text: # Copyright (c) Microsoft Corporation and contributors.
  # Licensed under the MIT License.
  
  """This module contains algorithms implementing the reductions approach to disparity mitigation.
  
  In this approach, disparity constraints are cast as Lagrange multipliers, which cause the
  reweighting and relabelling of the input data. This *reduces* the problem back to standard machine
  learning training.
  """
  
  from ._exponentiated_gradient import ExponentiatedGradient  # noqa: F401
  from ._grid_search import GridSearch  # noqa: F401
  from ._moments import AbsoluteLoss, Moment, ConditionalSelectionRate  # noqa: F401
  from ._moments import DemographicParity, EqualizedOdds, ErrorRateRatio, ErrorRate, TruePositiveRateDifference   # noqa: F401,E501
  from ._moments import GroupLossMoment, SquareLoss, ZeroOneLoss  # noqa: F401
  from ._moments import ClassificationMoment, LossMoment  # noqa: F401
  
  _exponentiated_gradient = [
      "ExponentiatedGradient",
  ]
  
  _grid_search = [
      "GridSearch",
  ]
  
  _moments = [
      "AbsoluteLoss",
      "Moment",
      "ClassificationMoment",
      "ConditionalSelectionRate",
      "DemographicParity",
      "EqualizedOdds",
      "TruePositiveRateDifference",
      "ErrorRateRatio",
      "ErrorRate",
      "GroupLossMoment",
      "LossMoment",
      "SquareLoss",
      "ZeroOneLoss"
  ]
  
  __all__ = [] + _exponentiated_gradient + _grid_search + _moments
  