Source code for bob.learn.boosting.ExponentialLoss

from .LossFunction import LossFunction

import numpy

class ExponentialLoss (LossFunction):
  """ The class implements the exponential loss function for the boosting framework."""


  def loss(self, targets, scores):
    """The function computes the exponential loss values using prediction scores and targets.
    It can be used in classification tasks, e.g., in combination with the StumpTrainer.

    Keyword parameters:

      targets (float <#samples, #outputs>): The target values that should be reached.

      scores (float <#samples, #outputs>): The scores provided by the classifier.

    Returns
      (float <#samples, #outputs>): The loss values for the samples, always >= 0
    """
    return numpy.exp(-(targets * scores))


  def loss_gradient(self, targets, scores):
    """The function computes the gradient of the exponential loss function using prediction scores and targets.

    Keyword parameters:

      targets (float <#samples, #outputs>): The target values that should be reached.

      scores (float <#samples, #outputs>): The scores provided by the classifier.

    Returns
      loss (float <#samples, #outputs>): The gradient of the loss based on the given scores and targets.
    """
    loss = numpy.exp(-(targets * scores))
    return -targets * loss