Fix normalize_D missing transpose and fix stage4 result path

code/bolsonaro/models/omp_forest.py

@@ -123,7 +123,9 @@ class SingleOmpForest(OmpForest):
         forest_predictions = self._base_estimator_predictions(X)
 
         if self._models_parameters.normalize_D:
+            forest_predictions = forest_predictions.T
             forest_predictions /= self._forest_norms
+            forest_predictions = forest_predictions.T
 
         return self._make_omp_weighted_prediction(forest_predictions, self._omp, self._models_parameters.normalize_weights)
 
@@ -139,7 +141,9 @@ class SingleOmpForest(OmpForest):
         forest_predictions = self._base_estimator_predictions(X).T
 
         if self._models_parameters.normalize_D:
+            forest_predictions = forest_predictions.T
             forest_predictions /= self._forest_norms
+            forest_predictions = forest_predictions.T
 
         weights = self._omp.coef_
         omp_trees_indices = np.nonzero(weights)[0]

code/bolsonaro/models/omp_forest_classifier.py

@@ -37,7 +37,9 @@ class OmpForestBinaryClassifier(SingleOmpForest):
         forest_predictions = np.array([tree.predict_proba(X) for tree in self._base_forest_estimator.estimators_])
 
         if self._models_parameters.normalize_D:
+            forest_predictions = forest_predictions.T
             forest_predictions /= self._forest_norms
+            forest_predictions = forest_predictions.T
 
         weights = self._omp.coef_
         omp_trees_indices = np.nonzero(weights)
@@ -119,7 +121,9 @@ class OmpForestMulticlassClassifier(OmpForest):
         forest_predictions = np.array([tree.predict_proba(X) for tree in self._base_forest_estimator.estimators_]).T
 
         if self._models_parameters.normalize_D:
+            forest_predictions = forest_predictions.T
             forest_predictions /= self._forest_norms
+            forest_predictions = forest_predictions.T
 
         label_names = []
         preds = []

code/compute_results.py

@@ -389,7 +389,7 @@ if __name__ == "__main__":
             raise ValueError('Score metrics of all experiments must be the same.')
         experiments_score_metric = base_with_params_experiment_score_metric
 
-        output_path = os.path.join(args.results_dir, args.dataset_name, 'stage4_fix')
+        output_path = os.path.join(args.results_dir, args.dataset_name, 'stage4')
         pathlib.Path(output_path).mkdir(parents=True, exist_ok=True)
 
         Plotter.plot_stage2_losses(