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

Speedup similarity forest regressor and add parallelization at the extracted forest size level in the training

Fix parallelization and estimator default hyperparams in kmeans and similarity methods. Fix on resume mode in train.py. Fix stage5 saving (tmp) in compute_results.py

Add on resume mode for the experiment training (and set the overwrite of the resulting model of the experiment optional)

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.

Add quick working version of diamonds dataset to test that the improvement of OMP are consistent in other regression tasks.

- Remove extracted_forest_sizes_number parameter from compute_results.py and retreive the value instead;
- Add almost all remaining experiment config files of stages 1, 2 and 3;
- Add almost all remaining result plots of stages 1, 2 and 3;
- Add some temporary scripts to run all stages experiments.

- Add command lines example for stage 3;
- Add experiment_id option that is useful sometimes;
- Fix subsets_used param;
- Remove experiment_id in config experiment file names;
- Add config experiment files for stages 2 and 3;
- Add results for stages 2 and 3 (california_housing).

- Fix possible issues for extracted forest sizes computation: around to reduce possible zeroes and remove duplicates;
- Create output experiment stage dir if not exists;
- Add base_score_metric to model raw results class;
- Add best params for lfw_pairs (maybe try with a larger number of random seeds since the score is not that high).