SMAPE - Symmetric Mean Absolute Percentage Error

\[\text{SMAPE}(y, \hat{y}) = \frac{100\%}{N} \sum_{i=0}^{N - 1} \frac{ 2*|y_i - \hat{y}_i|}{|y| + |\hat{y}|}\]

• Symmetric Mean Absolute Percentage Error (SMAPE): Best possible score is 0.0, smaller value is better. Range = [0, 1].

• If you want percentage, multiply the result with 100%

• Link: https://en.wikipedia.org/wiki/Symmetric_mean_absolute_percentage_error

Latex equation code:

\text{SMAPE}(y, \hat{y}) = \frac{100\%}{N} \sum_{i=0}^{N - 1} \frac{ 2*|y_i - \hat{y}_i|}{|y| + |\hat{y}|}

Example to use SMAPE metric:

from numpy import array
from permetrics.regression import RegressionMetric

## For 1-D array
y_true = array([3, -0.5, 2, 7])
y_pred = array([2.5, 0.0, 2, 8])

evaluator = RegressionMetric(y_true, y_pred, decimal=5)
print(evaluator.symmetric_mean_absolute_percentage_error())

## For > 1-D array
y_true = array([[0.5, 1], [-1, 1], [7, -6]])
y_pred = array([[0, 2], [-1, 2], [8, -5]])

evaluator = RegressionMetric(y_true, y_pred, decimal=5)
print(evaluator.SMAPE(multi_output="raw_values"))