From 3f64a7297e6ef1633e54384e603566c04fcd3a69 Mon Sep 17 00:00:00 2001 From: VincentAURIAU Date: Thu, 4 Apr 2024 11:37:54 +0200 Subject: [PATCH] attempt at getting images side bby side --- docs/paper/paper.md | 4 +--- 1 file changed, 1 insertion(+), 3 deletions(-) diff --git a/docs/paper/paper.md b/docs/paper/paper.md index 323ea6d2..fd7d223c 100644 --- a/docs/paper/paper.md +++ b/docs/paper/paper.md @@ -82,9 +82,7 @@ Choice-Learn also ambitions to offer a set of tools revolving around choice mode ## RAM usage comparison -![Memory usage comparison. \label{fig:ram_usage}](../illustrations/fbid_RAM.png) - -![Memory usage comparison on the Expedia Dataset. \label{fig:exp_ram_usage}](../illustrations/expedia_RAM.png) +![RAM usage with and without FeaturesByIDs. \label{fig:ram_usage}](../illustrations/fbid_RAM.png){ width=60% } ![Memory usage comparison on the Expedia Dataset. \label{fig:exp_ram_usage}](../illustrations/expedia_RAM.png){ width=60% } We conduct a small study on datasets memory usage in order to showcase the efficiency of Features by IDs provided by Choice-Learn. We consider a case where we have a feature that repeats itself over the dataset. For example if we represent a location with one-hot encoding, the different locations can be represented by a matrix of shape (n_locations, n_locations) that are repeated over the dataset of size dataset_size. In the Figure \autoref{fig:ram_usage} we compare the memory usage for different dataset sizes and n_locations=10 and 100. It shows how Choice-learn can save several magnitude of memory usage.