refactor: move kb interrupt handling to autoencoder classes

src/easyvae/autoencoder.py

@@ -3,6 +3,7 @@ from tqdm import tqdm
 from .layers import DeepNNLayer, SampleLayer
 from .activations import ActivationFunc, Identity
 from .plotters import Plotter, CAPlotter, VAEPlotter
+from .utils import interruptable
 from abc import ABC, abstractmethod
 
 LOADER = ['⡿', '⣟', '⣯', '⣷', '⣾', '⣽', '⣻', '⢿']
@@ -86,6 +87,7 @@ class ClassicalAutoencoder(AAutoencoder):
         )
         return np.sum(np.abs(error)) / len(v)
 
+    @interruptable
     def train_dataset(self,
                       data_set: list[np.ndarray],
                       max_epoch: int,
@@ -119,8 +121,6 @@ class ClassicalAutoencoder(AAutoencoder):
                     break
                 plotter.update()
                 epoch += 1
-        plotter.close()
-        return self.losses
 
     def encode(self, v: np.ndarray) -> np.ndarray:
         return self.encoder.forward(v)
@@ -174,6 +174,7 @@ class VariationalAutoencoder(AAutoencoder):
         )
         return np.mean(error ** 2), self.sampler.DKL()
 
+    @interruptable
     def train_dataset(self,
                       data_set: list[np.ndarray],
                       max_epoch: int,
@@ -215,8 +216,6 @@ class VariationalAutoencoder(AAutoencoder):
                     break
                 plotter.update()
                 epoch += 1
-        plotter.close()
-        return self.recon_losses
 
     def forward(self, v: np.ndarray) -> tuple[np.ndarray, np.ndarray]:
         code = self.encoder.forward(v)