Github user yanboliang commented on a diff in the pull request:
https://github.com/apache/spark/pull/9413#discussion_r43634982
--- Diff:
mllib/src/main/scala/org/apache/spark/ml/regression/LinearRegression.scala ---
@@ -471,6 +484,59 @@ class LinearRegressionSummary private[regression] (
predictions.select(t(col(predictionCol),
col(labelCol)).as("residuals"))
}
+ lazy val numInstances: Long = predictions.count()
+
+ lazy val dfe = if (model.getFitIntercept) {
+ numInstances - model.weights.size -1
+ } else {
+ numInstances - model.weights.size
+ }
+
+ lazy val devianceResiduals: Array[Double] = {
+ val weighted = if (model.getWeightCol.isEmpty) lit(1.0) else
sqrt(col(model.getWeightCol))
+ val dr =
predictions.select(col(model.getLabelCol).minus(col(model.getPredictionCol))
+ .multiply(weighted).as("weightedResiduals"))
+ .select(min(col("weightedResiduals")).as("min"),
max(col("weightedResiduals")).as("max"))
+ .take(1)(0)
+ Array(dr.getDouble(0), dr.getDouble(1))
--- End diff --
DataFrame currently does not provide interface to calculate percentile
(only Hive UDAF), so here we only provide max and min value of deviance
residuals. [SPARK-9299](https://issues.apache.org/jira/browse/SPARK-9299) works
on providing ```percentile``` and ```percentile_approx``` aggregate functions,
after it was resolved we can provide deviance residuals of quantile (0.25, 0.5,
0.75).
---
If your project is set up for it, you can reply to this email and have your
reply appear on GitHub as well. If your project does not have this feature
enabled and wishes so, or if the feature is enabled but not working, please
contact infrastructure at infrastruct...@apache.org or file a JIRA ticket
with INFRA.
---
---------------------------------------------------------------------
To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org
For additional commands, e-mail: reviews-h...@spark.apache.org
