geval/README.md

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# GEval
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GEval is a Haskell library and a stand-alone tool for evaluating the
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results of solutions to machine learning challenges as defined on the
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[Gonito](http://gonito.net) 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.
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Note that GEval is only about machine learning evaluation. No actual
machine learning algorithms are available here.
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The official repository is `git://gonito.net/geval`, browsable at
<https://gonito.net/gitlist/geval.git/>.
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## Installing
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You need [Haskell Stack](https://github.com/commercialhaskell/stack).
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You could install Stack with your package manager or with:
curl -sSL https://get.haskellstack.org/ | sh
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When you've got Haskell Stack, install GEval with:
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git clone git://gonito.net/geval
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cd geval
stack setup
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stack test
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stack install
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(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.)
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By default, `geval` binary 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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`$PATH` in your configuration or to type:
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PATH="$HOME/.local/bin" geval ...
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### 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
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## 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
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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 FECH_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.
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## Preparing a Gonito challenge
### Directory structure of a Gonito challenge
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A definition of a [Gonito](http://gonito.net) challenge should be put in a separate
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directory. Such a directory should
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have the following structure:
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* `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
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* `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)
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* `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
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* `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
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`dev-0/expected.tsv`), note that this file should be “hidden” by the
organisers of a Gonito challenge, see notes on the structure of
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commits below
* `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](http://gonito.net) challenge:
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geval --init --expected-directory my-challenge
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(This will generate a sample toy challenge about guessing planet masses).
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A metric (other than the default `RMSE` — root-mean-square error) can
be given to generate another type of toy challenge:
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geval --init --expected-directory my-machine-translation-challenge --metric BLEU
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### Preparing a Git repository
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[Gonito](http://gonito.net) platform expects a Git repository with a challenge to be
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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
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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 source codes and data used to generate the train/dev/test sets.
(Use [git-annex](https://git-annex.branchable.com/) if you have really big files there.)
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Branch (1) should be the parent of the branch (2), for instance, the
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repo (for the toy “planets” challenge) could be created as follows:
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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'
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git remote add origin ssh://gitolite@gonito.net/filipg/planets
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git push origin master
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git branch dont-peek
git checkout dont-peek
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git add test-A/expected.tsv
git commit -m 'with expected results'
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git push origin dont-peek
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## Taking up a Gonito challenge
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Clone the repo with a challenge, as given on the [Gonito](http://gonito.net) web-site, e.g.
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for the toy “planets” challenge (as generated with `geval --init`):
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git clone git://gonito.net/planets
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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,
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respectively, `dev-0/in.tsv` and `test-0/in.tsv`. They should contain
only the predicted values.)
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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
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“cheat” and check the results for the test set:
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cd ..
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git clone git://gonito.net/planets planets-secret --branch dont-peek
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cd planets
geval --expected-directory ../planets-secret
### 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. On [Gonito](http://gonito.net) you are encouraged to share your code, so
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be nice and commit also your source codes.
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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 add Makefile magic-bullet.py ... # whatever scripts/source codes you have
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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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## `geval` options
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```
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Usage: geval ([--init] | [-l|--line-by-line] | [-d|--diff OTHER-OUT])
([-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]
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
-l,--line-by-line Give scores for each line rather than the whole test
set
-d,--diff OTHER-OUT compare results
-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.), 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
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```
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If you need another metric, let me know, or do it yourself!
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## Licence
Apache License 2.0
## Authors
Filip Graliński