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。
最佳答案
至少有两个选择:
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", $"features".as("_", attrGroup.toMetadata))
DataFrame
转换 AttributeGroup
toStructField
并将其用作给定列的架构:import org.apache.spark.sql.types.{StructType, StructField, IntegerType}
val schema = StructType(Array(
StructField("label", IntegerType, false),
attrGroup.toStructField()
))
spark.createDataFrame(
rdd.map(row => Row.fromSeq(row.productIterator.toSeq)),
schema)
如果使用
VectorAssembler
创建了向量列应该已经附加了描述父列的列元数据。import org.apache.spark.ml.feature.VectorAssembler
val raw = sc.parallelize(Seq(
(1, 1.0, 2.0, 3.0)
)).toDF("id", "feat1", "feat2", "feat3")
val assembler = new VectorAssembler()
.setInputCols(Array("feat1", "feat2", "feat3"))
.setOutputCol("features")
val dfWithMeta = assembler.transform(raw).select($"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
关于scala - 将元数据附加到 Spark 中的向量列,我们在Stack Overflow上找到一个类似的问题: https://stackoverflow.com/questions/35305154/