Source code for torcheeg.transforms.label.fix
from typing import Dict, List, Union
from ..base_transform import LabelTransform
[docs]class FixCategory(LabelTransform):
r'''
Returns a pre-set label for all samples, usually used to supplement the dataset with new categories.
.. code-block:: python
transform = FixCategory(value=0)
transform(y=3)['y']
>>> 0
:obj:`FixCategory` allows multiple values to be selected and returned as a list. Suitable for multi-classification tasks or multi-task learning.
.. code-block:: python
transform = FixCategory(value=[0, 1])
transform(y=[1, 2])['y']
>>> [0, 1]
Args:
value (str or list): The pre-set label.
.. automethod:: __call__
'''
def __init__(self, value: Union[int, str, List]):
super(FixCategory, self).__init__()
self.value = value
[docs] def __call__(self, *args, y: Dict, **kwargs) -> Union[int, float, List]:
r'''
Args:
y (any): A label or label list describing the EEG signal samples.
Returns:
any: FixCategoryeded value pre-set by `value`.
'''
return super().__call__(*args, y=y, **kwargs)
def apply(self, y: Dict, **kwargs) -> Union[int, float, List]:
return self.value
@property
def repr_body(self) -> Dict:
return dict(super().repr_body, **{
'value': self.value
})
