fix sklearn 1.8 and pytorch 2.9 issues

Author: dholzmueller

README.md

@@ -201,6 +201,7 @@ and https://docs.ray.io/en/latest/cluster/vms/user-guides/community/slurm.html
 ## Releases (see git tags)
 
 - v1.7.2: 
+    - Added scikit-learn 1.8 compatibility.
     - Removed debug print in RealMLP.
     - fixed device memory estimation error in the scheduler when `CUDA_VISIBLE_DEVICES` was used.
 - v1.7.1:

pytabkit/models/alg_interfaces/rtdl_interfaces.py

@@ -69,7 +69,8 @@ def _fit_sklearn(self, x_df: pd.DataFrame, y: np.ndarray, val_idxs: np.ndarray,
             # missing values were encoded as zero, we need to make them missing again
             self.replace_zero_by_nans = SimpleImputer(missing_values=0.,
                                                       strategy="constant",
-                                                      fill_value=np.nan)
+                                                      fill_value=np.nan,
+                                                      keep_empty_features=True)
             x_train[:, self.categorical_indicator] = self.replace_zero_by_nans.fit_transform(
                 x_train[:, self.categorical_indicator])
             self.ord_enc = OrdinalEncoder(dtype=np.float32, handle_unknown='use_encoded_value', unknown_value=-1,

pytabkit/models/nn_models/nn.py

@@ -787,11 +787,11 @@ def forward_tensors(self, tensors):
             for i, cs in enumerate(y_cs):
                 if cs == 0:
                     # already continuous
-                    new_y_cols.append(y[[slice(None)] * (y.dim() - 1) + [slice(i, i + 1)]])
+                    new_y_cols.append(y[tuple([slice(None)] * (y.dim() - 1) + [slice(i, i + 1)])])
                 else:
                     # make continuous
                     # todo: is there a better one-hot function without the long -> float conversion?
-                    new_y_cols.append(F.one_hot(y[[slice(None)] * (y.dim() - 1) + [i]], num_classes=cs).float())
+                    new_y_cols.append(F.one_hot(y[tuple([slice(None)] * (y.dim() - 1) + [i])], num_classes=cs).float())
             return utils.join_dicts(tensors, {'y': torch.cat(new_y_cols, dim=-1)})