Fix hyperparams bugs in base and random. Fix extracted forest size used in random. Factorize random fitting

Add Paolo's first implementation of this paper: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2822360/

- Add new results for stage4.

- Add stage4 (results and experiments);
- Do not save model object.

- Remove useless getter in Dataset class.

- Reduce the extracted forest sizes upper bound and number because OMP seems to converge only with small forest sizes;
- Add extraction_strategy parameter in order to save base forest and the forests trained with the same size as the extracted forest sizes used in the experiment that used OMP.

- Update TODO list.

Big changes: Create intermediate classes OMPForest and SingleOmpForest for code factoring: share code between OmpForestRegressor and OmpForestBinaryClassifer. Remove set_wweights and set_forest which are not relevant anymore. load function from model_factory isn't trustfull now: raises an error. TODO: multiclass classifier

Replace use_dev_subset by subsets_used parameter, in order to specify more clearly which combination of train dev to used to train the forest and OMP

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

- 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.

- Add train_on_subset option to specify on which subset the model will be trained (either train or dev);
- find_dotenv() only working by specifying the example env on my machine?
- Add the seeds option to specify the seed(s) to use, and remove the use_random_seed, because it's obv if random_seed_number is used;
- Use a logger in train.py instead of prints.

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