def test_adaptation_all_equal(self):
# 100 equal records. Test that with a decaying learning rate we converge to
# that record and bounce around it.
records = [tf.constant(5.0)] * 20
learning_rate = tf.Variable(1.0)
query = quantile_adaptive_clip_sum_query.QuantileAdaptiveClipSumQuery(
initial_l2_norm_clip=0.0,
noise_multiplier=0.0,
target_unclipped_quantile=0.5,
learning_rate=learning_rate,
clipped_count_stddev=0.0,
expected_num_records=2.0)
global_state = query.initial_global_state()
for t in range(50):
tf.assign(learning_rate, 1.0 / np.sqrt(t+1))
_, global_state = test_utils.run_query(query, records, global_state)
actual_clip = global_state.l2_norm_clip
if t > 40:
self.assertNear(actual_clip, 5.0, 0.25)
def test_adaptation_linspace(self):
# 100 records equally spaced from 0 to 10 in 0.1 increments.
# Test that with a decaying learning rate we converge to the correct
# median with error at most 0.1.
records = [tf.constant(x) for x in np.linspace(
0.0, 10.0, num=21, dtype=np.float32)]
learning_rate = tf.Variable(1.0)
query = quantile_adaptive_clip_sum_query.QuantileAdaptiveClipSumQuery(
initial_l2_norm_clip=0.0,
noise_multiplier=0.0,
target_unclipped_quantile=0.5,
learning_rate=learning_rate,
clipped_count_stddev=0.0,
expected_num_records=2.0)
global_state = query.initial_global_state()
for t in range(50):
tf.assign(learning_rate, 1.0 / np.sqrt(t+1))
_, global_state = test_utils.run_query(query, records, global_state)
actual_clip = global_state.l2_norm_clip
if t > 40:
self.assertNear(actual_clip, 5.0, 0.25)
def test_adaptation_target_one(self):
record1 = tf.constant([-1.5])
record2 = tf.constant([2.75])
query = quantile_adaptive_clip_sum_query.QuantileAdaptiveClipSumQuery(
initial_l2_norm_clip=0.0,
noise_multiplier=0.0,
target_unclipped_quantile=1.0,
learning_rate=1.0,
clipped_count_stddev=0.0,
expected_num_records=2.0)
global_state = query.initial_global_state()
initial_clip = global_state.l2_norm_clip
self.assertAllClose(initial_clip, 0.0)
# On the first two iterations, both are clipped, so the clip goes up
# by 1.0 (the learning rate). When the clip reaches 2.0, only one record is
# clipped, so the clip goes up by only 0.5. After two more iterations,
# both records are clipped, and the clip norm stays there (at 3.0).
expected_sums = [0.0, 0.0, 0.5, 1.0, 1.25]
expected_clips = [1.0, 2.0, 2.5, 3.0, 3.0]
for expected_sum, expected_clip in zip(expected_sums, expected_clips):
actual_sum, global_state = test_utils.run_query(
query, [record1, record2], global_state)
actual_clip = global_state.l2_norm_clip
self.assertAllClose(actual_clip.numpy(), expected_clip)
self.assertAllClose(actual_sum.numpy(), (expected_sum,))
def test_sum_no_clip_no_noise(self):
record1 = tf.constant([2.0, 0.0])
record2 = tf.constant([-1.0, 1.0])
query = quantile_adaptive_clip_sum_query.QuantileAdaptiveClipSumQuery(
initial_l2_norm_clip=10.0,
noise_multiplier=0.0,
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 = [1.0, 1.0]
self.assertAllClose(result, expected)
def test_ledger(self):
record1 = tf.constant([8.5])
record2 = tf.constant([-7.25])
population_size = tf.Variable(0)
selection_probability = tf.Variable(0.0)
ledger = privacy_ledger.PrivacyLedger(
population_size, selection_probability, 50, 50)
query = quantile_adaptive_clip_sum_query.QuantileAdaptiveClipSumQuery(
initial_l2_norm_clip=10.0,
noise_multiplier=1.0,
target_unclipped_quantile=0.0,
learning_rate=1.0,
clipped_count_stddev=0.0,
expected_num_records=2.0,
ledger=ledger)
query = privacy_ledger.QueryWithLedger(query, ledger)
# First sample.
tf.assign(population_size, 10)
tf.assign(selection_probability, 0.1)
_, global_state = test_utils.run_query(query, [record1, record2])
expected_queries = [[10.0, 10.0], [0.5, 0.0]]
formatted = ledger.get_formatted_ledger_eager()
sample_1 = formatted[0]
self.assertAllClose(sample_1.population_size, 10.0)
self.assertAllClose(sample_1.selection_probability, 0.1)
self.assertAllClose(sample_1.queries, expected_queries)
# Second sample.
tf.assign(population_size, 20)
tf.assign(selection_probability, 0.2)
test_utils.run_query(query, [record1, record2], global_state)
formatted = ledger.get_formatted_ledger_eager()
sample_1, sample_2 = formatted
self.assertAllClose(sample_1.population_size, 10.0)
self.assertAllClose(sample_1.selection_probability, 0.1)
self.assertAllClose(sample_1.queries, expected_queries)
expected_queries_2 = [[9.0, 9.0], [0.5, 0.0]]
self.assertAllClose(sample_2.population_size, 20.0)
self.assertAllClose(sample_2.selection_probability, 0.2)
self.assertAllClose(sample_2.queries, expected_queries_2)
def test_sum_with_noise(self):
record1, record2 = 2.71828, 3.14159
stddev = 1.0
clip = 5.0
query = quantile_adaptive_clip_sum_query.QuantileAdaptiveClipSumQuery(
initial_l2_norm_clip=clip,
noise_multiplier=stddev / clip,
target_unclipped_quantile=1.0,
learning_rate=0.0,
clipped_count_stddev=0.0,
expected_num_records=2.0)
noised_sums = []
for _ in xrange(1000):
query_result, _ = test_utils.run_query(query, [record1, record2])
noised_sums.append(query_result.numpy())
result_stddev = np.std(noised_sums)
self.assertNear(result_stddev, stddev, 0.1)
