在Spark DataFrames中創建隨機特征數組

Question

創建ALS模型時，我們可以提取userFactors框和itemFactors框。 這些數據框包含帶有數組的列。

我想生成一些隨機數據並將其userFactors到userFactors DataFrame。

這是我的代碼：

 val df1: DataFrame  = Seq((123, 456, 4.0), (123, 789, 5.0), (234, 456, 4.5), (234, 789, 1.0)).toDF("user", "item", "rating")
val model1 = (new ALS()
 .setImplicitPrefs(true)
 .fit(df1))

val iF = model1.itemFactors
val uF = model1.userFactors

然后，我使用具有以下功能的VectorAssembler創建一個隨機DataFrame：

def makeNew(df: DataFrame, rank: Int): DataFrame = {
    var df_dummy = df
    var i: Int = 0
    var inputCols: Array[String] = Array()
    for (i <- 0 to rank) {
       df_dummy = df_dummy.withColumn("feature".concat(i.toString), rand())
       inputCols = inputCols :+ "feature".concat(i.toString)
      }
    val assembler = new VectorAssembler()
      .setInputCols(inputCols)
      .setOutputCol("userFeatures")
    val output = assembler.transform(df_dummy)
    output.select("user", "userFeatures")
  }

然后，我使用新的用戶ID創建DataFrame並添加隨機向量和偏倚：

val usersDf: DataFrame = Seq(567), (678)).toDF("user")
var usersFactorsNew: DataFrame = makeNew(usersDf, 20)

當我合並兩個DataFrame時會出現問題。

usersFactorsNew.union(uF)產生錯誤：

 org.apache.spark.sql.AnalysisException: Union can only be performed on tables with the compatible column types. struct<type:tinyint,size:int,indices:array<int>,values:array<double>> <> array<float> at the second column of the second table;;

如果我打印模式，則uF DataFrame具有類型為Array[Float]的特征向量，而usersFactorsNew DataFrame作為類型為Vector的特征Vector 。

我的問題是如何將Vector的類型更改為Array以便執行聯合。

我嘗試編寫此udf幾乎沒有成功：

val toArr: org.apache.spark.ml.linalg.Vector => Array[Double] = _.toArray
val toArrUdf = udf(toArr)

也許VectorAssembler不是此任務的最佳選擇。 但是，目前，這是我找到的唯一選擇。 我希望得到一些更好的建議。

Answer 1

無需創建虛擬數據VectorAssembler並使用VectorAssembler生成隨機特征向量，您只需直接使用UDF 。 ALS模型的userFactors將返回Array[Float]因此UDF的輸出應與此匹配。

val createRandomArray = udf((rank: Int) => {
  Array.fill(rank)(Random.nextFloat)
})

請注意，這將給出區間[0.0，1.0]中的數字（與問題中使用的rand()相同），如果需要其他數字，請修改為合適。

使用3的等級和userDf ：

val usersFactorsNew = usersDf.withColumn("userFeatures", createRandomArray(lit(3)))

將給出如下數據框（當然具有隨機特征值）

+----+----------------------------------------------------------+
|user|userFeatures                                              |
+----+----------------------------------------------------------+
|567 |[0.6866711267486822,0.7257031656127676,0.983562255688249] |
|678 |[0.7013908820314967,0.41029552817665327,0.554591149586789]|
+----+----------------------------------------------------------+

現在應該可以將此數據框與uF數據框連接起來。

---

的原因UDF沒有工作，應該是由於它是一個Array[Double] while you need an數組[浮點型] for the工會. It should be possible to fix with a . It should be possible to fix with a map（_。toFloat）` . It should be possible to fix with a 。

val toArr: org.apache.spark.ml.linalg.Vector => Array[Float] = _.toArray.map(_.toFloat)
val toArrUdf = udf(toArr)

Answer 2

您的所有過程都是正確的。 甚至udf函數也可以成功運行。 您需要做的就是將makeNew函數的最后一部分更改為

def makeNew(df: DataFrame, rank: Int): DataFrame = {
  var df_dummy = df
  var i: Int = 0
  var inputCols: Array[String] = Array()
  for (i <- 0 to rank) {
    df_dummy = df_dummy.withColumn("feature".concat(i.toString), rand())
    inputCols = inputCols :+ "feature".concat(i.toString)
  }
  val assembler = new VectorAssembler()
    .setInputCols(inputCols)
    .setOutputCol("userFeatures")
  val output = assembler.transform(df_dummy)
  output.select(col("id"), toArrUdf(col("userFeatures")).as("features"))
}

並且您應該走得很完美，以便這樣做（我用id列而不是user列 創建了userDf ）

val usersDf: DataFrame = Seq((567), (678)).toDF("id")
var usersFactorsNew: DataFrame = makeNew(usersDf, 20)
usersFactorsNew.union(uF).show(false)

你應該得到

+---+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
|id |features                                                                                                                                                                                                                                                                                                                                                                                                                                  |
+---+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
|567|[0.8259185719733708, 0.327713892339658, 0.049547223031371046, 0.056661808506210054, 0.5846626163454274, 0.038497936270104005, 0.8970865088803417, 0.8840660648882804, 0.837866669938156, 0.9395263094918058, 0.09179528484355126, 0.4915430644129799, 0.11083447052043116, 0.5122858182953718, 0.4302683812966408, 0.3862741815833828, 0.6189322403095068, 0.3000371006293433, 0.09331299668168902, 0.7421838728601371, 0.855867963988993]|
|678|[0.7686514248005568, 0.5473580740023187, 0.072945344124282, 0.36648594574355287, 0.9780202082328863, 0.5289221651923784, 0.3719451099963028, 0.2824660794505932, 0.4873197501260199, 0.9364676464120849, 0.011539929543513794, 0.5240615794930654, 0.6282546154521298, 0.995256022569878, 0.6659179561266975, 0.8990775317754092, 0.08650071017556926, 0.5190186149992805, 0.056345335742325475, 0.6465357505620791, 0.17913532817943245] |
|123|[0.04177388548851013, 0.26762014627456665, -0.19617630541324615, 0.34298020601272583, 0.19632814824581146, -0.2748605012893677, 0.07724890112876892, 0.4277132749557495, 0.1927199512720108, -0.40271613001823425]                                                                                                                                                                                                                        |
|234|[0.04139673709869385, 0.26520395278930664, -0.19440513849258423, 0.3398836553096771, 0.1945556253194809, -0.27237895131111145, 0.07655145972967148, 0.42385169863700867, 0.19098000228405, -0.39908021688461304]                                                                                                                                                                                                                          |
+---+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+