# 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 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 *
[docs]@inherit_doc
class ValueIndexerModel(ComplexParamsMixin, JavaMLReadable, JavaMLWritable, JavaTransformer):
"""
Args:
dataType (str): The datatype of the levels as a Json string (default: string)
inputCol (str): The name of the input column (default: input)
levels (object): Levels in categorical array
outputCol (str): The name of the output column (default: [self.uid]_output)
"""
@keyword_only
def __init__(self, dataType="string", inputCol="input", levels=None, outputCol=None):
super(ValueIndexerModel, self).__init__()
self._java_obj = self._new_java_obj("com.microsoft.ml.spark.featurize.ValueIndexerModel")
self.dataType = Param(self, "dataType", "dataType: The datatype of the levels as a Json string (default: string)")
self._setDefault(dataType="string")
self.inputCol = Param(self, "inputCol", "inputCol: The name of the input column (default: input)")
self._setDefault(inputCol="input")
self.levels = Param(self, "levels", "levels: Levels in categorical array")
self.outputCol = Param(self, "outputCol", "outputCol: The name of the output column (default: [self.uid]_output)")
self._setDefault(outputCol=self.uid + "_output")
if hasattr(self, "_input_kwargs"):
kwargs = self._input_kwargs
else:
kwargs = self.__init__._input_kwargs
self.setParams(**kwargs)
[docs] @keyword_only
def setParams(self, dataType="string", inputCol="input", levels=None, outputCol=None):
"""
Set the (keyword only) parameters
Args:
dataType (str): The datatype of the levels as a Json string (default: string)
inputCol (str): The name of the input column (default: input)
levels (object): Levels in categorical array
outputCol (str): The name of the output column (default: [self.uid]_output)
"""
if hasattr(self, "_input_kwargs"):
kwargs = self._input_kwargs
else:
kwargs = self.__init__._input_kwargs
return self._set(**kwargs)
[docs] def getDataType(self):
"""
Returns:
str: The datatype of the levels as a Json string (default: string)
"""
return self.getOrDefault(self.dataType)
[docs] def getLevels(self):
"""
Returns:
object: Levels in categorical array
"""
return self.getOrDefault(self.levels)
[docs] def getOutputCol(self):
"""
Returns:
str: The name of the output column (default: [self.uid]_output)
"""
return self.getOrDefault(self.outputCol)
[docs] def setDataType(self, value):
"""
Args:
dataType: The datatype of the levels as a Json string (default: 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 (default: [self.uid]_output)
"""
self._set(outputCol=value)
return self
[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.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)