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/*
* Licensed to the Apache Software Foundation (ASF) under one or more
* contributor license agreements. See the NOTICE file distributed with
* this work for additional information regarding copyright ownership.
* The ASF licenses this file to You under the Apache License, Version 2.0
* (the "License"); you may not use this file except in compliance with
* the License. You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package org.apache.spark.ml.feature
import scala.collection.mutable.ArrayBuilder
import org.apache.spark.SparkException
import org.apache.spark.annotation.Experimental
import org.apache.spark.ml.attribute._
import org.apache.spark.ml.param._
import org.apache.spark.ml.param.shared._
import org.apache.spark.ml.util.Identifiable
import org.apache.spark.ml.Transformer
import org.apache.spark.mllib.linalg.{Vector, VectorUDT, Vectors}
import org.apache.spark.sql.{DataFrame, Row}
import org.apache.spark.sql.functions._
import org.apache.spark.sql.types._
/**
* :: Experimental ::
* Implements the feature interaction transform. This transformer takes in Double and Vector type
* columns and outputs a flattened vector of their feature interactions. To handle interaction,
* we first one-hot encode any nominal features. Then, a vector of the feature cross-products is
* produced.
*
* For example, given the input feature values `Double(2)` and `Vector(3, 4)`, the output would be
* `Vector(6, 8)` if all input features were numeric. If the first feature was instead nominal
* with four categories, the output would then be `Vector(0, 0, 0, 0, 3, 4, 0, 0)`.
*/
@Experimental
class Interaction(override val uid: String) extends Transformer
with HasInputCols with HasOutputCol {
def this() = this(Identifiable.randomUID("interaction"))
/** @group setParam */
def setInputCols(values: Array[String]): this.type = set(inputCols, values)
/** @group setParam */
def setOutputCol(value: String): this.type = set(outputCol, value)
// optimistic schema; does not contain any ML attributes
override def transformSchema(schema: StructType): StructType = {
validateParams()
StructType(schema.fields :+ StructField($(outputCol), new VectorUDT, false))
}
override def transform(dataset: DataFrame): DataFrame = {
validateParams()
val inputFeatures = $(inputCols).map(c => dataset.schema(c))
val featureEncoders = getFeatureEncoders(inputFeatures)
val featureAttrs = getFeatureAttrs(inputFeatures)
def interactFunc = udf { row: Row =>
var indices = ArrayBuilder.make[Int]
var values = ArrayBuilder.make[Double]
var size = 1
indices += 0
values += 1.0
var featureIndex = row.length - 1
while (featureIndex >= 0) {
val prevIndices = indices.result()
val prevValues = values.result()
val prevSize = size
val currentEncoder = featureEncoders(featureIndex)
indices = ArrayBuilder.make[Int]
values = ArrayBuilder.make[Double]
size *= currentEncoder.outputSize
currentEncoder.foreachNonzeroOutput(row(featureIndex), (i, a) => {
var j = 0
while (j < prevIndices.length) {
indices += prevIndices(j) + i * prevSize
values += prevValues(j) * a
j += 1
}
})
featureIndex -= 1
}
Vectors.sparse(size, indices.result(), values.result()).compressed
}
val featureCols = inputFeatures.map { f =>
f.dataType match {
case DoubleType => dataset(f.name)
case _: VectorUDT => dataset(f.name)
case _: NumericType | BooleanType => dataset(f.name).cast(DoubleType)
}
}
dataset.select(
col("*"),
interactFunc(struct(featureCols: _*)).as($(outputCol), featureAttrs.toMetadata()))
}
/**
* Creates a feature encoder for each input column, which supports efficient iteration over
* one-hot encoded feature values. See also the class-level comment of [[FeatureEncoder]].
*
* @param features The input feature columns to create encoders for.
*/
private def getFeatureEncoders(features: Seq[StructField]): Array[FeatureEncoder] = {
def getNumFeatures(attr: Attribute): Int = {
attr match {
case nominal: NominalAttribute =>
math.max(1, nominal.getNumValues.getOrElse(
throw new SparkException("Nominal features must have attr numValues defined.")))
case _ =>
1 // numeric feature
}
}
features.map { f =>
val numFeatures = f.dataType match {
case _: NumericType | BooleanType =>
Array(getNumFeatures(Attribute.fromStructField(f)))
case _: VectorUDT =>
val attrs = AttributeGroup.fromStructField(f).attributes.getOrElse(
throw new SparkException("Vector attributes must be defined for interaction."))
attrs.map(getNumFeatures).toArray
}
new FeatureEncoder(numFeatures)
}.toArray
}
/**
* Generates ML attributes for the output vector of all feature interactions. We make a best
* effort to generate reasonable names for output features, based on the concatenation of the
* interacting feature names and values delimited with `_`. When no feature name is specified,
* we fall back to using the feature index (e.g. `foo:bar_2_0` may indicate an interaction
* between the numeric `foo` feature and a nominal third feature from column `bar`.
*
* @param features The input feature columns to the Interaction transformer.
*/
private def getFeatureAttrs(features: Seq[StructField]): AttributeGroup = {
var featureAttrs: Seq[Attribute] = Nil
features.reverse.foreach { f =>
val encodedAttrs = f.dataType match {
case _: NumericType | BooleanType =>
val attr = Attribute.decodeStructField(f, preserveName = true)
if (attr == UnresolvedAttribute) {
encodedFeatureAttrs(Seq(NumericAttribute.defaultAttr.withName(f.name)), None)
} else if (!attr.name.isDefined) {
encodedFeatureAttrs(Seq(attr.withName(f.name)), None)
} else {
encodedFeatureAttrs(Seq(attr), None)
}
case _: VectorUDT =>
val group = AttributeGroup.fromStructField(f)
encodedFeatureAttrs(group.attributes.get, Some(group.name))
}
if (featureAttrs.isEmpty) {
featureAttrs = encodedAttrs
} else {
featureAttrs = encodedAttrs.flatMap { head =>
featureAttrs.map { tail =>
NumericAttribute.defaultAttr.withName(head.name.get + ":" + tail.name.get)
}
}
}
}
new AttributeGroup($(outputCol), featureAttrs.toArray)
}
/**
* Generates the output ML attributes for a single input feature. Each output feature name has
* up to three parts: the group name, feature name, and category name (for nominal features),
* each separated by an underscore.
*
* @param inputAttrs The attributes of the input feature.
* @param groupName Optional name of the input feature group (for Vector type features).
*/
private def encodedFeatureAttrs(
inputAttrs: Seq[Attribute],
groupName: Option[String]): Seq[Attribute] = {
def format(
index: Int,
attrName: Option[String],
categoryName: Option[String]): String = {
val parts = Seq(groupName, Some(attrName.getOrElse(index.toString)), categoryName)
parts.flatten.mkString("_")
}
inputAttrs.zipWithIndex.flatMap {
case (nominal: NominalAttribute, i) =>
if (nominal.values.isDefined) {
nominal.values.get.map(
v => BinaryAttribute.defaultAttr.withName(format(i, nominal.name, Some(v))))
} else {
Array.tabulate(nominal.getNumValues.get)(
j => BinaryAttribute.defaultAttr.withName(format(i, nominal.name, Some(j.toString))))
}
case (a: Attribute, i) =>
Seq(NumericAttribute.defaultAttr.withName(format(i, a.name, None)))
}
}
override def copy(extra: ParamMap): Interaction = defaultCopy(extra)
override def validateParams(): Unit = {
require(get(inputCols).isDefined, "Input cols must be defined first.")
require(get(outputCol).isDefined, "Output col must be defined first.")
require($(inputCols).length > 0, "Input cols must have non-zero length.")
require($(inputCols).distinct.length == $(inputCols).length, "Input cols must be distinct.")
}
}
/**
* This class performs on-the-fly one-hot encoding of features as you iterate over them. To
* indicate which input features should be one-hot encoded, an array of the feature counts
* must be passed in ahead of time.
*
* @param numFeatures Array of feature counts for each input feature. For nominal features this
* count is equal to the number of categories. For numeric features the count
* should be set to 1.
*/
private[ml] class FeatureEncoder(numFeatures: Array[Int]) extends Serializable {
assert(numFeatures.forall(_ > 0), "Features counts must all be positive.")
/** The size of the output vector. */
val outputSize = numFeatures.sum
/** Precomputed offsets for the location of each output feature. */
private val outputOffsets = {
val arr = new Array[Int](numFeatures.length)
var i = 1
while (i < arr.length) {
arr(i) = arr(i - 1) + numFeatures(i - 1)
i += 1
}
arr
}
/**
* Given an input row of features, invokes the specific function for every non-zero output.
*
* @param value The row value to encode, either a Double or Vector.
* @param f The callback to invoke on each non-zero (index, value) output pair.
*/
def foreachNonzeroOutput(value: Any, f: (Int, Double) => Unit): Unit = value match {
case d: Double =>
assert(numFeatures.length == 1, "DoubleType columns should only contain one feature.")
val numOutputCols = numFeatures.head
if (numOutputCols > 1) {
assert(
d >= 0.0 && d == d.toInt && d < numOutputCols,
s"Values from column must be indices, but got $d.")
f(d.toInt, 1.0)
} else {
f(0, d)
}
case vec: Vector =>
assert(numFeatures.length == vec.size,
s"Vector column size was ${vec.size}, expected ${numFeatures.length}")
vec.foreachActive { (i, v) =>
val numOutputCols = numFeatures(i)
if (numOutputCols > 1) {
assert(
v >= 0.0 && v == v.toInt && v < numOutputCols,
s"Values from column must be indices, but got $v.")
f(outputOffsets(i) + v.toInt, 1.0)
} else {
f(outputOffsets(i), v)
}
}
case null =>
throw new SparkException("Values to interact cannot be null.")
case o =>
throw new SparkException(s"$o of type ${o.getClass.getName} is not supported.")
}
}
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