[codeboy7432]

I authored this package as I needed to generate confidence intervals for time series data without using SciPy. Sharing this here, as this could be a useful package for others :)

Included Models: - Linear regression - Ridge regression - Linear spline - Isotonic regression - Bin regression - Cubic spline - Natural cubic spline - Exponential moving average - Kernel functions (Gaussian, KNN, Weighted average)

[gkhartman]

Thanks for releasing this. I was just wondering if something like this exists. I've worked on a few projects where scipy was banned due to the large dependencies it pulls in.

[clircle]

Have you compared these to StatsModels? Or R? (which includes most of these ootb)

[codeboy7432]

I did look into stats models, but this was a large library with more than I needed. I did not look into R.. Are there any lightweight alternatives in this language?

[clircle]

I think most or all of these models are in the R Standard lib

[wdkrnls]

Not all as far as I know. For ridge regression you will want to install glmnet, for example. mgcv which is usually shipped with R provides access to a few common fast kernels which seem to be the ones python programmers are familiar with.

[clircle]

Can use lm.ridge from MASS instead of glmnet, but yeah there’s going to be some smoother not in R standard library

[wdkrnls]

mgcv also provides many more varieties of splines than base R.

[iamcreasy]

Am I guess correct to assume that you could not use sklearn/scikit as well because it depends on SciPy? (I am under the impression that sklearn/scikit is the dominant library for an implementation of these algorithms.)

[codeboy7432]

That is correct. I had to generate confidence intervals on over 8000 univariate data sets using very small VMs, so I needed to limit large dependancies as much as I could. This package was the result of this!

[t6jvcereio]

Two questions:

Why the requirement that you can't use scipy?

Have you heard of the package stats model?

[versteegen]

Well, SciPy depends heavily on NumPy, which as a CPython-specific extension won't run on other Python interpreters in general. Although for example there is ulab for MicroPython which replicates part of NumPy, and PyPy has a compatibility layer for CPython extensions.

Edit: well, Regessio itself also depends on NumPy, but might be able to run on top of ulab whereas I really doubt SciPy would.

[nothrowaways]

The repo on OP also depends on numpy

[t6jvcereio]

Responding that there's something out there called ulab doesn't really answer my question, which was: where does op's requirement to not use scipy come from.

[codeboy7432]

(Same as comment above)

".. I had to generate confidence intervals on over 8000 univariate data sets using very small VMs, so I needed to limit large dependancies as much as I could. This package was the result of this!"

Based on the comments in this thread, it may be worth trying to make this package not dependant on Numpy as well?