1 files changed, 10 insertions, 7 deletions
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 36c242bb5f..3ebb78f792 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
@@ -20,14 +20,14 @@ package org.apache.spark.ml.regression
 import scala.collection.mutable
 
 import breeze.linalg.{DenseVector => BDV, norm => brzNorm}
-import breeze.optimize.{CachedDiffFunction, DiffFunction, LBFGS => BreezeLBFGS,
-  OWLQN => BreezeOWLQN}
+import breeze.optimize.{CachedDiffFunction, DiffFunction, LBFGS => BreezeLBFGS, OWLQN => BreezeOWLQN}
 
 import org.apache.spark.Logging
 import org.apache.spark.annotation.AlphaComponent
 import org.apache.spark.ml.PredictorParams
 import org.apache.spark.ml.param.ParamMap
 import org.apache.spark.ml.param.shared.{HasElasticNetParam, HasMaxIter, HasRegParam, HasTol}
+import org.apache.spark.ml.util.Identifiable
 import org.apache.spark.mllib.linalg.{Vector, Vectors}
 import org.apache.spark.mllib.linalg.BLAS._
 import org.apache.spark.mllib.regression.LabeledPoint
@@ -59,9 +59,12 @@ private[regression] trait LinearRegressionParams extends PredictorParams
  *  - L2 + L1 (elastic net)
  */
 @AlphaComponent
-class LinearRegression extends Regressor[Vector, LinearRegression, LinearRegressionModel]
+class LinearRegression(override val uid: String)
+  extends Regressor[Vector, LinearRegression, LinearRegressionModel]
   with LinearRegressionParams with Logging {
 
+  def this() = this(Identifiable.randomUID("linReg"))
+
   /**
    * Set the regularization parameter.
    * Default is 0.0.
@@ -128,7 +131,7 @@ class LinearRegression extends Regressor[Vector, LinearRegression, LinearRegress
       logWarning(s"The standard deviation of the label is zero, so the weights will be zeros " +
         s"and the intercept will be the mean of the label; as a result, training is not needed.")
       if (handlePersistence) instances.unpersist()
-      return new LinearRegressionModel(this, Vectors.sparse(numFeatures, Seq()), yMean)
+      return new LinearRegressionModel(uid, Vectors.sparse(numFeatures, Seq()), yMean)
     }
 
     val featuresMean = summarizer.mean.toArray
@@ -182,7 +185,7 @@ class LinearRegression extends Regressor[Vector, LinearRegression, LinearRegress
     if (handlePersistence) instances.unpersist()
 
     // TODO: Converts to sparse format based on the storage, but may base on the scoring speed.
-    new LinearRegressionModel(this, weights.compressed, intercept)
+    copyValues(new LinearRegressionModel(uid, weights.compressed, intercept))
   }
 }
 
@@ -193,7 +196,7 @@ class LinearRegression extends Regressor[Vector, LinearRegression, LinearRegress
  */
 @AlphaComponent
 class LinearRegressionModel private[ml] (
-    override val parent: LinearRegression,
+    override val uid: String,
     val weights: Vector,
     val intercept: Double)
   extends RegressionModel[Vector, LinearRegressionModel]
@@ -204,7 +207,7 @@ class LinearRegressionModel private[ml] (
   }
 
   override def copy(extra: ParamMap): LinearRegressionModel = {
-    copyValues(new LinearRegressionModel(parent, weights, intercept), extra)
+    copyValues(new LinearRegressionModel(uid, weights, intercept), extra)
   }
 }