evaluate_sigmoid: Evaluate matrices of true targets and predictions from layer...

In GenomeNet/deepG: Deep Learning for Genome Sequence Data

Evaluate matrices of true targets and predictions from layer with sigmoid activation.

Description

Compute accuracy, binary crossentropy and (optionally) AUC or AUPRC, given predictions and true targets. Outputs columnwise average.

Usage

evaluate_sigmoid(y, y_conf, auc = FALSE, auprc = FALSE, label_names = NULL)

Arguments

y	Matrix of true target.
y_conf	Matrix of predictions.
auc	Whether to include AUC metric.
auprc	Whether to include AUPRC metric.
label_names	Names of corresponding labels. Length must be equal to number of columns of y.

Value

A list of evaluation results.

Examples

y <- matrix(sample(c(0, 1), 30, replace = TRUE), ncol = 3)
y_conf <- matrix(runif(n = 30), ncol = 3)
evaluate_sigmoid(y, y_conf, auc = TRUE, auprc = TRUE)

GenomeNet/deepG documentation built on Dec. 24, 2024, 12:11 p.m.