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Sum per partition histogram #512

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Dec 4, 2023
Merged

Sum per partition histogram #512

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7a0a38d
wip
dvadym Nov 30, 2023
5493675
Sum per partition histogram
dvadym Dec 1, 2023
f091000
tests
dvadym Dec 1, 2023
dc3b884
fix tests
dvadym Dec 1, 2023
94d642a
fix
dvadym Dec 1, 2023
293841d
addressed comments
dvadym Dec 4, 2023
1e03f07
formatting
dvadym Dec 4, 2023
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20 changes: 11 additions & 9 deletions analysis/tests/parameter_tuning_test.py
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Original file line number Diff line number Diff line change
Expand Up @@ -74,7 +74,7 @@ def test_find_candidate_parameters_maximum_number_of_candidates_is_respected_whe

mock_histograms = histograms.DatasetHistograms(mock_l0_histogram, None,
mock_linf_histogram,
None, None, None)
None, None, None, None)
parameters_to_tune = parameter_tuning.ParametersToTune(
max_partitions_contributed=True,
max_contributions_per_partition=True)
Expand All @@ -99,7 +99,7 @@ def test_find_candidate_parameters_more_candidates_for_l_0_when_not_so_many_l_in

mock_histograms = histograms.DatasetHistograms(mock_l0_histogram, None,
mock_linf_histogram,
None, None, None)
None, None, None, None)
parameters_to_tune = parameter_tuning.ParametersToTune(
max_partitions_contributed=True,
max_contributions_per_partition=True)
Expand Down Expand Up @@ -128,7 +128,7 @@ def test_find_candidate_parameters_more_candidates_for_l_inf_when_not_so_many_l_

mock_histograms = histograms.DatasetHistograms(mock_l0_histogram, None,
mock_linf_histogram,
None, None, None)
None, None, None, None)
parameters_to_tune = parameter_tuning.ParametersToTune(
max_partitions_contributed=True,
max_contributions_per_partition=True)
Expand Down Expand Up @@ -185,7 +185,8 @@ def test_find_candidate_parameters_count(self, max_value, max_candidates,
mock_l0_histogram.max_value = mock.Mock(return_value=max_value)

mock_histograms = histograms.DatasetHistograms(mock_l0_histogram, None,
None, None, None, None)
None, None, None, None,
None)
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weird formatting but maybe that's what the tool generates

parameters_to_tune = parameter_tuning.ParametersToTune(
max_partitions_contributed=True,
max_contributions_per_partition=False)
Expand Down Expand Up @@ -257,7 +258,8 @@ def test_find_candidate_parameters_sum(self, bins, max_candidates,
mock_linf_sum_contributions_histogram = histograms.Histogram(
histograms.HistogramType.LINF_SUM_CONTRIBUTIONS, bins)
mock_histograms = histograms.DatasetHistograms(
None, None, None, mock_linf_sum_contributions_histogram, None, None)
None, None, None, mock_linf_sum_contributions_histogram, None, None,
None)
parameters_to_tune = parameter_tuning.ParametersToTune(
max_partitions_contributed=False,
min_sum_per_partition=False,
Expand Down Expand Up @@ -286,7 +288,7 @@ def test_find_candidate_parameters_both_l0_and_linf_sum_to_be_tuned(self):

mock_histograms = histograms.DatasetHistograms(
mock_l0_histogram, None, None,
mock_linf_sum_contributions_histogram, None, None)
mock_linf_sum_contributions_histogram, None, None, None)
parameters_to_tune = parameter_tuning.ParametersToTune(
max_partitions_contributed=True,
min_sum_per_partition=False,
Expand Down Expand Up @@ -332,7 +334,7 @@ def test_find_candidate_parameters_generate_linf(
histograms.HistogramType.LINF_CONTRIBUTIONS, bins=[])
mock_histograms = histograms.DatasetHistograms(mock_l0_histogram, None,
mock_linf_histogram,
None, None, None)
None, None, None, None)

mock_find_candidate_from_histogram.return_value = [1, 2]

Expand Down Expand Up @@ -538,7 +540,7 @@ def test_tune_params_validation(self, error_msg,
is_public_partitions: bool):
tune_options = _get_tune_options(metrics)
contribution_histograms = histograms.DatasetHistograms(
None, None, None, None, None, None)
None, None, None, None, None, None, None)
data_extractors = pipeline_dp.DataExtractors(
privacy_id_extractor=lambda _: 0, partition_extractor=lambda _: 0)
public_partitions = [1] if is_public_partitions else None
Expand All @@ -554,7 +556,7 @@ def test_tune_min_sum_per_partition_is_not_supported(self):
min_sum_per_partition=True,
max_sum_per_partition=True))
contribution_histograms = histograms.DatasetHistograms(
None, None, None, None, None, None)
None, None, None, None, None, None, None)
data_extractors = pipeline_dp.DataExtractors(
privacy_id_extractor=lambda _: 0, partition_extractor=lambda _: 0)
with self.assertRaisesRegex(
Expand Down
92 changes: 84 additions & 8 deletions pipeline_dp/dataset_histograms/computing_histograms.py
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Original file line number Diff line number Diff line change
Expand Up @@ -201,6 +201,7 @@ def _list_to_contribution_histograms(
l0_contributions = l1_contributions = None
linf_contributions = linf_sum_contributions = None
count_per_partition = privacy_id_per_partition_count = None
sum_per_partition_histogram = None
for histogram in histograms:
if histogram.name == hist.HistogramType.L0_CONTRIBUTIONS:
l0_contributions = histogram
Expand All @@ -214,10 +215,14 @@ def _list_to_contribution_histograms(
count_per_partition = histogram
elif histogram.name == hist.HistogramType.COUNT_PRIVACY_ID_PER_PARTITION:
privacy_id_per_partition_count = histogram
elif histogram.name == hist.HistogramType.SUM_PER_PARTITION:
sum_per_partition_histogram = histogram

return hist.DatasetHistograms(l0_contributions, l1_contributions,
linf_contributions, linf_sum_contributions,
count_per_partition,
privacy_id_per_partition_count)
privacy_id_per_partition_count,
sum_per_partition_histogram)


def _to_dataset_histograms(histogram_list,
Expand Down Expand Up @@ -321,7 +326,7 @@ def _compute_linf_sum_contributions_histogram(
NUMBER_OF_BUCKETS_IN_LINF_SUM_CONTRIBUTIONS_HISTOGRAM.

Args:
col: collection with elements (privacy_id, partition_key, value).
col: collection with elements ((privacy_id, partition_key), value).
backend: PipelineBackend to run operations on the collection.
Returns:
1 element collection, which contains the computed hist.Histogram.
Expand Down Expand Up @@ -355,11 +360,15 @@ def _min_max_lowers(col, number_of_buckets,
"""
min_max_values = pipeline_functions.min_max_elements(
backend, col, "Min and max value in dataset")

# min_max_values: 1 element collection with a pair (min, max)
return backend.map(
min_max_values, lambda min_max: np.linspace(min_max[0], min_max[1],
(number_of_buckets + 1)),
"map to lowers")
def generate_lowers(min_max: Tuple[float, float]) -> List[float]:
min_, max_ = min_max
if min_ == max_:
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will it generate min_ number_of_buckets + 1 times otherweise?

return [min_, min_]
return list(np.linspace(min_, max_, (number_of_buckets + 1)))

return backend.map(min_max_values, generate_lowers, "map to lowers")


def _compute_partition_count_histogram(
Expand Down Expand Up @@ -417,6 +426,34 @@ def _compute_partition_privacy_id_count_histogram(
col, backend, hist.HistogramType.COUNT_PRIVACY_ID_PER_PARTITION)


def _compute_partition_sum_histogram(col,
backend: pipeline_backend.PipelineBackend):
"""Computes histogram of counts per partition.

This histogram contains: todo the number of partitions with total count of
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contributions = 1, 2 etc.

Args:
col: collection with elements ((privacy_id, partition_key), value).
backend: PipelineBackend to run operations on the collection.
Returns:
1 element collection, which contains the computed hist.Histogram.
"""

col = backend.map_tuple(col, lambda pid_pk, value: (pid_pk[1], value),
"Drop privacy id")
col = backend.sum_per_key(col, "Sum of contributions per partition")
# col: (pk, sum_per_partition)
col = backend.values(col, "Drop keys")
# col: (float)
col = backend.to_multi_transformable_collection(col)
lowers = _min_max_lowers(
col, NUMBER_OF_BUCKETS_IN_LINF_SUM_CONTRIBUTIONS_HISTOGRAM, backend)

return _compute_frequency_histogram_helper_with_lowers(
col, backend, hist.HistogramType.SUM_PER_PARTITION, lowers)


def compute_dataset_histograms(col, data_extractors: pipeline_dp.DataExtractors,
backend: pipeline_backend.PipelineBackend):
"""Computes dataset histograms.
Expand Down Expand Up @@ -464,13 +501,16 @@ def compute_dataset_histograms(col, data_extractors: pipeline_dp.DataExtractors,
partition_count_histogram = _compute_partition_count_histogram(col, backend)
partition_privacy_id_count_histogram = _compute_partition_privacy_id_count_histogram(
col_distinct, backend)
partition_sum_histogram = _compute_partition_sum_histogram(
col_with_values, backend)
# all histograms are 1 element collections which contains ContributionHistogram

# Combine histograms to histograms.DatasetHistograms.
return _to_dataset_histograms([
l0_contributions_histogram, l1_contributions_histogram,
linf_contributions_histogram, linf_sum_contributions_histogram,
partition_count_histogram, partition_privacy_id_count_histogram
partition_count_histogram, partition_privacy_id_count_histogram,
partition_sum_histogram
], backend)


Expand Down Expand Up @@ -613,6 +653,39 @@ def _compute_partition_count_histogram_on_preaggregated_data(
hist.HistogramType.COUNT_PER_PARTITION)


def _compute_partition_sum_histogram_on_preaggregated_data(
col, backend: pipeline_backend.PipelineBackend):
"""Computes histogram of counts per partition.

This histogram contains: the number of partitions with total count of
contributions = 1, 2 etc.

Args:
col: collection with a pre-aggregated dataset, each element is
(partition_key, (count, sum, n_partitions, n_contributions)).
backend: PipelineBackend to run operations on the collection.
Returns:
1 element collection, which contains the computed histograms.Histogram.
"""
col = backend.map_values(
col,
lambda x: x[1], # x is (count, sum, n_partitions, n_contributions)
"Extract sum per partition contribution")
# col: (pk, int)
col = backend.sum_per_key(col, "Sum per partition")
# col: (pk, int), where each element is the total count per partition.
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col = backend.values(col, "Drop partition keys")
# col: (int,)
col = backend.to_multi_transformable_collection(col)
lowers = _min_max_lowers(
col, NUMBER_OF_BUCKETS_IN_LINF_SUM_CONTRIBUTIONS_HISTOGRAM, backend)
# lowers: (float,) where each value defines a lower of a bin in the
# generated histogram.

return _compute_frequency_histogram_helper_with_lowers(
col, backend, hist.HistogramType.SUM_PER_PARTITION, lowers)


def _compute_partition_privacy_id_count_histogram_on_preaggregated_data(
col, backend: pipeline_backend.PipelineBackend):
"""Computes a histogram of privacy id counts per partition.
Expand Down Expand Up @@ -675,10 +748,13 @@ def compute_dataset_histograms_on_preaggregated_data(
col, backend)
partition_privacy_id_count_histogram = _compute_partition_privacy_id_count_histogram_on_preaggregated_data(
col, backend)
partition_sum_histogram = _compute_partition_sum_histogram_on_preaggregated_data(
col, backend)

# Combine histograms to histograms.DatasetHistograms.
return _to_dataset_histograms([
l0_contributions_histogram, l1_contributions_histogram,
linf_contributions_histogram, linf_sum_contributions_histogram,
partition_count_histogram, partition_privacy_id_count_histogram
partition_count_histogram, partition_privacy_id_count_histogram,
partition_sum_histogram
], backend)
7 changes: 6 additions & 1 deletion pipeline_dp/dataset_histograms/histograms.py
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Expand Up @@ -73,6 +73,7 @@ class HistogramType(enum.Enum):
LINF_SUM_CONTRIBUTIONS = 'linf_sum_contributions'
COUNT_PER_PARTITION = 'count_per_partition'
COUNT_PRIVACY_ID_PER_PARTITION = 'privacy_id_per_partition_count'
SUM_PER_PARTITION = 'sum_per_partition'


@dataclass
Expand Down Expand Up @@ -112,7 +113,10 @@ def is_integer(self) -> bool:
* Floating histograms, which is used for sums per partition. Which
has the same size bins.
"""
return self.name != HistogramType.LINF_SUM_CONTRIBUTIONS
return self.name not in [
HistogramType.LINF_SUM_CONTRIBUTIONS,
HistogramType.SUM_PER_PARTITION
]

def total_count(self):
return sum([bin.count for bin in self.bins])
Expand Down Expand Up @@ -209,3 +213,4 @@ class DatasetHistograms:
linf_sum_contributions_histogram: Histogram
count_per_partition_histogram: Histogram
count_privacy_id_per_partition: Histogram
sum_per_partition_histogram: Histogram
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