Yanbo, Thanks for the reply.
Is there a JIRA for exposing featureImportances on org.apache.spark.mllib.tree.RandomForest?, or could you create one? I am unable to create an issue on JIRA against Spark. Thanks. Asim On Thu, Dec 17, 2015 at 12:07 AM, Yanbo Liang <yblia...@gmail.com> wrote: > Hi Asim, > > The "featureImportances" is only exposed at ML not MLlib. > You need to update your code to use RandomForestClassifier of ML to train > and get one RandomForestClassificationModel. Then you can call > RandomForestClassificationModel.featureImportances > <https://github.com/apache/spark/blob/master/mllib/src/main/scala/org/apache/spark/ml/classification/RandomForestClassifier.scala#L237> > to get the importances of each feature. > > For how to use RandomForestClassifier, you can refer this example > <https://github.com/apache/spark/blob/master/examples/src/main/scala/org/apache/spark/examples/ml/RandomForestClassifierExample.scala> > . > > Yanbo > > 2015-12-17 13:41 GMT+08:00 Asim Jalis <asimja...@gmail.com>: > >> I wanted to use get feature importances related to a Random Forest as >> described in this JIRA: https://issues.apache.org/jira/browse/SPARK-5133 >> >> However, I don’t see how to call this. I don't see any methods exposed on >> >> org.apache.spark.mllib.tree.RandomForest >> >> How can I get featureImportances when I generate a RandomForest model in >> this code? >> >> import org.apache.spark.mllib.linalg.Vectors >> import org.apache.spark.mllib.regression.LabeledPoint >> import org.apache.spark.mllib.tree.RandomForest >> import org.apache.spark.mllib.tree.model.RandomForestModel >> import org.apache.spark.mllib.util.MLUtils >> import org.apache.spark.rdd.RDD >> import util.Random >> >> def displayModel(model:RandomForestModel) = { >> // Display model. >> println("Learned classification tree model:\n" + model.toDebugString) >> } >> >> def saveModel(model:RandomForestModel,path:String) = { >> // Save and load model. >> model.save(sc, path) >> val sameModel = DecisionTreeModel.load(sc, path) >> } >> >> def testModel(model:RandomForestModel,testData:RDD[LabeledPoint]) = { >> // Test model. >> val labelAndPreds = testData.map { point => >> val prediction = model.predict(point.features) >> (point.label, prediction) >> } >> val testErr = labelAndPreds. >> filter(r => r._1 != r._2).count.toDouble / testData.count() >> println("Test Error = " + testErr) >> } >> >> def buildModel(trainingData:RDD[LabeledPoint], >> numClasses:Int,categoricalFeaturesInfo:Map[Int,Int]) = { >> val numTrees = 30 >> val featureSubsetStrategy = "auto" >> val impurity = "gini" >> val maxDepth = 4 >> val maxBins = 32 >> >> // Build model. >> val model = RandomForest.trainClassifier( >> trainingData, numClasses, categoricalFeaturesInfo, >> numTrees, featureSubsetStrategy, impurity, maxDepth, >> maxBins) >> >> model >> } >> >> // Create plain RDD. >> val rdd = sc.parallelize(Range(0,1000)) >> >> // Convert to LabeledPoint RDD. >> val data = rdd. >> map(x => { >> val label = x % 2 >> val feature1 = x % 5 >> val feature2 = x % 7 >> val features = Seq(feature1,feature2). >> map(_.toDouble). >> zipWithIndex. >> map(_.swap) >> val vector = Vectors.sparse(features.size, features) >> val point = new LabeledPoint(label, vector) >> point }) >> >> // Split data into training (70%) and test (30%). >> val splits = data.randomSplit(Array(0.7, 0.3)) >> val (trainingData, testData) = (splits(0), splits(1)) >> >> // Set up parameters for training. >> val numClasses = data.map(_.label).distinct.count.toInt >> val categoricalFeaturesInfo = Map[Int, Int]() >> >> val model = buildModel( >> trainingData, >> numClasses, >> categoricalFeaturesInfo) >> testModel(model,testData) >> >> >
