Ridge Regression and Cross Validation in scikit

My dissertation work, Ridge Restricted Maximum Likelihood (RREML) is an extension of Ridge Regression applied to parametric covariance structures.  In my dissertation we applied it to Spatial Statistics, but it would also apply to Time Series as well.

One of the things that I struggled with in my dissertation approach was choosing the appropriate ridge constant.  I never got around to the much more favorable Cross Validation approach, but I instead used a secondary likelihood approach to estimate the ridge constant.

As I'm writing my algorithm in Python, I'm definitely going to leverage the scikit Ridge Regression approach to apply it to my RREML model.

http://scikit-learn.org/stable/modules/linear_model.html#ridge-regression

Spatial Distances in Python with GeoPy

So as I'm redoing my dissertation work, one of the functions I used was to calculate the distance between two locations based upon their coordinates (latitude and longitude).  At the time, I was referencing and using what is commonly referred to as the Great Circle distance.  So in the middle of my re-write into Python I stumbled across:

GeoPy

They not only provide the Great Circle distance calculation in their module, they introduced me to the Vincenty Distance.  According to the module authors this is a more accurate approach for calculating the distance.

It looks like I have a new way to calculate my spatial distances, and even better, I don't have to program it up myself.  I'm loving this Python re-write process already.

Time Series Analysis in Python with statsmodels

Wow, what a phenomenal discussion on Time Series analysis in Python.  I was unaware of this statsmodels project, but now I'm psyched to find it.  First of all, let me link to the talk that I'm referring to in my title:

Time Series Analysis in Python with statsmodels

Of course, this lead me to tracking down these experts, to learn more about what they do and wow I'm quite impressed.  Here are links to their blogs:

Wes McKinney
Josef Perktold
Skipper Seabold

These three seem to be very involved in Scientific Computing in Python, check out their blogs and links to talks, etc.  I know I will.

Navigating Python documentation in Emacs

I've been a bit frustrated with my ability to navigate Python documentation in Emacs.  I came across the pydoc command, which seems very useful.  Then I wondered how I could leverage this in emacs, maybe write a lisp function in my .emacs file?

Enter a simple google search for "emacs pydoc", and you come across this fine project by John Kitchin.  He has a summary of it here:

http://kitchingroup.cheme.cmu.edu/blog/2014/12/20/A-new-mode-for-Python-documentation/

Now, I love my elpy setup, so I thought I'd propose adding this into it.  No promises from Jorgen, but he did say that at the very least it needs to be its own package.

John, of course is very busy, and I've always wanted to do something like this in Emacs, so I volunteered to take on this project.  So get ready to follow along on how I create a MELPA distributed Emacs package. Yay, this should be fun. :)

You can follow along on github:

https://github.com/statmobile/pydoc

Copying Git files/directories to a new project.

I had recently done something similar when forking off my dissertation work from a subversion repository to a Git repository and then separating out the R library completely.  Anyway, I'm in the process of trying to port someone's previous work into its own project as a soon to be MELPA installable Emacs package.

Long story short, I need to get this guy's file and I want to keep his history in tact.  So, without further ado, this fine gentleman has a quick way of doing that.  See his post here.

http://blog.neutrino.es/2012/git-copy-a-file-or-directory-from-another-repository-preserving-history/