LabelDecoder
- class falcon.tabular.processors.LabelDecoder
Label encoder/decoder to be used for encoding labels as integers and vice versa.
- __init__() None
does not take any arguments
- fit(X: ndarray[Any, dtype[ScalarType]], y: Optional[Any] = None, *args: Any, **kwargs: Any) None
Fits the decoder.
- Parameters
X (npt.NDArray) – labels to be encoded as integers
y (Any, optional) – dummy argument, by default None
- fit_pipe(X: Any, y: Any, *args: Any, **kwargs: Any) None
Since label decoder should initially be fitted and applied before the main training phase of pipeline, this method does nothing.
- Parameters
X (Any) – dummy argument
y (Any) – dummy argument
- forward(X: ndarray[Any, dtype[ScalarType]], *args: Any, **kwargs: Any) ndarray[Any, dtype[ScalarType]]
Equivalent to .transform(X, inverse=True).
- Parameters
X (npt.NDArray) – labels to decode
- Returns
labels decoded to strings
- Return type
npt.NDArray
- get_input_type() Type
- Returns
Int64Array
- Return type
Type
- get_output_type() Type
- Returns
NDArray[str]
- Return type
Type
- predict(X: ndarray[Any, dtype[ScalarType]], inverse: bool = True, *args: Any, **kwargs: Any) ndarray[Any, dtype[ScalarType]]
Equivalent of .transform().
- Parameters
X (npt.NDArray) – labels
inverse (bool, optional) – if True, encode strings as integers, else convert integers back to strings, by default True
- Returns
encoded/decoded labels
- Return type
npt.NDArray
- to_onnx() SerializedModelRepr
Serializes the encoder to onnx.
- Return type
SerializedModelRepr
- transform(X: ndarray[Any, dtype[ScalarType]], inverse: bool = True, *args: Any, **kwargs: Any) ndarray[Any, dtype[ScalarType]]
Encodes/decodes the labels.
- Parameters
X (npt.NDArray) – labels
inverse (bool, optional) – if True, encode strings as integers, else convert integers back to strings, by default True
- Returns
encoded/decoded labels
- Return type
npt.NDArray