Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/2667#discussion_r18876004
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/evaluation/RankingMetrics.scala ---
@@ -0,0 +1,125 @@
+/*
+ * 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.mllib.evaluation
+
+import scala.reflect.ClassTag
+
+import org.apache.spark.SparkContext._
+import org.apache.spark.annotation.Experimental
+import org.apache.spark.rdd.RDD
+
+
+
+/**
+ * ::Experimental::
+ * Evaluator for ranking algorithms.
+ *
+ * @param predictionAndLabels an RDD of (predicted ranking, ground truth
set) pairs.
+ */
+@Experimental
+class RankingMetrics[T: ClassTag](predictionAndLabels: RDD[(Array[T],
Array[T])]) {
+
+ /**
+ * Returns the precsion@k for each query
+ */
+ lazy val precAtK: RDD[Array[Double]] = predictionAndLabels.map {case
(pred, lab)=>
+ val labSet = lab.toSet
+ val n = pred.length
+ val topKPrec = Array.fill[Double](n)(0.0)
+ var (i, cnt) = (0, 0)
+
+ while (i < n) {
+ if (labSet.contains(pred(i))) {
+ cnt += 1
+ }
+ topKPrec(i) = cnt.toDouble / (i + 1)
+ i += 1
+ }
+ topKPrec
+ }
+
+ /**
+ * @param k the position to compute the truncated precision
+ * @return the average precision at the first k ranking positions
+ */
+ def precision(k: Int): Double = precAtK.map {topKPrec =>
+ val n = topKPrec.length
+ if (k <= n) {
+ topKPrec(k - 1)
+ } else {
+ topKPrec(n - 1) * n / k
+ }
+ }.mean
+
+ /**
+ * Returns the average precision for each query
+ */
+ lazy val avePrec: RDD[Double] = predictionAndLabels.map {case (pred,
lab) =>
+ val labSet = lab.toSet
+ var (i, cnt, precSum) = (0, 0, 0.0)
+ val n = pred.length
+
+ while (i < n) {
+ if (labSet.contains(pred(i))) {
+ cnt += 1
+ precSum += cnt.toDouble / (i + 1)
+ }
+ i += 1
+ }
+ precSum / labSet.size
+ }
+
+ /**
+ * Returns the mean average precision (MAP) of all the queries
+ */
+ lazy val meanAvePrec: Double = avePrec.mean
+
+ /**
+ * Returns the normalized discounted cumulative gain for each query
+ */
+ lazy val ndcgAtK: RDD[Array[Double]] = predictionAndLabels.map {case
(pred, lab) =>
+ val labSet = lab.toSet
+ val labSetSize = labSet.size
+ val n = math.max(pred.length, labSetSize)
+ val topKNdcg = Array.fill[Double](n)(0.0)
+ var (maxDcg, dcg, i) = (0.0, 0.0, 0)
+ while (i < n) {
+ /* Calculate 1/log2(i + 2) */
+ val gain = math.log(2) / math.log(i + 2)
+ if (labSet.contains(pred(i))) {
+ dcg += gain
+ }
+ if (i < labSetSize) {
+ maxDcg += gain
+ }
+ topKNdcg(i) = dcg / maxDcg
+ i += 1
+ }
+ topKNdcg
+ }
+
+ /**
+ * @param k the position to compute the truncated ndcg
--- End diff --
Need a one sentence summary about the method. Also need a link to the paper
that describes how to handle the case when we don't have k items.
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