feat(Bagging): Implemented Bagging load (re-using result directory) f…

…unctionality

aucmedi/ensemble/bagging.py

@@ -233,23 +233,25 @@ def dump(self, directory_path):
             self.cache_dir = directory_path
 
     # Load model from file
-    def load(self, file_path, custom_objects={}):
-        """ Load neural network model and its weights from a file.
-
-        After loading, the model will be compiled.
-
-        If loading a model in ".hdf5" format, it is not necessary to define any custom_objects.
+    def load(self, directory_path):
+        """ Load a Bagging model directory which can be used for aggregated inference.
 
         Args:
-            file_path (str):            Input path, from which the model will be loaded.
-            custom_objects (dict):      Dictionary of custom objects for compiling
-                                        (e.g. non-TensorFlow based loss functions or architectures).
+            directory_path (str):       Input path, from which the Bagging models will be loaded.
         """
-        # Create model input path
-        self.model = load_model(file_path, custom_objects, compile=False)
-        # Compile model
-        self.model.compile(optimizer=Adam(learning_rate=self.learning_rate),
-                           loss=self.loss, metrics=self.metrics)
+        # Check directory existence
+        if not os.path.exists(directory_path):
+            raise FileNotFoundError("Provided model directory path does not exist!",
+                                    directory_path)
+        # Check model existence
+        for i in range(self.k_fold):
+            path_model = os.path.join(directory_path,
+                                      "cv_" + str(i) + ".model.hdf5")
+            if not os.path.exists(path_model):
+                raise FileNotFoundError("Bagging model for fold " + str(i) + \
+                                        " does not exist!", path_model)
+        # Update model directory
+        self.cache_dir = directory_path
 
 #-----------------------------------------------------#
 #                     Subroutines                     #