Github user viirya commented on a diff in the pull request:
https://github.com/apache/spark/pull/23263#discussion_r240003952
--- Diff: mllib/src/main/scala/org/apache/spark/ml/Estimator.scala ---
@@ -65,7 +65,19 @@ abstract class Estimator[M <: Model[M]] extends
PipelineStage {
* Fits a model to the input data.
*/
@Since("2.0.0")
- def fit(dataset: Dataset[_]): M
+ def fit(dataset: Dataset[_]): M = MLEvents.withFitEvent(this, dataset) {
+ fitImpl(dataset)
+ }
+
+ /**
+ * `fit()` handles events and then calls this method. Subclasses should
override this
+ * method to implement the actual fiting a model to the input data.
+ */
+ @Since("3.0.0")
+ protected def fitImpl(dataset: Dataset[_]): M = {
+ // Keep this default body for backward compatibility.
+ throw new UnsupportedOperationException("fitImpl is not implemented.")
--- End diff --
For current change, Spark ML developers can still choose to override `fit`
instead `fitImpl` so their ML model can work without ML event?
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