aif360.metrics.utils
.compute_num_gen_TF_PN¶
-
aif360.metrics.utils.
compute_num_gen_TF_PN
(X, y_true, y_score, w, feature_names, favorable_label, unfavorable_label, condition=None)[source]¶ Compute the number of generalized true/false positives/negatives optionally conditioned on protected attributes. Generalized counts are based on scores and not on the hard predictions.
Parameters: - X (numpy.ndarray) – Dataset features.
- y_true (numpy.ndarray) – True label vector.
- y_score (numpy.ndarray) – Predicted score vector. Values range from 0 to 1. 0 implies prediction for unfavorable label and 1 implies prediction for favorable label.
- 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).