# Auto-generated. Do not edit!
'''
The ``combinators`` module provides functions for combining transformations and measurements.
For more context, see :ref:`combinators in the User Guide <combinators-user-guide>`.
For convenience, all the functions of this module are also available from :py:mod:`opendp.prelude`.
We suggest importing under the conventional name ``dp``:
.. code:: python
>>> import opendp.prelude as dp
The methods of this module will then be accessible at ``dp.c``.
'''
from opendp._convert import *
from opendp._lib import *
from opendp.mod import *
from opendp.typing import *
from opendp.core import *
from opendp.domains import *
from opendp.metrics import *
from opendp.measures import *
__all__ = [
"make_basic_composition",
"make_chain_mt",
"make_chain_pm",
"make_chain_tt",
"make_fix_delta",
"make_population_amplification",
"make_pureDP_to_fixed_approxDP",
"make_pureDP_to_zCDP",
"make_sequential_composition",
"make_zCDP_to_approxDP",
"then_sequential_composition"
]
[docs]
def make_basic_composition(
measurements
) -> Measurement:
r"""Construct the DP composition \[`measurement0`, `measurement1`, ...\].
Returns a Measurement that when invoked, computes `[measurement0(x), measurement1(x), ...]`
All metrics and domains must be equivalent.
**Composition Properties**
* sequential: all measurements are applied to the same dataset
* basic: the composition is the linear sum of the privacy usage of each query
* noninteractive: all mechanisms specified up-front (but each can be interactive)
* compositor: all privacy parameters specified up-front (via the map)
[make_basic_composition in Rust documentation.](https://docs.rs/opendp/latest/opendp/combinators/fn.make_basic_composition.html)
:param measurements: A vector of Measurements to compose.
:rtype: Measurement
:raises TypeError: if an argument's type differs from the expected type
:raises UnknownTypeException: if a type argument fails to parse
:raises OpenDPException: packaged error from the core OpenDP library
"""
assert_features("contrib")
# No type arguments to standardize.
# Convert arguments to c types.
c_measurements = py_to_c(measurements, c_type=AnyObjectPtr, type_name=RuntimeType(origin='Vec', args=[AnyMeasurementPtr]))
# Call library function.
lib_function = lib.opendp_combinators__make_basic_composition
lib_function.argtypes = [AnyObjectPtr]
lib_function.restype = FfiResult
output = c_to_py(unwrap(lib_function(c_measurements), Measurement))
output._depends_on(get_dependencies_iterable(measurements))
return output
[docs]
def make_chain_mt(
measurement1: Measurement,
transformation0: Transformation
) -> Measurement:
r"""Construct the functional composition (`measurement1` ○ `transformation0`).
Returns a Measurement that when invoked, computes `measurement1(transformation0(x))`.
[make_chain_mt in Rust documentation.](https://docs.rs/opendp/latest/opendp/combinators/fn.make_chain_mt.html)
:param measurement1: outer mechanism
:type measurement1: Measurement
:param transformation0: inner transformation
:type transformation0: Transformation
:rtype: Measurement
:raises TypeError: if an argument's type differs from the expected type
:raises UnknownTypeException: if a type argument fails to parse
:raises OpenDPException: packaged error from the core OpenDP library
"""
assert_features("contrib")
# No type arguments to standardize.
# Convert arguments to c types.
c_measurement1 = py_to_c(measurement1, c_type=Measurement, type_name=None)
c_transformation0 = py_to_c(transformation0, c_type=Transformation, type_name=None)
# Call library function.
lib_function = lib.opendp_combinators__make_chain_mt
lib_function.argtypes = [Measurement, Transformation]
lib_function.restype = FfiResult
output = c_to_py(unwrap(lib_function(c_measurement1, c_transformation0), Measurement))
output._depends_on(get_dependencies(measurement1), get_dependencies(transformation0))
return output
[docs]
def make_chain_pm(
postprocess1: Function,
measurement0: Measurement
) -> Measurement:
r"""Construct the functional composition (`postprocess1` ○ `measurement0`).
Returns a Measurement that when invoked, computes `postprocess1(measurement0(x))`.
Used to represent non-interactive postprocessing.
[make_chain_pm in Rust documentation.](https://docs.rs/opendp/latest/opendp/combinators/fn.make_chain_pm.html)
:param postprocess1: outer postprocessor
:type postprocess1: Function
:param measurement0: inner measurement/mechanism
:type measurement0: Measurement
:rtype: Measurement
:raises TypeError: if an argument's type differs from the expected type
:raises UnknownTypeException: if a type argument fails to parse
:raises OpenDPException: packaged error from the core OpenDP library
"""
assert_features("contrib")
# No type arguments to standardize.
# Convert arguments to c types.
c_postprocess1 = py_to_c(postprocess1, c_type=Function, type_name=None)
c_measurement0 = py_to_c(measurement0, c_type=Measurement, type_name=None)
# Call library function.
lib_function = lib.opendp_combinators__make_chain_pm
lib_function.argtypes = [Function, Measurement]
lib_function.restype = FfiResult
output = c_to_py(unwrap(lib_function(c_postprocess1, c_measurement0), Measurement))
output._depends_on(get_dependencies(postprocess1), get_dependencies(measurement0))
return output
[docs]
def make_chain_tt(
transformation1: Transformation,
transformation0: Transformation
) -> Transformation:
r"""Construct the functional composition (`transformation1` ○ `transformation0`).
Returns a Transformation that when invoked, computes `transformation1(transformation0(x))`.
[make_chain_tt in Rust documentation.](https://docs.rs/opendp/latest/opendp/combinators/fn.make_chain_tt.html)
:param transformation1: outer transformation
:type transformation1: Transformation
:param transformation0: inner transformation
:type transformation0: Transformation
:rtype: Transformation
:raises TypeError: if an argument's type differs from the expected type
:raises UnknownTypeException: if a type argument fails to parse
:raises OpenDPException: packaged error from the core OpenDP library
"""
assert_features("contrib")
# No type arguments to standardize.
# Convert arguments to c types.
c_transformation1 = py_to_c(transformation1, c_type=Transformation, type_name=None)
c_transformation0 = py_to_c(transformation0, c_type=Transformation, type_name=None)
# Call library function.
lib_function = lib.opendp_combinators__make_chain_tt
lib_function.argtypes = [Transformation, Transformation]
lib_function.restype = FfiResult
output = c_to_py(unwrap(lib_function(c_transformation1, c_transformation0), Transformation))
output._depends_on(get_dependencies(transformation1), get_dependencies(transformation0))
return output
[docs]
def make_fix_delta(
measurement: Measurement,
delta
) -> Measurement:
r"""Fix the delta parameter in the privacy map of a `measurement` with a SmoothedMaxDivergence output measure.
[make_fix_delta in Rust documentation.](https://docs.rs/opendp/latest/opendp/combinators/fn.make_fix_delta.html)
:param measurement: a measurement with a privacy curve to be fixed
:type measurement: Measurement
:param delta: parameter to fix the privacy curve with
:rtype: Measurement
:raises TypeError: if an argument's type differs from the expected type
:raises UnknownTypeException: if a type argument fails to parse
:raises OpenDPException: packaged error from the core OpenDP library
"""
assert_features("contrib")
# No type arguments to standardize.
# Convert arguments to c types.
c_measurement = py_to_c(measurement, c_type=Measurement, type_name=None)
c_delta = py_to_c(delta, c_type=AnyObjectPtr, type_name=get_atom(measurement_output_distance_type(measurement)))
# Call library function.
lib_function = lib.opendp_combinators__make_fix_delta
lib_function.argtypes = [Measurement, AnyObjectPtr]
lib_function.restype = FfiResult
output = c_to_py(unwrap(lib_function(c_measurement, c_delta), Measurement))
output._depends_on(get_dependencies(measurement))
return output
[docs]
def make_population_amplification(
measurement: Measurement,
population_size: int
) -> Measurement:
r"""Construct an amplified measurement from a `measurement` with privacy amplification by subsampling.
This measurement does not perform any sampling.
It is useful when you have a dataset on-hand that is a simple random sample from a larger population.
The DIA, DO, MI and MO between the input measurement and amplified output measurement all match.
Protected by the "honest-but-curious" feature flag
because a dishonest adversary could set the population size to be arbitrarily large.
[make_population_amplification in Rust documentation.](https://docs.rs/opendp/latest/opendp/combinators/fn.make_population_amplification.html)
:param measurement: the computation to amplify
:type measurement: Measurement
:param population_size: the size of the population from which the input dataset is a simple sample
:type population_size: int
:rtype: Measurement
:raises TypeError: if an argument's type differs from the expected type
:raises UnknownTypeException: if a type argument fails to parse
:raises OpenDPException: packaged error from the core OpenDP library
"""
assert_features("contrib", "honest-but-curious")
# No type arguments to standardize.
# Convert arguments to c types.
c_measurement = py_to_c(measurement, c_type=Measurement, type_name=AnyMeasurement)
c_population_size = py_to_c(population_size, c_type=ctypes.c_size_t, type_name=usize)
# Call library function.
lib_function = lib.opendp_combinators__make_population_amplification
lib_function.argtypes = [Measurement, ctypes.c_size_t]
lib_function.restype = FfiResult
output = c_to_py(unwrap(lib_function(c_measurement, c_population_size), Measurement))
output._depends_on(get_dependencies(measurement))
return output
[docs]
def make_pureDP_to_fixed_approxDP(
measurement: Measurement
) -> Measurement:
r"""Constructs a new output measurement where the output measure
is casted from `MaxDivergence<QO>` to `FixedSmoothedMaxDivergence<QO>`.
[make_pureDP_to_fixed_approxDP in Rust documentation.](https://docs.rs/opendp/latest/opendp/combinators/fn.make_pureDP_to_fixed_approxDP.html)
:param measurement: a measurement with a privacy measure to be casted
:type measurement: Measurement
:rtype: Measurement
:raises TypeError: if an argument's type differs from the expected type
:raises UnknownTypeException: if a type argument fails to parse
:raises OpenDPException: packaged error from the core OpenDP library
"""
assert_features("contrib")
# No type arguments to standardize.
# Convert arguments to c types.
c_measurement = py_to_c(measurement, c_type=Measurement, type_name=AnyMeasurement)
# Call library function.
lib_function = lib.opendp_combinators__make_pureDP_to_fixed_approxDP
lib_function.argtypes = [Measurement]
lib_function.restype = FfiResult
output = c_to_py(unwrap(lib_function(c_measurement), Measurement))
return output
[docs]
def make_pureDP_to_zCDP(
measurement: Measurement
) -> Measurement:
r"""Constructs a new output measurement where the output measure
is casted from `MaxDivergence<QO>` to `ZeroConcentratedDivergence<QO>`.
[make_pureDP_to_zCDP in Rust documentation.](https://docs.rs/opendp/latest/opendp/combinators/fn.make_pureDP_to_zCDP.html)
**Citations:**
- [BS16 Concentrated Differential Privacy: Simplifications, Extensions, and Lower Bounds](https://arxiv.org/pdf/1605.02065.pdf#subsection.3.1)
:param measurement: a measurement with a privacy measure to be casted
:type measurement: Measurement
:rtype: Measurement
:raises TypeError: if an argument's type differs from the expected type
:raises UnknownTypeException: if a type argument fails to parse
:raises OpenDPException: packaged error from the core OpenDP library
"""
assert_features("contrib")
# No type arguments to standardize.
# Convert arguments to c types.
c_measurement = py_to_c(measurement, c_type=Measurement, type_name=AnyMeasurement)
# Call library function.
lib_function = lib.opendp_combinators__make_pureDP_to_zCDP
lib_function.argtypes = [Measurement]
lib_function.restype = FfiResult
output = c_to_py(unwrap(lib_function(c_measurement), Measurement))
return output
[docs]
def make_sequential_composition(
input_domain: Domain,
input_metric: Metric,
output_measure: Measure,
d_in,
d_mids
) -> Measurement:
r"""Construct a Measurement that when invoked,
returns a queryable that interactively composes measurements.
**Composition Properties**
* sequential: all measurements are applied to the same dataset
* basic: the composition is the linear sum of the privacy usage of each query
* interactive: mechanisms can be specified based on answers to previous queries
* compositor: all privacy parameters specified up-front
If the privacy measure supports concurrency,
this compositor allows you to spawn multiple interactive mechanisms
and interleave your queries amongst them.
[make_sequential_composition in Rust documentation.](https://docs.rs/opendp/latest/opendp/combinators/fn.make_sequential_composition.html)
**Supporting Elements:**
* Input Domain: `DI`
* Output Type: `Queryable<Measurement<DI, TO, MI, MO>, TO>`
* Input Metric: `MI`
* Output Measure: `MO`
:param input_domain: indicates the space of valid input datasets
:type input_domain: Domain
:param input_metric: how distances are measured between members of the input domain
:type input_metric: Metric
:param output_measure: how privacy is measured
:type output_measure: Measure
:param d_in: maximum distance between adjacent input datasets
:param d_mids: maximum privacy expenditure of each query
:rtype: Measurement
:raises TypeError: if an argument's type differs from the expected type
:raises UnknownTypeException: if a type argument fails to parse
:raises OpenDPException: packaged error from the core OpenDP library
"""
assert_features("contrib")
# Standardize type arguments.
QO = get_distance_type(output_measure) # type: ignore
# Convert arguments to c types.
c_input_domain = py_to_c(input_domain, c_type=Domain, type_name=None)
c_input_metric = py_to_c(input_metric, c_type=Metric, type_name=None)
c_output_measure = py_to_c(output_measure, c_type=Measure, type_name=None)
c_d_in = py_to_c(d_in, c_type=AnyObjectPtr, type_name=get_distance_type(input_metric))
c_d_mids = py_to_c(d_mids, c_type=AnyObjectPtr, type_name=RuntimeType(origin='Vec', args=[QO]))
# Call library function.
lib_function = lib.opendp_combinators__make_sequential_composition
lib_function.argtypes = [Domain, Metric, Measure, AnyObjectPtr, AnyObjectPtr]
lib_function.restype = FfiResult
output = c_to_py(unwrap(lib_function(c_input_domain, c_input_metric, c_output_measure, c_d_in, c_d_mids), Measurement))
return output
[docs]
def then_sequential_composition(
output_measure: Measure,
d_in,
d_mids
):
r"""partial constructor of make_sequential_composition
.. seealso::
Delays application of `input_domain` and `input_metric` in :py:func:`opendp.combinators.make_sequential_composition`
:param output_measure: how privacy is measured
:type output_measure: Measure
:param d_in: maximum distance between adjacent input datasets
:param d_mids: maximum privacy expenditure of each query
"""
return PartialConstructor(lambda input_domain, input_metric: make_sequential_composition(
input_domain=input_domain,
input_metric=input_metric,
output_measure=output_measure,
d_in=d_in,
d_mids=d_mids))
[docs]
def make_zCDP_to_approxDP(
measurement: Measurement
) -> Measurement:
r"""Constructs a new output measurement where the output measure
is casted from `ZeroConcentratedDivergence<QO>` to `SmoothedMaxDivergence<QO>`.
[make_zCDP_to_approxDP in Rust documentation.](https://docs.rs/opendp/latest/opendp/combinators/fn.make_zCDP_to_approxDP.html)
:param measurement: a measurement with a privacy measure to be casted
:type measurement: Measurement
:rtype: Measurement
:raises TypeError: if an argument's type differs from the expected type
:raises UnknownTypeException: if a type argument fails to parse
:raises OpenDPException: packaged error from the core OpenDP library
"""
assert_features("contrib")
# No type arguments to standardize.
# Convert arguments to c types.
c_measurement = py_to_c(measurement, c_type=Measurement, type_name=AnyMeasurement)
# Call library function.
lib_function = lib.opendp_combinators__make_zCDP_to_approxDP
lib_function.argtypes = [Measurement]
lib_function.restype = FfiResult
output = c_to_py(unwrap(lib_function(c_measurement), Measurement))
output._depends_on(get_dependencies(measurement))
return output
