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added mean absolute percentage error (#10464)

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* added mean absolute percentage error

* [pre-commit.ci] auto fixes from pre-commit.com hooks

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* added mean_absolute_percentage_error

* added mean_absolute_percentage_error

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

* added mean_absolute_percentage_error

* added mean_absolute_percentage_error

* added mean absolute percentage error

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

* added mean absolute percentage error

* added mean absolute percentage error

* added mean absolute percentage error

* added mean absolute percentage error

* added mean absolute percentage error

* Update machine_learning/loss_functions.py

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Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Tianyi Zheng <tianyizheng02@gmail.com>
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Humzafazal72 2023-10-26 04:05:35 +05:00 committed by GitHub
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machine_learning/loss_functions.py
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@ -297,6 +297,51 @@ def mean_squared_logarithmic_error(y_true: np.ndarray, y_pred: np.ndarray) -> fl
return np.mean(squared_logarithmic_errors) return np.mean(squared_logarithmic_errors)
def mean_absolute_percentage_error(
y_true: np.ndarray, y_pred: np.ndarray, epsilon: float = 1e-15
) -> float:
"""
Calculate the Mean Absolute Percentage Error between y_true and y_pred.
Mean Absolute Percentage Error calculates the average of the absolute
percentage differences between the predicted and true values.
Formula = (Σ|y_true[i]-Y_pred[i]/y_true[i]|)/n
Source: https://stephenallwright.com/good-mape-score/
Parameters:
y_true (np.ndarray): Numpy array containing true/target values.
y_pred (np.ndarray): Numpy array containing predicted values.
Returns:
float: The Mean Absolute Percentage error between y_true and y_pred.
Examples:
>>> y_true = np.array([10, 20, 30, 40])
>>> y_pred = np.array([12, 18, 33, 45])
>>> mean_absolute_percentage_error(y_true, y_pred)
0.13125
>>> y_true = np.array([1, 2, 3, 4])
>>> y_pred = np.array([2, 3, 4, 5])
>>> mean_absolute_percentage_error(y_true, y_pred)
0.5208333333333333
>>> y_true = np.array([34, 37, 44, 47, 48, 48, 46, 43, 32, 27, 26, 24])
>>> y_pred = np.array([37, 40, 46, 44, 46, 50, 45, 44, 34, 30, 22, 23])
>>> mean_absolute_percentage_error(y_true, y_pred)
0.064671076436071
"""
if len(y_true) != len(y_pred):
raise ValueError("The length of the two arrays should be the same.")
y_true = np.where(y_true == 0, epsilon, y_true)
absolute_percentage_diff = np.abs((y_true - y_pred) / y_true)
return np.mean(absolute_percentage_diff)
if __name__ == "__main__": if __name__ == "__main__":
import doctest import doctest