collection of tools for deep learning with transcription factors
from evaluation_metrics import EvaluationMetrics
true_labels = [[1, 0, 1], [0, 1, 1]] predicted_labels = [[0.8, 0.2, 0.6], [0.2, 0.9, 0.7]] objective = "binary"
mean, std = EvaluationMetrics.calculate_metrics(true_labels, predicted_labels, objective)
The following metrics are available for calculation:
Binary classification: Accuracy Area Under the Receiver Operating Characteristic curve (AUROC) Area Under the Precision-Recall curve (AUPR) Categorical classification: Accuracy Area Under the Receiver Operating Characteristic curve (AUROC) Area Under the Precision-Recall curve (AUPR) Regression: Pearson correlation coefficient R-squared Slope