CrossEntropy¶
Cross entropy loss.
This is a generalization of logistic loss to multiple classes.
Parameters¶
-
class_weight
Type → dict[base.typing.ClfTarget, float] | None
Default →
NoneA dictionary that indicates what weight to associate with each class.
Examples¶
from river import optim
y_true = [0, 1, 2, 2]
y_pred = [
{0: 0.29450637, 1: 0.34216758, 2: 0.36332605},
{0: 0.21290077, 1: 0.32728332, 2: 0.45981591},
{0: 0.42860913, 1: 0.33380113, 2: 0.23758974},
{0: 0.44941979, 1: 0.32962558, 2: 0.22095463}
]
loss = optim.losses.CrossEntropy()
for yt, yp in zip(y_true, y_pred):
print(loss(yt, yp))
1.222454
1.116929
1.437209
1.509797
for yt, yp in zip(y_true, y_pred):
print(loss.gradient(yt, yp))
{0: -0.70549363, 1: 0.34216758, 2: 0.36332605}
{0: 0.21290077, 1: -0.67271668, 2: 0.45981591}
{0: 0.42860913, 1: 0.33380113, 2: -0.76241026}
{0: 0.44941979, 1: 0.32962558, 2: -0.77904537}
Methods¶
call
Returns the loss.
Parameters
- y_true
- y_pred
Returns
The loss(es).
gradient
Return the gradient with respect to y_pred.
Parameters
- y_true
- y_pred
Returns
The gradient(s).
mean_func
Mean function.
This is the inverse of the link function. Typically, a loss function takes as input the raw output of a model. In the case of classification, the raw output would be logits. The mean function can be used to convert the raw output into a value that makes sense to the user, such as a probability.
Parameters
- y_pred
Returns
The adjusted prediction(s).