refactor: code de-dup __str__ method

examples/mnist_test.py

@@ -123,7 +123,7 @@ def mnist_test(model: str | AAutoencoder | LabelingVAE):
     idx = np.random.randint(0, len(x_test))
     example: np.ndarray = x_test[idx]
     labels_train = [str(int(i)) for i in y_train]
-    if isinstance(model, LabelingVAE):
+    if isinstance(autoencoder, LabelingVAE):
         autoencoder.learn_labels(x_train, labels_train)
         labeling_accuracy(autoencoder, x_test, y_test)
         res = autoencoder.label(example)

src/easyvae/autoencoder.py

@@ -35,7 +35,7 @@ class AAutoencoder(ABC):
         path = path.removesuffix('.npy')
         np.save(path, self)
 
-    def load(path: str) -> 'ClassicalAutoencoder':
+    def load(path: str) -> 'AAutoencoder':
         path = path.removesuffix('.npy') + '.npy'
         data = np.load(path, allow_pickle=True)
         return data.item()
@@ -56,6 +56,16 @@ class AAutoencoder(ABC):
     def train_dataset(self, *args, **kwargs) -> list[float]:
         pass
 
+    def __str__(self):
+        return "\n".join((
+                    f"Type: {self.__class__.__name__}",
+                    "Encoder:",
+                    f"{self.encoder}",
+                    "Decoder:",
+                    f"{self.decoder}"
+                )
+            )
+
 
 class ClassicalAutoencoder(AAutoencoder):
     plotter_cls = CAPlotter
@@ -64,16 +74,6 @@ class ClassicalAutoencoder(AAutoencoder):
         super().__init__(*args, **kwargs)
         self.losses = []
 
-    def __str__(self):
-        return "\n".join((
-                    f"Type: {__class__.__name__}",
-                    "Encoder:",
-                    f"{self.encoder}",
-                    "Decoder:",
-                    f"{self.decoder}"
-                )
-            )
-
     def loss(self, data_set: list[np.ndarray]) -> float:
         loss = 0
         for x in data_set:
@@ -149,15 +149,6 @@ class VariationalAutoencoder(AAutoencoder):
         self.KL_losses = []
         self.recon_losses = []
 
-    def __str__(self):
-        return "\n".join((
-                f"Type: {__class__.__name__}",
-                "Encoder:",
-                f"{self.encoder}",
-                "Decoder:",
-                f"{self.decoder}"
-            ))
-
     def loss(self, data_set: list[np.ndarray]) -> float:
         kl_loss = 0
         recon_loss = 0