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Reference implementation of the Vanishing Ranking Kernels (VRK) method
Example ROC curve on a hold-out test set. The test set had 38 active molecules and 664 inactives. ROC AUC: 0.861; BEDROC AUC: 0.766; PR AUC: 0.678. The ROC curve is in purple; the precision-recall (PR) curve in cyan. The probability of activity given a raw score is the red curve. The green curve is the number of actives divided by the number of decoys as a function of the scores filtering threshold.
Train and test a model:
rankers_bwmine -i data/tox21_nrar_ligands_std_rand_01.txt
Same, but using 16 cores :
rankers_bwmine -np 16 -i data/tox21_nrar_ligands_std_rand_01.txt
rankers_bwmine -i <train.txt>
[-p <float>]: proportion of the (randomized) dataset
used to train (default=0.80)
[-k {uni|tri|epa|biw}]: kernel function choice (default=biw)
[-np <int>]: max number of processes (default=1)
[-o <filename>]: write raw test scores to file
[--train <train.txt>]: training set (overrides -p)
[--valid <valid.txt>]: validation set (overrides -p)
[--test <test.txt>]: test set (overrides -p)
[-n <int>]: max number of optimization steps; default=150
[--capf <float>]: keep only fraction of decoys
[--capx <int>]: keep only X decoys per active
[--capi <int>]: limit total number of molecules
(but keep all actives)
[--seed <int>: fix random seed]
[--pr]: use PR AUC instead of ROC AUC during optimization
[-kb <float>]: user-chosen kernel bandwidth
[--mcc-scan]: scan classif. threshold to maximize MCC
[--tap]: tap the train-valid-test partitions to disk
[-q|--quick]: exit early; just after model training
[--noplot]: turn off gnuplot
[-v]: verbose/debug mode
[-h|--help]: show this help message