# Auto-generated. Do not edit.
from opendp._convert import *
from opendp._lib import *
from opendp.mod import *
from opendp.typing import *
__all__ = [
"make_base_laplace",
"make_base_gaussian",
"make_base_geometric",
"make_base_discrete_laplace_linear",
"make_base_discrete_laplace_cks20",
"make_base_discrete_laplace",
"make_base_discrete_gaussian",
"make_randomized_response_bool",
"make_randomized_response",
"make_base_ptr"
]
[docs]
def make_base_laplace(
scale,
k: int = -1074,
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.
:param scale: Noise scale parameter for the laplace distribution. `scale` == sqrt(2) * standard_deviation.
:param k: The noise granularity in terms of 2^k. Larger values are more computationally efficient, but have a looser privacy map. Defaults to the smallest granularity.
:type k: int
:param D: Domain of the data type to be privatized. Valid values are VectorDomain<AllDomain<T>> or AllDomain<T>
:type D: :ref:`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", "contrib")
# Standardize type arguments.
D = RuntimeType.parse(type_name=D, generics=["T"])
T = get_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)
k = py_to_c(k, c_type=ctypes.c_int32)
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_int32, ctypes.c_char_p]
function.restype = FfiResult
return c_to_py(unwrap(function(scale, k, D), Measurement))
[docs]
def make_base_gaussian(
scale,
k: int = -1074,
D: RuntimeTypeDescriptor = "AllDomain<T>",
MO: RuntimeTypeDescriptor = "ZeroConcentratedDivergence<T>"
) -> Measurement:
"""Make a Measurement that adds noise from the gaussian(`scale`) distribution to the input.
Adjust D to noise vector-valued data.
The output epsilon may be no greater than one.
:param scale: noise scale parameter for the gaussian distribution. `scale` == standard_deviation.
:param k: The noise granularity in terms of 2^k. Larger values are more computationally efficient, but have a looser privacy map. Defaults to the smallest granularity.
:type k: int
:param D: Domain of the data type to be privatized. Valid values are VectorDomain<AllDomain<T>> or AllDomain<T>
:type D: :ref:`RuntimeTypeDescriptor`
:param MO: Output measure. The only valid measure is ZeroConcentratedDivergence<T>.
:type MO: :ref:`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", "contrib")
# Standardize type arguments.
D = RuntimeType.parse(type_name=D, generics=["T"])
MO = RuntimeType.parse(type_name=MO, generics=["T"])
T = get_atom_or_infer(D, scale)
D = D.substitute(T=T)
MO = MO.substitute(T=T)
# Convert arguments to c types.
scale = py_to_c(scale, c_type=ctypes.c_void_p, type_name=T)
k = py_to_c(k, c_type=ctypes.c_int32)
D = py_to_c(D, c_type=ctypes.c_char_p)
MO = py_to_c(MO, c_type=ctypes.c_char_p)
# Call library function.
function = lib.opendp_meas__make_base_gaussian
function.argtypes = [ctypes.c_void_p, ctypes.c_int32, ctypes.c_char_p, ctypes.c_char_p]
function.restype = FfiResult
return c_to_py(unwrap(function(scale, k, D, MO), Measurement))
[docs]
def make_base_geometric(
scale,
bounds: Any = None,
D: RuntimeTypeDescriptor = "AllDomain<int>",
QO: RuntimeTypeDescriptor = None
) -> Measurement:
"""Deprecated. Use `make_base_discrete_laplace` instead (more efficient). `make_base_discrete_laplace_linear` has a similar interface with the optional constant-time bounds.
:param scale: noise scale parameter for the geometric distribution. `scale` == sqrt(2) * standard_deviation.
: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: :ref:`RuntimeTypeDescriptor`
:param QO: Data type of the sensitivity, scale, and budget.
:type QO: :ref:`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
"""
assert_features("contrib")
# Standardize type arguments.
D = RuntimeType.parse(type_name=D)
QO = RuntimeType.parse_or_infer(type_name=QO, public_example=scale)
T = get_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_discrete_laplace_linear(
scale,
bounds: Any = None,
D: RuntimeTypeDescriptor = "AllDomain<int>",
QO: RuntimeTypeDescriptor = None
) -> Measurement:
"""Make a Measurement that adds noise from the discrete_laplace(`scale`) distribution to the input.
This algorithm can be executed in constant time if bounds are passed.
Adjust D to noise vector-valued data.
:param scale: noise scale parameter for the distribution. `scale` == sqrt(2) * standard_deviation.
: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: :ref:`RuntimeTypeDescriptor`
:param QO: Data type of the sensitivity, scale, and budget.
:type QO: :ref:`RuntimeTypeDescriptor`
:return: A base_discrete_laplace_linear 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("contrib")
# Standardize type arguments.
D = RuntimeType.parse(type_name=D)
QO = RuntimeType.parse_or_infer(type_name=QO, public_example=scale)
T = get_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_discrete_laplace_linear
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_discrete_laplace_cks20(
scale,
D: RuntimeTypeDescriptor = "AllDomain<int>",
QO: RuntimeTypeDescriptor = None
) -> Measurement:
"""Make a Measurement that adds noise from the discrete_laplace(`scale`) distribution to the input.
Uses the sampling algorithm from CKS20, The Discrete Gaussian for Differential Privacy.
Adjust D to noise vector-valued data.
:param scale: noise scale parameter for the distribution. `scale` == sqrt(2) * standard_deviation.
:param D: Domain of the data type to be privatized. Valid values are VectorDomain<AllDomain<T>> or AllDomain<T>
:type D: :ref:`RuntimeTypeDescriptor`
:param QO: Data type of the sensitivity, scale, and budget.
:type QO: :ref:`RuntimeTypeDescriptor`
:return: A base_discrete_laplace_cks20 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("contrib")
# Standardize type arguments.
D = RuntimeType.parse(type_name=D)
QO = RuntimeType.parse_or_infer(type_name=QO, public_example=scale)
# Convert arguments to c types.
scale = py_to_c(scale, c_type=ctypes.c_void_p, type_name=QO)
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_discrete_laplace_cks20
function.argtypes = [ctypes.c_void_p, ctypes.c_char_p, ctypes.c_char_p]
function.restype = FfiResult
return c_to_py(unwrap(function(scale, D, QO), Measurement))
[docs]
def make_base_discrete_laplace(
scale,
D: RuntimeTypeDescriptor = "AllDomain<int>",
QO: RuntimeTypeDescriptor = None
) -> Measurement:
"""Make a Measurement that adds noise from the discrete_laplace(`scale`) distribution to the input.
Adjust D to noise vector-valued data.
This uses `make_base_discrete_laplace_cks20` if scale is greater than 10, otherwise it uses `make_base_discrete_laplace_linear`.
:param scale: noise scale parameter for the distribution. `scale` == sqrt(2) * standard_deviation.
:param D: Domain of the data type to be privatized. Valid values are VectorDomain<AllDomain<T>> or AllDomain<T>
:type D: :ref:`RuntimeTypeDescriptor`
:param QO: Data type of the sensitivity, scale, and budget.
:type QO: :ref:`RuntimeTypeDescriptor`
:return: A base_discrete_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("contrib")
# Standardize type arguments.
D = RuntimeType.parse(type_name=D)
QO = RuntimeType.parse_or_infer(type_name=QO, public_example=scale)
# Convert arguments to c types.
scale = py_to_c(scale, c_type=ctypes.c_void_p, type_name=QO)
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_discrete_laplace
function.argtypes = [ctypes.c_void_p, ctypes.c_char_p, ctypes.c_char_p]
function.restype = FfiResult
return c_to_py(unwrap(function(scale, D, QO), Measurement))
[docs]
def make_base_discrete_gaussian(
scale,
D: RuntimeTypeDescriptor = "AllDomain<int>",
MO: RuntimeTypeDescriptor = "ZeroConcentratedDivergence<Q>"
) -> Measurement:
"""Make a Measurement that adds noise from the discrete_gaussian(`scale`) distribution to the input.
Adjust D to noise vector-valued data.
:param scale: noise scale parameter for the distribution. `scale` == standard_deviation.
:param D: Domain of the data type to be privatized. Valid values are VectorDomain<AllDomain<T>> or AllDomain<T>
:type D: :ref:`RuntimeTypeDescriptor`
:param MO: Output measure. The only valid measure is ZeroConcentratedDivergence<Q>, but Q can be f32 or f64
:type MO: :ref:`RuntimeTypeDescriptor`
:return: A base_discrete_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("contrib")
# Standardize type arguments.
D = RuntimeType.parse(type_name=D)
MO = RuntimeType.parse(type_name=MO, generics=["Q"])
Q = get_atom_or_infer(MO, scale)
MO = MO.substitute(Q=Q)
# Convert arguments to c types.
scale = py_to_c(scale, c_type=ctypes.c_void_p, type_name=Q)
D = py_to_c(D, c_type=ctypes.c_char_p)
MO = py_to_c(MO, c_type=ctypes.c_char_p)
# Call library function.
function = lib.opendp_meas__make_base_discrete_gaussian
function.argtypes = [ctypes.c_void_p, ctypes.c_char_p, ctypes.c_char_p]
function.restype = FfiResult
return c_to_py(unwrap(function(scale, D, MO), Measurement))
[docs]
def make_randomized_response_bool(
prob,
constant_time: bool = False,
Q: RuntimeTypeDescriptor = None
) -> Measurement:
"""Make a Measurement that implements randomized response on a boolean value.
:param prob: Probability of returning the correct answer. Must be in [0.5, 1)
:param constant_time: Set to true to enable constant time
:type constant_time: bool
:param Q: Data type of probability and budget.
:type Q: :ref:`RuntimeTypeDescriptor`
:return: A randomized_response_bool 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("contrib")
# Standardize type arguments.
Q = RuntimeType.parse_or_infer(type_name=Q, public_example=prob)
# Convert arguments to c types.
prob = py_to_c(prob, c_type=ctypes.c_void_p, type_name=Q)
constant_time = py_to_c(constant_time, c_type=ctypes.c_bool)
Q = py_to_c(Q, c_type=ctypes.c_char_p)
# Call library function.
function = lib.opendp_meas__make_randomized_response_bool
function.argtypes = [ctypes.c_void_p, ctypes.c_bool, ctypes.c_char_p]
function.restype = FfiResult
return c_to_py(unwrap(function(prob, constant_time, Q), Measurement))
[docs]
def make_randomized_response(
categories: Any,
prob,
constant_time: bool = False,
T: RuntimeTypeDescriptor = None,
Q: RuntimeTypeDescriptor = None
) -> Measurement:
"""Make a Measurement that implements randomized response on a categorical value.
:param categories: Set of valid outcomes
:type categories: Any
:param prob: Probability of returning the correct answer. Must be in [1/num_categories, 1)
:param constant_time: Set to true to enable constant time
:type constant_time: bool
:param T: Data type of a category.
:type T: :ref:`RuntimeTypeDescriptor`
:param Q: Data type of probability and budget.
:type Q: :ref:`RuntimeTypeDescriptor`
:return: A randomized_response 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("contrib")
# Standardize type arguments.
T = RuntimeType.parse_or_infer(type_name=T, public_example=get_first(categories))
Q = RuntimeType.parse_or_infer(type_name=Q, public_example=prob)
# Convert arguments to c types.
categories = py_to_c(categories, c_type=AnyObjectPtr, type_name=RuntimeType(origin='Vec', args=[T]))
prob = py_to_c(prob, c_type=ctypes.c_void_p, type_name=Q)
constant_time = py_to_c(constant_time, c_type=ctypes.c_bool)
T = py_to_c(T, c_type=ctypes.c_char_p)
Q = py_to_c(Q, c_type=ctypes.c_char_p)
# Call library function.
function = lib.opendp_meas__make_randomized_response
function.argtypes = [AnyObjectPtr, ctypes.c_void_p, ctypes.c_bool, ctypes.c_char_p, ctypes.c_char_p]
function.restype = FfiResult
return c_to_py(unwrap(function(categories, prob, constant_time, T, Q), Measurement))
[docs]
def make_base_ptr(
scale,
threshold,
TK: RuntimeTypeDescriptor,
k: int = -1074,
TV: RuntimeTypeDescriptor = None
) -> Measurement:
"""Make a Measurement that uses propose-test-release to privatize a hashmap of counts.
:param scale: Noise scale parameter for the laplace distribution. `scale` == sqrt(2) * standard_deviation.
:param threshold: Exclude counts that are less than this minimum value.
:param k: The noise granularity in terms of 2^k. Larger values are more computationally efficient, but have a looser privacy map. Defaults to the smallest granularity.
:type k: int
:param TK: Type of Key. Must be hashable/categorical.
:type TK: :ref:`RuntimeTypeDescriptor`
:param TV: Type of Value. Must be float.
:type TV: :ref:`RuntimeTypeDescriptor`
:return: A base_ptr 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", "contrib")
# Standardize type arguments.
TK = RuntimeType.parse(type_name=TK)
TV = RuntimeType.parse_or_infer(type_name=TV, public_example=scale)
# Convert arguments to c types.
scale = py_to_c(scale, c_type=ctypes.c_void_p, type_name=TV)
threshold = py_to_c(threshold, c_type=ctypes.c_void_p, type_name=TV)
k = py_to_c(k, c_type=ctypes.c_int32)
TK = py_to_c(TK, c_type=ctypes.c_char_p)
TV = py_to_c(TV, c_type=ctypes.c_char_p)
# Call library function.
function = lib.opendp_meas__make_base_ptr
function.argtypes = [ctypes.c_void_p, ctypes.c_void_p, ctypes.c_int32, ctypes.c_char_p, ctypes.c_char_p]
function.restype = FfiResult
return c_to_py(unwrap(function(scale, threshold, k, TK, TV), Measurement))
