GEval

GEval is a library (and a stand-alone tool) for evaluating the results of solutions to machine learning challenges as defined in the Gonito platform.

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

Installing

You need Haskell Stack, then install GEval with:

git clone https://github.com/filipg/geval
cd geval
stack setup
stack install

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

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

Preparing a Gonito challenge

Directory structure of a Gonito challenge

A definition of a Gonito challenge should be put in a separate directory (preferably as a separate Git repo). Such a directory should have the following structure:

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)
README.md — description of a challenge in Markdown
train/ — subdirectory with training data (if training data are supplied for a given Gonito challenge at all)
train/train.tsv — the usual name of training data (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 organizers 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 with guessing the mass of a planet).

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

geval --init --expected-directory my-mt-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:

Prepare a branch with all the files without test-A/expected.tsv. This branch will be cloned by people taking up the challenge.
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.

Branch (1) should be the parent of the branch (2), for instance, the repos (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 git@github.com:filipg/planets
git push origin master
git add test-A/expected.tsv
git commit -m 'with expected results'
git push origin dont-peek-here

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 https://github.com/filipg/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.)

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 https://github.com/filipg/planets planets-secret --branch secret
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.

git remote add mine git@github.com:johnsmith/planets-johnsmith
git add {dev-0,test-A}/out.tsv
git commit -m 'my solution to the challenge'
git push mine master

Then let Gonito pull them and evaluate your results.

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