set random results linear to ease the read of coherence preds plot reading

code/bolsonaro/models/kmeans_forest_regressor.py

@@ -19,7 +19,7 @@ class KMeansForestRegressor(BaseEstimator, metaclass=ABCMeta):
     def __init__(self, models_parameters, score_metric=mean_squared_error):
         self._models_parameters = models_parameters
         self._estimator = RandomForestRegressor(**self._models_parameters.hyperparameters,
-            random_state=self._models_parameters.seed, n_jobs=2)
+            random_state=self._models_parameters.seed, n_jobs=1)
         self._extracted_forest_size = self._models_parameters.extracted_forest_size
         self._score_metric = score_metric
         self._selected_trees = list()
@@ -46,7 +46,7 @@ class KMeansForestRegressor(BaseEstimator, metaclass=ABCMeta):
         # For each cluster select the best tree on the validation set
         extracted_forest_sizes = list(range(self._extracted_forest_size))
         with tqdm_joblib(tqdm(total=self._extracted_forest_size, disable=True)) as prune_forest_job_pb:
-            pruned_forest = Parallel(n_jobs=2)(delayed(self._prune_forest_job)(prune_forest_job_pb,
+            pruned_forest = Parallel(n_jobs=1)(delayed(self._prune_forest_job)(prune_forest_job_pb,
                 extracted_forest_sizes[i], labels, X_val, y_val, self._score_metric)
                 for i in range(self._extracted_forest_size))
 
@@ -56,7 +56,7 @@ class KMeansForestRegressor(BaseEstimator, metaclass=ABCMeta):
     def _prune_forest_job(self, prune_forest_job_pb, c, labels, X_val, y_val, score_metric):
         index = np.where(labels == c)[0]
         with tqdm_joblib(tqdm(total=len(index), disable=True)) as cluster_job_pb:
-            cluster = Parallel(n_jobs=2)(delayed(self._cluster_job)(cluster_job_pb, index[i], X_val, 
+            cluster = Parallel(n_jobs=1)(delayed(self._cluster_job)(cluster_job_pb, index[i], X_val, 
                 y_val, score_metric) for i in range(len(index)))
         best_tree_index = np.argmax(cluster)
         prune_forest_job_pb.update()

code/bolsonaro/trainer.py

@@ -77,7 +77,7 @@ class Trainer(object):
         else:
             raise ValueError("Unknown specified subsets_used parameter '{}'".format(model.models_parameters.subsets_used))
 
-    def train(self, model, extracted_forest_size=None):
+    def train(self, model, extracted_forest_size=None, seed=None):
         """
         :param model: An instance of either RandomForestRegressor, RandomForestClassifier, OmpForestRegressor,
             OmpForestBinaryClassifier, OmpForestMulticlassClassifier.
@@ -88,6 +88,7 @@ class Trainer(object):
         if type(model) in [RandomForestRegressor, RandomForestClassifier]:
             if extracted_forest_size is not None:
                 estimators_index = np.arange(len(model.estimators_))
+                np.random.seed(seed)
                 np.random.shuffle(estimators_index)
                 choosen_estimators = estimators_index[:extracted_forest_size]
                 model.estimators_ = np.array(model.estimators_)[choosen_estimators]

code/compute_results.py

@@ -521,7 +521,7 @@ if __name__ == "__main__":
             ylabel=base_with_params_experiment_score_metric,
             title='Loss values of {}\nusing best params of previous stages'.format(args.dataset_name))
 
-    if args.plot_weight_density or args.plot_preds_coherence:
+    """if args.plot_weight_density or args.plot_preds_coherence:
         root_output_path = os.path.join(args.results_dir, args.dataset_name, f'stage{args.stage}')
 
         if args.stage == 1:
@@ -551,28 +551,28 @@ if __name__ == "__main__":
                     continue
 
                 current_experiment_id = int(args.experiment_ids[i].split('=')[1])
-                omp_experiment_ids.append((label, current_experiment_id))
+                omp_experiment_ids.append((label, current_experiment_id))"""
 
-        for (experiment_label, experiment_id) in omp_experiment_ids:
+        #for (experiment_label, experiment_id) in omp_experiment_ids:
     if args.plot_weight_density:
         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'))
     if args.plot_preds_coherence:
-                all_labels = ['random', 'omp']
+        root_output_path = os.path.join(args.results_dir, args.dataset_name, f'stage4')
+        all_labels = ['random', 'omp', 'omp_normalize_D']
         random_with_params_train_scores, random_with_params_dev_scores, random_with_params_test_scores, \
             with_params_extracted_forest_sizes, random_with_params_experiment_score_metric = \
-                    extract_scores_across_seeds_and_extracted_forest_sizes(args.models_dir, args.results_dir, int(args.experiment_ids[1]))
-                coherence_values = [extract_coherences_across_seeds(args.models_dir, args.results_dir, i) for i in range(2, 4)]
-                print(coherence_values[1])
+            extract_scores_across_seeds_and_extracted_forest_sizes(args.models_dir, args.results_dir, 2)
+        coherence_values = [extract_coherences_across_seeds(args.models_dir, args.results_dir, i) for i in [2, 3, 4]]
         Plotter.plot_stage2_losses(
-                    file_path=root_output_path + os.sep + f"coherences_{'-'.join(all_labels)}.png",
+            file_path=root_output_path + os.sep + f"coherences_{'-'.join(all_labels)}_30_all.png",
             all_experiment_scores=coherence_values,
             all_labels=all_labels,
             x_value=with_params_extracted_forest_sizes,
             xlabel='Number of trees extracted',
             ylabel='Coherence',
             title='Coherence values of {}'.format(args.dataset_name))
-                logger.info(f'Computing preds coherence plot for experiment {experiment_label}...')
+        logger.info(f'Computing preds coherence plot...')
 
     logger.info('Done.')

code/train.py

@@ -169,7 +169,7 @@ def extracted_forest_size_job(extracted_forest_size_job_pb, extracted_forest_siz
         pretrained_model_parameters.save(sub_models_dir, experiment_id)
 
     trainer.init(model, subsets_used=parameters['subsets_used'])
-    trainer.train(model, extracted_forest_size=extracted_forest_size)
+    trainer.train(model, extracted_forest_size=extracted_forest_size, seed=seed)
     #trainer.compute_preds_coherence(model)
     trainer.compute_results(model, sub_models_dir)