Improve logging

benchmark/trainer/load_data.py

@@ -174,7 +174,7 @@ def _resolve_import(self, args: Namespace) -> Tuple[str, str, dict]:
             else:
                 self.metric = 's_f1i'
             if self.data not in ['snap-patents']:
-                self._T_insert(T.ToUndirected(), index=0)
+                kwargs['transform'] = self._T_insert(T.ToUndirected(), index=0)
         elif self.data in ['penn94', 'amherst41', 'cornell5', 'johns_hopkins55', 'reed98']:
             module_name = 'dataset_process'
             class_name = 'FB100'
@@ -202,7 +202,7 @@ def _resolve_import(self, args: Namespace) -> Tuple[str, str, dict]:
             kwargs = dict(
                 root=DATAPATH.joinpath('PyG'),
                 name=self.data,
-                transform=self.transform,)
+                transform=self._T_insert(T.ToUndirected(), index=0),)
             pyg_mapping = {
                 'cora':         'Planetoid',
                 'citeseer':     'Planetoid',

benchmark/utils/logger.py

@@ -219,6 +219,7 @@ def merge(self,
             logger.data.index = rows
         if suffix:
             logger.data.columns = [f'{coli}_{suffix}' for coli in logger.data.columns]
+            logger.fmt.index = [f'{coli}_{suffix}' for coli in logger.fmt.index]
 
         self._set(logger.data, logger.fmt)
         return self
@@ -336,3 +337,6 @@ def get_str(self,
     def __str__(self) -> str:
         r"""String for print on screen."""
         return self.get_str(maxlen=80)
+
+    def __repr__(self) -> str:
+        return self.get_str()

docs/source/index.rst

@@ -9,7 +9,7 @@ pyg_spectral
 
    | 🔍 `Documentation <https://gdmnl.github.io/Spectral-GNN-Benchmark/>`__
    | 👾 `GitHub <https://github.com/gdmnl/Spectral-GNN-Benchmark/>`__ |gh-bn|
-   | 📄 `Paper <https://arxiv.org/abs/2106.06100>`__
+   | 📄 `Paper <https://arxiv.org/abs/2406.09675>`__
    | 📎 `Citation <https://github.com/gdmnl/Spectral-GNN-Benchmark#misc>`__
 
 ``pyg_spectral`` is a `PyG <https://pyg.org>`_-based framework for analyzing, implementing, and benchmarking spectral GNNs with effectiveness and efficiency evaluations.