NNSE - Normalized NSE

\[\text{NNSE}(y, \hat{y}) = \frac{1}{2 - NSE}\]

• Normalize Nash-Sutcliffe Efficiency (NNSE): Best possible score is 1.0, greater value is better. Range = [0, 1]

• Link: https://agrimetsoft.com/calculators/Nash%20Sutcliffe%20model%20Efficiency%20coefficient

Latex equation code:

\text{E}(y, \hat{y}) = \frac{1}{2 - NSE}

Example to use NNSE 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.normalized_nash_sutcliffe_efficiency())

## For > 1-D array
y_true = array([[0.5, 1], [-1, 1], [7, -6], [1, 2], [2.1, 2.2], [3.4, 5.5]])
y_pred = array([[0, 2], [-1, 2], [8, -5], [1.1, 1.9], [2.0, 2.3], [3.0, 4.2]])

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