diff options
| -rw-r--r-- | numpy/core/tests/test_numeric.py | 65 |
1 files changed, 65 insertions, 0 deletions
diff --git a/numpy/core/tests/test_numeric.py b/numpy/core/tests/test_numeric.py index 3bc4cd187..135acc51d 100644 --- a/numpy/core/tests/test_numeric.py +++ b/numpy/core/tests/test_numeric.py @@ -14,6 +14,9 @@ from numpy.testing import ( assert_warns, HAS_REFCOUNT ) +from hypothesis import assume, given, strategies as st +from hypothesis.extra import numpy as hynp + class TestResize: def test_copies(self): @@ -2018,6 +2021,68 @@ class TestClip: actual = np.clip(arr, amin, amax) assert_equal(actual, expected) + @given(data=st.data(), shape=hynp.array_shapes()) + def test_clip_property(self, data, shape): + """A property-based test using Hypothesis. + + This aims for maximum generality: it could in principle generate *any* + valid inputs to np.clip, and in practice generates much more varied + inputs than human testers come up with. + + Because many of the inputs have tricky dependencies - compatible dtypes + and mutually-broadcastable shapes - we use `st.data()` strategy draw + values *inside* the test function, from strategies we construct based + on previous values. An alternative would be to define a custom strategy + with `@st.composite`, but until we have duplicated code inline is fine. + + That accounts for most of the function; the actual test is just three + lines to calculate and compare actual vs expected results! + """ + # Our base array and bounds should not need to be of the same type as + # long as they are all compatible - so we allow any int or float type. + dtype_strategy = hynp.integer_dtypes() | hynp.floating_dtypes() + + # The following line is a total hack to disable the varied-dtypes + # component of this test, because result != expected if dtypes can vary. + dtype_strategy = st.just(data.draw(dtype_strategy)) + + # Generate an arbitrary array of the chosen shape and dtype + # This is the value that we clip. + arr = data.draw(hynp.arrays(dtype=dtype_strategy, shape=shape)) + + # Generate shapes for the bounds which can be broadcast with each other + # and with the base shape. Below, we might decide to use scalar bounds, + # but it's clearer to generate these shapes unconditionally in advance. + in_shapes, result_shape = data.draw( + hynp.mutually_broadcastable_shapes( + num_shapes=2, + base_shape=shape, + # Commenting out the min_dims line allows zero-dimensional arrays, + # and zero-dimensional arrays containing NaN make the test fail. + min_dims=1 + + ) + ) + amin = data.draw( + dtype_strategy.flatmap(hynp.from_dtype) + | hynp.arrays(dtype=dtype_strategy, shape=in_shapes[0]) + ) + amax = data.draw( + dtype_strategy.flatmap(hynp.from_dtype) + | hynp.arrays(dtype=dtype_strategy, shape=in_shapes[1]) + ) + # If we allow either bound to be a scalar `nan`, the test will fail - + # so we just "assume" that away (if it is, this raises a special + # exception and Hypothesis will try again with different inputs) + assume(not np.isscalar(amin) or not np.isnan(amin)) + assume(not np.isscalar(amax) or not np.isnan(amax)) + + # Then calculate our result and expected result and check that they're + # equal! See gh-12519 for discussion deciding on this property. + result = np.clip(arr, amin, amax) + expected = np.minimum(amax, np.maximum(arr, amin)) + assert_array_equal(result, expected) + class TestAllclose: rtol = 1e-5 |