1. 06 Mar, 2020 7 commits
  2. 05 Mar, 2020 1 commit
  3. 28 Feb, 2020 1 commit
  4. 04 Feb, 2020 1 commit
  5. 09 Jan, 2020 1 commit
  6. 08 Jan, 2020 1 commit
  7. 29 Dec, 2019 1 commit
  8. 26 Dec, 2019 2 commits
    • Charly Lamothe's avatar
      - Add code for stages 2 and 3 results; · 8de5e96a
      Charly Lamothe authored
      - 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).
    • Charly Lamothe's avatar
      - Add command lines for stage2 experiments; · 58061ea4
      Charly Lamothe authored
      - 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).
  9. 20 Dec, 2019 1 commit
    • Charly Lamothe's avatar
      - Unignore results; · 51ba8a0e
      Charly Lamothe authored
      - Even if hyperparameters file is ignore with skip_best_hyperparams option, still use the same forest_size to be comparable;
      - Update experiment files for stage1 wo_param experiments (using the same forest size as the with_params experiments);
      - In compute_results: remove useless folder creation; temporary add extracted_forest_sizes_number option to specify the extracted forest sizes number; temporary not plotting train and dev losses in stage1 loss values figure;
      - In plotter, clean-up stage1 figure generation;
      - Add first unbiased losses plot (stage1: best params vs default params in california housing dataset).
  10. 19 Dec, 2019 2 commits
  11. 18 Dec, 2019 3 commits
  12. 01 Dec, 2019 1 commit
  13. 22 Nov, 2019 3 commits
  14. 21 Nov, 2019 1 commit
    • Luc Giffon's avatar
      Big changes: Create intermediate classes OMPForest and SingleOmpForest for... · 3f5cdf68
      Luc Giffon authored
      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
  15. 20 Nov, 2019 1 commit
  16. 09 Nov, 2019 3 commits
  17. 08 Nov, 2019 1 commit
  18. 06 Nov, 2019 1 commit
  19. 05 Nov, 2019 6 commits
  20. 04 Nov, 2019 2 commits
    • Charly LAMOTHE's avatar
      - In compute_results, add loadenv, load raw results, and update experiment_ids... · 8b8eb9a5
      Charly LAMOTHE authored
      - 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.
    • Charly LAMOTHE's avatar
      - Add train_on_subset option to specify on which subset the model will be... · 3c6dc3e5
      Charly LAMOTHE authored
      - 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.