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Adds hinge loss function algorithm (#10628)
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* Adds exponential moving average algorithm

* code clean up

* spell correction

* Modifies I/O types of function

* Replaces generator function

* Resolved mypy type error

* readibility of code and documentation

* Update exponential_moving_average.py

* Adds hinge loss function

* suggested doc and refactoring changes

* refactoring

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Co-authored-by: Christian Clauss <cclauss@me.com>
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PoojanSmart and cclauss authored Oct 18, 2023
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64 changes: 64 additions & 0 deletions machine_learning/loss_functions/hinge_loss.py
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"""
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, y_pred: np.ndarray) -> float:
"""
Calculate the mean hinge loss for y_true and y_pred for binary classification.
Args:
y_true: Array of actual values (ground truth) encoded as -1 and 1.
y_pred: Array of predicted values.
Returns:
The hinge loss between y_true and y_pred.
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.
>>> y_true = np.array([-1, 1, 10, -1, 1])
>>> pred = np.array([-4, -0.3, 0.7, 5, 10])
>>> hinge_loss(y_true, pred)
Traceback (most recent call last):
...
ValueError: y_true can have values -1 or 1 only.
"""

if len(y_true) != len(y_pred):
raise ValueError("Length of predicted and actual array must be same.")

# Raise value error when y_true (encoded labels) have any other values
# than -1 and 1
if np.any((y_true != -1) & (y_true != 1)):
raise ValueError("y_true can have values -1 or 1 only.")

hinge_losses = np.maximum(0, 1.0 - (y_true * y_pred))
return np.mean(hinge_losses)


if __name__ == "__main__":
import doctest

doctest.testmod()

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