GEval

GEval is a Haskell library and a stand-alone tool for evaluating the results of solutions to machine learning challenges as defined on the Gonito platform. Also could be used outside the context of Gonito.net challenges, assuming the test data is given in simple TSV (tab-separated values) files.

Note that GEval is only about machine learning evaluation. No actual machine learning algorithms are available here.

The official repository is git://gonito.net/geval, browsable at https://gonito.net/gitlist/geval.git/.

Installing

You need Haskell Stack. You could install Stack with your package manager or with:

curl -sSL https://get.haskellstack.org/ | sh

When you've got Haskell Stack, install GEval with:

git clone git://gonito.net/geval
cd geval
stack setup
stack test
stack install

(Note that when you're running Haskell Stack for the first time it will take some time and a couple of gigabytes on your disk.)

By default, geval binary is installed in $HOME/.local/bin, so in order to run geval you need to either add $HOME/.local/bin to $PATH in your configuration or to type:

PATH="$HOME/.local/bin" geval ...

Plan B — just download the GEval binary

(Assuming you have a standard 64-bit Linux.)

wget https://gonito.net/get/bin/geval
chmod u+x geval
./geval --help

Examples

Let us download a Gonito.net challenge:

git clone git://gonito.net/sentiment-by-emoticons

The task is to predict the sentiment of a Polish short text -- whether it is positive or negative (or to be precise: to guess whether a positive or negative emoticon was used). The train set is given in the train/train.tsv.xz file, each item is given in a separate file, have a look at the first 5 items:

xzcat train/train.tsv.xz | head -n 5

Now let's try to evaluate some solution to this challenge. Let's fetch it:

git fetch git://gonito.net/sentiment-by-emoticons submission-01865
git reset --hard FETCH_HEAD

and now run geval:

geval -t dev-0

(You need to run dev-0 test as the expected results for the test-A test is hidden from you.) The evaluation result is 0.47481. This might be hard to interpret, so you could try other metrics.

geval -t dev-0 --metric Accuracy --metric Likelihood

So now you can see that the accuracy is over 78% and the likelihood (i.e. geometric mean of probabilities of the correct classes) is 0.62.

Preparing a Gonito challenge

Directory structure of a Gonito challenge

A definition of a Gonito challenge should be put in a separate directory. Such a directory should have the following structure:

• README.md — description of a challenge in Markdown, the first header will be used as the challenge title, the first paragraph — as its short description
• config.txt — simple configuration file with options the same as the ones accepted by geval binary (see below), usually just a metric is specified here (e.g. --metric BLEU), also non-default file names could be given here (e.g. --test-name test-B for a non-standard test subdirectory)
• train/ — subdirectory with training data (if training data are supplied for a given Gonito challenge at all)
• train/train.tsv — the usual name of the training data file (this name is not required and could be more than one file), the first column is the target (predicted) value, the other columns represent features, no header is assumed
• dev-0/ — subdirectory with a development set (a sample test set, which won't be used for the final evaluation)
• dev-0/in.tsv — input data (the same format as train/train.tsv, but without the first column)
• dev-0/expected.tsv — values to be guessed (note that paste dev-0/expected.tsv dev-0/in.tsv should give the same format as train/train.tsv)
• dev-1/, dev-2, ... — other dev sets (if supplied)
• test-A/ — subdirectory with the test set
• test-A/in.tsv — test input (the same format as dev-0/in.tsv)
• test-A/expected.tsv — values to be guessed (the same format as dev-0/expected.tsv), note that this file should be “hidden” by the organisers of a Gonito challenge, see notes on the structure of commits below
• test-B, test-C, ... — other alternative test sets (if supplied)

Initiating a Gonito challenge with geval

You can use geval to initiate a Gonito challenge:

geval --init --expected-directory my-challenge

(This will generate a sample toy challenge about guessing planet masses).

A metric (other than the default RMSE — root-mean-square error) can be given to generate another type of toy challenge:

geval --init --expected-directory my-machine-translation-challenge --metric BLEU

Preparing a Git repository

Gonito platform expects a Git repository with a challenge to be submitted. The suggested way to do this is as follows:

1. Prepare a branch with all the files without test-A/expected.tsv. This branch will be cloned by people taking up the challenge.
2. Prepare a separate branch (or even a repo) with test-A/expected.tsv added. This branch should be accessible by Gonito platform, but should be kept “hidden” for regular users (or at least they should be kindly asked not to peek there). It is recommended (though not obligatory) that this branch contain all the source codes and data used to generate the train/dev/test sets. (Use git-annex if you have really big files there.)

Branch (1) should be the parent of the branch (2), for instance, the repo (for the toy “planets” challenge) could be created as follows:

geval --init --expected-directory planets
cd planets
git init
git add .gitignore config.txt README.md train/train.tsv dev-0/{in,expected}.tsv test-A/in.tsv
git commit -m 'init challenge'
git remote add origin ssh://gitolite@gonito.net/filipg/planets
git push origin master
git branch dont-peek
git checkout dont-peek
git add test-A/expected.tsv
git commit -m 'with expected results'
git push origin dont-peek

Taking up a Gonito challenge

Clone the repo with a challenge, as given on the Gonito web-site, e.g. for the toy “planets” challenge (as generated with geval --init):

git clone git://gonito.net/planets

Now use the train data and whatever machine learning tools you like to guess the values for the dev set and the test set, put them, respectively, as:

• dev-0/out.tsv
• test-A/out.tsv

(These files must have exactly the same number of lines as, respectively, dev-0/in.tsv and test-0/in.tsv. They should contain only the predicted values.)

Check the result for the dev set with geval:

geval --test-name dev-0

(the current directory is assumed for --out-directory and --expected-directory).

If you'd like and if you have access to the test set results, you can “cheat” and check the results for the test set:

cd ..
git clone git://gonito.net/planets planets-secret --branch dont-peek
cd planets
geval --expected-directory ../planets-secret

Uploading your results to Gonito platform

Uploading is via Git — commit your “out” files and push the commit to your own repo. On Gonito you are encouraged to share your code, so be nice and commit also your source codes.

git remote add mine git@github.com/johnsmith/planets-johnsmith
git add {dev-0,test-A}/out.tsv
git add Makefile magic-bullet.py ... # whatever scripts/source codes you have
git commit -m 'my solution to the challenge'
git push mine master

Then let Gonito pull them and evaluate your results, either manually clicking "submit" at the Gonito web site or using --submit option (see below).

Submitting a solution to a Gonito platform with GEval

A solution to a machine learning challenge can be submitted with the special --submit option:

geval --submit --host HOST --token TOKEN

where:

• HOST is the name of the host with a Gonito platform
• TOKEN is a special per-user authorisation token (can be copied from "your account" page)

HOST must be given when --submit is used (unless the creator of the challenge put --host option in the config.txt file, note that in such a case using --host option will result in an error).

If TOKEN was not given, GEval attempts to read it from the .token file, and if the .token file does not exist, the user is asked to type it (and then the token is cached in .token file).

GEval with --submit does not commit or push changes, this needs to be done before running geval --submit. On the other hand, GEval will check whether the changes were committed and pushed.

Note that using --submit option for the main instance at https://gonito.net is usually NOT needed, as the git repositories are configured there in such a way that an evaluation is triggered with each push anyway.

geval options

geval - stand-alone evaluation tool for tests in Gonito platform

Usage: geval ([--init] | [-v|--version] | [-l|--line-by-line] |
             [-w|--worst-features] | [-d|--diff OTHER-OUT] |
             [-m|--most-worsening-features ARG] | [-j|--just-tokenize] |
             [-S|--submit]) ([-s|--sort] | [-r|--reverse-sort])
             [--out-directory OUT-DIRECTORY]
             [--expected-directory EXPECTED-DIRECTORY] [-t|--test-name NAME]
             [-o|--out-file OUT] [-e|--expected-file EXPECTED]
             [-i|--input-file INPUT] [-a|--alt-metric METRIC]
             [-m|--metric METRIC] [-p|--precision NUMBER-OF-FRACTIONAL-DIGITS]
             [-T|--tokenizer TOKENIZER] [--gonito-host GONITO_HOST]
             [--token TOKEN]
  Run evaluation for tests in Gonito platform

Available options:
  -h,--help                Show this help text
  --init                   Init a sample Gonito challenge rather than run an
                           evaluation
  -v,--version             Print GEval version
  -l,--line-by-line        Give scores for each line rather than the whole test
                           set
  -w,--worst-features      Print a ranking of worst features, i.e. features that
                           worsen the score significantly. Features are sorted
                           using p-value for Mann-Whitney U test comparing the
                           items with a given feature and without it. For each
                           feature the number of occurrences, average score and
                           p-value is given.
  -d,--diff OTHER-OUT      Compare results of evaluations (line by line) for two
                           outputs.
  -m,--most-worsening-features ARG
                           Print a ranking of the "most worsening" features,
                           i.e. features that worsen the score the most when
                           comparing outputs from two systems.
  -j,--just-tokenize       Just tokenise standard input and print out the tokens
                           (separated by spaces) on the standard output. rather
                           than do any evaluation. The --tokenizer option must
                           be given.
  -S,--submit              Submit current solution for evaluation to an external
                           Gonito instance specified with --gonito-host option.
                           Optionally, specify --token.
  -s,--sort                When in line-by-line or diff mode, sort the results
                           from the worst to the best
  -r,--reverse-sort        When in line-by-line or diff mode, sort the results
                           from the best to the worst
  --out-directory OUT-DIRECTORY
                           Directory with test results to be
                           evaluated (default: ".")
  --expected-directory EXPECTED-DIRECTORY
                           Directory with expected test results (the same as
                           OUT-DIRECTORY, if not given)
  -t,--test-name NAME      Test name (i.e. subdirectory with results or expected
                           results) (default: "test-A")
  -o,--out-file OUT        The name of the file to be
                           evaluated (default: "out.tsv")
  -e,--expected-file EXPECTED
                           The name of the file with expected
                           results (default: "expected.tsv")
  -i,--input-file INPUT    The name of the file with the input (applicable only
                           for some metrics) (default: "in.tsv")
  -a,--alt-metric METRIC   Alternative metric (overrides --metric option)
  -m,--metric METRIC       Metric to be used - RMSE, MSE, Accuracy, LogLoss,
                           Likelihood, F-measure (specify as F1, F2, F0.25,
                           etc.), multi-label F-measure (specify as
                           MultiLabel-F1, MultiLabel-F2, MultiLabel-F0.25,
                           etc.), MAP, BLEU, NMI, ClippEU, LogLossHashed,
                           LikelihoodHashed, BIO-F1, BIO-F1-Labels or CharMatch
  -p,--precision NUMBER-OF-FRACTIONAL-DIGITS
                           Arithmetic precision, i.e. the number of fractional
                           digits to be shown
  -T,--tokenizer TOKENIZER Tokenizer on expected and actual output before
                           running evaluation (makes sense mostly for metrics
                           such BLEU), minimalistic, 13a and v14 tokenizers are
                           implemented so far. Will be also used for tokenizing
                           text into features when in --worst-features and
                           --most-worsening-features modes.
  --gonito-host GONITO_HOST
                           Submit ONLY: Gonito instance location.
  --token TOKEN            Submit ONLY: Token for authorization with Gonito
                           instance.

If you need another metric, let me know, or do it yourself!

Licence

Apache License 2.0

Authors

Filip Graliński

Contributors

Piotr Halama