Github user jkbradley commented on a diff in the pull request:
https://github.com/apache/spark/pull/15148#discussion_r81602057
--- Diff: mllib/src/main/scala/org/apache/spark/ml/feature/LSH.scala ---
@@ -0,0 +1,338 @@
+/*
+ * 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.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]].
+ */
+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. This
+ * param is the dimension of the amplification. The higher the dimension
is, the lower the false
+ * negative rate.
+ * @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, 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
+ */
+ protected[this] final def validateAndTransformSchema(schema:
StructType): StructType = {
+ SchemaUtils.appendColumn(schema, $(outputCol), new VectorUDT)
+ }
+}
+
+/**
+ * Model produced by [[LSH]].
+ */
+abstract class LSHModel[T <: LSHModel[T]] private[ml]
+ extends Model[T] with LSHParams {
+ override def copy(extra: ParamMap): T = defaultCopy(extra)
+ /**
+ * :: DeveloperApi ::
+ *
+ * The hash function of LSH, mapping a predefined KeyType to a Vector
+ * @return The mapping of LSH function.
+ */
+ protected[this] val hashFunction: Vector => Vector
+
+ /**
+ * :: DeveloperApi ::
+ *
+ * Calculate the distance between two different keys using the distance
metric corresponding
+ * to the hashFunction
+ * @param x One of the point in the metric space
+ * @param y Another the point in the metric space
+ * @return The distance between x and y in double
+ */
+ protected[ml] def keyDistance(x: Vector, y: Vector): Double
+
+ /**
+ * :: DeveloperApi ::
+ *
+ * Calculate the distance between two different hash Vectors. By
default, the distance is the
+ * minimum distance of two hash values in any dimension.
+ *
+ * @param x One of the hash vector
+ * @param y Another hash vector
+ * @return The distance between hash vectors x and y in double
+ */
+ protected[ml] def hashDistance(x: Vector, y: Vector): Double
+
+ /**
+ * Transforms the input dataset.
+ */
+ override def transform(dataset: Dataset[_]): DataFrame = {
+ transformSchema(dataset.schema, logging = true)
+ val transformUDF = udf(hashFunction, new VectorUDT)
+ dataset.withColumn($(outputCol), transformUDF(dataset($(inputCol))))
+ }
+
+ /**
+ * :: DeveloperApi ::
+ *
+ * Check transform validity and derive the output schema from the input
schema.
+ *
+ * Typical implementation should first conduct verification on schema
change and parameter
+ * validity, including complex parameter interaction checks.
+ */
+ 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
--- End diff --
This method needs to document that it checks for the outputCol and
transforms the data if it is missing, allowing caching of the transformed data
when necessary.
---
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 [email protected] or file a JIRA ticket
with INFRA.
---
---------------------------------------------------------------------
To unsubscribe, e-mail: [email protected]
For additional commands, e-mail: [email protected]
