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# Copyright (C) IBM Corporation 2020
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"""
Validation functions for the differential privacy library
"""
from numbers import Real, Integral
import numpy as np
from diffprivlib.utils import warn_unused_args
[docs]
def check_epsilon_delta(epsilon, delta, allow_zero=False):
"""Checks that epsilon and delta are valid values for differential privacy. Throws an error if checks fail,
otherwise returns nothing.
As well as the requirements of epsilon and delta separately, both cannot be simultaneously zero, unless
``allow_zero`` is set to ``True``.
Parameters
----------
epsilon : float
Epsilon parameter for differential privacy. Must be non-negative.
delta : float
Delta parameter for differential privacy. Must be on the unit interval, [0, 1].
allow_zero : bool, default: False
Allow epsilon and delta both be zero.
"""
if not isinstance(epsilon, Real) or not isinstance(delta, Real):
raise TypeError("Epsilon and delta must be numeric")
if epsilon < 0:
raise ValueError("Epsilon must be non-negative")
if not 0 <= delta <= 1:
raise ValueError("Delta must be in [0, 1]")
if not allow_zero and epsilon + delta == 0:
raise ValueError("Epsilon and Delta cannot both be zero")
[docs]
def check_bounds(bounds, shape=0, min_separation=0.0, dtype=float):
"""Input validation for the ``bounds`` parameter.
Checks that ``bounds`` is composed of a list of tuples of the form (lower, upper), where lower <= upper and both
are numeric. Also checks that ``bounds`` contains the appropriate number of dimensions, and that there is a
``min_separation`` between the bounds.
Parameters
----------
bounds : tuple
Tuple of bounds of the form (min, max). `min` and `max` can either be scalars or 1-dimensional arrays.
shape : int, default: 0
Number of dimensions to be expected in ``bounds``.
min_separation : float, default: 0.0
The minimum separation between `lower` and `upper` of each dimension. This separation is enforced if not
already satisfied.
dtype : data-type, default: float
Data type of the returned bounds.
Returns
-------
bounds : tuple
"""
if not isinstance(bounds, tuple):
raise TypeError(f"Bounds must be specified as a tuple of (min, max), got {type(bounds)}.")
if not isinstance(shape, Integral):
raise TypeError(f"shape parameter must be integer-valued, got {type(shape)}.")
lower, upper = bounds
if np.asarray(lower).size == 1 or np.asarray(upper).size == 1:
lower = np.ravel(lower).astype(dtype)
upper = np.ravel(upper).astype(dtype)
else:
lower = np.asarray(lower, dtype=dtype)
upper = np.asarray(upper, dtype=dtype)
if lower.shape != upper.shape:
raise ValueError("lower and upper bounds must be the same shape array")
if lower.ndim > 1:
raise ValueError("lower and upper bounds must be scalar or a 1-dimensional array")
if lower.size not in (1, shape):
raise ValueError(f"lower and upper bounds must have {shape or 1} element(s), got {lower.size}.")
n_bounds = lower.shape[0]
for i in range(n_bounds):
_lower = lower[i]
_upper = upper[i]
if not isinstance(_lower, Real) or not isinstance(_upper, Real):
raise TypeError(f"Each bound must be numeric, got {_lower} ({type(_lower)}) and {_upper} ({type(_upper)}).")
if _lower > _upper:
raise ValueError(f"For each bound, lower bound must be smaller than upper bound, got {lower}, {upper})")
if _upper - _lower < min_separation:
mid = (_upper + _lower) / 2
lower[i] = mid - min_separation / 2
upper[i] = mid + min_separation / 2
if shape == 0:
return lower.item(), upper.item()
if n_bounds == 1:
lower = np.ones(shape, dtype=dtype) * lower.item()
upper = np.ones(shape, dtype=dtype) * upper.item()
return lower, upper
[docs]
def clip_to_norm(array, clip):
"""Clips the examples of a 2-dimensional array to a given maximum norm.
Parameters
----------
array : np.ndarray
Array to be clipped. After clipping, all examples have a 2-norm of at most `clip`.
clip : float
Norm at which to clip each example
Returns
-------
array : np.ndarray
The clipped array.
"""
if not isinstance(array, np.ndarray):
raise TypeError(f"Input array must be a numpy array, got {type(array)}.")
if array.ndim != 2:
raise ValueError(f"input array must be 2-dimensional, got {array.ndim} dimensions.")
if not isinstance(clip, Real):
raise TypeError(f"Clip value must be numeric, got {type(clip)}.")
if clip <= 0:
raise ValueError(f"Clip value must be strictly positive, got {clip}.")
norms = np.linalg.norm(array, axis=1) / clip
norms[norms < 1] = 1
return array / norms[:, np.newaxis]
[docs]
def clip_to_bounds(array, bounds):
"""Clips the examples of a 2-dimensional array to given bounds.
Parameters
----------
array : np.ndarray
Array to be clipped. After clipping, all examples have a 2-norm of at most `clip`.
bounds : tuple
Tuple of bounds of the form (min, max) which the array is to be clipped to. `min` and `max` must be scalar,
unless array is 2-dimensional.
Returns
-------
array : np.ndarray
The clipped array.
"""
if not isinstance(array, np.ndarray):
raise TypeError(f"Input array must be a numpy array, got {type(array)}.")
lower, upper = check_bounds(bounds, np.size(bounds[0]), min_separation=0)
clipped_array = array.copy()
if np.allclose(lower, np.min(lower)) and np.allclose(upper, np.max(upper)):
clipped_array = np.clip(clipped_array, np.min(lower), np.max(upper))
else:
if array.ndim != 2:
raise ValueError(f"For non-scalar bounds, input array must be 2-dimensional. Got {array.ndim} dimensions.")
for feature in range(array.shape[1]):
clipped_array[:, feature] = np.clip(array[:, feature], lower[feature], upper[feature])
return clipped_array
class DiffprivlibMixin: # pylint: disable=too-few-public-methods
"""Mixin for Diffprivlib models."""
_check_bounds = staticmethod(check_bounds)
_clip_to_norm = staticmethod(clip_to_norm)
_clip_to_bounds = staticmethod(clip_to_bounds)
_warn_unused_args = staticmethod(warn_unused_args)
# todo: remove when scikit-learn v1.2 is a min requirement
def _validate_params(self):
pass
@staticmethod
def _copy_parameter_constraints(cls, *args):
"""Copies the parameter constraints for `*args` from `cls`
"""
if not hasattr(cls, "_parameter_constraints"):
return {}
return {k: cls._parameter_constraints[k] for k in args if k in cls._parameter_constraints}