[GitHub] spark pull request: [MLLIB] SPARK-4231, SPARK-3066: Add RankingMet...

[debasish83 <gi...@git.apache.org>]

Github user debasish83 commented on a diff in the pull request:

    https://github.com/apache/spark/pull/3098#discussion_r27528071
  
    --- Diff: examples/src/main/scala/org/apache/spark/examples/mllib/MovieLensALS.scala ---
    @@ -171,18 +175,62 @@ object MovieLensALS {
     
         println(s"Test RMSE = $rmse.")
     
    +    if (params.validateRecommendation) {
    +      val (map, users) = computeRankingMetrics(model,
    +        training, test, numMovies.toInt)
    +      println(s"Test users $users MAP $map")
    +    }
    +    
         sc.stop()
       }
     
       /** Compute RMSE (Root Mean Squared Error). */
       def computeRmse(model: MatrixFactorizationModel, data: RDD[Rating], implicitPrefs: Boolean) = {
    -
    -    def mapPredictedRating(r: Double) = if (implicitPrefs) math.max(math.min(r, 1.0), 0.0) else r
    -
         val predictions: RDD[Rating] = model.predict(data.map(x => (x.user, x.product)))
    -    val predictionsAndRatings = predictions.map{ x =>
    -      ((x.user, x.product), mapPredictedRating(x.rating))
    +    val predictionsAndRatings = predictions.map { x =>
    +      ((x.user, x.product), mapPredictedRating(x.rating, implicitPrefs))
         }.join(data.map(x => ((x.user, x.product), x.rating))).values
         math.sqrt(predictionsAndRatings.map(x => (x._1 - x._2) * (x._1 - x._2)).mean())
       }
    +
    +  def mapPredictedRating(r: Double, implicitPrefs: Boolean) = {
    +    if (implicitPrefs) math.max(math.min(r, 1.0), 0.0)
    +    else r
    +  }
    +
    +  /**
    +   * Compute MAP (Mean Average Precision) statistics for top N product Recommendation
    +   */
    +  def computeRankingMetrics(model: MatrixFactorizationModel,
    --- End diff --
    
    followed the indentation from current code


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