Testing Your Assistant
Testing Your Assistant
Overview
These docs are for version 1.x of Rasa Open Source.
Docs for the new version 2.0 can be found here.
User Guide
- Installation
- Tutorial: Rasa Basics
- Tutorial: Building Assistants
- Command Line Interface
- Architecture
- Messaging and Voice Channels
- Testing Your Assistant
- Validate Data
- Configuring the HTTP API
- Deploying Your Rasa Assistant
- Cloud Storage
NLU
- About
- Using NLU Only
- Training Data Format
- Language Support
- Choosing a Pipeline
- Components
- Entity Extraction
Core
- About
- Stories
- Domains
- Responses
- Actions
- Reminders and External Events
- Policies
- Slots
- Forms
- Retrieval Actions
- Interactive Learning
- Fallback Actions
- Knowledge Base Actions
Conversation Design
API Reference
- Action Server
- HTTP API
- Jupyter Notebooks
- Agent
- Custom NLU Components
- Rasa SDK
- Events
- Tracker
- Tracker Stores
- Event Brokers
- Lock Stores
- Training Data Importers
- Featurization of Conversations
- TensorFlow Configuration
- Migration Guide
- Rasa Open Source Change Log
Migrate from (beta)
Reference
Versions
viewing: 1.9.4
Warning
This document is for an old version of Rasa. The latest version is 1.10.26.
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
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
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.
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.
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
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. Rasa Core has some scripts to help you choose and fine-tune your policy configuration.