I don't have any opinion on whether this is a good idea or not, but in the
meantime, you can monkey-patch numpy:
np.concat = np.concatenate
And you get your compatibility. And, since modules are singletons, I
believe you only have to do it once and it will propagate across your
modules.
/David
On Mon, 1 Jun 2020, 8:24 pm Iordanis Fostiropoulos, <
danny.fostiropou...@gmail.com> wrote:
> In regard to Feature Request: https://github.com/numpy/numpy/issues/16469
>
> It was suggested to sent to the mailing list. I think I can make a strong
> point as to why the support for this naming convention would make sense.
> Such as it would follow other frameworks that often work alongside numpy
> such as tensorflow. For backward compatibility, it can simply be an alias
> to np.concatenate
>
> I often convert portions of code from tf to np, it is as simple as
> changing the base module from tf to np. e.g. np.expand_dims ->
> tf.expand_dims. This is done either in debugging (e.g. converting tf to np
> without eager execution to debug portion of the code), or during
> prototyping, e.g. develop in numpy and convert in tf.
>
> I find myself more than at one occasion to getting syntax errors because
> of this particular function np.concatenate. It is unnecessarily long. I
> imagine there are more people that also run into the same problems. Pandas
> uses concat (torch on the other extreme uses simply cat, which I don't
> think is as descriptive).
>
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