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