feat: test label accuracy in mnist example

examples/mnist_test.py

@@ -8,6 +8,7 @@ from easyvae.autoencoder import ( # noqa
         AAutoencoder
     )
 from easyvae.activations import LeakyReLU
+from tqdm import tqdm
 
 
 def load_mnist() -> list[np.ndarray]:
@@ -90,6 +91,21 @@ def plot_random_reconstruction(
     print(f'{code.tolist()}')
 
 
+def labeling_accuracy(autoencoder: LabelingVAE, x_test, y_test):
+    accuracy = 0
+    for x, y in tqdm(
+            zip(x_test, y_test),
+            desc="Testing labeling",
+            total=len(x_test)
+    ):
+        res = autoencoder.label(x)
+        res = list(res.items())[0][0]
+        if res == str(int(y)):
+            accuracy += 1
+    accuracy /= len(y_test)
+    print(f"Accuracy : {accuracy * 100:.2f}%")
+
+
 def mnist_test(model: str | AAutoencoder | LabelingVAE):
     x_train, y_train, x_test, y_test = load_mnist()
     in_len = x_train[0].shape[0] * x_train[0].shape[0]
@@ -107,10 +123,12 @@ 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]
-    autoencoder.learn_labels(x_train, labels_train)
-    res = autoencoder.label(example)
-    for k, v in res.items():
-        print(f"{k} => {v}")
+    if isinstance(model, LabelingVAE):
+        autoencoder.learn_labels(x_train, labels_train)
+        labeling_accuracy(autoencoder, x_test, y_test)
+        res = autoencoder.label(example)
+        for k, v in res.items():
+            print(f"{k} => {v}")
     plot_random_reconstruction(autoencoder, example, img_shape, y_test[idx])
     if autoencoder.space_dim == 2:
         plot_mnist_latent_space(autoencoder, x_test, y_test)