On Mon, Mar 7, 2011 at 4:10 AM, Pauli Virtanen <p...@iki.fi> wrote: > Mon, 07 Mar 2011 11:03:17 +0800, Ralf Gommers wrote: > [clip] >> If anyone has new deprecations they want to put in for 1.6, discussing >> them now would be good. I found one item in Trac, #1543. The proposal in >> the ticket is to deprecate assert_almost_equal because it is quite badly >> behaved. This function is quite widely used however (also in scipy and >> presumably in third party code), so it will be a real pain if it starts >> spewing out warnings. >> >> I propose instead to just add a note at the top of the >> assert_almost_equal docstring that assert_array_max_ulp or >> assert_tol_equal (to be added from scipy.special in 1.6) should be used >> instead for new code. > > Numpy 1.5 has `assert_allclose` which is functionally equivalent to the > proposed `assert_tol_equal`, so no new functions need to be added. I'm OK > with documentation-only recommendation.
I also think assert_almost_equal should not be depreciated. Besides being very heavily used, it is also the most appropriate test for many statistical tests. Test statistics like t-tests and p-values have an absolute scale, and only a few decimals make statistical sense. The tolerance for calculations (minimization, numerical differentiation and integration) is often only targeted for this. Changing the signature just to replicate it with assert_allclose is a pain without any gain. And going over all the tests to see what the appropriate relative error is, is a lot of work. But we have some tickets in scipy stats to improve numerical precision, beyond some decimals, and relative tolerance would be useful in those cases. I didn't know about assert_allclose and more advertising will help to use it more often for new tests. Why does assert_allclose have atol=0, while np.allclose has rtol=1.e-5, atol=1.e-8 ? What's the status on np.testing.assert_approx_equal, I would have liked to use it more often, except it doesn't work on arrays. Josef > > Pauli > > _______________________________________________ > NumPy-Discussion mailing list > NumPy-Discussion@scipy.org > http://mail.scipy.org/mailman/listinfo/numpy-discussion > _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
