scala - 将元数据附加到 Spark 中的向量列

Question

上下文:
我有一个包含两列的数据框:标签和特征。

org.apache.spark.sql.DataFrame = [label: int, features: vector]

其中 features 是使用 VectorAssembler 构建的数字类型的 mllib.linalg.VectorUDT。

问题:
有没有办法为特征向量分配模式？我想跟踪每个功能的名称。

到目前为止尝试过:

val defaultAttr = NumericAttribute.defaultAttr
val attrs = Array("feat1", "feat2", "feat3").map(defaultAttr.withName)
val attrGroup = new AttributeGroup("userFeatures", attrs.asInstanceOf[Array[Attribute]])

scala> attrGroup.toMetadata 
res197: org.apache.spark.sql.types.Metadata = {"ml_attr":{"attrs":{"numeric":[{"idx":0,"name":"f1"},{"idx":1,"name":"f2"},{"idx":2,"name":"f3"}]},"num_attrs":3}}

但不确定如何将其应用于现有数据框。

Answer 1

至少有两个选择:

在现有 DataFrame您可以使用 as方法与 metadata争论:

import org.apache.spark.ml.attribute._

val rdd = sc.parallelize(Seq(
  (1, Vectors.dense(1.0, 2.0, 3.0))
))
val df = rdd.toDF("label", "features")

df.withColumn("features", $"features".as("_", attrGroup.toMetadata))

创建新时 DataFrame转换 AttributeGroup toStructField并将其用作给定列的架构:

import org.apache.spark.sql.types.{StructType, StructField, IntegerType}

val schema = StructType(Array(
  StructField("label", IntegerType, false),
  attrGroup.toStructField()
))

spark.createDataFrame(
  rdd.map(row => Row.fromSeq(row.productIterator.toSeq)),
  schema)

如果使用 VectorAssembler 创建了向量列应该已经附加了描述父列的列元数据。

import org.apache.spark.ml.feature.VectorAssembler

val raw = sc.parallelize(Seq(
  (1, 1.0, 2.0, 3.0)
)).toDF("id", "feat1", "feat2", "feat3")

val assembler = new VectorAssembler()
  .setInputCols(Array("feat1", "feat2", "feat3"))
  .setOutputCol("features")

val dfWithMeta = assembler.transform(raw).select($"id", $"features")
dfWithMeta.schema.fields(1).metadata

// org.apache.spark.sql.types.Metadata = {"ml_attr":{"attrs":{"numeric":[
//   {"idx":0,"name":"feat1"},{"idx":1,"name":"feat2"},
//   {"idx":2,"name":"feat3"}]},"num_attrs":3}

矢量字段不能使用点语法直接访问(如 $features.feat1 )，但可以由专业工具使用，如 VectorSlicer :

import org.apache.spark.ml.feature.VectorSlicer

val slicer = new VectorSlicer()
  .setInputCol("features")
  .setOutputCol("featuresSubset")
  .setNames(Array("feat1", "feat3"))

slicer.transform(dfWithMeta).show
// +---+-------------+--------------+
// | id|     features|featuresSubset|
// +---+-------------+--------------+
// |  1|[1.0,2.0,3.0]|     [1.0,3.0]|
// +---+-------------+--------------+

对于 PySpark，请参阅 How can I declare a Column as a categorical feature in a DataFrame for use in ml