def test_accountant(self):
from diffprivlib.accountant import BudgetAccountant
acc = BudgetAccountant(1.5, 0)
a = np.random.random((1000, 5))
mean(a, epsilon=1, bounds=(0, 1), accountant=acc)
self.assertEqual((1.0, 0), acc.total())
with acc:
with self.assertRaises(BudgetError):
mean(a, epsilon=1, bounds=(0, 1))
def test_large_epsilon_axis(self):
a = np.random.random((1000, 5))
res = np.mean(a, axis=0)
res_dp = mean(a, epsilon=1, range=1, axis=0)
for i in range(res.shape[0]):
self.assertAlmostEqual(res[i], res_dp[i], delta=0.01)
def test_large_epsilon(self):
a = np.random.random(1000)
res = float(np.mean(a))
res_dp = mean(a, epsilon=1, range=1)
self.assertAlmostEqual(res, res_dp, delta=0.01)
def test_missing_range(self):
a = np.array([1, 2, 3])
with self.assertWarns(PrivacyLeakWarning):
res = mean(a, epsilon=1, range=None)
self.assertIsNotNone(res)
def test_negative_range(self):
a = np.array([1, 2, 3])
with self.assertRaises(ValueError):
mean(a, epsilon=1, range=-1)
def test_no_epsilon(self):
a = np.array([1, 2, 3])
self.assertIsNotNone(mean(a, range=1))
def test_no_params(self):
a = np.array([1, 2, 3])
with self.assertWarns(PrivacyLeakWarning):
res = mean(a)
self.assertIsNotNone(res)
def test_clipped_output(self):
a = np.random.random((10, ))
for i in range(100):
self.assertTrue(0 <= mean(a, epsilon=1e-5, bounds=(0, 1)) <= 1)
def test_nan(self):
a = np.random.random((5, 5))
a[2, 2] = np.nan
res = mean(a, bounds=(0, 1))
self.assertTrue(np.isnan(res))
def test_array_like(self):
self.assertIsNotNone(mean([1, 2, 3], bounds=(1, 3)))
self.assertIsNotNone(mean((1, 2, 3), bounds=(1, 3)))
def test_bad_bounds(self):
a = np.array([1, 2, 3])
with self.assertRaises(ValueError):
mean(a, epsilon=1, bounds=(0, -1))
def test_no_bounds(self):
a = np.array([1, 2, 3])
with self.assertWarns(PrivacyLeakWarning):
res = mean(a, epsilon=1)
self.assertIsNotNone(res)
def test_no_epsilon(self):
a = np.array([1, 2, 3])
self.assertIsNotNone(mean(a, bounds=(0, 1)))
def test_nan(self):
a = np.random.random((5, 5))
a[2, 2] = np.nan
res = mean(a, range=1)
self.assertTrue(np.isnan(res))
