These docs are for version 1.x of Rasa Open Source.

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

Note: Custom Actions are not executed as part of end-to-end tests. If your custom actions append any events to the tracker, this has to be reflected in your end-to-end tests (e.g., by adding slot events to your end-to-end story).

If you have any questions or problems, please share them with us in the dedicated testing section on our forum!

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

If you don’t want to create a separate test set, you can still estimate how well your model generalizes using cross-validation. To do this, add the flag --cross-validation:

rasa test nlu -u data/nlu.md --config config.yml --cross-validation

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. The report logs precision, recall, and f1 measure for each intent and entity. Any samples which have been incorrectly predicted are logged and saved to a file called errors.json for easier debugging.

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

This will print the failed stories to results/failed_stories.md. We count any story as failed if at least one of the actions was predicted incorrectly.

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 generalize to conversations which it hasn’t seen before. Rasa Core has some scripts to help you choose and fine-tune your policy configuration.

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