Delete Figures/background.txt

Figures/background.txt

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-https://gitlab.com/university-of-prince-edward-isalnd/explanation-aware-optimization-and-automl/-/tree/main/src?ref_type=heads
-
-
-
-############################################################################################################################################################
-Code File Structure
-
-Shell scripts
-
-            h20_batch.sh ->   
-            nsga_batch.sh ->
-            grid_search_batch.sh ->
-
-
-
-
-############################################################################################################################################################
-Code Changes:
-
-- SHAP KernelExplainer
-        Use shap.TreeExplainer on tree-based models instead
-
-- AutoML search size
-        Reduce max_models or max_runtime_secs per fold or pre-select algorithms
-
-- Data transformations
-        Cache intermediate NumPy arrays to skip repeated fit_transform calls in each fold
-
-- Parallel folds
-        if CPU has many cores, parallelize the K-fold loop with joblib.parallel to fully use a higher core count CPU                        
-
-############################################################################################################################################################
-Notes
-- The Slurm headers indicate that the programs should be run on a system with 4 cores per task and 10GB of RAM. 
-  This is quite conservative and would not need to be directed towards a cloud-computing environment to run
-
-- The three jobs run with a run time limit of 11 hours. Considering average Compute Canada / AceNet servers (approx 2.5GHz CPUs), 
-  allocate a time limit of at least 5 hours to run on a 13600KF system (assuming no hyperthreading and E-core processing)
-
-- H20 AutoML supports GPU compute using CUDA libraries. A CUDA accelerate GPU may see performance gains for this computation
-
--