Github user gaborgsomogyi commented on a diff in the pull request:
https://github.com/apache/spark/pull/20362#discussion_r170072578
--- Diff:
mllib/src/test/scala/org/apache/spark/ml/recommendation/ALSSuite.scala ---
@@ -413,34 +411,36 @@ class ALSSuite
.setSeed(0)
val alpha = als.getAlpha
val model = als.fit(training.toDF())
- val predictions = model.transform(test.toDF()).select("rating",
"prediction").rdd.map {
- case Row(rating: Float, prediction: Float) =>
- (rating.toDouble, prediction.toDouble)
+ testTransformerByGlobalCheckFunc[Rating[Int]](test.toDF(), model,
"rating", "prediction") {
+ case rows: Seq[Row] =>
+ val predictions = rows.map(row => (row.getFloat(0).toDouble,
row.getFloat(1).toDouble))
+
+ val rmse =
+ if (implicitPrefs) {
+ // TODO: Use a better (rank-based?) evaluation metric for
implicit feedback.
+ // We limit the ratings and the predictions to interval [0,
1] and compute the
+ // weighted RMSE with the confidence scores as weights.
+ val (totalWeight, weightedSumSq) = predictions.map { case
(rating, prediction) =>
+ val confidence = 1.0 + alpha * math.abs(rating)
+ val rating01 = math.max(math.min(rating, 1.0), 0.0)
+ val prediction01 = math.max(math.min(prediction, 1.0), 0.0)
+ val err = prediction01 - rating01
+ (confidence, confidence * err * err)
+ }.reduce[(Double, Double)] { case ((c0, e0), (c1, e1)) =>
+ (c0 + c1, e0 + e1)
+ }
+ math.sqrt(weightedSumSq / totalWeight)
+ } else {
+ val errorSquares = predictions.map { case (rating,
prediction) =>
+ val err = rating - prediction
+ err * err
+ }
+ val mse = errorSquares.sum / errorSquares.length
+ math.sqrt(mse)
+ }
+ logInfo(s"Test RMSE is $rmse.")
+ assert(rmse < targetRMSE)
}
- val rmse =
--- End diff --
Mainly move but there was no mean function so implemented.
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