Github user Yunni commented on a diff in the pull request:
https://github.com/apache/spark/pull/15148#discussion_r82635879
--- Diff: mllib/src/main/scala/org/apache/spark/ml/feature/MinHash.scala ---
@@ -0,0 +1,107 @@
+/*
+ * 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.spark.annotation.{Experimental, Since}
+import org.apache.spark.ml.linalg.{Vector, Vectors, VectorUDT}
+import org.apache.spark.ml.util.Identifiable
+import org.apache.spark.sql.types.StructType
+
+/**
+ * Model produced by [[MinHash]]
+ */
+@Experimental
+@Since("2.1.0")
+class MinHashModel private[ml] (override val uid: String, hashFunctions:
Seq[Int => Long])
+ extends LSHModel[MinHashModel] {
+
+ @Since("2.1.0")
+ override protected[this] val hashFunction: Vector => Vector = {
+ elems: Vector =>
+ require(elems.numNonzeros > 0, "Must have at least 1 non zero
entry.")
+ Vectors.dense(hashFunctions.map(
+ func => elems.toSparse.indices.toList.map(func).min.toDouble
+ ).toArray)
+ }
+
+ @Since("2.1.0")
+ override protected[ml] def keyDistance(x: Vector, y: Vector): Double = {
+ val xSet = x.toSparse.indices.toSet
+ val ySet = y.toSparse.indices.toSet
+ 1 - xSet.intersect(ySet).size.toDouble / xSet.union(ySet).size.toDouble
+ }
+
+ @Since("2.1.0")
+ override protected[ml] def hashDistance(x: Vector, y: Vector): Double = {
+ // Since it's generated by hashing, it will be a pair of dense vectors.
+ x.toDense.values.zip(y.toDense.values).map(x => math.abs(x._1 -
x._2)).min
+ }
+}
+
+/**
+ * LSH class for Jaccard distance
+ * The input set should be represented in sparse vector form. 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 elem 2,
elem 3 and elem 5
+ */
+@Experimental
+@Since("2.1.0")
+class MinHash private[ml] (override val uid: String) extends
LSH[MinHashModel] {
+
+ private[this] val prime = 2038074743
+
+ @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 setOutputDim(value: Int): this.type =
super.setOutputDim(value)
+
+ private[this] lazy val randSeq: Seq[Int] = {
+ Seq.fill($(outputDim))(1 + Random.nextInt(prime -
1)).take($(outputDim))
+ }
+
+ @Since("2.1.0")
+ private[ml] def this() = {
+ this(Identifiable.randomUID("min hash"))
+ }
+
+ @Since("2.1.0")
+ override protected[this] def createRawLSHModel(inputDim: Int):
MinHashModel = {
+ val numEntry = inputDim * 2
+ require(numEntry < prime, "The input vector dimension is too large for
MinHash to handle.")
+ val hashFunctions: Seq[Int => Long] = {
+ (0 until $(outputDim)).map { i: Int =>
+ // Perfect Hash function, use 2n buckets to reduce collision.
+ elem: Int => (1 + elem) * randSeq(i).toLong % prime % numEntry
+ }
+ }
+ new MinHashModel(uid, hashFunctions)
+ }
+
+ @Since("2.1.0")
+ override def transformSchema(schema: StructType): StructType = {
+ require(schema.apply($(inputCol)).dataType.sameType(new VectorUDT),
--- End diff --
Done.
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