[mlthoughts2018]

> “I have mentioned earlier that it is not about precision but about index space.”

Right, and if you read in my comments it shows I have also been only talking about the index space as well, where int64 vs int32 is a matter of int precision for representing large amounts of indices, but where npy_intp will be of the higher precision (to match the platform’s address space) and will not be able to suffer the overflow issue you described unless it’s a custom compilation of numpy defining npy_intp as int32 even on a 64-bit system (which you seem confused about by repeatedly saying compilation isn’t a part of it, as if anyone is suggesting your personal workflow involves compiling anything, when I’m talking about how the numpy you have installed was compiled. If it’s standard numpy compiled for a 64-bit system, then the evidence suggests your claim is just wrong.)

You claim that indptr and indices arrays are silently converted from int64 to int32 on 64-bit platforms but you offer no evidence. You just keep saying that it happened to you, despite the actual code you linked indicating that it couldn’t happen. And I do actually work with indptr and indices arrays with tens of billions of nonzero elements in an Alexa top 300 website search engine every day, and have never encountered any such silent type conversion.

Given that this example of index int precision actually seems unfounded in the code, it just doesn’t seem relevant to any sort of static vs dynamic typing debate. There’s no such issue here that static typing would help with, because it’s clearly not causing the problem you think it’s causing in the dynamic typing code.

[srean]

All I can say is you will learn a little bit more if your "know it all" persona is in proportion with your actual extensive knowledge :)

There was a recent thread on HN on Ousterhout on exactly that https://news.ycombinator.com/item?id=17779953

Re evidence, you cant possibly expect me to replicate the entire software library stack here on HN or elsewhere to show the loss of type information.

Hint you are still looking at functions in isolation and repeating loss of type info cannot happen and I have dealt with hundreds of counter examples. You can look up the conversation, I did not say the pointed Github code is to blame. I pointed the code saying we have to make sure that piece of code is eventually called with the right type. We have to ensure that at the time of creation of the sparse matrices. With the dynamic type handling this gets lost. Int64 gets dropped to Int32.