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

Warning

This document is for an old version of Rasa.

Evaluating Models

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 test_set.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

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, 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. 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.

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 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

End-to-End Evaluation

Rasa lets you evaluate dialogues end-to-end, 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 is an example:

## end-to-end story 1
* greet: hello
   - utter_ask_howcanhelp
* inform: show me [chinese](cuisine) restaurants
   - utter_ask_location
* inform: in [Paris](location)
   - utter_ask_price

If you’ve saved end-to-end stories as a file called e2e_stories.md, you can evaluate your model against them by running:

$ rasa test --stories e2e_stories.md --e2e