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

Integrate Paolo's code of method 'Ensemble selection from libraries of models' by Rich Caruana et al

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

- 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.

- Add new best params for 7 datasets.

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

- 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