Megam-Teaching

Category

Other

Program On

Teaching

Version

0.92

Author / Distributor

Please see http://users.umiacs.umd.edu/~hal/megam/

Description

From http://users.umiacs.umd.edu/~hal/megam/: "MEGA Model Optimization Package: Maximum entropy models are very popular, especially in natural language processing. The software here is an implementation of maximum likelihood and maximum a posterior optimization of the parameters of these models."

Running Program

Also refer to Running Jobs on the teaching cluster

• Version 0.92, compiled with OCaml 4.07, installed in /usr/local/apps/gb/megam/0.92-foss-2018a

To use this version of megam, please first load the module with

ml megam/0.92-foss-2018a

This module will automatically load the foss/2018a toolchain.

Sample job submission script (sub.sh) to run megam:

#!/bin/bash
#SBATCH --job-name=megamjob
#SBATCH --partition=batch
#SBATCH --mail-type=ALL
#SBATCH --mail-user=username@uga.edu
#SBATCH --ntasks=1
#SBATCH --mem=1gb
#SBATCH --time=08:00:00
#SBATCH --output=megamjob.%j.out
#SBATCH --error=megamjob.%j.err

cd $SLURM_SUBMIT_DIR
ml megam/0.92-foss-2018a
megam [options] <model-type> <input-file>

where [options] <model-type> <input-file> need to be replaced by the options, the model and the input file that you want to use.

In the real submission script, at least all the above underlined values need to be reviewed or to be replaced by the proper values.

Sample job submission command:

sbatch sub.sh

Documentation

Please see http://users.umiacs.umd.edu/~hal/megam/

ml megam/0.92-foss-2018a

megam -h
usage: megam [options] <model-type> <input-file>
[options] are any of:
   -filespec        treat <input-file> as a filespec, not a normal
                    input file

   -fvals           data is in not in bernoulli format (i.e. feature
                    values appear next to their features int the file)

   -explicit        this specifies that each class gets its own feature
                    vector explicitely, rather than using the same one
                    independently of class
                    (only valid for multiclass problems)

   -quiet           don't generate per-iteration output

   -maxi <int>      specify the maximum number of iterations
                    (default: 100 for maxent, 20 for perceptron)

   -dpp <float>     specify the minimum change in perplexity
                    (default: -99999)

   -memory <int>    specify the memory size for LM-BFGS (multiclass only)
                    (default: 5)

   -lambda <float>  specify the precision of the Gaussian prior for maxent;
                    or the value for C for passive-aggressive algorithms
                    (default: 1)

   -tune            tune lambda using repeated optimizations (starts with
                    specified -lambda value and drops by half each time
                    until optimal dev error rate is achieved)

   -sprog <prog>    for density estimation problems, specify the program 
                    that will generate samples for us (see also -sfile)

   -sfile <files>   for de problems, instead of -sprog, just read from a
                    (set of) file(s); specify as file1:file2:...:fileN

   -sargs <string>  set the arguments for -sprog; default ""

   -sspec <i,i,i>   set the <burn-in time, number of samples, sample space>
                    parameters; default: 1000,500,50

   -sforcef <file>  include features listed in <file> in feature vectors
                    (even if they don't exist in the training data)

   -predict <file>  load parameters from <file> and do prediction
                    (will not optimize a model)

   -mean <file>     the Gaussian prior typically assumes mu=0 for all features;
                    you can instead list means in <file> in the same format
                    as is output by this program (baseline adaptation)

   -init <file>     initialized weights as in <file>

   -minfc <int>     remove all features with frequency <= <int>

   -bnsfs <int>     keep only <int> features by the BNS selection criteria

   -abffs <int>     use approximate Bayes factor feature selection; add features
                    in batches of (at most) <int> size

   -curve <spec>    produce a learning curve, where spec = "min,step"
                    and we start with min examples and increase (multiply!)
                    by step each time; eg: -curve 2,2

   -nobias          do not use the bias features

   -repeat <int>    repeat optimization <int> times (sometimes useful because
                    bfgs thinks it converges before it actually does)

   -lastweight      if there is DEV data, we will by default output the best
                    weight vector; use -lastweight to get the last one

   -multilabel      for multiclass problems, optimize a weighted multiclass
                    problem; labels should be of the form "c1:c2:c3:...:cN"
                    where there are N classes and ci is the cost for
                    predicting class i... if ci is 'x' then it forbids this
                    class from being correct (even during test)

   -bitvec          optimize bit vectors; implies multilabel input format,
                    but costs must be 0, 1 or 'x'

   -nc              use named classes (rather than numbered) for multi{class,tron};
                    incompatible with -multilabel

   -pa              perform passive-aggressive updates (should be used only
                    with perceptron or multitron inference; also see -lambda)

   -kernel <spec>   perform kernel mapping; <spec> should be one of:
                    '#:linear', '#:poly:#', or '#:rbf:#'; the first # should
                    be the desired dimensionality; the # for poly
                    is the degree and the # for rbf is the width of the
                    Gaussian.  Any of these options can be followed by
                    ':dist' (to select by distance) or ':class' (to select
                    by class)

   -norm[1|2]       l1 (or l2) normalization on instances

<model-type> is one of:
   binary           this is a binary classification problem; classes
                    are determined at a threshold of 0.5 (anything
                    less is negative class, anything greater is positive)

   perceptron       binary classification with averaged perceptron

   multitron        multiclass classification with averaged perceptron

   binomial         this is a binomial problem; all values should be
                    in the range [0,1]

   multiclass       this is a multiclass problem; classes should be
                    numbered [0, 1, 2, ...]; anything < 0 is mapped
                    to class 0

   density          this is a density estimation problem and thus the
                    partition function must be calculated through samples
                    (must use -sprog or -sfile arguments, above)

Fatal error: exception Failure("Error: not enough arguments")

Installation

Version 0.92, downloaded from http://hal3.name/megam/megam_src.tgz

System

64-bit Linux