binary_cross_entropy.py

import numpy as np


def binary_cross_entropy(
    y_true: np.ndarray, y_pred: np.ndarray, epsilon: float = 1e-15
) -> float:
    """
    Calculate the BCE Loss between true labels and predicted probabilities.

    Parameters:
    - y_true: True binary labels (0 or 1).
    - y_pred: Predicted probabilities for class 1.
    - epsilon: Small constant to avoid numerical instability.

    Returns:
    - bce_loss: Binary Cross-Entropy Loss.

    Example Usage:
    >>> true_labels = np.array([0, 1, 1, 0, 1])
    >>> predicted_probs = np.array([0.2, 0.7, 0.9, 0.3, 0.8])
    >>> bce_loss = binary_cross_entropy(true_labels, predicted_probs)
    >>> bce_loss
    0.6785203447911846
    """
    # Clip predicted probabilities to avoid log(0) and log(1)
    y_pred = np.clip(y_pred, epsilon, 1 - epsilon)

    # Calculate binary cross-entropy loss
    bce_loss = -(y_true * np.log(y_pred) + (1 - y_true) * np.log(1 - y_pred))

    # Take the mean over all samples
    bce_loss = np.mean(bce_loss)

    return bce_loss


if __name__ == "__main__":
    import doctest

    doctest.testmod()