Documentation for LabelEncoder

Suitable version of the scikit-learn LabelEncoder for PHOTONAI. Since the pipeline process streams the underlying samples to every transformer, this class is required.

Source code in photonai/modelwrapper/label_encoder.py

class LabelEncoder(SKLabelEncoder):
    """
    Suitable version of the scikit-learn LabelEncoder for PHOTONAI.
    Since the pipeline process streams the underlying samples to
    every transformer, this class is required.

    """
    def __init__(self):
        """Initialize the object."""
        super(LabelEncoder, self).__init__()
        self.needs_y = True

    def fit(self, X: np.ndarray, y: np.ndarray = None, **kwargs):
        """
        Call of the underlying sklearn.fit(y) method.

        Parameters:
            X:
                The input samples of shape [n_samples, n_features].

            y:
                The input targets of shape [n_samples, 1].

            **kwargs:
                Ignored input.

        """
        super(LabelEncoder, self).fit(y)
        return self

    def transform(self, X: np.ndarray, y: np.ndarray = None, **kwargs) -> (np.ndarray, np.ndarray):
        """
        Call of the underlying sklearn.transform(y) method.

        Parameters:
            X:
                The input samples of shape [n_samples, n_features].

            y:
                The input targets of shape [n_samples, 1].

            **kwargs:
                Ignored input.

        Returns:
            Original X and encoded y.

        """
        yt = super(LabelEncoder, self).transform(y)
        return X, yt

    def fit_transform(self, X: np.ndarray, y: np.ndarray = None, **kwargs) -> (np.ndarray, np.ndarray):
        """
        Call of the underlying sklearn.fit_transform(y) method.

        Parameters:
            X:
                The input samples of shape [n_samples, n_features].

            y:
                The input targets of shape [n_samples, 1].

            **kwargs:
                Ignored input.

        Returns:
            Original X and encoded y.

        """
        return super(LabelEncoder, self).fit_transform(y)

__init__(self) special

Initialize the object.

Source code in photonai/modelwrapper/label_encoder.py

def __init__(self):
    """Initialize the object."""
    super(LabelEncoder, self).__init__()
    self.needs_y = True

fit(self, X, y=None, **kwargs)

Call of the underlying sklearn.fit(y) method.

Parameters:

Name	Type	Description	Default
X	ndarray	The input samples of shape [n_samples, n_features].	required
y	ndarray	The input targets of shape [n_samples, 1].	None
**kwargs		Ignored input.	{}

Source code in photonai/modelwrapper/label_encoder.py

def fit(self, X: np.ndarray, y: np.ndarray = None, **kwargs):
    """
    Call of the underlying sklearn.fit(y) method.

    Parameters:
        X:
            The input samples of shape [n_samples, n_features].

        y:
            The input targets of shape [n_samples, 1].

        **kwargs:
            Ignored input.

    """
    super(LabelEncoder, self).fit(y)
    return self

fit_transform(self, X, y=None, **kwargs)

Call of the underlying sklearn.fit_transform(y) method.

Parameters:

Name	Type	Description	Default
X	ndarray	The input samples of shape [n_samples, n_features].	required
y	ndarray	The input targets of shape [n_samples, 1].	None
**kwargs		Ignored input.	{}

Returns:

Type	Description
(<class 'numpy.ndarray'>, <class 'numpy.ndarray'>)	Original X and encoded y.

Source code in photonai/modelwrapper/label_encoder.py

def fit_transform(self, X: np.ndarray, y: np.ndarray = None, **kwargs) -> (np.ndarray, np.ndarray):
    """
    Call of the underlying sklearn.fit_transform(y) method.

    Parameters:
        X:
            The input samples of shape [n_samples, n_features].

        y:
            The input targets of shape [n_samples, 1].

        **kwargs:
            Ignored input.

    Returns:
        Original X and encoded y.

    """
    return super(LabelEncoder, self).fit_transform(y)

transform(self, X, y=None, **kwargs)

Call of the underlying sklearn.transform(y) method.

Parameters:

Name	Type	Description	Default
X	ndarray	The input samples of shape [n_samples, n_features].	required
y	ndarray	The input targets of shape [n_samples, 1].	None
**kwargs		Ignored input.	{}

Returns:

Type	Description
(<class 'numpy.ndarray'>, <class 'numpy.ndarray'>)	Original X and encoded y.

Source code in photonai/modelwrapper/label_encoder.py

def transform(self, X: np.ndarray, y: np.ndarray = None, **kwargs) -> (np.ndarray, np.ndarray):
    """
    Call of the underlying sklearn.transform(y) method.

    Parameters:
        X:
            The input samples of shape [n_samples, n_features].

        y:
            The input targets of shape [n_samples, 1].

        **kwargs:
            Ignored input.

    Returns:
        Original X and encoded y.

    """
    yt = super(LabelEncoder, self).transform(y)
    return X, yt