clean scripts

@leo.bouscarrat @luc.giffon

Il reste encore à ajouter plus de plots dans compute_results.py (pour l'instant il y a un exemple qui plot les loss sur train/dev/test).

Question : Dans le notebook il y a une version de la prédiction calculée avec une division de la somme des poids, et une version sans. C'était laquelle la bonne déjà ?

EDIT 1 - Exemple d'utilisation :

python code/train.py --dataset_name=california_housing --normalize_D --dataset_normalizer=standard --extracted_forest_size 10 20 30 --random_seed_number=2
python code/compute_results.py --experiment_ids 1

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',