refactor: make FITS able to apply customized loss func;

pypots/imputation/fits/core.py

@@ -46,7 +46,7 @@ def __init__(
         self.output_projection = nn.Linear(n_features, n_features)
         self.saits_loss_func = SaitsLoss(ORT_weight, MIT_weight)
 
-    def forward(self, inputs: dict, training: bool = True) -> dict:
+    def forward(self, inputs: dict) -> dict:
         X, missing_mask = inputs["X"], inputs["missing_mask"]
 
         if self.apply_nonstationary_norm:
@@ -75,7 +75,7 @@ def forward(self, inputs: dict, training: bool = True) -> dict:
         }
 
         # if in training mode, return results with losses
-        if training:
+        if self.training:
             X_ori, indicating_mask = inputs["X_ori"], inputs["indicating_mask"]
             loss, ORT_loss, MIT_loss = self.saits_loss_func(reconstruction, X_ori, missing_mask, indicating_mask)
             results["ORT_loss"] = ORT_loss

pypots/imputation/fits/model.py

@@ -33,26 +33,11 @@ class FITS(BaseNNImputer):
     n_features :
         The number of features in the time-series data sample.
 
-    n_layers :
-        The number of layers in the FITS model.
+    cut_freq :
+        The cut-off frequency for the Fourier transformation.
 
-    d_model :
-        The dimension of the model.
-
-    n_heads :
-        The number of heads in each layer of FITS.
-
-    d_ffn :
-        The dimension of the feed-forward network.
-
-    factor :
-        The factor of the auto correlation mechanism for the FITS model.
-
-    moving_avg_window_size :
-        The window size of moving average.
-
-    dropout :
-        The dropout rate for the model.
+    individual :
+        Whether to use individual Fourier transformation for each feature.
 
     ORT_weight :
         The weight for the ORT loss, the same as SAITS.
@@ -71,6 +56,14 @@ class FITS(BaseNNImputer):
         stopped when the model does not perform better after that number of epochs.
         Leaving it default as None will disable the early-stopping.
 
+    train_loss_func:
+        The customized loss function designed by users for training the model.
+        If not given, will use the default loss as claimed in the original paper.
+
+    val_metric_func:
+        The customized metric function designed by users for validating the model.
+        If not given, will use the default MSE metric.
+
     optimizer :
         The optimizer for model training.
         If not given, will use a default Adam optimizer.
@@ -115,6 +108,8 @@ def __init__(
         batch_size: int = 32,
         epochs: int = 100,
         patience: int = None,
+        train_loss_func: Optional[dict] = None,
+        val_metric_func: Optional[dict] = None,
         optimizer: Optional[Optimizer] = Adam(),
         num_workers: int = 0,
         device: Optional[Union[str, torch.device, list]] = None,
@@ -123,14 +118,16 @@ def __init__(
         verbose: bool = True,
     ):
         super().__init__(
-            batch_size,
-            epochs,
-            patience,
-            num_workers,
-            device,
-            saving_path,
-            model_saving_strategy,
-            verbose,
+            batch_size=batch_size,
+            epochs=epochs,
+            patience=patience,
+            train_loss_func=train_loss_func,
+            val_metric_func=val_metric_func,
+            num_workers=num_workers,
+            device=device,
+            saving_path=saving_path,
+            model_saving_strategy=model_saving_strategy,
+            verbose=verbose,
         )
 
         self.n_steps = n_steps
@@ -272,7 +269,7 @@ def predict(
         with torch.no_grad():
             for idx, data in enumerate(test_loader):
                 inputs = self._assemble_input_for_testing(data)
-                results = self.model.forward(inputs, training=False)
+                results = self.model.forward(inputs)
                 imputation_collector.append(results["imputed_data"])
 
         # Step 3: output collection and return