# Auto-generated. Do not edit.
from opendp._convert import *
from opendp._lib import *
from opendp.mod import *
from opendp.typing import *
__all__ = [
"make_cast",
"make_cast_default",
"make_is_equal",
"make_is_null",
"make_cast_inherent",
"make_cast_metric",
"make_clamp",
"make_unclamp",
"make_count",
"make_count_distinct",
"make_count_by",
"make_count_by_categories",
"make_split_lines",
"make_split_records",
"make_create_dataframe",
"make_split_dataframe",
"make_select_column",
"make_identity",
"make_impute_constant",
"make_impute_uniform_float",
"make_sized_bounded_mean",
"make_resize",
"make_bounded_resize",
"make_bounded_sum",
"make_sized_bounded_sum",
"make_sized_bounded_variance"
]
[docs]
def make_cast(
TIA: RuntimeTypeDescriptor,
TOA: RuntimeTypeDescriptor
) -> Transformation:
"""Make a Transformation that casts a vector of data from type `TIA` to type `TOA`.
Failure to parse results in None, else Some<TOA>.
:param TIA: atomic input data type to cast from
:type TIA: RuntimeTypeDescriptor
:param TOA: atomic data type to cast into
:type TOA: RuntimeTypeDescriptor
:return: A cast step.
:rtype: Transformation
: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.
TIA = RuntimeType.parse(type_name=TIA)
TOA = RuntimeType.parse(type_name=TOA)
# Convert arguments to c types.
TIA = py_to_c(TIA, c_type=ctypes.c_char_p)
TOA = py_to_c(TOA, c_type=ctypes.c_char_p)
# Call library function.
function = lib.opendp_trans__make_cast
function.argtypes = [ctypes.c_char_p, ctypes.c_char_p]
function.restype = FfiResult
return c_to_py(unwrap(function(TIA, TOA), Transformation))
[docs]
def make_cast_default(
TIA: RuntimeTypeDescriptor,
TOA: RuntimeTypeDescriptor
) -> Transformation:
"""Make a Transformation that casts a vector of data from type `TIA` to type `TOA`. If cast fails, fill with default.
:param TIA: atomic input data type to cast from
:type TIA: RuntimeTypeDescriptor
:param TOA: atomic data type to cast into
:type TOA: RuntimeTypeDescriptor
:return: A cast_default step.
:rtype: Transformation
: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.
TIA = RuntimeType.parse(type_name=TIA)
TOA = RuntimeType.parse(type_name=TOA)
# Convert arguments to c types.
TIA = py_to_c(TIA, c_type=ctypes.c_char_p)
TOA = py_to_c(TOA, c_type=ctypes.c_char_p)
# Call library function.
function = lib.opendp_trans__make_cast_default
function.argtypes = [ctypes.c_char_p, ctypes.c_char_p]
function.restype = FfiResult
return c_to_py(unwrap(function(TIA, TOA), Transformation))
[docs]
def make_is_equal(
value: Any,
TIA: RuntimeTypeDescriptor = None
) -> Transformation:
"""Make a Transformation that checks if each element is equal to `value`.
:param value: value to check against
:type value: Any
:param TIA: atomic input data type
:type TIA: RuntimeTypeDescriptor
:return: A is_equal step.
:rtype: Transformation
: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.
TIA = RuntimeType.parse_or_infer(type_name=TIA, public_example=value)
# Convert arguments to c types.
value = py_to_c(value, c_type=AnyObjectPtr, type_name=TIA)
TIA = py_to_c(TIA, c_type=ctypes.c_char_p)
# Call library function.
function = lib.opendp_trans__make_is_equal
function.argtypes = [AnyObjectPtr, ctypes.c_char_p]
function.restype = FfiResult
return c_to_py(unwrap(function(value, TIA), Transformation))
[docs]
def make_is_null(
DIA: RuntimeTypeDescriptor
) -> Transformation:
"""Make a Transformation that checks if each element in a vector is null.
:param DIA: atomic input domain
:type DIA: RuntimeTypeDescriptor
:return: A is_null step.
:rtype: Transformation
: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.
DIA = RuntimeType.parse(type_name=DIA)
# Convert arguments to c types.
DIA = py_to_c(DIA, c_type=ctypes.c_char_p)
# Call library function.
function = lib.opendp_trans__make_is_null
function.argtypes = [ctypes.c_char_p]
function.restype = FfiResult
return c_to_py(unwrap(function(DIA), Transformation))
[docs]
def make_cast_inherent(
TIA: RuntimeTypeDescriptor,
TOA: RuntimeTypeDescriptor
) -> Transformation:
"""Make a Transformation that casts a vector of data from type `TI` to a type that can represent nullity `TO`.
If cast fails, fill with `TO`'s null value.
:param TIA: input data type to cast from
:type TIA: RuntimeTypeDescriptor
:param TOA: data type to cast into
:type TOA: RuntimeTypeDescriptor
:return: A cast_inherent step.
:rtype: Transformation
: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.
TIA = RuntimeType.parse(type_name=TIA)
TOA = RuntimeType.parse(type_name=TOA)
# Convert arguments to c types.
TIA = py_to_c(TIA, c_type=ctypes.c_char_p)
TOA = py_to_c(TOA, c_type=ctypes.c_char_p)
# Call library function.
function = lib.opendp_trans__make_cast_inherent
function.argtypes = [ctypes.c_char_p, ctypes.c_char_p]
function.restype = FfiResult
return c_to_py(unwrap(function(TIA, TOA), Transformation))
[docs]
def make_cast_metric(
MI: DatasetMetric,
MO: DatasetMetric,
TA: RuntimeTypeDescriptor
) -> Transformation:
"""Make a Transformation that converts the dataset metric from type `MI` to type `MO`.
:param MI: input dataset metric
:type MI: DatasetMetric
:param MO: output dataset metric
:type MO: DatasetMetric
:param TA: atomic type of data
:type TA: RuntimeTypeDescriptor
:return: A cast_metric step.
:rtype: Transformation
: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.
MI = RuntimeType.parse(type_name=MI)
MO = RuntimeType.parse(type_name=MO)
TA = RuntimeType.parse(type_name=TA)
# Convert arguments to c types.
MI = py_to_c(MI, c_type=ctypes.c_char_p)
MO = py_to_c(MO, c_type=ctypes.c_char_p)
TA = py_to_c(TA, c_type=ctypes.c_char_p)
# Call library function.
function = lib.opendp_trans__make_cast_metric
function.argtypes = [ctypes.c_char_p, ctypes.c_char_p, ctypes.c_char_p]
function.restype = FfiResult
return c_to_py(unwrap(function(MI, MO, TA), Transformation))
[docs]
def make_clamp(
bounds: Tuple[Any, Any],
TA: RuntimeTypeDescriptor = None
) -> Transformation:
"""Make a Transformation that clamps numeric data in Vec<`T`> to `bounds`.
If datum is less than lower, let datum be lower.
If datum is greater than upper, let datum be upper.
:param bounds: Tuple of inclusive lower and upper bounds.
:type bounds: Tuple[Any, Any]
:param TA: atomic data type
:type TA: RuntimeTypeDescriptor
:return: A clamp step.
:rtype: Transformation
: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.
TA = RuntimeType.parse_or_infer(type_name=TA, public_example=get_first(bounds))
# Convert arguments to c types.
bounds = py_to_c(bounds, c_type=AnyObjectPtr, type_name=RuntimeType(origin='Tuple', args=[TA, TA]))
TA = py_to_c(TA, c_type=ctypes.c_char_p)
# Call library function.
function = lib.opendp_trans__make_clamp
function.argtypes = [AnyObjectPtr, ctypes.c_char_p]
function.restype = FfiResult
return c_to_py(unwrap(function(bounds, TA), Transformation))
[docs]
def make_unclamp(
bounds: Tuple[Any, Any],
TA: RuntimeTypeDescriptor = None
) -> Transformation:
"""Make a Transformation that unclamps a VectorDomain<BoundedDomain<T>> to a VectorDomain<AllDomain<T>>.
:param bounds: Tuple of inclusive lower and upper bounds.
:type bounds: Tuple[Any, Any]
:param TA: atomic data type
:type TA: RuntimeTypeDescriptor
:return: A unclamp step.
:rtype: Transformation
: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.
TA = RuntimeType.parse_or_infer(type_name=TA, public_example=get_first(bounds))
# Convert arguments to c types.
bounds = py_to_c(bounds, c_type=AnyObjectPtr, type_name=RuntimeType(origin='Tuple', args=[TA, TA]))
TA = py_to_c(TA, c_type=ctypes.c_char_p)
# Call library function.
function = lib.opendp_trans__make_unclamp
function.argtypes = [AnyObjectPtr, ctypes.c_char_p]
function.restype = FfiResult
return c_to_py(unwrap(function(bounds, TA), Transformation))
[docs]
def make_count(
TIA: RuntimeTypeDescriptor,
TO: RuntimeTypeDescriptor = "i32"
) -> Transformation:
"""Make a Transformation that computes a count of the number of records in data.
:param TIA: Atomic Input Type. Input data is expected to be of the form Vec<TIA>.
:type TIA: RuntimeTypeDescriptor
:param TO: Output Type. Must be an integer.
:type TO: RuntimeTypeDescriptor
:return: A count step.
:rtype: Transformation
: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.
TIA = RuntimeType.parse(type_name=TIA)
TO = RuntimeType.parse(type_name=TO)
# Convert arguments to c types.
TIA = py_to_c(TIA, c_type=ctypes.c_char_p)
TO = py_to_c(TO, c_type=ctypes.c_char_p)
# Call library function.
function = lib.opendp_trans__make_count
function.argtypes = [ctypes.c_char_p, ctypes.c_char_p]
function.restype = FfiResult
return c_to_py(unwrap(function(TIA, TO), Transformation))
[docs]
def make_count_distinct(
TIA: RuntimeTypeDescriptor,
TO: RuntimeTypeDescriptor = "i32"
) -> Transformation:
"""Make a Transformation that computes a count of the number of unique, distinct records in data.
:param TIA: Atomic Input Type. Input data is expected to be of the form Vec<TIA>.
:type TIA: RuntimeTypeDescriptor
:param TO: Output Type. Must be an integer.
:type TO: RuntimeTypeDescriptor
:return: A count_distinct step.
:rtype: Transformation
: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.
TIA = RuntimeType.parse(type_name=TIA)
TO = RuntimeType.parse(type_name=TO)
# Convert arguments to c types.
TIA = py_to_c(TIA, c_type=ctypes.c_char_p)
TO = py_to_c(TO, c_type=ctypes.c_char_p)
# Call library function.
function = lib.opendp_trans__make_count_distinct
function.argtypes = [ctypes.c_char_p, ctypes.c_char_p]
function.restype = FfiResult
return c_to_py(unwrap(function(TIA, TO), Transformation))
[docs]
def make_count_by(
size: int,
MO: SensitivityMetric,
TIA: RuntimeTypeDescriptor,
TOA: RuntimeTypeDescriptor = "i32"
) -> Transformation:
"""Make a Transformation that computes the count of each unique value in data.
This assumes that the category set is unknown.
This uses a restricted-sensitivity proof that takes advantage of known dataset size.
Use `make_resize` to establish dataset size.
Use meas.make_base_stability to release this query.
:param size: Number of records in input data.
:type size: int
:param MO: Output Metric.
:type MO: SensitivityMetric
:param TIA: Atomic Input Type. Categorical/hashable input data type. Input data must be Vec<TI>.
:type TIA: RuntimeTypeDescriptor
:param TOA: Atomic Output Type. Express counts in terms of this integral type.
:type TOA: RuntimeTypeDescriptor
:return: The carrier type is HashMap<TI, TO>- the counts for each unique data input.
:rtype: Transformation
: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.
MO = RuntimeType.parse(type_name=MO)
TIA = RuntimeType.parse(type_name=TIA)
TOA = RuntimeType.parse(type_name=TOA)
# Convert arguments to c types.
size = py_to_c(size, c_type=ctypes.c_uint)
MO = py_to_c(MO, c_type=ctypes.c_char_p)
TIA = py_to_c(TIA, c_type=ctypes.c_char_p)
TOA = py_to_c(TOA, c_type=ctypes.c_char_p)
# Call library function.
function = lib.opendp_trans__make_count_by
function.argtypes = [ctypes.c_uint, ctypes.c_char_p, ctypes.c_char_p, ctypes.c_char_p]
function.restype = FfiResult
return c_to_py(unwrap(function(size, MO, TIA, TOA), Transformation))
[docs]
def make_count_by_categories(
categories: Any,
MO: SensitivityMetric = "L1Distance<i32>",
TIA: RuntimeTypeDescriptor = None,
TOA: RuntimeTypeDescriptor = "i32"
) -> Transformation:
"""Make a Transformation that computes the number of times each category appears in the data.
This assumes that the category set is known.
:param categories: The set of categories to compute counts for.
:type categories: Any
:param MO: output sensitivity metric
:type MO: SensitivityMetric
:param TIA: categorical/hashable input type. Input data must be Vec<TIA>.
:type TIA: RuntimeTypeDescriptor
:param TOA: express counts in terms of this integral type
:type TOA: RuntimeTypeDescriptor
:return: A count_by_categories step.
:rtype: Transformation
: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.
MO = RuntimeType.parse(type_name=MO)
TIA = RuntimeType.parse_or_infer(type_name=TIA, public_example=next(iter(categories), None))
TOA = RuntimeType.parse(type_name=TOA)
# Convert arguments to c types.
categories = py_to_c(categories, c_type=AnyObjectPtr, type_name=RuntimeType(origin='Vec', args=[TIA]))
MO = py_to_c(MO, c_type=ctypes.c_char_p)
TIA = py_to_c(TIA, c_type=ctypes.c_char_p)
TOA = py_to_c(TOA, c_type=ctypes.c_char_p)
# Call library function.
function = lib.opendp_trans__make_count_by_categories
function.argtypes = [AnyObjectPtr, ctypes.c_char_p, ctypes.c_char_p, ctypes.c_char_p]
function.restype = FfiResult
return c_to_py(unwrap(function(categories, MO, TIA, TOA), Transformation))
[docs]
def make_split_lines(
) -> Transformation:
"""Make a Transformation that takes a string and splits it into a Vec<String> of its lines.
:return: A split_lines step.
:rtype: Transformation
: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")
# No type arguments to standardize.
# No arguments to convert to c types.
# Call library function.
function = lib.opendp_trans__make_split_lines
function.argtypes = []
function.restype = FfiResult
return c_to_py(unwrap(function(), Transformation))
[docs]
def make_split_records(
separator: str
) -> Transformation:
"""Make a Transformation that splits each record in a Vec<String> into a Vec<Vec<String>>.
:param separator: The token(s) that separate entries in each record.
:type separator: str
:return: A split_records step.
:rtype: Transformation
: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")
# No type arguments to standardize.
# Convert arguments to c types.
separator = py_to_c(separator, c_type=ctypes.c_char_p)
# Call library function.
function = lib.opendp_trans__make_split_records
function.argtypes = [ctypes.c_char_p]
function.restype = FfiResult
return c_to_py(unwrap(function(separator), Transformation))
[docs]
def make_create_dataframe(
col_names: Any,
K: RuntimeTypeDescriptor = None
) -> Transformation:
"""Make a Transformation that constructs a dataframe from a Vec<Vec<String>>.
:param col_names: Column names for each record entry.
:type col_names: Any
:param K: categorical/hashable data type of column names
:type K: RuntimeTypeDescriptor
:return: A create_dataframe step.
:rtype: Transformation
: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.
K = RuntimeType.parse_or_infer(type_name=K, public_example=next(iter(col_names), None))
# Convert arguments to c types.
col_names = py_to_c(col_names, c_type=AnyObjectPtr, type_name=RuntimeType(origin='Vec', args=[K]))
K = py_to_c(K, c_type=ctypes.c_char_p)
# Call library function.
function = lib.opendp_trans__make_create_dataframe
function.argtypes = [AnyObjectPtr, ctypes.c_char_p]
function.restype = FfiResult
return c_to_py(unwrap(function(col_names, K), Transformation))
[docs]
def make_split_dataframe(
separator: str,
col_names: Any,
K: RuntimeTypeDescriptor = None
) -> Transformation:
"""Make a Transformation that splits each record in a String into a Vec<Vec<String>>,
and loads the resulting table into a dataframe keyed by `col_names`.
:param separator: The token(s) that separate entries in each record.
:type separator: str
:param col_names: Column names for each record entry.
:type col_names: Any
:param K: categorical/hashable data type of column names
:type K: RuntimeTypeDescriptor
:return: A split_dataframe step.
:rtype: Transformation
: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.
K = RuntimeType.parse_or_infer(type_name=K, public_example=next(iter(col_names), None))
# Convert arguments to c types.
separator = py_to_c(separator, c_type=ctypes.c_char_p)
col_names = py_to_c(col_names, c_type=AnyObjectPtr, type_name=RuntimeType(origin='Vec', args=[K]))
K = py_to_c(K, c_type=ctypes.c_char_p)
# Call library function.
function = lib.opendp_trans__make_split_dataframe
function.argtypes = [ctypes.c_char_p, AnyObjectPtr, ctypes.c_char_p]
function.restype = FfiResult
return c_to_py(unwrap(function(separator, col_names, K), Transformation))
[docs]
def make_select_column(
key: Any,
TOA: RuntimeTypeDescriptor,
K: RuntimeTypeDescriptor = None
) -> Transformation:
"""Make a Transformation that retrieves the column `key` from a dataframe as Vec<`TOA`>.
:param key: categorical/hashable data type of the key/column name
:type key: Any
:param K: data type of the key
:type K: RuntimeTypeDescriptor
:param TOA: atomic data type to downcast to
:type TOA: RuntimeTypeDescriptor
:return: A select_column step.
:rtype: Transformation
: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.
K = RuntimeType.parse_or_infer(type_name=K, public_example=key)
TOA = RuntimeType.parse(type_name=TOA)
# Convert arguments to c types.
key = py_to_c(key, c_type=AnyObjectPtr, type_name=K)
K = py_to_c(K, c_type=ctypes.c_char_p)
TOA = py_to_c(TOA, c_type=ctypes.c_char_p)
# Call library function.
function = lib.opendp_trans__make_select_column
function.argtypes = [AnyObjectPtr, ctypes.c_char_p, ctypes.c_char_p]
function.restype = FfiResult
return c_to_py(unwrap(function(key, K, TOA), Transformation))
[docs]
def make_identity(
D: RuntimeTypeDescriptor,
M: RuntimeTypeDescriptor
) -> Transformation:
"""Make a Transformation that simply passes the data through.
:param D: Domain of the identity function. Must be VectorDomain<AllDomain<_>> or AllDomain<_>
:type D: RuntimeTypeDescriptor
:param M: metric. Must be a dataset metric if D is a VectorDomain or a sensitivity metric if D is an AllDomain
:type M: RuntimeTypeDescriptor
:return: A transformation where the input and output domain are D and the input and output metric are M
:rtype: Transformation
: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)
M = RuntimeType.parse(type_name=M)
# Convert arguments to c types.
D = py_to_c(D, c_type=ctypes.c_char_p)
M = py_to_c(M, c_type=ctypes.c_char_p)
# Call library function.
function = lib.opendp_trans__make_identity
function.argtypes = [ctypes.c_char_p, ctypes.c_char_p]
function.restype = FfiResult
return c_to_py(unwrap(function(D, M), Transformation))
[docs]
def make_impute_constant(
constant: Any,
DA: RuntimeTypeDescriptor = "OptionNullDomain<AllDomain<TA>>"
) -> Transformation:
"""Make a Transformation that replaces null/None data with `constant`.
By default, the input type is Vec<Option<TA>>, as emitted by make_cast.
Set `DA` to InherentNullDomain<AllDomain<TA>> for imputing on types that have an inherent representation of nullity, like floats.
:param constant: Value to replace nulls with.
:type constant: Any
:param DA: domain of data being imputed. This is OptionNullDomain<AllDomain<TA>> or InherentNullDomain<AllDomain<TA>>
:type DA: RuntimeTypeDescriptor
:return: A impute_constant step.
:rtype: Transformation
: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.
DA = RuntimeType.parse(type_name=DA, generics=["TA"])
TA = get_domain_atom_or_infer(DA, constant)
DA = DA.substitute(TA=TA)
# Convert arguments to c types.
constant = py_to_c(constant, c_type=AnyObjectPtr, type_name=TA)
DA = py_to_c(DA, c_type=ctypes.c_char_p)
# Call library function.
function = lib.opendp_trans__make_impute_constant
function.argtypes = [AnyObjectPtr, ctypes.c_char_p]
function.restype = FfiResult
return c_to_py(unwrap(function(constant, DA), Transformation))
[docs]
def make_sized_bounded_mean(
size: int,
bounds: Tuple[Any, Any],
T: RuntimeTypeDescriptor = None
) -> Transformation:
"""Make a Transformation that computes the mean of bounded data.
This uses a restricted-sensitivity proof that takes advantage of known dataset size.
Use `make_clamp` to bound data and `make_bounded_resize` to establish dataset size.
:param size: Number of records in input data.
:type size: int
:param bounds: Tuple of inclusive lower and upper bounds of the input data.
:type bounds: Tuple[Any, Any]
:param T: atomic data type
:type T: RuntimeTypeDescriptor
:return: A sized_bounded_mean step.
:rtype: Transformation
: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(bounds))
# Convert arguments to c types.
size = py_to_c(size, c_type=ctypes.c_uint)
bounds = py_to_c(bounds, c_type=AnyObjectPtr, type_name=RuntimeType(origin='Tuple', args=[T, T]))
T = py_to_c(T, c_type=ctypes.c_char_p)
# Call library function.
function = lib.opendp_trans__make_sized_bounded_mean
function.argtypes = [ctypes.c_uint, AnyObjectPtr, ctypes.c_char_p]
function.restype = FfiResult
return c_to_py(unwrap(function(size, bounds, T), Transformation))
[docs]
def make_resize(
size: int,
constant: Any,
TA: RuntimeTypeDescriptor = None
) -> Transformation:
"""Make a Transformation that either truncates or imputes records with `constant` in a Vec<`TA`> to match a provided `size`.
:param size: Number of records in output data.
:type size: int
:param constant: Value to impute with.
:type constant: Any
:param TA: Atomic type.
:type TA: RuntimeTypeDescriptor
:return: A vector of the same type `TA`, but with the provided `size`.
:rtype: Transformation
: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.
TA = RuntimeType.parse_or_infer(type_name=TA, public_example=constant)
# Convert arguments to c types.
size = py_to_c(size, c_type=ctypes.c_uint)
constant = py_to_c(constant, c_type=AnyObjectPtr, type_name=TA)
TA = py_to_c(TA, c_type=ctypes.c_char_p)
# Call library function.
function = lib.opendp_trans__make_resize
function.argtypes = [ctypes.c_uint, AnyObjectPtr, ctypes.c_char_p]
function.restype = FfiResult
return c_to_py(unwrap(function(size, constant, TA), Transformation))
[docs]
def make_bounded_resize(
size: int,
bounds: Tuple[Any, Any],
constant,
TA: RuntimeTypeDescriptor = None
) -> Transformation:
"""Make a Transformation that either truncates or imputes records with `constant` in a Vec<`TA`> to match a provided `size`.
:param size: Number of records in output data.
:type size: int
:param bounds: Tuple of lower and upper bounds for data in the input domain
:type bounds: Tuple[Any, Any]
:param constant: Value to impute with.
:param TA: Atomic type. If not passed, TA is inferred from the lower bound.
:type TA: RuntimeTypeDescriptor
:return: A vector of the same type `TA`, but with the provided `size`.
:rtype: Transformation
: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.
TA = RuntimeType.parse_or_infer(type_name=TA, public_example=get_first(bounds))
# Convert arguments to c types.
size = py_to_c(size, c_type=ctypes.c_uint)
bounds = py_to_c(bounds, c_type=AnyObjectPtr, type_name=RuntimeType(origin='Tuple', args=[TA, TA]))
constant = py_to_c(constant, c_type=ctypes.c_void_p, type_name=TA)
TA = py_to_c(TA, c_type=ctypes.c_char_p)
# Call library function.
function = lib.opendp_trans__make_bounded_resize
function.argtypes = [ctypes.c_uint, AnyObjectPtr, ctypes.c_void_p, ctypes.c_char_p]
function.restype = FfiResult
return c_to_py(unwrap(function(size, bounds, constant, TA), Transformation))
[docs]
def make_bounded_sum(
bounds: Tuple[Any, Any],
T: RuntimeTypeDescriptor = None
) -> Transformation:
"""Make a Transformation that computes the sum of bounded data.
Use `make_clamp` to bound data.
:param bounds: Tuple of lower and upper bounds for data in the input domain
:type bounds: Tuple[Any, Any]
:param T: atomic type of data
:type T: RuntimeTypeDescriptor
:return: A bounded_sum step.
:rtype: Transformation
: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(bounds))
# Convert arguments to c types.
bounds = py_to_c(bounds, c_type=AnyObjectPtr, type_name=RuntimeType(origin='Tuple', args=[T, T]))
T = py_to_c(T, c_type=ctypes.c_char_p)
# Call library function.
function = lib.opendp_trans__make_bounded_sum
function.argtypes = [AnyObjectPtr, ctypes.c_char_p]
function.restype = FfiResult
return c_to_py(unwrap(function(bounds, T), Transformation))
[docs]
def make_sized_bounded_sum(
size: int,
bounds: Tuple[Any, Any],
T: RuntimeTypeDescriptor = None
) -> Transformation:
"""Make a Transformation that computes the sum of bounded data with known dataset size.
This uses a restricted-sensitivity proof that takes advantage of known dataset size for better utility.
Use `make_clamp` to bound data and `make_bounded_resize` to establish dataset size.
:param size: Number of records in input data.
:type size: int
:param bounds: Tuple of lower and upper bounds for input data
:type bounds: Tuple[Any, Any]
:param T: atomic type of data
:type T: RuntimeTypeDescriptor
:return: A sized_bounded_sum step.
:rtype: Transformation
: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(bounds))
# Convert arguments to c types.
size = py_to_c(size, c_type=ctypes.c_uint)
bounds = py_to_c(bounds, c_type=AnyObjectPtr, type_name=RuntimeType(origin='Tuple', args=[T, T]))
T = py_to_c(T, c_type=ctypes.c_char_p)
# Call library function.
function = lib.opendp_trans__make_sized_bounded_sum
function.argtypes = [ctypes.c_uint, AnyObjectPtr, ctypes.c_char_p]
function.restype = FfiResult
return c_to_py(unwrap(function(size, bounds, T), Transformation))
[docs]
def make_sized_bounded_variance(
size: int,
bounds: Tuple[Any, Any],
ddof: int = 1,
T: RuntimeTypeDescriptor = None
) -> Transformation:
"""Make a Transformation that computes the variance of bounded data.
This uses a restricted-sensitivity proof that takes advantage of known dataset size.
Use `make_clamp` to bound data and `make_bounded_resize` to establish dataset size.
:param size: Number of records in input data.
:type size: int
:param bounds: Tuple of lower and upper bounds for input data
:type bounds: Tuple[Any, Any]
:param ddof: Delta degrees of freedom. Set to 0 if not a sample, 1 for sample estimate.
:type ddof: int
:param T: atomic data type
:type T: RuntimeTypeDescriptor
:return: A sized_bounded_variance step.
:rtype: Transformation
: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(bounds))
# Convert arguments to c types.
size = py_to_c(size, c_type=ctypes.c_uint)
bounds = py_to_c(bounds, c_type=AnyObjectPtr, type_name=RuntimeType(origin='Tuple', args=[T, T]))
ddof = py_to_c(ddof, c_type=ctypes.c_uint)
T = py_to_c(T, c_type=ctypes.c_char_p)
# Call library function.
function = lib.opendp_trans__make_sized_bounded_variance
function.argtypes = [ctypes.c_uint, AnyObjectPtr, ctypes.c_uint, ctypes.c_char_p]
function.restype = FfiResult
return c_to_py(unwrap(function(size, bounds, ddof, T), Transformation))
