将元数据附加到 Spark 中的向量列

问题描述

背景:我有一个包含两列的数据框:标签和特征.

Context:I have a data frame with two columns: label, and features.

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.

Where features is a mllib.linalg.VectorUDT of numeric type built using VectorAssembler.

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.

Question:Is there a way to assign a schema to the features vector? I want to keep track of the name of each feature.

目前尝试过:

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.

But was not sure how to apply this to an existing data frame.

推荐答案

至少有两个选择:

1. 在现有的 DataFrame 上，您可以使用带有 metadata 参数的 as 方法:

1. On existing DataFrame you can use as method with metadata argument:

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))

当您创建新的 DataFrame 时，转换 AttributeGroup toStructField 并将其用作给定列的架构:

When you create new DataFrame convert AttributeGroup toStructField and use it as a schema for a given column:

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，则应该已经附加了描述父列的列元数据.

If vector column has been created using VectorAssembler column metadata describing parent columns should be already attached.

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)，但可以由诸如 VectorSlicer 之类的专门工具使用:

Vector fields are not directly accessible using dot syntax (like $features.feat1) but can used by specialized tools like 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，请参阅如何将列声明为 DataFrame 中的分类特征以用于 ml

For PySpark see How can I declare a Column as a categorical feature in a DataFrame for use in ml

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