Github user Yunni commented on a diff in the pull request:
https://github.com/apache/spark/pull/15148#discussion_r85417885
--- Diff: mllib/src/main/scala/org/apache/spark/ml/feature/LSH.scala ---
@@ -0,0 +1,339 @@
+/*
+ * 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.{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.ml.util.SchemaUtils
+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]].
+ */
+@Experimental
+@Since("2.1.0")
+private[ml] trait LSHParams extends HasInputCol with HasOutputCol {
+ /**
+ * Param for the dimension of LSH OR-amplification.
+ *
+ * In this implementation, we use LSH OR-amplification to reduce the
false negative rate. The
+ * higher the dimension is, the lower the false negative rate.
+ * @group param
+ */
+ @Since("2.1.0")
+ final val outputDim: IntParam = new IntParam(this, "outputDim", "output
dimension, where" +
+ "increasing dimensionality lowers the false negative rate, and
decreasing dimensionality" +
+ " improves the running performance", ParamValidators.gt(0))
+
+ /** @group getParam */
+ @Since("2.1.0")
+ final def getOutputDim: Int = $(outputDim)
+
+ // TODO: Decide about this default. It should probably depend on the
particular LSH algorithm.
+ setDefault(outputDim -> 1, outputCol -> "lshFeatures")
+
+ /**
+ * Transform the Schema for LSH
+ * @param schema The schema of the input dataset without [[outputCol]]
+ * @return A derived schema with [[outputCol]] added
+ */
+ @Since("2.1.0")
+ protected[this] final def validateAndTransformSchema(schema:
StructType): StructType = {
+ SchemaUtils.appendColumn(schema, $(outputCol), new VectorUDT)
+ }
+}
+
+/**
+ * Model produced by [[LSH]].
+ */
+@Experimental
+@Since("2.1.0")
+private[ml] abstract class LSHModel[T <: LSHModel[T]] extends Model[T]
with LSHParams {
+ self: T =>
+
+ @Since("2.1.0")
+ override def copy(extra: ParamMap): T = defaultCopy(extra)
+
+ /**
+ * The hash function of LSH, mapping a predefined KeyType to a Vector
+ * @return The mapping of LSH function.
+ */
+ @Since("2.1.0")
+ protected[this] val hashFunction: Vector => Vector
+
+ /**
+ * Calculate the distance between two different keys using the distance
metric corresponding
+ * to the hashFunction
+ * @param x One of the point in the metric space
+ * @param y Another the point in the metric space
+ * @return The distance between x and y
+ */
+ @Since("2.1.0")
+ protected[ml] def keyDistance(x: Vector, y: Vector): Double
+
+ /**
+ * Calculate the distance between two different hash Vectors.
+ *
+ * @param x One of the hash vector
+ * @param y Another hash vector
+ * @return The distance between hash vectors x and y
+ */
+ @Since("2.1.0")
+ protected[ml] def hashDistance(x: Vector, y: Vector): Double
+
+ @Since("2.1.0")
+ override def transform(dataset: Dataset[_]): DataFrame = {
+ transformSchema(dataset.schema, logging = true)
+ val transformUDF = udf(hashFunction, new VectorUDT)
+ dataset.withColumn($(outputCol), transformUDF(dataset($(inputCol))))
+ }
+
+ @Since("2.1.0")
+ override def transformSchema(schema: StructType): StructType = {
+ validateAndTransformSchema(schema)
+ }
+
+ /**
+ * Given a large dataset and an item, approximately find at most k items
which have the closest
+ * distance to the item. If the [[outputCol]] is missing, the method
will transform the data; if
+ * the [[outputCol]] exists, it will use the [[outputCol]]. This allows
caching of the
+ * transformed data when necessary.
+ *
+ * This method implements two ways of fetching k nearest neighbors:
+ * - Single Probing: Fast, return at most k elements (Probing only one
buckets)
+ * - Multiple Probing: Slow, return exact k elements (Probing multiple
buckets close to the key)
+ *
+ * @param dataset the dataset to search for nearest neighbors of the key
+ * @param key Feature vector representing the item to search for
+ * @param numNearestNeighbors The maximum number of nearest neighbors
+ * @param singleProbing True for using Single Probing; false for
multiple probing
+ * @param distCol Output column for storing the distance between each
result record and the key
+ * @return A dataset containing at most k items closest to the key. A
distCol is added to show
+ * the distance between each record and the key.
+ */
+ @Since("2.1.0")
+ def approxNearestNeighbors(
+ dataset: Dataset[_],
+ key: Vector,
+ numNearestNeighbors: Int,
+ singleProbing: Boolean,
+ distCol: String): Dataset[_] = {
+ require(numNearestNeighbors > 0, "The number of nearest neighbors
cannot be less than 1")
+ // Get Hash Value of the key
+ val keyHash = hashFunction(key)
+ val modelDataset: DataFrame = if
(!dataset.columns.contains($(outputCol))) {
+ transform(dataset)
+ } else {
+ dataset.toDF()
+ }
+
+ // In the origin dataset, find the hash value that is closest to the
key
+ val hashDistUDF = udf((x: Vector) => hashDistance(x, keyHash),
DataTypes.DoubleType)
+ val hashDistCol = hashDistUDF(col($(outputCol)))
+
+ val modelSubset = if (singleProbing) {
+ modelDataset.filter(hashDistCol === 0.0)
+ } else {
+ // Compute threshold to get exact k elements.
+ val modelDatasetSortedByHash =
modelDataset.sort(hashDistCol).limit(numNearestNeighbors)
+ val thresholdDataset =
modelDatasetSortedByHash.select(max(hashDistCol))
+ val hashThreshold = thresholdDataset.take(1).head.getDouble(0)
+
+ // Filter the dataset where the hash value is less than the
threshold.
+ modelDataset.filter(hashDistCol <= hashThreshold)
+ }
--- End diff --
Not really, `modelDataset.filter(hashDistCol === 0.0)` might return < k
rows but the second method will guarantee >= k rows are returned
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