8000 Merge pull request #24680 from mdhaber/gh21595 · numpy/numpy@224b28f · GitHub
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Merge pull request #24680 from mdhaber/gh21595
ENH: add parameter `strict` to `assert_allclose`
2 parents 95d35dc + 9dc5865 commit 224b28f

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‎doc/release/upcoming_changes/24680.new_feature.rst

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``strict`` option for `testing.assert_allclose`
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-----------------------------------------------
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The ``strict`` option is now available for `testing.assert_allclose`.
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Setting ``strict=True`` will disable the broadcasting behaviour for scalars
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and ensure that input arrays have the same data type.

‎numpy/testing/_private/utils.py

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@@ -1436,7 +1436,7 @@ def _assert_valid_refcount(op):
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def assert_allclose(actual, desired, rtol=1e-7, atol=0, equal_nan=True,
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err_msg='', verbose=True):
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err_msg='', verbose=True, *, strict=False):
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"""
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Raises an AssertionError if two objects are not equal up to desired
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tolerance.
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The error message to be printed in case of failure.
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verbose : bool, optional
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If True, the conflicting values are appended to the error message.
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strict : bool, optional
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If True, raise an ``AssertionError`` when either the shape or the data
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type of the arguments does not match. The special handling of scalars
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mentioned in the Notes section is disabled.
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.. versionadded:: 2.0.0
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Raises
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------
@@ -1484,13 +1490,47 @@ def assert_allclose(actual, desired, rtol=1e-7, atol=0, equal_nan=True,
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When one of `actual` and `desired` is a scalar and the other is
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array_like, the function checks that each element of the array_like
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object is equal to the scalar.
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This behaviour can be disabled with the `strict` parameter.
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Examples
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--------
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>>> x = [1e-5, 1e-3, 1e-1]
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>>> y = np.arccos(np.cos(x))
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>>> np.testing.assert_allclose(x, y, rtol=1e-5, atol=0)
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As mentioned in the Notes section, `assert_allclose` has special
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handling for scalars. Here, the test checks that the value of `numpy.sin`
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is nearly zero at integer multiples of π.
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>>> x = np.arange(3) * np.pi
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>>> np.testing.assert_allclose(np.sin(x), 0, atol=1e-15)
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Use `strict` to raise an ``AssertionError`` when comparing an array
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with one or more dimensions against a scalar.
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>>> np.testing.assert_allclose(np.sin(x), 0, atol=1e-15, strict=True)
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Traceback (most recent call last):
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...
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AssertionError:
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Not equal to tolerance rtol=1e-07, atol=1e-15
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<BLANKLINE>
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(shapes (3,), () mismatch)
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x: array([ 0.000000e+00, 1.224647e-16, -2.449294e-16])
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y: array(0)
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The `strict` parameter also ensures that the array data types match:
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>>> y = np.zeros(3, dtype=np.float32)
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>>> np.testing.assert_allclose(np.sin(x), y, atol=1e-15, strict=True)
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Traceback (most recent call last):
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...
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AssertionError:
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Not equal to tolerance rtol=1e-07, atol=1e-15
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<BLANKLINE>
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(dtypes float64, float32 mismatch)
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x: array([ 0.000000e+00, 1.224647e-16, -2.449294e-16])
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y: array([0., 0., 0.], dtype=float32)
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"""
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__tracebackhide__ = True # Hide traceback for py.test
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import numpy as np
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actual, desired = np.asanyarray(actual), np.asanyarray(desired)
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header = f'Not equal to tolerance rtol={rtol:g}, atol={atol:g}'
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assert_array_compare(compare, actual, desired, err_msg=str(err_msg),
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verbose=verbose, header=header, equal_nan=equal_nan)
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verbose=verbose, header=header, equal_nan=equal_nan,
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strict=strict)
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def assert_array_almost_equal_nulp(x, y, nulp=1):

‎numpy/testing/_private/utils.pyi

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@@ -312,6 +312,8 @@ def assert_allclose(
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equal_nan: bool = ...,
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err_msg: str = ...,
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verbose: bool = ...,
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*,
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strict: bool = ...
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) -> None: ...
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@overload
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def assert_allclose(
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equal_nan: bool = ...,
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err_msg: str = ...,
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verbose: bool = ...,
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*,
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strict: bool = ...
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) -> None: ...
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def assert_array_almost_equal_nulp(

‎numpy/testing/tests/test_utils.py

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msgs = str(exc_info.value).split('\n')
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assert_equal(msgs[4], 'Max absolute difference: 4')
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def test_strict(self):
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"""Test the behavior of the `strict` option."""
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x = np.ones(3)
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y = np.ones(())
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assert_allclose(x, y)
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with pytest.raises(AssertionError):
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assert_allclose(x, y, strict=True)
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assert_allclose(x, x)
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with pytest.raises(AssertionError):
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assert_allclose(x, x.astype(np.float32), strict=True)
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class TestArrayAlmostEqualNulp:
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