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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.mllib.feature
import scala.collection.mutable.ArrayBuilder
import org.json4s._
import org.json4s.JsonDSL._
import org.json4s.jackson.JsonMethods._
import org.apache.spark.annotation.Since
import org.apache.spark.mllib.linalg.{DenseVector, SparseVector, Vector, Vectors}
import org.apache.spark.mllib.regression.LabeledPoint
import org.apache.spark.mllib.stat.Statistics
import org.apache.spark.mllib.util.{Loader, Saveable}
import org.apache.spark.rdd.RDD
import org.apache.spark.SparkContext
import org.apache.spark.sql.{Row, SparkSession}
/**
* Chi Squared selector model.
*
* @param selectedFeatures list of indices to select (filter). Must be ordered asc
*/
@Since("1.3.0")
class ChiSqSelectorModel @Since("1.3.0") (
@Since("1.3.0") val selectedFeatures: Array[Int]) extends VectorTransformer with Saveable {
require(isSorted(selectedFeatures), "Array has to be sorted asc")
protected def isSorted(array: Array[Int]): Boolean = {
var i = 1
val len = array.length
while (i < len) {
if (array(i) < array(i-1)) return false
i += 1
}
true
}
/**
* Applies transformation on a vector.
*
* @param vector vector to be transformed.
* @return transformed vector.
*/
@Since("1.3.0")
override def transform(vector: Vector): Vector = {
compress(vector, selectedFeatures)
}
/**
* Returns a vector with features filtered.
* Preserves the order of filtered features the same as their indices are stored.
* Might be moved to Vector as .slice
* @param features vector
* @param filterIndices indices of features to filter, must be ordered asc
*/
private def compress(features: Vector, filterIndices: Array[Int]): Vector = {
features match {
case SparseVector(size, indices, values) =>
val newSize = filterIndices.length
val newValues = new ArrayBuilder.ofDouble
val newIndices = new ArrayBuilder.ofInt
var i = 0
var j = 0
var indicesIdx = 0
var filterIndicesIdx = 0
while (i < indices.length && j < filterIndices.length) {
indicesIdx = indices(i)
filterIndicesIdx = filterIndices(j)
if (indicesIdx == filterIndicesIdx) {
newIndices += j
newValues += values(i)
j += 1
i += 1
} else {
if (indicesIdx > filterIndicesIdx) {
j += 1
} else {
i += 1
}
}
}
// TODO: Sparse representation might be ineffective if (newSize ~= newValues.size)
Vectors.sparse(newSize, newIndices.result(), newValues.result())
case DenseVector(values) =>
val values = features.toArray
Vectors.dense(filterIndices.map(i => values(i)))
case other =>
throw new UnsupportedOperationException(
s"Only sparse and dense vectors are supported but got ${other.getClass}.")
}
}
@Since("1.6.0")
override def save(sc: SparkContext, path: String): Unit = {
ChiSqSelectorModel.SaveLoadV1_0.save(sc, this, path)
}
override protected def formatVersion: String = "1.0"
}
object ChiSqSelectorModel extends Loader[ChiSqSelectorModel] {
@Since("1.6.0")
override def load(sc: SparkContext, path: String): ChiSqSelectorModel = {
ChiSqSelectorModel.SaveLoadV1_0.load(sc, path)
}
private[feature]
object SaveLoadV1_0 {
private val thisFormatVersion = "1.0"
/** Model data for import/export */
case class Data(feature: Int)
private[feature]
val thisClassName = "org.apache.spark.mllib.feature.ChiSqSelectorModel"
def save(sc: SparkContext, model: ChiSqSelectorModel, path: String): Unit = {
val spark = SparkSession.builder().sparkContext(sc).getOrCreate()
val metadata = compact(render(
("class" -> thisClassName) ~ ("version" -> thisFormatVersion)))
sc.parallelize(Seq(metadata), 1).saveAsTextFile(Loader.metadataPath(path))
// Create Parquet data.
val dataArray = Array.tabulate(model.selectedFeatures.length) { i =>
Data(model.selectedFeatures(i))
}
spark.createDataFrame(dataArray).repartition(1).write.parquet(Loader.dataPath(path))
}
def load(sc: SparkContext, path: String): ChiSqSelectorModel = {
implicit val formats = DefaultFormats
val spark = SparkSession.builder().sparkContext(sc).getOrCreate()
val (className, formatVersion, metadata) = Loader.loadMetadata(sc, path)
assert(className == thisClassName)
assert(formatVersion == thisFormatVersion)
val dataFrame = spark.read.parquet(Loader.dataPath(path))
val dataArray = dataFrame.select("feature")
// Check schema explicitly since erasure makes it hard to use match-case for checking.
Loader.checkSchema[Data](dataFrame.schema)
val features = dataArray.rdd.map {
case Row(feature: Int) => (feature)
}.collect()
new ChiSqSelectorModel(features)
}
}
}
/**
* Creates a ChiSquared feature selector.
* @param numTopFeatures number of features that selector will select
* (ordered by statistic value descending)
* Note that if the number of features is less than numTopFeatures,
* then this will select all features.
*/
@Since("1.3.0")
class ChiSqSelector @Since("1.3.0") (
@Since("1.3.0") val numTopFeatures: Int) extends Serializable {
/**
* Returns a ChiSquared feature selector.
*
* @param data an `RDD[LabeledPoint]` containing the labeled dataset with categorical features.
* Real-valued features will be treated as categorical for each distinct value.
* Apply feature discretizer before using this function.
*/
@Since("1.3.0")
def fit(data: RDD[LabeledPoint]): ChiSqSelectorModel = {
val indices = Statistics.chiSqTest(data)
.zipWithIndex.sortBy { case (res, _) => -res.statistic }
.take(numTopFeatures)
.map { case (_, indices) => indices }
.sorted
new ChiSqSelectorModel(indices)
}
}
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