Nkululeko: How to evaluate a test set with a given best model

Nululeko has two modules for testing and unknown data set, despite train and development/evaluation set.

Let's recap the concept of train/dev/test splits:

  • train is used to train a supervised model
  • dev is a set to evaluate this model, i.e. know when it is a good model (that doesn't overfit)
  • test is a set to be used ONLY once: for the real use of the model. If you would use the test as a dev set, you can't be sure if you're not overfitting again (because you used the dev set to adjust the meta parameters of your model).

So, in order to evaluate a third dataset ( beneath train and dev) you might have situations:
a) you have a labeled test set and want to evaluate it
b) you have an unknown test set (no labels) and want to add predictions (without evaluation)

For a),
you can use the test module, and set a tests entry in the configuration [DATA] section like so:

tests = ['my_testdb']
my_testdb = /mypath/my_testdb
my_testdb.split_strategy = test

and then call Nkululeko's test module

python -m nkululeko.test --config mycoonfg.ini --outfile myresults.csv

For b),
you can use the demo module and state your test set as a list of files like so:

python -m nkululeko.demo --config my_config.ini --list my_testsamples.csv --outfile my_results.csv

In order to use a model, of course you do need to have it trained and saved before. So you need a run with the nkululeko module before.

python -m nkululeko.nkululeko --config my_config.ini

with my_config,ini containing:

save = True
save = True

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