A JupyterLab extension to visualize and explain AutoML optimization procedures.
Project description
XAutoML: A Visual Analytics Tool for Establishing Trust in Automated Machine Learning
XAutoML is an interactive visual analytics tool for explaining AutoML optimisation procedures and ML pipelines constructed by AutoML. It combines interactive visualizations with established techniques from explainable AI (XAI) to make the complete AutoML procedure transparent and explainable. We integrate XAutoML with Jupyter to enable experienced users to extend the visual analytics with advanced ad-hoc visualizations based on information extracted from XAutoML
Currently, XAutoML supports only
but we plan to add support for further AutoML systems. You can find a video introducing XAutoML on YouTube.
Install
To install the extension, execute:
pip install xautoml
Usage
XAutoML currently only works with JupyterLab. You can find ready to use Notebook examples in the examples folder.
cd examples
jupyter lab
To use XAutoML, three steps are necessary:
- Perform an optimization in one of the supported AutoML frameworks
- Import the RunHistory of the optimizer via the corresponding adapter
- Create the XAutoML visualization
from xautoml.main import XAutoML
from xautoml.adapter import import_sklearn
from xautoml.util.datasets import openml_task
from sklearn.model_selection import RandomizedSearchCV
# 1) Perform AutoML optimization
random_search = RandomizedSearchCV(...).fit(...)
# 2) Use Adapter to create RunHistory
rh = import_sklearn(random_search)
# 3) Create Visualization
X_test, y_test = openml_task(31, 0, test=True)
main = XAutoML(rh, X_test, y_test)
main.explain()
Uninstall
To remove the extension, execute:
pip uninstall xautoml
Troubleshoot
If you are seeing the frontend extension, but it is not working, check that the server extension is enabled:
jupyter server extension list
If the server extension is installed and enabled, but you are not seeing the frontend extension, check the frontend extension is installed:
jupyter labextension list
Contributing
Development install
Note: You will need NodeJS to build the extension package.
The jlpm
command is JupyterLab's pinned version of
yarn that is installed with JupyterLab. You may use
yarn
or npm
in lieu of jlpm
below.
# Clone the repo to your local environment
# Change directory to the xautoml directory
# Install package in development mode
pip install -e .
# Link your development version of the extension with JupyterLab
jupyter labextension develop . --overwrite
# Server extension must be manually installed in develop mode
jupyter server extension enable xautoml
# Rebuild extension Typescript source after making changes
jlpm install
jlpm run build
You can watch the source directory and run JupyterLab at the same time in different terminals to watch for changes in the extension's source and automatically rebuild the extension.
# Watch the source directory in one terminal, automatically rebuilding when needed
jlpm run watch
# Run JupyterLab in another terminal
jupyter lab
With the watch command running, every saved change will immediately be built locally and available in your running JupyterLab. Refresh JupyterLab to load the change in your browser (you may need to wait several seconds for the extension to be rebuilt).
By default, the jlpm run build
command generates the source maps for this extension to make it easier to debug using the browser dev tools. To also generate source maps for the JupyterLab core extensions, you can run the following command:
jupyter lab build --minimize=False
Development uninstall
# Server extension must be manually disabled in develop mode
jupyter server extension disable xautoml
pip uninstall xautoml
In development mode, you will also need to remove the symlink created by jupyter labextension develop
command. To find its location, you can run jupyter labextension list
to figure out where the labextensions
folder is located. Then you can remove the symlink named xautoml
within that folder.
Release new version
Increase version number in package.json
and upload the latest build to pypi.
python setup.py sdist
python -m twine upload dist/*
Project details
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