import numpy
from . import LossFunction
[docs]class ExponentialLoss(LossFunction):
""" The class implements the exponential loss function for the boosting framework."""
[docs] 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))
[docs] 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