A snippet on how to calculate mean squared logarithmic error (MSLE) from scratch.

calculate-msle.py

import numpy as np

actual = np.array([1, 2, 3, 4, 5])
predicted = np.array([1.1, 1.9, 2.7, 4.5, 6])

def msle(actual: np.ndarray, predicted: np.ndarray) -> float:
    log_differences = np.subtract(np.log(1 + actual), np.log(1 + predicted))
    squared_log_differences = np.square(log_differences)
    return np.mean(squared_log_differences)

msle(actual, predicted)