What did I do this week?

This week I compiled all the code and experimentation notebooks into a Github web page to be submitted as a part of the final work product for GSoC. The cover page includes all the links to the different parts of the project and all the experiments that went into devising the algorithm for each part.

What is coming up next?

I will continue to work on the 'ongoing' sections of the project beyond the GSoC period and will hope to continue contributing to the PySAL repository.

Did I get stuck anywhere?

I wanted to compile the notebooks and markdown files into a Jupyter book but due to Jekyll dependency issues on my Windows machine was unable to do it. I then resorted to use the simple github web-pages from notebooks option and that worked out well.

What did I do this week?

This week I attempted to understand and code the out of sample predictions for the MGWR model which is the third part of my proposal. After a discussion with the mentors I realized the scope of the work to be larger than I had first anticipated. I experimented with the options to implement the algorithm and with the help of the mentors we were able to center down on an approach. This part of the project will be defined as an ongoing part which I will continue to work on beyond the Google Summer of Code period.

What is coming up next?

In the coming week I will work on compiling all the results and work done so far for the Poisson and Binomial models into a Github web-page/s to submit as a work product for the final evaluations.

Did I get stuck anywhere?

No major issues were encountered this week. Through experimentation and discussions I learnt the technique currently used for out of sample predictions for the GWR model which was very interesting 

Looking forward to the progress update next week!

What did I do this week?

After a discussion with the mentors we decided on testing the implementation of local scoring algorithm for the Binomial MGWR model. After an adjustment to the parameters, the results from the model were as expected. I implemented a Monte Carlo experiment to check for the bandwidth and parameter behavior and the results look very close to expectation. The implementation was decided to be kept internal until further checks were performed and the implementation was theoretically checked and confirmed.

What is coming up next?

In the coming week I will work on compiling all the results and work for the Poisson and Binomial models to complete its implementation in the MGWR pysal package.

Did I get stuck anywhere?

No major issues were encountered this week.

Looking forward to the progress update next week!

What did I do this week?

After a productive discussion with my mentors last week, we agreed to proceed coding the local scoring algorithm for Binomial MGWR and testing the results from that. After familiarizing myself with the literature on the local scoring procedure, I coded it in the context of local models for MGWR. After multiple iterations, the model is converging and the bandwidth results are looking as expected. Though the parameter coefficients have values close to expected but not as accurate as needed. There could be possible issues with the weights associated in the model, and some adjustments need to be made for the coefficients which need to be figured out.

What is coming up next?

In the coming week I will work on resolving the coefficient value issue discussed above and design and implement a Monte-Carlo design for the Binomial model as was done for the Poisson MGWR model.

Did I get stuck anywhere?

The modeling of the binary response variable with MGWR is still not resolved and issues have been encountered continuously in it, though that is expected from research. Hoping to resolve these final issues soon and work further on the predictions with GWR and MGWR.

Looking forward to the progress update next week!

What did I do this week?

This week I read and experimented with the literature on Logistic Regression GAMs. After trying many approaches to model a binomial dependent variable, none of those seem to work to give the expected bandwidths or parameters. Looking forward to the discussion with the mentors to maybe understand the best next steps to resolve the model convergence. Also read and understood the predictions for un-sampled locations in the GWR code and will attempt to resolve the recurring errors and build the prediction model for MGWR.

What is coming up next?

In the coming week I will work on finalizing the approach or way forward for the Logistic Regression within MGWR with the mentors' guidance and build on the final part of the project around predictions.

Did I get stuck anywhere?

The modeling of the binary response variable with MGWR is not resolved and has been a blocker for a little bit. Hoping to find an approach that works with the mentors' advice soon and work further on the predictions with GWR and MGWR.

Looking forward to the progress update next week!