Testing Your Assistant

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 !

Note

Make sure your model file in models is a combined core and nlu model. If it does not contain an NLU model, Core will use the default RegexInterpreter.

End-to-End Testing

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 generalises using cross-validation. To do this, add the flag --cross-validation:

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

The full list of options for the script is:

usage: rasa test nlu [-h] [-v] [-vv] [--quiet] [-m MODEL] [-u NLU] [--out OUT]
                     [--successes] [--no-errors] [--histogram HISTOGRAM]
                     [--confmat CONFMAT] [-c CONFIG [CONFIG ...]]
                     [--cross-validation] [-f FOLDS] [-r RUNS]
                     [-p PERCENTAGES [PERCENTAGES ...]] [--no-plot]

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

The command in the example above will create a train/test split from your data, then train each pipeline multiple times with 0, 25, 50, 70 and 90% of your intent data excluded from the training set. The models are then evaluated on the test set and the f1-score for each exclusion percentage is recorded. This process runs three times (i.e. with 3 test sets in total) and then a graph is plotted using the means and standard deviations of the f1-scores.

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, as well as providing an overall average. You can save these reports as JSON files using the --report argument.

The confusion matrix shows you which intents are mistaken for others; any samples which have been incorrectly predicted are logged and saved to a file called errors.json for easier debugging.

The histogram that the script produces allows you to visualise the confidence distribution for all predictions, with the volume of correct and incorrect predictions being displayed by blue and red bars respectively. Improving the quality of your training data will move the blue histogram bars to the right and the red histogram bars to the left of the plot.

Response Selection

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

The report logs precision, recall and f1 measure for each response, as well as providing an overall average. You can save these reports as JSON files using the --report argument.

Entity Extraction

The CRFEntityExtractor is the only entity extractor which you train using your own data, and so is the only one that will be evaluated. If you use the spaCy or duckling pre-trained entity extractors, Rasa NLU will not include these in the evaluation.

Rasa NLU will report recall, precision, and f1 measure for each entity type that CRFEntityExtractor is trained to recognize.

Entity Scoring

To evaluate entity extraction we apply a simple tag-based approach. We don’t consider BILOU tags, but only the entity type tags on a per token basis. Our approach is more lenient when it comes to evaluation, as it rewards partial extraction and does not punish the splitting of entities.

Here’s a comparison between the two scoring mechanisms for the phrase "near Alexanderplatz tonight":

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.

In addition, this will save a confusion matrix to a file called results/story_confmat.pdf. For each action in your domain, the confusion matrix shows how often the action was correctly predicted and how often an incorrect action was predicted instead.

The full list of options for the script is:

usage: rasa test core [-h] [-v] [-vv] [--quiet] [-m MODEL [MODEL ...]]
                      [-s STORIES] [--max-stories MAX_STORIES] [--out OUT]
                      [--e2e] [--endpoints ENDPOINTS]
                      [--fail-on-prediction-errors] [--url URL]
                      [--evaluate-model-directory] [--no-plot]

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. 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

This will evaluate each of the models on the provided stories and plot some graphs to show you which policy performs best.