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Merge pull request #22847 from UnixJunkie/linwrap_915
new file: packages/linwrap/linwrap.9.1.5/opam
authored by
Marcello Seri
and committed by
GitHub
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packages/linwrap/linwrap.9.1.5/opam
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packages/linwrap/linwrap.9.1.5/opam
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opam-version: "2.0"
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authors: "Francois Berenger"
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maintainer: "unixjunkie@sdf.org"
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homepage: "https://github.com/UnixJunkie/linwrap"
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bug-reports: "https://github.com/UnixJunkie/linwrap/issues"
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dev-repo: "git+https://github.com/UnixJunkie/linwrap.git"
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license: "BSD-3-Clause"
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build: ["dune" "build" "-p" name "-j" jobs]
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install: ["cp" "bin/ecfp6.py" "%{bin}%/linwrap_ecfp6.py"]
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depends: [
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"base-unix"
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"batteries" {>= "3.3.0"}
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"bst"
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"conf-liblinear-tools"
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"cpm" {>= "11.0.0"}
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"dokeysto" # possible perf. regr.: dokeysto_camltc -> dokeysto
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"ocaml" {>= "5.0.0"} # because camltc not yet ready for ocaml>=5.0.0
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"dolog" {>= "6.0.0"}
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"dune" {>= "1.10"}
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"minicli" {>= "5.0.0"}
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"molenc"
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"parany" {>= "11.0.0"}
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]
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# the software can compile and install without the depopts.
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# however, some tools and options will not work anymore at run-time
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depopts: [
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"conf-gnuplot"
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"conf-python-3"
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"conf-rdkit"
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]
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synopsis: "Wrapper on top of liblinear-tools"
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description: """
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Linwrap can be used to train a L2-regularized logistic regression classifier
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or a linear Support Vector Regressor.
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You can optimize C (the L2 regularization parameter), w (the class weight)
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or k (the number of bags, i.e. use bagging).
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You can also find the optimal classification threshold using MCC maximization,
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use k-folds cross validation, parallelization, etc.
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In the regression case, you can only optimize C and epsilon.
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When using bagging, each model is trained on balanced bootstraps
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from the training set (one bootstrap for the positive class,
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one for the negative class).
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The size of the bootstrap is the size of the smallest (under-represented)
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class.
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usage: linwrap
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-i <filename>: training set or DB to screen
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[-o <filename>]: predictions output file
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[-np <int>]: ncores
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[-c <float>]: fix C
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[-e <float>]: fix epsilon (for SVR);
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(0 <= epsilon <= max_i(|y_i|))
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[-w <float>]: fix w1
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[--no-plot]: no gnuplot
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[-k <int>]: number of bags for bagging (default=off)
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[{-n|--NxCV} <int>]: folds of cross validation
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[--mcc-scan]: MCC scan for a trained model (requires n>1)
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also requires (c, w, k) to be known
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[--seed <int>]: fix random seed
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[-p <float>]: training set portion (in [0.0:1.0])
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[--pairs]: read from .AP files (atom pairs; will offset feat. indexes by 1)
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[--train <train.liblin>]: training set (overrides -p)
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[--valid <valid.liblin>]: validation set (overrides -p)
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[--test <test.liblin>]: test set (overrides -p)
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[{-l|--load} <filename>]: prod. mode; use trained models
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[{-s|--save} <filename>]: train. mode; save trained models
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[-f]: force overwriting existing model file
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[--scan-c]: scan for best C
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[--scan-e <int>]: epsilon scan #steps for SVR
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[--regr]: regression (SVR); also, implied by -e and --scan-e
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[--scan-w]: scan weight to counter class imbalance
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[--w-range <float>:<int>:<float>]: specific range for w
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(semantic=start:nsteps:stop)
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[--e-range <float>:<int>:<float>]: specific range for e
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(semantic=start:nsteps:stop)
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[--c-range <float,float,...>] explicit scan range for C
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(example='0.01,0.02,0.03')
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[--k-range <int,int,...>] explicit scan range for k
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(example='1,2,3,5,10')
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[--scan-k]: scan number of bags (advice: optim. k rather than w)
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"""
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url {
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src: "https://github.com/UnixJunkie/linwrap/archive/v9.1.5.tar.gz"
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checksum: "md5=f59e8b0452a5bb33f0fe239e524b5b40"
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}