(y_true, y_pred, *, sample_weight=None)
return np.sum(mae) / len(mae)
Compute Macro-Averaged MAE for imbalanced ordinal classification.

    This function computes each MAE for each class and average them,
    giving an equal weight to each class.

    Read more in the :ref:`User Guide <macro_averaged_mean_absolute_error>`.

    .. versionadded:: 0.8

    Parameters
    ----------
    y_true : array-like of shape (n_samples,) or (n_samples, n_outputs)
        Ground truth (correct) target values.

    y_pred : array-like of shape (n_samples,) or (n_samples, n_outputs)
        Estimated targets as returned by a classifier.

    sample_weight : array-like of shape (n_samples,), default=None
        Sample weights.

    Returns
    -------
    loss : float or ndarray of floats
        Macro-Averaged MAE output is non-negative floating point.
        The best value is 0.0.

    Examples
    --------
    >>> import numpy as np
    >>> from sklearn.metrics import mean_absolute_error
    >>> from imblearn.metrics import macro_averaged_mean_absolute_error
    >>> y_true_balanced = [1, 1, 2, 2]
    >>> y_true_imbalanced = [1, 2, 2, 2]
    >>> y_pred = [1, 2, 1, 2]
    >>> mean_absolute_error(y_true_balanced, y_pred)
    0.5
    >>> mean_absolute_error(y_true_imbalanced, y_pred)
    0.25
    >>> macro_averaged_mean_absolute_error(y_true_balanced, y_pred)
    0.5
    >>> macro_averaged_mean_absolute_error(y_true_imbalanced, y_pred)
    0.16...
Compute Macro-Averaged MAE for imbalanced ordinal classification.
Compute Macro - Averaged MAE for imbalanced ordinal classification .
def macro_averaged_mean_absolute_error(y_true, y_pred, *, sample_weight=None):
    """Compute Macro-Averaged MAE for imbalanced ordinal classification.

    This function computes each MAE for each class and average them,
    giving an equal weight to each class.

    Read more in the :ref:`User Guide <macro_averaged_mean_absolute_error>`.

    .. versionadded:: 0.8

    Parameters
    ----------
    y_true : array-like of shape (n_samples,) or (n_samples, n_outputs)
        Ground truth (correct) target values.

    y_pred : array-like of shape (n_samples,) or (n_samples, n_outputs)
        Estimated targets as returned by a classifier.

    sample_weight : array-like of shape (n_samples,), default=None
        Sample weights.

    Returns
    -------
    loss : float or ndarray of floats
        Macro-Averaged MAE output is non-negative floating point.
        The best value is 0.0.

    Examples
    --------
    >>> import numpy as np
    >>> from sklearn.metrics import mean_absolute_error
    >>> from imblearn.metrics import macro_averaged_mean_absolute_error
    >>> y_true_balanced = [1, 1, 2, 2]
    >>> y_true_imbalanced = [1, 2, 2, 2]
    >>> y_pred = [1, 2, 1, 2]
    >>> mean_absolute_error(y_true_balanced, y_pred)
    0.5
    >>> mean_absolute_error(y_true_imbalanced, y_pred)
    0.25
    >>> macro_averaged_mean_absolute_error(y_true_balanced, y_pred)
    0.5
    >>> macro_averaged_mean_absolute_error(y_true_imbalanced, y_pred)
    0.16...
    """
    _, y_true, y_pred = _check_targets(y_true, y_pred)
    if sample_weight is not None:
        sample_weight = column_or_1d(sample_weight)
    else:
        sample_weight = np.ones(y_true.shape)
    check_consistent_length(y_true, y_pred, sample_weight)
    labels = unique_labels(y_true, y_pred)
    mae = []
    for possible_class in labels:
        indices = np.flatnonzero(y_true == possible_class)

        mae.append(
            mean_absolute_error(
                y_true[indices],
                y_pred[indices],
                sample_weight=sample_weight[indices],
            )
        )

    return np.sum(mae) / len(mae)
def macro_averaged_mean_absolute_error ( y_true , y_pred , * , sample_weight = None ) : _ , y_true , y_pred = _check_targets ( y_true , y_pred ) if sample_weight is not None : sample_weight = column_or_1d ( sample_weight ) else : sample_weight = np . ones ( y_true . shape ) check_consistent_length ( y_true , y_pred , sample_weight ) labels = unique_labels ( y_true , y_pred ) mae = [ ] for possible_class in labels : indices = np . flatnonzero ( y_true == possible_class ) mae . append ( mean_absolute_error ( y_true [ indices ] , y_pred [ indices ] , sample_weight = sample_weight [ indices ] , ) ) return np . sum ( mae ) / len ( mae )