/JavaKMeansSuite.java
+++ b/mllib/src/test/java/org/apache/spark/ml/clustering/JavaKMeansSuite.java
@@ -28,7 +28,7 @@ import org.junit.After;
 import org.junit.Before;
 import org.junit.Test;
 
-import org.apache.spark.mllib.linalg.Vector;
+import org.apache.spark.ml.linalg.Vector;
 import org.apache.spark.sql.Dataset;
 import org.apache.spark.sql.Row;
 import org.apache.spark.sql.SparkSession;
diff --git a/mllib/src/test/java/org/apache/spark/ml/feature/JavaDCTSuite.java b/mllib/src/test/java/org/apache/spark/ml/feature/JavaDCTSuite.java
index 06482d8f0d..9d8c09b30c 100644
--- a/mllib/src/test/java/org/apache/spark/ml/feature/JavaDCTSuite.java
+++ b/mllib/src/test/java/org/apache/spark/ml/feature/JavaDCTSuite.java
@@ -27,9 +27,9 @@ import org.junit.Assert;
 import org.junit.Before;
 import org.junit.Test;
 
-import org.apache.spark.mllib.linalg.Vector;
-import org.apache.spark.mllib.linalg.VectorUDT;
-import org.apache.spark.mllib.linalg.Vectors;
+import org.apache.spark.ml.linalg.Vector;
+import org.apache.spark.ml.linalg.VectorUDT;
+import org.apache.spark.ml.linalg.Vectors;
 import org.apache.spark.sql.Dataset;
 import org.apache.spark.sql.Row;
 import org.apache.spark.sql.RowFactory;
diff --git a/mllib/src/test/java/org/apache/spark/ml/feature/JavaHashingTFSuite.java b/mllib/src/test/java/org/apache/spark/ml/feature/JavaHashingTFSuite.java
index 0e21d4a94f..3c37441a77 100644
--- a/mllib/src/test/java/org/apache/spark/ml/feature/JavaHashingTFSuite.java
+++ b/mllib/src/test/java/org/apache/spark/ml/feature/JavaHashingTFSuite.java
@@ -25,7 +25,7 @@ import org.junit.Assert;
 import org.junit.Before;
 import org.junit.Test;
 
-import org.apache.spark.mllib.linalg.Vector;
+import org.apache.spark.ml.linalg.Vector;
 import org.apache.spark.sql.Dataset;
 import org.apache.spark.sql.Row;
 import org.apache.spark.sql.RowFactory;
diff --git a/mllib/src/test/java/org/apache/spark/ml/feature/JavaNormalizerSuite.java b/mllib/src/test/java/org/apache/spark/ml/feature/JavaNormalizerSuite.java
index 04b2897b18..b3e213a497 100644
--- a/mllib/src/test/java/org/apache/spark/ml/feature/JavaNormalizerSuite.java
+++ b/mllib/src/test/java/org/apache/spark/ml/feature/JavaNormalizerSuite.java
@@ -25,7 +25,7 @@ import org.junit.Test;
 
 import org.apache.spark.api.java.JavaRDD;
 import org.apache.spark.api.java.JavaSparkContext;
-import org.apache.spark.mllib.linalg.Vectors;
+import org.apache.spark.ml.linalg.Vectors;
 import org.apache.spark.sql.Dataset;
 import org.apache.spark.sql.Row;
 import org.apache.spark.sql.SparkSession;
diff --git a/mllib/src/test/java/org/apache/spark/ml/feature/JavaPCASuite.java b/mllib/src/test/java/org/apache/spark/ml/feature/JavaPCASuite.java
index 32f6b4375e..a4bce2283b 100644
--- a/mllib/src/test/java/org/apache/spark/ml/feature/JavaPCASuite.java
+++ b/mllib/src/test/java/org/apache/spark/ml/feature/JavaPCASuite.java
@@ -31,9 +31,9 @@ import org.junit.Test;
 import org.apache.spark.api.java.JavaRDD;
 import org.apache.spark.api.java.JavaSparkContext;
 import org.apache.spark.api.java.function.Function;
+import org.apache.spark.ml.linalg.Vector;
+import org.apache.spark.ml.linalg.Vectors;
 import org.apache.spark.mllib.linalg.Matrix;
-import org.apache.spark.mllib.linalg.Vector;
-import org.apache.spark.mllib.linalg.Vectors;
 import org.apache.spark.mllib.linalg.distributed.RowMatrix;
 import org.apache.spark.sql.Dataset;
 import org.apache.spark.sql.Row;
@@ -88,9 +88,25 @@ public class JavaPCASuite implements Serializable {
     );
     JavaRDD<Vector> dataRDD = jsc.parallelize(points, 2);
 
-    RowMatrix mat = new RowMatrix(dataRDD.rdd());
+    RowMatrix mat = new RowMatrix(dataRDD.map(
+            new Function<Vector, org.apache.spark.mllib.linalg.Vector>() {
+              public org.apache.spark.mllib.linalg.Vector call(Vector vector) {
+                return new org.apache.spark.mllib.linalg.DenseVector(vector.toArray());
+              }
+            }
+    ).rdd());
+    
     Matrix pc = mat.computePrincipalComponents(3);
-    JavaRDD<Vector> expected = mat.multiply(pc).rows().toJavaRDD();
+
+    mat.multiply(pc).rows().toJavaRDD();
+
+    JavaRDD<Vector> expected = mat.multiply(pc).rows().toJavaRDD().map(
+      new Function<org.apache.spark.mllib.linalg.Vector, Vector>() {
+        public Vector call(org.apache.spark.mllib.linalg.Vector vector) {
+          return vector.asML();
+        }
+      }
+    );
 
     JavaRDD<VectorPair> featuresExpected = dataRDD.zip(expected).map(
       new Function<Tuple2<Vector, Vector>, VectorPair>() {
diff --git a/mllib/src/test/java/org/apache/spark/ml/feature/JavaPolynomialExpansionSuite.java b/mllib/src/test/java/org/apache/spark/ml/feature/JavaPolynomialExpansionSuite.java
index 8f726077a2..a28f73f10a 100644
--- a/mllib/src/test/java/org/apache/spark/ml/feature/JavaPolynomialExpansionSuite.java
+++ b/mllib/src/test/java/org/apache/spark/ml/feature/JavaPolynomialExpansionSuite.java
@@ -26,9 +26,9 @@ import org.junit.Before;
 import org.junit.Test;
 
 import org.apache.spark.api.java.JavaSparkContext;
-import org.apache.spark.mllib.linalg.Vector;
-import org.apache.spark.mllib.linalg.VectorUDT;
-import org.apache.spark.mllib.linalg.Vectors;
+import org.apache.spark.ml.linalg.Vector;
+import org.apache.spark.ml.linalg.VectorUDT;
+import org.apache.spark.ml.linalg.Vectors;
 import org.apache.spark.sql.Dataset;
 import org.apache.spark.sql.Row;
 import org.apache.spark.sql.RowFactory;
diff --git a/mllib/src/test/java/org/apache/spark/ml/feature/JavaStandardScalerSuite.java b/mllib/src/test/java/org/apache/spark/ml/feature/JavaStandardScalerSuite.java
index c7397bdd68..8415fdb84f 100644
--- a/mllib/src/test/java/org/apache/spark/ml/feature/JavaStandardScalerSuite.java
+++ b/mllib/src/test/java/org/apache/spark/ml/feature/JavaStandardScalerSuite.java
@@ -25,7 +25,7 @@ import org.junit.Before;
 import org.junit.Test;
 
 import org.apache.spark.api.java.JavaSparkContext;
-import org.apache.spark.mllib.linalg.Vectors;
+import org.apache.spark.ml.linalg.Vectors;
 import org.apache.spark.sql.Dataset;
 import org.apache.spark.sql.Row;
 import org.apache.spark.sql.SparkSession;
diff --git a/mllib/src/test/java/org/apache/spark/ml/feature/JavaVectorAssemblerSuite.java b/mllib/src/test/java/org/apache/spark/ml/feature/JavaVectorAssemblerSuite.java
index 8774cd0c69..fedaa77176 100644
--- a/mllib/src/test/java/org/apache/spark/ml/feature/JavaVectorAssemblerSuite.java
+++ b/mllib/src/test/java/org/apache/spark/ml/feature/JavaVectorAssemblerSuite.java
@@ -25,9 +25,9 @@ import org.junit.Before;
 import org.junit.Test;
 
 import org.apache.spark.SparkConf;
-import org.apache.spark.mllib.linalg.Vector;
-import org.apache.spark.mllib.linalg.VectorUDT;
-import org.apache.spark.mllib.linalg.Vectors;
+import org.apache.spark.ml.linalg.Vector;
+import org.apache.spark.ml.linalg.VectorUDT;
+import org.apache.spark.ml.linalg.Vectors;
 import org.apache.spark.sql.Dataset;
 import org.apache.spark.sql.Row;
 import org.apache.spark.sql.RowFactory;
diff --git a/mllib/src/test/java/org/apache/spark/ml/feature/JavaVectorIndexerSuite.java b/mllib/src/test/java/org/apache/spark/ml/feature/JavaVectorIndexerSuite.java
index c386c9a45b..a8dd44608d 100644
--- a/mllib/src/test/java/org/apache/spark/ml/feature/JavaVectorIndexerSuite.java
+++ b/mllib/src/test/java/org/apache/spark/ml/feature/JavaVectorIndexerSuite.java
@@ -29,7 +29,7 @@ import org.junit.Test;
 
 import org.apache.spark.api.java.JavaSparkContext;
 import org.apache.spark.ml.feature.VectorIndexerSuite.FeatureData;
-import org.apache.spark.mllib.linalg.Vectors;
+import org.apache.spark.ml.linalg.Vectors;
 import org.apache.spark.sql.Dataset;
 import org.apache.spark.sql.Row;
 import org.apache.spark.sql.SparkSession;
diff --git a/mllib/src/test/java/org/apache/spark/ml/feature/JavaVectorSlicerSuite.java b/mllib/src/test/java/org/apache/spark/ml/feature/JavaVectorSlicerSuite.java
index 59ad3c2f61..a565c77af4 100644
--- a/mllib/src/test/java/org/apache/spark/ml/feature/JavaVectorSlicerSuite.java
+++ b/mllib/src/test/java/org/apache/spark/ml/feature/JavaVectorSlicerSuite.java
@@ -28,8 +28,8 @@ import org.junit.Test;
 import org.apache.spark.ml.attribute.Attribute;
 import org.apache.spark.ml.attribute.AttributeGroup;
 import org.apache.spark.ml.attribute.NumericAttribute;
-import org.apache.spark.mllib.linalg.Vector;
-import org.apache.spark.mllib.linalg.Vectors;
+import org.apache.spark.ml.linalg.Vector;
+import org.apache.spark.ml.linalg.Vectors;
 import org.apache.spark.sql.Dataset;
 import org.apache.spark.sql.Row;
 import org.apache.spark.sql.RowFactory;
diff --git a/mllib/src/test/java/org/apache/spark/ml/feature/JavaWord2VecSuite.java b/mllib/src/test/java/org/apache/spark/ml/feature/JavaWord2VecSuite.java
index 392aabc96d..bef7eb0f99 100644
--- a/mllib/src/test/java/org/apache/spark/ml/feature/JavaWord2VecSuite.java
+++ b/mllib/src/test/java/org/apache/spark/ml/feature/JavaWord2VecSuite.java
@@ -24,7 +24,7 @@ import org.junit.Assert;
 import org.junit.Before;
 import org.junit.Test;
 
-import org.apache.spark.mllib.linalg.Vector;
+import org.apache.spark.ml.linalg.Vector;
 import org.apache.spark.sql.Dataset;
 import org.apache.spark.sql.Row;
 import org.apache.spark.sql.RowFactory;
diff --git a/mllib/src/test/java/org/apache/spark/ml/regression/JavaDecisionTreeRegressorSuite.java b/mllib/src/test/java/org/apache/spark/ml/regression/JavaDecisionTreeRegressorSuite.java
index bbd59a04ec..4ea3f2255e 100644
--- a/mllib/src/test/java/org/apache/spark/ml/regression/JavaDecisionTreeRegressorSuite.java
+++ b/mllib/src/test/java/org/apache/spark/ml/regression/JavaDecisionTreeRegressorSuite.java
@@ -27,9 +27,9 @@ import org.junit.Test;
 
 import org.apache.spark.api.java.JavaRDD;
 import org.apache.spark.api.java.JavaSparkContext;
+import org.apache.spark.ml.classification.LogisticRegressionSuite;
+import org.apache.spark.ml.feature.LabeledPoint;
 import org.apache.spark.ml.tree.impl.TreeTests;
-import org.apache.spark.mllib.classification.LogisticRegressionSuite;
-import org.apache.spark.mllib.regression.LabeledPoint;
 import org.apache.spark.sql.Dataset;
 import org.apache.spark.sql.Row;
 import org.apache.spark.sql.SparkSession;
diff --git a/mllib/src/test/java/org/apache/spark/ml/regression/JavaGBTRegressorSuite.java b/mllib/src/test/java/org/apache/spark/ml/regression/JavaGBTRegressorSuite.java
index 5370b58e8f..3b5edf1e15 100644
--- a/mllib/src/test/java/org/apache/spark/ml/regression/JavaGBTRegressorSuite.java
+++ b/mllib/src/test/java/org/apache/spark/ml/regression/JavaGBTRegressorSuite.java
@@ -27,9 +27,9 @@ import org.junit.Test;
 
 import org.apache.spark.api.java.JavaRDD;
 import org.apache.spark.api.java.JavaSparkContext;
+import org.apache.spark.ml.classification.LogisticRegressionSuite;
+import org.apache.spark.ml.feature.LabeledPoint;
 import org.apache.spark.ml.tree.impl.TreeTests;
-import org.apache.spark.mllib.classification.LogisticRegressionSuite;
-import org.apache.spark.mllib.regression.LabeledPoint;
 import org.apache.spark.sql.Dataset;
 import org.apache.spark.sql.Row;
 import org.apache.spark.sql.SparkSession;
diff --git a/mllib/src/test/java/org/apache/spark/ml/regression/JavaLinearRegressionSuite.java b/mllib/src/test/java/org/apache/spark/ml/regression/JavaLinearRegressionSuite.java
index 00c59f08b6..d3ef5f6fca 100644
--- a/mllib/src/test/java/org/apache/spark/ml/regression/JavaLinearRegressionSuite.java
+++ b/mllib/src/test/java/org/apache/spark/ml/regression/JavaLinearRegressionSuite.java
@@ -27,11 +27,11 @@ import static org.junit.Assert.assertEquals;
 
 import org.apache.spark.api.java.JavaRDD;
 import org.apache.spark.api.java.JavaSparkContext;
-import org.apache.spark.mllib.regression.LabeledPoint;
+import static org.apache.spark.ml.classification.LogisticRegressionSuite.generateLogisticInputAsList;
+import org.apache.spark.ml.feature.LabeledPoint;
 import org.apache.spark.sql.Dataset;
 import org.apache.spark.sql.Row;
 import org.apache.spark.sql.SparkSession;
-import static org.apache.spark.mllib.classification.LogisticRegressionSuite.generateLogisticInputAsList;
 
 public class JavaLinearRegressionSuite implements Serializable {
 
diff --git a/mllib/src/test/java/org/apache/spark/ml/regression/JavaRandomForestRegressorSuite.java b/mllib/src/test/java/org/apache/spark/ml/regression/JavaRandomForestRegressorSuite.java
index fdb41ffc10..d601e7c540 100644
--- a/mllib/src/test/java/org/apache/spark/ml/regression/JavaRandomForestRegressorSuite.java
+++ b/mllib/src/test/java/org/apache/spark/ml/regression/JavaRandomForestRegressorSuite.java
@@ -28,10 +28,10 @@ import org.junit.Test;
 
 import org.apache.spark.api.java.JavaRDD;
 import org.apache.spark.api.java.JavaSparkContext;
+import org.apache.spark.ml.classification.LogisticRegressionSuite;
+import org.apache.spark.ml.feature.LabeledPoint;
+import org.apache.spark.ml.linalg.Vector;
 import org.apache.spark.ml.tree.impl.TreeTests;
-import org.apache.spark.mllib.classification.LogisticRegressionSuite;
-import org.apache.spark.mllib.linalg.Vector;
-import org.apache.spark.mllib.regression.LabeledPoint;
 import org.apache.spark.sql.Dataset;
 import org.apache.spark.sql.Row;
 import org.apache.spark.sql.SparkSession;
diff --git a/mllib/src/test/java/org/apache/spark/ml/source/libsvm/JavaLibSVMRelationSuite.java b/mllib/src/test/java/org/apache/spark/ml/source/libsvm/JavaLibSVMRelationSuite.java
index 058f2ddafd..022dcf94bd 100644
--- a/mllib/src/test/java/org/apache/spark/ml/source/libsvm/JavaLibSVMRelationSuite.java
+++ b/mllib/src/test/java/org/apache/spark/ml/source/libsvm/JavaLibSVMRelationSuite.java
@@ -28,8 +28,8 @@ import org.junit.Assert;
 import org.junit.Before;
 import org.junit.Test;
 
-import org.apache.spark.mllib.linalg.DenseVector;
-import org.apache.spark.mllib.linalg.Vectors;
+import org.apache.spark.ml.linalg.DenseVector;
+import org.apache.spark.ml.linalg.Vectors;
 import org.apache.spark.sql.Dataset;
 import org.apache.spark.sql.Row;
 import org.apache.spark.sql.SparkSession;
diff --git a/mllib/src/test/java/org/apache/spark/ml/tuning/JavaCrossValidatorSuite.java b/mllib/src/test/java/org/apache/spark/ml/tuning/JavaCrossValidatorSuite.java
index 8b4d034ffe..b874ccd48b 100644
--- a/mllib/src/test/java/org/apache/spark/ml/tuning/JavaCrossValidatorSuite.java
+++ b/mllib/src/test/java/org/apache/spark/ml/tuning/JavaCrossValidatorSuite.java
@@ -27,13 +27,13 @@ import org.junit.Test;
 
 import org.apache.spark.api.java.JavaSparkContext;
 import org.apache.spark.ml.classification.LogisticRegression;
+import static org.apache.spark.ml.classification.LogisticRegressionSuite.generateLogisticInputAsList;
+import org.apache.spark.ml.feature.LabeledPoint;
 import org.apache.spark.ml.evaluation.BinaryClassificationEvaluator;
 import org.apache.spark.ml.param.ParamMap;
-import org.apache.spark.mllib.regression.LabeledPoint;
 import org.apache.spark.sql.Dataset;
 import org.apache.spark.sql.Row;
 import org.apache.spark.sql.SparkSession;
-import static org.apache.spark.mllib.classification.LogisticRegressionSuite.generateLogisticInputAsList;
 
 public class JavaCrossValidatorSuite implements Serializable {
 
diff --git a/mllib/src/test/scala/org/apache/spark/ml/PipelineSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/PipelineSuite.scala
index 55448325e4..3b490cdf56 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/PipelineSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/PipelineSuite.scala
@@ -27,9 +27,9 @@ import org.scalatest.mock.MockitoSugar.mock
 import org.apache.spark.SparkFunSuite
 import org.apache.spark.ml.Pipeline.SharedReadWrite
 import org.apache.spark.ml.feature.{HashingTF, MinMaxScaler}
-import org.apache.spark.ml.param.{IntParam, ParamMap, ParamPair}
+import org.apache.spark.ml.linalg.Vectors
+import org.apache.spark.ml.param.{IntParam, ParamMap}
 import org.apache.spark.ml.util._
-import org.apache.spark.mllib.linalg.Vectors
 import org.apache.spark.mllib.util.MLlibTestSparkContext
 import org.apache.spark.sql.{DataFrame, Dataset}
 import org.apache.spark.sql.types.StructType
diff --git a/mllib/src/test/scala/org/apache/spark/ml/ann/ANNSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/ann/ANNSuite.scala
index dc91fc5f9e..35586320cb 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/ann/ANNSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/ann/ANNSuite.scala
@@ -18,10 +18,9 @@
 package org.apache.spark.ml.ann
 
 import org.apache.spark.SparkFunSuite
-import org.apache.spark.mllib.linalg.Vectors
+import org.apache.spark.ml.linalg.Vectors
+import org.apache.spark.ml.util.TestingUtils._
 import org.apache.spark.mllib.util.MLlibTestSparkContext
-import org.apache.spark.mllib.util.TestingUtils._
-
 
 class ANNSuite extends SparkFunSuite with MLlibTestSparkContext {
 
diff --git a/mllib/src/test/scala/org/apache/spark/ml/ann/GradientSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/ann/GradientSuite.scala
index 04cc426c40..f0c0183323 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/ann/GradientSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/ann/GradientSuite.scala
@@ -20,7 +20,7 @@ package org.apache.spark.ml.ann
 import breeze.linalg.{DenseMatrix => BDM}
 
 import org.apache.spark.SparkFunSuite
-import org.apache.spark.mllib.linalg.Vectors
+import org.apache.spark.ml.linalg.Vectors
 import org.apache.spark.mllib.util.MLlibTestSparkContext
 
 class GradientSuite extends SparkFunSuite with MLlibTestSparkContext {
diff --git a/mllib/src/test/scala/org/apache/spark/ml/classification/ClassifierSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/classification/ClassifierSuite.scala
index 98116656ba..4db5f03fb0 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/classification/ClassifierSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/classification/ClassifierSuite.scala
@@ -19,10 +19,10 @@ package org.apache.spark.ml.classification
 
 import org.apache.spark.{SparkException, SparkFunSuite}
 import org.apache.spark.ml.classification.ClassifierSuite.MockClassifier
+import org.apache.spark.ml.feature.LabeledPoint
+import org.apache.spark.ml.linalg.{Vector, Vectors}
 import org.apache.spark.ml.param.ParamMap
 import org.apache.spark.ml.util.Identifiable
-import org.apache.spark.mllib.linalg.{Vector, Vectors}
-import org.apache.spark.mllib.regression.LabeledPoint
 import org.apache.spark.mllib.util.MLlibTestSparkContext
 import org.apache.spark.rdd.RDD
 import org.apache.spark.sql.{DataFrame, Dataset}
diff --git a/mllib/src/test/scala/org/apache/spark/ml/classification/DecisionTreeClassifierSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/classification/DecisionTreeClassifierSuite.scala
index 91a947f44b..089d30abb5 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/classification/DecisionTreeClassifierSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/classification/DecisionTreeClassifierSuite.scala
@@ -18,12 +18,13 @@
 package org.apache.spark.ml.classification
 
 import org.apache.spark.SparkFunSuite
+import org.apache.spark.ml.feature.LabeledPoint
+import org.apache.spark.ml.linalg.{Vector, Vectors}
 import org.apache.spark.ml.param.ParamsSuite
 import org.apache.spark.ml.tree.{CategoricalSplit, InternalNode, LeafNode}
 import org.apache.spark.ml.tree.impl.TreeTests
 import org.apache.spark.ml.util.{DefaultReadWriteTest, MLTestingUtils}
-import org.apache.spark.mllib.linalg.{Vector, Vectors}
-import org.apache.spark.mllib.regression.LabeledPoint
+import org.apache.spark.mllib.regression.{LabeledPoint => OldLabeledPoint}
 import org.apache.spark.mllib.tree.{DecisionTree => OldDecisionTree, DecisionTreeSuite => OldDecisionTreeSuite}
 import org.apache.spark.mllib.util.MLlibTestSparkContext
 import org.apache.spark.rdd.RDD
@@ -44,17 +45,18 @@ class DecisionTreeClassifierSuite
   override def beforeAll() {
     super.beforeAll()
     categoricalDataPointsRDD =
-      sc.parallelize(OldDecisionTreeSuite.generateCategoricalDataPoints())
+      sc.parallelize(OldDecisionTreeSuite.generateCategoricalDataPoints()).map(_.asML)
     orderedLabeledPointsWithLabel0RDD =
-      sc.parallelize(OldDecisionTreeSuite.generateOrderedLabeledPointsWithLabel0())
+      sc.parallelize(OldDecisionTreeSuite.generateOrderedLabeledPointsWithLabel0()).map(_.asML)
     orderedLabeledPointsWithLabel1RDD =
-      sc.parallelize(OldDecisionTreeSuite.generateOrderedLabeledPointsWithLabel1())
+      sc.parallelize(OldDecisionTreeSuite.generateOrderedLabeledPointsWithLabel1()).map(_.asML)
     categoricalDataPointsForMulticlassRDD =
-      sc.parallelize(OldDecisionTreeSuite.generateCategoricalDataPointsForMulticlass())
+      sc.parallelize(OldDecisionTreeSuite.generateCategoricalDataPointsForMulticlass()).map(_.asML)
     continuousDataPointsForMulticlassRDD =
-      sc.parallelize(OldDecisionTreeSuite.generateContinuousDataPointsForMulticlass())
+      sc.parallelize(OldDecisionTreeSuite.generateContinuousDataPointsForMulticlass()).map(_.asML)
     categoricalDataPointsForMulticlassForOrderedFeaturesRDD = sc.parallelize(
       OldDecisionTreeSuite.generateCategoricalDataPointsForMulticlassForOrderedFeatures())
+      .map(_.asML)
   }
 
   test("params") {
@@ -395,7 +397,7 @@ private[ml] object DecisionTreeClassifierSuite extends SparkFunSuite {
       numClasses: Int): Unit = {
     val numFeatures = data.first().features.size
     val oldStrategy = dt.getOldStrategy(categoricalFeatures, numClasses)
-    val oldTree = OldDecisionTree.train(data, oldStrategy)
+    val oldTree = OldDecisionTree.train(data.map(OldLabeledPoint.fromML), oldStrategy)
     val newData: DataFrame = TreeTests.setMetadata(data, categoricalFeatures, numClasses)
     val newTree = dt.fit(newData)
     // Use parent from newTree since this is not checked anyways.
diff --git a/mllib/src/test/scala/org/apache/spark/ml/classification/GBTClassifierSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/classification/GBTClassifierSuite.scala
index 5a5e5c15fc..8d588ccfd3 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/classification/GBTClassifierSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/classification/GBTClassifierSuite.scala
@@ -18,13 +18,14 @@
 package org.apache.spark.ml.classification
 
 import org.apache.spark.{SparkException, SparkFunSuite}
+import org.apache.spark.ml.feature.LabeledPoint
+import org.apache.spark.ml.linalg.Vectors
 import org.apache.spark.ml.param.ParamsSuite
 import org.apache.spark.ml.regression.DecisionTreeRegressionModel
 import org.apache.spark.ml.tree.LeafNode
 import org.apache.spark.ml.tree.impl.TreeTests
 import org.apache.spark.ml.util.{DefaultReadWriteTest, MLTestingUtils}
-import org.apache.spark.mllib.linalg.Vectors
-import org.apache.spark.mllib.regression.LabeledPoint
+import org.apache.spark.mllib.regression.{LabeledPoint => OldLabeledPoint}
 import org.apache.spark.mllib.tree.{EnsembleTestHelper, GradientBoostedTrees => OldGBT}
 import org.apache.spark.mllib.tree.configuration.{Algo => OldAlgo}
 import org.apache.spark.mllib.util.MLlibTestSparkContext
@@ -51,10 +52,13 @@ class GBTClassifierSuite extends SparkFunSuite with MLlibTestSparkContext
   override def beforeAll() {
     super.beforeAll()
     data = sc.parallelize(EnsembleTestHelper.generateOrderedLabeledPoints(numFeatures = 10, 100), 2)
+      .map(_.asML)
     trainData =
       sc.parallelize(EnsembleTestHelper.generateOrderedLabeledPoints(numFeatures = 20, 120), 2)
+        .map(_.asML)
     validationData =
       sc.parallelize(EnsembleTestHelper.generateOrderedLabeledPoints(numFeatures = 20, 80), 2)
+        .map(_.asML)
   }
 
   test("params") {
@@ -229,7 +233,7 @@ private object GBTClassifierSuite extends SparkFunSuite {
     val oldBoostingStrategy =
       gbt.getOldBoostingStrategy(categoricalFeatures, OldAlgo.Classification)
     val oldGBT = new OldGBT(oldBoostingStrategy, gbt.getSeed.toInt)
-    val oldModel = oldGBT.run(data)
+    val oldModel = oldGBT.run(data.map(OldLabeledPoint.fromML))
     val newData: DataFrame = TreeTests.setMetadata(data, categoricalFeatures, numClasses = 2)
     val newModel = gbt.fit(newData)
     // Use parent from newTree since this is not checked anyways.
diff --git a/mllib/src/test/scala/org/apache/spark/ml/classification/LogisticRegressionSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/classification/LogisticRegressionSuite.scala
index 69650ebb36..a1b48539c4 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/classification/LogisticRegressionSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/classification/LogisticRegressionSuite.scala
@@ -17,18 +17,19 @@
 
 package org.apache.spark.ml.classification
 
+import scala.collection.JavaConverters._
 import scala.language.existentials
 import scala.util.Random
+import scala.util.control.Breaks._
 
 import org.apache.spark.SparkFunSuite
-import org.apache.spark.ml.feature.Instance
+import org.apache.spark.ml.classification.LogisticRegressionSuite._
+import org.apache.spark.ml.feature.{Instance, LabeledPoint}
+import org.apache.spark.ml.linalg.{Vector, Vectors}
 import org.apache.spark.ml.param.ParamsSuite
 import org.apache.spark.ml.util.{DefaultReadWriteTest, MLTestingUtils}
-import org.apache.spark.mllib.classification.LogisticRegressionSuite._
-import org.apache.spark.mllib.linalg.{Vector, Vectors}
-import org.apache.spark.mllib.regression.LabeledPoint
+import org.apache.spark.ml.util.TestingUtils._
 import org.apache.spark.mllib.util.MLlibTestSparkContext
-import org.apache.spark.mllib.util.TestingUtils._
 import org.apache.spark.sql.{DataFrame, Dataset, Row}
 import org.apache.spark.sql.functions.lit
 
@@ -967,4 +968,122 @@ object LogisticRegressionSuite {
     "standardization" -> false,
     "threshold" -> 0.6
   )
+
+  def generateLogisticInputAsList(
+    offset: Double,
+    scale: Double,
+    nPoints: Int,
+    seed: Int): java.util.List[LabeledPoint] = {
+    generateLogisticInput(offset, scale, nPoints, seed).asJava
+  }
+
+  // Generate input of the form Y = logistic(offset + scale*X)
+  def generateLogisticInput(
+      offset: Double,
+      scale: Double,
+      nPoints: Int,
+      seed: Int): Seq[LabeledPoint] = {
+    val rnd = new Random(seed)
+    val x1 = Array.fill[Double](nPoints)(rnd.nextGaussian())
+
+    val y = (0 until nPoints).map { i =>
+      val p = 1.0 / (1.0 + math.exp(-(offset + scale * x1(i))))
+      if (rnd.nextDouble() < p) 1.0 else 0.0
+    }
+
+    val testData = (0 until nPoints).map(i => LabeledPoint(y(i), Vectors.dense(Array(x1(i)))))
+    testData
+  }
+
+  /**
+   * Generates `k` classes multinomial synthetic logistic input in `n` dimensional space given the
+   * model weights and mean/variance of the features. The synthetic data will be drawn from
+   * the probability distribution constructed by weights using the following formula.
+   *
+   * P(y = 0 | x) = 1 / norm
+   * P(y = 1 | x) = exp(x * w_1) / norm
+   * P(y = 2 | x) = exp(x * w_2) / norm
+   * ...
+   * P(y = k-1 | x) = exp(x * w_{k-1}) / norm
+   * where norm = 1 + exp(x * w_1) + exp(x * w_2) + ... + exp(x * w_{k-1})
+   *
+   * @param weights matrix is flatten into a vector; as a result, the dimension of weights vector
+   *                will be (k - 1) * (n + 1) if `addIntercept == true`, and
+   *                if `addIntercept != true`, the dimension will be (k - 1) * n.
+   * @param xMean the mean of the generated features. Lots of time, if the features are not properly
+   *              standardized, the algorithm with poor implementation will have difficulty
+   *              to converge.
+   * @param xVariance the variance of the generated features.
+   * @param addIntercept whether to add intercept.
+   * @param nPoints the number of instance of generated data.
+   * @param seed the seed for random generator. For consistent testing result, it will be fixed.
+   */
+  def generateMultinomialLogisticInput(
+      weights: Array[Double],
+      xMean: Array[Double],
+      xVariance: Array[Double],
+      addIntercept: Boolean,
+      nPoints: Int,
+      seed: Int): Seq[LabeledPoint] = {
+    val rnd = new Random(seed)
+
+    val xDim = xMean.length
+    val xWithInterceptsDim = if (addIntercept) xDim + 1 else xDim
+    val nClasses = weights.length / xWithInterceptsDim + 1
+
+    val x = Array.fill[Vector](nPoints)(Vectors.dense(Array.fill[Double](xDim)(rnd.nextGaussian())))
+
+    x.foreach { vector =>
+      // This doesn't work if `vector` is a sparse vector.
+      val vectorArray = vector.toArray
+      var i = 0
+      val len = vectorArray.length
+      while (i < len) {
+        vectorArray(i) = vectorArray(i) * math.sqrt(xVariance(i)) + xMean(i)
+        i += 1
+      }
+    }
+
+    val y = (0 until nPoints).map { idx =>
+      val xArray = x(idx).toArray
+      val margins = Array.ofDim[Double](nClasses)
+      val probs = Array.ofDim[Double](nClasses)
+
+      for (i <- 0 until nClasses - 1) {
+        for (j <- 0 until xDim) margins(i + 1) += weights(i * xWithInterceptsDim + j) * xArray(j)
+        if (addIntercept) margins(i + 1) += weights((i + 1) * xWithInterceptsDim - 1)
+      }
+      // Preventing the overflow when we compute the probability
+      val maxMargin = margins.max
+      if (maxMargin > 0) for (i <- 0 until nClasses) margins(i) -= maxMargin
+
+      // Computing the probabilities for each class from the margins.
+      val norm = {
+        var temp = 0.0
+        for (i <- 0 until nClasses) {
+          probs(i) = math.exp(margins(i))
+          temp += probs(i)
+        }
+        temp
+      }
+      for (i <- 0 until nClasses) probs(i) /= norm
+
+      // Compute the cumulative probability so we can generate a random number and assign a label.
+      for (i <- 1 until nClasses) probs(i) += probs(i - 1)
+      val p = rnd.nextDouble()
+      var y = 0
+      breakable {
+        for (i <- 0 until nClasses) {
+          if (p < probs(i)) {
+            y = i
+            break
+          }
+        }
+      }
+      y
+    }
+
+    val testData = (0 until nPoints).map(i => LabeledPoint(y(i), x(i)))
+    testData
+  }
 }
diff --git a/mllib/src/test/scala/org/apache/spark/ml/classification/MultilayerPerceptronClassifierSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/classification/MultilayerPerceptronClassifierSuite.scala
index d5282e07d6..85f325f076 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/classification/MultilayerPerceptronClassifierSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/classification/MultilayerPerceptronClassifierSuite.scala
@@ -18,14 +18,16 @@
 package org.apache.spark.ml.classification
 
 import org.apache.spark.SparkFunSuite
+import org.apache.spark.ml.classification.LogisticRegressionSuite._
+import org.apache.spark.ml.linalg.{Vector, Vectors}
 import org.apache.spark.ml.util.DefaultReadWriteTest
 import org.apache.spark.ml.util.MLTestingUtils
-import org.apache.spark.mllib.classification.LogisticRegressionSuite._
+import org.apache.spark.ml.util.TestingUtils._
 import org.apache.spark.mllib.classification.LogisticRegressionWithLBFGS
 import org.apache.spark.mllib.evaluation.MulticlassMetrics
-import org.apache.spark.mllib.linalg.{Vector, Vectors}
+import org.apache.spark.mllib.linalg.{Vectors => OldVectors}
+import org.apache.spark.mllib.regression.{LabeledPoint => OldLabeledPoint}
 import org.apache.spark.mllib.util.MLlibTestSparkContext
-import org.apache.spark.mllib.util.TestingUtils._
 import org.apache.spark.sql.{Dataset, Row}
 
 class MultilayerPerceptronClassifierSuite
@@ -134,12 +136,13 @@ class MultilayerPerceptronClassifierSuite
       .setNumClasses(numClasses)
     lr.optimizer.setRegParam(0.0)
       .setNumIterations(numIterations)
-    val lrModel = lr.run(rdd)
-    val lrPredictionAndLabels = lrModel.predict(rdd.map(_.features)).zip(rdd.map(_.label))
+    val lrModel = lr.run(rdd.map(OldLabeledPoint.fromML))
+    val lrPredictionAndLabels =
+      lrModel.predict(rdd.map(p => OldVectors.fromML(p.features))).zip(rdd.map(_.label))
     // MLP's predictions should not differ a lot from LR's.
     val lrMetrics = new MulticlassMetrics(lrPredictionAndLabels)
     val mlpMetrics = new MulticlassMetrics(mlpPredictionAndLabels)
-    assert(mlpMetrics.confusionMatrix ~== lrMetrics.confusionMatrix absTol 100)
+    assert(mlpMetrics.confusionMatrix.asML ~== lrMetrics.confusionMatrix.asML absTol 100)
   }
 
   test("read/write: MultilayerPerceptronClassifier") {
diff --git a/mllib/src/test/scala/org/apache/spark/ml/classification/NaiveBayesSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/classification/NaiveBayesSuite.scala
index 2a05c446e5..ff52115ec0 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/classification/NaiveBayesSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/classification/NaiveBayesSuite.scala
@@ -17,16 +17,20 @@
 
 package org.apache.spark.ml.classification
 
-import breeze.linalg.{Vector => BV}
+import scala.util.Random
+
+import breeze.linalg.{DenseVector => BDV, Vector => BV}
+import breeze.stats.distributions.{Multinomial => BrzMultinomial}
 
 import org.apache.spark.SparkFunSuite
+import org.apache.spark.ml.classification.NaiveBayesSuite._
+import org.apache.spark.ml.feature.LabeledPoint
+import org.apache.spark.ml.linalg._
 import org.apache.spark.ml.param.ParamsSuite
 import org.apache.spark.ml.util.{DefaultReadWriteTest, MLTestingUtils}
+import org.apache.spark.ml.util.TestingUtils._
 import org.apache.spark.mllib.classification.NaiveBayes.{Bernoulli, Multinomial}
-import org.apache.spark.mllib.classification.NaiveBayesSuite._
-import org.apache.spark.mllib.linalg._
 import org.apache.spark.mllib.util.MLlibTestSparkContext
-import org.apache.spark.mllib.util.TestingUtils._
 import org.apache.spark.sql.{DataFrame, Dataset, Row}
 
 class NaiveBayesSuite extends SparkFunSuite with MLlibTestSparkContext with DefaultReadWriteTest {
@@ -206,4 +210,48 @@ object NaiveBayesSuite {
     "predictionCol" -> "myPrediction",
     "smoothing" -> 0.1
   )
+
+  private def calcLabel(p: Double, pi: Array[Double]): Int = {
+    var sum = 0.0
+    for (j <- 0 until pi.length) {
+      sum += pi(j)
+      if (p < sum) return j
+    }
+    -1
+  }
+
+  // Generate input of the form Y = (theta * x).argmax()
+  def generateNaiveBayesInput(
+    pi: Array[Double],            // 1XC
+    theta: Array[Array[Double]],  // CXD
+    nPoints: Int,
+    seed: Int,
+    modelType: String = Multinomial,
+    sample: Int = 10): Seq[LabeledPoint] = {
+    val D = theta(0).length
+    val rnd = new Random(seed)
+    val _pi = pi.map(math.pow(math.E, _))
+    val _theta = theta.map(row => row.map(math.pow(math.E, _)))
+
+    for (i <- 0 until nPoints) yield {
+      val y = calcLabel(rnd.nextDouble(), _pi)
+      val xi = modelType match {
+        case Bernoulli => Array.tabulate[Double] (D) { j =>
+            if (rnd.nextDouble () < _theta(y)(j) ) 1 else 0
+        }
+        case Multinomial =>
+          val mult = BrzMultinomial(BDV(_theta(y)))
+          val emptyMap = (0 until D).map(x => (x, 0.0)).toMap
+          val counts = emptyMap ++ mult.sample(sample).groupBy(x => x).map {
+            case (index, reps) => (index, reps.size.toDouble)
+          }
+          counts.toArray.sortBy(_._1).map(_._2)
+        case _ =>
+          // This should never happen.
+          throw new UnknownError(s"Invalid modelType: $modelType.")
+      }
+
+      LabeledPoint(y, Vectors.dense(xi))
+    }
+  }
 }
diff --git a/mllib/src/test/scala/org/apache/spark/ml/classification/OneVsRestSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/classification/OneVsRestSuite.scala
index 5044d40998..361dd74cb0 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/classification/OneVsRestSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/classification/OneVsRestSuite.scala
@@ -19,14 +19,16 @@ package org.apache.spark.ml.classification
 
 import org.apache.spark.SparkFunSuite
 import org.apache.spark.ml.attribute.NominalAttribute
+import org.apache.spark.ml.classification.LogisticRegressionSuite._
+import org.apache.spark.ml.feature.LabeledPoint
 import org.apache.spark.ml.feature.StringIndexer
+import org.apache.spark.ml.linalg.Vectors
 import org.apache.spark.ml.param.{ParamMap, ParamsSuite}
 import org.apache.spark.ml.util.{DefaultReadWriteTest, MetadataUtils, MLTestingUtils}
-import org.apache.spark.mllib.classification.LogisticRegressionSuite._
 import org.apache.spark.mllib.classification.LogisticRegressionWithLBFGS
 import org.apache.spark.mllib.evaluation.MulticlassMetrics
-import org.apache.spark.mllib.linalg.Vectors
-import org.apache.spark.mllib.regression.LabeledPoint
+import org.apache.spark.mllib.linalg.{Vectors => OldVectors}
+import org.apache.spark.mllib.regression.{LabeledPoint => OldLabeledPoint}
 import org.apache.spark.mllib.util.MLlibTestSparkContext
 import org.apache.spark.mllib.util.TestingUtils._
 import org.apache.spark.rdd.RDD
@@ -88,8 +90,8 @@ class OneVsRestSuite extends SparkFunSuite with MLlibTestSparkContext with Defau
     val lr = new LogisticRegressionWithLBFGS().setIntercept(true).setNumClasses(numClasses)
     lr.optimizer.setRegParam(0.1).setNumIterations(100)
 
-    val model = lr.run(rdd)
-    val results = model.predict(rdd.map(_.features)).zip(rdd.map(_.label))
+    val model = lr.run(rdd.map(OldLabeledPoint.fromML))
+    val results = model.predict(rdd.map(p => OldVectors.fromML(p.features))).zip(rdd.map(_.label))
     // determine the #confusion matrix in each class.
     // bound how much error we allow compared to multinomial logistic regression.
     val expectedMetrics = new MulticlassMetrics(results)
diff --git a/mllib/src/test/scala/org/apache/spark/ml/classification/ProbabilisticClassifierSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/classification/ProbabilisticClassifierSuite.scala
index cfa75ecf38..b3bd2b3e57 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/classification/ProbabilisticClassifierSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/classification/ProbabilisticClassifierSuite.scala
@@ -18,7 +18,7 @@
 package org.apache.spark.ml.classification
 
 import org.apache.spark.SparkFunSuite
-import org.apache.spark.mllib.linalg.{Vector, Vectors}
+import org.apache.spark.ml.linalg.{Vector, Vectors}
 
 final class TestProbabilisticClassificationModel(
     override val uid: String,
diff --git a/mllib/src/test/scala/org/apache/spark/ml/classification/RandomForestClassifierSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/classification/RandomForestClassifierSuite.scala
index 8002a2f4f2..2e99ee157a 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/classification/RandomForestClassifierSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/classification/RandomForestClassifierSuite.scala
@@ -18,12 +18,13 @@
 package org.apache.spark.ml.classification
 
 import org.apache.spark.SparkFunSuite
+import org.apache.spark.ml.feature.LabeledPoint
+import org.apache.spark.ml.linalg.{Vector, Vectors}
 import org.apache.spark.ml.param.ParamsSuite
 import org.apache.spark.ml.tree.LeafNode
 import org.apache.spark.ml.tree.impl.TreeTests
 import org.apache.spark.ml.util.{DefaultReadWriteTest, MLTestingUtils}
-import org.apache.spark.mllib.linalg.{Vector, Vectors}
-import org.apache.spark.mllib.regression.LabeledPoint
+import org.apache.spark.mllib.regression.{LabeledPoint => OldLabeledPoint}
 import org.apache.spark.mllib.tree.{EnsembleTestHelper, RandomForest => OldRandomForest}
 import org.apache.spark.mllib.tree.configuration.{Algo => OldAlgo}
 import org.apache.spark.mllib.util.MLlibTestSparkContext
@@ -46,8 +47,10 @@ class RandomForestClassifierSuite
     super.beforeAll()
     orderedLabeledPoints50_1000 =
       sc.parallelize(EnsembleTestHelper.generateOrderedLabeledPoints(numFeatures = 50, 1000))
+        .map(_.asML)
     orderedLabeledPoints5_20 =
       sc.parallelize(EnsembleTestHelper.generateOrderedLabeledPoints(numFeatures = 5, 20))
+        .map(_.asML)
   }
 
   /////////////////////////////////////////////////////////////////////////////
@@ -233,7 +236,8 @@ private object RandomForestClassifierSuite extends SparkFunSuite {
     val oldStrategy =
       rf.getOldStrategy(categoricalFeatures, numClasses, OldAlgo.Classification, rf.getOldImpurity)
     val oldModel = OldRandomForest.trainClassifier(
-      data, oldStrategy, rf.getNumTrees, rf.getFeatureSubsetStrategy, rf.getSeed.toInt)
+      data.map(OldLabeledPoint.fromML), oldStrategy, rf.getNumTrees, rf.getFeatureSubsetStrategy,
+      rf.getSeed.toInt)
     val newData: DataFrame = TreeTests.setMetadata(data, categoricalFeatures, numClasses)
     val newModel = rf.fit(newData)
     // Use parent from newTree since this is not checked anyways.
diff --git a/mllib/src/test/scala/org/apache/spark/ml/clustering/KMeansSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/clustering/KMeansSuite.scala
index 2832db2f99..88f31a1cd2 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/clustering/KMeansSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/clustering/KMeansSuite.scala
@@ -18,9 +18,9 @@
 package org.apache.spark.ml.clustering
 
 import org.apache.spark.SparkFunSuite
+import org.apache.spark.ml.linalg.{Vector, Vectors}
 import org.apache.spark.ml.util.DefaultReadWriteTest
 import org.apache.spark.mllib.clustering.{KMeans => MLlibKMeans}
-import org.apache.spark.mllib.linalg.{Vector, Vectors}
 import org.apache.spark.mllib.util.MLlibTestSparkContext
 import org.apache.spark.sql.{DataFrame, Dataset, SparkSession}
 
diff --git a/mllib/src/test/scala/org/apache/spark/ml/clustering/LDASuite.scala b/mllib/src/test/scala/org/apache/spark/ml/clustering/LDASuite.scala
index 34e8964286..ddfa875554 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/clustering/LDASuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/clustering/LDASuite.scala
@@ -20,10 +20,10 @@ package org.apache.spark.ml.clustering
 import org.apache.hadoop.fs.Path
 
 import org.apache.spark.SparkFunSuite
+import org.apache.spark.ml.linalg.{Vector, Vectors}
 import org.apache.spark.ml.util.{DefaultReadWriteTest, MLTestingUtils}
-import org.apache.spark.mllib.linalg.{Vector, Vectors}
+import org.apache.spark.ml.util.TestingUtils._
 import org.apache.spark.mllib.util.MLlibTestSparkContext
-import org.apache.spark.mllib.util.TestingUtils._
 import org.apache.spark.sql._
 
 
diff --git a/mllib/src/test/scala/org/apache/spark/ml/evaluation/BinaryClassificationEvaluatorSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/evaluation/BinaryClassificationEvaluatorSuite.scala
index a8766f9035..9ee3df5eb5 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/evaluation/BinaryClassificationEvaluatorSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/evaluation/BinaryClassificationEvaluatorSuite.scala
@@ -18,9 +18,9 @@
 package org.apache.spark.ml.evaluation
 
 import org.apache.spark.SparkFunSuite
+import org.apache.spark.ml.linalg.Vectors
 import org.apache.spark.ml.param.ParamsSuite
 import org.apache.spark.ml.util.{DefaultReadWriteTest, MLTestingUtils}
-import org.apache.spark.mllib.linalg.Vectors
 import org.apache.spark.mllib.util.MLlibTestSparkContext
 
 class BinaryClassificationEvaluatorSuite
diff --git a/mllib/src/test/scala/org/apache/spark/ml/evaluation/RegressionEvaluatorSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/evaluation/RegressionEvaluatorSuite.scala
index dcc004358d..42ff8adf6b 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/evaluation/RegressionEvaluatorSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/evaluation/RegressionEvaluatorSuite.scala
@@ -44,7 +44,7 @@ class RegressionEvaluatorSuite
      */
     val dataset = spark.createDataFrame(
       sc.parallelize(LinearDataGenerator.generateLinearInput(
-        6.3, Array(4.7, 7.2), Array(0.9, -1.3), Array(0.7, 1.2), 100, 42, 0.1), 2))
+        6.3, Array(4.7, 7.2), Array(0.9, -1.3), Array(0.7, 1.2), 100, 42, 0.1), 2).map(_.asML))
 
     /**
      * Using the following R code to load the data, train the model and evaluate metrics.
diff --git a/mllib/src/test/scala/org/apache/spark/ml/feature/BinarizerSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/feature/BinarizerSuite.scala
index e91f758112..9cb84a6ee9 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/feature/BinarizerSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/feature/BinarizerSuite.scala
@@ -18,9 +18,9 @@
 package org.apache.spark.ml.feature
 
 import org.apache.spark.SparkFunSuite
+import org.apache.spark.ml.linalg.{Vector, Vectors}
 import org.apache.spark.ml.param.ParamsSuite
 import org.apache.spark.ml.util.DefaultReadWriteTest
-import org.apache.spark.mllib.linalg.{Vector, Vectors}
 import org.apache.spark.mllib.util.MLlibTestSparkContext
 import org.apache.spark.sql.{DataFrame, Row}
 
diff --git a/mllib/src/test/scala/org/apache/spark/ml/feature/BucketizerSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/feature/BucketizerSuite.scala
index 98b2316d78..cd10c78311 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/feature/BucketizerSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/feature/BucketizerSuite.scala
@@ -20,11 +20,11 @@ package org.apache.spark.ml.feature
 import scala.util.Random
 
 import org.apache.spark.{SparkException, SparkFunSuite}
+import org.apache.spark.ml.linalg.Vectors
 import org.apache.spark.ml.param.ParamsSuite
 import org.apache.spark.ml.util.{DefaultReadWriteTest, MLTestingUtils}
-import org.apache.spark.mllib.linalg.Vectors
+import org.apache.spark.ml.util.TestingUtils._
 import org.apache.spark.mllib.util.MLlibTestSparkContext
-import org.apache.spark.mllib.util.TestingUtils._
 import org.apache.spark.sql.{DataFrame, Row}
 
 class BucketizerSuite extends SparkFunSuite with MLlibTestSparkContext with DefaultReadWriteTest {
diff --git a/mllib/src/test/scala/org/apache/spark/ml/feature/ChiSqSelectorSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/feature/ChiSqSelectorSuite.scala
index 4fcc9745b7..40d5b4881f 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/feature/ChiSqSelectorSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/feature/ChiSqSelectorSuite.scala
@@ -18,12 +18,11 @@
 package org.apache.spark.ml.feature
 
 import org.apache.spark.SparkFunSuite
+import org.apache.spark.ml.linalg.{Vector, Vectors}
 import org.apache.spark.ml.util.{DefaultReadWriteTest, MLTestingUtils}
+import org.apache.spark.ml.util.TestingUtils._
 import org.apache.spark.mllib.feature
-import org.apache.spark.mllib.linalg.{Vector, Vectors}
-import org.apache.spark.mllib.regression.LabeledPoint
 import org.apache.spark.mllib.util.MLlibTestSparkContext
-import org.apache.spark.mllib.util.TestingUtils._
 import org.apache.spark.sql.{Row, SparkSession}
 
 class ChiSqSelectorSuite extends SparkFunSuite with MLlibTestSparkContext
diff --git a/mllib/src/test/scala/org/apache/spark/ml/feature/CountVectorizerSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/feature/CountVectorizerSuite.scala
index b82e3e90b4..a59203c33d 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/feature/CountVectorizerSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/feature/CountVectorizerSuite.scala
@@ -17,11 +17,11 @@
 package org.apache.spark.ml.feature
 
 import org.apache.spark.SparkFunSuite
+import org.apache.spark.ml.linalg.{Vector, Vectors}
 import org.apache.spark.ml.param.ParamsSuite
 import org.apache.spark.ml.util.DefaultReadWriteTest
-import org.apache.spark.mllib.linalg.{Vector, Vectors}
+import org.apache.spark.ml.util.TestingUtils._
 import org.apache.spark.mllib.util.MLlibTestSparkContext
-import org.apache.spark.mllib.util.TestingUtils._
 import org.apache.spark.sql.Row
 
 class CountVectorizerSuite extends SparkFunSuite with MLlibTestSparkContext
diff --git a/mllib/src/test/scala/org/apache/spark/ml/feature/DCTSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/feature/DCTSuite.scala
index dbd5ae8345..c02e961041 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/feature/DCTSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/feature/DCTSuite.scala
@@ -22,8 +22,8 @@ import scala.beans.BeanInfo
 import edu.emory.mathcs.jtransforms.dct.DoubleDCT_1D
 
 import org.apache.spark.SparkFunSuite
+import org.apache.spark.ml.linalg.{Vector, Vectors}
 import org.apache.spark.ml.util.DefaultReadWriteTest
-import org.apache.spark.mllib.linalg.{Vector, Vectors}
 import org.apache.spark.mllib.util.MLlibTestSparkContext
 import org.apache.spark.sql.Row
 
diff --git a/mllib/src/test/scala/org/apache/spark/ml/feature/ElementwiseProductSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/feature/ElementwiseProductSuite.scala
index fc1c05de23..a4cca27be7 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/feature/ElementwiseProductSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/feature/ElementwiseProductSuite.scala
@@ -18,8 +18,8 @@
 package org.apache.spark.ml.feature
 
 import org.apache.spark.SparkFunSuite
+import org.apache.spark.ml.linalg.Vectors
 import org.apache.spark.ml.util.DefaultReadWriteTest
-import org.apache.spark.mllib.linalg.Vectors
 import org.apache.spark.mllib.util.MLlibTestSparkContext
 
 class ElementwiseProductSuite
diff --git a/mllib/src/test/scala/org/apache/spark/ml/feature/HashingTFSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/feature/HashingTFSuite.scala
index 89d67d8e6f..99b800776b 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/feature/HashingTFSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/feature/HashingTFSuite.scala
@@ -19,12 +19,12 @@ package org.apache.spark.ml.feature
 
 import org.apache.spark.SparkFunSuite
 import org.apache.spark.ml.attribute.AttributeGroup
+import org.apache.spark.ml.linalg.{Vector, Vectors}
 import org.apache.spark.ml.param.ParamsSuite
 import org.apache.spark.ml.util.DefaultReadWriteTest
+import org.apache.spark.ml.util.TestingUtils._
 import org.apache.spark.mllib.feature.{HashingTF => MLlibHashingTF}
-import org.apache.spark.mllib.linalg.{Vector, Vectors}
 import org.apache.spark.mllib.util.MLlibTestSparkContext
-import org.apache.spark.mllib.util.TestingUtils._
 import org.apache.spark.util.Utils
 
 class HashingTFSuite extends SparkFunSuite with MLlibTestSparkContext with DefaultReadWriteTest {
diff --git a/mllib/src/test/scala/org/apache/spark/ml/feature/IDFSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/feature/IDFSuite.scala
index 208ea84913..09dc8b9b93 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/feature/IDFSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/feature/IDFSuite.scala
@@ -18,12 +18,13 @@
 package org.apache.spark.ml.feature
 
 import org.apache.spark.SparkFunSuite
+import org.apache.spark.ml.linalg.{DenseVector, SparseVector, Vector, Vectors}
 import org.apache.spark.ml.param.ParamsSuite
 import org.apache.spark.ml.util.DefaultReadWriteTest
+import org.apache.spark.ml.util.TestingUtils._
 import org.apache.spark.mllib.feature.{IDFModel => OldIDFModel}
-import org.apache.spark.mllib.linalg.{DenseVector, SparseVector, Vector, Vectors}
+import org.apache.spark.mllib.linalg.VectorImplicits._
 import org.apache.spark.mllib.util.MLlibTestSparkContext
-import org.apache.spark.mllib.util.TestingUtils._
 import org.apache.spark.sql.Row
 
 class IDFSuite extends SparkFunSuite with MLlibTestSparkContext with DefaultReadWriteTest {
diff --git a/mllib/src/test/scala/org/apache/spark/ml/feature/InteractionSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/feature/InteractionSuite.scala
index 3409928007..3429172a8c 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/feature/InteractionSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/feature/InteractionSuite.scala
@@ -21,9 +21,9 @@ import scala.collection.mutable.ArrayBuilder
 
 import org.apache.spark.{SparkException, SparkFunSuite}
 import org.apache.spark.ml.attribute._
+import org.apache.spark.ml.linalg.{Vector, Vectors}
 import org.apache.spark.ml.param.ParamsSuite
 import org.apache.spark.ml.util.DefaultReadWriteTest
-import org.apache.spark.mllib.linalg.{Vector, Vectors}
 import org.apache.spark.mllib.util.MLlibTestSparkContext
 import org.apache.spark.sql.functions.col
 
diff --git a/mllib/src/test/scala/org/apache/spark/ml/feature/MaxAbsScalerSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/feature/MaxAbsScalerSuite.scala
index 73d69ebfee..d6400ee02f 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/feature/MaxAbsScalerSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/feature/MaxAbsScalerSuite.scala
@@ -17,8 +17,8 @@
 package org.apache.spark.ml.feature
 
 import org.apache.spark.SparkFunSuite
+import org.apache.spark.ml.linalg.{Vector, Vectors}
 import org.apache.spark.ml.util.{DefaultReadWriteTest, MLTestingUtils}
-import org.apache.spark.mllib.linalg.{Vector, Vectors}
 import org.apache.spark.mllib.util.MLlibTestSparkContext
 import org.apache.spark.sql.Row
 
diff --git a/mllib/src/test/scala/org/apache/spark/ml/feature/MinMaxScalerSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/feature/MinMaxScalerSuite.scala
index e495c8e571..5da8471175 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/feature/MinMaxScalerSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/feature/MinMaxScalerSuite.scala
@@ -18,8 +18,8 @@
 package org.apache.spark.ml.feature
 
 import org.apache.spark.SparkFunSuite
+import org.apache.spark.ml.linalg.{Vector, Vectors}
 import org.apache.spark.ml.util.{DefaultReadWriteTest, MLTestingUtils}
-import org.apache.spark.mllib.linalg.{Vector, Vectors}
 import org.apache.spark.mllib.util.MLlibTestSparkContext
 import org.apache.spark.sql.Row
 
diff --git a/mllib/src/test/scala/org/apache/spark/ml/feature/NormalizerSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/feature/NormalizerSuite.scala
index 241a1e9fb5..b692831714 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/feature/NormalizerSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/feature/NormalizerSuite.scala
@@ -18,10 +18,10 @@
 package org.apache.spark.ml.feature
 
 import org.apache.spark.SparkFunSuite
+import org.apache.spark.ml.linalg.{DenseVector, SparseVector, Vector, Vectors}
 import org.apache.spark.ml.util.DefaultReadWriteTest
-import org.apache.spark.mllib.linalg.{DenseVector, SparseVector, Vector, Vectors}
+import org.apache.spark.ml.util.TestingUtils._
 import org.apache.spark.mllib.util.MLlibTestSparkContext
-import org.apache.spark.mllib.util.TestingUtils._
 import org.apache.spark.sql.{DataFrame, Row}
 
 
diff --git a/mllib/src/test/scala/org/apache/spark/ml/feature/OneHotEncoderSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/feature/OneHotEncoderSuite.scala
index 06ffbc386f..7841b4fbc7 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/feature/OneHotEncoderSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/feature/OneHotEncoderSuite.scala
@@ -19,9 +19,9 @@ package org.apache.spark.ml.feature
 
 import org.apache.spark.SparkFunSuite
 import org.apache.spark.ml.attribute.{AttributeGroup, BinaryAttribute, NominalAttribute}
+import org.apache.spark.ml.linalg.Vector
 import org.apache.spark.ml.param.ParamsSuite
 import org.apache.spark.ml.util.DefaultReadWriteTest
-import org.apache.spark.mllib.linalg.Vector
 import org.apache.spark.mllib.util.MLlibTestSparkContext
 import org.apache.spark.sql.DataFrame
 import org.apache.spark.sql.functions.col
diff --git a/mllib/src/test/scala/org/apache/spark/ml/feature/PCASuite.scala b/mllib/src/test/scala/org/apache/spark/ml/feature/PCASuite.scala
index 4befa84dbb..ddb51fb170 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/feature/PCASuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/feature/PCASuite.scala
@@ -18,12 +18,13 @@
 package org.apache.spark.ml.feature
 
 import org.apache.spark.SparkFunSuite
+import org.apache.spark.ml.linalg._
 import org.apache.spark.ml.param.ParamsSuite
 import org.apache.spark.ml.util.{DefaultReadWriteTest, MLTestingUtils}
-import org.apache.spark.mllib.linalg._
+import org.apache.spark.ml.util.TestingUtils._
+import org.apache.spark.mllib.linalg.{Vectors => OldVectors}
 import org.apache.spark.mllib.linalg.distributed.RowMatrix
 import org.apache.spark.mllib.util.MLlibTestSparkContext
-import org.apache.spark.mllib.util.TestingUtils._
 import org.apache.spark.sql.Row
 
 class PCASuite extends SparkFunSuite with MLlibTestSparkContext with DefaultReadWriteTest {
@@ -45,9 +46,9 @@ class PCASuite extends SparkFunSuite with MLlibTestSparkContext with DefaultRead
 
     val dataRDD = sc.parallelize(data, 2)
 
-    val mat = new RowMatrix(dataRDD)
+    val mat = new RowMatrix(dataRDD.map(OldVectors.fromML))
     val pc = mat.computePrincipalComponents(3)
-    val expected = mat.multiply(pc).rows
+    val expected = mat.multiply(pc).rows.map(_.asML)
 
     val df = spark.createDataFrame(dataRDD.zip(expected)).toDF("features", "expected")
 
diff --git a/mllib/src/test/scala/org/apache/spark/ml/feature/PolynomialExpansionSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/feature/PolynomialExpansionSuite.scala
index e3adbba9d5..8e1f9ddb36 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/feature/PolynomialExpansionSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/feature/PolynomialExpansionSuite.scala
@@ -20,11 +20,11 @@ package org.apache.spark.ml.feature
 import org.scalatest.exceptions.TestFailedException
 
 import org.apache.spark.SparkFunSuite
+import org.apache.spark.ml.linalg.{DenseVector, SparseVector, Vector, Vectors}
 import org.apache.spark.ml.param.ParamsSuite
 import org.apache.spark.ml.util.DefaultReadWriteTest
-import org.apache.spark.mllib.linalg.{DenseVector, SparseVector, Vector, Vectors}
+import org.apache.spark.ml.util.TestingUtils._
 import org.apache.spark.mllib.util.MLlibTestSparkContext
-import org.apache.spark.mllib.util.TestingUtils._
 import org.apache.spark.sql.Row
 
 class PolynomialExpansionSuite
diff --git a/mllib/src/test/scala/org/apache/spark/ml/feature/RFormulaSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/feature/RFormulaSuite.scala
index c623a6210b..c12ab8fe9e 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/feature/RFormulaSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/feature/RFormulaSuite.scala
@@ -19,9 +19,9 @@ package org.apache.spark.ml.feature
 
 import org.apache.spark.SparkFunSuite
 import org.apache.spark.ml.attribute._
+import org.apache.spark.ml.linalg.Vectors
 import org.apache.spark.ml.param.ParamsSuite
 import org.apache.spark.ml.util.{DefaultReadWriteTest, MLTestingUtils}
-import org.apache.spark.mllib.linalg.Vectors
 import org.apache.spark.mllib.util.MLlibTestSparkContext
 import org.apache.spark.sql.types.DoubleType
 
diff --git a/mllib/src/test/scala/org/apache/spark/ml/feature/StandardScalerSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/feature/StandardScalerSuite.scala
index d62301be14..2243a0f972 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/feature/StandardScalerSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/feature/StandardScalerSuite.scala
@@ -18,11 +18,11 @@
 package org.apache.spark.ml.feature
 
 import org.apache.spark.SparkFunSuite
+import org.apache.spark.ml.linalg.{Vector, Vectors}
 import org.apache.spark.ml.param.ParamsSuite
 import org.apache.spark.ml.util.DefaultReadWriteTest
-import org.apache.spark.mllib.linalg.{Vector, Vectors}
+import org.apache.spark.ml.util.TestingUtils._
 import org.apache.spark.mllib.util.MLlibTestSparkContext
-import org.apache.spark.mllib.util.TestingUtils._
 import org.apache.spark.sql.{DataFrame, Row}
 
 class StandardScalerSuite extends SparkFunSuite with MLlibTestSparkContext
diff --git a/mllib/src/test/scala/org/apache/spark/ml/feature/VectorAssemblerSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/feature/VectorAssemblerSuite.scala
index 250011c859..14973e79bf 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/feature/VectorAssemblerSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/feature/VectorAssemblerSuite.scala
@@ -19,9 +19,9 @@ package org.apache.spark.ml.feature
 
 import org.apache.spark.{SparkException, SparkFunSuite}
 import org.apache.spark.ml.attribute.{AttributeGroup, NominalAttribute, NumericAttribute}
+import org.apache.spark.ml.linalg.{DenseVector, SparseVector, Vector, Vectors}
 import org.apache.spark.ml.param.ParamsSuite
 import org.apache.spark.ml.util.DefaultReadWriteTest
-import org.apache.spark.mllib.linalg.{DenseVector, SparseVector, Vector, Vectors}
 import org.apache.spark.mllib.util.MLlibTestSparkContext
 import org.apache.spark.sql.Row
 import org.apache.spark.sql.functions.col
diff --git a/mllib/src/test/scala/org/apache/spark/ml/feature/VectorIndexerSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/feature/VectorIndexerSuite.scala
index d1c0270a02..7071423323 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/feature/VectorIndexerSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/feature/VectorIndexerSuite.scala
@@ -22,9 +22,9 @@ import scala.beans.{BeanInfo, BeanProperty}
 import org.apache.spark.{SparkException, SparkFunSuite}
 import org.apache.spark.internal.Logging
 import org.apache.spark.ml.attribute._
+import org.apache.spark.ml.linalg.{SparseVector, Vector, Vectors}
 import org.apache.spark.ml.param.ParamsSuite
 import org.apache.spark.ml.util.{DefaultReadWriteTest, MLTestingUtils}
-import org.apache.spark.mllib.linalg.{SparseVector, Vector, Vectors}
 import org.apache.spark.mllib.util.MLlibTestSparkContext
 import org.apache.spark.rdd.RDD
 import org.apache.spark.sql.DataFrame
diff --git a/mllib/src/test/scala/org/apache/spark/ml/feature/VectorSlicerSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/feature/VectorSlicerSuite.scala
index 88a077f9a1..1746ce5310 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/feature/VectorSlicerSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/feature/VectorSlicerSuite.scala
@@ -19,9 +19,9 @@ package org.apache.spark.ml.feature
 
 import org.apache.spark.SparkFunSuite
 import org.apache.spark.ml.attribute.{Attribute, AttributeGroup, NumericAttribute}
+import org.apache.spark.ml.linalg.{Vector, Vectors, VectorUDT}
 import org.apache.spark.ml.param.ParamsSuite
 import org.apache.spark.ml.util.DefaultReadWriteTest
-import org.apache.spark.mllib.linalg.{Vector, Vectors, VectorUDT}
 import org.apache.spark.mllib.util.MLlibTestSparkContext
 import org.apache.spark.sql.{DataFrame, Row}
 import org.apache.spark.sql.types.{StructField, StructType}
diff --git a/mllib/src/test/scala/org/apache/spark/ml/feature/Word2VecSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/feature/Word2VecSuite.scala
index 8cbe0f3def..280a36f56e 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/feature/Word2VecSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/feature/Word2VecSuite.scala
@@ -18,12 +18,12 @@
 package org.apache.spark.ml.feature
 
 import org.apache.spark.SparkFunSuite
+import org.apache.spark.ml.linalg.{Vector, Vectors}
 import org.apache.spark.ml.param.ParamsSuite
 import org.apache.spark.ml.util.{DefaultReadWriteTest, MLTestingUtils}
+import org.apache.spark.ml.util.TestingUtils._
 import org.apache.spark.mllib.feature.{Word2VecModel => OldWord2VecModel}
-import org.apache.spark.mllib.linalg.{Vector, Vectors}
 import org.apache.spark.mllib.util.MLlibTestSparkContext
-import org.apache.spark.mllib.util.TestingUtils._
 import org.apache.spark.sql.Row
 
 class Word2VecSuite extends SparkFunSuite with MLlibTestSparkContext with DefaultReadWriteTest {
diff --git a/mllib/src/test/scala/org/apache/spark/ml/linalg/VectorUDTSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/linalg/VectorUDTSuite.scala
index 7b50876d33..6ddb12cb76 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/linalg/VectorUDTSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/linalg/VectorUDTSuite.scala
@@ -17,19 +17,11 @@
 
 package org.apache.spark.ml.linalg
 
-import scala.beans.BeanInfo
-
 import org.apache.spark.SparkFunSuite
+import org.apache.spark.ml.feature.LabeledPoint
 import org.apache.spark.sql.catalyst.JavaTypeInference
 import org.apache.spark.sql.types._
 
-@BeanInfo
-case class LabeledPoint(label: Double, features: Vector) {
-  override def toString: String = {
-    s"($label,$features)"
-  }
-}
-
 class VectorUDTSuite extends SparkFunSuite {
 
   test("preloaded VectorUDT") {
diff --git a/mllib/src/test/scala/org/apache/spark/ml/optim/IterativelyReweightedLeastSquaresSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/optim/IterativelyReweightedLeastSquaresSuite.scala
index 604021220a..b30d995794 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/optim/IterativelyReweightedLeastSquaresSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/optim/IterativelyReweightedLeastSquaresSuite.scala
@@ -19,9 +19,9 @@ package org.apache.spark.ml.optim
 
 import org.apache.spark.SparkFunSuite
 import org.apache.spark.ml.feature.Instance
-import org.apache.spark.mllib.linalg.Vectors
+import org.apache.spark.ml.linalg.Vectors
+import org.apache.spark.ml.util.TestingUtils._
 import org.apache.spark.mllib.util.MLlibTestSparkContext
-import org.apache.spark.mllib.util.TestingUtils._
 import org.apache.spark.rdd.RDD
 
 class IterativelyReweightedLeastSquaresSuite extends SparkFunSuite with MLlibTestSparkContext {
diff --git a/mllib/src/test/scala/org/apache/spark/ml/optim/WeightedLeastSquaresSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/optim/WeightedLeastSquaresSuite.scala
index 0b58a9821f..c8de796b2d 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/optim/WeightedLeastSquaresSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/optim/WeightedLeastSquaresSuite.scala
@@ -19,9 +19,9 @@ package org.apache.spark.ml.optim
 
 import org.apache.spark.SparkFunSuite
 import org.apache.spark.ml.feature.Instance
-import org.apache.spark.mllib.linalg.Vectors
+import org.apache.spark.ml.linalg.Vectors
+import org.apache.spark.ml.util.TestingUtils._
 import org.apache.spark.mllib.util.MLlibTestSparkContext
-import org.apache.spark.mllib.util.TestingUtils._
 import org.apache.spark.rdd.RDD
 
 class WeightedLeastSquaresSuite extends SparkFunSuite with MLlibTestSparkContext {
diff --git a/mllib/src/test/scala/org/apache/spark/ml/param/ParamsSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/param/ParamsSuite.scala
index a3366c0e59..aa9c53ca30 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/param/ParamsSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/param/ParamsSuite.scala
@@ -17,11 +17,11 @@
 
 package org.apache.spark.ml.param
 
-import java.io.{ByteArrayOutputStream, NotSerializableException, ObjectOutputStream}
+import java.io.{ByteArrayOutputStream, ObjectOutputStream}
 
 import org.apache.spark.SparkFunSuite
+import org.apache.spark.ml.linalg.{Vector, Vectors}
 import org.apache.spark.ml.util.MyParams
-import org.apache.spark.mllib.linalg.{Vector, Vectors}
 
 class ParamsSuite extends SparkFunSuite {
 
diff --git a/mllib/src/test/scala/org/apache/spark/ml/recommendation/ALSSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/recommendation/ALSSuite.scala
index 9da0c32dee..bbfc415cbb 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/recommendation/ALSSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/recommendation/ALSSuite.scala
@@ -31,11 +31,11 @@ import org.apache.commons.io.filefilter.TrueFileFilter
 
 import org.apache.spark._
 import org.apache.spark.internal.Logging
+import org.apache.spark.ml.linalg.Vectors
 import org.apache.spark.ml.recommendation.ALS._
 import org.apache.spark.ml.util.{DefaultReadWriteTest, MLTestingUtils}
-import org.apache.spark.mllib.linalg.Vectors
+import org.apache.spark.ml.util.TestingUtils._
 import org.apache.spark.mllib.util.MLlibTestSparkContext
-import org.apache.spark.mllib.util.TestingUtils._
 import org.apache.spark.rdd.RDD
 import org.apache.spark.scheduler.{SparkListener, SparkListenerStageCompleted}
 import org.apache.spark.sql.{DataFrame, Row, SparkSession}
diff --git a/mllib/src/test/scala/org/apache/spark/ml/regression/AFTSurvivalRegressionSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/regression/AFTSurvivalRegressionSuite.scala
index e4772df622..05aae80c66 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/regression/AFTSurvivalRegressionSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/regression/AFTSurvivalRegressionSuite.scala
@@ -20,12 +20,12 @@ package org.apache.spark.ml.regression
 import scala.util.Random
 
 import org.apache.spark.SparkFunSuite
+import org.apache.spark.ml.linalg.{Vector, Vectors}
 import org.apache.spark.ml.param.ParamsSuite
 import org.apache.spark.ml.util.{DefaultReadWriteTest, MLTestingUtils}
-import org.apache.spark.mllib.linalg.{Vector, Vectors}
+import org.apache.spark.ml.util.TestingUtils._
 import org.apache.spark.mllib.random.{ExponentialGenerator, WeibullGenerator}
 import org.apache.spark.mllib.util.MLlibTestSparkContext
-import org.apache.spark.mllib.util.TestingUtils._
 import org.apache.spark.sql.{DataFrame, Row}
 
 class AFTSurvivalRegressionSuite
diff --git a/mllib/src/test/scala/org/apache/spark/ml/regression/DecisionTreeRegressorSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/regression/DecisionTreeRegressorSuite.scala
index 2d30cbf367..9afb742406 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/regression/DecisionTreeRegressorSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/regression/DecisionTreeRegressorSuite.scala
@@ -18,10 +18,11 @@
 package org.apache.spark.ml.regression
 
 import org.apache.spark.SparkFunSuite
+import org.apache.spark.ml.feature.LabeledPoint
+import org.apache.spark.ml.linalg.Vector
 import org.apache.spark.ml.tree.impl.TreeTests
 import org.apache.spark.ml.util.{DefaultReadWriteTest, MLTestingUtils}
-import org.apache.spark.mllib.linalg.Vector
-import org.apache.spark.mllib.regression.LabeledPoint
+import org.apache.spark.mllib.regression.{LabeledPoint => OldLabeledPoint}
 import org.apache.spark.mllib.tree.{DecisionTree => OldDecisionTree,
   DecisionTreeSuite => OldDecisionTreeSuite}
 import org.apache.spark.mllib.util.MLlibTestSparkContext
@@ -38,7 +39,7 @@ class DecisionTreeRegressorSuite
   override def beforeAll() {
     super.beforeAll()
     categoricalDataPointsRDD =
-      sc.parallelize(OldDecisionTreeSuite.generateCategoricalDataPoints())
+      sc.parallelize(OldDecisionTreeSuite.generateCategoricalDataPoints().map(_.asML))
   }
 
   /////////////////////////////////////////////////////////////////////////////
@@ -170,7 +171,7 @@ private[ml] object DecisionTreeRegressorSuite extends SparkFunSuite {
       categoricalFeatures: Map[Int, Int]): Unit = {
     val numFeatures = data.first().features.size
     val oldStrategy = dt.getOldStrategy(categoricalFeatures)
-    val oldTree = OldDecisionTree.train(data, oldStrategy)
+    val oldTree = OldDecisionTree.train(data.map(OldLabeledPoint.fromML), oldStrategy)
     val newData: DataFrame = TreeTests.setMetadata(data, categoricalFeatures, numClasses = 0)
     val newTree = dt.fit(newData)
     // Use parent from newTree since this is not checked anyways.
diff --git a/mllib/src/test/scala/org/apache/spark/ml/regression/GBTRegressorSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/regression/GBTRegressorSuite.scala
index ac833b833d..7b5df8f31b 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/regression/GBTRegressorSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/regression/GBTRegressorSuite.scala
@@ -18,10 +18,11 @@
 package org.apache.spark.ml.regression
 
 import org.apache.spark.SparkFunSuite
+import org.apache.spark.ml.feature.LabeledPoint
+import org.apache.spark.ml.linalg.Vectors
 import org.apache.spark.ml.tree.impl.TreeTests
 import org.apache.spark.ml.util.{DefaultReadWriteTest, MLTestingUtils}
-import org.apache.spark.mllib.linalg.Vectors
-import org.apache.spark.mllib.regression.LabeledPoint
+import org.apache.spark.mllib.regression.{LabeledPoint => OldLabeledPoint}
 import org.apache.spark.mllib.tree.{EnsembleTestHelper, GradientBoostedTrees => OldGBT}
 import org.apache.spark.mllib.tree.configuration.{Algo => OldAlgo}
 import org.apache.spark.mllib.util.MLlibTestSparkContext
@@ -48,10 +49,13 @@ class GBTRegressorSuite extends SparkFunSuite with MLlibTestSparkContext
   override def beforeAll() {
     super.beforeAll()
     data = sc.parallelize(EnsembleTestHelper.generateOrderedLabeledPoints(numFeatures = 10, 100), 2)
+      .map(_.asML)
     trainData =
       sc.parallelize(EnsembleTestHelper.generateOrderedLabeledPoints(numFeatures = 20, 120), 2)
+        .map(_.asML)
     validationData =
       sc.parallelize(EnsembleTestHelper.generateOrderedLabeledPoints(numFeatures = 20, 80), 2)
+        .map(_.asML)
   }
 
   test("Regression with continuous features") {
@@ -197,7 +201,7 @@ private object GBTRegressorSuite extends SparkFunSuite {
     val numFeatures = data.first().features.size
     val oldBoostingStrategy = gbt.getOldBoostingStrategy(categoricalFeatures, OldAlgo.Regression)
     val oldGBT = new OldGBT(oldBoostingStrategy, gbt.getSeed.toInt)
-    val oldModel = oldGBT.run(data)
+    val oldModel = oldGBT.run(data.map(OldLabeledPoint.fromML))
     val newData: DataFrame = TreeTests.setMetadata(data, categoricalFeatures, numClasses = 0)
     val newModel = gbt.fit(newData)
     // Use parent from newTree since this is not checked anyways.
diff --git a/mllib/src/test/scala/org/apache/spark/ml/regression/GeneralizedLinearRegressionSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/regression/GeneralizedLinearRegressionSuite.scala
index 3d9aeb8c0a..a4568e83fa 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/regression/GeneralizedLinearRegressionSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/regression/GeneralizedLinearRegressionSuite.scala
@@ -20,15 +20,15 @@ package org.apache.spark.ml.regression
 import scala.util.Random
 
 import org.apache.spark.SparkFunSuite
+import org.apache.spark.ml.classification.LogisticRegressionSuite._
 import org.apache.spark.ml.feature.Instance
+import org.apache.spark.ml.feature.LabeledPoint
+import org.apache.spark.ml.linalg.{BLAS, DenseVector, Vector, Vectors}
 import org.apache.spark.ml.param.ParamsSuite
 import org.apache.spark.ml.util.{DefaultReadWriteTest, MLTestingUtils}
-import org.apache.spark.mllib.classification.LogisticRegressionSuite._
-import org.apache.spark.mllib.linalg.{BLAS, DenseVector, Vector, Vectors}
+import org.apache.spark.ml.util.TestingUtils._
 import org.apache.spark.mllib.random._
-import org.apache.spark.mllib.regression.LabeledPoint
 import org.apache.spark.mllib.util.MLlibTestSparkContext
-import org.apache.spark.mllib.util.TestingUtils._
 import org.apache.spark.sql.{DataFrame, Row}
 import org.apache.spark.sql.functions._
 
diff --git a/mllib/src/test/scala/org/apache/spark/ml/regression/IsotonicRegressionSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/regression/IsotonicRegressionSuite.scala
index bed4978b25..14d8a4e4e3 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/regression/IsotonicRegressionSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/regression/IsotonicRegressionSuite.scala
@@ -18,9 +18,9 @@
 package org.apache.spark.ml.regression
 
 import org.apache.spark.SparkFunSuite
+import org.apache.spark.ml.linalg.Vectors
 import org.apache.spark.ml.param.ParamsSuite
 import org.apache.spark.ml.util.{DefaultReadWriteTest, MLTestingUtils}
-import org.apache.spark.mllib.linalg.Vectors
 import org.apache.spark.mllib.util.MLlibTestSparkContext
 import org.apache.spark.sql.{DataFrame, Row}
 
diff --git a/mllib/src/test/scala/org/apache/spark/ml/regression/LinearRegressionSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/regression/LinearRegressionSuite.scala
index a98227d2c1..332d331a47 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/regression/LinearRegressionSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/regression/LinearRegressionSuite.scala
@@ -21,12 +21,12 @@ import scala.util.Random
 
 import org.apache.spark.SparkFunSuite
 import org.apache.spark.ml.feature.Instance
+import org.apache.spark.ml.feature.LabeledPoint
+import org.apache.spark.ml.linalg.{DenseVector, Vector, Vectors}
 import org.apache.spark.ml.param.ParamsSuite
 import org.apache.spark.ml.util.{DefaultReadWriteTest, MLTestingUtils}
-import org.apache.spark.mllib.linalg.{DenseVector, Vector, Vectors}
-import org.apache.spark.mllib.regression.LabeledPoint
+import org.apache.spark.ml.util.TestingUtils._
 import org.apache.spark.mllib.util.{LinearDataGenerator, MLlibTestSparkContext}
-import org.apache.spark.mllib.util.TestingUtils._
 import org.apache.spark.sql.{DataFrame, Row}
 
 class LinearRegressionSuite
@@ -45,7 +45,7 @@ class LinearRegressionSuite
     datasetWithDenseFeature = spark.createDataFrame(
       sc.parallelize(LinearDataGenerator.generateLinearInput(
         intercept = 6.3, weights = Array(4.7, 7.2), xMean = Array(0.9, -1.3),
-        xVariance = Array(0.7, 1.2), nPoints = 10000, seed, eps = 0.1), 2))
+        xVariance = Array(0.7, 1.2), nPoints = 10000, seed, eps = 0.1), 2).map(_.asML))
     /*
        datasetWithDenseFeatureWithoutIntercept is not needed for correctness testing
        but is useful for illustrating training model without intercept
@@ -53,7 +53,7 @@ class LinearRegressionSuite
     datasetWithDenseFeatureWithoutIntercept = spark.createDataFrame(
       sc.parallelize(LinearDataGenerator.generateLinearInput(
         intercept = 0.0, weights = Array(4.7, 7.2), xMean = Array(0.9, -1.3),
-        xVariance = Array(0.7, 1.2), nPoints = 10000, seed, eps = 0.1), 2))
+        xVariance = Array(0.7, 1.2), nPoints = 10000, seed, eps = 0.1), 2).map(_.asML))
 
     val r = new Random(seed)
     // When feature size is larger than 4096, normal optimizer is choosed
@@ -64,7 +64,7 @@ class LinearRegressionSuite
         intercept = 0.0, weights = Seq.fill(featureSize)(r.nextDouble()).toArray,
         xMean = Seq.fill(featureSize)(r.nextDouble()).toArray,
         xVariance = Seq.fill(featureSize)(r.nextDouble()).toArray, nPoints = 200,
-        seed, eps = 0.1, sparsity = 0.7), 2))
+        seed, eps = 0.1, sparsity = 0.7), 2).map(_.asML))
 
     /*
        R code:
@@ -795,7 +795,7 @@ class LinearRegressionSuite
     Seq("auto", "l-bfgs", "normal").foreach { solver =>
       val (data, weightedData) = {
         val activeData = LinearDataGenerator.generateLinearInput(
-          6.3, Array(4.7, 7.2), Array(0.9, -1.3), Array(0.7, 1.2), 500, 1, 0.1)
+          6.3, Array(4.7, 7.2), Array(0.9, -1.3), Array(0.7, 1.2), 500, 1, 0.1).map(_.asML)
 
         val rnd = new Random(8392)
         val signedData = activeData.map { case p: LabeledPoint =>
@@ -822,7 +822,7 @@ class LinearRegressionSuite
         }
 
         val noiseData = LinearDataGenerator.generateLinearInput(
-          2, Array(1, 3), Array(0.9, -1.3), Array(0.7, 1.2), 500, 1, 0.1)
+          2, Array(1, 3), Array(0.9, -1.3), Array(0.7, 1.2), 500, 1, 0.1).map(_.asML)
         val weightedNoiseData = noiseData.map {
           case LabeledPoint(label, features) => Instance(label, weight = 0, features)
         }
diff --git a/mllib/src/test/scala/org/apache/spark/ml/regression/RandomForestRegressorSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/regression/RandomForestRegressorSuite.scala
index 7a3a3698f9..c08335f9f8 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/regression/RandomForestRegressorSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/regression/RandomForestRegressorSuite.scala
@@ -18,9 +18,10 @@
 package org.apache.spark.ml.regression
 
 import org.apache.spark.SparkFunSuite
+import org.apache.spark.ml.feature.LabeledPoint
 import org.apache.spark.ml.tree.impl.TreeTests
 import org.apache.spark.ml.util.{DefaultReadWriteTest, MLTestingUtils}
-import org.apache.spark.mllib.regression.LabeledPoint
+import org.apache.spark.mllib.regression.{LabeledPoint => OldLabeledPoint}
 import org.apache.spark.mllib.tree.{EnsembleTestHelper, RandomForest => OldRandomForest}
 import org.apache.spark.mllib.tree.configuration.{Algo => OldAlgo}
 import org.apache.spark.mllib.util.MLlibTestSparkContext
@@ -40,7 +41,8 @@ class RandomForestRegressorSuite extends SparkFunSuite with MLlibTestSparkContex
   override def beforeAll() {
     super.beforeAll()
     orderedLabeledPoints50_1000 =
-      sc.parallelize(EnsembleTestHelper.generateOrderedLabeledPoints(numFeatures = 50, 1000))
+      sc.parallelize(EnsembleTestHelper.generateOrderedLabeledPoints(numFeatures = 50, 1000)
+        .map(_.asML))
   }
 
   /////////////////////////////////////////////////////////////////////////////
@@ -139,8 +141,8 @@ private object RandomForestRegressorSuite extends SparkFunSuite {
     val numFeatures = data.first().features.size
     val oldStrategy =
       rf.getOldStrategy(categoricalFeatures, numClasses = 0, OldAlgo.Regression, rf.getOldImpurity)
-    val oldModel = OldRandomForest.trainRegressor(
-      data, oldStrategy, rf.getNumTrees, rf.getFeatureSubsetStrategy, rf.getSeed.toInt)
+    val oldModel = OldRandomForest.trainRegressor(data.map(OldLabeledPoint.fromML), oldStrategy,
+      rf.getNumTrees, rf.getFeatureSubsetStrategy, rf.getSeed.toInt)
     val newData: DataFrame = TreeTests.setMetadata(data, categoricalFeatures, numClasses = 0)
     val newModel = rf.fit(newData)
     // Use parent from newTree since this is not checked anyways.
diff --git a/mllib/src/test/scala/org/apache/spark/ml/source/libsvm/LibSVMRelationSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/source/libsvm/LibSVMRelationSuite.scala
index 7d0e01fd8f..2517de59fe 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/source/libsvm/LibSVMRelationSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/source/libsvm/LibSVMRelationSuite.scala
@@ -23,7 +23,7 @@ import java.nio.charset.StandardCharsets
 import com.google.common.io.Files
 
 import org.apache.spark.{SparkException, SparkFunSuite}
-import org.apache.spark.mllib.linalg.{DenseVector, SparseVector, Vector, Vectors}
+import org.apache.spark.ml.linalg.{DenseVector, SparseVector, Vector, Vectors}
 import org.apache.spark.mllib.util.MLlibTestSparkContext
 import org.apache.spark.sql.{Row, SaveMode}
 import org.apache.spark.util.Utils
diff --git a/mllib/src/test/scala/org/apache/spark/ml/tree/impl/GradientBoostedTreesSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/tree/impl/GradientBoostedTreesSuite.scala
index de92b51eb0..5c50a88c83 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/tree/impl/GradientBoostedTreesSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/tree/impl/GradientBoostedTreesSuite.scala
@@ -19,7 +19,7 @@ package org.apache.spark.ml.tree.impl
 
 import org.apache.spark.SparkFunSuite
 import org.apache.spark.internal.Logging
-import org.apache.spark.mllib.regression.LabeledPoint
+import org.apache.spark.ml.feature.LabeledPoint
 import org.apache.spark.mllib.tree.{GradientBoostedTreesSuite => OldGBTSuite}
 import org.apache.spark.mllib.tree.configuration.{BoostingStrategy, Strategy}
 import org.apache.spark.mllib.tree.configuration.Algo._
@@ -35,8 +35,8 @@ class GradientBoostedTreesSuite extends SparkFunSuite with MLlibTestSparkContext
   test("runWithValidation stops early and performs better on a validation dataset") {
     // Set numIterations large enough so that it stops early.
     val numIterations = 20
-    val trainRdd = sc.parallelize(OldGBTSuite.trainData, 2)
-    val validateRdd = sc.parallelize(OldGBTSuite.validateData, 2)
+    val trainRdd = sc.parallelize(OldGBTSuite.trainData, 2).map(_.asML)
+    val validateRdd = sc.parallelize(OldGBTSuite.validateData, 2).map(_.asML)
     val trainDF = spark.createDataFrame(trainRdd)
     val validateDF = spark.createDataFrame(validateRdd)
 
diff --git a/mllib/src/test/scala/org/apache/spark/ml/tree/impl/RandomForestSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/tree/impl/RandomForestSuite.scala
index 9739e6c05d..dcc2f305df 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/tree/impl/RandomForestSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/tree/impl/RandomForestSuite.scala
@@ -21,14 +21,14 @@ import scala.collection.mutable
 
 import org.apache.spark.SparkFunSuite
 import org.apache.spark.ml.classification.DecisionTreeClassificationModel
+import org.apache.spark.ml.feature.LabeledPoint
+import org.apache.spark.ml.linalg.{Vector, Vectors}
 import org.apache.spark.ml.tree._
-import org.apache.spark.mllib.linalg.{Vector, Vectors}
-import org.apache.spark.mllib.regression.LabeledPoint
+import org.apache.spark.ml.util.TestingUtils._
 import org.apache.spark.mllib.tree.{DecisionTreeSuite => OldDTSuite, EnsembleTestHelper}
 import org.apache.spark.mllib.tree.configuration.{Algo => OldAlgo, QuantileStrategy, Strategy => OldStrategy}
 import org.apache.spark.mllib.tree.impurity.{Entropy, Gini, GiniCalculator}
 import org.apache.spark.mllib.util.MLlibTestSparkContext
-import org.apache.spark.mllib.util.TestingUtils._
 import org.apache.spark.util.collection.OpenHashMap
 
 /**
@@ -43,7 +43,7 @@ class RandomForestSuite extends SparkFunSuite with MLlibTestSparkContext {
   /////////////////////////////////////////////////////////////////////////////
 
   test("Binary classification with continuous features: split calculation") {
-    val arr = OldDTSuite.generateOrderedLabeledPointsWithLabel1()
+    val arr = OldDTSuite.generateOrderedLabeledPointsWithLabel1().map(_.asML)
     assert(arr.length === 1000)
     val rdd = sc.parallelize(arr)
     val strategy = new OldStrategy(OldAlgo.Classification, Gini, 3, 2, 100)
@@ -55,7 +55,7 @@ class RandomForestSuite extends SparkFunSuite with MLlibTestSparkContext {
   }
 
   test("Binary classification with binary (ordered) categorical features: split calculation") {
-    val arr = OldDTSuite.generateCategoricalDataPoints()
+    val arr = OldDTSuite.generateCategoricalDataPoints().map(_.asML)
     assert(arr.length === 1000)
     val rdd = sc.parallelize(arr)
     val strategy = new OldStrategy(OldAlgo.Classification, Gini, maxDepth = 2, numClasses = 2,
@@ -72,7 +72,7 @@ class RandomForestSuite extends SparkFunSuite with MLlibTestSparkContext {
 
   test("Binary classification with 3-ary (ordered) categorical features," +
     " with no samples for one category: split calculation") {
-    val arr = OldDTSuite.generateCategoricalDataPoints()
+    val arr = OldDTSuite.generateCategoricalDataPoints().map(_.asML)
     assert(arr.length === 1000)
     val rdd = sc.parallelize(arr)
     val strategy = new OldStrategy(OldAlgo.Classification, Gini, maxDepth = 2, numClasses = 2,
@@ -148,7 +148,7 @@ class RandomForestSuite extends SparkFunSuite with MLlibTestSparkContext {
   }
 
   test("Multiclass classification with unordered categorical features: split calculations") {
-    val arr = OldDTSuite.generateCategoricalDataPoints()
+    val arr = OldDTSuite.generateCategoricalDataPoints().map(_.asML)
     assert(arr.length === 1000)
     val rdd = sc.parallelize(arr)
     val strategy = new OldStrategy(
@@ -189,7 +189,7 @@ class RandomForestSuite extends SparkFunSuite with MLlibTestSparkContext {
   }
 
   test("Multiclass classification with ordered categorical features: split calculations") {
-    val arr = OldDTSuite.generateCategoricalDataPointsForMulticlassForOrderedFeatures()
+    val arr = OldDTSuite.generateCategoricalDataPointsForMulticlassForOrderedFeatures().map(_.asML)
     assert(arr.length === 3000)
     val rdd = sc.parallelize(arr)
     val strategy = new OldStrategy(OldAlgo.Classification, Gini, maxDepth = 2, numClasses = 100,
@@ -334,7 +334,7 @@ class RandomForestSuite extends SparkFunSuite with MLlibTestSparkContext {
   }
 
   test("Second level node building with vs. without groups") {
-    val arr = OldDTSuite.generateOrderedLabeledPoints()
+    val arr = OldDTSuite.generateOrderedLabeledPoints().map(_.asML)
     assert(arr.length === 1000)
     val rdd = sc.parallelize(arr)
     // For tree with 1 group
@@ -378,7 +378,7 @@ class RandomForestSuite extends SparkFunSuite with MLlibTestSparkContext {
   def binaryClassificationTestWithContinuousFeaturesAndSubsampledFeatures(strategy: OldStrategy) {
     val numFeatures = 50
     val arr = EnsembleTestHelper.generateOrderedLabeledPoints(numFeatures, 1000)
-    val rdd = sc.parallelize(arr)
+    val rdd = sc.parallelize(arr).map(_.asML)
 
     // Select feature subset for top nodes.  Return true if OK.
     def checkFeatureSubsetStrategy(
diff --git a/mllib/src/test/scala/org/apache/spark/ml/tree/impl/TreeTests.scala b/mllib/src/test/scala/org/apache/spark/ml/tree/impl/TreeTests.scala
index 12ade4c92f..8cbd652bac 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/tree/impl/TreeTests.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/tree/impl/TreeTests.scala
@@ -22,9 +22,9 @@ import scala.collection.JavaConverters._
 import org.apache.spark.{SparkContext, SparkFunSuite}
 import org.apache.spark.api.java.JavaRDD
 import org.apache.spark.ml.attribute.{AttributeGroup, NominalAttribute, NumericAttribute}
+import org.apache.spark.ml.feature.LabeledPoint
+import org.apache.spark.ml.linalg.Vectors
 import org.apache.spark.ml.tree._
-import org.apache.spark.mllib.linalg.Vectors
-import org.apache.spark.mllib.regression.LabeledPoint
 import org.apache.spark.rdd.RDD
 import org.apache.spark.sql.{DataFrame, SparkSession}
 
diff --git a/mllib/src/test/scala/org/apache/spark/ml/tuning/CrossValidatorSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/tuning/CrossValidatorSuite.scala
index 85df6da7a1..30bd390381 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/tuning/CrossValidatorSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/tuning/CrossValidatorSuite.scala
@@ -20,17 +20,17 @@ package org.apache.spark.ml.tuning
 import org.apache.spark.SparkFunSuite
 import org.apache.spark.ml.{Estimator, Model, Pipeline}
 import org.apache.spark.ml.classification.{LogisticRegression, LogisticRegressionModel}
+import org.apache.spark.ml.classification.LogisticRegressionSuite.generateLogisticInput
 import org.apache.spark.ml.evaluation.{BinaryClassificationEvaluator, Evaluator, RegressionEvaluator}
 import org.apache.spark.ml.feature.HashingTF
+import org.apache.spark.ml.linalg.Vectors
 import org.apache.spark.ml.param.{ParamMap, ParamPair}
 import org.apache.spark.ml.param.shared.HasInputCol
 import org.apache.spark.ml.regression.LinearRegression
 import org.apache.spark.ml.util.{DefaultReadWriteTest, MLTestingUtils}
-import org.apache.spark.mllib.classification.LogisticRegressionSuite.generateLogisticInput
-import org.apache.spark.mllib.linalg.Vectors
 import org.apache.spark.mllib.util.{LinearDataGenerator, MLlibTestSparkContext}
-import org.apache.spark.sql.{DataFrame, Dataset}
-import org.apache.spark.sql.types.{StructField, StructType}
+import org.apache.spark.sql.Dataset
+import org.apache.spark.sql.types.StructType
 
 class CrossValidatorSuite
   extends SparkFunSuite with MLlibTestSparkContext with DefaultReadWriteTest {
@@ -69,7 +69,7 @@ class CrossValidatorSuite
   test("cross validation with linear regression") {
     val dataset = spark.createDataFrame(
       sc.parallelize(LinearDataGenerator.generateLinearInput(
-        6.3, Array(4.7, 7.2), Array(0.9, -1.3), Array(0.7, 1.2), 100, 42, 0.1), 2))
+        6.3, Array(4.7, 7.2), Array(0.9, -1.3), Array(0.7, 1.2), 100, 42, 0.1), 2).map(_.asML))
 
     val trainer = new LinearRegression().setSolver("l-bfgs")
     val lrParamMaps = new ParamGridBuilder()
diff --git a/mllib/src/test/scala/org/apache/spark/ml/tuning/TrainValidationSplitSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/tuning/TrainValidationSplitSuite.scala
index f8d3de19b0..c1e9c2fc1d 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/tuning/TrainValidationSplitSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/tuning/TrainValidationSplitSuite.scala
@@ -20,15 +20,15 @@ package org.apache.spark.ml.tuning
 import org.apache.spark.SparkFunSuite
 import org.apache.spark.ml.{Estimator, Model}
 import org.apache.spark.ml.classification.{LogisticRegression, LogisticRegressionModel}
+import org.apache.spark.ml.classification.LogisticRegressionSuite.generateLogisticInput
 import org.apache.spark.ml.evaluation.{BinaryClassificationEvaluator, Evaluator, RegressionEvaluator}
+import org.apache.spark.ml.linalg.Vectors
 import org.apache.spark.ml.param.ParamMap
 import org.apache.spark.ml.param.shared.HasInputCol
 import org.apache.spark.ml.regression.LinearRegression
 import org.apache.spark.ml.util.DefaultReadWriteTest
-import org.apache.spark.mllib.classification.LogisticRegressionSuite.generateLogisticInput
-import org.apache.spark.mllib.linalg.Vectors
 import org.apache.spark.mllib.util.{LinearDataGenerator, MLlibTestSparkContext}
-import org.apache.spark.sql.{DataFrame, Dataset}
+import org.apache.spark.sql.Dataset
 import org.apache.spark.sql.types.StructType
 
 class TrainValidationSplitSuite
@@ -60,7 +60,7 @@ class TrainValidationSplitSuite
   test("train validation with linear regression") {
     val dataset = spark.createDataFrame(
         sc.parallelize(LinearDataGenerator.generateLinearInput(
-            6.3, Array(4.7, 7.2), Array(0.9, -1.3), Array(0.7, 1.2), 100, 42, 0.1), 2))
+            6.3, Array(4.7, 7.2), Array(0.9, -1.3), Array(0.7, 1.2), 100, 42, 0.1), 2).map(_.asML))
 
     val trainer = new LinearRegression().setSolver("l-bfgs")
     val lrParamMaps = new ParamGridBuilder()
diff --git a/mllib/src/test/scala/org/apache/spark/ml/util/MLTestingUtils.scala b/mllib/src/test/scala/org/apache/spark/ml/util/MLTestingUtils.scala
index ad7d2c9b8d..6aae625fc8 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/util/MLTestingUtils.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/util/MLTestingUtils.scala
@@ -20,9 +20,9 @@ package org.apache.spark.ml.util
 import org.apache.spark.SparkFunSuite
 import org.apache.spark.ml.{Estimator, Model}
 import org.apache.spark.ml.evaluation.Evaluator
+import org.apache.spark.ml.linalg.Vectors
 import org.apache.spark.ml.param.ParamMap
 import org.apache.spark.ml.tree.impl.TreeTests
-import org.apache.spark.mllib.linalg.Vectors
 import org.apache.spark.sql.{DataFrame, SparkSession}
 import org.apache.spark.sql.functions._
 import org.apache.spark.sql.types._
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/linalg/MatricesSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/linalg/MatricesSuite.scala
index b7df02e6c0..8c5b4bda25 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/linalg/MatricesSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/linalg/MatricesSuite.scala
@@ -560,4 +560,55 @@ class MatricesSuite extends SparkFunSuite {
     compare(oldSM0, newSM0)
     compare(oldDM0, newDM0)
   }
+
+  test("implicit conversions between new local linalg and mllib linalg") {
+
+    def mllibMatrixToTriple(m: Matrix): (Array[Double], Int, Int) =
+      (m.toArray, m.numCols, m.numRows)
+
+    def mllibDenseMatrixToTriple(m: DenseMatrix): (Array[Double], Int, Int) =
+      (m.toArray, m.numCols, m.numRows)
+
+    def mllibSparseMatrixToTriple(m: SparseMatrix): (Array[Double], Int, Int) =
+      (m.toArray, m.numCols, m.numRows)
+
+    def mlMatrixToTriple(m: newlinalg.Matrix): (Array[Double], Int, Int) =
+      (m.toArray, m.numCols, m.numRows)
+
+    def mlDenseMatrixToTriple(m: newlinalg.DenseMatrix): (Array[Double], Int, Int) =
+      (m.toArray, m.numCols, m.numRows)
+
+    def mlSparseMatrixToTriple(m: newlinalg.SparseMatrix): (Array[Double], Int, Int) =
+      (m.toArray, m.numCols, m.numRows)
+
+    def compare(m1: (Array[Double], Int, Int), m2: (Array[Double], Int, Int)): Unit = {
+      assert(m1._1 === m2._1)
+      assert(m1._2 === m2._2)
+      assert(m1._3 === m2._3)
+    }
+
+    val dm: DenseMatrix = new DenseMatrix(3, 2, Array(0.0, 0.0, 1.0, 0.0, 2.0, 3.5))
+    val sm: SparseMatrix = dm.toSparse
+    val sm0: Matrix = sm.asInstanceOf[Matrix]
+    val dm0: Matrix = dm.asInstanceOf[Matrix]
+
+    val newSM: newlinalg.SparseMatrix = sm.asML
+    val newDM: newlinalg.DenseMatrix = dm.asML
+    val newSM0: newlinalg.Matrix = sm0.asML
+    val newDM0: newlinalg.Matrix = dm0.asML
+
+    import org.apache.spark.mllib.linalg.MatrixImplicits._
+
+    compare(mllibMatrixToTriple(dm0), mllibMatrixToTriple(newDM0))
+    compare(mllibMatrixToTriple(sm0), mllibMatrixToTriple(newSM0))
+
+    compare(mllibDenseMatrixToTriple(dm), mllibDenseMatrixToTriple(newDM))
+    compare(mllibSparseMatrixToTriple(sm), mllibSparseMatrixToTriple(newSM))
+
+    compare(mlMatrixToTriple(dm0), mlMatrixToTriple(newDM))
+    compare(mlMatrixToTriple(sm0), mlMatrixToTriple(newSM0))
+
+    compare(mlDenseMatrixToTriple(dm), mlDenseMatrixToTriple(newDM))
+    compare(mlSparseMatrixToTriple(sm), mlSparseMatrixToTriple(newSM))
+  }
 }
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/linalg/VectorsSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/linalg/VectorsSuite.scala
index a7c1a07604..2e9c40ab88 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/linalg/VectorsSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/linalg/VectorsSuite.scala
@@ -422,4 +422,43 @@ class VectorsSuite extends SparkFunSuite with Logging {
     assert(oldSV0.toArray === newSV0.toArray)
     assert(oldDV0.toArray === newDV0.toArray)
   }
+
+  test("implicit conversions between new local linalg and mllib linalg") {
+
+    def mllibVectorToArray(v: Vector): Array[Double] = v.toArray
+
+    def mllibDenseVectorToArray(v: DenseVector): Array[Double] = v.toArray
+
+    def mllibSparseVectorToArray(v: SparseVector): Array[Double] = v.toArray
+
+    def mlVectorToArray(v: newlinalg.Vector): Array[Double] = v.toArray
+
+    def mlDenseVectorToArray(v: newlinalg.DenseVector): Array[Double] = v.toArray
+
+    def mlSparseVectorToArray(v: newlinalg.SparseVector): Array[Double] = v.toArray
+
+    val dv: DenseVector = new DenseVector(Array(1.0, 2.0, 3.5))
+    val sv: SparseVector = new SparseVector(5, Array(1, 2, 4), Array(1.1, 2.2, 4.4))
+    val sv0: Vector = sv.asInstanceOf[Vector]
+    val dv0: Vector = dv.asInstanceOf[Vector]
+
+    val newSV: newlinalg.SparseVector = sv.asML
+    val newDV: newlinalg.DenseVector = dv.asML
+    val newSV0: newlinalg.Vector = sv0.asML
+    val newDV0: newlinalg.Vector = dv0.asML
+
+    import org.apache.spark.mllib.linalg.VectorImplicits._
+
+    assert(mllibVectorToArray(dv0) === mllibVectorToArray(newDV0))
+    assert(mllibVectorToArray(sv0) === mllibVectorToArray(newSV0))
+
+    assert(mllibDenseVectorToArray(dv) === mllibDenseVectorToArray(newDV))
+    assert(mllibSparseVectorToArray(sv) === mllibSparseVectorToArray(newSV))
+
+    assert(mlVectorToArray(dv0) === mlVectorToArray(newDV0))
+    assert(mlVectorToArray(sv0) === mlVectorToArray(newSV0))
+
+    assert(mlDenseVectorToArray(dv) === mlDenseVectorToArray(newDV))
+    assert(mlSparseVectorToArray(sv) === mlSparseVectorToArray(newSV))
+  }
 }
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/regression/LabeledPointSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/regression/LabeledPointSuite.scala
index f8d0af8820..252a068dcd 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/regression/LabeledPointSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/regression/LabeledPointSuite.scala
@@ -18,6 +18,7 @@
 package org.apache.spark.mllib.regression
 
 import org.apache.spark.SparkFunSuite
+import org.apache.spark.ml.feature.{LabeledPoint => NewLabeledPoint}
 import org.apache.spark.mllib.linalg.Vectors
 
 class LabeledPointSuite extends SparkFunSuite {
@@ -40,4 +41,16 @@ class LabeledPointSuite extends SparkFunSuite {
     val point = LabeledPoint.parse("1.0,1.0 0.0 -2.0")
     assert(point === LabeledPoint(1.0, Vectors.dense(1.0, 0.0, -2.0)))
   }
+
+  test("conversions between new ml LabeledPoint and mllib LabeledPoint") {
+    val points: Seq[LabeledPoint] = Seq(
+      LabeledPoint(1.0, Vectors.dense(1.0, 0.0)),
+      LabeledPoint(0.0, Vectors.sparse(2, Array(1), Array(-1.0))))
+
+    val newPoints: Seq[NewLabeledPoint] = points.map(_.asML)
+
+    points.zip(newPoints).foreach { case (p1, p2) =>
+      assert(p1 === LabeledPoint.fromML(p2))
+    }
+  }
 }
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/tree/DecisionTreeSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/tree/DecisionTreeSuite.scala
index 49cb7e1f24..441d0f7614 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/tree/DecisionTreeSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/tree/DecisionTreeSuite.scala
@@ -73,7 +73,7 @@ class DecisionTreeSuite extends SparkFunSuite with MLlibTestSparkContext {
       maxBins = 100,
       categoricalFeaturesInfo = Map(0 -> 3, 1 -> 3))
 
-    val metadata = DecisionTreeMetadata.buildMetadata(rdd, strategy)
+    val metadata = DecisionTreeMetadata.buildMetadata(rdd.map(_.asML), strategy)
     assert(!metadata.isUnordered(featureIndex = 0))
     assert(!metadata.isUnordered(featureIndex = 1))
 
@@ -100,7 +100,7 @@ class DecisionTreeSuite extends SparkFunSuite with MLlibTestSparkContext {
       maxDepth = 2,
       maxBins = 100,
       categoricalFeaturesInfo = Map(0 -> 2, 1 -> 2))
-    val metadata = DecisionTreeMetadata.buildMetadata(rdd, strategy)
+    val metadata = DecisionTreeMetadata.buildMetadata(rdd.map(_.asML), strategy)
     assert(!metadata.isUnordered(featureIndex = 0))
     assert(!metadata.isUnordered(featureIndex = 1))
 
@@ -116,7 +116,7 @@ class DecisionTreeSuite extends SparkFunSuite with MLlibTestSparkContext {
     val rdd = sc.parallelize(arr)
     val strategy = new Strategy(Classification, Gini, maxDepth = 3,
       numClasses = 2, maxBins = 100)
-    val metadata = DecisionTreeMetadata.buildMetadata(rdd, strategy)
+    val metadata = DecisionTreeMetadata.buildMetadata(rdd.map(_.asML), strategy)
     assert(!metadata.isUnordered(featureIndex = 0))
     assert(!metadata.isUnordered(featureIndex = 1))
 
@@ -133,7 +133,7 @@ class DecisionTreeSuite extends SparkFunSuite with MLlibTestSparkContext {
     val rdd = sc.parallelize(arr)
     val strategy = new Strategy(Classification, Gini, maxDepth = 3,
       numClasses = 2, maxBins = 100)
-    val metadata = DecisionTreeMetadata.buildMetadata(rdd, strategy)
+    val metadata = DecisionTreeMetadata.buildMetadata(rdd.map(_.asML), strategy)
     assert(!metadata.isUnordered(featureIndex = 0))
     assert(!metadata.isUnordered(featureIndex = 1))
 
@@ -150,7 +150,7 @@ class DecisionTreeSuite extends SparkFunSuite with MLlibTestSparkContext {
     val rdd = sc.parallelize(arr)
     val strategy = new Strategy(Classification, Entropy, maxDepth = 3,
       numClasses = 2, maxBins = 100)
-    val metadata = DecisionTreeMetadata.buildMetadata(rdd, strategy)
+    val metadata = DecisionTreeMetadata.buildMetadata(rdd.map(_.asML), strategy)
     assert(!metadata.isUnordered(featureIndex = 0))
     assert(!metadata.isUnordered(featureIndex = 1))
 
@@ -167,7 +167,7 @@ class DecisionTreeSuite extends SparkFunSuite with MLlibTestSparkContext {
     val rdd = sc.parallelize(arr)
     val strategy = new Strategy(Classification, Entropy, maxDepth = 3,
       numClasses = 2, maxBins = 100)
-    val metadata = DecisionTreeMetadata.buildMetadata(rdd, strategy)
+    val metadata = DecisionTreeMetadata.buildMetadata(rdd.map(_.asML), strategy)
     assert(!metadata.isUnordered(featureIndex = 0))
     assert(!metadata.isUnordered(featureIndex = 1))
 
@@ -183,7 +183,7 @@ class DecisionTreeSuite extends SparkFunSuite with MLlibTestSparkContext {
     val rdd = sc.parallelize(arr)
     val strategy = new Strategy(algo = Classification, impurity = Gini, maxDepth = 4,
       numClasses = 3, categoricalFeaturesInfo = Map(0 -> 3, 1 -> 3))
-    val metadata = DecisionTreeMetadata.buildMetadata(rdd, strategy)
+    val metadata = DecisionTreeMetadata.buildMetadata(rdd.map(_.asML), strategy)
     assert(strategy.isMulticlassClassification)
     assert(metadata.isUnordered(featureIndex = 0))
     assert(metadata.isUnordered(featureIndex = 1))
@@ -240,7 +240,7 @@ class DecisionTreeSuite extends SparkFunSuite with MLlibTestSparkContext {
       numClasses = 3, maxBins = maxBins,
       categoricalFeaturesInfo = Map(0 -> 3, 1 -> 3))
     assert(strategy.isMulticlassClassification)
-    val metadata = DecisionTreeMetadata.buildMetadata(rdd, strategy)
+    val metadata = DecisionTreeMetadata.buildMetadata(rdd.map(_.asML), strategy)
     assert(metadata.isUnordered(featureIndex = 0))
     assert(metadata.isUnordered(featureIndex = 1))
 
@@ -288,7 +288,7 @@ class DecisionTreeSuite extends SparkFunSuite with MLlibTestSparkContext {
     val strategy = new Strategy(algo = Classification, impurity = Gini, maxDepth = 4,
       numClasses = 3, maxBins = 100, categoricalFeaturesInfo = Map(0 -> 3))
     assert(strategy.isMulticlassClassification)
-    val metadata = DecisionTreeMetadata.buildMetadata(rdd, strategy)
+    val metadata = DecisionTreeMetadata.buildMetadata(rdd.map(_.asML), strategy)
     assert(metadata.isUnordered(featureIndex = 0))
 
     val model = DecisionTree.train(rdd, strategy)
@@ -310,7 +310,7 @@ class DecisionTreeSuite extends SparkFunSuite with MLlibTestSparkContext {
       numClasses = 3, maxBins = 100,
       categoricalFeaturesInfo = Map(0 -> 10, 1 -> 10))
     assert(strategy.isMulticlassClassification)
-    val metadata = DecisionTreeMetadata.buildMetadata(rdd, strategy)
+    val metadata = DecisionTreeMetadata.buildMetadata(rdd.map(_.asML), strategy)
     assert(!metadata.isUnordered(featureIndex = 0))
     assert(!metadata.isUnordered(featureIndex = 1))
author	DB Tsai <dbt@netflix.com>	2016-05-17 12:51:07 -0700
committer	Xiangrui Meng <meng@databricks.com>	2016-05-17 12:51:07 -0700
commit	e2efe0529acd748f26dbaa41331d1733ed256237 (patch)
tree	fe1a5aeeadfbf220b5dbe1429e0235153db8117b /mllib
parent	9f176dd3918129a72282a6b7a12e2899cbb6dac9 (diff)
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