# Copyright (C) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License. See LICENSE in project root for information.
import sys
if sys.version >= '3':
basestring = str
from pyspark.ml.param.shared import *
from pyspark import keyword_only
from pyspark.ml.util import JavaMLReadable, JavaMLWritable
from mmlspark.core.serialize.java_params_patch import *
from pyspark.ml.wrapper import JavaTransformer, JavaEstimator, JavaModel
from pyspark.ml.common import inherit_doc
from mmlspark.core.schema.Utils import *
from mmlspark.core.schema.TypeConversionUtils import generateTypeConverter, complexTypeConverter
[docs]@inherit_doc
class CustomOutputParser(ComplexParamsMixin, JavaMLReadable, JavaMLWritable, JavaTransformer):
"""
Args:
inputCol (str): The name of the input column
outputCol (str): The name of the output column
udfPython (object): User Defined Python Function to be applied to the DF input col
udfScala (object): User Defined Function to be applied to the DF input col
"""
@keyword_only
def __init__(self, inputCol=None, outputCol=None, udfPython=None, udfScala=None):
super(CustomOutputParser, self).__init__()
self._java_obj = self._new_java_obj("com.microsoft.ml.spark.io.http.CustomOutputParser")
self._cache = {}
self.inputCol = Param(self, "inputCol", "inputCol: The name of the input column")
self.outputCol = Param(self, "outputCol", "outputCol: The name of the output column")
self.udfPython = Param(self, "udfPython", "udfPython: User Defined Python Function to be applied to the DF input col")
self.udfScala = Param(self, "udfScala", "udfScala: User Defined Function to be applied to the DF input col", generateTypeConverter("udfScala", self._cache, complexTypeConverter))
if hasattr(self, "_input_kwargs"):
kwargs = self._input_kwargs
else:
kwargs = self.__init__._input_kwargs
self.setParams(**kwargs)
[docs] @keyword_only
def setParams(self, inputCol=None, outputCol=None, udfPython=None, udfScala=None):
"""
Set the (keyword only) parameters
Args:
inputCol (str): The name of the input column
outputCol (str): The name of the output column
udfPython (object): User Defined Python Function to be applied to the DF input col
udfScala (object): User Defined Function to be applied to the DF input col
"""
if hasattr(self, "_input_kwargs"):
kwargs = self._input_kwargs
else:
kwargs = self.__init__._input_kwargs
return self._set(**kwargs)
[docs] def setOutputCol(self, value):
"""
Args:
outputCol (str): The name of the output column
"""
self._set(outputCol=value)
return self
[docs] def getOutputCol(self):
"""
Returns:
str: The name of the output column
"""
return self.getOrDefault(self.outputCol)
[docs] def setUdfPython(self, value):
"""
Args:
udfPython (object): User Defined Python Function to be applied to the DF input col
"""
self._set(udfPython=value)
return self
[docs] def getUdfPython(self):
"""
Returns:
object: User Defined Python Function to be applied to the DF input col
"""
return self.getOrDefault(self.udfPython)
[docs] def setUdfScala(self, value):
"""
Args:
udfScala (object): User Defined Function to be applied to the DF input col
"""
self._set(udfScala=value)
return self
[docs] def getUdfScala(self):
"""
Returns:
object: User Defined Function to be applied to the DF input col
"""
return self._cache.get("udfScala", None)
[docs] @classmethod
def read(cls):
""" Returns an MLReader instance for this class. """
return JavaMMLReader(cls)
[docs] @staticmethod
def getJavaPackage():
""" Returns package name String. """
return "com.microsoft.ml.spark.io.http.CustomOutputParser"
@staticmethod
def _from_java(java_stage):
module_name=CustomOutputParser.__module__
module_name=module_name.rsplit(".", 1)[0] + ".CustomOutputParser"
return from_java(java_stage, module_name)