Github user Yunni commented on a diff in the pull request:
https://github.com/apache/spark/pull/15148#discussion_r84586831
--- Diff: mllib/src/main/scala/org/apache/spark/ml/feature/LSH.scala ---
@@ -0,0 +1,343 @@
+/*
+ * 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.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.functions._
+import org.apache.spark.sql.types._
+
+/**
+ * Params for [[LSH]].
+ */
+@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)
+
+ 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, $(outputCol), new VectorUDT)
+ }
+}
+
+/**
+ * Model produced by [[LSH]].
+ */
+@Since("2.1.0")
+private[ml] abstract class LSHModel[T <: LSHModel[T]] extends Model[T]
with LSHParams {
+ self: T =>
+
+ @Since("2.1.0")
+ override def copy(extra: ParamMap): T = defaultCopy(extra)
+
+ /**
+ * The hash function of LSH, mapping a predefined KeyType to a Vector
+ * @return The mapping of LSH function.
+ */
+ @Since("2.1.0")
+ protected[this] val hashFunction: Vector => Vector
+
+ /**
+ * Calculate the distance between two different keys using the distance
metric corresponding
+ * to the hashFunction
+ * @param x One input vector in the metric space
+ * @param y One input vector in the metric space
+ * @return The distance between x and y
+ */
+ @Since("2.1.0")
+ protected[ml] def keyDistance(x: Vector, y: Vector): Double
+
+ /**
+ * Calculate the distance between two different hash Vectors.
+ *
+ * @param x One of the hash vector
+ * @param y Another hash vector
+ * @return The distance between hash vectors x and y
+ */
+ @Since("2.1.0")
+ protected[ml] def hashDistance(x: Vector, y: Vector): Double
+
+ @Since("2.1.0")
+ override def transform(dataset: Dataset[_]): DataFrame = {
+ transformSchema(dataset.schema, logging = true)
+ val transformUDF = udf(hashFunction, new VectorUDT)
+ dataset.withColumn($(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($(outputCol)))
+
+ val modelSubset = if (singleProbing) {
+ modelDataset.filter(hashDistCol === 0.0)
+ } else {
+ // Compute threshold to get exact k elements.
+ val modelDatasetSortedByHash =
modelDataset.sort(hashDistCol).limit(numNearestNeighbors)
+ val thresholdDataset =
modelDatasetSortedByHash.select(max(hashDistCol))
+ val hashThreshold = thresholdDataset.take(1).head.getDouble(0)
+
+ // Filter the dataset where the hash value is less than the
threshold.
+ modelDataset.filter(hashDistCol <= hashThreshold)
+ }
+
+ // Get the top k nearest neighbor by their distance to the key
+ val keyDistUDF = udf((x: Vector) => keyDistance(x, key),
DataTypes.DoubleType)
+ val modelSubsetWithDistCol = modelSubset.withColumn(distCol,
keyDistUDF(col($(inputCol))))
+ modelSubsetWithDistCol.sort(distCol).limit(numNearestNeighbors)
+ }
+
+ /**
+ * Overloaded method for approxNearestNeighbors. Use Single Probing as
default way to search
+ * nearest neighbors and "distCol" as default distCol.
+ */
+ @Since("2.1.0")
+ def approxNearestNeighbors(
+ dataset: Dataset[_],
+ key: Vector,
+ numNearestNeighbors: Int): Dataset[_] = {
+ approxNearestNeighbors(dataset, key, numNearestNeighbors, true,
"distCol")
+ }
+
+ /**
+ * Preprocess step for approximate similarity join. Transform and
explode the [[outputCol]] to
+ * two explodeCols: entry and value. "entry" is the index in hash
vector, and "value" is the
+ * value of corresponding value of the index in the vector.
+ *
+ * @param dataset The dataset to transform and explode.
+ * @param explodeCols The alias for the exploded columns, must be a seq
of two strings.
+ * @return A dataset containing idCol, inputCol and explodeCols
+ */
+ @Since("2.1.0")
+ private[this] def processDataset(
+ dataset: Dataset[_],
+ inputName: String,
+ explodeCols: Seq[String]): Dataset[_] = {
+ require(explodeCols.size == 2, "explodeCols must be two strings.")
+ val vectorToMap = udf((x: Vector) => x.asBreeze.iterator.toMap,
+ MapType(DataTypes.IntegerType, DataTypes.DoubleType))
+ val modelDataset: DataFrame = if
(!dataset.columns.contains($(outputCol))) {
+ transform(dataset)
+ } else {
+ dataset.toDF()
+ }
+ modelDataset.select(
+ struct(col("*")).as(inputName),
+ explode(vectorToMap(col($(outputCol)))).as(explodeCols))
+ }
+
+ /**
+ * Recreate a column using the same column name but different attribute
id. Used in approximate
+ * similarity join.
+ * @param dataset The dataset where a column need to recreate
+ * @param colName The name of the column to recreate
+ * @param tmpColName A temporary column name which does not conflict
with existing columns
+ * @return
+ */
+ @Since("2.1.0")
+ private[this] def recreateCol(
+ dataset: Dataset[_],
+ colName: String,
+ tmpColName: String): Dataset[_] = {
+ dataset
+ .withColumnRenamed(colName, tmpColName)
+ .withColumn(colName, col(tmpColName))
+ .drop(tmpColName)
+ }
+
+ /**
+ * Join two dataset to approximately find all pairs of records whose
distance are smaller than
+ * the threshold. 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.
+ *
+ * @param datasetA One of the datasets to join
+ * @param datasetB Another dataset to join
+ * @param threshold The threshold for the distance of record pairs
+ * @param distCol Output column for storing the distance between each
result record and the key
+ * @param leftColName The alias of all columns of datasetA in the output
Dataset
--- End diff --
Done.
---
If your project is set up for it, you can reply to this email and have your
reply appear on GitHub as well. If your project does not have this feature
enabled and wishes so, or if the feature is enabled but not working, please
contact infrastructure at infrastruct...@apache.org or file a JIRA ticket
with INFRA.
---
---------------------------------------------------------------------
To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org
For additional commands, e-mail: reviews-h...@spark.apache.org
