First upload

background.txt

@@ -0,0 +1,42 @@
+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
+
+-