Testing Your Assistant

End-to-End Testing

Rasa Open Source lets you test dialogues end-to-end by running through test conversations and making sure that both NLU and Core make correct predictions.

To do this, you need some stories in the end-to-end format, which includes both the NLU output and the original text. Here are some examples:

By default Rasa Open Source saves conversation tests to tests/conversation_tests.md. You can test your assistant against them by running:

$ rasa test

Evaluating an NLU Model

A standard technique in machine learning is to keep some data separate as a test set. You can split your NLU training data into train and test sets using:

rasa data split nlu

If you’ve done this, you can see how well your NLU model predicts the test cases using this command:

rasa test nlu -u train_test_split/test_data.md --model models/nlu-20180323-145833.tar.gz

Comparing NLU Pipelines

By passing multiple pipeline configurations (or a folder containing them) to the CLI, Rasa will run a comparative examination between the pipelines.

$ rasa test nlu --config pretrained_embeddings_spacy.yml supervised_embeddings.yml \
  --nlu data/nlu.md --runs 3 --percentages 0 25 50 70 90

Intent Classification

The evaluation script will produce a report, confusion matrix, and confidence histogram for your model.

Response Selection

The evaluation script will produce a combined report for all response selector models in your pipeline.

Evaluating a Core Model

You can evaluate your trained model on a set of test stories by using the evaluate script:

rasa test core --stories test_stories.md --out results

Comparing Core Configurations

To choose a configuration for your core model, or to choose hyperparameters for a specific policy, you want to measure how well Rasa Core will generalise to conversations which it hasn’t seen before. Especially in the beginning of a project, you do not have a lot of real conversations to use to train your bot, so you don’t just want to throw some away to use as a test set.

Rasa Core has some scripts to help you choose and fine-tune your policy configuration. Once you are happy with it, you can then train your final configuration on your full data set. To do this, you first have to train models for your different configurations. Create two (or more) config files including the policies you want to compare, and then use the compare mode of the train script to train your models:

$ rasa train core -c config_1.yml config_2.yml \
  -d domain.yml -s stories_folder --out comparison_models --runs 3 \
  --percentages 0 5 25 50 70 95

Once this script has finished, you can use the evaluate script in compare mode to evaluate the models you just trained:

$ rasa test core -m comparison_models --stories stories_folder
--out comparison_results --evaluate-model-directory

Note

This training process can take a long time, so we’d suggest letting it run somewhere in the background where it can’t be interrupted.

Additional Notes

If you are looking to tune the hyperparameters of your NLU model, check out this tutorial.