Github user MLnick commented on a diff in the pull request:
https://github.com/apache/spark/pull/3098#discussion_r20002546
--- Diff:
examples/src/main/scala/org/apache/spark/examples/mllib/MovieLensALS.scala ---
@@ -165,22 +169,60 @@ object MovieLensALS {
.setProductBlocks(params.numProductBlocks)
.run(training)
- val rmse = computeRmse(model, test, params.implicitPrefs)
-
- println(s"Test RMSE = $rmse.")
-
+ val (rmse, userMap, productMap) =
+ computeRecommendationMetrics(model, test, params.implicitPrefs)
+
+ println(s"Test RMSE = $rmse user MAP = $userMap product MAP =
$productMap.")
+
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
-
+
+ /**
+ * Threshold for predictions are at 0.5
+ */
+ def mapPredictedRating(r: Double, implicitPrefs: Boolean) = {
+ if (implicitPrefs) math.max(math.min(r, 1.0), 0.0)
+ else math.max(round(r), 0.0)
+ }
+
+ /**
+ * Compute MAP (Mean Average Precision) statistics
+ */
+ def computeMap(predictedAndLabels: RDD[(Int, (Double, Double))]) = {
+ val ranking = predictedAndLabels.groupByKey.map {
+ case (user, entries) => {
+ val predictionValues = entries.toArray
--- End diff --
I was going to comment on this point too - MAP has a max of 1.0.
The input to `RankingMetrics` should be RDD[(predicted IDs array), (ground
truth IDs array)], where the predictions are ordered by score (position matters
for avg precision at K).
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