def test_raises_with_empty_value(self):
value_type = type_conversions.type_from_tensors(())
with self.assertRaises(ValueError):
adaptive_zeroing.build_adaptive_zeroing_mean_process(
value_type=value_type,
initial_quantile_estimate=100.0,
target_quantile=0.99,
multiplier=2.0,
increment=1.0,
learning_rate=1.0,
norm_order=np.inf)
def test_adaptation_achieved_with_multiplier(self, shapes):
value_type = type_conversions.type_from_tensors(_make_value(0, shapes))
mean_process = adaptive_zeroing.build_adaptive_zeroing_mean_process(
value_type=value_type,
initial_quantile_estimate=100.0,
target_quantile=0.5,
multiplier=2.0,
increment=10.0,
learning_rate=np.log(4.0),
norm_order=np.inf)
global_state = mean_process.initialize()
values = [_make_value(x, shapes) for x in [30, 60]]
# With target 0.5, learning rate λ=ln(4), estimate should be cut in
# half in first round: exp(ln(4)(-0.5)) = 1/sqrt(4) = 0.5.
output = mean_process.next(global_state, values, [1, 1])
self._check_result(output, 45, shapes)
global_state = output.state
metrics = output.measurements
self.assertAllClose(metrics.zeroing_threshold, 110.0)
self.assertEqual(metrics.num_zeroed, 0)
# In second round, target is achieved, no adaptation occurs, and no updates
# are zeroed becaues of multiplier.
output = mean_process.next(global_state, values, [1, 1])
self._check_result(output, 45, shapes)
global_state = output.state
metrics = output.measurements
self.assertAllClose(metrics.zeroing_threshold, 110.0)
self.assertEqual(metrics.num_zeroed, 0)
def test_adaptation_achieved(self, shapes):
value_type = type_conversions.type_from_tensors(_make_value(0, shapes))
mean_process = adaptive_zeroing.build_adaptive_zeroing_mean_process(
value_type, 100.0, 0.5, 1.0, np.log(4.0), np.inf)
global_state = mean_process.initialize()
self.assertEqual(global_state.current_estimate, 100.0)
values = [_make_value(x, shapes) for x in [30, 60]]
# With target 0.5, learning rate λ=ln(4), estimate should be cut in
# half in first round: exp(ln(4)(-0.5)) = 1/sqrt(4) = 0.5.
output = mean_process.next(global_state, values, [1, 1])
self._check_result(output, 45, shapes)
global_state = output['state']
self.assertAllClose(global_state.current_estimate, 50.0)
metrics = output['measurements']
self.assertAllClose(metrics.current_threshold, 50.0)
self.assertEqual(metrics.num_zeroed, 0)
# In second round, target is achieved, no adaptation occurs, but one update
# is zeroed.
output = mean_process.next(global_state, values, [1, 1])
self._check_result(output, 30, shapes)
global_state = output['state']
self.assertAllClose(global_state.current_estimate, 50.0)
metrics = output['measurements']
self.assertAllClose(metrics.current_threshold, 50.0)
self.assertEqual(metrics.num_zeroed, 1)
def test_adaptation_up(self, shapes):
value_type = type_conversions.type_from_tensors(_make_value(0, shapes))
mean_process = adaptive_zeroing.build_adaptive_zeroing_mean_process(
value_type=value_type,
initial_quantile_estimate=1.0,
target_quantile=1.0,
multiplier=1.0,
increment=1.0,
learning_rate=np.log(2.0),
norm_order=np.inf)
global_state = mean_process.initialize()
values = [_make_value(x, shapes) for x in [90, 91, 92]]
output = mean_process.next(global_state, values, [1, 1, 1])
self._check_result(output, 0, shapes)
global_state = output.state
metrics = output.measurements
self.assertAllClose(metrics.zeroing_threshold, 3.0)
self.assertEqual(metrics.num_zeroed, 3)
output = mean_process.next(global_state, values, [1, 1, 1])
self._check_result(output, 0, shapes)
global_state = output.state
metrics = output.measurements
self.assertAllClose(metrics.zeroing_threshold, 5.0)
self.assertEqual(metrics.num_zeroed, 3)
def test_simple_average(self, shapes):
value_type = type_conversions.type_from_tensors(_make_value(0, shapes))
mean_process = adaptive_zeroing.build_adaptive_zeroing_mean_process(
value_type=value_type,
initial_quantile_estimate=100.0,
target_quantile=1.0,
multiplier=1.0,
increment=0.0,
learning_rate=0.0,
norm_order=np.inf)
# Basic average.
global_state = mean_process.initialize()
values = [_make_value(x, shapes) for x in [0, 1, 2]]
output = mean_process.next(global_state, values, [1, 1, 1])
self._check_result(output, 1, shapes)
metrics = output.measurements
self.assertEqual(metrics.zeroing_threshold, 100.0)
self.assertEqual(metrics.num_zeroed, 0)
# Weighted average.
global_state = output.state
values = [_make_value(x, shapes) for x in [3, 5, 1, 0]]
output = mean_process.next(global_state, values, [1, 2, 3, 2])
self._check_result(output, 2, shapes)
metrics = output.measurements
self.assertEqual(metrics.zeroing_threshold, 100.0)
self.assertEqual(metrics.num_zeroed, 0)
# One value zeroed.
global_state = output.state
values = [_make_value(x, shapes) for x in [50, 150]]
output = mean_process.next(global_state, values, [1, 1])
self._check_result(output, 50, shapes)
metrics = output.measurements
self.assertEqual(metrics.zeroing_threshold, 100.0)
self.assertEqual(metrics.num_zeroed, 1)
# Both values zeroed.
global_state = mean_process.initialize()
values = [_make_value(x, shapes) for x in [250, 150]]
output = mean_process.next(global_state, values, [1, 1])
self._check_result(output, 0, shapes)
metrics = output.measurements
self.assertEqual(metrics.zeroing_threshold, 100.0)
self.assertEqual(metrics.num_zeroed, 2)
def test_process_type_signature(self, value_template):
value_type = type_conversions.type_from_tensors(value_template)
mean_process = adaptive_zeroing.build_adaptive_zeroing_mean_process(
value_type=value_type,
initial_quantile_estimate=100.0,
target_quantile=0.99,
multiplier=2.0,
increment=1.0,
learning_rate=1.0,
norm_order=np.inf)
dummy_quantile_query = tensorflow_privacy.NoPrivacyQuantileEstimatorQuery(
50.0, 0.99, 1.0, True)
quantile_query_state = dummy_quantile_query.initial_global_state()
server_state_type = computation_types.FederatedType(
type_conversions.type_from_tensors(quantile_query_state),
placements.SERVER)
self.assertEqual(
mean_process.initialize.type_signature,
computation_types.FunctionType(parameter=None,
result=server_state_type))
client_value_type = computation_types.FederatedType(
value_type, placements.CLIENTS)
client_value_weight_type = computation_types.FederatedType(
tf.float32, placements.CLIENTS)
server_result_type = computation_types.FederatedType(
value_type, placements.SERVER)
server_metrics_type = computation_types.FederatedType(
adaptive_zeroing.AdaptiveZeroingMetrics(
zeroing_threshold=tf.float32, num_zeroed=tf.int32),
placements.SERVER)
self.assertTrue(
mean_process.next.type_signature.is_equivalent_to(
computation_types.FunctionType(
parameter=collections.OrderedDict(
global_state=server_state_type,
value=client_value_type,
weight=client_value_weight_type),
result=collections.OrderedDict(
state=server_state_type,
result=server_result_type,
measurements=server_metrics_type,
))))
def test_adaptation_up(self, shapes):
value_type = type_conversions.type_from_tensors(_make_value(0, shapes))
mean_process = adaptive_zeroing.build_adaptive_zeroing_mean_process(
value_type, 1.0, 1.0, 1.0, np.log(2.0), np.inf)
global_state = mean_process.initialize()
values = [_make_value(x, shapes) for x in [90, 91, 92]]
output = mean_process.next(global_state, values, [1, 1, 1])
self._check_result(output, 0, shapes)
global_state = output.state
metrics = output.measurements
self.assertAllClose(metrics.current_threshold, 2.0)
self.assertEqual(metrics.num_zeroed, 3)
output = mean_process.next(global_state, values, [1, 1, 1])
self._check_result(output, 0, shapes)
global_state = output.state
metrics = output.measurements
self.assertAllClose(metrics.current_threshold, 4.0)
self.assertEqual(metrics.num_zeroed, 3)
def test_simple_average(self, shapes):
value_type = type_conversions.type_from_tensors(_make_value(0, shapes))
mean_process = adaptive_zeroing.build_adaptive_zeroing_mean_process(
value_type, 100.0, 1.0, 1.0, 0.0, np.inf)
# Basic average.
global_state = mean_process.initialize()
values = [_make_value(x, shapes) for x in [0, 1, 2]]
output = mean_process.next(global_state, values, [1, 1, 1])
self._check_result(output, 1, shapes)
metrics = output['measurements']
self.assertEqual(metrics.num_zeroed, 0)
# Weighted average.
global_state = output['state']
values = [_make_value(x, shapes) for x in [3, 5, 1, 0]]
output = mean_process.next(global_state, values, [1, 2, 3, 2])
self._check_result(output, 2, shapes)
metrics = output['measurements']
self.assertEqual(metrics.num_zeroed, 0)
# One value zeroed.
global_state = output['state']
values = [_make_value(x, shapes) for x in [50, 150]]
output = mean_process.next(global_state, values, [1, 1])
self._check_result(output, 50, shapes)
metrics = output['measurements']
self.assertEqual(metrics.num_zeroed, 1)
# Both values zeroed.
global_state = mean_process.initialize()
values = [_make_value(x, shapes) for x in [250, 150]]
output = mean_process.next(global_state, values, [1, 1])
self._check_result(output, 0, shapes)
metrics = output['measurements']
self.assertEqual(metrics.num_zeroed, 2)
def test_adaptation_down(self, shapes):
value_type = type_conversions.type_from_tensors(_make_value(0, shapes))
mean_process = adaptive_zeroing.build_adaptive_zeroing_mean_process(
value_type, 100.0, 0.0, 1.0, np.log(2.0), np.inf)
global_state = mean_process.initialize()
self.assertEqual(global_state.current_estimate, 100.0)
values = [_make_value(x, shapes) for x in [0, 1, 2]]
output = mean_process.next(global_state, values, [1, 1, 1])
self._check_result(output, 1, shapes)
global_state = output['state']
self.assertAllClose(global_state.current_estimate, 50.0)
metrics = output['measurements']
self.assertAllClose(metrics.current_threshold, 50.0)
self.assertEqual(metrics.num_zeroed, 0)
output = mean_process.next(global_state, values, [1, 1, 1])
self._check_result(output, 1, shapes)
global_state = output['state']
self.assertAllClose(global_state.current_estimate, 25.0)
metrics = output['measurements']
self.assertAllClose(metrics.current_threshold, 25.0)
self.assertEqual(metrics.num_zeroed, 0)
def test_raises_with_empty_value(self):
value_type = type_conversions.type_from_tensors(())
with self.assertRaises(ValueError):
adaptive_zeroing.build_adaptive_zeroing_mean_process(
value_type, 100.0, 0.99, 2.0, 1.0, np.inf)
