# Auto-generated. Do not edit.
from opendp._convert import *
from opendp._lib import *
from opendp.mod import *
from opendp.typing import *
[docs]
def make_base_laplace(
scale,
D: RuntimeTypeDescriptor = "AllDomain<T>"
) -> Measurement:
"""Make a Measurement that adds noise from the laplace(`scale`) distribution to a scalar value.
Adjust D to noise vector-valued data.
`This constructor is supported by the linked proof. <https://www.overleaf.com/read/brvrprjhrhwb>`_
:param scale: Noise scale parameter of the laplace distribution.
:param D: Domain of the data type to be privatized. Valid values are VectorDomain<AllDomain<T>> or AllDomain<T>
:type D: RuntimeTypeDescriptor
:return: A base_laplace step.
:rtype: Measurement
:raises AssertionError: 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("floating-point")
# Standardize type arguments.
D = RuntimeType.parse(type_name=D, generics=["T"])
T = get_domain_atom_or_infer(D, scale)
D = D.substitute(T=T)
# Convert arguments to c types.
scale = py_to_c(scale, c_type=ctypes.c_void_p, type_name=T)
D = py_to_c(D, c_type=ctypes.c_char_p)
# Call library function.
function = lib.opendp_meas__make_base_laplace
function.argtypes = [ctypes.c_void_p, ctypes.c_char_p]
function.restype = FfiResult
return c_to_py(unwrap(function(scale, D), Measurement))
[docs]
def make_base_gaussian(
scale,
D: RuntimeTypeDescriptor = "AllDomain<T>"
) -> Measurement:
"""Make a Measurement that adds noise from the gaussian(`scale`) distribution to the input.
Adjust D to noise vector-valued data.
:param scale: noise scale parameter to the gaussian distribution
:param D: Domain of the data type to be privatized. Valid values are VectorDomain<AllDomain<T>> or AllDomain<T>
:type D: RuntimeTypeDescriptor
:return: A base_gaussian step.
:rtype: Measurement
:raises AssertionError: 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("floating-point")
# Standardize type arguments.
D = RuntimeType.parse(type_name=D, generics=["T"])
T = get_domain_atom_or_infer(D, scale)
D = D.substitute(T=T)
# Convert arguments to c types.
scale = py_to_c(scale, c_type=ctypes.c_void_p, type_name=T)
D = py_to_c(D, c_type=ctypes.c_char_p)
# Call library function.
function = lib.opendp_meas__make_base_gaussian
function.argtypes = [ctypes.c_void_p, ctypes.c_char_p]
function.restype = FfiResult
return c_to_py(unwrap(function(scale, D), Measurement))
[docs]
def make_base_geometric(
scale,
bounds: Any = None,
D: RuntimeTypeDescriptor = "AllDomain<i32>",
QO: RuntimeTypeDescriptor = None
) -> Measurement:
"""Make a Measurement that adds noise from the geometric(`scale`) distribution to the input.
Adjust D to noise vector-valued data.
:param scale: noise scale parameter to the geometric distribution
:param bounds: Set bounds on the count to make the algorithm run in constant-time.
:type bounds: Any
:param D: Domain of the data type to be privatized. Valid values are VectorDomain<AllDomain<T>> or AllDomain<T>
:type D: RuntimeTypeDescriptor
:param QO: Data type of the sensitivity, scale, and budget.
:type QO: RuntimeTypeDescriptor
:return: A base_geometric step.
:rtype: Measurement
:raises AssertionError: 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
"""
# Standardize type arguments.
D = RuntimeType.parse(type_name=D)
QO = RuntimeType.parse_or_infer(type_name=QO, public_example=scale)
T = get_domain_atom(D)
OptionT = RuntimeType(origin='Option', args=[RuntimeType(origin='Tuple', args=[T, T])])
# Convert arguments to c types.
scale = py_to_c(scale, c_type=ctypes.c_void_p, type_name=QO)
bounds = py_to_c(bounds, c_type=AnyObjectPtr, type_name=OptionT)
D = py_to_c(D, c_type=ctypes.c_char_p)
QO = py_to_c(QO, c_type=ctypes.c_char_p)
# Call library function.
function = lib.opendp_meas__make_base_geometric
function.argtypes = [ctypes.c_void_p, AnyObjectPtr, ctypes.c_char_p, ctypes.c_char_p]
function.restype = FfiResult
return c_to_py(unwrap(function(scale, bounds, D, QO), Measurement))
[docs]
def make_base_stability(
n: int,
scale,
threshold,
MI: SensitivityMetric,
TIK: RuntimeTypeDescriptor,
TIC: RuntimeTypeDescriptor = "i32"
) -> Measurement:
"""Make a Measurement that implements a stability-based filtering and noising.
:param n: Number of records in the input vector.
:type n: int
:param scale: Noise scale parameter.
:param threshold: Exclude counts that are less than this minimum value.
:param MI: Input metric.
:type MI: SensitivityMetric
:param TIK: Data type of input key- must be hashable/categorical.
:type TIK: RuntimeTypeDescriptor
:param TIC: Data type of input count- must be integral.
:type TIC: RuntimeTypeDescriptor
:return: A base_stability step.
:rtype: Measurement
:raises AssertionError: 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("floating-point")
# Standardize type arguments.
MI = RuntimeType.parse(type_name=MI)
TIK = RuntimeType.parse(type_name=TIK)
TIC = RuntimeType.parse(type_name=TIC)
# Convert arguments to c types.
n = py_to_c(n, c_type=ctypes.c_uint)
scale = py_to_c(scale, c_type=ctypes.c_void_p, type_name=MI.args[0])
threshold = py_to_c(threshold, c_type=ctypes.c_void_p, type_name=MI.args[0])
MI = py_to_c(MI, c_type=ctypes.c_char_p)
TIK = py_to_c(TIK, c_type=ctypes.c_char_p)
TIC = py_to_c(TIC, c_type=ctypes.c_char_p)
# Call library function.
function = lib.opendp_meas__make_base_stability
function.argtypes = [ctypes.c_uint, ctypes.c_void_p, ctypes.c_void_p, ctypes.c_char_p, ctypes.c_char_p, ctypes.c_char_p]
function.restype = FfiResult
return c_to_py(unwrap(function(n, scale, threshold, MI, TIK, TIC), Measurement))
