def _from_java_impl(cls, java_stage): """ Return Python estimator, estimatorParamMaps, and evaluator from a Java ValidatorParams. """ # Load information from java_stage to the instance. estimator = JavaWrapper._from_java(java_stage.getEstimator()) evaluator = JavaWrapper._from_java(java_stage.getEvaluator()) epms = [estimator._transfer_param_map_from_java(epm) for epm in java_stage.getEstimatorParamMaps()] return estimator, epms, evaluator
def _from_java_impl(cls, java_stage): """ Return Python estimator, estimatorParamMaps, and evaluator from a Java ValidatorParams. """ # Load information from java_stage to the instance. estimator = JavaWrapper._from_java(java_stage.getEstimator()) evaluator = JavaWrapper._from_java(java_stage.getEvaluator()) epms = [ estimator._transfer_param_map_from_java(epm) for epm in java_stage.getEstimatorParamMaps() ] return estimator, epms, evaluator
def _from_java(cls, java_stage): """ Given a Java PipelineModel, create and return a Python wrapper of it. Used for ML persistence. """ # Load information from java_stage to the instance. py_stages = [JavaWrapper._from_java(s) for s in java_stage.stages()] # Create a new instance of this stage. py_stage = cls(py_stages) py_stage._resetUid(java_stage.uid()) return py_stage
def _from_java(cls, java_stage): """ Given a Java CrossValidatorModel, 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(CrossValidatorModel, cls)._from_java_impl(java_stage) # Create a new instance of this stage. py_stage = cls(bestModel=bestModel)\ .setEstimator(estimator).setEstimatorParamMaps(epms).setEvaluator(evaluator) py_stage._resetUid(java_stage.uid()) return py_stage
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_impl(java_stage) # Create a new instance of this stage. py_stage = cls(bestModel=bestModel)\ .setEstimator(estimator).setEstimatorParamMaps(epms).setEvaluator(evaluator) py_stage._resetUid(java_stage.uid()) return py_stage