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)