Github user avulanov commented on a diff in the pull request:
https://github.com/apache/spark/pull/12943#discussion_r63086277
--- Diff: python/pyspark/ml/classification.py ---
@@ -1117,6 +1137,56 @@ def getBlockSize(self):
"""
return self.getOrDefault(self.blockSize)
+ @since("2.0.0")
+ def setStepSize(self, value):
+ """
+ Sets the value of :py:attr:`stepSize`.
+ """
+ return self._set(stepSize=value)
+
+ @since("2.0.0")
+ def getStepSize(self):
+ """
+ Gets the value of stepSize or its default value.
+ """
+ return self.getOrDefault(self.stepSize)
+
+ @since("2.0.0")
+ def setSolver(self, value):
+ """
+ Sets the value of :py:attr:`solver`.
+ """
+ return self._set(solver=value)
+
+ @since("2.0.0")
+ def getSolver(self):
+ """
+ Gets the value of solver or its default value.
+ """
+ return self.getOrDefault(self.solver)
+
+ @property
+ @since("2.0.0")
+ def getOptimizer(self):
--- End diff --
Thank you for bringing that issue, @MLnick. `getOptimizer` is there to let
the user know explicitly which optimizer is used. The reason why I did not
implement `setOpmizer` is because minibatch gradient descent in not very
efficient in Spark and can be used only in very specific cases. So the default
choice for Spark is L-BFGS. However, if we label `setOpmizer` as `expertParam`
then it is OK to have it. I believe that we should proceed as suggested by
@yanboliang
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