Creating a Random Feature Array in Spark DataFrames - arrays

Question

When creating an ALS model, we can extract a userFactors DataFrame and an itemFactors DataFrame. These DataFrames contain a column with an Array.
I would like to generate some random data and union it to the userFactors DataFrame.
Here is my code:
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
I then create a random DataFrame using a VectorAssembler with this function:
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")
}
I then create the DataFrame with new user IDs and add the random vectors and bias:
val usersDf: DataFrame = Seq(567), (678)).toDF("user")
var usersFactorsNew: DataFrame = makeNew(usersDf, 20)
The problem arises when I union the two DataFrames.
usersFactorsNew.union(uF) produces the error:
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;;
If I print the schema, the uF DataFrame has a feature vector of type Array[Float] and the usersFactorsNew DataFrame as a feature vector of type Vector.
My question is how to change the type of the Vector to an Array in order to perform the union.
I tried writing this udf with little success:
val toArr: org.apache.spark.ml.linalg.Vector => Array[Double] = _.toArray
val toArrUdf = udf(toArr)
Perhaps the VectorAssembler is not the best option for this task. However, at the moment, it is the only option I have found. I would love to get some recommendations for something better.

Answer 1

Instead of creating a dummy dataframe and using VectorAssembler to generate a random feature vector, you can simply use an UDF directly. The userFactors from the ALS model will return an Array[Float] so the output from the UDF should match that.
val createRandomArray = udf((rank: Int) => {
Array.fill(rank)(Random.nextFloat)
})
Note that this will give numbers in the interval [0.0, 1.0] (same as the rand() used in the question), if other numbers are required, modify as fit.
Using a rank of 3 and the userDf:
val usersFactorsNew = usersDf.withColumn("userFeatures", createRandomArray(lit(3)))
will give a dataframe as follows (of course with random feature values)
+----+----------------------------------------------------------+
|user|userFeatures |
+----+----------------------------------------------------------+
|567 |[0.6866711267486822,0.7257031656127676,0.983562255688249] |
|678 |[0.7013908820314967,0.41029552817665327,0.554591149586789]|
+----+----------------------------------------------------------+
Joining this dataframe with the uF dataframe should now be possible.
The reason the UDF did not work should be due to it being an Array[Double] while you need anArray[Float]for theunion. It should be possible to fix with amap(_.toFloat)`.
val toArr: org.apache.spark.ml.linalg.Vector => Array[Float] = _.toArray.map(_.toFloat)
val toArrUdf = udf(toArr)

Answer 2

All of your process are all correct. Even the udf function is working successfully. All you need to do is change the last part of makeNew function as
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"))
}
and you should be perfect to go so that when you do (I created userDf with id column and not user column)
val usersDf: DataFrame = Seq((567), (678)).toDF("id")
var usersFactorsNew: DataFrame = makeNew(usersDf, 20)
usersFactorsNew.union(uF).show(false)
you should be getting
+---+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
|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] |
+---+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+