Github user imatiach-msft commented on a diff in the pull request:
https://github.com/apache/spark/pull/16630#discussion_r101357001
--- Diff:
mllib/src/main/scala/org/apache/spark/ml/regression/GeneralizedLinearRegression.scala
---
@@ -915,6 +919,23 @@ class GeneralizedLinearRegressionSummary
private[regression] (
/** Number of instances in DataFrame predictions. */
private[regression] lazy val numInstances: Long = predictions.count()
+
+ /**
+ * Name of features. If the name cannot be retrieved from attributes,
+ * set default names to feature column name with numbered suffix "_0",
"_1", and so on.
+ */
+ @Since("2.2.0")
+ lazy val featureNames: Array[String] = {
+ val featureAttrs = AttributeGroup.fromStructField(
+ dataset.schema(model.getFeaturesCol)).attributes
+ if (featureAttrs == None) {
+ Array.tabulate[String](origModel.numFeatures)(
+ (x: Int) => (model.getFeaturesCol + "_" + x))
--- End diff --
in general I would have preferred to create a platform-level function (or
use one if it exists) to format the strings in the same way, so there is no
duplicate code in VectorAssembler vs here that can diverge (and which other
functions in spark can generally use). However, this seems a bit out of scope
of this code review, so I don't think you need to do this.
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