def test_smoke_extra_arg(self, transform_y_t, transform_y_p, transform_gid):
y_t = transform_y_t([0, 0, 1, 1, 0, 1, 1, 1])
y_p = transform_y_p([0, 1, 1, 1, 1, 0, 0, 1])
gid = transform_gid([0, 0, 0, 0, 1, 1, 1, 1])
# Run with the argument defaulted
result = metrics.group_summary(mock_func_extra_arg,
y_t,
y_p,
sensitive_features=gid)
assert result.overall == 5
assert len(result.by_group) == 2
assert result.by_group[0] == 2
assert result.by_group[1] == 3
# Run with the argument speficied
result = metrics.group_summary(mock_func_extra_arg,
y_t,
y_p,
sensitive_features=gid,
my_arg=2)
assert result.overall == 10
assert len(result.by_group) == 2
assert result.by_group[0] == 4
assert result.by_group[1] == 6
def test_true_weight_length_mismatch(self, transform_y_t, transform_s_w):
y_t = transform_y_t([0, 0, 1, 1, 0, 1, 1, 1])
y_p = [0, 1, 1, 1, 1, 0, 0, 0]
gid = [0, 0, 0, 0, 1, 1, 2, 3]
s_w = transform_s_w([1, 1, 1, 1, 2, 2, 3])
with pytest.raises(ValueError) as exception_context:
_ = metrics.group_summary(
mock_func_weight, y_t, y_p, sensitive_features=gid, sample_weight=s_w)
expected = "Array sample_weight is not the same size as y_true"
assert exception_context.value.args[0] == expected
def test_matrix_metric(self, transform_y_t, transform_y_p, transform_gid):
a = "ABC"
b = "DEF"
c = "GHI"
y_t = transform_y_t([0, 0, 1, 1, 0, 1, 1, 1])
y_p = transform_y_p([0, 1, 1, 1, 1, 0, 0, 1])
gid = transform_gid([a, a, a, b, b, c, c, c])
result = metrics.group_summary(mock_func_matrix_return, y_t, y_p, sensitive_features=gid)
assert np.array_equal(result.overall, np.ones([8, 5]))
assert np.array_equal(result.by_group[a], np.ones([3, 2]))
assert np.array_equal(result.by_group[b], np.ones([2, 2]))
assert np.array_equal(result.by_group[c], np.ones([3, 1]))
def test_smoke(self, transform_y_t, transform_y_p, transform_gid):
y_t = transform_y_t([0, 0, 1, 1, 0, 1, 1, 1])
y_p = transform_y_p([0, 1, 1, 1, 1, 0, 0, 1])
gid = transform_gid([0, 0, 0, 0, 1, 1, 1, 1])
result = metrics.group_summary(mock_func, y_t, y_p, sensitive_features=gid)
assert result.overall == 5
assert len(result.by_group) == 2
assert result.by_group[0] == 2
assert result.by_group[1] == 3
assert metrics.group_min_from_summary(result) == 2
assert metrics.group_max_from_summary(result) == 3
assert metrics.difference_from_summary(result) == 1
assert metrics.ratio_from_summary(result) == pytest.approx(0.6666666667)
def test_true_predict_length_mismatch(self, transform_y_a, transform_y_p):
y_a = transform_y_a([0, 0, 1, 1, 0, 1, 1, 1])
y_p = transform_y_p([0, 1, 1, 1, 1, 0, 0])
gid = [0, 0, 0, 0, 1, 1, 2, 2]
s_w = [1, 1, 1, 1, 2, 2, 3, 3]
with pytest.raises(ValueError) as exception_context:
_ = metrics.group_summary(mock_func_weight,
y_a,
y_p,
gid,
sample_weight=s_w)
expected = "Array y_pred is not the same size as y_true"
assert exception_context.value.args[0] == expected
def test_groups_only_one_element(self):
y_t = [1, 2]
y_p = [1, 2]
gid = [0, 1]
def sum_lengths(y_true, y_pred):
return len(y_true) + len(y_pred)
result = metrics.group_summary(sum_lengths, y_t, y_p, sensitive_features=gid)
assert result.overall == 4
assert result.by_group[0] == 2
assert result.by_group[1] == 2
assert metrics.group_min_from_summary(result) == 2
assert metrics.group_max_from_summary(result) == 2
assert metrics.difference_from_summary(result) == 0
assert metrics.ratio_from_summary(result) == 1
def test_single_element_input(self):
y_t = [0]
y_p = [0]
gid = [0]
s_w = [0]
def sum_lengths(y_true, y_pred, sample_weight):
return len(y_true) + len(y_pred) + len(sample_weight)
result = metrics.group_summary(
sum_lengths, y_t, y_p, sensitive_features=gid, sample_weight=s_w)
assert result.overall == 3
assert result.by_group[0] == 3
assert metrics.group_min_from_summary(result) == 3
assert metrics.group_max_from_summary(result) == 3
assert metrics.difference_from_summary(result) == 0
assert metrics.ratio_from_summary(result) == 1
def test_negative_results(self):
y_t = [0, 0, 1, 1, 0, 1, 1, 1]
y_p = [0, 1, 1, 1, 1, 0, 0, 1]
gid = [0, 0, 0, 0, 0, 1, 1, 1]
def negative_results(y_true, y_pred):
return -(len(y_true) + len(y_pred))
result = metrics.group_summary(negative_results, y_t, y_p, sensitive_features=gid)
assert result.overall == -16
assert result.by_group[0] == -10
assert result.by_group[1] == -6
assert metrics.group_min_from_summary(result) == -10
assert metrics.group_max_from_summary(result) == -6
assert metrics.difference_from_summary(result) == 4
assert np.isnan(metrics.ratio_from_summary(result))
def test_with_weights(self, transform_y_t, transform_y_p, transform_gid, transform_s_w):
y_t = transform_y_t([0, 0, 1, 1, 0, 1, 1, 1])
y_p = transform_y_p([0, 1, 1, 1, 1, 0, 0, 1])
gid = transform_gid([0, 0, 0, 0, 1, 1, 2, 2])
s_w = transform_s_w([1, 1, 1, 1, 2, 2, 3, 3])
result = metrics.group_summary(
mock_func_weight, y_t, y_p, sensitive_features=gid, sample_weight=s_w)
assert result.overall == 10
assert len(result.by_group) == 3
assert result.by_group[0] == 2
assert result.by_group[1] == 2
assert result.by_group[2] == 6
assert metrics.group_min_from_summary(result) == 2
assert metrics.group_max_from_summary(result) == 6
assert metrics.difference_from_summary(result) == 4
assert metrics.ratio_from_summary(result) == pytest.approx(0.33333333333333)
def test_string_groups(self, transform_y_t, transform_y_p, transform_gid):
a = "ABC"
b = "DEF"
c = "GHI"
y_t = transform_y_t([0, 0, 1, 1, 0, 1, 1, 1])
y_p = transform_y_p([0, 1, 1, 1, 1, 0, 0, 1])
gid = transform_gid([a, a, a, b, b, c, c, c])
result = metrics.group_summary(mock_func, y_t, y_p, sensitive_features=gid)
assert result.overall == 5
assert len(result.by_group) == 3
assert result.by_group[a] == 1
assert result.by_group[b] == 1
assert result.by_group[c] == 3
assert metrics.group_min_from_summary(result) == 1
assert metrics.group_max_from_summary(result) == 3
assert metrics.difference_from_summary(result) == 2
assert metrics.ratio_from_summary(result) == pytest.approx(0.33333333333333)
def test_metric_results_zero(self):
y_t = [0, 0, 1, 1, 0, 1, 1, 1]
y_p = [0, 1, 1, 1, 1, 0, 0, 1]
gid = [0, 0, 0, 0, 0, 1, 1, 1]
def zero_results(y_true, y_pred):
# Arrays will always be same length
return len(y_true)-len(y_pred)
result = metrics.group_summary(zero_results, y_t, y_p, sensitive_features=gid)
assert result.overall == 0
assert result.by_group[0] == 0
assert result.by_group[1] == 0
assert metrics.group_min_from_summary(result) == 0
assert metrics.group_max_from_summary(result) == 0
assert metrics.difference_from_summary(result) == 0
# Following is special case
assert metrics.ratio_from_summary(result) == 1
def test_matrix_metric_other_properties(self):
a = "ABC"
b = "DEF"
c = "GHI"
y_t = [0, 0, 1, 1, 0, 1, 1, 1]
y_p = [0, 1, 1, 1, 1, 0, 0, 1]
gid = [a, a, a, b, b, c, c, c]
result = metrics.group_summary(mock_func_matrix_return, y_t, y_p, sensitive_features=gid)
# Other fields should fail
with pytest.raises(ValueError):
_ = metrics.group_min_from_summary(result)
with pytest.raises(ValueError):
_ = metrics.group_max_from_summary(result)
with pytest.raises(ValueError):
_ = metrics.difference_from_summary(result)
with pytest.raises(ValueError):
_ = metrics.ratio_from_summary(result)
def evaluate(eps, X_train, y_train, X_test, y_test, sex_train, sex_test,
index):
estimator = GradientBoostingClassifier()
constraints = DemographicParity()
egsolver = ExponentiatedGradient(estimator, constraints, eps=eps)
egsolver.fit(X_train, y_train, sensitive_features=sex_train)
y_pred = egsolver.predict(X_test)
# print("y_pred",y_pred)
group_summary_adult = group_summary(accuracy_score,
y_test,
y_pred,
sensitive_features=sex_test)
selection_rate_summary = selection_rate_group_summary(
y_test, y_pred, sensitive_features=sex_test)
error = 1 - group_summary_adult["overall"]
dp = demographic(selection_rate_summary)
errorlist[index].append(error)
dplist[index].append(dp)
print("error:%f,dp:%f" % (error, dp))
def evaluate(weight, X_train, y_train, X_test, y_test, sex_train, sex_test,
index):
estimator = GradientBoostingClassifier()
constraints = DemographicParity()
gssolver = GridSearch(estimator,
constraints,
grid_size=10,
constraint_weight=weight)
gssolver.fit(X_train, y_train, sensitive_features=sex_train)
y_pred = gssolver.predict(X_test)
# print("y_pred",y_pred)
group_summary_adult = group_summary(accuracy_score,
y_test,
y_pred,
sensitive_features=sex_test)
selection_rate_summary = selection_rate_group_summary(
y_test, y_pred, sensitive_features=sex_test)
error = 1 - group_summary_adult["overall"]
dp = demographic(selection_rate_summary)
errorlist[index].append(error)
dplist[index].append(dp)
print("error:%f,dp:%f" % (error, dp))
else:
y_true.append(0)
# print(data["Sex"][:10])
# print(y_true[:100])
return pd.DataFrame(data), np.array(y_true)'''
X, y_true = shap.datasets.adult() #readfrom("adult.data")
y_true = y_true * 1
sex = X['Sex'].apply(lambda sex: "female" if sex == 0 else "male")
classifier = DecisionTreeClassifier()
classifier.fit(X, y_true)
y_pred = classifier.predict(X)
result1 = metrics.group_summary(accuracy_score,
y_true,
y_pred,
sensitive_features=sex)
print("group_summary", result1)
result2 = metrics.selection_rate_group_summary(y_true,
y_pred,
sensitive_features=sex)
print("selection_rate_group_summary", result2)
# FairlearnDashboard(sensitive_features=sex,
# sensitive_feature_names=['sex'],
# y_true=y_true,
# y_pred={"initial model": y_pred})
np.random.seed(0)
constraint = DemographicParity()
classifier = DecisionTreeClassifier()
mitigator = ExponentiatedGradient(classifier, constraint)
# linear regression
from sklearn.metrics import mean_squared_error, r2_score
from sklearn.linear_model import LinearRegression
reg = LinearRegression()
reg.fit(X_train, Y_train)
lr_pred = reg.predict(X_test)
print("MSE: ", mean_squared_error(y_pred=lr_pred, y_true=Y_test))
print("RMSE: ", mean_squared_error(y_pred=lr_pred,
y_true=Y_test,
squared=False))
print("R^2: ", r2_score(y_true=Y_test, y_pred=lr_pred))
from fairlearn.metrics import group_summary
print("Under Bounded Group Loss constraint, MSE summary: {}".format(
group_summary(mean_squared_error,
Y_test,
lr_pred,
sensitive_features=A_test)))
results = group_summary(mean_squared_error,
Y_test,
lr_pred,
sensitive_features=A_test)
cls_error = results['overall']
error_0 = results['by_group'][0]
error_1 = results['by_group'][1]
print("err_gap: ", np.abs(error_0 - error_1))
# save to file
f_out_np = 'data/insurance.npz'
np.savez(f_out_np,
x_train=X_train,
x_test=X_test,
