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noxer.base module

A set of abstract classes defining interfaces.

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"""
A set of abstract classes defining interfaces.
"""

class AugmentMixin(object):
    """Mixin class for augmentation of data in noxer."""

    def fit_transform(self, X, Y, **fit_params):
        """Fit to data, then transform it.

        Fits transformer to X and Y with optional parameters fit_params
        and returns a transformed version of X and Y.

        Parameters
        ----------
        X : array of shape [n_samples, ...]
            Training set.

        Y : array of shape [n_samples, ...]
            Target values.

        Returns
        -------
        X_new : array of shape [n_samples, ...]
            Transformed array.

        Y_new : array of shape [n_samples, ...]
            Transformed outputs.

        """
        return self.fit(X, Y, **fit_params).transform(X, Y)

Classes

class AugmentMixin

Mixin class for augmentation of data in noxer.

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class AugmentMixin(object):
    """Mixin class for augmentation of data in noxer."""

    def fit_transform(self, X, Y, **fit_params):
        """Fit to data, then transform it.

        Fits transformer to X and Y with optional parameters fit_params
        and returns a transformed version of X and Y.

        Parameters
        ----------
        X : array of shape [n_samples, ...]
            Training set.

        Y : array of shape [n_samples, ...]
            Target values.

        Returns
        -------
        X_new : array of shape [n_samples, ...]
            Transformed array.

        Y_new : array of shape [n_samples, ...]
            Transformed outputs.

        """
        return self.fit(X, Y, **fit_params).transform(X, Y)

Ancestors (in MRO)

Static methods

def fit_transform(

self, X, Y, **fit_params)

Fit to data, then transform it.

Fits transformer to X and Y with optional parameters fit_params and returns a transformed version of X and Y.

Parameters

X : array of shape [n_samples, ...] Training set.

Y : array of shape [n_samples, ...] Target values.

Returns

X_new : array of shape [n_samples, ...] Transformed array.

Y_new : array of shape [n_samples, ...] Transformed outputs.

Show source ≡

def fit_transform(self, X, Y, **fit_params):
    """Fit to data, then transform it.
    Fits transformer to X and Y with optional parameters fit_params
    and returns a transformed version of X and Y.
    Parameters
    ----------
    X : array of shape [n_samples, ...]
        Training set.
    Y : array of shape [n_samples, ...]
        Target values.
    Returns
    -------
    X_new : array of shape [n_samples, ...]
        Transformed array.
    Y_new : array of shape [n_samples, ...]
        Transformed outputs.
    """
    return self.fit(X, Y, **fit_params).transform(X, Y)