Github user mengxr commented on a diff in the pull request: https://github.com/apache/spark/pull/2667#discussion_r19129863 --- Diff: mllib/src/main/scala/org/apache/spark/mllib/evaluation/RankingMetrics.scala --- @@ -0,0 +1,157 @@ +/* + * 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.Logging +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])]) + extends Logging with Serializable { + + /** + * Compute the average precision of all the queries, truncated at ranking position k. + * + * If for a query, the ranking algorithm returns n (n < k) results, the precision value will be + * computed as #(relevant items retrived) / k. This formula also applies when the size of the + * ground truth set is less than k. + * + * If a query has an empty ground truth set, zero will be returned together with a log warning. + * + * See the following paper for detail: + * + * IR evaluation methods for retrieving highly relevant documents. + * K. Jarvelin and J. Kekalainen + * + * @param k the position to compute the truncated precision, must be positive + * @return the average precision at the first k ranking positions + */ + def precisionAt(k: Int): Double = { + require (k > 0,"ranking position k should be positive") + predictionAndLabels.map { case (pred, lab) => + val labSet = lab.toSet + val n = math.min(pred.length, k) + var i = 0 + var cnt = 0 + + while (i < n) { + if (labSet.contains(pred(i))) { + cnt += 1 + } + i += 1 + } + if (labSet.size == 0) { + logWarning("Empty ground truth set, check input data") + 0.0 + } else { + cnt.toDouble / k + } + }.mean + } + + /** + * 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 + * warining is generated. + */ + lazy val meanAveragePrecision: Double = { + predictionAndLabels.map { case (pred, lab) => + val labSet = lab.toSet + val labSetSize = labSet.size + var i = 0 + var cnt = 0 + var precSum = 0.0 + val n = pred.length + + while (i < n) { + if (labSet.contains(pred(i))) { + cnt += 1 + precSum += cnt.toDouble / (i + 1) + } + i += 1 + } + if (labSetSize == 0) { + logWarning("Empty ground truth set, check input data") + 0.0 + } else { + precSum / labSet.size + } + }.mean + } + + /** + * 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 ith 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 for a query, the ranking algorithm returns n (n < k) results, the NDCG value at position n + * will be used. If the ground truth set contains n (n < k) results, the first n items will be + * used to compute the DCG value on the ground truth set. --- End diff -- This paragraph is not necessary because those cases are compatible with the definition of NDCG.
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