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53 lines
1.5 KiB
Python
53 lines
1.5 KiB
Python
"""
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Huber Loss Function
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Description:
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Huber loss function describes the penalty incurred by an estimation procedure.
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It serves as a measure of the model's accuracy in regression tasks.
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Formula:
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Huber Loss = if |y_true - y_pred| <= delta then 0.5 * (y_true - y_pred)^2
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else delta * |y_true - y_pred| - 0.5 * delta^2
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Source:
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[Wikipedia - Huber Loss](https://en.wikipedia.org/wiki/Huber_loss)
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"""
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import numpy as np
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def huber_loss(y_true: np.ndarray, y_pred: np.ndarray, delta: float) -> float:
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"""
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Calculate the mean of Huber Loss.
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Parameters:
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- y_true: The true values (ground truth).
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- y_pred: The predicted values.
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Returns:
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- huber_loss: The mean of Huber Loss between y_true and y_pred.
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Example usage:
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>>> true_values = np.array([0.9, 10.0, 2.0, 1.0, 5.2])
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>>> predicted_values = np.array([0.8, 2.1, 2.9, 4.2, 5.2])
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>>> np.isclose(huber_loss(true_values, predicted_values, 1.0), 2.102)
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True
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>>> true_labels = np.array([11.0, 21.0, 3.32, 4.0, 5.0])
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>>> predicted_probs = np.array([8.3, 20.8, 2.9, 11.2, 5.0])
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>>> np.isclose(huber_loss(true_labels, predicted_probs, 1.0), 1.80164)
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True
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"""
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if len(y_true) != len(y_pred):
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raise ValueError("Input arrays must have the same length.")
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huber_mse = 0.5 * (y_true - y_pred) ** 2
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huber_mae = delta * (np.abs(y_true - y_pred) - 0.5 * delta)
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return np.where(np.abs(y_true - y_pred) <= delta, huber_mse, huber_mae).mean()
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if __name__ == "__main__":
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import doctest
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doctest.testmod()
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