Github user jkbradley commented on a diff in the pull request: https://github.com/apache/spark/pull/15148#discussion_r85616890 --- 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 -- Right, good point about the last bucket
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