Source code for torcheeg.transforms.label.mapping
from typing import Dict, List, Union
from ..base_transform import LabelTransform
[docs]class Mapping(LabelTransform):
r'''
Mapping the label according to a certain dictionary.
.. code-block:: python
from torcheeg import transforms
t = Mapping({
'left_hand': 0,
'right_hand': 1,
})
t(y='left_hand')['y']
>>> 0
:obj:`Mapping` allows simultaneous binarization using the same threshold for multiple labels.
.. code-block:: python
from torcheeg import transforms
t = Mapping({
'left_hand': 0,
'right_hand': 1,
'left_feet': 0,
'right_feet': 1
})
t(y=['left_hand', 'left_feet'])['y']
>>> [0, 0]
Args:
map_dict (float): The mapping dictionary.
default (float, optional): The default value when the input label is not in the dictionary. (default: :obj:`-1`)
.. automethod:: __call__
'''
def __init__(self, map_dict: float, default: float = -1) -> None:
super(Mapping, self).__init__()
self.map_dict = map_dict
self.default = default
[docs] def __call__(self, *args, y: Union[int, float, List],
**kwargs) -> Union[int, List]:
r'''
Args:
label (int, float, or list): The input label or list of labels.
Returns:
int, float, or list: The output label or list of labels after binarization.
'''
return super().__call__(*args, y=y, **kwargs)
def apply(self, y: Union[int, float, List], **kwargs) -> Union[int, List]:
if isinstance(y, list):
return [self.map_dict.get(label, self.default) for label in y]
else:
return self.map_dict.get(y, self.default)
@property
def repr_body(self) -> Dict:
return dict(super().repr_body, **{'map_dict': self.map_dict})
