scala - 如何将 spark DataFrame 转换为 RDD mllib LabeledPoints？

Question

我尝试将 PCA 应用于我的数据，然后将 RandomForest 应用于转换后的数据。但是，PCA.transform(data) 给了我一个 DataFrame，但我需要一个 mllib LabeledPoints 来提供我的 RandomForest。我怎样才能做到这一点？
我的代码:

    import org.apache.spark.mllib.util.MLUtils
    import org.apache.spark.{SparkConf, SparkContext}
    import org.apache.spark.mllib.tree.RandomForest
    import org.apache.spark.mllib.tree.model.RandomForestModel
    import org.apache.spark.ml.feature.PCA
    import org.apache.spark.mllib.regression.LabeledPoint
    import org.apache.spark.mllib.linalg.Vectors


    val dataset = MLUtils.loadLibSVMFile(sc, "data/mnist/mnist.bz2")

    val splits = dataset.randomSplit(Array(0.7, 0.3))

    val (trainingData, testData) = (splits(0), splits(1))

    val trainingDf = trainingData.toDF()

    val pca = new PCA()
    .setInputCol("features")
    .setOutputCol("pcaFeatures")
    .setK(100)
    .fit(trainingDf)

    val pcaTrainingData = pca.transform(trainingDf)

    val numClasses = 10
    val categoricalFeaturesInfo = Map[Int, Int]()
    val numTrees = 10 // Use more in practice.
    val featureSubsetStrategy = "auto" // Let the algorithm choose.
    val impurity = "gini"
    val maxDepth = 20
    val maxBins = 32

    val model = RandomForest.trainClassifier(pcaTrainingData, numClasses, categoricalFeaturesInfo,
        numTrees, featureSubsetStrategy, impurity, maxDepth, maxBins)


     error: type mismatch;
     found   : org.apache.spark.sql.DataFrame
     required: org.apache.spark.rdd.RDD[org.apache.spark.mllib.regression.LabeledPoint]

我尝试了以下两种可能的解决方案，但没有奏效:

 scala> val pcaTrainingData = trainingData.map(p => p.copy(features = pca.transform(p.features)))
 <console>:39: error: overloaded method value transform with alternatives:
   (dataset: org.apache.spark.sql.DataFrame)org.apache.spark.sql.DataFrame <and>
   (dataset: org.apache.spark.sql.DataFrame,paramMap: org.apache.spark.ml.param.ParamMap)org.apache.spark.sql.DataFrame <and>
   (dataset: org.apache.spark.sql.DataFrame,firstParamPair: org.apache.spark.ml.param.ParamPair[_],otherParamPairs: org.apache.spark.ml.param.ParamPair[_]*)org.apache.spark.sql.DataFrame
  cannot be applied to (org.apache.spark.mllib.linalg.Vector)

和:

     val labeled = pca
    .transform(trainingDf)
    .map(row => LabeledPoint(row.getDouble(0), row(4).asInstanceOf[Vector[Int]]))

     error: type mismatch;
     found   : scala.collection.immutable.Vector[Int]
     required: org.apache.spark.mllib.linalg.Vector

(我在上面的例子中导入了 org.apache.spark.mllib.linalg.Vectors)

有什么帮助吗？

Answer 1

这里的正确方法是您尝试的第二个方法 - 将每个 Row 映射到 LabeledPoint 以获得 RDD[LabeledPoint] 。但是，它有两个错误:

正确的 Vector 类( org.apache.spark.mllib.linalg.Vector )不接受类型参数(例如 Vector[Int] ) - 所以即使你有正确的导入，编译器得出结论你的意思是 scala.collection.immutable.Vector

从 PCA.fit() 返回的 DataFrame 有 3 列，您尝试提取列号 4。例如，显示前 4 行:

+-----+--------------------+--------------------+
|label|            features|         pcaFeatures|
+-----+--------------------+--------------------+
|  5.0|(780,[152,153,154...|[880.071111851977...|
|  1.0|(780,[158,159,160...|[-41.473039034112...|
|  2.0|(780,[155,156,157...|[931.444898405036...|
|  1.0|(780,[124,125,126...|[25.5114585648411...|
+-----+--------------------+--------------------+

为了使这更容易 - 我更喜欢使用列名而不是它们的索引。

所以这是您需要的转换:

val labeled = pca.transform(trainingDf).rdd.map(row => LabeledPoint(
   row.getAs[Double]("label"),   
   row.getAs[org.apache.spark.mllib.linalg.Vector]("pcaFeatures")
))