I am trying to take columns from a DataFrame
and convert it to an RDD[Vector]
.
The problem is that I have columns with a "dot" in their name as the following dataset :
"col0.1","col1.2","col2.3","col3.4"
1,2,3,4
10,12,15,3
1,12,10,5
This is what I'm doing :
val df = spark.read.format("csv").options(Map("header" -> "true", "inferSchema" -> "true")).load("C:/Users/mhattabi/Desktop/donnee/test.txt")
val column=df.columns.map(c=>s"`${c}`")
val rows = new VectorAssembler().setInputCols(column).setOutputCol("vs")
.transform(df)
.select("vs")
.rdd
val data =rows.map(_.getAs[org.apache.spark.ml.linalg.Vector](0))
.map(org.apache.spark.mllib.linalg.Vectors.fromML)
val mat: RowMatrix = new RowMatrix(data)
//// Compute the top 5 singular values and corresponding singular vectors.
val svd: SingularValueDecomposition[RowMatrix, Matrix] = mat.computeSVD(mat.numCols().toInt, computeU = true)
val U: RowMatrix = svd.U // The U factor is a RowMatrix.
val s: Vector = svd.s // The singular values are stored in a local dense vector.
val V: Matrix = svd.V // The V factor is a local dense matrix.
println(V)
Please any help to get me consider columns with dot in their names.Thanks
If your problem is the .(dot)
in the column name, you could use `(backticks)
to enclose the column name.
df.select("`col0.1`")
The problem here is VectorAssembler
implementation, not the columns per se. You can for example skip the header:
val df = spark.read.format("csv")
.options(Map("inferSchema" -> "true", "comment" -> "\""))
.load(path)
new VectorAssembler()
.setInputCols(df.columns)
.setOutputCol("vs")
.transform(df)
or rename columns before passing to VectorAssembler
:
val renamed = df.toDF(df.columns.map(_.replace(".", "_")): _*)
new VectorAssembler()
.setInputCols(renamed.columns)
.setOutputCol("vs")
.transform(renamed)
Finally the best approach is to provide schema explicitly:
import org.apache.spark.sql.types._
val schema = StructType((0 until 4).map(i => StructField(s"_$i", DoubleType)))
val dfExplicit = spark.read.format("csv")
.options(Map("header" -> "true"))
.schema(schema)
.load(path)
new VectorAssembler()
.setInputCols(dfExplicit.columns)
.setOutputCol("vs")
.transform(dfExplicit)
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