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[SPARK-10479] [SPARK-10480] [ML] Fix ML.LinearRegressionModel.copy() #8641

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[SPARK-10479] [SPARK-10480] [ML] Fix ML.LinearRegressionModel.copy() #8641

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8e1d219
fix ml.LinearRegressionModel.copy
yanboliang Sep 7, 2015
891d29e
LogisticRegression copy should copy model summary if available
yanboliang Sep 8, 2015
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4 changes: 3 additions & 1 deletion mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala
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Original file line number Diff line number Diff line change
Expand Up @@ -468,7 +468,9 @@ class LogisticRegressionModel private[ml] (
}

override def copy(extra: ParamMap): LogisticRegressionModel = {
copyValues(new LogisticRegressionModel(uid, weights, intercept), extra).setParent(parent)
val newModel = copyValues(new LogisticRegressionModel(uid, weights, intercept), extra)
if (trainingSummary.isDefined) newModel.setSummary(trainingSummary.get)
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Just checking, was this intended as well? does it need to happen for LinearRegression too? LGTM otherwise.

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Yes, it's needed. Because after we new LogisticRegressionModel or LinearRegressionModel, the default value of trainingSummary is None. So we should also copy trainingSummary explicitly to the new model.

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nit. newModel.trainingSummary = trainingSummary should work the same.

newModel.setParent(parent)
}

override protected def raw2prediction(rawPrediction: Vector): Double = {
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2 changes: 1 addition & 1 deletion mllib/src/main/scala/org/apache/spark/ml/regression/LinearRegression.scala
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Expand Up @@ -310,7 +310,7 @@ class LinearRegressionModel private[ml] (
}

override def copy(extra: ParamMap): LinearRegressionModel = {
val newModel = copyValues(new LinearRegressionModel(uid, weights, intercept))
val newModel = copyValues(new LinearRegressionModel(uid, weights, intercept), extra)
if (trainingSummary.isDefined) newModel.setSummary(trainingSummary.get)
newModel.setParent(parent)
}
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