ENH: Change sparse .sum and .mean dtype casting to match NumPy.
#5907
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Change the sparse `.sum` and `.mean` methods to behave like their NumPy matrix counterparts in terms of dtype casting, and update the corresponding tests in scipy/sparse/test_base.py. This should solve issue scipy#2534.
| rtol = 1e-04 | ||
| else: | ||
| rtol = 1e-07 | ||
| assert_allclose(NC.A, M.A, rtol=rtol) |
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I included it because the assert_allclose test failed at the default rtol for _NonCanonicalMixin subclasses initialized with np.random.rand(40, 40) and sparse.rand(40, 40, density=1e-2).A (used in test_sum) for the dtypes np.float32 and np.complex64.
Comparisons between the results of the sparse and dense matrix `.sum` methods in `sparse.test_base.test_sum` fail for matrices containing single-precision floats (`np.float32` and `np.complex64`) when the sparse and dense sums are calculated differently and the resulting sum values have too many digits to be considered equal in single-precision float format with the default value of the `decimal` parameter of `assert_array_almost_equal`. This is fixed by using smaller test matrices, so that the number of digits of their sums remains small enough in all cases for the values to be considered equal at the default value of the `decimal` parameter.
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perimosocordiae
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Mar 6, 2016
ENH: Change sparse `.sum` and `.mean` dtype casting to match NumPy.
|
Merged. Thanks, @Tiepies! |
rgommers
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Change the sparse
.sumand.meanmethods to behave like their NumPymatrix counterparts in terms of dtype casting, and update the
corresponding tests in scipy/sparse/test_base.py. This should resolve #2534.