from typing import List
from typing import Union
import numpy as np
ArrayLike = List[Union[float, List[float]]]
[docs]def mape(y_true: ArrayLike, y_pred: ArrayLike, eps: float = 1e-15) -> float:
"""Mean absolute percentage error.
`Wikipedia entry on the Mean absolute percentage error
<https://en.wikipedia.org/wiki/Mean_absolute_percentage_error>`_
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 target values.
eps: float=1e-15
MAPE is undefined for ``y_true[i]==0`` for any ``i``, so all zeros ``y_true[i]`` are
clipped to ``max(eps, abs(y_true))``.
Returns
-------
float
A non-negative floating point value (the best value is 0.0).
"""
y_true_array, y_pred_array = np.asarray(y_true), np.asarray(y_pred)
if len(y_true_array.shape) != len(y_pred_array.shape):
raise ValueError("Shapes of the labels must be the same")
y_true_array = y_true_array.clip(eps)
return np.mean(np.abs((y_true_array - y_pred_array) / y_true_array)) * 100
[docs]def smape(y_true: ArrayLike, y_pred: ArrayLike, eps: float = 1e-15) -> float:
"""Symmetric mean absolute percentage error.
`Wikipedia entry on the Symmetric mean absolute percentage error
<https://en.wikipedia.org/wiki/Symmetric_mean_absolute_percentage_error>`_
.. math::
SMAPE = \dfrac{100}{n}\sum_{t=1}^{n}\dfrac{|ytrue_{t}-ypred_{t}|}{(|ypred_{t}|+|ytrue_{t}|) / 2}
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 target values.
eps: float=1e-15
SMAPE is undefined for ``y_true[i] + y_pred[i] == 0`` for any ``i``, so all zeros ``y_true[i] + y_pred[i]`` are
clipped to ``max(eps, abs(y_true) + abs(y_pred))``.
Returns
-------
float
A non-negative floating point value (the best value is 0.0).
"""
y_true_array, y_pred_array = np.asarray(y_true), np.asarray(y_pred)
if len(y_true_array.shape) != len(y_pred_array.shape):
raise ValueError("Shapes of the labels must be the same")
return 100 * np.mean(
2 * np.abs(y_pred_array - y_true_array) / (np.abs(y_true_array) + np.abs(y_pred_array)).clip(eps)
)