A Python implementation of Macroaveraged MAE and RMSE

macroaveraged_error_rates.py

"""Macroaveraged MAE and RMSE ([Baccianella et al 2009](http://nmis.isti.cnr.it/sebastiani/Publications/ISDA09.pdf)) for evaluation of ordinal classifiers.

"""

import numpy as np


def groupby_labels(y, yhat):
    """Based on https://stackoverflow.com/questions/38013778/is-there-any-numpy-group-by-function
    """
    m = np.stack([y, yhat]).T
    m = m[m[:, 0].argsort()]
    grouped_preds = np.split(m[:, 1], np.unique(m[:, 0], return_index=True)[1])[1:]
    labels = np.unique(m[:, 0])
    return labels, grouped_preds

def mae_macro(y, yhat):
    """Macroaveraged MAE
    """
    labels, preds = groupby_labels(y, yhat)
    mean_diff = np.array([np.abs(label - pred).mean() for label, pred in zip(labels, preds)]).mean()
    return mean_diff

def rmse_macro(y, yhat):
    """Macroaveraged RMSE
    """
    labels, preds = groupby_labels(y, yhat)
    mean_diff = np.array([np.power(label - pred, 2).mean() for label, pred in zip(labels, preds)]).mean()
    return np.sqrt(mean_diff)


if __name__ == "__main__":

	y = np.array([1, 2, 3, 1, 2])
	yhat = np.array([3, 2, 2, 1, 2])

	print(mae_macro(y, yhat))
	print(rmse_macro(y, yhat))