aif360.metrics.utils.compute_num_TF_PN
- aif360.metrics.utils.compute_num_TF_PN(X, y_true, y_pred, w, feature_names, favorable_label, unfavorable_label, condition=None)[source]
Compute the number of true/false positives/negatives optionally conditioned on protected attributes.
- Parameters:
X (numpy.ndarray) – Dataset features.
y_true (numpy.ndarray) – True label vector.
y_pred (numpy.ndarray) – Predicted label vector.
w (numpy.ndarray) – Instance weight vector - the true and predicted datasets are supposed to have same instance level weights.
feature_names (list) – names of the features.
favorable_label (float) – Value of favorable/positive label.
unfavorable_label (float) – Value of unfavorable/negative label.
condition (list(dict)) – Same format as
compute_boolean_conditioning_vector().
- Returns:
Number of positives/negatives (optionally conditioned).