srowen commented on a change in pull request #24656: [SPARK-27787][ML]
Eliminate uncessary job to compute SSreg
URL: https://github.com/apache/spark/pull/24656#discussion_r286080206
##########
File path:
mllib/src/main/scala/org/apache/spark/mllib/evaluation/RegressionMetrics.scala
##########
@@ -60,29 +60,23 @@ class RegressionMetrics @Since("2.0.0") (
* Use MultivariateOnlineSummarizer to calculate summary statistics of
observations and errors.
*/
private lazy val summary: MultivariateStatisticalSummary = {
- val summary: MultivariateStatisticalSummary =
predictionAndObservations.map {
+ predictionAndObservations.map {
case (prediction: Double, observation: Double, weight: Double) =>
- (Vectors.dense(observation, observation - prediction), weight)
+ (Vectors.dense(observation, observation - prediction, prediction),
weight)
case (prediction: Double, observation: Double) =>
- (Vectors.dense(observation, observation - prediction), 1.0)
+ (Vectors.dense(observation, observation - prediction, prediction), 1.0)
}.treeAggregate(new MultivariateOnlineSummarizer())(
(summary, sample) => summary.add(sample._1, sample._2),
(sum1, sum2) => sum1.merge(sum2)
)
- summary
}
private lazy val SSy = math.pow(summary.normL2(0), 2)
private lazy val SSerr = math.pow(summary.normL2(1), 2)
private lazy val SStot = summary.variance(0) * (summary.weightSum - 1)
- private lazy val SSreg = {
- val yMean = summary.mean(0)
- predictionAndObservations.map {
- case (prediction: Double, _: Double, weight: Double) =>
- math.pow(prediction - yMean, 2) * weight
- case (prediction: Double, _: Double) => math.pow(prediction - yMean, 2)
- }.sum()
- }
+ private lazy val SSreg = math.pow(summary.normL2(2), 2) +
Review comment:
It took me a minute but yes I think this is correct.
It feels like this code would be more straightforward if the summarizer
would just expose a sum and squaredL2Norm method, but, maybe for another time.
A few comments about the computations here wouldn't hurt.
----------------------------------------------------------------
This is an automated message from the Apache Git Service.
To respond to the message, please log on to GitHub and use the
URL above to go to the specific comment.
For queries about this service, please contact Infrastructure at:
[email protected]
With regards,
Apache Git Services
---------------------------------------------------------------------
To unsubscribe, e-mail: [email protected]
For additional commands, e-mail: [email protected]
