# 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.platform import running_on_synapse_internal
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 DistributionBalanceMeasure(ComplexParamsMixin, JavaMLReadable, JavaMLWritable, JavaTransformer):
"""
Args:
featureNameCol (str): Output column name for feature names.
outputCol (str): output column name
referenceDistribution (object): An ordered list of reference distributions that correspond to each of the sensitive columns.
sensitiveCols (list): Sensitive columns to use.
verbose (bool): Whether to show intermediate measures and calculations, such as Positive Rate.
"""
featureNameCol = Param(Params._dummy(), "featureNameCol", "Output column name for feature names.", typeConverter=TypeConverters.toString)
outputCol = Param(Params._dummy(), "outputCol", "output column name", typeConverter=TypeConverters.toString)
referenceDistribution = Param(Params._dummy(), "referenceDistribution", "An ordered list of reference distributions that correspond to each of the sensitive columns.")
sensitiveCols = Param(Params._dummy(), "sensitiveCols", "Sensitive columns to use.", typeConverter=TypeConverters.toListString)
verbose = Param(Params._dummy(), "verbose", "Whether to show intermediate measures and calculations, such as Positive Rate.", typeConverter=TypeConverters.toBoolean)
@keyword_only
def __init__(
self,
java_obj=None,
featureNameCol="FeatureName",
outputCol="DistributionBalanceMeasure",
referenceDistribution=None,
sensitiveCols=None,
verbose=False
):
super(DistributionBalanceMeasure, self).__init__()
if java_obj is None:
self._java_obj = self._new_java_obj("com.microsoft.azure.synapse.ml.exploratory.DistributionBalanceMeasure", self.uid)
else:
self._java_obj = java_obj
self._setDefault(featureNameCol="FeatureName")
self._setDefault(outputCol="DistributionBalanceMeasure")
self._setDefault(verbose=False)
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,
featureNameCol="FeatureName",
outputCol="DistributionBalanceMeasure",
referenceDistribution=None,
sensitiveCols=None,
verbose=False
):
"""
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.exploratory.DistributionBalanceMeasure"
@staticmethod
def _from_java(java_stage):
module_name=DistributionBalanceMeasure.__module__
module_name=module_name.rsplit(".", 1)[0] + ".DistributionBalanceMeasure"
return from_java(java_stage, module_name)
[docs] def setFeatureNameCol(self, value):
"""
Args:
featureNameCol: Output column name for feature names.
"""
self._set(featureNameCol=value)
return self
[docs] def setOutputCol(self, value):
"""
Args:
outputCol: output column name
"""
self._set(outputCol=value)
return self
[docs] def setReferenceDistribution(self, value):
"""
Args:
referenceDistribution: An ordered list of reference distributions that correspond to each of the sensitive columns.
"""
self._set(referenceDistribution=value)
return self
[docs] def setSensitiveCols(self, value):
"""
Args:
sensitiveCols: Sensitive columns to use.
"""
self._set(sensitiveCols=value)
return self
[docs] def setVerbose(self, value):
"""
Args:
verbose: Whether to show intermediate measures and calculations, such as Positive Rate.
"""
self._set(verbose=value)
return self
[docs] def getFeatureNameCol(self):
"""
Returns:
featureNameCol: Output column name for feature names.
"""
return self.getOrDefault(self.featureNameCol)
[docs] def getOutputCol(self):
"""
Returns:
outputCol: output column name
"""
return self.getOrDefault(self.outputCol)
[docs] def getReferenceDistribution(self):
"""
Returns:
referenceDistribution: An ordered list of reference distributions that correspond to each of the sensitive columns.
"""
return self.getOrDefault(self.referenceDistribution)
[docs] def getSensitiveCols(self):
"""
Returns:
sensitiveCols: Sensitive columns to use.
"""
return self.getOrDefault(self.sensitiveCols)
[docs] def getVerbose(self):
"""
Returns:
verbose: Whether to show intermediate measures and calculations, such as Positive Rate.
"""
return self.getOrDefault(self.verbose)