update

Signed-off-by: minmingzhu <minming.zhu@intel.com>
oap-project · Apr 21, 2023 · c965934 · c965934
1 parent bb7ec7e
commit c965934
Showing 1 changed file with 3 additions and 67 deletions.
diff --git a/...ation/MLRandomForestClassifierSuite.scala → ...on/MLlibRandomForestClassifierSuite.scala b/...ation/MLRandomForestClassifierSuite.scala → ...on/MLlibRandomForestClassifierSuite.scala
@@ -17,7 +17,8 @@
 
 package org.apache.spark.ml.classification
 
-import org.apache.spark.{SparkConf, SparkFunSuite}
+import com.intel.oneapi.dal.table.Common
+import org.apache.spark.{SparkConf, SparkFunSuite, TestCommon}
 import org.apache.spark.ml.classification.LinearSVCSuite.generateSVMInput
 import org.apache.spark.ml.feature.LabeledPoint
 import org.apache.spark.ml.linalg.{Vector, Vectors}
@@ -42,7 +43,7 @@ class RandomForestClassifierSuite extends MLTest with DefaultReadWriteTest {
   import testImplicits._
   override def sparkConf: SparkConf = {
     val conf = super.sparkConf
-    conf.set("spark.oap.mllib.device", "GPU")
+    conf.set("spark.oap.mllib.device", Common.ComputeDevice.GPU.toString)
   }
 
   private var orderedLabeledPoints50_1000: RDD[LabeledPoint] = _
@@ -65,78 +66,13 @@ class RandomForestClassifierSuite extends MLTest with DefaultReadWriteTest {
   // Tests calling train()
   /////////////////////////////////////////////////////////////////////////////
 
-//  def binaryClassificationTestWithContinuousFeatures(rf: RandomForestClassifier): Unit = {
-//    val categoricalFeatures = Map.empty[Int, Int]
-//    val numClasses = 2
-//    val newRF = rf
-//      .setImpurity("Gini")
-//      .setMaxDepth(2)
-//      .setNumTrees(1)
-//      .setFeatureSubsetStrategy("auto")
-//      .setSeed(123)
-//    compareAPIs(orderedLabeledPoints50_1000, newRF, categoricalFeatures, numClasses)
-//  }
-
   test("params") {
     ParamsSuite.checkParams(new RandomForestClassifier)
     val model = new RandomForestClassificationModel("rfc",
       Array(new DecisionTreeClassificationModel("dtc", new LeafNode(0.0, 0.0, null), 1, 2)), 2, 2)
     ParamsSuite.checkParams(model)
   }
 
-//  test("Binary classification with continuous features:" +
-//    " comparing DecisionTree vs. RandomForest(numTrees = 1)") {
-//    val rf = new RandomForestClassifier()
-//      .setBootstrap(false)
-//    binaryClassificationTestWithContinuousFeatures(rf)
-//  }
-
-//  test("Binary classification with continuous features and node Id cache:" +
-//    " comparing DecisionTree vs. RandomForest(numTrees = 1)") {
-//    val rf = new RandomForestClassifier()
-//      .setBootstrap(false)
-//      .setCacheNodeIds(true)
-//    binaryClassificationTestWithContinuousFeatures(rf)
-//  }
-
-//  test("alternating categorical and continuous features with multiclass labels to test indexing") {
-//    val arr = Array(
-//      LabeledPoint(0.0, Vectors.dense(1.0, 0.0, 0.0, 3.0, 1.0)),
-//      LabeledPoint(1.0, Vectors.dense(0.0, 1.0, 1.0, 1.0, 2.0)),
-//      LabeledPoint(0.0, Vectors.dense(2.0, 0.0, 0.0, 6.0, 3.0)),
-//      LabeledPoint(2.0, Vectors.dense(0.0, 2.0, 1.0, 3.0, 2.0))
-//    )
-//    val rdd = sc.parallelize(arr)
-//    val categoricalFeatures = Map(0 -> 3, 2 -> 2, 4 -> 4)
-//    val numClasses = 3
-//
-//    val rf = new RandomForestClassifier()
-//      .setImpurity("Gini")
-//      .setMaxDepth(5)
-//      .setNumTrees(2)
-//      .setFeatureSubsetStrategy("sqrt")
-//      .setSeed(12345)
-//    compareAPIs(rdd, rf, categoricalFeatures, numClasses)
-//  }
-
-//  test("subsampling rate in RandomForest") {
-//    val rdd = orderedLabeledPoints5_20
-//    val categoricalFeatures = Map.empty[Int, Int]
-//    val numClasses = 2
-//
-//    val rf1 = new RandomForestClassifier()
-//      .setImpurity("Gini")
-//      .setMaxDepth(2)
-//      .setCacheNodeIds(true)
-//      .setNumTrees(3)
-//      .setFeatureSubsetStrategy("auto")
-//      .setSeed(123)
-//    compareAPIs(rdd, rf1, categoricalFeatures, numClasses)
-//
-//    val rf2 = rf1.setSubsamplingRate(0.5)
-//    compareAPIs(rdd, rf2, categoricalFeatures, numClasses)
-//  }
-
   test("predictRaw and predictProbability") {
     val rdd = orderedLabeledPoints5_20
     val rf = new RandomForestClassifier()