How to transform a csv string into a Spark-ML comp

问题:

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.

回答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...
}

回答2:

You have different ways of achieving this.

Create a schema as per your CSV file.

public class CSVData implements Serializable {
  String col1;
  String col2;
  long col3;
  String col4;
  //getters and setters  
}

Then convert the file into an RDD.

JavaSparkContext sc;
JavaRDD<String> data = sc.textFile("path-to-csv-file");
JavaSQLContext sqlContext = new JavaSQLContext(sc);

JavaRDD<Record> csv_rdd = sc.textFile(data).map(
  new Function<String, Record>() {
      public Record call(String line) throws Exception {
         String[] fields = line.split(",");
         Record sd = new Record(fields[0], fields[1], fields[2].trim(), fields[3]);
         return sd;
      }
});

Or

Create a Spark Session to read the file as a Dataset.

SparkSession spark = SparkSession
                .builder()
                .appName("SparkSample")
                .master("local[*]")
                .getOrCreate();
//Read file
Dataset<Row> ds = spark.read().text("path-to-csv-file");
 or
Dataset<Row> ds = spark.read().csv("path-to-csv-file");
ds.show();