Github user jkbradley commented on a diff in the pull request: https://github.com/apache/spark/pull/12020#discussion_r58602540 --- Diff: python/pyspark/ml/tuning.py --- @@ -480,19 +653,87 @@ def copy(self, extra=None): extra = dict() return TrainValidationSplitModel(self.bestModel.copy(extra)) + @since("2.0.0") + def write(self): + """Returns an JavaMLWriter instance for this ML instance.""" + return JavaMLWriter(self) + + @since("2.0.0") + def save(self, path): + """Save this ML instance to the given path, a shortcut of `write().save(path)`.""" + self.write().save(path) + + @classmethod + @since("2.0.0") + def read(cls): + """Returns an MLReader instance for this class.""" + return JavaMLReader(cls) + + @classmethod + def _from_java(cls, java_stage): + """ + Given a Java TrainValidationSplitModel, create and return a Python wrapper of it. + Used for ML persistence. + """ + + # Load information from java_stage to the instance. + bestModel = JavaWrapper._from_java(java_stage.bestModel()) + estimator, epms, evaluator = super(TrainValidationSplitModel, cls)._from_java(java_stage) + # Create a new instance of this stage. + py_stage = cls(bestModel=bestModel)\ + .setEstimator(estimator).setEstimatorParamMaps(epms).setEvaluator(evaluator) --- End diff -- Use _copyValues
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