Source code for opendp.context

The ``context`` module provides :py:class:`opendp.context.Context` and supporting utilities.

For more context, see :ref:`context in the User Guide <context-user-guide>`.

For convenience, all the functions of this module are also available from :py:mod:`opendp.prelude`.
We suggest importing under the conventional name ``dp``:

.. code:: python

    >>> import opendp.prelude as dp

import logging
from typing import Any, Callable, Optional, Union
import importlib
from inspect import signature
from functools import partial
from opendp.combinators import (
from import atom_domain, vector_domain, with_margin
from opendp.measurements import make_laplace, make_gaussian
from opendp.measures import (
from opendp.metrics import (
from opendp.mod import (
from opendp.typing import RuntimeType
from opendp._lib import indent, import_optional_dependency
from opendp.polars import LazyFrameQuery, Margin
from dataclasses import asdict

__all__ = [

logger = logging.getLogger(__name__)

# a dictionary of "constructor name" -> (constructor_function, is_partial)
# "constructor name" is the name of the constructor without the "make_" prefix
# constructor_function is the partial version if is_partial is True
constructors = {}
for module_name in ["transformations", "measurements"]:
    module = importlib.import_module(f"opendp.{module_name}")
    for name in module.__all__:
        if not name.startswith("make_"):
        partial_name = "then_" + name[5:]
        make_func = getattr(module, name)

        is_partial = partial_name in module.__all__
        constructor = getattr(module, partial_name if is_partial else name)

        constructors[name[5:]] = constructor, is_partial

[docs] def space_of(T, M=None, infer: bool = False) -> tuple[Domain, Metric]: """A shorthand for building a metric space. A metric space consists of a domain and a metric. >>> import opendp.prelude as dp ... >>> dp.space_of(list[int]) (VectorDomain(AtomDomain(T=i32)), SymmetricDistance()) >>> # the verbose form allows greater control: >>> (dp.vector_domain(dp.atom_domain(T=dp.i32)), dp.symmetric_distance()) (VectorDomain(AtomDomain(T=i32)), SymmetricDistance()) :param T: carrier type (the type of members in the domain) :param M: metric type :param infer: if True, ``T`` is an example of the sensitive dataset. Passing sensitive data may result in a privacy violation. """ import opendp.typing as ty domain = domain_of(T, infer=infer) D = domain.type # choose a metric type if not set if M is None: if D.origin == "VectorDomain": # type: ignore[union-attr] M = ty.SymmetricDistance elif D.origin == "AtomDomain" and ty.get_atom(D) in ty.NUMERIC_TYPES: # type: ignore[union-attr] M = ty.AbsoluteDistance else: raise TypeError(f"no default metric for domain {D}. Please set `M`") # choose a distance type if not set if isinstance(M, ty.RuntimeType) and not M.args: M = M[ty.get_atom(D)] # type: ignore[index] return domain, metric_of(M)
[docs] def domain_of(T, infer: bool = False) -> Domain: """Constructs an instance of a domain from carrier type ``T``, or from an example. Accepts a limited set of Python type expressions: >>> import opendp.prelude as dp >>> dp.domain_of(list[int]) VectorDomain(AtomDomain(T=i32)) As well as strings representing types in the underlying Rust syntax: >>> dp.domain_of('Vec<int>') VectorDomain(AtomDomain(T=i32)) Dictionaries, optional types, and a range of primitive types are supported: >>> dp.domain_of(dict[str, int]) MapDomain { key_domain: AtomDomain(T=String), value_domain: AtomDomain(T=i32) } .. TODO: Support python syntax for Option: >>> dp.domain_of('Option<int>') # Python's `Optional` is not supported. OptionDomain(AtomDomain(T=i32)) >>> dp.domain_of(dp.i32) AtomDomain(T=i32) More complex types are not supported: >>> dp.domain_of(list[list[int]]) # doctest: +IGNORE_EXCEPTION_DETAIL Traceback (most recent call last): ... opendp.mod.OpenDPException: FFI("VectorDomain constructor only supports AtomDomain or UserDomain inner domains") Alternatively, an example of the data can be provided, but note that passing sensitive data may result in a privacy violation: >>> dp.domain_of([1, 2, 3], infer=True) VectorDomain(AtomDomain(T=i32)) :param T: carrier type :param infer: if True, ``T`` is an example of the sensitive dataset. Passing sensitive data may result in a privacy violation. """ import opendp.typing as ty from import vector_domain, atom_domain, option_domain, map_domain if infer: pl = import_optional_dependency("polars", raise_error=False) if pl is not None and isinstance(T, pl.LazyFrame): from opendp.polars import _lazyframe_domain_from_schema return _lazyframe_domain_from_schema(T.schema) # normalize to a type descriptor if infer: T = ty.RuntimeType.infer(T) else: T = ty.RuntimeType.parse(T) # construct the domain if isinstance(T, ty.RuntimeType): if T.origin == "Vec": return vector_domain(domain_of(T.args[0])) if T.origin == "HashMap": return map_domain(domain_of(T.args[0]), domain_of(T.args[1])) if T.origin == "Option": return option_domain(domain_of(T.args[0])) if T in ty.PRIMITIVE_TYPES: return atom_domain(T=T) raise TypeError(f"unrecognized carrier type: {T}")
[docs] def metric_of(M) -> Metric: """Constructs an instance of a metric from metric type ``M``.""" import opendp.typing as ty import opendp.metrics as metrics if isinstance(M, Metric): return M M = ty.RuntimeType.parse(M) if isinstance(M, ty.RuntimeType): if M.origin == "AbsoluteDistance": return metrics.absolute_distance(T=M.args[0]) if M.origin == "L1Distance": return metrics.l1_distance(T=M.args[0]) if M.origin == "L2Distance": return metrics.l2_distance(T=M.args[0]) if M == ty.HammingDistance: return metrics.hamming_distance() if M == ty.SymmetricDistance: return metrics.symmetric_distance() if M == ty.InsertDeleteDistance: return metrics.insert_delete_distance() if M == ty.ChangeOneDistance: return metrics.change_one_distance() if M == ty.DiscreteDistance: return metrics.discrete_distance() raise TypeError(f"unrecognized metric: {M}")
[docs] def loss_of( epsilon: Optional[float] = None, delta: Optional[float] = None, rho: Optional[float] = None, U = None) -> tuple[Measure, Union[float, tuple[float, float]]]: """Constructs a privacy loss, consisting of a privacy measure and a privacy loss parameter. >>> import opendp.prelude as dp >>> dp.loss_of(epsilon=1.0) (MaxDivergence(f64), 1.0) >>> dp.loss_of(epsilon=1.0, delta=1e-9) (FixedSmoothedMaxDivergence(f64), (1.0, 1e-09)) >>> dp.loss_of(rho=1.0) (ZeroConcentratedDivergence(f64), 1.0) :param epsilon: Parameter for pure ε-DP. :param delta: Parameter for approximate (ε,δ)-DP. :param rho: Parameter for zero-concentrated ρ-DP. :param U: The type of the privacy parameter; Inferred if not provided. """ def range_warning(name, value, info_level, warn_level): if value > warn_level: if info_level == warn_level: logger.warning(f'{name} should be less than or equal to {warn_level}') else: logger.warning(f'{name} should be less than or equal to {warn_level}, and is typically less than or equal to {info_level}') elif value > info_level:'{name} is typically less than or equal to {info_level}') if rho: range_warning('rho', rho, 0.25, 0.5) U = RuntimeType.parse_or_infer(U, rho) return zero_concentrated_divergence(T=U), rho if epsilon is None: raise ValueError("Either epsilon or rho must be specified.") range_warning('epsilon', epsilon, 1, 5) if delta is None: U = RuntimeType.parse_or_infer(U, epsilon) return max_divergence(T=U), epsilon range_warning('delta', delta, 1e-6, 1e-6) U = RuntimeType.parse_or_infer(U, epsilon) return fixed_smoothed_max_divergence(T=U), (epsilon, delta)
[docs] def unit_of( *, contributions: Optional[int] = None, changes: Optional[int] = None, absolute: Optional[float] = None, l1: Optional[float] = None, l2: Optional[float] = None, ordered: bool = False, U=None, ) -> tuple[Metric, float]: """Constructs a unit of privacy, consisting of a metric and a dataset distance. The parameters are mutually exclusive. >>> import opendp.prelude as dp >>> dp.unit_of(contributions=3) (SymmetricDistance(), 3) >>> dp.unit_of(l1=2.0) (L1Distance(f64), 2.0) :param contributions: Greatest number of records a privacy unit may contribute to microdata :param changes: Greatest number of records a privacy unit may change in microdata :param absolute: Greatest absolute distance a privacy unit can influence a scalar aggregate data set :param l1: Greatest l1 distance a privacy unit can influence a vector aggregate data set :param l2: Greatest l2 distance a privacy unit can influence a vector aggregate data set :param ordered: Set to ``True`` to use ``InsertDeleteDistance`` instead of ``SymmetricDistance``, or ``HammingDistance`` instead of ``ChangeOneDistance``. :param U: The type of the dataset distance.""" if ordered and contributions is None and changes is None: raise ValueError('"ordered" is only valid with "changes" or "contributions"') def _is_distance(p, v): return p not in ["ordered", "U", "_is_distance"] and v is not None if sum(1 for p, v in locals().items() if _is_distance(p, v)) != 1: raise ValueError("Must specify exactly one distance.") if contributions is not None: metric = insert_delete_distance() if ordered else symmetric_distance() return metric, contributions if changes is not None: metric = hamming_distance() if ordered else change_one_distance() return metric, changes if absolute is not None: metric = absolute_distance(T=RuntimeType.parse_or_infer(U, absolute)) return metric, absolute if l1 is not None: metric = l1_distance(T=RuntimeType.parse_or_infer(U, l1)) return metric, l1 if l2 is not None: metric = l2_distance(T=RuntimeType.parse_or_infer(U, l2)) return metric, l2 raise Exception('No matching metric found')
[docs] class Context(object): """A Context coordinates queries to an instance of a privacy :py:attr:`accountant`. It is recommended to use the :py:func:`make_sequential_composition <opendp.combinators.make_sequential_composition>` constructor instead of this one. :param accountant: The measurement used to spawn the queryable. :param queryable: Executes the queries and tracks the privacy expenditure. :param d_in: An upper bound on the distance between adjacent datasets. :param d_mids: A sequence of privacy losses for each query to be sent to the queryable. Used for compositors. :param d_out: An upper bound on the overall privacy loss. Used for filters.""" accountant: Measurement # union Odometer once merged """The accountant is the measurement used to spawn the queryable. It contains information about the queryable, such as the input domain, input metric, and output measure expected of measurement queries sent to the queryable.""" queryable: Queryable """The queryable executes the queries and tracks the privacy expenditure.""" def __init__( self, accountant: Measurement, queryable: Queryable, d_in: float, d_mids: Optional[list[float]] = None, d_out: Optional[float] = None, space_override: Optional[tuple[Domain, Metric]] = None, # TODO: Document or add leading underscore and explain that is is for internal use only. ): = accountant self.queryable = queryable self.d_in = d_in self.d_mids = d_mids self.d_out = d_out self.space_override = space_override def __repr__(self) -> str: return f"""Context( accountant = {indent(repr(}, d_in = {self.d_in}, d_mids = {self.d_mids})""" # TODO: Add "d_out" when filters are implemented.
[docs] @staticmethod def compositor( data: Any, privacy_unit: tuple[Metric, float], privacy_loss: tuple[Measure, Any], split_evenly_over: Optional[int] = None, split_by_weights: Optional[list[float]] = None, domain: Optional[Domain] = None, margins: Optional[dict[tuple[str, ...], Margin]] = None, ) -> "Context": """Constructs a new context containing a sequential compositor with the given weights. If the domain is not specified, it will be inferred from the data. This makes the assumption that the structure of the data is public information. ``split_evenly_over`` and ``split_by_weights`` are mutually exclusive. :param data: The data to be analyzed. :param privacy_unit: The privacy unit of the compositor. :param privacy_loss: The privacy loss of the compositor. :param split_evenly_over: The number of parts to evenly distribute the privacy loss. :param split_by_weights: A list of weights for each intermediate privacy loss. :param domain: The domain of the data. :param margins: A dictionary where the keys are grouping columns and values describe known properties of the respective margins.""" if domain is None: domain = domain_of(data, infer=True) if margins: for by, margin in margins.items(): domain = with_margin(domain, by=list(by), **asdict(margin)) accountant, d_mids = _sequential_composition_by_weights( domain, privacy_unit, privacy_loss, split_evenly_over, split_by_weights ) try: queryable = accountant(data) except TypeError as e: inferred_domain = domain_of(data, infer=True) if vector_domain(domain) == inferred_domain: # With Python 3.11, add_note is available, but pytest.raises doesn't see notes. e.args = (e.args[0] + '; To fix, wrap domain kwarg with dp.vector_domain()',) raise e return Context( accountant=accountant, queryable=queryable, d_in=privacy_unit[1], d_mids=d_mids, )
def __call__(self, query: Measurement): """Executes the given query on the context.""" answer = self.queryable(query) if self.d_mids is not None: self.d_mids.pop(0) return answer
[docs] def query(self, **kwargs) -> Union["Query", LazyFrameQuery]: """Starts a new Query to be executed in this context. If the context has been constructed with a sequence of privacy losses, the next loss will be used. Otherwise, the loss will be computed from the kwargs. :param kwargs: The privacy loss to use for the query. Passed directly into :py:func:`loss_of`. """ d_query = None if self.d_mids is not None: if kwargs: raise ValueError(f"Expected no privacy arguments but got {kwargs}") if not self.d_mids: raise ValueError("Privacy allowance has been exhausted") d_query = self.d_mids[0] elif kwargs: # pragma: no cover # TODO: Is there a way to reach this? The usual ways of constructing a Context will populate d_mids. # TODO: Update the docstring if we do remove this. measure, d_query = loss_of(**kwargs) # type: ignore[assignment] if measure != self.output_measure: # type: ignore[attr-defined] raise ValueError( f"Expected output measure {self.output_measure} but got {measure}" # type: ignore[attr-defined] ) chain = self.space_override or query = Query( chain=chain,, d_in=self.d_in, d_out=d_query, context=self, ) # return a LazyFrameQuery when dealing with Polars data, to better mimic the Polars API if chain[0].type == "LazyFrameDomain": from import _lazyframe_from_domain # creates an empty lazyframe to hold the query plan lf_plan = _lazyframe_from_domain( return LazyFrameQuery(lf_plan, query) return query
Chain = Union[tuple[Domain, Metric], Transformation, Measurement, "PartialChain"]
[docs] class Query(object): """A helper API to build a measurement.""" _chain: Chain """The current chain of transformations and measurements.""" _output_measure: Measure """The output measure of the query.""" _context: Optional["Context"] """The context that the query is part of. ``query.release()`` submits ``_chain`` to ``_context``.""" _wrap_release: Optional[Callable[[Any], Any]] """For internal use. A function that wraps the release of the query. Used to wrap the response of compositor/odometer queries in another ``Analysis``.""" def __init__( self, chain: Chain, output_measure: Measure = None, # type: ignore[assignment] d_in: Optional[float] = None, d_out: Optional[float] = None, context: "Context" = None, # type: ignore[assignment] _wrap_release=None, ) -> None: """Initializes the query with the given chain and output measure. It is more convenient to use the ``context.query()`` constructor than this one. However, this can be used stand-alone to help build a transformation/measurement that is not part of a context. :param chain: an initial metric space (tuple of domain and metric) or transformation :param output_measure: how privacy will be measured on the output of the query :param d_in: an upper bound on the distance between adjacent datasets :param d_out: an upper bound on the overall privacy loss :param context: if specified, then when the query is released, the chain will be submitted to this context :param _wrap_release: for internal use only """ self._chain = chain self._output_measure = output_measure self._d_in = d_in self._d_out = d_out self._context = context self._wrap_release = _wrap_release def __repr__(self) -> str: return f"""Query( chain = {indent(repr(self._chain))}, output_measure = {self._output_measure}, d_in = {self._d_in}, d_out = {self._d_out}, context = {indent(repr(self._context))})""" def __getattr__(self, name: str) -> Callable[..., "Query"]: """Creates a new query by applying a transformation or measurement to the current chain.""" if name not in constructors: raise AttributeError(f"Unrecognized constructor: '{name}'") def make(*args, **kwargs) -> "Query": """Wraps the ``make_{name}`` constructor to allow one optional parameter and chains it to the current query. This function will be called when the user calls ``query.{name}(...)``. """ constructor, is_partial = constructors[name] # determine how many parameters are missing param_diff = len(args) for param in signature(constructor).parameters.values(): if in kwargs: continue if param.default is not param.empty: break param_diff -= 1 if param_diff == -1 and not isinstance(self._chain, PartialChain): constructor = PartialChain.wrap(constructor) elif param_diff < 0: raise ValueError(f"{name} is missing {-param_diff} parameter(s).") elif param_diff > 0: raise ValueError(f"{name} has {param_diff} parameter(s) too many.") new_chain = constructor(*args, **kwargs) if is_partial or not isinstance(self._chain, tuple): new_chain = self._chain >> new_chain return self.new_with(chain=new_chain) return make
[docs] def new_with(self, *, chain: Chain, wrap_release=None) -> "Query": """Convenience constructor that creates a new query with a different chain.""" return Query( chain=chain, output_measure=self._output_measure, d_in=self._d_in, d_out=self._d_out, context=self._context, # type: ignore[arg-type] _wrap_release=wrap_release or self._wrap_release, )
def __dir__(self): """Returns the list of available constructors. Used by Python's error suggestion mechanism. Without this, none of the transformations or measument methods are listed. """ return super().__dir__() + list(constructors.keys()) # type: ignore[operator]
[docs] def resolve(self, allow_transformations: bool = False): """Resolve the query into a measurement. :param allow_transformations: If true, allow the response to be a transformation instead of a measurement. """ # resolve a partial chain into a measurement, by fixing the input and output distances if isinstance(self._chain, PartialChain): assert self._d_in is not None assert self._d_out is not None chain = self._chain.fix(self._d_in, self._d_out, self._output_measure) else: chain = self._chain if not allow_transformations and isinstance(chain, Transformation): raise ValueError("Query is not yet a measurement") return _cast_measure(chain, self._output_measure, self._d_out)
[docs] def release(self) -> Any: """Release the query. The query must be part of a context.""" # TODO: consider adding an optional `data` parameter for when _context is None answer = self._context(self.resolve()) # type: ignore[misc] if self._wrap_release: answer = self._wrap_release(answer) return answer
[docs] def param(self): """Returns the discovered parameter, if there is one.""" return getattr(self.resolve(), "param", None)
[docs] def compositor( self, split_evenly_over: Optional[int] = None, split_by_weights: Optional[list[float]] = None, d_out: Optional[float] = None, output_measure: Optional[Measure] = None, ) -> "Query": """Constructs a new context containing a sequential compositor with the given weights. ``split_evenly_over`` and ``split_by_weights`` are mutually exclusive. :param split_evenly_over: The number of parts to evenly distribute the privacy loss :param split_by_weights: A list of weights for each intermediate privacy loss """ if d_out is not None and self._d_out is not None: raise ValueError("`d_out` has already been specified in query") if d_out is None and self._d_out is None: raise ValueError("`d_out` has not yet been specified in the query") d_out = d_out or self._d_out if output_measure is not None: d_out = _translate_measure_distance( d_out, self._output_measure, output_measure ) def compositor(chain: Union[tuple[Domain, Metric], Transformation], d_in): if isinstance(chain, tuple): input_domain, input_metric = chain elif isinstance(chain, Transformation): input_domain, input_metric = chain.output_domain, chain.output_metric d_in = privacy_unit = input_metric, d_in assert d_out is not None privacy_loss = output_measure or self._output_measure, d_out accountant, d_mids = _sequential_composition_by_weights( input_domain, privacy_unit, privacy_loss, split_evenly_over, split_by_weights, ) if isinstance(chain, Transformation): accountant = chain >> accountant def wrap_release(queryable): return Context( accountant=accountant, queryable=queryable, d_in=d_in, d_mids=d_mids, space_override=(input_domain, input_metric) ) return self.new_with(chain=accountant, wrap_release=wrap_release) return self._compose_context(compositor)
def _compose_context(self, compositor): """Helper function for composition in a context.""" if isinstance(self._chain, PartialChain): # TODO: Can we exercise this? return PartialChain(lambda x: compositor(self._chain(x), self._d_in)) # pragma: no cover else: return compositor(self._chain, self._d_in)
[docs] class PartialChain(object): """A partial chain is a transformation or measurement that is missing one numeric parameter. The parameter can be solved for by calling the fix method, which returns the closest transformation or measurement that satisfies the given stability or privacy constraint. """ partial: Callable[[float], Union[Transformation, Measurement]] """The partial transformation or measurement.""" def __init__(self, f, *args, **kwargs): self.partial = partial(f, *args, **kwargs) def __call__(self, v): """Returns the transformation or measurement with the given parameter.""" # TODO: Can we exercise this? return self.partial(v) # pragma: no cover
[docs] def fix(self, d_in: float, d_out: float, output_measure: Optional[Measure] = None, T=None): """Returns the closest transformation or measurement that satisfies the given stability or privacy constraint. The discovered parameter is assigned to the param attribute of the returned transformation or measurement. """ param = binary_search( lambda x: _cast_measure(self.partial(x), output_measure, d_out).check( d_in, d_out ), T=T, ) chain = self.partial(param) chain.param = param return chain
def __rshift__(self, other: Union[Transformation, Measurement]): # partials may be chained with other transformations or measurements to form a new partial # TODO: Can we exercise this? if isinstance(other, (Transformation, Measurement)): # pragma: no cover return PartialChain(lambda x: self.partial(x) >> other) raise ValueError("At most one parameter may be missing at a time")
[docs] @classmethod def wrap(cls, f): """Wraps a constructor for a transformation or measurement to return a partial chain instead.""" def inner(*args, **kwargs): return cls(f, *args, **kwargs) return inner
def _sequential_composition_by_weights( domain: Domain, privacy_unit: tuple[Metric, float], privacy_loss: tuple[Measure, float], split_evenly_over: Optional[int] = None, split_by_weights: Optional[list[float]] = None, ) -> tuple[Measurement, list[Any]]: """Constructs a sequential composition measurement where the ``d_mids`` are proportional to the weights. ``split_evenly_over`` and ``split_by_weights`` are mutually exclusive. :param domain: the domain of the data :param privacy_unit: a tuple of the input metric and the data distance (``d_in``) :param privacy_loss: a tuple of the output measure and the privacy parameter (``d_out``) :param split_evenly_over: The number of parts to evenly distribute the privacy loss :param split_by_weights: A list of weights for each intermediate privacy loss """ input_metric, d_in = privacy_unit output_measure, d_out = privacy_loss if split_evenly_over is not None and split_by_weights is not None: raise ValueError( "Cannot specify both `split_evenly_over` and `split_by_weights`" ) if split_evenly_over is not None: weights = [d_out] * split_evenly_over elif split_by_weights is not None: weights = [d_out * w for w in split_by_weights] else: raise ValueError( "Must specify either `split_evenly_over` or `split_by_weights`" ) def mul(dist, scale: float): if isinstance(dist, tuple): return dist[0] * scale, dist[1] * scale else: return dist * scale def scale_weights(scale: float, weights): return [mul(w, scale) for w in weights] def scale_sc(scale: float): return make_sequential_composition( input_domain=domain, input_metric=input_metric, output_measure=output_measure, d_in=d_in, d_mids=scale_weights(scale, weights), ) scale = binary_search_param(scale_sc, d_in=d_in, d_out=d_out, T=float) # return the accountant and d_mids return scale_sc(scale), scale_weights(scale, weights) def _cast_measure(chain, to_measure: Optional[Measure] = None, d_to=None): """Casts the output measure of a given ``chain`` to ``to_measure``. If provided, ``d_to`` is the privacy loss wrt the new measure. """ if to_measure is None or chain.output_measure == to_measure: return chain from_to = chain.output_measure.type.origin, to_measure.type.origin if from_to == ("MaxDivergence", "FixedSmoothedMaxDivergence"): return make_pureDP_to_fixed_approxDP(chain) if from_to == ("MaxDivergence", "ZeroConcentratedDivergence"): return make_pureDP_to_zCDP(chain) if from_to == ( "ZeroConcentratedDivergence", "FixedSmoothedMaxDivergence", ): return make_fix_delta(make_zCDP_to_approxDP(chain), d_to[1]) raise ValueError(f"Unable to cast measure from {from_to[0]} to {from_to[1]}") def _translate_measure_distance(d_from, from_measure: Measure, to_measure: Measure): """Translate a privacy loss ``d_from`` from ``from_measure`` to ``to_measure``. >>> _translate_measure_distance(1, dp.max_divergence(dp.f64), dp.max_divergence(dp.f64)) 1 >>> _translate_measure_distance(1, dp.max_divergence(dp.f64), dp.fixed_smoothed_max_divergence(dp.f64)) (1, 0.0) >>> _translate_measure_distance((1.5, 5e-07), dp.fixed_smoothed_max_divergence(dp.f64), dp.zero_concentrated_divergence(dp.f64)) 0.0489... >>> _translate_measure_distance(0.05, dp.zero_concentrated_divergence(dp.f64), dp.max_divergence(dp.f64)) 0.316... """ if from_measure == to_measure: return d_from from_to = from_measure.type.origin, to_measure.type.origin T = to_measure.type.args[0] constant = 1.0 # the choice of constant doesn't matter if from_to == ("MaxDivergence", "FixedSmoothedMaxDivergence"): return (d_from, 0.0) if from_to == ("ZeroConcentratedDivergence", "MaxDivergence"): space = atom_domain(T=T), absolute_distance(T=T) scale = binary_search_param( lambda eps: make_pureDP_to_zCDP(make_laplace(*space, eps)), d_in=constant, d_out=d_from, T=float, ) return make_laplace(*space, scale).map(constant) if from_to == ( "FixedSmoothedMaxDivergence", "ZeroConcentratedDivergence", ): def caster(measurement): return make_fix_delta(make_zCDP_to_approxDP(measurement), delta=d_from[1]) space = atom_domain(T=int), absolute_distance(T=T) scale = binary_search_param( lambda scale: caster(make_gaussian(*space, scale)), d_in=constant, d_out=d_from, T=float, ) return make_gaussian(*space, scale).map(constant) raise ValueError(f"Unable to translate distance from {from_to[0]} to {from_to[1]}")