On Sat, Feb 22, 2020 at 9:41 AM <josef.pktd@gmail.com> wrote:
On Sat, Feb 22, 2020 at 9:34 AM <josef.pktd@gmail.com> wrote:
not having a hashable tuple conversion would be a strong limitation
a = tuple(np.arange(5)) versus a = tuple([np.array(i) for i in range(5)]) {a:5}
also there is the question of which scalar
.item() versus [()]
This was used in the old times in scipy.stats, and I just saw https://github.com/scipy/scipy/pull/11165#issuecomment-589952838
aside: AFAIR, I use 0-dim arrays also to ensure that I have a numpy dtype and not, e.g. some equivalent python type
0-dim as mutable pseudo-scalar a = np.asarray(5) a, id(a) (array(5), 844574884528) a[()] = 1 a, id(a) (array(1), 844574884528) maybe I never used that, In a recent similar case, I could use just a 1-d list or array to work around python's muting or mutability behavior
Josef
Josef
On Sat, Feb 22, 2020 at 9:28 AM Evgeni Burovski < evgeny.burovskiy@gmail.com> wrote:
Hi Sebastian,
Just to clarify the difference:
x = np.float64(42) y = np.array(42, dtype=float)
Here `x` is a scalar and `y` is a 0D array, correct? If that's the case, not having the former would be very confusing for users (at least, that would be very confusing to me, FWIW).
If anything, I think it'd be cleaner to not have the latter, and only have either scalars or 1D arrays (i.e., N-D arrays with N>=1), but it is probably way too late to even think about it anyway.
Cheers,
Evgeni
On Sat, Feb 22, 2020 at 4:37 AM Sebastian Berg <sebastian@sipsolutions.net> wrote:
Hi all,
When we create new datatypes, we have the option to make new choices for the new datatypes [0] (not the existing ones).
The question is: Should every NumPy datatype have a scalar associated and should operations like indexing return a scalar or a 0-D array?
This is in my opinion a complex, almost philosophical, question, and we do not have to settle anything for a long time. But, if we do not decide a direction before we have many new datatypes the decision will make itself... So happy about any ideas, even if its just a gut feeling :).
There are various points. I would like to mostly ignore the technical ones, but I am listing them anyway here:
* Scalars are faster (although that can be optimized likely)
* Scalars have a lower memory footprint
* The current implementation incurs a technical debt in NumPy. (I do not think that is a general issue, though. We could automatically create scalars for each new datatype probably.)
Advantages of having no scalars:
* No need to keep track of scalars to preserve them in ufuncs, or libraries using `np.asarray`, do they need `np.asarray_or_scalar`? (or decide they return always arrays, although ufuncs may not)
* Seems simpler in many ways, you always know the output will be an array if it has to do with NumPy.
Advantages of having scalars:
* Scalars are immutable and we are used to them from Python. A 0-D array cannot be used as a dictionary key consistently [1].
I.e. without scalars as first class citizen `dict[arr1d[0]]` cannot work, `dict[arr1d[0].item()]` may (if `.item()` is defined, and e.g. `dict[arr1d[0].frozen()]` could make a copy to work. [2]
* Object arrays as we have them now make sense, `arr1d[0]` can reasonably return a Python object. I.e. arrays feel more like container if you can take elements out easily.
Could go both ways:
* Scalar math `scalar = arr1d[0]; scalar += 1` modifies the array without scalars. With scalars `arr1d[0, ...]` clarifies the meaning. (In principle it is good to never use `arr2d[0]` to get a 1D slice, probably more-so if scalars exist.)
Note: array-scalars (the current NumPy scalars) are not useful in my opinion [3]. A scalar should not be indexed or have a shape. I do not believe in scalars pretending to be arrays.
I personally tend towards liking scalars. If Python was a language where the array (array-programming) concept was ingrained into the language itself, I would lean the other way. But users are used to scalars, and they "put" scalars into arrays. Array objects are in some ways strange in Python, and I feel not having scalars detaches them further.
Having scalars, however also means we should preserve them. I feel in principle that is actually fairly straight forward. E.g. for ufuncs:
* np.add(scalar, scalar) -> scalar * np.add.reduce(arr, axis=None) -> scalar * np.add.reduce(arr, axis=1) -> array (even if arr is 1d) * np.add.reduce(scalar, axis=()) -> array
Of course libraries that do `np.asarray` would/could basically chose to not preserve scalars: Their signature is defined as taking strictly array input.
Cheers,
Sebastian
[0] At best this can be a vision to decide which way they may evolve.
[1] E.g. PyTorch uses `hash(tensor) == id(tensor)` which is arguably strange. E.g. Quantity defines hash correctly, but does not fully ensure immutability for 0-D Quantities. Ensuring immutability in a world where "views" are a central concept requires a write-only copy.
[2] Arguably `.item()` would always return a scalar, but it would be a second class citizen. (Although if it returns a scalar, at least we already have a scalar implementation.)
[3] They are necessary due to technical debt for NumPy datatypes though. _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@python.org https://mail.python.org/mailman/listinfo/numpy-discussion
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