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package cpm
Parameters
module SL : SCORE_LABELSignature
val rank_order_by_score_a : SL.t array -> unitin-place sort of score labels; putting high scores first
val cumulated_actives_curve : SL.t list -> int listcompute the cumulated actives curve given an already sorted list of score labels
val roc_curve : SL.t list -> (float * float) listcompute Area Under the ROC curve given an already sorted list of score labels
val fast_auc : SL.t list -> floatROC curve (list of (FPR,TPR) values) corresponding to those score labels
val auc : SL.t list -> floatcompute Area Under the ROC curve given an unsorted list of score labels
val auc_a : SL.t array -> floatcompute Area Under the ROC curve given an unsorted array of score labels; array will be sorted in-place
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 -> floatinitial_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 -> floatsame as initial_enhancement but does not reorder the list of score_labels
val power_metric : float -> SL.t list -> floatpower metric at given threshold given an unsorted list of score labels
val bedroc_auc : ?alpha:float -> SL.t list -> floatbedroc_auc at given alpha. Default alpha = 20.0.
val mcc : float -> SL.t list -> floatMatthews' Correlation Coefficient (MCC) use: mcc classif_threshold score_labels. scores >= threshold are predicted as targets; scores < threshold are predicted as non targets.
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