<div dir="ltr"><div dir="ltr"><div dir="ltr"><br><br><div class="gmail_quote"><div dir="ltr">On Fri, Oct 19, 2018 at 8:24 PM Marten van Kerkwijk <<a href="mailto:m.h.vankerkwijk@gmail.com">m.h.vankerkwijk@gmail.com</a>> wrote:<br></div><blockquote class="gmail_quote" style="margin:0px 0px 0px 0.8ex;border-left:1px solid rgb(204,204,204);padding-left:1ex"><div dir="ltr"><div>Hi All,<br></div><div><br></div><div>It seems there are two extreme possibilities for general functions:</div><div>1. Put `asarray` everywhere. The main benefit that I can see is that even if people put in list instead of arrays, one is guaranteed to have shape, dtype, etc. But it seems a bit like calling `int` on everything that might get used as an index, instead of letting the actual indexing do the proper thing and call `__index__`.<br></div></div></blockquote><div><br></div><div>Yes, actually getting a proper "array protocol" into Python would be a fantastic approach. We have been working with Lenore Mullin who is a researcher on the mathematics of arrays on what it means to be an array and believe we can come up with an actual array protocol that perhaps could be put into Python itself (though that isn't our immediate goal right now). </div><div><br></div><div> <br></div><blockquote class="gmail_quote" style="margin:0px 0px 0px 0.8ex;border-left:1px solid rgb(204,204,204);padding-left:1ex"><div dir="ltr"><div></div><div>2. Do not coerce at all, but rather write code assuming something is an array already. This will often, but not always, just work for array mimics, with coercion done only where necessary (e.g., in lower-lying C code such as that of the ufuncs which has a smaller API surface and can be overridden more easily).</div><div><br></div><div>The current __array_function__ work may well provide us with a way to combine both, if we (over time) move the coercion inside `ndarray.__array_function__` so that the actual implementation *can* assume it deals with pure ndarray - then, when relevant, calling that implementation will be what subclasses/duck arrays can happily do (and it is up to them to ensure this works).</div></div></blockquote><div><br></div><div>Also, we could get rid of asarray entirely by changing expectations. This automatic conversion code throughout NumPy and SciPy is an example of the confusion in both of these libraries between "user-oriented interfaces" and "developer-oriented interfaces". A developer just wants the library to use duck-typing and then raise errors if you don't provide the right type (i.e. a list instead of an array). The user-interface could happen in Jupyter, or be isolated to a high-level library or meta-code approach (of which there are several possibilities for Python). </div><br class="gmail-Apple-interchange-newline"><div> </div><blockquote class="gmail_quote" style="margin:0px 0px 0px 0.8ex;border-left:1px solid rgb(204,204,204);padding-left:1ex"><div dir="ltr"><div><br></div><div>Of course, the above does not really answer what to do in the meantime. But perhaps it helps in thinking of what we are actually aiming for. </div></div></blockquote><blockquote class="gmail_quote" style="margin:0px 0px 0px 0.8ex;border-left:1px solid rgb(204,204,204);padding-left:1ex"><div dir="ltr"><div><br></div><div>One last thing: could we please stop bashing subclasses? One can subclass essentially everything in python, often to great advantage. Subclasses such as MaskedArray and, yes, Quantity, are widely used, and if they cause problems perhaps that should be seen as a sign that ndarray subclassing should be made easier and clearer. <br></div><div><br></div></div></blockquote><div><br></div><div>I agree that we can stop bashing subclasses in general. The problem with numpy subclasses is that they were made without adherence to SOLID: <a href="https://en.wikipedia.org/wiki/SOLID">https://en.wikipedia.org/wiki/SOLID</a>. In particular the Liskov substitution principle: <a href="https://en.wikipedia.org/wiki/Liskov_substitution_principle">https://en.wikipedia.org/wiki/Liskov_substitution_principle</a> . Much of this is my fault. Being a scientist/engineer more than a computer scientist, I had no idea what these principles were and did not properly apply them in creating np.matrix which clearly violates the substitution principle. </div><div><br></div><div>We can clean all this and more up.</div><div><br></div><div>But, we really need to start talking about NumPy 2.0 to do it. Now that Python 3.x is really here, we can raise the money for it and get it done. We don't have to just rely on volunteer time. </div><div><br></div><div>The world will thank us for actually pushing NumPy 2.0. I know not everyone agrees, but for whatever its worth, I feel very, very strongly about this, and despite not being very active on this list for the past years, I do have a lot of understanding about how the current code actually works (and where and why its warts are).</div><div><br></div><div>-Travis</div><div><br></div><div><br></div><div><br></div><div><br></div><div> <br></div><blockquote class="gmail_quote" style="margin:0px 0px 0px 0.8ex;border-left:1px solid rgb(204,204,204);padding-left:1ex"><div dir="ltr"><div></div><div>All the best,</div><div><br></div><div>Marten</div><div><div><br></div></div></div><br><div class="gmail_quote"><div dir="ltr">On Fri, Oct 19, 2018 at 7:02 PM Ralf Gommers <<a href="mailto:ralf.gommers@gmail.com" target="_blank">ralf.gommers@gmail.com</a>> wrote:<br></div><blockquote class="gmail_quote" style="margin:0px 0px 0px 0.8ex;border-left:1px solid rgb(204,204,204);padding-left:1ex"><div dir="ltr"><div dir="ltr"><br><br><div class="gmail_quote"><div dir="ltr">On Fri, Oct 19, 2018 at 10:28 PM Ralf Gommers <<a href="mailto:ralf.gommers@gmail.com" target="_blank">ralf.gommers@gmail.com</a>> wrote:<br></div><blockquote class="gmail_quote" style="margin:0px 0px 0px 0.8ex;border-left:1px solid rgb(204,204,204);padding-left:1ex"><div dir="ltr"><br><br><div class="gmail_quote"><div dir="ltr">On Fri, Oct 19, 2018 at 4:15 PM Hameer Abbasi <<a href="mailto:einstein.edison@gmail.com" target="_blank">einstein.edison@gmail.com</a>> wrote:<br></div><blockquote class="gmail_quote" style="margin:0px 0px 0px 0.8ex;border-left:1px solid rgb(204,204,204);padding-left:1ex"> <div style="font-family:Helvetica;color:rgb(0,0,0);font-size:13px"><div id="gmail-m_-709599305993225282m_6396869678531188226gmail-m_5627656941379967435m_256686967757922553CanaryBody"> <div> Hi!</div><div><br></div></div> <div id="gmail-m_-709599305993225282m_6396869678531188226gmail-m_5627656941379967435m_256686967757922553CanaryDropbox"> </div> <blockquote id="gmail-m_-709599305993225282m_6396869678531188226gmail-m_5627656941379967435m_256686967757922553CanaryBlockquote"> <div> <div>On Friday, Oct 19, 2018 at 6:09 PM, Stephan Hoyer <<a href="mailto:shoyer@gmail.com" target="_blank">shoyer@gmail.com</a>> wrote:<br></div> <div><div dir="ltr">I don't think it makes much sense to change NumPy's existing usage of asarray() to asanyarray() unless we add subok=True arguments (which default to False). But this ends up cluttering NumPy's public API, which is also undesirable.</div><div dir="ltr"><br></div></div></div></blockquote>Agreed so far.<br></div></blockquote><div><br></div><div>I'm not sure I agree. "subok" is very unpythonic; the average numpy library function should work fine for a well-behaved subclass (i.e. most things out there except np.matrix).<br></div><blockquote class="gmail_quote" style="margin:0px 0px 0px 0.8ex;border-left:1px solid rgb(204,204,204);padding-left:1ex"><div style="font-family:Helvetica;color:rgb(0,0,0);font-size:13px"><blockquote><div><div><div dir="ltr"><br></div><div dir="ltr">The preferred way to override NumPy functions going forward should be __array_function__.</div><div dir="ltr"><br></div></div></div></blockquote><br>I think we should “soft support” i.e. allow but consider unsupported, the case where one of NumPy’s functions is implemented in terms of others and “passing through” an array results in the correct behaviour for that array.</div></blockquote><div><br></div><div>I don't think we have or want such a concept as "soft support". We intend to not break anything that now has asanyarray, i.e. it's supported and ideally we have regression tests for all such functions. For anything we transition over from asarray to asanyarray, PRs should come with new tests.<br></div><div> <br></div><blockquote class="gmail_quote" style="margin:0px 0px 0px 0.8ex;border-left:1px solid rgb(204,204,204);padding-left:1ex"><div style="font-family:Helvetica;color:rgb(0,0,0);font-size:13px"><div><br><blockquote><div><div><br><div class="gmail_quote"><div dir="ltr">On Fri, Oct 19, 2018 at 8:13 AM Marten van Kerkwijk <<a href="mailto:m.h.vankerkwijk@gmail.com" target="_blank">m.h.vankerkwijk@gmail.com</a>> wrote:<br></div><blockquote class="gmail_quote" style="margin:0px 0px 0px 0.8ex;border-left:1px solid rgb(204,204,204);padding-left:1ex"><div dir="ltr"><div><div>There are exceptions for `matrix` in quite a few places, and there now is warning for `maxtrix` - it might not be bad to use `asanyarray` and add an exception for `maxtrix`. Indeed, I quite like the suggestion by Eric Wieser to just add the exception to `asanyarray` itself - that way when matrix is truly deprecated, it will be a very easy change.<br></div></div></div></blockquote></div></div></div></blockquote></div></div></blockquote><div>I don't quite understand this. Adding exceptions is not deprecation - we then may as well just rip np.matrix out straight away.</div><div><br></div><div>What I suggested in the call about this issue is that it's not very effective to treat functions like percentile/quantile one by one without an overarching strategy. A way forward could be for someone to write an overview of which sets of functions now have asanyarray (and actually work with subclasses), which ones we can and want to change now, and which ones we can and want to change after np.matrix is gone. Also, some guidelines for new functions that we add to numpy would be handy. I suspect we've been adding new functions that use asarray rather than asanyarray, which is probably undesired.</div></div></div></blockquote><div><br></div><div>Thanks Nathaniel and Stephan. Your comments on my other two points are both clear and correct (and have been made a number of times before). I think the "write an overview so we can stop making ad-hoc decisions and having these discussions" is the most important point I was trying to make though. If we had such a doc and it concluded "hence we don't change anything, __array_function__ is the only way to go" then we can just close PRs like <a href="https://github.com/numpy/numpy/pull/11162" target="_blank">https://github.com/numpy/numpy/pull/11162</a> straight away.</div><div><br></div><div>Cheers,<br></div><div>Ralf</div><div><br></div></div></div></div>
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