Externalize BlackboxClassifier dataset (x and predictions)

apt/utils/models/model.py

@@ -316,10 +316,20 @@ class BlackboxClassifierPredictions(BlackboxClassifier):
 
         self._nb_classes = get_nb_classes(y_pred)
         self._input_shape = x_pred.shape[1:]
+        self._x_pred = x_pred
+        self._y_pred = y_pred
         predict_fn = (x_pred, y_pred)
         self._art_model = BlackBoxClassifier(predict_fn, self._input_shape, self._nb_classes, fuzzy_float_compare=True,
                                              preprocessing=None)
 
+    def get_predictions(self) -> Tuple[OUTPUT_DATA_ARRAY_TYPE, OUTPUT_DATA_ARRAY_TYPE]:
+        """
+        Return all the data for which the model contains predictions.
+
+        :return: Tuple containing data and predictions as numpy arrays.
+        """
+        return self._x_pred, self._y_pred
+
 
 class BlackboxClassifierPredictFunction(BlackboxClassifier):
     """

tests/test_model.py

@@ -12,6 +12,8 @@ from sklearn.ensemble import RandomForestClassifier
 from tensorflow.keras.models import Sequential
 from tensorflow.keras.layers import Dense, Input
 
+from art.utils import check_and_transform_label_format
+
 
 from art.utils import to_categorical
 
@@ -158,6 +160,10 @@ def test_blackbox_classifier_no_test():
     score = model.score(train)
     assert (score == 1.0)
 
+    predictions_x, predictions_y = model.get_predictions()
+    assert np.array_equal(predictions_x, x_train)
+    assert np.array_equal(predictions_y, check_and_transform_label_format(y_train, nb_classes=3))
+
 
 def test_blackbox_classifier_no_train():
     (_, _), (x_test, y_test) = dataset_utils.get_iris_dataset_np()
@@ -171,6 +177,10 @@ def test_blackbox_classifier_no_train():
     score = model.score(test)
     assert (score == 1.0)
 
+    predictions_x, predictions_y = model.get_predictions()
+    assert np.array_equal(predictions_x, x_test)
+    assert np.array_equal(predictions_y, check_and_transform_label_format(y_test, nb_classes=3))
+
 
 def test_blackbox_classifier_no_test_y():
     (x_train, y_train), (x_test, _) = dataset_utils.get_iris_dataset_np()