update hyper related

easy-graph · Nov 24, 2023 · 7e43e3a · 7e43e3a
1 parent 9561f7b
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Showing 4 changed files with 8 additions and 8 deletions.
diff --git a/easygraph/datasets/utils.py b/easygraph/datasets/utils.py
@@ -288,7 +288,7 @@ def generate_mask_tensor(mask):
     """
     assert isinstance(
         mask, np.ndarray
-    ), "input for generate_mask_tensorshould be an numpy ndarray"
+    ), "input for generate_mask_tensor should be an numpy ndarray"
     return tensor(mask, dtype=data_type_dict()["bool"])
 
 

diff --git a/easygraph/experiments/base.py b/easygraph/experiments/base.py
@@ -27,9 +27,9 @@ class BaseTask:
         ``data`` (``dict``): The dictionary to store input data that used in the experiment.
         ``model_builder`` (``Callable``): The function to build a model with a fixed parameter ``trial``.
         ``train_builder`` (``Callable``): The function to build a training configuration with two fixed parameters ``trial`` and ``model``.
-        ``evaluator`` (``dhg.ml_metrics.BaseEvaluator``): The EasyGraph evaluator object to evaluate performance of the model in the experiment.
+        ``evaluator`` (``eg.ml_metrics.BaseEvaluator``): The EasyGraph evaluator object to evaluate performance of the model in the experiment.
         ``device`` (``torch.device``): The target device to run the experiment.
-        ``structure_builder`` (``Optional[Callable]``): The function to build a structure with a fixed parameter ``trial``. The structure can be ``dhg.Graph``, ``dhg.DiGraph``, ``dhg.BiGraph``, and ``dhg.Hypergraph``.
+        ``structure_builder`` (``Optional[Callable]``): The function to build a structure with a fixed parameter ``trial``. The structure can be ``eg.Graph``, ``eg.DiGraph``, ``eg.BiGraph``, and ``eg.Hypergraph``.
         ``study_name`` (``Optional[str]``): The name of this study. If set to ``None``, the study name will be generated automatically according to current time. Defaults to ``None``.
         ``overwrite`` (``bool``): The flag that whether to overwrite the existing study. Different studies are identified by the ``study_name``. Defaults to ``True``.
     """
@@ -199,6 +199,6 @@ def test(self, data: Optional[dict] = None, model: Optional[nn.Module] = None):
         r"""Test the model.
 
         Args:
-            ``data`` (``dict``, optional): The input data if set to ``None``, the specified ``data`` in the intialization of the experiments will be used. Defaults to ``None``.
+            ``data`` (``dict``, optional): The input data if set to ``None``, the specified ``data`` in the initialization of the experiments will be used. Defaults to ``None``.
             ``model`` (``nn.Module``, optional): The model if set to ``None``, the trained best model will be used. Defaults to ``None``.
         """
diff --git a/easygraph/experiments/vertex_classification.py b/easygraph/experiments/vertex_classification.py
@@ -142,7 +142,7 @@ def test(self, data: Optional[dict] = None, model: Optional[nn.Module] = None):
         r"""Test the model.
 
         Args:
-            ``data`` (``dict``, optional): The input data if set to ``None``, the specified ``data`` in the intialization of the experiments will be used. Defaults to ``None``.
+            ``data`` (``dict``, optional): The input data if set to ``None``, the specified ``data`` in the initialization of the experiments will be used. Defaults to ``None``.
             ``model`` (``nn.Module``, optional): The model if set to ``None``, the trained best model will be used. Defaults to ``None``.
         """
         if data is None:

diff --git a/easygraph/functions/hypergraph/hypergraph_clustering.py b/easygraph/functions/hypergraph/hypergraph_clustering.py
@@ -155,8 +155,8 @@ def hypergraph_local_clustering_coefficient(H):
                     if len(D1.union(D2)) == 0:
                         eo = 0
                     else:
-                        # otherwise we have to look at their neighbours
-                        # the neighbours of D1 and D2, respectively.
+                        # otherwise we have to look at their neighbors
+                        # the neighbors of D1 and D2, respectively.
                         neighD1 = {i for d in D1 for i in H.neighbor_of_node(d)}
                         neighD2 = {i for d in D2 for i in H.neighbor_of_node(d)}
                         # compute extra overlap [len() is used for cardinality of edges]
@@ -169,7 +169,7 @@ def hypergraph_local_clustering_coefficient(H):
                     # add it up
                     total_eo = total_eo + eo
 
-            # include normalisation by degree k*(k-1)/2
+            # include normalization by degree k*(k-1)/2
             result[n] = 2 * total_eo / (dv * (dv - 1))
     return result