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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.util.Random
import org.apache.hadoop.fs.Path
import org.apache.spark.annotation.Since
import org.apache.spark.ml.linalg.{Vector, Vectors, VectorUDT}
import org.apache.spark.ml.param.ParamMap
import org.apache.spark.ml.param.shared.HasSeed
import org.apache.spark.ml.util._
import org.apache.spark.sql.types.StructType
/**
* Model produced by [[MinHashLSH]], where multiple hash functions are stored. Each hash function
* is picked from the following family of hash functions, where a_i and b_i are randomly chosen
* integers less than prime:
* `h_i(x) = ((x \cdot a_i + b_i) \mod prime)`
*
* This hash family is approximately min-wise independent according to the reference.
*
* Reference:
* Tom Bohman, Colin Cooper, and Alan Frieze. "Min-wise independent linear permutations."
* Electronic Journal of Combinatorics 7 (2000): R26.
*
* @param randCoefficients Pairs of random coefficients. Each pair is used by one hash function.
*/
@Since("2.1.0")
class MinHashLSHModel private[ml](
override val uid: String,
private[ml] val randCoefficients: Array[(Int, Int)])
extends LSHModel[MinHashLSHModel] {
/** @group setParam */
@Since("2.4.0")
override def setInputCol(value: String): this.type = super.set(inputCol, value)
/** @group setParam */
@Since("2.4.0")
override def setOutputCol(value: String): this.type = super.set(outputCol, value)
@Since("2.1.0")
override protected[ml] def hashFunction(elems: Vector): Array[Vector] = {
require(elems.nonZeroIterator.nonEmpty, "Must have at least 1 non zero entry.")
val hashValues = randCoefficients.map { case (a, b) =>
elems.nonZeroIterator.map { case (i, _) =>
((1L + i) * a + b) % MinHashLSH.HASH_PRIME
}.min.toDouble
}
// TODO: Output vectors of dimension numHashFunctions in SPARK-18450
hashValues.map(Vectors.dense(_))
}
@Since("2.1.0")
override protected[ml] def keyDistance(x: Vector, y: Vector): Double = {
val xSet = x.nonZeroIterator.map(_._1).toSet
val ySet = y.nonZeroIterator.map(_._1).toSet
val intersectionSize = xSet.intersect(ySet).size.toDouble
val unionSize = xSet.size + ySet.size - intersectionSize
assert(unionSize > 0, "The union of two input sets must have at least 1 elements")
1 - intersectionSize / unionSize
}
@Since("2.1.0")
override protected[ml] def hashDistance(x: Seq[Vector], y: Seq[Vector]): Double = {
// Since it's generated by hashing, it will be a pair of dense vectors.
// TODO: This hashDistance function requires more discussion in SPARK-18454
x.zip(y).map(vectorPair =>
vectorPair._1.toArray.zip(vectorPair._2.toArray).count(pair => pair._1 != pair._2)
).min
}
@Since("2.1.0")
override def copy(extra: ParamMap): MinHashLSHModel = {
val copied = new MinHashLSHModel(uid, randCoefficients).setParent(parent)
copyValues(copied, extra)
}
@Since("2.1.0")
override def write: MLWriter = new MinHashLSHModel.MinHashLSHModelWriter(this)
@Since("3.0.0")
override def toString: String = {
s"MinHashLSHModel: uid=$uid, numHashTables=${$(numHashTables)}"
}
}
/**
* LSH class for Jaccard distance.
*
* The input can be dense or sparse vectors, but it is more efficient if it is sparse. For example,
* `Vectors.sparse(10, Array((2, 1.0), (3, 1.0), (5, 1.0)))`
* means there are 10 elements in the space. This set contains elements 2, 3, and 5. Also, any
* input vector must have at least 1 non-zero index, and all non-zero values are
* treated as binary "1" values.
*
* References:
* <a href="https://en.wikipedia.org/wiki/MinHash">Wikipedia on MinHash</a>
*/
@Since("2.1.0")
class MinHashLSH(override val uid: String) extends LSH[MinHashLSHModel] with HasSeed {
@Since("2.1.0")
override def setInputCol(value: String): this.type = super.setInputCol(value)
@Since("2.1.0")
override def setOutputCol(value: String): this.type = super.setOutputCol(value)
@Since("2.1.0")
override def setNumHashTables(value: Int): this.type = super.setNumHashTables(value)
@Since("2.1.0")
def this() = {
this(Identifiable.randomUID("mh-lsh"))
}
/** @group setParam */
@Since("2.1.0")
def setSeed(value: Long): this.type = set(seed, value)
@Since("2.1.0")
override protected[ml] def createRawLSHModel(inputDim: Int): MinHashLSHModel = {
require(inputDim <= MinHashLSH.HASH_PRIME,
s"The input vector dimension $inputDim exceeds the threshold ${MinHashLSH.HASH_PRIME}.")
val rand = new Random($(seed))
val randCoefs: Array[(Int, Int)] = Array.fill($(numHashTables)) {
(1 + rand.nextInt(MinHashLSH.HASH_PRIME - 1), rand.nextInt(MinHashLSH.HASH_PRIME - 1))
}
new MinHashLSHModel(uid, randCoefs)
}
@Since("2.1.0")
override def transformSchema(schema: StructType): StructType = {
SchemaUtils.checkColumnType(schema, $(inputCol), new VectorUDT)
validateAndTransformSchema(schema)
}
@Since("2.1.0")
override def copy(extra: ParamMap): this.type = defaultCopy(extra)
}
@Since("2.1.0")
object MinHashLSH extends DefaultParamsReadable[MinHashLSH] {
// A large prime smaller than sqrt(2^63 − 1)
private[ml] val HASH_PRIME = 2038074743
@Since("2.1.0")
override def load(path: String): MinHashLSH = super.load(path)
}
@Since("2.1.0")
object MinHashLSHModel extends MLReadable[MinHashLSHModel] {
@Since("2.1.0")
override def read: MLReader[MinHashLSHModel] = new MinHashLSHModelReader
@Since("2.1.0")
override def load(path: String): MinHashLSHModel = super.load(path)
private[MinHashLSHModel] class MinHashLSHModelWriter(instance: MinHashLSHModel)
extends MLWriter {
private case class Data(randCoefficients: Array[Int])
override protected def saveImpl(path: String): Unit = {
DefaultParamsWriter.saveMetadata(instance, path, sc)
val data = Data(instance.randCoefficients.flatMap(tuple => Array(tuple._1, tuple._2)))
val dataPath = new Path(path, "data").toString
sparkSession.createDataFrame(Seq(data)).repartition(1).write.parquet(dataPath)
}
}
private class MinHashLSHModelReader extends MLReader[MinHashLSHModel] {
/** Checked against metadata when loading model */
private val className = classOf[MinHashLSHModel].getName
override def load(path: String): MinHashLSHModel = {
val metadata = DefaultParamsReader.loadMetadata(path, sc, className)
val dataPath = new Path(path, "data").toString
val data = sparkSession.read.parquet(dataPath).select("randCoefficients").head()
val randCoefficients = data.getAs[Seq[Int]](0).grouped(2)
.map(tuple => (tuple(0), tuple(1))).toArray
val model = new MinHashLSHModel(metadata.uid, randCoefficients)
metadata.getAndSetParams(model)
model
}
}
}