Github user srowen commented on a diff in the pull request:
https://github.com/apache/spark/pull/12461#discussion_r60474181
--- Diff:
mllib/src/main/scala/org/apache/spark/ml/evaluation/RankingEvaluator.scala ---
@@ -0,0 +1,287 @@
+/*
+ * 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.evaluation
+
+import scala.reflect.ClassTag
+
+import org.apache.spark.SparkContext
+import org.apache.spark.annotation.{Experimental, Since}
+import org.apache.spark.internal.Logging
+import org.apache.spark.ml.param.{IntParam, Param, ParamMap,
ParamValidators}
+import org.apache.spark.ml.param.shared.{HasLabelCol, HasPredictionCol}
+import org.apache.spark.ml.util.{DefaultParamsReadable,
DefaultParamsWritable, Identifiable}
+import org.apache.spark.sql.{DataFrame, Dataset, Row, SQLContext}
+import org.apache.spark.sql.functions._
+
+/**
+ * :: Experimental ::
+ * Evaluator for ranking, which expects two input columns: prediction and
label.
+ */
+@Since("2.0.0")
+@Experimental
+final class RankingEvaluator[T: ClassTag] @Since("2.0.0") (@Since("2.0.0")
override val uid: String)
+ extends Evaluator with HasPredictionCol with HasLabelCol with
DefaultParamsWritable with Logging {
+
+ @Since("2.0.0")
+ def this() = this(Identifiable.randomUID("rankingEval"))
+
+ @Since("2.0.0")
+ final val k = new IntParam(this, "k", "Top-K cutoff", (x: Int) => x > 0)
+
+ /** @group getParam */
+ @Since("2.0.0")
+ def getK: Int = $(k)
+
+ /** @group setParam */
+ @Since("2.0.0")
+ def setK(value: Int): this.type = set(k, value)
+
+ setDefault(k -> 1)
+
+ /**
+ * Param for metric name in evaluation. Supports:
+ * - `"map"` (default): Mean Average Precision
+ * - `"mapk"`: Mean Average Precision@K
+ * - `"ndcg"`: Normalized Discounted Cumulative Gain
+ * - `"mrr"`: Mean Reciprocal Rank
+ *
+ * @group param
+ */
+ @Since("2.0.0")
+ val metricName: Param[String] = {
+ val allowedParams = ParamValidators.inArray(Array("map", "mapk",
"ndcg", "mrr"))
+ new Param(this, "metricName", "metric name in evaluation
(map|mapk|ndcg||mrr)", allowedParams)
+ }
+
+ /** @group getParam */
+ @Since("2.0.0")
+ def getMetricName: String = $(metricName)
+
+ /** @group setParam */
+ @Since("2.0.0")
+ def setMetricName(value: String): this.type = set(metricName, value)
+
+ /** @group setParam */
+ @Since("2.0.0")
+ def setPredictionCol(value: String): this.type = set(predictionCol,
value)
+
+ /** @group setParam */
+ @Since("2.0.0")
+ def setLabelCol(value: String): this.type = set(labelCol, value)
+
+ setDefault(metricName -> "map")
+
+ @Since("2.0.0")
+ override def evaluate(dataset: Dataset[_]): Double = {
+ val schema = dataset.schema
+ val predictionColName = $(predictionCol)
+ val predictionType = schema($(predictionCol)).dataType
+ val labelColName = $(labelCol)
+ val labelType = schema($(labelCol)).dataType
+ require(predictionType == labelType,
+ s"Prediction column $predictionColName and Label column
$labelColName " +
+ s"must be of the same type, but Prediction column
$predictionColName is $predictionType " +
+ s"and Label column $labelColName is $labelType")
+
+ val metric = $(metricName) match {
+ case "map" => meanAveragePrecision(dataset)
+ case "ndcg" => normalizedDiscountedCumulativeGain(dataset)
+ case "mapk" => meanAveragePrecisionAtK(dataset)
+ case "mrr" => meanReciprocalRank(dataset)
+ }
+ metric
+ }
+
+ /**
+ * Returns the mean average precision (MAP) of all the queries.
+ * If a query has an empty ground truth set, the average precision will
be zero and a log
+ * warning is generated.
+ */
+ private def meanAveragePrecision(dataset: Dataset[_]): Double = {
+ val sc = SparkContext.getOrCreate()
+ val sqlContext = SQLContext.getOrCreate(sc)
+ import sqlContext.implicits._
+
+ dataset.map{ case (prediction: Array[T], label: Array[T]) =>
+ val labSet = label.toSet
+
+ if (labSet.nonEmpty) {
+ var i = 0
+ var cnt = 0
+ var precSum = 0.0
+ val n = prediction.length
+ while (i < n) {
+ if (labSet.contains(prediction(i))) {
+ cnt += 1
+ precSum += cnt.toDouble / (i + 1)
+ }
+ i += 1
+ }
+ precSum / labSet.size
+ } else {
+ logWarning("Empty ground truth set, check input data")
+ 0.0
+ }
+ }.reduce{ (a, b) => a + b } / dataset.count
+ }
+
+ /**
+ * Compute the average NDCG value of all the queries, truncated at
ranking position k.
+ * The discounted cumulative gain at position k is computed as:
+ * sum,,i=1,,^k^ (2^{relevance of ''i''th item}^ - 1) / log(i + 1),
+ * and the NDCG is obtained by dividing the DCG value on the ground
truth set. In the current
+ * implementation, the relevance value is binary.
+
+ * If a query has an empty ground truth set, zero will be used as ndcg
together with
+ * a log warning.
+ *
+ * See the following paper for detail:
+ *
+ * IR evaluation methods for retrieving highly relevant documents. K.
Jarvelin and J. Kekalainen
+ */
+ private def normalizedDiscountedCumulativeGain(dataset: Dataset[_]):
Double = {
+ val sc = SparkContext.getOrCreate()
+ val sqlContext = SQLContext.getOrCreate(sc)
+ import sqlContext.implicits._
+
+ dataset.map{ case (prediction: Array[T], label: Array[T]) =>
+ val labSet = label.toSet
+
+ if (labSet.nonEmpty) {
+ val labSetSize = labSet.size
+ val n = math.min(math.max(prediction.length, labSetSize), $(k))
+ var maxDcg = 0.0
+ var dcg = 0.0
+ var i = 0
+ while (i < n) {
+ val gain = 1.0 / math.log(i + 2)
+ if (labSet.contains(prediction(i))) {
+ dcg += gain
+ }
+ if (i < labSetSize) {
+ maxDcg += gain
--- End diff --
Relevance here is 0/1. It doesn't matter if you calculate the ideal DCG to
the number of all relevant items, or all the way to n, since everything after
the number of relevant items has relevance 0 and does not add to the sum.
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