# 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_analytic_gaussian",
"make_base_geometric",
"make_randomized_response_bool",
"make_randomized_response",
"make_base_ptr"
]
[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.
: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", "contrib")
# 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", "contrib")
# 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_analytic_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.
The privacy relation is based on the analytic gaussian mechanism.
: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_analytic_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"])
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_analytic_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
"""
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_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_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: 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: RuntimeTypeDescriptor
:param Q: Data type of probability and budget.
:type Q: 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,
TV: RuntimeTypeDescriptor = None
) -> Measurement:
"""Make a Measurement that uses propose-test-release to privatize a hashmap of counts.
:param scale: Noise scale parameter.
:param threshold: Exclude counts that are less than this minimum value.
:param TK: Type of Key. Must be hashable/categorical.
:type TK: RuntimeTypeDescriptor
:param TV: Type of Value. Must be float.
:type TV: 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)
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_char_p, ctypes.c_char_p]
function.restype = FfiResult
return c_to_py(unwrap(function(scale, threshold, TK, TV), Measurement))
