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 !

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.

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]

optional arguments:
  -h, --help            show this help message and exit
  -m MODEL, --model MODEL
                        Path to a trained Rasa model. If a directory is
                        specified, it will use the latest model in this
                        directory. (default: models)
  -u NLU, --nlu NLU     File or folder containing your NLU data. (default:
                        data)
  --out OUT             Output path for any files created during the
                        evaluation. (default: results)
  --successes           If set successful predictions (intent and entities)
                        will be written to a file. (default: False)
  --no-errors           If set incorrect predictions (intent and entities)
                        will NOT be written to a file. (default: False)
  --histogram HISTOGRAM
                        Output path for the confidence histogram. (default:
                        hist.png)
  --confmat CONFMAT     Output path for the confusion matrix plot. (default:
                        confmat.png)
  -c CONFIG [CONFIG ...], --config CONFIG [CONFIG ...]
                        Model configuration file. If a single file is passed
                        and cross validation mode is chosen, cross-validation
                        is performed, if multiple configs or a folder of
                        configs are passed, models will be trained and
                        compared directly. (default: None)
  --no-plot             Don't render evaluation plots (default: False)

Python Logging Options:
  -v, --verbose         Be verbose. Sets logging level to INFO. (default:
                        None)
  -vv, --debug          Print lots of debugging statements. Sets logging level
                        to DEBUG. (default: None)
  --quiet               Be quiet! Sets logging level to WARNING. (default:
                        None)

Cross Validation:
  --cross-validation    Switch on cross validation mode. Any provided model
                        will be ignored. (default: False)
  -f FOLDS, --folds FOLDS
                        Number of cross validation folds (cross validation
                        only). (default: 5)

Comparison Mode:
  -r RUNS, --runs RUNS  Number of comparison runs to make. (default: 3)
  -p PERCENTAGES [PERCENTAGES ...], --percentages PERCENTAGES [PERCENTAGES ...]
                        Percentages of training data to exclude during
                        comparison. (default: [0, 25, 50, 75])

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.

Response Selection

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

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.