def test_average_no_noise(self):
record1 = tf.constant([5.0, 0.0]) # Clipped to [3.0, 0.0].
record2 = tf.constant([-1.0, 2.0]) # Not clipped.
query = quantile_adaptive_clip_sum_query.QuantileAdaptiveClipAverageQuery(
initial_l2_norm_clip=3.0,
noise_multiplier=0.0,
denominator=2.0,
target_unclipped_quantile=1.0,
learning_rate=0.0,
clipped_count_stddev=0.0,
expected_num_records=2.0)
query_result, _ = test_utils.run_query(query, [record1, record2])
result = query_result.numpy()
expected_average = [1.0, 1.0]
self.assertAllClose(result, expected_average)
def test_average_with_noise(self):
record1, record2 = 2.71828, 3.14159
sum_stddev = 1.0
denominator = 2.0
clip = 3.0
query = quantile_adaptive_clip_sum_query.QuantileAdaptiveClipAverageQuery(
initial_l2_norm_clip=clip,
noise_multiplier=sum_stddev / clip,
denominator=denominator,
target_unclipped_quantile=1.0,
learning_rate=0.0,
clipped_count_stddev=0.0,
expected_num_records=2.0)
noised_averages = []
for _ in range(1000):
query_result, _ = test_utils.run_query(query, [record1, record2])
noised_averages.append(query_result.numpy())
result_stddev = np.std(noised_averages)
avg_stddev = sum_stddev / denominator
self.assertNear(result_stddev, avg_stddev, 0.1)