diff --git a/code/compute_results.py b/code/compute_results.py index 78d30272fcf56620249208f356c3d04bb9177f9d..dad99922deaa802bd2e794b96f4f3b0f42c97239 100644 --- a/code/compute_results.py +++ b/code/compute_results.py @@ -116,6 +116,44 @@ if __name__ == "__main__": title='Loss values of the trained model' ) + """ + TODO: + For each dataset: + 0) A figure for the selection of the best base forest model hyperparameters (best vs default/random hyperparams) + 1) A figure for the selection of the best dataset normalization method + 2) A figure for the selection of the best combination of dataset: normalization vs D normalization vs weights normalization + 3) A figure for the selection of the most relevant subsets combination: train,dev vs train+dev,train+dev vs train,train+dev + 4) A figure to finally compare the perf of our approach using the previous selected parameters vs the baseline vs other papers + + 2) + In all axis: + - untrained forest + - trained base forest (straight line cause it doesn't depend on the number of extracted trees) + + Axis 1: + - test with forest on train+dev and OMP on train+dev + - test with forest on train+dev and OMP on train+dev with dataset normalization + - test with forest on train+dev and OMP on train+dev with dataset normalization + D normalization + - test with forest on train+dev and OMP on train+dev with dataset normalization + weights normalization + - test with forest on train+dev and OMP on train+dev with dataset normalization + D normalization + weights normalization + + Axis 2: + - test with forest on train and OMP on dev + - test with forest on train and OMP on dev with dataset normalization + - test with forest on train and OMP on dev with dataset normalization + D normalization + - test with forest on train and OMP on dev with dataset normalization + weights normalization + - test with forest on train and OMP on dev with dataset normalization + D normalization + weights normalization + + Axis 3: + - test with forest on train and OMP train+dev + - test with forest on train and OMP train+dev with dataset normalization + - test with forest on train and OMP train+dev with dataset normalization + D normalization + - test with forest on train and OMP train+dev with dataset normalization + weights normalization + - test with forest on train and OMP train+dev with dataset normalization + D normalization + weights normalization + + IMPORTANT: Same seeds used in all axis. + """ + # Plot the density of the weights Plotter.weight_density( file_path=args.results_dir + os.sep + experiment_id + os.sep + 'density_weight.png',