this repo has no description
anil.recoil.org
1opam-version: "2.0"
2authors: "Francois Berenger"
3maintainer: "unixjunkie@sdf.org"
4homepage: "https://github.com/UnixJunkie/linwrap"
5bug-reports: "https://github.com/UnixJunkie/linwrap/issues"
6dev-repo: "git+https://github.com/UnixJunkie/linwrap.git"
7license: "BSD-3-Clause"
8build: ["dune" "build" "-p" name "-j" jobs]
9install: [
10 ["cp" "bin/ecfp6.py" "%{bin}%/linwrap_ecfp6.py"]
11]
12depends: [
13 "base-unix"
14 "batteries"
15 "conf-liblinear-tools"
16 "conf-liblinear-tools"
17 "conf-python-3"
18 "conf-rdkit"
19 "cpm" {>= "4.0.0"}
20 "dokeysto_camltc"
21 "dolog" {>= "4.0.0" & < "5.0.0"}
22 "dune" {>= "1.10"}
23 "minicli"
24 "parany" {>= "6.0.0" & < "10.0.0"}
25]
26synopsis: "Wrapper around liblinear-tools"
27description: """
28Only L2-regularized logistic regression is supported currently.
29When using bagging, each model is trained on balanced bootstraps
30from the training set (one bootstrap for the positive class,
31one for the negative class).
32The size of the bootstrap is the size of the smallest (under-represented)
33class.
34
35usage: linwrap
36 -i <filename>: training set or DB to screen
37 [-o <filename>]: predictions output file
38 [-np <int>]: ncores
39 [-c <float>]: fix C
40 [-w <float>]: fix w1
41 [-k <int>]: number of bags for bagging (default=off)
42 [-n <int>]: folds of cross validation
43 [--seed <int>]: fix random seed
44 [-p <float>]: training set portion (in [0.0:1.0])
45 [--train <train.liblin>]: training set (overrides -p)
46 [--valid <valid.liblin>]: validation set (overrides -p)
47 [--test <test.liblin>]: test set (overrides -p)
48 [{-l|--load} <filename>]: prod. mode; use trained models
49 [{-s|--save} <filename>]: train. mode; save trained models
50 [-f]: force overwriting existing model file
51 [--scan-c]: scan for best C
52 [--scan-w]: scan weight to counter class imbalance
53 [--scan-k]: scan number of bags (advice: optim. k rather than w)
54"""
55url {
56 src: "https://github.com/UnixJunkie/linwrap/archive/v4.0.0.tar.gz"
57 checksum: [
58 "sha256=1d0c96b7c0dde9ae8101947bbff321c5e64aaf99d7b790e680f8e9fee77cd16a"
59 "md5=217f5f1cac3f521a8877f0dbc9bd2c8a"
60 ]
61}