def test_bgl_lagrange_specifications(A_two_dim):
a0_count = 13
a1_count = 4
a0_label = 5
a1_label = 3
a0_factor = 1
a1_factor = 16
X, y, A = _simple_regression_data(a0_count, a1_count,
a0_factor, a1_factor,
a0_label, a1_label, A_two_dim)
estimator = LinearRegression()
# Do the grid search with a zero Lagrange multiplier
idx = pd.Int64Index(sorted([a0_label, a1_label]))
l0_series = pd.Series([2.0, 0.0], index=idx)
l1_series = pd.Series([1.5, 0.5], index=idx)
l2_series = pd.Series([1.0, 1.0], index=idx)
l3_series = pd.Series([0.5, 1.5], index=idx)
l4_series = pd.Series([0.0, 2.0], index=idx)
grid_df = pd.concat([l0_series,
l1_series,
l2_series,
l3_series,
l4_series],
axis=1)
grid_search1 = GridSearch(copy.deepcopy(estimator),
constraints=BoundedGroupLoss(ZeroOneLoss()),
grid_size=5)
grid_search2 = GridSearch(copy.deepcopy(estimator),
constraints=BoundedGroupLoss(ZeroOneLoss()),
grid=grid_df)
tradeoffs = [0, 0.25, 0.5, 0.75, 1]
grid_search1.fit(X, y, sensitive_features=A)
grid_search2.fit(X, y, sensitive_features=A)
assert_n_grid_search_results(len(tradeoffs), grid_search1)
assert_n_grid_search_results(len(tradeoffs), grid_search2)
# Check we generated the same multipliers
for i in range(len(tradeoffs)):
lm1 = grid_search1.lambda_vecs_[i]
lm2 = grid_search2.lambda_vecs_[i]
assert lm1.equals(lm2)
# Check the models are the same
for i in range(len(tradeoffs)):
coef1 = grid_search1.predictors_[i].coef_
coef2 = grid_search2.predictors_[i].coef_
assert np.array_equal(coef1, coef2)
def test_bgl_unmitigated_same(A_two_dim):
a0_count = 4
a1_count = 4
a0_label = 2
a1_label = 3
a0_factor = 1
a1_factor = 16
X, y, A = _simple_regression_data(a0_count, a1_count, a0_factor, a1_factor,
a0_label, a1_label, A_two_dim)
estimator = LinearRegression()
unmitigated_estimator = copy.deepcopy(estimator)
unmitigated_estimator.fit(X, y)
# Do the grid search with a zero Lagrange multiplier
idx = pd.Int64Index(sorted([a0_label, a1_label]))
lagrange_balanced_series = pd.Series([1.0, 1.0], index=idx)
grid_df = pd.DataFrame(lagrange_balanced_series)
target = GridSearch(estimator,
constraints=GroupLossMoment(ZeroOneLoss()),
grid=grid_df)
target.fit(X, y, sensitive_features=A)
raw_coef = unmitigated_estimator.coef_
gs_coef = target.best_result.predictor.coef_
# Can't quite get exact match, but this should be very close
assert np.allclose(raw_coef, gs_coef, rtol=1e-10, atol=1e-7)
def test_call_oracle(Constraints, eps, estimator, mocker):
X, y, A = _get_data(A_two_dim=False)
# Using a real estimator here with a mocked `fit` method since we don't actually
# want to fit one, but rather care about having that object's fit method called exactly once.
estimator.fit = mocker.MagicMock(name="fit")
if issubclass(Constraints, LossMoment):
constraints = Constraints(ZeroOneLoss())
else:
constraints = Constraints()
lagrangian = _Lagrangian(
X=X,
y=y,
estimator=estimator,
constraints=deepcopy(constraints),
B=1 / eps,
sensitive_features=A,
)
# Set up initial lambda vector based on a 0-initialized theta and use separate constraints
# object for it to avoid the dependence on the lagrangian object.
lambda_vec, new_weights, new_labels = get_lambda_new_weights_and_labels(
constraints, X, y, A)
_ = lagrangian._call_oracle(lambda_vec)
# Ideally we'd prefer calling assert_called_once_with(args) but that is not compatible with
# pandas data structures.
assert len(estimator.fit.mock_calls) == 1
_, args, kwargs = estimator.fit.mock_calls[0]
assert (args[0] == X).all().all()
assert (args[1] == new_labels).all()
assert (kwargs["sample_weight"] == new_weights).all()
assert lagrangian.n_oracle_calls == 1
assert len(lagrangian.oracle_execution_times) == 1
def test_objective_constraints_compatibility(Constraints, Objective):
X, y, A = _get_data(A_two_dim=False)
estimator = LeastSquaresBinaryClassifierLearner()
if issubclass(Constraints, LossMoment):
constraints = Constraints(ZeroOneLoss())
else:
constraints = Constraints()
if issubclass(Objective, LossMoment):
objective = Objective(ZeroOneLoss())
else:
objective = Objective()
if objective._moment_type() != constraints._moment_type():
with pytest.raises(ValueError) as execInfo:
_ = _Lagrangian(
X=X,
y=y,
estimator=estimator,
constraints=deepcopy(constraints),
objective=objective,
B=1.0,
sensitive_features=A,
)
assert (_MESSAGE_BAD_OBJECTIVE.format(objective._moment_type(),
constraints._moment_type())
in execInfo.value.args[0])
else:
# No exception raised
_ = _Lagrangian(
X=X,
y=y,
estimator=estimator,
constraints=deepcopy(constraints),
objective=objective,
B=1.0,
sensitive_features=A,
)
def test_call_oracle_single_y_value(Constraints, eps, y_value, mocker):
X_dict = {
"c": [0, 1, 4, 1, 5, 1, 6, 0, 2, 4],
"d": [1, 5, 1, 6, 2, 3, 5, 1, 5, 2],
}
X = pd.DataFrame(X_dict)
# Try with both possible y values for binary classification to ensure that
# constraints that focus only on positives or negatives can handle the
# case where none of the rows apply to them.
y = pd.Series([y_value] * 10)
A = pd.Series([0, 0, 0, 0, 0, 0, 0, 0, 0, 1])
# We mock the estimator, but we only patch it for pickling
estimator = mocker.MagicMock()
if issubclass(Constraints, LossMoment):
constraints = Constraints(ZeroOneLoss(), upper_bound=eps)
else:
constraints = Constraints(difference_bound=eps)
lagrangian = _Lagrangian(
X=X,
y=y,
estimator=estimator,
constraints=deepcopy(constraints),
B=1 / eps,
sensitive_features=A,
)
# Set up initial lambda vector based on a 0-initialized theta and use separate constraints
# object for it to avoid the dependence on the lagrangian object.
lambda_vec = get_lambda_vec(constraints, X, y, A)
test_X_dict = {"c": [10000], "d": [2000000]}
test_X = pd.DataFrame(test_X_dict)
result_estimator = lagrangian._call_oracle(lambda_vec)
assert isinstance(result_estimator, DummyClassifier)
assert result_estimator.predict(test_X) == y_value
assert lagrangian.n_oracle_calls_dummy_returned == 1
# Make sure the mocked estimator wasn't called
assert len(estimator.method_calls) == 0
def test_call_oracle(Constraints, eps, mocker):
X, y, A = _get_data(A_two_dim=False)
# Using a mocked estimator here since we don't actually want to fit one, but rather care about
# having that object's fit method called exactly once.
estimator = mocker.MagicMock()
if issubclass(Constraints, LossMoment):
constraints = Constraints(ZeroOneLoss())
else:
constraints = Constraints()
# ExponentiatedGradient pickles and unpickles the estimator, which isn't possible for the mock
# object, so we mock that process as well. It sets the result from pickle.loads as the
# estimator, so we can simply overwrite the return value to be our mocked estimator object.
mocker.patch('pickle.dumps')
pickle.loads = mocker.MagicMock(return_value=estimator)
lagrangian = _Lagrangian(X, A, y, estimator, deepcopy(constraints),
1 / eps)
# Set up initial lambda vector based on a 0-initialized theta and use separate constraints
# object for it to avoid the dependence on the lagrangian object.
lambda_vec, new_weights, new_labels = get_lambda_new_weights_and_labels(
constraints, X, y, A)
_ = lagrangian._call_oracle(lambda_vec)
# Ideally we'd prefer calling assert_called_once_with(args) but that is not compatible with
# pandas data structures.
assert len(estimator.method_calls) == 1
name, args, kwargs = estimator.method_calls[0]
assert name == 'fit'
assert len(args) == 2
assert len(kwargs) == 1
assert (args[0] == X).all().all()
assert (args[1] == new_labels).all()
assert (kwargs['sample_weight'] == new_weights).all()
assert lagrangian.n_oracle_calls == 1
assert len(lagrangian.oracle_execution_times) == 1
def setup_method(self, method):
self.estimator = LinearRegression()
self.disparity_criterion = GroupLossMoment(ZeroOneLoss())
def setup_method(self, method):
self.estimator = LinearRegression()
eps = 0.01
self.disparity_criterion = BoundedGroupLoss(ZeroOneLoss(), upper_bound=eps)
self.sample_weight_name = 'sample_weight'
def test_lagrangian_eval(eps, Constraints, use_Q_callable, opt_lambda):
X, y, A = _get_data(A_two_dim=False)
estimator = LeastSquaresBinaryClassifierLearner()
if issubclass(Constraints, LossMoment):
task_type = "regression"
constraints = Constraints(ZeroOneLoss(), upper_bound=eps)
else:
task_type = "classification"
constraints = Constraints(difference_bound=eps)
# epsilon (and thereby also B) only affects L_high and L
B = 1 / eps
lagrangian = _Lagrangian(
X=X,
y=y,
estimator=estimator,
constraints=deepcopy(constraints),
B=B,
opt_lambda=opt_lambda,
sensitive_features=A,
)
lambda_vec = get_lambda_vec(constraints, X, y, A)
# call oracle to determine error and gamma and calculate exp
fitted_estimator = lagrangian._call_oracle(lambda_vec)
def h(X):
return fitted_estimator.predict(X)
best_h_error = lagrangian.obj.gamma(h)[0]
best_h_gamma = lagrangian.constraints.gamma(h)
# opt_lambda affects only the calculation of L
if opt_lambda:
projected_lambda = constraints.project_lambda(lambda_vec)
L_expected = (best_h_error + np.sum(projected_lambda * best_h_gamma) -
eps * np.sum(projected_lambda))
else:
L_expected = (best_h_error + np.sum(lambda_vec * best_h_gamma) -
eps * np.sum(lambda_vec))
L_high_expected = best_h_error + B * (best_h_gamma.max() - eps)
# manually set errors and gammas which would otherwise be done in the best_h step
lagrangian.errors = pd.Series([best_h_error])
lagrangian.gammas = pd.Series([best_h_gamma])
# call _eval to get the desired results L, L_high, gamma, error;
# _eval is compatible with a callable h or a vector Q
Q_vec = pd.Series([1.0])
L, L_high, gamma, error = lagrangian._eval(h if use_Q_callable else Q_vec,
lambda_vec)
# in this particular example the estimator is always the same
expected_estimator_weights = {
"regression":
pd.Series({
"X1": 0.541252,
"X2": 0.454293,
"X3": 0.019203
}),
"classification":
pd.Series({
"X1": 0.538136,
"X2": 0.457627,
"X3": 0.021186
}),
}
assert (np.isclose(
fitted_estimator.weights,
expected_estimator_weights[task_type],
atol=1.0e-6,
)).all()
assert L == pytest.approx(L_expected, abs=_PRECISION)
assert L_high == pytest.approx(L_high_expected, abs=_PRECISION)
assert error == 0.25
assert (gamma == best_h_gamma).all()
def setup_method(self, method):
self.estimator = LinearRegression()
eps = 0.01
self.disparity_criterion = GroupLossMoment(ZeroOneLoss(),
upper_bound=eps)
class TestExponentiatedGradientSmoke:
smoke_test_data = [
{
"constraint_class": DemographicParity,
"eps": 0.100,
"best_gap": 0.000000,
"last_iter": 5,
"best_iter": 5,
"disp": 0.100000,
"error": 0.250000,
"n_oracle_calls": 32,
"n_oracle_calls_dummy_returned": 0,
"n_predictors": 3
},
{
"constraint_class": DemographicParity,
"eps": 0.100,
"best_gap": 0.000000,
"last_iter": 5,
"best_iter": 5,
"disp": -0.020000,
"error": 0.250000,
"n_oracle_calls": 17,
"n_oracle_calls_dummy_returned": 12,
"n_predictors": 2,
"ratio": 0.8
},
{
"constraint_class": DemographicParity,
"eps": 0.050,
"best_gap": 0.000000,
"last_iter": 5,
"best_iter": 5,
"disp": 0.050000,
"error": 0.266522,
"n_oracle_calls": 23,
"n_oracle_calls_dummy_returned": 0,
"n_predictors": 6
},
{
"constraint_class": DemographicParity,
"eps": 0.050,
"best_gap": 0.000000,
"last_iter": 5,
"best_iter": 5,
"disp": -0.020000,
"error": 0.25,
"n_oracle_calls": 17,
"n_oracle_calls_dummy_returned": 12,
"n_predictors": 2,
"ratio": 0.8
},
{
"constraint_class": DemographicParity,
"eps": 0.020,
"best_gap": 0.000000,
"last_iter": 5,
"best_iter": 5,
"disp": 0.020000,
"error": 0.332261,
"n_oracle_calls": 22,
"n_oracle_calls_dummy_returned": 0,
"n_predictors": 5
},
# ================================================
{
"constraint_class": DemographicParity,
"eps": 0.020,
"best_gap": 0.000000,
"last_iter": 5,
"best_iter": 5,
"disp": -0.020000,
"error": 0.25,
"n_oracle_calls": 17,
"n_oracle_calls_dummy_returned": 12,
"n_predictors": 2,
"ratio": 0.8
},
{
"constraint_class": DemographicParity,
"eps": 0.010,
"best_gap": 0.000000,
"last_iter": 5,
"best_iter": 5,
"disp": 0.010000,
"error": 0.354174,
"n_oracle_calls": 22,
"n_oracle_calls_dummy_returned": 0,
"n_predictors": 5
},
{
"constraint_class": DemographicParity,
"eps": 0.010,
"best_gap": 0.000000,
"last_iter": 5,
"best_iter": 5,
"disp": -0.020000,
"error": 0.25,
"n_oracle_calls": 17,
"n_oracle_calls_dummy_returned": 12,
"n_predictors": 2,
"ratio": 0.8
},
{
"constraint_class": DemographicParity,
"eps": 0.005,
"best_gap": 0.000000,
"last_iter": 5,
"best_iter": 5,
"disp": 0.005000,
"error": 0.365130,
"n_oracle_calls": 22,
"n_oracle_calls_dummy_returned": 0,
"n_predictors": 5
},
{
"constraint_class": DemographicParity,
"eps": 0.005,
"best_gap": 0.000000,
"last_iter": 5,
"best_iter": 5,
"disp": -0.020000,
"error": 0.25,
"n_oracle_calls": 17,
"n_oracle_calls_dummy_returned": 12,
"n_predictors": 2,
"ratio": 0.8
},
# ================================================
{
"constraint_class": EqualizedOdds,
"eps": 0.100,
"best_gap": 0.000000,
"last_iter": 5,
"best_iter": 5,
"disp": 0.100000,
"error": 0.309333,
"n_oracle_calls": 21,
"n_oracle_calls_dummy_returned": 0,
"n_predictors": 4
},
{
"constraint_class": EqualizedOdds,
"eps": 0.100,
"best_gap": 0.000000,
"last_iter": 5,
"best_iter": 5,
"disp": 0.100000,
"error": 0.25,
"n_oracle_calls": 22,
"n_oracle_calls_dummy_returned": 12,
"n_predictors": 4,
"ratio": 0.8
},
{
"constraint_class": EqualizedOdds,
"eps": 0.050,
"best_gap": 0.000000,
"last_iter": 5,
"best_iter": 5,
"disp": 0.050000,
"error": 0.378827,
"n_oracle_calls": 19,
"n_oracle_calls_dummy_returned": 0,
"n_predictors": 6
},
{
"constraint_class": EqualizedOdds,
"eps": 0.050,
"best_gap": 0.000000,
"last_iter": 5,
"best_iter": 5,
"disp": 0.050000,
"error": 0.277016,
"n_oracle_calls": 22,
"n_oracle_calls_dummy_returned": 12,
"n_predictors": 4,
"ratio": 0.8
},
{
"constraint_class": EqualizedOdds,
"eps": 0.020,
"best_gap": 0.000000,
"last_iter": 5,
"best_iter": 5,
"disp": 0.020000,
"error": 0.421531,
"n_oracle_calls": 19,
"n_oracle_calls_dummy_returned": 0,
"n_predictors": 6
},
# ================================================
{
"constraint_class": EqualizedOdds,
"eps": 0.020,
"best_gap": 0.000000,
"last_iter": 5,
"best_iter": 5,
"disp": 0.020000,
"error": 0.296612,
"n_oracle_calls": 22,
"n_oracle_calls_dummy_returned": 12,
"n_predictors": 4,
"ratio": 0.8
},
{
"constraint_class": EqualizedOdds,
"eps": 0.010,
"best_gap": 0.000000,
"last_iter": 5,
"best_iter": 5,
"disp": 0.010000,
"error": 0.435765,
"n_oracle_calls": 19,
"n_oracle_calls_dummy_returned": 0,
"n_predictors": 6
},
{
"constraint_class": EqualizedOdds,
"eps": 0.010,
"best_gap": 0.000000,
"last_iter": 5,
"best_iter": 5,
"disp": 0.010000,
"error": 0.303145,
"n_oracle_calls": 22,
"n_oracle_calls_dummy_returned": 12,
"n_predictors": 4,
"ratio": 0.8
},
{
"constraint_class": EqualizedOdds,
"eps": 0.005,
"best_gap": 0.000000,
"last_iter": 5,
"best_iter": 5,
"disp": 0.005000,
"error": 0.442883,
"n_oracle_calls": 19,
"n_oracle_calls_dummy_returned": 0,
"n_predictors": 6
},
{
"constraint_class": EqualizedOdds,
"eps": 0.005,
"best_gap": 0.000000,
"last_iter": 5,
"best_iter": 5,
"disp": 0.005000,
"error": 0.306411,
"n_oracle_calls": 22,
"n_oracle_calls_dummy_returned": 12,
"n_predictors": 4,
"ratio": 0.8
},
# ================================================
{
"constraint_class": ErrorRateParity,
"eps": 0.1,
"best_gap": 0.000000,
"last_iter": 5,
"best_iter": 5,
"disp": 0.100000,
"error": 0.25625,
"n_oracle_calls": 17,
"n_oracle_calls_dummy_returned": 0,
"n_predictors": 3
},
{
"constraint_class": ErrorRateParity,
"eps": 0.1,
"best_gap": 0.000000,
"last_iter": 5,
"best_iter": 5,
"disp": 0.092857,
"error": 0.25,
"n_oracle_calls": 17,
"n_oracle_calls_dummy_returned": 0,
"n_predictors": 3,
"ratio": 0.8
},
{
"constraint_class": ErrorRateParity,
"eps": 0.05,
"best_gap": 0.000000,
"last_iter": 5,
"best_iter": 5,
"disp": 0.049999,
"error": 0.3,
"n_oracle_calls": 17,
"n_oracle_calls_dummy_returned": 0,
"n_predictors": 3
},
{
"constraint_class": ErrorRateParity,
"eps": 0.05,
"best_gap": 0.000000,
"last_iter": 5,
"best_iter": 5,
"disp": 0.050000,
"error": 0.253472,
"n_oracle_calls": 26,
"n_oracle_calls_dummy_returned": 0,
"n_predictors": 6,
"ratio": 0.8
},
{
"constraint_class": ErrorRateParity,
"eps": 0.02,
"best_gap": 0.000000,
"last_iter": 5,
"best_iter": 5,
"disp": 0.019999,
"error": 0.326250,
"n_oracle_calls": 17,
"n_oracle_calls_dummy_returned": 0,
"n_predictors": 3
},
# ================================================
{
"constraint_class": ErrorRateParity,
"eps": 0.02,
"best_gap": 0.000000,
"last_iter": 5,
"best_iter": 5,
"disp": 0.020000,
"error": 0.268055,
"n_oracle_calls": 26,
"n_oracle_calls_dummy_returned": 0,
"n_predictors": 5,
"ratio": 0.8
},
{
"constraint_class": ErrorRateParity,
"eps": 0.01,
"best_gap": 0.000000,
"last_iter": 5,
"best_iter": 5,
"disp": 0.010000,
"error": 0.325555,
"n_oracle_calls": 18,
"n_oracle_calls_dummy_returned": 0,
"n_predictors": 4
},
{
"constraint_class": ErrorRateParity,
"eps": 0.01,
"best_gap": 0.000000,
"last_iter": 5,
"best_iter": 5,
"disp": 0.010000,
"error": 0.272916,
"n_oracle_calls": 26,
"n_oracle_calls_dummy_returned": 0,
"n_predictors": 5,
"ratio": 0.8
},
{
"constraint_class": ErrorRateParity,
"eps": 0.005,
"best_gap": 0.000000,
"last_iter": 5,
"best_iter": 5,
"disp": 0.005000,
"error": 0.329444,
"n_oracle_calls": 19,
"n_oracle_calls_dummy_returned": 0,
"n_predictors": 5
},
{
"constraint_class": ErrorRateParity,
"eps": 0.005,
"best_gap": 0.000000,
"last_iter": 5,
"best_iter": 5,
"disp": 0.005000,
"error": 0.275347,
"n_oracle_calls": 26,
"n_oracle_calls_dummy_returned": 0,
"n_predictors": 5,
"ratio": 0.8
},
# ================================================
{
"constraint_class": TruePositiveRateParity,
"eps": 0.005,
"best_gap": 0.0,
"last_iter": 5,
"best_iter": 5,
"disp": 0.005000,
"error": 0.25,
"n_oracle_calls": 16,
"n_oracle_calls_dummy_returned": 0,
"n_predictors": 2
},
{
"constraint_class": FalsePositiveRateParity,
"eps": 0.005,
"best_gap": 0.0,
"last_iter": 5,
"best_iter": 5,
"disp": 0.005000,
"error": 0.427133,
"n_oracle_calls": 17,
"n_oracle_calls_dummy_returned": 0,
"n_predictors": 3
}
]
smoke_test_data_flipped = [{
"constraint_class": TruePositiveRateParity,
"eps": 0.005,
"best_gap": 0.0,
"last_iter": 5,
"best_iter": 5,
"disp": 0.005000,
"error": 0.427133,
"n_oracle_calls": 17,
"n_oracle_calls_dummy_returned": 0,
"n_predictors": 3
}, {
"constraint_class": FalsePositiveRateParity,
"eps": 0.005,
"best_gap": 0.0,
"last_iter": 5,
"best_iter": 5,
"disp": 0.005000,
"error": 0.25,
"n_oracle_calls": 16,
"n_oracle_calls_dummy_returned": 0,
"n_predictors": 2
}, {
"constraint_class": EqualizedOdds,
"eps": 0.005,
"best_gap": 0.000000,
"last_iter": 5,
"best_iter": 5,
"disp": 0.005000,
"error": 0.442883,
"n_oracle_calls": 19,
"n_oracle_calls_dummy_returned": 0,
"n_predictors": 6
}]
smoke_test_data_regression = [
{
"constraint_class":
BoundedGroupLoss,
"loss":
SquareLoss(0, 1),
"eps":
0.01,
"best_gap":
0.003905,
"last_iter":
6,
"best_iter":
6,
"upper_bound":
0.01, # infeasible
"disp": [
0.178333, 0.178333, 0.178333, 0.178333, 0.178333, 0.178333,
0.028045, 0.178333, 0.178333, 0.178333, 0.030853, 0.178333,
0.178333, 0.178333, 0.178333, 0.178333
],
"error": [
0.1035, 0.1035, 0.1035, 0.1035, 0.1035, 0.1035, 0.024412,
0.1035, 0.1035, 0.1035, 0.025691, 0.1035, 0.1035, 0.1035,
0.1035, 0.1035
],
"weights":
[0, 0, 0, 0, 0, 0, 0.956748, 0, 0, 0, 0.043251, 0, 0, 0, 0, 0, 0],
"n_oracle_calls":
23,
"n_oracle_calls_dummy_returned":
0,
"n_predictors":
16
},
{
"constraint_class":
BoundedGroupLoss,
"loss":
SquareLoss(0, 1),
"eps":
0.01,
"best_gap":
0.0,
"last_iter":
5,
"best_iter":
5,
"upper_bound":
0.05, # feasible
"disp": [
0.178333, 0.178333, 0.036690, 0.178333, 0.178333, 0.178333,
0.178333
],
"error":
[0.1035, 0.1035, 0.021988, 0.1035, 0.1035, 0.1035, 0.1035],
"weights": [0, 0, 1, 0, 0, 0, 0],
"n_oracle_calls":
32,
"n_oracle_calls_dummy_returned":
0,
"n_predictors":
7
},
{
"constraint_class":
BoundedGroupLoss,
"loss":
SquareLoss(0, 1),
"eps":
0.01,
"best_gap":
0.0,
"last_iter":
5,
"best_iter":
5,
"max_iter":
20,
"nu":
1e-6,
"upper_bound":
0.05, # feasible
"disp": [
0.178333, 0.178333, 0.036690, 0.178333, 0.178333, 0.178333,
0.178333
],
"error":
[0.1035, 0.1035, 0.021988, 0.1035, 0.1035, 0.1035, 0.1035],
"weights": [0, 0, 1, 0, 0, 0, 0],
"n_oracle_calls":
29,
"n_oracle_calls_dummy_returned":
0,
"n_predictors":
7
},
{
"constraint_class":
BoundedGroupLoss,
"loss":
ZeroOneLoss(),
"eps":
0.01,
"best_gap":
0.007185,
"last_iter":
5,
"best_iter":
5,
"upper_bound":
0.01, # infeasible
"disp": [
0.383333, 0.383333, 0.383333, 0.383333, 0.1479, 0.383333,
0.383333, 0.383333, 0.140256, 0.383333, 0.383333, 0.383333,
0.383333, 0.383333
],
"error": [
0.255, 0.255, 0.255, 0.255, 0.140198, 0.255, 0.255, 0.255,
0.135674, 0.255, 0.255, 0.255, 0.255, 0.255
],
"weights":
[0, 0, 0, 0, 0.221468, 0, 0, 0, 0.778531, 0, 0, 0, 0, 0],
"n_oracle_calls":
20,
"n_oracle_calls_dummy_returned":
0,
"n_predictors":
14
},
{
"constraint_class": BoundedGroupLoss,
"loss": ZeroOneLoss(),
"eps": 0.01,
"best_gap": 0.0,
"last_iter": 5,
"best_iter": 5,
"upper_bound": 0.2, # feasible
"disp": [0.383333, 0.383333, 0.166918],
"error": [0.255, 0.255, 0.116949],
"weights": [0, 0, 1],
"n_oracle_calls": 20,
"n_oracle_calls_dummy_returned": 0,
"n_predictors": 3
},
{
"constraint_class": BoundedGroupLoss,
"loss": ZeroOneLoss(),
"eps": 0.01,
"best_gap": 0.0,
"last_iter": 5,
"best_iter": 5,
"max_iter": 20,
"nu": 1e-6,
"upper_bound": 0.2, # feasible
"disp": [0.383333, 0.383333, 0.166918],
"error": [0.255, 0.255, 0.116949],
"weights": [0, 0, 1],
"n_oracle_calls": 17,
"n_oracle_calls_dummy_returned": 0,
"n_predictors": 3
},
{
"constraint_class":
BoundedGroupLoss,
"loss":
AbsoluteLoss(0, 1),
"eps":
0.01,
"best_gap":
0.007185,
"last_iter":
5,
"best_iter":
5,
"upper_bound":
0.01, # infeasible
"disp": [
0.383333, 0.383333, 0.383333, 0.383333, 0.1479, 0.383333,
0.383333, 0.383333, 0.140256, 0.383333, 0.383333, 0.383333,
0.383333, 0.383333
],
"error": [
0.255, 0.255, 0.255, 0.255, 0.140198, 0.255, 0.255, 0.255,
0.135674, 0.255, 0.255, 0.255, 0.255, 0.255
],
"weights":
[0, 0, 0, 0, 0.221468, 0, 0, 0, 0.778531, 0, 0, 0, 0, 0],
"n_oracle_calls":
20,
"n_oracle_calls_dummy_returned":
0,
"n_predictors":
14
},
]
def run_smoke_test_binary_classification(self, data, flipped=False):
learner = LeastSquaresBinaryClassifierLearner()
if "ratio" in data.keys():
disparity_moment = data["constraint_class"](
ratio_bound_slack=data["eps"], ratio_bound=data["ratio"])
else:
disparity_moment = data["constraint_class"](
difference_bound=data["eps"])
# 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"])
X, y, A = _get_data(A_two_dim=False, flip_y=flipped)
expgrad.fit(X, y, sensitive_features=A)
self._assert_expgrad_state(expgrad, data)
# select probability of predicting 1
def Q(X):
return expgrad._pmf_predict(X)[:, 1]
default_objective = ErrorRate()
disparity_moment.load_data(X, y, sensitive_features=A)
default_objective.load_data(X, y, sensitive_features=A)
disparity = disparity_moment.gamma(Q).max()
error = default_objective.gamma(Q)[0]
assert disparity == pytest.approx(data["disp"], abs=_PRECISION)
assert error == pytest.approx(data["error"], abs=_PRECISION)
@pytest.mark.parametrize("testdata", smoke_test_data)
def test_smoke(self, testdata):
self.run_smoke_test_binary_classification(testdata)
@pytest.mark.parametrize("testdata", smoke_test_data_flipped)
def test_smoke_flipped(self, testdata):
self.run_smoke_test_binary_classification(testdata, flipped=True)
@pytest.mark.parametrize("data", smoke_test_data_regression)
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)[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)
@pytest.mark.parametrize("Constraints", [
TruePositiveRateParity, FalsePositiveRateParity, DemographicParity,
EqualizedOdds, ErrorRateParity
])
def test_simple_fit_predict_binary_classification(self, Constraints):
X, y, sensitive_features = _get_data()
estimator = LeastSquaresBinaryClassifierLearner()
expgrad = ExponentiatedGradient(estimator, Constraints())
expgrad.fit(X, y, sensitive_features=sensitive_features)
expgrad.predict(X)
@pytest.mark.parametrize("constraints", [
BoundedGroupLoss(loss=SquareLoss(0, 1), upper_bound=0.01),
BoundedGroupLoss(loss=AbsoluteLoss(0, 1), upper_bound=0.01),
BoundedGroupLoss(loss=ZeroOneLoss(), upper_bound=0.01)
])
def test_simple_fit_predict_regression(self, constraints):
X, y, sensitive_features = _get_data(y_as_scores=True)
estimator = LeastSquaresRegressor()
expgrad = ExponentiatedGradient(estimator, constraints)
expgrad.fit(X, y, sensitive_features=sensitive_features)
expgrad.predict(X)
def test_single_y_value(self):
# Setup with data designed to result in "all single class"
# at some point in the grid
X_dict = {"c": [10, 50, 10]}
X = pd.DataFrame(X_dict)
y = [1, 1, 1]
A = ['a', 'b', 'b']
estimator = LogisticRegression(solver='liblinear',
fit_intercept=True,
random_state=97)
expgrad = ExponentiatedGradient(estimator, DemographicParity())
# Following line should not throw an exception
expgrad.fit(X, y, sensitive_features=A)
# Check the predictors for a ConstantPredictor
test_X_dict = {"c": [1, 2, 3, 4, 5, 6]}
test_X = pd.DataFrame(test_X_dict)
assert expgrad.n_oracle_calls_dummy_returned_ > 0
assert len(expgrad.oracle_execution_times_) == expgrad.n_oracle_calls_
for p in expgrad.predictors_:
assert isinstance(p, DummyClassifier)
assert np.array_equal(p.predict(test_X), [1, 1, 1, 1, 1, 1])
def _assert_expgrad_state(self, expgrad, data):
n_predictors = len(expgrad.predictors_)
assert expgrad.best_gap_ == pytest.approx(data["best_gap"],
abs=_PRECISION)
assert expgrad.best_gap_ < expgrad.nu
assert expgrad.last_iter_ == data["last_iter"]
assert expgrad.best_iter_ == data["best_iter"]
assert expgrad.last_iter_ >= _MIN_ITER
assert expgrad.n_oracle_calls_ == data["n_oracle_calls"]
assert expgrad.n_oracle_calls_dummy_returned_ == data[
"n_oracle_calls_dummy_returned"]
assert n_predictors == data["n_predictors"]
assert len(expgrad.oracle_execution_times_) == expgrad.n_oracle_calls_
class TestExponentiatedGradientSmoke:
smoke_test_data = [
{
"constraint_class": DemographicParity,
"eps": 0.100,
"best_gap": 0.000000,
"last_iter": 5,
"best_iter": 5,
"disp": 0.100000,
"error": 0.250000,
"n_oracle_calls": 32,
"n_oracle_calls_dummy_returned": 0,
"n_predictors": 3,
},
{
"constraint_class": DemographicParity,
"eps": 0.100,
"best_gap": 0.000000,
"last_iter": 5,
"best_iter": 5,
"disp": -0.020000,
"error": 0.250000,
"n_oracle_calls": 17,
"n_oracle_calls_dummy_returned": 12,
"n_predictors": 2,
"ratio": 0.8,
},
{
"constraint_class": DemographicParity,
"eps": 0.050,
"best_gap": 0.000000,
"last_iter": 5,
"best_iter": 5,
"disp": 0.050000,
"error": 0.266522,
"n_oracle_calls": 23,
"n_oracle_calls_dummy_returned": 0,
"n_predictors": 6,
},
{
"constraint_class": DemographicParity,
"eps": 0.050,
"best_gap": 0.000000,
"last_iter": 5,
"best_iter": 5,
"disp": -0.020000,
"error": 0.25,
"n_oracle_calls": 17,
"n_oracle_calls_dummy_returned": 12,
"n_predictors": 2,
"ratio": 0.8,
},
{
"constraint_class": DemographicParity,
"eps": 0.020,
"best_gap": 0.000000,
"last_iter": 5,
"best_iter": 5,
"disp": 0.020000,
"error": 0.332261,
"n_oracle_calls": 22,
"n_oracle_calls_dummy_returned": 0,
"n_predictors": 5,
},
{
"constraint_class": DemographicParity,
"eps": 0.020,
"best_gap": 0.000000,
"last_iter": 5,
"best_iter": 5,
"disp": -0.020000,
"error": 0.25,
"n_oracle_calls": 17,
"n_oracle_calls_dummy_returned": 12,
"n_predictors": 2,
"ratio": 0.8,
},
{
"constraint_class": DemographicParity,
"eps": 0.010,
"best_gap": 0.000000,
"last_iter": 5,
"best_iter": 5,
"disp": 0.010000,
"error": 0.354174,
"n_oracle_calls": 22,
"n_oracle_calls_dummy_returned": 0,
"n_predictors": 5,
},
{
"constraint_class": DemographicParity,
"eps": 0.010,
"best_gap": 0.000000,
"last_iter": 5,
"best_iter": 5,
"disp": -0.020000,
"error": 0.25,
"n_oracle_calls": 17,
"n_oracle_calls_dummy_returned": 12,
"n_predictors": 2,
"ratio": 0.8,
},
{
"constraint_class": DemographicParity,
"eps": 0.005,
"best_gap": 0.000000,
"last_iter": 5,
"best_iter": 5,
"disp": 0.005000,
"error": 0.365130,
"n_oracle_calls": 22,
"n_oracle_calls_dummy_returned": 0,
"n_predictors": 5,
},
{
"constraint_class": DemographicParity,
"eps": 0.005,
"best_gap": 0.000000,
"last_iter": 5,
"best_iter": 5,
"disp": -0.020000,
"error": 0.25,
"n_oracle_calls": 17,
"n_oracle_calls_dummy_returned": 12,
"n_predictors": 2,
"ratio": 0.8,
},
# ================================================
{
"constraint_class": DemographicParity,
"eps": 0.050,
"objective": ErrorRate(costs={"fn": 2.0, "fp": 1.0}),
"best_gap": 0.000000,
"last_iter": 5,
"best_iter": 5,
"disp": 0.050000,
"error": 0.407142,
"n_oracle_calls": 18,
"n_oracle_calls_dummy_returned": 0,
"n_predictors": 4,
},
{
"constraint_class": DemographicParity,
"eps": 0.050,
"objective": ErrorRate(costs={"fn": 2.0, "fp": 1.0}),
"best_gap": 0.000000,
"last_iter": 5,
"best_iter": 5,
"disp": 0.050000,
"error": 0.263830,
"n_oracle_calls": 21,
"n_oracle_calls_dummy_returned": 12,
"n_predictors": 3,
"ratio": 0.8,
},
{
"constraint_class": DemographicParity,
"eps": 0.020,
"objective": ErrorRate(costs={"fn": 2.0, "fp": 1.0}),
"best_gap": 0.000000,
"last_iter": 5,
"best_iter": 5,
"disp": 0.020000,
"error": 0.422,
"n_oracle_calls": 19,
"n_oracle_calls_dummy_returned": 0,
"n_predictors": 5,
},
{
"constraint_class": DemographicParity,
"eps": 0.020,
"objective": ErrorRate(costs={"fn": 2.0, "fp": 1.0}),
"best_gap": 0.000000,
"last_iter": 5,
"best_iter": 5,
"disp": 0.020000,
"error": 0.286170,
"n_oracle_calls": 21,
"n_oracle_calls_dummy_returned": 12,
"n_predictors": 3,
"ratio": 0.8,
},
# ================================================
{
"constraint_class": EqualizedOdds,
"eps": 0.100,
"best_gap": 0.000000,
"last_iter": 5,
"best_iter": 5,
"disp": 0.100000,
"error": 0.309333,
"n_oracle_calls": 21,
"n_oracle_calls_dummy_returned": 0,
"n_predictors": 4,
},
{
"constraint_class": EqualizedOdds,
"eps": 0.100,
"best_gap": 0.000000,
"last_iter": 5,
"best_iter": 5,
"disp": 0.100000,
"error": 0.25,
"n_oracle_calls": 22,
"n_oracle_calls_dummy_returned": 12,
"n_predictors": 4,
"ratio": 0.8,
},
{
"constraint_class": EqualizedOdds,
"eps": 0.050,
"best_gap": 0.000000,
"last_iter": 5,
"best_iter": 5,
"disp": 0.050000,
"error": 0.378827,
"n_oracle_calls": 19,
"n_oracle_calls_dummy_returned": 0,
"n_predictors": 6,
},
{
"constraint_class": EqualizedOdds,
"eps": 0.050,
"best_gap": 0.000000,
"last_iter": 5,
"best_iter": 5,
"disp": 0.050000,
"error": 0.277016,
"n_oracle_calls": 22,
"n_oracle_calls_dummy_returned": 12,
"n_predictors": 4,
"ratio": 0.8,
},
{
"constraint_class": EqualizedOdds,
"eps": 0.020,
"best_gap": 0.000000,
"last_iter": 5,
"best_iter": 5,
"disp": 0.020000,
"error": 0.421531,
"n_oracle_calls": 19,
"n_oracle_calls_dummy_returned": 0,
"n_predictors": 6,
},
{
"constraint_class": EqualizedOdds,
"eps": 0.020,
"best_gap": 0.000000,
"last_iter": 5,
"best_iter": 5,
"disp": 0.020000,
"error": 0.296612,
"n_oracle_calls": 22,
"n_oracle_calls_dummy_returned": 12,
"n_predictors": 4,
"ratio": 0.8,
},
{
"constraint_class": EqualizedOdds,
"eps": 0.010,
"best_gap": 0.000000,
"last_iter": 5,
"best_iter": 5,
"disp": 0.010000,
"error": 0.435765,
"n_oracle_calls": 19,
"n_oracle_calls_dummy_returned": 0,
"n_predictors": 6,
},
{
"constraint_class": EqualizedOdds,
"eps": 0.010,
"best_gap": 0.000000,
"last_iter": 5,
"best_iter": 5,
"disp": 0.010000,
"error": 0.303145,
"n_oracle_calls": 22,
"n_oracle_calls_dummy_returned": 12,
"n_predictors": 4,
"ratio": 0.8,
},
{
"constraint_class": EqualizedOdds,
"eps": 0.005,
"best_gap": 0.000000,
"last_iter": 5,
"best_iter": 5,
"disp": 0.005000,
"error": 0.442883,
"n_oracle_calls": 19,
"n_oracle_calls_dummy_returned": 0,
"n_predictors": 6,
},
{
"constraint_class": EqualizedOdds,
"eps": 0.005,
"best_gap": 0.000000,
"last_iter": 5,
"best_iter": 5,
"disp": 0.005000,
"error": 0.306411,
"n_oracle_calls": 22,
"n_oracle_calls_dummy_returned": 12,
"n_predictors": 4,
"ratio": 0.8,
},
# ================================================
{
"constraint_class": EqualizedOdds,
"eps": 0.050,
"objective": ErrorRate(costs={"fn": 2.0, "fp": 1.0}),
"best_gap": 0.000000,
"last_iter": 5,
"best_iter": 5,
"disp": 0.050000,
"error": 0.4125,
"n_oracle_calls": 23,
"n_oracle_calls_dummy_returned": 0,
"n_predictors": 6,
},
{
"constraint_class": EqualizedOdds,
"eps": 0.050,
"objective": ErrorRate(costs={"fn": 2.0, "fp": 1.0}),
"best_gap": 0.000000,
"last_iter": 5,
"best_iter": 5,
"disp": 0.050000,
"error": 0.324067,
"n_oracle_calls": 22,
"n_oracle_calls_dummy_returned": 12,
"n_predictors": 4,
"ratio": 0.8,
},
{
"constraint_class": EqualizedOdds,
"eps": 0.020,
"objective": ErrorRate(costs={"fn": 2.0, "fp": 1.0}),
"best_gap": 0.000000,
"last_iter": 5,
"best_iter": 5,
"disp": 0.020000,
"error": 0.435,
"n_oracle_calls": 23,
"n_oracle_calls_dummy_returned": 0,
"n_predictors": 6,
},
{
"constraint_class": EqualizedOdds,
"eps": 0.020,
"objective": ErrorRate(costs={"fn": 2.0, "fp": 1.0}),
"best_gap": 0.000000,
"last_iter": 5,
"best_iter": 5,
"disp": 0.020000,
"error": 0.339179,
"n_oracle_calls": 22,
"n_oracle_calls_dummy_returned": 12,
"n_predictors": 4,
"ratio": 0.8,
},
# ================================================
{
"constraint_class": ErrorRateParity,
"eps": 0.1,
"best_gap": 0.000000,
"last_iter": 5,
"best_iter": 5,
"disp": 0.100000,
"error": 0.25625,
"n_oracle_calls": 17,
"n_oracle_calls_dummy_returned": 0,
"n_predictors": 3,
},
{
"constraint_class": ErrorRateParity,
"eps": 0.1,
"best_gap": 0.000000,
"last_iter": 5,
"best_iter": 5,
"disp": 0.092857,
"error": 0.25,
"n_oracle_calls": 17,
"n_oracle_calls_dummy_returned": 0,
"n_predictors": 3,
"ratio": 0.8,
},
{
"constraint_class": ErrorRateParity,
"eps": 0.05,
"best_gap": 0.000000,
"last_iter": 5,
"best_iter": 5,
"disp": 0.049999,
"error": 0.3,
"n_oracle_calls": 17,
"n_oracle_calls_dummy_returned": 0,
"n_predictors": 3,
},
{
"constraint_class": ErrorRateParity,
"eps": 0.05,
"best_gap": 0.000000,
"last_iter": 5,
"best_iter": 5,
"disp": 0.050000,
"error": 0.253472,
"n_oracle_calls": 26,
"n_oracle_calls_dummy_returned": 0,
"n_predictors": 6,
"ratio": 0.8,
},
{
"constraint_class": ErrorRateParity,
"eps": 0.02,
"best_gap": 0.000000,
"last_iter": 5,
"best_iter": 5,
"disp": 0.019999,
"error": 0.326250,
"n_oracle_calls": 17,
"n_oracle_calls_dummy_returned": 0,
"n_predictors": 3,
},
{
"constraint_class": ErrorRateParity,
"eps": 0.02,
"best_gap": 0.000000,
"last_iter": 5,
"best_iter": 5,
"disp": 0.020000,
"error": 0.268055,
"n_oracle_calls": 26,
"n_oracle_calls_dummy_returned": 0,
"n_predictors": 5,
"ratio": 0.8,
},
{
"constraint_class": ErrorRateParity,
"eps": 0.01,
"best_gap": 0.000000,
"last_iter": 5,
"best_iter": 5,
"disp": 0.010000,
"error": 0.325555,
"n_oracle_calls": 18,
"n_oracle_calls_dummy_returned": 0,
"n_predictors": 4,
},
{
"constraint_class": ErrorRateParity,
"eps": 0.01,
"best_gap": 0.000000,
"last_iter": 5,
"best_iter": 5,
"disp": 0.010000,
"error": 0.272916,
"n_oracle_calls": 26,
"n_oracle_calls_dummy_returned": 0,
"n_predictors": 5,
"ratio": 0.8,
},
{
"constraint_class": ErrorRateParity,
"eps": 0.005,
"best_gap": 0.000000,
"last_iter": 5,
"best_iter": 5,
"disp": 0.005000,
"error": 0.329444,
"n_oracle_calls": 19,
"n_oracle_calls_dummy_returned": 0,
"n_predictors": 5,
},
{
"constraint_class": ErrorRateParity,
"eps": 0.005,
"best_gap": 0.000000,
"last_iter": 5,
"best_iter": 5,
"disp": 0.005000,
"error": 0.275347,
"n_oracle_calls": 26,
"n_oracle_calls_dummy_returned": 0,
"n_predictors": 5,
"ratio": 0.8,
},
# ================================================
{
"constraint_class": TruePositiveRateParity,
"eps": 0.005,
"best_gap": 0.0,
"last_iter": 5,
"best_iter": 5,
"disp": 0.005000,
"error": 0.25,
"n_oracle_calls": 16,
"n_oracle_calls_dummy_returned": 0,
"n_predictors": 2,
},
{
"constraint_class": FalsePositiveRateParity,
"eps": 0.005,
"best_gap": 0.0,
"last_iter": 5,
"best_iter": 5,
"disp": 0.005000,
"error": 0.427133,
"n_oracle_calls": 17,
"n_oracle_calls_dummy_returned": 0,
"n_predictors": 3,
},
]
smoke_test_data_flipped = [
{
"constraint_class": TruePositiveRateParity,
"eps": 0.005,
"best_gap": 0.0,
"last_iter": 5,
"best_iter": 5,
"disp": 0.005000,
"error": 0.427133,
"n_oracle_calls": 17,
"n_oracle_calls_dummy_returned": 0,
"n_predictors": 3,
},
{
"constraint_class": FalsePositiveRateParity,
"eps": 0.005,
"best_gap": 0.0,
"last_iter": 5,
"best_iter": 5,
"disp": 0.005000,
"error": 0.25,
"n_oracle_calls": 16,
"n_oracle_calls_dummy_returned": 0,
"n_predictors": 2,
},
{
"constraint_class": EqualizedOdds,
"eps": 0.005,
"best_gap": 0.000000,
"last_iter": 5,
"best_iter": 5,
"disp": 0.005000,
"error": 0.442883,
"n_oracle_calls": 19,
"n_oracle_calls_dummy_returned": 0,
"n_predictors": 6,
},
]
smoke_test_data_regression = [
{
"constraint_class": BoundedGroupLoss,
"loss": SquareLoss(0, 1),
"eps": 0.01,
"best_gap": 0.003905,
"last_iter": 6,
"best_iter": 6,
"upper_bound": 0.01, # infeasible
"disp": [
0.178333,
0.178333,
0.178333,
0.178333,
0.178333,
0.178333,
0.028045,
0.178333,
0.178333,
0.178333,
0.030853,
0.178333,
0.178333,
0.178333,
0.178333,
0.178333,
],
"error": [
0.1035,
0.1035,
0.1035,
0.1035,
0.1035,
0.1035,
0.024412,
0.1035,
0.1035,
0.1035,
0.025691,
0.1035,
0.1035,
0.1035,
0.1035,
0.1035,
],
"weights": [
0,
0,
0,
0,
0,
0,
0.956748,
0,
0,
0,
0.043251,
0,
0,
0,
0,
0,
0,
],
"n_oracle_calls": 23,
"n_oracle_calls_dummy_returned": 0,
"n_predictors": 16,
},
{
"constraint_class": BoundedGroupLoss,
"loss": SquareLoss(0, 1),
"eps": 0.01,
"best_gap": 0.0,
"last_iter": 5,
"best_iter": 5,
"upper_bound": 0.05, # feasible
"disp": [
0.178333,
0.178333,
0.036690,
0.178333,
0.178333,
0.178333,
0.178333,
],
"error": [0.1035, 0.1035, 0.021988, 0.1035, 0.1035, 0.1035, 0.1035],
"weights": [0, 0, 1, 0, 0, 0, 0],
"n_oracle_calls": 32,
"n_oracle_calls_dummy_returned": 0,
"n_predictors": 7,
},
{
"constraint_class": BoundedGroupLoss,
"loss": SquareLoss(0, 1),
"eps": 0.01,
"best_gap": 0.0,
"last_iter": 5,
"best_iter": 5,
"max_iter": 20,
"nu": 1e-6,
"upper_bound": 0.05, # feasible
"disp": [
0.178333,
0.178333,
0.036690,
0.178333,
0.178333,
0.178333,
0.178333,
],
"error": [0.1035, 0.1035, 0.021988, 0.1035, 0.1035, 0.1035, 0.1035],
"weights": [0, 0, 1, 0, 0, 0, 0],
"n_oracle_calls": 29,
"n_oracle_calls_dummy_returned": 0,
"n_predictors": 7,
},
{
"constraint_class": BoundedGroupLoss,
"loss": ZeroOneLoss(),
"eps": 0.01,
"best_gap": 0.007185,
"last_iter": 5,
"best_iter": 5,
"upper_bound": 0.01, # infeasible
"disp": [
0.383333,
0.383333,
0.383333,
0.383333,
0.1479,
0.383333,
0.383333,
0.383333,
0.140256,
0.383333,
0.383333,
0.383333,
0.383333,
0.383333,
],
"error": [
0.255,
0.255,
0.255,
0.255,
0.140198,
0.255,
0.255,
0.255,
0.135674,
0.255,
0.255,
0.255,
0.255,
0.255,
],
"weights": [0, 0, 0, 0, 0.221468, 0, 0, 0, 0.778531, 0, 0, 0, 0, 0],
"n_oracle_calls": 20,
"n_oracle_calls_dummy_returned": 0,
"n_predictors": 14,
},
{
"constraint_class": BoundedGroupLoss,
"loss": ZeroOneLoss(),
"eps": 0.01,
"best_gap": 0.0,
"last_iter": 5,
"best_iter": 5,
"upper_bound": 0.2, # feasible
"disp": [0.383333, 0.383333, 0.166918],
"error": [0.255, 0.255, 0.116949],
"weights": [0, 0, 1],
"n_oracle_calls": 20,
"n_oracle_calls_dummy_returned": 0,
"n_predictors": 3,
},
{
"constraint_class": BoundedGroupLoss,
"loss": ZeroOneLoss(),
"eps": 0.01,
"best_gap": 0.0,
"last_iter": 5,
"best_iter": 5,
"max_iter": 20,
"nu": 1e-6,
"upper_bound": 0.2, # feasible
"disp": [0.383333, 0.383333, 0.166918],
"error": [0.255, 0.255, 0.116949],
"weights": [0, 0, 1],
"n_oracle_calls": 17,
"n_oracle_calls_dummy_returned": 0,
"n_predictors": 3,
},
{
"constraint_class": BoundedGroupLoss,
"loss": AbsoluteLoss(0, 1),
"eps": 0.01,
"best_gap": 0.007185,
"last_iter": 5,
"best_iter": 5,
"upper_bound": 0.01, # infeasible
"disp": [
0.383333,
0.383333,
0.383333,
0.383333,
0.1479,
0.383333,
0.383333,
0.383333,
0.140256,
0.383333,
0.383333,
0.383333,
0.383333,
0.383333,
],
"error": [
0.255,
0.255,
0.255,
0.255,
0.140198,
0.255,
0.255,
0.255,
0.135674,
0.255,
0.255,
0.255,
0.255,
0.255,
],
"weights": [0, 0, 0, 0, 0.221468, 0, 0, 0, 0.778531, 0, 0, 0, 0, 0],
"n_oracle_calls": 20,
"n_oracle_calls_dummy_returned": 0,
"n_predictors": 14,
},
]
def run_smoke_test_binary_classification(self, data, flipped=False):
learner = LeastSquaresBinaryClassifierLearner()
if "ratio" in data.keys():
disparity_moment = data["constraint_class"](
ratio_bound_slack=data["eps"], ratio_bound=data["ratio"]
)
else:
disparity_moment = data["constraint_class"](difference_bound=data["eps"])
if "objective" in data.keys():
objective_moment = deepcopy(data["objective"])
else:
objective_moment = ErrorRate()
# 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),
objective=deepcopy(objective_moment),
eps=data["eps"],
)
X, y, A = _get_data(A_two_dim=False, flip_y=flipped)
expgrad.fit(X, y, sensitive_features=A)
self._assert_expgrad_state(expgrad, data)
# select probability of predicting 1
def Q(X):
return expgrad._pmf_predict(X)[:, 1]
disparity_moment.load_data(X, y, sensitive_features=A)
objective_moment.load_data(X, y, sensitive_features=A)
disparity = disparity_moment.gamma(Q).max()
error = objective_moment.gamma(Q)[0]
assert disparity == pytest.approx(data["disp"], abs=_PRECISION)
assert error == pytest.approx(data["error"], abs=_PRECISION)
@pytest.mark.parametrize("testdata", smoke_test_data)
def test_smoke(self, testdata):
self.run_smoke_test_binary_classification(testdata)
@pytest.mark.parametrize("testdata", smoke_test_data_flipped)
def test_smoke_flipped(self, testdata):
self.run_smoke_test_binary_classification(testdata, flipped=True)
@pytest.mark.parametrize("data", smoke_test_data_regression)
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)[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)
@pytest.mark.parametrize(
"Constraints",
[
TruePositiveRateParity,
FalsePositiveRateParity,
DemographicParity,
EqualizedOdds,
ErrorRateParity,
],
)
def test_simple_fit_predict_binary_classification(self, Constraints):
X, y, sensitive_features = _get_data()
estimator = LeastSquaresBinaryClassifierLearner()
expgrad = ExponentiatedGradient(estimator, Constraints())
expgrad.fit(X, y, sensitive_features=sensitive_features)
expgrad.predict(X)
@pytest.mark.parametrize(
"constraints",
[
BoundedGroupLoss(loss=SquareLoss(0, 1), upper_bound=0.01),
BoundedGroupLoss(loss=AbsoluteLoss(0, 1), upper_bound=0.01),
BoundedGroupLoss(loss=ZeroOneLoss(), upper_bound=0.01),
],
)
def test_simple_fit_predict_regression(self, constraints):
X, y, sensitive_features = _get_data(y_as_scores=True)
estimator = LeastSquaresRegressor()
expgrad = ExponentiatedGradient(estimator, constraints)
expgrad.fit(X, y, sensitive_features=sensitive_features)
expgrad.predict(X)
def test_single_y_value(self):
# Setup with data designed to result in "all single class"
# at some point in the grid
X_dict = {"c": [10, 50, 10]}
X = pd.DataFrame(X_dict)
y = [1, 1, 1]
A = ["a", "b", "b"]
estimator = LogisticRegression(
solver="liblinear", fit_intercept=True, random_state=97
)
expgrad = ExponentiatedGradient(estimator, DemographicParity())
# Following line should not throw an exception
expgrad.fit(X, y, sensitive_features=A)
# Check the predictors for a ConstantPredictor
test_X_dict = {"c": [1, 2, 3, 4, 5, 6]}
test_X = pd.DataFrame(test_X_dict)
assert expgrad.n_oracle_calls_dummy_returned_ > 0
assert len(expgrad.oracle_execution_times_) == expgrad.n_oracle_calls_
for p in expgrad.predictors_:
assert isinstance(p, DummyClassifier)
assert np.array_equal(p.predict(test_X), [1, 1, 1, 1, 1, 1])
def _assert_expgrad_state(self, expgrad, data):
n_predictors = len(expgrad.predictors_)
assert expgrad.best_gap_ == pytest.approx(data["best_gap"], abs=_PRECISION)
assert expgrad.best_gap_ < expgrad.nu
assert expgrad.last_iter_ == data["last_iter"]
assert expgrad.best_iter_ == data["best_iter"]
assert expgrad.last_iter_ >= _MIN_ITER
assert expgrad.n_oracle_calls_ == data["n_oracle_calls"]
assert (
expgrad.n_oracle_calls_dummy_returned_
== data["n_oracle_calls_dummy_returned"]
)
assert n_predictors == data["n_predictors"]
assert len(expgrad.oracle_execution_times_) == expgrad.n_oracle_calls_
@pytest.mark.parametrize("eps", [0.05, 0.02])
@pytest.mark.parametrize("ratio", [None, 0.8])
@pytest.mark.parametrize("pos_copies", [0, 1, 2])
def test_error_rate_consistency(self, eps, ratio, pos_copies):
learner = LeastSquaresBinaryClassifierLearner()
if ratio is None:
constraints_moment = EqualizedOdds(difference_bound=eps)
else:
constraints_moment = EqualizedOdds(ratio_bound=ratio, ratio_bound_slack=eps)
results = {}
for method in ["costs", "sampling"]:
X, y, A = _get_data()
if method == "sampling":
select = y == 1
X = pd.concat((X,) + (X.loc[select, :],) * pos_copies).values
y = pd.concat((y,) + (y[select],) * pos_copies).values
A = pd.concat((A,) + (A[select],) * pos_copies).values
objective_moment = ErrorRate()
else:
objective_moment = ErrorRate(costs={"fn": 1.0 + pos_copies, "fp": 1.0})
expgrad = ExponentiatedGradient(
learner,
constraints=deepcopy(constraints_moment),
objective=deepcopy(objective_moment),
eps=eps,
nu=1e-3,
)
expgrad.fit(X, y, sensitive_features=A)
# select probability of predicting 1
def Q(X):
return expgrad._pmf_predict(X)[:, 1]
constraints_eval = deepcopy(constraints_moment)
constraints_eval.load_data(X, y, sensitive_features=A)
disparity = constraints_eval.gamma(Q).max()
objective_eval = deepcopy(objective_moment)
objective_eval.load_data(X, y, sensitive_features=A)
total_error = objective_eval.gamma(Q)[0] * len(y)
results[method] = {
"error": objective_eval.gamma(Q)[0],
"total_error": total_error,
"disp": disparity,
"n_predictors": len(expgrad.predictors_),
"best_gap": expgrad.best_gap_,
"last_iter": expgrad.last_iter_,
"best_iter": expgrad.best_iter_,
"n_oracle_calls": expgrad.n_oracle_calls_,
"n_oracle_calls_dummy_returned": expgrad.n_oracle_calls_dummy_returned_,
}
self._assert_expgrad_two_states(results["costs"], results["sampling"])
def _assert_expgrad_two_states(self, state1, state2):
assert state1["total_error"] == pytest.approx(
state2["total_error"], abs=_PRECISION
)
assert state1["disp"] == pytest.approx(state2["disp"], abs=_PRECISION)
assert state1["n_predictors"] == state2["n_predictors"]
assert state1["best_gap"] == pytest.approx(state2["best_gap"], abs=_PRECISION)
assert state1["last_iter"] == state2["last_iter"]
assert state1["best_iter"] == state2["best_iter"]
assert state1["n_oracle_calls"] == state2["n_oracle_calls"]
assert (
state1["n_oracle_calls_dummy_returned"]
== state2["n_oracle_calls_dummy_returned"]
)
