Introduce the notion of "stage" in the experiments (see TODO comment in compute_results.py file. Add example of experiment configuration file tree in experiments/boston/stage3 for stage 3 (unoptimized parameters due to missing stage 1 and 2 results).

- Add a TODO for the subset in train
- Remove an useless new line

- Fix bug of resolve_experiment_id after 10 exp ids;
- Display the new experiment id at the beginning of the training;
- For now there's only a simple losses plot in compute_results.

- In compute_results, add loadenv, load raw results, and update experiment_ids so that it's possible to specify a list of experiments ids. The default behavior is to load all exp ids;
- Fix normalization option in train.py. By default it normalizes D, and it doesn't when specify wo_normalization option;
- Fix logger.warn to logger.warning in train.py
- Replace the dumping of result in trainer.py by a dedicated class to save and load the trained model and training metadatas: model_raw_results.py;
- Rename too long func DatasetLoader.load_from_name to DatasetLoader.load;
- Add loading functions in dataset_parameters and model_parameters;
- Set console logging level to INFO to summarize the most important console logs;
- Add a load function in model_factory;
- In omp_forest_regressor, move private funcs to the bottom of the file.

TODO: compute the plot from the loaded raw results in compute_results file.

rassemble le code dans un répertoire 'code' + update setup.py en conséquence + ajoute scikit-learn à requirements.txt

- Add error handling module (TODO: add logging over the code)
- Record dataset parameters and model parameters
- Begin compute_results, plotter and visualize files