Add the weights normalization parameter (but not implemented yet)

code/bolsonaro/models/model_parameters.py

@@ -5,11 +5,12 @@ import os
 
 class ModelParameters(object):
 
-    def __init__(self, forest_size, extracted_forest_size, normalize_D, subsets_used, seed=None):
+    def __init__(self, forest_size, extracted_forest_size, normalize_D, subsets_used, normalize_weights, seed):
         self._forest_size = forest_size
         self._extracted_forest_size = extracted_forest_size
         self._normalize_D = normalize_D
         self._subsets_used = subsets_used
+        self._normalize_weights = normalize_weights
         self._seed = seed
 
     @property
@@ -28,6 +29,10 @@ class ModelParameters(object):
     def subsets_used(self):
         return self._subsets_used
 
+    @property
+    def normalize_weights(self):
+        return self._normalize_weights
+
     @property
     def seed(self):
         return self._seed

code/bolsonaro/models/omp_forest_regressor.py

@@ -56,7 +56,8 @@ class OmpForestRegressor(BaseEstimator):
         if self._models_parameters.normalize_D:
             D /= self._forest_norms
 
-        predictions = D @ self.weights
+        # TODO: use self._models_parameters.normalize_weights here
+        predictions = D @ self._weights
 
         return predictions
 

code/train.py

@@ -31,6 +31,7 @@ if __name__ == "__main__":
     DEFAULT_RANDOM_SEED_NUMBER = 1
     DEFAULT_SUBSETS_USED = 'train,dev'
     DEFAULT_DISABLE_PROGRESS = False
+    DEFAULT_normalize_weights = False
 
     begin_random_seed_range = 1
     end_random_seed_range = 2000
@@ -48,6 +49,7 @@ if __name__ == "__main__":
     parser.add_argument('--seeds', nargs='+', type=int, default=None, help='Specific a list of seeds instead of generate them randomly')
     parser.add_argument('--subsets_used', nargs='+', type=str, default=DEFAULT_SUBSETS_USED, help='train,dev: forest on train, OMP on dev. train+dev,train+dev: both forest and OMP on train+dev. train,train+dev: forest on train+dev and OMP on dev.')
     parser.add_argument('--disable_progress', action='store_true', default=DEFAULT_DISABLE_PROGRESS, help='Disable the progress bars.')
+    parser.add_argument('--normalize_weights', action='store_true', default=DEFAULT_normalize_weights, help='Divide the predictions by the weights sum.')
     args = parser.parse_args()
 
     pathlib.Path(args.models_dir).mkdir(parents=True, exist_ok=True)
@@ -102,6 +104,7 @@ if __name__ == "__main__":
                         extracted_forest_size=extracted_forest_size,
                         normalize_D=args.normalize_D,
                         subsets_used=args.subsets_used,
+                        normalize_weights=args.normalize_weights,
                         seed=seed
                     )
                     model_parameters.save(sub_models_dir, experiment_id)