Merge pull request #239 from changbiHub/self_training_fix

Self_training_fix

Author: jrzaurin

pytorch_widedeep/losses.py

@@ -959,7 +959,7 @@ def forward(self, x_true: Tensor, x_pred: Tensor, mask: Tensor) -> Tensor:
         x_true_means[x_true_means == 0] = 1
 
         x_true_stds = torch.std(x_true, dim=0) ** 2
-        x_true_stds[x_true_stds == 0] = x_true_means[x_true_stds == 0]
+        x_true_stds[x_true_stds == 0] = torch.abs(x_true_means[x_true_stds == 0])
 
         features_loss = torch.matmul(reconstruction_errors, 1 / x_true_stds)
         nb_reconstructed_variables = torch.sum(mask, dim=1)

pytorch_widedeep/models/tabular/self_supervised/encoder_decoder_model.py

@@ -82,7 +82,7 @@ def _forward_tabnet(self, X: Tensor) -> Tuple[Tensor, Tensor, Tensor]:
             x_embed_rec = self.decoder(steps_out)
             mask = torch.ones(x_embed.shape).to(X.device)
 
-        return x_embed_rec, x_embed, mask
+        return x_embed, x_embed_rec, mask
 
     def _build_decoder(self, encoder: ModelWithoutAttention) -> DecoderWithoutAttention:
         if isinstance(encoder, TabMlp):