Add support for numpy arrays (and dicts) to approx.
This fixes #1994. It turned out to require a lot of refactoring because subclassing numpy.ndarray was necessary to coerce python into calling the right `__eq__` operator.
This commit is contained in:
Kale Kundert
parent
467c526307
commit
9f3122fec6
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@ -1117,7 +1117,6 @@ def raises(expected_exception, *args, **kwargs):
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...
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Failed: Expecting ZeroDivisionError
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.. note::
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When using ``pytest.raises`` as a context manager, it's worthwhile to
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@ -1150,7 +1149,6 @@ def raises(expected_exception, *args, **kwargs):
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>>> with raises(ValueError, match=r'must be \d+$'):
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... raise ValueError("value must be 42")
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Or you can specify a callable by passing a to-be-called lambda::
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>>> raises(ZeroDivisionError, lambda: 1/0)
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@ -1230,10 +1228,8 @@ def raises(expected_exception, *args, **kwargs):
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return _pytest._code.ExceptionInfo()
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fail(message)
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raises.Exception = fail.Exception
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class RaisesContext(object):
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def __init__(self, expected_exception, message, match_expr):
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self.expected_exception = expected_exception
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@ -1265,9 +1261,271 @@ 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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class ApproxBase(object):
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"""
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Provide shared utilities for making approximate comparisons between numbers
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or sequences of numbers.
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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(
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repr(self._approx_scalar(x))
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for x in self._yield_expected())
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def __eq__(self, actual):
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return all(
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a == self._approx_scalar(x)
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for a, x in self._yield_comparisons(actual))
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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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def _approx_scalar(self, x):
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return ApproxScalar(x, rel=self.rel, abs=self.abs)
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def _yield_expected(self, actual):
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"""
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Yield all the expected values associated with this object. This is
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used to implement the `__repr__` method.
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"""
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raise NotImplementedError
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def _yield_comparisons(self, actual):
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"""
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Yield all the pairs of numbers to be compared. This is used to
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implement the `__eq__` method.
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"""
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raise NotImplementedError
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try:
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import numpy as np
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class ApproxNumpy(ApproxBase, np.ndarray):
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"""
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Perform approximate comparisons for numpy arrays.
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This class must inherit from numpy.ndarray in order to allow the approx
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to be on either side of the `==` operator. The reason for this has to
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do with how python decides whether to call `a.__eq__()` or `b.__eq__()`
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when it encounters `a == b`.
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If `a` and `b` are not related by inheritance, `a` gets priority. So
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as long as `a.__eq__` is defined, it will be called. Because most
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implementations of `a.__eq__` end up calling `b.__eq__`, this detail
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usually doesn't matter. However, `numpy.ndarray.__eq__` raises an
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error complaining that "the truth value of an array with more than
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one element is ambiguous. Use a.any() or a.all()" when compared with a
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custom class, so `b.__eq__` never gets called.
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The trick is that the priority rules change if `a` and `b` are related
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by inheritance. Specifically, `b.__eq__` gets priority if `b` is a
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subclass of `a`. So we can guarantee that `ApproxNumpy.__eq__` gets
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called by inheriting from `numpy.ndarray`.
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"""
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def __new__(cls, expected, rel=None, abs=None):
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"""
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Numpy uses __new__ (rather than __init__) to initialize objects.
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The `expected` argument must be a numpy array. This should be
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ensured by the approx() delegator function.
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"""
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assert isinstance(expected, np.ndarray)
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obj = super(ApproxNumpy, cls).__new__(cls, expected.shape)
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obj.__init__(expected, rel, abs)
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return obj
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def __repr__(self):
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# It might be nice to rewrite this function to account for the
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# shape of the array...
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return '[' + ApproxBase.__repr__(self) + ']'
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def __eq__(self, actual):
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try:
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actual = np.array(actual)
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except:
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raise ValueError("cannot cast '{0}' to numpy.ndarray".format(actual))
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if actual.shape != self.expected.shape:
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return False
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return ApproxBase.__eq__(self, actual)
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def _yield_expected(self):
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for x in self.expected:
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yield x
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def _yield_comparisons(self, actual):
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# We can be sure that `actual` is a numpy array, because it's
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# casted in `__eq__` before being passed to `ApproxBase.__eq__`,
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# which is the only method that calls this one.
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for i in np.ndindex(self.expected.shape):
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yield actual[i], self.expected[i]
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except ImportError:
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np = None
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class ApproxMapping(ApproxBase):
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"""
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Perform approximate comparisons for mappings where the values are numbers
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(the keys can be anything).
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"""
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def __repr__(self):
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item = lambda k, v: "'{0}': {1}".format(k, self._approx_scalar(v))
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return '{' + ', '.join(item(k,v) for k,v in self.expected.items()) + '}'
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def __eq__(self, actual):
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if actual.keys() != self.expected.keys():
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return False
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return ApproxBase.__eq__(self, actual)
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def _yield_comparisons(self, actual):
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for k in self.expected.keys():
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yield actual[k], self.expected[k]
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class ApproxSequence(ApproxBase):
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"""
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Perform approximate comparisons for sequences of numbers.
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"""
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def __repr__(self):
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open, close = '()' if isinstance(self.expected, tuple) else '[]'
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return open + ApproxBase.__repr__(self) + close
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def __eq__(self, actual):
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if len(actual) != len(self.expected):
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return False
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return ApproxBase.__eq__(self, actual)
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def _yield_expected(self):
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return iter(self.expected)
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def _yield_comparisons(self, actual):
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return zip(actual, self.expected)
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class ApproxScalar(ApproxBase):
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"""
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Perform approximate comparisons for single numbers only.
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"""
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def __repr__(self):
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"""
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Return a string communicating both the expected value and the tolerance
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for the comparison being made, e.g. '1.0 +- 1e-6'. Use the unicode
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plus/minus symbol if this is python3 (it's too hard to get right for
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python2).
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"""
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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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"""
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Return true if the given value is equal to the expected value within
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the pre-specified tolerance.
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"""
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from numbers import Number
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# Give a good error message we get values to compare that aren't
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# numbers, rather than choking on them later on.
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if not isinstance(actual, Number):
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raise ValueError("approx can only compare numbers, not '{0}'".format(actual))
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if not isinstance(self.expected, Number):
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raise ValueError("approx can only compare numbers, not '{0}'".format(self.expected))
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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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@property
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def tolerance(self):
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"""
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Return the tolerance for the comparison. This could be either an
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absolute tolerance or a relative tolerance, depending on what the user
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specified or which would be larger.
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"""
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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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def approx(expected, rel=None, abs=None):
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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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@ -1307,6 +1565,8 @@ class approx(object):
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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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>>> {'a': 0.1 + 0.2, 'b': 0.2 + 0.4} == approx({'a': 0.3, 'b': 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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@ -1381,138 +1641,36 @@ class approx(object):
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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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from collections import Mapping, Sequence
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try:
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String = basestring # python2
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except NameError:
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String = str, bytes # python3
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def __repr__(self):
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return ', '.join(repr(x) for x in self.expected)
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# Delegate the comparison to a class that knows how to deal with the type
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# of the expected value (e.g. int, float, list, dict, numpy.array, etc).
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#
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# This architecture is really driven by the need to support numpy arrays.
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# The only way to override `==` for arrays without requiring that approx be
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# the left operand is to inherit the approx object from `numpy.ndarray`.
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# But that can't be a general solution, because it requires (1) numpy to be
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# installed and (2) the expected value to be a numpy array. So the general
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# solution is to delegate each type of expected value to a different class.
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#
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# This has the advantage that it made it easy to support mapping types
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# (i.e. dict). The old code accepted mapping types, but would only compare
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# their keys, which is probably not what most people would expect.
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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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if np and isinstance(expected, np.ndarray):
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cls = ApproxNumpy
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elif isinstance(expected, Mapping):
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cls = ApproxMapping
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elif isinstance(expected, Sequence) and not isinstance(expected, String):
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cls = ApproxSequence
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else:
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cls = ApproxScalar
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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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return cls(expected, rel, abs)
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#
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|
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@ -9,7 +9,6 @@ from decimal import Decimal
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from fractions import Fraction
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inf, nan = float('inf'), float('nan')
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class MyDocTestRunner(doctest.DocTestRunner):
|
||||
|
||||
def __init__(self):
|
||||
|
|
@ -29,12 +28,19 @@ class TestApprox(object):
|
|||
if sys.version_info[:2] == (2, 6):
|
||||
tol1, tol2, infr = '???', '???', '???'
|
||||
assert repr(approx(1.0)) == '1.0 {pm} {tol1}'.format(pm=plus_minus, tol1=tol1)
|
||||
assert repr(approx([1.0, 2.0])) == '1.0 {pm} {tol1}, 2.0 {pm} {tol2}'.format(pm=plus_minus, tol1=tol1, tol2=tol2)
|
||||
assert repr(approx([1.0, 2.0])) == '[1.0 {pm} {tol1}, 2.0 {pm} {tol2}]'.format(pm=plus_minus, tol1=tol1, tol2=tol2)
|
||||
assert repr(approx((1.0, 2.0))) == '(1.0 {pm} {tol1}, 2.0 {pm} {tol2})'.format(pm=plus_minus, tol1=tol1, tol2=tol2)
|
||||
assert repr(approx(inf)) == 'inf'
|
||||
assert repr(approx(1.0, rel=nan)) == '1.0 {pm} ???'.format(pm=plus_minus)
|
||||
assert repr(approx(1.0, rel=inf)) == '1.0 {pm} {infr}'.format(pm=plus_minus, infr=infr)
|
||||
assert repr(approx(1.0j, rel=inf)) == '1j'
|
||||
|
||||
# Dictionaries aren't ordered, so we need to check both orders.
|
||||
assert repr(approx({'a': 1.0, 'b': 2.0})) in (
|
||||
"{{'a': 1.0 {pm} {tol1}, 'b': 2.0 {pm} {tol2}}}".format(pm=plus_minus, tol1=tol1, tol2=tol2),
|
||||
"{{'b': 2.0 {pm} {tol2}, 'a': 1.0 {pm} {tol1}}}".format(pm=plus_minus, tol1=tol1, tol2=tol2),
|
||||
)
|
||||
|
||||
def test_operator_overloading(self):
|
||||
assert 1 == approx(1, rel=1e-6, abs=1e-12)
|
||||
assert not (1 != approx(1, rel=1e-6, abs=1e-12))
|
||||
|
|
@ -228,18 +234,38 @@ class TestApprox(object):
|
|||
# tolerance, so only an absolute tolerance is calculated.
|
||||
assert a != approx(x, abs=inf)
|
||||
|
||||
def test_expecting_sequence(self):
|
||||
within_1e8 = [
|
||||
(1e8 + 1e0, 1e8),
|
||||
(1e0 + 1e-8, 1e0),
|
||||
(1e-8 + 1e-16, 1e-8),
|
||||
def test_int(self):
|
||||
within_1e6 = [
|
||||
(1000001, 1000000),
|
||||
(-1000001, -1000000),
|
||||
]
|
||||
actual, expected = zip(*within_1e8)
|
||||
assert actual == approx(expected, rel=5e-8, abs=0.0)
|
||||
for a, x in within_1e6:
|
||||
assert a == approx(x, rel=5e-6, abs=0)
|
||||
assert a != approx(x, rel=5e-7, abs=0)
|
||||
assert approx(x, rel=5e-6, abs=0) == a
|
||||
assert approx(x, rel=5e-7, abs=0) != a
|
||||
|
||||
def test_expecting_sequence_wrong_len(self):
|
||||
assert [1, 2] != approx([1])
|
||||
assert [1, 2] != approx([1,2,3])
|
||||
def test_decimal(self):
|
||||
within_1e6 = [
|
||||
(Decimal('1.000001'), Decimal('1.0')),
|
||||
(Decimal('-1.000001'), Decimal('-1.0')),
|
||||
]
|
||||
for a, x in within_1e6:
|
||||
assert a == approx(x, rel=Decimal('5e-6'), abs=0)
|
||||
assert a != approx(x, rel=Decimal('5e-7'), abs=0)
|
||||
assert approx(x, rel=Decimal('5e-6'), abs=0) == a
|
||||
assert approx(x, rel=Decimal('5e-7'), abs=0) != a
|
||||
|
||||
def test_fraction(self):
|
||||
within_1e6 = [
|
||||
(1 + Fraction(1, 1000000), Fraction(1)),
|
||||
(-1 - Fraction(-1, 1000000), Fraction(-1)),
|
||||
]
|
||||
for a, x in within_1e6:
|
||||
assert a == approx(x, rel=5e-6, abs=0)
|
||||
assert a != approx(x, rel=5e-7, abs=0)
|
||||
assert approx(x, rel=5e-6, abs=0) == a
|
||||
assert approx(x, rel=5e-7, abs=0) != a
|
||||
|
||||
def test_complex(self):
|
||||
within_1e6 = [
|
||||
|
|
@ -251,33 +277,86 @@ class TestApprox(object):
|
|||
for a, x in within_1e6:
|
||||
assert a == approx(x, rel=5e-6, abs=0)
|
||||
assert a != approx(x, rel=5e-7, abs=0)
|
||||
assert approx(x, rel=5e-6, abs=0) == a
|
||||
assert approx(x, rel=5e-7, abs=0) != a
|
||||
|
||||
def test_int(self):
|
||||
within_1e6 = [
|
||||
(1000001, 1000000),
|
||||
(-1000001, -1000000),
|
||||
]
|
||||
for a, x in within_1e6:
|
||||
assert a == approx(x, rel=5e-6, abs=0)
|
||||
assert a != approx(x, rel=5e-7, abs=0)
|
||||
def test_list(self):
|
||||
actual = [1 + 1e-7, 2 + 1e-8]
|
||||
expected = [1, 2]
|
||||
|
||||
def test_decimal(self):
|
||||
within_1e6 = [
|
||||
(Decimal('1.000001'), Decimal('1.0')),
|
||||
(Decimal('-1.000001'), Decimal('-1.0')),
|
||||
]
|
||||
for a, x in within_1e6:
|
||||
assert a == approx(x, rel=Decimal('5e-6'), abs=0)
|
||||
assert a != approx(x, rel=Decimal('5e-7'), abs=0)
|
||||
# Return false if any element is outside the tolerance.
|
||||
assert actual == approx(expected, rel=5e-7, abs=0)
|
||||
assert actual != approx(expected, rel=5e-8, abs=0)
|
||||
assert approx(expected, rel=5e-7, abs=0) == actual
|
||||
assert approx(expected, rel=5e-8, abs=0) != actual
|
||||
|
||||
def test_fraction(self):
|
||||
within_1e6 = [
|
||||
(1 + Fraction(1, 1000000), Fraction(1)),
|
||||
(-1 - Fraction(-1, 1000000), Fraction(-1)),
|
||||
]
|
||||
for a, x in within_1e6:
|
||||
assert a == approx(x, rel=5e-6, abs=0)
|
||||
assert a != approx(x, rel=5e-7, abs=0)
|
||||
def test_list_wrong_len(self):
|
||||
assert [1, 2] != approx([1])
|
||||
assert [1, 2] != approx([1,2,3])
|
||||
|
||||
def test_tuple(self):
|
||||
actual = (1 + 1e-7, 2 + 1e-8)
|
||||
expected = (1, 2)
|
||||
|
||||
# Return false if any element is outside the tolerance.
|
||||
assert actual == approx(expected, rel=5e-7, abs=0)
|
||||
assert actual != approx(expected, rel=5e-8, abs=0)
|
||||
assert approx(expected, rel=5e-7, abs=0) == actual
|
||||
assert approx(expected, rel=5e-8, abs=0) != actual
|
||||
|
||||
def test_tuple_wrong_len(self):
|
||||
assert (1, 2) != approx((1,))
|
||||
assert (1, 2) != approx((1,2,3))
|
||||
|
||||
def test_dict(self):
|
||||
actual = {'a': 1 + 1e-7, 'b': 2 + 1e-8}
|
||||
expected = {'b': 2, 'a': 1} # Dictionaries became ordered in python3.6,
|
||||
# so make sure the order doesn't matter
|
||||
|
||||
# Return false if any element is outside the tolerance.
|
||||
assert actual == approx(expected, rel=5e-7, abs=0)
|
||||
assert actual != approx(expected, rel=5e-8, abs=0)
|
||||
assert approx(expected, rel=5e-7, abs=0) == actual
|
||||
assert approx(expected, rel=5e-8, abs=0) != actual
|
||||
|
||||
def test_dict_wrong_len(self):
|
||||
assert {'a': 1, 'b': 2} != approx({'a': 1})
|
||||
assert {'a': 1, 'b': 2} != approx({'a': 1, 'c': 2})
|
||||
assert {'a': 1, 'b': 2} != approx({'a': 1, 'b': 2, 'c': 3})
|
||||
|
||||
def test_numpy_array(self):
|
||||
try:
|
||||
import numpy as np
|
||||
except ImportError:
|
||||
pytest.skip("numpy not installed")
|
||||
|
||||
actual = np.array([1 + 1e-7, 2 + 1e-8])
|
||||
expected = np.array([1, 2])
|
||||
|
||||
# Return false if any element is outside the tolerance.
|
||||
assert actual == approx(expected, rel=5e-7, abs=0)
|
||||
assert actual != approx(expected, rel=5e-8, abs=0)
|
||||
assert approx(expected, rel=5e-7, abs=0) == expected
|
||||
assert approx(expected, rel=5e-8, abs=0) != actual
|
||||
|
||||
def test_numpy_array_wrong_shape(self):
|
||||
try:
|
||||
import numpy as np
|
||||
except ImportError:
|
||||
pytest.skip("numpy not installed")
|
||||
|
||||
import numpy as np
|
||||
a12 = np.array([[1, 2]])
|
||||
a21 = np.array([[1],[2]])
|
||||
|
||||
assert a12 != approx(a21)
|
||||
assert a21 != approx(a12)
|
||||
|
||||
def test_non_number(self):
|
||||
with pytest.raises(ValueError):
|
||||
1 == approx("1")
|
||||
with pytest.raises(ValueError):
|
||||
"1" == approx(1)
|
||||
|
||||
def test_doctests(self):
|
||||
parser = doctest.DocTestParser()
|
||||
|
|
|
|||
Loading…
Reference in New Issue