Github user karlhigley commented on a diff in the pull request:
https://github.com/apache/spark/pull/15148#discussion_r80392464
--- Diff: mllib/src/main/scala/org/apache/spark/ml/feature/lsh/LSH.scala ---
@@ -0,0 +1,290 @@
+/*
+ * 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.lsh
+
+import scala.util.Random
+
+import org.apache.spark.ml.{Estimator, Model}
+import org.apache.spark.ml.linalg.{Vector, VectorUDT}
+import org.apache.spark.ml.param.{IntParam, ParamMap, ParamValidators}
+import org.apache.spark.ml.param.shared.{HasInputCol, HasOutputCol}
+import org.apache.spark.sql._
+import org.apache.spark.sql.expressions.UserDefinedFunction
+import org.apache.spark.sql.functions._
+import org.apache.spark.sql.types._
+
+/**
+ * Params for [[LSH]].
+ */
+private[ml] trait LSHParams extends HasInputCol with HasOutputCol {
+ /**
+ * Param for output dimension.
+ *
+ * @group param
+ */
+ final val outputDim: IntParam = new IntParam(this, "outputDim", "output
dimension",
+ ParamValidators.gt(0))
+
+ /** @group getParam */
+ final def getOutputDim: Int = $(outputDim)
+
+ setDefault(outputDim -> 1)
+
+ setDefault(outputCol -> "lsh_output")
+
+ /**
+ * Transform the Schema for LSH
+ * @param schema The schema of the input dataset without outputCol
+ * @return A derived schema with outputCol added
+ */
+ final def transformLSHSchema(schema: StructType): StructType = {
+ val outputFields = schema.fields :+
+ StructField($(outputCol), new VectorUDT, nullable = false)
+ StructType(outputFields)
+ }
+}
+
+/**
+ * Model produced by [[LSH]].
+ */
+abstract class LSHModel[KeyType, T <: LSHModel[KeyType, T]] private[ml]
+ extends Model[T] with LSHParams {
+ override def copy(extra: ParamMap): T = defaultCopy(extra)
+ /**
+ * :: DeveloperApi ::
+ *
+ * The hash function of LSH, mapping a predefined KeyType to a Vector
+ * @return The mapping of LSH function.
+ */
+ protected[this] val hashFunction: KeyType => Vector
+
+ /**
+ * :: DeveloperApi ::
+ *
+ * Calculate the distance between two different keys using the distance
metric corresponding
+ * to the hashFunction
+ * @param x One of the point in the metric space
+ * @param y Another the point in the metric space
+ * @return The distance between x and y in double
+ */
+ protected[ml] def keyDistance(x: KeyType, y: KeyType): Double
+
+ /**
+ * :: DeveloperApi ::
+ *
+ * Calculate the distance between two different hash Vectors. By
default, the distance is the
+ * minimum distance of two hash values in any dimension.
+ *
+ * @param x One of the hash vector
+ * @param y Another hash vector
+ * @return The distance between hash vectors x and y in double
+ */
+ 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
--- End diff --
By default, this is computing the Manhattan distance between hash values,
which probably works as a proxy for the distance between hash buckets when
using LSH based on p-stable distributions and any other approach that produces
vectors of integers/doubles as hash signatures (e.g. MinHash).
However, the default won't work for approaches that produce vectors of
booleans as hash signatures (e.g. sign random projection for cosine distance).
It could be overridden to compute Hamming distance in that case, though.
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