code: def test_smoke_regression(self, data):
      learner = LeastSquaresRegressor()
      disparity_moment = data["constraint_class"](
          loss=data["loss"], upper_bound=data["upper_bound"]
      )
  
      # Create Exponentiated Gradient object with a copy of the constraint.
      # The original disparity_moment object is used for validation, so the
      # assumption is that the moment logic is correct in these tests.
      expgrad = ExponentiatedGradient(
          learner,
          constraints=deepcopy(disparity_moment),
          eps=data["eps"],
          nu=data.get("nu"),
          max_iter=data.get("max_iter", 50),
      )
  
      X, y, A = _get_data(A_two_dim=False, y_as_scores=True)
  
      expgrad.fit(X, y, sensitive_features=A)
  
      self._assert_expgrad_state(expgrad, data)
  
      # check all predictors
      disparity_moment.load_data(X, y, sensitive_features=A)
      for i in range(len(expgrad.predictors_)):
  
          def Q(X):
              return expgrad._pmf_predict(X)[i]
  
          default_objective = MeanLoss(data["loss"])
          default_objective.load_data(X, y, sensitive_features=A)
          disparity = disparity_moment.gamma(Q).max()
          error = default_objective.gamma(Q).iloc[0]
          assert disparity == pytest.approx(
              data["disp"][i], abs=_PRECISION
          )
          assert error == pytest.approx(
              data["error"][i], abs=_PRECISION
          )
          assert expgrad.weights_[i] == pytest.approx(
              data["weights"][i], abs=_PRECISION
          )
  
      assert sum(expgrad.weights_) == pytest.approx(1, abs=_PRECISION)
  
imports: from copy import deepcopy
  import numpy
  import pandas
  import pytest
  from sklearn.dummy import DummyClassifier
  from sklearn.linear_model import LogisticRegression
  from fairlearn.reductions import AbsoluteLoss, BoundedGroupLoss, DemographicParity, EqualizedOdds, ErrorRate, ErrorRateParity, ExponentiatedGradient, FalsePositiveRateParity, SquareLoss, TruePositiveRateParity, ZeroOneLoss
  from fairlearn.reductions._exponentiated_gradient._constants import _MIN_ITER
  from fairlearn.reductions._moments.bounded_group_loss import MeanLoss
  from .simple_learners import LeastSquaresBinaryClassifierLearner, LeastSquaresRegressor
  from .test_utilities import _get_data
label_desc: Use image node_id 4 for calling the TestExponentiatedGradientSmoke obj's underlying member method code with example usage: obj.test_smoke_regression(data) and returns: expgrad