def test_distrib(self):
epsilon = 1.0
runs = 10000
mech = StringToInt(Geometric().set_sensitivity(1).set_epsilon(epsilon))
count = [0, 0]
for _ in range(runs):
if mech.randomise("0") == "0":
count[0] += 1
if mech.randomise("1") == "0":
count[1] += 1
self.assertGreater(count[0], count[1])
self.assertLessEqual(count[0] / runs,
count[1] * np.exp(epsilon) / runs + 0.05)
def test_not_none(self):
mech = StringToInt(Geometric())
self.assertIsNotNone(mech)
_mech = mech.copy()
self.assertIsNotNone(_mech)
def test_randomise(self):
mech = StringToInt(Geometric().set_sensitivity(1).set_epsilon(1))
self.assertIsInstance(mech.randomise("1"), str)
def setup_method(self, method):
if method.__name__.endswith("prob"):
global_seed(314159)
self.mech = Geometric()
class TestGeometric(TestCase):
def setup_method(self, method):
if method.__name__.endswith("prob"):
global_seed(314159)
self.mech = Geometric()
def teardown_method(self, method):
del self.mech
def test_not_none(self):
self.assertIsNotNone(self.mech)
def test_class(self):
from diffprivlib.mechanisms import DPMechanism
self.assertTrue(issubclass(Geometric, DPMechanism))
def test_no_params(self):
with self.assertRaises(ValueError):
self.mech.randomise(1)
def test_default_sensitivity(self):
self.mech.set_epsilon(1)
self.assertEqual(1, self.mech._sensitivity)
self.assertIsNotNone(self.mech.randomise(1))
def test_zero_sensitivity(self):
self.mech.set_epsilon(1).set_sensitivity(0)
for i in range(1000):
self.assertAlmostEqual(self.mech.randomise(1), 1)
def test_neg_sensitivity(self):
self.mech.set_epsilon(1)
with self.assertRaises(ValueError):
self.mech.set_sensitivity(-1)
def test_non_integer_sensitivity(self):
self.mech.set_epsilon(1)
with self.assertRaises(TypeError):
self.mech.set_sensitivity(0.5)
def test_no_epsilon(self):
self.mech.set_sensitivity(1)
with self.assertRaises(ValueError):
self.mech.randomise(1)
def test_non_zero_delta(self):
self.mech.set_sensitivity(1)
with self.assertRaises(ValueError):
self.mech.set_epsilon_delta(1, 0.5)
def test_neg_epsilon(self):
self.mech.set_sensitivity(1)
with self.assertRaises(ValueError):
self.mech.set_epsilon(-1)
def test_inf_epsilon(self):
self.mech.set_sensitivity(1).set_epsilon(float("inf"))
for i in range(1000):
self.assertEqual(self.mech.randomise(1), 1)
def test_complex_epsilon(self):
with self.assertRaises(TypeError):
self.mech.set_epsilon(1 + 2j)
def test_string_epsilon(self):
with self.assertRaises(TypeError):
self.mech.set_epsilon("Two")
def test_non_numeric(self):
self.mech.set_sensitivity(1).set_epsilon(1)
with self.assertRaises(TypeError):
self.mech.randomise("Hello")
def test_non_integer(self):
self.mech.set_sensitivity(1).set_epsilon(1)
with self.assertRaises(TypeError):
self.mech.randomise(1.0)
def test_zero_median(self):
self.mech.set_sensitivity(1).set_epsilon(1)
vals = []
for i in range(10000):
vals.append(self.mech.randomise(0))
median = float(np.median(vals))
self.assertAlmostEqual(np.abs(median), 0.0, delta=0.1)
def test_neighbors_prob(self):
epsilon = 1
runs = 10000
self.mech.set_sensitivity(1).set_epsilon(epsilon)
count = [0, 0]
for i in range(runs):
val0 = self.mech.randomise(0)
if val0 <= 0:
count[0] += 1
val1 = self.mech.randomise(1)
if val1 <= 0:
count[1] += 1
self.assertGreater(count[0], count[1])
self.assertLessEqual(count[0] / runs,
np.exp(epsilon) * count[1] / runs + 0.1)
def test_repr(self):
repr_ = repr(self.mech.set_epsilon(1).set_sensitivity(1))
self.assertIn(".Geometric(", repr_)
def test_bias(self):
self.assertEqual(0.0, self.mech.get_bias(0))
def test_variance(self):
self.mech.set_epsilon(-np.log(0.5))
# Expected answer gives \sum_{i \in Z} i^2 (1/2)^i = 2 \sum_{i > 0} i^2 (1/2)^i
self.assertAlmostEqual(6 * 2 / 3, self.mech.get_variance(0))
def test_set_epsilon_locally(self):
mech = StringToInt(Geometric().set_sensitivity(1))
mech.set_epsilon(1)
self.assertIsNotNone(mech)
def test_empty_mechanism(self):
mech = StringToInt(Geometric())
with self.assertRaises(ValueError):
mech.randomise("1")
def test_randomise(self):
mech = StringToInt(Geometric(epsilon=1))
self.assertIsInstance(mech.randomise("1"), str)