[SPARK-16177][ML] model loading backward compatibility for ml.regression

## What changes were proposed in this pull request?
jira: https://issues.apache.org/jira/browse/SPARK-16177
model loading backward compatibility for ml.regression

## How was this patch tested?

existing ut and manual test for loading 1.6 models.

Author: Yuhao Yang <hhbyyh@gmail.com>

Closes #13879 from hhbyyh/regreComp.

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

@@ -33,6 +33,7 @@ import org.apache.spark.ml.param.shared._
 import org.apache.spark.ml.util._
 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
 import org.apache.spark.sql.{DataFrame, Dataset, Row}
 import org.apache.spark.sql.functions._
@@ -389,10 +390,10 @@ object AFTSurvivalRegressionModel extends MLReadable[AFTSurvivalRegressionModel]
 
       val dataPath = new Path(path, "data").toString
       val data = sparkSession.read.parquet(dataPath)
-        .select("coefficients", "intercept", "scale").head()
-      val coefficients = data.getAs[Vector](0)
-      val intercept = data.getDouble(1)
-      val scale = data.getDouble(2)
+      val Row(coefficients: Vector, intercept: Double, scale: Double) =
+        MLUtils.convertVectorColumnsToML(data, "coefficients")
+          .select("coefficients", "intercept", "scale")
+          .head()
       val model = new AFTSurvivalRegressionModel(metadata.uid, coefficients, intercept, scale)
 
       DefaultParamsReader.getAndSetParams(model, metadata)

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

@@ -39,6 +39,7 @@ import org.apache.spark.mllib.evaluation.RegressionMetrics
 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.mllib.util.MLUtils
 import org.apache.spark.rdd.RDD
 import org.apache.spark.sql.{DataFrame, Dataset, Row}
 import org.apache.spark.sql.functions._
@@ -500,9 +501,10 @@ object LinearRegressionModel extends MLReadable[LinearRegressionModel] {
 
       val dataPath = new Path(path, "data").toString
       val data = sparkSession.read.format("parquet").load(dataPath)
-        .select("intercept", "coefficients").head()
-      val intercept = data.getDouble(0)
-      val coefficients = data.getAs[Vector](1)
+      val Row(intercept: Double, coefficients: Vector) =
+        MLUtils.convertVectorColumnsToML(data, "coefficients")
+          .select("intercept", "coefficients")
+          .head()
       val model = new LinearRegressionModel(metadata.uid, coefficients, intercept)
 
       DefaultParamsReader.getAndSetParams(model, metadata)