如何将 csv 字符串转换为 Spark-ML 兼容的数据集<Row>格式? [英] How to transform a csv string into a Spark-ML compatible Dataset<Row> format?

问题描述

我有一个 Datasetdf，包含 string 类型的两列(key"和value").df.printSchema();给我以下输出:

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)

value 列的内容实际上是一个 csv 格式的行(来自 kafka 主题)，该行的最后一个条目代表类标签，所有之前的条目代表特征(第一行不包含在数据集中):

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

因为我想在这个数据上训练一个分类器，所以我需要把这个表示转换成一行密集向量，包含所有的特征值和一个 double 类型的列，包含标签值:

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)

如何使用 java 1.8 和 Spark 2.2.0 执行此操作?

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

我走得更远，但在尝试使用灵活数量的特征尺寸时，我再次陷入困境.我创建了一个 后续问题.

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.

推荐答案

A VectorAssembler (javadocs) 可以将数据集转换为所需的格式.

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

首先，输入分为三列:

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));

然后由 VectorAssembler 转换结果:

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");

结果数据框具有所需的格式:

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 是一个简单的 Java bean，用于确保列名正确:

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...
}

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