def _create_compound_combiner(
self, aggregate_params: pipeline_dp.AggregateParams
) -> combiners.CompoundCombiner:
mechanism_type = aggregate_params.noise_kind.convert_to_mechanism_type(
)
internal_combiners = []
if not self._is_public_partitions:
budget = self._budget_accountant.request_budget(
mechanism_type=pipeline_dp.MechanismType.GENERIC)
internal_combiners.append(
utility_analysis_combiners.PartitionSelectionCombiner(
combiners.CombinerParams(budget, aggregate_params)))
if pipeline_dp.Metrics.COUNT in aggregate_params.metrics:
budget = self._budget_accountant.request_budget(
mechanism_type, weight=aggregate_params.budget_weight)
internal_combiners.append(
utility_analysis_combiners.UtilityAnalysisCountCombiner(
combiners.CombinerParams(budget, aggregate_params)))
if pipeline_dp.Metrics.SUM in aggregate_params.metrics:
budget = self._budget_accountant.request_budget(
mechanism_type, weight=aggregate_params.budget_weight)
internal_combiners.append(
utility_analysis_combiners.UtilityAnalysisSumCombiner(
combiners.CombinerParams(budget, aggregate_params)))
if pipeline_dp.Metrics.PRIVACY_ID_COUNT in aggregate_params.metrics:
budget = self._budget_accountant.request_budget(
mechanism_type, weight=aggregate_params.budget_weight)
internal_combiners.append(
utility_analysis_combiners.
UtilityAnalysisPrivacyIdCountCombiner(
combiners.CombinerParams(budget, aggregate_params)))
return combiners.CompoundCombiner(internal_combiners,
return_named_tuple=False)
def _create_combiner(self, no_noise):
mechanism_spec = _create_mechanism_spec(no_noise)
aggregate_params = _create_aggregate_params()
params = dp_combiners.CombinerParams(mechanism_spec, aggregate_params)
return dp_combiners.CompoundCombiner([
dp_combiners.CountCombiner(params),
dp_combiners.SumCombiner(params)
])
def _create_combiner(self, no_noise, per_partition_bound):
mechanism_spec = _create_mechanism_spec(no_noise)
if per_partition_bound:
aggregate_params = self._create_aggregate_params_per_partition_bound(
)
else:
aggregate_params = _create_aggregate_params()
aggregate_params.metrics = [pipeline_dp.Metrics.SUM]
params = dp_combiners.CombinerParams(mechanism_spec, aggregate_params)
return dp_combiners.SumCombiner(params)
def _create_combiner(self, no_noise):
mechanism_spec = _create_mechanism_spec(no_noise)
aggregate_params = _create_aggregate_params(max_value=4)
metrics_to_compute = ['count', 'sum', 'mean']
params = dp_combiners.CombinerParams(mechanism_spec, aggregate_params)
return dp_combiners.MeanCombiner(params, metrics_to_compute)
def _create_combiner(self, no_noise):
mechanism_spec = _create_mechanism_spec(no_noise)
aggregate_params = _create_aggregate_params()
params = dp_combiners.CombinerParams(mechanism_spec, aggregate_params)
return dp_combiners.PrivacyIdCountCombiner(params)
def _create_combiner(self, no_noise, vector_size):
mechanism_spec = _create_mechanism_spec(no_noise)
aggregate_params = _create_aggregate_params(vector_size=vector_size)
params = dp_combiners.CombinerParams(mechanism_spec, aggregate_params)
return dp_combiners.VectorSumCombiner(params)