from typing import Union, Dict, List
import torch
from ..classification import VertexClassificationEvaluator
[docs]class GraphVertexClassificationEvaluator(VertexClassificationEvaluator):
r"""Return the metric evaluator for vertex classification task on the graph structure. The supported 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 dhg.metrics as dm
>>> evaluator = dm.GraphVertexClassificationEvaluator(
[
"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)
[docs] def validate(self, y_true: torch.LongTensor, y_pred: torch.Tensor):
return super().validate(y_true, y_pred)
[docs] def test(self, y_true: torch.LongTensor, y_pred: torch.Tensor):
return super().test(y_true, y_pred)