Why Spark ML ALS algorithm print RMSE = NaN?

Question

I use ALS to predict rating, this is my code:

val als = new ALS()
  .setMaxIter(5)
  .setRegParam(0.01)
  .setUserCol("user_id")
  .setItemCol("business_id")
  .setRatingCol("stars")
val model = als.fit(training)

// Evaluate the model by computing the RMSE on the test data
val predictions = model.transform(testing)
predictions.sort("user_id").show(1000)
val evaluator = new RegressionEvaluator()
  .setMetricName("rmse")
  .setLabelCol("stars")
  .setPredictionCol("prediction")
val rmse = evaluator.evaluate(predictions)
println(s"Root-mean-square error = $rmse")

But get some negative scores and RMSE is Nan:

+-------+-----------+---------+------------+
|user_id|business_id|    stars|  prediction|
+-------+-----------+---------+------------+
|      0|       2175|      4.0|   4.0388923|
|      0|       5753|      3.0|   2.6875196|
|      0|       9199|      4.0|   4.1753435|
|      0|      16416|      2.0|   -2.710618|
|      0|       6063|      3.0|         NaN|
|      0|      23076|      2.0|  -0.8930751|

Root-mean-square error = NaN

How to get a good result?

Answer 1

Negative values don't matter as RMSE squares the values first. Probably you have empty prediction values. You could drop them:

predictions.na().drop(["prediction"])

Although, that can be a bit misleading, alternatively you could fill those values with your lowest/highest/average rating.

I'd also recommend to round x < min_rating and x > max_rating to the lowest/highest ratings, which would improve your RMSE.

EDIT:

Some extra info here: https://issues.apache.org/jira/browse/SPARK-14489