Start to add back density plot

code/bolsonaro/models/model_raw_results.py

@@ -6,12 +6,12 @@ import datetime
 
 class ModelRawResults(object):
 
-    def __init__(self, model_object, training_time,
+    def __init__(self, model_weights, training_time,
         datetime, train_score, dev_score, test_score,
         train_score_base, dev_score_base,
         test_score_base, score_metric, base_score_metric):
 
-        self._model_object = model_object
+        self._model_weights = model_weights
         self._training_time = training_time
         self._datetime = datetime
         self._train_score = train_score
@@ -24,8 +24,8 @@ class ModelRawResults(object):
         self._base_score_metric = base_score_metric
     
     @property
-    def model_object(self):
-        return self.model_object
+    def model_weights(self):
+        return self.model_weights
 
     @property
     def training_time(self):

code/bolsonaro/models/omp_forest.py

@@ -8,6 +8,7 @@ from sklearn.base import BaseEstimator
 
 
 class OmpForest(BaseEstimator, metaclass=ABCMeta):
+
     def __init__(self, models_parameters, base_forest_estimator):
         self._base_forest_estimator = base_forest_estimator
         self._models_parameters = models_parameters
@@ -95,6 +96,7 @@ class OmpForest(BaseEstimator, metaclass=ABCMeta):
         pass
 
 class SingleOmpForest(OmpForest):
+
     def __init__(self, models_parameters, base_forest_estimator):
         # fit_intercept shouldn't be set to False as the data isn't necessarily centered here
         # normalization is handled outsite OMP

code/bolsonaro/trainer.py

@@ -126,8 +126,15 @@ class Trainer(object):
         :param model: Object with
         :param models_dir: Where the results will be saved
         """
+
+        model_weights = ''
+        if type(model) == RandomForestRegressor:
+            model_weights = model.coef_
+        elif type(model) == OmpForestRegressor:
+            model_weights = model._omp.coef_
+
         results = ModelRawResults(
-            model_object='',
+            model_weights=model_weights,
             training_time=self._end_time - self._begin_time,
             datetime=datetime.datetime.now(),
             train_score=self.__score_func(model, self._dataset.X_train, self._dataset.y_train),

code/compute_results.py

@@ -33,6 +33,8 @@ def extract_scores_across_seeds_and_extracted_forest_sizes(models_dir, results_d
     # Used to check if all losses were computed using the same metric (it should be the case)
     experiment_score_metrics = list()
 
+    all_weights = list()
+
     # For each seed results stored in models/{experiment_id}/seeds
     seeds = os.listdir(experiment_seed_root_path)
     seeds.sort(key=int)
@@ -120,6 +122,7 @@ if __name__ == "__main__":
 
     DEFAULT_RESULTS_DIR = os.environ["project_dir"] + os.sep + 'results'
     DEFAULT_MODELS_DIR = os.environ["project_dir"] + os.sep + 'models'
+    DEFAULT_PLOT_WEIGHT_DENSITY = False
 
     parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
     parser.add_argument('--stage', nargs='?', type=int, required=True, help='Specify the stage number among [1, 5].')
@@ -130,6 +133,7 @@ if __name__ == "__main__":
     parser.add_argument('--dataset_name', nargs='?', type=str, required=True, help='Specify the dataset name. TODO: read it from models dir directly.')
     parser.add_argument('--results_dir', nargs='?', type=str, default=DEFAULT_RESULTS_DIR, help='The output directory of the results.')
     parser.add_argument('--models_dir', nargs='?', type=str, default=DEFAULT_MODELS_DIR, help='The output directory of the trained models.')
+    parser.add_argument('--plot_weight_density', action='store_true', default=DEFAULT_PLOT_WEIGHT_DENSITY, help='Plot the weight density. Only working for regressor models for now.')
     args = parser.parse_args()
 
     if args.stage not in list(range(1, 6)):
@@ -224,6 +228,8 @@ if __name__ == "__main__":
             ylabel=experiments_score_metric,
             title='Loss values of {}\nusing best and default hyperparameters'.format(args.dataset_name)
         )
+
+        Plotter.plot_weight_density()
     elif args.stage == 2:
         if len(args.experiment_ids) != 4:
             raise ValueError('In the case of stage 2, the number of specified experiment ids must be 4.')