Function assert_allclose

sklearn/utils/_testing.py:174–244

dtype-aware variant of numpy.testing.assert_allclose This variant introspects the least precise floating point dtype in the input argument and automatically sets the relative tolerance parameter to 1e-4 float32 and use 1e-7 otherwise (typically float64 in scikit-learn). `atol`

(
    actual, desired, rtol=None, atol=0.0, equal_nan=True, err_msg="", verbose=True
)

def assert_allclose(
    actual, desired, rtol=None, atol=0.0, equal_nan=True, err_msg="", verbose=True
):
    """dtype-aware variant of numpy.testing.assert_allclose

    This variant introspects the least precise floating point dtype
    in the input argument and automatically sets the relative tolerance
    parameter to 1e-4 float32 and use 1e-7 otherwise (typically float64
    in scikit-learn).

    `atol` is always left to 0. by default. It should be adjusted manually
    to an assertion-specific value in case there are null values expected
    in `desired`.

    The aggregate tolerance is `atol + rtol * abs(desired)`.

    Parameters
    ----------
    actual : array_like
        Array obtained.
    desired : array_like
        Array desired.
    rtol : float, optional, default=None
        Relative tolerance.
        If None, it is set based on the provided arrays' dtypes.
    atol : float, optional, default=0.
        Absolute tolerance.
    equal_nan : bool, optional, default=True
        If True, NaNs will compare equal.
    err_msg : str, optional, default=''
        The error message to be printed in case of failure.
    verbose : bool, optional, default=True
        If True, the conflicting values are appended to the error message.

    Raises
    ------
    AssertionError
        If actual and desired are not equal up to specified precision.

    See Also
    --------
    numpy.testing.assert_allclose

    Examples
    --------
    >>> import numpy as np
    >>> from sklearn.utils._testing import assert_allclose
    >>> x = [1e-5, 1e-3, 1e-1]
    >>> y = np.arccos(np.cos(x))
    >>> assert_allclose(x, y, rtol=1e-5, atol=0)
    >>> a = np.full(shape=10, fill_value=1e-5, dtype=np.float32)
    >>> assert_allclose(a, 1e-5)
    """
    dtypes = []

    actual, desired = np.asanyarray(actual), np.asanyarray(desired)
    dtypes = [actual.dtype, desired.dtype]