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* Added Mean Squared Logarithmic Error (MSLE) * Added Mean Squared Logarithmic Error (MSLE) * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --------- Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
56 lines
1.7 KiB
Python
56 lines
1.7 KiB
Python
"""
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Mean Squared Logarithmic Error (MSLE) Loss Function
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Description:
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MSLE measures the mean squared logarithmic difference between
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true values and predicted values, particularly useful when
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dealing with regression problems involving skewed or large-value
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targets. It is often used when the relative differences between
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predicted and true values are more important than absolute
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differences.
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Formula:
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MSLE = (1/n) * Σ(log(1 + y_true) - log(1 + y_pred))^2
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Source:
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(https://insideaiml.com/blog/MeanSquared-Logarithmic-Error-Loss-1035)
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"""
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import numpy as np
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def mean_squared_logarithmic_error(y_true: np.ndarray, y_pred: np.ndarray) -> float:
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"""
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Calculate the Mean Squared Logarithmic Error (MSLE) between two arrays.
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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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- msle: The Mean Squared Logarithmic Error between y_true and y_pred.
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Example usage:
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>>> true_values = np.array([1.0, 2.0, 3.0, 4.0, 5.0])
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>>> predicted_values = np.array([0.8, 2.1, 2.9, 4.2, 5.2])
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>>> mean_squared_logarithmic_error(true_values, predicted_values)
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0.0030860877925181344
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>>> true_labels = np.array([1.0, 2.0, 3.0, 4.0, 5.0])
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>>> predicted_probs = np.array([0.3, 0.8, 0.9, 0.2])
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>>> mean_squared_logarithmic_error(true_labels, predicted_probs)
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Traceback (most recent call last):
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...
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ValueError: Input arrays must have the same length.
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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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squared_logarithmic_errors = (np.log1p(y_true) - np.log1p(y_pred)) ** 2
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return np.mean(squared_logarithmic_errors)
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if __name__ == "__main__":
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import doctest
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doctest.testmod()
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