Fix bugs for feature engineers but issues of solver still exists

autogl/module/feature/__init__.py

@@ -33,6 +33,9 @@ from ._graph import (
 from ._selectors import (
     FilterConstant, GBDTFeatureSelector
 )
+from ._auto_feature import (
+    IdentityFeature, OnlyConstFeature, AutoFeatureEngineer
+)
 
 __all__ = [
     "BaseFeatureEngineer",
@@ -61,5 +64,8 @@ __all__ = [
     "NXGlobalEfficiency",
     "NXIsEulerian",
     "FilterConstant",
-    "GBDTFeatureSelector"
+    "GBDTFeatureSelector",
+    "IdentityFeature",
+    "OnlyConstFeature",
+    "AutoFeatureEngineer"
 ]

autogl/module/feature/_auto_feature.py

@@ -166,7 +166,7 @@ class AutoFeatureEngineer(BaseFeatureEngineer):
             verbosity: int = 0,
             *args, **kwargs
     ):
-        super(AutoFeatureEngineer, self).__init__(multi_graph=False)
+        super(AutoFeatureEngineer, self).__init__()
         self._ops = [op_sum, op_mean, op_max, op_min]
         self._sim = cosine_similarity
         self._fixlen = fix_length
@@ -208,7 +208,7 @@ class AutoFeatureEngineer(BaseFeatureEngineer):
         for u, v in homogeneous_static_graph.edges.connections.t().numpy():
             neighbours[u].append(v)
         self.__neighbours: _typing.Sequence[np.ndarray] = tuple(
-            [np.ndarray(v) for v in neighbours]
+            [np.array(v) for v in neighbours]
         )
 
         x: np.ndarray = _original_features.numpy()

autogl/module/feature/_base_feature_engineer/_base_feature_engineer.py

@@ -29,7 +29,11 @@ class _ComposedFeatureEngineer(_AbstractBaseFeatureEngineer):
         return dataset
 
     def fit_transform(self, dataset, inplace: bool = True):
-        return self.fit_transform(dataset, inplace)
+        for fe in self.fe_components:
+            dataset = fe.fit(dataset)
+        for fe in self.fe_components:
+            dataset = fe.fit(dataset)
+        return dataset
 
     def __init__(self, feature_engineers: _typing.Iterable[_AbstractBaseFeatureEngineer]):
         self.__fe_components: _typing.List[_AbstractBaseFeatureEngineer] = []

autogl/module/feature/_base_feature_engineer/_base_feature_engineer_dgl.py

@@ -40,7 +40,7 @@ class BaseFeatureEngineer(
         with torch.no_grad():
             for i, data in enumerate(dataset):
                 dataset[i] = self.__postprocess(
-                    self._postprocess(self._transform(self._fit(self._preprocess(self.__preprocess(data)))))
+                    self._postprocess(self._fit(self._preprocess(self.__preprocess(data))))
                 )
         return dataset
 

autogl/module/feature/_base_feature_engineer/_base_feature_engineer_pyg.py

@@ -25,7 +25,7 @@ class BaseFeatureEngineer(
         with torch.no_grad():
             for i, data in enumerate(dataset):
                 dataset[i] = self.__postprocess(
-                    self._postprocess(self._transform(self._fit(self._preprocess(self.__preprocess(data)))))
+                    self._postprocess(self._fit(self._preprocess(self.__preprocess(data))))
                 )
         return dataset
 

autogl/module/feature/_selectors/_basic.py

@@ -16,16 +16,12 @@ class BaseFeatureSelector(BaseFeatureEngineer):
     ) -> GeneralStaticGraph:
         if (
                 'x' in static_graph.nodes.data and
-                self._selection not in (Ellipsis, None) and
-                isinstance(self._selection, torch.Tensor) and
-                torch.is_tensor(self._selection) and self._selection.dim() == 1
+                isinstance(self._selection, (torch.Tensor, np.ndarray))
         ):
             static_graph.nodes.data['x'] = static_graph.nodes.data['x'][:, self._selection]
         if (
                 'feat' in static_graph.nodes.data and
-                self._selection not in (Ellipsis, None) and
-                isinstance(self._selection, torch.Tensor) and
-                torch.is_tensor(self._selection) and self._selection.dim() == 1
+                isinstance(self._selection, (torch.Tensor, np.ndarray))
         ):
             static_graph.nodes.data['feat'] = static_graph.nodes.data['feat'][:, self._selection]
         return static_graph

autogl/module/feature/_selectors/_gbdt.py

@@ -41,6 +41,7 @@ def _gbdt_generator(
     )
 
     num_classes: int = torch.max(data.y).item() + 1
+    parameters["num_class"] = num_classes
     __optimizer_parameters = {
         "num_boost_round": 100,
         "early_stopping_rounds": 5,
@@ -78,7 +79,7 @@ def _gbdt_generator(
         train_x = pd.DataFrame(x, columns=feature_index, index=None)
         dtrain = lightgbm.Dataset(train_x, label=label)
         clf = lightgbm.train(
-            train_set=dtrain, params=params,
+            train_set=dtrain, params=parameters,
             **__optimizer_parameters
         )