How to transform a csv string into a Spark-ML compatible Dataset<Row> format?

Question

I have a Dataset<Row> df, that contains two columns ("key" and "value") of type string. df.printSchema(); is giving me the following output:

root
 |-- key: string (nullable = true)
 |-- value: string (nullable = true)

The content of the value column is actually a csv formated line (coming from a kafka topic), with the last entry of that line representing the class label and all the previous entries beeing the features (first row not included in the dataset):

feature0,feature1,label
0.6720004294237854,-0.4033586564886893,0
0.6659082469383558,0.07688976580256132,0
0.8086502311695247,0.564354801275521,1

Since I would like to train a classifier on this data, I need to transform this representation into a row of type dense vector, containing all the feature values and a column of type double, containing the label value:

root
 |-- indexedFeatures: vector (nullable = false)
 |-- indexedLabel: double (nullable = false)

How can I do this, using java 1.8 and Spark 2.2.0?

Edit: I got further, but while attempting to make it work with a flexible amount feature dimensions, I got stuck again. I created a follow-up question.

Answer 1

A VectorAssembler (javadocs) can transform the dataset into the required format.

First, the input is split into three columns:

Dataset<FeaturesAndLabelData> featuresAndLabelData = inputDf.select("value").as(Encoders.STRING())
  .flatMap(s -> {
    String[] splitted = s.split(",");
    if (splitted.length == 3) {
      return Collections.singleton(new FeaturesAndLabelData(
        Double.parseDouble(splitted[0]),
        Double.parseDouble(splitted[1]), 
        Integer.parseInt(splitted[2]))).iterator();
    } else {
      // apply some error handling...
      return Collections.emptyIterator();
    }
  }, Encoders.bean(FeaturesAndLabelData.class));

The result is then transformed by a VectorAssembler:

VectorAssembler assembler = new VectorAssembler()
  .setInputCols(new String[] { "feature1", "feature2" })
  .setOutputCol("indexedFeatures");
Dataset<Row> result = assembler.transform(featuresAndLabelData)
  .withColumn("indexedLabel", functions.col("label").cast("double"))
  .select("indexedFeatures", "indexedLabel");

The result dataframe has the required format:

+----------------------------------------+------------+
|indexedFeatures                         |indexedLabel|
+----------------------------------------+------------+
|[0.6720004294237854,-0.4033586564886893]|0.0         |
|[0.6659082469383558,0.07688976580256132]|0.0         |
|[0.8086502311695247,0.564354801275521]  |1.0         |
+----------------------------------------+------------+

root
 |-- indexedFeatures: vector (nullable = true)
 |-- indexedLabel: double (nullable = true)

FeaturesAndLabelData is a simple Java bean to make sure that the column names are correct:

public class FeaturesAndLabelData {
  private double feature1;
  private double feature2;
  private int label;

  //getters and setters...
}