move approx to own file
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Ronny Pfannschmidt
parent
467c526307
commit
36251e0db4
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@ -1265,256 +1265,6 @@ class RaisesContext(object):
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return suppress_exception
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# builtin pytest.approx helper
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class approx(object):
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"""
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Assert that two numbers (or two sets of numbers) are equal to each other
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within some tolerance.
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Due to the `intricacies of floating-point arithmetic`__, numbers that we
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would intuitively expect to be equal are not always so::
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>>> 0.1 + 0.2 == 0.3
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False
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__ https://docs.python.org/3/tutorial/floatingpoint.html
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This problem is commonly encountered when writing tests, e.g. when making
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sure that floating-point values are what you expect them to be. One way to
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deal with this problem is to assert that two floating-point numbers are
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equal to within some appropriate tolerance::
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>>> abs((0.1 + 0.2) - 0.3) < 1e-6
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True
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However, comparisons like this are tedious to write and difficult to
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understand. Furthermore, absolute comparisons like the one above are
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usually discouraged because there's no tolerance that works well for all
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situations. ``1e-6`` is good for numbers around ``1``, but too small for
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very big numbers and too big for very small ones. It's better to express
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the tolerance as a fraction of the expected value, but relative comparisons
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like that are even more difficult to write correctly and concisely.
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The ``approx`` class performs floating-point comparisons using a syntax
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that's as intuitive as possible::
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>>> from pytest import approx
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>>> 0.1 + 0.2 == approx(0.3)
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True
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The same syntax also works on sequences of numbers::
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>>> (0.1 + 0.2, 0.2 + 0.4) == approx((0.3, 0.6))
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True
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By default, ``approx`` considers numbers within a relative tolerance of
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``1e-6`` (i.e. one part in a million) of its expected value to be equal.
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This treatment would lead to surprising results if the expected value was
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``0.0``, because nothing but ``0.0`` itself is relatively close to ``0.0``.
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To handle this case less surprisingly, ``approx`` also considers numbers
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within an absolute tolerance of ``1e-12`` of its expected value to be
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equal. Infinite numbers are another special case. They are only
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considered equal to themselves, regardless of the relative tolerance. Both
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the relative and absolute tolerances can be changed by passing arguments to
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the ``approx`` constructor::
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>>> 1.0001 == approx(1)
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False
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>>> 1.0001 == approx(1, rel=1e-3)
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True
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>>> 1.0001 == approx(1, abs=1e-3)
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True
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If you specify ``abs`` but not ``rel``, the comparison will not consider
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the relative tolerance at all. In other words, two numbers that are within
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the default relative tolerance of ``1e-6`` will still be considered unequal
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if they exceed the specified absolute tolerance. If you specify both
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``abs`` and ``rel``, the numbers will be considered equal if either
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tolerance is met::
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>>> 1 + 1e-8 == approx(1)
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True
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>>> 1 + 1e-8 == approx(1, abs=1e-12)
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False
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>>> 1 + 1e-8 == approx(1, rel=1e-6, abs=1e-12)
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True
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If you're thinking about using ``approx``, then you might want to know how
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it compares to other good ways of comparing floating-point numbers. All of
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these algorithms are based on relative and absolute tolerances and should
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agree for the most part, but they do have meaningful differences:
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- ``math.isclose(a, b, rel_tol=1e-9, abs_tol=0.0)``: True if the relative
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tolerance is met w.r.t. either ``a`` or ``b`` or if the absolute
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tolerance is met. Because the relative tolerance is calculated w.r.t.
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both ``a`` and ``b``, this test is symmetric (i.e. neither ``a`` nor
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``b`` is a "reference value"). You have to specify an absolute tolerance
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if you want to compare to ``0.0`` because there is no tolerance by
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default. Only available in python>=3.5. `More information...`__
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__ https://docs.python.org/3/library/math.html#math.isclose
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- ``numpy.isclose(a, b, rtol=1e-5, atol=1e-8)``: True if the difference
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between ``a`` and ``b`` is less that the sum of the relative tolerance
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w.r.t. ``b`` and the absolute tolerance. Because the relative tolerance
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is only calculated w.r.t. ``b``, this test is asymmetric and you can
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think of ``b`` as the reference value. Support for comparing sequences
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is provided by ``numpy.allclose``. `More information...`__
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__ http://docs.scipy.org/doc/numpy-1.10.0/reference/generated/numpy.isclose.html
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- ``unittest.TestCase.assertAlmostEqual(a, b)``: True if ``a`` and ``b``
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are within an absolute tolerance of ``1e-7``. No relative tolerance is
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considered and the absolute tolerance cannot be changed, so this function
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is not appropriate for very large or very small numbers. Also, it's only
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available in subclasses of ``unittest.TestCase`` and it's ugly because it
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doesn't follow PEP8. `More information...`__
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__ https://docs.python.org/3/library/unittest.html#unittest.TestCase.assertAlmostEqual
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- ``a == pytest.approx(b, rel=1e-6, abs=1e-12)``: True if the relative
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tolerance is met w.r.t. ``b`` or if the absolute tolerance is met.
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Because the relative tolerance is only calculated w.r.t. ``b``, this test
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is asymmetric and you can think of ``b`` as the reference value. In the
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special case that you explicitly specify an absolute tolerance but not a
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relative tolerance, only the absolute tolerance is considered.
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"""
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def __init__(self, expected, rel=None, abs=None):
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self.expected = expected
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self.abs = abs
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self.rel = rel
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def __repr__(self):
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return ', '.join(repr(x) for x in self.expected)
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def __eq__(self, actual):
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from collections import Iterable
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if not isinstance(actual, Iterable):
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actual = [actual]
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if len(actual) != len(self.expected):
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return False
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return all(a == x for a, x in zip(actual, self.expected))
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__hash__ = None
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def __ne__(self, actual):
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return not (actual == self)
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@property
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def expected(self):
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# Regardless of whether the user-specified expected value is a number
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# or a sequence of numbers, return a list of ApproxNotIterable objects
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# that can be compared against.
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from collections import Iterable
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approx_non_iter = lambda x: ApproxNonIterable(x, self.rel, self.abs)
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if isinstance(self._expected, Iterable):
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return [approx_non_iter(x) for x in self._expected]
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else:
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return [approx_non_iter(self._expected)]
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@expected.setter
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def expected(self, expected):
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self._expected = expected
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class ApproxNonIterable(object):
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"""
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Perform approximate comparisons for single numbers only.
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In other words, the ``expected`` attribute for objects of this class must
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be some sort of number. This is in contrast to the ``approx`` class, where
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the ``expected`` attribute can either be a number of a sequence of numbers.
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This class is responsible for making comparisons, while ``approx`` is
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responsible for abstracting the difference between numbers and sequences of
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numbers. Although this class can stand on its own, it's only meant to be
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used within ``approx``.
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"""
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def __init__(self, expected, rel=None, abs=None):
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self.expected = expected
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self.abs = abs
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self.rel = rel
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def __repr__(self):
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if isinstance(self.expected, complex):
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return str(self.expected)
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# Infinities aren't compared using tolerances, so don't show a
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# tolerance.
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if math.isinf(self.expected):
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return str(self.expected)
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# If a sensible tolerance can't be calculated, self.tolerance will
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# raise a ValueError. In this case, display '???'.
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try:
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vetted_tolerance = '{:.1e}'.format(self.tolerance)
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except ValueError:
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vetted_tolerance = '???'
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if sys.version_info[0] == 2:
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return '{0} +- {1}'.format(self.expected, vetted_tolerance)
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else:
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return u'{0} \u00b1 {1}'.format(self.expected, vetted_tolerance)
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def __eq__(self, actual):
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# Short-circuit exact equality.
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if actual == self.expected:
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return True
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# Infinity shouldn't be approximately equal to anything but itself, but
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# if there's a relative tolerance, it will be infinite and infinity
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# will seem approximately equal to everything. The equal-to-itself
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# case would have been short circuited above, so here we can just
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# return false if the expected value is infinite. The abs() call is
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# for compatibility with complex numbers.
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if math.isinf(abs(self.expected)):
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return False
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# Return true if the two numbers are within the tolerance.
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return abs(self.expected - actual) <= self.tolerance
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__hash__ = None
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def __ne__(self, actual):
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return not (actual == self)
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@property
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def tolerance(self):
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set_default = lambda x, default: x if x is not None else default
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# Figure out what the absolute tolerance should be. ``self.abs`` is
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# either None or a value specified by the user.
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absolute_tolerance = set_default(self.abs, 1e-12)
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if absolute_tolerance < 0:
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raise ValueError("absolute tolerance can't be negative: {}".format(absolute_tolerance))
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if math.isnan(absolute_tolerance):
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raise ValueError("absolute tolerance can't be NaN.")
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# If the user specified an absolute tolerance but not a relative one,
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# just return the absolute tolerance.
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if self.rel is None:
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if self.abs is not None:
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return absolute_tolerance
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# Figure out what the relative tolerance should be. ``self.rel`` is
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# either None or a value specified by the user. This is done after
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# we've made sure the user didn't ask for an absolute tolerance only,
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# because we don't want to raise errors about the relative tolerance if
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# we aren't even going to use it.
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relative_tolerance = set_default(self.rel, 1e-6) * abs(self.expected)
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if relative_tolerance < 0:
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raise ValueError("relative tolerance can't be negative: {}".format(absolute_tolerance))
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if math.isnan(relative_tolerance):
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raise ValueError("relative tolerance can't be NaN.")
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# Return the larger of the relative and absolute tolerances.
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return max(relative_tolerance, absolute_tolerance)
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#
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# the basic pytest Function item
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#
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@ -22,10 +22,12 @@ from _pytest.skipping import xfail
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from _pytest.main import Item, Collector, File, Session
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from _pytest.fixtures import fillfixtures as _fillfuncargs
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from _pytest.python import (
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raises, approx,
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raises,
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Module, Class, Instance, Function, Generator,
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)
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from _pytest.python_api import approx
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set_trace = __pytestPDB.set_trace
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__all__ = [
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