chore: import upstream snapshot with attribution

easygraph/ml_metrics/hypergraphs/hypergraph.py

@@ -0,0 +1,47 @@
+from typing import Dict
+from typing import List
+from typing import Union
+
+import torch
+
+from ..classification import VertexClassificationEvaluator
+
+
+class HypergraphVertexClassificationEvaluator(VertexClassificationEvaluator):
+    r"""Return the metric evaluator for vertex classification task on the hypergraph structure. The supported ml_metrics includes: ``accuracy``, ``f1_score``, ``confusion_matrix``.
+
+    Args:
+        ``metric_configs`` (``List[Union[str, Dict[str, dict]]]``): The metric configurations. The key is the metric name and the value is the metric parameters.
+        ``validate_index`` (``int``): The specified metric index used for validation. Defaults to ``0``.
+
+    Examples:
+        >>> import torch
+        >>> import easygraph.ml_metrics as dm
+        >>> evaluator = dm.HypergraphVertexClassificationEvaluator(
+                [
+                    "accuracy",
+                    {"f1_score": {"average": "macro"}},
+                ],
+                0
+            )
+        >>> y_true = torch.tensor([0, 0, 1, 1, 2, 2])
+        >>> y_pred = torch.tensor([0, 2, 1, 2, 1, 2])
+        >>> evaluator.validate(y_true, y_pred)
+        0.5
+        >>> evaluator.test(y_true, y_pred)
+        {
+            'accuracy': 0.5,
+            'f1_score -> average@macro': 0.5222222222222221
+        }
+    """
+
+    def __init__(
+        self, metric_configs: List[Union[str, Dict[str, dict]]], validate_index: int = 0
+    ):
+        super().__init__(metric_configs, validate_index)
+
+    def validate(self, y_true: torch.LongTensor, y_pred: torch.Tensor):
+        return super().validate(y_true, y_pred)
+
+    def test(self, y_true: torch.LongTensor, y_pred: torch.Tensor):
+        return super().test(y_true, y_pred)