rebase

Author: huaxingao

mllib/src/main/scala/org/apache/spark/ml/classification/LinearSVC.scala

@@ -394,27 +394,6 @@ class LinearSVCModel private[classification] (
   @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) = {
-    val model = if ($(rawPredictionCol).isEmpty && $(predictionCol).isEmpty) {
-      copy(ParamMap.empty)
-        .setRawPredictionCol("rawPrediction_" + java.util.UUID.randomUUID.toString)
-        .setPredictionCol("prediction_" + java.util.UUID.randomUUID.toString)
-    } else if ($(rawPredictionCol).isEmpty) {
-      copy(ParamMap.empty).setRawPredictionCol("rawPrediction_" +
-        java.util.UUID.randomUUID.toString)
-    } else if ($(predictionCol).isEmpty) {
-      copy(ParamMap.empty).setPredictionCol("prediction_" + java.util.UUID.randomUUID.toString)
-    } else {
-      this
-    }
-    (model, model.getRawPredictionCol, model.getPredictionCol)
-  }
-
   /**
    * Evaluates the model on a test dataset.
    *

mllib/src/main/scala/org/apache/spark/ml/classification/ProbabilisticClassifier.scala

@@ -19,8 +19,8 @@ package org.apache.spark.ml.classification
 
 import org.apache.spark.annotation.Since
 import org.apache.spark.ml.linalg.{DenseVector, Vector, VectorUDT}
-import org.apache.spark.ml.param.shared._
 import org.apache.spark.ml.param.ParamMap
+import org.apache.spark.ml.param.shared._
 import org.apache.spark.ml.util.SchemaUtils
 import org.apache.spark.sql.{DataFrame, Dataset}
 import org.apache.spark.sql.functions._