Adds hinge loss function algorithm

machine_learning/loss_functions/hinge_loss.py

@@ -0,0 +1,55 @@
+"""
+Hinge Loss
+
+Description:
+Compute the Hinge loss used for training SVM (Support Vector Machine).
+
+Formula:
+loss = max(0, 1 - true * pred)
+
+Reference: https://en.wikipedia.org/wiki/Hinge_loss
+
+Author: Poojan Smart
+Email: smrtpoojan@gmail.com
+"""
+
+import numpy as np
+
+
+def hinge_loss(y_true: np.ndarray, pred: np.ndarray) -> float:
+    """
+    Calculate the hinge loss for y_true and pred for binary classification.
+
+    Args:
+        y_true: Array of actual values (ground truth) encoded as -1 and 1.
+        pred: Array of predicted values.
+
+    Returns:
+        Hinge loss
+
+    Examples:
+    >>> y_true = np.array([-1, 1, 1, -1, 1])
+    >>> pred = np.array([-4, -0.3, 0.7, 5, 10])
+    >>> hinge_loss(y_true, pred)
+    1.52
+    >>> y_true = np.array([-1, 1, 1, -1, 1, 1])
+    >>> pred = np.array([-4, -0.3, 0.7, 5, 10])
+    >>> hinge_loss(y_true, pred)
+    Traceback (most recent call last):
+    ...
+    ValueError: Length of predicted and actual array must be same.
+    """
+
+    if len(y_true) != len(pred):
+        raise ValueError("Length of predicted and actual array must be same.")
+
+    intermidiate_result = 1.0 - (y_true * pred)
+    intermidiate_result[intermidiate_result < 0] = 0
+    loss = np.mean(intermidiate_result)
+    return loss
+
+
+if __name__ == "__main__":
+    import doctest
+
+    doctest.testmod()