zhengruifeng commented on a change in pull request #28884:
URL: https://github.com/apache/spark/pull/28884#discussion_r443924069
##########
File path:
mllib/src/main/scala/org/apache/spark/ml/classification/LinearSVC.scala
##########
@@ -439,3 +502,55 @@ object LinearSVCModel extends MLReadable[LinearSVCModel] {
}
}
}
+
+/**
+ * Abstraction for LinearSVC results for a given model.
+ */
+sealed trait LinearSVCSummary extends BinaryClassificationSummary {
Review comment:
nit: uncessary braces
##########
File path:
mllib/src/main/scala/org/apache/spark/ml/classification/LinearSVC.scala
##########
@@ -439,3 +502,55 @@ object LinearSVCModel extends MLReadable[LinearSVCModel] {
}
}
}
+
+/**
+ * Abstraction for LinearSVC results for a given model.
+ */
+sealed trait LinearSVCSummary extends BinaryClassificationSummary {
+}
+
+/**
+ * Abstraction for LinearSVC training results.
+ */
+sealed trait LinearSVCTrainingSummary extends LinearSVCSummary with
TrainingSummary {
+}
Review comment:
ditto
##########
File path:
mllib/src/main/scala/org/apache/spark/ml/classification/LinearSVC.scala
##########
@@ -368,6 +388,49 @@ class LinearSVCModel private[classification] (
BLAS.dot(features, coefficients) + intercept
}
+ /**
+ * Gets summary of model on training set. An exception is thrown
+ * if `hasSummary` is false.
+ */
+ @Since("3.1.0")
+ override def summary: LinearSVCTrainingSummary = super.summary
+
+ /**
+ * If the rawPrediction and prediction columns are set, this method returns
the current model,
+ * otherwise it generates new columns for them and sets them as columns on a
new copy of
+ * the current model
+ */
+ private[classification] def findSummaryModel():
+ (LinearSVCModel, String, String) = {
Review comment:
nit:
` private[classification] def findSummaryModel(): (LinearSVCModel, String,
String) = {` ?
##########
File path:
mllib/src/main/scala/org/apache/spark/ml/classification/LinearSVC.scala
##########
@@ -267,7 +267,27 @@ class LinearSVC @Since("2.2.0") (
if (featuresStd(i) != 0.0) rawCoefficients(i) / featuresStd(i) else 0.0
}
val intercept = if ($(fitIntercept)) rawCoefficients.last else 0.0
- copyValues(new LinearSVCModel(uid, Vectors.dense(coefficientArray),
intercept))
+ val vec = Vectors.dense(coefficientArray)
+ return createModel(dataset, vec, intercept, objectiveHistory)
Review comment:
I'd like: `createModel(dataset, Vectors.dense(coefficientArray),
intercept, objectiveHistory)`
var `vec` and `return` are unnecessary
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