Source code for opendp.combinators

# Auto-generated. Do not edit.
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"
]


[docs]@versioned def make_basic_composition( measurements: Any ) -> Measurement: """Construct the DP composition [`measurement0`, `measurement1`, ...]. Returns a Measurement that when invoked, computes `[measurement0(x), measurement1(x), ...]` All metrics and domains must be equivalent, except for the output domain. [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. :type measurements: Any :rtype: Measurement :raises TypeError: if an argument's type differs from the expected type :raises UnknownTypeError: 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]@versioned def make_chain_mt( measurement1: Measurement, transformation0: Transformation ) -> Measurement: """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 UnknownTypeError: 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]@versioned def make_chain_pm( postprocess1: Function, measurement0: Measurement ) -> Measurement: """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 UnknownTypeError: 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]@versioned def make_chain_tt( transformation1: Transformation, transformation0: Transformation ) -> Transformation: """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 UnknownTypeError: 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]@versioned def make_fix_delta( measurement: Measurement, delta: Any ) -> Measurement: """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 :type delta: Any :rtype: Measurement :raises TypeError: if an argument's type differs from the expected type :raises UnknownTypeError: 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]@versioned def make_population_amplification( measurement: Measurement, population_size: int ) -> Measurement: """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 UnknownTypeError: 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]@versioned def make_pureDP_to_fixed_approxDP( measurement: Measurement ) -> Measurement: """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 UnknownTypeError: 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]@versioned def make_pureDP_to_zCDP( measurement: Measurement ) -> Measurement: """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 UnknownTypeError: 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]@versioned def make_sequential_composition( input_domain, input_metric, output_measure, d_in: Any, d_mids: Any ) -> Measurement: """Construct a queryable that interactively composes interactive measurements. [make_sequential_composition in Rust documentation.](https://docs.rs/opendp/latest/opendp/combinators/fn.make_sequential_composition.html) :param input_domain: indicates the space of valid input datasets :param input_metric: how distances are measured between members of the input domain :param output_measure: how privacy is measured :param d_in: maximum distance between adjacent input datasets :type d_in: Any :param d_mids: maximum privacy expenditure of each query :type d_mids: Any :rtype: Measurement :raises TypeError: if an argument's type differs from the expected type :raises UnknownTypeError: 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) # Convert arguments to c types. c_input_domain = py_to_c(input_domain, c_type=Domain, type_name=AnyDomain) c_input_metric = py_to_c(input_metric, c_type=Metric, type_name=AnyMetric) c_output_measure = py_to_c(output_measure, c_type=Measure, type_name=AnyMeasure) 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]@versioned def make_zCDP_to_approxDP( measurement: Measurement ) -> Measurement: """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 UnknownTypeError: 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