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 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
