Library
Module
Module type
Parameter
Class
Class type
module SL : SCORE_LABEL
val cumulated_actives_curve : SL.t list -> int list
compute the cumulated actives curve given an already sorted list of score labels
val roc_curve : SL.t list -> (float * float) list
compute Area Under the ROC curve given an already sorted list of score labels
val fast_auc : SL.t list -> float
ROC curve (list of (FPR,TPR) values) corresponding to those score labels
val auc : SL.t list -> float
compute Area Under the ROC curve given an unsorted list of score labels
val enrichment_factor : float -> SL.t list -> float
(early) enrichment factor at given threshold (database percentage) given an unsorted list of score labels
val initial_enhancement : float -> SL.t list -> float
initial_enhancement a score_labels
will compute S = sum_over_i (exp (-rank(active_i) / a)) given an unsorted list of score labels. Robust Initial Enhancement (RIE) = S/<S> where <S> is the average S for randomly ordered score labels. RIE = 1.0 <=> random performance. Cf. DOI:10.1021/ci0100144 for details.
val fast_initial_enhancement : float -> SL.t list -> float
same as initial_enhancement
but does not reorder the list of score_labels
val power_metric : float -> SL.t list -> float
power metric at given threshold given an unsorted list of score labels
val bedroc_auc : ?alpha:float -> SL.t list -> float
bedroc_auc at given alpha. Default alpha = 20.0.