WeichenXu123 commented on a change in pull request #30471:
URL: https://github.com/apache/spark/pull/30471#discussion_r531111061
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File path: python/pyspark/ml/tuning.py
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@@ -207,6 +210,205 @@ def _to_java_impl(self):
return java_estimator, java_epms, java_evaluator
+class _ValidatorSharedReadWrite:
+
+ @staticmethod
+ def saveImpl(path, instance, sc, extraMetadata=None):
+ from pyspark.ml.classification import OneVsRest
+ numParamsNotJson = 0
+ jsonEstimatorParamMaps = []
+ for paramMap in instance.getEstimatorParamMaps():
+ jsonParamMap = []
+ for p, v in paramMap.items():
+ jsonParam = {'parent': p.parent, 'name': p.name}
+ if (isinstance(v, Estimator) and not (
+ isinstance(v, _ValidatorParams) or
+ isinstance(v, OneVsRest))
+ ) or isinstance(v, Transformer) or \
Review comment:
> It's easy to add the extension when that case is explicitly supported
Seems no.
Suppose there're some 3rd party libraries depends on spark. And 3rd party
estimators may include an evaluator param (e.g. for supervising in training
early stop) . We need to support them.
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