# Path: diffprivlib/tools/utils.py
# def nanvar(array, epsilon=1.0, bounds=None, axis=None, dtype=None, keepdims=False, accountant=None, **unused_args):
#     r"""
#     Compute the differentially private variance along the specified axis, ignoring NaNs.
# 
#     Returns the variance of the array elements, a measure of the spread of a distribution, with differential privacy.
#     The variance is computer for the flattened array by default, otherwise over the specified axis.  Noise is added
#     using :class:`.LaplaceBoundedDomain` to satisfy differential privacy, where sensitivity is calculated using
#     `bounds`.  Users are advised to consult the documentation of :obj:`numpy.var` for further details, as the behaviour
#     of `var` closely follows its Numpy variant.
# 
#     For all-NaN slices, NaN is returned and a `RuntimeWarning` is raised.
# 
#     Parameters
#     ----------
#     array : array_like
#         Array containing numbers whose variance is desired.  If `array` is not an array, a conversion is attempted.
# 
#     epsilon : float, default: 1.0
#         Privacy parameter :math:`\epsilon`.
# 
#     bounds : tuple, optional
#         Bounds of the values of the array, of the form (min, max).
# 
#     axis : int or tuple of ints, optional
#         Axis or axes along which the variance is computed.  The default is to compute the variance of the flattened
#         array.
# 
#         If this is a tuple of ints, a variance is performed over multiple axes, instead of a single axis or all the axes
#         as before.
# 
#     dtype : data-type, optional
#         Type to use in computing the variance.  For arrays of integer type the default is `float32`; for arrays of float
#         types it is the same as the array type.
# 
#     keepdims : bool, default: False
#         If this is set to True, the axes which are reduced are left in the result as dimensions with size one.  With
#         this option, the result will broadcast correctly against the input array.
# 
#     accountant : BudgetAccountant, optional
#         Accountant to keep track of privacy budget.
# 
#     Returns
#     -------
#     variance : ndarray, see dtype parameter above
#         If ``out=None``, returns a new array containing the variance; otherwise, a reference to the output array is
#         returned.
# 
#     See Also
#     --------
#     std , mean, var
# 
#     """
#     warn_unused_args(unused_args)
# 
#     return _var(array, epsilon=epsilon, bounds=bounds, axis=axis, dtype=dtype, keepdims=keepdims, accountant=accountant,
#                 nan=True)
# 
# Path: diffprivlib/utils.py
# class PrivacyLeakWarning(RuntimeWarning):
#     """Custom warning to capture privacy leaks resulting from incorrect parameter setting.
# 
#     For example, this warning may occur when the user:
# 
#         - fails to specify the bounds or range of data to a model where required (e.g., `bounds=None` to
#           :class:`.GaussianNB`).
#         - inputs data to a model that falls outside the bounds or range originally specified.
# 
#     """
# 
# class BudgetError(ValueError):
#     """Custom exception to capture the privacy budget being exceeded, typically controlled by a
#     :class:`.BudgetAccountant`.
# 
#     For example, this exception may be raised when the user:
# 
#         - Attempts to execute a query which would exceed the privacy budget of the accountant.
#         - Attempts to change the slack of the accountant in such a way that the existing budget spends would exceed the
#           accountant's budget.
# 
#     """


from unittest import TestCase
from diffprivlib.tools.utils import nanvar
from diffprivlib.utils import PrivacyLeakWarning, BudgetError
        from diffprivlib.accountant import BudgetAccountant
import numpy as np

    def test_missing_bounds(self):
        a = np.array([1, 2, 3])
        with self.assertWarns(PrivacyLeakWarning):
            res = nanvar(a, 1, None)
        self.assertIsNotNone(res)

    def test_large_epsilon(self):
        a = np.random.random(1000)
        res = float(np.var(a))
        res_dp = nanvar(a, epsilon=5, bounds=(0, 1))

        self.assertAlmostEqual(res, res_dp, delta=0.01)

    def test_large_epsilon_axis(self):
        a = np.random.random((1000, 5))
        res = np.var(a, axis=0)
        res_dp = nanvar(a, epsilon=15, bounds=(0, 1), axis=0)

        for i in range(res.shape[0]):
            self.assertAlmostEqual(res[i], res_dp[i], delta=0.01)

    def test_array_like(self):
        self.assertIsNotNone(nanvar([1, 2, 3], bounds=(1, 3)))
        self.assertIsNotNone(nanvar((1, 2, 3), bounds=(1, 3)))

    def test_clipped_output(self):
        a = np.random.random((10,))

        for i in range(100):

# Path: diffprivlib/tools/utils.py
# def nanvar(array, epsilon=1.0, bounds=None, axis=None, dtype=None, keepdims=False, accountant=None, **unused_args):
#     r"""
#     Compute the differentially private variance along the specified axis, ignoring NaNs.
# 
#     Returns the variance of the array elements, a measure of the spread of a distribution, with differential privacy.
#     The variance is computer for the flattened array by default, otherwise over the specified axis.  Noise is added
#     using :class:`.LaplaceBoundedDomain` to satisfy differential privacy, where sensitivity is calculated using
#     `bounds`.  Users are advised to consult the documentation of :obj:`numpy.var` for further details, as the behaviour
#     of `var` closely follows its Numpy variant.
# 
#     For all-NaN slices, NaN is returned and a `RuntimeWarning` is raised.
# 
#     Parameters
#     ----------
#     array : array_like
#         Array containing numbers whose variance is desired.  If `array` is not an array, a conversion is attempted.
# 
#     epsilon : float, default: 1.0
#         Privacy parameter :math:`\epsilon`.
# 
#     bounds : tuple, optional
#         Bounds of the values of the array, of the form (min, max).
# 
#     axis : int or tuple of ints, optional
#         Axis or axes along which the variance is computed.  The default is to compute the variance of the flattened
#         array.
# 
#         If this is a tuple of ints, a variance is performed over multiple axes, instead of a single axis or all the axes
#         as before.
# 
#     dtype : data-type, optional
#         Type to use in computing the variance.  For arrays of integer type the default is `float32`; for arrays of float
#         types it is the same as the array type.
# 
#     keepdims : bool, default: False
#         If this is set to True, the axes which are reduced are left in the result as dimensions with size one.  With
#         this option, the result will broadcast correctly against the input array.
# 
#     accountant : BudgetAccountant, optional
#         Accountant to keep track of privacy budget.
# 
#     Returns
#     -------
#     variance : ndarray, see dtype parameter above
#         If ``out=None``, returns a new array containing the variance; otherwise, a reference to the output array is
#         returned.
# 
#     See Also
#     --------
#     std , mean, var
# 
#     """
#     warn_unused_args(unused_args)
# 
#     return _var(array, epsilon=epsilon, bounds=bounds, axis=axis, dtype=dtype, keepdims=keepdims, accountant=accountant,
#                 nan=True)
# 
# Path: diffprivlib/utils.py
# class PrivacyLeakWarning(RuntimeWarning):
#     """Custom warning to capture privacy leaks resulting from incorrect parameter setting.
# 
#     For example, this warning may occur when the user:
# 
#         - fails to specify the bounds or range of data to a model where required (e.g., `bounds=None` to
#           :class:`.GaussianNB`).
#         - inputs data to a model that falls outside the bounds or range originally specified.
# 
#     """
# 
# class BudgetError(ValueError):
#     """Custom exception to capture the privacy budget being exceeded, typically controlled by a
#     :class:`.BudgetAccountant`.
# 
#     For example, this exception may be raised when the user:
# 
#         - Attempts to execute a query which would exceed the privacy budget of the accountant.
#         - Attempts to change the slack of the accountant in such a way that the existing budget spends would exceed the
#           accountant's budget.
# 
#     """


# Path: tests/tools/test_nanvar.py
from unittest import TestCase
from diffprivlib.tools.utils import nanvar
from diffprivlib.utils import PrivacyLeakWarning, BudgetError
        from diffprivlib.accountant import BudgetAccountant
import numpy as np


    def test_missing_bounds(self):
        a = np.array([1, 2, 3])
        with self.assertWarns(PrivacyLeakWarning):
            res = nanvar(a, 1, None)
        self.assertIsNotNone(res)

    def test_large_epsilon(self):
        a = np.random.random(1000)
        res = float(np.var(a))
        res_dp = nanvar(a, epsilon=5, bounds=(0, 1))

        self.assertAlmostEqual(res, res_dp, delta=0.01)

    def test_large_epsilon_axis(self):
        a = np.random.random((1000, 5))
        res = np.var(a, axis=0)
        res_dp = nanvar(a, epsilon=15, bounds=(0, 1), axis=0)

        for i in range(res.shape[0]):
            self.assertAlmostEqual(res[i], res_dp[i], delta=0.01)

    def test_array_like(self):
        self.assertIsNotNone(nanvar([1, 2, 3], bounds=(1, 3)))
        self.assertIsNotNone(nanvar((1, 2, 3), bounds=(1, 3)))

    def test_clipped_output(self):
        a = np.random.random((10,))

        for i in range(100):

            self.assertTrue(0 <= nanvar(a, epsilon=1e-5, bounds=(0, 1)) <= 1)