def test_proportion_multiple(self):
data = np.array([[True, False, True, True] * 10] * 3)
expected_values = np.count_nonzero(data, axis=1)
expected_values = np.divide(expected_values, np.size(data, axis=1))
self.set_seed()
value = DiffPrivStatistics.proportion(data, self.epsilon, axis=1)
np.testing.assert_almost_equal(value, expected_values, self.decimal_places)
def test_proportion_single(self):
data = np.array([True, False, True, True] * 10)
expected_value = np.count_nonzero(data)
expected_value = np.divide(expected_value, np.size(data))
self.set_seed()
value = DiffPrivStatistics.proportion(data, self.epsilon)
self.assertAlmostEqual(value, expected_value, self.decimal_places)
def test_proportion_single_with_condition(self):
data = np.array([0.1, -0.1, 0.1, 0.1] * 10)
expected_value = np.count_nonzero(data >= 0)
expected_value = np.divide(expected_value, np.size(data))
def condition(data):
return data >= 0.0
self.set_seed()
value = DiffPrivStatistics.proportion(data, self.epsilon, condition=condition)
self.assertAlmostEqual(value, expected_value, self.decimal_places)
def test_proportion_multiple_with_condition(self):
data = np.array([[0.1, -0.1, 0.1, 0.1] * 10] * 3)
expected_values = np.count_nonzero(data >= 0.0, axis=1)
expected_values = np.divide(expected_values, np.size(data, axis=1))
def condition(data):
return data >= 0.0
self.set_seed()
value = DiffPrivStatistics.proportion(
data, self.epsilon, condition=condition, axis=1
)
np.testing.assert_almost_equal(value, expected_values, self.decimal_places)