Warning: This document is for an old version of Rasa Core. The latest version is 0.14.5.

Evaluating and Testing

Evaluating a Trained Model

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

python -m rasa_core.evaluate -d models/dialogue \
  -s test_stories.md -o matrix.pdf --failed failed_stories.md

This will print the failed stories to 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 matrix.pdf. The confusion matrix shows, for each action in your domain, how often that action was predicted, and how often an incorrect action was predicted instead.

The full list of options for the script is:

Calling `rasa_core.evaluate` is deprecated. Please use `rasa_core.test` instead.
usage: evaluate.py [-h] {default,compare} ...

evaluates a dialogue model

positional arguments:
  {default,compare}  mode
    default          default mode: evaluate a dialogue model
    compare          compare mode: evaluate multiple dialogue models to
                     compare policies

optional arguments:
  -h, --help         show this help message and exit

Comparing Policies

To choose a specific policy, 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. Once you are happy with it, you can then train your final policy on your full data set. To do this, split your training data into multiple files in a single directory. You can then use the train_paper script to train multiple policies on the same data. You can choose one of the files to be partially excluded. This means that Rasa Core will be trained multiple times, with 0, 5, 25, 50, 70, 90, 95, and 100% of the stories in that file removed from the training data. By evaluating on the full set of stories, you can measure how well Rasa Core is predicting the held-out stories.

The full list of options for the script is:

/home/travis/virtualenv/python3.5.6/bin/python: No module named rasa_core.train_paper

The full list of options for the evaluation script is:

/home/travis/virtualenv/python3.5.6/bin/python: No module named rasa_core.evaluate_paper

Have questions or feedback?

We have a very active support community on Rasa Community Forum that is happy to help you with your questions. If you have any feedback for us or a specific suggestion for improving the docs, feel free to share it by creating an issue on Rasa Core GitHub repository.