bugfix in storing/loading KerasSequenceCNN (deepcopy on keras object …

…crashes in newer versions)

immuneML/ml_methods/KerasSequenceCNN.py

@@ -69,7 +69,6 @@ def __init__(self, units_per_layer: list = None, activation: str = None, trainin
         self.training_percentage = training_percentage
 
         self.background_probabilities = None
-        self.CNN = None
         self.label = None
         self.class_mapping = None
         self.result_path = None
@@ -202,7 +201,7 @@ def store(self, path: Path, feature_names=None, details_path: Path = None):
 
         self.model.save(path / "model.keras")
 
-        custom_vars = copy.deepcopy(vars(self))
+        custom_vars = self.get_params()
         del custom_vars["model"]
         del custom_vars["result_path"]
 
@@ -234,8 +233,15 @@ def check_if_exists(self, path):
         return self.model is not None
 
     def get_params(self):
-        params = copy.deepcopy(vars(self))
-        params["model"] = copy.deepcopy(self.model).state_dict()
+        params = dict()
+
+        # using 'deepcopy' on the model directly results in an error, therefore loop over all other items
+        for key, value in vars(self).items():
+            if key != "model":
+                params[key] = copy.deepcopy(value)
+
+        params["model"] = copy.deepcopy(self.model.get_config())
+
         return params
 
     def get_label_name(self):

test/ml_methods/test_kerasSequenceCNN.py

@@ -75,21 +75,17 @@ def _test_fit(self):
         cnn2 = KerasSequenceCNN()
         cnn2.load(path / "model_storage")
 
-        cnn2_vars = vars(cnn2)
-        del cnn2_vars["CNN"]
-        cnn_vars = vars(cnn)
-        del cnn_vars["CNN"]
+        cnn2_params = cnn2.get_params()
+        cnn_params = cnn.get_params()
 
-        for item, value in cnn_vars.items():
+        for item, value in cnn_params.items():
             if isinstance(value, Label):
-                self.assertDictEqual(vars(value), (vars(cnn2_vars[item])))
-            elif not isinstance(value, keras.Sequential):
-                self.assertEqual(value, cnn2_vars[item])
+                self.assertDictEqual(vars(value), (vars(cnn2_params[item])))
+            else:
+                self.assertEqual(value, cnn2_params[item])
 
         predictions_proba2 = cnn2.predict_proba(enc_dataset.encoded_data, label)
 
-        print(predictions_proba2)
-
         self.assertTrue(all(predictions_proba["CMV"]["yes"] == predictions_proba2["CMV"]["yes"]))
         self.assertTrue(all(predictions_proba["CMV"]["no"] == predictions_proba2["CMV"]["no"]))