Yellowbrick is an open source project that is supported by a community who will gratefully and humbly accept any contributions you might make to the project. Large or small, any contribution makes a big difference; and if you’ve never contributed to an open source project before, we hope you will start with Yellowbrick!

Principally, Yellowbrick development is about the addition and creation of visualizers — objects that learn from data and create a visual representation of the data or model. Visualizers integrate with scikit-learn estimators, transformers, and pipelines for specific purposes and as a result, can be simple to build and deploy. The most common contribution is a new visualizer for a specific model or model family. We’ll discuss in detail how to build visualizers later.

Beyond creating visualizers, there are many ways to contribute:

As you can see, there are lots of ways to get involved and we would be very happy for you to join us! The only thing we ask is that you abide by the principles of openness, respect, and consideration of others as described in our Code of Conduct.


If you’re unsure where to start, perhaps the best place is to drop the maintainers a note via our mailing list:

Getting Started on GitHub

Yellowbrick is hosted on GitHub at

The typical workflow for a contributor to the codebase is as follows:

  1. Discover a bug or a feature by using Yellowbrick.
  2. Discuss with the core contributors by adding an issue.
  3. Assign yourself the task by pulling a card from our Waffle Kanban
  4. Fork the repository into your own GitHub account.
  5. Create a Pull Request first thing to connect with us about your task.
  6. Code the feature, write the tests and documentation, add your contribution.
  7. Review the code with core contributors who will guide you to a high quality submission.
  8. Merge your contribution into the Yellowbrick codebase.


Please create a pull request as soon as possible, even before you’ve started coding. This will allow the core contributors to give you advice about where to add your code or utilities and discuss other style choices and implementation details as you go. Don’t wait!

We believe that contribution is collaboration and therefore emphasize communication throughout the open source process. We rely heavily on GitHub’s social coding tools to allow us to do this.

Forking the Repository

The first step is to fork the repository into your own account. This will create a copy of the codebase that you can edit and write to. Do so by clicking the “fork” button in the upper right corner of the Yellowbrick GitHub page.

Once forked, use the following steps to get your development environment set up on your computer:

  1. Clone the repository.

    After clicking the fork button, you should be redirected to the GitHub page of the repository in your user account. You can then clone a copy of the code to your local machine.:

    $ git clone[YOURUSERNAME]/yellowbrick
    $ cd yellowbrick
  2. Create a virtual environment.

    Yellowbrick developers typically use virtualenv (and virtualenvwrapper), pyenv or conda envs in order to manage their Python version and dependencies. Using the virtual environment tool of your choice, create one for Yellowbrick. Here’s how with virtualenv:

    $ virtualenv venv
  3. Install dependencies.

    Yellowbrick’s dependencies are in the requirements.txt document at the root of the repository. Open this file and uncomment the dependencies that are for development only. Then install the dependencies with pip:

    $ pip install -r requirements.txt

    Note that there may be other dependencies required for development and testing; you can simply install them with pip. For example to install the additional dependencies for building the documentation or to run the test suite, use the requirements.txt files in those directories:

    $ pip install -r tests/requirements.txt
    $ pip install -r docs/requirements.txt
  4. Switch to the develop branch.

    The Yellowbrick repository has a develop branch that is the primary working branch for contributions. It is probably already the branch you’re on, but you can make sure and switch to it as follows:

    $ git fetch
    $ git checkout develop

At this point you’re ready to get started writing code. If you’re going to take on a specific task, we’d strongly encourage you to check out the issue on Waffle and create a pull request before you start coding to better foster communication with other contributors. More on this in the next section.

Pull Requests

A pull request (PR) is a GitHub tool for initiating an exchange of code and creating a communication channel for Yellowbrick maintainers to discuss your contribution. In essenence, you are requesting that the maintainers merge code from your forked repository into the develop branch of the primary Yellowbrick repository. Once completed, your code will be part of Yellowbrick!

When starting a Yellowbrick contribution, open the pull request as soon as possible. We use your PR issue page to discuss your intentions and to give guidance and direction. Every time you push a commit into your forked repository, the commit is automatically included with your pull request, therefore we can review as you code. The earlier you open a PR, the more easily we can incorporate your updates, we’d hate for you to do a ton of work only to discover someone else already did it or that you went in the wrong direction and need to refactor.


For a great example of a pull request for a new feature visualizer, check out this one by Carlo Morales.

When you open a pull request, ensure it is from your forked repository to the develop branch of; we will not merge a PR into the master branch. Title your Pull Request so that it is easy to understand what you’re working on at a glance. Also be sure to include a reference to the issue that you’re working on so that correct references are set up.

After you open a PR, you should get a message from one of the maintainers. Use that time to discuss your idea and where best to implement your work. Feel free to go back and forth as you are developing with questions in the comment thread of the PR. Once you are ready, please ensure that you explicitly ping the maintainer to do a code review. Before code review, your PR should contain the following:

  1. Your code contribution
  2. Tests for your contribution
  3. Documentation for your contribution
  4. A PR comment describing the changes you made and how to use them
  5. A PR comment that includes an image/example of your visualizer

At this point your code will be formally reviewed by one of the contributors. We use GitHub’s code review tool, starting a new code review and adding comments to specific lines of code as well as general global comments. Please respond to the comments promptly, and don’t be afraid to ask for help implementing any requested changes! You may have to go back and forth a couple of times to complete the code review.

When the following is true:

  1. Code is reviewed by at least one maintainer
  2. Continuous Integration tests have passed
  3. Code coverage and quality have not decreased
  4. Code is up to date with the yellowbrick develop branch

Then we will “Squash and Merge” your contribution, combining all of your commits into a single commit and merging it into the develop branch of Yellowbrick. Congratulations! Once your contribution has been merged into master, you will be officially listed as a contributor.

Developing Visualizers

In this section, we’ll discuss the basics of developing visualizers. This of course is a big topic, but hopefully these simple tips and tricks will help make sense. First thing though, check out this presentation that we put together on yellowbrick development, it discusses the expected user workflow, our integration with scikit-learn, our plans and roadmap, etc:

One thing that is necessary is a good understanding of scikit-learn and Matplotlib. Because our API is intended to integrate with scikit-learn, a good start is to review “APIs of scikit-learn objects” and “rolling your own estimator”. In terms of matplotlib, use Yellowbrick’s guide Effective Matplotlib. Additional resources include Nicolas P. Rougier’s Matplotlib tutorial and Chris Moffitt’s Effectively Using Matplotlib.

Visualizer API

There are two basic types of Visualizers:

  • Feature Visualizers are high dimensional data visualizations that are essentially transformers.
  • Score Visualizers wrap a scikit-learn regressor, classifier, or clusterer and visualize the behavior or performance of the model on test data.

These two basic types of visualizers map well to the two basic objects in scikit-learn:

  • Transformers take input data and return a new data set.
  • Estimators are fit to training data and can make predictions.

The scikit-learn API is object oriented, and estimators and transformers are initialized with parameters by instantiating their class. Hyperparameters can also be set using the set_attrs() method and retrieved with the corresponding get_attrs() method. All scikit-learn estimators have a fit(X, y=None) method that accepts a two dimensional data array, X, and optionally a vector y of target values. The fit() method trains the estimator, making it ready to transform data or make predictions. Transformers have an associated transform(X) method that returns a new dataset, Xprime and models have a predict(X) method that returns a vector of predictions, yhat. Models also have a score(X, y) method that evaluate the performance of the model.

Visualizers interact with scikit-learn objects by intersecting with them at the methods defined above. Specifically, visualizers perform actions related to fit(), transform(), predict(), and score() then call a draw() method which initializes the underlying figure associated with the visualizer. The user calls the visualizer’s poof() method, which in turn calls a finalize() method on the visualizer to draw legends, titles, etc. and then poof() renders the figure. The Visualizer API is therefore:

  • draw(): add visual elements to the underlying axes object
  • finalize(): prepare the figure for rendering, adding final touches such as legends, titles, axis labels, etc.
  • poof(): render the figure for the user (or saves it to disk).

Creating a visualizer means defining a class that extends Visualizer or one of its subclasses, then implementing several of the methods described above. A barebones implementation is as follows:

import matplotlib.pyplot as plt

from yellowbrick.base import Visualizer

class MyVisualizer(Visualizer):

    def __init__(self, ax=None, **kwargs):
        super(MyVisualizer, self).__init__(ax, **kwargs)

    def fit(self, X, y=None):
        return self

    def draw(self, X):
        if is None:
   = self.gca()

    def finalize(self):
        self.set_title("My Visualizer")

This simple visualizer simply draws a line graph for some input dataset X, intersecting with the scikit-learn API at the fit() method. A user would use this visualizer in the typical style:

visualizer = MyVisualizer()

Score visualizers work on the same principle but accept an additional required model argument. Score visualizers wrap the model (which can be either instantiated or uninstantiated) and then pass through all attributes and methods through to the underlying model, drawing where necessary.


The test package mirrors the yellowbrick package in structure and also contains several helper methods and base functionality. To add a test to your visualizer, find the corresponding file to add the test case, or create a new test file in the same place you added your code.

Visual tests are notoriously difficult to create — how do you test a visualization or figure? Moreover, testing scikit-learn models with real data can consume a lot of memory. Therefore the primary test you should create is simply to test your visualizer from end to end and make sure that no exceptions occur. To assist with this, we have two primary helpers, VisualTestCase and DatasetMixin. Create your unittest as follows:

import pytest
from tests.base import VisualTestCase
from tests.dataset import DatasetMixin

class MyVisualizerTests(VisualTestCase, DatasetMixin):

    def test_my_visualizer(self):
        Test MyVisualizer on a real dataset
        # Load the data from the fixture
        dataset = self.load_data('occupancy')

        # Get the data
        X = dataset[[
            "temperature", "relative_humidity", "light", "C02", "humidity"
        y = dataset['occupancy'].astype(int)

            visualizer = MyVisualizer()
        except Exception as e:
  "my visualizer didn't work")

Tests can be run as follows:

$ make test

The Makefile uses the pytest runner and testing suite as well as the coverage library, so make sure you have those dependencies installed! The DatasetMixin also requires to fetch data from our Amazon S3 account.

Image Comparison Tests

Writing an image based comparison test is only a little more difficult than the simple testcase presented above. We have adapted matplotlib’s image comparison test utility into an easy to use assert method : self.assert_images_similar(visualizer)

The main consideration is that you must specify the “baseline”, or expected, image in the tests/baseline_images/ folder structure.

For example, create your unittest located in tests/test_regressor/ as follows:

from tests.base import VisualTestCase
    def test_my_visualizer_output(self):
        visualizer = MyVisualizer()

The first time this test is run, there will be no baseline image to compare against, so the test will fail. Copy the output images (in this case tests/actual_images/test_regressor/test_myvisualizer/test_my_visualizer_output.png) to the correct subdirectory of baseline_images tree in the source directory (in this case tests/baseline_images/test_regressor/test_myvisualizer/test_my_visualizer_output.png). Put this new file under source code revision control (with git add). When rerunning the tests, they should now pass.

We also have a helper script, tests/ to clean up and manage baseline images automatically. It is run using the python -m command to execute a module as main, and it takes as an argument the path to your test file. To copy the figures as above:

$ python -m tests.images tests/test_regressor/

This will move all related test images from actual_images to baseline_images on your behalf (note you’ll have had to run the tests at least once to generate the images). You can also clean up images from both actual and baseline as follows:

$ python -m tests.images -C tests/test_regressor/

This is useful particularly if you’re stuck trying to get an image comparison to work. For more information on the images helper script, use python -m tests.images --help.


The initial documentation for your visualizer will be a well structured docstring. Yellowbrick uses Sphinx to build documentation, therefore docstrings should be written in reStructuredText in numpydoc format (similar to scikit-learn). The primary location of your docstring should be right under the class definition, here is an example:

class MyVisualizer(Visualizer):
    This initial section should describe the visualizer and what
    it's about, including how to use it. Take as many paragraphs
    as needed to get as much detail as possible.

    In the next section describe the parameters to __init__.


    model : a scikit-learn regressor
        Should be an instance of a regressor, and specifically one whose name
        ends with "CV" otherwise a will raise a YellowbrickTypeError exception
        on instantiation. To use non-CV regressors see:

    ax : matplotlib Axes, default: None
        The axes to plot the figure on. If None is passed in the current axes
        will be used (or generated if required).

    kwargs : dict
        Keyword arguments that are passed to the base class and may influence
        the visualization as defined in other Visualizers.


    >>> model = MyVisualizer()
    >>> model.poof()


    In the notes section specify any gotchas or other info.

When your visualizer is added to the API section of the documentation, this docstring will be rendered in HTML to show the various options and functionality of your visualizer!

To add the visualizer to the documentation it needs to be added to the docs/api folder in the correct subdirectory. For example if your visualizer is a model score visualizer related to regression it would go in the docs/api/regressor subdirectory. If you have a question where your documentation should be located, please ask the maintainers via your pull request, we’d be happy to help!

There are two primary files that need to be created:

  1. mymodule.rst: the reStructuredText document
  2. a python file that generates images for the rst document

There are quite a few examples in the documentation on which you can base your files of similar types. The primary format for the API section is as follows:

.. -*- mode: rst -*-

My Visualizer

Intro to my visualizer

.. code:: python

    # Example to run MyVisualizer
    visualizer = MyVisualizer(LinearRegression()), y)
    g = visualizer.poof()

.. image:: images/my_visualizer.png

Discussion about my visualizer

API Reference

.. automodule:: yellowbrick.regressor.mymodule
    :members: MyVisualizer

This is a pretty good structure for a documentation page; a brief introduction followed by a code example with a visualization included (using the to generate the images into the local directory’s images subdirectory). The primary section is wrapped up with a discussion about how to interpret the visualizer and use it in practice. Finally the API Reference section will use automodule to include the documentation from your docstring.

At this point there are several places where you can list your visualizer, but to ensure it is included in the documentation it must be listed in the TOC of the local index. Find the index.rst file in your subdirectory and add your rst file (without the .rst extension) to the ..toctree:: directive. This will ensure the documentation is included when it is built.

Speaking of, you can build your documentation by changing into the docs directory and running make html, the documentation will be built and rendered in the _build/html directory. You can view it by opening _build/html/index.html then navigating to your documentation in the browser.

There are several other places that you can list your visualizer including:

  • docs/index.rst for a high level overview of our visualizers
  • DESCRIPTION.rst for inclusion on PyPI
  • for inclusion on GitHub

Please ask for the maintainer’s advice about how to include your visualizer in these pages.

Advanced Development

In this section we discuss more advanced contributing guidelines including setting up branches for development as well as the release cycle. This section is intended for maintainers and core contributors of the Yellowbrick project. If you would like to be a maintainer please contact one of the current maintainers of the project.

Branching Convention

The Yellowbrick repository is set up in a typical production/release/development cycle as described in “A Successful Git Branching Model.” The primary working branch is the develop branch. This should be the branch that you are working on and from, since this has all the latest code. The master branch contains the latest stable version and release, which is pushed to PyPI. No one but core contributors will generally push to master.


All pull requests should be into the yellowbrick/develop branch from your forked repository.

You can work directly in your fork and create a pull request from your fork’s develop branch into ours. We also recommend setting up an upstream remote so that you can easily pull the latest development changes from the main Yellowbrick repository (see configuring a remote for a fork). You can do that as follows:

$ git remote add upstream
$ git remote -v
origin (fetch)
origin (push)
upstream (fetch)
upstream (push)

When you’re ready, request a code review for your pull request. Then, when reviewed and approved, you can merge your fork into our main branch. Make sure to use the “Squash and Merge” option in order to create a Git history that is understandable.


When merging a pull request, use the “squash and merge” option.

Core contributors have write access to the repository. In order to reduce the number of merges (and merge conflicts) we recommend that you utilize a feature branch off of develop to do intermediate work in:

$ git checkout -b feature-myfeature develop

Once you are done working (and everything is tested) merge your feature into develop.:

$ git checkout develop
$ git merge --no-ff feature-myfeature
$ git branch -d feature-myfeature
$ git push origin develop

Head back to Waffle and checkout another issue!


When ready to create a new release we branch off of develop as follows:

$ git checkout -b release-x.x

This creates a release branch for version x.x. At this point do the version bump by modifying and the test version in tests/ Make sure all tests pass for the release and that the documentation is up to date. There may be style changes or deployment options that have to be done at this phase in the release branch. At this phase you’ll also modify the changelog with the features and changes in the release.

Once the release is ready for prime-time, merge into master:

$ git checkout master
$ git merge --no-ff --no-edit release-x.x

Tag the release in GitHub:

$ git tag -a vx.x
$ git push origin vx.x

You’ll have to go to the release page to edit the release with similar information as added to the changelog. Once done, push the release to PyPI:

$ make build
$ make deploy

Check that the PyPI page is updated with the correct version and that pip install -U yellowbrick updates the version and works correctly. Also check the documentation on PyHosted, ReadTheDocs, and on our website to make sure that it was correctly updated. Finally merge the release into develop and clean up:

$ git checkout develop
$ git merge --no-ff --no-edit release-x.x
$ git branch -d release-x.x

Hotfixes and minor releases also follow a similar pattern; the goal is to effectively get new code to users as soon as possible!