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Consolidate low-level DP primitives into dpsynth.local_mode.primitives #182

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Consolidate low-level DP primitives into dpsynth.local_mode.primitives #182

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2 changes: 1 addition & 1 deletion docs/mechanism_api.md
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Original file line number Diff line number Diff line change
Expand Up @@ -280,7 +280,7 @@ composed or split as cleanly:
bisections, whose composite `dp_event` is a composition of exponential
mechanisms.

[dp-quantiles-src]: https://github.com/google/dpsynth/blob/main/dpsynth/local_mode/_quantiles.py
[dp-quantiles-src]: https://github.com/google/dpsynth/blob/main/dpsynth/local_mode/primitives.py

Because these heterogeneous parameters cannot be directly combined,
`configure()` accepts a single scalar `zcdp_rho` and translates it into each
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153 changes: 0 additions & 153 deletions dpsynth/local_mode/_quantiles.py
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5 changes: 2 additions & 3 deletions dpsynth/local_mode/initialization.py
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Expand Up @@ -22,7 +22,6 @@
import dp_accounting
from dpsynth import api
from dpsynth import domain
from dpsynth.local_mode import _quantiles
from dpsynth.local_mode import primitives
from dpsynth.local_mode import vectorized_transformations as vtx
import numpy as np
Expand Down Expand Up @@ -90,7 +89,7 @@ def compute_grid_spec(
"""Returns (lower, upper, grid_size) for the quantile candidate grid."""
min_value = float(attribute.min_value)
if attribute.dtype == 'int':
m = _quantiles.jitter_factor(num_partitions)
m = primitives.jitter_factor(num_partitions)
budget = max(2, max_grid_size // m)
int_range = int(attribute.max_value - attribute.min_value + 1)
step = max(1, math.ceil(int_range / budget))
Expand Down Expand Up @@ -212,7 +211,7 @@ def from_summary(
) -> NumericalMeasurement:
"""Returns a NumericalMeasurement from pre-aggregated histogram counts."""
jitter_strategy = 'refine' if self.attribute.dtype == 'int' else 'symmetric'
indices = _quantiles.quantiles_from_histogram(
indices = primitives.quantiles_from_histogram(
rng,
counts,
epsilon_levels=np.asarray(self.epsilon_levels),
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