Better to take non zero values of list as indicated in the numpy doc

code/bolsonaro/models/omp_forest.py

@@ -136,14 +136,11 @@ class SingleOmpForest(OmpForest):
         :param X: a Forest
         :return: a np.array of the predictions of the entire forest
         """
-        forest_predictions = self._base_estimator_predictions(X).T
+        forest_predictions = np.array([tree.predict(X) for tree in self._base_forest_estimator.estimators_])
 
         if self._models_parameters.normalize_D:
             forest_predictions /= self._forest_norms
 
         weights = self._omp.coef_
-        omp_trees_indices = np.nonzero(weights)[0]
-
-        select_trees = np.mean(forest_predictions[omp_trees_indices], axis=0)
-        print(len(omp_trees_indices))
+        select_trees = np.mean(forest_predictions[weights != 0], axis=0)
         return select_trees

code/bolsonaro/models/omp_forest_classifier.py

@@ -40,9 +40,7 @@ class OmpForestBinaryClassifier(SingleOmpForest):
             forest_predictions /= self._forest_norms
 
         weights = self._omp.coef_
-        omp_trees_indices = np.nonzero(weights)
-
-        omp_trees_predictions = forest_predictions[omp_trees_indices].T[1]
+        omp_trees_predictions = forest_predictions[weights != 0].T[1]
 
         # Here forest_pred is the probability of being class 1.