# 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 import SparkContext, SQLContext
from pyspark.sql import DataFrame
from pyspark.ml.param.shared import *
from pyspark import keyword_only
from pyspark.ml.util import JavaMLReadable, JavaMLWritable
from synapse.ml.core.serialize.java_params_patch import *
from pyspark.ml.wrapper import JavaTransformer, JavaEstimator, JavaModel
from pyspark.ml.evaluation import JavaEvaluator
from pyspark.ml.common import inherit_doc
from synapse.ml.core.schema.Utils import *
from pyspark.ml.param import TypeConverters
from synapse.ml.core.schema.TypeConversionUtils import generateTypeConverter, complexTypeConverter
[docs]@inherit_doc
class ValueIndexerModel(ComplexParamsMixin, JavaMLReadable, JavaMLWritable, JavaModel):
"""
Args:
dataType (str): The datatype of the levels as a Json string
inputCol (str): The name of the input column
levels (object): Levels in categorical array
outputCol (str): The name of the output column
"""
dataType = Param(Params._dummy(), "dataType", "The datatype of the levels as a Json string", typeConverter=TypeConverters.toString)
inputCol = Param(Params._dummy(), "inputCol", "The name of the input column", typeConverter=TypeConverters.toString)
levels = Param(Params._dummy(), "levels", "Levels in categorical array")
outputCol = Param(Params._dummy(), "outputCol", "The name of the output column", typeConverter=TypeConverters.toString)
@keyword_only
def __init__(
self,
java_obj=None,
dataType="string",
inputCol="input",
levels=None,
outputCol="ValueIndexerModel_49b1aaa418d2_output"
):
super(ValueIndexerModel, self).__init__()
if java_obj is None:
self._java_obj = self._new_java_obj("com.microsoft.azure.synapse.ml.featurize.ValueIndexerModel", self.uid)
else:
self._java_obj = java_obj
self._setDefault(dataType="string")
self._setDefault(inputCol="input")
self._setDefault(outputCol="ValueIndexerModel_49b1aaa418d2_output")
if hasattr(self, "_input_kwargs"):
kwargs = self._input_kwargs
else:
kwargs = self.__init__._input_kwargs
if java_obj is None:
for k,v in kwargs.items():
if v is not None:
getattr(self, "set" + k[0].upper() + k[1:])(v)
[docs] @keyword_only
def setParams(
self,
dataType="string",
inputCol="input",
levels=None,
outputCol="ValueIndexerModel_49b1aaa418d2_output"
):
"""
Set the (keyword only) parameters
"""
if hasattr(self, "_input_kwargs"):
kwargs = self._input_kwargs
else:
kwargs = self.__init__._input_kwargs
return self._set(**kwargs)
[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.azure.synapse.ml.featurize.ValueIndexerModel"
@staticmethod
def _from_java(java_stage):
module_name=ValueIndexerModel.__module__
module_name=module_name.rsplit(".", 1)[0] + ".ValueIndexerModel"
return from_java(java_stage, module_name)
[docs] def setDataType(self, value):
"""
Args:
dataType: The datatype of the levels as a Json string
"""
self._set(dataType=value)
return self
[docs] def setLevels(self, value):
"""
Args:
levels: Levels in categorical array
"""
self._set(levels=value)
return self
[docs] def setOutputCol(self, value):
"""
Args:
outputCol: The name of the output column
"""
self._set(outputCol=value)
return self
[docs] def getDataType(self):
"""
Returns:
dataType: The datatype of the levels as a Json string
"""
return self.getOrDefault(self.dataType)
[docs] def getLevels(self):
"""
Returns:
levels: Levels in categorical array
"""
return self.getOrDefault(self.levels)
[docs] def getOutputCol(self):
"""
Returns:
outputCol: The name of the output column
"""
return self.getOrDefault(self.outputCol)