- Definitely use the correct forest size (either the one from best hyperparameters or the one specified in parameter);
- Use a number of extracted forest sizes proportional as the forest size instead of fixed forest size;
- Add an option to save the current command line name instead of using the unamed directory;
- Add new california housing dataset best hyperparameters, and convert all value types that are number from string to int/float in other best hyperparameter files;
- Remove useless code from compute_results.py in prevision of the changes;
- Before best hyperparameters saving, save number as int or float instead of string;
- Add job_number option for parallelisation in both train.py and compute_hyperparameters.py scripts;
- Clean-up TODO list.

- Add new best params for 7 datasets.

- Ignore unamed experiment configuration file backups;
- Factorize default dataset loading parameters;
- Add missing return_X_y in basic dataset loaders.

- Move bolsonaro imports on top of the file in compute_hyperparam file;
- Move change_binary_func_load to utils file.

When training, look if there is bayesian search results, if yes use this. Exception: forest_size use the one given by parser if applicable