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From: mengxr <git@git.apache.org>
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Subject: [GitHub] spark pull request: SPARK-3568 [mllib] add ranking metrics
Content-Type: text/plain
Message-Id: <20141017222236.D19489C7027@tyr.zones.apache.org>
Date: Fri, 17 Oct 2014 22:22:36 +0000 (UTC)

Github user mengxr commented on a diff in the pull request:

    https://github.com/apache/spark/pull/2667#discussion_r19046954
  
    --- Diff: mllib/src/main/scala/org/apache/spark/mllib/evaluation/RankingMetrics.scala ---
    @@ -0,0 +1,115 @@
    +/*
    + * 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])]) {
    +
    +  /**
    +   * 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.
    +   * 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
    +   * @return the average precision at the first k ranking positions
    +   */
    +  def precisionAt(k: Int): Double = 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
    +    }
    +    cnt.toDouble / k
    +  }.mean
    +
    +  /**
    +   * Returns the mean average precision (MAP) of all the queries
    +   */
    +  lazy val meanAveragePrecision: Double = predictionAndLabels.map { case (pred, lab) =>
    +    val labSet = lab.toSet
    +    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
    +    }
    +    precSum / labSet.size
    +  }.mean
    +
    +  /**
    +   * Compute the average NDCG value of all the queries, truncated at ranking position k.
    +   * If for a query, the ranking algorithm returns n (n < k) results, the NDCG value at
    +   * at position n will be used. 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 ndcg
    +   * @return the average ndcg at the first k ranking positions
    +   */
    +  def ndcgAt(k: Int): Double = predictionAndLabels.map { case (pred, lab) =>
    +    val labSet = lab.toSet
    +    val labSetSize = labSet.size
    +    val n = math.min(math.max(pred.length, labSetSize), k)
    +    var maxDcg = 0.0
    +    var dcg = 0.0
    +    var i = 0
    +
    +    while (i < n) {
    +      // Calculate 1/log2(i + 2)
    --- End diff --
    
    the comment doesn't provide any extra information


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