On 1/3/20, Sebastian Berg <sebast...@sipsolutions.net> wrote: > On Fri, 2020-01-03 at 07:11 -0500, Warren Weckesser wrote: >> In response to some work on improving the documentation of >> `numpy.linalg` and how it compares to `scipy.linalg`, Kevin Sheppard >> suggested that the documentation of the module `numpy.dual` should >> also be improved. When I mentioned this suggestion in the community >> meeting on December 11, it was suggested that we should probably >> deprecate `numpy.dual`. >> >> I think some current NumPy developers (myself included at the time >> the topic came up) are unfamiliar with the history and purpose of >> this module, so I spent some time reading code and github issues and >> wrote up some notes. These notes are available at >> >> >> https://github.com/WarrenWeckesser/numpy-notes/blob/master/numpy-dual.md >> >> If you are not familiar with `numpy.dual`, you might find those notes >> useful. >> >> Now that I know a bit more about `numpy.dual`, I'm not sure it should >> be deprecated. It provides a hook for other libraries to selectively >> replace the use of the exposed functions in internal NumPy code, so >> if a library has a better version of, say, `linalg.eigh`, it can >> configure `numpy.dual` to use its version. Then, for example, NumPy >> multivariate normal distribution code could benefit from the use of >> that library's version of `eigh`. > > That is in principle true, but I do not think we use `dual` at all > internally right now in numpy, and I doubt there is more than a hand > full uses out there.
In the notes, I listed the internal uses of `numpy.dual` within numpy that I found: 1. In the code that generates random variates from the multivariate normal distribution, one of `svd`, `eigh` or `cholesky` are used from `numpy.dual`. 2. In `matrixlib/defmatrix.py`, the `.I` property of the `matrix` class uses either `inv` or `pinv` from `numpy.dual` to compute its value. 3. The window function `numpy.kaiser` uses `numpy.dual.i0`. > > Dual is an override mechanism for functionality on ndarrays implemented > also by numpy. > > In either case, I still tend towards deprecation. It seems to have > issues and the main use case probably was to improve the situation when > NumPy was compiled without an optimized BLAS/LAPACK. That probably was > a common problem at some point, but I am not sure it is still an issue. > > Overriding functionality with faster implementations is of course a > valid use-case and maybe `dual` is not a bad solution to the problem > [0]. But I think we should discuss this more generally with other > options. IMO deprecating this practically unused thing now does not > mean we cannot do something similar in the future. It probably makes sense to have the general discussion before deprecating `numpy.dual`--there is a (slim?) chance that `numpy.dual` will turn out to be the best option. Warren > > - Sebastian > > > [0] It has its own namespace, so is opt-in for the end user. You can > only support a single backend at a time, although I am not sure that > matters too much. If overrides provide a function to override, it is > explicit to the end user as to what gets executed as well. > > >> The NumPy documentation of `numpy.dual` refers specifically to SciPy, >> but it could be used by any library. Does anyone know if any other >> libraries use `register_func` to put their functions into the >> `numpy.dual` namespace? >> >> SciPy currently registers some functions, but there is an open issue >> in which it is proposed that SciPy no longer register its functions >> with `numpy.dual`: >> >> https://github.com/scipy/scipy/issues/10441 >> >> This email is to start the discussion of the future of `numpy.dual`. >> Some of the options: >> >> 1. Quietly continue the status quo. >> 2. Deprecate `numpy.dual`. >> 3. Spend time improving the documentation of this feature, and >> perhaps even expand the functions that are supported. >> >> What do you think? For those who were involved in the creation of >> `numpy.dual`: is it working out like you expected? If not, is it >> worthwhile maintaining it? >> >> Warren >> >> _______________________________________________ >> NumPy-Discussion mailing list >> NumPy-Discussion@python.org >> https://mail.python.org/mailman/listinfo/numpy-discussion > _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@python.org https://mail.python.org/mailman/listinfo/numpy-discussion
