- Add the metric name used in trainer.py logging;
- Fix arg "subsets_used" in train.py.

- Remove useless load function of model_factory
- Replace hyperparameters files with new ones

- Update TODO list.

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

- Update wrong load_dotenv in compute_results and compute_hyperparams;
- Replace old remaining task.CLASSIFICATION enum.

- Fix small typos and issues;
- Add some best hyperparameters (TODO: fix score for regression task, and separate best parameters for regression and classif tasks).

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

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

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 experiment_configuration parameter to run an experiment from a json configuration file. If the experiment configuration are commnig from the arguments, save it to a file to keep trace of it;
- Add few comments in train.py.

- Use as much CPU as possible when training a random forest regressor.

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

Resolve "Add plots"

See merge request !5

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