Closed tomvars closed 3 years ago
Hi tomvars,
Could you please let me know what is the dimensionality of your feature space?
There are plans for a more efficient implementation but I am afraid I have no scheduled updates soon as I am busy with other projects. However, I may be able to help you speed up your experiment if you provide me with more details on the data dimensions.
Thanks.
Hi Ahmed,
At the moment we're using 27 features. How does the algorithm's complexity grow with feature size K?
I should also mention this is for a COVID-19 research project.
Best, Tom
Hi Tom,
I see. 27 features is a lot because the algorithm searches for many interaction terms between subsets of features so the complexity grows by (num_features choose 2) just to cover pairwise interactions. With your number of features, this means that the final expression will have 351 G functions and that's why it is slow. The alternative Kolmogorov representation also grows exponentially with number of features. I think that our approach is most useful when you have only few features but these interact in a complex way so you can get a precise mathematical equation that describes their interaction.
To proceed with your analysis, I suggest you first do feature selection to include only relevant features into your model. If these relevant features are less than 10, then the algorithm should run in a reasonable amount of time. In my experience, most medical data sets are usually dominated by only few strong predictors so I guess feature selection as a pre-processing step may give you s thinner data set.
Thanks for working on COVID-19! Wish you good luck with your analysis!
Hi Ahmed,
Thank you for making your code open-source and simple to use. We're using this to explain some black-box models and it works as you would expect. The problem is that the code runs on a single CPU thread, and takes a very long time to run. Is there any existing approach to getting faster training? Are there any plans to improve performance?
Best, Tom