- Definitely use the correct forest size (either the one from best hyperparameters or the one specified in parameter);
- Use a number of extracted forest sizes proportional as the forest size instead of fixed forest size;
- Add an option to save the current command line name instead of using the unamed directory;
- Add new california housing dataset best hyperparameters, and convert all value types that are number from string to int/float in other best hyperparameter files;
- Remove useless code from compute_results.py in prevision of the changes;
- Before best hyperparameters saving, save number as int or float instead of string;
- Add job_number option for parallelisation in both train.py and compute_hyperparameters.py scripts;
- Clean-up TODO list.

- Add new best params for 7 datasets.

- Ignore unamed experiment configuration file backups;
- Factorize default dataset loading parameters;
- Add missing return_X_y in basic dataset loaders.

Finish to fix sklearn dataset fetchers. Keep 20newsgroups_vectorized instead of non vectorized version that is the preprocessed version of this text dataset. Remove kddcup99 dataset.

Fix most of the missing dataset fetchers. Remainings: 20newsgroups, 20newsgroups_vectorized, kddcup99

- Update TODO list.

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

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

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

- Save and load json files from __dict__.