In this update I added TimeSeriesInitialSplit. This will split the data in a convenient way for time series testing.
I love Stan, but I had to drop it as the backend sampler.
A lot of problems were solved as this package was developed. Multiprocessing
with cmdstanpy folder structures was a big one. As I used this package more
in production I started to dread it more than love it. Why? A lot of small
simple things. Keeping tabs on cmdstanpy, getting a version of python
that all of the dependancies were happy with felt tedious. In prod environments
you also have to figure out if you have C++ compilation tools like gcc.
This is not always the case. If those tools are not available then install_cmdstanpy
will not work. This is additional overhead, and something I did not want to
have to worry about. Once I got
a python version 3.8>= to work well, I found that when I wanted to make
a notebook example with Google Colab I had to wrestle with the notebook
to get a more recent version of python -- Colab comes with 3.7 by default.
The version was not all though. I had to make sure that the priority was
reset to run the more recent version. This was not the end. I also had
to install additional software that comes with the newer version of
python, but does not come with the notebook -- something called dsutils?
Why would I care so much about the notebook environment? Well, I think there are a population of people who like to run code on notebooks. This could be how they 'kick the tires' when trying a new package. If that experience goes well then maybe the chances of making into production is higher.
Though I love Stan, and it hurts to remove it, I think that these models are simple enough to survive just fine without stans sampler.
If you are interested in how to use stan with a python then checkout out
version 4.4 of this package commit hash 49066323c08915e90b8b1e06a88cbddb52a726ae
I think these changes will make the package easier for me to maintain and very simple to integrate for production runs.