Update fix of normalize_D in the case of wo weights. Add density plot in...

Update fix of normalize_D in the case of wo weights. Add density plot in compute results for all stages and add some plots in results.

code/bolsonaro/models/omp_forest.py

@@ -123,9 +123,7 @@ 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)
 

code/bolsonaro/models/omp_forest_classifier.py

@@ -119,9 +119,7 @@ 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 = []
@@ -149,7 +147,9 @@ 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

@@ -156,6 +156,7 @@ 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
+    DEFAULT_WO_LOSS_PLOTS = 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].')
@@ -168,6 +169,7 @@ if __name__ == "__main__":
     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.')
+    parser.add_argument('--wo_loss_plots', action='store_true', default=DEFAULT_WO_LOSS_PLOTS, help='Do not compute the loss plots.')
     args = parser.parse_args()
 
     if args.stage not in list(range(1, 6)):
@@ -181,7 +183,7 @@ if __name__ == "__main__":
     # Create recursively the results dir tree
     pathlib.Path(args.results_dir).mkdir(parents=True, exist_ok=True)
 
-    if args.stage == 1:
+    if args.stage == 1 and not args.wo_loss_plots:
         if len(args.experiment_ids) != 6:
             raise ValueError('In the case of stage 1, the number of specified experiment ids must be 6.')
 
@@ -221,8 +223,8 @@ if __name__ == "__main__":
             wo_params_extracted_forest_sizes, random_wo_params_experiment_score_metric = \
                 extract_scores_across_seeds_and_extracted_forest_sizes(
                 args.models_dir, args.results_dir, int(args.experiment_ids[4]))
-        # base_wo_params
-        logger.info('Loading base_wo_params experiment scores...')
+        # omp_wo_params
+        logger.info('Loading omp_wo_params experiment scores...')
         omp_wo_params_train_scores, omp_wo_params_dev_scores, omp_wo_params_test_scores, _, \
             omp_wo_params_experiment_score_metric = extract_scores_across_seeds_and_extracted_forest_sizes(
                 args.models_dir, args.results_dir, int(args.experiment_ids[5]))
@@ -262,7 +264,7 @@ if __name__ == "__main__":
             ylabel=experiments_score_metric,
             title='Loss values of {}\nusing best and default hyperparameters'.format(args.dataset_name)
         )
-    elif args.stage == 2:
+    elif args.stage == 2 and not args.wo_loss_plots:
         if len(args.experiment_ids) != 4:
             raise ValueError('In the case of stage 2, the number of specified experiment ids must be 4.')
 
@@ -308,7 +310,7 @@ if __name__ == "__main__":
             xlabel='Number of trees extracted',
             ylabel=experiments_score_metric,
             title='Loss values of {}\nusing different normalizations'.format(args.dataset_name))
-    elif args.stage == 3:
+    elif args.stage == 3 and not args.wo_loss_plots:
         if len(args.experiment_ids) != 3:
             raise ValueError('In the case of stage 3, the number of specified experiment ids must be 3.')
 
@@ -365,7 +367,7 @@ if __name__ == "__main__":
             xlabel='Number of trees extracted',
             ylabel=experiments_score_metric,
             title='Loss values of {}\nusing different training subsets'.format(args.dataset_name))"""
-    elif args.stage == 4:
+    elif args.stage == 4 and not args.wo_loss_plots:
         if len(args.experiment_ids) != 3:
             raise ValueError('In the case of stage 4, the number of specified experiment ids must be 3.')
 
@@ -427,11 +429,7 @@ if __name__ == "__main__":
             xlabel='Number of trees extracted',
             ylabel=experiments_score_metric,
             title='Loss values of {}\nusing best params of previous stages'.format(args.dataset_name))
-
-        experiment_weights = extract_weights_across_seeds(args.models_dir, args.results_dir, args.experiment_ids[2])
-
-        Plotter.weight_density(experiment_weights, os.path.join(output_path, 'weight_density.png'))
-    elif args.stage == 5:
+    elif args.stage == 5 and not args.wo_loss_plots:
         # Retreive the extracted forest sizes number used in order to have a base forest axis as long as necessary
         extracted_forest_sizes_number = retreive_extracted_forest_sizes_number(args.models_dir, int(args.experiment_ids[1]))
         all_labels = list()
@@ -475,8 +473,9 @@ if __name__ == "__main__":
                 continue
 
             logger.info(f'Loading {label} experiment scores...')
+            current_experiment_id = int(args.experiment_ids[i].split('=')[1])
             _, _, current_test_scores, _, _ = extract_scores_across_seeds_and_extracted_forest_sizes(
-                args.models_dir, args.results_dir, int(args.experiment_ids[i].split('=')[1]))
+                args.models_dir, args.results_dir, current_experiment_id)
             all_labels.append(label)
             all_scores.append(current_test_scores)
 
@@ -491,7 +490,42 @@ if __name__ == "__main__":
             xlabel='Number of trees extracted',
             ylabel=base_with_params_experiment_score_metric,
             title='Loss values of {}\nusing best params of previous stages'.format(args.dataset_name))
-    else:
-        raise ValueError('This stage number is not supported yet, but it will be!')
+
+    if args.plot_weight_density:
+        root_output_path = os.path.join(args.results_dir, args.dataset_name, f'stage{args.stage}')
+
+        if args.stage == 1:
+            omp_experiment_ids = [('omp_with_params', args.experiment_ids[2]), ('omp_wo_params', args.experiment_ids[2])]
+        elif args.stage == 2:
+            omp_experiment_ids = [('no_normalization', args.experiment_ids[0]),
+                ('normalize_D', args.experiment_ids[1]),
+                ('normalize_weights', args.experiment_ids[2]),
+                ('normalize_D_and_weights', args.experiment_ids[3])]
+        elif args.stage == 3:
+            omp_experiment_ids = [('train-dev_subset', args.experiment_ids[0]),
+                ('train-dev_train-dev_subset', args.experiment_ids[1]),
+                ('train-train-dev_subset', args.experiment_ids[2])]
+        elif args.stage == 4:
+            omp_experiment_ids = [('omp_with_params', args.experiment_ids[2])]
+        elif args.stage == 5:
+            omp_experiment_ids = [('omp_with_params', args.experiment_ids[2])]
+            for i in range(3, len(args.experiment_ids)):
+                if 'kmeans' in args.experiment_ids[i]:
+                    label = 'kmeans'
+                elif 'similarity' in args.experiment_ids[i]:
+                    label = 'similarity'
+                elif 'ensemble' in args.experiment_ids[i]:
+                    label = 'ensemble'
+                else:
+                    logger.error('Invalid value encountered')
+                    continue
+
+                current_experiment_id = int(args.experiment_ids[i].split('=')[1])
+                omp_experiment_ids.append((label, current_experiment_id))
+
+        for (experiment_label, experiment_id) in omp_experiment_ids:
+            logger.info(f'Computing weight density plot for experiment {experiment_label}...')
+            experiment_weights = extract_weights_across_seeds(args.models_dir, args.results_dir, experiment_id)
+            Plotter.weight_density(experiment_weights, os.path.join(root_output_path, f'weight_density_{experiment_label}.png'))
 
     logger.info('Done.')