luz (version 0.5.0)

luz_metric_multiclass_auroc: Computes the multi-class AUROC

Description

The same definition as Keras is used by default. This is equivalent to the 'micro' method in SciKit Learn too. See docs.

Usage

luz_metric_multiclass_auroc(
  num_thresholds = 200,
  thresholds = NULL,
  from_logits = FALSE,
  average = c("micro", "macro", "weighted", "none")
)

Arguments

num_thresholds

Number of thresholds used to compute confusion matrices. In that case, thresholds are created by getting num_thresholds values linearly spaced in the unit interval.

thresholds

(optional) If threshold are passed, then those are used to compute the confusion matrices and num_thresholds is ignored.

from_logits

If TRUE then we call torch::nnf_softmax() in the predictions before computing the metric.

average

The averaging method:

• 'micro': Stack all classes and computes the AUROC as if it was a binary classification problem.

• 'macro': Finds the AUCROC for each class and computes their mean.

• 'weighted': Finds the AUROC for each class and computes their weighted mean pondering by the number of instances for each class.

• 'none': Returns the AUROC for each class in a list.

Details

Note that class imbalance can affect this metric unlike the AUC for binary classification.

Currently the AUC is approximated using the 'interpolation' method described in Keras.

See Also

Other luz_metrics: luz_metric(), luz_metric_accuracy(), luz_metric_binary_accuracy(), luz_metric_binary_accuracy_with_logits(), luz_metric_binary_auroc(), luz_metric_mae(), luz_metric_mse(), luz_metric_rmse()

Examples

if (torch::torch_is_installed()) {
library(torch)
actual <- c(1, 1, 1, 0, 0, 0) + 1L
predicted <- c(0.9, 0.8, 0.4, 0.5, 0.3, 0.2)
predicted <- cbind(1-predicted, predicted)

y_true <- torch_tensor(as.integer(actual))
y_pred <- torch_tensor(predicted)

m <- luz_metric_multiclass_auroc(thresholds = as.numeric(predicted),
                                 average = "micro")
m <- m$new()

m$update(y_pred[1:2,], y_true[1:2])
m$update(y_pred[3:4,], y_true[3:4])
m$update(y_pred[5:6,], y_true[5:6])
m$compute()
}