Source code for synapse.ml.exploratory.FeatureBalanceMeasure

# 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 FeatureBalanceMeasure(ComplexParamsMixin, JavaMLReadable, JavaMLWritable, JavaTransformer): """ Args: classACol (object): Output column name for the first feature value to compare. classBCol (object): Output column name for the second feature value to compare. featureNameCol (object): Output column name for feature names. labelCol (object): label column name outputCol (object): output column name sensitiveCols (list): Sensitive columns to use. verbose (bool): Whether to show intermediate measures and calculations, such as Positive Rate. """ classACol = Param(Params._dummy(), "classACol", "Output column name for the first feature value to compare.") classBCol = Param(Params._dummy(), "classBCol", "Output column name for the second feature value to compare.") featureNameCol = Param(Params._dummy(), "featureNameCol", "Output column name for feature names.") labelCol = Param(Params._dummy(), "labelCol", "label column name") outputCol = Param(Params._dummy(), "outputCol", "output column name") 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, classACol="ClassA", classBCol="ClassB", featureNameCol="FeatureName", labelCol="label", outputCol="FeatureBalanceMeasure", sensitiveCols=None, verbose=False ): super(FeatureBalanceMeasure, self).__init__() if java_obj is None: self._java_obj = self._new_java_obj("com.microsoft.azure.synapse.ml.exploratory.FeatureBalanceMeasure", self.uid) else: self._java_obj = java_obj self._setDefault(classACol="ClassA") self._setDefault(classBCol="ClassB") self._setDefault(featureNameCol="FeatureName") self._setDefault(labelCol="label") self._setDefault(outputCol="FeatureBalanceMeasure") 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, classACol="ClassA", classBCol="ClassB", featureNameCol="FeatureName", labelCol="label", outputCol="FeatureBalanceMeasure", 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.FeatureBalanceMeasure"
@staticmethod def _from_java(java_stage): module_name=FeatureBalanceMeasure.__module__ module_name=module_name.rsplit(".", 1)[0] + ".FeatureBalanceMeasure" return from_java(java_stage, module_name)
[docs] def setClassACol(self, value): """ Args: classACol: Output column name for the first feature value to compare. """ self._set(classACol=value) return self
[docs] def setClassBCol(self, value): """ Args: classBCol: Output column name for the second feature value to compare. """ self._set(classBCol=value) return self
[docs] def setFeatureNameCol(self, value): """ Args: featureNameCol: Output column name for feature names. """ self._set(featureNameCol=value) return self
[docs] def setLabelCol(self, value): """ Args: labelCol: label column name """ self._set(labelCol=value) return self
[docs] def setOutputCol(self, value): """ Args: outputCol: output column name """ self._set(outputCol=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 getClassACol(self): """ Returns: classACol: Output column name for the first feature value to compare. """ return self.getOrDefault(self.classACol)
[docs] def getClassBCol(self): """ Returns: classBCol: Output column name for the second feature value to compare. """ return self.getOrDefault(self.classBCol)
[docs] def getFeatureNameCol(self): """ Returns: featureNameCol: Output column name for feature names. """ return self.getOrDefault(self.featureNameCol)
[docs] def getLabelCol(self): """ Returns: labelCol: label column name """ return self.getOrDefault(self.labelCol)
[docs] def getOutputCol(self): """ Returns: outputCol: output column name """ return self.getOrDefault(self.outputCol)
[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)