fix: AutoencoderPipeline logged loss mean (#55)

Signed-off-by: Diego Ponce <[email protected]>

numalogic/models/autoencoder/pipeline.py

@@ -66,6 +66,8 @@ def __init__(
  ):
  if not (model and seq_len):
  raise ValueError("No model and seq len provided!")
+ if num_epochs < 1:
+ raise ValueError("num_epochs must be a positive interger")
 
  self._model = model
  self.seq_len = seq_len
@@ -147,18 +149,18 @@ def fit(self, X: NDArray[float], y=None, log_freq: int = 5) -> "AutoencoderPipel
  dataset = self._model.construct_dataset(X, self.seq_len)
  loader = DataLoader(dataset, batch_size=self.batch_size, shuffle=False)
  self._model.train()
-
- loss = torch.Tensor([0.0])
+ losses = []
  for epoch in range(1, self.num_epochs + 1):
  for x_batch in loader:
  self.optimizer.zero_grad()
  _, decoded = self._model(x_batch)
  loss = self.criterion(decoded, x_batch)
  loss.backward()
  self.optimizer.step()
-
+ losses.append(loss.item())
  if epoch % log_freq == 0:
- _LOGGER.info(f"epoch : {epoch}, loss_mean : {loss.item():.7f}")
+ _LOGGER.info(f"epoch : {epoch}, loss_mean : {np.mean(losses):.7f}")
+ losses = []
 
  self._thresholds, _mean, _std = self.find_thresholds(X)
  self._stats["mean"] = _mean

numalogic/tests/models/autoencoder/test_pipeline.py

@@ -300,6 +300,11 @@ def test_exception_in_load_model(self):
  )
  self.assertEqual(model_pl2.err_stats["std"], model_pl1.err_stats["std"])
 
+ def test_exception_invalid_epoch(self):
+ model = VanillaAE(10)
+ with self.assertRaises(ValueError):
+ AutoencoderPipeline(model, 10, num_epochs=-10)
+
 
 class TestSparseAEPipeline(unittest.TestCase):
  X_train = None