Vector Slicer

VectorSlicer feature selection, which takes a feature vector and outputs a new feature vector with a sub-array of the original features. It is useful for extracting features from a vector column

Type

ml-transformer

Class

fire.nodes.ml.NodeVectorSlicer

Fields

Name	Title	Description
inputCol	Features Column	The features column name
outputCol	Output Column	The output column name
indices	Indices	comma seprated
names	Names	The output column name

Details

VectorSlicer is a transformer that takes a feature vector and outputs a new feature vector with a sub-array of the original features. It is useful for extracting features from a vector column.

VectorSlicer accepts a vector column with specified indices, then outputs a new vector column whose values are selected via those indices.

More details are available at : http://spark.apache.org/docs/latest/ml-features.html#vectorslicer

Examples

The below example is available at : http://spark.apache.org/docs/latest/ml-features.html#vectorslicer

import java.util.Arrays

import org.apache.spark.ml.attribute.{Attribute, AttributeGroup, NumericAttribute}

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

import org.apache.spark.ml.linalg.Vectors

import org.apache.spark.sql.Row

import org.apache.spark.sql.types.StructType

val data = Arrays.asList(Row(Vectors.dense(-2.0, 2.3, 0.0)))

val defaultAttr = NumericAttribute.defaultAttr

val attrs = Array(“f1”, “f2”, “f3”).map(defaultAttr.withName)

val attrGroup = new AttributeGroup(“userFeatures”, attrs.asInstanceOf[Array[Attribute]])

val dataset = spark.createDataFrame(data, StructType(Array(attrGroup.toStructField())))

val slicer = new VectorSlicer().setInputCol(“userFeatures”).setOutputCol(“features”)

slicer.setIndices(Array(1)).setNames(Array(“f3”))

// or slicer.setIndices(Array(1, 2)), or slicer.setNames(Array(“f2”, “f3”))

val output = slicer.transform(dataset)

println(output.select(“userFeatures”, “features”).first())