Marcelo/x y renaming

.github/workflows/integration_tests.yml

@@ -64,4 +64,4 @@ jobs:
 
       - name: Install dependencies and check code
         run: |
-          uv run pytest -m "integration_test" --log-cli-level=WARNING
+          uv run pytest -m "integration_test" --log-cli-level=WARNING -s -vv

src/midst_toolkit/attacks/ensemble/clavaddpm_fine_tuning.py

@@ -99,7 +99,9 @@ def fine_tune_model(
     if len(category_sizes) == 0 or transformations.categorical_encoding == CategoricalEncoding.ONE_HOT:
         category_sizes = np.array([0])
 
-    num_numerical_features = dataset.x_num[DataSplit.TRAIN.value].shape[1] if dataset.x_num is not None else 0
+    num_numerical_features = (
+        dataset.numerical_features[DataSplit.TRAIN.value].shape[1] if dataset.numerical_features is not None else 0
+    )
 
     train_loader = prepare_fast_dataloader(dataset, split=DataSplit.TRAIN, batch_size=batch_size)
 
@@ -110,7 +112,7 @@ def fine_tune_model(
     trainer = ClavaDDPMTrainer(
         diffusion,
         train_loader,
-        lr=lr,
+        learning_rate=lr,
         weight_decay=weight_decay,
         steps=steps,
         device=str(device),
@@ -193,11 +195,11 @@ def fine_tune_classifier(
     if len(category_sizes) == 0 or transformations.categorical_encoding == CategoricalEncoding.ONE_HOT:
         category_sizes = np.array([0])
 
-    if dataset.x_num is None:
+    if dataset.numerical_features is None:
         log(WARNING, "dataset.x_num is None. num_numerical_features will be set to 0")
         num_numerical_features = 0
     else:
-        num_numerical_features = dataset.x_num[DataSplit.TRAIN.value].shape[1]
+        num_numerical_features = dataset.numerical_features[DataSplit.TRAIN.value].shape[1]
 
     if model_params.is_target_conditioned == IsTargetConditioned.CONCAT:
         num_numerical_features -= 1
@@ -279,7 +281,7 @@ def child_fine_tuning(
     child_info = get_table_info(child_df_with_cluster, child_domain_dict, target_col)
     child_model_params = ModelParameters(
         diffusion_parameters=DiffusionParameters(
-            d_layers=diffusion_config["d_layers"],
+            layers_dimensions=diffusion_config["d_layers"],
             dropout=diffusion_config["dropout"],
         ),
     )

src/midst_toolkit/models/clavaddpm/data_loaders.py

@@ -537,23 +537,25 @@ def prepare_fast_dataloader(
     Returns:
         A generator of batches of data from the dataset.
     """
-    if dataset.x_cat is not None:
-        if dataset.x_num is not None:
-            concatenated_features = np.concatenate([dataset.x_num[split.value], dataset.x_cat[split.value]], axis=1)
-            x = torch.from_numpy(concatenated_features).float()
+    if dataset.categorical_features is not None:
+        if dataset.numerical_features is not None:
+            concatenated_features = np.concatenate(
+                [dataset.numerical_features[split.value], dataset.categorical_features[split.value]], axis=1
+            )
+            features = torch.from_numpy(concatenated_features).float()
         else:
-            x = torch.from_numpy(dataset.x_cat[split.value]).float()
+            features = torch.from_numpy(dataset.categorical_features[split.value]).float()
     else:
-        assert dataset.x_num is not None
-        x = torch.from_numpy(dataset.x_num[split.value]).float()
+        assert dataset.numerical_features is not None
+        features = torch.from_numpy(dataset.numerical_features[split.value]).float()
 
     if target_type == TargetType.FLOAT:
-        y = torch.from_numpy(dataset.y[split.value]).float()
+        target = torch.from_numpy(dataset.target[split.value]).float()
     elif target_type == TargetType.LONG:
-        y = torch.from_numpy(dataset.y[split.value]).long()
+        target = torch.from_numpy(dataset.target[split.value]).long()
     else:
         raise ValueError(f"Unsupported target type: {target_type}")
 
-    dataloader = FastTensorDataLoader([x, y], batch_size=batch_size, shuffle=(split == DataSplit.TRAIN))
+    dataloader = FastTensorDataLoader([features, target], batch_size=batch_size, shuffle=(split == DataSplit.TRAIN))
     while True:
         yield from dataloader