Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/7987#discussion_r39792732
--- Diff:
mllib/src/main/scala/org/apache/spark/ml/feature/Interaction.scala ---
@@ -0,0 +1,276 @@
+/*
+ * 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.{ArrayBuffer, 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.{Estimator, Model, Pipeline, PipelineModel,
PipelineStage, 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.fromStructField(f)
+ 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]) {
+ 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)
+ for (i <- 1 until arr.length) {
--- End diff --
minor: Use `while` instead of `for`. The latter is slow in Scala. Another
option is using `foldLeft` (not as fast as `while` but saves some code)
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