146 lines
5.3 KiB
Markdown
146 lines
5.3 KiB
Markdown
# GEval
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GEval is a library (and a stand-alone tool) for evaluating the results
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of solutions to machine learning challenges as defined in the Gonito
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platform.
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Note that GEval is only about machine learning evaluation. No actual
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machine learning algorithms are available here.
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## Installing
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You need [Haskell Stack](https://github.com/commercialhaskell/stack),
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then install GEval with:
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git clone https://github.com/filipg/geval
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cd geval
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stack setup
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stack install
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By default `geval` library is installed in `$HOME/.local/bin`, so in
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order to run `geval` you need to either add `$HOME/.local/bin` to
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`$PATH` or to type:
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PATH="$HOME/.local/bin" geval
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## Preparing a Gonito challenge
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### Directory structure of a Gonito challenge
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A definition of a Gonito challenge should be put in a separate
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directory (preferably as a separate Git repo). Such a directory should
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have the following structure:
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* `config.txt` — simple configuration file with options the same as
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the ones accepted by `geval` binary (see below), usually just a
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metric is specified here (e.g. `--metric BLEU`), also non-default
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file names could be given here (e.g. `--test-name test-B` for a
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non-standard test subdirectory)
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* `README.md` — description of a challenge in Markdown
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* `train/` — subdirectory with training data (if training data are
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supplied for a given Gonito challenge at all)
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* `train/train.tsv` — the usual name of training data (this name is
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not required and could be more than one file), the first column is the
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target (predicted) value, the other columns represent features, no
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header is assumed
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* `dev-0/` — subdirectory with a development set (a sample test set,
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which won't be used for the final evaluation)
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* `dev-0/in.tsv` — input data (the same format as `train/train.tsv`,
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but without the first column)
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* `dev-0/expected.tsv` — values to be guessed (note that `paste
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dev-0/expected.tsv dev-0/in.tsv` should give the same format as
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`train/train.tsv`)
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* `dev-1/`, `dev-2`, ... — other dev sets (if supplied)
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* `test-A/` — subdirectory with the test set
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* `test-A/in.tsv` — test input (the same format as `dev-0/in.tsv`)
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* `test-A/expected.tsv` — values to be guessed (the same format as
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`dev-0/expected.tsv`), note that this file should be "hidden" by the
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organizers of a Gonito challenge, see notes on the structure of
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commits below
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* `test-B`, `test-C`, ... — other alternative test sets (if supplied)
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### Initiating a Gonito challenge with geval
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You can use `geval` to initiate a Gonito challenge:
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geval --init --expected-directory my-challenge
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(This will generate a sample toy challenge with guessing the mass of a planet).
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A metric (other than the default root-mean-square error) can be given
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to generate another type of a toy challenge:
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geval --init --expected-directory my-mt-challenge --metric BLEU
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### Preparing a Git repository
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Gonito platform expects a Git repository with a challenge to be
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submitted. The suggested way to do this is as follows:
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1. Prepare a branch with all the files _without_
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`test-A/expected.tsv`. This branch will be cloned by people taking
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up the challenge.
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2. Prepare a separate branch (or even a repo) with
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`test-A/expected.tsv` added. This branch should be accessible by
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Gonito platform, but should be kept "hidden" for regular users (or
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at least they should be kindly asked not to peek there). It is
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recommended (though not obligatory) that this branch contain all the
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source codes and data used to generate the train/dev/test sets.
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Branch (1) should be the parent of the branch (2), for instance, the
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repos (for the toy "planets" challenge) could be created as follows:
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geval --init --expected-directory planets
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cd planets
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git init
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git add .gitignore config.txt README.md train/train.tsv dev-0/{in,expected}.tsv test-A/in.tsv
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git commit -m 'init challenge'
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git remote add origin git@github.com:filipg/planets
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git push origin master
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git add test-A/expected.tsv
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git commit -m 'with expected results'
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git push origin dont-peek-here
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## Taking up a Gonito challenge
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Clone the repo with a challenge, as given on the Gonito web-site, e.g.
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for the toy "planets" challenge (as generated with `geval --init`):
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git clone https://github.com/filipg/planets
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Now use the train data and whatever machine learning tools you like to
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guess the values for the dev set and the test set, put them,
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respectively, as:
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* `dev-0/out.tsv`
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* `test-A/out.tsv`
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(These files must have exactly the same number of lines as,
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respectively, `dev-0/in.tsv` and `test-0/in.tsv`.)
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Check the result for the dev set with `geval`:
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geval --test-name dev-0
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(the current directory is assumed for `--out-directory` and `--expected-directory`).
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If you'd like and if you have access to the test set results, you can
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"cheat" and check the results for the test set:
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cd ..
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git clone https://github.com/filipg/planets planets-secret --branch secret
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cd planets
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geval --expected-directory ../planets-secret
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### Uploading your results to Gonito platform
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Uploading is via Git — commit your "out" files and push the commit to
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your own repo.
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git remote add mine git@github.com:johnsmith/planets-johnsmith
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git add {dev-0,test-A}/out.tsv
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git commit -m 'my solution to the challenge'
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git push mine master
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Then let Gonito pull them and evaluate your results.
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